A machine-learning algorithm for diagnosis of multisystem inflammatory syndrome in children and Kawasaki disease in the USA: a retrospective model development and validation study.
A machine-learning algorithm for diagnosis of multisystem inflammatory syndrome in children and Kawasaki disease in the USA: a retrospective model development and validation study.
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DOI：
10.1016/s2589-7500(22)00149-2
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发表时间：
2022-10
影响因子：
30.8
通讯作者：
Burns, Jane C.
中科院分区：
文献类型：
--
作者：
Lam, Jonathan Y.;Shimizu, Chisato;Tremoulet, Adriana H.;Bainto, Emelia;Roberts, Samantha C.;Sivilay, Nipha;Gardiner, Michael A.;Kanegaye, John T.;Hogan, Alexander H.;Salazar, Juan C.;Mohandas, Sindhu;Szmuszkovicz, Jacqueline R.;Mahanta, Simran;Dionne, Audrey;Newburger, Jane W.;Ansusinha, Emily;DeBiasi, Roberta L.;Hao, Shiying;Ling, Xuefeng B.;Cohen, Harvey J.;Nemati, Shamim;Burns, Jane C.
儿童多系统炎症综合征（MIS-C）是一种新型疾病，在COVID-19大流行期间被发现，其特征是SARS-CoV-2感染后的全身炎症。MIS-C的早期检测是一个挑战，因为它与川崎和其他急性发热性儿童疾病的临床相似性。我们的目标是开发和验证一种人工智能算法，该算法可以区分MIS-C、川崎和其他类似的发热性疾病，并有助于急诊科和急诊室患者的诊断。在这项回顾性模型开发和验证研究中，我们开发了一种名为KIDMATCH（儿童川崎病与多系统炎症综合征）的深度学习算法，使用患者年龄，五种经典的临床川崎病体征和17种实验室测量值。所有特征均在2009年1月1日至2019年12月31日期间在圣地亚哥Rady儿童医院（美国加利福尼亚州）诊断为川崎或其他发热性疾病的患者进行初始评估时前瞻性收集。对于MIS-C患者，从2020年5月7日至2021年7月20日期间在Rady儿童医院、哈特福德康涅狄格儿童医疗中心（CT，美国）和洛杉矶儿童医院（CA，美国）的患者中收集了相同的数据。我们训练了一个由前馈神经网络组成的两阶段模型，以区分MIS-C患者和没有MIS-C的患者，然后区分川崎和其他发热性疾病。在使用分层十重交叉验证对算法进行内部验证后，我们采用了一个适形预测框架来标记具有错误数据或分布偏移的患者。我们最终在2020年4月22日至2021年7月21日期间入组的MIS-C患者中对KIDMATCH进行了外部验证，这些患者来自波士顿儿童医院（MA，美国）、国立儿童医院（华盛顿，DC，美国）和由14家美国医院组成的CHARMS研究组联盟。确定了2009年1月1日至2021年6月7日期间在Rady儿童医院诊断为MIS-C（n=69）、川崎（n=775）或其他发热性疾病（n=673）的1517例患者进行内部验证，5月7日，康涅狄格州儿童医疗中心和洛杉矶儿童医院分别增加了16名和50名MIS-C患者，2020年，2021年7月20日。KIDMATCH在第一阶段和第二阶段的内部验证期间，接受者工作特征曲线下面积中位数分别为98.8%（IQR 98.0 - 99.3）和96.0%（95.6 - 97.2）。我们对来自波士顿儿童医院（n=50）、国立儿童医院（n=42）和14家美国医院的CHARMS研究组联盟（n=83）的175名MIS-C患者进行了KIDMATCH外部验证。KIDMATCH对MIS-C患者的外部验证正确分类了81例患者中的76例（准确率94%，两例被适形预测拒绝）来自CHARMS研究小组联盟的14家医院，49名患者中的47名（96%的准确性，一个被适形预测拒绝），来自波士顿儿童医院，40名患者中有36名（90%的准确性，两个拒绝适形预测）从儿童的国家医院。KIDMATCH有可能帮助一线临床医生区分MIS-C、川崎和其他类似的发热性疾病，以便及时治疗并预防严重并发症。美国尤尼斯·肯尼迪·施莱佛国家儿童健康和人类发展研究所、美国国家心脏、肺和血液研究所、美国以患者为中心的结果研究所、美国国家医学图书馆、McCance基金会以及Gordon和Marilyn Macklin基金会。
Multisystem inflammatory syndrome in children (MIS-C) is a novel disease that was identified during the COVID-19 pandemic and is characterised by systemic inflammation following SARS-CoV-2 infection. Early detection of MIS-C is a challenge given its clinical similarities to Kawasaki disease and other acute febrile childhood illnesses. We aimed to develop and validate an artificial intelligence algorithm that can distinguish among MIS-C, Kawasaki disease, and other similar febrile illnesses and aid in the diagnosis of patients in the emergency department and acute care setting. In this retrospective model development and validation study, we developed a deep-learning algorithm called KIDMATCH (Kawasaki Disease vs Multisystem Inflammatory Syndrome in Children) using patient age, the five classic clinical Kawasaki disease signs, and 17 laboratory measurements. All features were prospectively collected at the time of initial evaluation from patients diagnosed with Kawasaki disease or other febrile illness between Jan 1, 2009, and Dec 31, 2019, at Rady Children's Hospital in San Diego (CA, USA). For patients with MIS-C, the same data were collected from patients between May 7, 2020, and July 20, 2021, at Rady Children's Hospital, Connecticut Children's Medical Center in Hartford (CT, USA), and Children's Hospital Los Angeles (CA, USA). We trained a two-stage model consisting of feedforward neural networks to distinguish between patients with MIS-C and those without and then those with Kawasaki disease and other febrile illnesses. After internally validating the algorithm using stratified tenfold cross-validation, we incorporated a conformal prediction framework to tag patients with erroneous data or distribution shifts. We finally externally validated KIDMATCH on patients with MIS-C enrolled between April 22, 2020, and July 21, 2021, from Boston Children's Hospital (MA, USA), Children's National Hospital (Washington, DC, USA), and the CHARMS Study Group consortium of 14 US hospitals. 1517 patients diagnosed at Rady Children's Hospital between Jan 1, 2009, and June 7, 2021, with MIS-C (n=69), Kawasaki disease (n=775), or other febrile illnesses (n=673) were identified for internal validation, with an additional 16 patients with MIS-C included from Connecticut Children's Medical Center and 50 from Children's Hospital Los Angeles between May 7, 2020, and July 20, 2021. KIDMATCH achieved a median area under the receiver operating characteristic curve during internal validation of 98·8% (IQR 98·0–99·3) in the first stage and 96·0% (95·6–97·2) in the second stage. We externally validated KIDMATCH on 175 patients with MIS-C from Boston Children's Hospital (n=50), Children's National Hospital (n=42), and the CHARMS Study Group consortium of 14 US hospitals (n=83). External validation of KIDMATCH on patients with MIS-C correctly classified 76 of 81 patients (94% accuracy, two rejected by conformal prediction) from 14 hospitals in the CHARMS Study Group consortium, 47 of 49 patients (96% accuracy, one rejected by conformal prediction) from Boston Children's Hospital, and 36 of 40 patients (90% accuracy, two rejected by conformal prediction) from Children's National Hospital. KIDMATCH has the potential to aid front-line clinicians to distinguish between MIS-C, Kawasaki disease, and other similar febrile illnesses to allow prompt treatment and prevent severe complications. US Eunice Kennedy Shriver National Institute of Child Health and Human Development, US National Heart, Lung, and Blood Institute, US Patient-Centered Outcomes Research Institute, US National Library of Medicine, the McCance Foundation, and the Gordon and Marilyn Macklin Foundation.