Association between the platelet to white blood cell ratio and short term mortality in critically ill patients with atherosclerotic cardiovascular disease: A retrospective study and machine learning with external validation.
The platelet to white blood cell ratio (PWR) has shown prognostic value in many diseases. Yet its predictive utility for patients with atherosclerotic cardiovascular disease (ASCVD) who receive care in the intensive care unit (ICU) remains uncertain. We examined whether PWR at ICU admission is associated with short term all cause mortality among ICU patients with ASCVD.
We used the MIMIC IV and eICU databases to study the association between PWR and 30 day all cause mortality in critically ill patients with ASCVD. Patients were grouped by PWR quartiles. Collinearity was checked with the variance inflation factor (VIF). Nonlinearity was assessed with restricted cubic splines(RCS). Survival was compared with Kaplan Meier(KM) curves and the log rank test. Hazard ratios were estimated with stratified and adjusted Cox models. We also built machine learning models that included PWR and clinical features selected with the Boruta algorithm to predict 30 day mortality. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). External validation in the eICU database was used to assess generalizability.
A total of 10 943 ICU patients with ASCVD were included, 62 % were men, and the median age was 71 years. The highest PWR quartile had a lower 30 day all cause mortality than the lowest quartile, 11 % versus 15 % (p < 0.001). In multivariable Cox models the highest quartile had a lower risk (HR 0.80, 95 % CI 0.67 to 0.95, p = 0.012). RCS suggested a nonlinear association. Age modified the association, with a stronger protective effect in patients younger than 70 years (HR 0.64, 95 % CI 0.47 to 0.87, interaction p < 0.001). The best machine learning model achieved an AUC of 0.812 in internal validation and 0.80 in external validation. SHAP analysis showed that higher PWR was linked to a lower predicted risk of death.
PWR independently predicts 30 day all cause mortality in ICU patients with ASCVD. These findings support the use of PWR for risk stratification and to inform management in critical care for ASCVD.
We used the MIMIC IV and eICU databases to study the association between PWR and 30 day all cause mortality in critically ill patients with ASCVD. Patients were grouped by PWR quartiles. Collinearity was checked with the variance inflation factor (VIF). Nonlinearity was assessed with restricted cubic splines(RCS). Survival was compared with Kaplan Meier(KM) curves and the log rank test. Hazard ratios were estimated with stratified and adjusted Cox models. We also built machine learning models that included PWR and clinical features selected with the Boruta algorithm to predict 30 day mortality. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). External validation in the eICU database was used to assess generalizability.
A total of 10 943 ICU patients with ASCVD were included, 62 % were men, and the median age was 71 years. The highest PWR quartile had a lower 30 day all cause mortality than the lowest quartile, 11 % versus 15 % (p < 0.001). In multivariable Cox models the highest quartile had a lower risk (HR 0.80, 95 % CI 0.67 to 0.95, p = 0.012). RCS suggested a nonlinear association. Age modified the association, with a stronger protective effect in patients younger than 70 years (HR 0.64, 95 % CI 0.47 to 0.87, interaction p < 0.001). The best machine learning model achieved an AUC of 0.812 in internal validation and 0.80 in external validation. SHAP analysis showed that higher PWR was linked to a lower predicted risk of death.
PWR independently predicts 30 day all cause mortality in ICU patients with ASCVD. These findings support the use of PWR for risk stratification and to inform management in critical care for ASCVD.