Circulation-Cardiovascular Quality and Outcomes最新文献

Background: Type 2 myocardial infarction (MI) is common among older adults and is associated with adverse outcomes in single-center studies. We aimed to examine temporal trends and compare outcomes between type 1 and type 2 MI in Medicare beneficiaries.

Methods: Medicare beneficiaries with type 1 or type 2 MI were identified using International Classification of Diseases, Tenth Revision codes from Medicare Provider Analysis and Review 100% inpatient files. Temporal trends were assessed from 2018 to 2021. Patients with type 2 MI were matched 1:1 to type 1 MI by age, sex, race, and year. Outcomes included all-cause mortality, recurrent MI, heart failure hospitalization (HFH), and stroke. Time-to-event analyses used Cox models for mortality and Fine-Gray models for hospitalization outcomes, with short-term (≤30 days) and long-term (>30 days) outcomes assessed using landmark analysis.

Results: Among 1 816 926 Medicare beneficiaries, the proportion of type 2 MI increased from 19.4% in 2018 to 26.8% in 2021 (P_trend<0.001). In propensity-matched analyses of 94 132 patients (mean age 77.3±11 years, 53.3% male), patients with type 2 (versus type 1) MI had lower short-term all-cause mortality (hazard ratio, 0.61 [95% CI, 0.59-0.63]), recurrent MI (subdistribution hazard ratio [sHR], 0.56 [95% CI, 0.54-0.59]), HFH (sHR, 0.56 [95% CI, 0.47-0.67]), and similar risk of stroke (sHR, 1.04 [95% CI, 0.90-1.21]). In long-term, patients with type 2 (versus type 1) MI had higher risk of all-cause mortality (hazard ratio, 1.23 [95% CI, 1.20-1.26]) and stroke (sHR, 1.20 [95% CI, 1.10-1.31]). The risk of recurrent MI and HFH associated with type 2 (versus type 1) MI in long-term follow-up was lower but considerably attenuated in magnitude compared with short-term risk (recurrent MI: sHR, 0.89 [95% CI, 0.86-0.94]; HFH; sHR, 0.73 [95% CI, 0.66-0.81]).

Conclusions: Type 2 MI now accounts for over one-quarter of all MIs and is increasingly diagnosed. While patients with type 1 MI had higher short-term all-cause mortality risk, those with type 2 MI demonstrated higher long-term all-cause mortality and stroke risk, with lower risk of recurrent MI and HFH. These results highlight an urgent need for evidence-based strategies in this high-risk population.

Background: Infections following durable left ventricular assist device (dLVAD) implantation are common and associated with increased morbidity and mortality. Despite documented interhospital variability, few studies have identified strategies to mitigate their occurrence. This national study uses a multistage mixed methods design to develop a customizable and deployable toolkit of expert-guided recommendations to reduce infections post-dLVAD.

Methods: Using purposeful sampling, participants (eg, clinical and operational ventricular assist device [VAD] team members) from low, medium, and high-performance hospitals (based on their risk-adjusted, 90-day post-implantation infection rates) across the United States were interviewed to assess factors contributing to postdLVAD infections. Draft toolkit recommendations were iteratively developed after integrating thematically analyzed qualitative and quantitative data from a merged national registry with Medicare and hospital survey data. A national advisory team of VAD subject matter experts provided mixed methods input to refine the toolkit's content and structure.

Results: Seventy-three clinical and operational VAD team members across 8 US hospitals were interviewed, spanning low (n=4), medium (n=1) and high (n=3) performance groups. Fourteen subject matter experts provided stakeholder feedback to refine the toolkit. The resulting toolkit contains 39 infection prevention recommendations that address VAD program care processes (eg, real-time provider communication), clinicians (eg, multidisciplinary protocol development), patients and caregivers (eg, engaging patient advisors in patient education), and VAD leadership (eg, unit and service level data reporting). Accompanying resources (eg, team-based exercises, data collection worksheets) support implementing and evaluating site-specific strategies.

Conclusions: Using mixed methods approaches, an infection prevention toolkit was developed to enhance care coordination among VAD team members and mitigate postdLVAD infections. Future work should evaluate the effectiveness of implementing this infection prevention toolkit within the dLVAD setting.

Background: Digital remote patient monitoring (RPM), such as home-based blood pressure, heart rate, or weight monitoring, enables longitudinal care outside traditional health care settings, especially in the vulnerable period after hospitalizations, with broad coverage of the service by payers. We sought to evaluate patterns of RPM service availability at US hospitals and the characteristics of hospitals and the counties they serve that are associated with the availability of these services.

Methods: We used national data from the American Hospital Association Annual Survey from 2018 to 2022 to ascertain US hospitals offering RPM services for postdischarge or chronic care. We linked hospitals with their census-based county-level data to define the characteristics of the counties they serve, including sociodemographic features such as age distribution, racial/ethnic composition, median household income, education level, and disability status. We used multivariable logistic regression to assess associations between hospital- and county-level characteristics and RPM availability, adjusting for hospital size, region, teaching status, and ownership.

Results: The study included 5644 hospitals. Over 5 years of study, there was a 40.3% increase in the number of hospitals offering RPM services, rising from 1364 (33.0%) hospitals in 2018 to 1797 (46.3%) in 2022. In 2022, hospitals with >300 beds had 3.7-fold odds of offering RPM compared with those with <100 beds (adjusted odds ratio, 3.71 [95% CI, 2.90-4.74]). Nonteaching hospitals had lower odds of RPM availability than teaching hospitals (adjusted odds ratio, 0.29 [95% CI, 0.19-0.44]), and rural hospitals had lower odds than urban hospitals (adjusted odds ratio, 0.49 [95% CI, 0.32-0.77]).

Conclusions: In this national study of US hospitals, there has been a large increase in the availability of RPM services but with large variation among hospitals, with lower availability in hospitals serving low-income and rural counties.

Background: The benefits of switching from warfarin to direct oral anticoagulants in atrial fibrillation remain unclear.

Methods: This retrospective study used the Medicare fee-for-service (2013-2020) and Optum Deidentified Clinformatics Data Mart databases (2013-2023). Among patients with atrial fibrillation who received warfarin for at least 180 days, we created 2 cohorts: (1) patients switching to apixaban versus continuing warfarin (the apixaban cohort) and (2) patients switching to rivaroxaban versus continuing warfarin (the rivaroxaban cohort). The index date was the switch date for switchers and a matched date based on warfarin duration for warfarin continuers. After 1:1 propensity score matching, we estimated the rate ratios (RR) for a composite of ischemic stroke, major bleeding, and death in each database and pooled the results using meta-analysis. Subgroup analyses by claims-based frailty and by follow-up time (first 60 days versus beyond 60 days) were performed.

Results: In the apixaban cohort (n=164 480; mean age, 80.5 years; 55.5% female; median follow-up, 354 days), switching to apixaban was associated with a lower rate of composite outcome (97.1 versus 104.9 per 1000 person-years; rate ratio, 0.92 [95% CI, 0.89-0.95]) compared with continuing warfarin. In the rivaroxaban cohort (n=96 030, mean age 79.7 years, 54.8% female, median follow-up 365 days), switching to rivaroxaban was associated with an increased rate of composite outcome (105.8 versus 99.3 per 1000 person-years; rate ratio, 1.08 [95% CI, 1.04-1.13]). No heterogeneity by frailty levels was observed. However, switching was associated with an initial risk increase within the first 60 days, followed by risk attenuation beyond 60 days, for both apixaban and rivaroxaban.

Conclusions: In patients with atrial fibrillation on warfarin therapy, switching to apixaban may reduce the risk of ischemic stroke, major bleeding, and death, whereas switching to rivaroxaban may increase the risk. For both apixaban and rivaroxaban, switching may temporarily increase risk during the first 60 days.

Background: Prediction models determining expected outcomes are infrequently updated (ie, static), which may reduce accuracy and misclassify hospital performance over time. Dynamic models incorporate changes over time and may improve accuracy and fairness in hospital comparisons. This study evaluated whether dynamic updating, compared with a static model, altered hospital rankings and outlier detection among surgical aortic valve replacement patients.

Methods: This retrospective cohort study assessed performance across 53 hospitals using claims data from the Pennsylvania Health Care Cost Containment Council. A multivariable logistic regression model using clinical and demographic variables was developed on data from 1999 to 2006 to predict 30-day postoperative mortality, then applied to testing data from 2007 to 2018 to compare 4 strategies: (1) a static model with fixed parameters, (2) an annual correction factor based on The Society of Thoracic Surgeons methodology, (3) calibration regression for annual recalibration, and (4) dynamic logistic state space model to continuously update model coefficients. Performance was evaluated using observed-to-expected ratios and Z scores. Lower values indicate better-than-expected outcomes.

Results: The training sample included 14 070 patients (mean age 66.6; 43.1% women); the testing sample included 29 127 patients (mean age 67.4; 39.1% women). The static model had the widest Z score variability (range -6.97 to 1.38), compared with calibration regression (-3.04 to 2.85), correction factor (-2.87 to 3.24), and dynamic logistic state space model (-2.57 to 3.03). The static model labeled 15 hospitals as significantly better-than-expected; only 3 (20.0%) maintained this classification with the correction factor and dynamic logistic state space model, and 5 (33.3%) with calibration regression. No hospitals were classified as significantly worse-than-expected under the static model, whereas calibration regression identified 6, and both dynamic logistic state space model and the correction factor identified 7.

Conclusions: Static models may misclassify hospital performance and rankings. Dynamic strategies influence outlier detection and change hospital rankings over time. Regular model updates may better reflect current performance, supporting fairer hospital comparisons.

Background: Randomized trials have clearly demonstrated the benefits of anticoagulant therapy in patients with atrial fibrillation who are at high risk of ischemic stroke. However, less is known about the benefit of anticoagulation in low-risk patients, and exactly how low baseline stroke risk justifies further attempts to reduce it with direct oral anticoagulants (DOACs) remains unclear.

Methods: We developed a Markov decision model to estimate the impact of initiating DOACs on quality-adjusted life years (QALYs) on a 20-year time horizon in patients with atrial fibrillation across a range of nonanticoagulated ischemic stroke risk. The model incorporated data from randomized controlled trials on the effects of DOACs on the severity and risk of ischemic stroke, major bleeding, and mortality, as well as previous evidence on their impact on quality of life. Nonanticoagulated event rates were averaged from previous observational studies.

Results: The tipping point in the annual nonanticoagulated ischemic stroke rate, at which DOAC treatment resulted in equal cumulative QALYs as withholding therapy, was 0.65%. Below this risk threshold, DOAC therapy yielded slightly fewer QALYs, while, above it, DOAC therapy resulted in increasingly higher QALYs. At nonanticoagulated stroke risk levels of 1%, 2%, and 3%, the mean QALY gains with DOACs per patient during a 20-year simulation were 0.13, 0.53, and 1.00, respectively, whereas, at the stroke risk level of 0.4%, DOAC therapy resulted in 0.01 lower QALYs per patient.

Conclusions: In this simulation, DOAC therapy versus no anticoagulation was associated with a net benefit on QALYs in patients with atrial fibrillation with an annual nonanticoagulated stroke risk >0.65%, with the magnitude of benefit increasing with higher stroke risk.

Background: Outcome reporting in cardio-obstetrics studies is inconsistent. The objective of the COSCarP (Core Outcome Set on Cardiac Diseases in Pregnancy) study was to develop a core outcome set through international consensus and harmonize outcome reporting in cardio-obstetrics studies.

Methods: We conducted a multimethod study between February 2021 and April 2023 that included an online 2-round Delphi survey, 3 small group discussions and a consensus meeting with health service users (people with lived experience of pregnancy and heart disease) and health care professionals.

Results: A total of 110 participants (22 health service users and 88 health care professionals) from 13 countries in Africa, Asia, Europe, and North America scored 71 candidate items obtained through literature reviews and qualitative interviews. Participants identified 12 core outcomes and 12 core reporting checklist items (which may not always represent outcomes) for inclusion in all cardio-obstetrics studies. Core outcomes included: maternal mortality, cardiac arrest, cerebrovascular events, heart failure, arrhythmias requiring treatment or change in treatment, thromboembolism, syncope, maternal intensive care unit admission, cardiovascular interventions, fetal/neonatal loss (miscarriage, stillbirth, neonatal death), severe neonatal morbidity, and prolonged neonatal intensive care unit admission. Reporting checklist items included: hypertensive disorders, major bleeding, anesthetic complications, adverse reactions, labor and birth details, maternal hospitalization and re-admissions, treatment compliance, fetal growth restriction, congenital malformations, gestational age at birth, and nonsevere neonatal morbidity. Participants also identified 7 condition-specific outcomes and 7 other patient-important outcomes, which may be challenging to measure in all studies and should be reported when feasible and relevant.

Conclusions: The COSCarP study has identified core outcomes, reporting checklist items, and patient-important outcomes, which researchers are encouraged to measure and report in future cardio-obstetrics studies. Widespread use of the COSCarP checklists could support data harmonization, enable meaningful comparisons between studies, facilitate meta-analyses, and ensure that future guidelines incorporate patient-important outcomes while making clinical practice recommendations.

Background: Over 35% of Danish children with congenital heart disease (CHD) are diagnosed with or treated for a neurodevelopmental or mental health condition. We examined child clinical, parent socioeconomic, and family health factors associated with 4 common diagnostic groups in children with CHD: developmental disorders, intellectual disability, attention-deficit/hyperactivity disorder, and anxiety and mood disorders.

Methods: This population-based cohort study identified children aged <18 years with CHD from 1996 to 2017 by linking individual-level data across Danish health and social registries, excluding children with a neurodevelopmental or mental health diagnosis, by International Classification of Diseases, Tenth Revision codes, before the index date (ie, CHD diagnosis). Using age as a time scale, we computed cumulative incidence by the age of 18 years, incidence rates, and crude and adjusted hazard ratios for each diagnostic group. Hazard ratios were adjusted for child sex and year of CHD diagnosis. In addition, cumulative days in hospital were adjusted for CHD complexity.

Results: In 16 473 children with CHD (male, 50.9%; median age at index date, 0.1 [interquartile range, 0.0-1.3] years), the cumulative incidence by the age of 18 years was 7.5% (95% CI, 7.0%-8.1%), 5.0% (95% CI, 4.6%-5.5%), 5.8% (95% CI, 5.3%-6.3%), and 10.3% (95% CI, 9.6%-11.1%) for these 4 diagnostic groups, respectively. Cumulative days in hospital within the first year after CHD diagnosis were the strongest clinical predictor of neurodevelopmental and mental health diagnoses, followed by sex, the presence of a genetic syndrome, and small for gestational age birthweight. Multiple socioeconomic metrics, including maternal or paternal age <25 years, low education (9-10 years), unemployment, and maternal marital status (eg, single or divorced), were predictors, particularly for attention-deficit/hyperactivity disorder and intellectual disability. Maternal and paternal mental health diagnoses were predictors of all 4 diagnostic groups.

Conclusions: We identified clinical, socioeconomic, and parent mental health factors associated with neurodevelopmental and mental health diagnoses in children with CHD. These data may inform early identification of these conditions and guide prevention and resource allocation.