Utilizing longitudinal data in assessing all‐cause mortality in patients hospitalized with heart failure

Herman, Robert; Vanderheyden, Marc; Vavrik, Boris; Beles, Monika; Palus, Timotej; Nelis, Olivier; Goethals, Marc; Verstreken, Sofie; Dierckx, Riet; Pěnička, Martin; Heggermont, Ward; Bartúnek, Jozef

doi:10.1002/ehf2.14011

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…Recent years have seen efforts dedicated to developing automated models identifying patients at high risk of mortality, in order to improve end-of-life care and align patient preferences with the provided care. Recent works have explored the use of machine learning models to integrate patients' longitudinal data in several clinical contexts, [27][28][29] and presented interesting improvements over single-visit models. However, to date, these techniques have not been used for models predicting the HOMR score to enhance palliative care.…”

Section: Discussionmentioning

confidence: 99%

Leveraging patients’ longitudinal data to improve the Hospital One-year Mortality Risk

Laribi,

Raymond,

Taseen

et al. 2024

Preprint

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ObjectiveTo develop and validate an Ensemble Long Short-term Memory neural network (ELSTM) that integrates patients’ longitudinal data to predict the Hospital One-year Mortality Risk using patients’ information collected routinely at admission. The aim is to identify patients at the end of life who may benefit from goals of care (GOC) discussions.Materials and MethodsWe evaluated our ELSTM (i) when including only predictors that can be reported upon admission (Ad-mDemo), and (ii) when adding also diagnoses available later during patients’ stay (AdmDemoDx). We used records of 82,104 patients admitted between 2011 and 2017 to compare the temporal and non-temporal strategies. We also quantified the clinical utility of the best strategy on 33,898 patients eligible for GOC discussions admitted between 2017 and 2021.ResultsOur ELSTM used with AdmDemo and AdmDemoDx predictors demonstrated an increased performance with AUROCs between 0.73-0.90 and 0.79-0.93, respectively. The ELSTM-based decision-making increased prediction precision by up to 12.1% compared to the usual decision-making process, but it also reduced sensitivity by up to 3.8%.DiscussionThe integration of patients’ longitudinal data provides better insights into the severity of illness and the overall condition of patients, especially when limited information is available during their hospitalization.ConclusionThe proposed ELSTM is an automated and accurate model able to identify patients at high risk of one-year mortality, potentially usable in clinical decision support systems to improve end-of-life care.

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Section: Discussionmentioning

confidence: 99%

Leveraging patients’ longitudinal data to improve the Hospital One-year Mortality Risk

Laribi,

Raymond,

Taseen

et al. 2024

Preprint

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“…The application of artificial intelligence (AI) to ECG waveforms has demonstrated increased diagnostic accuracy in various conditions and may offer a significant improvement in the timely detection of OMI. [16][17][18][19][20] 1.…”

Section: Introductionmentioning

confidence: 99%

International Evaluation of an Artificial Intelligence-Powered Ecg Model Detecting Occlusion Myocardial Infarction

Herman

Meyers

Smith

et al. 2023

Preprint

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Background. One third of Non-ST-elevation myocardial infarction (NSTEMI) patients present with an acutely occluded culprit coronary artery (occlusion myocardial infarction [OMI]), which is associated with poor short and long-term outcomes due to delayed identification and consequent delayed invasive management. We sought to develop and validate a versatile artificial intelligence (AI)-model detecting OMI on single standard 12-lead electrocardiograms (ECGs) and compare its performance to existing state-of-the-art diagnostic criteria. Methods. An AI model was developed using 18,616 ECGs from 10,692 unique contacts (22.9% OMI) of 10,543 patients (age 66±14 years, 65.9% males) with acute coronary syndrome (ACS) originating from an international online database and a tertiary care center. This AI model was tested on an international test set of 3,254 ECGs from 2,263 unique contacts (20% OMI) of 2,222 patients (age 62±14 years, 67% males) and compared with STEMI criteria and annotations of ECG experts in detecting OMI on 12-lead ECGs using sensitivity, specificity, predictive values and time to OMI diagnosis. OMI was based on a combination of angiographic and biomarker outcomes. Results. The AI model achieved an area under the curve (AUC) of 0.941 (95% CI: 0.926-0.954) in identifying the primary outcome of OMI, with superior performance (accuracy 90.7% [95% CI: 89.5-91.9], sensitivity 82.6% [95% CI: 78.9-86.1], specificity 92.8 [95% CI: 91.5-93.9]) compared to STEMI criteria (accuracy 84.9% [95% CI: 83.5-86.3], sensitivity 34.4% [95% CI: 30.0-38.8], specificity 97.6% [95% CI: 96.8-98.2]) and similar performance compared to ECG experts (accuracy 91.2% [95% CI: 90.0-92.4], sensitivity 75.9% [95% CI: 71.9-80.0], specificity 95.0 [95% CI: 94.0-96.0]). The average time from presentation to a correct diagnosis of OMI was significantly shorter when relying on the AI model compared to STEMI criteria (2.0 vs. 4.9 hours, p<0.001). Conclusions. The present novel ECG AI model demonstrates superior accuracy and earlier diagnosis of AI to detect acute OMI when compared to the STEMI criteria. Its external and international validation suggests its potential to improve ACS patient triage with timely referral for immediate revascularization.

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“…Studies were carried out to predict HF-related death and hospital readmission using AI and ML algorithms [24]. In some of those studies, echocardiographic data [25], or many parameters such as clinical phenotyping, laboratory, ECG, and echocardiography were used simultaneously [26]. Congestive heart failure (CHF) includes HFrEF and HFpEF.…”

Section: Introductionmentioning

confidence: 99%

A New Medical Decision Support System for Diagnosing HFrEF and HFpEF Using ECG and Machine Learning Techniques

et al. 2022

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As heart failure (HF) is a growing epidemic, no case should be overlooked in the diagnosis of HF. Two subtypes by left ventricular ejection fraction (LVEF) of HF are HF with reduced ejection fraction (HFrEF) (LVEF ≤ 40%) and HF with preserved ejection fraction (HFpEF) (LVEF ≥ 50%). HFrEF is easier to diagnose. However, the diagnosis of HFpEF is more complex and difficult even for specialists. The diagnosis of HFpEF is a problem that is being tried to be solved in medicine. Since LVEF appears normal (LVEF ≥ 50% in healthy individuals), HFpEF can be confused with chest diseases due to some similar symptoms. The diagnosis of HF subtypes is ideally made using echocardiography. Echocardiography should be performed in all patients with HF; however, it is expensive and requires specialists. Even in high-resource regions, this test is not always performed, and treatment may need to be initiated before the echocardiographic data are obtained. For such situations, economical and practical systems are required. In this study, a medical decision support system was developed to detect HFrEF and HFpEF cases using only 3-lead ECG. From the ECG data of 61 volunteers, 37 features were extracted, of which 16 were Yule-Walker and Burg's method parameters, and 21 were in the time domain. Consequently, 37 features were reduced by feature selection and triple classification was performed with only 4 features with maximum accuracy. The aim of this study was to determine whether the individuals with HF symptoms were HFrEF, HFpEF, or healthy. Four machine learning algorithms were used for classification. The best classification accuracy rate was 100% for k-NN, and significant results were obtained from the other three algorithms: SVMs, Decision Trees, and Ensemble Bagged Trees.

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Utilizing longitudinal data in assessing all‐cause mortality in patients hospitalized with heart failure

Cited by 7 publications

References 25 publications

Leveraging patients’ longitudinal data to improve the Hospital One-year Mortality Risk

Leveraging patients’ longitudinal data to improve the Hospital One-year Mortality Risk

International Evaluation of an Artificial Intelligence-Powered Ecg Model Detecting Occlusion Myocardial Infarction

A New Medical Decision Support System for Diagnosing HFrEF and HFpEF Using ECG and Machine Learning Techniques

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