{"id":27074,"date":"2025-02-18T04:58:00","date_gmt":"2025-02-17T20:58:00","guid":{"rendered":"http:\/\/csccm.org.cn\/?p=27074"},"modified":"2025-02-18T06:19:56","modified_gmt":"2025-02-17T22:19:56","slug":"jama-netw-open%e5%8f%91%e8%a1%a8%e8%ae%ba%e6%96%87%ef%bc%9a%e4%bc%b4%e6%88%96%e4%b8%8d%e4%bc%b4%e4%ba%ba%e5%b7%a5%e6%99%ba%e8%83%bd%e7%9a%84%e6%97%a9%e6%9c%9f%e9%a2%84%e8%ad%a6%e8%af%84%e5%88%86","status":"publish","type":"post","link":"https:\/\/csccm.org.cn\/?p=27074","title":{"rendered":"[JAMA Netw Open\u53d1\u8868\u8bba\u6587]\uff1a\u4f34\u6216\u4e0d\u4f34\u4eba\u5de5\u667a\u80fd\u7684\u65e9\u671f\u9884\u8b66\u8bc4\u5206"},"content":{"rendered":"\n

Original Investigation <\/p>\n\n\n\n

Health Informatics<\/p>\n\n\n\n

October 15, 2024<\/p>\n\n\n\n

Early Warning Scores With and Without Artificial Intelligence<\/h1>\n\n\n\n

Dana P. Edelson, Matthew M. Churpek, Kyle A. Carey, et al<\/h3>\n\n\n\n

JAMA Netw Open. <\/em>2024;7(10):e2438986. doi:10.1001\/jamanetworkopen.2024.38986<\/h3>\n\n\n\n

<\/a>Key Points<\/p>\n\n\n\n

Question<\/strong>  How do hospital early warning scores compare with one another?<\/p>\n\n\n\n

Findings<\/strong>  In this cohort study that compared 6 early warning scores across 362\u202f926 patient encounters, eCARTv5, a machine learning model, identified clinical deterioration best with an area under the receiver operating characteristics curve (AUROC) of 0.895 and the highest positive predictive values at both the moderate- and high-risk matched thresholds. The National Early Warning Score, a non\u2013artificial intelligence score with an AUROC of 0.831, was the second-best performer at both thresholds, while the Epic Deterioration Index was one of the worst, with an AUROC of 0.808 and the lowest positive predictive values.<\/p>\n\n\n\n

Meaning<\/strong>  Given the wide variation in accuracy, these findings suggest that additional transparency and oversight of early warning tools may be warranted.<\/a><\/p>\n\n\n\n

Abstract<\/p>\n\n\n\n

Importance<\/strong>  Early warning decision support tools to identify clinical deterioration in the hospital are widely used, but there is little information on their comparative performance.<\/p>\n\n\n\n

Objective<\/strong>  To compare 3 proprietary artificial intelligence (AI) early warning scores and 3 publicly available simple aggregated weighted scores.<\/p>\n\n\n\n

Design, Setting, and Participants<\/strong>  This retrospective cohort study was performed at 7 hospitals in the Yale New Haven Health System. All consecutive adult medical-surgical ward hospital encounters between March 9, 2019, and November 9, 2023, were included.<\/p>\n\n\n\n

Exposures<\/strong>  Simultaneous Epic Deterioration Index (EDI), Rothman Index (RI), eCARTv5 (eCART), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and NEWS2 scores.<\/p>\n\n\n\n

Main Outcomes and Measures<\/strong>  Clinical deterioration, defined as a transfer from ward to intensive care unit or death within 24 hours of an observation.<\/p>\n\n\n\n

Results<\/strong>\u00a0\u00a0Of the 362\u202f926 patient encounters (median patient age, 64 [IQR, 47-77] years; 200\u202f642 [55.3%] female), 16\u202f693 (4.6%) experienced a clinical deterioration event. eCART had the highest area under the receiver operating characteristic curve at 0.895 (95% CI, 0.891-0.900), followed by NEWS2 at 0.831 (95% CI, 0.826-0.836), NEWS at 0.829 (95% CI, 0.824-0.835), RI at 0.828 (95% CI, 0.823-0.834), EDI at 0.808 (95% CI, 0.802-0.812), and MEWS at 0.757 (95% CI, 0.750-0.764). After matching scores at the moderate-risk sensitivity level for a NEWS score of 5, overall positive predictive values (PPVs) ranged from a low of 6.3% (95% CI, 6.1%-6.4%) for an EDI score of 41 to a high of 17.3% (95% CI, 16.9%-17.8%) for an eCART score of 94. Matching scores at the high-risk specificity of a NEWS score of 7 yielded overall PPVs ranging from a low of 14.5% (95% CI, 14.0%-15.2%) for an EDI score of 54 to a high of 23.3% (95% CI, 22.7%-24.2%) for an eCART score of 97. The moderate-risk thresholds provided a median of at least 20 hours of lead time for all the scores. Median lead time at the high-risk threshold was 11 (IQR, 0-69) hours for eCART, 8 (IQR, 0-63) hours for NEWS, 6 (IQR, 0-62) hours for NEWS2, 5 (IQR, 0-56) hours for MEWS, 1 (IQR, 0-39) hour for EDI, and 0 (IQR, 0-42) hours for RI.<\/p>\n\n\n\n

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Conclusions and Relevance<\/strong>  In this cohort study of inpatient encounters, eCART outperformed the other AI and non-AI scores, identifying more deteriorating patients with fewer false alarms and sufficient time to intervene. NEWS, a non-AI, publicly available early warning score, significantly outperformed EDI. Given the wide variation in accuracy, additional transparency and oversight of early warning tools may be warranted.<\/p>\n","protected":false},"excerpt":{"rendered":"

Original Investigation  Health Informatics October […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[32,23],"tags":[],"_links":{"self":[{"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/posts\/27074"}],"collection":[{"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=27074"}],"version-history":[{"count":3,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/posts\/27074\/revisions"}],"predecessor-version":[{"id":27714,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/posts\/27074\/revisions\/27714"}],"wp:attachment":[{"href":"https:\/\/csccm.org.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=27074"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=27074"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=27074"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}