Elie Sarraf , Alireza Vafaei Sadr , Vida Abedi , Anthony S. Bonavia
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A Random Forest model incorporating SOFA scores, demographic/social data, and the Charlson Comorbidity Index was trained and validated.</p></div><div><h3>Findings</h3><p>In the MIMIC-IV dataset of 32,970 sepsis patients, 6,824 (20.7%) died within 30 days. A model including demographic, socioeconomic, and comorbidity data with SOFA scores improved predictive accuracy beyond SOFA scores alone. Day 2 SOFA, age, weight, and comorbidities were significant predictors. External validation showed consistent performance, highlighting the importance of delta SOFA between days 1 and 3.</p></div><div><h3>Conclusion</h3><p>Adding patient-specific demographic and socioeconomic information to clinical metrics significantly improves sepsis mortality prediction. This suggests a more comprehensive, multidimensional prognostic approach is needed for accurate sepsis outcome predictions.</p></div>","PeriodicalId":15451,"journal":{"name":"Journal of critical care","volume":"83 ","pages":"Article 154857"},"PeriodicalIF":3.2000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0883944124003447/pdfft?md5=36b45867a08adfca3877080e37534580&pid=1-s2.0-S0883944124003447-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Enhancing Sepsis prognosis: Integrating social determinants and demographic variables into a comprehensive model for critically ill patients\",\"authors\":\"Elie Sarraf , Alireza Vafaei Sadr , Vida Abedi , Anthony S. 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A Random Forest model incorporating SOFA scores, demographic/social data, and the Charlson Comorbidity Index was trained and validated.</p></div><div><h3>Findings</h3><p>In the MIMIC-IV dataset of 32,970 sepsis patients, 6,824 (20.7%) died within 30 days. A model including demographic, socioeconomic, and comorbidity data with SOFA scores improved predictive accuracy beyond SOFA scores alone. Day 2 SOFA, age, weight, and comorbidities were significant predictors. External validation showed consistent performance, highlighting the importance of delta SOFA between days 1 and 3.</p></div><div><h3>Conclusion</h3><p>Adding patient-specific demographic and socioeconomic information to clinical metrics significantly improves sepsis mortality prediction. 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Enhancing Sepsis prognosis: Integrating social determinants and demographic variables into a comprehensive model for critically ill patients
Background
The Sequential Organ Failure Assessment (SOFA) score monitors organ failure and defines sepsis but may not fully capture factors influencing sepsis mortality. Socioeconomic and demographic impacts on sepsis outcomes have been highlighted recently.
Objective
To evaluate the prognostic value of SOFA scores against demographic and social health determinants for predicting sepsis mortality in critically ill patients, and to assess if a combined model increases predictive accuracy.
Methods
The study utilized retrospective data from the MIMIC-IV database and prospective external validation from the Penn State Health cohort. A Random Forest model incorporating SOFA scores, demographic/social data, and the Charlson Comorbidity Index was trained and validated.
Findings
In the MIMIC-IV dataset of 32,970 sepsis patients, 6,824 (20.7%) died within 30 days. A model including demographic, socioeconomic, and comorbidity data with SOFA scores improved predictive accuracy beyond SOFA scores alone. Day 2 SOFA, age, weight, and comorbidities were significant predictors. External validation showed consistent performance, highlighting the importance of delta SOFA between days 1 and 3.
Conclusion
Adding patient-specific demographic and socioeconomic information to clinical metrics significantly improves sepsis mortality prediction. This suggests a more comprehensive, multidimensional prognostic approach is needed for accurate sepsis outcome predictions.
期刊介绍:
The Journal of Critical Care, the official publication of the World Federation of Societies of Intensive and Critical Care Medicine (WFSICCM), is a leading international, peer-reviewed journal providing original research, review articles, tutorials, and invited articles for physicians and allied health professionals involved in treating the critically ill. The Journal aims to improve patient care by furthering understanding of health systems research and its integration into clinical practice.
The Journal will include articles which discuss:
All aspects of health services research in critical care
System based practice in anesthesiology, perioperative and critical care medicine
The interface between anesthesiology, critical care medicine and pain
Integrating intraoperative management in preparation for postoperative critical care management and recovery
Optimizing patient management, i.e., exploring the interface between evidence-based principles or clinical insight into management and care of complex patients
The team approach in the OR and ICU
System-based research
Medical ethics
Technology in medicine
Seminars discussing current, state of the art, and sometimes controversial topics in anesthesiology, critical care medicine, and professional education
Residency Education.