Development of a prediction model for in-hospital mortality in immunocompromised chronic kidney diseases patients with severe infection

Abstract

Background

Immunosuppressive agents, although indispensable in the treatment of chronic kidney diseases (CKD), could compromise the patient’s immune function. The risk factor for in-hospital mortality in immunocompromised CKD patients with severe infections remain elusive.

Methods

We conducted a retrospective analysis of the clinical data of CKD patients who received immunosuppressive agents and presented severe infections. The cohort comprised 272 patients, among whom 73 experienced mortalities during their hospitalization. Logistic regression was employed on the training set to identify key feature variables and construct a predictive model for in-hospital mortality among immunocompromised CKD patients following severe infections. To facilitate clinical application, we constructed a nomogram to visually represent the predictive model.

Results

Our findings indicate that ventilator use, vasoactive drug administration, elevated lactate dehydrogenase (LDH), total bilirubin (TBIL) levels, and persistent lymphopenia(PL) are effective predictors of in-hospital mortality in immunocompromised patients with severe infections. These variables were subsequently incorporated to construct a robust prognostic model. Our model demonstrated excellent discriminative ability (AUC = 0.959, 95% CI, 0.924–0.994), significantly outperforming the Sequential Organ Failure Assessment (SOFA) score (AUC = 0.878, 95% CI, 0.825–0.930) and quick Pitt Bacteremia Score (qPBS) (AUC = 0.897, 95% CI, 0.846–0.947). Calibration curve analysis and the Hosmer-Lemeshow (HL) test corroborate the concordance of our model with empirical observations. Furthermore, decision curve analysis (DCA) underscores the superior clinical utility of our predictive model when compared to the SOFA score and qPBS score. Most importantly, our results showed that PL is the most important predictor of in-hospital mortality in immunocompromised patients following severe infection.

Conclusion

Our findings highlight PL as the most significant predictor of in-hospital mortality in immunocompromised CKD patients. A clinical prediction model incorporating PL as a key variable exhibited robust performance in terms of diagnostic accuracy and clinical utility.