Opioid-induced constipation: a stepwise treatment algorithm feasibility study.

BACKGROUND: Opioid-induced constipation (OIC) is frequently undertreated in patients with advanced cancer. Our hypothesis is that the use of a stepwise treatment algorithm, supported by regular patient-reported outcome measures, should improve the management of OIC. The aim of this feasibility study was to determine whether a definitive study could be successfully completed. METHODS: Patients with OIC (Rome Foundation diagnostic criteria positive), and a Bowel Function Index (BFI) score of ≥30, were recruited to the study. The study involved weekly assessments, and decisions about management were based on the current BFI score (and the tolerability of the current treatment). Management was based on a four-step treatment algorithm, developed from recent international guidelines. RESULTS: One hundred patients entered the study, and 79 patients completed the study. Fifty-seven (72%) participants responded to treatment, with 34 (43%) participants having a 'complete' response (ie, final BFI<30) and 23 (29%) participants having a 'partial' response (ie, change in BFI≥12). In participants with a complete response, 73.5% were prescribed conventional laxatives, 12% were prescribed a peripherally acting mu-opioid receptor antagonist (PAMORA) and 14.5% were prescribed a PAMORA and conventional laxative. DISCUSSION: The feasibility study suggests that a definitive study can be successfully completed. However, we will amend the methodology to try to improve participant recruitment, participant retention and adherence to the treatment algorithm. The feasibility study also suggests that the use of the BFI to monitor OIC, and the use of a treatment algorithm to manage OIC, can result in clinically important improvements in OIC.Trial registration number NCT04404933.

Prognostication in Advanced Cancer by Combining Actigraphy-Derived Rest-Activity and Sleep Parameters with Routine Clinical Data: An Exploratory Machine Learning Study.

Survival prediction is integral to oncology and palliative care, yet robust prognostic models remain elusive. We assessed the feasibility of combining actigraphy, sleep diary data, and routine clinical parameters to prognosticate. Fifty adult outpatients with advanced cancer and estimated prognosis of <1 year were recruited. Patients were required to wear an Actiwatch® (wrist actigraph) for 8 days, and complete a sleep diary. Univariate and regularised multivariate regression methods were used to identify predictors from 66 variables and construct predictive models of survival. A total of 49 patients completed the study, and 34 patients died within 1 year. Forty-two patients had disrupted rest-activity rhythms (dichotomy index (I < O ≤ 97.5%) but I < O did not have prognostic value in univariate analyses. The Lasso regularised derived algorithm was optimal and able to differentiate participants with shorter/longer survival (log rank p < 0.0001). Predictors associated with increased survival time were: time of awakening sleep efficiency, subjective sleep quality, clinician's estimate of survival and global health status score, and haemoglobin. A shorter survival time was associated with self-reported sleep disturbance, neutrophil count, serum urea, creatinine, and C-reactive protein. Applying machine learning to actigraphy and sleep data combined with routine clinical data is a promising approach for the development of prognostic tools.