Methodological issues for measuring pharmacotherapy treatment and its calibration with patient outcomes using real-world data

Introduction Health system and administrative data sources, although collected for process/procedural healthcare use, have recently been leveraged for research purposes. Complex methodologies such as data linkages, defined algorithms, and proxy measures are needed to validate the use of these data. Alberta has exceptionally rich health system data to be explored. Objectives and Approach Our objectives were to explore data sources in Alberta regarding pharmacotherapy treatments, patient outcomes, and develop methods to calibrate associations. We scoped data sources for pharmacotherapy treatment and patient outcomes in Alberta, Canada. Using a cohort of patients with multiple sclerosis (MS) as an example, we developed algorithms to measure medication possession ratio (MPR), proportion of days covered (PDC), and treatment discontinuation (TD). Patient outcomes included: ambulatory care visits, physician claims and hospitalizations. We explored different algorithms for statistical modeling of patient treatments and outcomes. Optimal cut-points and statistical model performance of MPR/PDC and TD with patient outcomes were evaluated. Results We utilized six Albertan data sources to examine treatment patterns and patient outcomes. From the year 2000, the pharmaceutical information network (PIN) and Alberta Blue Cross drug datasets collected: service dates, dosages, drug identification numbers, anatomical therapeutic chemical classification codes, and days of supplies; which were used to calculate MPR, PDC and TD. MPR and PDC were calculated using three different methods: (1) fixed follow-up period; (2) with and without last fill; and (3) adjustment for hospitalization periods. No significant differences between methods were found. In addition, TD using 60- and 90-day gaps, while considering the medication day supplies, were calculated. Adherence with optimal cut-point 0.8 of MPR/PDC and TD were significantly associated with ambulatory care visits, physician claims, and hospitalizations (p=0.000; C-statistic range: 0.78-0.89). Conclusion/Implications The PIN data can be utilized for measuring pharmacotherapy treatment in patient outcomes research. To understand the impact of therapies on outcomes in a real-world setting, however, comprehensive methods for measuring treatment indicators as well as patient outcomes should be developed to control for inherent biases in observational data

Landscape of somatic single nucleotide variants and indels in colorectal cancer and impact on survival

Colorectal cancer (CRC) is a biologically heterogeneous disease. To characterize its mutational profile, we conduct targeted sequencing of 205 genes for 2,105 CRC cases with survival data. Our data shows several findings in addition to enhancing the existing knowledge of CRC. We identify PRKCI, SPZ1, MUTYH, MAP2K4, FETUB, and TGFBR2 as additional genes significantly mutated in CRC. We find that among hypermutated tumors, an increased mutation burden is associated with improved CRC-specific survival (HR=0.42, 95% CI: 0.21-0.82). Mutations in TP53 are associated with poorer CRC-specific survival, which is most pronounced in cases carrying TP53 mutations with predicted 0% transcriptional activity (HR=1.53, 95% CI: 1.21-1.94). Furthermore, we observe differences in mutational frequency of several genes and pathways by tumor location, stage, and sex. Overall, this large study provides deep insights into somatic mutations in CRC, and their potential relationships with survival and tumor features. Large scale sequencing study is of paramount importance to unravel the heterogeneity of colorectal cancer. Here, the authors sequenced 205 cancer genes in more than 2000 tumours and identified additional mutated driver genes, determined that mutational burden and specific mutations in TP53 are associated with survival odds

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p<0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (<1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (<1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline

Interventions outside the workplace for reducing sedentary behaviour in adults under 60 years of age

Background Adults spend a majority of their time outside the workplace being sedentary. Large amounts of sedentary behaviour increase the risk of type 2 diabetes, cardiovascular disease, and both all‐cause and cardiovascular disease mortality. Objectives Primary • To assess effects on sedentary time of non‐occupational interventions for reducing sedentary behaviour in adults under 60 years of age Secondary • To describe other health effects and adverse events or unintended consequences of these interventions • To determine whether specific components of interventions are associated with changes in sedentary behaviour • To identify if there are any differential effects of interventions based on health inequalities (e.g. age, sex, income, employment) Search methods We searched CENTRAL, MEDLINE, Embase, Cochrane Database of Systematic Reviews, CINAHL, PsycINFO, SportDiscus, and ClinicalTrials.gov on 14 April 2020. We checked references of included studies, conducted forward citation searching, and contacted authors in the field to identify additional studies. Selection criteria We included randomised controlled trials (RCTs) and cluster RCTs of interventions outside the workplace for community‐dwelling adults aged 18 to 59 years. We included studies only when the intervention had a specific aim or component to change sedentary behaviour. Data collection and analysis Two review authors independently screened titles/abstracts and full‐text articles for study eligibility. Two review authors independently extracted data and assessed risk of bias. We contacted trial authors for additional information or data when required. We examined the following primary outcomes: device‐measured sedentary time, self‐report sitting time, self‐report TV viewing time, and breaks in sedentary time. Main results We included 13 trials involving 1770 participants, all undertaken in high‐income countries. Ten were RCTs and three were cluster RCTs. The mean age of study participants ranged from 20 to 41 years. A majority of participants were female. All interventions were delivered at the individual level. Intervention components included personal monitoring devices, information or education, counselling, and prompts to reduce sedentary behaviour. We judged no study to be at low risk of bias across all domains. Seven studies were at high risk of bias for blinding of outcome assessment due to use of self‐report outcomes measures. Primary outcomes Interventions outside the workplace probably show little or no difference in device‐measured sedentary time in the short term (mean difference (MD) ‐8.36 min/d, 95% confidence interval (CI) ‐27.12 to 10.40; 4 studies; I² = 0%; moderate‐certainty evidence). We are uncertain whether interventions reduce device‐measured sedentary time in the medium term (MD ‐51.37 min/d, 95% CI ‐126.34 to 23.59; 3 studies; I² = 84%; very low‐certainty evidence) We are uncertain whether interventions outside the workplace reduce self‐report sitting time in the short term (MD ‐64.12 min/d, 95% CI ‐260.91 to 132.67; I² = 86%; very low‐certainty evidence). Interventions outside the workplace may show little or no difference in self‐report TV viewing time in the medium term (MD ‐12.45 min/d, 95% CI ‐50.40 to 25.49; 2 studies; I² = 86%; low‐certainty evidence) or in the long term (MD 0.30 min/d, 95% CI ‐0.63 to 1.23; 2 studies; I² = 0%; low‐certainty evidence). It was not possible to pool the five studies that reported breaks in sedentary time given the variation in definitions used. Secondary outcomes Interventions outside the workplace probably have little or no difference on body mass index in the medium term (MD ‐0.25 kg/m², 95% CI ‐0.48 to ‐0.01; 3 studies; I² = 0%; moderate‐certainty evidence). Interventions may have little or no difference in waist circumference in the medium term (MD ‐2.04 cm, 95% CI ‐9.06 to 4.98; 2 studies; I² = 65%; low‐certainty evidence). Interventions probably have little or no difference on glucose in the short term (MD ‐0.18 mmol/L, 95% CI ‐0.30 to ‐0.06; 2 studies; I² = 0%; moderate‐certainty evidence) and medium term (MD ‐0.08 mmol/L, 95% CI ‐0.21 to 0.05; 2 studies, I² = 0%; moderate‐certainty evidence) Interventions outside the workplace may have little or no difference in device‐measured MVPA in the short term (MD 1.99 min/d, 95% CI ‐4.27 to 8.25; 4 studies; I² = 23%; low‐certainty evidence). We are uncertain whether interventions improve device‐measured MVPA in the medium term (MD 6.59 min/d, 95% CI ‐7.35 to 20.53; 3 studies; I² = 70%; very low‐certainty evidence). We are uncertain whether interventions outside the workplace improve self‐reported light‐intensity PA in the short‐term (MD 156.32 min/d, 95% CI 34.34 to 278.31; 2 studies; I² = 79%; very low‐certainty evidence). Interventions may have little or no difference on step count in the short‐term (MD 226.90 steps/day, 95% CI ‐519.78 to 973.59; 3 studies; I² = 0%; low‐certainty evidence) No data on adverse events or symptoms were reported in the included studies. Authors' conclusions Interventions outside the workplace to reduce sedentary behaviour probably lead to little or no difference in device‐measured sedentary time in the short term, and we are uncertain if they reduce device‐measured sedentary time in the medium term. We are uncertain whether interventions outside the workplace reduce self‐reported sitting time in the short term. Interventions outside the workplace may result in little or no difference in self‐report TV viewing time in the medium or long term. The certainty of evidence is moderate to very low, mainly due to concerns about risk of bias, inconsistent findings, and imprecise results. Future studies should be of longer duration; should recruit participants from varying age, socioeconomic, or ethnic groups; and should gather quality of life, cost‐effectiveness, and adverse event data. We strongly recommend that standard methods of data preparation and analysis are adopted to allow comparison of the effects of interventions to reduce sedentary behaviour

Quality of Life After Prostate Cancer Diagnosis: A Longitudinal Prospective Cohort Study in Alberta, Canada

OBJECTIVES: First, we examined the associations of post-diagnosis physical activity and change in pre-diagnosis physical activity on quality of life (QoL) in prostate cancer survivors. Then, we identified post-prostate cancer diagnosis QoL trajectories over time in the population. METHODS: 830 prostate cancer survivors were derived from a prior case-control study where information at diagnosis was collected, then survivors were re-consented into a follow-up study. Three repeated measurements of physical activity and QoL were undertaken post-diagnosis. RESULTS: We observed improvements in physical QoL in prostate cancer survivors who maintained or adopted higher levels of physical activity pre- and post-diagnosis, according to the cancer prevention physical activity guidelines compared to those who were non-exercisers. In the trajectory analysis, three physical and three mental trajectory groups were identified. CONCLUSION: With additional research, these established trajectory groups may help healthcare professionals in improving treatment and follow-up for this population of prostate cancer survivors