Objective To investigate the influencing factors associated with the comorbidity of inflammatory bowel disease (IBD) and depression. Methods A case-control study was conducted based on the ^“Healthcare Big Data Platform^” of a tertiary class-A comprehensive hospital in Shandong Province. IBD comorbid with depression was served as the case group and IBD without depression was served as the control group. Propensity score matching (PSM) was performed by matching the case group with the control group in a ratio of 1:2 according to the age and gender of the patients. Conditional logistic regression model was used to explore the influencing factors associated with the comorbidity of IBD and depression. Results A total of 405 patients with IBD were enrolled in this study, including 270 patients without depression and 135 patients comorbid with depression. The results of conditional logistic regression showed that the use of immunosuppressants (OR=2.84, 95% CI: 1.00-8.07) and glucocorticoids (OR=2.05, 95% CI: 1.17-3.58), dementia (OR=5.20, 95% CI:1.59-17.05), cardiovascular disease (OR=3.58, 95% CI: 1.84-6.98) and cancer (OR=2.63, 95% CI: 1.16-5.95) were associated with the comorbidity of depression and IBD. Conclusion Attention should be paid to the use of immunosuppressants and glucocorticoids in the population of IBD comorbid with depression, and the coexistence of physical diseases such as dementia, cardiovascular disease and cancer. Early prevention and targeted treatment measures should be taken for high-risk populations to reduce their risk of depression and improve their quality of life and health.

Objective:To analyze the influencing factors on the occurrence of acute kidney injury (AKI) in hospitalized patients treated with esomeprazole and to construct a risk prediction model to predict the occurrence of esomeprazole-associated AKI.Methods:The study was designed as a retrospective study. The subjects were selected from patients who were hospitalized in the First Affiliated Hospital of Shandong First Medical University from January 2018 to December 2020 and received treatment with esomeprazole. The clinical data of patients, including basic information, operations, intervention measures, medication, and laboratory test results, was collected through the hospital′s electronic medical record system. Patients were divided into AKI and non-AKI groups according to the occurrence of esomeprazole-associated AKI, and the clinical characteristics between the 2 groups were compared. The least absolute shrinkage and selection operator (LASSO regression) was used to analyze the influencing factors of esomeprazole-associated AKI. Patients were randomly divided into the training set and the test set at a 8∶2 ratio. Based on data in the training set, 5 machine learning algorithms were used to build esomeprazole-associated AKI prediction models, including logistic regression, random forest, gradient boosting machine (GBM), extreme gradient boosting, and light gradient boosting machine. Based on data in the test set, the performance of 5 models was validated through the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.Results:A total of 5 436 patients were enrolled in the study, including 3 231 males and 2 205 females, with an age of 61(51, 70) years. Esomeprazole-associated AKI occurred in 393 patients, with an incidence of 7.23%. The results of LASSO regression analysis identified 24 variables closely related to esomeprazole-associated, such as hepatic insufficiency, chronic renal insufficiency, hypoproteinemia. Based on data in the training set (4 349 patients), the esomeprazole-associated AKI risk prediction models were constructed and their predictive performance was good (all AUC>0.900). The predictive performance validation was conducted using the data in the test set (1 087 patients), and the results showed that the GBM model has the highest AUC (0.922) and relatively stable performance, with small differences in various indicators between the training and the test sets.Conclusions:The use of esomeprazole is significantly associated with AKI, and the risk is influenced by factors such as baseline renal function, comorbidities, and combined medications. The risk prediction model based on GBM algorithm is helpful for early assessment of the risk of esomeprazole-related AKI in clinical practice.

Objective:To analyze the influencing factors on the occurrence of acute kidney injury (AKI) in hospitalized patients treated with esomeprazole and to construct a risk prediction model to predict the occurrence of esomeprazole-associated AKI.Methods:The study was designed as a retrospective study. The subjects were selected from patients who were hospitalized in the First Affiliated Hospital of Shandong First Medical University from January 2018 to December 2020 and received treatment with esomeprazole. The clinical data of patients, including basic information, operations, intervention measures, medication, and laboratory test results, was collected through the hospital′s electronic medical record system. Patients were divided into AKI and non-AKI groups according to the occurrence of esomeprazole-associated AKI, and the clinical characteristics between the 2 groups were compared. The least absolute shrinkage and selection operator (LASSO regression) was used to analyze the influencing factors of esomeprazole-associated AKI. Patients were randomly divided into the training set and the test set at a 8∶2 ratio. Based on data in the training set, 5 machine learning algorithms were used to build esomeprazole-associated AKI prediction models, including logistic regression, random forest, gradient boosting machine (GBM), extreme gradient boosting, and light gradient boosting machine. Based on data in the test set, the performance of 5 models was validated through the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.Results:A total of 5 436 patients were enrolled in the study, including 3 231 males and 2 205 females, with an age of 61(51, 70) years. Esomeprazole-associated AKI occurred in 393 patients, with an incidence of 7.23%. The results of LASSO regression analysis identified 24 variables closely related to esomeprazole-associated, such as hepatic insufficiency, chronic renal insufficiency, hypoproteinemia. Based on data in the training set (4 349 patients), the esomeprazole-associated AKI risk prediction models were constructed and their predictive performance was good (all AUC>0.900). The predictive performance validation was conducted using the data in the test set (1 087 patients), and the results showed that the GBM model has the highest AUC (0.922) and relatively stable performance, with small differences in various indicators between the training and the test sets.Conclusions:The use of esomeprazole is significantly associated with AKI, and the risk is influenced by factors such as baseline renal function, comorbidities, and combined medications. The risk prediction model based on GBM algorithm is helpful for early assessment of the risk of esomeprazole-related AKI in clinical practice.

Objective: To study the lag effect of temperature and the source of heterogeneity on other infectious diarrhea (OID) in Zhejiang province, so as to identify related vulnerable populations at risk. Methods: Data on OID and meteorology in Zhejiang province from 2014 to 2016 were collected. A two-stage model was conducted, including: 1) using the distributed lag non-linear model to estimate the city-specific lag effect of temperature on OID, 2) applying the multivariate Meta- analysis to pool the estimated city-specific effect, 3) using the multivariate Meta-regression to explore the sources of heterogeneity. Results: There were 301 593 cases of OID in Zhejiang province during the study period. At the provincial level, temperature that corresponding to the lowest risk of OID was 16.7 ℃, and the temperature corresponding to the highest risk was 6.2℃ (RR=2.298, 95%CI: 1.527- 3.459). 16.7 ℃ was recognized as the reference temperature. P₅ and P₉₅ of the average daily temperature represented low and high temperature respectively. When the temperature was cold, the risk was delayed by 2 days, with the highest risk found on the 5^th day (RR=1.057, 95%CI: 1.030-1.084) before decreasing to the 23^rd day. When the temperature got hot, the risk of OID occurred on the first day (RR=1.081, 95%CI: 1.045-1.118) and gradually decreasing to the 8^th day. Differences on heterogeneous sources related to the risks of OID in different regions, presented on urban latitude and the rate of ageing in the population. Conclusions Both high or low temperature could increase the risk of OID, with a lag effect noticed. Prevention program on OID should be focusing on populations living in the high latitude and the elderly population at the low temperature areas.