Ambulatory Cardiology or General Internal Medicine Assessment Prior to Scheduled Major Vascular Surgery is Associated with Improved Outcomes.

OBJECTIVE: To characterize the association between ambulatory cardiology or general internal medicine (GIM) assessment prior to surgery and outcomes following scheduled major vascular surgery. BACKGROUND: Cardiovascular risk assessment and management prior to high-risk surgery remains an evolving area of care. METHODS: This is population-based retrospective cohort study of all adults who underwent scheduled major vascular surgery in Ontario, Canada, April 1, 2004-March 31, 2019. Patients who had an ambulatory cardiology and/or GIM assessment within 6 months prior to surgery were compared to those who did not. The primary outcome was 30-day mortality. Secondary outcomes included: composite of 30-day mortality, myocardial infarction or stroke; 30-day cardiovascular death; 1-year mortality; composite of 1-year mortality, myocardial infarction or stroke; and 1-year cardiovascular death. Cox proportional hazard regression using inverse probability of treatment weighting (IPTW) was used to mitigate confounding by indication. RESULTS: Among 50,228 patients, 20,484 (40.8%) underwent an ambulatory assessment prior to surgery: 11,074 (54.1%) with cardiology, 8,071 (39.4%) with GIM and 1,339 (6.5%) with both. Compared to patients who did not, those who underwent an assessment had a higher Revised Cardiac Risk Index (N with Index over 2= 4,989[24.4%] vs. 4,587[15.4%], P<0.001) and more frequent pre-operative cardiac testing (N=7,772[37.9%] vs. 6,113[20.6%], P<0.001) but, lower 30-day mortality (N=551[2.7%] vs. 970[3.3%], P<0.001). After application of IPTW, cardiology or GIM assessment prior to surgery remained associated with a lower 30-day mortality (weighted Hazard Ratio [95%CI] = 0.73 [0.65-0.82]) and a lower rate of all secondary outcomes. CONCLUSIONS: Major vascular surgery patients assessed by a cardiology or GIM physician prior to surgery have better outcomes than those who are not. Further research is needed to better understand potential mechanisms of benefit.

Comparison of Machine Learning and Conventional Statistical Modeling for Predicting Readmission Following Acute Heart Failure Hospitalization.

INTRODUCTION: Developing accurate models for predicting the risk of 30-day readmission is a major healthcare interest. Evidence suggests that models developed using machine learning (ML) may have better discrimination than conventional statistical models (CSM), but the calibration of such models is unclear. OBJECTIVES: To compare models developed using ML with those developed using CSM to predict 30-day readmission for cardiovascular and non-cardiovascular causes in HF patients. METHODS: We retrospectively enrolled 10,919 patients with HF (> 18 years) discharged alive from a hospital or emergency department (2004-2007) in Ontario, Canada. The study sample was randomly divided into training and validation sets in a 2:1 ratio. CSMs to predict 30-day readmission were developed using Fine-Gray subdistribution hazards regression (treating death as a competing risk), and the ML algorithm employed random survival forests for competing risks (RSF-CR). Models were evaluated in the validation set using both discrimination and calibration metrics. RESULTS: In the validation sample of 3602 patients, RSF-CR (c-statistic=0.620) showed similar discrimination to the Fine-Gray competing risk model (c-statistic=0.621) for 30-day cardiovascular readmission. In contrast, for 30-day non-cardiovascular readmission, the Fine-Gray model (c-statistic=0.641) slightly outperformed the RSF-CR model (c-statistic=0.632). For both outcomes, The Fine-Gray model displayed better calibration than RSF-CR using calibration plots of observed vs. predicted risks across the deciles of predicted risk. CONCLUSIONS: Fine-Gray models had similar discrimination but superior calibration to the RSF-CR model, highlighting the importance of reporting calibration metrics for ML-based prediction models. The discrimination was modest in all readmission prediction models regardless of the methods used.

The Effect of Using a Remote Patient Management Platform in Optimizing Guideline-Directed Medical Therapy in Heart Failure Patients: A Randomized Controlled Trial.

BACKGROUND: Guideline-directed medical therapy (GDMT) remains underutilized in patients with heart failure with reduced ejection fraction, leading to morbidity and mortality. OBJECTIVES: The Medly Titrate (Use of Telemonitoring to Facilitate Heart Failure Mediation Titration) study was an open-label, randomized controlled trial to determine whether remote medication titration for patients with heart failure with reduced ejection fraction was more effective than usual care (UC). METHODS: In this study, 108 patients were randomized to remote GDMT titration through the Medly heart failure program (n = 56) vs UC (n = 52). The primary outcome was the proportion of patients completing GDMT titration at 6 months. Secondary outcomes included the number of clinic visits and time required to achieve titration, patient health outcomes, and health care utilization, including urgent clinic/emergency department visits and hospitalization. RESULTS: At 6 months, GDMT titration was completed in 82.1% (95% CI: 71.2%-90.8%) of patients in the intervention arm vs 53.8% in UC (95% CI: 41.1%-67.7%; P = 0.001). Remote titration required fewer in-person (1.62 ± 1.09 vs 2.42 ± 1.65; P = 0.004) and virtual clinic visits (0.50 ± 1.08 vs 1.29 ± 1.86; P = 0.009) to complete titration. Median time to optimization was shorter with remote titration (3.42 months [Q1-Q3: 2.99-4.04 months] vs 5.47 months [Q1-Q3: 4.14-7.33 months]; P < 0.001). The number of urgent clinic/emergency department visits (incidence rate ratio of remote vs control groups: 0.90 [95% CI: 0.53-1.56]; P = 0.70) were similar between groups, with a reduction in all-cause hospitalization with remote titration (incidence rate ratio: 0.55 [95% CI: 0.31-0.97]; P = 0.042). CONCLUSIONS: Remote titration of GDMT in heart failure with reduced ejection fraction was effective, safe, feasible, and increased the proportion of patients achieving target doses, in a shorter period of time with no excess adverse events compared with UC. (Use of Telemonitoring to Facilitate Heart Failure Mediation Titration [Medly Titrate]; NCT04205513).

Enhancing the Prediction of Cardiac Allograft Vasculopathy Using Intravascular Ultrasound and Machine Learning: A Proof of Concept.

BACKGROUND: Cardiac allograft vasculopathy (CAV) is the leading cause of late graft dysfunction in heart transplantation. Building on previous unsupervised learning models, we sought to identify CAV clusters using serial maximal intimal thickness and baseline clinical risk factors to predict the development of early CAV. METHODS: This is a single-center retrospective study including adult heart transplantation recipients. A latent class mixed-effects model was used to identify patient clusters with similar trajectories of maximal intimal thickness posttransplant and pretransplant covariates associated with each cluster. RESULTS: Among 186 heart transplantation recipients, we identified 4 patient phenotypes: very low, low, moderate, and high risk. The 5-year risk (95% CI) of the International Society for Heart and Lung Transplantation-defined CAV in the high, moderate, low, and very low risk groups was 49.1% (35.2%-68.5%), 23.4% (13.3%-41.2%), 5.0% (1.3%-19.6%), and 0%, respectively. Only patients in the moderate to high risk cluster developed the International Society for Heart and Lung Transplantation CAV 2-3 at 5 years (P=0.02). Of the 4 groups, the low risk group had significantly younger female recipients, shorter ischemic time, and younger female donors compared with the high risk group. CONCLUSIONS: We identified 4 clusters characterized by distinct maximal intimal thickness trajectories. These clusters were shown to discriminate against the development of angiographic CAV. This approach allows for the personalization of surveillance and CAV-directed treatment before the development of angiographically apparent disease.

Predicting heart failure outcomes by integrating breath-by-breath measurements from cardiopulmonary exercise testing and clinical data through a deep learning survival neural network.

Aims: Mathematical models previously developed to predict outcomes in patients with heart failure (HF) generally have limited performance and have yet to integrate complex data derived from cardiopulmonary exercise testing (CPET), including breath-by-breath data. We aimed to develop and validate a time-to-event prediction model using a deep learning framework using the DeepSurv algorithm to predict outcomes of HF. Methods and results: Inception cohort of 2490 adult patients with high-risk cardiac conditions or HF underwent CPET with breath-by-breath measurements. Potential predictive features included known clinical indicators, standard summary statistics from CPETs, and mathematical features extracted from the breath-by-breath time series of 13 measurements. The primary outcome was a composite of death, heart transplant, or mechanical circulatory support treated as a time-to-event outcomes. Predictive features ranked as most important included many of the features engineered from the breath-by-breath data in addition to traditional clinical risk factors. The prediction model showed excellent performance in predicting the composite outcome with an area under the curve of 0.93 in the training and 0.87 in the validation data sets. Both the predicted vs. actual freedom from the composite outcome and the calibration of the prediction model were excellent. Model performance remained stable in multiple subgroups of patients. Conclusion: Using a combined deep learning and survival algorithm, integrating breath-by-breath data from CPETs resulted in improved predictive accuracy for long-term (up to 10 years) outcomes in HF. DeepSurv opens the door for future prediction models that are both highly performing and can more fully use the large and complex quantity of data generated during the care of patients with HF.

Better Respiratory Function in Heart Failure Patients With Use of Central-Acting Therapeutics.

Background: Diaphragm atrophy can contribute to dyspnea in patients with heart failure (HF) with its link to central neurohormonal overactivation. HF medications that cross the blood-brain barrier could act centrally and improve respiratory function, potentially alleviating diaphragmatic atrophy. Therefore, we compared the benefit of central- vs peripheral-acting HF drugs on respiratory function, as assessed by a single cardiopulmonary exercise test (CPET) and outcomes in HF patients. Methods: A retrospective study was conducted of 624 ambulatory adult HF patients (80% male) with reduced left ventricular ejection fraction ≤ 40% and a complete CPET, followed at a single institution between 2001 and 2017. CPET parameters, and the outcomes all-cause death, a composite endpoint (all-cause death, need for left ventricular assist device, heart transplantation), and all-cause and/or HF hospitalizations, were compared in patients receiving central-acting (n = 550) vs peripheral-acting (n = 74) drugs. Results: Compared to patients who receive peripheral-acting drugs, patients who receive central-acting drugs had better respiratory function (peak breath-by breath oxygen uptake [VO2], P = 0.020; forced expiratory volume in 1 second [FEV1], P = 0.007), and ventilatory efficiency (minute ventilation / carbon dioxide production [VE/VCO2], P < 0.001; end-tidal carbon dioxide tension [PETCO2], P = 0.015; and trend for forced vital capacity [FVC], P = 0.056). Many of the associations between the CPET parameters and drug type remained significant after multivariate adjustment. Moreover, patients receiving central-acting drugs had fewer composite events (P = 0.023), and HF hospitalizations (P = 0.044), although significance after multivariant correction was not achieved, despite the hazard ratio being 0.664 and 0.757, respectively. Conclusions: Central-acting drugs were associated with better respiratory function as measured by CPET parameters in HF patients. This could extend to clinically meaningful composite outcomes and hospitalizations but required more power to be definitive in linking to drug effect. Central-acting HF drugs show a role in mitigating diaphragm weakness.

Contexte: L'atrophie du diaphragme peut contribuer à la dyspnée chez les personnes atteintes d'insuffisance cardiaque (IC), compte tenu de son lien avec la suractivation neuro-hormonale centrale. Or, les médicaments contre l'IC qui franchissent la barrière hématoencéphalique pourraient exercer une action centrale, améliorer la respiration et ainsi éventuellement atténuer l'atrophie du diaphragme. C'est pourquoi nous avons voulu comparer, au moyen d'une seule épreuve d'effort cardiopulmonaire (EECP), les effets bénéfiques exercés par des médicaments à action périphérique et des médicaments à action centrale sur la fonction respiratoire, de même que l'issue des patients atteints d'IC auxquels ils ont été administrés. Méthodologie: Nous avons réalisé une étude rétrospective auprès de 624 adultes ambulatoires atteints d'IC (80 % d'hommes) dont la fraction d'éjection ventriculaire gauche était réduite (≤ 40 %), qui se sont prêtés à une EECP complète et qui ont été suivis dans le même établissement entre 2001 et 2017. Les paramètres de l'EECP et la mortalité toutes causes confondues, un critère d'évaluation composé (décès toutes causes confondues, nécessité de recourir à un dispositif d'assistance ventriculaire gauche, transplantation cardiaque), et les hospitalisations toutes causes confondues et/ou liées à l'IC ont été comparés entre les patients qui recevaient des médicaments à action centrale (n = 550) et ceux qui recevaient des médicaments à action périphérique (n = 74). Résultats: Comparativement aux patients ayant reçu des médicaments à action périphérique, ceux qui ont reçu des médicaments à action centrale ont bénéficié d'une meilleure fonction respiratoire (consommation maximale d'oxygène [VO2], p = 0,020; volume expiratoire maximal par seconde [VEMS], p = 0,007) et d'une meilleure efficacité ventilatoire (ventilation minute/production de dioxyde de carbone [VE/VCO2], p < 0,001; pression partielle de dioxyde de carbone en fin d'expiration [PETCO2], p = 0,015; et tendance de la capacité vitale forcée [CVF], p = 0,056). De plus, bon nombre des associations entre les paramètres de l'EECP et le type de médicament sont demeurées significatives après ajustement multivarié. Les patients qui ont reçu des médicaments à action centrale ont également présenté moins d'événements faisant partie du critère d'évaluation composé (p = 0,023) et moins d'hospitalisations liées à l'IC (p = 0,044), même si la différence après correction multivariée n'a pas été significative et que les rapports de risques étaient respectivement de 0,664 et de 0,757. Conclusions: Les médicaments à action centrale ont été associés à une meilleure fonction respiratoire, mesurée à l'aide des paramètres d'une EECP, chez les patients atteints d'IC. Ce résultat pourrait également s'appliquer au critère d'évaluation composé et aux hospitalisations, mais une étude plus puissante est nécessaire pour établir un lien cliniquement significatif avec l'effet des médicaments. Les médicaments à action centrale contre l'IC ont donc un rôle à jouer dans la correction de la faiblesse du diaphragme.

Factors Impacting Physician Prognostic Accuracy in Heart Failure Patients With Reduced Left Ventricular Ejection Fraction.

BACKGROUND: A recent study showed that the accuracy of heart failure (HF) cardiologists and family doctors to predict mortality in outpatients with HF proved suboptimal, performing less well than models. OBJECTIVES: The authors sought to evaluate patient and physician factors associated with physician accuracy. METHODS: The authors included outpatients with HF from 11 HF clinics. Family doctors and HF cardiologists estimated patient 1-year mortality. They calculated predicted mortality using the Seattle HF Model and followed patients for 1 year to record mortality (or urgent heart transplant or ventricular assist device implant as mortality-equivalent events). Using multivariable logistic regression, the authors evaluated associations among physician experience and confidence in estimates, duration of patient-physician relationship, patient-physician sex concordance, patient race, and predicted risk, with concordant results between physician and model predictions. RESULTS: Among 1,643 patients, 1-year event rate was 10% (95% CI: 8%-12%). One-half of the estimates showed discrepant results between model and physician predictions, mainly owing to physician risk overestimation. Discrepancies were more frequent with increasing patient risk from 38% in low-risk to ∼75% in high-risk patients. When making predictions on male patients, female HF cardiologists were 26% more likely to have discrepant predictions (OR: 0.74; 95% CI: 0.58-0.94). HF cardiologist estimates in Black patients were 33% more likely to be discrepant (OR: 0.67; 95% CI: 0.45-0.99). Low confidence in predictions was associated with discrepancy. Analyses restricted to high-confidence estimates showed inferior calibration to the model, with risk overestimation across risk groups. CONCLUSIONS: Discrepant physician and model predictions were more frequent in cases with perceived increased risk. Model predictions outperform physicians even when they are confident in their predictions. (Predicted Prognosis in Heart Failure [INTUITION]; NCT04009798).

Association Between the Incidence of Hospitalizations for Acute Cardiovascular Events, Weather, and Air Pollution.

Background: The incidence of hospitalizations for cardiovascular events has been associated with specific weather conditions and air pollution. A comprehensive model including the interactions between various environmental factors remains to be developed. Objectives: The purpose of this study was to develop a comprehensive model of the association between weather patterns and the incidence of cardiovascular events and use this model to forecast near-term spatiotemporal risk. Methods: We present a spatiotemporal analysis of the association between atmospheric data and the incidence rate of hospital admissions related to heart failure (922,132 episodes), myocardial infarction (521,988 episodes), and ischemic stroke (263,529 episodes) in ∼24 million people in Canada between 2007 and 2017. Our hierarchical Bayesian model captured the spatiotemporal distribution of hospitalizations and identified weather and air pollution-related factors that could partially explain fluctuations in incidence. Results: Models that included weather and air pollution variables outperformed models without those covariates for most event types. Our results suggest that environmental factors may interact in complex ways on human physiology. The impact of environmental factors was magnified with increasing age. The weather and air pollution variables included in our models were predictive of the future incidence of heart failure, myocardial infarction, and ischemic strokes. Conclusions: The increasing importance of environmental factors on cardiovascular events with increasing age raises the need for the development of educational materials for older patients to recognize environmental conditions where exacerbations are more likely. This model could be the basis of a forecasting system used for local, short-term clinical resource planning based on the anticipated incidence of events.