Clinical and Statistical Validation of a Probabilistic Prediction Tool of Total Knee Arthroplasty Outcome

Twiggs, Joshua; Wakelin, Edgar A.; Fritsch, Brett; Liu, David W.; Solomon, Michael; Parker, David; Klasan, Antonio; Miles, Brad

doi:10.1016/j.arth.2019.06.007

Cited by 29 publications

(67 citation statements)

References 46 publications

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“…There has been much interest in risk stratification for TKA. Many of the variables used in other studies on risk of TKA, including body mass index (BMI), sex, and laboratory values, are included in the CAN score 7 , 15 - 17 .…”

Section: Discussionmentioning

confidence: 99%

“…As a result, there have been efforts to design preoperative risk assessment tools to help surgeons to risk-stratify and counsel patients 3 . Unfortunately, currently available surgical risk assessment tools are often cumbersome, requiring patients or providers to manually input various data elements; have limited accuracy; and are poorly adopted 4 - 7 . Thus, there remains a need for a preoperative risk assessment tool that is both accurate and efficient for providers.…”

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confidence: 99%

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Patient Electronic Health Records Score for Preoperative Risk Assessment Before Total Knee Arthroplasty

et al. 2020

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Background: Current preoperative risk assessment tools are often cumbersome, have limited accuracy, and are poorly adopted. The Care Assessment Need (CAN) score, an existing tool developed for primary care providers in the U.S. Veterans Administration health-care system (VA), is automatically calculated for individual patients using electronic health record data. Therefore, it could present an efficient preoperative risk assessment tool. The aim of this project was to determine if the CAN score can be repurposed as a preoperative risk assessment tool for patients undergoing total knee arthroplasty (TKA). Methods: A multicenter retrospective observational study was conducted using national VA data from 2013 to 2016. The cohort included veterans who underwent TKA identified through ICD-9 (International Classification of Diseases, Ninth Revision), ICD-10, and CPT (Current Procedural Terminology) codes. The focus of the study was the preoperative patient CAN score, a single numerical value ranging from 0 to 99 (with a higher score representing greater risk) that is automatically calculated each week using multiple data points in the VA electronic health record. Study outcomes of interest were 90-day readmission, prolonged hospital stay (>5 days), 1-year mortality, and non-routine patient discharge. Results: The study included 17,210 veterans. Their median preoperative CAN score was 75, although there was substantial variability in patient CAN scores among different facilities. A preoperative CAN score of >75 was significantly associated with mortality (odds ratio [OR] = 3.54), prolonged length of stay (OR = 1.97), 90-day readmission (OR = 1.65), and non-routine discharge (OR = 1.57). The CAN score had good accuracy with a receiver operating characteristic (ROC) curve value of >0.7 for all outcomes except 90-day readmission. Conclusions: The CAN score can be leveraged as an extremely efficient way to risk-stratify patients before TKA, with results that surpass other commonly available and labor-intensive alternatives. As a result, this simple and efficient solution is well positioned for broad adoption as a standardized decision support tool. Level of Evidence: Prognostic Level IV . See Instructions for Authors for a complete description of levels of evidence.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Patient Electronic Health Records Score for Preoperative Risk Assessment Before Total Knee Arthroplasty

et al. 2020

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“…Gap data investigated here is not normalized for patient specific anatomic, demographic or preoperative PROMS data. Previous literature has reported a number of pre-operative factors that can affect and be used to predict patient outcome [15][16][17][18]. Major demographic factors such as age, gender and BMI were not found to be significantly different between the joint gap groups identified.…”

Section: Discussionmentioning

confidence: 78%

Intra-Operative Soft Tissue Targets For Improved Outcomes In Total Knee Arthroplasty

Wakelin¹,

Shalhoub²,

Lawrence³

et al.

EPiC Series in Health Sciences

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A poorly balanced, unstable or stiff joint is a leading cause of dissatisfaction and revision after surgery. The quantitative definition of a well-balanced joint, however, remains a source of controversy. This study investigates joint gaps measured by a digitally-controlled ligament tensioning device throughout flexion and its effect on post- operative outcome.Surgeries were performed using the OMNIBotics robot-assisted TKA platform and BalanceBot device. Joint gaps were recorded by the BalanceBot throughout flexion during trialing. Knee Injury and Osteoarthritis Outcome Score (KOOS) was collected at 1-year post-op. Correlations between joint gaps and KOOS outcome were investigated.Knees that reported: a medial gap ≤ lateral gap in extension (p = 0.007, median ∆ = 8.3); an average joint gap of between 1 mm tighter and 1 mm looser than the final insert thickness in midflexion (p = 0.006, median ∆ = 5.5); and an imbalance of less than 1.5 mm in flexion (p = 0.012, median ∆ = 2.8) reported significantly improved pain scores. Patients which satisfied both extension and flexion criteria, or midflexion and flexion criteria reported improved outcomes compared to those which satisfied only one or neither criterion (p = 0.0002, median ∆ = 9.7, p = 0.0019, median ∆ = 8.4 respectively).KOOS Pain scores correlated with joint gap measurements across all flexion angles investigated. Combining joint gap windows, subgroups of patients were found that reported a change in the median KOOS Pain outcome beyond the minimally clinically important difference.

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“…From this survey, a prediction of final state and assessment of current state was generated of a similar form to that shown in Figure 1. The predictive model used is a Tree Augmented Naive Bayes Network and has been previously published on [14].…”

Section: Methodsmentioning

confidence: 99%

“…Such models can further be used to set patient expectations to an appropriate level based on patient specific risk factors and in doing so reduce incidence of unmet expectations. This study presents validation relative to surgeon predictions of such a predictive model, the Patient Expectation Management tool [14].…”

Section: Introductionmentioning

confidence: 99%

Preliminary Validation of a Patient Outcome Prediction Tool Relative to Surgeon Predictions of Patient Outcome

Twiggs¹,

Liu²,

Roe³

et al.

EPiC Series in Health Sciences

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Currently, patient selection for Total Knee Arthroplasties (TKA) is surgeon specific. A combination of patient reported symptoms, clinical examination findings, and radiological criteria are combined in an idiosyncratic fashion based on the surgeon’s individual clinical experiences. Predictive models offer an alternative by providing more detailed information on expected patient outcome. This study presents validation relative to surgeon predictions of such a predictive model, the Patient Expectation Management (PEM) tool.A cohort of patients undertook a survey covering the KOOS questionnaire and a number of other questions pertaining to comorbidities prior to their consultation. From this survey, a prediction of final state and assessment of current state was generated. Prior to seeing the prediction but after consulting the patient, surgeons were asked to score out of 100 a) their understanding of the patient’s current pain state and b) their prediction of the patient’s pain level following surgery. 35 of the patients were selected for TKA surgery and have gone on to have 12 month Knee Osteoarthritis and Outcome Scores (KOOS) captured.The predicted change in the PEM predicted score (preop to postop difference) had a relatively high correlation with the actual KOOS pain improvement achieved (r=0.71, p<0.001), compared to no significant correlation for the surgeon prediction (r=0.24, p=0.20). Significant correlations also existed for changes in KOOS symptoms score (r=0.70, p<0.001) and KOOS Activities of Daily Living (ADL) score (r=0.42, p=0.02).This study showed that, compared to a set of surgeon predictions of outcome following a consultation with patients, a predictive analytics tool was able to outperform in terms of predicting the improvement patients are likely to report following TKA.

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Clinical and Statistical Validation of a Probabilistic Prediction Tool of Total Knee Arthroplasty Outcome

Cited by 29 publications

References 46 publications

Patient Electronic Health Records Score for Preoperative Risk Assessment Before Total Knee Arthroplasty

Patient Electronic Health Records Score for Preoperative Risk Assessment Before Total Knee Arthroplasty

Intra-Operative Soft Tissue Targets For Improved Outcomes In Total Knee Arthroplasty

Preliminary Validation of a Patient Outcome Prediction Tool Relative to Surgeon Predictions of Patient Outcome

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