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Journal articles on the topic 'Hospital mortality'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 March 2023

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1

Crossingham, Iain. "Measuring Hospital Mortality." Acute Medicine Journal 12, no. 3 (July 1, 2013): 129–34. http://dx.doi.org/10.52964/amja.0304.

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The hospital standardised mortality ratio (HSMR) and the summary hospital mortality index (SHMI) are both in current use in the UK as measures of the performance of acute hospitals. Characteristics of both the acute hospital itself and of its local healthcare environment influence these indices. Whilst many hope that measures of mortality can be used as a surrogate for healthcare quality, this is an evolving area.

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2

Dubois, Robert W., William H. Rogers, John H. Moxley, David Draper, and Robert H. Brook. "Hospital Inpatient Mortality." New England Journal of Medicine 317, no. 26 (December 24, 1987): 1674–80. http://dx.doi.org/10.1056/nejm198712243172626.

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3

Green, Jesse. "Analyzing Hospital Mortality." JAMA 265, no. 14 (April 10, 1991): 1849. http://dx.doi.org/10.1001/jama.1991.03460140077030.

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4

Thengal, Dhuldev, Preeti Umate, and Mangala Shinde. "Determinants of Perinatal Mortality: A Hospital Based Study." Indian Journal of Obstetrics and Gynecology 5, no. 4 (2017): 490–95. http://dx.doi.org/10.21088/ijog.2321.1636.5417.8.

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5

Stewart, Kevin, Mohsin I. Choudry, and Rhona Buckingham. "Learning from hospital mortality." Clinical Medicine 16, no. 6 (December 2016): 530–34. http://dx.doi.org/10.7861/clinmedicine.16-6-530.

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6

Jacobson, B. "Hospital mortality league tables." BMJ 326, no. 7393 (April 12, 2003): 777–78. http://dx.doi.org/10.1136/bmj.326.7393.777.

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7

Iezzoni, Lisa I., Arlene S. Ash, Gerald A. Coffman, and Mark A. Moskowitz. "Predicting In-Hospital Mortality." Medical Care 30, no. 4 (April 1992): 347–59. http://dx.doi.org/10.1097/00005650-199204000-00005.

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8

Webster, G., and E. W. MD DrPH. "Hospital standardized mortality ratios." Canadian Medical Association Journal 179, no. 10 (November 4, 2008): 1036–37. http://dx.doi.org/10.1503/cmaj.1080101.

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9

Shojania, K. G., and A. J. Forster. "Hospital standardized mortality ratios." Canadian Medical Association Journal 179, no. 10 (November 4, 2008): 1037. http://dx.doi.org/10.1503/cmaj.1080102.

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10

Jencks, Stephen F. "Interpreting Hospital Mortality Data." JAMA 260, no. 24 (December 23, 1988): 3611. http://dx.doi.org/10.1001/jama.1988.03410240081036.

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11

Kahn, Katherine L. "Interpreting Hospital Mortality Data." JAMA 260, no. 24 (December 23, 1988): 3625. http://dx.doi.org/10.1001/jama.1988.03410240095038.

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12

Cheungpasitporn, Wisit, Charat Thongprayoon, Api Chewcharat, Tananchai Petnak, Michael A. Mao, Paul W. Davis, Tarun Bathini, Saraschandra Vallabhajosyula, Fawad Qureshi, and Stephen B. Erickson. "Hospital-Acquired Dysmagnesemia and In-Hospital Mortality." Medical Sciences 8, no. 3 (September 1, 2020): 37. http://dx.doi.org/10.3390/medsci8030037.

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Background and Objectives: This study aimed to report the incidence of hospital-acquired dysmagnesemia and its association with in-hospital mortality in adult general hospitalized patients. Materials and Methods: We studied 26,020 adult hospitalized patients from 2009 to 2013 who had normal admission serum magnesium levels and at least two serum magnesium measurements during hospitalization. The normal range of serum magnesium was 1.7–2.3 mg/dL. We categorized in-hospital serum magnesium levels based on the occurrence of hospital-acquired hypomagnesemia and/or hypermagnesemia. We assessed the association between hospital-acquired dysmagnesemia and in-hospital mortality using multivariable logistic regression. Results: 28% of patients developed hospital-acquired dysmagnesemia. Fifteen per cent had hospital-acquired hypomagnesemia only, 10% had hospital-acquired hypermagnesemia only, and 3% had both hospital-acquired hypomagnesemia and hypermagnesemia. Compared with patients with persistently normal serum magnesium levels in hospital, those with hospital-acquired hypomagnesemia only (OR 1.77; p < 0.001), hospital-acquired hypermagnesemia only (OR 2.31; p < 0.001), and both hospital-acquired hypomagnesemia and hypermagnesemia (OR 2.14; p < 0.001) were significantly associated with higher in-hospital mortality. Conclusions: Hospital-acquired dysmagnesemia affected approximately one-fourth of hospitalized patients. Hospital-acquired hypomagnesemia and hypermagnesemia were significantly associated with increased in-hospital mortality.

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13

van Lanschot, J. Jan B., Jan B. F. Hulscher, Christianne J. Buskens, Hugo W. Tilanus, Fiebo J. W. ten Kate, and Hugo Obertop. "Hospital volume and hospital mortality for esophagectomy." Cancer 91, no. 8 (2001): 1574–78. http://dx.doi.org/10.1002/1097-0142(20010415)91:8<1574::aid-cncr1168>3.0.co;2-2.

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14

Elia, Marinos. "Nutrition, hospital food and in-hospital mortality." Clinical Nutrition 28, no. 5 (October 2009): 481–83. http://dx.doi.org/10.1016/j.clnu.2009.06.010.

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15

Angood, Peter B. "Hospital Mortality and Leapfrog Hospital Survey Results." JAMA 302, no. 6 (August 12, 2009): 625. http://dx.doi.org/10.1001/jama.2009.1101.

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16

Pouw, Maurice E., Linda M. Peelen, Hester F. Lingsma, Daniel Pieter, Ewout Steyerberg, Cor J. Kalkman, and Karel G. M. Moons. "Hospital Standardized Mortality Ratio: Consequences of Adjusting Hospital Mortality with Indirect Standardization." PLoS ONE 8, no. 4 (April 9, 2013): e59160. http://dx.doi.org/10.1371/journal.pone.0059160.

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17

Kim, Yoon, Juhwan Oh, and Ashish Jha. "Contribution of hospital mortality variations to socioeconomic disparities in in-hospital mortality." BMJ Quality & Safety 23, no. 9 (March 7, 2014): 741–48. http://dx.doi.org/10.1136/bmjqs-2013-002744.

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18

Cross, DeWitte T., David L. Tirschwell, Mary Ann Clark, Dan Tuden, Colin P. Derdeyn, Christopher J. Moran, and Ralph G. Dacey. "Mortality rates after subarachnoid hemorrhage: variations according to hospital case volume in 18 states." Journal of Neurosurgery 99, no. 5 (November 2003): 810–17. http://dx.doi.org/10.3171/jns.2003.99.5.0810.

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Object. The goal of this study was to determine whether a hospital's volume of subarachnoid hemorrhage (SAH) cases affects mortality rates in patients with SAH. For certain serious illnesses and surgical procedures, outcome has been associated with hospital case volume. Subarachnoid hemorrhage, usually resulting from a ruptured cerebral aneurysm, yields a high mortality rate. There has been no multistate study of a diverse set of hospitals to determine whether in-hospital mortality rates are influenced by hospital volume of SAH cases. Methods. The authors conducted an analysis of a retrospective, administrative database of 16,399 hospitalizations for SAH (9290 admitted through emergency departments). These hospitalizations were from acute-care hospitals in 18 states representing 58% of the US population. Both univariate and multivariate analyses were used to assess the case volume—mortality rate relationship. The authors used patient age, sex, Medicaid status, hospital region, data source year, hospital case volume quartile, and a comorbidity index in multivariate generalized estimating equations to model the relationship between hospital volume and mortality rates after SAH. Patients with SAH who were treated in hospitals in which low volumes of patients with SAH are admitted through the emergency department had 1.4 times the odds of dying in the hospital (95% confidence interval 1.2–1.6) as patients admitted to high-volume hospitals after controlling for patient age, sex, Medicaid status, hospital region, database year, and comorbid conditions. Conclusions. Patients with a diagnosis of SAH on their discharge records who initially presented through the emergency department of a hospital with a high volume of SAH cases had significantly lower mortality rates. Concentrating care for this disease in high-volume SAH treatment centers may improve overall survival.

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19

Aziz, O., D. Fink, L. Hobbs L, G. Williams, and TC Holme. "Hospital mortality under surgical care." Annals of The Royal College of Surgeons of England 93, no. 3 (April 2011): 193–200. http://dx.doi.org/10.1308/003588411x563411.

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INTRODUCTION The ‘hospital standardised mortality ratio’ (HSMR) has been used in England since 1999 to measure NHS hospital performance. Large variations in reported HSMR between English hospitals have recently led to heavy criticism of their use as a surrogate measure of hospital performance. This paper aims to review the mortality data for a consultant general surgeon contributed by his NHS trust over a 3-year period as part of the trust's HSMR calculation and evaluate the accuracy of coding the diagnoses and covariates for case mix adjustment. SUBJECTS AND METHODS The Dr Foster Intelligence database was interrogated to extract the NHS trust's HSMR benchmark data on inpatient mortality for the surgeon from 1 April 2006 to 31 March 2009 and compared to the hospital notes. RESULTS 30 patients were identified of whom 12 had no evidence of being managed by the surgeon. This represents a potential 40% inaccuracy rate in designating consultant responsibility. The remaining 18 patients could be separated into ‘operative’ (11 patients) and ‘non-operative’ (7 patients) groups. Only 27% in the operative group and 43% of the non-operative mortality group respectively had a Charlson co-morbidity index recorded despite 94% of the cases having significant co-morbidities CONCLUSIONS Highlighting crude and inaccurate clinician-specific mortality data when only 1-5% of deaths under surgical care may be associated with avoidable adverse events seems potentially irresponsible.

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20

Gandhi, Rashmika. "Review of Maternal Mortality at A Tertiary Care Hospital." Indian Journal of Obstetrics and Gynecology 8, no. 2 (June 1, 2020): 67–63. http://dx.doi.org/10.21088/ijog.2321.1636.8220.12.

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21

Marshall, V. R. "IN-HOSPITAL MORTALITY AFTER TRANSURETHRAL PROSTATECTOMY IN VICTORIAN HOSPITALS." ANZ Journal of Surgery 70, no. 5 (May 6, 2000): 321. http://dx.doi.org/10.1046/j.1440-1622.2000.01818.x.

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22

Coe, Taylor M., Samuel E. Wilson, and David C. Chang. "Do past mortality rates predict future hospital mortality?" American Journal of Surgery 211, no. 1 (January 2016): 159–65. http://dx.doi.org/10.1016/j.amjsurg.2015.04.001.

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23

Coe, Taylor M., Samuel E. Wilson, and David C. Chang. "Do Past Mortality Rates Predict Future Hospital Mortality?" Journal of the American College of Surgeons 219, no. 3 (September 2014): S98—S99. http://dx.doi.org/10.1016/j.jamcollsurg.2014.07.235.

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24

Hernandez Fustes, Otto J., and Carlos Arteaga Rodriguez. "Stay Hospital and In-hospital Mortality by Stroke." Journal of Neuroscience Nursing 53, no. 5 (October 2021): 189. http://dx.doi.org/10.1097/jnn.0000000000000602.

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25

Kernisan, Leslie P. "Hospital Mortality and Leapfrog Hospital Survey Results—Reply." JAMA 302, no. 6 (August 12, 2009): 625. http://dx.doi.org/10.1001/jama.2009.1102.

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26

Sawayama, Yuichi, Kyohei Yamaji, Shun Kohsaka, Takashi Yamamoto, Yosuke Higo, Yohei Numasawa, Taku Inohara, et al. "Variation in in-hospital mortality and its association with percutaneous coronary intervention-related bleeding complications: A report from nationwide registry in Japan." PLOS ONE 16, no. 12 (December 13, 2021): e0261371. http://dx.doi.org/10.1371/journal.pone.0261371.

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Large-scale registries have demonstrated that in-hospital mortality after percutaneous coronary intervention (PCI) varies widely across institutions. However, whether this variation is related to major procedural complications (e.g., bleeding) is unclear. In this study, institutional variation in in-hospital mortality and its association with PCI-related bleeding complications were investigated. We analyzed 388,866 procedures at 718 hospitals performed from 2017 to 2018, using data from a nationwide PCI registry in Japan. Hospitals were stratified into quintiles according to risk-adjusted in-hospital mortality (very low, low, medium, high, and very high). Incidence of bleeding complications, defined as procedure-related bleeding events that required a blood transfusion, and in-hospital mortality in patients who developed bleeding complications were calculated for each quintile. Overall, 4,048 (1.04%) in-hospital deaths and 1,535 (0.39%) bleeding complications occurred. Among patients with bleeding complications, 270 (17.6%) died during hospitalization. In-hospital mortality ranged from 0.22% to 2.46% in very low to very high mortality hospitals. The rate of bleeding complications varied modestly from 0.27% to 0.57% (odds ratio, 1.95; 95% confidence interval, 1.58–2.39). However, mortality after bleeding complications markedly increased by quintile and was 6-fold higher in very high mortality hospitals than very low mortality hospitals (29.0% vs. 4.8%; odds ratio, 12.2; 95% confidence interval, 6.90–21.7). In conclusion, institutional variation in in-hospital mortality after PCI was associated with procedure-related bleeding complications, and this variation was largely driven by differences in mortality after bleeding complications rather than difference in their incidence. These findings underscore the importance of efforts toward reducing not only bleeding complications but also, even more importantly, subsequent mortality once they have occurred.

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27

Moundzika-Kibamba, JC, and F. L. Nakwa. "Neonatal mortality at Leratong Hospital." South African Journal of Child Health 12, no. 1 (April 11, 2018): 24. http://dx.doi.org/10.7196/sajch.2018.v12i1.1436.

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28

Mamytova, Elmira, Toktobay Maanaev, Dariha Bakaeva, and Khalida Musaeva. "Stroke-associated in-hospital mortality." Heart, Vessels and Transplantation 6, Issue 2 (June 11, 2022): 84. http://dx.doi.org/10.24969/hvt.2022.323.

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In this article, we narrated the epidemiological indicators of stroke in the world and in Kyrgyzstan as well. The data on morbidity, mortality and hospital mortality in acute cerebrovascular events, such as ischemic, hemorrhagic strokes, were shared.

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29

Kutner, Michael H. "Predictions of Hospital Mortality Rates." Annals of Internal Medicine 127, no. 9 (November 1, 1997): 846. http://dx.doi.org/10.7326/0003-4819-127-9-199711010-00017.

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30

Lamarche-Vadel, Agathe, Marcus Ngantcha, Marie-Annick Le Pogam, Walid Ghosn, Catherine Grenier, Laurence Meyer, and Grégoire Rey. "Hospital Comparisons Based on Mortality." Medical Care 53, no. 8 (August 2015): 736–42. http://dx.doi.org/10.1097/mlr.0000000000000376.

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31

Maddern, G., J. Smith, W. Babidge, and G. Guy. "Hospital mortality under surgical care." Annals of The Royal College of Surgeons of England 94, no. 1 (March 2012): 66. http://dx.doi.org/10.1308/003588412x13171221499900.

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32

McClish, Donna Katzman. "Prediction Model for Hospital Mortality." Critical Care Medicine 14, no. 4 (April 1986): 311. http://dx.doi.org/10.1097/00003246-198604000-00020.

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33

Soelch, Luke A., and Allen B. Repp. "Hospital Responses to Mortality Measures." Quality Management in Health Care 28, no. 2 (2019): 78–83. http://dx.doi.org/10.1097/qmh.0000000000000209.

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34

HOLTHOF, BRUNO, and PETER PRINS. "Comparing Hospital Perinatal Mortality Rates." Medical Care 31, no. 9 (September 1993): 801–7. http://dx.doi.org/10.1097/00005650-199309000-00005.

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35

SPLETE, HEIDI. "Mortality Doubles After Hospital Readmission." Caring for the Ages 10, no. 4 (April 2009): 7. http://dx.doi.org/10.1016/s1526-4114(09)60085-8.

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36

Bolsin, Stephen. "Hospital mortality and staff workload." Lancet 356, no. 9238 (October 2000): 1356. http://dx.doi.org/10.1016/s0140-6736(05)74268-9.

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37

Miró, Oscar, Miquel Sánchez, and José Millá. "Hospital mortality and staff workload." Lancet 356, no. 9238 (October 2000): 1356–57. http://dx.doi.org/10.1016/s0140-6736(05)74269-0.

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38

Hartz, Arthur J., Henry Krakauer, Evelyn M. Kuhn, Mark Young, Steven J. Jacobsen, Greer Gay, Larry Muenz, Myron Katzoff, R. Clifton Bailey, and Alfred A. Rimm. "Hospital Characteristics and Mortality Rates." New England Journal of Medicine 321, no. 25 (December 21, 1989): 1720–25. http://dx.doi.org/10.1056/nejm198912213212506.

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39

Jones, Rod. "Bed occupancy and hospital mortality." British Journal of Healthcare Management 22, no. 7 (July 2, 2016): 380–81. http://dx.doi.org/10.12968/bjhc.2016.22.7.380.

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40

Sills, Marion R., Anne M. Libby, and Heather D. Orton. "Prehospital and In-Hospital Mortality." Archives of Pediatrics & Adolescent Medicine 159, no. 7 (July 1, 2005): 665. http://dx.doi.org/10.1001/archpedi.159.7.665.

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41

Leung, Andrew. "Hospital Proximity and Mortality in Australia." Risks 7, no. 3 (July 17, 2019): 81. http://dx.doi.org/10.3390/risks7030081.

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It is intuitive that proximity to hospitals can only improve the chances of survival from a range of medical conditions. This study examines the empirical evidence for this assertion, based on Australian data. While hospital proximity might serve as a proxy for other factors, such as indigenity, income, wealth or geography, the evidence suggests that proximity provides the most direct link to these factors. In addition, as it turns out, a very statistically significant one that transcends economies.

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42

Wu, Xiaoting, Min Zhang, Ruyun Jin, Gary L. Grunkemeier, Charles Maynard, Ravi S. Hira, Todd MacKenzie, et al. "A Comparison of statistical methods for hospital performance assessment." Journal of Hospital Administration 10, no. 3 (May 24, 2021): 32. http://dx.doi.org/10.5430/jha.v10n3p32.

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During hospital quality improvement activities, statistical approaches are critical to help assess hospital performance for benchmarking. Current statistical approaches are used primarily for research and reimbursement purposes. In this multiinstitutional study, these established statistical methods were evaluated for quality improvement applications. Leveraging a dataset of 42,199 patients who underwent coronary artery bypass grafting surgery from 2014 to 2016 across 90 hospitals, six statistical approaches were applied. The non-shrinkage methods were: (1) indirect standardization without hospital effect; (2) indirect standardization with hospital fixed effect; (3) direct standardization with hospital fixed effect. The shrinkage methods were: (4) indirect standardization with hospital random effect; (5) direct standardization with hospital random effect; (6) Bayesian method. Hospital performance related to operative mortality and major morbidity or mortality was compared across methods based on variation in adjusted rates, rankings, and performance outliers. Method performance was evaluated across procedure volume terciles: small (< 96 cases/year), medium (96-171), and large (> 171). Shrinkage methods reduced inter-hospital variation (min-max) for mortality (observed: 0%-10%; adjusted: 1.5%-2.4%) and major morbidity or mortality (observed: 2.6%-35%; adjusted: 6.9%-17.5%). Shrinkage methods shrunk hospital rates toward the group mean. Direct standardization with hospital random effect, compared to fixed effect, resulted in 16.7%-38.9% of hospitals changing quintile mortality ranking. Indirect standardization with hospital random effect resulted in no performance outliers among small and medium hospitals for mortality, while logistic and fixed effect methods identified one small and three medium outlier hospitals. The choice of statistical method greatly impacts hospital ranking and performance outlier’ status. These findings should be considered when benchmarking hospital performance for hospital quality improvement activities.

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43

Pérez-Cuevas, Ricardo, Saúl Eduardo Contreras-Sánchez, Svetlana V. Doubova, Sebastián García-Saisó, Odet Sarabia-González, Paulina Pacheco-Estrello, and Alexandra Arias-Mendoza. "Gaps between supply and demand of acute myocardial infarction treatment in Mexico." Salud Pública de México 62, no. 5, sep-oct (August 29, 2020): 540–49. http://dx.doi.org/10.21149/11032.

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Objective. To analyze acute myocardial infarction (AMI) admissions and in-hospital mortality rates and evaluate the competence of the Ministry of Health (MOH) hospitals to provide AMI treatment. Materials and methods. We used a mixed-methods approach: 1) Joinpoint analysis of hospitalizations and in-hospital mortality trends between 2005 and 2017; 2) a nation-wide cross-sectional MOH hospital survey. Results. AMI hospitalizations are increasing among men and patients aged >60 years; women have higher mortality rates. The survey included 527 hospitals (2nd level =471; 3rd level =56). We identified insufficient competence to diagnose AMI (2nd level 37%, 3rd level 51%), perform pharmacological perfusion (2nd level 8.7%, 3rd level 26.8%), and mechanical reperfusion (2nd level 2.8%, 3rd level 17.9%). Conclusions. There are wide disparities in demand, supply, and health outcomes of AMI in Mexico. It is advisable to build up the competence with gender and age perspectives in order to diagnose and manage AMI and reduce AMI mortality effectively.

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44

Jones, Daryl A. "Long term mortality of medical emergency team patients in regional Australia." Critical Care and Resuscitation 24, no. 2 (June 6, 2022): 100–101. http://dx.doi.org/10.51893/2022.2.e.

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Medical emergency teams (METs) have been introduced into hospitals worldwide to improve the recognition of and response to deteriorating hospitalised patients. Australia was an early adopter of this model ofcare, 1 which is now mandatory and linked to hospital accreditation.

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45

Jiménez-Puente, A., F. Rivas-Ruíz, and J. Agulló-García. "Variability in hospital mortality prior to admission in Spanish hospitals." Revista Clínica Española (English Edition) 213, no. 4 (May 2013): 194–99. http://dx.doi.org/10.1016/j.rceng.2013.04.001.

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46

Grubb, N. R., K. A. A. Fox, and R. A. Elton. "In-hospital mortality after out-of-hospital cardiac arrest." Lancet 346, no. 8972 (August 1995): 417–21. http://dx.doi.org/10.1016/s0140-6736(95)92784-0.

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47

Shahsavari, Dariush, Adam C. Ehrlich, Bryan Zoll, Zubair A. Malik, and Henry P. Parkman. "Sa1086 - Hospital Admissions for Gastroparesis: Demographics and Hospital Mortality." Gastroenterology 154, no. 6 (May 2018): S—236. http://dx.doi.org/10.1016/s0016-5085(18)31167-3.

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48

Wübker, Ansgar, and Christiane Wuckel. "The Impact of Private for-Profit Hospital Ownership on Costs and Quality of Care – Evidence from Germany." CESifo Economic Studies 65, no. 4 (April 14, 2019): 373–401. http://dx.doi.org/10.1093/cesifo/ifz005.

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Abstract What is the impact of private for-profit (PfP) hospital ownership on costs and quality of care? In light of a substantial and increasing share of PfP hospitals in many hospital markets like the USA or Germany, this is an important question. We estimate the effect of PfP ownership on hospital 30-day- and 1-year-mortality outcomes and hospital costs by focusing on heart attacks and pneumonia, two very common conditions in healthcare markets. We use rich administrative hospital data from Germany for the years 2006–2015. Applying differential distance as instrument for hospital choice, we imitate randomization of patients into PfP hospitals. Our results suggest that PfP hospitals have no higher mortality rates for heart attack treatment than public ones. For pneumonia patients, we even find lower 30-day-mortality rates of PfP hospitals compared to public hospitals. Finally, we show that PfP hospitals have higher hospital costs than public or private not-for-profit hospitals for both conditions.

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49

Alali, Aziz S., David Gomez, Victoria McCredie, Todd G. Mainprize, and Avery B. Nathens. "Understanding Hospital Volume–Outcome Relationship in Severe Traumatic Brain Injury." Neurosurgery 80, no. 4 (January 28, 2017): 534–42. http://dx.doi.org/10.1093/neuros/nyw098.

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Abstract BACKGROUND: The hospital volume–outcome relationship in severe traumatic brain injury (TBI) population remains unclear. OBJECTIVE: To examine the relationship between volume of patients with severe TBI per hospital and in-hospital mortality, major complications, and mortality following a major complication (ie, failure to rescue). METHODS: In a multicenter cohort study, data on 9255 adults with severe TBI were derived from 111 hospitals participating in the American College of Surgeons Trauma Quality Improvement Program over 2009-2011. Hospitals were ranked into quartiles based on their volume of severe TBI during the study period. Random-intercept multilevel models were used to examine the association between hospital quartile of severe TBI volume and in-hospital mortality, major complications, and mortality following a major complication after adjusting for patient and hospital characteristics. In sensitivity analyses, we examined these associations after excluding transferred cases. RESULTS: Overall mortality was 37.2% (n = 3447). Two thousand ninety-eight patients (22.7%) suffered from 1 or more major complication. Among patients with major complications, 27.8% (n = 583) died. Higher-volume hospitals were associated with lower mortality; the adjusted odds ratio of death was 0.50 (95% confidence interval: 0.29-0.85) in the highest volume quartile compared to the lowest. There was no significant association between hospital-volume quartile and the odds of a major complication or the odds of death following a major complication. After excluding transferred cases, similar results were found. CONCLUSION: High-volume hospitals might be associated with lower in-hospital mortality following severe TBI. However, this mortality reduction was not associated with lower risk of major complications or death following a major complication.

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50

Martsevich, S. Yu, M. L. Ginsburg, N. P. Kutishenko, A. D. Deev, A. V. Fokina, and E. V. Daniels. "Lyubertsy Study of mortality in patients with acute myocardial infarction (LIS): the analysis of anamnestic predictors of in-hospital mortality." Cardiovascular Therapy and Prevention 11, no. 1 (February 20, 2012): 45–48. http://dx.doi.org/10.15829/1728-8800-2012-1-45-48.

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Abstract:

Aim. To identify the main anamnestic predictors of mortality in the acute phase of acute myocardial infarction (AMI). Material and methods. The study included all patients admitted to Lyubertsy District hospitals and diagnosed with AMI (n=1133). Results. Out of 1133 hospitalised patients, 172 died in the hospital; in-hospital lethality was 15,2%. Mean age of diseased patients was significantly higher than that in those survived. The risk of in-hospital death was significantly and independently associated with older age (relative risk 1,07). After adjustment for age and sex, other independent predictors of in-hospital AMI death included diabetes mellitus (DM), low physical activity, and selected psychosocial factors. Conclusion. The in-hospital lethality levels, observed in the LIS Study, were typical for the Russian Federation. The main anamnestic predictors of in-hospital death were low physical activity, DM, and psychosocial risk factors.

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