Perioperative predictors of prolonged length of hospital stay following total knee arthroplasty: a retrospective study from a single center in China

Song, Xiaoxiao; Xia, Caiwei; Li, Qiangqiang; Yao, Chen; Yao, Yao; Chen, Dongyang; Jiang, Qing

doi:10.1186/s12891-020-3042-x

Cited by 24 publications

(28 citation statements)

References 28 publications

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“…In our study, the mean in-hospital stay duration was nine days and was significantly longer in patients transferred to an acute rehabilitation facility compared to those discharged at home. This was in accordance with previous studies reporting in-hospital stay duration ranging from eight to 11 days after TKA in elderly patients [ 35 – 37 ]. However, the difference of three days we observed between these two groups of patients was mainly related to the delay to obtain a bed in a geriatric rehabilitation facility.…”

Section: Discussionsupporting

confidence: 93%

Total knee arthroplasty improves the quality-adjusted life years in patients who exceeded their estimated life expectancy

Palazzuolo

Antoniadis

Mahlouly

et al. 2021

International Orthopaedics (SICOT)

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Purpose Total knee arthroplasty (TKA) is the treatment of choice for end-stage osteoarthritis though its risk-benefit ratio in elderly patients remains debated. This study aimed to evaluate the functional outcome, rates of complication and mortality, and quality-adjusted life years (QALY) in patients who exceeded their estimated life expectancy. Methods Ninety-seven TKA implanted in 86 patients who exceeded their estimated life expectancy at the time of TKA were prospectively included in our institutional joint registry and retrospectively analyzed. At latest follow-up, the functional outcome with the Knee Society Score (KSS), rates of complication and mortality, and QALY with utility value of EuroQol-5D score were evaluated. Results At a mean follow-up of three ± one years, the pre- to post-operative KSS improved significantly (p < 0.01). The rates of surgical and major medical complications related to TKA were 3% and 10%, respectively. The re-operation rate with readmission was 3% while no TKA was revised. The 30-day and one year mortality was 1% and 3%, respectively. The pre- to one year post-operative QALY improved significantly (p < 0.01). The cumulative QALY five years after TKA was four years. Assuming that these patients did not undergo TKA, their cumulative QALY at five years would have been only two years. Conclusion TKA is an effective procedure for the treatment of end-stage osteoarthritis in patients who exceeded their estimated life expectancy. TKA provided significant improvement in function and quality of life without adversely affecting overall morbidity and mortality. Therefore, TKA should not be contra-indicated in elderly patients based on their advanced age alone.

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Section: Discussionsupporting

confidence: 93%

Total knee arthroplasty improves the quality-adjusted life years in patients who exceeded their estimated life expectancy

Palazzuolo

Antoniadis

Mahlouly

et al. 2021

International Orthopaedics (SICOT)

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“…Patients with pLOS may have serious medical situations and thus need a longer hospital stay. We adopted this pLOS definition in our study by referring to existing studies [44][45][46] and consulting with experienced clinicians. The pLOS prediction models developed in our study aimed to assist physicians in identifying patients at a high pLOS risk and thus to provide early and timely interventions for these high-risk patients.…”

Section: Outcome and Predictor Variablesmentioning

confidence: 99%

“…In this study, pLOS was defined as an LOS longer than the average LOS by referring to existing studies [44][45][46] and consulting with experienced clinicians. In the literature, there is no consensus on the definition of pLOS for general patients or PD-treated patients.…”

Section: Definition Of Plosmentioning

confidence: 99%

“…In the literature, there is no consensus on the definition of pLOS for general patients or PD-treated patients. Existing studies have defined pLOS as an LOS longer than the average LOS [44][45][46], longer than the median LOS [68], or longer than a specific LOS according to experiences [69]. After consulting with experienced clinicians, we know that the average LOS is a more important metric for PD-treated patients, and it is also a more commonly used metric in assessing medical efficiency around the world.…”

Section: Definition Of Plosmentioning

confidence: 99%

“…After consulting with experienced clinicians, we know that the average LOS is a more important metric for PD-treated patients, and it is also a more commonly used metric in assessing medical efficiency around the world. In addition, pLOS has been defined as an LOS longer than the average LOS in various medical fields by researchers from different countries [44][45][46]. Among the 3 cited references that defined pLOS as an LOS longer than the average LOS, one study [44] was of trauma patients in the United States, another study [45] was of critically ill patients in Switzerland, and the third study [46] was of surgery patients in China.…”

Section: Definition Of Plosmentioning

confidence: 99%

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Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis–Treated Patients Using Stacked Generalization: Model Development and Validation Study

Kong¹,

Wu²,

Chu³

et al. 2021

JMIR Med Inform

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Background The increasing number of patients treated with peritoneal dialysis (PD) and their consistently high rate of hospital admissions have placed a large burden on the health care system. Early clinical interventions and optimal management of patients at a high risk of prolonged length of stay (pLOS) may help improve the medical efficiency and prognosis of PD-treated patients. If timely clinical interventions are not provided, patients at a high risk of pLOS may face a poor prognosis and high medical expenses, which will also be a burden on hospitals. Therefore, physicians need an effective pLOS prediction model for PD-treated patients. Objective This study aimed to develop an optimal data-driven model for predicting the pLOS risk of PD-treated patients using basic admission data. Methods Patient data collected using the Hospital Quality Monitoring System (HQMS) in China were used to develop pLOS prediction models. A stacking model was constructed with support vector machine, random forest (RF), and K-nearest neighbor algorithms as its base models and traditional logistic regression (LR) as its meta-model. The meta-model used the outputs of all 3 base models as input and generated the output of the stacking model. Another LR-based pLOS prediction model was built as the benchmark model. The prediction performance of the stacking model was compared with that of its base models and the benchmark model. Five-fold cross-validation was employed to develop and validate the models. Performance measures included the Brier score, area under the receiver operating characteristic curve (AUROC), estimated calibration index (ECI), accuracy, sensitivity, specificity, and geometric mean (Gm). In addition, a calibration plot was employed to visually demonstrate the calibration power of each model. Results The final cohort extracted from the HQMS database consisted of 23,992 eligible PD-treated patients, among whom 30.3% had a pLOS (ie, longer than the average LOS, which was 16 days in our study). Among the models, the stacking model achieved the best calibration (ECI 8.691), balanced accuracy (Gm 0.690), accuracy (0.695), and specificity (0.701). Meanwhile, the stacking and RF models had the best overall performance (Brier score 0.174 for both) and discrimination (AUROC 0.757 for the stacking model and 0.756 for the RF model). Compared with the benchmark LR model, the stacking model was superior in all performance measures except sensitivity, but there was no significant difference in sensitivity between the 2 models. The 2-sided t tests revealed significant performance differences between the stacking and LR models in overall performance, discrimination, calibration, balanced accuracy, and accuracy. Conclusions This study is the first to develop data-driven pLOS prediction models for PD-treated patients using basic admission data from a national database. The results indicate the feasibility of utilizing a stacking-based pLOS prediction model for PD-treated patients. The pLOS prediction tools developed in this study have the potential to assist clinicians in identifying patients at a high risk of pLOS and to allocate resources optimally for PD-treated patients.

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Can machine learning models predict prolonged length of hospital stay following primary total knee arthroplasty based on a national patient cohort data?

Chen,

Buddhiraju,

Seo

et al. 2023

Arch Orthop Trauma Surg

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Perioperative predictors of prolonged length of hospital stay following total knee arthroplasty: a retrospective study from a single center in China

Cited by 24 publications

References 28 publications

Total knee arthroplasty improves the quality-adjusted life years in patients who exceeded their estimated life expectancy

Total knee arthroplasty improves the quality-adjusted life years in patients who exceeded their estimated life expectancy

Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis–Treated Patients Using Stacked Generalization: Model Development and Validation Study

Can machine learning models predict prolonged length of hospital stay following primary total knee arthroplasty based on a national patient cohort data?

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