Initial arterial pH predicts survival of out-of-hospital cardiac arrest in South Korea

• Published in: Acute and critical care Vol. 40; no. 3; pp. 444 - 451
• Main Authors: Jeong, Daun, Shin, Sang Do, Shin, Tae Gun, Lee, Gun Tak, Park, Jong Eun, Hwang, Sung Yeon, Choi, Jin-Ho
• Format: Journal Article
• Language: English
• Published: Korea (South) 대한중환자의학회 01.08.2025
• Subjects: 마취과학; resuscitation; machine learning; prognosis; blood pH; hydrogen-ion concentration; out-of-hospital cardiac arrest
• ISSN: 2586-6052; 2586-6060; 2586-6060
• DOI: 10.4266/acc.001050

Background: Arterial pH reflects both metabolic and respiratory distress in cardiac arrest and has prognostic implications. However, it was excluded from the 2024 update of the Utstein out-of-hospital cardiac arrest (OHCA) registry template. We investigated the rationale for including arterial pH into models predicting clinical outcomes.Methods: Data were sourced from the Korean Cardiac Arrest Research Consortium, a nationwide OHCA registry (NCT03222999). Prediction models were constructed using logistic regression, random forest, and eXtreme Gradient Boosting frameworks. Each framework included three model types: pH, low-flow time, and combined models. Then the area under the receiver operating characteristic curve (AUROC) of each predicting model was compared. The primary outcome was 30-day death or neurologically unfavorable status (cerebral performance category ≥3).Results: Among the 15,765 patients analyzed, 92.2% experienced death or unfavorable neurological outcomes. The predicting performance of the models including pH (AUROC=0.92–0.94) were comparable to the models including low-flow time in all frameworks (0.93–0.94) (all P>0.05). Inclusion of pH into low-flow time models consistently showed higher AUROCs than individual models in all frameworks (AUROC=0.93–0.95, all P<0.05). Conclusions: The predicting performance of models including arterial pH was comparable to models including low-flow time, and addition of arterial pH into low-flow time models could increase the performance of the models.