2f:I[703058,["/_next/static/immutable/chunks/3bhq_5eh1d760.js","/_next/static/immutable/chunks/2iy5wruuedsh6.js","/_next/static/immutable/chunks/2pfsjpn6k6zlg.js","/_next/static/immutable/chunks/26c7v3_3uwnwu.js","/_next/static/immutable/chunks/00yc7rcvua6a7.js","/_next/static/immutable/chunks/37ktb71aq3480.js","/_next/static/immutable/chunks/2d50b-7eu4558.js","/_next/static/immutable/chunks/06_yt270h41rg.js","/_next/static/immutable/chunks/2d6noqpp6ehas.js","/_next/static/immutable/chunks/1sx186c93bpzg.js","/_next/static/immutable/chunks/3dc-1n866s65i.js","/_next/static/immutable/chunks/20x2y96ldu5wx.js","/_next/static/immutable/chunks/2w1bvggzb0ga8.js","/_next/static/immutable/chunks/2051ffx3xzio9.js"],"PaperListPageClient"] 30:T911,Early detection of risk of heart failure with reduced ejection fraction remains challenging in resource-limited settings due to limited access to echocardiography. Artificial intelligence electrocardiogram (AI-ECG) algorithms have demonstrated promise for identifying left ventricular systolic dysfunction (LVSD), but their feasibility in resource-constrained settings remains unknown. To determine the frequency of patients in Kenya with a high probability of LVSD by AI-ECG and assess AI-ECG algorithm performance against the gold standard of echocardiography. This was a cross-sectional study with enrollment from June to December 2024. Participants underwent baseline assessment and 12-lead ECG, and a subset completed echocardiography within 7 days. The echocardiography subset included participants from 3 prespecified risk strata: those with prior cardiovascular disease, those at high cardiovascular risk (Framingham Risk Score [FRS] ≥10%), and those at low risk (FRS <10%). The study took place at 8 outpatient health care facilities across Kenya. A total of 1444 patients 18 years and older seeking routine care were enrolled and completed paired echocardiogram. Exclusion criteria included inability to provide informed consent. Risk of LVSD was identified using a validated convolutional neural network AI-ECG algorithm (AiTiALVSD). Key outcomes were the diagnostic performance (sensitivity, specificity, and positive and negative predictive values) of the AI-ECG algorithm for detecting LVSD (LVEF <40%) when confirmed on echocardiography. Among 1444 participants (mean [SD] age, 59.0 [16.7] years; 907 [62.8%] female; 1118 [77.4%] at high risk), LVSD was identified in 204 (14.1%). The AI-ECG algorithm had a sensitivity of 95.6% (95% CI, 91.8-97.7), specificity of 79.4% (95% CI, 77.0-81.5), positive predictive value of 43.2% (95% CI, 38.7-47.9), negative predictive value of 99.1% (95% CI, 98.3-99.5), and area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI, 0.95-0.97). Performance remained consistent across cardiovascular risk strata (AUC, 0.96-0.98). In this study, the AI-ECG algorithm demonstrated the potential clinical utility for screening of LVSD risk with high sensitivity and negative predictive value and may be particularly scalable in a resource-limited setting.31:Tdfa, Importance Early detection of risk of heart failure with reduced ejection fraction remains challenging in resource-limited settings due to limited access to echocardiography. Artificial intelligence electrocardiogram (AI-ECG) algorithms have demonstrated promise for identifying left ventricular systolic dysfunction (LVSD), but their feasibility in resource-constrained settings remains unknown. Objective To determine the frequency of patients in Kenya with a high probability of LVSD by AI-ECG and assess AI-ECG algorithm performance against the gold standard of echocardiography. Design, Setting, and Participants This was a cross-sectional study with enrollment from June to December 2024. Participants underwent baseline assessment and 12-lead ECG, and a subset completed echocardiography within 7 days. The echocardiography subset included participants from 3 prespecified risk strata: those with prior cardiovascular disease, those at high cardiovascular risk (Framingham Risk Score [FRS] ≥10%), and those at low risk (FRS &lt;10%). The study took place at 8 outpatient health care facilities across Kenya. A total of 1444 patients 18 years and older seeking routine care were enrolled and completed paired echocardiogram. Exclusion criteria included inability to provide informed consent. Exposure Risk of LVSD was identified using a validated convolutional neural network AI-ECG algorithm (AiTiALVSD). Main Outcomes and Measures Key outcomes were the diagnostic performance (sensitivity, specificity, and positive and negative predictive values) of the AI-ECG algorithm for detecting LVSD (LVEF &lt;40%) when confirmed on echocardiography. Results Among 1444 participants (mean [SD] age, 59.0 [16.7] years; 907 [62.8%] female; 1118 [77.4%] at high risk), LVSD was identified in 204 (14.1%). The AI-ECG algorithm had a sensitivity of 95.6% (95% CI, 91.8-97.7), specificity of 79.4% (95% CI, 77.0-81.5), positive predictive value of 43.2% (95% CI, 38.7-47.9), negative predictive value of 99.1% (95% CI, 98.3-99.5), and area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI, 0.95-0.97). Performance remained consistent across cardiovascular risk strata (AUC, 0.96-0.98). Conclusions and Relevance In this study, the AI-ECG algorithm demonstrated the potential clinical utility for screening of LVSD risk with high sensitivity and negative predictive value and may be particularly scalable in a resource-limited setting. 25:[[["$","script","0",{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"{\"@context\":\"https://schema.org\",\"@type\":\"CollectionPage\",\"name\":\"AiTiA LVSD algorithm: generic Research & Clinical Takeaways\",\"description\":\"Explore peer-reviewed papers on AiTiA LVSD algorithm (generic). Clinician-written summaries and actionable practice insights. 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Kenya","updated":"2026-05-12T08:54:10+00:00","published_date":"2026-05-06","best_oa_location":{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13150732/","pmh_id":"oai:pubmedcentral.nih.gov:13150732","url_for_pdf":null,"url_for_landing_page":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13150732/"}}},"created_at":"2026-05-12T13:18:02.788Z","updated_at":"2026-05-14T15:13:26.918Z","tags":[{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"age_group","value":"adult","value_numeric":null},{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"category","value":"Cardiac & Cardiovascular Systems","value_numeric":null},{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"condition","value":"Heart Failure with Reduced Ejection Fraction","value_numeric":null},{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"condition","value":"Left Ventricular Systolic Dysfunction","value_numeric":null},{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"generic","value":"AiTiA LVSD algorithm","value_numeric":null},{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"impact_flag","value":"practice_changing","value_numeric":null},{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"level_of_evidence","value":"3","value_numeric":null},{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"modality","value":"Artificial Intelligence Electrocardiogram","value_numeric":null},{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"sample_size","value":"n=1444","value_numeric":1444},{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"study_design","value":"Cross-Sectional Study","value_numeric":null},{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"warning","value":"AiTiA LVSD is listed as a generic name here for lack of a separate brand name in the source text; its exact commercial/brand status could not be confirmed.","value_numeric":null},{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"warning","value":"The full text was not accessible; summary is based solely on the abstract and results section provided. Some methodological details (e.g., ECG acquisition protocol, algorithm training data origin) could not be verified.","value_numeric":null},{"paper_doi":"10.1001/jamacardio.2026.0908","tag_type":"warning","value":"The 'practice_changing' impact flag is applied cautiously — while results are compelling, external validation in other low-resource settings is needed before broad implementation.","value_numeric":null}],"summaries":{"what_studied":"A cross-sectional study (June–December 2024) evaluated an AI-ECG algorithm (AiTiA LVSD) for detecting left ventricular systolic dysfunction (LVEF <40%) against echocardiography as the gold standard in 1,444 adult outpatients across 8 facilities in Kenya, stratified by prior cardiovascular disease, high CVD risk (FRS ≥10%), and low risk (FRS <10%).","key_result":"LVSD was found in 204 of 1,444 participants (14.1%). The AI-ECG achieved sensitivity 95.6%, specificity 79.4%, PPV 43.2%, NPV 99.1%, and AUC 0.96 (95% CI, 0.95–0.97), with consistent performance across all cardiovascular risk strata (AUC 0.96–0.98).","limitations":"- Cross-sectional design limits causal or longitudinal inference.\n- Study enrolled patients seeking routine care at outpatient facilities, which may not represent the general Kenyan population.\n- Low PPV (43.2%) means most AI-positive screens still require confirmatory echocardiography, which remains a resource constraint.","practice_takeaway":"The AI-ECG algorithm's very high sensitivity (95.6%) and NPV (99.1%) make it a strong rule-out tool for LVSD in resource-limited settings where echocardiography is scarce. Clinicians can use a negative AI-ECG result to confidently defer echocardiography, reserving limited imaging for screen-positive patients."}}],"initialOffset":1,"fetchConfig":{"kind":"tags","tags":[{"tag_type":"generic","value":"AiTiA LVSD algorithm"}]},"breadcrumbItems":[{"name":"Home","href":"/"},{"name":"Research Hub","href":"/hub"},{"name":"Generics","href":"/hub/tag/generic"},{"name":"AiTiA LVSD algorithm"}],"hrefConfig":{"basePath":"/hub/papers","webViewQueryParams":{"hub_ctx":"tag","hub_tag_type":"generic","hub_tag_value":"AiTiA LVSD algorithm"}}}]]

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