Key result
A machine learning-derived TAVI readmission score successfully identified patients at high risk for 30-day readmission, demonstrating good performance in an external validation cohort (AUC 0.74).
Why the study?
Identifying predictors of readmissions after transcatheter aortic valve implantation is an important unmet need, prompting exploration of machine learning to predict these events.
Can a machine learning-derived risk score accurately predict 30-day and 90-day readmissions in patients undergoing TAVI?
Cohort (n=117,398)
Yes
Can a machine learning-derived risk score accurately predict 30-day and 90-day readmissions in patients undergoing TAVI?
A machine learning-derived risk score using administrative data can effectively predict 30-day readmission after TAVI, potentially improving post-procedural care.
ML readmission score shows promise in TAVI validation; leaves open whether implementation reduces readmissions without prospective trials.
Background: Identifying predictors of readmissions after transcatheter aortic valve implantation (TAVI) is an important unmet need. Objectives: We sought to explore the role of machine learning (ML) in predicting readmissions after TAVI. Methods: We included patients who underwent TAVI between 2016 and 2019 in the Nationwide Readmission Database. A total of 917 candidate predictors representing all International Classification of Diseases, Tenth Revision, diagnosis and procedure codes were included. First, we used lasso regression to remove noninformative variables and rank informative ones. Next, we used an unsupervised ML model (K-means) to identify patterns/clusters in the data. Furthermore, we used Light Gradient Boosting Machine and Shapley Additive exPlanations to specify the impact of individual predictors. Finally, we built a parsimonious model to predict 30-day readmission. Results: A total of 117,398 and 93,800 index TAVI hospitalizations were included in the 30- and 90-day analyses, respectively. Lasso regression identified 138 and 199 informative predictors for the 30- and 90-day readmission, respectively. Next, K-means recognized 2 distinct clusters: low risk and high risk. In the 30-day cohort, the readmission rate was 10.1% in the low risk group and 23.3% in the high risk group. In the 90-day cohort, the rates were 17.4% and 35.3%, respectively. The top predictors were the length of stay, frailty score, total discharge diagnoses, acute kidney injury, and Elixhauser score. These predictors were incorporated into a risk score (TAVI readmission score), which exhibited good performance in an external validation cohort (area under the curve 0.74 [0.7-0.78]). Conclusions: ML methods can leverage widely available administrative databases to identify patients at risk for readmission after TAVI, which could inform and improve post-TAVI care.
No takes yet. Share an insight, caveat, or question.
Sulaiman et al. (2022) conducted a cohort in Transcatheter aortic valve implantation (TAVI) (n=117,398). High risk cluster identified by machine learning vs. Low risk cluster was evaluated on 30-day readmission (AUC 0.74, 95% CI 0.7-0.78). A machine learning-derived TAVI readmission score successfully identified patients at high risk for 30-day readmission, demonstrating good performance in an external validation cohort (AUC 0.74).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: