Choi Seohyun, Kim Young Jae, Lee Seon Min, Kim Kwang Gi
Department of Medicine, College of Medicine, Gachon University, Incheon, Republic of Korea.
Gachon Biomedical & Convergence Institute, Gil Medical Center, Gachon University, Incheon, Republic of Korea.
Digit Health. 2025 Apr 3;11:20552076251325990. doi: 10.1177/20552076251325990. eCollection 2025 Jan-Dec.
Identifying factors that increase the risk of hospital readmission will help determine high-risk patients and decrease the socioeconomic burden. Pneumonia is associated with high readmission rates. Although residential greenness has been reported to have beneficial health effects, no studies have investigated its importance in predicting readmission in patients with pneumonia. This study aimed to build prediction models for 30-day readmission in patients with pneumonia and to analyze the importance of risk factors for readmission, mainly residential greenness.
Data on 47 risk factors were collected from 22,600 patients diagnosed with pneumonia. Residential greenness was quantified as the mean of normalized difference vegetation index of the district in which the patient resides. Prediction models were built using logistic regression, support vector machine, random forest, and extreme gradient boosting.
Residential greenness was selected from the top 21 risk factors after feature selection. The area under the curves of the four models were 0.6919, 0.6931, 0.7117, and 0.7044. Age, red blood cell distribution width, and history of cancer were the top three risk factors affecting readmission prediction. Residential greenness was the 15th important factor.
We constructed prediction models for 30-day readmission of patients with pneumonia by incorporating residential greenness as a risk factor. The models demonstrated sufficient performance, and residential greenness was significant in predicting readmission. Incorporating residential greenness into the identification of groups at high risk for readmission can complement the possible loss of information when using data from electronic health records.
识别增加医院再入院风险的因素将有助于确定高危患者并减轻社会经济负担。肺炎与高再入院率相关。尽管已有报道称居住环境绿化对健康有益，但尚无研究调查其在预测肺炎患者再入院方面的重要性。本研究旨在建立肺炎患者30天再入院的预测模型，并分析再入院风险因素的重要性，主要是居住环境绿化。
从22600例诊断为肺炎的患者中收集了47个风险因素的数据。居住环境绿化被量化为患者居住地区归一化植被指数的平均值。使用逻辑回归、支持向量机、随机森林和极端梯度提升建立预测模型。
经过特征选择后，居住环境绿化从21个最重要的风险因素中被选出。四个模型的曲线下面积分别为0.6919、0.6931、0.7117和0.7044。年龄、红细胞分布宽度和癌症病史是影响再入院预测的前三大风险因素。居住环境绿化是第15个重要因素。
我们通过将居住环境绿化作为一个风险因素纳入，构建了肺炎患者30天再入院的预测模型。这些模型表现出足够的性能，并且居住环境绿化在预测再入院方面具有重要意义。将居住环境绿化纳入再入院高危人群的识别中，可以补充使用电子健康记录数据时可能丢失的信息。
