Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in China
BackgroundTotal knee arthroplasty (TKA) is the ultimate option for end-stage osteoarthritis, and the demand of this procedure are increasing every year. The length of hospital stay (LOS) greatly affects the overall cost of joint arthroplasty. The purpose of this study was to develop and validate a p...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-04-01
|
| Series: | Frontiers in Surgery |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fsurg.2023.1102371/full |
| _version_ | 1797851364403970048 |
|---|---|
| author | Bo Zhu Dejun Zhang Maocheng Sang Long Zhao Chaoqun Wang Yunqiang Xu |
| author_facet | Bo Zhu Dejun Zhang Maocheng Sang Long Zhao Chaoqun Wang Yunqiang Xu |
| author_sort | Bo Zhu |
| collection | DOAJ |
| description | BackgroundTotal knee arthroplasty (TKA) is the ultimate option for end-stage osteoarthritis, and the demand of this procedure are increasing every year. The length of hospital stay (LOS) greatly affects the overall cost of joint arthroplasty. The purpose of this study was to develop and validate a predictive model using perioperative data to estimate the risk of prolonged LOS in patients undergoing TKA.MethodsData for 694 patients after TKA collected retrospectively in our department were analyzed by logistic regression models. Multi-variable logistic regression modeling with forward stepwise elimination was used to determine reduced parameters and establish a prediction model. The discrimination efficacy, calibration efficacy, and clinical utility of the prediction model were evaluated.ResultsEight independent predictors were identified: non-medical insurance payment, Charlson Comorbidity Index (CCI) ≥ 3, body mass index (BMI) > 25.2, surgery on Monday, age > 67.5, postoperative complications, blood transfusion, and operation time > 120.5 min had a higher probability of hospitalization for ≥6 days. The model had good discrimination [area under the curve (AUC), 0.802 95% CI, 0.754–0.850]] and good calibration (p = 0.929). A decision curve analysis proved that the nomogram was clinically effective.ConclusionThis study identified risk factors for prolonged hospital stay in patients after TKA. It is important to recognize all the factors that affect hospital LOS to try to maximize the use of medical resources, optimize hospital LOS and ultimately optimize the care of our patients. |
| first_indexed | 2024-04-09T19:16:43Z |
| format | Article |
| id | doaj.art-4b9d52e10aad4e29bc9d1cdacdb2f2a6 |
| institution | Directory Open Access Journal |
| issn | 2296-875X |
| language | English |
| last_indexed | 2024-04-09T19:16:43Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Surgery |
| spelling | doaj.art-4b9d52e10aad4e29bc9d1cdacdb2f2a62023-04-06T05:22:31ZengFrontiers Media S.A.Frontiers in Surgery2296-875X2023-04-011010.3389/fsurg.2023.11023711102371Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in ChinaBo ZhuDejun ZhangMaocheng SangLong ZhaoChaoqun WangYunqiang XuBackgroundTotal knee arthroplasty (TKA) is the ultimate option for end-stage osteoarthritis, and the demand of this procedure are increasing every year. The length of hospital stay (LOS) greatly affects the overall cost of joint arthroplasty. The purpose of this study was to develop and validate a predictive model using perioperative data to estimate the risk of prolonged LOS in patients undergoing TKA.MethodsData for 694 patients after TKA collected retrospectively in our department were analyzed by logistic regression models. Multi-variable logistic regression modeling with forward stepwise elimination was used to determine reduced parameters and establish a prediction model. The discrimination efficacy, calibration efficacy, and clinical utility of the prediction model were evaluated.ResultsEight independent predictors were identified: non-medical insurance payment, Charlson Comorbidity Index (CCI) ≥ 3, body mass index (BMI) > 25.2, surgery on Monday, age > 67.5, postoperative complications, blood transfusion, and operation time > 120.5 min had a higher probability of hospitalization for ≥6 days. The model had good discrimination [area under the curve (AUC), 0.802 95% CI, 0.754–0.850]] and good calibration (p = 0.929). A decision curve analysis proved that the nomogram was clinically effective.ConclusionThis study identified risk factors for prolonged hospital stay in patients after TKA. It is important to recognize all the factors that affect hospital LOS to try to maximize the use of medical resources, optimize hospital LOS and ultimately optimize the care of our patients.https://www.frontiersin.org/articles/10.3389/fsurg.2023.1102371/fulltotal knee arthroplasty (TKA)length of hospital stay (LOS)nomogramprediction modelpayment method |
| spellingShingle | Bo Zhu Dejun Zhang Maocheng Sang Long Zhao Chaoqun Wang Yunqiang Xu Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in China Frontiers in Surgery total knee arthroplasty (TKA) length of hospital stay (LOS) nomogram prediction model payment method |
| title | Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in China |
| title_full | Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in China |
| title_fullStr | Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in China |
| title_full_unstemmed | Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in China |
| title_short | Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in China |
| title_sort | establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty a single center retrospective study in china |
| topic | total knee arthroplasty (TKA) length of hospital stay (LOS) nomogram prediction model payment method |
| url | https://www.frontiersin.org/articles/10.3389/fsurg.2023.1102371/full |
| work_keys_str_mv | AT bozhu establishmentandevaluationofapredictivemodelforlengthofhospitalstayaftertotalkneearthroplastyasinglecenterretrospectivestudyinchina AT dejunzhang establishmentandevaluationofapredictivemodelforlengthofhospitalstayaftertotalkneearthroplastyasinglecenterretrospectivestudyinchina AT maochengsang establishmentandevaluationofapredictivemodelforlengthofhospitalstayaftertotalkneearthroplastyasinglecenterretrospectivestudyinchina AT longzhao establishmentandevaluationofapredictivemodelforlengthofhospitalstayaftertotalkneearthroplastyasinglecenterretrospectivestudyinchina AT chaoqunwang establishmentandevaluationofapredictivemodelforlengthofhospitalstayaftertotalkneearthroplastyasinglecenterretrospectivestudyinchina AT yunqiangxu establishmentandevaluationofapredictivemodelforlengthofhospitalstayaftertotalkneearthroplastyasinglecenterretrospectivestudyinchina |