OBJECTIVE To analyze the incidence of cyclosporine （CsA）-associated acute kidney injury （AKI） in hospitalized patients， identify influencing factors， and construct a risk prediction model. METHODS A single-center retrospective study was conducted， enrolling clinical data from hospitalized patients treated with CsA at the First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital from January 2018 to July 2024. The patients were classified into AKI group and non-AKI group based on the occurrence of CsA-related AKI. Univariate analysis and multivariate Logistic regression analysis were used to identify independent risk factors for CsA-related AKI， and a risk prediction model was constructed and its performance was evaluated. RESULTS A total of 439 patients were included， of whom 54 developed CsA-related AKI， with an incidence rate of 12.30%. The occurrence of CsA-associated AKI was positively correlated with concurrent bacterial pulmonary infection， cytomegalovirus viremia， respiratory failure， renal insufficiency， gastrointestinal bleeding， and peripheral central venous catheterization （odds ratios of 763.750， 16.944， 41.933， 236.806， 17.537 and 212.789， respectively； P ＜0.05）； while uric acid， prealbumin， and calcium levels were negatively associated with it （odds ratios of 0.983， 0.967 and 0.058， respectively； P ＜0.05）. The prediction model constructed based on the above factors yielded a χ 2 value of 10.254 （ P ＞0.05） in the Hosmer-Lemeshow test. The average area under the curve （AUC） from 10-fold cross-validation was 0.885. The AUC of the receiver operating characteristic curve was 0.883， with a sensitivity of 84.3% and a specificity of 80.4%， respectively， at the optimal cutoff value of 0.1. CONCLUSIONS Six factors， including concurrent bacterial pulmonary infection and cytomegalovirus viremia， are positively associated with the occurrence of CsA-related AKI； while uric acid， prealbumin， and calcium levels are negatively associated. The Logistic regression model constructed based on these factors demonstrates good predictive performance and can assist clinic in conducting early risk assessment and personalized interventions.

Objective To explore the association between meteorological factors and suicide in cancer patients. Methods Based on the Shandong Multicenter Healthcare Big Data Platform, data on suicide deaths of cancer patients were collected from January 2010 to December 2021. Meteorological data were extracted for the corresponding time. Conditional logistic regression models were constructed using case-crossover design to explore the association between meteorological factors and suicide risk. Results A total of 80 patients with cancer suicide deaths were included, including 48 males, 32 females, and 43 patients aged 60 and above. Suicide risk in cancer patients increased with increasing monthly average temperatures, with an OR of 1.20 (95% CI: 1.12 - 1.28). The ORs for monthly average maximum and minimum temperatures were 1.20 (95% CI: 1.13 - 1.29) and 1.19 (95% CI: 1.12 - 1.27), respectively. Monthly average sunshine duration and monthly average precipitation were negatively associated with the risk of suicide in cancer patients. However, no associations were observed on daily and weekly scales. In all subgroups, the increase in monthly mean temperature, monthly mean minimum temperature, and monthly mean maximum temperature was associated with the increased suicide risk of cancer patients. The risk of suicide among males, females and cancer patients ≥ 60 years were affected by monthly mean sunshine duration, while cancer patients < 60 years were more susceptible to the influence of monthly mean precipitation. Conclusion The meteorological factors are highly related to the risk of suicide in cancer patients, and psychological interventions for cancer patients during weather changes should be strengthened to reduce their suicide risk.

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.