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Journal articles on the topic 'Statistic risk'
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Shen, Xi, Chang-Xing Ma, Kam C. Yuen, and Guo-Liang Tian. "Common risk difference test and interval estimation of risk difference for stratified bilateral correlated data." Statistical Methods in Medical Research 28, no. 8 (June 19, 2018): 2418–38. http://dx.doi.org/10.1177/0962280218781988.
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Bilateral correlated data are often encountered in medical researches such as ophthalmologic (or otolaryngologic) studies, in which each unit contributes information from paired organs to the data analysis, and the measurements from such paired organs are generally highly correlated. Various statistical methods have been developed to tackle intra-class correlation on bilateral correlated data analysis. In practice, it is very important to adjust the effect of confounder on statistical inferences, since either ignoring the intra-class correlation or confounding effect may lead to biased results. In this article, we propose three approaches for testing common risk difference for stratified bilateral correlated data under the assumption of equal correlation. Five confidence intervals of common difference of two proportions are derived. The performance of the proposed test methods and confidence interval estimations is evaluated by Monte Carlo simulations. The simulation results show that the score test statistic outperforms other statistics in the sense that the former has robust type [Formula: see text] error rates with high powers. The score confidence interval induced from the score test statistic performs satisfactorily in terms of coverage probabilities with reasonable interval widths. A real data set from an otolaryngologic study is used to illustrate the proposed methodologies.
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Tian, Dejian, and Xinli Suo. "A note on convex risk statistic." Operations Research Letters 40, no. 6 (November 2012): 551–53. http://dx.doi.org/10.1016/j.orl.2012.09.011.
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Deng, Xiaochuan, and Fei Sun. "Regulator-Based Risk Statistics for Portfolios." Discrete Dynamics in Nature and Society 2020 (July 9, 2020): 1–6. http://dx.doi.org/10.1155/2020/7015267.
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Risk statistic is a critical factor not only for risk analysis but also for financial application. However, the traditional risk statistics may fail to describe the characteristics of regulator-based risk. In this paper, we consider the regulator-based risk statistics for portfolios. By further developing the properties related to regulator-based risk statistics, we are able to derive dual representation for such risk.
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Ocak, Gurbey, Chava Ramspek, Maarten B. Rookmaaker, Peter J. Blankestijn, Marianne C. Verhaar, Willem Jan W. Bos, Friedo W. Dekker, and Merel van Diepen. "Performance of bleeding risk scores in dialysis patients." Nephrology Dialysis Transplantation 34, no. 7 (January 4, 2019): 1223–31. http://dx.doi.org/10.1093/ndt/gfy387.
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Abstract Background Bleeding risk scores have been created to identify patients with an increased bleeding risk, which could also be useful in dialysis patients. However, the predictive performances of these bleeding risk scores in dialysis patients are unknown. Therefore, the aim of this study was to validate existing bleeding risk scores in dialysis patients. Methods A cohort of 1745 incident dialysis patients was prospectively followed for 3 years during which bleeding events were registered. We evaluated the discriminative performance of the Hypertension, Abnormal kidney and liver function, Stroke, Bleeding, Labile INR, Elderly and Drugs or alcohol (HASBLED), the AnTicoagulation and Risk factors In Atrial fibrillation (ATRIA), the Hepatic or kidney disease, Ethanol abuse, Malignancy, Older age, Reduced platelet count or Reduced platelet function, Hypertension, Anaemia, Genetic factors, Excessive fall risk and Stroke (HEMORR2HAGES) and the Outcomes Registry for Better Informed Treatment (ORBIT) bleeding risk scores by calculating C-statistics with 95% confidence intervals (CI). In addition, calibration was evaluated by comparing predicted and observed risks. Results Of the 1745 dialysis patients, 183 patients had a bleeding event, corresponding to an incidence rate of 5.23/100 person-years. The HASBLED [C-statistic of 0.58 (95% CI 0.54–0.62)], ATRIA [C-statistic of 0.55 (95% CI 0.51–0.60)], HEMORR2HAGES [C-statistic of 0.56 (95% CI 0.52–0.61)] and ORBIT [C-statistic of 0.56 (95% CI 0.52–0.61)] risk scores had poor discriminative performances in dialysis patients. Furthermore, the calibration analyses showed that patients with a low risk of bleeding according to the HASBLED, ATRIA, HEMORR2HAGES and ORBIT bleeding risk scores had higher incidence rates for bleeding in our cohort than predicted. Conclusions The HASBLED, ATRIA, HEMORR2HAGES and ORBIT bleeding risk scores had poor predictive abilities in dialysis patients. Therefore, these bleeding risk scores may not be useful in this population.
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Molinari, Nicolas. "A U-statistic test in competing risk models." Comptes Rendus Mathematique 341, no. 5 (September 2005): 317–22. http://dx.doi.org/10.1016/j.crma.2005.07.016.
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Zhu, Hong Han. "Financial Market Risk Overflow Modeling and Inspection Based on Support Vector Machine." Applied Mechanics and Materials 571-572 (June 2014): 1189–94. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.1189.
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Combined granger test statistics based on VaR and CCF and machine learning theory to establish financial market risk overflow model of support vector machine. To analyze risk information overflow by the statistic characteristics of risk information overflow structure. The model can more effective to test variety forms of risk overflow, Main performance is the extreme risk for information received peripheral selectivity and market volatility non-stability. Emerging markets characteristics in A Shares is evident, the performance are the selective reception of outside extreme risk information. Empirical results demonstrate that models have certain value to the management and control of overflow risks in financial markets.
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Überla, K., W. Ahlborn, and H. J. Tuz. "Risk Analysis in Cohort Studies with Heterogeneous Strata. A Global χ2-Test for Dose-Response Relationship, Generalizing the Mantel-Haenszel Procedure." Methods of Information in Medicine 29, no. 02 (1990): 113–21. http://dx.doi.org/10.1055/s-0038-1636366.
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AbstractIn cohort studies the Mantel-Haenszel estimator OR̂ MH is computed from sample data and is used as a point estimator of relative risk. Test-based confidence intervals are estimated with the help of the asymptotic chi-squared distributed MH-statistic χ2 MHS . The Mantel-exten-sion-chi-squared is used as a test statistic for a dose-response relationship. Both test statistics – the Mantel-Haenszel-chi as well as the Mantel-extension-chi – assume homogeneity of risk across strata, which is rarely present. Also an extended nonparametric statistic, proposed by Terpstra, which is based on the Mann-Whitney-statistics assumes homogeneity of risk across strata.We have earlier defined four risk measures RR k j (k = 1,2,...,4) in the population and considered their estimates and the corresponding asymptotic distributions. In order to overcome the homogeneity assumption we use the δ-method to get “test-based” confidence intervals. Because the four risk measures RR k j are presented as functions of four weights gik we give, consequently, the asymptotic variances of these risk estimators also as functions of the weights g ik in a closed form. Approximations to these variances are given.For testing a dose-response relationship we propose a new class of χ2(1)-distributed global measures Ĝk and the corresponding global χ2-test. In contrast to the Mantel-extension-chi homogeneity of risk across strata must not be assumed. These global test statistics are of the Wald type for composite hypotheses. The Mantel-extension-chi is a special case of the global-chi statistic and it is further shown that the Mantel-extension-chi can be expressed as a special weighted Terpstra statistic. Formulas for computing estimators of the global measures are provided. Three elaborated examples with hypothetical data of varying structure show, that the Mantel-extension-chi is systematically biased, generally overestimates the dose-response relation and leads to wrong conclusions, when heterogeneity is present. This is consistent with our theoretical considerations. In case of heterogeneity, when one wants to test an association between exposure and effect, or a dose-response relationship, the new global-χ2-test should be used in cohort studies.
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McKian, Kevin P., Carol A. Reynolds, Daniel W. Visscher, Aziza Nassar, Derek C. Radisky, Robert A. Vierkant, Amy C. Degnim, et al. "Novel Breast Tissue Feature Strongly Associated With Risk of Breast Cancer." Journal of Clinical Oncology 27, no. 35 (December 10, 2009): 5893–98. http://dx.doi.org/10.1200/jco.2008.21.5079.
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Purpose Accurate, individualized risk prediction for breast cancer is lacking. Tissue-based features may help to stratify women into different risk levels. Breast lobules are the anatomic sites of origin of breast cancer. As women age, these lobular structures should regress, which results in reduced breast cancer risk. However, this does not occur in all women. Methods We have quantified the extent of lobule regression on a benign breast biopsy in 85 patients who developed breast cancer and 142 age-matched controls from the Mayo Benign Breast Disease Cohort, by determining number of acini per lobule and lobular area. We also calculated Gail model 5-year predicted risks for these women. Results There is a step-wise increase in breast cancer risk with increasing numbers of acini per lobule (P = .0004). Adjusting for Gail model score, parity, histology, and family history did not attenuate this association. Lobular area was similarly associated with risk. The Gail model estimates were associated with risk of breast cancer (P = .03). We examined the individual accuracy of these measures using the concordance (c) statistic. The Gail model c statistic was 0.60 (95% CI, 0.50 to 0.70); the acinar count c statistic was 0.65 (95% CI, 0.54 to 0.75). Combining acinar count and lobular area, the c statistic was 0.68 (95% CI, 0.58 to 0.78). Adding the Gail model to these measures did not improve the c statistic. Conclusion Novel, tissue-based features that reflect the status of a woman's normal breast lobules are associated with breast cancer risk. These features may offer a novel strategy for risk prediction.
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Gaprindashvili, George, Jianping Guo, Panisara Daorueang, Tian Xin, and Pooyan Rahimy. "A New Statistic Approach towards Landslide Hazard Risk Assessment." International Journal of Geosciences 05, no. 01 (2014): 38–49. http://dx.doi.org/10.4236/ijg.2014.51006.
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Vaziri, Sasha, Jacob Wilson, Joseph Abbatematteo, Paul Kubilis, Saptarshi Chakraborty, Khare Kshitij, and Daniel J. Hoh. "Predictive performance of the American College of Surgeons universal risk calculator in neurosurgical patients." Journal of Neurosurgery 128, no. 3 (March 2018): 942–47. http://dx.doi.org/10.3171/2016.11.jns161377.
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OBJECTIVEThe American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) universal Surgical Risk Calculator is an online decision-support tool that uses patient characteristics to estimate the risk of adverse postoperative events. Further validation of this risk calculator in the neurosurgical population is needed; therefore, the object of this study was to assess the predictive performance of the ACS NSQIP Surgical Risk Calculator in neurosurgical patients treated at a tertiary care center.METHODSA single-center retrospective review of 1006 neurosurgical patients treated in the period from September 2011 through December 2014 was performed. Individual patient characteristics were entered into the NSQIP calculator. Predicted complications were compared with actual occurrences identified through chart review and administrative quality coding data. Statistical models were used to assess the predictive performance of risk scores. Traditionally, an ideal risk prediction model demonstrates good calibration and strong discrimination when comparing predicted and observed events.RESULTSThe ACS NSQIP risk calculator demonstrated good calibration between predicted and observed risks of death (p = 0.102), surgical site infection (SSI; p = 0.099), and venous thromboembolism (VTE; p = 0.164) Alternatively, the risk calculator demonstrated a statistically significant lack of calibration between predicted and observed risk of pneumonia (p = 0.044), urinary tract infection (UTI; p < 0.001), return to the operating room (p < 0.001), and discharge to a rehabilitation or nursing facility (p < 0.001). The discriminative performance of the risk calculator was assessed using the c-statistic. Death (c-statistic 0.93), UTI (0.846), and pneumonia (0.862) demonstrated strong discriminative performance. Discharge to a rehabilitation facility or nursing home (c-statistic 0.794) and VTE (0.767) showed adequate discrimination. Return to the operating room (c-statistic 0.452) and SSI (0.556) demonstrated poor discriminative performance. The risk prediction model was both well calibrated and discriminative only for 30-day mortality.CONCLUSIONSThis study illustrates the importance of validating universal risk calculators in specialty-specific surgical populations. The ACS NSQIP Surgical Risk Calculator could be used as a decision-support tool for neurosurgical informed consent with respect to predicted mortality but was poorly predictive of other potential adverse events and clinical outcomes.
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Nimigean, Victor, Valentin Daniel Sîrbu, Vanda Roxana Nimigean, Lavinia Buţincu, Dan Ionuţ Sălăvăstru, Alexandru Poll, and Roxana Ivaşcu. "Statistic study regarding mandibular canal in dentate human specimens." Romanian Journal of Stomatology 61, no. 4 (December 31, 2015): 300–303. http://dx.doi.org/10.37897/rjs.2015.4.9.
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The major risk of dental implant treatment in the mandible is represented by the mandibular canal. Precise location of the mandibular canal is essential for oral rehabilitation with dental implants. The aim of this study was to analyze the topography of the mandibular canal in order to increase the long-term performance of oral rehabilitation with dental implants in „poor areas“. The topography of the mandibular canal was statistically studied on 11 human mandibles. The results obtained show similarities but also differences with data reported in other specialized references.
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Al-Marshadi, Ali Hussein, and Muhammad Aslam. "Statistical Analysis for Food Quality in the Presence of Vague Information." Journal of Food Quality 2021 (September 16, 2021): 1–5. http://dx.doi.org/10.1155/2021/7373620.
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The present study introduces the neutrosophic statistical test to see investigate the difference between variances of two populations when correlation exits in pair observations. The procedure and statistic of the proposed test under neutrosophic statistics are introduced in the paper. The application of the proposed test is given using the food industry data. The efficiency of the proposed test is compared with that of the existing test in terms of the measure of indeterminacy, flexibility, and information. From the real application and comparative studies, it is concluded that the proposed test is quite reasonable to apply in uncertainty.
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Li, Xiao-Zhou, Jin-Feng Wang, Wei-Zhong Yang, Zhong-Jie Li, and Sheng-Jie Lai. "A spatial scan statistic for nonisotropic two-level risk cluster." Statistics in Medicine 31, no. 2 (August 18, 2011): 177–87. http://dx.doi.org/10.1002/sim.4341.
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Mehrotra, Devan V., and Arthur J. Roth. "Relative risk estimation and inference using a generalized logrank statistic." Statistics in Medicine 20, no. 14 (2001): 2099–113. http://dx.doi.org/10.1002/sim.854.
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Saar, Aet, Kristi Läll, Maris Alver, Toomas Marandi, Tiia Ainla, Jaan Eha, Andres Metspalu, and Krista Fischer. "Estimating the performance of three cardiovascular disease risk scores: the Estonian Biobank cohort study." Journal of Epidemiology and Community Health 73, no. 3 (January 11, 2019): 272–77. http://dx.doi.org/10.1136/jech-2017-209965.
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BackgroundWe aim to investigate the predictive ability of PCE (Pooled Cohort Equations), QRISK2 and SCORE (Systematic COronary Risk Estimation) scoring systems for atherosclerotic cardiovascular disease (ASCVD) risk prediction in Estonia, a country with one of the highest ASCVD event rates in Europe.MethodsSeven-year risk estimates were calculated in risk score–specific subsets of the Estonian Biobank cohort. Calibration was assessed by standardised incidence ratios (SIRs) and discrimination by Harrell’s C-statistics. In addition, a head-to-head comparison of the scores was performed in the intersection of the three score-specific subcohorts.ResultsPCE, QRISK2 and SCORE risk estimates were calculated for 4356, 7191 and 3987 eligible individuals, respectively. During the 7-year follow-up, 220 hard ASCVD events (PCE outcome), 671 ASCVD events (QRISK2 outcome) and 94 ASCVD deaths (SCORE outcome) occurred among the score-specific subsets of the cohort. While PCE (SIR 1.03, 95% CI 0.90 to 1.18) and SCORE (SIR 0.99, 95% CI 0.81 to 1.21) were calibrated well for the cohort, QRISK2 underestimated the risk by 48% (SIR 0.52, 95% CI 0.48 to 0.56). In terms of discrimination, PCE (C-statistic 0.778) was inferior to QRISK2 (C-statistic 0.812) and SCORE (C-statistic 0.865). All three risk scores performed at similar level in the head-to-head comparison.ConclusionOf three widely used ASCVD risk scores, PCE and SCORE performed at acceptable level, while QRISK2 underestimated ASCVD risk markedly. These results highlight the need for evaluating the accuracy of ASCVD risk scores prior to use in high-risk populations.
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Carobbio, Alessandra, Elisabetta Antonioli, Alessandro M. Vannucchi, Federica Delaini, Vittoria Guerini, Guido Finazzi, Alessandro Rambaldi, and Tiziano Barbui. "Leukocytosis and Risk Stratification Assessment in Essential Thrombocythemia." Blood 110, no. 11 (November 16, 2007): 681. http://dx.doi.org/10.1182/blood.v110.11.681.681.
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Abstract Established risk factors for thrombosis in Essential Thrombocythemia (ET) including age and previous vascular events have been incorporated into algorithms for risk assessment in clinical trials. Our interest is now to refine this risk stratification by adding, to this established predictive model, leukocytosis, found to be a new risk factor for these events. For this purpose we used the C statistic estimate that defines, from a Cox multivariable model, the probability of concordance between leukocytosis and events among comparable patients during the time. C statistic values range from 0.5 (usefulness of the test) to 1 (perfect discriminatory test) and allow to evaluate the specificity and sensitivity of the test (leukocytosis) in analogy of the area under the curve (AUC) used for assessing the accuracy of the diagnostic tests. This analysis provides an assessment of the incremental role of leukocytosis, in addition to conventional risk factors, for discriminating ET patients with or without thrombosis. Finally, by a receiver operating characteristic (ROC) curve, we looked for the best cut-off of leukocytes to stratify patients into risk categories. During follow-up (median 4.5 years) 657 ET (PVSG and WHO diagnostic criteria) patients, 212 males, 445 females, median age 52 years (range 8–93), seen in two academic Italian institutions, had 72 major thrombotic events (28 venous, 44 arterial). Cox proportional hazard model was performed to analyse the thrombotic risk, adjusted for the following baseline variables: centre, sex, standard risk factors (age ≥ 60 years and/or prior thrombosis), hemoglobin, hematocrit, platelet, leukocytes and JAK2V617F allele burden. Results confirmed that age, prior events and leukocytosis are independent risk factors for thrombosis. Interestingly, a gradient between white blood cell (WBC) number and venous and arterial events was documented [Reference category: WBC <7.2 x109/L, WBC 7.2 − 8.7x109/L: RR=2.4, WBC 8.7 − 10.4x109/L: RR=2.7, WBC >10.4 x109/L: RR=3.0, all p-values <0.05]. On the contrary, no significant (p>0.05) association was found either for JAK2V617F allele burden [Reference category: JAK2V617F 0%, JAK2V617F 1–25%: RR=1.2, JAK2V617F 26–50%: RR=1.5, JAK2V617F >50%: RR=1.8] and for the other laboratory parameters. No centre-confounding effect was found. C statistics were then calculated on two Cox models for the prediction of major thrombosis in the follow-up of individual patients. The first model, including age over 60 years and/or prior thrombosis, showed a C statistic of 0.63. In the second one, by adding leukocytes at diagnosis, the C statistic was significantly improved (0.67). The best leukocyte cut-off values for predicting the events (ROC curve) ranged from 9.0 to 9.5 (x109/L), which corresponded to the highest sensitivity and specificity rates. In conclusion, we confirmed in this large retrospective cohort of ET patients that leukocytosis is an independent risk-factor for thrombosis. Moreover, we demonstrated by C statistic that leukocytosis has an incremental prognostic role in addition to conventional risk factors and found the best leukocytes cut-off able to discriminate between the group that will have thrombosis and the group that will not. These findings constitute a solid background to stratify patients in future clinical trials in ET.
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Lau, Kui Kai, Linxin Li, Ursula Schulz, Michela Simoni, Koon Ho Chan, Shu Leong Ho, Raymond Tak Fai Cheung, Wilhelm Küker, Henry Ka Fung Mak, and Peter M. Rothwell. "Total small vessel disease score and risk of recurrent stroke." Neurology 88, no. 24 (May 17, 2017): 2260–67. http://dx.doi.org/10.1212/wnl.0000000000004042.
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Objective:In patients with TIA and ischemic stroke, we validated the total small vessel disease (SVD) score by determining its prognostic value for recurrent stroke.Methods:Two independent prospective studies were conducted, one comprising predominantly Caucasian patients with TIA/ischemic stroke (Oxford Vascular Study [OXVASC]) and one predominantly Chinese patients with ischemic stroke (University of Hong Kong [HKU]). Cerebral MRI was performed and assessed for lacunes, microbleeds, white matter hyperintensities (WMH), and perivascular spaces (PVS). Predictive value of total SVD score for risk of recurrent stroke was determined and potential refinements considered.Results:In 2,002 patients with TIA/ischemic stroke (OXVASC n = 1,028, HKU n = 974, 6,924 patient-years follow-up), a higher score was associated with an increased risk of recurrent ischemic stroke (adjusted hazard ratio [HR] per unit increase: 1.32, 1.16–1.51, p < 0.0001; c statistic 0.61, 0.56–0.65, p < 0.0001) and intracerebral hemorrhage (ICH) (HR 1.54, 1.11–2.13, p = 0.009; c statistic 0.65, 0.54–0.76, p = 0.006). A higher score predicted recurrent stroke in SVD and non-SVD TIA/ischemic stroke subtypes (c statistic 0.67, 0.59–0.74, p < 0.0001 and 0.60, 0.55–0.65, p < 0.0001). Including burden of microbleeds and WMH and adjusting the cutoff of basal ganglia PVS potentially improved predictive power for ICH (c statistic 0.71, 0.60–0.81, phet = 0.45), but not for recurrent ischemic stroke (c statistic 0.60, 0.56–0.65, phet = 0.76) on internal validation.Conclusions:The total SVD score has predictive value for recurrent stroke after TIA/ischemic stroke. Prediction of recurrence in patients with nonlacunar events highlights the potential role of SVD in wider stroke etiology.
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van Walraven, Carl, Timothy D. Jackson, and Nick Daneman. "Derivation and Validation of the Surgical Site Infections Risk Model Using Health Administrative Data." Infection Control & Hospital Epidemiology 37, no. 4 (January 20, 2016): 455–65. http://dx.doi.org/10.1017/ice.2015.327.
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OBJECTIVESurgical site infections (SSIs) are common hospital-acquired infections. Tracking SSIs is important to monitor their incidence, and this process requires primary data collection. In this study, we derived and validated a method using health administrative data to predict the probability that a person who had surgery would develop an SSI within 30 days.METHODSAll patients enrolled in the National Surgical Quality Improvement Program (NSQIP) from 2 sites were linked to population-based administrative datasets in Ontario, Canada. We derived a multivariate model, stratified by surgical specialty, to determine the independent association of SSI status with patient and hospitalization covariates as well as physician claim codes. This SSI risk model was validated in 2 cohorts.RESULTSThe derivation cohort included 5,359 patients with a 30-day SSI incidence of 6.0% (n=118). The SSI risk model predicted the probability that a person had an SSI based on 7 covariates: index hospitalization diagnostic score; physician claims score; emergency visit diagnostic score; operation duration; surgical service; and potential SSI codes. More than 90% of patients had predicted SSI risks lower than 10%. In the derivation group, model discrimination and calibration was excellent (C statistic, 0.912; Hosmer-Lemeshow [H-L] statistic, P=.47). In the 2 validation groups, performance decreased slightly (C statistics, 0.853 and 0.812; H-L statistics, 26.4 [P=.0009] and 8.0 [P=.42]), but low-risk patients were accurately identified.CONCLUSIONHealth administrative data can effectively identify postoperative patients with a very low risk of surgical site infection within 30 days of their procedure. Records of higher-risk patients can be reviewed to confirm SSI status.Infect. Control Hosp. Epidemiol. 2016;37(4):455–465
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Yuan, Xiaoyong, Zheng Feng, Matthew Norton, and Xiaolin Li. "Generalized Batch Normalization: Towards Accelerating Deep Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1682–89. http://dx.doi.org/10.1609/aaai.v33i01.33011682.
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Utilizing recently introduced concepts from statistics and quantitative risk management, we present a general variant of Batch Normalization (BN) that offers accelerated convergence of Neural Network training compared to conventional BN. In general, we show that mean and standard deviation are not always the most appropriate choice for the centering and scaling procedure within the BN transformation, particularly if ReLU follows the normalization step. We present a Generalized Batch Normalization (GBN) transformation, which can utilize a variety of alternative deviation measures for scaling and statistics for centering, choices which naturally arise from the theory of generalized deviation measures and risk theory in general. When used in conjunction with the ReLU non-linearity, the underlying risk theory suggests natural, arguably optimal choices for the deviation measure and statistic. Utilizing the suggested deviation measure and statistic, we show experimentally that training is accelerated more so than with conventional BN, often with improved error rate as well. Overall, we propose a more flexible BN transformation supported by a complimentary theoretical framework that can potentially guide design choices.
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Hou, Peng, Xiaojian Yi, and Haiping Dong. "A Spatial Statistic Based Risk Assessment Approach to Prioritize the Pipeline Inspection of the Pipeline Network." Energies 13, no. 3 (February 5, 2020): 685. http://dx.doi.org/10.3390/en13030685.
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The identification of high risk regions is an important aim of risk-based inspections (RBIs) in pipeline networks. As the most vital part of risk-based inspections, risk assessment makes a significant contribution to achieving this aim. Accurate assessment can target high risk inspected regions so that limited resources can mitigate considerable risks in the face of increased spatial distribution of a pipeline network. However, the existing approaches for risk assessment face grave challenges due to a lack of sufficient data and an assessment’s vulnerability to human biases and errors. This paper attempts to tackle those challenges through spatial statistics, which is used to estimate the uncertainty of risk based on a dataset of locations of pipeline network failure events without having to acquire additional data. The consequence of risk in each inspected region is measured by the total cost caused by the failure events that have occurred in the region, which is also calculated in the assessment. Then, the risks of the different inspected regions are obtained by integrating the uncertainty and consequences. Finally, the feasibility of our approach is validated in a case study. Our results in the case study demonstrate that uncertainty is less instructive for prioritizing pipeline inspections than the consequences of risk due to the low significant difference in risk uncertainty in different regions. Our results also have implications for understanding the correlation between the spatial location and consequences of risk.
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Benkovskaya, Ludmila V. "STATISTIC INDICATORS OF THE ASSESSMENT OF RISK OF PRODUCTION OF GRAIN." Statistics and Economics, no. 3 (January 1, 2015): 151–56. http://dx.doi.org/10.21686/2500-3925-2015-3-151-156.
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Biswas, Biplab, Maidul Husain, and M. Shafiqur Rahman. "Review and evaluation of the concordance measures for assessing discrimination in the logistic regression methods." Journal of Statistical Research 53, no. 1 (August 1, 2019): 63–77. http://dx.doi.org/10.47302/jsr.2019530104.
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Concordance statistic (C-statistic), which is equivalent to the area under a receiver operating characteristic curve (AUC), is frequently used to quantify the discriminatory power (the ability of the model to distinguish low and high risk patient) of a risk prediction model developed in the logistic regression framework. Several methods for estimating concordance statistics including both non-parametric and parametric have been proposed in the literature. Despite the several proposals of the C-statistic, it is still unclear to the practical users which approaches should be applied in practice. This paper reviewed and evaluated some commonly used C-statistics by illustrating them using two datasets with different prognostic abilities and an extensive simulation study and compared their results to make some practical recommendations. Several simulation scenarios were considered by varying the sample size, prevalence of the binary outcome, and distribution of prognostic index (or log-odds) derived from the model, to mimic the scenarios in practice. The results revealed that both non-parametric and Kernel-smoothing based methods showed comparable results in most simulation scenarios but performed better than the parametric approach particularly for small sample situation and skewed distribution of the prognostic index. Based on the findings of the study, some practical recommendations are discussed.
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Merkow, Ryan P., Thomas E. Kmiecik, David J. Bentrem, Mark E. Cohen, Bruce L. Hall, Warren B. Chow, Clifford Y. Ko, and Karl Y. Bilimoria. "Do cancer-specific variables improve risk-adjusted hospital quality comparisons?" Journal of Clinical Oncology 30, no. 4_suppl (February 1, 2012): 585. http://dx.doi.org/10.1200/jco.2012.30.4_suppl.585.
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585 Background: For patients undergoing surgery for cancer, it has been suggested that risk-adjustment with cancer-specific variables is needed when evaluating short-term outcomes. Our objectives were to assess the influence of cancer-related variables on postoperative complications and hospital quality comparisons. Methods: Patients from ACS NSQIP and NCDB who underwent colorectal resection for cancer were linked (2006-2008) to create a dataset containing robust information on comorbidities, complications, and oncologic variables. Three hierarchical models were developed predicting the NSQIP outcome 30-day mortality or any serious morbidity using variables from (1) NSQIP only, (2) NCDB only, and (3) a combined model using NSQIP and NCDB. Models were compared with fit statistics and hospital outlier agreement. Results: From 146 NSQIP hospitals, 11401 patients underwent a colorectal resection for cancer, of which, 1954 (17%) experienced a mortality or serious morbidity event. The first five variables selected in the NCDB-only model were Charlson comorbidity score, neoadjuvant therapy use, T stage, primary payer, and M stage (c-statistic, 0.64; AIC, 9886). The first five variables selected in the NSQIP-only model were ASA class, preop sepsis, albumin, surgical procedure, and COPD (c-statistic, 0.66; AIC, 9787). In the combined model, neoadjuvant therapy use was the only cancer-specific variable selected in the top five. The remaining variables were ASA class, preop sepsis, albumin, and wound class (c-statistic, 0.67; AIC, 9455). At the hospital-level, the NCDB-only model identified three high outliers (worse than expected) and one low outlier (better than expected). Both the NSQIP-only and combined models identified the same four high and two low outlying hospitals (kappa: 1.0), which agreed marginally with the NCDB-only model (kappa: 0.59). Conclusions: Addition of cancer-specific variables to NSQIP models slightly improved model fit; however, hospital outcome comparisons were identical. For patients with colorectal cancer undergoing resection, cancer-related factors have limited predictive ability for short-term outcomes and did not influence hospital quality comparisons.
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Wada, Cicilia Yuko, Pranab Kumar Sen, and Silvia Emiko Shimakura. "A Bivariate Exponential Model with Covariates in Competing Risk Data." Calcutta Statistical Association Bulletin 46, no. 3-4 (September 1996): 197–210. http://dx.doi.org/10.1177/0008068319960305.
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Sarkar's bivariate exponential distribution is incorporated in a competing risk model with two causes of failure. In view of the nonidentifiability of the parameters of this distribution under competing risk, reparameterization is advocated and covariates are related to the reparameterized parameters through logistic and loglinear models. The restricted alternative hypothe,is is considered for the comparison of the survival distributions of the two causes of failure, the test statistic based on Roy's unionintersection principle is used and compared with the score test statistic. An application is also considered,
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Olchanski, Natalia, Joshua T. Cohen, Peter J. Neumann, John B. Wong, and David M. Kent. "Understanding the Value of Individualized Information: The Impact of Poor Calibration or Discrimination in Outcome Prediction Models." Medical Decision Making 37, no. 7 (April 11, 2017): 790–801. http://dx.doi.org/10.1177/0272989x17704855.
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Background. Risk prediction models allow for the incorporation of individualized risk and clinical effectiveness information to identify patients for whom therapy is most appropriate and cost-effective. This approach has the potential to identify inefficient (or harmful) care in subgroups at different risks, even when the overall results appear favorable. Here, we explore the value of personalized risk information and the factors that influence it. Methods. Using an expected value of individualized care (EVIC) framework, which monetizes the value of customizing care, we developed a general approach to calculate individualized incremental cost effectiveness ratios (ICERs) as a function of individual outcome risk. For a case study (tPA v. streptokinase to treat possible myocardial infarction), we used a simulation to explore how an EVIC is influenced by population outcome prevalence, model discrimination (c-statistic) and calibration, and willingness-to-pay (WTP) thresholds. Results. In our simulations, for well-calibrated models, which do not over- or underestimate predicted v. observed event risk, the EVIC ranged from $0 to $700 per person, with better discrimination (higher c-statistic values) yielding progressively higher EVIC values. For miscalibrated models, the EVIC ranged from −$600 to $600 in different simulated scenarios. The EVIC values decreased as discrimination improved from a c-statistic of 0.5 to 0.6, before becoming positive as the c-statistic reached values of ~0.8. Conclusions. Individualizing treatment decisions using risk may produce substantial value but also has the potential for net harm. Good model calibration ensures a non-negative EVIC. Improvements in discrimination generally increase the EVIC; however, when models are miscalibrated, greater discriminating power can paradoxically reduce the EVIC under some circumstances.
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Carobbio, Alessandra, Elisabetta Antonioli, Paola Guglielmelli, Alessandro M. Vannucchi, Federica Delaini, Vittoria Guerini, Guido Finazzi, Alessandro Rambaldi, and Tiziano Barbui. "Leukocytosis and Risk Stratification Assessment in Essential Thrombocythemia." Journal of Clinical Oncology 26, no. 16 (June 1, 2008): 2732–36. http://dx.doi.org/10.1200/jco.2007.15.3569.
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Purpose Established risk factors for thrombosis in essential thrombocythemia (ET) include age and previous vascular events. We aimed to refine this risk stratification by adding baseline leukocytosis. Patients and Methods We enrolled 657 patients with ET followed for a median of 4.5 years who developed 72 major thrombosis. Cox proportional hazard model was performed to analyze the thrombotic risk and to discriminate ET patients with or without thrombosis, multivariable C statistic index was used. We searched for leukocytes cutoff with the best sensitivity and specificity by a receiver operating characteristic curve. Results Results confirmed that age and prior events are independent risk factors for thrombosis and showed a gradient between baseline leukocytosis and thrombosis. On the contrary, no significant association was found either for JAK2V617F allele burden and for other laboratory parameters, including platelet number. In the model with conventional risk factors alone, C statistic ratio for total thrombosis was 0.63 and when leukocytosis was added, the change was small (C = 0.67). In contrast, in younger and asymptomatic patients (low-risk category), C statistic value indicated an high risk for thrombosis in patients with leukocytosis, similar to that calculated in conventionally defined high-risk group (C = 0.65). The best leukocyte cutoff values for predicting the events was found to be 9.4 (× 109/L). Conclusion We suggest to include baseline leukocytosis in the risk stratification of ET patients enrolled in clinical trials.
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Katona, Szilvia, and Imre Ertsey. "Simulation of optimizing decisions and risk analysis in investment plans." Acta Agraria Debreceniensis, no. 27 (November 15, 2007): 160–64. http://dx.doi.org/10.34101/actaagrar/27/3120.
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Investments always contain risks, as data referring to the future are planned and uncertain. Therefore, besides feasibility analyses we need to perform risk analyses, as well. Through statistic simulation methods, our aim is to examine how uncertain and prospective data as risk factors affect investment-profitability indices. On the other hand, our aim is to find out the optimal innovation – financing decisions by using decision optimizingmethods.
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Kleinstern, Geffen, J. Brice Weinberg, Sameer A. Parikh, Esteban Braggio, Dennis P. Robinson, Aaron D. Norman, Kari G. Rabe, et al. "Polygenic Risk Score and Risk of Chronic Lymphocytic Leukemia, Monoclonal B-Cell Lymphocytosis (MBL), and MBL Subtypes." Blood 136, Supplement 1 (November 5, 2020): 35–36. http://dx.doi.org/10.1182/blood-2020-136548.
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Background MBL is a precursor to chronic lymphocytic leukemia (CLL) and is subclassified into low-count (LC) MBL (absolute B-cell count<0.5x109/L) and high-count (HC) MBL (absolute B-cell count between 0.5 and 5x109/L). We previously reported that a polygenic risk score (PRS) based on a weighted average of 41 CLL-susceptibility variants was associated with risk of both MBL and CLL among a cohort of individuals from CLL families. Here we evaluate this PRS in an independent cohort of MBL and CLL individuals of European ancestry (EA), all of whom were ascertained agnostic to CLL family-history status. We also evaluate the PRS by MBL subtype (LC/HC), and in African American (AA) CLL cases and controls. Methods We genotyped 535 EA MBLs (139 HC-MBL, 396 LC-MBLs), 735 CLLs (640 EA, 95 AA), and 2,866 controls (2,631 EA, 235 AA) from the Mayo Clinic CLL Resource, Duke University, and Weill Cornell Medical College. We computed the CLL-PRS for each individual and used logistic regression to estimate odds ratios (OR) and 95% confidence intervals, adjusting for age and sex. To assess discriminatory accuracy, we computed the c-statistic. Among EA individuals, we calculated a trend test among LC-MBL, HC-MBL, and CLL risk using the P-value for heterogeneity from a polytomous logistic regression analysis. Moreover, we plotted a boxplot for the PRS among controls, LC-MBL, HC-MBL, and EA CLL, as well as for AA CLL cases and controls, and tested the statistical difference using the Kruskal Wallis test and Mann-Whitney test, respectively. Results We found a significant association of PRS with overall MBL risk (OR=1.87, P=1.1x10-28) with good discrimination (c-statistic=0.72). Significant associations were also found for LC-MBL (OR=1.75, P=7.5x10-19, c-statistic=0.72), HC-MBL (OR=2.22, P=1.4x10-17, c-statistic=0.74), and CLL of EA (OR=2.60, P=1.2x10-62, c-statistic=0.78), with a significant difference among these cohorts (Figure 1.A) and a significant positive trend across these cohorts (Pheterogeneity=8.4x10-6). Although we observed a 33% increased risk of CLL in AA (c-statistic=0.57), the PRS was borderline significant (P=0.07, Figure 1.B). Conclusion The CLL-PRS is a strong prediction-tool for risk of CLL and MBL among individuals of EA. Future studies are needed to improve the PRS for AAs including performing GWAS of AA in order to identify CLL-susceptibility SNPs that are more representative within known CLL loci and to discover novel CLL loci that are unique for AAs. Disclosures Parikh: GlaxoSmithKline: Honoraria; Janssen: Honoraria, Research Funding; Ascentage Pharma: Research Funding; AbbVie: Honoraria, Research Funding; Merck: Research Funding; TG Therapeutics: Research Funding; Genentech: Honoraria; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; AstraZeneca: Honoraria, Research Funding; Verastem Oncology: Honoraria. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Brander:Genentech: Consultancy, Honoraria, Other, Research Funding; Juno/Celgene/BMS: Other, Research Funding; MEI Pharma: Other, Research Funding; Ascentage: Other, Research Funding; ArQule: Consultancy, Other, Research Funding; NCCN: Other; Teva: Consultancy, Honoraria; Tolero: Research Funding; AstraZeneca: Consultancy, Honoraria, Other, Research Funding; Pharmacyclics LLC, an AbbVie Company: Consultancy, Honoraria, Other, Research Funding; Pfizer: Consultancy, Other; TG Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Novartis: Consultancy, Other; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Tolero: Research Funding; Teva: Consultancy, Honoraria; DTRM: Other, Research Funding; BeiGene: Other, Research Funding; Novartis: Consultancy, Other; NCCN: Other; Verastem: Consultancy, Honoraria, Other, Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding. Kay:Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; MEI Pharma: Research Funding; Abbvie: Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees. Furman:Acerta: Consultancy; AstraZeneca: Consultancy, Research Funding; Beigene: Consultancy; Abbvie: Consultancy; Pharmacyclics: Consultancy; Sunesis: Consultancy; TG Therapeutics: Consultancy, Research Funding; Verastem: Consultancy; Incyte: Consultancy; Genentech: Consultancy; Janssen: Consultancy, Speakers Bureau; Loxo Oncology: Consultancy; Oncotarget: Consultancy. Shanafelt:Mayo Clinic: Patents & Royalties: and other intellectual property; Genentech, Pharmacyclics LLC, an AbbVie Company, AbbVie, GlaxoSmithKline, and Merck: Research Funding.
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Lambie, Mark, Lucy Teece, David W. Johnson, Michaela Petrie, Robert Mactier, Ivonne Solis-Trapala, John Belcher, et al. "Estimating risk of encapsulating peritoneal sclerosis accounting for the competing risk of death." Nephrology Dialysis Transplantation 34, no. 9 (February 28, 2019): 1585–91. http://dx.doi.org/10.1093/ndt/gfz034.
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AbstractBackgroundRisk of encapsulating peritoneal sclerosis (EPS) is strongly associated with the duration of peritoneal dialysis (PD), such that patients who have been on PD for some time may consider elective transfer to haemodialysis to mitigate the risk of EPS. There is a need to determine this risk to better inform clinical decision making, but previous studies have not allowed for the competing risk of death.MethodsThis study included new adult PD patients in Australia and New Zealand (ANZ; 1990–2010) or Scotland (2000–08) followed until 2012. Age, time on PD, primary renal disease, gender, data set and diabetic status were evaluated as predictors at the start of PD, then at 3 and 5 years after starting PD using flexible parametric competing risks models.ResultsIn 17 396 patients (16 162 ANZ, 1234 Scotland), EPS was observed in 99 (0.57%) patients, less frequently in ANZ patients (n = 65; 0.4%) than in Scottish patients (n = 34; 2.8%). The estimated risk of EPS was much lower when the competing risk of death was taken into account (1 Kaplan–Meier = 0.0126, cumulative incidence function = 0.0054). Strong predictors of EPS included age, primary renal disease and time on PD. The risk of EPS was reasonably discriminated at the start of PD (C-statistic = 0.74–0.79) and this improved at 3 and 5 years after starting PD (C-statistic = 0.81–0.92).ConclusionsEPS risk estimates are lower when calculated using competing risk of death analyses. A patient’s estimated risk of EPS is country-specific and can be predicted using age, primary renal disease and duration of PD.
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Szmukler, G., B. Everitt, and M. Leese. "Risk assessment and receiver operating characteristic curves." Psychological Medicine 42, no. 5 (October 3, 2011): 895–98. http://dx.doi.org/10.1017/s003329171100208x.
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Risk assessment is now regarded as a necessary competence in psychiatry. The area under the curve (AUC) statistic of the receiver operating characteristic curve is increasingly offered as the main evidence for accuracy of risk assessment instruments. But, even a highly statistically significant AUC is of limited value in clinical practice.
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Tango, Toshiro. "Spatial scan statistics can be dangerous." Statistical Methods in Medical Research 30, no. 1 (January 2021): 75–86. http://dx.doi.org/10.1177/0962280220930562.
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Spatial scan statistics are widely used tools for the detection of disease clusters. Especially, the circular spatial scan statistic proposed by Kulldorff along with SaTScan software has been used in a wide variety of epidemiological studies and disease surveillance. However, as it cannot detect non-circular, irregularly shaped clusters, many authors have proposed non-circular spatial scan statistics. Above all, the flexible spatial scan statistic proposed by Tango and Takahashi along with FleXScan software has also been used. However, it does not seem to be well recognized that these spatial scan statistics, especially SaTScan, tend to detect the most likely cluster, much larger than the true cluster by absorbing neighboring regions with nonelevated risk of disease occurrence. Therefore, if researchers reported the detected most likely cluster as they are, it might lead to a criticism to them due to the fact that some regions with nonelevated risk are included in the detected most likely cluster. In this paper, to avoid detecting such undesirable and misleading clusters which might cause a social concern, we shall propose the use of the restricted likelihood ratio proposed by Tango and illustrate the procedure with two kinds of mortality data in Japan.
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Swanson, G. M. "Breast Cancer Risk Estimation: A Translational Statistic for Communication to the Public." JNCI Journal of the National Cancer Institute 85, no. 11 (June 2, 1993): 848–49. http://dx.doi.org/10.1093/jnci/85.11.848.
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Merkow, Ryan P., Bruce L. Hall, Mark E. Cohen, Justin B. Dimick, Edward Wang, Warren B. Chow, Clifford Y. Ko, and Karl Y. Bilimoria. "Relevance of the C-Statistic When Evaluating Risk-Adjustment Models in Surgery." Journal of the American College of Surgeons 214, no. 5 (May 2012): 822–30. http://dx.doi.org/10.1016/j.jamcollsurg.2011.12.041.
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Zhuo, Zirong, Jixiang Liu, and Wenmin Luo. "Credit Default Risk Assessment of Local Government Debts Based on KMV Model." International Journal of Economics and Finance 8, no. 5 (April 25, 2016): 230. http://dx.doi.org/10.5539/ijef.v8n5p230.
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With the continuing expansion of Chinese local government debts, its credit risk issues raise the public attention. According to the overall statistics data in Chinese Statistic Bureau, there’re various scales of debts exist, undertaken by Chinese prefecture-level cities’ local government. Some of them exceed the alerting level of international line. In an effort to measure the credit default risk level of Chinese local governments, this paper makes a moderate assessment of credit default risk based on modified KMV model. In conditions of a variety of local government revenue, this model calculates the distance from default and default possibility of local government debts under different guarantee proportion. Meanwhile, this paper also explores the variation of local governments’ credit default risk when they use different financial ratio of financing for the construction of urban infrastructure. Finally, we reach the conclusion that the expected default probability shrinks as guarantee proportion raises, and increases as financing proportion raises; under a 40% of guarantee proportion, expected default rates are low with controllable risks; And within a financing proportion of 50%, chances of default as well as risks, are low.
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Hong, Fangxin, Brad S. Kahl, and Robert Gray. "Incremental Value in Outcome Prediction with Molecular Signatures in Diffuse Large B-Cell Lymphoma,." Blood 118, no. 21 (November 18, 2011): 3687. http://dx.doi.org/10.1182/blood.v118.21.3687.3687.
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Abstract Abstract 3687 INTRODUCTION: Multiple gene expression-based biomarkers have been identified in diffuse large B-cell lymphoma (DLBCL) that are predictive for survival outcomes. Most studies assess predictive significance based on p-value from multivariate Cox regression; few investigations have evaluated the incremental usefulness of these biomarkers in risk prediction. Using the recently developed concordance measures (e.g., C-statistics) on censored survival data, we assessed the usefulness of two published gene-based risk signatures and compared them to the known clinical prognostic factors; with an overall goal of investigating the added value. METHOD: The added value of biomarkers was assessed by C-statistic and the Integrated Discrimination Improvement (IDI). The overall C-statistic is an estimated concordance between prediction and observation (event vs. non-event) - the probability that predicted risk score is higher for subject with earlier time of event. The IDI measures overall improvement in sensitivity and specificity. The signatures we selected are a six-gene predictor by Lossos et al. (2004) and a three-component signature (∼400 genes) by Lenz et al. (2008). We used the Lenz dataset which include 233 patients with DLBCL who received R-CHOP therapy (median follow-up=2.81 yr), and focused on predicting 3-year survival outcome (42% censored). Clinical prognostic factors evaluated are the traditional IPI components (stage, performance status, age, LDH, and number of extra nodal sites). RESULTS: The C-statistic was 0.60 and 0.721 for six-gene predictor and three-component signature, suggesting good discrimination ability by molecular signature when used alone. However, the performance is inferior to IPI risk factors, with a C-statistic of 0.733. When integrating gene signatures with IPI risk factors, the C-statistic was increased to 0.744 and 0.762, an improvement of only 0.011 (95% CI, -0.049 to 0.071) and 0.029 (95% CI, -0.033 to 0.091) for six-gene predictor and three-component signature, respectively. Furthermore, assessment by IDI reveals an added value of only 0.011 (95% CI, -0.008 to 0.081) and 0.076 (95% CI, 0.013 to 0.16) for the two molecular signatures. Kaplan-Meier survival curves for the four quartile groups based on the predictor scores confirms the marginal benefit in risk prediction using molecular signatures. (Figure 1). CONCLUSIONS: These results indicate that molecular biomarkers are inferior to clinical factors for risk assessment in DLBCL and provide little added value in risk prediction. These calculations suggest we will need to consider more than gene expression to develop highly discriminatory risk prediction models. However, the study of gene expression and clinical outcomes retains considerable potential to enhance understanding of disease mechanisms and uncover new therapeutic targets. Disclosures: No relevant conflicts of interest to declare.
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Solomon, Daniel H., Joel Kremer, Jeffrey R. Curtis, Marc C. Hochberg, George Reed, Peter Tsao, Michael E. Farkouh, Soko Setoguchi, and Jeffrey D. Greenberg. "Explaining the cardiovascular risk associated with rheumatoid arthritis: traditional risk factors versus markers of rheumatoid arthritis severity." Annals of the Rheumatic Diseases 69, no. 11 (May 5, 2010): 1920–25. http://dx.doi.org/10.1136/ard.2009.122226.
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BackgroundCardiovascular (CV) disease has a major impact on patients with rheumatoid arthritis (RA), however, the relative contributions of traditional CV risk factors and markers of RA severity are unclear. The authors examined the relative importance of traditional CV risk factors and RA markers in predicting CV events.MethodsA prospective longitudinal cohort study was conducted in the setting of the CORRONA registry in the USA. Baseline data from subjects with RA enrolled in the CORRONA registry were examined to determine predictors of CV outcomes, including myocardial infarction, stroke or transient ischemic attack. Possible predictors were of two types: traditional CV risk factors and markers of RA severity. The discriminatory value of these variables was assessed by calculating the area under the receiver operating characteristic curve (c-statistic) in logistic regression. The authors then assessed the incidence rate for CV events among subjects with an increasing number of traditional CV risk factors and/or RA severity markers.ResultsThe cohort consisted of 10 156 patients with RA followed for a median of 22 months. The authors observed 76 primary CV events during follow-up for a composite event rate of 3.98 (95% CI 3.08 to 4.88) per 1000 patient-years. The c-statistic improved from 0.57 for models with only CV risk factors to 0.67 for models with CV risk factors plus age and gender. The c-statistic improved further to 0.71 when markers of RA severity were also added. The incidence rate for CV events was 0 (95% CI 0 to 5.98) for persons without any CV risk factors or markers of RA severity, while in the group with two or more CV risk factors and three or more markers of RA severity the incidence was 7.47 (95% CI 4.21 to 10.73) per 1000 person-years.ConclusionsTraditional CV risk factors and markers of RA severity both contribute to models predicting CV events. Increasing numbers of both types of factors are associated with greater risk.
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Jackson, Sarah S., Surbhi Leekha, Lisa Pineles, Laurence S. Magder, Kerri A. Thom, Yuan Wang, and Anthony D. Harris. "Improving Risk Adjustment Above Current Centers for Disease Control and Prevention Methodology Using Electronically Available Comorbid Conditions." Infection Control & Hospital Epidemiology 37, no. 10 (July 15, 2016): 1173–78. http://dx.doi.org/10.1017/ice.2016.140.
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OBJECTIVETo identify comorbid conditions associated with surgical site infection (SSI) among patients undergoing renal transplantation and improve existing risk adjustment methodology used by the Centers for Disease Control and Prevention National Healthcare Safety Network (NHSN).PATIENTSPatients (≥18 years) who underwent renal transplantation at University of Maryland Medical Center January 1, 2010-December 31, 2011.METHODSTrained infection preventionists reviewed medical records to identify surgical site infections that developed within 30 days after transplantation, using NHSN criteria. Patient demographic characteristics and risk factors for surgical site infections were identified through a central data repository. International Statistical Classification of Disease, Ninth Revision, Clinical Modification codes were used to analyze individual component comorbid conditions and calculate the Charlson and Elixhauser comorbidity indices. These indices were compared with the current NHSN risk adjustment methodology.RESULTSA total of 441 patients were included in the final cohort. In bivariate analysis, the Charlson components of cerebrovascular disease, peripheral vascular disease, and rheumatologic disorders and Elixhauser components of obesity, rheumatoid arthritis, and weight loss were significantly associated with the outcome. A model utilizing the variables from the NHSN methodology had a c-statistic of 0.56 (95% CI, 0.48–0.63), whereas a model that also included comorbidities from the Charlson and Elixhauser indices had a c-statistic of 0.65 (95% CI, 0.58–0.73). The model with all 3 risk adjustment scores performed best and was statistically different from the NHSN model alone, demonstrated by improvement in the c statistic (0.65 vs 0.56).CONCLUSIONRisk adjustment models should incorporate electronically available comorbid conditions.Infect Control Hosp Epidemiol 2016;1–6
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Nam, Byung-Ho, Ami Yu, Sang Myung Woo, Hye-Ryung Yang, Jungnam Joo, Woo Jin Lee, and Sang Jae Park. "Development and validation of a predictive model to assess an individual's risk of pancreatic cancer." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 4045. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.4045.
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4045 Background: Due to the very low survival of pancreatic cancer (PC), early detection is a critical strategy to improve the outcome of PC.Screening individuals with genetic syndromes associated with a high incidence of PC or families predisposed to PC is increasing. However, those populations account for only 10% of all PC cases. A different approach for developing an effective surveillance tool is needed to identify high-risk individuals without hereditary risks. The goal of this study was to develop and validate risk prediction models for filtering purposes as part of the sporadic PC surveillance activities. Methods: Based on an eight-year follow-up of a cohort study involving 1,289,933 men and 557,701 women in Korea who had biennial examinations in 1996-1997, gender-specific risk prediction models were developed using the Cox proportional hazards model. The models were validated using independent data of 500,046 men and 627,629 women who had biennial examinations in 1998-1999. The models’ performance was evaluated with respect to their discrimination and calibration abilities based on the C-statistic and the Hosmer-Lemeshow (H-L) type χ2-statistic. Results: Age, height, BMI, fasting glucose, urine glucose, smoking, and age at smoking initiation were included in the model for men. In the model for women, height, BMI, fasting glucose, urine glucose, smoking, and drinking habits were included. Smoking was the most significant risk factor for developing pancreatic cancer in both men and women. Model validation showed excellent model performance with C-statistics (95% confidence interval) of 0.813 (0.800−0.826) and 0.804 (0.788−0.820) for men and women, respectively. The H-L type χ2-statistics (P-values) were 7.478 (0.587) and 10.297 (0.327) for men and women, respectively. Five different risk groups could be identified with hazard ratios (HR) greater than 20 in the highest risk group compared to the lowest risk group in both the men and women. Conclusions: Gender-specific individualized risk prediction models for PC were developed and validated with a high level of performance. These models can be used to identify high-risk individuals who may benefit from increased surveillance of PC.
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Oreyemi, A. B., R. A. Sanusi, L. O. Okojie, A. O. Olaiya, and D. Akerele. "Produce certification and income risk management strategies of cocoa farming households in South-West Nigeria." Applied Studies in Agribusiness and Commerce 9, no. 3 (September 30, 2015): 75–79. http://dx.doi.org/10.19041/apstract/2015/3/10.
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Agricultural produce certification is synonymous to farm assurance of which cocoa certification is an example; dealing with issues of Good Agricultural, Environmental and Social Practices (GAP, GEP and GSP) in cocoa production. Essentially, GAP, GEP and GSP packages had in-built mechanism that can aid farmers mitigate factors that could lead to farm income risks in cocoa production. Consequently, this study examined the influence of cocoa certification on income risks of cocoa farming households in South-west Nigeria. A multistage sampling technique was used to select 180 cocoa farming households from whose heads data were obtained with interview schedule in Southwest Nigeria. Data were analyzed with Chi-square Statistic, Income Risk Management Diversification Index (IRD) and Mann-Whitney-U Test Statistic. Chi-square analysis shows that (52.3%) certified cocoa farming households employed more risk management strategies than (94.2%) uncertified cocoa farming households (p<0.01). The Mann-Whitney-U test revealed a significant difference (p>0.05) between the income risk management practices of certified and uncertified cocoa farming households. Therefore, produce certification has been helping cocoa farming households in mitigating farm income risk in cocoa production through the employment of diverse (risk) management strategies. Hence, stakeholders should intensify efforts in encouraging farming households to embrace (cocoa) produce certification.
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Lakeman, Inge M. M., Mar Rodríguez-Girondo, Andrew Lee, Rikje Ruiter, Bruno H. Stricker, Sara R. A. Wijnant, Maryam Kavousi, et al. "Validation of the BOADICEA model and a 313-variant polygenic risk score for breast cancer risk prediction in a Dutch prospective cohort." Genetics in Medicine 22, no. 11 (July 6, 2020): 1803–11. http://dx.doi.org/10.1038/s41436-020-0884-4.
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Abstract Purpose We evaluated the performance of the recently extended Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA version 5) in a Dutch prospective cohort, using a polygenic risk score (PRS) based on 313 breast cancer (BC)–associated variants (PRS313) and other, nongenetic risk factors. Methods Since 1989, 6522 women without BC aged 45 or older of European descent have been included in the Rotterdam Study. The PRS313 was calculated per 1 SD in controls from the Breast Cancer Association Consortium (BCAC). Cox regression analysis was performed to estimate the association between the PRS313 and incident BC risk. Cumulative 10-year risks were calculated with BOADICEA including different sets of variables (age, risk factors and PRS313). C-statistics were used to evaluate discriminative ability. Results In total, 320 women developed BC. The PRS313 was significantly associated with BC (hazard ratio [HR] per SD of 1.56, 95% confidence interval [CI] [1.40–1.73]). Using 10-year risk estimates including age and the PRS313, other risk factors improved the discriminatory ability of the BOADICEA model marginally, from a C-statistic of 0.636 to 0.653. Conclusions The effect size of the PRS313 is highly reproducible in the Dutch population. Our results validate the BOADICEA v5 model for BC risk assessment in the Dutch general population.
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Qiu, Baoling, Dong WU, Qi Zhou, Dan Hong, Jian Pan, and Shaoyan Hu. "The Family Members of Mrps Expression and significance in Childhood Acute Lymphoblastic Leukemia." Blood 124, no. 21 (December 6, 2014): 5191. http://dx.doi.org/10.1182/blood.v124.21.5191.5191.
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Abstract Objective Multidrug resistance-associated proteins 1 to 6 have been reported involved in a large number of tumors and have a close correlation with tumor multi-drug resistance. In this work, we detect the MRP2-6 genes expression in childhood acute lymphoblastic leukemia (ALL) by Q-RT-PCR and explore their clinical significance. Methods 156 patients at different stages of ALL were enrolled in this study and treated by the protocol (CCLG-2008) during 2012 to 2013, including 67 cases at initial stage, 70 cases at complete remission, and 9 cases at relapse, 10 patients diagnosed as idiopathic thrombocytopenic purpura (ITP) as control. MRP1-6 genes’ expressions were detected using real-time quantitative PCR (QRT-PCR)and their clinical significance was analyzed by the SPSS software 16.0. P value below 0.05 was regarded as statistic significance. Results The median expression of MRP1 was 5.82 and 8.49 for initial and relapse group,respectively, which was statistic higher than that at complete remission, the latter was 1.99. MRP1 expression level had close correlation with ALL risk, the median of MRP1 expression was 4.28, 5.62 and 7.56 for standard-risk group (SR), intermediate-risk group (IR) and high-risk group (HR), respectively. Initial ALL children were divided into two groups including high expression group and low expression group by the median expression, the rate of sensitivity of blast cells to prednisone on 7th day was 70.6% in high expression group (n=34), which was statistic lower than that in low expression group which was 90.9% (n=33, P=0.035). The rate of complete remission on 33th day in high expression group was 64.7%, while 87.9% in low expression group, which showed a significant difference between them (P=0.026). The rate of complete remission on 15th day in high expression group was 68.8%, and 69.7% in low expression group, which showed no significance between them (P=0.664). The transcription level of MRP1 in initial group of T-ALL (median=7.71) was statistic higher than that in B-ALL (median=5.18) (P=0.007). Correlation analysis indicated that mRNA expression level of MRP1 didn’t show any relationship with gender, age, WBC count, hemoglobin, platelet and blast percentage in bone narrow and peripheral blood at diagnosis. The median expression of MRP2 at initial stage was highest, higher than that at relapse and complete remission, but did not reach statistic significance. However, the median expression of MRP3 at initial stage was highest and statistic higher than complete remission. MRP4 and MRP5 showed a similar pattern in their expression, namely, high expression for relapse, intermediate expression for initial stage and low expression at complete remission which reached statistic significance. The median expression of MRP6 for relapse was 2.003, which was higher than initial and complete remission group, but the differences among them were not significant. The median of MRP2 and MRP5 expression for intermediate-risk group (IR) (Median MRP2=4.622, Median MRP5=1.712) was higher than standard- risk group (SR) (Median MRP2=3.279, Median MRP5=1.277) and high risk group (HR) (Median MRP2=2.145, Median MRP5=1.673). However, there was no statistical significance among them. The median of MRP4 expression was increase continually with the risk of ALL, but did not reach statistical significance among them; the median of MRP6 expression for high-risk group (HR) was higher than standard-risk group (0.8812 vs 0.6205) and intermediate-risk group (0.8812 vs 0.4053), but the differences among them were not significant either. Initial ALL children were divided into two group including high expression group and low expression group by median expression, and evaluated their prediction on the treatment response on 7th, 15th and 33th day. The results revealed no significant difference between them, neither between B-ALL and T-ALL. Single factor analysis showed that MRP2 has relationship with platelet count at diagnosis and MRP4 has relationship with gender. Conclusions In children ALL, the expression of MRP1 is closely related with immunophenotyping, treatment response, hazard level and disease relapse which was a poor biomarker for ALL prognosis. MRP2-6 has a different expression pattern. MRP4 and MRP6 mRNA expression showed a close relation with relapse. Disclosures No relevant conflicts of interest to declare.
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Bakir, Saad T. "A Nonparametric Shewhart-Type Quality Control Chart for Monitoring Broad Changes in a Process Distribution." International Journal of Quality, Statistics, and Reliability 2012 (September 11, 2012): 1–10. http://dx.doi.org/10.1155/2012/147520.
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This paper develops a distribution-free (or nonparametric) Shewhart-type statistical quality control chart for detecting a broad change in the probability distribution of a process. The proposed chart is designed for grouped observations, and it requires the availability of a reference (or training) sample of observations taken when the process was operating in-control. The charting statistic is a modified version of the two-sample Kolmogorov-Smirnov test statistic that allows the exact calculation of the conditional average run length using the binomial distribution. Unlike the traditional distribution-based control charts (such as the Shewhart X-Bar), the proposed chart maintains the same control limits and the in-control average run length over the class of all (symmetric or asymmetric) continuous probability distributions. The proposed chart aims at monitoring a broad, rather than a one-parameter, change in a process distribution. Simulation studies show that the chart is more robust against increased skewness and/or outliers in the process output. Further, the proposed chart is shown to be more efficient than the Shewhart X-Bar chart when the underlying process distribution has tails heavier than those of the normal distribution.
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Tangri, Navdeep, Thomas W. Ferguson, Chris Wiebe, Frederick Eng, Michelle Nash, Brad C. Astor, Ngan N. Lam, et al. "Validation of the Kidney Failure Risk Equation in Kidney Transplant Recipients." Canadian Journal of Kidney Health and Disease 7 (January 2020): 205435812092262. http://dx.doi.org/10.1177/2054358120922627.
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Background: Predicting allograft failure in kidney transplant recipients can help plan renal replacement therapy and guide patient-provider communication. The kidney failure risk equation (KFRE) accurately predicts the need for dialysis in patients with chronic kidney disease (CKD), but has not been validated in kidney transplant recipients. Objective: We sought to validate the 4-variable KFRE (age, sex, estimated glomerular filtration rate [eGFR], and urine albumin-to-creatinine ratio [ACR]) for prediction of 2- and 5-year death-censored allograft failure. Design: Retrospective cohort study. Setting: Four independent North American Cohorts from Ontario, Canada; Alberta, Canada; Manitoba, Canada; and Wisconsin, United States, between January 1999 and December 2017. Patients: Adult kidney transplant patients at 1-year posttransplantation. Measurements: Kidney failure risk as measured by the KFRE (eGFR, urine ACR, age, and sex). Methods: We included all adult patients who had at least 1 serum creatinine and at least 1 urine ACR measurement approximately 1 year following kidney transplantation. The performance of the KFRE was evaluated using the area under the receiver operating characteristic curve (C-statistic). C-statistics from the 4 cohorts were meta-analyzed using random-effects models. Results: A total of 3659 patients were included. Pooled C-statistics were good in the entire population, at 0.81 (95% confidence interval: 0.72-0.91) for the 2-year KFRE and 0.73 (0.67-0.80) for the 5-year KFRE. Discrimination improved among patients with poorer kidney function (eGFR < 45 mL/min/1.73 m2), with a C-statistic of 0.88 (0.78-0.98) for the 2-year KFRE and 0.83 (0.74-0.91) for the 5-year KFRE. Limitations: The KFRE does not predict episodes of acute rejection and there was heterogeneity between cohorts. Conclusions: The KFRE accurately predicts kidney failure in kidney transplant recipients at 1-year posttransplantation. Further validation in larger cohorts with longer follow-up times can strengthen the case for clinical implementation.
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Miyata, T., S. Mii, H. Kumamaru, A. Takahashi, H. Miyata, K. Shigematsu, N. Azuma, et al. "Risk prediction model for early outcomes of revascularization for chronic limb-threatening ischaemia." British Journal of Surgery 108, no. 8 (March 6, 2021): 941–50. http://dx.doi.org/10.1093/bjs/znab036.
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Abstract Background Quantifying the risks and benefits of revascularization for chronic limb-threatening ischaemia (CLTI) is important. The aim of this study was to create a risk prediction model for treatment outcomes 30 days after revascularization in patients with CLTI. Methods Consecutive patients with CLTI who had undergone revascularization between 2013 and 2016 were collected from the JAPAN Critical Limb Ischemia Database (JCLIMB). The cohort was divided into a development and a validation cohort. In the development cohort, multivariable risk models were constructed to predict major amputation and/or death and major adverse limb events using least absolute shrinkage and selection operator logistic regression. This developed model was applied to the validation cohort and its performance was evaluated using c-statistic and calibration plots. Results Some 2906 patients were included in the analysis. The major amputation and/or mortality rate within 30 days of arterial reconstruction was 5.0 per cent (144 of 2906), and strong predictors were abnormal white blood cell count, emergency procedure, congestive heart failure, body temperature of 38°C or above, and hemodialysis. Conversely, moderate, low or no risk in the Geriatric Nutritional Risk Index (GNRI) and ambulatory status were associated with improved results. The c-statistic value was 0.82 with high prediction accuracy. The rate of major adverse limb events was 6.4 per cent (185 of 2906), and strong predictors were abnormal white blood cell count and body temperature of 38°C or above. Moderate, low or no risk in the GNRI, and age greater than 84 years were associated with improved results. The c-statistic value was 0.79, with high prediction accuracy. Conclusion This risk prediction model can help in deciding on the treatment strategy in patients with CLTI and serve as an index for evaluating the quality of each medical facility.
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Muller, David C., Mattias Johansson, and Paul Brennan. "Lung Cancer Risk Prediction Model Incorporating Lung Function: Development and Validation in the UK Biobank Prospective Cohort Study." Journal of Clinical Oncology 35, no. 8 (March 10, 2017): 861–69. http://dx.doi.org/10.1200/jco.2016.69.2467.
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Purpose Several lung cancer risk prediction models have been developed, but none to date have assessed the predictive ability of lung function in a population-based cohort. We sought to develop and internally validate a model incorporating lung function using data from the UK Biobank prospective cohort study. Methods This analysis included 502,321 participants without a previous diagnosis of lung cancer, predominantly between 40 and 70 years of age. We used flexible parametric survival models to estimate the 2-year probability of lung cancer, accounting for the competing risk of death. Models included predictors previously shown to be associated with lung cancer risk, including sex, variables related to smoking history and nicotine addiction, medical history, family history of lung cancer, and lung function (forced expiratory volume in 1 second [FEV1]). Results During accumulated follow-up of 1,469,518 person-years, there were 738 lung cancer diagnoses. A model incorporating all predictors had excellent discrimination (concordance (c)-statistic [95% CI] = 0.85 [0.82 to 0.87]). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected c-statistic = 0.84). The full model, including FEV1, also had modestly superior discriminatory power than one that was designed solely on the basis of questionnaire variables (c-statistic = 0.84 [0.82 to 0.86]; optimism-corrected c-statistic = 0.83; pFEV1 = 3.4 × 10−13). The full model had better discrimination than standard lung cancer screening eligibility criteria (c-statistic = 0.66 [0.64 to 0.69]). Conclusion A risk prediction model that includes lung function has strong predictive ability, which could improve eligibility criteria for lung cancer screening programs.
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Kuzmak, Olena, Oleh Kuzmak, Anna Tarasova, and Yana Buchkovska. "Present-day realities of risk management in the activity of Ukrainian banks." Banks and Bank Systems 13, no. 1 (April 13, 2018): 150–61. http://dx.doi.org/10.21511/bbs.13(1).2018.14.
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Modern development of banking business is connected with significant risks, which, taking into account globalization processes, political, economic problems in Ukraine and worldwide, development of technological and information systems, tend to transform, therefore it is very difficult to identify them and take preventive measures concerning their smoothing. Taking the abovementioned into account, it is reasonable to assess the modern state of risk management in the activity of Ukrainian banks and the influence on banking system development. For this purpose, the authors analyzed the performance of Ukrainian banks in the period 2017–2018 based on official statistic data of the National Bank of Ukraine and measures of economic standard of banking activity; studied the modern state of performing risk management in Ukrainian banks. The authors offer the process of effective organization of risk management system in national banks, which is a prerequisite for safe management of the bank. During the study, the authors found the significant decrease in the share of credits in total assets of Ukrainian banks and low quality of assets of Ukrainian banks during 2017–2018. This is caused by the significant amount of loan arrears, during the study period, the amount of loan arrears in 2016 increased by 36 times in comparison with 2008. The authors point to the need for improvement of assessment of banks’ riskiness, as a result of which they offer to use the methods of descriptive statistics for assessing risks and identifying them at all levels of banking activity.
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Story, D. A., M. Fink, K. Leslie, P. S. Myles, S. J. Yap, V. Beavis, R. K. Kerridge, and P. L. Mcnicol. "Perioperative Mortality Risk Score using Pre- and Post-operative Risk Factors in Older Patients." Anaesthesia and Intensive Care 37, no. 3 (May 2009): 392–98. http://dx.doi.org/10.1177/0310057x0903700310.
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We developed a risk score for 30-day postoperative mortality: the Perioperative Mortality risk score. We used a derivation cohort from a previous study of surgical patients aged 70 years or more at three large metropolitan teaching hospitals, using the significant risk factors for 30-day mortality from multivariate analysis. We summed the risk score for each of six factors creating an overall Perioperative Mortality score. We included 1012 patients and the 30-day mortality was 6%. The three preoperative factors and risk scores were (“three A's”): 1) age, years: 70 to 79=1, 80 to 89=3, 90+=6; 2) ASA physical status: ASA I or II=0, ASA III=3, ASA IV=6, ASA V=15; and 3) preoperative albumin <30 g/l=2.5. The three postoperative factors and risk scores were (“three I's”) 1) unplanned intensive care unit admission =4.0; 2) systemic inflammation =3; and 3) acute renal impairment=2.5. Scores and mortality were: <5=1%, 5 to 9.5=7% and ≥10=26%. We also used a preliminary validation cohort of 256 patients from a regional hospital. The area under the receiver operating characteristic curve (C-statistic) for the derivation cohort was 0.80 (95% CI 0.74 to 0.86) similar to the validation C-statistic: 0.79 (95% CI 0.70 to 0.88), P=0.88. The Hosmer-Lemeshow test (P=0.35) indicated good calibration in the validation cohort. The Perioperative Mortality score is straightforward and may assist progressive risk assessment and management during the perioperative period. Risk associated with surgical complexity and urgency could be added to this baseline patient factor Perioperative Mortality score.
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Kagan, E. S., K. N. Belogai, I. S. Morozova, Yu V. Borisenko, E. V. Evseenkova, S. G. Gutova, and A. A. Kindyakov. "The Approbation of Hopelessness Scale for Children (HLPS) on Non-Clinical Sample of Russian Students." Experimental Psychology (Russia) 13, no. 2 (2020): 210–23. http://dx.doi.org/10.17759/exppsy.2020130214.
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In this paper we analyze the suicide risk predictors among adolescents and consider interrelation between hopelessness and suicide risk to be especially important in this context. Also we provide the results of the empirical approbation of “Hopelessness Scale for Children” (HLPS) by А. Kazdin, A. Rodgers, D. Colbus on non-clinical sample of Russian students. 627 school and college students from Kuzbass region aged 13—18 years old participated in the study. Statistic analyses contained descriptive statistics analyses, correlation analyses, ANOVA, exploratory and confirmatory factor analysis, Student’s t-test. All analyses were performed with STATISTICA 10 and SPSS17. The statistics analyses helped to find among HLPS results two factors emerged using loadings greater than or equal to 0,30 to define the factors. The first factor includes items focused on negative self reflections and giving up. Second factor includes items phrased as positive reflections overall happiness as well as future expectations, which according to measure’s key in the lowest points may be interpreted as negative future expectations. Also the obtained factors and the summary hopelessness positively correlated with depression (Beck Depression Inventory, BDI) and mental stress (Nemchin’s Questionnaire), as well as personal and behavioral characteristics such as coping strategies, self esteem, and deviant tendencies among adolescents (Amirkchan’s Questionnaire). We have found the statistical differences in hopelessness between different samples of adolescents, such as school students, lyceum students and college students. Russian version of Hopelessness Scale for Children (HLPS) was proved to be valid and compact measurement for the adolescents screening. And its first factor — negative self reflections and giving up — has the greatest predictive value as predictor of depression and suicide risk among adolescents.
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Boeck, Lucas, Joan B. Soriano, Marjolein Brusse-Keizer, Francesco Blasi, Konstantinos Kostikas, Wim Boersma, Branislava Milenkovic, et al. "Prognostic assessment in COPD without lung function: the B-AE-D indices." European Respiratory Journal 47, no. 6 (April 21, 2016): 1635–44. http://dx.doi.org/10.1183/13993003.01485-2015.
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Several composite markers have been proposed for risk assessment in chronic obstructive pulmonary disease (COPD). However, choice of parameters and score complexity restrict clinical applicability. Our aim was to provide and validate a simplified COPD risk index independent of lung function.The PROMISE study (n=530) was used to develop a novel prognostic index. Index performance was assessed regarding 2-year COPD-related mortality and all-cause mortality. External validity was tested in stable and exacerbated COPD patients in the ProCOLD, COCOMICS and COMIC cohorts (total n=2988).Using a mixed clinical and statistical approach, body mass index (B), severe acute exacerbations of COPD frequency (AE), modified Medical Research Council dyspnoea severity (D) and copeptin (C) were identified as the most suitable simplified marker combination. 0, 1 or 2 points were assigned to each parameter and totalled to B-AE-D or B-AE-D-C. It was observed that B-AE-D and B-AE-D-C were at least as good as BODE (body mass index, airflow obstruction, dyspnoea, exercise capacity), ADO (age, dyspnoea, airflow obstruction) and DOSE (dyspnoea, obstruction, smoking, exacerbation) indices for predicting 2-year all-cause mortality (c-statistic: 0.74, 0.77, 0.69, 0.72 and 0.63, respectively; Hosmer–Lemeshow test all p>0.05). Both indices were COPD specific (c-statistic for predicting COPD-related 2-year mortality: 0.87 and 0.89, respectively). External validation of B-AE-D was performed in COCOMICS and COMIC (c-statistic for 1-year all-cause mortality: 0.68 and 0.74; c-statistic for 2-year all-cause mortality: 0.65 and 0.67; Hosmer–Lemeshow test all p>0.05).The B-AE-D index, plus copeptin if available, allows a simple and accurate assessment of COPD-related risk.
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Likun, Z., and Y. Ba. "A meta-analysis of bevacizumab plus interferon alfa for previously untreated patients with metastatic clear-cell renal carcinoma (mRCC)." Journal of Clinical Oncology 29, no. 7_suppl (March 1, 2011): 366. http://dx.doi.org/10.1200/jco.2011.29.7_suppl.366.
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366 Background: mRCC was highly resistant to conventional chemotherapy. Both CALGB study and AVOREN study failed to detect statistic differences in overall survival (OS). To evaluate the effectiveness and adverse-effect of bevacizumab plus IFN-α for untreated mRCC, we performed a meta-analysis. Methods: We searched RCTs that compared bevacizumab plus IFN- alpha with other treatments. For trials without heterogeneity, meta-analysis was carried out by using review manager (Revman 4.2) with fixed or random-effects models. Hazard ratio (HR), relative risk (RR), and 95 percent confidence intervals (CI) were calculated. The primary outcome was overall survival. The secondary outcomes were PFS and the adverse effects. Subgroup analyses were performed by MSKCC risk factors. Results: Two RCTs (CALGB study and AVOREN study) were identified as eligible for inclusion in our meta-analysis. Overall survival: Meta- analysis including 1,381 patients showed a trend in favor of bevacizumab plus IFN-alpha with an HR of 0.89 (95% CI, 0.80-1.00, p=0.05). Meta-analysis of OS which adjusted on stratification factors showed extent improvement in favor of bevacizumab plus IFN-alpha (HR=0.86, 95% CI, 0.76-0.97, p=0.02). Progress-free survival: Meta-analyses showed statistic differences in favor of bevacizumab plus IFN-alpha in both unstratified PFS (HR=0.68, 95% CI, 0.61-0.77, p<0.00001) and stratified PFS (HR=0.67, 95% CI, 0.59-0.75, p<0.00001). Subgroup analysis: There were no statistic differences between bevacizumab plus IFN-alpha and IFN-alpha in MSKCC favorable risk group (HR=0.91, 95% CI, 0.70-1.17, p=0.45) and MSKCC poor risk group (HR=0.79, 95% CI, 0.55-1.14, p=0.21). For MSKCC intermediate risk group, there was statistic difference in favor of bevacizumab plus IFN-alpha (HR=0.85, 95% CI, 0.73-0.99, p=0.04). Adverse effects: There were statistic differences in favor ofIFN-alpha in anorexia, fatigue proteinuria, and bleeding, while no statistic difference in gastrointestinal perforation. Conclusions: Bevacizumab plus IFN-alpha prolonged both OS and PFS as first line therapy for mRCC, especially for MSKCC intermediate risk group. The toxicity was acceptable and well tolerated. No significant financial relationships to disclose.