Elsevier

The Journal of Arthroplasty

Volume 35, Issue 7, July 2020, Pages 1840-1846.e2
The Journal of Arthroplasty

Primary Knee
Development and Validation of a Model for Predicting Rehabilitation Care Location Among Patients Discharged Home After Total Knee Arthroplasty

https://doi.org/10.1016/j.arth.2020.02.032Get rights and content

Abstract

Background

Demand for joint replacement is increasing, with many patients receiving postsurgical physical therapy (PT) in non-inpatient settings. Clinicians need a reliable tool to guide decisions about the appropriate PT setting for patients discharged home after surgery. We developed and validated a model to predict PT location for patients in our health system discharged home after total knee arthroplasty.

Methods

We analyzed data for patients who completed a preoperative total knee risk assessment in 2017 (model development cohort) or during the first 6 months of 2018 (model validation cohort). The initial total knee risk assessment, to guide rehabilitation disposition, included 28 variables in mobility, social, and environment domains, and on patient demographics and comorbidities. Multivariable logistic regression was used to identify factors that best predict discharge to home health service (HHS) vs home with outpatient PT. Model performance was assessed by standard criteria.

Results

The development cohort included 259 patients (19%) discharged to HHS and 1129 patients (81%) discharged to home with outpatient PT. The validation cohort included 609 patients, with 91 (15%) discharged to HHS. The final model included age, gender, motivation for outpatient PT, and reliable transportation. Patients without motivation for outpatient PT had the highest probability of discharge to HHS, followed by those without reliable transportation. Model performance was excellent in the development and validation cohort, with c-statistics of 0.91 and 0.86, respectively.

Conclusion

We developed and validated a predictive model for total knee arthroplasty PT discharge location. This model includes 4 variables with accurate prediction to guide patient-clinician preoperative decision making.

Section snippets

Study Population

This was a retrospective analysis of patient data collected before TKA surgery from an integrated health care system in Colorado. Data for patients who completed a preoperative TKRA and underwent TKA at 1 of 3 orthopedics surgery sites in our health system were included. Primary TKA patients were identified using Current Procedural Terminology code 27447 or an International Classification of Diseases, Tenth Revision (ICD-10) procedure code starting with 0SRC or 0SRD. Patients with body mass

Model Development

We followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for model development and validation [20]. To avoid overfitting, we required at least 10 HHS cases per degree of freedom [21]. For the continuous predictors age and BMI, a linear relationship with outcome was found to be a good approximation after assessment of nonlinearity using restricted cubic splines [22]. We conducted bivariate comparisons using t tests for normally

Model Development

There were 1388 patients in the development cohort (Table 1). Overall, 259 (19%) patients were discharged to HHS. The bivariate analysis of the development cohort showed that all 28 variables identified initially were significantly different between patients discharged to HHS and those discharged to HOPT, except BMI (P = .71) and chronic pain (P = .08) (Table 2). Backward elimination and goodness-of-fit statistics in the multivariable logistic regression modeling succeeded in paring down the

Discussion

As the demand for TKA continues to grow, understanding the factors associated with determining the appropriate venue for rehabilitation may allow for increased discharge rates to outpatient rehabilitation, leading to shorter recovery time, high-quality care and outcomes, improved patient satisfaction, and efficient and cost-effective operations [27]. In this study, we developed and validated a predictive model for PT discharge location after TKA using patient demographic and comorbidity data

Conclusions

In conclusion, this study identified preoperative patient-reported variables and objective clinical variables that predict higher likelihood of discharge to home with HHS after TKA. Our findings suggested that patient motivation and lack of reliable transportation are key predictors. Efforts to promote TKA patients' motivation for outpatient PT and access to transportation could help decrease the need for HHS and the cost of postoperative care, as well as improve patient outcomes.

Acknowledgments

This study was conducted by Institute for Health Research of Kaiser Permanente Colorado with funding from the Kaiser Permanente Health Plan of Colorado.

References (35)

Cited by (6)

  • Pre-Operative Predictors for Discharge to Post-Acute Care Facilities After Total Knee Arthroplasty

    2022, Journal of Arthroplasty
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    In addition, RAPT separates patient expectation of discharge destination in the final scoring system, as patient expectation could be modified by providing patients with education and advice. However, we found that lack of motivation to participate in outpatient PT was not a significant risk factor for PACF discharge, although in our previous study it was the strongest predictor for HHS among those who discharged to home [9]. Based on the findings from this study on predictors of PACF discharge and our previous study on predictors of HHS discharge, we have implemented a 2-step pre-operative planning process for discharge disposition among patients undergoing TKA in our healthcare system.

  • Medicare Total Knee Arthroplasty Patients Need Not Stay 2 Midnights for Full Facility Reimbursement

    2021, Journal of Arthroplasty
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    In fact, those patients in which a penalized facility reimbursement occurred were slightly younger, had a lower body mass index, and had a similar CCI (Table 1) compared with those patients for whom our facility received a full reimbursement. Although surgeons can use a variety of tools and checklists to best determine where a patient may be discharged after their procedure [29], most data seem to indicate that the most important factor in determining where a patient is discharged is their desire to be discharged to a specific location [9,30,31]. Therefore, educating, optimizing, and planning with patients preoperatively so that they can be safely discharged home appears to be critical in terms of giving patients the best chance to be discharged home.

No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work. For full disclosure statements refer to https://doi.org/10.1016/j.arth.2020.02.032.

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