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Comparison of the FRIEND and Wasserman-Hansen Equations in Predicting Outcomes in Heart Failure.

ABSTRACT: Background Percentage of age-predicted peak oxygen uptake (VO₂) achieved (ppVO₂) has been widely used to stratify risk in patients with heart failure. However, there are limitations to traditional normal standards. We compared the recently derived FRIEND (Fitness Registry and the Importance of Exercise: A National Data Base) equation to the widely used Wasserman-Hansen (WH) ppVO₂ equation to predict outcomes in patients with heart failure. Methods and Results A subgroup of 4055 heart failure patients from the FRIEND registry (mean age 53±15 years) was followed for a mean of 28±16 months. The FRIEND and WH equations along with measured peak VO₂ expressed in mL/kg^-1 per min^-1 were compared for mortality and composite cardiovascular events. ppVO₂ was higher for the FRIEND versus the WH equation (66±30% versus 58±25%; P<0.001). The areas under the receiver operating characteristic curves were slightly but significantly higher for the FRIEND equation for mortality (0.74 versus 0.72; P=0.03) and cardiac events (0.70 versus 0.68; P=0.008). Area under the receiver operating characteristic curve for measured peak VO₂ was 0.70 (P<0.001) for mortality and 0.73 (P<0.001) for cardiovascular events. For each 1-SD higher ppVO₂ for the FRIEND equation, mortality was reduced by 18% (hazard ratio, 0.82; 95% CI, 0.69-0.97; P<0.02); for each 1-SD higher ppVO₂ for the WH equation, the mortality was reduced by 17% (hazard ratio, 0.83; 95% CI, 0.71-0.97; P=0.02). The corresponding reductions in risk per 1 SD for cardiovascular events for the FRIEND and WH equations were 23 and 21%, respectively (both P<0.001). Conclusions Peak VO₂ expressed as percentage of an age-predicted standard strongly predicts mortality and major cardiovascular events in patients with heart failure. The FRIEND registry equation exhibited test characteristics slightly superior to the commonly used WH equation.

SUBMITTER: Myers J

PROVIDER: S-EPMC8751827 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Patient-reported outcome measures (PROMs) are relevant independent outcomes in heart failure (HF) care and are predictive of subsequent hospitalization and death in HF. The Kansas City Cardiomyopathy Questionnaire (KCCQ) and the Minnesota Living with Heart Failure Questionnaire (MLHFQ) are the 2 most widely adopted PROMs specific to HF. We compared their prognostic abilities in a prospective cohort of HF patients. A prospective cohort of subjects from a single-center registry was analyzed with regard to baseline KCCQ and MLHFQ scores and the outcomes of death, transplant, or left ventricular assist device implantation and hospitalization. A total of 516 subjects with reduced left ventricular ejection fraction (HFrEF) and 151 subjects with preserved left ventricular ejection fraction (HFpEF) were included. Discrimination was assessed using c-statistics based on time-to-event analyses and receiver-operator curves. The additive contribution of MLHFQ was assessed through the change in c-statistic, incremental discrimination index, and category-free net reclassification index. Overall, KCCQ was superior to MLHFQ for predicting death/transplant/ventricular assist device (c-statistic 0.702 [0.666 to 0.738] and 0.658 [0.621 to 0.695] respectively, p value for difference <0.001) and hospitalization (c-statistic 0.640 [0.613 to 0.666] and 0.624 [0.597 to 0.651], respectively, p value for difference 0.022). However, this difference was statistically nonsignificant in the HFpEF group alone. When analyzing the additional prognostic information afforded by adding MLHFQ to KCCQ in the overall, HFrEF, and HFpEF groups there was no significant improvement, although adding KCCQ to MLHFQ did significantly improve risk stratification. Scoring based upon the abbreviated KCCQ-12 did not reduce the prognostic accuracy of KCCQ. In conclusion, KCCQ is more prognostic of death/transplant/left ventricular assist device and hospitalization than MLHFQ in a combined cohort of patients with HFrEF and HFpEF, although the effect in HFpEF was less pronounced. KCCQ should be the preferred PROM for patients with HF if prognostication is a desired goal of using the PROMs.

Project description:ImportanceThe Pooled Cohort Equations to Prevent Heart Failure (PCP-HF) estimate the 10-year risk for symptomatic heart failure (HF) from routine clinical data. The PCP-HF score should detect asymptomatic individuals with cardiac maladaptation preceding HF symptoms for it to be a useful HF prediction tool in primary prevention.ObjectiveTo assess the concordance between PCP-HF risk scoring and the presence of subclinical cardiac maladaptation in the community.Design, setting, and participantsThis cross-sectional analysis included participants enrolled in the Flemish Study on Environment, Genes and Health Outcomes who underwent a clinical examination including echocardiography between May 2005 and January 2015. Participants younger than 30 years, older than 79 years, had prevalent cardiovascular disease, and/or had suboptimal echocardiographic imaging quality were excluded. Analysis began February 2020 and ended April 2020.ExposuresTen-year HF risk as calculated from routine clinical data using the PCP-HF. Based on tertile limits, participants were categorized as having low (≤0.4%), intermediate (0.4%-2.4%), and high (≥2.4%) 10-year HF risk score.Main outcomes and measuresEchocardiographic profiles of subclinical heart remodeling and dysfunction.ResultsA total of 1020 individuals were analyzed (mean [SD] age, 52.8 [11.4] years; 541 female [53.0%]). The prevalence of left ventricular (LV) remodeling and dysfunction was significantly higher from low to intermediate and high 10-year HF risk score. A doubling in 10-year HF risk score was associated with higher odds for LV concentric remodeling (odds ratio [OR], 1.48; 95% CI, 1.36-1.61; P < .001), LV hypertrophy (OR, 1.66; 95% CI, 1.51-1.83; P < .001), abnormal LV longitudinal strain (OR, 1.12; 95% CI, 1.05-1.19; P < .001), and LV diastolic dysfunction (OR, 2.28; 95% CI, 1.94-2.69; P < .001). Moreover, the PCP-HF score detected echocardiographic abnormalities with an accuracy of 74% (LV concentric remodeling), 78% (LV hypertrophy), 59% (abnormal LV longitudinal strain), and 87% (LV diastolic dysfunction). The likelihood of LV concentric remodeling, hypertrophy, and diastolic dysfunction were 3.1, 3.8, and 9.4 times higher in participants with high 10-year HF risk score than the average population risk, respectively (P < .001). Of all PCP-HF score components, age, body mass index, and systolic blood pressure were key correlates of echocardiographic abnormalities in multivariable-adjusted analysis.Conclusions and relevancePCP-HF risk scoring adequately detected individuals with subclinical heart maladaptation that precedes HF symptoms by years. Thus, it may be a valuable HF prediction tool in primary prevention.

Project description:BackgroundHeart failure (HF) is highly prevalent in the United States. Approximately one-third to one-half of HF cases are categorized as HF with reduced ejection fraction (HFrEF). Patients with HFrEF are at risk of worsening HF, have a high risk of adverse outcomes, and experience higher health care use and costs. Therefore, it is crucial to identify patients with HFrEF who are at high risk of subsequent events after HF hospitalization.ObjectiveMachine learning (ML) has been used to predict HF-related outcomes. The objective of this study was to compare different ML prediction models and feature construction methods to predict 30-, 90-, and 365-day hospital readmissions and worsening HF events (WHFEs).MethodsWe used the Veradigm PINNACLE outpatient registry linked to Symphony Health's Integrated Dataverse data from July 1, 2013, to September 30, 2017. Adults with a confirmed diagnosis of HFrEF and HF-related hospitalization were included. WHFEs were defined as HF-related hospitalizations or outpatient intravenous diuretic use within 1 year of the first HF hospitalization. We used different approaches to construct ML features from clinical codes, including frequencies of clinical classification software (CCS) categories, Bidirectional Encoder Representations From Transformers (BERT) trained with CCS sequences (BERT + CCS), BERT trained on raw clinical codes (BERT + raw), and prespecified features based on clinical knowledge. A multilayer perceptron neural network, extreme gradient boosting (XGBoost), random forest, and logistic regression prediction models were applied and compared.ResultsA total of 30,687 adult patients with HFrEF were included in the analysis; 11.41% (3184/27,917) of adults experienced a hospital readmission within 30 days of their first HF hospitalization, and nearly half (9231/21,562, 42.81%) of the patients experienced at least 1 WHFE within 1 year after HF hospitalization. The prediction models and feature combinations with the best area under the receiver operating characteristic curve (AUC) for each outcome were XGBoost with CCS frequency (AUC=0.595) for 30-day readmission, random forest with CCS frequency (AUC=0.630) for 90-day readmission, XGBoost with CCS frequency (AUC=0.649) for 365-day readmission, and XGBoost with CCS frequency (AUC=0.640) for WHFEs. Our ML models could discriminate between readmission and WHFE among patients with HFrEF. Our model performance was mediocre, especially for the 30-day readmission events, most likely owing to limitations of the data, including an imbalance between positive and negative cases and high missing rates of many clinical variables and outcome definitions.ConclusionsWe predicted readmissions and WHFEs after HF hospitalizations in patients with HFrEF. Features identified by data-driven approaches may be comparable with those identified by clinical domain knowledge. Future work may be warranted to validate and improve the models using more longitudinal electronic health records that are complete, are comprehensive, and have a longer follow-up time.

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Comparison of the FRIEND and Wasserman-Hansen Equations in Predicting Outcomes in Heart Failure.

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