Predictors Of Heart Failure Research Articles

Artificial intelligence assessment of heart failure and mortality after acute stroke

Abstract Background/Introduction The adverse cardiovascular outcomes following a stroke have shown significant associations with post-stroke mortality. However, studies focusing on predicting heart failure and its impact on long-term mortality are scarce. Purpose To address this gap, we employed an commercially approved artificial intelligence/machine learning enabled software as a medical device (AiTiALVSD version 1.00.00) which uses only 12 lead electrocardiogram data, resulting probability score for predicting left ventricular systolic dysfunction. We derived the AiTiALVSD probability score from initial ECGs at stroke admission, and assess its implications for prediction of heart failure and long-term mortality. Methods We collected data from 335 ischemic stroke patients admitted to a cardiovascular center during acute periods between 2013 and 2019. The AiTiALVSD score was employed to predict heart failure with reduced ejection fraction and heart failure with mild reduced ejection fraction, analyzed through logistic regression. Subsequently, we used the Cox Hazard proportional model, incorporating the AiTiALVSD score and clinical factors, to predict long-term mortality. Results The model incorporating the AiTiALVSD score with clinical variables demonstrated a higher AUC for predicting heart failure (0.905 [95% CI, 0.827-0.927] vs. 0.828 [95% CI, 0.749-0.907], p<0.004) than clinical variables alone. Moreover, for long-term mortality prediction, this model outperformed the conventional mortality score (AUC, 0.884 [95% CI, 0.836-0.933] vs. 0.755 [95% CI, 0.690-0.820], p<0.001). Conclusion Multivariable models which combine the AiTiALVSD score with clinical factors, effectively predicts heart failure and long-term mortality. This model could enhance therapeutic strategy tailoring by providing acute phase prognostic predictions for individual stroke patients.Prediction performance for heart failurePrediction performance for mortality

Beyond resting measurements: exercise pulse pressure and its association with heart failure hospitalization

Abstract Background Pulse pressure (PP), the difference between systolic and diastolic blood pressure, is a known predictor of heart failure (HF). While high resting PP has been linked to increased HF risk, the implications of exercise-induced PP changes remain less understood. This study investigates the contrasting roles of exercise PP in predicting hospitalization for heart failure (hHF). Methods We conducted a retrospective analysis of adults referred for treadmill exercise testing between 2003 and 2012. ΔMaximal BP referred to the BP change from rest to peak exercise. Cox proportional hazards models adjusted for demographic and clinical variables were used to assess the predictive value of PP changes for hHF. Results Among 15,249 participants, 117 (0.7%) were hospitalized for HF over a median follow-up of 112 months. Higher resting PP was significantly associated with an increased risk of hHF, whereas increased Exercise PP was inversely related to hHF risk independently (adjusted HR: 0.76, 95% CI: 0.67-0.86, P<0.001). Conclusions Resting PP and Exercise PP exhibit opposite effects on the risk of heart failure hospitalization. These findings suggest the importance of considering both resting and exercise-induced PP changes in the assessment and management of heart failure risk.

Impact of different P2Y12 inhibitors in acute coronary syndrome patients with severe chronic kidney disease: insights from the CORALYS registry

Abstract Background The impact of the most potent P2Y12i ticagrelor and prasugrel after acute coronary syndrome in patients with chronic kidney disease (CKD) remains unclear. Most evidence on dual antiplatelet therapy (DAPT) comes from real-world study but is limited to glomerular filtration rate (GFR) under 60 ml/min whereas no data are available regarding patients with severe CKD (stages IV-V) Methods Consecutive patients with acute coronary syndrome (ACS) treated with percutaneous coronary intervention (PCI) and severe CKD defined as GFR under 30 ml/min not on dialysis from the CORALYS (Incidence and Predictors of Heart Failure After Acute Coronary Syndrome) registry were included. Incidence of Net Adverse Clinical Events (NACE) defined as major adverse cardiac events (MACE, a composite of cardiovascular death, myocardial infarction and unplanned revascularizations) in addition to major bleedings (classified as BARC scale 3 to 5) was our primary endpoint. Overall MACE and their single components, all bleedings, major bleedings and hospitalizations for heart failure were the secondary ones. Results Of 14699 patients in the CORALYS registry, 257 presented with severe CKD not on dialysis and had available data on antiplatelet regimen at discharge. Patients were divided into two groups according to the type of P2Y12i prescribed at discharge: 193 (75.1%) were given clopidogrel while 64 (24.9%) with ticagrelor or prasugrel. Our populaiton was predominantly male (153, 59.5%) with an average age 75.8 ± 10.9 years old. Average GFR was 21.8 ± 6.3 ml/min. Over a median follow-up of 459 days, Ticagrelor and Prasugrel were associated to lower rates of NACE compared to Clopidogrel (39.9% vs 25.0%, p=0.036). This was mainly driven by an excess of both CV mortality (31.1% vs 6.3%) and myocardial infarction (20.7% vs 9.4%) reported in the clopidogrel group. No difference was reported regarding overall bleeding events (5.7% vs 4.6% with clopidogrel and new P2Y12i respectively, p=0.81) and major bleedings (1.7% vs 4.6% with clopidogrel and new P2Y12i respectively, p=0.15). At multivariate Cox regression analysis DAPT with Ticagrelor/Prasugrel resulted independently associated with a lower risk of NACE (Adj Hr 0.41, 95%CI 0.23-0.74, p=0.003) whereas left main involvement or multivessel coronary disease led to higher risk of NACE (Adj HR 1.96 95%CI 1.21-3.18, p=0.006) Conclusions In ACS patients treated with PCI with severe CKD ticagrelor and prasugrel were associated to lower risk of NACE, MACE, CV death and myocardial infarction compared to clopidogrel. No significative difference was reported in overall and major bleedings despite a relative increase of major bleedings in the group treated with Ticagrelor or Prasugrel.

AI-enabled cardiac chambers volumetry in coronary calcium scans (AI-CAC) predicts heart failure and outperforms NT-proBNP: The Multi-Ethnic Study of Atherosclerosis

Abstract Introduction Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC for prediction of incident heart failure (HF) and compared it with 10 known clinical risk factors, NT-proBNP, and the Agatston CAC score. Methods We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% women, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC versus NT-proBNP, Agatston score, and 10 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL, total cholesterol, and hs-CRP) for predicting incident HF over 15 years. Results Over 15 years of follow-up, 256 HF events accrued. The AUC for predicting HF with AI-CAC (0.826) was significantly higher than NT-proBNP (0.742) and Agatston score (0.712) (p<.0001), and comparable to clinical risk factors (0.818, p=0.4141). AI-CAC category-free Net Reclassification Index (NRI) significantly improved on clinical risk factors (0.32), NT-proBNP (0.46), and Agatston score (0.71) for HF prediction at 15 years (p<0.0001). Conclusion AI-CAC significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.

Genotype predicts heart failure independent of left ventricular ejection fraction and NT-proBNP levels in hypertrophic cardiomyopathy

Abstract Background/Introduction Hypertrophic cardiomyopathy (HCM) is a myocardial disease associated with progression to heart failure. In around 40% of cases, the disease is caused by variants in genes encoding sarcomere proteins. Purpose To evaluate the role of genotype in predicting heart failure (HF) outcomes. Methods In this observational single-centre cohort study, consecutive patients with HCM were assessed for heart failure clinically stratified by genetic status into genotype-elusive which included those with no significant variants (G-) or variants of unknown significance (VUS) and genotype-positive (G+) with pathogenic/likely pathogenic (LP/P) variants. Heart failure endpoint was defined as left ventricular assist device implantation, heart transplantation or heart-failure-related death. The incidence of HF endpoint was compared between genotypes during follow-up using a time-to-event analysis. Results 474 patients (50.9 ±14.5 years, 67.3 males) with HCM (25% with left ventricular (LV) outflow obstruction) were followed for 10.9 years (IQR 5.1-13.5). G+ LP/P patients (201/474 (42.4%)) were younger (45.8. ±14.0 years) compared to G- (225/474 (47.5%), 55.2 ±13.4 years) and VUS (48/474 (10.1%), 52.0 ± 15 years); 29/474 (6.1%) reached the heart failure endpoint. The incidence of heart failure endpoints was 12.4% (25/201) in the G+ LP/P group compared to 1.5% (4/273) in the gene elusive group. A multivariate Cox proportional hazards model including NT-proBNP and LV ejection fraction (LVEF) and genetic status showed that G+ LP/P was associated with increased risk of heart failure outcomes (HR: 5.11, 95% CI: 1.43–18.25, p=0.012). Every 100-unit rise in NT-proBNP was associated with a 41% increase in risk (HR: 1.41, 95% CI: 1.20–1.66, p<0.0001). A one-unit (1%) reduction in LVEF correlated with a 10% increase in heart failure risk (HR: -0.90, 95% CI: 0.87–0.93, p<0.0001). Conclusion(s) Genetic status is a predictor of heart failure outcomes independent of LVEF and NT-proBNP levels in HCM. This underscores the importance of genetic testing in the clinical management of HCM.

Predicting heart failure in cancer survivors: use of polygenic risk vs clinical score

Abstract Background The risk of heart failure (HF) is increased among cancer survivors, but predicting individual HF risk is difficult. Polygenic risk scores (PRS) for HF prediction summarize the combined effect of multiple genetic variants, specific to the individual. We sought to compare clinical HF prediction models with PRS in both cancer- and non-cancer populations. Methods Cancer and HF diagnoses were identified in the UK-Biobank from International Classification of Diseases (ICD)-10 codes. HF risk was calculated using the Atherosclerosis Risk in Communities score (ARIC-HF). PRS-HF was calculated using the Global Biobank Meta-analysis Initiative. The predictive performance of ARIC-HF and PRS-HF were compared using the area under the curve (AUC) in both cancer- and non-cancer populations. Results After excluding 2,644 participants with HF prior to consent, we identified 440,813 non-cancer participants (mean age 57 years, 53% female) and 43,720 cancer survivors (mean age 60 years, 65% female). Both ARIC-HF and PRS-HF were significant predictors for HF occurrence after adjustment for chronic kidney disease, overall health rating and total cholesterol level. The PRS-HF performed poorly in predicting HF among cancer (AUC: 0.552) and non-cancer populations (AUC: 0.571). However, the ARIC-HF predicted HF incidence in the non-cancer population (AUC: 0.806) and a reasonable performance among the cancer population (AUC: 0.748). Conclusions Prediction of HF occurrence in cancer survivors based on conventional risk factors is superior to PRS. However, both models demonstrate suboptimal discriminative capabilities in forecasting HF, emphasizing the imperative for the development of a cancer-specific HF predictive model.

An ensemble machine learning approach for predicting heart failure risk in acute coronary syndrome patients: heartguardai

Abstract Background Heart failure (HF) constitutes a major post-event morbidity in patients experiencing Acute Coronary Syndrome (ACS), underscoring the imperative for precise risk stratification methodologies. Traditional prognostic models, although beneficial, are limited by their reliance on a narrow scope of variables, thereby overlooking the comprehensive analytical potential afforded by extensive clinical datasets. Aims To develop "HeartGuardAI," an ensemble machine learning model that predicts HF risk in ACS patients. By incorporating a wide array of variables including ejection fraction (EF), glomerular filtration rate (GFR), body mas index(BMI), male sex, this model seeks to offer a more practical tool for clinical decision-making. Methods We queried ACTION-ACS a large-scale multicenter database of 3335 patients at 10-year follow-up. We employed a feature selection process informed by Random Forest Classifier to identify the most predictive variables. An ensemble model combining the strengths of Random Forest and Gradient Boosting methodologies was then developed. The model's predictive performance was validated through Stratified K-Fold cross-validation, focusing on Area Under the Curve (AUC) and confidence intervals to assess its efficacy. The model's predictive performance was validated through Stratified K-Fold cross-validation, focusing on Area Under the Curve (AUC) and confidence intervals to assess its efficacy. Results Demonstrating a mean AUC of 0.76 ± 0.036, "HeartGuardAI" showed a high capability to predict HF risk in post-ACS patients and emphasizes the significance of incorporating multifactorial clinical assessments Conclusions HF risk can be predicted by a handy tool developed with advanced machine learning methods. Future studies are needed to prospectively validate this algorithm.Graphic 1Graphic 2

A standards-based software tool to support prognostic modelling: application to exemplar heart failure risk scores

Abstract Introduction Heart failure prognosis is an active research area, with the development of several new risk scores each year. While risk score derivation and evaluation have become common practices, these processes currently lack standardisation. Purpose We aimed to: i) develop a standards-based software tool to support prognostic modelling; and ii) apply the tool to derive, evaluate and compare exemplar heart failure risk models to address a hypothetical question of whether using only non-invasive measurements provides similar prognosis value compared to adding invasive measurements. Methods A software tool was developed based on a prognostic modelling workflow defined using the Common Workflow Language and containing 7 steps: univariate Cox proportional hazard (PH) regression, multivariable Cox PH regression with assessment of proportional hazards and linearity with outcome (Schoenfeld and Martingale residuals), derivation of points per risk factor, risk calculation and stratification, discrimination and calibration assessment. Using data from the PEOPLE heart failure cohort [1], two integer-based risk scores were developed to predict 2-year all-cause mortality. The first model utilised 12 variables available at recruitment informed by prior analysis of the PEOPLE cohort to provide a benchmark for comparison, while the second model is based on 9 non-invasive parameters. The analysis was conducted on cases with complete data (n=781), with no internal or external validation. Results By inputting the dataset and indicating the variables of interest for each model, our software tool automatically generated the patient-specific risk scores, risk strata, c-index, and discrimination and calibration plots. A 12-parameter model based on age, sex, ejection fraction, hypertension, diabetes, atrial fibrillation, ischaemic history, NYHA class, heart rate, systolic blood pressure, creatinine and NT-proBNP yielded reasonable discrimination (c-index=0.77). Graphical assessment of risk strata and calibration (Fig. 1.a and c) revealed good performance overall, apart from the highest risk decile where survival was overestimated. The reduced 9-parameter model excluded creatinine, NT-proBNP, and automatically dropped diabetes status for non-linearity. It showed a substantial drop in discrimination (c-index=0.67), confirmed in graphical assessment of risk strata (Fig. 1.b). Calibration was more variable per decile than seen with the 12-parameter model but was more robust to survival overestimation (Fig. 1.d). Conclusion Our software tool ensures the uniformity and comparability of the two risk models derived by a common statistical process. In this example, we showed how the addition of NT-proBNP, creatinine, and diabetes status improves discrimination for predicting heart failure survival. This tool can be leveraged to test new hypotheses in a standardised and reproducible manner to enhance research in heart failure prognosis.

Prediction of Cardiac ATTR Depletion by NI006 (ALXN2220) Using Mechanistic PK/PD Modeling.

NI006 (aka ALXN2220) is a therapeutic antibody candidate in phase III clinical development for the depletion of amyloid transthyretin (ATTR) in patients with ATTR cardiomyopathy, an infiltrative cardiomyopathy leading to increased left ventricular wall thickness (LVWT). The mode-of-action consists in removal of disease-causing amyloid accumulations by activating phagocytic immune cells, a mechanism without precedent in cardiology. To select a safe and potentially efficacious dose range and treatment duration for a combined first-in-human and proof-of-concept clinical phase Ib study, we developed a mechanistic pharmacokinetic and pharmacodynamic (PK/PD) model that can predict NI006 exposure, its effects on cardiac amyloid load and on LWVT, which is a predictor of heart failure in this disease. The PK/PD model predictions supported 0.3 mg/kg monthly dosing as a safe starting dose and identified 10-60 mg/kg monthly as the potentially efficacious dose range with substantial and dose dependent cardiac amyloid burden reduction within 4 months for 60 mg/kg and 10 months for 10 mg/kg. These predictions were in good agreement with the observed primary results of the clinical phase Ib study where amyloid burden was measured by imaging. This novel translational PK/PD model provided important predictions to guide the design of the phase Ib study of NI006, indicating the value of this approach to integrate preclinical results into clinical trial design and increase translational success.

Myocardial extracellular volume measurement using cardiac computed tomography.

Myocardial fibrosis is a common endpoint of many cardiac diseases and increasingly recognized as a predictor of heart failure, arrhythmia, and death. Recent studies have utilised cardiac computed tomography (CT) scans with delayed phase imaging to quantify diffuse fibrosis of the myocardium. CT extracellular volume (CT-ECV) measurement correlates well with CMR and histological myocardial fibrosis. Furthermore, CT-ECV predicts outcomes such as death, heart failure and arrhythmia in various disease states. This review summarizes the rationale and methodology behind CT-ECV measurement and provides a detailed summary of the current clinical evidence for the use of CT-ECV.

The prognostic value of left atrial strain and strain rate in predicting heart failure outcomes in patients with hypertrophic cardiomyopathy and a left ventricular ejection fraction of 50% or higher

The prognostic value of left atrial strain and strain rate in predicting heart failure outcomes in patients with hypertrophic cardiomyopathy and a left ventricular ejection fraction of 50% or higher

Cardiomyopathy characterizing and heart failure risk predicting by echocardiography and pathoanatomy in aged male mice.

Correlation between echocardiographic and pathoanatomic variables and their prognostic value in murine cardiomyopathy models remain unknown. Using echocardiography, morphometrics, and survival monitoring, we characterized transgenic (TG) mice with dilated cardiomyopathy due to cardiac overexpression of β2-adrenoceptors focusing on predicting heart failure (HF) risk and HF mortality. In 12-month-old non-TG and TG mice, echocardiography was performed to determine left ventricular (LV) dimensions (d), wall thickness (h), and fractional shortening (FS). Animals were monitored for 3 months for survival. Organ weights and pathological events indicating left HF were determined. TG mice (n = 76) had reduced FS and enlarged LV, and 79% died of HF or likely arrhythmias during the follow-up period while all non-TG mice (n = 26) survived. These mice with left HF also had pulmonary congestion and hypertrophy/dilatation of the right ventricle (RV). Weights of lungs, RV, and atria were intercorrelated (r = 0.79-0.83) and also negatively correlated with FS × (h/d) index (r = -0.502 to -0.609). By FS × (h/d) tertiles, TG mice of low tertiles were identified with the highest mortality (96%) largely due to HF (76%). In conclusion, in aged cardiomyopathy mice a good correlation existed between echocardiographic and pathoanatomic variables. Echocardiography-derived LV function and remodeling were useful in identifying a subgroup of TG mice with a high risk of HF and HF fatality.

Differentiating Sensor Changes in a Composite Heart Failure ICD Monitoring Index: Clinical Correlates and Implications

The HeartLogic algorithm integrates data from implantable defibrillator(ICD) sensors to predict heart failure(HF) decompensation: first(S1) and third(S3) heart sounds, intrathoracic impedance, respiration rate, ratio of respiration rate to tidal volume(RSBI), and night heart rate. This study assessed the relative changes in ICD sensors at the onset of HeartLogic alerts, their association with patient characteristics, and outcomes. The study included 568 HF patients carrying ICDs(CRT-D,n=410) across 26 centers, with a median follow-up of 26 months. HeartLogic alerts triggered patient contact and potential treatment. A total of 1200 HeartLogic alerts were recorded in 370 patients. The sensor with the highest change at the alert's onset was S3 in 27% of alerts, followed by S3/S1(25%). Patients with atrial fibrillation(AF) and chronic kidney disease(CKD) at implantation had higher alert prevalence(AF,84% vs. no-AF,58%; CKD,72% vs. no-CKD,59%; p <0.05) and rate (AF,1.51/patient-year vs. no-AF,0.88/patient-year; CKD,1.30/patient-year vs. no-CKD,0.89/patient-year; p<0.05). During follow-up, 247 patients experienced more than one alert; in 85%, the sensor with the highest change varied between successive alerts. Of the 88(7%) alerts associated with HF hospitalization or death, respiration rate or RSBI(11%, p=0.007 vs. S3/S1) and night heart rate(11%, p=0.031 vs. S3/S1) were more commonly the sensors showing the highest change. Clinical events were more common with the first alert(12.6%) than subsequent alerts(5.2%,p <0.001). HeartLogic alerts are mostly triggered by changes in heart sounds, but clinical events are more linked to respiration rate, RSBI, and night heart rate. Recurrent alerts often involve different sensors, indicating diverse mechanisms of HF progression.

Explainable machine learning and online calculators to predict heart failure mortality in intensive care units

Explainable machine learning and online calculators to predict heart failure mortality in intensive care units

A Study of Tpeak-Tend/QT Interval Ratio in Predicting Heart Failure in ST-Elevation Myocardial Infarction and Its Correlation With N-terminal Pro B-type Natriuretic Peptide (NT-proBNP).

Background Acute ST-elevation myocardial infarction (STEMI) is the most common cause of mortality across the world. The electrocardiogram (ECG) Tpeak-Tend/QT interval appears to be a measure of the left ventricle's transmural dispersion of repolarization (TDR). Prolongation of this time could indicate adverse cardiac events like heart failure, arrhythmias, etc. Heart failure is a clinical syndrome that includes signs and symptoms such as peripheral edema and high jugular vein pressure. N-terminal pro B-type natriuretic peptide (NT-proBNP) is an effective predictor of left ventricular function and also helps in predicting the disease prognosis. Elevated levels of NT-proBNP are seen in many cases of left ventricular dysfunction. This study aims to evaluate the predictive utility of the Tpeak-Tend/QT interval ratio in predicting major adverse cardiac events (MACEs), such as heart failure, by examining the Tend/QT interval ratio values with NT-proBNP in STEMI. Methodology This cross-sectional study was conducted at a tertiary hospital between April 2024 and June 2024. It included STEMI patients, excluding those with non-STEMI (NSTEMI), valvular heart diseases, bundle branch block, or pacemakers. The patients with a Tpeak-Tend/QT ratio < 0.3 were included in group A, and the Tpeak-Tend/QT ratio > 0.3 in group B. They were monitored for MACE-like heart failure during hospitalization and were compared with a study of the Tpeak-Tend/QT interval ratio in predicting heart failure in STEMI and its correlation with NT-proBNP levels. Results In this study, out of 45 patients, male predominance was observed, with 35 (78%) being men and 10 (22%) being women. In group A, the most common age group was 60-70 years, with 16 (51%) patients; in Group B, it was 50-60 years, with six (42.8%) patients. Out of 31 patients in group A, 25 were male, and six were female. In group B, out of 14 patients, 10 were male, and four were female. In this study, out of the 45 patients included, 12 (85%) among 14 patients who had MACEs like heart failure had a Tpeak-Tend/QT interval ratio of more than 0.3, and their measured NT-proBNP levels were also more than 900 pg/mL, thus showing a statistically significant association between Tpeak-Tend/QT interval ratio and NT-proBNP. Conclusion The present study showed an increased Tpeak-Tend/QT interval ratio and NT-proBNP in patients who developed heart failure in STEMI. As ECG is a readily available and affordable tool, the Tpeak-Tend/QT interval ratio can be used along with conventional markers like NT-proBNP to predict MACE in patients with STEMI.

Vectorcardiography Predicts Heart Failure in Patients Following ST Elevation Myocardial Infarction.

Modeling outcomes, such as onset of heart failure (HF) or mortality, in patients following ST elevation myocardial infarction (STEMI) is challenging but clinically very useful. The acute insult following a myocardial infarction and chronic degeneration seen in HF involve a similar process where a loss of cardiomyocytes and abnormal remodeling lead to pump failure. This process may alter the strength and direction of the heart's net depolarization signal. We hypothesize that changes over time in unique parameters extracted using vectorcardiography (VCG) have the potential to predict outcomes in patients post-STEMI and could eventually be used as a noninvasive and cost-effective surveillance tool for characterizing the severity and progression of HF to guide evidence-based therapies. We identified 162 patients discharged from Michigan Medicine between 2016 and 2021 with a diagnosis of acute STEMI. For each patient, a single 12-lead ECG > 1 week pre-STEMI and > 1 week post-STEMI were collected. A set of unique VCG parameters were derived by analyzing features of the QRS complex. We used LASSO regression analysis incorporating clinical variables and VCG parameters to create a predictive model for HF, mortality, or the composite at 90, 180, and 365 days post-STEMI. The VCG model is most predictive for HF onset at 90 days with a robust AUC. Variables from the HF model mitigating or driving risk, at a p < 0.05, were primarily parameters that assess the area swept by the depolarization vector including the 3D integral and convex hull in select spatial octants and quadrants.

The significance of plasma biomarkers in predicting heart failure among patients with acute myocardial infarction

Background: Acute myocardial infarction (AMI) is characterized by high mortality and disability rates. Timely and effective revascularization has saved the lives of many patients. However, some patients develop complications such as heart failure (HF) following myocardial infarction, which significantly affects their quality of life and may even be life-threatening. Therefore, the search for effective biomarkers to predict the occurrence of HF after AMI, along with early prevention and treatment strategies, will greatly improve clinical outcomes for patients with post-myocardial infarction HF Methods: A total of 170 patients with acute myocardial infarction (AMI) were enrolled in this study, including 44 patients with post-AMI heart failure (HF) and 126 patients with post-AMI non-HF. We measured the serum biomarkers NT-proBNP, cTNT, PLR, NLR, and GDF-15 in all patients using enzyme-linked immunosorbent assay (ELISA). Subsequently, we performed t-tests to assess the predictive value of these biomarkers for post-AMI heart failure (HF). Results: In the group of HF patients, cTNT, NT-proBNP, GDF-15 and NLR was significantly higher than in the group without HF, but PLR was not .The AUC of NLR to predict HF after AMI was 0.632 (95%CI:0.542-0.723), P=0.010, with a cut-off value of (3.86×109)/L , GDF-15 was 0.661 (95%CI0.560-0.763), P=0.002, with a cut-off value of 1.35 ng/mL, and NT-proBNP was 0.82 (95%CI0.723-0.876), P&lt;0.001, with a cut-off value of 1444 pg/mL. Conclusions: In five biomarkers, there is predictive value in NT-proBNP , NLR, GDF-15 for patients with HF after AMI .

A HEART FAILURE PREDICTION ALGORITHM BASED ON IMPROVED REINFORCEMENT LEARNING FRAMEWORK

Reducing the occurrence of diseases has become an important research direction in today’s social medicine. Accurate prediction of heart failure can greatly reduce the complication rate, allowing patients to know the condition in advance and receive treatment. To deal with heart failure, this paper proposes a heart failure prediction algorithm based on a reinforcement learning framework. This paper combines and improves the reinforcement learning algorithm and the swarm intelligence (SI) optimization algorithm, aiming to use the reinforcement learning algorithm to improve the global optimization capability of the SI optimization algorithm. In order to better improve the global search ability of the SI optimization algorithm, the penalty function will be replaced by a combination mode of dynamic and static reward. The improved reinforcement learning algorithm framework is significantly better than the previous algorithm framework and has been used to predict heart failure. The experimental results have proven the effectiveness of the algorithm and its accuracy in prediction of heart failure.

Machine Learning Models for Predicting Heart Failure: Unveiling Patterns and Enhancing Precision in Cardiac Risk Assessment

Purpose This study aims to evaluate the efficacy of various machine learning models in predicting heart failure incidence using medical data, focusing on the innovative aspect of a novel dataset. Unlike previous studies that predominantly examined features such as smoking status or age, this research explores novel features. The primary challenge addressed is the utilization of these new features, coupled with machine learning techniques, to accurately diagnose heart failure. Methods Five machine learning models, including logistic regression, support vector classifier, decision tree classifier, random forest classifier, and K-nearest neighbors, were applied to analyze medical data from a dataset comprising over 900 individuals. The dataset encompasses diverse parameters such as age, sex, chest pain severity, blood pressure, cholesterol levels, blood sugar levels, and electrocardiogram results, introducing a novel approach to feature selection. Results The evaluation of machine learning models unveiled varying performances in predicting heart failure. Logistic regression and support vector classifier exhibited the highest accuracy of 88%, followed by the decision tree classifier (below 85%), random forest classifier (84%), and K-nearest neighbors (82%). Additionally, the analysis revealed a balanced dataset distribution and highlighted sex-based disparities in heart failure incidence, along with significant correlations with factors such as age, chest pain severity, blood glucose levels, and physical activity. Conclusions The findings underscore the potential of integrating multiple machine learning models for early detection of heart failure, leveraging the inclusion of novel features in the dataset. However, careful model selection is crucial to account for discrepancies in accuracy among different models, emphasizing the importance of tailoring approaches based on specific project requirements.

Systolic blood pressure time in target range within 24 hours and incident heart failure: insights from the real-world setting.

Systolic blood pressure (SBP) time in target (TTR) over months were associated with lower risk of adverse clinical outcomes in hypertensive patients, whether short-term of 24-h SBP TTR was effective in predicting heart failure (HF) risk in the general population remained unclear. This prospective study aimed to investigate the association of 24-h SBP TTR with HF in the real-world settings. Based on Kailuan study, 24-h SBP target range defined as 110-140 mmHg was calculated with linear interpolation. Among 5152 participants included in the analysis, 186 (3.61%) cases of incident HF occurred during a median follow-up of 6.96 years. Compared with participants with SBP TTR of 0 to <25%, those with TTR of 75% to 100% had 47% lower risk of HF (hazard ratio [HR], 0.53; 95% confidence interval [CI], 0.32-0.89). The restricted spline curve depicted an inverse relationship between SBP TTR and incident HF. Additionally, the addition of SBP TTR, rather than mean SBP and SBP variation, to a conventional risk model had an incremental effect on the predictive value for HF, with integrated discrimination improvement value of 0.31% (P = 0.0003) and category-free net reclassification improvement value of 19.79% (P = 0.0081). Higher SBP TTR was associated with a lower risk of incident HF. Efforts to attain SBP within 110 to 140 mmHg may be an effective strategy to prevent HF.