Association Between the Platelet-Albumin-Bilirubin Score and All-Cause Mortality in ICU-Admitted Heart Failure Patients: A Retrospective Cohort Analysis and Machine Learning-Based Prognostic Modeling

Zhantao Cao^1,2Jian Li^1,2Guanfa Yuan²Jinghua Ren²Jingting Chen^1,2Kailin Zheng^1,2Yunsu Wang^1,2*Zhonghui Lin^2*

• ¹Fujian University of Traditional Chinese Medicine, Fuzhou, China
• ²Affiliated Xiamen Hospital of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Xiamen, China

Background The platelet-albumin-bilirubin (PALBI) score has shown prognostic value across multiple medical conditions; nevertheless, its effectiveness in forecasting prognoses among severely ill heart failure (HF) patients treated in Intensive Care Unit (ICU) has yet to be fully established. This study explores the relationship between PALBI scores at ICU admission and all-cause mortality in HF patients admitted to the ICU. Methods Drawing on records from the MIMIC-IV version 3.1 critical care database, we included ICU-admitted HF patients and calculated their PALBI scores at admission. Kaplan–Meier survival curves and log-rank tests were used to assess differences in overall mortality at 30 and 360 days across the PALBI tertile groups. Cox regression models based on the proportional hazards assumption were utilized to control for possible confounding variables. In addition, predictive models based on machine learning were constructed using PALBI alongside other clinical features to estimate 30-day mortality, with model performance evaluated through the area under the ROC curve (AUC). Results A total of 4,318 participants were included in the study cohort(57% male; median age 73 years). The cumulative incidence of all-cause mortality was 24% at 30 days and 44% at 360 days. Individuals in the top PALBI tertile exhibited markedly higher mortality rates compared to those in the lowest tertile (30% vs. 20% at 30 days and 52% vs. 39% at 360 days). Multivariate Cox regression analysis revealed significant associations of elevated PALBI scores with higher mortality risk at both 30 days (HR 1.36; 95% CI 1.12-1.64; p = 0.002) and 360 days (HR 1.22; 95% CI 1.03-1.44; p = 0.019). Machine learning models effectively discriminated patients at risk of 30-day mortality, with the best performance achieved by Ridge regression (AUC = 0.76). Conclusion The PALBI score independently predicts 30-day and 360-day all-cause mortality among ICU-admitted HF patients. These findings suggest that the PALBI score has potential utility for risk stratification and for guiding treatment decisions in the intensive care management of HF.