These authors point out that TSA confers improvement in pain and function for most patients, however some will remain unsatisfied postoperatively. The purpose of their study was to (1) train supervised machine learning (SML) algorithms to predict satisfaction after TSA and (2) develop a clinical tool for individualized assessment of patient-specific risk factors.
They conducted a retrospective review of 413 primary anatomic and reverse TSA patients of which of which 331 (82.6%) were satisfied at two years postoperatively.
The most 22 important factors for predicting satisfaction were baseline single assessment numeric evaluation (SANE) score, exercise and activity, workers compensation status, diagnosis, symptom duration prior to surgery, body mass index, age, smoking status, anatomic vs. reverse TSA, and diabetes.
Their algorithm was incorporated into an open-access digital application for patient-level explanations of risk and predictions available at this link:
They concluded that the best performing model demonstrated excellent discrimination and adequate calibration for predicting satisfaction following TSA. They caution, however, that rigorous external validation in different geographic locations and patient populations is essential prior to assessment of clinical utility.
Comment: This is a commendable effort and readers are encouraged to try out the tool. We ran a series of five hypothetical patients having anatomic TSA for primary glenohumeral osteoarthritis with symptoms for > 2 yrs, a preoperative SANE of 30 and a BMI of 25. The predictions are shown in the chart and figures below. Importantly it suggested that - even in the ideal candidate - the probability of satisfaction was only 84%. These results did not demonstrate a difference between the satisfaction of older and younger patients, and did not demonstrate a detrimental effect of adding Worker's Compensation insurance or diabetes to the 65 year old patient who smoked.
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