PTOTCohortArchives of rehabilitation research and clinical translation2024

Predicting Delayed In-Hospital Recovery of Physical Function After Total Knee Arthroplasty.

Louisa T M A Mulder, Danielle D P Berghmans, Peter Z Feczko and 3 others

PMID 38482109

WHAT IT FOUND

A prediction model for delayed recovery after knee replacement performed poorly.

Preoperative walking aid use and mobility scores were associated with recovery time, but the model could not reliably distinguish high-risk patients from low-risk patients.

Key findings

01The prediction model had poor discrimination, with an optimism-corrected area under the curve of 61.2%.

02Using a walking aid indoors before surgery was the most important predictor of delayed recovery, increasing the predicted probability by at least 20%.

03The model misclassified patients: using the most sensitive cutoff, 145 patients who recovered quickly were incorrectly flagged as high risk.

STILL TO COME

How it was doneWhat they foundWhat it means for PTsWhat it means for OTs

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What it does not show

Patients scheduled for inpatient rehabilitation or nursing home care were excluded, creating a homogeneous sample of patients who were likely to return home. This limits the generalizability of the findings to patients with more severe disability or social support issues. The model failed to perform well despite a large sample size, likely because the included predictors (physical fitness measures) did not capture the full complexity of recovery, which includes social and medical factors not measured here. Missing data for 15 patients regarding the primary outcome required multiple imputation, which can introduce uncertainty, although sensitivity analysis showed no large differences. The study was retrospective, relying on data collected during usual care, which may have inconsistencies in assessment timing or method compared to a controlled trial setting.

The easy way to misread this

Do not use the finding that walking aid use and mobility scores are associated with recovery time to assume this model can reliably predict individual patient outcomes. The model's poor discrimination and high rate of misclassification mean it is not ready for clinical use.

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