Table 1_Association between lactate dehydrogenase-to-albumin ratio and short-term mortality in critically ill patients with urosepsis: evidence from dual retrospective cohorts.docx

Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) quantified incremental value of adding logLDAR; Machine learning selected predictors for a parsimonious model validated internally and externally. decision curve analysis (DCA) evaluated the 7-variable model’s utility.</p>Results<p>Among 3,060 patients, higher logLDAR was associated with significantly elevated 28-day ICU (23.4% vs. 9.02%) and in-hospital (22.1% vs. 8.24%) mortality (both P<0.001).

In the primary multivariable-adjusted model, per-unit increases in logLDAR yielded hazard ratios (HRs) of 1.26 (95% CI: 1.17–1.36) for ICU mortality and 1.35 (95% CI: 1.25–1.45) for in-hospital mortality. Compared with the lowest tertile, the highest tertile showed HRs of 1.79 (1.40–2.30) and 2.29 (1.77–2.96), respectively. RCS analysis revealed linear dose-response relationships (P<0.001), and the Fine-Gray model confirmed robust associations (subdistribution hazard ratio (sHR)=1.36, 1.27–1.46).

Adding logLDAR significantly improved the AUCs of all six conventional scores (DeLong P<0.01), with NRI/IDI showing substantial reclassification improvement (P<0.001). A parsimonious 7-variable model achieved AUCs of 0.674, 0.677, and 0.783 in the internal training, internal validation, and external cohorts, respectively, with adequate calibration (Hosmer-Lemeshow P > 0.05) and DCA demonstrating net clinical benefit. These findings were fully validated in an external real-world cohort.</p>Conclusions<p>LDAR independently predicts short-term mortality in critically ill patients with urosepsis, supplementing conventional severity scores.

The parsimonious risk model showed moderate performance with acceptable cross-cohort generalizability.</p>