Treffer: EVALUATION OF PROGNOSTIC RISK MODELS BASED ON AGE AND COMORBIDITY IN SEPTIC PATIENTS: INSIGHTS FROM MACHINE LEARNING AND TRADITIONAL METHODS IN A LARGE-SCALE, MULTICENTER, RETROSPECTIVE STUDY.
Title:
EVALUATION OF PROGNOSTIC RISK MODELS BASED ON AGE AND COMORBIDITY IN SEPTIC PATIENTS: INSIGHTS FROM MACHINE LEARNING AND TRADITIONAL METHODS IN A LARGE-SCALE, MULTICENTER, RETROSPECTIVE STUDY.
Authors:
Liu G; Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Shang Z; Department of Emergency, Huashan Hospital, Fudan University School of Medicine, Shanghai, China., Ning N; Departments of Emergency, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China., Li J; Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Sun W, Fan Y; Department of Pathology Medicine Biology, University Medical Centre Groningen, The Netherlands., Guo Y; Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Ye J; Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Zhou W; Department of Emergency, Huashan Hospital, Fudan University School of Medicine, Shanghai, China., Qian J; Department of Emergency, Huashan Hospital, Fudan University School of Medicine, Shanghai, China., Ma C; Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Zhang J; Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Jiang X; Department of Emergency, Huashan Hospital, Fudan University School of Medicine, Shanghai, China., Zhu C; Department of Emergency, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Mao E; Departments of Emergency, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China., Chen M; Department of Emergency, Huashan Hospital, Fudan University School of Medicine, Shanghai, China., Gao C; Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Source:
Shock (Augusta, Ga.) [Shock] 2025 Jul 01; Vol. 64 (1), pp. 56-64.
Publication Type:
Journal Article; Multicenter Study
Language:
English
Journal Info:
Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 9421564 Publication Model: Print Cited Medium: Internet ISSN: 1540-0514 (Electronic) Linking ISSN: 10732322 NLM ISO Abbreviation: Shock Subsets: MEDLINE
Imprint Name(s):
Publication: 2002- : Philadelphia : Lippincott Williams & Wilkins
Original Publication: Augusta, GA : BioMedical Press, [1994-
Original Publication: Augusta, GA : BioMedical Press, [1994-
MeSH Terms:
References:
Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA . 2016;315(8):801–810.
Xie J, Wang H, Kang Y, et al. The epidemiology of sepsis in Chinese ICUs: a national cross-sectional survey. Crit Care Med . 2020;48(3):e209–e218.
Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the global burden of disease study. Lancet . 2020;395(10219):200–211.
Fleischmann-Struzek C, Mellhammar L, Rose N, et al. Incidence and mortality of hospital- and ICU-treated sepsis: results from an updated and expanded systematic review and meta-analysis. Intensive Care Med . 2020;46(8):1552–1562.
Cui Y, Wang T, Bao J, et al. Comparison of Charlson's weighted index of comorbidities with the chronic health score for the prediction of mortality in septic patients. Chin Med J (Engl) . 2014;127(14):2623–2627.
Alrawashdeh M, Klompas M, Simpson SQ, et al. Prevalence and outcomes of previously healthy adults among patients hospitalized with community-onset sepsis. Chest . 2022;162(1):101–110.
Skei NV, Nilsen TIL, Mohus RM, et al. Trends in mortality after a sepsis hospitalization: a nationwide prospective registry study from 2008 to 2021. Infection . 2023;51(6):1773–1786.
Pandolfi F, Brun-Buisson C, Guillemot D, et al. One-year hospital readmission for recurrent sepsis: associated risk factors and impact on 1-year mortality-a French nationwide study. Crit Care . 2022;26(1):371.
Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis . 1987;40(5):373–383.
Charlson M, Szatrowski TP, Peterson J, et al. Validation of a combined comorbidity index. J Clin Epidemiol . 1994;47(11):1245–1251.
Kurien SS, David R, Varma RP, et al. Correlation between biomarkers and age-adjusted Charlson Comorbidity Index in patients with covid-19: a cross-sectional study in a tertiary care center in South India. Cureus . 2023;15(3):e36000.
Wei D, Sun Y, Chen R, et al. Age-adjusted Charlson Comorbidity Index and in-hospital mortality in critically ill patients with cardiogenic shock: a retrospective cohort study. Exp Ther Med . 2023;25(6):299.
Chen Z, Zha L, Feng G, et al. Prognostic value of serum cholinesterase levels for in-hospital mortality among patients with acute exacerbation of chronic obstructive pulmonary disease. COPD . 2023;20(1):178–185.
Sun B, Lei M, Wang L, et al. Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study. Int J Surg . 2024;111:467–480. doi:10.1097/js9.0000000000001866. (PMID: 10.1097/js9.0000000000001866)
Moor M, Bennett N, Plečko D, et al. Predicting sepsis using deep learning across international sites: a retrospective development and validation study. EClinicalMedicine . 2023;62:102124.
Choudhary T, Upadhyaya P, Davis CM, et al. Derivation and validation of generalized sepsis-induced acute respiratory failure phenotypes among critically ill patients: a retrospective study. Crit Care . 2024;28(1):321.
Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med . 2023;388(13):1201–1208.
Lundberg SM, Erion G, Chen H, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell . 2020;2(1):56–67.
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ . 2024;385:q902.
Stekhoven DJ, Bühlmann P. MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics . 2012;28(1):112–118.
Komorowski M, Green A, Tatham KC, et al. Sepsis biomarkers and diagnostic tools with a focus on machine learning. EBioMedicine . 2022;86:104394.
Chang Z, Lu J, Zhang Q, et al. Clinical biomarker profiles reveals gender differences and mortality factors in sepsis. Front Immunol . 2024;15:1413729.
Bhavani SV, Spicer A, Sinha P, et al. Distinct immune profiles and clinical outcomes in sepsis subphenotypes based on temperature trajectories. Intensive Care Med . 2024;50(12):2094–2104.
Liu YC, Yao Y, Yu MM, et al. Frequency and mortality of sepsis and septic shock in China: a systematic review and meta-analysis. BMC Infect Dis . 2022;22(1):564.
Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ . 2020;368:m441.
Ali S, Akhlaq F, Imran AS, et al. The enlightening role of explainable artificial intelligence in medical & healthcare domains: a systematic literature review. Comput Biol Med . 2023;166:107555.
Wu J, Tian S, Xu J, et al. Association of high-risk comorbidity with overall survival among patients with gastric cancer and its sex-specific differences in China: a retrospective observational cohort study. BMC Cancer . 2023;23(1):916.
Yang Y, Yang KS, Hsann YM, et al. The effect of comorbidity and age on hospital mortality and length of stay in patients with sepsis. J Crit Care . 2010;25(3):398–405.
Esper AM, Moss M, Lewis CA, et al. The role of infection and comorbidity: factors that influence disparities in sepsis. Crit Care Med . 2006;34(10):2576–2582.
Liyanarachi KV, Mohus RM, Rogne T, et al. Chronic kidney disease and risk of bloodstream infections and sepsis: a 17-year follow-up of the population-based Trøndelag Health Study in Norway. Infection . 2024;52(5):1983–1993.
McDonald HI, Nitsch D, Millett ERC, et al. Are pre-existing markers of chronic kidney disease associated with short-term mortality following acute community-acquired pneumonia and sepsis? A cohort study among older people with diabetes using electronic health records. Nephrol Dial Transplant . 2015;30(6):1002–1009.
Cuenca JA, Manjappachar NK, Ramírez CM, et al. Outcomes and predictors of 28-day mortality in patients with solid tumors and septic shock defined by third international consensus definitions for sepsis and septic shock criteria. Chest . 2022;162(5):1063–1073.
Lu H. Inflammatory liver diseases and susceptibility to sepsis. Clin Sci (Lond) . 2024;138(7):435–487.
Walker AMN, Drozd M, Hall M, et al. Prevalence and predictors of sepsis death in patients with chronic heart failure and reduced left ventricular ejection fraction. J Am Heart Assoc . 2018;7(20):e009684.
Mirouse A, Vigneron C, Llitjos JF, et al. Sepsis and cancer: an interplay of friends and foes. Am J Respir Crit Care Med . 2020;202(12):1625–1635.
Philips CA, Sarin SK. Sepsis in cirrhosis: emerging concepts in pathogenesis, diagnosis and management. Hepatol Int . 2016;10(6):871–882.
Wong F, Bernardi M, Balk R, et al. Sepsis in cirrhosis: report on the 7th meeting of the international ascites club. Gut . 2005;54(5):718–725.
Dounousi E, Torino C, Pizzini P, et al. Intact FGF23 and α-Klotho during acute inflammation/sepsis in CKD patients. Eur J Clin Invest . 2016;46(3):234–241.
Lu KJ, Kearney LG, Ord M, et al. Age adjusted Charlson Co-morbidity Index is an independent predictor of mortality over long-term follow-up in infective endocarditis. Int J Cardiol . 2013;168(6):5243–5248.
Imam Z, Odish F, Gill I, et al. Older age and comorbidity are independent mortality predictors in a large cohort of 1305 covid-19 patients in Michigan, United States. J Intern Med . 2020;288(4):469–476.
Piscator E, Hedberg P, Göransson K, et al. Survival after in-hospital cardiac arrest is highly associated with the age-combined Charlson Co-morbidity Index in a cohort study from a two-site Swedish University hospital. Resuscitation . 2016;99:79–83.
Cho SI, Yoon S, Lee HJ. Impact of comorbidity burden on mortality in patients with covid-19 using the Korean health insurance database. Sci Rep . 2021;11(1):6375.
Huang Y, Gao Y, Quan S, et al. Development and internal-external validation of the ACCI-SOFA model for predicting in-hospital mortality of patients with sepsis-3 in the ICU: a multicenter retrospective cohort study. Shock . 2024;61(3):367–374.
Torvik MA, Nymo SH, Nymo SH, et al. Patient characteristics in sepsis-related deaths: prevalence of advanced frailty, comorbidity, and age in a Norwegian hospital trust. Infection . 2023;51(4):1103–1115.
Gonçalves-Pereira J, Oliveira A, Vieira T, et al. Critically ill patient mortality by age: long-term follow-up (CIMbA-LT). Ann Intensive Care . 2023;13(1):7.
Gan Q, Li Z, Li X, et al. Analysis of the effects of early screening combined with blood lactate on the severity of patients with sepsis. Heliyon . 2024;10(11):e31907.
Fu Y, He Y, Zheng C, et al. A predictive model for disseminated intravascular coagulopathy in sepsis: an observational study. Int J Gen Med . 2024;17:4845–4855.
Baldirà J, Ruiz-Rodríguez JC, Ruiz-Sanmartin A, et al. Use of biomarkers to improve 28-day mortality stratification in patients with sepsis and SOFA ≤6. Biomedicine . 2023;11(8):2149.
Karakike E, Kyriazopoulou E, Tsangaris I, et al. The early change of SOFA score as a prognostic marker of 28-day sepsis mortality: analysis through a derivation and a validation cohort. Crit Care . 2019;23(1):387.
Ye Q, Wang X, Xu X, et al. Serial platelet count as a dynamic prediction marker of hospital mortality among septic patients. Burns Dent Traumatol . 2024;12:tkae016.
Filho RR, Rocha LL, Corrêa TD, et al. Blood lactate levels cutoff and mortality prediction in sepsis-time for a reappraisal? A retrospective cohort study. Shock . 2016;46(5):480–485.
Zaki HA, Bensliman S, Bashir K, et al. Accuracy of procalcitonin for diagnosing sepsis in adult patients admitted to the emergency department: a systematic review and meta-analysis. Syst Rev . 2024;13(1):37.
Xie J, Wang H, Kang Y, et al. The epidemiology of sepsis in Chinese ICUs: a national cross-sectional survey. Crit Care Med . 2020;48(3):e209–e218.
Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the global burden of disease study. Lancet . 2020;395(10219):200–211.
Fleischmann-Struzek C, Mellhammar L, Rose N, et al. Incidence and mortality of hospital- and ICU-treated sepsis: results from an updated and expanded systematic review and meta-analysis. Intensive Care Med . 2020;46(8):1552–1562.
Cui Y, Wang T, Bao J, et al. Comparison of Charlson's weighted index of comorbidities with the chronic health score for the prediction of mortality in septic patients. Chin Med J (Engl) . 2014;127(14):2623–2627.
Alrawashdeh M, Klompas M, Simpson SQ, et al. Prevalence and outcomes of previously healthy adults among patients hospitalized with community-onset sepsis. Chest . 2022;162(1):101–110.
Skei NV, Nilsen TIL, Mohus RM, et al. Trends in mortality after a sepsis hospitalization: a nationwide prospective registry study from 2008 to 2021. Infection . 2023;51(6):1773–1786.
Pandolfi F, Brun-Buisson C, Guillemot D, et al. One-year hospital readmission for recurrent sepsis: associated risk factors and impact on 1-year mortality-a French nationwide study. Crit Care . 2022;26(1):371.
Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis . 1987;40(5):373–383.
Charlson M, Szatrowski TP, Peterson J, et al. Validation of a combined comorbidity index. J Clin Epidemiol . 1994;47(11):1245–1251.
Kurien SS, David R, Varma RP, et al. Correlation between biomarkers and age-adjusted Charlson Comorbidity Index in patients with covid-19: a cross-sectional study in a tertiary care center in South India. Cureus . 2023;15(3):e36000.
Wei D, Sun Y, Chen R, et al. Age-adjusted Charlson Comorbidity Index and in-hospital mortality in critically ill patients with cardiogenic shock: a retrospective cohort study. Exp Ther Med . 2023;25(6):299.
Chen Z, Zha L, Feng G, et al. Prognostic value of serum cholinesterase levels for in-hospital mortality among patients with acute exacerbation of chronic obstructive pulmonary disease. COPD . 2023;20(1):178–185.
Sun B, Lei M, Wang L, et al. Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study. Int J Surg . 2024;111:467–480. doi:10.1097/js9.0000000000001866. (PMID: 10.1097/js9.0000000000001866)
Moor M, Bennett N, Plečko D, et al. Predicting sepsis using deep learning across international sites: a retrospective development and validation study. EClinicalMedicine . 2023;62:102124.
Choudhary T, Upadhyaya P, Davis CM, et al. Derivation and validation of generalized sepsis-induced acute respiratory failure phenotypes among critically ill patients: a retrospective study. Crit Care . 2024;28(1):321.
Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med . 2023;388(13):1201–1208.
Lundberg SM, Erion G, Chen H, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell . 2020;2(1):56–67.
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ . 2024;385:q902.
Stekhoven DJ, Bühlmann P. MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics . 2012;28(1):112–118.
Komorowski M, Green A, Tatham KC, et al. Sepsis biomarkers and diagnostic tools with a focus on machine learning. EBioMedicine . 2022;86:104394.
Chang Z, Lu J, Zhang Q, et al. Clinical biomarker profiles reveals gender differences and mortality factors in sepsis. Front Immunol . 2024;15:1413729.
Bhavani SV, Spicer A, Sinha P, et al. Distinct immune profiles and clinical outcomes in sepsis subphenotypes based on temperature trajectories. Intensive Care Med . 2024;50(12):2094–2104.
Liu YC, Yao Y, Yu MM, et al. Frequency and mortality of sepsis and septic shock in China: a systematic review and meta-analysis. BMC Infect Dis . 2022;22(1):564.
Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ . 2020;368:m441.
Ali S, Akhlaq F, Imran AS, et al. The enlightening role of explainable artificial intelligence in medical & healthcare domains: a systematic literature review. Comput Biol Med . 2023;166:107555.
Wu J, Tian S, Xu J, et al. Association of high-risk comorbidity with overall survival among patients with gastric cancer and its sex-specific differences in China: a retrospective observational cohort study. BMC Cancer . 2023;23(1):916.
Yang Y, Yang KS, Hsann YM, et al. The effect of comorbidity and age on hospital mortality and length of stay in patients with sepsis. J Crit Care . 2010;25(3):398–405.
Esper AM, Moss M, Lewis CA, et al. The role of infection and comorbidity: factors that influence disparities in sepsis. Crit Care Med . 2006;34(10):2576–2582.
Liyanarachi KV, Mohus RM, Rogne T, et al. Chronic kidney disease and risk of bloodstream infections and sepsis: a 17-year follow-up of the population-based Trøndelag Health Study in Norway. Infection . 2024;52(5):1983–1993.
McDonald HI, Nitsch D, Millett ERC, et al. Are pre-existing markers of chronic kidney disease associated with short-term mortality following acute community-acquired pneumonia and sepsis? A cohort study among older people with diabetes using electronic health records. Nephrol Dial Transplant . 2015;30(6):1002–1009.
Cuenca JA, Manjappachar NK, Ramírez CM, et al. Outcomes and predictors of 28-day mortality in patients with solid tumors and septic shock defined by third international consensus definitions for sepsis and septic shock criteria. Chest . 2022;162(5):1063–1073.
Lu H. Inflammatory liver diseases and susceptibility to sepsis. Clin Sci (Lond) . 2024;138(7):435–487.
Walker AMN, Drozd M, Hall M, et al. Prevalence and predictors of sepsis death in patients with chronic heart failure and reduced left ventricular ejection fraction. J Am Heart Assoc . 2018;7(20):e009684.
Mirouse A, Vigneron C, Llitjos JF, et al. Sepsis and cancer: an interplay of friends and foes. Am J Respir Crit Care Med . 2020;202(12):1625–1635.
Philips CA, Sarin SK. Sepsis in cirrhosis: emerging concepts in pathogenesis, diagnosis and management. Hepatol Int . 2016;10(6):871–882.
Wong F, Bernardi M, Balk R, et al. Sepsis in cirrhosis: report on the 7th meeting of the international ascites club. Gut . 2005;54(5):718–725.
Dounousi E, Torino C, Pizzini P, et al. Intact FGF23 and α-Klotho during acute inflammation/sepsis in CKD patients. Eur J Clin Invest . 2016;46(3):234–241.
Lu KJ, Kearney LG, Ord M, et al. Age adjusted Charlson Co-morbidity Index is an independent predictor of mortality over long-term follow-up in infective endocarditis. Int J Cardiol . 2013;168(6):5243–5248.
Imam Z, Odish F, Gill I, et al. Older age and comorbidity are independent mortality predictors in a large cohort of 1305 covid-19 patients in Michigan, United States. J Intern Med . 2020;288(4):469–476.
Piscator E, Hedberg P, Göransson K, et al. Survival after in-hospital cardiac arrest is highly associated with the age-combined Charlson Co-morbidity Index in a cohort study from a two-site Swedish University hospital. Resuscitation . 2016;99:79–83.
Cho SI, Yoon S, Lee HJ. Impact of comorbidity burden on mortality in patients with covid-19 using the Korean health insurance database. Sci Rep . 2021;11(1):6375.
Huang Y, Gao Y, Quan S, et al. Development and internal-external validation of the ACCI-SOFA model for predicting in-hospital mortality of patients with sepsis-3 in the ICU: a multicenter retrospective cohort study. Shock . 2024;61(3):367–374.
Torvik MA, Nymo SH, Nymo SH, et al. Patient characteristics in sepsis-related deaths: prevalence of advanced frailty, comorbidity, and age in a Norwegian hospital trust. Infection . 2023;51(4):1103–1115.
Gonçalves-Pereira J, Oliveira A, Vieira T, et al. Critically ill patient mortality by age: long-term follow-up (CIMbA-LT). Ann Intensive Care . 2023;13(1):7.
Gan Q, Li Z, Li X, et al. Analysis of the effects of early screening combined with blood lactate on the severity of patients with sepsis. Heliyon . 2024;10(11):e31907.
Fu Y, He Y, Zheng C, et al. A predictive model for disseminated intravascular coagulopathy in sepsis: an observational study. Int J Gen Med . 2024;17:4845–4855.
Baldirà J, Ruiz-Rodríguez JC, Ruiz-Sanmartin A, et al. Use of biomarkers to improve 28-day mortality stratification in patients with sepsis and SOFA ≤6. Biomedicine . 2023;11(8):2149.
Karakike E, Kyriazopoulou E, Tsangaris I, et al. The early change of SOFA score as a prognostic marker of 28-day sepsis mortality: analysis through a derivation and a validation cohort. Crit Care . 2019;23(1):387.
Ye Q, Wang X, Xu X, et al. Serial platelet count as a dynamic prediction marker of hospital mortality among septic patients. Burns Dent Traumatol . 2024;12:tkae016.
Filho RR, Rocha LL, Corrêa TD, et al. Blood lactate levels cutoff and mortality prediction in sepsis-time for a reappraisal? A retrospective cohort study. Shock . 2016;46(5):480–485.
Zaki HA, Bensliman S, Bashir K, et al. Accuracy of procalcitonin for diagnosing sepsis in adult patients admitted to the emergency department: a systematic review and meta-analysis. Syst Rev . 2024;13(1):37.
Contributed Indexing:
Keywords: ACCI — age-adjusted Charlson Comorbidity Index; ALB — albumin; APTT — activated partial thromboplastin time; AUC — area under the receiver operating characteristic curve; AdaBoost — Adaptive Boosting; BUN — blood urea nitrogen; CI — confidence interval; Cr — creatinine; DDI — D-dimer; DT — Decision Tree; ED — emergency department; EMR — electronic medical records; FDP — fibrin degradation products; FIB — fibrinogen; GB — Gradient Boosting; HCT — hematocrit; HDL — high-density lipoprotein; HR — hazard ratio; Hba1C — glycated hemoglobin; ICD-10 — the Tenth Revision of the International Classification of Diseases; ICU — intensive care unit; INR — international normalized ratio; IQR — interquartile range; LR — logistic regression; LightGBM — Light Gradient Boosting; ML — machine learning; NB — Naive Bayes; NT-proBNP — N-terminal pro-brain natriuretic peptide; PLT — platelet; PT — prothrombin time; RF — Random Forest; ROC — receiver operating characteristic; SHAP — SHapley Additive exPlanations; SOFA — Sequential Organ Failure Assessment; Sepsis; TG — triglyceride; XGBoost — Extreme Gradient Boosting; age-adjusted Charlson Comorbidity Index; comorbidity; prognostic risk models; qACCI — quick age-adjusted Charlson Comorbidity Index; qSOFA — quick Sequential Organ Failure Assessment; quick age-adjusted Charlson Comorbidity Index
Entry Date(s):
Date Created: 20250218 Date Completed: 20250611 Latest Revision: 20250626
Update Code:
20250630
DOI:
10.1097/SHK.0000000000002562
PMID:
39965627
Database:
MEDLINE
Weitere Informationen
Abstract: Background: Age and comorbidity significantly impact the prognosis of septic patients and inform treatment decisions. To provide clinicians with effective tools for identifying high-risk patients, this study assesses the predictive value of the age-adjusted Charlson Comorbidity Index (ACCI) and its simplified version, the quick ACCI (qACCI), for mortality in septic patients. Methods: This retrospective study included septic patients from four Chinese medical centers. The internal validation cohort comprised patients from Xinhua Hospital, Ruijin Hospital, and Huashan Hospital, while participants from Renji Hospital served as the external validation cohort. Machine learning models identified ACCI's feature importance. Restricted cubic spline regression and subgroup analysis assess the correlation between ACCI and mortality risk. The qACCI, derived from the ACCI components, was also evaluated for predictive reliability. Results: A total of 3,287 septic patients were included: 2,974 in the internal cohort (mean age 67.96 years; 37.5% male) and 313 in the external cohort (mean age 67.90 years; 48.2% male). Machine learning models identified ACCI as a key predictor of in-hospital mortality. A linear correlation was confirmed between ACCI and risks of in-hospital, 30-day, and ICU mortality. Sensitivity analysis revealed consistent results across subgroups, demonstrating significantly higher mortality risks in the moderate- (hazard ratio [HR] 2.18, 95% CI 1.77-2.70) and high-ACCI (HR 3.72, 95% CI 2.99-4.65) groups compared to the low-ACCI group (HR 1, reference). The ACCI achieved an AUC of 0.788 for in-hospital mortality, outperforming the SOFA in gastrointestinal (0.831 vs. 0.794) and central nervous system infections (0.803 vs. 0.739). The qACCI showed moderate predictive performance in both the internal (AUC, 0.734) and external (AUC, 0.758) cohorts. Conclusions: As composite indicators of age and comorbidity, ACCI and qACCI provide valuable and reliable tools for clinicians to identify high-risk patients early.
(Copyright © 2025 by the Shock Society.)
The authors report no conflicts of interest.