Khan, Muhammad Shahzeb

Abstract Aims We aimed to perform a retrospective cohort study using the Centers for Disease Control and Prevention’s (CDC’s) Wide-Ranging Online Data for Epidemiologic Research (WONDER) database to analyse the trends in cardiovascular disease (CVD)–related mortality in patients with myeloproliferative neoplasms (MPNs) from 1999 to 2020. Methods and results We analysed the death certificate data from the CDC WONDER database from 1999 to 2020 for CVD with co-morbid myeloproliferative disorders in the US population. Age-adjusted mortality rates (AAMRs) and 95% confidence intervals (CIs) were computed per 1 million population by standardizing crude mortality rates to the 2000 US census population. To assess annual national mortality trends, we employed the Joinpoint regression model, calculating the annual per cent change in AAMR and corresponding 95% CIs. A total of 15 269 deaths related to CVD occurred in patients with co-morbid MPNs from 1999 to 2020. Overall, there was a decreasing trend in CVD-related AAMRs throughout these years. Males contributed to 51% of total deaths, and their AAMR was persistently higher than women throughout the study. Non-Hispanic (NH) Whites had the highest overall AAMR, followed by NH Blacks, NH American Indians or Alaska Natives, Hispanics or Latinos, and NH Asian or Pacific Islanders. Conclusion Our findings indicate a significant decline with notable gender, racial/ethnic, and regional differences in CVD-related mortality among patients with MPN over the past two decades. We emphasize the importance of a collaborative approach between oncologists and cardiologists in managing these patients, highlighting the potential benefits of integrating cardio-oncology services to enhance patient outcomes.

Abstract Endpoint adjudication (EA) is a common feature of contemporary randomized controlled trials (RCTs) in cardiovascular medicine. Endpoint adjudication refers to a process wherein a group of expert reviewers, known as the clinical endpoint committee (CEC), verify potential endpoints identified by site investigators. Events that are determined by the CEC to meet pre-specified trial definitions are then utilized for analysis. The rationale behind the use of EA is that it may lessen the potential misclassification of clinical events, thereby reducing statistical noise and bias. However, it has been questioned whether this is universally true, especially given that EA significantly increases the time, effort, and resources required to conduct a trial. Herein, we compare the summary estimates obtained using adjudicated vs. non-adjudicated site designated endpoints in major cardiovascular RCTs in which both were reported. Based on these data, we lay out a framework to determine which trials may warrant EA and where it may be redundant. The value of EA is likely greater when cardiovascular trials have nuanced primary endpoints, endpoint definitions that align poorly with practice, sub-optimal data completeness, greater operator variability, and lack of blinding. EA may not be needed if the primary endpoint is all-cause death or all-cause hospitalization. In contrast, EA is likely merited for more nuanced endpoints such as myocardial infarction, bleeding, worsening heart failure as an outpatient, unstable angina, or transient ischaemic attack. A risk-based approach to adjudication can potentially allow compromise between costs and accuracy. This would involve adjudication of a small proportion of events, with further adjudication done if inconsistencies are detected.

Various pharmacotherapies exist for heart failure with preserved ejection fraction (HFpEF), but with unclear comparative efficacy. We searched EMBASE, Medline, and Cochrane Library from inception through August 2021 for all randomized clinical trials in HFpEF (EF >40%) that evaluated beta-blockers, mineralocorticoid receptor antagonist (MRA), angiotensin-converting enzyme inhibitors (ACE), angiotensin receptor blockers (ARB), angiotensin receptor-neprilysin inhibitor (ARNI), and sodium-glucose cotransporter-2 inhibitors (SGLT2i). Outcomes assessed were cardiovascular mortality, all-cause mortality, and HF hospitalization. A frequentist network meta-analysis was performed with a random-effects model. We included 22 randomized clinical trials (30,673 participants; mean age = 71.7 ± 4.2 years; females = 49.3 ± 7.7%; median follow-up = 24.4 ± 11.1 months). Compared with placebo, there was no statistically significant difference in cardiovascular mortality [beta-blockers; odds ratio (OR) 0.79 (0.46–1.34), MRA; OR 0.90 (0.70–1.14), ACE OR 0.95 (0.59–1.53), ARB; OR 1.02 (0.87–1.19), ARNI; OR 0.97 (0.74–1.26) and SGLT2i; OR 1.00 (0.84–1.18)] or all-cause mortality [beta blockers; OR 0.75 (0.54–1.04), MRA; OR 0.90 (0.75–1.08) ACE; OR 1.05 (0.71–1.54), ARB; OR 1.03 (0.91–1.15), ARNI; OR 0.99 (0.82–1.20) and SGLT2i; OR 1.00 (0.89–1.13)]. The certainty in these estimates was low or very low. There was a significantly reduction in HF hospitalization with the use of SGLT2i [OR 0.71 (0.62–0.82), moderate certainty], ARNI [OR 0.77 (0.63–0.94), low certainty], and MRA [OR 0.81 (0.66–0.98), moderate certainty]; with corresponding P scores of 0.84, 0.68, and 0.58, respectively. In HFpEF, the use of beta-blockers, MRA, ACE/ARB/ARNI, or SGLT2i was not associated with improved cardiovascular or all-cause mortality. SGLT2i, ARNI, and MRA reduced the risk of HF hospitalizations.

Abstract Aims There are several risk scores designed to predict mortality in patients with heart failure (HF). This study aimed to assess performance of risk scores validated for mortality prediction in patients with acute HF (AHF) and chronic HF. Methods and results MEDLINE and Scopus were searched from January 2015 to January 2021 for studies which internally or externally validated risk models for predicting all-cause mortality in patients with AHF and chronic HF. Discrimination data were analysed using C-statistics, and pooled using generic inverse-variance random-effects model. Nineteen studies (n = 494 156 patients; AHF: 24 762; chronic HF mid-term mortality: 62 000; chronic HF long-term mortality: 452 097) and 11 risk scores were included. Overall, discrimination of risk scores was good across the three subgroups: AHF mortality [C-statistic: 0.76 (0.68–0.83)], chronic HF mid-term mortality [1 year; C-statistic: 0.74 (0.68–0.79)], and chronic HF long-term mortality [≥2 years; C-statistic: 0.71 (0.69–0.73)]. MEESSI-AHF [C-statistic: 0.81 (0.80–0.83)] and MARKER-HF [C-statistic: 0.85 (0.80–0.89)] had an excellent discrimination for AHF and chronic HF mid-term mortality, respectively, whereas MECKI had good discrimination [C-statistic: 0.78 (0.73–0.83)] for chronic HF long-term mortality relative to other models. Overall, risk scores predicting short-term mortality in patients with AHF did not have evidence of poor calibration (Hosmer–Lemeshow P > 0.05). However, risk models predicting mid-term and long-term mortality in patients with chronic HF varied in calibration performance. Conclusions The majority of recently validated risk scores showed good discrimination for mortality in patients with HF. MEESSI-AHF demonstrated excellent discrimination in patients with AHF, and MARKER-HF and MECKI displayed an excellent discrimination in patients with chronic HF. However, modest reporting of calibration and lack of head-to-head comparisons in same populations warrant future studies.

AbstractBackgroundRepresentation trends of women, older adults, and ethnic/racial minorities in randomized controlled trials (RCTs) of atrial fibrillation (AF) are uncertain.MethodsWe systematically reviewed 134 AF related RCTs (phase II and III) encompassing 149,162 participants using Medline and ClinicalTrials.gov through April 2019 to determine representation trends of women, older patients (≥75 years), and ethnic/racial minorities. Weighted data on the prevalence of AF from epidemiological studies were used to compare the representation of the studied groups of interest in AF RCTs to their expected burden of the disease.ResultsOnly 18.7% of the RCTs reported proportion of older patients, and 12.7% RCTs reported ethnic/racial minorities. The proportions of women in RCTs versus general population were 35.2% and 35.1%, of Hispanics were 11.9% and 5.2%, of Blacks were 1.2% and 5.7%, of American Indian/Alaskans were 0.2% and 0.2%, of Asians were 14.2% and 2.4%, of native Hawaiian/Pacific Islanders were 0.05% and 0.1% and of non‐Whites were 19.5% and 22.5%, respectively. The weighted mean age (SD) across the trials was 65.3 (3.2) years which was less than the corresponding weighted mean age of 71.1 (4.5) years in the comparative epidemiological data.ConclusionThe reporting of older patients and ethnic/racial minorities was poor in RCTs of AF. The representation of women and American Indian/Alaskan natives matched their expected population share of disease burden. Hispanics and Asians were over‐represented and Blacks, native Hawaiian/Pacific Islanders and non‐Whites were under‐represented in RCTs of AF.

Abstract Aim The 2018 American Heart Association/American College of Cardiology/Multi-Society Cholesterol Guidelines recommended the addition of non-statins to statin therapy for high-risk secondary prevention patients above a low-density lipoprotein cholesterol (LDL-C) threshold of ≥70 mg/dL (1.8 mmol/L). We compared effectiveness and safety of treatment to achieve lower (<70) vs. higher (≥70 mg/dL) LDL-C among patients receiving intensive lipid-lowering therapy (statins alone or plus ezetimibe or proprotein convertase subtilisin/kexin type 9 inhibitors). Methods and results Eleven randomized controlled trials (130 070 patients), comparing intensive vs. less-intensive lipid-lowering therapy, with follow-up ≥6 months and sample size ≥1000 patients were selected. Meta-analysis was reported as random effects risk ratios (RRs) [95% confidence intervals] and absolute risk differences (ARDs) as incident cases per 1000 person-years. The median LDL-C levels achieved in lower LDL-C vs. higher LDL-C groups were 62 and 103 mg/dL, respectively. At median follow-up of 2 years, the lower LDL-C vs. higher LDL-C group was associated with significant reduction in all-cause mortality [ARD −1.56; RR 0.94 (0.89–1.00)], cardiovascular mortality [ARD −1.49; RR 0.90 (0.81–1.00)], and reduced risk of myocardial infarction, cerebrovascular events, revascularization, and major adverse cardiovascular events (MACE). These benefits were achieved without increasing the risk of incident cancer, diabetes mellitus, or haemorrhagic stroke. All-cause mortality benefit in lower LDL-C group was limited to statin therapy and those with higher baseline LDL-C (≥100 mg/dL). However, the RR reduction in ischaemic and safety endpoints was independent of baseline LDL-C or drug therapy. Conclusion This meta-analysis showed that treatment to achieve LDL-C levels below 70 mg/dL using intensive lipid-lowering therapy can safely reduce the risk of mortality and MACE.

Background: Assessment of heterogeneity in meta-analyses is critical to ensure the consistency of pooled results. Therefore, we sought to assess the evaluation and reporting of heterogeneity in heart failure (HF) meta-analyses. Methods: Study level meta-analyses pertaining to HF were selected from January 2009 to July 2019, published in 11 high impact factor journals. We tabulated the overall proportion of the meta-analyses reporting statistical heterogeneity and specific metrics and methods employed to quantify and explore heterogeneity. Results: Of 126 HF meta-analyses (612 outcomes), heterogeneity was reported for 422 outcomes (68.9 %) in 108 meta-analyses. Out of the 422 outcomes reporting statistical heterogeneity, 137 outcomes (32.5%) had no observable heterogeneity: ( I 2 =0%), 40 outcomes (9.5%) had low heterogeneity ( I 2 <25%), 76 outcomes (18%) had moderate heterogeneity ( I 2 =25%–50%), and 169 outcomes (40%) had high heterogeneity ( I 2 >50%). Reporting of statistical heterogeneity was not significantly associated with year of publication, funding source, disclosure information, or the type of studies pooled. Sensitivity analysis (n=68) was the most common statistical technique employed to evaluate the source of heterogeneity followed by subgroup analyses (n=59) and meta-regression (n=40). Conclusions: Despite being an essential component of meta-analyses, heterogeneity was not reported for nearly 30% of outcomes and variably handled in contemporary HF meta-analyses. As meta-analyses increase across HF science, interpreting and handling of heterogeneity should be standardized.

Background Aortic stenosis–related mortality might vary across demographic subsets, regions, and states in the United States. Methods and Results We reviewed the death certificate data from the Centers for Disease Control and Prevention Wide‐Ranging OnLine Data for Epidemiologic Research database to examine aortic stenosis–related mortality trends from 2008 to 2018. Crude and age‐adjusted mortality rates (AAMRs) per 100 000 people and annual percentage change with 95% CIs were calculated. Between 2008 and 2018, AAMR reduced from 12.7 to 11.5 (average annual percentage change, −1.0 [95% CI, −1.5 to −0.5]), because of an accelerated decline between 2015 and 2018 (annual percentage change, −4.4 [95% CI, −6.0 to −2.7]). Older (aged >85 years), male, and White patients had higher death rates than younger, female, and non‐White patients, respectively. Although mortality reduction was similar across sexes, significant mortality reduction was limited to White patients only. The AAMRs were higher in rural than urban areas. States with AAMRs >90th percentile were distributed in the West and the Northeast, and <10th percentile in the South. The AAMRs for sex and race were highest in the West and lowest in the South. None of the states located in the Midwest showed a significant reduction in mortality. Mortality remained stable for hospital setting and nursing home/long‐term care facility, except that the number of deaths increased at home and hospice facility since 2014. Conclusions The reduction in mortality in patients with aortic stenosis was not consistent among demographic subsets and states. The substantial public health and economic implications call for determination of underlying clinical and socioeconomic factors to narrow the gap.