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Background\ud The LACE index—length of stay (L), acuity (A), Charlson co-morbidities (C), and emergent visits (E)—predicts 30-day outcomes following heart failure (HF) hospitalization but is complex to score. A simpler LE index (length of stay and emergent visits) could offer a practical advantage in point-of-care risk prediction.\ud \ud Methods and results\ud This was a sub-study of the patient-centred care transitions in HF (PACT-HF) multicentre trial. The derivation cohort comprised patients hospitalized for HF, enrolled in the trial, and followed prospectively. External validation was performed retrospectively in a cohort of patients hospitalized for HF. We used log-binomial regression models with LACE or LE as the predictor and either 30-day composite all-cause readmission or death or 30-day all-cause readmission as the outcomes, adjusting only for post-discharge services. There were 1985 patients (mean [SD] age 78.1 [12.1] years) in the derivation cohort and 378 (mean [SD] age 73.1 [13.2] years) in the validation cohort. Increments in the LACE and LE indices were associated with 17% (RR 1.17; 95% CI 1.12, 1.21; C-statistic 0.64) and 21% (RR 1.21; 95% CI 1.15, 1.26; C-statistic 0.63) increases, respectively, in 30-day composite all-cause readmission or death; and 16% (RR 1.16; 95% CI 1.11, 1.20; C-statistic 0.64) and 18% (RR 1.18; 95% CI 1.13, 1.24; C-statistic 0.62) increases, respectively, in 30-day all-cause readmission. The LE index provided better risk discrimination for the 30-day outcomes than did the LACE index in the external validation cohort.\ud \ud Conclusions\ud The LE index predicts 30-day outcomes following HF hospitalization with similar or better performance than the more complex LACE index.

Aims The LE index (Length of hospitalization plus number of Emergent visits ≤6 months) predicts 30 day all‐cause readmission or death following hospitalization for heart failure (HF). We combined N‐terminal pro‐B type natriuretic peptide (NT‐proBNP) levels with the LE index to derive and validate the LENT index for risk prediction at the point of care on the day of hospital discharge. Methods and results In this prospective cohort sub‐study of the Patient‐centred Care Transitions in HF clinical trial, we used log‐binomial regression models with LE index and either admission or discharge NT‐proBNP as the predictors and 30 day composite all‐cause readmission or death as the primary outcome. No other variables were added to the model. We used regression coefficients to derive the LENT index and bootstrapping analysis for internal validation. There were 772 patients (mean [SD] age 77.0 [12.4] years, 49.9% female). Each increment in the LE index was associated with a 25% increased risk of the primary outcome (RR 1.25, 95% CI 1.16–1.35; C‐statistic 0.63). Adjusted for the LE index, every 10‐fold increase in admission and discharge NT‐proBNP was associated with a 48% (RR 1.48; 95% CI 1.10, 1.99; C‐statistic 0.64; net reclassification index [NRI] 0.19) and 56% (RR 1.56; 95% CI 1.08, 2.25; C‐statistic 0.64; NRI 0.21) increased risk of the primary outcome, respectively. The predicted probability of the primary outcome increased to a similar extent with incremental LENT, regardless of whether admission or discharge NT‐proBNP level was used. Conclusions The point‐of‐care LENT index predicts 30 day composite all‐cause readmission or death among patients hospitalized with HF, with improved risk reclassification compared with the LE index. The performance of this simple, 3‐variable index ‐ without adjustment for comorbidities ‐ is comparable to complex risk prediction models in HF. Trial Registration: ClinicalTrials.gov Identifier: NCT02112227

Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected. In this review, we discuss the rationale for the use and applications of ML in heart failure, including disease classification, early diagnosis, early detection of decompensation, risk stratification, optimal titration of medical therapy, effective patient selection for devices, and clinical trial recruitment. We discuss how ML can be used to expedite implementation and close healthcare gaps in learning healthcare systems. We review the limitations of ML, including opaque logic and unreliable model performance in the setting of data errors or data shift. Whilst ML has great potential to improve clinical care and research in HF, the applications must be externally validated in prospective studies for broad uptake to occur.

Background: The association between socioeconomic status (SES), sex, race / ethnicity and outcomes during hospitalization for heart failure (HF) has not previously been investigated. Methods and Results: We analyzed HF hospitalizations in the United States National Inpatient Sample between 2015 and 2017. Using a hierarchical, multivariable Poisson regression model to adjust for hospital- and patient-level factors, we assessed the association between SES, sex, and race / ethnicity and all-cause in-hospital mortality. We estimated the direct costs (USD) across SES groups. Among 4,287,478 HF hospitalizations, 40.8% were in high SES, 48.7% in female, and 70.0% in White patients. Relative to these comparators, low SES (homelessness or lowest quartile of median neighborhood income) (relative risk [RR] 1.02, 95% confidence interval [CI] 1.00–1.05) and male sex (RR 1.09, 95% CI 1.07–1.11) were associated with increased risk, whereas Black (RR 0.79, 95% CI 0.76–0.81) and Hispanic (RR 0.90, 95% CI 0.86–0.93) race / ethnicity were associated with a decreased risk of in-hospital mortality (5.1% of all hospitalizations). There were significant interactions between race / ethnicity and both, SES (P < .01) and sex (P = .04), such that racial/ethnic differences in outcome were more pronounced in low SES groups and in male patients. The median direct cost of admission was lower in low vs high SES groups ($9324.60 vs $10,940.40), female vs male patients ($9866.60 vs $10,217.10), and Black vs White patients ($9077.20 vs $10,019.80). The median costs increased with SES in all demographic groups primarily related to greater procedural utilization. Conclusions: SES, sex, and race / ethnicity were independently associated with in-hospital mortality during HF hospitalization, highlighting possible care disparities. Racial/ethnic differences in outcome were more pronounced in low SES groups and in male patients.

Background Risk prediction models in heart failure (HF) are typically complex, derived retrospectively from administrative databases, and modest in their ability to discriminate between high, medium, and low risk categories. The complexity of these models makes them difficult to use at the point of care. Purpose To determine if a simple risk index using Length of hospital stay (L), number of Emergency department visits in the preceding 6 months (E), and either admission or discharge N-Terminal (NT) prohormone of Brain Natriuretic Peptide (pro-BNP) at the point of care can predict 30-day readmissions in patients hospitalized for HF. Methods This is a sub-study of the Patient-Centered Care Transitions in HF (PACT-HF) stepped-wedge cluster randomized trial. We included 772 patients hospitalized for HF at 10 Canadian hospitals. We used log-binomial regression models with Length of stay, Emergency department visits in the preceding 6 months, and either admission or discharge N-Terminal prohormone of Brain Natriuretic Peptide (NT-pro-BNP) as the predictor variables and 30-day all-cause readmission as the outcome. We derived the LENT risk score from the β-coefficients of the regression model (Fig. 1). All the models were adjusted for post-discharge services. We assessed model discrimination with C-statistics and model calibration with the net reclassification index (NRI). We used the bootstrapping approach with 100 runs for internal validation. Results The LENT index had a possible score ranging from 1 to 13 (Fig 1). Increments in the LENT risk score were associated with an increased risk of 30-day readmission; a 1-point increase in the LENT index using the admission and discharge NT-pro-BNP predicted a 23% and 19% increase in 30-day readmission risk, respectively. The internal validation produced similar results. Compared to a null model, the LE index had an NRI of 0.35 [95% CI 0.18, 0.53], and admission and discharge NT-pro-BNP further improved calibration of the LE index (NRI 0.15 [95% CI 0, 0.32] and 0.20 [95% CI 0.03, 0.37], respectively). The LENT index offered modest discrimination for 30-day readmission (C-statistic 0.64 [95% CI 0.59, 0.69]), similar to more complex risk models. Figure 1. The LENT index scoring system Conclusion A simple risk index based on Length of stay, Emergent visits, and NT-pro-BNP at the point of care can reliably predict 30-day readmissions. The LENT index offers ease of use over traditional risk prediction models. Acknowledgement/Funding Canadian Institutes of Health Research, Ontario MOHLTC, Roche Diagnostics

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