Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty
Abstract Venous thromboembolism (VTE) and major bleeding (MBE) are feared complications that are influenced by numerous host and surgical related factors. Using machine learning on contemporary data, our aim was to develop and validate a practical, easy-to-use algorithm to predict risk for VTE and M...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-26032-1 |
| _version_ | 1811165923793436672 |
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| author | Noam Shohat Leanne Ludwick Matthew B. Sherman Yale Fillingham Javad Parvizi |
| author_facet | Noam Shohat Leanne Ludwick Matthew B. Sherman Yale Fillingham Javad Parvizi |
| author_sort | Noam Shohat |
| collection | DOAJ |
| description | Abstract Venous thromboembolism (VTE) and major bleeding (MBE) are feared complications that are influenced by numerous host and surgical related factors. Using machine learning on contemporary data, our aim was to develop and validate a practical, easy-to-use algorithm to predict risk for VTE and MBE following total joint arthroplasty (TJA). This was a single institutional study of 35,963 primary and revision total hip (THA) and knee arthroplasty (TKA) patients operated between 2009 and 2020. Fifty-six variables related to demographics, comorbidities, operative factors as well as chemoprophylaxis were included in the analysis. The cohort was divided to training (70%) and test (30%) sets. Four machine learning models were developed for each of the outcomes assessed (VTE and MBE). Models were created for all VTE grouped together as well as for pulmonary emboli (PE) and deep vein thrombosis (DVT) individually to examine the need for distinct algorithms. For each outcome, the model that best performed using repeated cross validation was chosen for algorithm development, and predicted versus observed incidences were evaluated. Of the 35,963 patients included, 308 (0.86%) developed VTE (170 PE’s, 176 DVT’s) and 293 (0.81%) developed MBE. Separate models were created for PE and DVT as they were found to outperform the prediction of VTE. Gradient boosting trees had the highest performance for both PE (AUC-ROC 0.774 [SD 0.055]) and DVT (AUC-ROC 0.759 [SD 0.039]). For MBE, least absolute shrinkage and selection operator (Lasso) analysis had the highest AUC (AUC-ROC 0.803 [SD 0.035]). An algorithm that provides the probability for PE, DVT and MBE for each specific patient was created. All 3 algorithms had good discriminatory capability and cross-validation showed similar probabilities comparing predicted and observed failures indicating high accuracy of the model. We successfully developed and validated an easy-to-use algorithm that accurately predicts VTE and MBE following TJA. This tool can be used in every-day clinical decision making and patient counseling. |
| first_indexed | 2024-04-10T15:45:12Z |
| format | Article |
| id | doaj.art-4f557c047b76458a8d4a39b0553b9972 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T15:45:12Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4f557c047b76458a8d4a39b0553b99722023-02-12T12:10:35ZengNature PortfolioScientific Reports2045-23222023-02-0113111110.1038/s41598-022-26032-1Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplastyNoam Shohat0Leanne Ludwick1Matthew B. Sherman2Yale Fillingham3Javad Parvizi4Rothman Orthopaedic Institute at Thomas Jefferson UniversityRothman Orthopaedic Institute at Thomas Jefferson UniversityRothman Orthopaedic Institute at Thomas Jefferson UniversityRothman Orthopaedic Institute at Thomas Jefferson UniversityRothman Orthopaedic Institute at Thomas Jefferson UniversityAbstract Venous thromboembolism (VTE) and major bleeding (MBE) are feared complications that are influenced by numerous host and surgical related factors. Using machine learning on contemporary data, our aim was to develop and validate a practical, easy-to-use algorithm to predict risk for VTE and MBE following total joint arthroplasty (TJA). This was a single institutional study of 35,963 primary and revision total hip (THA) and knee arthroplasty (TKA) patients operated between 2009 and 2020. Fifty-six variables related to demographics, comorbidities, operative factors as well as chemoprophylaxis were included in the analysis. The cohort was divided to training (70%) and test (30%) sets. Four machine learning models were developed for each of the outcomes assessed (VTE and MBE). Models were created for all VTE grouped together as well as for pulmonary emboli (PE) and deep vein thrombosis (DVT) individually to examine the need for distinct algorithms. For each outcome, the model that best performed using repeated cross validation was chosen for algorithm development, and predicted versus observed incidences were evaluated. Of the 35,963 patients included, 308 (0.86%) developed VTE (170 PE’s, 176 DVT’s) and 293 (0.81%) developed MBE. Separate models were created for PE and DVT as they were found to outperform the prediction of VTE. Gradient boosting trees had the highest performance for both PE (AUC-ROC 0.774 [SD 0.055]) and DVT (AUC-ROC 0.759 [SD 0.039]). For MBE, least absolute shrinkage and selection operator (Lasso) analysis had the highest AUC (AUC-ROC 0.803 [SD 0.035]). An algorithm that provides the probability for PE, DVT and MBE for each specific patient was created. All 3 algorithms had good discriminatory capability and cross-validation showed similar probabilities comparing predicted and observed failures indicating high accuracy of the model. We successfully developed and validated an easy-to-use algorithm that accurately predicts VTE and MBE following TJA. This tool can be used in every-day clinical decision making and patient counseling.https://doi.org/10.1038/s41598-022-26032-1 |
| spellingShingle | Noam Shohat Leanne Ludwick Matthew B. Sherman Yale Fillingham Javad Parvizi Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty Scientific Reports |
| title | Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty |
| title_full | Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty |
| title_fullStr | Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty |
| title_full_unstemmed | Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty |
| title_short | Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty |
| title_sort | using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty |
| url | https://doi.org/10.1038/s41598-022-26032-1 |
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