Arthroplasty Care Redesign Impacts the Predictive Accuracy of the Risk Assessment and Prediction Tool

Dibra, Florian; Silverberg, Arnold; Vasilopoulos, Terrie; Gray, Chancellor F.; Parvataneni, Hari K.; Prieto, Hernan A.

doi:10.1016/j.arth.2019.06.035

Cited by 23 publications

(22 citation statements)

References 15 publications

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“…Hansen et al after reviewing 3,213 patients after THA and TKA revealed a strong prediction of RAPT to pick up those who can directly go home. Similar results were reported by Dibra et al, who validated the predictive value of the score in terms of discharge destination; however, they recommended modifying the score as the predictive expectation increased the predictive value [6,7] . On the contrary , and in the US as well Cizmic et al look mainly into the patient with extended stay over 3 days and found that the score is less predictive regarding discharge destination in those cohort , which probably coincide though partially with our study [8].…”

Section: Discussionsupporting

confidence: 83%

“…It has been utilized in Europe by Dauty et al and Coudeyre et al, in which both revealed strong predictive value of the score regarding the LOS and the discharge destination [ 4 , 5 ]. The reports from the US were contradicting, where Dibra et al [ 6 ] and Hansen et al [ 7 ] found the score reliable for discharge planning and to identify the need for extended postoperative rehabilitation for low score patients. On the other hand, in the US as well, Cizmic et al [ 8 ] revealed low predictive accuracy for discharge destination for patients with extended LOS despite good prediction for the LOS itself.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Value of the Risk Assessment and Prediction Tool (RAPT) Score for Primary Hip and Knee Arthroplasty Patients: A Single-Center Study

Alshahwani¹,

Dungey²,

Lillie³

et al. 2021

Cureus

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The Risk Assessment and Prediction Tool (RAPT) was developed to predict patient discharge destination for arthroplasty operations. However, since Enhanced Recovery After Surgery (ERAS) programs have been utilized in the UK, the RAPT score has not been validated for use. The aim of the current study was to evaluate the predictive validity of the RAPT score in an ERAS environment with short length of stay. Data were compiled from 545 patients receiving a primary elective total hip or total knee arthroplasty in a district general hospital over 12 months. RAPT scores, length of stay, and discharge destinations were recorded.Patients were classified as low, intermediate, or high risk as per their RAPT score. Length of stay was significantly different between groups (p = 0.008), with low-risk patients having shorter length of stay. However, RAPT scores did not predict discharge destination; the overall correct prediction was only 31.9%. Furthermore, the most likely discharge destination was directly home in ≤3 days in all groups (68.5%, 60.2%, and 40% for the low-, intermediate-, and high-risk groups, respectively).The RAPT score is not an adequate tool to predict the discharge disposition following primary total knee and hip replacement surgery in a UK hospital with a standardized modern ERAS program. Alternative predictive tools are required.

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Section: Discussionsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Predictive Value of the Risk Assessment and Prediction Tool (RAPT) Score for Primary Hip and Knee Arthroplasty Patients: A Single-Center Study

Alshahwani¹,

Dungey²,

Lillie³

et al. 2021

Cureus

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“…While the work evaluated the ability of the National Surgical Quality Improvement Program data to generate a discharge prediction tool, the authors acknowledged the study lacked validation of their model [ 12 ]. Probably the most cited discharge prediction tool in TJA is the Risk Assessment and Prediction Tool (RAPT) ( Table 2 ) [ [13] , [14] , [15] , [16] , [17] , [18] ]. The RAPT has been applied prospectively with intermediate success within the literature, including within the setting of the bundled care for TJA [ 16 , 17 ].…”

Section: Discussionmentioning

confidence: 99%

“…[ 11 ] AM-PAC Retrospective institutional n/a 0.78 39% Dibra et al. [ 15 ] RAPT Retrospective institutional 88%, however 52.2-78.7% in intermediate risk patients n/a 14% Hansen et al. [ 16 ] RAPT Prospective institutional 78%, however 65.2% in intermediate risk patients n/a 15% Slover et al.…”

Section: Discussionmentioning

confidence: 99%

An Electronic Medical Record–Based Discharge Disposition Tool Gets Bundle Busted: Decaying Relevance of Clinical Data Accuracy in Machine Learning

et al. 2020

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Background Determining discharge disposition after total joint arthroplasty (TJA) has been a challenge. Advances in machine learning (ML) have produced computer models that learn by example to generate predictions on future events. We hypothesized a trained ML algorithm’s diagnostic accuracy will be better than that of current predictive tools to predict discharge disposition after primary TJA. Methods This study was a retrospective cohort study from a single, tertiary referral center for primary TJA. We trained and validated an artificial neural network (ANN) based on 4368 distinct surgical encounters between 1/1/2013 and 6/28/2016. The ANN’s ability to identify discharge disposition was then tested on 1452 distinct surgical encounters between 1/3/17 and 11/30/17. Results The area under the curve and accuracy achieved during model validation were 0.973 and 91.7%, respectively, with 25% of patients being discharged to skilled nursing facilities (SNFs). Within our testing data set, 6.7% of patients went to SNFs. The performance in the testing set included an area under the curve of 0.804, accuracy of 61.3%, sensitivity of 28.9%, and specificity of 93.8%. Conclusions This is the first prediction tool using an electronic medical record–integrated ANN to predict discharge disposition after TJA based on locally generated data. Dramatically reduced numbers of patients discharged to SNFs due to implementation of a bundled payment model lead to poor recall in the testing model. This model serves as a proof of concept for developing an ML prediction tool using a relatively small data set and subsequent integration into the electronic medical record.

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“…As the number of annual total joint arthroplasty (TJA) procedures continues to rise, so does health-care expenditures on arthroplasty services annually [1][2][3]. As a result, modern arthroplasty care has focused on controlling costs, improving outcomes, and optimizing the value of care [4].…”

Section: Introductionmentioning

confidence: 99%

Modifying the RAPT Score to Reflect Discharge Destination in Current Practice

et al. 2021

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Background: The Risk Assessment Prediction Tool (RAPT) is a validated 6-question survey designed to predict primary total joint arthroplasty (TJA) patients' discharge disposition. It is scored from 1 to 12 with patients stratified into high-, intermediate-, and low-risk groups. Given recent advancements in rapiddischarge protocols and increasing utilization of home services, the RAPT score may require modified scoring cutoffs. Methods: A retrospective chart review of all patients undergoing primary TJA at a single academic center over 14 months was performed. The RAPT score was implemented during the sixth month. Patients undergoing revision TJA, complex TJA, and TJA after resection of malignancy were excluded. Outcomes before and after RAPT implementation were analyzed with additional subanalysis investigating of post-RAPT data. Results: A total of 1264 patients (624 Pre-RAPT and 640 Post-RAPT) were evaluated. The post-RAPT group (245 total hip arthroplasty and 395 total knee arthroplasty) experienced significant decreases in mean hospital length of stay (2.22 days pre-RAPT to 1.82 days post-RAPT, P < .001) and the proportion of patients discharged to facility (21.8% pre-RAPT to 15.2% post-RAPT, P ¼ .002). The modified system demonstrated the highest overall predictive accuracy at 92% and was found to be predictive of hospital length of stay. Conclusion:Owing to the recent trends favoring in-home services over rehab facility after discharge, previously published RAPT scoring cutoffs are inaccurate for modern practice. Using mRAPT cutoffs maximizes the number of patients for whom a discharge prediction can be made, while maintaining excellent predictive accuracy.

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Arthroplasty Care Redesign Impacts the Predictive Accuracy of the Risk Assessment and Prediction Tool

Cited by 23 publications

References 15 publications

Predictive Value of the Risk Assessment and Prediction Tool (RAPT) Score for Primary Hip and Knee Arthroplasty Patients: A Single-Center Study

Predictive Value of the Risk Assessment and Prediction Tool (RAPT) Score for Primary Hip and Knee Arthroplasty Patients: A Single-Center Study

An Electronic Medical Record–Based Discharge Disposition Tool Gets Bundle Busted: Decaying Relevance of Clinical Data Accuracy in Machine Learning

Modifying the RAPT Score to Reflect Discharge Destination in Current Practice

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