Clustering out-of-hospital cardiac arrest patients with non-shockable rhythm by machine learning latent class analysis

被引：7

作者：

Okada, Yohei ^{[1
,2
]}

Komukai, Sho ^{[3
]}

Kitamura, Tetsuhisa ^{[4
]}

Kiguchi, Takeyuki ^{[5
]}

Irisawa, Taro ^{[6
]}

Yamada, Tomoki ^{[7
]}

Yoshiya, Kazuhisa ^{[8
]}

Park, Changhwi ^{[9
]}

Nishimura, Tetsuro ^{[10
]}

Ishibe, Takuya ^{[11
]}

Yagi, Yoshiki ^{[12
]}

Kishimoto, Masafumi ^{[13
]}

Inoue, Toshiya ^{[14
]}

Hayashi, Yasuyuki ^{[15
]}

Sogabe, Taku ^{[16
]}

Morooka, Takaya ^{[17
]}

Sakamoto, Haruko ^{[18
]}

Suzuki, Keitaro ^{[19
]}

Nakamura, Fumiko ^{[20
]}

Matsuyama, Tasuku ^{[21
]}

Nishioka, Norihiro ^{[1
]}

Kobayashi, Daisuke ^{[1
]}

Matsui, Satoshi ^{[4
]}

Hirayama, Atsushi ^{[22
]}

Yoshimura, Satoshi ^{[1
]}

Kimata, Shunsuke ^{[1
]}

Shimazu, Takeshi

Ohtsuru, Shigeru ^{[2
]}

Iwami, Taku ^{[1
]}

机构：

[1] Kyoto Univ, Sch Publ Hlth, Dept Prevent Serv, Kyoto, Japan

[2] Kyoto Univ, Grad Sch Med, Dept Primary Care & Emergency Med, Kyoto, Japan

[3] Osaka Univ, Grad Sch Med, Dept Integrated Med, Div Biomed Stat, Suita, Osaka, Japan

[4] Osaka Univ, Grad Sch Med, Div Environm Med & Populat Sci, Dept Social & Environm Med, Osaka, Japan

[5] Osaka Gen Med Ctr, Crit Care & Trauma Ctr, Osaka, Japan

[6] Osaka Univ, Dept Traumatol & Acute Crit Med, Grad Sch Med, Suita, Japan

[7] Osaka Police Hosp, Emergency & Crit Care Med Ctr, Osaka, Japan

[8] Kansai Med Univ, Takii Hosp, Dept Emergency & Crit Care Med, Moriguchi, Osaka, Japan

[9] Tane Gen Hosp, Dept Emergency Med, Osaka, Japan

[10] Osaka City Univ, Dept Crit Care Med, Osaka, Japan

[11] Kindai Univ, Dept Emergency & Crit Care Med, Sch Med, Osaka, Japan

[12] Osaka Mishima Emergency Crit Care Ctr, Takatsuki, Osaka, Japan

[13] Osaka Prefectural Nakakawachi Med Ctr Acute Med, Higashiosaka, Osaka, Japan

[14] Senshu Trauma & Crit Care Ctr, Osaka, Japan

[15] Saiseikai Senri Hosp, Senri Crit Care Med Ctr, Suita, Osaka, Japan

[16] Osaka Natl Hosp, Traumatol & Crit Care Med Ctr, Natl Hosp Org, Osaka, Japan

[17] Osaka City Gen Hosp, Emergency & Crit Care Med Ctr, Osaka, Japan

[18] Osaka Red Cross Hosp, Dept Pediat, Osaka, Japan

[19] Kishiwada Tokushukai Hosp, Emergency & Crit Care Med Ctr, Osaka, Japan

[20] Kansai Med Univ, Dept Emergency & Crit Care Med, Hirakata, Osaka, Japan

[21] Kyoto Prefectural Univ Med, Dept Emergency Med, Kyoto, Japan

[22] Osaka Univ, Dept Social & Environm Med, Publ Hlth, Grad Sch Med, Osaka, Japan

来源：

ACUTE MEDICINE & SURGERY | 2022年 / 9卷 / 01期

关键词：

Asystole; cardiac arrest; clustering; latent class analysis; pulseless electrical activity; subphenotype; AMERICAN-HEART-ASSOCIATION; INTERNATIONAL LIAISON COMMITTEE; EUROPEAN-RESUSCITATION-COUNCIL; PUBLIC-ACCESS DEFIBRILLATION; HEALTH-CARE PROFESSIONALS; CHRONIC KIDNEY-DISEASE; CARDIOPULMONARY-RESUSCITATION; STROKE-FOUNDATION; TASK-FORCE; OUTCOME REPORTS;

D O I：

10.1002/ams2.760

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

Aim: We aimed to identify subphenotypes among patients with out-of-hospital cardiac arrest (OHCA) with initial non-shockable rhythm by applying machine learning latent class analysis and examining the associations between subphenotypes and neurological outcomes. Methods: This study was a retrospective analysis within a multi-institutional prospective observational cohort study of OHCA patients in Osaka, Japan (the CRITICAL study). The data of adult OHCA patients with medical causes and initial non-shockable rhythm presenting with OHCA between 2012 and 2016 were included in machine learning latent class analysis models, which identified subphenotypes, and patients who presented in 2017 were included in a dataset validating the subphenotypes. We investigated associations between subphenotypes and 30-day neurological outcomes. Results: Among the 12,594 patients in the CRITICAL study database, 4,849 were included in the dataset used to classify subphenotypes (median age: 75 years, 60.2% male), and 1,465 were included in the validation dataset (median age: 76 years, 59.0% male). Latent class analysis identified four subphenotypes. Odds ratios and 95% confidence intervals for a favorable 30-day neurological outcome among patients with these subphenotypes, using group 4 for comparison, were as follows; group 1, 0.01 (0.001-0.046); group 2, 0.097 (0.051-0.171); and group 3, 0.175 (0.073-0.358). Associations between subphenotypes and 30-day neurological outcomes were validated using the validation dataset. Conclusion: We identified four subphenotypes of OHCA patients with initial non-shockable rhythm. These patient subgroups presented with different characteristics associated with 30-day survival and neurological outcomes.

引用

页数：11

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