Key result
A machine learning model predicted 30-day hospital readmissions with an F1-score of 0.386, outperforming the LACE index by 12.5% and the PARR model by 22.9%.
Why the study?
The study was designed to develop a machine learning-based model to predict acute hospital readmissions within 30 days of discharge and validate it against existing models.
Does a machine learning predictive model improve the prediction of acute hospital readmissions within 30 days of discharge compared to LACE and PARR models in hospitalized patients?
Cohort (n=180,118)
Yes
Does a machine learning predictive model improve the prediction of acute hospital readmissions within 30 days of discharge compared to LACE and PARR models in hospitalized patients?
A machine learning-based predictive model demonstrated superior performance compared to traditional LACE and PARR indices for predicting 30-day acute hospital readmissions.
May support ML integration into readmission tools; leaves open prospective validation before clinical use.
The objective of this study was to design and develop a 30-day risk of hospital readmission predictive model using machine learning techniques. The proposed risk of readmission predictive model was then validated with the two most commonly used risk of readmission models - LACE index and patient at-risk of hospital readmission (PARR). The study cohort consisted of 180,118 admissions with 22565 (12.5%) of actual readmissions within 30-day of hospital discharge, from 01 Jan 2015 to 31 Dec 2016 from two Auckland-region hospitals. We developed a machine learning model to predict 30-day readmissions using the model types: XGBoost, Random Forests and Adaboost with decision stumps as a base learner with different feature combinations and preprocessing procedures. The proposed model achieved the F1-score (0.386 ± 0.006), sensitivity (0.598 ± 0.013), positive predictive value (PPV) (0.285 ± 0.004) and negative predictive value (NPV) (0.932 ± 0.002). When compared with LACE and PARR (NZ) models, the proposed model achieved better F1-score by 12.5% compared to LACE and 22.9% compared to PARR (NZ). The mean sensitivity of the proposed model was 6.0% higher than LACE and 42.4% higher than PARR (NZ). The mean PPV was 15.9% and 13.5% higher than LACE and PARR (NZ) respectively.
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Baig et al. (2019) conducted a cohort in Hospital readmission (n=180,118). Machine learning predictive model vs. LACE index and PARR models was evaluated on 30-day hospital readmission prediction performance. A machine learning model predicted 30-day hospital readmissions with an F1-score of 0.386, outperforming the LACE index by 12.5% and the PARR model by 22.9%.
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