Objective: Comorbidities are diseases that coexist with a disease of interest or an index disease, which can directly affect the prognosis of the disease of interest or indirectly affect the choice of treatment. The Charlson comorbidity index (CCI) is the most widely used comorbidity index. In this study, it was aimed to examine the predictive role of the CCI score on the mortality of patients with COVID-19.
Methods: We have retrospectively analyzed COVID-19 patients whose diagnosis was confirmed by PCR and who were hospitalized in two centers between April 2020 and December 2020. The severity of comorbidity of the patients was categorized into five groups according to the CCI score: CCI score 0, CCI score 1-2, CCI score 3-4, CCI score 5-6, and CCI score ≥7. Factors affecting mortality and differences between groups classified by CCI were determined by logistic regression analysis and one-way analysis of variance.
Results: A total of 1,559 COVID-19 patients were included in the study and 70 (4.49%) patients had deceased. Half of the study population (n=793, 50.9%) had different comorbidities. The CCI score was 3.8±2.7 in deceased patients and 1.3±1.9 in surviving individuals. There was a positive correlation between CCI scores and mortality in COVID-19 patients, with each point increase in the CCI score increasing the risk of death by 2.5%. CCI score of 4 and above predicted mortality with 87.2% sensitivity and 97.9% negative predictive value. Five (0.6%) of 766 patients with CCI scores of 0, 16 (3.6%) of 439 patients with CCI scores of 1-2, 13 (6.9%) of 189 patients with CCI scores of 3-4, and a CCI score of 5, 13 (15.7%) of 83 patients with -6 and 23 (28.0%) of 82 patients with a CCI score of ≥7 died.
Conclusion: CCI is a simple, easily applicable, and valid method for classifying comorbidities and estimating COVID-19 mortality. The close relationship between the CCI score and mortality reveals the reality of how important vaccination is, especially in this group of patients. Increasing awareness of potential comorbidities in COVID-19 patients can provide insight into the disease and to improve outcomes by identifying and treating patients earlier and more effectively.
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http://dx.doi.org/10.14744/nci.2022.33349 | DOI Listing |
Cureus
December 2024
Urology, SSM Health Saint Louis University Hospital, Saint Louis, USA.
Introduction Fournier's gangrene (FG) is a rapidly progressing necrotizing fasciitis. The Fournier's Gangrene Severity Index (FGSI), in conjunction with the Charlson Comorbidity Index (CCI), has been used as a mortality predictor during hospitalization. Patients with diabetes have also been shown to be at an increased risk for the development of FG.
View Article and Find Full Text PDFSci Rep
January 2025
China Academy of Chinese Medical Sciences, Beijing, China.
Heart failure is a common complication in patients with sepsis, and individuals who experience both sepsis and heart failure are at a heightened risk for adverse outcomes. This study aims to develop an effective nomogram model to predict the 7-day, 15-day, and 30-day survival probabilities of septic patients with heart failure in the intensive care unit (ICU). This study extracted the pertinent clinical data of septic patients with heart failure from the Critical Medical Information Mart for Intensive Care (MIMIC-IV) database.
View Article and Find Full Text PDFJ Arthroplasty
January 2025
Helios ENDO-Klinik, Hamburg, Germany.
Background: Periprosthetic joint infections (PJI) are one of the most devastating complications of total knee arthroplasty (TKA). Patients who have chronic kidney disease (CKD) are more vulnerable to PJI. We aimed to answer the following questions: 1) What are the commonly observed pathogens in PJI after TKA in CKD patients, and do they differ from those in non-CKD patients? and 2) What are the risk factors for PJI after TKA in CKD patients?
Methods: Patients who underwent surgery due to a chronic PJI of the TKA were retrospectively enrolled.
BMC Health Serv Res
January 2025
Institute for Health and Nursing Science, Faculty of Medicine, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
Background: Cancer requires interdisciplinary intersectoral care. The Care Coordination Instrument (CCI) captures patients' perspectives on cancer care coordination. We aimed to translate, adapt, and validate the CCI for Germany (CCI German version).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Pulmonology, Yokohama City University, Yokohama, Japan.
Community-acquired pneumonia (CAP) is associated with high mortality rates and often results in prolonged hospital stays. The potential of machine learning to enhance prediction accuracy in this context is significant, yet clinicians often lack the programming skills required for effective data mining. This study aimed to assess the effectiveness of a low-code approach for assisting clinicians with data mining for mortality and length of stay (LOS) prediction in patients with CAP.
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