Panchangam Prasad V R, A Tejas, B U Thejas, Maniaci Michael J
Data Science Team, Saigeware Inc., Karnataka 560070, India.
Enterprise Physician Lead, Advanced Care at Home Program, Mayo Clinic Hospital, Jacksonville, FL 32224, USA.
Healthcare (Basel). 2024 Jul 28;12(15):1497. doi: 10.3390/healthcare12151497.
The primary objective of this study was to develop a risk-based readmission prediction model using the EMR data available at discharge. This model was then validated with the LACE plus score. The study cohort consisted of about 310,000 hospital admissions of patients with cardiovascular and cerebrovascular conditions. The EMR data of the patients consisted of lab results, vitals, medications, comorbidities, and admit/discharge settings. These data served as the input to an XGBoost model v1.7.6, which was then used to predict the number of days until the next readmission. Our model achieved remarkable results, with a precision score of 0.74 (±0.03), a recall score of 0.75 (±0.02), and an overall accuracy of approximately 82% (±5%). Notably, the model demonstrated a high accuracy rate of 78.39% in identifying the patients readmitted within 30 days and 80.81% accuracy for those with readmissions exceeding six months. The model was able to outperform the LACE plus score; of the people who were readmitted within 30 days, only 47.70 percent had a LACE plus score greater than 70, and, for people with greater than 6 months, only 10.09 percent had a LACE plus score less than 30. Furthermore, our analysis revealed that the patients with a higher comorbidity burden and lower-than-normal hemoglobin levels were associated with increased readmission rates. This study opens new doors to the world of differential patient care, helping both clinical decision makers and healthcare providers make more informed and effective decisions. This model is comparatively more robust and can potentially substitute the LACE plus score in cardiovascular and cerebrovascular settings for predicting the readmission risk.
本研究的主要目的是利用出院时可用的电子病历（EMR）数据开发一种基于风险的再入院预测模型。然后，该模型用LACE+评分进行验证。研究队列包括约310,000例心血管和脑血管疾病患者的住院病例。患者的EMR数据包括实验室检查结果、生命体征、用药情况、合并症以及入院/出院情况。这些数据作为XGBoost模型v1.7.6的输入，该模型随后用于预测下次再入院前的天数。我们的模型取得了显著成果，精确率得分为0.74（±0.03），召回率得分为0.75（±0.02），总体准确率约为82%（±5%）。值得注意的是，该模型在识别30天内再入院的患者方面准确率高达78.39%，对于再入院时间超过6个月的患者，准确率为80.81%。该模型的表现优于LACE+评分；在30天内再入院的患者中，只有47.70%的患者LACE+评分大于70，而对于再入院时间超过6个月的患者，只有10.09%的患者LACE+评分低于30。此外，我们的分析表明，合并症负担较高且血红蛋白水平低于正常的患者再入院率较高。这项研究为差异化患者护理领域打开了新的大门，有助于临床决策者和医疗服务提供者做出更明智、更有效的决策。该模型相对更稳健，在心血管和脑血管疾病环境中预测再入院风险时，有可能替代LACE+评分。
