A Precision Medicine Approach to Develop and Internally Validate Optimal Exercise and Weight Loss Treatments for Overweight and Obese Adults with Knee Osteoarthritis

We proposed a precision medicine approach to determine the optimal treatment regime for participants in an exercise (E), dietary weight loss (D), and D+E trial for knee osteoarthritis (KOA) that would have maximized their expected outcomes. Using data from 343 participants of the Intensive Diet and Exercise for Arthritis (IDEA) trial, we applied 24 machine-learning models to develop individualized treatment rules on seven outcomes: SF-36 physical component score, weight loss, WOMAC pain/function/stiffness scores, compressive force, and IL-6. The optimal model was selected based on jackknife value function estimates that indicate improvement in the outcome(s) if future participants follow the estimated decision rule compared against the optimal single, fixed treatment model. Multiple outcome random forest was the optimal model for the WOMAC outcomes. For the other outcomes, list-based models were optimal. For example, the estimated optimal decision rule for weight loss assigns the D+E intervention to participants with baseline weight not exceeding 109.35 kg and waist circumference above 90.25 cm, and assigns D to all other participants except those with history of a heart attack. If applied to future participants, the optimal rule for weight loss is estimated to increase average weight loss to 11.2 kg at 18 months, contrasted with 9.8 kg if all received D+E (p = 0.01). The precision medicine models supported the overall findings from IDEA that the D+E intervention was optimal for most participants, but there was evidence that a subgroup of participants would likely benefit more from diet alone for two outcomes.