Circulation. Cardiovascular Quality and Outcomes最新文献

Background: Heart failure (HF) is a leading cause of hospitalization in older adults. Medicare data have been used to assess HF outcomes. However, the validity of ICD-10 diagnosis codes (used since 2015) to identify acute HF hospitalization or distinguish reduced (heart failure with reduced ejection fraction) versus preserved ejection fraction (HFpEF) is unknown in Medicare data.

Methods: Using Medicare data (2015-2017), we randomly sampled 200 HF hospitalizations with ICD-10 diagnosis codes for HF in the first/second claim position in a 1:1:2 ratio for systolic HF (I50.2), diastolic HF (I50.3), and other HF (I50.X). The primary gold standards included recorded HF diagnosis by a treating physician for HF hospitalization, ejection fraction (EF)≤50 for heart failure with reduced ejection fraction, and EF>50 for HFpEF. If the quantitative EF was not present, then qualitative descriptions of EF were used for heart failure with reduced ejection fraction/HFpEF gold standards. Multiple secondary gold standards were also tested. Gold standard data were extracted from medical records using standardized forms and adjudicated by cardiology fellows/staff. We calculated positive predictive values with 95% CIs.

Results: The 200-chart validation sample included 50 systolic, 50 diastolic, 47 combined dysfunction, and 53 unspecified HF patients. The positive predictive values of acute HF hospitalization was 98% [95% CI, 95-100] for first-position ICD-10 HF diagnosis and 66% [95% CI, 58-74] for first/second-position diagnosis. Quantitative EF was available for ≥80% of patients with systolic, diastolic, or combined dysfunction ICD-10 codes. The positive predictive value of systolic HF codes was 90% [95% CI, 82-98] for EFs≤50% and 72% [95% CI, 60-85] for EFs≤40%. The positive predictive value was 92% [95% CI, 85-100] for HFpEF for EFs>50%. The ICD-10 codes for combined or unspecified HF poorly predicted heart failure with reduced ejection fraction or HFpEF.

Conclusions: ICD-10 principal diagnosis identified acute HF hospitalization with a high positive predictive value. Systolic and diastolic ICD-10 diagnoses reliably identified heart failure with reduced ejection fraction and HFpEF when EF 50% was used as the cutoff.

Background: A better understanding is needed of the burdens and benefits of left ventricular assist device (LVAD) implantation on patients' physical, mental, and social well-being. The purpose of this report was to evaluate the validity of Patient-Reported Outcomes Measurement Information System (PROMIS) measures for LVAD patients and to estimate clinically important score differences likely to have implications for patient treatment or care.

Methods: Adults from 12 sites across all US geographic regions completed PROMIS measures ≥3 months post-LVAD implantation. Other patient-reported outcomes (eg, Kansas City Cardiomyopathy Questionnaire-12 item), clinician ratings, performance tests, and clinical adverse events were used as validity indicators. Criterion and construct validity and clinically important differences were estimated with Pearson correlations, ANOVA methods, and Cohen d effect sizes.

Results: Participants' (n=648) mean age was 58 years, and the majority were men (78%), non-Hispanic White people (68%), with dilated cardiomyopathy (55%), long-term implantation strategy (57%), and New York Heart Association classes I and II (54%). Most correlations between validity indicators and PROMIS measures were medium to large (≥0.3; p<0.01). Most validity analyses demonstrated medium-to-large effect sizes (≥0.5) and clinically important differences in mean PROMIS scores (up to 14.8 points). Ranges of minimally important differences for 4 PROMIS measures were as follows: fatigue (3-5 points), physical function (2-3), ability to participate in social roles and activities (3), and satisfaction with social roles and activities (3-5).

Conclusions: The findings provide convincing evidence for the relevance and validity of PROMIS physical, mental, and social health measures in patients from early-to-late post-LVAD implantation. Findings may inform shared decision-making when patients consider treatment options. Patients with an LVAD, their caregivers, and their clinicians should find it useful to interpret the meaning of their PROMIS scores in relation to the general population, that is, PROMIS may help to monitor a return to normalcy in everyday life.

Background: Previous studies have observed a higher out-of-hospital cardiac arrest (OHCA) risk among lower socioeconomic groups. However, due to the cross-sectional and ecological designs used in these studies, the magnitude of these inequalities is uncertain. This study is the first to assess the individual-level association between income and OHCA using a large-scale longitudinal study.

Methods: This retrospective cohort study followed 1 688 285 adults aged 25 and above, living in the catchment area of an OHCA registry in a Dutch province. OHCA cases (n=5493) were linked to demographic and income registries. Cox proportional hazard models were conducted to determine hazard ratios of OHCA for household and personal income quintiles, stratified by sex and age.

Results: The total incidence of OHCA per 100 000 person years was 30.9 in women and 87.1 in men. A higher OHCA risk was observed with lower household and personal income. Compared with the highest household income quintile, the adjusted hazard ratios from the second highest to the lowest household income quintiles ranged from 1.24 (CI=1.01-1.51) to 1.75 (CI=1.46-2.10) in women and from 0.95 (CI=0.68-1.34) to 2.30 (CI=1.74-3.05) in men. For personal income, this ranged from 0.95 (CI=0.68-1.34) to 2.30 (CI=1.74-3.05) in women and between 1.28 (CI=1.16-1.42) and 1.68 (CI=1.48-1.89) in men. Comparable household and personal income gradients were found across age groups except in the highest (>84 years) age group. For example, household income in women aged 65 to 74 ranged from 1.25 (CI=1.02-1.52) to 1.65 (CI=1.36-2.00). Sensitivity analyses assessing the prevalence of comorbidities at baseline and different lengths of follow-up yielded similar estimates.

Conclusions: This study provides new evidence for a substantial increase in OHCA risk with lower income in different age and sex groups. Low-income groups are likely to be a suitable target for intervention strategies to reduce OHCA risk.

Background: Establishing registries to collect demographic characteristics, processes of care, and outcomes of patients with out-of-hospital cardiac arrest (OHCA) can better understand epidemiological trends, measure care quality, and identify opportunities for improvement. This study aimed to describe the design, implementation, and scientific significance of a nationwide registry-the BASIC-OHCA (Baseline Investigation of Out-of-Hospital Cardiac Arrest)-in China.

Methods: BASIC-OHCA was designed as a prospective, multicenter, observational, population-based study. The BASIC-OHCA registry was developed based on Utstein templates. BASIC-OHCA includes all OHCA patients confirmed by emergency medical services (EMS) personnel regardless of age, sex, or cause. Patients declared dead at the scene by EMS personnel for any reasons are also included. To fully characterize an OHCA event, BASIC-OHCA collects data from 3 sources-EMS, the receiving hospital, and patient follow-up-and links them to form a single record. Once data entry is completed and quality is checked, individual identifiers are stripped from the record.

Results: Currently, 32 EMS agencies in 7 geographic regions contribute data to BASIC-OHCA. They are distributed in the urban and rural areas, covering ≈9% of the population of mainland China. Data collection started on August 1, 2019. By July 31, 2020, a total of 92 913 EMS-assessed OHCA patients were enrolled. Among 28969 (31.18%) EMS-treated OHCAs, the mean age was 65.79±17.36 years, and 68.35% were males. The majority of OHCAs (76.85%) occurred at home or residence. A shockable initial rhythm was reported in 5.43% of patients. Any return of spontaneous circulation, survival to hospital discharge, and favorable neurological outcome at hospital discharge were 5.98%, 1.15%, and 0.83%, respectively.

Conclusions: BASIC-OHCA is the first nationwide registry on OHCA in China. It can be used as a public health surveillance system and as a platform to produce evidence-based practices to help identify opportunities for improvement.

Registration: URL: https://www.

Clinicaltrials: gov; Unique identifier: NCT03926325.

Background: Hospitals are increasingly likely to implement clinical informatics tools to improve quality of care, necessitating rigorous approaches to evaluate effectiveness. We leveraged a multi-institutional data repository and applied causal inference methods to assess implementation of a commercial data visualization software in our pediatric cardiac intensive care unit.

Methods: Natural experiment in the University of Michigan (UM) Cardiac Intensive Care Unit pre and postimplementation of data visualization software analyzed within the Pediatric Cardiac Critical Care Consortium clinical registry; we identified N=21 control hospitals that contributed contemporaneous registry data during the study period. We used the platform during multiple daily rounds to visualize clinical data trends. We evaluated outcomes-case-mix adjusted postoperative mortality, cardiac arrest and unplanned readmission rates, and postoperative length of stay-most likely impacted by this change. There were no quality improvement initiatives focused specifically on these outcomes nor any organizational changes at UM in either era. We performed a difference-in-differences analysis to compare changes in UM outcomes to those at control hospitals across the pre versus postimplementation eras.

Results: We compared 1436 pre versus 779 postimplementation admissions at UM to 19 854 (pre) versus 14 160 (post) at controls. Admission characteristics were similar between eras. Postimplementation at UM we observed relative reductions in cardiac arrests among medical admissions, unplanned readmissions, and postoperative length of stay by -14%, -41%, and -18%, respectively. The difference-in-differences estimate for each outcome was statistically significant (P<0.05), suggesting the difference in outcomes at UM pre versus postimplementation is statistically significantly different from control hospitals during the same time.

Conclusions: Clinical registries provide opportunities to thoroughly evaluate implementation of new informatics tools at single institutions. Borrowing strength from multi-institutional data and drawing ideas from causal inference, our analysis solidified greater belief in the effectiveness of this software across our institution.

Background: Cardiovascular disease disproportionately affects persons living in low- and middle-income countries and heart failure (HF) is thought to be a leading cause. Population-based studies characterizing the epidemiology of HF in these settings are lacking. We describe the age-standardized prevalence, survival, subtypes, risk factors, and 1-year mortality of HF in the population-based Haiti Cardiovascular Disease Cohort.

Methods: Participants were recruited using multistage cluster-area random sampling in Port-au-Prince, Haiti. A total of 2981 completed standardized history and exam, laboratory measures, and cardiac imaging. Clinical HF was defined by Framingham criteria. Kaplan-Meier and Cox proportional hazard regression assessed mortality among participants with and without HF; logistic regression identified associated factors.

Results: Among all participants, the median age was 40 years (interquartile range, 27-55), and 58.2% were female. Median follow-up was 15.4 months (interquartile range, 9-22). The age-standardized HF prevalence was 3.2% (93/2981 [95% CI, 2.6-3.9]). The average age of participants with HF was 57 years (interquartile range, 45-65), and 67.7% were female. The first significant increase in HF prevalence occurred between 30 to 39 and 40 to 49 years (1.1% versus 3.7%, P=0.003). HF with preserved ejection fraction was the most common HF subtype (71.0%). Age (adjusted odds ratio, 1.36 [1.12-1.66] per 10-year increase), hypertension (2.14 [1.26-3.66]), obesity (3.35 [95% CI, 1.99-5.62]), poverty (2.10 [1.18-3.72]), and renal dysfunction (5.42 [2.94-9.98]) were associated with HF. One-year HF mortality was 6.6% versus 0.8% (hazard ratio, 7.7 [95% CI, 2.9-20.6]; P<0.0001).

Conclusions: The age-standardized prevalence of HF in this low-income setting was alarmingly high at 3.2%-5-fold higher than modeling estimates for low- and middle-income countries. Adults with HF were two decades younger and 7.7× more likely to die at 1 year compared with those in the community without HF. Further research characterizing the population burden of HF in low- and middle-income countries can guide resource allocation and development of pragmatic HF prevention and treatment interventions, ultimately reducing global cardiovascular disease health disparities.

Registration: URL: https://www.

Clinicaltrials: gov; Unique identifier: NCT03892265.

Background: Direct oral anticoagulants are first-line therapy for common thrombotic conditions, including atrial fibrillation and venous thromboembolism. Despite their strong efficacy and safety profile, evidence-based prescribing can be challenging given differences in dosing based on indication, renal function, and drug-drug interactions. The Veterans Health Affairs developed and implemented a population management dashboard to support pharmacist review of anticoagulant prescribing. The dashboard includes information about direct oral anticoagulants and dose prescribed, renal function, age, and weight, potential interacting medications, and the need for direct oral anticoagulant medication refills. It is a stand-alone system.

Methods: Using login data from the dashboard, nationwide implementation was evaluated using elements from the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework.

Results: Between August 2016 and June 2020, 150/164 sites within the Veterans Health Affairs system used the dashboard, averaging 1875 patients per site. The dashboard was made available to sites on a staggered basis. Moderate or high adoption, defined as at least one login on at least 2 separate days per month, began slowly with 3/5 sites in the pilot phase but rapidly grew to 142/150 (94.7%) sites by June 2020. The average number of unique users per site increased from 2.4 to 7.5 over the study period. Moderate to high adoption of the dashboard's use was maintained for > 6 months in 126/150 (84.0%) sites by the end of the study period.

Conclusions: There was rapid and sustained implementation and adoption of a population health dashboard for evidence-based anticoagulant prescribing across the national United States Veterans Health Administration health system. The impact of this tool on clinical outcomes and strategies to replicate this care model in other health systems will be important for broad dissemination and uptake.