Evidence map›Paper›PMID 42232069›Full record

ArticleResuscitation plus2026

Development and preliminary validation of an explainable machine learning model for early ROSC detection during chest compression in a swine model of cardiac arrest.

Jing Yang, Jing Shi, Hanqi Tang, Xingliang Jin, Fei Zhang, Xinming Mei, Wenlin Hao, Ruoxue Wu, Yan Li, Huadong Zhu and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Resuscitation plus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Jing YangEmergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Jing ShiEmergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Hanqi TangEmergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Xingliang JinShenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen, China.
Fei ZhangShenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen, China.
Xinming MeiShenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen, China.
Wenlin HaoEmergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Ruoxue WuEmergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Yan LiEmergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Huadong ZhuEmergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Shengyong XuEmergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Jun XuEmergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to develop a novel multi-parameter-based system for the early and accurate identification of restoration of spontaneous circulation (ROSC) during chest compression using a machine learning (ML) approach in cardiac arrest (CA) animal models. Methods: Thirteen CA mini-swine models were established by inducing ventricular fibrillation, followed by cardiopulmonary resuscitation. Electrocardiograph (ECG), end-tidal carbon dioxide (ETCO Results: Among the eight ML models, the Adaboost model performed the best. The Adaboost model accurately identified ROSC without the interruption of chest compression [AUROC 0.891, sensitivity 0.813, specificity 0.957, PPV 0.929, F1-score 0.867 and leading time 18.2 s]. SHAP analysis identified the top dominant predictors of ROSC: ECG high-frequency energy, ECG mean slope, heart rate, respiration rate, perfusion index, and median ETCO Conclusion: A machine learning model integrating non-invasive ECG, ETCO

Indexed as

Cardiopulmonary resuscitationElectrocardiogramEnd-tidal carbon dioxideMachine learningPhotoplethysmography waveformReturn of spontaneous circulation

Identifiers

PMID42232069
PMCPMC13223806

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.