Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial

Nigeria 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 dysfunc...

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Veröffentlicht in:Nature medicine 2024-10, Vol.30 (10), p.2897-2906
Hauptverfasser: Adedinsewo, Demilade A., Morales-Lara, Andrea Carolina, Afolabi, Bosede B., Kushimo, Oyewole A., Mbakwem, Amam C., Ibiyemi, Kehinde F., Ogunmodede, James Ayodele, Raji, Hadijat Olaide, Ringim, Sadiq H., Habib, Abdullahi A., Hamza, Sabiu M., Ogah, Okechukwu S., Obajimi, Gbolahan, Saanu, Olugbenga Oluseun, Jagun, Olusoji E., Inofomoh, Francisca O., Adeolu, Temitope, Karaye, Kamilu M., Gaya, Sule A., Alfa, Isiaka, Yohanna, Cynthia, Venkatachalam, K. L., Dugan, Jennifer, Yao, Xiaoxi, Sledge, Hanna J., Johnson, Patrick W., Wieczorek, Mikolaj A., Attia, Zachi I., Phillips, Sabrina D., Yamani, Mohamad H., Tobah, Yvonne Butler, Rose, Carl H., Sharpe, Emily E., Lopez-Jimenez, Francisco, Friedman, Paul A., Noseworthy, Peter A., Carter, Rickey E.
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container_title Nature medicine
container_volume 30
creator Adedinsewo, Demilade A.
Morales-Lara, Andrea Carolina
Afolabi, Bosede B.
Kushimo, Oyewole A.
Mbakwem, Amam C.
Ibiyemi, Kehinde F.
Ogunmodede, James Ayodele
Raji, Hadijat Olaide
Ringim, Sadiq H.
Habib, Abdullahi A.
Hamza, Sabiu M.
Ogah, Okechukwu S.
Obajimi, Gbolahan
Saanu, Olugbenga Oluseun
Jagun, Olusoji E.
Inofomoh, Francisca O.
Adeolu, Temitope
Karaye, Kamilu M.
Gaya, Sule A.
Alfa, Isiaka
Yohanna, Cynthia
Venkatachalam, K. L.
Dugan, Jennifer
Yao, Xiaoxi
Sledge, Hanna J.
Johnson, Patrick W.
Wieczorek, Mikolaj A.
Attia, Zachi I.
Phillips, Sabrina D.
Yamani, Mohamad H.
Tobah, Yvonne Butler
Rose, Carl H.
Sharpe, Emily E.
Lopez-Jimenez, Francisco
Friedman, Paul A.
Noseworthy, Peter A.
Carter, Rickey E.
description Nigeria 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. 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. 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. 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). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration: NCT05438576 In this pragmatic, randomized clinical trial involving 1,196 pregnant and postpartum women from 6 hospitals in Nigeria, AI-based electrocardiogram screening proved accurate in detecting cardiomyopathies and suggests that it could improve detection of these conditions.
doi_str_mv 10.1038/s41591-024-03243-9
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L. ; Dugan, Jennifer ; Yao, Xiaoxi ; Sledge, Hanna J. ; Johnson, Patrick W. ; Wieczorek, Mikolaj A. ; Attia, Zachi I. ; Phillips, Sabrina D. ; Yamani, Mohamad H. ; Tobah, Yvonne Butler ; Rose, Carl H. ; Sharpe, Emily E. ; Lopez-Jimenez, Francisco ; Friedman, Paul A. ; Noseworthy, Peter A. ; Carter, Rickey E. ; SPEC-AI Nigeria Investigators ; on behalf of the SPEC-AI Nigeria Investigators</creatorcontrib><description>Nigeria 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. 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. 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. 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). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. 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L.</creatorcontrib><creatorcontrib>Dugan, Jennifer</creatorcontrib><creatorcontrib>Yao, Xiaoxi</creatorcontrib><creatorcontrib>Sledge, Hanna J.</creatorcontrib><creatorcontrib>Johnson, Patrick W.</creatorcontrib><creatorcontrib>Wieczorek, Mikolaj A.</creatorcontrib><creatorcontrib>Attia, Zachi I.</creatorcontrib><creatorcontrib>Phillips, Sabrina D.</creatorcontrib><creatorcontrib>Yamani, Mohamad H.</creatorcontrib><creatorcontrib>Tobah, Yvonne Butler</creatorcontrib><creatorcontrib>Rose, Carl H.</creatorcontrib><creatorcontrib>Sharpe, Emily E.</creatorcontrib><creatorcontrib>Lopez-Jimenez, Francisco</creatorcontrib><creatorcontrib>Friedman, Paul A.</creatorcontrib><creatorcontrib>Noseworthy, Peter A.</creatorcontrib><creatorcontrib>Carter, Rickey E.</creatorcontrib><creatorcontrib>SPEC-AI Nigeria Investigators</creatorcontrib><creatorcontrib>on behalf of the SPEC-AI Nigeria Investigators</creatorcontrib><title>Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial</title><title>Nature medicine</title><addtitle>Nat Med</addtitle><addtitle>Nat Med</addtitle><description>Nigeria 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. 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. 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. 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). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration: NCT05438576 In this pragmatic, randomized clinical trial involving 1,196 pregnant and postpartum women from 6 hospitals in Nigeria, AI-based electrocardiogram screening proved accurate in detecting cardiomyopathies and suggests that it could improve detection of these conditions.</description><subject>692/308/174</subject><subject>692/308/409</subject><subject>692/699/75/74</subject><subject>692/700/478/2772</subject><subject>Adult</subject><subject>Artificial Intelligence</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Cancer Research</subject><subject>Cardiomyopathies - diagnosis</subject><subject>Cardiomyopathies - diagnostic imaging</subject><subject>Cardiomyopathy</subject><subject>Clinical trials</subject><subject>Diagnosis</subject><subject>Echocardiography</subject><subject>EKG</subject><subject>Electrocardiography</subject><subject>Female</subject><subject>Heart</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Infectious Diseases</subject><subject>Intervention</subject><subject>Mass Screening - methods</subject><subject>Medical instruments</subject><subject>Metabolic Diseases</subject><subject>Molecular Medicine</subject><subject>Neurosciences</subject><subject>Nigeria - epidemiology</subject><subject>Patients</subject><subject>Postpartum</subject><subject>Postpartum period</subject><subject>Pregnancy</subject><subject>Pregnancy Complications, Cardiovascular - diagnosis</subject><subject>Subgroups</subject><subject>Ventricular Dysfunction, Left - diagnosis</subject><subject>Ventricular Dysfunction, Left - diagnostic imaging</subject><issn>1078-8956</issn><issn>1546-170X</issn><issn>1546-170X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><recordid>eNp9kctu1jAQhS0EoqXwAiyQJTZsAr7FidmgquImVWIDEjtr4jjpVIkdbAepbHh1XP5SLgtWtjXfOTPjQ8hjzp5zJvsXWfHW8IYJ1TAplGzMHXLMW6Ub3rHPd-uddX3Tm1YfkQc5XzLGJGvNfXIkjRBS9OqYfD9NBSd0CAvFUPyy4OyD83TecfQjzS55HzDMdIqJOkgjxvUqblAu0OcqoRBoHHLxJaGjW9z2BQrG8JIC3RLMa305miCMccVv1dEtGNDVdlUAy0Nyb4Il-0c35wn59Ob1x7N3zfmHt-_PTs8bJ1tdGsF5Z7reCM-MEqOAnulBy27Uo_OuH6TkxgtmJq04KDBKDdJxKabJm0FCL0_Iq4Pvtg-rr6JQEix2S7hCurIR0P5dCXhh5_jVcq76VrSiOjy7cUjxy-5zsStmVz8Mgo97tpIzJjrZ8utmT_9BL-OeQt2vUryrc2qmKyUOlEsx5-Sn22k4s9cB20PAtgZsfwZsTRU9-XOPW8mvRCsgD0CupTD79Lv3f2x_APocs-0</recordid><startdate>202410</startdate><enddate>202410</enddate><creator>Adedinsewo, Demilade A.</creator><creator>Morales-Lara, Andrea Carolina</creator><creator>Afolabi, Bosede B.</creator><creator>Kushimo, Oyewole A.</creator><creator>Mbakwem, Amam C.</creator><creator>Ibiyemi, Kehinde F.</creator><creator>Ogunmodede, James Ayodele</creator><creator>Raji, Hadijat Olaide</creator><creator>Ringim, Sadiq H.</creator><creator>Habib, Abdullahi A.</creator><creator>Hamza, Sabiu M.</creator><creator>Ogah, Okechukwu S.</creator><creator>Obajimi, Gbolahan</creator><creator>Saanu, Olugbenga Oluseun</creator><creator>Jagun, Olusoji E.</creator><creator>Inofomoh, Francisca O.</creator><creator>Adeolu, Temitope</creator><creator>Karaye, Kamilu M.</creator><creator>Gaya, Sule A.</creator><creator>Alfa, Isiaka</creator><creator>Yohanna, Cynthia</creator><creator>Venkatachalam, K. 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L. ; Dugan, Jennifer ; Yao, Xiaoxi ; Sledge, Hanna J. ; Johnson, Patrick W. ; Wieczorek, Mikolaj A. ; Attia, Zachi I. ; Phillips, Sabrina D. ; Yamani, Mohamad H. ; Tobah, Yvonne Butler ; Rose, Carl H. ; Sharpe, Emily E. ; Lopez-Jimenez, Francisco ; Friedman, Paul A. ; Noseworthy, Peter A. ; Carter, Rickey E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-211797892e0942d2a806b637d6dcec8b3319e209f641a4a944b3c132ffe9b3a83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>692/308/174</topic><topic>692/308/409</topic><topic>692/699/75/74</topic><topic>692/700/478/2772</topic><topic>Adult</topic><topic>Artificial Intelligence</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Cancer Research</topic><topic>Cardiomyopathies - diagnosis</topic><topic>Cardiomyopathies - diagnostic imaging</topic><topic>Cardiomyopathy</topic><topic>Clinical trials</topic><topic>Diagnosis</topic><topic>Echocardiography</topic><topic>EKG</topic><topic>Electrocardiography</topic><topic>Female</topic><topic>Heart</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Infectious Diseases</topic><topic>Intervention</topic><topic>Mass Screening - methods</topic><topic>Medical instruments</topic><topic>Metabolic Diseases</topic><topic>Molecular Medicine</topic><topic>Neurosciences</topic><topic>Nigeria - epidemiology</topic><topic>Patients</topic><topic>Postpartum</topic><topic>Postpartum period</topic><topic>Pregnancy</topic><topic>Pregnancy Complications, Cardiovascular - diagnosis</topic><topic>Subgroups</topic><topic>Ventricular Dysfunction, Left - diagnosis</topic><topic>Ventricular Dysfunction, Left - diagnostic imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Adedinsewo, Demilade A.</creatorcontrib><creatorcontrib>Morales-Lara, Andrea Carolina</creatorcontrib><creatorcontrib>Afolabi, Bosede B.</creatorcontrib><creatorcontrib>Kushimo, Oyewole A.</creatorcontrib><creatorcontrib>Mbakwem, Amam C.</creatorcontrib><creatorcontrib>Ibiyemi, Kehinde F.</creatorcontrib><creatorcontrib>Ogunmodede, James Ayodele</creatorcontrib><creatorcontrib>Raji, Hadijat Olaide</creatorcontrib><creatorcontrib>Ringim, Sadiq H.</creatorcontrib><creatorcontrib>Habib, Abdullahi A.</creatorcontrib><creatorcontrib>Hamza, Sabiu M.</creatorcontrib><creatorcontrib>Ogah, Okechukwu S.</creatorcontrib><creatorcontrib>Obajimi, Gbolahan</creatorcontrib><creatorcontrib>Saanu, Olugbenga Oluseun</creatorcontrib><creatorcontrib>Jagun, Olusoji E.</creatorcontrib><creatorcontrib>Inofomoh, Francisca O.</creatorcontrib><creatorcontrib>Adeolu, Temitope</creatorcontrib><creatorcontrib>Karaye, Kamilu M.</creatorcontrib><creatorcontrib>Gaya, Sule A.</creatorcontrib><creatorcontrib>Alfa, Isiaka</creatorcontrib><creatorcontrib>Yohanna, Cynthia</creatorcontrib><creatorcontrib>Venkatachalam, K. L.</creatorcontrib><creatorcontrib>Dugan, Jennifer</creatorcontrib><creatorcontrib>Yao, Xiaoxi</creatorcontrib><creatorcontrib>Sledge, Hanna J.</creatorcontrib><creatorcontrib>Johnson, Patrick W.</creatorcontrib><creatorcontrib>Wieczorek, Mikolaj A.</creatorcontrib><creatorcontrib>Attia, Zachi I.</creatorcontrib><creatorcontrib>Phillips, Sabrina D.</creatorcontrib><creatorcontrib>Yamani, Mohamad H.</creatorcontrib><creatorcontrib>Tobah, Yvonne Butler</creatorcontrib><creatorcontrib>Rose, Carl H.</creatorcontrib><creatorcontrib>Sharpe, Emily E.</creatorcontrib><creatorcontrib>Lopez-Jimenez, Francisco</creatorcontrib><creatorcontrib>Friedman, Paul A.</creatorcontrib><creatorcontrib>Noseworthy, Peter A.</creatorcontrib><creatorcontrib>Carter, Rickey E.</creatorcontrib><creatorcontrib>SPEC-AI Nigeria Investigators</creatorcontrib><creatorcontrib>on behalf of the SPEC-AI Nigeria Investigators</creatorcontrib><collection>Springer Nature OA/Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium &amp; Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Nature medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Adedinsewo, Demilade A.</au><au>Morales-Lara, Andrea Carolina</au><au>Afolabi, Bosede B.</au><au>Kushimo, Oyewole A.</au><au>Mbakwem, Amam C.</au><au>Ibiyemi, Kehinde F.</au><au>Ogunmodede, James Ayodele</au><au>Raji, Hadijat Olaide</au><au>Ringim, Sadiq H.</au><au>Habib, Abdullahi A.</au><au>Hamza, Sabiu M.</au><au>Ogah, Okechukwu S.</au><au>Obajimi, Gbolahan</au><au>Saanu, Olugbenga Oluseun</au><au>Jagun, Olusoji E.</au><au>Inofomoh, Francisca O.</au><au>Adeolu, Temitope</au><au>Karaye, Kamilu M.</au><au>Gaya, Sule A.</au><au>Alfa, Isiaka</au><au>Yohanna, Cynthia</au><au>Venkatachalam, K. L.</au><au>Dugan, Jennifer</au><au>Yao, Xiaoxi</au><au>Sledge, Hanna J.</au><au>Johnson, Patrick W.</au><au>Wieczorek, Mikolaj A.</au><au>Attia, Zachi I.</au><au>Phillips, Sabrina D.</au><au>Yamani, Mohamad H.</au><au>Tobah, Yvonne Butler</au><au>Rose, Carl H.</au><au>Sharpe, Emily E.</au><au>Lopez-Jimenez, Francisco</au><au>Friedman, Paul A.</au><au>Noseworthy, Peter A.</au><au>Carter, Rickey E.</au><aucorp>SPEC-AI Nigeria Investigators</aucorp><aucorp>on behalf of the SPEC-AI Nigeria Investigators</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial</atitle><jtitle>Nature medicine</jtitle><stitle>Nat Med</stitle><addtitle>Nat Med</addtitle><date>2024-10</date><risdate>2024</risdate><volume>30</volume><issue>10</issue><spage>2897</spage><epage>2906</epage><pages>2897-2906</pages><issn>1078-8956</issn><issn>1546-170X</issn><eissn>1546-170X</eissn><abstract>Nigeria 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. 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. 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. 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). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration: NCT05438576 In this pragmatic, randomized clinical trial involving 1,196 pregnant and postpartum women from 6 hospitals in Nigeria, AI-based electrocardiogram screening proved accurate in detecting cardiomyopathies and suggests that it could improve detection of these conditions.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>39223284</pmid><doi>10.1038/s41591-024-03243-9</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-9906-7106</orcidid><orcidid>https://orcid.org/0000-0001-5780-6070</orcidid><orcidid>https://orcid.org/0000-0003-4859-5405</orcidid><orcidid>https://orcid.org/0000-0001-7992-2398</orcidid><orcidid>https://orcid.org/0000-0003-1851-8724</orcidid><orcidid>https://orcid.org/0000-0002-8629-2029</orcidid><orcidid>https://orcid.org/0000-0002-9706-7900</orcidid><orcidid>https://orcid.org/0000-0002-0760-8014</orcidid><orcidid>https://orcid.org/0000-0002-0125-7276</orcidid><orcidid>https://orcid.org/0000-0002-4308-0456</orcidid><orcidid>https://orcid.org/0000-0002-0665-1045</orcidid><orcidid>https://orcid.org/0000-0002-5595-195X</orcidid><orcidid>https://orcid.org/0000-0003-4232-0718</orcidid><orcidid>https://orcid.org/0000-0001-5052-2948</orcidid><orcidid>https://orcid.org/0000-0002-7511-7567</orcidid><oa>free_for_read</oa></addata></record>
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identifier ISSN: 1078-8956
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issn 1078-8956
1546-170X
1546-170X
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11485252
source MEDLINE; Nature Journals Online; Alma/SFX Local Collection
subjects 692/308/174
692/308/409
692/699/75/74
692/700/478/2772
Adult
Artificial Intelligence
Biomedical and Life Sciences
Biomedicine
Cancer Research
Cardiomyopathies - diagnosis
Cardiomyopathies - diagnostic imaging
Cardiomyopathy
Clinical trials
Diagnosis
Echocardiography
EKG
Electrocardiography
Female
Heart
Hospitals
Humans
Infectious Diseases
Intervention
Mass Screening - methods
Medical instruments
Metabolic Diseases
Molecular Medicine
Neurosciences
Nigeria - epidemiology
Patients
Postpartum
Postpartum period
Pregnancy
Pregnancy Complications, Cardiovascular - diagnosis
Subgroups
Ventricular Dysfunction, Left - diagnosis
Ventricular Dysfunction, Left - diagnostic imaging
title Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial
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