Construction and internal validation of a predictive model for early acute kidney injury in patients with sepsis / 中华急诊医学杂志

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.

Construction of a predictive model for in-hospital mortality of sepsis patients in intensive care unit based on machine learning / 中华危重病急救医学

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.

Detection of IL-2 and IL-6 in human breast tumor Bcap-37 cells treated by hematoporphyrin nonomethyl ether mediated photodynamic therapy / 肿瘤研究与临床

Objective To observe IL-2 and IL-6 changes in the breast tumor Bcap-37 cells reated by hematoporphyrin nonomethyl ether mediated photodynamic therapy (HMME-PDT).Methods Cells in logarithmic growth phase were collected among breast cancer cells cultured in conventional methods.According to blank control group or the experimental group (laser irradiation group,photosensitive agent group and HMME-PDT group),PDT in addition to HMME and HMME-PDT were conducted.The changes of IL-2,IL-6 were detected by radioimmunoassay.Results After HMME-PDT,IL-2 was increased as time passed.After 12,24 and 48 h,compared with IL-2 level in the control group,in laser irradiation group or photosensitive agent group,the levels of IL-2 in HMME-PDT group was significantly differences (P ＜ 0.05).But IL-6 levels decreased.The most obvious changes of IL-6 levels happened at 12h and 24h.There was significant differences between IL-6 in HMME-PDT group with the control group,laser irradiation group or photosensitive agent group (P ＜ 0.05).Conclusion HMME-PDT maybe have destruction effect by altering IL-2,IL-6 activity on breast tumor cells,which provides objective indicators for clinical patients to regulate immune function and auxiliary diagnosis.