OBJECTIVE: To explore the risk factors affecting the prognosis of patients with sepsis complicated with acute pulmonary embolism, and to construct and validate a nomogram predictive model for in-hospital mortality risk. METHODS: Based on the American Medical Information Mart for Intensive Care (MIMIC-III, MIMIC-IV) databases, the data were collected on patients with sepsis complicated with acute pulmonary embolism from 2001 to 2019, including baseline characteristics, and vital signs, disease scores, laboratory tests within 24 hours of admission to the intensive care unit (ICU), and interventions. In-hospital mortality was the outcome event. The total samples were divided into training and testing sets in a 7:3 ratio by random sampling. Univariate Cox regression analysis was used to verify the impact of all variables on the risk of in-hospital mortality, thereby screen potential influencing factors. Subsequently, a stepwise bi-directional regression method was applied to select factors one by one, leading to the construction of a nomogram prediction model. Collinearity testing was used to demonstrate the absence of strong multicollinearity among the influencing factors in the nomogram prediction model. The discrimination of the nomogram model, sequential organ failure assessment (SOFA), and simplified pulmonary embolism severity index (sPESI) was evaluated using C-index in the test set. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of various models for in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism. RESULTS: A total of 562 patients with sepsis complicated with acute pulmonary embolism were included, including 393 in the training set and 169 in the testing set. Univariate Cox regression analysis showed that 30 factors associated with in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism. Through stepwise bi-directional regression, 12 variables were ultimately selected, including gender, presence of malignant tumors, body temperature, red cell distribution width (RDW), blood urea nitrogen (BUN), serum potassium, prothrombin time (PT), 24-hour urine output, mechanical ventilation, vasoactive drugs, warfarin use, and sepsis-induced coagulopathy (SIC). Collinearity testing indicated no strong multicollinearity among the influencing factors [all variance inflation factor (VIF) > 10]. A nomogram model was constructed using the 12 variables mentioned above. The nomogram model predicted the C-index and its 95% confidence interval (95%CI) of in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism better than SOFA score and sPESI [0.771 (0.725-0.816) vs. 0.579 (0.519-0.639), 0.608 (0.554-0.663)]. The ROC curve showed that the area under the curve (AUC) and its 95%CI of the nomogram model were higher than those of the SOFA score and sPESI [0.811 (0.766-0.857) vs. 0.630 (0.568-0.691), 0.623 (0.566-0.680)]. These findings were consistently replicated in the internal validation of the testing set. In both the training and testing sets, Delong's test showed that the AUC of the nomogram model was significantly higher than the SOFA score and sPESI (both P < 0.05). CONCLUSION The nomogram model demonstrated good predictive effectiveness for the risk of in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism, enabling clinicians to predict mortality risk in advance and take timely interventions to reduce mortality.
Humans ; Pulmonary Embolism/mortality* ; Hospital Mortality ; Nomograms ; Sepsis/complications* ; Prognosis ; Risk Factors ; Intensive Care Units ; Male ; Female ; Middle Aged ; Aged

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

Objective:To develop and evaluate a nomogram prediction model for the 3-month mortality risk of patients with sepsis-associated acute kidney injury (S-AKI).Methods:Based on the American Medical Information Mart for Intensive Care-Ⅳ (MIMIC-Ⅳ), clinical data of S-AKI patients from 2008 to 2021 were collected.Initially, 58 relevant predictive factors were included, with all-cause mortality within 3 months as the outcome event. The data were divided into training and testing sets at a 7∶3 ratio. In the training set, univariate Logistic regression analysis was used for preliminary variable screening. Multicollinearity analysis, Lasso regression, and random forest algorithm were employed for variable selection, combined with the clinical application value of variables, to establish a multivariable Logistic regression model, visualized using a nomogram. In the testing set, the predictive value of the model was evaluated through internal validation. The receiver operator characteristic curve (ROC curve) was drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of nomogram model and Oxford acute severity of illness score (OASIS), sequential organ failure assessment (SOFA), and systemic inflammatory response syndrome score (SIRS). The calibration curve was used to evaluate the calibration, and decision curve analysis (DCA) was performed to assess the net benefit at different probability thresholds.Results:Based on the survival status at 3 months after diagnosis, patients were divided into 7?768 (68.54%) survivors and 3?566 (31.46%) death. In the training set, after multiple screenings, 7 variables were finally included in the nomogram model: Logistic organ dysfunction system (LODS), Charlson comorbidity index, urine output, international normalized ratio (INR), respiratory support mode, blood urea nitrogen, and age. Internal validation in the testing set showed that the AUC of nomogram model was 0.81 [95% confidence interval (95% CI) was 0.80-0.82], higher than the OASIS score's 0.70 (95% CI was 0.69-0.71) and significantly higher than the SOFA score's 0.57 (95% CI was 0.56-0.58) and SIRS score's 0.56 (95% CI was 0.55-0.57), indicating good discrimination. The calibration curve demonstrated that the nomogram model's calibration was better than the OASIS, SOFA, and SIRS scores. The DCA curve suggested that the nomogram model's clinical net benefit was better than the OASIS, SOFA, and SIRS scores at different probability thresholds. Conclusions:A nomogram prediction model for the 3-month mortality risk of S-AKI patients, based on clinical big data from MIMIC-Ⅳ and including seven variables, demonstrates good discriminative ability and calibration, providing an effective new tool for assessing the prognosis of S-AKI patients.

Malaria remains one of the most serious public health problems in tropical and subtropical countries and regions. In the control of the vector Anopheles, insecticide-treated bed nets and indoor residual spraying, which have been promoted to interrupt malaria transmission by only preventing indoor blood-sucking by adult mosquitoes, consequently have been widely used in malaria-endemic areas. However, the efficacy of these measures in interrupting malaria transmission is gradually decreasing due to the development of mosquito resistance. In contrast, Anopheles larval source management can effectively reduce the population density of indoor and outdoor blood-sucking, wild and domestic mosquitoes. It can also be combined with adult mosquito control to become an important adjunct to existing adult mosquito control measures. In more than 70 years of malaria control and elimination in China, according to different conditions in different places, exploration and practice have been carried out in different types of control of malaria Anopheles larvae, such as habitat modification, habitat manipulation, larviciding, and biological control. These efforts have accumulated rich experience and resulted in strategies tailored to local conditions, integrated control, community involvement, and methodological innovation. This paper outlines the main practices and basic strategies of Anopheles larval source management for malaria control in China, with the aim of providing references for malaria control in other regions where malaria is still endemic.

Objective:To investigate the risk factors and construct a nomogram model for predicting the occurrence of cirrhotic portal vein thrombosis in patients combined with esophagogastric variceal bleeding (EVB).Methods:Clinical data on 416 cirrhotic PVT cases was collected from the First Hospital of Lanzhou University between January 2016 and January 2022. A total of 385 cases were included after excluding 31 cases for retrospective analysis. They were divided into an esophagogastric variceal bleeding group and a non-esophagogastric variceal bleeding group based on the clinical diagnosis. The esophagogastric variceal group was then further divided into an EVB group and a non-bleeding group. All patients underwent gastroscopy, serology, and imaging examinations. The risk factors of PVT combined with EVB were identified by univariate analysis using SPSS 26. The prediction model of cirrhotic PVT in patients combined with EVB was constructed by R 4.0.4. The prediction efficiency and clinical benefits of the model were evaluated by the C-index, area under the receiver operating characteristic curve, calibration plots, and decision curve. The measurement data were examined by a t-test or Mann-Whitney U test. The counting data were tested using the χ2 test or the Fisher exact probability method. Results:There were statistically significant differences in the etiology, Child-Pugh grade,erythrocyte count, hematocrit, globulin, and serum lipids between the esophageal and non-esophageal varices groups ( P < 0.05). There were statistically significant differences in etiology, erythrocyte count, hemoglobin, hematocrit, neutrophil percentage, total protein, globulin, albumin/globulin, urea, high-density lipoprotein cholesterol, calcium, and neutrophil lymphocyte ratio (NLR) between the EVB and non-bleeding groups ( P < 0.05). Multivariate logistic regression analysis showed that etiology ( OR = 3.287, 95% CI: 1.497 ~ 7.214), hematocrit ( OR = 0.897, 95% CI: 0.853 ~ 0.943), and high-density lipoprotein cholesterol ( OR = 0.229, 95% CI: 0.071 ~ 0.737) were independent risk factors for cirrhotic PVT patients combined with EVB. The constructed normogram model predicted the probability of bleeding in patients. The nomogram model had shown good consistency and differentiation (AUC = 0.820, 95% CI: 0.707 ~ 0.843), as verified by 10-fold cross-validation (C-index = 0.799) and the Hosmer-Lemeshow goodness of fit test ( P = 0.915). The calibration plot and the decision curve suggested that the prediction model had good stability and clinical practicability. Conclusion:The risk factors for EVB occurrence include etiology, erythrocyte, hemoglobin, hematocrit, percentage of neutrophils, total protein, globulin, albumin/globulin, urea, high-density lipoprotein cholesterol, calcium, and NLR in patients with cirrhotic liver. The constructed prediction model has good predictive value, and it can provide a reference for medical personnel to screen patients with high bleeding risk for targeted treatment.

ObjectiveTo understand the pathogenic spectrum and epidemiological characteristics of severe acute respiratory infection （SARI） in adult inpatients in Yangpu District， Shanghai， China， in order to explore strategies for the prevention and treatment of respiratory infectious diseases. MethodsIndividual cases were from adult inpatients with SARI in Yangpu District， Shanghai， China from January 2019 to July 2021. Their respiratory samples were collected for etiological pathogen testing. ResultsA total of 681 SARI cases were enrolled for sampling and lab testing. Among them， 79.00% were aged 60 years and older， and 75.48% had confirmed chronic disease history. A total of 163 infection inpatients （23.94%） were positive for at least one pathogen. The pathogens identified most frequently were influenza A virus （6.75%）， followed by rhinovirus/enterovirus （3.23%）， parainfluenza virus （PIV） （2.79%）， Mycoplasma pneumoniae （2.35%）， coronavirus （CoV） （2.06%）. The positive rates of adenovirus （AdV）， human metapneumovirus （hMPV）， respiratory syncytial virus and bocavirus were all less than 2%. Bacterial strains were identified in eleven SARI cases， including Staphylococcus aureus and Pseudomonas aeruginosa （4 strains）， Klebsiella pneumoniae （3 strains）. Legionella pneumophila was detected in 9 cases （1.32%） and Bordetella pertussis in 5 cases （0.73%）. Two pathogens were co-detected from 11 cases， accounting for 1.62% of 163 positive cases. The most common co-detected pathogens were influenza A virus and other pathogens， accounting for 54.55% of the mixed infection. The positive rates of pathogens were not significantly different between less than 60 years old and over 60 years old groups except for Bordetella pertussis， adenovirus and Mycoplasma pneumonia（P<0.05）. Influenza virus had epidemic peak in winter and spring， but not in summer from 2019 to 2021. ConclusionVarious respiratory pathogens are detected from adult SARI cases. It is mainly influenza virus， with co-detected pathogens and rare pathogens. This study provides helpful information for targeted prevention and control measures including vaccination.