Tolerability of maintenance olaparib in newly diagnosed patients with advanced ovarian cancer and a BRCA mutation in the randomized phase III SOLO1 trial.

Objectives: In the phase III SOLO1 trial (NCT01844986), maintenance olaparib provided a substantial progression-free survival benefit in patients with newly diagnosed, advanced ovarian cancer and a BRCA mutation who were in response after platinum-based chemotherapy. We analyzed the timing, duration and grade of the most common hematologic and non-hematologic adverse events in SOLO1.

Methods: Eligible patients were randomized to olaparib tablets 300 mg twice daily (N = 260) or placebo (N = 131), with a 2-year treatment cap in most patients.

Molecular and clinical predictors of improvement in progression-free survival with maintenance PARP inhibitor therapy in women with platinum-sensitive, recurrent ovarian cancer: A meta-analysis.

Background: The authors performed a meta-analysis to better quantify the benefit of maintenance poly(ADP-ribose) polymerase inhibitor (PARPi) therapy to inform practice in platinum-sensitive, recurrent, high-grade ovarian cancer for patient subsets with the following characteristics: germline BRCA mutation (gBRCAm), somatic BRCA mutation (sBRCAm), wild-type BRCA but homologous recombinant-deficient (HRD), homologous recombinant-proficient (HRP), and baseline clinical prognostic characteristics.

Methods: Randomized trials comparing a PARPi versus placebo as maintenance treatment were identified from electronic databases. Treatment estimates of progression-free survival were pooled across trials using the inverse variance weighted method.

Machine learning and wearable sensors at preoperative assessments: Functional recovery prediction to set realistic expectations for knee replacements.

Unmet expectations contribute to a high patient dissatisfaction rate following total knee replacement but clinicians currently do not have the tools to confidently adjust expectations. In this study, supervised machine learning was applied to multi-variate wearable sensor data from preoperative timed-up-and-go tests. Participants (n=82) were instrumented three months after surgery and patients showing relevant improvement were designated as "responders" while the remainder were labelled "maintainers".

Machine Learning Predicts the Fall Risk of Total Hip Arthroplasty Patients Based on Wearable Sensor Instrumented Performance Tests.

Background: The prevalence of falls affects the wellbeing of aging adults and places an economic burden on the healthcare system. Integration of wearable sensors into existing fall risk assessment tools enables objective data collection that describes the functional ability of patients. In this study, supervised machine learning was applied to sensor-derived metrics to predict the fall risk of patients following total hip arthroplasty.