Stopping renin-angiotensin system blockers after acute kidney injury and risk of adverse outcomes: parallel population-based cohort studies in English and Swedish routine care.

BACKGROUND: The safety of restarting angiotensin-converting enzyme inhibitors (ACEI) or angiotensin II receptor blockers (ARB) after acute kidney injury (AKI) is unclear. There is concern that previous users do not restart ACEI/ARB despite ongoing indications. We sought to determine the risk of adverse events after an episode of AKI, comparing prior ACEI/ARB users who stop treatment to those who continue. METHODS: We conducted two parallel cohort studies in English and Swedish primary and secondary care, 2006-2016. We used multivariable Cox regression to estimate hazard ratios (HR) for hospital admission with heart failure (primary analysis), AKI, stroke, or death within 2 years after hospital discharge following a first AKI episode. We compared risks of admission between people who stopped ACEI/ARB treatment to those who were prescribed ACEI/ARB within 30 days of AKI discharge. We undertook sensitivity analyses, including propensity score-matched samples, to explore the robustness of our results. RESULTS: In England, we included 7303 people with AKI hospitalisation following recent ACEI/ARB therapy for the primary analysis. Four thousand three (55%) were classified as stopping ACEI/ARB based on no prescription within 30 days of discharge. In Sweden, we included 1790 people, of whom 1235 (69%) stopped treatment. In England, no differences were seen in subsequent risk of heart failure (HR 1.10; 95% confidence intervals (CI) 0.93-1.30), AKI (HR 0.90; 95% CI 0.77-1.05), or stroke (HR 0.99; 95% CI 0.71-1.38), but there was an increased risk of death (HR 1.27; 95% CI 1.15-1.41) in those who stopped ACEI/ARB compared to those who continued. Results were similar in Sweden: no differences were seen in risk of heart failure (HR 0.91; 95% CI 0.73-1.13) or AKI (HR 0.81; 95% CI 0.54-1.21). However, no increased risk of death was seen (HR 0.94; 95% CI 0.78-1.13) and stroke was less common in people who stopped ACEI/ARB (HR 0.56; 95% CI 0.34-0.93). Results were similar across all sensitivity analyses. CONCLUSIONS: Previous ACEI/ARB users who continued treatment after an episode of AKI did not have an increased risk of heart failure or subsequent AKI compared to those who stopped the drugs

Economic methods for valuing the outcomes of genetic testing: beyond cost-effectiveness analysis.

Genetic testing in health care can provide information to help with disease prediction, diagnosis, prognosis, and treatment. Assessing the clinical utility of genetic testing requires a process to value and weight different outcomes. This article discusses the relative merits of different economic measures and methods to inform recommendations relative to genetic testing for risk of disease, including cost-effectiveness analysis and cost-benefit analysis. Cost-effectiveness analyses refer to analyses that calculate the incremental cost per unit of health outcomes, such as deaths prevented or life-years saved because of some intervention. Cost-effectiveness analyses that use preference-based measures of health state utility such as quality-adjusted life-years to define outcomes are referred to as cost-utility analyses. Cost-effectiveness analyses presume that health policy decision makers seek to maximize health subject to resource constraints. Cost-benefit analyses can incorporate monetary estimates of willingness-to-pay for genetic testing, including the perceived value of information independent of health outcomes. These estimates can be derived from contingent valuation or discrete choice experiments. Because important outcomes of genetic testing do not fit easily within traditional measures of health, cost-effectiveness analyses do not necessarily capture the full range of outcomes of genetic testing that are important to decision makers and consumers. We recommend that health policy decision makers consider the value to consumers of information and other nonhealth attributes of genetic testing strategies