AbstractNigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period.

摘要尼日利亚是全球围产期心肌病发病率最高的国家。这项开放标签，实用的临床试验将孕妇和产后妇女随机分为常规护理或人工智能（AI）指导的筛查，以评估其对围产期左心室收缩功能障碍（LVSD）诊断的影响。

The study intervention included digital stethoscope recordings with point of-care AI predictions and a 12-lead electrocardiogram with asynchronous AI predictions for LVSD. The primary end point was identification of LVSD during the study period. In the intervention arm, the primary end point was defined as the number of identified participants with LVSD as determined by a positive AI screen, confirmed by echocardiography.

研究干预包括数字听诊器记录和即时AI预测，以及12导联心电图和LVSD异步AI预测。。在干预组中，主要终点定义为通过超声心动图证实的阳性AI筛查确定的LVSD参与者人数。

In the control arm, this was the number of participants with clinical recognition and documentation of LVSD on echocardiography in keeping with current standard of care. Participants in the intervention arm had a confirmatory echocardiogram at baseline for AI model validation. A total of 1,232 (616 in each arm) participants were randomized and 1,195 participants (587 intervention arm and 608 control arm) completed the baseline visit at 6 hospitals in Nigeria between August 2022 and September 2023 with follow-up through May 2024.

在对照组中，这是根据当前的护理标准，在超声心动图上对LVSD进行临床识别和记录的参与者人数。干预组的参与者在基线时进行了确认性超声心动图检查，以进行AI模型验证。2022年8月至2023年9月，共有1232名（每组616名）参与者被随机分配，1195名参与者（587名干预组和608名对照组）在尼日利亚的6家医院完成了基线访问，随访至2024年5月。

Using the AI-enabled digital stethoscope, the primary study end point was met with detection of 24 out of 587 (4.1%) versus 12 out of 608 (2.0%) patients with LVSD (intervention versus control odds ratio 2.12, 95% CI 1.05–4.27; P = 0.032). With the 12-lead AI-electrocardiogram model, the primary end point was detected in 20 out of 587 (3.4%) versus 12 out of 608 (2.0%) patients (odds ratio 1.75, 95% CI 0.85–3.62; P = 0.125).

。使用12导联AI心电图模型，587例患者中有20例（3.4%）检测到主要终点，608例患者中有12例（2.0%）检测到主要终点（优势比1.75,95%CI 0.85-3.62；P=0.125）。

A similar direction of effect was observed in prespecified subgro.

在预先指定的subgro中观察到类似的作用方向。

MainIn the United States, cardiomyopathy is a leading cause of maternal mortality and the number one cause of death in the postpartum period. Its incidence is estimated to be 1 in 2,000 (refs. 1,2) and as high as 1 in ~700 among African American women with milder forms likely going undetected in the absence of screening2.

Main在美国，心肌病是孕产妇死亡的主要原因，也是产后死亡的头号原因。据估计，其发病率为2000年的1/1（参考文献1,2），在没有筛查的情况下可能未被发现的较轻形式的非裔美国女性中，其发病率高达700/1。

In Nigeria, the incidence is reported to be 1 in 96 deliveries3,4, the highest reported worldwide5. Cardiomyopathy occurring during pregnancy and postpartum is challenging to diagnose due to similarities between heart failure symptoms and those related to physiologic changes of pregnancy6,7. This leads to a delay in diagnosis and consequently, adverse maternal outcomes6.

在尼日利亚，据报道发病率为96次分娩中的1次[3,4]，这是全球报道的最高水平[5]。由于心力衰竭症状与妊娠生理变化相关的症状相似，妊娠期和产后发生的心肌病难以诊断[6,7]。这导致诊断延迟，从而导致不良的孕产妇结局6。

While cardiomyopathy can occur de novo during pregnancy, hemodynamic changes of pregnancy can also unmask previously undiagnosed or asymptomatic LVSD8.AI-enabled electrocardiograms (ECGs) have shown effectiveness in identifying multiple cardiovascular pathologies9,10,11,12, including detection of low left ventricular ejection fraction (LVEF)13,14.

虽然心肌病可能在怀孕期间从头发生，但妊娠的血流动力学变化也可以掩盖以前未诊断或无症状的LVSD8.AI启用的心电图（ECG）已显示出识别多种心血管疾病的有效性9,10,11,12，包括检测左心室射血分数低（LVEF）13,14。

A retrospective study (area under the curve (AUC) = 0.89)15 and a pilot prospective study among pregnant and postpartum women in the United States showed AI-based screening to be effective (AUC = 1.00 using a 12-lead ECG and 0.98 using a digital stethoscope)16 in identifying pregnancy-related LVSD with LVEF < 45%.

一项回顾性研究（曲线下面积（AUC）=0.89）15和一项针对美国孕妇和产后妇女的前瞻性试验研究显示，基于AI的筛查是有效的（使用12导联心电图的AUC=1.00，使用数字听诊器的AUC=0.98）16在识别LVEF<45%的妊娠相关LVSD方面。

Other retrospective studies in the United States and the Republic of Korea have also shown good performance of separate AI-ECG models for detecting perinatal LVSD17,18. Based on these previous studies, we surmised that this new technology has the potential to enhance screening and identification of LVSD in the peripartum period; however, it remains unknown whether AI-guided screening improves cardiomyopathy detection in obstetric pa.

美国和大韩民国的其他回顾性研究也显示，单独的AI-ECG模型在检测围产期LVSD17,18方面表现良好。基于这些先前的研究，我们推测这项新技术有可能在围产期加强LVSD的筛查和鉴定；然而，AI指导的筛查是否能改善产科pa的心肌病检测尚不清楚。

US FDA-cleared AI-ECG algorithm

美国FDA批准的AI-ECG算法

At study entry, 18 cases (3.1%) of LVSD (LVEF < 50%) were identified in the intervention arm (positive 12-lead ECG AI prediction for LVSD, confirmed with echocardiography) compared to 11 (1.8%) in the control arm; odds ratio 1.72, 95% CI 0.80–3.67; P = 0.158. At the study end, three additional LVSD cases were identified with two in the intervention arm and one in the control arm, resulting in a total of 20 cases (3.4%) of LVSD (LVEF < 50%) identified in the intervention arm compared to 12 (2.0%) in the control arm; odds ratio 1.75, 95% CI 0.85–3.62; P = 0.125 (Table 2).

在研究开始时，干预组发现18例（3.1%）LVSD（LVEF<50%）（LVSD 12导联心电图AI预测阳性，超声心动图证实），而对照组为11例（1.8%）；优势比1.72，95%可信区间0.80-3.67；P=0.158。在研究结束时，另外3例LVSD病例被确定为干预组2例，对照组1例，干预组共有20例（3.4%）LVSD（LVEF＜50%），而对照组为12例（2.0%）；优势比1.75，95%可信区间0.85-3.62；P=0.125（表2）。

Although a numerically higher number of LVSD cases were identified with the 12-lead AI-ECG screening, this did not reach statistical significance..

尽管通过12导联AI-ECG筛查发现了数量较多的LVSD病例，但这并没有达到统计学意义。。

Original Mayo Clinic AI-ECG algorithm

原始梅奥诊所AI-ECG算法

At study entry, 14 cases (2.4%) of LVSD (LVEF < 50%) were identified in the intervention arm (positive 12-lead AI-ECG prediction for LVSD, confirmed with echocardiography) compared to 11 (1.8%) in the control arm; odds ratio 1.33, 95% CI 0.60–2.94; P = 0.487. At the study end, five additional LVSD cases were identified with four in the intervention arm and one in the control arm resulting in a total of 18 cases (3.1%) of LVSD (LVEF < 50%) identified in the intervention arm compared to 12 (2.0%) in the control arm; odds ratio 1.57, 95% CI 0.75–3.29; P = 0.227 (Table 2).

在研究开始时，干预组发现14例（2.4%）LVSD（LVEF<50%）（LVSD阳性12导联AI-ECG预测，超声心动图证实），而对照组为11例（1.8%）；优势比1.33，95%可信区间0.60-2.94；P=0.487。在研究结束时，又发现了5例LVSD病例，其中干预组4例，对照组1例，干预组共发现18例（3.1%）LVSD（LVEF＜50%），而对照组为12例（2.0%）；优势比1.57，95%可信区间0.75-3.29；P=0.227（表2）。

Similar to the US FDA-cleared model, a numerically higher number of LVSD cases were identified with this AI model but this did not reach statistical significance..

与美国FDA批准的模型类似，该AI模型在数字上识别出更多的LVSD病例，但这没有达到统计学意义。。

Secondary outcomesDigital stethoscope performance by subgroupsEvaluation of the primary outcome within prespecified subgroups were consistent with a tendency toward improved detection of LVSD in the intervention arm compared to the control arm across all prespecified subgroups (age group, ethnicity, region, presence of hypertensive disorders and pregnancy/postpartum status).

次要结果数字听诊器在预先设定的亚组中的主要结果的亚组评估表现与干预组与所有预先设定的亚组（年龄组，种族，地区，高血压疾病的存在和妊娠/产后状态）的对照组相比改善LVSD检测的趋势一致。

A summary figure is provided in Fig. 3a.Fig. 3: Forest plots showing primary outcome stratified by subgroups.a, Performance of the digital stethoscope. b, Performance of the US FDA-cleared 12-lead AI-ECG algorithm. c, Performance of the original Mayo Clinic 12-lead AI-ECG algorithm in detecting the primary outcome within each prespecified subgroup.

。图3：森林图显示了按亚组分层的主要结果。a，数字听诊器的性能。b、 美国FDA批准的12导联AI-ECG算法的性能。c、 原始梅奥诊所12导联AI-ECG算法在检测每个预先指定的亚组内的主要结果方面的表现。

Data in the columns are presented as frequencies and percentages with 95% exact CI in parenthesis. The column with error bars represents odds ratio estimates depicted as a black dot and the error bar represents the large sample 95% CI around the odds ratio estimate. The odds ratios and 95% large sample CI were estimated using logistic regression.

列中的数据以频率和百分比表示，括号中有95%的准确CI。带有误差线的列表示以黑点表示的优势比估计值，误差线表示优势比估计值周围的大样本95%CI。使用逻辑回归估计优势比和95%的大样本CI。

HDP, hypertensive disorder of pregnancy (includes chronic hypertension, gestational hypertension, pre-eclampsia and eclampsia).Full size image12-Lead ECG performance by subgroupsSubgroup analysis using the US FDA-cleared 12-lead AI-ECG algorithm also showed consistent results across the prespecified subgroups except for age group, where the odds ratio for identifying LVSD among those younger than 30 years was 0.9 (95% CI 0.4–2.5) and 4.2 (95% CI 1.2–15.0) for those aged 30 years and older (Fig.

HDP，妊娠高血压疾病（包括慢性高血压，妊娠高血压，先兆子痫和子痫）。使用美国FDA批准的12导联AI-ECG算法通过亚组分析的全尺寸图像12导联心电图表现也显示了预先指定的亚组的一致结果，但年龄组除外，其中30岁以下人群识别LVSD的优势比为0.9（95%CI 0.4-2.5）和4.2（95%CI 1.2-15.0）。

3b). This may suggest improved performance of the model in older compared to younger patients. We also provide a summary figure for the subgroup analysis using the original Mayo Clinic 12-lead AI-ECG algorithm (Fig. 3c) an.

3b）。这可能表明与年轻患者相比，老年患者的模型表现有所改善。我们还提供了使用原始Mayo Clinic 12导联AI-ECG算法（图3c）进行亚组分析的汇总图。

Data availability

数据可用性

The underlying data supporting the findings of this study can be made available to clinical investigators and researchers upon request. Written requests for data sharing including an analysis plan will be required before approval. These requests will be individually assessed in consultation with the study team leads and co-investigators as appropriate.

支持这项研究结果的基础数据可以根据要求提供给临床研究人员和研究人员。在批准之前，需要书面的数据共享请求，包括分析计划。这些请求将酌情与研究团队领导和联合调查人员协商进行单独评估。

If other investigators are interested in performing additional analyses, these requests can be made to the corresponding author (D.A.A.) and analyses will be performed in collaboration with the Mayo Clinic. In all cases, any data and materials to be shared will be released via a Material Transfer Agreement.

如果其他研究人员有兴趣进行其他分析，可以向通讯作者（D.A.A.）提出这些要求，并将与梅奥诊所合作进行分析。在任何情况下，共享的任何数据和材料都将通过材料转让协议发布。

Individual-level data will be available and data sharing will ensure that the rights and privacy of individuals participating in the research always remains protected. The anticipated time frame to respond to initial data requests is 1 month..

将提供个人层面的数据，数据共享将确保参与研究的个人的权利和隐私始终受到保护。响应初始数据请求的预期时间范围为1个月。。

Code availability

代码可用性

The AI algorithms used in this paper have been previously published and have recently received US FDA clearance35,36. The code itself cannot be shared because it is proprietary intellectual property that has been licensed to Anumana and Eko Health. The US FDA-cleared 12-lead AI-ECG algorithm can be accessed from Anumana and the digital stethoscope AI algorithm through Eko Health..

本文中使用的AI算法先前已经发布，最近获得了美国FDA的许可35,36。代码本身不能共享，因为它是专有知识产权，已被授权给Anumana和Eko Health。。。

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Download referencesAcknowledgementsWe express our sincere thanks to all the clinical staff and patients of the obstetric and cardiology departments at all participating sites in Nigeria, without whom this study would not be possible. We especially appreciate all research study staff who contributed to participant recruitment, data collection and study coordination efforts: D.

下载参考文献致谢我们衷心感谢尼日利亚所有参与站点的产科和心脏病学部门的所有临床工作人员和患者，没有他们，本研究将不可能进行。我们特别感谢所有为参与者招募，数据收集和研究协调工作做出贡献的研究人员：D。

Onietan (lead study coordinator, Lagos University Teaching Hospital), R. Quao (Lagos University Teaching Hospital), S. Aborisade (University College Ibadan (UCH)), O. Makinde (UCH), V. Ojo (UCH), K. Adenike Olatunde (UCH), O. Allison Orimolade (UCH), S. Taofeek Oladotun (UCH), O. Alabi (UCH), A. Teslim Sanusi (UCH), T.

Onietan（拉各斯大学教学医院首席研究协调员），R.Quao（拉各斯大学教学医院），S.Aborisade（伊巴丹大学学院（UCH）），O.Makinde（UCH），V.Ojo（UCH），K.Adenike Olatunde（UCH），O.Allison Orimolade（UCH），S.Taofeek Oladotun（UCH），O.Alabi（UCH），A.Teslim Sanusi（UCH），T。

Azeez Olukunle (UCH), I. Alabede (UCH), M.A. Ijaiya (University of Illorin Teaching Hospital (UITH)), A. Adeniran (UITH), A. Temilola (Olabisi Onabanjo University Teaching Hospital (OOUTH)), A. Adekolade (OOUTH), A. Akiseku (OOUTH), K. Hidaya (OOUTH), A. Adedokun (UITH), O. Ishola (UITH), N.A. Ishaq (Aminu Kano Teaching Hospital), Y.

Azeez-Olukunle（UCH），I.Alabede（UCH），M.A.Ijaiya（伊洛林大学教学医院（UITH）），A.Adeniran（UITH），A.Temilola（Olabisi-Onabanjo大学教学医院（OOTH）），A.Adekolade（OOTH），A.Akiseku（OOTH），K.Hidaya（OOTH），A.Adedokun（UITH），O.Ishola（UITH），N.A.Ishaq（阿米努卡诺教学医院），Y。

Sa’ad (Aminu Kano Teaching Hospital), A. Abdullahi (Rasheed Shekoni Specialist Hospital, (RSSH)), R. Musa (RSSH) and A. Ibrahim Aliyu (RSSH). This trial was funded by the Mayo Clinic (Centers for Digital Health and Community Health and Engagement Research, D.A.A.) and, in part, by the Mayo Clinic BIRCWH program funded by the NIH (grant no.

Sa'ad（阿米努卡诺教学医院），A.Abdullahi（拉希德谢科尼专科医院（RSSH）），R.Musa（RSSH）和A.Ibrahim Aliyu（RSSH）。这项试验由梅奥诊所（数字健康和社区健康与参与研究中心，D.A.A.）资助，部分由美国国立卫生研究院资助的梅奥诊所BIRCWH计划资助（批准号：。

K12 AR084222, D.A.A.) and Mayo Clinic’s Center for Clinical and Translational Sciences (grant no. UL1 TR002377, R.E.C.). Multiple co-authors are Mayo Clinic employees, otherwise funders were not directly involved in designing the trial, data collection, analysis, interpretation or manuscript writing.

K12 AR084222，D.A.A.）和梅奥诊所临床和转化科学中心（批准号UL1 TR002377，R.E.C.）。多名合著者是梅奥诊所的员工，否则资助者不会直接参与试验设计，数据收集，分析，解释或手稿撰写。

Portable ECG, phonocardiogram recordings and AI predictions using the digital stethoscope were extracted.

提取了使用数字听诊器的便携式心电图，心音图记录和AI预测。

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哈比卜（Habib）、萨比乌（Sabiu M.Hamza）、奥克丘库（Okechukwu S.Ogah）、格博拉汉（Gbolahan Obajimi）、奥卢格班加（Olugbenga Oluseun Saanu）、奥卢索吉（Olusoji E.Jagun）、方济各（Francisca O.Inofomoh）、特米托佩（Temitope Adeolu）、卡米卢（Kamilu M.Karaye）、苏勒（Sule A.Gaya）、伊萨卡（Is。五十、 文卡塔查兰（Venkatachalam），詹妮弗·杜根（JenniferDugan），姚晓曦（XiaoxiYao），汉娜·J·斯莱奇（HannaJ.Sledge），帕特里克·W·约翰逊（PatrickW.Johnson），米科拉杰（Mikolaj A。

Wieczorek, Zachi I. Attia, Sabrina D. Phillips, Mohamad H. Yamani, Yvonne Butler Tobah, Carl H. Rose, Emily E. Sharpe, Francisco Lopez-Jimenez, Paul A. Friedman, Peter A. Noseworthy & Rickey E. CarterContributionsD.A.A., R.E.C. and P.A.N. initially designed the trial and provided study oversight. D.A.A.

Wieczorek、Zachi I.Attia、Sabrina D.Phillips、Mohamad H.Yamani、Yvonne Butler Tobah、Carl H.Rose、Emily E.Sharpe、Francisco Lopez Jimenez、Paul A.Friedman、Peter A.Noseworthy和Rickey E.CarterContributionsD。A、 A.，R.E.C.和P.A.N.最初设计了试验并提供了研究监督。D、 A.A。

and A.C.M.L. performed site monitoring, all co-authors at participating institutions performed data acquisition, interpretation and entry. R.E.C., P.W.J., M.A.W. and H.J.S. performed data analysis. All authors contributed to study conceptualization, design and execution. D.A.A. wrote the initial paper draft and all co-authors contributed substantially to the writing and revising of the paper for key intellectual content and approved the final version for publication.Corresponding authorCorrespondence to.

A.C.M.L.进行了现场监测，参与机构的所有合著者都进行了数据采集，解释和输入。R、 E.C.，P.W.J.，M.A.W.和H.J.S.进行了数据分析。所有作者都为研究概念化，设计和执行做出了贡献。D、 。对应作者对应。

Demilade A. Adedinsewo.Ethics declarations

Demilade A.Adedinsewo。

Competing interests

相互竞争的利益

D.A.A. is supported by the Mayo Clinic BIRCWH program funded by the NIH (grant no. K12 AR084222). The content of the article is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Z.I.A. is a co-inventor of several AI algorithms (including screening for low LVEF, QT tool, aortic stenosis and atrial fibrillation detection during normal sinus rhythm).

D、 A.A.得到了美国国立卫生研究院资助的梅奥诊所BIRCWH计划的支持（批准号K12 AR084222）。。Z、 I.A.是几种AI算法的共同发明者（包括筛查低LVEF，QT工具，主动脉瓣狭窄和正常窦性心律期间的心房颤动检测）。

These have been licensed to Anumana, AliveCor and Eko. The Mayo Clinic and Z.I.A. may benefit from their commercialization. Z.I.A. is a member of the scientific advisory board for Anumana, an AI company, receives stock options for being an inventor of the ejection fraction algorithm and is a consultant for Anumana, AliveCor and XAI.health.

这些已被许可给Anumana，AliveCor和Eko。梅奥诊所和Z.I.A.可能会从其商业化中受益。Z、 I.A.是人工智能公司Anumana科学顾问委员会的成员，因其是射出分率算法的发明者而获得股票期权，并且是Anumana，AliveCor和XAI.health的顾问。

P.A.F. is a co-inventor of several AI algorithms (including screening for low LVEF, QT tool, aortic stenosis and atrial fibrillation detection during normal sinus rhythm). These have been licensed to Anumana, AliveCor and Eko. The Mayo Clinic and P.A.F. may benefit from their commercialization. P.A.F.

P、 A.F.是几种AI算法的共同发明者（包括筛查低LVEF，QT工具，主动脉瓣狭窄和正常窦性心律期间的心房颤动检测）。这些已被许可给Anumana，AliveCor和Eko。梅奥诊所和P.A.F.可能会从其商业化中受益。P、 A.F。

is a member of the scientific advisory board for Anumana, an AI company. F.L.J. in conjunction with the Mayo Clinic has filed patents related to the application of AI to ECG for diagnosis and risk stratification. F.L.J. is a member of the scientific advisory board for Anumana, an AI company. P.A.N. and the Mayo Clinic have filed patents related to the application of AI to ECG for diagnosis and risk stratification and have licensed several AI-ECG algorithms to Anumana.

是人工智能公司Anumana科学顾问委员会的成员。F、 L.J.与梅奥诊所（Mayo Clinic）共同申请了与AI在心电图诊断和风险分层中的应用相关的专利。F、 L.J.是人工智能公司Anumana科学顾问委员会的成员。P、 A.N.和梅奥诊所（Mayo Clinic）已经申请了与AI应用于心电图诊断和风险分层相关的专利，并向Anumana许可了几种AI-ECG算法。

P.A.N. and the Mayo Clinic are involved in potential equity/royalty relationship with AliveCor. P.A.N. is a study investigator in an ablation trial sponsored by Medtronic. P.A.N. also has served on an expert advisory panel for OptumLabs. Y.B.T. has sponsored research grants .

P、 A.N.和梅奥诊所与AliveCor存在潜在的股权/版税关系。P、 A.N.是美敦力赞助的消融试验的研究人员。P、 A.N.还曾担任OptumLabs的专家咨询小组成员。Y、 。

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Nature Medicine thanks Michael Honigberg, Antonio Luiz Ribeiro and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Lorenzo Righetto, in collaboration with the Nature Medicine team.

《自然医学》感谢迈克尔·霍尼格伯格（MichaelHonigberg）、安东尼奥·路易斯·里贝罗（AntonioLuizRibeiro）和另一位匿名审稿人对这项工作的同行评审做出的贡献。主要处理编辑：洛伦佐·里格托（LorenzoRighetto）与《自然医学》团队合作。

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