OBJECTIVES: To evaluate the correlation of mean arterial oxygen tension (PaO₂) during the first 24 h following intensive care unit (ICU) admission with mortality in critically ill patients with acute kidney injury (AKI) and determine the optimal PaO₂ threshold for devising oxygen therapy strategies for these patients. METHODS: We collected the clinical data of ICU patients with AKI from the MIMIC-IV database. Based on the optimal first 24-h PaO₂ threshold determined by receiver operating characteristic (ROC) curve analysis and the Youden index maximization principle, we classified the patients into hyperoxia group (with PaO₂ ≥137.029 mmHg) and hypoxemia group (PaO₂<137.029 mm Hg). Multivariable logistic regression and propensity score matching were used to evaluate the correlation of first 24-h PaO₂ levels with in-hospital mortality of the patients. RESULTS: Among the 18 335 patients, 46.7% were in the hyperoxia group, who had an overall mortality rate of 16.9%. The optimal PaO₂ threshold (137.029 mm Hg) had a sensitivity of 78.3%, a specificity of 63.7%, and an AUC of 0.76 (95% CI: 0.74=0.78). Hyperoxia within the first 24 h after ICU admission was associated with a significantly lower in-hospital mortality (OR=0.78) and 90-day mortality (OR=0.77), particularly in stage 1 AKI patients. A non-linear relationship was identified between PaO₂ and mortality of the patients (P<0.001). Kaplan-Meier survival curves indicated a significantly increased 90-day survival rate in the patients in hyperoxia group (P<0.001), who also had shorter durations of mechanical ventilation, less vasopressor use, and shorter lengths of hospital/ICU stay. CONCLUSIONS Maintenance of a PaO₂ level ≥137.029 mmHg within 24 h after ICU admission may improve clinical outcomes of critically ill AKI patients, which underscores the importance of targeted oxygen delivery in ICU care.
Humans ; Acute Kidney Injury/blood* ; Male ; Female ; Middle Aged ; Intensive Care Units ; Aged ; Oxygen/blood* ; Hospital Mortality ; Partial Pressure ; Adult ; Databases, Factual

Objective To explore the applicable conditions of the Cox-TEL(Cox PH-Taylor expansion adjustment for long-term survival data)method for analysis of survival data that contain cured patients.Methods The simulated survival data method based on Weibull distribution was used to simulate and generate the survival data with different cure rates,censored rates,and cure rate differences.The Cox-TEL method was used for analysis of the generated simulation data,and its performance was evaluated by calculating its type I error and power.Results Almost all the type I error of the hazard ratios(HRs)obtained by the Cox-TEL method under different conditions were slightly greater than 0.05,and this method showed a good test power for estimating the HRs for data with a large sample size and a large difference in proportions(DPs).For the data of cured patients,the type I error of the DPs obtained by the Cox-TEL method was well around 0.05,and its test power was robust in most of the scenarios.Conclusion The Cox-TEL method is effective for analyzing data of uncured patients and obtaining reliable HRs for most of the survival data with a sample size,a low censored rates,and a large difference in cure rates.The method is capable of accurately estimating the DPs regardless of the sample size,censored rates,or the cure rates.

Objective To explore the applicable conditions of the Cox-TEL(Cox PH-Taylor expansion adjustment for long-term survival data)method for analysis of survival data that contain cured patients.Methods The simulated survival data method based on Weibull distribution was used to simulate and generate the survival data with different cure rates,censored rates,and cure rate differences.The Cox-TEL method was used for analysis of the generated simulation data,and its performance was evaluated by calculating its type I error and power.Results Almost all the type I error of the hazard ratios(HRs)obtained by the Cox-TEL method under different conditions were slightly greater than 0.05,and this method showed a good test power for estimating the HRs for data with a large sample size and a large difference in proportions(DPs).For the data of cured patients,the type I error of the DPs obtained by the Cox-TEL method was well around 0.05,and its test power was robust in most of the scenarios.Conclusion The Cox-TEL method is effective for analyzing data of uncured patients and obtaining reliable HRs for most of the survival data with a sample size,a low censored rates,and a large difference in cure rates.The method is capable of accurately estimating the DPs regardless of the sample size,censored rates,or the cure rates.