Clinical Phenotyping of Out-of-Hospital Cardiac Arrest Patients With Shockable Rhythm ― Machine Learning-Based Unsupervised Cluster Analysis ―
Yohei Okada, Sho Komukai, Tetsuhisa Kitamura, Takeyuki Kiguchi, Taro Irisawa, Tomoki Yamada, Kazuhisa Yoshiya, Changhwi Park, Tetsuro Nishimura, Takuya Ishibe, Yoshiki Yagi, Masafumi Kishimoto, Toshiya Inoue, Yasuyuki Hayashi, Taku Sogabe, Takaya Morooka, Haruko Sakamoto, Keitaro Suzuki, Fumiko Nakamura, Tasuku Matsuyama, Norihiro Nishioka, Daisuke Kobayashi, Satoshi Matsui, Atsushi Hirayama, Satoshi Yoshimura, Shunsuke Kimata, Takeshi Shimazu, Shigeru Ohtsuru, Taku Iwami
Abstract
BACKGROUND: The hypothesis of this study is that latent class analysis could identify the subphenotypes of out-of-hospital cardiac arrest (OHCA) patients associated with the outcomes and allow us to explore heterogeneity in the effects of extracorporeal cardiopulmonary resuscitation (ECPR). METHODS AND RESULTS: ) value of blood gas assessment, cardiac rhythm on hospital arrival, and estimated glomerular filtration rate. The 30-day survival outcomes were varied across the groups: 15.7% in Group 1; 30.7% in Group 2; and 85.9% in Group 3. Further, the association between ECPR and 30-day survival outcomes by subphenotype groups in the development dataset was as varied. These results were validated using the validation dataset. CONCLUSIONS: The latent class analysis identified 3 subphenotypes with different survival outcomes and potential heterogeneity in the effects of ECPR.