Objective To investigate the clinical value of serum cystatin C (sCysC) and APACHE Ⅱ score in predicting diagosis and prognosis of acute kidney injury(AKI) in patients with sepsis. Methods In this study, we prospectively enrolled 138 adult patients with sepsis who had been admitted to the mixed ICU of Xiaolan Hospital of Southern Medical University during March 2015 to January 2016. According to the Kidney Disease Improving Global Outcomes (KDIGO) criterion, the patients were divided into non-AKI group and AKI group (including mild AKI and severe AKI). The receiver operating characteristic(ROC) curve and the area under curve(AUC) were used to evaluate these indexes' capability of detecting septic AKI and its prognosis. Results In this study,72 patients (52.2％) developed AKI. The levels of sCysC and APACHE Ⅱ score were significantly higher in AKI than in non-AKI (P<0.05). In total, 33 patients (23.9％) developed severe AKI. The levels of sCysC and APACHE Ⅱscore were significantly higher in severe AKI than in non-AKI and mild AKI (P<0.05) . Combination of sCysC and APACHE Ⅱ score predicted AKI and severe AKI after ICU admission with a higherAUC value (0.880&0.930) than each biomarker alone. In this cohort, in-hospital mortality was 19.6％and renal replacement therapy rate was 9.4％,which were strikingly higher in AKI group than non AKI group (P<0.05). Conclusions sCysC is a novel indexes for predicting AKI and its prognosis in patients with sepsis. Combinating with APACHE Ⅱ score can further improve its predictive performance of AKI detection and short-term prognosis.

Objective:To construct a non-invasive pre-hospital screening model and early based on artificial intelligence algorithms to provide the severity of stroke in patients, provide screening, guidance and early warning for stroke patients and their families, and provide data support for clinical decision-making.Methods:A retrospective study was conducted. The clinical information of stroke patients ( n = 53?793) were extracted from the Yidu cloud big data server system of the Second Affiliated Hospital of Dalian Medical University from January 1, 2001 to July 31, 2023. Combined with the results of single factor screening and the opinions of experts with senior professional titles in neurology, the input variable was determined, and the output variable was the National Institutes of Health Stroke Scale (NIHSS) representing the severity of the disease at admission. Python 3.7 was used to build DeepFM algorithm model, and five data mining models including Logistic regression, CART decision tree, C5.0 decision tree, Bayesian network and deep neural network (DNN) were built at the same time. The original data were randomly divided into 80% training set and 20% test set, which were used to train and test the models, adjust the parameters of each model, respectively calculate the accuracy, sensitivity and F-index of the six models, carry out the comprehensive comparison and evaluation of the model. The receiver operator characteristic curve (ROC curve) and calibration curve were drawn, compared the prediction performance of DeepFM model and the other five algorithms. In addition, the data of stroke patients ( n = 1?028) were extracted from Dalian Central Hospital for external verification of the model. Results:A total of 14?015 stroke patients with complete information were selected, including 11?212 in the training set and 2?803 in the testing set. After univariate screening, 14 indicators were included to construct the model, including gender, age, recurrence, physical impairment, facial problems, speech disorders, head reactions, disturbance of consciousness, visual disorders, abnormal cough and swallowing, high risk factor, family history, smoking history and drinking history. DeepFM model adopted the two-order crossover feature. The number of hidden layers in DNN layer was 3. Dropout was used to discard the neurons in the neural network. Rule was used as the activation function. Each layer used Dense full connection. The objective function was random gradient descent. The number of iterations was 15. There were 133?922 training parameters in total. Comparing the predictive value of the six models showed that the accuracy of DeepFM model was 0.951, the sensitivity was 0.992, the specificity was 0.814, the F-index was 0.950, and the area under the curve (AUC) was 0.916. The accuracy of the other five data mining models were between 0.771-0.780, the sensitivity were between 0.978-0.987, the F-index were between 0.690-0.707, and the AUC were between 0.568-0.639. The calibration curve of the DeepFM model was more aligned with the ideal curve than the other five data mining models. Suggesting that the prediction performance of DeepFM model was the best. External validation was conducted on the DeepFM model, and its accuracy was 0.891, indicating good generalization performance of the model.Conclusion:The pre-hospital non-invasive screening prediction model based on DeepFM can accurately predict the severity grading of stroke patients, and has potential application value in rapid screening and early clinical decision-making of stroke.