OBJECTIVE: To investigate the predictive value of oxygenation index (PaO₂/FiO₂) at intensive care unit (ICU) admission on 30-day mortality in patients with sepsis. METHODS: A retrospective study was conducted. Patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to October 2023 were enrolled. The demographic information, comorbidities, sites of infection, vital signs and laboratory test indicators at the time of admission to the ICU, disease severity scores within 24 hours of admission to the ICU, treatment process and prognostic indicators were collected. According to the PaO₂/FiO₂ at ICU admission, patients were divided into Q1 group (PaO₂/FiO₂ of 4.1-16.4 cmHg, 1 cmHg ≈ 1.33 kPa), Q2 group (PaO₂/FiO₂ of 16.5-22.6 cmHg), Q3 group (PaO₂/FiO₂ of 22.7-32.9 cmHg), and Q4 group (PaO₂/FiO₂ of 33.0-94.8 cmHg). Differences in the indicators across the four groups were compared. Multifactorial Cox regression analysis was used to assess the relationship between PaO₂/FiO₂ and 30-day mortality of patients with sepsis. The predictive value of PaO₂/FiO₂, sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) on 30-day prognosis of patients with sepsis was analyzed by receiver operator characteristic curve (ROC curve). RESULTS: A total of 1 711 patients with sepsis were enrolled, including 428 patients in Q1 group, 424 patients in Q2 group, 425 patients in Q3 group, and 434 patients in Q4 group. 622 patients died at 30-day, the overall 30-day mortality was 36.35%. There were statistically significant differences in age, body mass index (BMI), history of smoking, history of alcohol consumption, admission heart rate, respiratory rate, APACHE II score, SOFA score, Glasgow coma score (GCS), site of infection, Combined chronic obstructive pulmonary disease (COPD), blood lactic acid (Lac), prothrombin time (PT), albumin (Alb), total bilirubin (TBil), pH, proportion of mechanical ventilation, duration of mechanical ventilation, proportion of vasoactive medication used, and maximal concentration, length of ICU stay, hospital stay, incidence of acute kidney injury, in-hospital mortality, 30-day mortality among the four groups. Multivariate Cox regression analysis showed that after adjusting for confounding factors, for every 1 cmHg increase in PaO₂/FiO₂ at ICU admission, the 30-day mortality risk decreased by 2% [hazard ratio (HR) = 0.98, 95% confidence interval (95%CI) was 0.98-0.99, P < 0.001]. The 30-day mortality risk in the Q4 group was reduced compared with the Q1 group by 41% (HR = 0.59, 95%CI was 0.46-0.76, P < 0.001). The fitted curve showed that a curvilinear relationship between PaO₂/FiO₂ and 30-day mortality after adjustment for confounders. In the inflection point analysis, for every 1 cmHg increase in PaO₂/FiO₂ at PaO₂/FiO₂ < 28.55 cmHg, the risk of 30-day death in sepsis patients was reduced by 5% (HR = 0.95, 95%CI was 0.94-0.97, P < 0.001); when PaO₂/FiO₂ ≥ 28.55 cmHg, there was no statistically significant association between PaO2/FiO2 and the increase in the risk of 30-day death in sepsis (HR = 1.01, 95%CI was 0.99-1.02, P = 0.512). ROC curve analysis showed that the area under the curve (AUC) for the prediction of 30-day mortality by admission PaO₂/FiO₂ in ICU sepsis patients was 0.650, which was lower than the predictive ability of the SOFA score (AUC = 0.698) and APACHE II score (AUC = 0.723). CONCLUSION In patients with sepsis, PaO₂/FiO₂ at ICU admission is strongly associated with 30-day mortality risk, alerting healthcare professionals to pay attention to patients with low PaO₂/FiO₂ for timely interventions.
Humans ; Sepsis/mortality* ; Intensive Care Units ; Retrospective Studies ; Prognosis ; Hospital Mortality ; Oxygen ; Male ; Predictive Value of Tests ; Female ; Middle Aged ; Aged

OBJECTIVE: To analyze the risk factors of in-hospital death in patients with sepsis in the intensive care unit (ICU) based on machine learning, and to construct a predictive model, and to explore the predictive value of the predictive model. METHODS: The clinical data of patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to April 2021 were retrospectively analyzed,including demographic information, vital signs, complications, laboratory examination indicators, diagnosis, treatment, etc. Patients were divided into death group and survival group according to whether in-hospital death occurred. The cases in the dataset (70%) were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. Based on seven machine learning models including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), a prediction model for in-hospital mortality of sepsis patients was constructed. The receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the seven models from the aspects of identification, calibration and clinical application, respectively. In addition, the predictive model based on machine learning was compared with the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) models. RESULTS: A total of 741 patients with sepsis were included, of which 390 were discharged after improvement, 351 died in hospital, and the in-hospital mortality was 47.4%. There were significant differences in gender, age, APACHE II score, SOFA score, Glasgow coma score (GCS), heart rate, oxygen index (PaO₂/FiO₂), mechanical ventilation ratio, mechanical ventilation time, proportion of norepinephrine (NE) used, maximum NE, lactic acid (Lac), activated partial thromboplastin time (APTT), albumin (ALB), serum creatinine (SCr), blood urea nitrogen (BUN), blood uric acid (BUA), pH value, base excess (BE), and K⁺ between the death group and the survival group. ROC curve analysis showed that the area under the curve (AUC) of RF, XGBoost, LR, ANN, DT, SVM, KNN models, SOFA score, and APACHE II score for predicting in-hospital mortality of sepsis patients were 0.871, 0.846, 0.751, 0.747, 0.677, 0.657, 0.555, 0.749 and 0.760, respectively. Among all the models, the RF model had the highest precision (0.750), accuracy (0.785), recall (0.773), and F1 score (0.761), and best discrimination. The calibration curve showed that the RF model performed best among the seven machine learning models. DCA curve showed that the RF model exhibited greater net benefit as well as threshold probability compared to other models, indicating that the RF model was the best model with good clinical utility. CONCLUSIONS The machine learning model can be used as a reliable tool for predicting in-hospital mortality in sepsis patients. RF models has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention to reduce mortality.
Humans ; Hospital Mortality ; Retrospective Studies ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Intensive Care Units

Objective:To construct a nomogram model predicting the occurrence of acute kidney injury (AKI) in patients with sepsis in the intensive care unit (ICU), and to verify its validity for early prediction.Methods:Sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to December 2021 were retrospectively included, and those who met the inclusion criteria were randomly divided into training and validation sets at a ratio of 7:3. Univariate and multivariate logistic regression models were used to identify independent risk factors for AKI in patients with sepsis, and a nomogram was constructed based on the independent risk factors. Calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the nomogram model.Results:741 patients with sepsis were included in the study, 335 patients developed AKI within 7 d of ICU admission, with an AKI incidence of 45.1%. Randomization was performed in the training set ( n=519) and internal validation set ( n=222). Multivariate logistic analysis revealed that acute physiology and chronic health status score Ⅱ, sequential organ failure score, serum lactate, calcitoninogen, norepinephrine dose, urea nitrogen, and neutrophil percentage were independent factors influencing the occurrence of AKI, and a nomogram model was constructed by combining these variables. In the training set, the AUC of the nomogram model ROC was 0.875 (95% CI: 0.767-0.835), the calibration curve showed consistency between the predicted and actual probabilities, and the DCA showed a good net clinical benefit. In the internal validation set, the nomogram model had a similar predictive value for AKI (AUC=0.871, 95% CI: 0.734-0.854). Conclusions:A nomogram model constructed based on the critical care score at admission combined with inflammatory markers can be used for the early prediction of AKI in sepsis patients in the ICU. The model is helpful for clinicians early identify AKI in sepsis patients.

Objective:To analyze the risk factors of acute kidney injury (AKI) in patients with septic shock in intensive care unit (ICU), construct a predictive model, and explore the predictive value of the predictive model.Methods:The clinical data of patients with septic shock who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical College from April 2015 to June 2019 were retrospectively analyzed. According to whether the patients had AKI within 7 days of admission to the ICU, they were divided into AKI group and non-AKI group. 70% of the cases were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. XGBoost model was used to integrate relevant parameters to predict the risk of AKI in patients with septic shock. The predictive ability was assessed through receiver operator characteristic curve (ROC curve), and was correlated with acute physiology and chronic health evaluationⅡ(APACHEⅡ), sequential organ failure assessment (SOFA), procalcitonin (PCT) and other comparative verification models to verify the predictive value.Results:A total of 303 patients with septic shock were enrolled, including 153 patients with AKI and 150 patients without AKI. The incidence of AKI was 50.50%. Compared with the non-AKI group, the AKI group had higher APACHEⅡscore, SOFA score and blood lactate (Lac), higher dose of norepinephrine (NE), higher proportion of mechanical ventilation, and tachycardiac. In the XGBoost prediction model of AKI risk in septic shock patients, the top 10 features were serum creatinine (SCr) level at ICU admission, NE use, drinking history, albumin, serum sodium, C-reactive protein (CRP), Lac, body mass index (BMI), platelet count (PLT), and blood urea nitrogen (BUN) levels. Area under the ROC curve (AUC) of the XGBoost model for predicting the risk of AKI in patients with septic shock was 0.816, with a sensitivity of 73.3%, a specificity of 71.7%, and an accuracy of 72.5%. Compared with the APACHEⅡscore, SOFA score and PCT, the performance of the model improved significantly. The calibration curve of the model showed that the goodness of fit of the XGBoost model was higher than the other scores (the calibration curve had the lowest score, with a score of 0.205).Conclusion:Compared with the commonly used clinical scores, the XGBoost model can more accurately predict the risk of AKI in patients with septic shock, which helps to make appropriate diagnosis, treatment and follow-up strategies while predicting the prognosis of patients.

Objective:To explore the effect of nursing model based on nursing quality evaluation system in patients with severe acute pancreatitis.Methods:From July 2019 to June 2020, convenience sampling was used to select 80 patients with severe acute pancreatitis admitted to the Affiliated Hospital of Jining Medical College as the research object. According to the random number table method, the patients were divided into the observation group and the control group, with 40 cases in each group. The control group implemented the conventional nursing model, and the observation group conducted the nursing model based on the nursing quality evaluation system on this basis. The treatment compliance and nursing satisfaction of the two groups of patients were compared.Results:The treatment compliance and nursing satisfaction of the observation group were higher than those of the control group, and the differences were statistically significant ( P<0.05) . Conclusions:The nursing model based on the nursing quality evaluation system can improve the treatment compliance of patients with severe acute pancreatitis and their satisfaction with nursing work, and it is worthy of clinical application.