Discontinuation versus continuation of statins: A systematic review.

BACKGROUND: Clinicians and patients often face a decision to continue or discontinue statins. We examined the impact of discontinuation of statins compared with continuation on clinical outcomes (all-cause mortality, cardiovascular [CV] mortality, CV events, and quality of life). METHODS: We conducted a systematic review. Randomized controlled trials (RCTs), cohort studies, case-control studies, and quasi-randomized studies among people ≥18 years were eligible. We searched MEDLINE, Embase, and Cochrane Central Registry (inception to August 2023). Two independent reviewers performed screening and extracted data. Quality assessment was performed by one author and verified by another. We summarized results narratively, performed meta-analysis for a subset of studies, and used GRADE to assess certainty of evidence. We summarized findings in the subgroup of persons ≥75 years. RESULTS: We retrieved 8369 titles/abstracts; 37 reports from 36 studies were eligible. This comprised 35 non-randomized studies (n = 1,708,684) and 1 RCT (n = 381). The 1 RCT was conducted among persons with life expectancy <1 year and showed there is probably no difference in 60-day mortality (risk difference = 3.5%, 90% CI -3.5 to 10.5) for statin discontinuation compared with continuation. Non-randomized studies varied in terms of population and setting, but consistently suggested that statin discontinuation might be associated with a relative increased risk of mortality (hazard ratio (HR) 1.92, 95% CI 1.52 to 2.44, nine studies), CV mortality (HR 1.63, 95% CI 1.27 to 2.10, five reports), and CV events (HR 1.31, 95% CI 1.23 to 1.39, eight reports). Findings in people ≥75 years were consistent with main results. There was a high degree of uncertainty in findings from non-randomized studies due to methodological limitations. CONCLUSIONS: Statin discontinuation does not appear to affect short-term mortality near end-of-life based on one RCT. Outside of this population, findings from non-randomized studies consistently suggested statin discontinuation may be associated with worse outcomes, though this is uncertain.

COVID-19 and the Genetics of Inflammation.

OBJECTIVE: Interindividual variability in the clinical progression of COVID-19 may be explained by host genetics. Emerging literature supports a potential inherited predisposition to severe forms of COVID-19. Demographic and inflammatory characteristics of COVID-19 suggest that acquired hematologic mutations leading to clonal hematopoiesis (CH) may further increase vulnerability to adverse sequelae. This review summarizes the available literature examining genetic predispositions to severe COVID-19 and describes how these findings could eventually be used to improve its clinical management. DATA SOURCES: A PubMed literature search was performed. STUDY SELECTION: Studies examining the significance of inherited genetic variation or acquired CH mutations in severe COVID-19 were selected for inclusion. DATA EXTRACTION: Relevant genetic association data and aspects of study design were qualitatively assessed and narratively synthesized. DATA SYNTHESIS: Genetic variants affecting inflammatory responses may increase susceptibility to severe COVID-19. Genome-wide association studies and candidate gene approaches have identified a list of inherited mutations, which likely alter cytokine and interferon secretion, and lung-specific mechanisms of immunity in COVID-19. The potential role of CH in COVID-19 is more uncertain at present; however, the available evidence suggests that the various types of acquired mutations and their differential influence on immune cell function must be carefully considered. CONCLUSIONS: The current literature supports the hypothesis that host genetic factors affect vulnerability to severe COVID-19. Further research is required to confirm the full scope of relevant variants and the causal mechanisms underlying these associations. Clinical approaches, which consider the genetic basis of interindividual variability in COVID-19 and potentially other causes of critical illness, could optimize hospital resource allocation, predict responsiveness to treatment, identify more efficacious drug targets, and ultimately improve outcomes.

Examination of Impact of After-Hours Admissions on Hospital Resource Use, Patient Outcomes, and Costs.

Background: Nighttime and weekends in hospital and intensive care unit (ICU) contexts are thought to present a greater risk for adverse events than daytime admissions. Although some studies exist comparing admission time with patient outcomes, the results are contradictory. No studies currently exist comparing costs with the time of admission. We investigated the differences in-hospital mortality, ICU length of stay, ICU mortality, and cost between daytime and nighttime admissions. Methods: All adult patients (≥18 years of age) admitted to a large academic medical-surgical ICU between 2011 and 2015 were included. Admission cohorts were defined as daytime (8:00-16:59) or nighttime (17:00-07:59). Student's t-tests and chi-squared tests were used to test for associations between days spent in the ICU, days on mechanical ventilation, comorbidities, diagnoses, and cohort membership. Regression analysis was used to test for associations between patient and hospitalization characteristics and in-hospital mortality and total ICU costs. Results: The majority of admissions occurred during nighttime hours (69.5%) with no difference in the overall Elixhauser comorbidity score between groups (p=0.22). Overall ICU length of stay was 7.96 days for daytime admissions compared to 7.07 days (p=0.001) for patients admitted during nighttime hours. Overall mortality was significantly higher in daytime admissions (22.5% vs 20.6, p=0.012); however, ICU mortality was not different. The average MODS was 2.9 with those admitted during the daytime having a significantly higher MODS (3.0, p=0.046). Total ICU cost was significantly higher for daytime admissions (p=0.003). Adjusted ICU mortality was similar in both groups despite an increased rate of adverse events for nighttime admissions. Daytime admissions were associated with increased cost. There was no difference in all hospital total cost or all hospital direct cost between groups. These findings are likely due to the higher severity of illness in daytime admissions. Conclusion: Daytime admissions were associated with a higher severity of illness, mortality rate, and ICU cost. To further account for the effect of staffing differences during off-hours, it may be beneficial to compare weekday and weeknight admission times with associated mortality rates.

Characteristics and resource utilization of high-cost users in the intensive care unit: a population-based cohort study.

BACKGROUND: Healthcare expenditure within the intensive care unit (ICU) is costly. A cost reduction strategy may be to target patients accounting for a disproportionate amount of healthcare spending, or high-cost users. This study aims to describe high-cost users in the ICU, including health outcomes and cost patterns. METHODS: We conducted a population-based retrospective cohort study of patients with ICU admissions in Ontario from 2011 to 2018. Patients with total healthcare costs in the year following ICU admission (including the admission itself) in the upper 10th percentile were defined as high-cost users. We compared characteristics and outcomes including length of stay, mortality, disposition, and costs between groups. RESULTS: Among 370,061 patients included, 37,006 were high-cost users. High-cost users were 64.2 years old, 58.3% male, and had more comorbidities (41.2% had ≥3) when likened to non-high cost users (66.1 years old, 57.2% male, 27.9% had ≥3 comorbidities). ICU length of stay was four times greater for high-cost users compared to non-high cost users (22.4 days, 95% confidence interval [CI] 22.0-22.7 days vs. 5.56 days, 95% CI 5.54-5.57 days). High-cost users had lower in-hospital mortality (10.0% vs.14.2%), but increased dispositioning outside of home (77.4% vs. 42.2%) compared to non-high-cost users. Total healthcare costs were five-fold higher for high-cost users ($238,231, 95% CI $237,020-$239,442) compared to non-high-cost users ($45,155, 95% CI $45,046-$45,264). High-cost users accounted for 37.0% of total healthcare costs. CONCLUSION: High-cost users have increased length of stay, lower in-hospital mortality, and higher total healthcare costs when compared to non-high-cost users. Further studies into cost patterns and predictors of high-cost users are necessary to identify methods of decreasing healthcare expenditure.