Evidence map›Paper›PMID 41642845›Full record

ArticlePloS one2026

Evaluation of oxygenation indices incorporating SpO₂ and PEEP for assessing ARDS severity: Evidence from the MIMIC-IV and eICU collaborative research database v2.0 databases.

Zekun Wei, Cunyang Li, Zhiyun Liu, Bolin Wang, Can Wang, Yang Liu, Tejin Ba, Li Kong, Feihu Zhang

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

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5 · Who and what money

Authors and funding

9 authors.

Zekun WeiThe First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China.
Cunyang LiThe First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China.
Zhiyun LiuThe First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China.
Bolin WangDepartment of Emergency Center, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China.
Can WangDepartment of Emergency Center, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China.
Yang LiuDepartment of Emergency Center, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China.
Tejin BaDepartment of Emergency and Critical Care Medicine, International Mongolian Medical Hospital of Inner Mongolia Autonomous Region, Hohhot, China.
Li KongDepartment of Emergency Center, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China.ORCID https://orcid.org/0009-0007-7267-2438
Feihu ZhangDepartment of Emergency Center, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe in-hospital mortality of acute respiratory distress syndrome can reach 35-45%, with patients requiring a more convenient and accurate way to assess the disease condition, which can change even more rapidly in patients undergoing mechanical ventilation in intensive care units.

methodsEligible patients in MIMIC-IV v3.0and eICU Collaborative Research Database v2.0were screened by the Berlin definition to examine the comparison of the diagnostic abilities of SpO2*10/FiO2*PEEP (S/F*P), PaO2*10/FiO2*PEEP (P/F*P), and SpO2/ FiO2 (S/F), with nine types of machine learning performed on S/F*P for 10 cross-validations, validating the diagnostic ability of the models for ARDS patients.

resultsROC_AUC = 0.700(95 CI:0.624 ~ 0.777) for S/F, ROC_AUC = 0.720(95 CI:0.668 ~ 0.772) for P/F*P, and ROC_AUC = 0.761(95 CI:0.693 ~ 0.830) for S/F*P showed that S/F had a better fit in diagnosing ARDS with slightly inferior efficacy to P/F*P, had superior diagnostic efficacy after incorporating peep into S/F, and S/F*P showed good diagnostic efficacy in 9 machine learning and 10 cross-validations. In terms of predicting the prognosis of patients, the ability of S/F*P is not as good as S/F, but the grading of S/F*P has a more positive significance for the evaluation of the prognosis of patients.

conclusionS/F*P provides a more convenient judgment for mechanically ventilated patients, avoiding the phenomenon of clinical diagnosis of PEEP and oxygenation index separation as much as possible, minimizing invasive operation of patients and improving the selection of ARDS treatment modalities. Therefore, S/F*P provides a reference for the early treatment of ARDS in the clinic to improve the resource allocation in the ICU and reduce the mortality of patients, Given all patients had ARDS diagnosis, this study evaluated relative diagnostic performance among indices rather than disease vs. non-disease discrimination.

Indexed as

OxygenPositive-Pressure RespirationRespiratory Distress SyndromeAgedDatabases, FactualFemaleHumansIntensive Care UnitsMachine LearningMiddle AgedROC CurveSeverity of Illness IndexOxygen

Identifiers

PMID41642845
PMCPMC12875482

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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.