Relevant bibliographies by topics / Comorbidity

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Comorbidity'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 July 2025

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Journal articles on the topic "Comorbidity"

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Ronzano, Francesco, Alba Gutiérrez-Sacristán, and Laura I. Furlong. "Comorbidity4j: a tool for interactive analysis of disease comorbidities over large patient datasets." Bioinformatics 35, no. 18 (January 25, 2019): 3530–32. http://dx.doi.org/10.1093/bioinformatics/btz061.

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Abstract Summary Pushed by the growing availability of Electronic Health Records for data mining, the identification of relevant patterns of co-occurring diseases over a population of individuals—referred to as comorbidity analysis—has become a common practice due to its great impact on life expectancy, quality of life and healthcare costs. In this scenario, the availability of scalable, easy-to-use software frameworks tailored to support the study of comorbidities over large datasets of patients is essential. We introduce Comorbidity4j, an open-source Java tool to perform systematic analyses of comorbidities by generating interactive Web visualizations to explore and refine results. Comorbidity4j processes user-provided clinical data by identifying significant disease co-occurrences and computing a comprehensive set of comorbidity indices. Patients can be stratified by sex, age and user-defined criteria. Comorbidity4j supports the analysis of the temporal directionality and the sex ratio of diseases. The incremental upload and validation of clinical input data and the customization of comorbidity analyses are performed by an interactive Web interface. With a Web browser, the results of such analyses can be filtered with respect to comorbidity indexes and disease names and explored by means of heat maps and network charts of disease associations. Comorbidity4j is optimized to efficiently process large datasets of clinical data. Besides a software tool for local execution, we provide Comorbidity4j as a Web service to enable users to perform online comorbidity analyses. Availability and implementation Doc: http://comorbidity4j.readthedocs.io/; Source code: https://github.com/fra82/comorbidity4j, Web tool: http://comorbidity.eu/comorbidity4web/.
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Angold, Adrian, E. Jane Costello, and Alaattin Erkanli. "Comorbidity." Journal of Child Psychology and Psychiatry 40, no. 1 (January 1999): 57–87. http://dx.doi.org/10.1111/1469-7610.00424.

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S Malhi, Anthony S Hale, Gin. "Comorbidity." International Journal of Psychiatry in Clinical Practice 4, no. 2 (January 2000): 163–64. http://dx.doi.org/10.1080/13651500050518370.

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Schmits, E., E. Quertemont, P. Maurage, Y. Briane, P. de Timary, and D. Grynberg. "COMORBIDITY." Alcohol and Alcoholism 48, suppl 1 (August 12, 2013): i49—i50. http://dx.doi.org/10.1093/alcalc/agt115.

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Saravay, Stephen M. "Comorbidity." JAMA 296, no. 2 (July 12, 2006): 223. http://dx.doi.org/10.1001/jama.296.2.226.

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Sari, Yeni Kartika, Thatit Nurmawati, Joko Ivnu Santoso, and Maratus Sholichah Fitriah Hajar Kusnianto. "Comorbidity and Fatality Among Covid Patients: A Hospital Based-Retrospective Cohort Study." Jurnal Ners dan Kebidanan (Journal of Ners and Midwifery) 10, no. 2 (September 5, 2023): 210–16. http://dx.doi.org/10.26699/jnk.v10i2.art.p210-216.

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People with comorbid diseases have a high risk of contracting COVID-19, because the immune system has decreased, thus increasing the risk of transmission of COVID-19 and even potentially increasing the risk of fatality. This study aimed to determine the comorbidity and mortality among COVID-19 confirmed cases during May to September, 2021. The study was a retrospective cohort with secondary data from the COVID-19 with in Hospital report from May to September, 2021, with a total sample of 178. We extracted demographic and clinical data, including hospital outcomes (discharge or death). The result of the research showed that the respondents separated in to two groups, half of them are patients with comorbidity (89 patients) and the rest are patients non comorbidity. From 89 patients with comorbidity, 62 % were died and 38 % were discharged. The most comorbidites were 67,4% diabetes mellitus, 33% hypertension. The fatality rate of COVID-19 patients was 34% devided to 61,8% were comorbidity patients and 5,5% were non comorbidity. The comorbidity related to fatality rate of COVID-19 patient’s (p=0.000). It is important for society to avoid and control comorbid factors of COVID-19
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Tulkinovna, Isanova Shoira. "Modern Views Of Obesity –Comorbidity." American Journal of Medical Sciences and Pharmaceutical Research 02, no. 08 (August 20, 2020): 27–36. http://dx.doi.org/10.37547/tajmspr/volume02issue08-04.

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Kaneko, Takeshi. "3. Comorbidity." Nihon Naika Gakkai Zasshi 104, no. 6 (2015): 1089–97. http://dx.doi.org/10.2169/naika.104.1089.

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Chrischilles, Elizabeth, Kathleen Schneider, June Wilwert, Gregory Lessman, Brian O’Donnell, Brian Gryzlak, Kara Wright, and Robert Wallace. "Beyond Comorbidity." Medical Care 52 (March 2014): S75—S84. http://dx.doi.org/10.1097/mlr.0000000000000026.

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Watt, Fiona E. "Comorbidity: arthritis." Maturitas 124 (June 2019): 123–24. http://dx.doi.org/10.1016/j.maturitas.2019.04.046.

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Dissertations / Theses on the topic "Comorbidity"

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Carmichael, Duncan Andrew. "Synaesthesia and comorbidity." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/15796.

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Synaesthesia is a hereditary, neurological condition in which common stimuli trigger unexpected secondary sensations. For example, reading letters may result in the visualisation of colour, a variant known as grapheme-colour synaesthesia. While synaesthesia is thought to confer a range of benefits such as improved memory, empathy, visual search and creativity to the synaesthete, there is a small, yet growing, body of evidence that suggests synaesthesia may also be associated with more clinical conditions. This thesis investigates potential associations between synaesthesia and a range of clinical conditions, identifying a set of cormorbidities, and exploring the possible genetic roots of these associations. First, I identified an increased prevalence of multiple sclerosis (MS) and its clinical precursor, radiologically isolated syndrome (RIS) in synaesthetes self-referring for participation in scientific studies. Furthermore, I identified an increased occurrence of anxiety disorder in randomly sampled synaesthetes. In addition, I show that synaesthetes with anxiety disorder experience reduced luminance in their synaesthetic colours. I also conducted an association study into the genetic origins of synaesthesia and propose the immune hypothesis of synaesthesia, which provides a theoretical basis for comorbidities (linked to the altered cortical connectivity thought to underlie the development of synaesthesia). Finally, in phenotyping synaesthesia in individuals, I also validated the most widely used online test for synaesthesia, and use this test to provide a reliable prevalence of grapheme-colour synaesthesia in the general population. Such baselines are important for establishing whether other (e.g., clinical) populations are showing rates of synaesthesia higher than otherwise expected. I also demonstrate there is no significant difference in grapheme-colour synaesthesia prevalence between the sexes and discuss its implications for genetic theories of synaesthesia.
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Ortiz-Dominguez, Tania Abigail. "Migraine comorbidity in bipolar disorder." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=116105.

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Introduction: Bipolar Disorder (BD) is a chronic mental illness associated with functional decline, mortality, and significant health care costs; furthermore, specific general medical conditions have been found to occur disproportionately within BD patient populations, among them, migraine is one of the most studied. Migraine has a global prevalence of 10%, and it is a disorder with elevated direct and indirect costs, the later mostly derived from its association with mood and anxiety disorders. Specifically, the reported prevalence of migraine in the BD population ranges from 24.8% to 39.8%, rates that are considerable higher than those found in the general population.<br>Objective: To explore the prevalence and clinical characteristics of BD patients with and without migraine (Study 1), and to examine the psychiatric comorbidity in patients suffering from migraine (Study 2).<br>Methods: 323 BD patients were studied, using SADS-L and SCID as diagnostic interviews, and ill-Migraine questionnaire to assess the presence of migraine. Statistical analyses were conducted using parametric analysis and the development of log-linear models. Additionally, 102 migraine patients were interviewed using SADS-L, and the descriptive characteristics of the sample were analyzed.<br>Results: For Study 1, we found that 24.5% of BD patients suffer from migraine, and it is significantly associated with BD 2, suicidal behaviour, and a variety of anxiety disorders. As well, over 70% of migraine patients showed a lifetime psychiatric diagnosis, mainly within the spheres of mood and anxiety disorders; specifically, the prevalence of BD among migraine patients was 12.7%.<br>Conclusions: Our study highlights the high prevalence of migraine among BD patients, and the elevated prevalence of psychiatric comorbidity among migraine sufferers. The study of this comorbidity will deepen our understanding of the mechanisms that underlie both disorders and provide a better framework for the developing of molecular techniques to further analyze the molecular physiopathology of Bipolar Disorder.
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Keeley, Jared Wayne Blashfield Roger K. "Clinicians' conceptual use of comorbidity." Auburn, Ala, 2009. http://hdl.handle.net/10415/1709.

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Pashkovskyy, V. M. "Comorbidity in neurological and mental disorders." Thesis, БДМУ, 2020. http://dspace.bsmu.edu.ua:8080/xmlui/handle/123456789/17640.

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Nika, O. M. "Comorbidity of migraine and psychiatric disorders." Thesis, БДМУ, 2021. http://dspace.bsmu.edu.ua:8080/xmlui/handle/123456789/18745.

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Gilbert, Elizabeth. "The Validity of Summary Comorbidity Measures." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/382997.

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Statistics<br>Ph.D.<br>Prognostic scores, and more specifically comorbidity scores, are important and widely used measures in the health care field and in health services research. A comorbidity is an existing disease an individual has in addition to a primary condition of interest, such as cancer. A comorbidity score is a summary score that can be created from these individual comorbidities for prognostic purposes, as well as for confounding adjustment. Despite their widespread use, the properties of and conditions under which comorbidity scores are valid dimension reduction tools in statistical models is largely unknown. This dissertation explores the use of summary comorbidity measures in statistical models. Three particular aspects are examined. First, it is shown that, under standard conditions, the predictive ability of these summary comorbidity measures remains as accurate as the individual comorbidities in regression models, which can include factors such as treatment variables and additional covariates. However, these results are only true when no interaction exists between the individual comorbidities and any additional covariate. The use of summary comorbidity measures in the presence of such interactions leads to biased results. Second, it is shown that these measures are also valid in the causal inference framework through confounding adjustment in estimating treatment effects. Lastly, we introduce a time dependent extension of summary comorbidity scores. This time dependent score can account for changes in patients' health over time and is shown to be a more accurate predictor of patient outcomes. A data example using breast cancer data from the SEER Medicare Database is used throughout this dissertation to illustrate the application of these results to the health care field.<br>Temple University--Theses
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Wittchen, Hans-Ulrich. "Implications of comorbidity: lessons from epidemiological studies." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-99635.

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Objective: The paper discusses conceptual, methodological and clinical issues of comorbidity from the perspective of more recent epidemiological studies. Further the potential causal and pathogenic role of temporally primary disorders for the onset of secondary disorders is evaluated. Results: The available data suggest so far that comorbidity (a) is not an artefact of assessment strategies, sampling or design features, (b) is specific in different disorders, (c) is particularly frequent in anxiety and affective disorders, (d) affects systematically the course of the comorbid conditions and (0 might be related to symptom progression models. Conclusions: Furthermore, evidence is presented that specific forms of primary anxiety disorders affect the risk for secondary depressive disorders, increase the likelihood of non-remission as well as the number of subsequent depressive episodes<br>Scopo: Lo studio affronta problemi concettuali, metodologici e clinici della comorbidità, alia luce dei piu recenti studi epidemiologici. Inoltre viene valutata la causa potenziale e il ruolo patogenetico dei disturbi temporaneamente primari per la comparsa dei disturbi secondari. Risultati: I risultati disponibili fino ad oggi suggeriscono che la comorbidità (a) non è un artefatto delle strategic di valutazione, del campionamento o del disegno, (b) è specifica nei differenti disturbi, (c) e particolarmente frequente nei disturbi affettivi e d'ansia, (d) influisce sistematicamente sull'andamento delle condizioni di comorbidita è (f) potrebbe essere correlata con modelli di evoluzione dei sintomi. Conclusioni: È inoltre evidente che forme specifiche di disturbi di ansia primaria comportano il rischio di sviluppare disturbi depressivi. secondari, aumentano la probability di non remissione cosi come il numero di successivi episodi depressivi
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Wittchen, Hans-Ulrich. "Implications of comorbidity: lessons from epidemiological studies." Il Pensiero Scientifico Ed, 1996. https://tud.qucosa.de/id/qucosa%3A26249.

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Objective: The paper discusses conceptual, methodological and clinical issues of comorbidity from the perspective of more recent epidemiological studies. Further the potential causal and pathogenic role of temporally primary disorders for the onset of secondary disorders is evaluated. Results: The available data suggest so far that comorbidity (a) is not an artefact of assessment strategies, sampling or design features, (b) is specific in different disorders, (c) is particularly frequent in anxiety and affective disorders, (d) affects systematically the course of the comorbid conditions and (0 might be related to symptom progression models. Conclusions: Furthermore, evidence is presented that specific forms of primary anxiety disorders affect the risk for secondary depressive disorders, increase the likelihood of non-remission as well as the number of subsequent depressive episodes.<br>Scopo: Lo studio affronta problemi concettuali, metodologici e clinici della comorbidità, alia luce dei piu recenti studi epidemiologici. Inoltre viene valutata la causa potenziale e il ruolo patogenetico dei disturbi temporaneamente primari per la comparsa dei disturbi secondari. Risultati: I risultati disponibili fino ad oggi suggeriscono che la comorbidità (a) non è un artefatto delle strategic di valutazione, del campionamento o del disegno, (b) è specifica nei differenti disturbi, (c) e particolarmente frequente nei disturbi affettivi e d'ansia, (d) influisce sistematicamente sull'andamento delle condizioni di comorbidita è (f) potrebbe essere correlata con modelli di evoluzione dei sintomi. Conclusioni: È inoltre evidente che forme specifiche di disturbi di ansia primaria comportano il rischio di sviluppare disturbi depressivi. secondari, aumentano la probability di non remissione cosi come il numero di successivi episodi depressivi.
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Herasymiuk, L. G. "Comorbidity of recurrent depressive disorder and insomnia." Thesis, БДМУ, 2021. http://dspace.bsmu.edu.ua:8080/xmlui/handle/123456789/18743.

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Musacchio, Katherine, Brandy Anders, Wallace E. Jr Dixon, and Jaima S. Price. "Infant Indicators of ADHD and Overweight Comorbidity." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etsu-works/4912.

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Books on the topic "Comorbidity"

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Brown, Rhonda, and Einar Thorsteinsson, eds. Comorbidity. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-32545-9.

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Schoenen, Jean, David W. Dodick, and Peter S. Sándor, eds. Comorbidity in Migraine. Oxford, UK: Wiley-Blackwell, 2011. http://dx.doi.org/10.1002/9781444394047.

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Schoenen, Jean. Comorbidity in migraine. Chichester, West Sussex, UK: Wiley-Blackwell, 2011.

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El Miedany, Yasser, ed. Comorbidity in Rheumatic Diseases. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59963-2.

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-U, Wittchen H., ed. Comorbidity of mood disorders. London: Royal College of Psychiatrists, 1996.

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Petry, Nancy M. Pathological gambling: Etiology, comorbidity, and treatment. Washington: American Psychological Association, 2005. http://dx.doi.org/10.1037/10894-000.

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Lee, Andrew G., Yi-Hsien Renee Yeh, Ashwini Kini, and Bayan Al Othman. Referred Comorbidity Diplopia in Geriatric Patients. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25945-7.

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B, Lipton Richard, and Silberstein Stephen D, eds. Neurologic and psychiatric comorbidity with migraine. New York: Advanstar Communications, 1994.

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D, Maser Jack, and Cloninger C. Robert, eds. Comorbidity of mood and anxiety disorders. Washington, DC: American Psychiatric Press, 1990.

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S, Miller Norman, and Stimmel Barry 1939-, eds. Comorbidity of addictive and psychiatric disorders. New York: Haworth Press, 1993.

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Book chapters on the topic "Comorbidity"

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Brown, Rhonda, and Einar Thorsteinsson. "Comorbidity: What Is It and Why Is It Important?" In Comorbidity, 1–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32545-9_1.

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Brown, Rhonda, and Einar Thorsteinsson. "Models of Comorbidity." In Comorbidity, 23–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32545-9_2.

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Brown, Rhonda, and Yasmine Umar. "Overweight/Obesity and Concurrent Disorders, Symptoms, Behaviour, and Body Temperature." In Comorbidity, 43–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32545-9_3.

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Nolan, Christopher J. "Overview of the Comorbidity Between Medical Illnesses and Overweight/Obesity." In Comorbidity, 79–114. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32545-9_4.

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Birmingham, C. Laird. "Comorbid Eating Disorders." In Comorbidity, 115–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32545-9_5.

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Thorsteinsson, Einar, and Rhonda Brown. "Comorbid Psychiatric Illnesses." In Comorbidity, 139–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32545-9_6.

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Brown, Rhonda, and Einar Thorsteinsson. "Arousal States, Symptoms, Behaviour, Sleep and Body Temperature." In Comorbidity, 179–219. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32545-9_7.

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Thorsteinsson, Einar, and Rhonda Brown. "Design, Statistical and Methodological Considerations: Comorbidity." In Comorbidity, 221–39. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32545-9_8.

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Brown, Rhonda, and Einar Thorsteinsson. "Typing It All Together." In Comorbidity, 241–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32545-9_9.

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Fountoulakis, Kostas N. "Comorbidity." In Bipolar Disorder, 225–340. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-37216-2_10.

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Conference papers on the topic "Comorbidity"

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Mukhlif, Dheyauldeen M., Dhafar Hamed Abd, Ridha Ejbali, Adel M. Alimi, and Mohammed Fadhil Mahdi. "Enhancing Comorbidity Diagnosis with Adversarial Ensemble Learning." In 2024 17th International Conference on Development in eSystem Engineering (DeSE), 381–86. IEEE, 2024. https://doi.org/10.1109/dese63988.2024.10912032.

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Permana, Angga Aditya, David Agustriawan, Wirawan Istiono, Analekta Tiara Perdana, Yaman Khaeruzzaman, Reynard Matthew Yaputra, Hans Philemon Limaza, Gilbert Evan Tilung, Sharone Angelica Jovans, and Farel Arden Jani. "Comorbidity Disease Identification of Diabetes Insipidus Using Graph Network Algorithms." In 2024 10th International Conference on Computing, Engineering and Design (ICCED), 1–7. IEEE, 2024. https://doi.org/10.1109/icced64257.2024.10983467.

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Näslund-Koch, Charlotte, Hannah Kaiser*, and Lone Skov. "Comorbidity in adult psoriasis." In 6th IFPA-WPPAC 2021. Baarn, the Netherlands: Medicom Medical Publishers, 2023. http://dx.doi.org/10.55788/723a4c4c.

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Kun’ain, Utin Ilma Agni, Setyo Sri Rahardjo, and Didik Gunawan Tamtomo. "Meta-Analysis: The Effect of Diabetes Mellitus Comorbidity on the Severity in Patients with Covid-19 Infection." In The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.05.57.

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Background: Previous studies reported that adults with certain comorbid conditions are at increased risk for severe illness of Covid-19. The purpose of this meta-analysis study was to assess the effect of diabetes mellitus comorbidity on the severity in patients with Covid-19 infection. Subjects and Method: Meta-analysis and systematic review was conducted by collecting articles from Pubmed, Google Scholar, and Springer Link online databases, from 2019 to 2020. Keywords used “COVID-19 and comorbidity”, “COVID-19 and severity and diabetes”, “comorbidity or severity or diabetes or adjusted odd ratio”. The inclusion criteria were full text, using English and Indonesian language, using observational study, primary study in hospital. The study population (P) was COVID-19 patients. Intervention (I) was diabetes mellitus with comparison (C) without diabetes mellitus. The study outcome (O) was risk of severity. The data were analyzed using random effect model in Revman 5.3. Results: There were 6 studies from South Korea, United States, and China. This study had high heterogeneity (I2= 65%, p= 0.01). DM comorbidity increased the risk of severity 2.74 times in patients with comorbidity than those without DM comorbidity (aOR=2.74; 95% CI= 1.37 to 5.48; p=0.004). Conclusion: Diabetes mellitus comorbidity increases the risk of severity in patients with COVID-19. Keywords: COVID-19, severity, diabetes mellitus, comorbidity Correspondence: Utin Ilma Agni Kun’ain. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java. Email: utinilmaagni8p@gmail.com Mobile: +628111044542. DOI: https://doi.org/10.26911/the7thicph.05.57
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STEFANIS, C. N., D. B. DIKEOS, and G. N. PAPADIMITRIOU. "COMORBIDITY AND MULTIFORMITY IN SCHIZOPHRENIA." In IX World Congress of Psychiatry. WORLD SCIENTIFIC, 1994. http://dx.doi.org/10.1142/9789814440912_0098.

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Darapaneni, Narayana, Michael Arul, Anwesh Reddy Paduri, Vignesh Meenakshisundaram, Prashanth Srinivasan, Najeen Joseph, and Deepak Sharma. "Comorbidity Impact on COVID-19." In 2020 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT). IEEE, 2020. http://dx.doi.org/10.1109/icmlant50963.2020.9355994.

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Gelfand, Joel, and Susanne Kammerer. "New insights into psoriasis comorbidity." In 2022 American Academy of Dermatology Annual Meeting, edited by Peter van de Kerkhof. Baarn, the Netherlands: Medicom Medical Publishers, 2022. http://dx.doi.org/10.55788/b8220ab9.

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Gelfand, Joel, and Susanne Kammerer. "New insights into psoriasis comorbidity." In 2022 American Academy of Dermatology Annual Meeting, edited by Peter van de Kerkhof. Baarn, the Netherlands: Medicom Medical Publishers, 2022. http://dx.doi.org/10.55788/83564db4.

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Boiculese, Lucian v., Laura mihaela Trandafir, and Mihaela Moscalu. "SENSITIVITY AND SPECIFICITY AFFECTED BY COVARIATES (FACTORS) - ONE WAY TO ANALYZE." In eLSE 2017. Carol I National Defence University Publishing House, 2017. http://dx.doi.org/10.12753/2066-026x-17-247.

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Taking into account the high frequency of elderly patients affected by colorectal cancer and the increased prevalence of comorbidities, there have been created different classification systems for the prediction of postoperative results which depends on the number and type of comorbidities. In order to be useful in clinical practice, a system has to be easily useable, to allow comparisons amongst patients, and to offer a good estimation of risk based on the preoperative score, for a correct evaluation of patients who will probably best benefit from the surgery. The Charlson comorbidity index (CCI) is widely used in the surgical fields. It comprises of a weighted scoring system based on 19 comorbidities. The absolute obtained number can be used for comparison and evaluation of the risk. The Elixhauser method is a more recent model which utilises the risk adjustment using 31 conditions. The comorbidities' evaluation index for adults (ACE-27) was developed by modifying the Kaplan-Feinstein comorbidity index (KFI). The modifications have been made following discussions with experts regarding clinical aspects and a review of literature. Only a few studies had compared the results about comorbidities index in estimating mortality after a colorectal cancer surgery. Recent studies compared Charlson comorbidity index and the Elixhauser method or ACE-27. Their aim was to investigate each comorbidity's impact and their prognostic ability in colorectal cancer regarding the survival causes. This study compares the punctual obtained estimations in accordance with the number of comorbidities and the adjusted indexes for the well-known preoperative risk factors, for a complete evaluation of the preoperative risk. We have evaluated the prediction capacity by ROC analysis and choose the cutoff for maximum accuracy. The results were not acceptable and the need of a proper score is mandatory. More than this by logistic regression (multiple regression) we found that sensitivity and specificity depends on other covariate like age and TNM stage. Therefore we have improved the prediction of death by a score that take into account beside Charlson score other covariates.
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Messer, T. "Therapeutic drug monitoring (TDM) and comorbidity." In XIIIth Symposium of the Task Force Therapeutic Drug Monitoring of the AGNP. Georg Thieme Verlag KG, 2018. http://dx.doi.org/10.1055/s-0038-1649538.

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Reports on the topic "Comorbidity"

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Kalinina, E. V., E. V. Krivospitskaya, A. R. Babaeva, and M. S. Zvonorenko. COMORBIDITY IN PATIENTS WITH RHEUMATOID ARTHRITIS. "PLANET", 2019. http://dx.doi.org/10.18411/978-5-907192-54-6-2019-xxxvi-112-119.

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Krebs, Erin E. Post-Traumatic Stress Disorder and Pain Comorbidity in Veterans. Fort Belvoir, VA: Defense Technical Information Center, August 2011. http://dx.doi.org/10.21236/ada553762.

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Krebs, Erin E. Post-Traumatic Stress Disorder and Pain Comorbidity in Veterans. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada554830.

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Galea, Sandro. PTSD Trajectory, Comorbidity, and Utilization of Mental Health Services Among Reserves. Fort Belvoir, VA: Defense Technical Information Center, November 2011. http://dx.doi.org/10.21236/ada568657.

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Galea, Sandro. PTSD Trajectory, Comorbidity, and Utilization of Mental Health Services among Reserves. Fort Belvoir, VA: Defense Technical Information Center, November 2012. http://dx.doi.org/10.21236/ada578786.

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Galea, Sandro. PTSD Trajectory, Comorbidity, and Utilization of Mental Health Services Among Reserves. Fort Belvoir, VA: Defense Technical Information Center, July 2010. http://dx.doi.org/10.21236/ada543842.

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Kowalewska, Ewelina, Magdalena Bzowska, and Michał Lew-Starowicz. Comorbidity of Binge Eating and other psychiatric disorders: a systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2023. http://dx.doi.org/10.37766/inplasy2023.7.0075.

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Ursano, Robert J. PTSD Trajectory, Comorbidity, and Utilization of Mental Health Services among National Guard Forces. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada578785.

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Chatterji, Pinka, Margarita Alegria, and David Takeuchi. Psychiatric Disorders and Labor Market Outcomes: Evidence from the National Comorbidity Survey - Replication. Cambridge, MA: National Bureau of Economic Research, October 2008. http://dx.doi.org/10.3386/w14404.

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Ursano, Robert J., and Sandro Galea. PTSD Trajectory, Comorbidity, and Utilization of Mental Health Services Among National Guard Soldiers. Fort Belvoir, VA: Defense Technical Information Center, October 2010. http://dx.doi.org/10.21236/ada544007.

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