tetano
Editor, Senior Moderator
Sci Rep
. 2025 Oct 17;15(1):36422.
doi: 10.1038/s41598-025-20468-x. Lactate/albumin ratio predicts mortality in critically ill COVID-19 patients: a retrospective machine learning study
Chaofan Liang[SUP] 1 [/SUP], Xinyan Zhang[SUP] 1 [/SUP], Qingqing Zhu[SUP] 2 [/SUP], Jianan Guo[SUP] 1 [/SUP], Yongjun Wu[SUP] 3 [/SUP], Ziqi Wang[SUP] 1 3 [/SUP], Xiaoju Zhang[SUP] 4 5 [/SUP], Yi Kang[SUP] 6 [/SUP]
Affiliations
Severe COVID-19 often progresses to critical illness, requiring accurate prognostic biomarkers. Lactate-to-albumin ratio (LAR) has been proposed as a novel indicator to estimate the likelihood of death. Using data from the MIMIC database, this retrospective study assessed lactate-to-albumin ratio (LAR) effectiveness in forecasting outcomes among severely ill COVID-19 patients. Patients were grouped into four quartiles based on their lactate-to-albumin ratio (LAR) values. Analysis using the Kaplan-Meier method revealed a clear difference in survival outcomes among the groups, with individuals with higher levels of LAR indicating a higher observed mortality rate. The RCS analysis identified a distinct nonlinear link between LAR values and overall 28-day death rates (P < 0.001), which retained statistical significance after covariate adjustment (P < 0.001). Multivariate Cox regression verified that LAR independently correlates with 28-day death risk (HR = 1.309, 95% CI: 1.113-1.540). Subgroup analyses consistently indicated increased mortality risks in Q4 across most strata. Both the Boruta and LASSO algorithms identified LAR as a key determinant of 28-day mortality. Among the machine learning models evaluated, the Random Survival Forest (RSF) model demonstrated strong overall predictive performance for 14-day (AUC: 0.948) and 28-day (AUC: 0.887) mortality prediction in both the training and test datasets. The prediction model incorporating LAR achieved outstanding results across multiple algorithmic methods, serving as an effective and simple clinical tool that enables risk stratification and guides therapeutic decision-making through the integration of multidimensional parameters.
Keywords: COVID−19; MIMIC database; Machine learning; Mortality prediction; Prognostic model.
. 2025 Oct 17;15(1):36422.
doi: 10.1038/s41598-025-20468-x. Lactate/albumin ratio predicts mortality in critically ill COVID-19 patients: a retrospective machine learning study
Chaofan Liang[SUP] 1 [/SUP], Xinyan Zhang[SUP] 1 [/SUP], Qingqing Zhu[SUP] 2 [/SUP], Jianan Guo[SUP] 1 [/SUP], Yongjun Wu[SUP] 3 [/SUP], Ziqi Wang[SUP] 1 3 [/SUP], Xiaoju Zhang[SUP] 4 5 [/SUP], Yi Kang[SUP] 6 [/SUP]
Affiliations
- PMID: 41107476
- PMCID: PMC12534539
- DOI: 10.1038/s41598-025-20468-x
Severe COVID-19 often progresses to critical illness, requiring accurate prognostic biomarkers. Lactate-to-albumin ratio (LAR) has been proposed as a novel indicator to estimate the likelihood of death. Using data from the MIMIC database, this retrospective study assessed lactate-to-albumin ratio (LAR) effectiveness in forecasting outcomes among severely ill COVID-19 patients. Patients were grouped into four quartiles based on their lactate-to-albumin ratio (LAR) values. Analysis using the Kaplan-Meier method revealed a clear difference in survival outcomes among the groups, with individuals with higher levels of LAR indicating a higher observed mortality rate. The RCS analysis identified a distinct nonlinear link between LAR values and overall 28-day death rates (P < 0.001), which retained statistical significance after covariate adjustment (P < 0.001). Multivariate Cox regression verified that LAR independently correlates with 28-day death risk (HR = 1.309, 95% CI: 1.113-1.540). Subgroup analyses consistently indicated increased mortality risks in Q4 across most strata. Both the Boruta and LASSO algorithms identified LAR as a key determinant of 28-day mortality. Among the machine learning models evaluated, the Random Survival Forest (RSF) model demonstrated strong overall predictive performance for 14-day (AUC: 0.948) and 28-day (AUC: 0.887) mortality prediction in both the training and test datasets. The prediction model incorporating LAR achieved outstanding results across multiple algorithmic methods, serving as an effective and simple clinical tool that enables risk stratification and guides therapeutic decision-making through the integration of multidimensional parameters.
Keywords: COVID−19; MIMIC database; Machine learning; Mortality prediction; Prognostic model.