OBJECTIVE: To assess the impact of albumin (Alb) administration on the prognosis of patients with acute kidney injury (AKI). METHODS: Clinical data of AKI patients in the intensive care unit (ICU) were retrospectively analyzed from the American Medical Information Mart of Intensive Care-IV (MIMIC-IV), including demographic data, acute physiology score (APS), comorbidities, vital signs, laboratory indicators, treatment status, ICU length of stay, and outcome indicators. The main outcome measure is ICU mortality. AKI patients were divided into Alb infusion group and Alb non infusion group based on whether they received Alb treatment. Multiple imputation was used to process missing data and eliminate variables that missing more than 30%. To ensure the stability of the results, propensity score matching (PSM) and inverse probability weighting (IPW) were used to correct the results. Using Kaplan-Meier survival curve and Cox proportional hazards regression model to evaluate the effect of Alb infusion on ICU survival rate in AKI patients. Perform subgroup analysis based on patient age, gender, and comorbidities to evaluate the prognostic effects of Alb on different patient subgroups. RESULTS: A total of 6 390 AKI patients were included, including 1 721 in the Alb infusion group and 4 669 in the Alb non infusion group. After adjusting for key covariates in the Cox regression model, compared with the Alb non infusion group, patients in the Alb infusion group were significantly younger in age, with APS III score, proportion of vasoactive drugs and continuous renal replacement therapy (CRRT) use, sepsis proportion, heart rate, respiratory frequency, aspartate aminotransferase (AST), alanine aminotransferase (ALT), creatinine (Cr), lactic acid (Lac), and arterial partial pressure of carbon dioxide (PaCO₂) levels significantly higher. The proportion of hypertension, myocardial infarction, and congestive heart failure, as well as blood pressure, urine output, platelet count (PLT), and Alb levels were significantly lower. The results of univariate and multivariate Cox regression analysis on the raw data showed that the risk of death in the Alb infusion group was significantly lower than that in the Alb non infusion group [hazard ratio (HR) = 0.69, 95% confidence interval (95%CI) was 0.60-0.80, all P < 0.05]. The results after propensity score matching (PSM) and inverse probability weighting (IPW) processing are consistent with the original data trend (both P < 0.05). The Kaplan-Meier survival curve showed that the cumulative survival rate during ICU stay in the Alb infusion group was significantly higher than that in the Alb non infusion group (24.48% vs. 12.17%, Log-Rank test: χ² = 74.26, P < 0.05). Subgroup analysis shows that Alb infusion has a more significant survival benefit for AKI patients who use vasoactive drugs, have concurrent sepsis, and do not have liver disease. CONCLUSION Albumin infusion can decrease the ICU mortality of AKI patients.
Humans ; Retrospective Studies ; Acute Kidney Injury/mortality* ; Prognosis ; Male ; Female ; Middle Aged ; Aged ; Intensive Care Units ; Albumins/therapeutic use* ; Proportional Hazards Models ; Adult ; Databases, Factual

Objective:To determine the motion detection uncertainty of the real-time CybeRay ? imaging system and patient intrafractional motion with thermoplastic mask-based immobilization. Methods:Real-time CybeRay ? imaging system was used for irradiation and treatment for head phantom and patients with brain tumors. All patients were immobilized with thermoplastic masks. Real-time imaging was delivered using kilovoltage projection images during radiotherapy. The detected patient motion data was collected from 5 head phantom measurements and 27 treatment fractions of 9 brain tumor patients admitted to Kaifeng Cancer Hospital. The accuracy and uncertainty of the motion monitoring system were determined. Results:The mean and standard deviation (SD) of the detected motion in the X, Y, and Z directions for phantom were (-0.02±0.41) mm, (-0.05±0.22) mm and (0.01±0.35) mm, respectively. The detected motion in the X, Y and Z directions for patents were (-0.13±0.48) mm, (-0.05±0.48) mm and (0.11±0.36) mm, respectively. After removing the motion detection uncertainty, the actual intrafractional motion of patients were (-0.11±0.25) mm, (0±0.43) mm and (0.10±0.08) mm in three directions, respectively. Conclusions:The uncertainty of real-time imaging-based motion monitoring system of CybeRay ? is less than 0.5 mm. It is feasible to apply thermoplastic masks for brain tumor patients in clinical practice, which can provide steady immobilization and limit the SD of patient intrafractional motion within 0.5 mm. Real-time imaging-based motion monitoring system of CybeRay ? is accurate for patient motion monitoring during frameless stereotactic radiosurgery/radiotherapy.

Objective To investigate the risk factors of 28-day mortality in septic patients and develop optimizing clinical prediction model based on machine learning algorithms.Methods Data from patients admitted to the department of intensive care unit(ICU)of the Affiliated Hospital of Nanjing University of Chinese Medicine from January 2019 to December 2023 were retrospectively analyzed.The data extracted included①gender,age,history of hypertension,diabetes,coronary heart disease,chronic obstructive pulmonary disease(COPD)and chronic kidney disease(CKD);②Vital signs and results of laboratory examination at admission were also collected,then acute physiology and chronic health evaluationⅡ(APACHEⅡ)score and sequential organ failure assessment(SOFA)score were calculated;③The other laboratory test results not included in APACHEⅡscore and SOFA score,such as blood lactate acid(Lac),alanine aminotransferase(AST),hemoglobin(Hb),procalcitonin(PCT),brian natriuretic peptide(BNP),C-reactive protein(CRP),activated partial thromboplastin time(APTT),D-dimer and troponin I(TNI)were also gathered.According to the 28-day survival,the patients were divided into a survival group and a death group.The difference of the clinical data and related loboratory indicators between the two groups of sepsis patients were compared.LASSO regression and Boruta algorithm were used to screen predictive variables.Models of Logistic regression(LG),neural network(NN)and light gradient boosting machine(LightGBM)were constructed.The data was divided into training set and verification set under a ratio of 7:3,and fivefold cross-validation was used to evaluate the stability of the models.Confusion matrix,receiver operator characteristic curve(ROC curve)and calibration curve were also used to assess the recognition ability and accuracy of three models.Decision curve analysis(DCA)was conducted to evaluate the models'utility in decision-making.Shapley additive explanations(SHAP)analysis was used to explain the best-performing model.Results A total of 426 patients were included in the study,of which 256 survived and 170 died.Compared with death group,the age(72.09±14.08 vs.76.88±11.32,P<0.05),COPD[11.33%(29/256)vs.20.00%(34/170)],CKD[20.31%(52/256)vs.31.77%(54/170)],Lac on admission[mmol/L:1.72(1.20,2.66)vs.2.25(1.60,3.50)],AST[U/L:32.00(18.00,59.75)vs.37.00(24.00,76.50)],CRP[mg/L:71.23(22.51,151.79)vs.87.00(37.00,173.36)],APACHEⅡscore(19.96±6.55 vs.22.83±6.92)and SOFA score[7(5,10)vs.9(5,12)]in surrial group were significantly decreased,the difference were statistically significant(all P<0.05).Age,APACHEⅡscore,Lac,PCT and CRP were revealed as independent predictors of 28-day mortality in sepsis by LASSO regression and Boruta algorithm,the above 5 variables were incorporated into the LG,NN and LightGBM models,and the five-fold cross-validation showed that the LightGBM model had the best stability.The confusion matrix,ROC curve and calibration curves of the 3 models were plotted,and the results showed that the F1 score of the 3 models were 0.61,0.63 and 0.74,respectively;area under the curve(AUC)was 0.68,0.74 and 0.87,respectively;the Log Loss was 0.62,0.41 and 0.34,respectively;and the Brier scores were 0.22,0.13 and 0.09,respectively,indicating that LightGBM model was optimal.DCA showed that LightGBM model had the greatest clinical net benefit.SHAP showed that the predicted results were in good agreement with the actual results.Conclusion The LightGBM model exhibited the best performance in predicting 28-day mortality in septic patients and has the potential to help clinicians identify high-risk patients and guide clinical decision-making.

Objective To investigate the risk factors of 28-day mortality in septic patients and develop optimizing clinical prediction model based on machine learning algorithms.Methods Data from patients admitted to the department of intensive care unit(ICU)of the Affiliated Hospital of Nanjing University of Chinese Medicine from January 2019 to December 2023 were retrospectively analyzed.The data extracted included①gender,age,history of hypertension,diabetes,coronary heart disease,chronic obstructive pulmonary disease(COPD)and chronic kidney disease(CKD);②Vital signs and results of laboratory examination at admission were also collected,then acute physiology and chronic health evaluationⅡ(APACHEⅡ)score and sequential organ failure assessment(SOFA)score were calculated;③The other laboratory test results not included in APACHEⅡscore and SOFA score,such as blood lactate acid(Lac),alanine aminotransferase(AST),hemoglobin(Hb),procalcitonin(PCT),brian natriuretic peptide(BNP),C-reactive protein(CRP),activated partial thromboplastin time(APTT),D-dimer and troponin I(TNI)were also gathered.According to the 28-day survival,the patients were divided into a survival group and a death group.The difference of the clinical data and related loboratory indicators between the two groups of sepsis patients were compared.LASSO regression and Boruta algorithm were used to screen predictive variables.Models of Logistic regression(LG),neural network(NN)and light gradient boosting machine(LightGBM)were constructed.The data was divided into training set and verification set under a ratio of 7:3,and fivefold cross-validation was used to evaluate the stability of the models.Confusion matrix,receiver operator characteristic curve(ROC curve)and calibration curve were also used to assess the recognition ability and accuracy of three models.Decision curve analysis(DCA)was conducted to evaluate the models'utility in decision-making.Shapley additive explanations(SHAP)analysis was used to explain the best-performing model.Results A total of 426 patients were included in the study,of which 256 survived and 170 died.Compared with death group,the age(72.09±14.08 vs.76.88±11.32,P<0.05),COPD[11.33%(29/256)vs.20.00%(34/170)],CKD[20.31%(52/256)vs.31.77%(54/170)],Lac on admission[mmol/L:1.72(1.20,2.66)vs.2.25(1.60,3.50)],AST[U/L:32.00(18.00,59.75)vs.37.00(24.00,76.50)],CRP[mg/L:71.23(22.51,151.79)vs.87.00(37.00,173.36)],APACHEⅡscore(19.96±6.55 vs.22.83±6.92)and SOFA score[7(5,10)vs.9(5,12)]in surrial group were significantly decreased,the difference were statistically significant(all P<0.05).Age,APACHEⅡscore,Lac,PCT and CRP were revealed as independent predictors of 28-day mortality in sepsis by LASSO regression and Boruta algorithm,the above 5 variables were incorporated into the LG,NN and LightGBM models,and the five-fold cross-validation showed that the LightGBM model had the best stability.The confusion matrix,ROC curve and calibration curves of the 3 models were plotted,and the results showed that the F1 score of the 3 models were 0.61,0.63 and 0.74,respectively;area under the curve(AUC)was 0.68,0.74 and 0.87,respectively;the Log Loss was 0.62,0.41 and 0.34,respectively;and the Brier scores were 0.22,0.13 and 0.09,respectively,indicating that LightGBM model was optimal.DCA showed that LightGBM model had the greatest clinical net benefit.SHAP showed that the predicted results were in good agreement with the actual results.Conclusion The LightGBM model exhibited the best performance in predicting 28-day mortality in septic patients and has the potential to help clinicians identify high-risk patients and guide clinical decision-making.

Objective To explore the disease management experience of patients with depression and their caregivers.Methods In February to July 2017, qualitative research method was adopted, and depth interviews were conducted among 16 patients with depression and 12 caregivers.Results After analysis, 4 themes were extracted. Emotion management: the patients and their caregivers were void of knowledge of the disease which affects the identification of emotional symptoms,and they were also deficient in methods and skills of emotional regulation. Drug management: depression emotion affected the self-management of drugs. The comprehending deviation of drug effects and adverse reaction will affect the patients to continue take medicine. Role management: patients had misunderstood the maintenance of social role function, and their attitude towards the maintenance of a good social role function was also lack of continuity. Social resources and utilization: supporting social resources were insufficient, and social resources cannot be effectively utilized. Conclusions There are some difficulties in the disease management of patients with depression, so patients and their caregivers should learn disease-related knowledge and management skills. At the same time, social support and help in multiple ways are required.