BACKGROUND:Real-world data regarding diuretic strategies and associated outcomes in patients hospitalized for heart failure (HF) in community health systems are limited. OBJECTIVES:Evaluate associations between initial diuretic therapy, markers of decongestion, and clinical outcomes in patients hospitalized for HF. METHODS:Patients hospitalized for HF from 2015-2022 across 30 health systems in the U.S. were identified in the Truveta national database. High dose loop diuretics were defined as >2.5X home dose or >160 mg IV furosemide equivalent over 24 hours. Four study groups were defined based on most intensive diuretic strategy used within 48 hours of admission: 1) high dose loop diuretics with adjuvant therapy (thiazide or acetazolamide), 2) high dose loop diuretics alone, 3) low dose loop diuretics with adjuvant therapy, and 4) low-dose loop diuretics alone. Multivariable logistic and linear regression models adjusted for clinical and demographic covariates were developed to evaluate associations between initial diuretic strategies and both in-hospital outcomes (acute kidney injury [AKI]), hemoconcentration, and weight change) and the composite of readmission or death. RESULTS:Patients were treated with low dose loop diuretics (N=81,734; 74.9%), high dose loop diuretics (N=17,187; 15.7%), low dose loop diuretics plus adjuvant therapy (N=7,027; 6.4%), and high dose loop diuretics plus adjuvant therapy (N=3,210; 2.9%). Patients treated with more intensive strategies had greater illness severity, including more frequent prior HF hospitalizations and worse kidney function. Adjusted weight loss during hospitalization was greater for patients treated with more intensive strategies (high dose loop with adjuvant: 4.6 lbs [4.0-5.2]; high dose loop alone: 2.1 lbs [1.9-2.4]; low dose loop with adjuvant: 1.4 lbs [1.0-1.8]), as were adjusted odds of AKI. The adjusted odds of 90-day readmission/death were not lower with more intensive initial diuretic strategies. However, odds of 90-day readmission/death were lower for every 5 lbs of achieved weight loss (adjusted odds ratio: 0.97, 95% confidence interval 0.96-0.97). CONCLUSIONS:More intensive diuretic strategies were used in sicker patients, improved in-hospital decongestion, but were not associated with improved 90-day outcomes. However, greater weight loss was associated with modestly lower risk of death or readmission. These data highlight the need for prospective studies to evaluate if enhancing decongestion can improve outcomes in patients hospitalized for HF.

Introduction/Background: Aortic valve calcification (AVC) quantification is recommended by guidelines as an imaging biomarker for aortic stenosis (AS) severity and progression yet remains underreported on routine chest computed tomography (CT). With nearly 20 million non-gated chest CTs performed annually in the U.S., automated AVC detection offers potential for opportunistic AS screening without additional radiation exposure or cost. Research Question/Hypothesis: We hypothesized that deep learning methods can accurately quantify AVC from non-gated, non-contrast chest CT scans with performance comparable to expert assessment. Methods/Approach: We developed a convolutional neural network to automatically detect and quantify AVC from non-gated chest CTs. The algorithm was trained and validated on 1,807 imaging studies across 8 large health systems in the U.S. and Brazil from 2021 to 2024. Model performance was evaluated on a holdout set of 239 CT studies from 33 sites across three U.S. geographic regions. The reference standard consisted of manual segmentations independently verified by at least two board-certified radiologists. Performance was evaluated by sensitivity, specificity, and Pearson correlation between algorithm-estimated and ground truth Agatston scores. Subgroup analyses across age categories, sex, geographic regions, CT manufacturers, and technical parameters were conducted. Results/Data: The deep learning algorithm demonstrated high correlation with expert reference standards (Pearson r = 0.99; 95% CI, 0.98-0.99; P <.001). Bland-Altman analysis showed minimal bias with mean difference of 5.2 AU (95% CI, -7.6 to 17.9 AU) and standard deviation of 99.9 AU. For detecting moderate-to-severe AS (>125 AU for females, >275 AU for males), sensitivity was 0.92 (95% CI, 0.80-0.97) and specificity was 0.98 (95% CI, 0.95-0.99). For severe AS (>600 AU for females, >1100 AU for males), sensitivity was 0.91 (95% CI, 0.62-0.98) and specificity was 1.00 (95% CI, 0.98-1.00). Performance remained consistent across demographic subgroups and CT technical parameters. Conclusion: This automated deep learning algorithm accurately quantifies AVC from routine chest CT scans with performance comparable to experts. Implementation into existing radiology workflows may enable opportunistic AS screening, potentially facilitating earlier identification and timely intervention. Prospective studies are needed to determine whether automated AVC screening improves clinical outcomes.

The use of an alteplase (Activase) purge solution to address Impella ventricular assist device “thrombosis” or “purge system occlusion” has been mainly documented with earlier generation Impella devices (CP, 2.5, 5.0). Here, we report the use of an alteplase purge solution to manage Impella 5.5 purge system occlusion in a 31-year-old male admitted to the cardiac care unit in cardiogenic shock and listed for a heart transplant. Throughout the purge system occlusion, the patient demonstrated hemodynamic stability and overall pump flow and cardiac output were preserved. Initially, there was a lack of response in purge flow and pressure observed at the lower concentration of alteplase purge solution (0.04 mg/ml), yet after using the alteplase 4 mg/50 ml purge (0.08 mg/ml) solution concentration, a response was seen. No bleeding or hemodynamic complications were observed. In addition, a suggested management workflow and review of the case reports and case series published to date regarding Impella purge system occlusion is included in this article.

BACKGROUND:The prevalence of heart failure with improved ejection fraction (HFimpEF) is growing. The association of ejection fraction (EF) recovery and changes in health status has not been previously reported. We aimed to characterize patient-reported health status among patients with HFimpEF, heart failure with reduced ejection fraction, and heart failure with midrange (HFmrEF) or preserved ejection fraction (HFpEF). METHODS:We identified patients with heart failure with 2 clinic visits, who completed a Kansas City Cardiomyopathy Questionnaire-12 from August 2020 to October 2023. HFimpEF was defined as most recent EF >40% from a preceding echocardiogram ≥30 days prior with EF ≤40%. We analyzed Kansas City Cardiomyopathy Questionnaire-12 Overall Summary Score across EF classifications with and without adjustment for patient characteristics via multivariable linear regression. We calculated the R2 to determine the impact of clinical characteristics on variation in health status among patients with HFimpEF. RESULTS:A total of 2519 patients were included, of which 18.7% had HFimpEF, 55.7% had HFmrEF/HFpEF, and 25.6% had HFrEF. Patients with HFimpEF were less likely to be women compared with patients with HFmrEF/HFpEF (41% versus 58%, P<0.01). Overall Summary Score was 5.2 points lower among patients with HFrEF versus HFimpEF (95% CI, -8.2 to -2.3); P=0.010) but patients with HFimpEF had similar scores as HFmrEF/HFpEF (-1.1 [95% CI, -3.7 to 1.6]; P=0.43). A multivariable regression model explained 16% of the variation in the Overall Summary Score in the HFimpEF subgroup. CONCLUSIONS:Despite improvement in EF, patients with HFimpEF continue to have impaired health status similar to HFpEF. Further research is needed to understand and improve health status among patients with HFimpEF.

This focused review examines the results of the PHARM-HF A F Study, a randomized trial evaluating audit and feedback interventions to optimize heart failure medication management among primary care pharmacists in the Veterans Affairs (VA) healthcare system. Despite strong evidence that quadruple guideline–directed medical therapy (GDMT) can reduce mortality by 70

BACKGROUND:Heart failure (HF) is a leading cause of hospitalization in the United States. Decongestion remains a central goal of inpatient management, but contemporary decongestion practices and associated weight loss have not been well characterized nationally. OBJECTIVES:This study aimed to describe contemporary inpatient diuretic practices and clinical predictors of weight loss in patients hospitalized for HF. METHODS:The authors identified HF hospitalizations from 2015 to 2022 in a U.S. national database aggregating deidentified patient-level electronic health record data across 31 geographically diverse community-based health systems. The authors report patient characteristics and inpatient weight change as a primary indicator of decongestion. Predictors of weight loss were evaluated using multivariable models. Temporal trends in inpatient diuretic practices, including augmented diuresis strategies such as adjunctive thiazides and continuous diuretic infusions, were assessed. RESULTS:The study cohort included 262,673 HF admissions across 165,482 unique patients. The median inpatient weight loss was 5.3 pounds (Q1-Q3: 0.0-12.8 pounds) or 2.4 kg (Q1-Q3: 0.0-5.8 kg). Discharge weight was higher than admission weight in 20% of encounters. An increase of ≥0.3 mg/dL in serum creatinine from admission to inpatient peak occurred in >30% of hospitalizations and was associated with less weight loss. Adjunctive diuretic agents were utilized in <20% of encounters but were associated with greater weight loss. CONCLUSIONS:In a large-scale U.S. community-based cohort study of HF hospitalizations, estimated weight loss from inpatient decongestion remains highly variable, with weight gain observed across many admissions. Augmented diuresis strategies were infrequently used. Comparative effectiveness trials are needed to establish optimal strategies for inpatient decongestion for acute HF.

BACKGROUND:The impact of routine clinic use of patient-reported outcome (PRO) measures on clinical outcomes in patients with heart failure (HF) has not been well-characterized. We tested if clinic-based use of a disease-specific PRO improves patient-reported quality of life at 1 year.METHODS:The PRO-HF trial (Patient-Reported Outcome Measurement in Heart Failure Clinic) was an open-label, parallel, patient-level randomized clinical trial of routine PRO assessment or usual care at an academic HF clinic between August 30, 2021, and June 30, 2022, with 1 year of follow-up. In the PRO assessment arm, participants completed the Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12) at each HF clinic visit, and results were shared with their treating clinician. The usual care arm completed the KCCQ-12 at randomization and 1 year later, which was not shared with the treating clinician. The primary outcome was the KCCQ-12 overall summary score (OSS) between 12 and 15 months after randomization. Secondary outcomes included domains of the KCCQ-12, hospitalization and emergency department visit rates, HF medication therapy, clinic visit frequency, and testing rates.RESULTS:Across 17 clinicians, 1248 participants were enrolled and randomized to PRO assessment (n=624) or usual care (n=624). The median age was 63.9 years (interquartile range [IQR], 51.8-72.8), 38.9% were women, and the median baseline KCCQ-12 OSS was 82.3 (IQR, 58.3-94.8). Final KCCQ-12 (available in 87.9% of the PRO arm and 85.1% in usual care; P=0.16) median OSS were 87.5 (IQR, 68.8-96.9) in the PRO arm and 87.6 (IQR, 69.7-96.9) in the usual care arm with a baseline-adjusted mean difference of 0.2 ([95% CI, -1.7 to 2.0]; P=0.85). The results were consistent across prespecified subgroups. A post hoc analysis demonstrated a significant interaction with greater benefit among participants with a baseline KCCQ-12 OSS of 60 to 80 but not in less or more symptomatic participants. No significant differences were found in 1-year mortality, hospitalizations, emergency department visits, medication therapy, clinic follow-up, or testing rates between arms.CONCLUSIONS:Routine PRO assessment in HF clinic visits did not impact patient-reported quality of life or other clinical outcomes. Alternate strategies and settings for embedding PROs into routine clinical care should be tested.REGISTRATION:URL: https://www.clinicaltrials.gov; Unique identifier: NCT04164004.

BACKGROUND:Guideline-directed medical therapy (GDMT) for heart failure with reduced ejection fraction (HFrEF) remains underused. Acute heart failure (HF) hospitalization represents a critical opportunity for rapid initiation of evidence-based medications. However, data on GDMT use at discharge are mostly derived from national quality improvement registries. OBJECTIVES:This study aimed to describe contemporary GDMT use patterns across HF hospitalizations at community-based health systems. METHODS:The authors identified HF hospitalizations from 2016 to 2022 in a U.S. database aggregating deidentified electronic health record data from more than 30 health systems. In-hospital and discharge rates of GDMT use were reported for eligible HFrEF patients. Factors associated with inpatient GDMT use and predischarge discontinuation were evaluated with the use of multivariable models. RESULTS:A total of 20,387 HF hospitalizations among 13,729 HFrEF patients were identified. Renin-angiotensin system inhibitors, beta-blockers, and mineralocorticoid receptor antagonists were administered during 70%, 86%, and 37% of eligible hospitalizations, respectively. Angiotensin receptor-neprilysin inhibitors and sodium-glucose cotransporter 2 inhibitors were used in 17% and 8% of eligible hospitalizations, respectively. Discharge GDMT rates were low. Triple/quadruple therapy was administered in 26% of hospitalizations, falling to 14% on discharge. Predischarge GDMT discontinuations were associated with inpatient hypotension, hyperkalemia, and worsening renal function, but 43%-57% had no medical contraindications. In adjusted analyses, use of 3 or more GDMT classes was associated with fewer 90-day all-cause deaths and HF readmissions compared with less comprehensive GDMT. CONCLUSIONS:Inpatient GDMT use in a national analysis of HF hospitalizations was lower than reported in quality improvement registries. High discontinuation rates emphasize an unmet need for inpatient and postdischarge strategies to optimize GDMT use.

BACKGROUND:Disparities in guideline-based quality measures likely contribute to differences in heart failure (HF) outcomes. We evaluated between- and within-hospital differences in the quality of care across sex, race, ethnicity, and insurance for patients hospitalized for HF.METHODS:This retrospective analysis included patients hospitalized for HF across 596 hospitals in the Get With the Guidelines-HF registry between 2016 and 2021. We evaluated performance across 7 measures stratified by patient sex, race, ethnicity, and insurance. We evaluated differences in performance with and without adjustment for the treating hospital. We also measured variation in hospital-specific disparities.RESULTS:Among 685 227 patients, the median patient age was 72 (interquartile range, 61-82) and 47.2% were women. Measure performance was significantly lower (worse) for women compared with men for all 7 measures before adjustment. For 4 of 7 measures, there were no significant sex-related differences after patient-level adjustment. For 20 of 25 other comparisons, racial and ethnic minorities and Medicaid/uninsured patients had similar or higher (better) adjusted measure performance compared with White and Medicare/privately insured patients, respectively. Angiotensin receptor neprilysin inhibitor measure performance was significantly lower for Asian, Hispanic, and Medicaid/uninsured patients, and cardiac resynchronization therapy implant/prescription was lower among women and Black patients after hospital adjustment, indicating within-hospital differences. There was hospital-level variation in these differences. For cardiac resynchronization therapy implantation/prescription, 278 hospitals (46.6%) had >= 2% lower implant/prescription for Black versus White patients compared with 109 hospitals (18.3%) with the same or higher cardiac resynchronization therapy implantation/prescription for Black patients.CONCLUSIONS:HF quality measure performance was equitable for most measures. There were within-hospital differences in angiotensin receptor neprilysin inhibitor and cardiac resynchronization therapy implant/prescription for historically marginalized groups. The magnitude of hospital-specific disparities varied across hospitals.

HomeJournal of the American Heart AssociationVol. 13, No. 2Patient Representativeness With Virtual Enrollment in the PRO‐HF Trial Open AccessRapid CommunicationPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toOpen AccessRapid CommunicationPDF/EPUBPatient Representativeness With Virtual Enrollment in the PRO‐HF Trial Anshal Gupta, Megan Skye, Jamie Calma, Natasha Din, Zahra Azizi, Mario Funes Hernandez, Jimmy Zheng, Neil M. Kalwani, Sanjay Malunjkar, Jessica Schirmer, Paul Wang, Fatima Rodriguez, Paul Heidenreich and Alexander T. Sandhu Anshal GuptaAnshal Gupta , Stanford University School of Medicine, , Stanford, , CA, , Megan SkyeMegan Skye , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Veterans Affairs Palo Alto Health Care System, , Palo Alto, , CA, , Jamie CalmaJamie Calma https://orcid.org/0000-0002-6426-1336 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Natasha DinNatasha Din https://orcid.org/0000-0001-5312-4451 , Veterans Affairs Palo Alto Health Care System, , Palo Alto, , CA, , Center for Digital Health, Department of Medicine, , Stanford University, , Stanford, , CA, , Zahra AziziZahra Azizi https://orcid.org/0000-0002-7897-0934 , Center for Digital Health, Department of Medicine, , Stanford University, , Stanford, , CA, , Mario Funes HernandezMario Funes Hernandez https://orcid.org/0000-0002-6545-3110 , Center for Digital Health, Department of Medicine, , Stanford University, , Stanford, , CA, , Jimmy ZhengJimmy Zheng https://orcid.org/0000-0002-2009-2059 , Stanford University School of Medicine, , Stanford, , CA, , Neil M. KalwaniNeil M. Kalwani https://orcid.org/0000-0003-0075-6206 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Veterans Affairs Palo Alto Health Care System, , Palo Alto, , CA, , Sanjay MalunjkarSanjay Malunjkar , Research Technology, Stanford Medicine, , Stanford, , CA, , Jessica SchirmerJessica Schirmer https://orcid.org/0009-0007-2623-7515 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Paul WangPaul Wang https://orcid.org/0000-0002-5467-5877 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Center for Digital Health, Department of Medicine, , Stanford University, , Stanford, , CA, , Fatima RodriguezFatima Rodriguez https://orcid.org/0000-0002-5226-0723 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Center for Digital Health, Department of Medicine, , Stanford University, , Stanford, , CA, , Paul HeidenreichPaul Heidenreich https://orcid.org/0000-0001-7730-8490 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Veterans Affairs Palo Alto Health Care System, , Palo Alto, , CA, and Alexander T. SandhuAlexander T. Sandhu * Correspondence to: Alexander T. Sandhu, MD, MS, Stanford University, 870 Quarry Rd, Stanford, CA 94305. Email: E-mail Address: [email protected] https://orcid.org/0000-0003-3208-1143 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Veterans Affairs Palo Alto Health Care System, , Palo Alto, , CA, , Center for Digital Health, Department of Medicine, , Stanford University, , Stanford, , CA, Originally published16 Jan 2024https://doi.org/10.1161/JAHA.123.030903Journal of the American Heart Association. 2024;13:e030903Patients with heart failure (HF) who are elderly, women, Black race, or Hispanic ethnicity are historically underrepresented in clinical trials.1 In‐person recruitment is a barrier to participation that virtual enrollment may overcome.2 Designed as a pragmatic trial with virtual enrollment (via email, text message, and telephone call) of patients seen in a HF clinic, the PRO‐HF (Patient‐Reported Outcome Measurement in Heart Failure Clinic) trial evaluates the impact of routine assessment of patient‐reported health status before HF clinic visits.3 The effect of pragmatic, virtual trial design on representation is unknown. We sought to identify shared characteristics of enrolled PRO‐HF trial patients.In the PRO‐HF trial, we recruited adults with Stanford HF clinic appointments between August 30, 2021 and June 30, 2022, via email 7 to 10 days previsit. Patients (n=372) were excluded because of competing trials with routine health status assessment. Consented patients completed baseline assessments via the Kansas City Cardiomyopathy Questionnaire‐12 online before their clinic visits. Patients who did not respond to 2 enrollment emails were contacted by telephone (telephone call followed by text message), repeated 3 to 5 days before their clinic visit. Enrolled patients were randomized to routine Kansas City Cardiomyopathy Questionnaire‐12 assessment or usual care. Detailed trial methods have been published previously.4 Data for this study are available from the corresponding author only on reasonable request.We obtained electronic health records of eligible patients from the Stanford Research Repository. Patient characteristics were defined by the date of first clinic visit during the recruitment period. Baseline demographics included age, sex, self‐reported race and ethnicity, comorbidities, and insurance. On the basis of home address 9‐digit zip code, we included the area deprivation index as a measure of neighborhood social risk. Clinical characteristics included a history of HF diagnosis, baseline comorbidities and medications, outpatient clinical encounters, hospitalizations in the prior year, and select laboratory measurements.We compared characteristics between those who did and did not enroll and by method of enrollment: email only versus follow‐up telephone (call, text, or both as secondary analysis). We used standardized mean differences (SMDs) via Cohen d to evaluate the magnitude of differences between groups.5 An SMD of 0.1 to 0.5 was considered small, and an SMD of >0.5 was considered medium to large.5 We compared statistical significance with t‐tests for continuous variables and χ2 tests for categorical variables. We performed a multivariable logistic regression model to determine associations between enrollment and select characteristics: age, sex, race, ethnicity, and prior ouptatient clinic visits. The Stanford Institutional Review Board approved this study.Of 5112 eligible patients, 1248 (24.4%) enrolled in the PRO‐HF trial (Table 1). This included 520 (41.7%) patients enrolled by email only and 728 (58.3%) enrolled by telephone. Enrolled patients were a median age of 63.8 years (interquartile range, 51.7–72.7 years), and 38.9% were women.Table 1. Baseline Characteristics of Patients Invited to Enroll in the PRO‐HF TrialVariableEnrolled (N=1248)Did not enroll (N=3864)SMD (enrolled vs did not enroll)Adjusted odds ratio (95% CI) of enrollment*Email enrollment (N=520)Telephone enrollment (N=728)SMD (email vs telephone)DemographicsAge, y63.8 (51.7–72.7)66.1 (52.1–75.8)0.08†0.99 (0.99–0.99)66.2 (55.2–74.3)61.9 (49.3–72.1)0.27†Female sex38.9 (485)38.7 (1496)0.011.03 (0.90–1.18)38.3 (199)39.3 (286)0.02Interpreter preferred0.1 (1)10.6 (409)0.48†0.0 (0)0.1 (1)0.05Race0.34†0.26†Asian12.1 (151)14.8 (570)0.56 (0.46–0.69)10.8 (56)13.0 (95)Black4.9 (61)6.9 (268)0.54 (0.40–0.73)3.5 (18)5.9 (43)Native American0.7 (9)0.3 (13)1.86 (0.77–4.50)0.2 (1)1.1 (8)Unknown14.7 (183)24.9 (864)0.49 (0.39–0.61)12.7 (66)15.1 (117)Pacific Islander1.5 (19)2.0 (78)0.47 (0.28–0.79)0.8 (4)2.1 (15)White66.1 (825)51.0 (1971)Reference72.1 (375)61.8 (450)Ethnicity0.19†0.23†Hispanic or Latinx8.0 (100)13.2 (509)0.76 (0.58–0.99)4.6 (24)10.4 (76)Non‐Hispanic87.7 (1095)81.2 (3139)Reference91.3 (475)85.2 (620)Unknown4.3 (53)5.6 (216)1.05 (0.73–1.51)4.1 (21)4.4 (32)ADI national ranking4.0 (1.0–13.0)5.0 (2.0–18.0)0.053.0 (1.0–11.0)5.0 (2.0–15.0)0.16†DiagnosesAtrial fibrillation35.0 (437)28.8 (1111)0.13†33.5 (174)36.1 (263)0.06CAD40.1 (500)32.6 (1261)0.15†41.5 (216)39.0 (284)0.05COPD14.1 (176)12.2 (473)0.0613.8 (72)14.3 (104)0.01Depression12.7 (159)9.2 (355)0.11†9.6 (50)15.0 (109)0.16†Diabetes18.8 (235)17.4 (671)0.0416.3 (85)20.6 (150)0.11HF or cardiomyopathy87.3 (1089)68.4 (2644)0.47†82.5 (429)90.7 (660)0.24†Hypertension52.7 (658)44.8 (1732)0.16†51.9 (270)53.3 (388)0.03PAD38.5 (481)24.2 (937)0.31†40.4 (210)37.2 (271)0.06Elixhauser comorbidity score4.0 (2.0–7.0)3.0 (2.0–6.0)0.16†4.0 (2.0–6.0)4.0 (2.0–7.0)0.16†Acute care servicesED visits in prior year13.5 (168)11.2 (431)0.07†12.3 (64)14.3 (104)0.06Hospitalizations, prior 90 d9.9 (123)9.0 (347)0.037.3 (38)11.7 (85)0.15†HF hospitalizations, prior 90 d4.7 (59)4.7 (180)0.003.3 (17)5.8 (42)0.12†Any hospitalization in prior year20.2 (252)16.8 (649)0.09†16.9 (88)22.5 (164)0.14†HF hospitalizations in prior year9.2 (115)8.1 (312)0.047.5 (39)10.4 (76)0.10Insurance0.11†0.26†Medicare47.2 (589)50.4 (1946)51.7 (269)44.0 (320)Medicaid7.7 (96)9.5 (369)4.2 (22)10.2 (74)Private37.7 (471)32.9 (1273)35.6 (185)39.3 (286)Other7.4 (92)7.2 (276)8.4 (44)6.6 (48)Outpatient encountersNew patients with HF22.9 (285)27.2 (1050)0.10†0.82 (0.76–0.86)21.3 (110)24.1 (175)0.07No. of prior HF clinic visits, prior year5.0 (1.0–11.0)2.0 (0.0–8.0)0.25†5.0 (1.0–12.0)5.0 (1.0–11.0)0.05Stanford primary care65.1 (809)46.1 (1781)0.39†2.11 (1.84–2.42)68.0 (351)63.1 (458)0.10Echocardiogram, prior year58.6 (728)53.6 (2072)0.10†56.6 (292)60.1 (436)0.07Left ventricular EF0.12†0.34†EF≤40%27.9 (348)17.3 (667)20.8 (108)33.0 (240)EF >40% and <50%17.1 (213)10.7 (413)14.6 (76)18.8 (137)EF≥50%54.8 (684)43.2 (1671)64.4 (335)47.9 (349)Missing0.2 (3)28.8 (1113)0.2 (1)0.3 (2)Vital signs and laboratory valuesBMI, kg/m227.0 (24.0–31.4)26.4 (23.3–30.6)0.15†26.7 (23.5–30.0)27.5 (24.2–32.4)0.26†eGFR <30 mL/min per 1.73 m23.4 (42)5.8 (226)0.19†2.5 (13)4.0 (29)0.10eGFR 30–44 mL/min per 1.73 m25.9 (74)6.6 (254)5.6 (29)6.2 (45)eGFR 45–59 mL/min per 1.73 m213.3 (166)11.0 (424)13.8 (72)12.9 (94)eGFR ≥60 mL/min per 1.73 m259.4 (741)48.9 (1889)60.0 (312)58.9 (429)eGFR missing18.0 (225)27.7 (1071)18.1 (94)18.0 (131)Medication therapiesACEI/ARB/ARNI45.7 (567)34.8 (1346)0.22†39.5 (204)50.0 (363)0.21†β‐Blocker54.1 (672)39.7 (1535)0.29†48.6 (251)58.0 (421)0.19†Loop diuretics27.0 (335)28.0 (1083)0.0222.5 (116)30.2 (219)0.18†MRA30.0 (372)22.7 (879)0.16†24.4 (126)33.9 (246)0.21†SGLT2i15.5 (192)10.0 (385)0.17†9.3 (48)19.8 (144)0.30†Continuous variables are expressed as median with 95% CI. Other variables are given as percentage (number). Email recruitment preceded telephone recruitment. ACEI indicates angiotensin‐converting enzyme inhibitor; ADI, area deprivation index; ARB, angiotensin receptor blocker; ARNI, angiotensin receptor/neprilysin inhibitor; BMI, body mass index; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; ED, emergency department; EF, ejection fraction; eGFR, estimated glomerular filtration rate; HF, heart failure; MRA, mineralocorticoid receptor antagonist; PAD, peripheral artery disease; PRO‐HF, Patient‐Reported Outcome Measurement in Heart Failure Clinic; and SGLT2i, sodium‐glucose cotransporter‐2 inhibitor.*On the basis of multivariable logistic regression model with select patient characteristics: age, sex, race, ethnicity, new Stanford HF clinic patient, and prior Stanford primary care visit.†The SMDs when the differences between groups have a P<0.05 (based on t tests and χ2 analyses for continuous and categorical variables, respectively).Enrolled and non‐enrolled patients were similar by age and sex but had differences across race (SMD=0.34) and ethnicity (SMD=0.19). Greater proportions of enrolled patients identified as White race (66.1% enrolled versus 51.0% nonenrolled) or non‐Hispanic ethnicity (87.7% versus 81.2%) compared with patients who identified as Asian race (12.1% versus 14.8%), Black race (4.9% versus 6.9%), or Hispanic or Latinx ethnicity (8.0% versus 13.2%). All non‐White (those who identified themselves from a race other than the White race [Asian, Black, Native American, and Pacific Islander]) patients enrolled in greater proportions by telephone than by email, with the largest difference among Hispanic or Latinx patients (10.4% versus 4.6%). Patients requiring an interpreter enrolled at lower rates (0.1% versus 10.6%; SMD=0.48). Enrolled and nonenrolled patients had similar area deprivation index (SMD=0.05).Enrolled patients were more likely to have an existing HF/cardiomyopathy diagnosis and be prescribed HF medications. Enrolled patients had more HF clinic encounters in the prior year than nonenrollees (SMD=0.25) and were more likely to be seen by Stanford primary care (SMD=0.39). Enrolled patients had a slightly higher comorbidity burden, as defined by the Elixhauser score (SMD=0.16).Patients enrolled via email were more likely to be older (SMD=0.27), White race (SMD=0.26), and non‐Hispanic ethnicity (SMD=0.23). Patients enrolled via email/telephone were more likely to have Medicaid (SMD=0.26) and higher area deprivation index scores (SMD=0.16).In an adjusted analysis, Asian, Black, Pacific Islander, and Hispanic patients had lower odds of enrollment, whereas greater odds of enrollment were associated with prior Stanford primary care or HF clinic visits (Table 1). No significant association was found between sex and enrollment.Virtual enrollment in the PRO‐HF trial varied by patient characteristics and enrollment method. Among the strongest predictors of enrollment were prior primary care and HF clinic visits. This may reflect how trusting patient‐physician relationships enhance patient perceptions of trial participation.6 We identified racial and ethnic disparities similar to traditional trials. Compared with email only, follow‐up telephone recruitment enrolled greater proportions of younger patients, historically marginalized racial and ethnic groups, and Medicaid recipients.This study has several limitations. First, multicollinearity across patient characteristics is likely. Second, race was unknown for 25% of patients who did not enroll. Third, independent effects of telephone versus email cannot be assessed. Finally, virtual enrollment may vary across trials with greater participant burden.Virtual clinical trials enable efficient enrollment of large populations. Leveraging multiple recruitment modalities is important for achieving diverse representation in virtual trials that may lead to improved generalizability.Sources of FundingThe PRO‐HF (Patient‐Reported Outcomes in Heart Failure Clinic) trial is supported by the National Heart, Lung, and Blood Institute (1K23HL151672‐01) and Stanford institutional funding. The data collection is supported by the National Institutes of Health (UL1 TR001085).DisclosuresDr Sandhu is supported by the National Heart, Lung, and Blood Institute (1K23HL151672‐03) and has consulting relationships with Lexicon Pharmaceuticals and Reprieve Cardiovascular. Dr Rodriguez reports consulting relationships with Healthpals, Novartis, NovoNordisk, and AstraZeneca outside the submitted work. Dr Kalwani reports stock options from Gordy Health and funding from the US Agency for Healthcare Research and Quality (T32 HS026128). Drs Azizi, Hernandez, and Wang were funded by American Heart Association Strategically Focused Research Network. The remaining authors have no disclosures to report.Footnotes* Correspondence to: Alexander T. Sandhu, MD, MS, Stanford University, 870 Quarry Rd, Stanford, CA 94305. Email: ats114@stanford.eduThis article was sent to Francoise A. Marvel, MD, Guest Editor, for review by expert referees, editorial decision, and final disposition.For Sources of Funding and Disclosures, see page 4.References1 Tahhan AS, Vaduganathan M, Greene SJ, Fonarow G, Fiuzat M, Jessup M, Lindenfeld J, O'Connor CM, Butler J. Enrollment of older patients, women, and racial and ethnic minorities in contemporary heart failure clinical trials: a systematic review. JAMA Cardiol. 2018; 3:1011–1019. doi: 10.1001/jamacardio.2018.2559CrossrefMedlineGoogle Scholar2 Martin SS, Ou FS, Newby LK, Sutton V, Adams P, Felker GM, Wang TY. Patient‐ and trial‐specific barriers to participation in cardiovascular randomized clinical trials. J Am Coll Cardiol. 2013; 61:762–769. doi: 10.1016/j.jacc.2012.10.046CrossrefMedlineGoogle Scholar3 Sandhu AT, Zheng J, Kalwani N, Gupta A, Calma J, Skye M, Lan R, Yu B, Spertus J, Heidenreich P. Impact of patient‐reported outcome measurement in heart failure clinic on clinician health status assessment and patient experience: a sub‐study of the PRO‐HF trial. Circ Heart Fail. 2023; 16:e010280. doi: 10.1161/CIRCHEARTFAILURE.122.010280LinkGoogle Scholar4 Kalwani NM, Calma J, Varghese GM, Gupta A, Zheng J, Brown‐Johnson C, Amano A, Vilendrer S, Winget M, Asch S, et al. The patient‐reported outcome measurement in heart failure clinic trial: rationale and methods of the PRO‐HF trial. Am Heart J. 2023; 255:137–146. doi: 10.1016/j.ahj.2022.10.081CrossrefMedlineGoogle Scholar5 Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed.Routledge; 1988.Google Scholar6 Houghton C, Dowling M, Meskell P, Hunter A, Gardner H, Conway A, Treweek S, Sutcliffe K, Noyes J, Devane D, et al. Factors that impact on recruitment to randomised trials in health care: a qualitative evidence synthesis. Cochrane Database Syst Rev. 2020; 10:MR000045. doi: 10.1002/14651858.MR000045.pub2CrossrefMedlineGoogle Scholar eLetters(0) eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate. Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page. Sign In to Submit a Response to This Article Previous Back to top Next FiguresReferencesRelatedDetails January 16, 2024Vol 13, Issue 2 Article Information Metrics Copyright © 2024 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley BlackwellThis is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.https://doi.org/10.1161/JAHA.123.030903PMID: 38226522 Manuscript receivedMay 7, 2023Manuscript acceptedOctober 3, 2023Originally publishedJanuary 16, 2024 Keywordsheart failureKansas City cardiomyopathy Questionnaire‐12virtual enrollmentPDF download Subjects Health Services Heart Failure Quality and Outcomes

BACKGROUND: Coronary artery disease (CAD) testing remains underutilized in patients with newly diagnosed heart failure (HF). The longitudinal clinical impact of early CAD testing has not been well-characterized. We investigated changes in clinical management and long-term outcomes after early CAD evaluation in patients with incident HF. METHODS: We identified Medicare patients with incident HF from 2006 to 2018. The exposure variable was early CAD testing within 1 month of initial HF diagnosis. Covariate-adjusted rates of cardiovascular interventions after testing, including CAD-related management, were modeled using mixed-effects regression with clinician as a random intercept. We assessed mortality and hospitalization outcomes using landmark analyses with inverse probability-weighted Cox proportional hazards models. Falsification end points and mediation analysis were employed for bias assessment. RESULTS: Among 309 559 patients with new-onset HF without prior CAD, 15.7% underwent early CAD testing. Patients who underwent prompt CAD evaluation had higher adjusted rates of subsequent antiplatelet/statin prescriptions and revascularization, guideline-directed therapy for HF, and stroke prophylaxis for atrial fibrillation/flutter than controls. In weighted Cox models, 1-month CAD testing was associated with significantly reduced all-cause mortality (hazard ratio, 0.93 [95% CI, 0.91–0.96]). Mediation analyses indicated that ≈70% of this association was explained by CAD management, largely from new statin prescriptions. Falsification end points (outpatient diagnoses of urinary tract infection and hospitalizations for hip/vertebral fracture) were nonsignificant. CONCLUSIONS: Early CAD testing after incident HF was associated with a modest mortality benefit, driven mostly by subsequent statin therapy. Further investigation on clinician barriers to testing and treating high-risk patients may improve adherence to guideline-recommended cardiovascular interventions.

Background Among patients with heart failure (HF), patient-reported health status provides information beyond stan-dard clinician assessment. Although HF management guidelines recommend collecting patient-reported health status as part of routine care, there is minimal data on the impact of this intervention.Study Design The Patient-Reported Outcomes in Heart Failure Clinic (PRO-HF) trial is a pragmatic, randomized, implementation-effectiveness trial testing the hypothesis that routine health status assessment via the Kansas City Cardiomy-opathy Questionnaire-12 (KCCQ-12) leads to an improvement in patient-reported health status among patients treated in a tertiary health system HF clinic. PRO-HF has completed randomization of 1,248 participants to routine KCCQ-12 assessment or usual care. Patients randomized to the KCCQ-12 arm complete KCCQ-12 assessments before each HF clinic visit with the results shared with their treating clinician. Clinicians received education regarding the interpretation and potential utility of the KCCQ-12. The primary endpoint is the change in KCCQ-12 over 1 year. Secondary outcomes are HF therapy patterns and health care utilization, including clinic visits, testing, hospitalizations, and emergency department visits. As a sub-study, PRO-HF will also evaluate the impact of routine KCCQ-12 assessment on patient experience and the accuracy of clinician -assessed health status. In addition, clinicians completed semi-structured interviews to capture their perceptions on the trial's implementation of routine KCCQ-12 assessment in clinical practice.Conclusions PRO-HF is a pragmatic, randomized trial based in a real-world HF clinic to determine the feasibility of routinely assessing patient-reported health status and the impact of this intervention on health status, care delivery, patient experience, and the accuracy of clinician health status assessment. (Am Heart J 2023;255:137-146.)

ABSTRACTIntroductionLeft ventricular ejection fraction (EF) is an important factor for treatment decisions for heart failure. The EF is unavailable in administrative claims. We sought to evaluate the predictive accuracy of claims diagnoses for classifying heart failure with reduced ejection fraction (HFrEF) versus heart failure with preserved ejection fraction (HFpEF) with International Classification of Disease-Tenth Revision codes.MethodsWe identified HF diagnoses for VA patients between 2017-2019 and extracted the EF from clinical notes and imaging reports using a VA natural language processing algorithm. We classified sets of codes as HFrEF-related, HFpEF-related, or non-specific based on the closest EF within 180 days. We selected a random heart failure diagnosis for each patient and tested the predictive accuracy of various algorithms for identifying HFrEF using the last 1 year of heart failure diagnoses. We performed sensitivity analyses on the EF thresholds, the cohort, and the diagnoses used.ResultsBetween 2017-2019, we identified 358,172 patients and 1,671,084 diagnoses with an EF recording within 180 days. After dividing diagnoses into HFrEF-related, HFpEF-related, or non-specific, we found using the proportion of specific diagnoses classified as HFrEF-related had an AUC of 0.76 for predicting EF≤40% and 0.80 for predicting EF<50%. However, 23.3% of patients could not be classified due to only having non-specific codes. Predictive accuracy increased among patients with ≥4 HF diagnoses over the preceding year.DiscussionIn a VA cohort, administrative claims with ICD-10 codes had moderate accuracy for identifying reduced ejection fraction. This level of specificity is likely inadequate for performance measures. Administrative claims need to better align terminology with relevant clinical definitions.

Background: Traditional approaches to guideline-directed medical therapy (GDMT) management often lead to delayed initiation and titration of therapies in patients with heart failure. This study sought to characterize alternative models of care involving nonphysician provider-led GDMT interventions and their associations with therapy use and clinical outcomes. Methods: We performed a systematic review and meta-analysis of randomized controlled trials (RCTs) and observational studies comparing nonphysician provider-led GDMT initiation and/or uptitration interventions vs usual physician care (PROSPERO ID: CRD42022334661). We queried PubMed, Embase, the Cochrane Library, and the World Health Organization International Clinical Trial Registry Platform for peer-reviewed studies from database inception to July 31, 2022. In the meta-analysis, we used RCT data only and leveraged random-effects models to estimate pooled outcomes. Primary outcomes were GDMT initiation and titration to target dosages by therapeutic class. Secondary outcomes included all-cause mortality and HF hospitalizations. Results: We reviewed 33 studies, of which 17 (52%) were randomized controlled trials with median follow-ups of 6 months; 14 (82%) trials evaluated nurse interventions, and the remainder assessed pharmacists' interventions. The primary analysis pooled data from 16 RCTs, which enrolled 5268 patients. Pooled risk ratios (RR) for renin-angiotensin system inhibitor (RASI) and beta-blocker initiation were 2.09 (95% CI 1.05-4.16; I-2 = 68%) and 1.91 (95% C11.35-2.70; I-2 = 37%), respectively. Outcomes were similar for uptitration of RASI (RR 1.99, 95% CI 1.24-3.20; I-2 = 77%) and beta-blocker (RR 2.22, 95% CI 1.29-3.83; I-2 = 66%). No association was found with mineralocorticoid receptor antagonist initiation (RR 1.01, 95% CI 0.47-2.19). There were lower rates of mortality (RR 0.82, 95% CI 0.67-1.04; I-2 = 12%) and hospitalization due to HF (RR 0.80, 95% CI 0.63-1.01; I-2 = 25%) across intervention arms, but these differences were small and not statistically significant. Prediction intervals were wide due to moderate-to-high heterogeneity across trial populations and interventions. Subgroup analyses by provider type did not show significant effect modification. Conclusions: Pharmacist- and nurse-led interventions for GDMT initiation and/or uptitration improved guideline concordance. Further research evaluating newer therapies and titration strategies integrated with pharmacist- and/or nurse-based care may be valuable.

Large data have accelerated advances in AI. While it is well known that population differences from genetics, sex, race, diet, and various environmental factors contribute significantly to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share in medicine and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We present three techniques: Fed Averaging (FedAvg), Incremental Institutional Learning (IIL), and Cyclical Incremental Institutional Learning (CIIL). We also propose an FL strategy that leverages synthetically generated data to overcome class imbalances and data size disparities across centers. We show that FL can achieve comparable performance to Centralized Data Sharing (CDS) while maintaining high performance across sites with small, underrepresented data. We investigate the strengths and weaknesses for all technical approaches on this heterogeneous dataset including the robustness to non-Independent and identically distributed (non-IID) diversity of data. We also describe the sources of data heterogeneity such as age, sex, and site locations in the context of FL and show how even among the correctly labeled populations, disparities can arise due to these biases.

While it is well known that population differences from genetics, sex, race, and environmental factors contribute to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We also propose an FL strategy that leverages synthetically generated data to overcome class and size imbalances. We also describe the sources of data heterogeneity in the context of FL, and show how even among the correctly labeled populations, disparities can arise due to these biases.

Background: Clinicians typically estimate heart failure health status using the New York Heart Association Class, which is often discordant with patient-reported health status. It is unknown whether collecting patient-reported health status improves the accuracy of clinician assessments. Methods: The PRO-HF trial (Patient-Reported Outcomes in Heart Failure Clinic) is a randomized, nonblinded trial evaluating routine Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12) collection in heart failure clinic. Patients with a scheduled visit to Stanford heart failure clinic between August 30, 2021 and June 30, 2022 were enrolled and randomized to KCCQ-12 assessment or usual care. In this prespecified substudy, we evaluated whether access to the KCCQ-12 improved the accuracy of clinicians’ New York Heart Association assessment or patients’ perspectives on their clinician interaction. We surveyed clinicians regarding their patients’ New York Heart Association Class, quality of life, and symptom frequency. Clinician responses were compared with patients’ KCCQ-12 responses. We surveyed patients regarding their clinician interactions. Results: Of the 1248 enrolled patients, 1051 (84.2%) attended a visit during the substudy. KCCQ-12 results were given to the clinicians treating the 528 patients in the KCCQ-12 arm; the 523 patients in the usual care arm completed the KCCQ-12 without the results being shared. The correlation between New York Heart Association Class and KCCQ-12 Overall Summary Score was stronger when clinicians had access to the KCCQ-12 (r=−0.73 versus r=−0.61, P <0.001). More patients in the KCCQ-12 arm strongly agreed that their clinician understood their symptoms (95.2% versus 89.7% of respondents [odds ratio‚ 2.27; 95% CI‚ 1.32–3.87]). However, patients in both arms reported similar quality of clinician communication and therapeutic alliance. Conclusions: Collecting the KCCQ-12 in heart failure clinic improved clinicians’ accuracy of health status assessment; correspondingly, patients believed their clinicians better understood their symptoms. Registration: URL: https://www.clinicaltrials .gov; Unique identifier: NCT04164004.