Developing and validating a multivariable prediction model for in-hospital mortality of pneumonia with advanced chronic kidney disease patients: a retrospective analysis using a nationwide database in Japan.

BACKGROUND: The prognosis of pneumonia in patients with advanced stage chronic kidney disease (CKD) remains unimproved for years. We attempt to develop a simple and more useful scoring system for predicting in-hospital mortality for advanced CKD patients with pneumonia.
METHODS: Using the Diagnosis Procedure Combination database, we identified the in-hospital adult patients both with a record of pneumonia and stage 5 or 5D CKD as a comorbidity on admission between April 1, 2012 and March 31, 2016. Predictive variable selection was analyzed by multivariable logistic regression analysis, stepwise method, LASSO method and random forest method, and then develop a new simple scoring system seeking for highest c-statistics combination of variables in one sample data set for model development. Finally, we compared c-statistics of univariate logistic regression about new scoring system with c-statistics about "A-DROP" in the other sample data set. RESULT: We identified 8402 patients in 707 hospitals, and the total in-hospital mortality was 11.0% (437 patients) in development data set. Seven variables were selected, which includes age (male ≥ 70 years, female ≥ 75 years), respiratory failure, orientation disturbance, low blood pressure, the need of assistance in feeding or bowel control, severe or moderate thinness and CRP 200 mg/L or extent of consolidation on chest X-ray ≥ 2/3 of one lung. The c-statistics of univariate logistic regression was 0.8017 using seven variables, while that was 0.7372 using "A-DROP"
CONCLUSION: In advanced CKD patients, if we select appropriate variables for predicting in-hospital mortality, simple scoring system may have better discrimination than "A-DROP".