Patterns of rates of mortality in the Clinical Practice Research Datalink

The Clinical Practice Research Datalink (CPRD) is a widely used data resource, representative in demographic profile, with accurate death recordings but it is unclear if mortality rates within CPRD GOLD are similar to rates in the general population. Rates may additionally be affected by selection bias caused by the requirement that a cohort have a minimum lookback window, i.e. observation time prior to start of at-risk follow-up. Standardised Mortality Ratios (SMRs) were calculated incorporating published population reference rates from the Office for National Statistics (ONS), using Poisson regression with rates in CPRD GOLD contrasted to ONS rates, stratified by age, calendar year and sex. An overall SMR was estimated along with SMRs presented for cohorts with different lookback windows (1, 2, 5, 10 years). SMRs were stratified by calendar year, length of follow-up and age group. Mortality rates in a random sample of 1 million CPRD GOLD patients were slightly lower than the national population [SMR = 0.980 95% confidence interval (CI) (0.973, 0.987)]. Cohorts with observational lookback had SMRs below one [1 year of lookback; SMR = 0.905 (0.898, 0.912), 2 years; SMR = 0.881 (0.874, 0.888), 5 years; SMR = 0.849 (0.841, 0.857), 10 years; SMR = 0.837 (0.827, 0.847)]. Mortality rates in the first two years after patient entry into CPRD were higher than the general population, while SMRs dropped below one thereafter. Mortality rates in CPRD, using simple entry requirements, are similar to rates seen in the English population. The requirement of at least a single year of lookback results in lower mortality rates compared to national estimates

Perils of Randomized Controlled Trial Survival Extrapolation Assuming Treatment Effect Waning: Why the Distinction Between Marginal and Conditional Estimates Matters

A long-term, constant, protective treatment effect is a strong assumption when extrapolating survival beyond clinical trial follow-up, hence sensitivity to treatment effect waning is commonly assessed for economic evaluations. Forcing a HR (hazard ratio) to 1 does not necessarily estimate loss of individual-level treatment effect accurately due to HR selection bias. A simulation study was designed to explore the behaviour of marginal HRs under a waning conditional (individual-level) treatment effect and demonstrate bias in forcing a marginal HR to 1 when the estimand is 'survival difference with individual-level waning'.Data were simulated under 4 parameter combinations (varying prognostic strength of heterogeneity and treatment effect). Time varying marginal HRs were estimated in scenarios where the true conditional HR attenuated to 1. ΔRMSTs (restricted mean survival time difference), estimated having constrained the marginal HR to 1, were compared to true values to assess bias induced by marginal constraints.Under loss of conditional treatment effect, the marginal HR took a value >1 due to covariate imbalances. Constraining this value to 1 lead to ΔRMST bias of up to 0.8 years (57% increase). Inflation of effect size estimates also increased with the magnitude of initial protective treatment effect.Important differences exist between survival extrapolations assuming marginal versus conditional treatment effect waning. When a marginal HR is constrained to 1 to assess efficacy under individual-level treatment effect waning, the survival benefits associated with the new treatment will be over-estimated and incremental cost-effectiveness ratios will be under-estimated. Objectives Methods Results Conclusions</p

Data Resource Profile: The Virtual Cardio-Oncology Research Initiative (VICORI) linking national English cancer registration and cardiovascular audits.

Key FeaturesThe Virtual Cardio-Oncology Research Initiative (VICORI) programme brings together English national cancer data and six national cardiovascular disease audits to investigate the interplay between cardiovascular disease and cancer.The VICORI data resource captures adults (aged 18+ years) who were hospitalized for cardiac disease, had a cardiac procedure and/or a cancer diagnosis alongside information on their treatment and outcomes. These data are routinely collected and submitted to health care registries and are linked using a unique health service number.Detailed data on cancer and cardiac diagnosis, treatment, outcomes, previous and subsequent hospital diagnoses and operations, and mortality are available from 6.2 million cancer diagnoses between 1995 and 2018, and 3.8 million cardiac hospital admissions/procedures between 1999 and 2018.The VICORI cohort will be updated on a rolling basis with annual updates from the audits.</ul

Acute heart failure presentation, management and outcomes in cancer patients: a national longitudinal study.

Aims Currently, little evidence exists on survival and quality of care in cancer patients presenting with acute heart failure (HF). The aim of the study is to investigate the presentation and outcomes of hospital admission with acute HF in a national cohort of patients with prior cancer. Methods and results This retrospective, population-based cohort study identified 221 953 patients admitted to a hospital in England for HF during 2012–2018 (12 867 with a breast, prostate, colorectal, or lung cancer diagnosis in the previous 10 years). We examined the impact of cancer on (i) HF presentation and in-hospital mortality, (ii) place of care, (iii) HF medication prescribing, and (iv) post-discharge survival, using propensity score weighting and model-based adjustment. Heart failure presentation was similar between cancer and non-cancer patients. A lower percentage of patients with prior cancer were cared for in a cardiology ward [−2.4% age point difference (ppd) (95% CI −3.3, −1.6)] or were prescribed angiotensin-converting enzyme inhibitors or angiotensin receptor antagonists (ACEi/ARB) for heart failure with reduced ejection fraction [−2.1 ppd (−3.3, −0.9)] than non-cancer patients. Survival after HF discharge was poor with median survival of 1.6 years in prior cancer and 2.6 years in non-cancer patients. Mortality in prior cancer patients was driven primarily by non-cancer causes (68% of post-discharge deaths). Conclusion Survival in prior cancer patients presenting with acute HF was poor, with a significant proportion due to non-cancer causes of death. Despite this, cardiologists were less likely to manage cancer patients with HF. Cancer patients who develop HF were less likely to be prescribed guideline-based HF medications compared with non-cancer patients. This was particularly driven by patients with a poorer cancer prognosis.</p