Development of a risk-adjusted in-hospital mortality prediction model for community-acquired pneumonia: a retrospective analysis using a Japanese administrative database

BMC Pulmonary Medicine

Yuichi Imanaka¹  Hiroshi Ikai¹  Noriko Sasaki¹  Susumu Kunisawa²  Hironori Uematsu¹ 

[1] Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Yoshida Konoe-cho, Sakyo-ku, Kyoto City, Kyoto 606-8501, Japan;Department of Biomedical Sciences, Ritsumeikan University, Norohigashi, Kusatsu City, Shiga 525-0058, Japan

关键词: Administrative database;    Scoring system;    Severity index;    Prognosis prediction model;    Risk-adjusted mortality;    Community-acquired pneumonia;   

Others  :  1090745
DOI  :  10.1186/1471-2466-14-203

 received in 2014-08-21, accepted in 2014-12-01,  发布年份 2014

【 摘 要 】

Background

Community-acquired pneumonia (CAP) is a common cause of patient hospitalization and death, and its burden on the healthcare system is increasing in aging societies. Here, we develop and internally validate risk-adjustment models and scoring systems for predicting mortality in CAP patients to enable more precise measurements of hospital performance.

Methods

Using a multicenter administrative claims database, we analyzed 35,297 patients hospitalized for CAP who had been discharged between April 1, 2012 and September 30, 2013 from 303 acute care hospitals in Japan. We developed hierarchical logistic regression models to analyze predictors of in-hospital mortality, and validated the models using the bootstrap method. Discrimination of the models was assessed using c-statistics. Additionally, we developed scoring systems based on predictors identified in the regression models.

Results

The 30-day in-hospital mortality rate was 5.8%. Predictors of in-hospital mortality included advanced age, high blood urea nitrogen level or dehydration, orientation disturbance, respiratory failure, low blood pressure, high C-reactive protein levels or high degree of pneumonic infiltration, cancer, and use of mechanical ventilation or vasopressors. Our models showed high levels of discrimination for mortality prediction, with a c-statistic of 0.89 (95% confidence interval: 0.89-0.90) in the bootstrap-corrected model. The scoring system based on 8 selected variables also showed good discrimination, with a c-statistic of 0.87 (95% confidence interval: 0.86-0.88).

Conclusions

Our mortality prediction models using administrative data showed good discriminatory power in CAP patients. These risk-adjustment models may support improvements in quality of care through accurate hospital evaluations and inter-hospital comparisons.

【 授权许可】

   
2014 Uematsu et al.; licensee BioMed Central.

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