Predictors of Length-of-Stay Among Transcatheter Aortic Valve Replacement Patients Using a Supervised Machine Learning Algorithm
Length of stay following transcatheter aortic valve replacement (TAVR) continues to improve, but significant gaps remain in predicting the length of stay following TAVR. This study aimed to develop a novel machine learning (ML) algorithm that would facilitate the understanding of the predictors of e...
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| Published in: | JACC. Advances (Online) Vol. 4; no. 8; p. 101902 |
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01.08.2025
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| ISSN: | 2772-963X, 2772-963X |
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| Abstract | Length of stay following transcatheter aortic valve replacement (TAVR) continues to improve, but significant gaps remain in predicting the length of stay following TAVR.
This study aimed to develop a novel machine learning (ML) algorithm that would facilitate the understanding of the predictors of early and late hospital discharge in patients who have undergone TAVR.
Using the Biome data set, 9,172 outpatient TAVR procedures were analyzed from 21 centers between 2017 and 2021 across the United States. Supervised random forest ML algorithms were developed to identify variables involved in short length of stay (SLOS) (length of stay <36 hours) and long length of stay (LLOS) (length of stay ≥72 hours) in a 70% sample of the Biome data set. The models were then tested on the remaining 30% of the data set and results compared to standard multivariable models in predicting LOS.
Twenty and 22 variables were identified and included as important predictors for the SLOS and LLOS multivariable models, respectively. The predictive power of both the SLOS (sensitivity 0.81, specificity 0.70, area under the curve [AUC] 0.82) and LLOS (sensitivity 0.45, specificity 0.94, AUC 0.85) ML models were more robust than the standard multivariable model (SLOS AUC 0.65, LLOS AUC 0.65). Our study uncovered several previously unreported predictors for length of stay following TAVR, such as procedural duration, postprocedure physical therapy, and procedure day of the week.
ML algorithms may have an important role in identifying novel predictors of short and prolonged length of stay following TAVR. These efforts may facilitate targeted quality improvement programs to decrease length of stay post-TAVR.
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| AbstractList | AbstractBackgroundLength of stay following transcatheter aortic valve replacement (TAVR) continues to improve, but significant gaps remain in predicting the length of stay following TAVR. ObjectivesThis study aimed to develop a novel machine learning (ML) algorithm that would facilitate the understanding of the predictors of early and late hospital discharge in patients who have undergone TAVR. MethodsUsing the Biome data set, 9,172 outpatient TAVR procedures were analyzed from 21 centers between 2017 and 2021 across the United States. Supervised random forest ML algorithms were developed to identify variables involved in short length of stay (SLOS) (length of stay <36 hours) and long length of stay (LLOS) (length of stay ≥72 hours) in a 70% sample of the Biome data set. The models were then tested on the remaining 30% of the data set and results compared to standard multivariable models in predicting LOS. ResultsTwenty and 22 variables were identified and included as important predictors for the SLOS and LLOS multivariable models, respectively. The predictive power of both the SLOS (sensitivity 0.81, specificity 0.70, area under the curve [AUC] 0.82) and LLOS (sensitivity 0.45, specificity 0.94, AUC 0.85) ML models were more robust than the standard multivariable model (SLOS AUC 0.65, LLOS AUC 0.65). Our study uncovered several previously unreported predictors for length of stay following TAVR, such as procedural duration, postprocedure physical therapy, and procedure day of the week. ConclusionsML algorithms may have an important role in identifying novel predictors of short and prolonged length of stay following TAVR. These efforts may facilitate targeted quality improvement programs to decrease length of stay post-TAVR. Length of stay following transcatheter aortic valve replacement (TAVR) continues to improve, but significant gaps remain in predicting the length of stay following TAVR. This study aimed to develop a novel machine learning (ML) algorithm that would facilitate the understanding of the predictors of early and late hospital discharge in patients who have undergone TAVR. Using the Biome data set, 9,172 outpatient TAVR procedures were analyzed from 21 centers between 2017 and 2021 across the United States. Supervised random forest ML algorithms were developed to identify variables involved in short length of stay (SLOS) (length of stay <36 hours) and long length of stay (LLOS) (length of stay ≥72 hours) in a 70% sample of the Biome data set. The models were then tested on the remaining 30% of the data set and results compared to standard multivariable models in predicting LOS. Twenty and 22 variables were identified and included as important predictors for the SLOS and LLOS multivariable models, respectively. The predictive power of both the SLOS (sensitivity 0.81, specificity 0.70, area under the curve [AUC] 0.82) and LLOS (sensitivity 0.45, specificity 0.94, AUC 0.85) ML models were more robust than the standard multivariable model (SLOS AUC 0.65, LLOS AUC 0.65). Our study uncovered several previously unreported predictors for length of stay following TAVR, such as procedural duration, postprocedure physical therapy, and procedure day of the week. ML algorithms may have an important role in identifying novel predictors of short and prolonged length of stay following TAVR. These efforts may facilitate targeted quality improvement programs to decrease length of stay post-TAVR. [Display omitted] Background: Length of stay following transcatheter aortic valve replacement (TAVR) continues to improve, but significant gaps remain in predicting the length of stay following TAVR. Objectives: This study aimed to develop a novel machine learning (ML) algorithm that would facilitate the understanding of the predictors of early and late hospital discharge in patients who have undergone TAVR. Methods: Using the Biome data set, 9,172 outpatient TAVR procedures were analyzed from 21 centers between 2017 and 2021 across the United States. Supervised random forest ML algorithms were developed to identify variables involved in short length of stay (SLOS) (length of stay <36 hours) and long length of stay (LLOS) (length of stay ≥72 hours) in a 70% sample of the Biome data set. The models were then tested on the remaining 30% of the data set and results compared to standard multivariable models in predicting LOS. Results: Twenty and 22 variables were identified and included as important predictors for the SLOS and LLOS multivariable models, respectively. The predictive power of both the SLOS (sensitivity 0.81, specificity 0.70, area under the curve [AUC] 0.82) and LLOS (sensitivity 0.45, specificity 0.94, AUC 0.85) ML models were more robust than the standard multivariable model (SLOS AUC 0.65, LLOS AUC 0.65). Our study uncovered several previously unreported predictors for length of stay following TAVR, such as procedural duration, postprocedure physical therapy, and procedure day of the week. Conclusions: ML algorithms may have an important role in identifying novel predictors of short and prolonged length of stay following TAVR. These efforts may facilitate targeted quality improvement programs to decrease length of stay post-TAVR. Length of stay following transcatheter aortic valve replacement (TAVR) continues to improve, but significant gaps remain in predicting the length of stay following TAVR.BACKGROUNDLength of stay following transcatheter aortic valve replacement (TAVR) continues to improve, but significant gaps remain in predicting the length of stay following TAVR.This study aimed to develop a novel machine learning (ML) algorithm that would facilitate the understanding of the predictors of early and late hospital discharge in patients who have undergone TAVR.OBJECTIVESThis study aimed to develop a novel machine learning (ML) algorithm that would facilitate the understanding of the predictors of early and late hospital discharge in patients who have undergone TAVR.Using the Biome data set, 9,172 outpatient TAVR procedures were analyzed from 21 centers between 2017 and 2021 across the United States. Supervised random forest ML algorithms were developed to identify variables involved in short length of stay (SLOS) (length of stay <36 hours) and long length of stay (LLOS) (length of stay ≥72 hours) in a 70% sample of the Biome data set. The models were then tested on the remaining 30% of the data set and results compared to standard multivariable models in predicting LOS.METHODSUsing the Biome data set, 9,172 outpatient TAVR procedures were analyzed from 21 centers between 2017 and 2021 across the United States. Supervised random forest ML algorithms were developed to identify variables involved in short length of stay (SLOS) (length of stay <36 hours) and long length of stay (LLOS) (length of stay ≥72 hours) in a 70% sample of the Biome data set. The models were then tested on the remaining 30% of the data set and results compared to standard multivariable models in predicting LOS.Twenty and 22 variables were identified and included as important predictors for the SLOS and LLOS multivariable models, respectively. The predictive power of both the SLOS (sensitivity 0.81, specificity 0.70, area under the curve [AUC] 0.82) and LLOS (sensitivity 0.45, specificity 0.94, AUC 0.85) ML models were more robust than the standard multivariable model (SLOS AUC 0.65, LLOS AUC 0.65). Our study uncovered several previously unreported predictors for length of stay following TAVR, such as procedural duration, postprocedure physical therapy, and procedure day of the week.RESULTSTwenty and 22 variables were identified and included as important predictors for the SLOS and LLOS multivariable models, respectively. The predictive power of both the SLOS (sensitivity 0.81, specificity 0.70, area under the curve [AUC] 0.82) and LLOS (sensitivity 0.45, specificity 0.94, AUC 0.85) ML models were more robust than the standard multivariable model (SLOS AUC 0.65, LLOS AUC 0.65). Our study uncovered several previously unreported predictors for length of stay following TAVR, such as procedural duration, postprocedure physical therapy, and procedure day of the week.ML algorithms may have an important role in identifying novel predictors of short and prolonged length of stay following TAVR. These efforts may facilitate targeted quality improvement programs to decrease length of stay post-TAVR.CONCLUSIONSML algorithms may have an important role in identifying novel predictors of short and prolonged length of stay following TAVR. These efforts may facilitate targeted quality improvement programs to decrease length of stay post-TAVR. Length of stay following transcatheter aortic valve replacement (TAVR) continues to improve, but significant gaps remain in predicting the length of stay following TAVR. This study aimed to develop a novel machine learning (ML) algorithm that would facilitate the understanding of the predictors of early and late hospital discharge in patients who have undergone TAVR. Using the Biome data set, 9,172 outpatient TAVR procedures were analyzed from 21 centers between 2017 and 2021 across the United States. Supervised random forest ML algorithms were developed to identify variables involved in short length of stay (SLOS) (length of stay <36 hours) and long length of stay (LLOS) (length of stay ≥72 hours) in a 70% sample of the Biome data set. The models were then tested on the remaining 30% of the data set and results compared to standard multivariable models in predicting LOS. Twenty and 22 variables were identified and included as important predictors for the SLOS and LLOS multivariable models, respectively. The predictive power of both the SLOS (sensitivity 0.81, specificity 0.70, area under the curve [AUC] 0.82) and LLOS (sensitivity 0.45, specificity 0.94, AUC 0.85) ML models were more robust than the standard multivariable model (SLOS AUC 0.65, LLOS AUC 0.65). Our study uncovered several previously unreported predictors for length of stay following TAVR, such as procedural duration, postprocedure physical therapy, and procedure day of the week. ML algorithms may have an important role in identifying novel predictors of short and prolonged length of stay following TAVR. These efforts may facilitate targeted quality improvement programs to decrease length of stay post-TAVR. |
| ArticleNumber | 101902 |
| Author | Lawrence, Skye Agrawal, Harsh Judson, Gregory L. Soni, Krishan Tolstrup, Kirsten Mahadevan, Vaikom S. Luck, Jeff Jaganathan, Vijayadithyan Khaki, Rakan |
| Author_xml | – sequence: 1 givenname: Gregory L. surname: Judson fullname: Judson, Gregory L. organization: Department of Medicine Division of Cardiology, University of California-San Francisco, San Francisco, California, USA – sequence: 2 givenname: Jeff surname: Luck fullname: Luck, Jeff organization: Biome Analytics, Chicago, Illinois, USA – sequence: 3 givenname: Skye surname: Lawrence fullname: Lawrence, Skye organization: Biome Analytics, Chicago, Illinois, USA – sequence: 4 givenname: Rakan surname: Khaki fullname: Khaki, Rakan organization: Biome Analytics, Chicago, Illinois, USA – sequence: 5 givenname: Harsh surname: Agrawal fullname: Agrawal, Harsh organization: Department of Medicine Division of Cardiology, University of California-San Francisco, San Francisco, California, USA – sequence: 6 givenname: Krishan surname: Soni fullname: Soni, Krishan organization: Department of Medicine Division of Cardiology, University of California-San Francisco, San Francisco, California, USA – sequence: 7 givenname: Kirsten surname: Tolstrup fullname: Tolstrup, Kirsten organization: Department of Medicine Division of Cardiology, University of California-San Francisco, San Francisco, California, USA – sequence: 8 givenname: Vijayadithyan surname: Jaganathan fullname: Jaganathan, Vijayadithyan organization: University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA – sequence: 9 givenname: Vaikom S. orcidid: 0000-0001-6354-1873 surname: Mahadevan fullname: Mahadevan, Vaikom S. email: Vaikom.Mahadevan@umassmed.edu organization: Department of Medicine Division of Cardiology, University of California-San Francisco, San Francisco, California, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40561612$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.jacc.2020.09.595 10.1080/21642583.2014.956265 10.1007/s00392-020-01647-4 10.21037/cdt-21-717 10.1080/14779072.2021.1920926 10.1016/j.ahj.2016.06.028 10.1186/s12872-017-0573-7 10.1016/j.jacc.2020.11.018 10.1016/j.ahj.2016.08.016 10.25270/jic/18.00280 10.1016/j.jcin.2019.04.040 10.1007/s10554-020-01944-z 10.1016/j.jcin.2014.04.005 |
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| Keywords | RFECV KCCQ LLOS machine learning TAVR AUC BSA LOS STS length of stay SLOS BMI ML long length of stay recursive feature elimination with cross-validation body mass index short length of stay Kansas City Cardiomyopathy Questionnaire area under curve Society of Thoracic Surgeon transcatheter aortic valve replacement body surface area |
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| SubjectTerms | Cardiovascular length of stay machine learning Original Research TAVR |
| Title | Predictors of Length-of-Stay Among Transcatheter Aortic Valve Replacement Patients Using a Supervised Machine Learning Algorithm |
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