ObjectiveTo evaluate the effects of Tongnaoyin on the blood-brain barrier status and neurological impairment in acute ischemic stroke （AIS） patients with the syndrome of phlegm-stasis blocking collaterals by dynamic contrast-enhanced magnetic resonance imaging （DCE-MRI）. MethodsA total of 63 patients diagnosed with AIS in the Jiangsu Province Hospital of Chinese Medicine from October 2022 to December 2023 were enrolled in this study. According to random number table method，the patients were assigned into a control group （32 cases） and an observation group （31 cases）. The control group received conventional Western medical treatment，and the observation group took 200 mL Tongnaoyin after meals，twice a day from day 2 of admission on the basis of the treatment in the control group. After 7 days of treatment，the patients were examined by DCE-MRI. The baseline data for two groups of patients before treatment were compared. The National Institute of Health Stroke Scale （NIHSS） score and modified Rankin Scale （mRS） score were recorded before treatment and after 90 days of treatment for both groups. The rKtrans，rKep，and rVe values were obtained from the region of interest （ROI） of the infarct zone/mirror area and compared between the two groups. ResultsThere was no significant difference in the NIHSS or mRS score between the two groups before treatment. After 90 days of treatment，the NIHSS and mRS scores declined in both groups，and the observation group had lower scores than the control group （P<0.05）. After treatment，the rKtrans and rVe in the observation group were lower than those in the control group （P<0.01）. ConclusionCompared with conventional Western medical treatment alone，conventional Western medical treatment combined with Tongnaoyin accelerates the repair of the blood-brain barrier in AIS patients，thereby ameliorating neurological impairment after AIS to improve the prognosis.

Objective:To explore the current situation and influencing factors of quality of life of septic patients in intensive care unit (ICU) after discharge, and to provide theoretical basis for clinical early psychological intervention and continuity of care.Methods:A prospective observational study was conducted. The septic patients who were hospitalized in the department of critical care medicine of the Affiliated Hospital of Jining Medical University and discharged with improvement from January 1 to December 31, 2022 were selected as the research objects. The demographic information, basic diseases, infection site, vital signs at ICU admission, severity scores of the condition within 24 hours after ICU admission, various biochemical indexes, treatment process, and prognostic indexes of all the patients were recorded. All patients were assessed by questionnaire at 3 months of discharge using the 36-item short-form health survey scale (SF-36 scale), the activities of daily living scale (ADL scale), and the Montreal cognitive assessment scale (MoCA scale). Multiple linear regression was used to analyze the factors influencing the quality of life of septic patients after discharge from the hospital.Results:A total of 200 septic patients were discharged with improvement and followed up at 3 months of discharge, of which 150 completed the questionnaire. Of the 150 patients, 57 had sepsis and 93 had septic shock. The total SF-36 scale score of septic patients at 3 months of discharge was 81.4±23.0, and the scores of dimensions were, in descending order, role-emotional (83.4±23.0), mental health (82.9±23.6), bodily pain (82.8±23.3), vitality (81.6±23.2), physical function (81.4±23.5), general health (81.1±23.3), role-physical (79.5±27.0), and social function (78.8±25.2). There was no statistically significant difference in the total SF-36 scale score between the patients with sepsis and septic shock (82.6±22.0 vs. 80.7±23.6, P > 0.05). Incorporating the statistically significant indicators from linear univariate analysis into multiple linear regression analysis, and the results showed that the factors influencing the quality of life of septic patients at 3 months after discharge included ADL scale score at 3 months after discharge [ β= 0.741, 95% confidence interval (95% CI) was 0.606 to 0.791, P < 0.001], length of ICU stay ( β= -0.209, 95% CI was -0.733 to -0.208, P = 0.001), duration of mechanical ventilation ( β= 0.147, 95% CI was 0.122 to 0.978, P = 0.012), total dosage of norepinephrine ( β= -0.111, 95% CI was -0.044 to -0.002, P = 0.028), mean arterial pressure (MAP) at ICU admission ( β= -0.102, 95% CI was -0.203 to -0.007, P = 0.036) and body weight ( β= 0.097, 95% CI was 0.005 to 0.345, P = 0.044). Conclusions:The quality of life of patients with sepsis at 3 months after discharge is at a moderately high level. The influencing factors of the quality of life of patients with sepsis at 3 months after discharge include the ADL scale score at 3 months after discharge, the length of ICU stay, the duration of mechanical ventilation, the total dosage of norepinephrine, MAP at ICU admission and body weight, and healthcare professionals should enhance the treatment and care of the patients during their hospitalization based on the above influencing factors, and pay attention to early psychological intervention and continued care for such patients.

ObjectiveTo investigate the changes in cerebral blood perfusion in patients with acute cerebral infarction after taking Tongnaoyin， a traditional Chinese medicine， based on head and neck computed tomography （CT） angiography （CTA） combined with brain CT perfusion imaging （CTP）. MethodA total of 240 patients with cerebral infarction of phlegm and blood stasis syndrome treated in Jiangsu Province Hospital of Traditional Chinese Medicine from March 2018 to September 2023 were randomly divided into a control group （99 cases） and a Tongnaoyin group （141 cases）. Based on the guidelines， the control group was treated with conventional treatment such as anti-aggregation， anticoagulation， lipid-lowering and plaque stabilization， brain protection， and supportive treatment. The Tongnaoyin group was treated with Tongnaoyin of 200 mL in warm conditions in the morning and evening on the basis of the control group. Both groups underwent CTA combined with CTP within 24 hours after admission， and they were reexamined by CTA and CTP in the sixth month after admission. The degree of intracranial artery stenosis was determined according to the North American Symptomatic Carotid Endarterectomy Trial （NASCET） method. The relative cerebral blood volume （rCBV）， relative cerebral blood flow （rCBF）， mean transit time （MTT）， and time to peak （TTP） of the lesion area before and after treatment were compared. The adverse outcomes of the two groups within six months after discharge were compared. ResultCompared with the group before treatment， the degree of vascular stenosis in the Tongnaoyin group was significantly reduced， and the difference was statistically significant （Z=105.369，P<0.05）. Compared with the control group after treatment， the improvement rate of vascular stenosis in the Tongnaoyin group was higher （χ²=84.179，P<0.01）， and the curative effect was better.After treatment， the rCBV and rCBF of patients in the Tongnaoyin group were significantly increased， and the difference was statistically significant （P<0.01）. MTT and TTP showed a trend of shortening， but the difference was not statistically significant. There was no statistically significant difference in rCBV， rCBF， MTT， and TTP in the control group. Compared with those in the control group after treatment， the rCBV and rCBF in the Tongnaoyin group were significantly increased， while MTT and TTP were significantly reduced （P<0.01）. After six months of discharge， the risk of poor prognosis in the Tongnaoyin group was significantly reduced compared with the control group （P<0.05）. ConclusionTongnaoyin has a good effect on improving cerebral blood perfusion in patients with acute cerebral infarction. It can be used as an effective supplement for the conventional treatment of ischemic stroke to improve clinical efficacy.

Objective:To describe the epidemiological characteristics of respiratory syncytial virus (RSV) infection in preschool children and explore the risk factors for severe pneumonia.Methods:Epidemiological data of 279 preschool children with RSV infection were investigated. The children were screened for severe pneumonia and separated into ordinary and severe types. General data and laboratory test data from both groups were compared, and binary logistic regression model analysis was applied to determine the risk factors for severe pneumonia.Results:Preschool children with RSV infection were mostly male (63.08%), <6 months old (65.95%) and had poor living environment (53.05%), with main symptoms of cough (91.04%) and wheezing (69.18%), the lung auscultation was mainly characterized by wheezing (86.74%), and imaging findings were mainly patchy shadows (76.34%), the onset season was concentrated in autumn (31.18%) and winter (43.37%). The detection rate of severe pneumonia in 279 pediatric patients was 20.27% (56/279). The proportions of onset season being autumn or winter, low birth weight infants, history of respiratory infections within 3 months, delayed treatment, neutrophils count <10×10 9/L, C-reactive protein≥10 mg/L, procalcitonin≥1.5 ng/mL, albumin<30 g/L, CD4 + /CD8 + <1.2 in the severe types were higher than those in the normal types ( P<0.05). Logistic regression analysis showed that the onset season was autumn or winter ( OR=2.316, 95% CI: 1.235-4.345), low birth weight infants ( OR=2.679, 95% CI: 1.442-4.977), history of respiratory infections within 3 months ( OR=2.815, 95% CI: 1.539-5.148), delayed treatment ( OR=2.869, 95% CI: 1.581-5.206), low albumin<30 g/L ( OR=2.756, 95% CI: 1.495-5.080), and low CD4 + /CD8 + <1.2 ( OR=3.016, 95% CI: 1.695-5.366) were risk factors for severe RSV pneumonia in preschool children ( P<0.05). Conclusions:Autumn and winter, low birth weight infants, history of respiratory infections within 3 months, delayed treatment, low albumin, and low CD4 + /CD8 + are related to the occurrence of severe RSV pneumonia in preschool children. Therefore, it is necessary to strengthen the attention to the condition of preschool RSV infected children with the above risk factors, and actively intervene in controllable factors to reduce the risk of severe pneumonia.

Objective:To investigate the value of nomogram based on radiomics features of CT enterography (CTE) combined with clinical characteristics to predict secondary loss of response (SLOR) after infliximab (IFX) treatment in patients with Crohn′s disease (CD).Methods:This study was a case-control study. Clinical and imaging data of 155 patients with CD diagnosed at the First Affiliated Hospital of Anhui Medical University from March 2015 to July 2022 were retrospectively collected. The patients were divided into a training set ( n=108) and a testing set ( n=47) in the ratio of 7∶3 by stratified sampling method. All patients were treated according to the standardized protocol and were classified as SLOR (43 in the training set and 18 in the testing set) and non-SLOR (65 in the training set and 29 in the testing set) according to treatment outcome. Based on the data from the training group, independent clinical predictors of SLOR after IFX treatment were screened in the clinical data using univariate and multivariate logistic regression analysis to establish a clinical model. Intestinal phase images were selected to be outlined layer by layer along the margin of the lesion to obtain the volume of the region of interest to extract the radiomics features. The radiomics features were screened using univariate analysis and the minimum absolute shrinkage and selection operator to establish the radiomics model. Multivariate logistic regression analysis was used to build a combined clinical-radiomics model based on the screened clinical independent predictors and radiomics characters, then a nomogram was drawn. The predictive efficacy of the 3 models for SLOR after IFX treatment was assessed by receiver operating characteristic curves, and the area under the curve (AUC) was calculated. The decision curve analysis was applied to evaluate the clinical utility of the models. Results:Disease duration ( OR=1.983, 95% CI 1.966-2.000, P=0.046) and intestinal stenosis ( OR=1.246, 95% CI 1.079-1.764, P=0.015) were identified as the independent predictors of SLOR in the clinical data, and a clinical model was established. Totally 9 radiomics features were included in the radiomics model. The AUCs of clinical, radiomics, and combined models for predicting SLOR after IFX treatment in CD patients were 0.691 (95% CI 0.591-0.792), 0.896 (95% CI 0.836-0.955), and 0.910 (95% CI 0.855-0.965) in the training set, and 0.722 (95% CI 0.574-0.871), 0.866 (95% CI 0.764-0.968), and 0.889 (95% CI 0.796-0.982) in the testing set. Decision curve analysis in the testing set showed higher net clinical benefits for both the radiomics model and combined model than the clinical model, and combined model had higher net clinical benefits than the radiomics model over most threshold probability intervals. Conclusions:CTE-based radiomics model can effectively predict SLOR after IFX treatment in patients with CD, and a combined model by incorporating clinical characteristics of disease duration and intestinal stenosis can further improve the predictive efficacy.

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.

Objective:To explore the effects of online supportive disclosure therapy on the self-expression level, professional identity, dimission intention, and turnover rate of pre-dimission nurses, in order to provide a reference for nursing managers to stabilize the nursing team.Methods:A quasi experimental research method was used, and a convenient sampling method was used to select 192 pre resigned nurses from Shandong Provincial Third Hospital, the First Affiliated Hospital of Shandong First Medical University, and Shandong First Medical University Affiliated Provincial Hospital from October 2021 to December 2022 as the research subjects. They were divided into a control group and an experimental group by random number table method, with 96 nurses in each group. The control group received routine exit interviews, while the experimental group received online supportive disclosure therapy intervention based on this. The self-expression level, professional identity, dimission intention, and turnover rate of two groups of nurses before and 1, 2 months after the intervention were evaluated.Results:Finally, 94 nurses in the control group and 92 nurses in the experimental group completed the study. There were no significant differences in the self-expression level, professional identity, dimission intention before the intervention between the two groups ( P>0.05). After 1 and 2 months of the intervention, the scores of the Pain Self Disclosure Index, Nurse Professional Identity Rating Scale, and Resignation Intention Scale of the experimental group were 36.33 ± 5.13, 73.88 ± 8.72, 14.18 ± 1.12 and 34.22 ± 6.78, 98.26 ± 11.29, 6.16 ± 1.19, respectively,and the control group were 28.06 ± 8.23, 64.72 ± 10.39, 17.82 ± 1.37 and 44.26 ± 7.62, 79.82 ± 8.66, 9.18 ± 1.06, there were statistically significant differences between the two groups ( t values were -13.54 to -2.11, all P<0.05); there were statistically significant differences in the inter group effects, time effects, and interaction effects of the scores on the Pain Self Disclosure Index, Professional Identity Rating Scale, and Resignation Intention Scale between the two groups ( F values were 5.12 to 14.82, all P<0.05). The turnover rate of nurses in the experimental group was 1.09% (1/92), lower than 8.51% (8/94) in the control group, and the difference between the two groups was statistically significant ( χ2=1.59, P<0.05). Conclusions:Online supportive disclosure therapy can improve the self-expression level and professional identity of pre-dimission nurses, and reduce their willingness to resign and turnover rate.

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.
Humans ; Hospital Mortality ; Retrospective Studies ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Intensive Care Units

Objective:To analyze the risk factors of acute kidney injury (AKI) in patients with septic shock in intensive care unit (ICU), construct a predictive model, and explore the predictive value of the predictive model.Methods:The clinical data of patients with septic shock who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical College from April 2015 to June 2019 were retrospectively analyzed. According to whether the patients had AKI within 7 days of admission to the ICU, they were divided into AKI group and non-AKI group. 70% of the cases were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. XGBoost model was used to integrate relevant parameters to predict the risk of AKI in patients with septic shock. The predictive ability was assessed through receiver operator characteristic curve (ROC curve), and was correlated with acute physiology and chronic health evaluationⅡ(APACHEⅡ), sequential organ failure assessment (SOFA), procalcitonin (PCT) and other comparative verification models to verify the predictive value.Results:A total of 303 patients with septic shock were enrolled, including 153 patients with AKI and 150 patients without AKI. The incidence of AKI was 50.50%. Compared with the non-AKI group, the AKI group had higher APACHEⅡscore, SOFA score and blood lactate (Lac), higher dose of norepinephrine (NE), higher proportion of mechanical ventilation, and tachycardiac. In the XGBoost prediction model of AKI risk in septic shock patients, the top 10 features were serum creatinine (SCr) level at ICU admission, NE use, drinking history, albumin, serum sodium, C-reactive protein (CRP), Lac, body mass index (BMI), platelet count (PLT), and blood urea nitrogen (BUN) levels. Area under the ROC curve (AUC) of the XGBoost model for predicting the risk of AKI in patients with septic shock was 0.816, with a sensitivity of 73.3%, a specificity of 71.7%, and an accuracy of 72.5%. Compared with the APACHEⅡscore, SOFA score and PCT, the performance of the model improved significantly. The calibration curve of the model showed that the goodness of fit of the XGBoost model was higher than the other scores (the calibration curve had the lowest score, with a score of 0.205).Conclusion:Compared with the commonly used clinical scores, the XGBoost model can more accurately predict the risk of AKI in patients with septic shock, which helps to make appropriate diagnosis, treatment and follow-up strategies while predicting the prognosis of patients.

Objective:To establish a new nurse standardized training management index system based on training transfer theory, and to provide reference for objective evaluation of standardized training management for new nurses.Methods:From August 2020 to April 2021, guided by the theory of training transfer, the standardized training management indexs for new nurses were preliminarily drawn up through literature review, semi-structured interviews. The Delphi method was used to conduct two rounds of expert consultation.Results:The effective questionnaire recovery rate of the two rounds of expert consultation was 92.00% (23/25) and 95.65% (22/23), respectively. The expert authority coefficients were 0.904 and 0.905, respectively. Kendall′s harmony coefficients were 0.228 and 0.250, respectively, both P<0.01. The final index system of standardized training management for new nurses based on training transfer theory included 4 first-level indexes, 14 second-level indexes and 59 third-level indexes. Conclusions:The new nurse standardized training management index system based on training transfer theory is scientific and reliable. It provides a tool for evaluating standardized training management of new nurses and a reference for perfecting the training management system of new nurses.