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首页> 外文期刊>BMC Pulmonary Medicine >Development of a risk-adjusted in-hospital mortality prediction model for community-acquired pneumonia: a retrospective analysis using a Japanese administrative database
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Development of a risk-adjusted in-hospital mortality prediction model for community-acquired pneumonia: a retrospective analysis using a Japanese administrative database

机译：社区获得性肺炎风险调整的院内死亡率预测模型的开发：使用日本行政数据库的回顾性分析

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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.
机译：背景技术社区获得性肺炎（CAP）是患者住院和死亡的常见原因，在老龄化社会中，其对医疗系统的负担正在增加。在这里，我们开发并内部验证了风险调整模型和评分系统，以预测CAP患者的死亡率，从而能够更精确地评​​估医院的绩效。方法使用多中心行政理赔数据库，我们分析了2012年4月1日至2013年9月30日期间在日本303所急诊医院出院的CAP住院患者35297例。我们开发了分层逻辑回归模型来分析院内死亡率的预测因素，并使用自举法对模型进行了验证。使用c统计量评估模型的区别。此外，我们基于回归模型中确定的预测变量开发了评分系统。结果30天住院死亡率为5.8％。院内死亡率的预测因素包括高龄，尿素氮水平高或脱水，定向障碍，呼吸衰竭，血压低，C反应蛋白水平高或肺炎浸润程度高，癌症以及使用机械通气或血管加压药。我们的模型显示出很高的判别死亡率预测能力，并且在经过bootstrap校正的模型中，c统计量为0.89（95％置信区间：0.89-0.90）。基于选择的8个变量的评分系统也显示出良好的辨别力，其c统计量为0.87（95％置信区间：0.86-0.88）。结论我们使用行政数据进行的死亡率预测模型显示出对CAP患者的良好区分能力。这些风险调整模型可以通过准确的医院评估和医院之间的比较来支持改善护理质量。

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