Machine learning classifiers do not improve prediction of hospitalization > 2 days after fast-track hip and knee arthroplasty compared with a classical statistical risk model
Background and purpose — Prediction of postoperative outcomes and length of hospital stay (LOS) of patients is vital for allocation of healthcare resources. We investigated the performance of prediction models based on machinelearning algorithms compared with a previous risk stratification model usi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Medical Journals Sweden
2022-01-01
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| Series: | Acta Orthopaedica |
| Subjects: | |
| Online Access: | https://actaorthop.org/actao/article/view/843 |
| _version_ | 1831658670701674496 |
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| author | Katrin B Johannesdottir Henrik Kehlet Pelle B Petersen Eske K Aasvang Helge B D Sørensen Christoffer C Jørgensen |
| author_facet | Katrin B Johannesdottir Henrik Kehlet Pelle B Petersen Eske K Aasvang Helge B D Sørensen Christoffer C Jørgensen |
| author_sort | Katrin B Johannesdottir |
| collection | DOAJ |
| description | Background and purpose — Prediction of postoperative outcomes and length of hospital stay (LOS) of patients is vital for allocation of healthcare resources. We investigated the performance of prediction models based on machinelearning algorithms compared with a previous risk stratification model using traditional multiple logistic regression, for predicting the risk of a LOS of > 2 days after fast-track total hip and knee replacement.
Patients and methods — 3 different machine learning classifiers were trained on data from the Lundbeck Centre for Fast-track Hip and Knee Replacement Database (LCDB) collected from 9,512 patients between 2016 and 2017. The chosen classifiers were a random forest classifier (RF), a support vector machine classifier with a polynomial kernel (SVM), and a multinomial Naïve-Bayes classifier (NB).
Results — Comparing performance measures of the classifiers with the traditional model revealed that all the models had a similar performance in terms of F1 score, accuracy, sensitivity, specificity, area under the receiver operating curve (AUC), and area under the precision-recall curve (AUPRC). A feature importance analysis of the RF classifier found hospital, age, use of walking aid, living alone, and joint operated on to be the most relevant input features. None of the classifiers reached a clinically relevant performance with the input data from the LCDB.
Interpretation — Despite the promising prospects of machine-learning practices for disease and risk prediction, none of the machine learning models tested outperformed the traditional multiple regression model in predicting which patients in this cohort had a LOS > 2 days. |
| first_indexed | 2024-12-19T17:47:54Z |
| format | Article |
| id | doaj.art-6b482f2a759f46d3906c23f3659f505d |
| institution | Directory Open Access Journal |
| issn | 1745-3674 1745-3682 |
| language | English |
| last_indexed | 2024-12-19T17:47:54Z |
| publishDate | 2022-01-01 |
| publisher | Medical Journals Sweden |
| record_format | Article |
| series | Acta Orthopaedica |
| spelling | doaj.art-6b482f2a759f46d3906c23f3659f505d2022-12-21T20:12:00ZengMedical Journals SwedenActa Orthopaedica1745-36741745-36822022-01-019310.2340/17453674.2021.843Machine learning classifiers do not improve prediction of hospitalization > 2 days after fast-track hip and knee arthroplasty compared with a classical statistical risk modelKatrin B Johannesdottir0Henrik Kehlet1Pelle B Petersen2Eske K Aasvang3Helge B D Sørensen4Christoffer C Jørgensen5Biomedical Signal Processing & AI research group, Digital Health Section, DTU Health Tech, Technical University of Denmark, LyngbySection of Surgical Pathophysiology 7621, Rigshospitalet, CopenhagenSection of Surgical Pathophysiology 7621, Rigshospitalet, CopenhagenSection of Surgical Pathophysiology 7621, Rigshospitalet, Copenhagen; Department of Anesthesiology, Center for Cancer and Organ Diseases, Copenhagen, DenmarkBiomedical Signal Processing & AI research group, Digital Health Section, DTU Health Tech, Technical University of Denmark, LyngbySection of Surgical Pathophysiology 7621, Rigshospitalet, CopenhagenBackground and purpose — Prediction of postoperative outcomes and length of hospital stay (LOS) of patients is vital for allocation of healthcare resources. We investigated the performance of prediction models based on machinelearning algorithms compared with a previous risk stratification model using traditional multiple logistic regression, for predicting the risk of a LOS of > 2 days after fast-track total hip and knee replacement. Patients and methods — 3 different machine learning classifiers were trained on data from the Lundbeck Centre for Fast-track Hip and Knee Replacement Database (LCDB) collected from 9,512 patients between 2016 and 2017. The chosen classifiers were a random forest classifier (RF), a support vector machine classifier with a polynomial kernel (SVM), and a multinomial Naïve-Bayes classifier (NB). Results — Comparing performance measures of the classifiers with the traditional model revealed that all the models had a similar performance in terms of F1 score, accuracy, sensitivity, specificity, area under the receiver operating curve (AUC), and area under the precision-recall curve (AUPRC). A feature importance analysis of the RF classifier found hospital, age, use of walking aid, living alone, and joint operated on to be the most relevant input features. None of the classifiers reached a clinically relevant performance with the input data from the LCDB. Interpretation — Despite the promising prospects of machine-learning practices for disease and risk prediction, none of the machine learning models tested outperformed the traditional multiple regression model in predicting which patients in this cohort had a LOS > 2 days.https://actaorthop.org/actao/article/view/843ArthroplastyHipKneeLength of hospital stayOutcomePrediction |
| spellingShingle | Katrin B Johannesdottir Henrik Kehlet Pelle B Petersen Eske K Aasvang Helge B D Sørensen Christoffer C Jørgensen Machine learning classifiers do not improve prediction of hospitalization > 2 days after fast-track hip and knee arthroplasty compared with a classical statistical risk model Acta Orthopaedica Arthroplasty Hip Knee Length of hospital stay Outcome Prediction |
| title | Machine learning classifiers do not improve prediction of hospitalization > 2 days after fast-track hip and knee arthroplasty compared with a classical statistical risk model |
| title_full | Machine learning classifiers do not improve prediction of hospitalization > 2 days after fast-track hip and knee arthroplasty compared with a classical statistical risk model |
| title_fullStr | Machine learning classifiers do not improve prediction of hospitalization > 2 days after fast-track hip and knee arthroplasty compared with a classical statistical risk model |
| title_full_unstemmed | Machine learning classifiers do not improve prediction of hospitalization > 2 days after fast-track hip and knee arthroplasty compared with a classical statistical risk model |
| title_short | Machine learning classifiers do not improve prediction of hospitalization > 2 days after fast-track hip and knee arthroplasty compared with a classical statistical risk model |
| title_sort | machine learning classifiers do not improve prediction of hospitalization 2 days after fast track hip and knee arthroplasty compared with a classical statistical risk model |
| topic | Arthroplasty Hip Knee Length of hospital stay Outcome Prediction |
| url | https://actaorthop.org/actao/article/view/843 |
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