A study of the trajectory of arterial oxygen tension dynamics after successful resuscitation of cardiac arrest patients and its impact on prognosis.
10.3760/cma.j.cn121430-20241015-00843
- Author:
Jie HU
1
,
2
;
Lei ZHONG
;
Dan ZONG
;
Jianhong LU
;
Bo XIE
;
Xiaowei JI
Author Information
1. Department of Critical Care Medicine, Huzhou Central Hospital, Huzhou 313000, China. Corresponding author: Ji Xiaowei, Email: oralwind@
2. com.
- Publication Type:Journal Article
- MeSH:
Humans;
Retrospective Studies;
Hospital Mortality;
Heart Arrest/blood*;
Prognosis;
Oxygen/blood*;
Intensive Care Units;
Cardiopulmonary Resuscitation;
Male;
Female;
Middle Aged
- From:
Chinese Critical Care Medicine
2025;37(9):843-847
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To construct a longitudinal trajectory model of arterial oxygen tension (PaO2) within 24 hours after cardiac arrest (CA).
METHODS:A retrospective cohort study was conducted. CA patients admitted to the ICU from 2014 to 2015 were selected from the eICU Collaborative Research Database (eICU-CRD). Data about patients' demographic characteristics, history of comorbidities, laboratory test indicators within 24 hours of intensive care unit (ICU) admission [including all PaO2 data and arterial carbon dioxide tension (PaCO2)], vasopressor use, and clinical outcomes were extracted from the database. The primary outcome variable was all-cause in-hospital mortality. Group-based trajectory model (GBTM) were built based on the changes in PaO2 within 24 hours of ICU admission, and patients were grouped according to their initial static PaO2 values upon ICU admission. Multivariable adjusted Poisson regression analysis was used to compare the in-hospital mortality risk among patients in different PaO2 dynamic trajectory groups. Sensitivity analyses were performed using multivariable logistic regression and multivariable adjusted Poisson regression without imputation of missing values.
RESULTS:A total of 3 866 CA patients were included. Three GBTM trajectory groups were identified based on PaO2 changes within 24 hours of ICU admission: Group-1 (low level first increased then decreased, 148 cases), Group-2 (sustained low level, 3 040 cases), and Group-3 (first high level then decreased, 678 cases). Significant differences were found among the three groups in age, body weight, maximum serum potassium, maximum PaCO2, minimum hemoglobin (Hb), vasopressor use, total hospitalization time, ICU stay, and hospital mortality. After incorporating variables with significant differences into the multivariable adjusted Poisson regression model, results showed that compared to Group-2 patients, patients in Group-1 and Group-3 had an increased risk of all-cause in-hospital mortality [Group-1 adjusted relative risk (aRR) = 1.20, 95% confidence interval (95%CI) was 1.02-1.41; Group-3 aRR = 1.11, 95%CI was 1.01-1.24]. Based on initial static PaO2 values at ICU admission, patients were divided into four groups: PaO2 < 100 mmHg (1 mmHg = 0.133 kPa; 1 217 cases), PaO2 100-200 mmHg (569 cases), PaO2 201-300 mmHg (547 cases), and PaO2 > 300 mmHg (1 082 cases). Multivariable adjusted Poisson regression analysis indicated a significant upward trend in aRR for the latter three groups compared to the PaO2 < 100 mmHg group. Sensitivity analyses revealed that compared to Group-2, patients in Group-1 and Group-3 had a significantly increased risk of all-cause in-hospital mortality (both P < 0.05).
CONCLUSIONS:Within 24 hours after return of spontaneous circulation in CA patients, PaO2 exhibits different dynamic trajectories, and patients with hyperoxia have an increased risk of in-hospital mortality.
