ObjectiveTo explore the risk factors affecting operation treatment selection of acute adhesive small bowel obstruction (ASBO), and establish a prediction model of surgical treatment selection to provide a guidance for clinical decision-making. MethodsThe patients with acute ASBO admitted to this hospital and met the inclusion and exclusion criteria, from January 2019 to December 2022, were retrospectively collected, and the patients were assigned into the surgical treatment and conservative treatment according to the treatment selection. The differences in the clinicopathologic factors between the patients with surgical treatment and conservative treatment were compared. Meanwhile, the factors with statistical differences (P<0.05) or the factors with clinical significance judged based on professional knowledge were included to screen the influencing factors of surgical treatment selection using the multivariate logistic regression analysis, and the selected influencing factors were used to construct the logistic regression prediction model equation. The area under the receiver operating characteristic curve (AUC) and its 95% confidence interval (95%CI) was used to evaluate the prediction efficiency of the prediction model equation. ResultsA total of 231 patients with acute ASBO were included, 117 (50.6%) of whom underwent surgical treatment and 114 (49.4%) underwent conservative treatment. In all 16 clinicopathologic factors between the patients with surgical treatment and conservative treatment had statistical differences (P<0.05) including the body mass index (BMI), preopeative high fever, intestinal type, sign of peritonitis, acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score excluded age scoring, abdominal surgery history and times of abdominal surgery history, times of pre-admission seek medical advice and preoperative conservative treatment time, the air-liquid level by X-ray plain film, and severe small bowel obstruction and adhesive bands by CT examination, as well as the white blood cell count (WBC), neutrophil percentage, albumin (ALB), and urea nitrogen. The multivariate logistic regression analysis showed that the acute ASBO accompanied by sign of peritonitis (β=1.778, P=0.028), history of abdominal surgery (β=1.394, P=0.022), and adhesive bands (β=1.321, P=0.010) and severe small bowel obstruction (β=1.183, P=0.018) by CT examination, WBC (β=0.524, P＜0.001), APACHEⅡ score excluded age scoring (β=0.291, P＜0.001), and BMI (β=0.191, P=0.011) had positive impacts on adopting surgical treatment, while preoperative ALB (β=–0.101, P=0.023) and conservative treatment time (β=–0.391, P＜0.001) had negative impacts on adopting surgical treatment. The accuracy, specificity, and sensitivity of the logistic regression prediction model equation constructed according to these 9 influencing factors were 84.8%, 71.1%, and 77.7%, respectively. The AUC (95%CI) of the prediction model equation to distinguish selection of surgical treatment from conservative treatment was 0.942 (0.914, 0.970). ConclusionsAccording to the preliminary results of this study, surgical treatment is recommended for patients with acute ASBO accompanied by signs of peritonitis, history of abdominal surgery, adhesive bands and severe small bowel obstruction by CT, increased preoperative WBC, high APACHEⅡ score excluded age scoring, high BMI, preoperative low ALB level, and shorter preoperative conservative treatment time. And the logistic prediction model equation constructed according to these characteristics in this study has a good discrimination for patients with surgical treatment or conservative treatment selection.
ObjectiveTo explore the application value of machine learning models in predicting postoperative survival of patients with thoracic squamous esophageal cancer. MethodsThe clinical data of 369 patients with thoracic esophageal squamous carcinoma who underwent radical esophageal cancer surgery at the Department of Thoracic Surgery of Northern Jiangsu People's Hospital from January 2014 to September 2015 were retrospectively analyzed. There were 279 (75.6%) males and 90 (24.4%) females aged 41-78 years. The patients were randomly divided into a training set (259 patients) and a test set (110 patients) with a ratio of 7 : 3. Variable screening was performed by selecting the best subset of features. Six machine learning models were constructed on this basis and validated in an independent test set. The performance of the models' predictions was evaluated by area under the curve (AUC), accuracy and logarithmic loss, and the fit of the models was reflected by calibration curves. The best model was selected as the final model. Risk stratification was performed using X-tile, and survival analysis was performed using the Kaplan-Meier method with log-rank test. ResultsThe 5-year postoperative survival rate of the patients was 67.5%. All clinicopathological characteristics of patients between the two groups in the training and test sets were not statistically different (P>0.05). A total of seven variables, including hypertension, history of smoking, history of alcohol consumption, degree of tissue differentiation, pN stage, vascular invasion and nerve invasion, were included for modelling. The AUC values for each model in the independent test set were: decision tree (AUC=0.796), support vector machine (AUC=0.829), random forest (AUC=0.831), logistic regression (AUC=0.838), gradient boosting machine (AUC=0.846), and XGBoost (AUC=0.853). The XGBoost model was finally selected as the best model, and risk stratification was performed on the training and test sets. Patients in the training and test sets were divided into a low risk group, an intermediate risk group and a high risk group, respectively. In both data sets, the differences in surgical prognosis among three groups were statistically significant (P<0.001). ConclusionMachine learning models have high value in predicting postoperative prognosis of thoracic squamous esophageal cancer. The XGBoost model outperforms common machine learning methods in predicting 5-year survival of patients with thoracic squamous esophageal cancer, and it has high utility and reliability.
ObjectiveTo explore the clinical value of three early predictive scale of lung injury (ALI) in patients with high risk of acute lung injury (ALI) after lung cancer surgery.MethodsA convenient sampling method was used in this study. A retrospective analysis was performed on patients with lung cancer underwent lung surgery. The patients were divided into an ALI group and a non-ALI group according to ALI diagnostic criteria. Three kinds of lung injury predictive scoring methods were used, including lung injury prediction score (LIPS), surgical lung injury prediction (SLIP) and SLIP-2. The differences in the scores of the two groups were compared. The correlation between the three scoring methods was also analyzed. The diagnostic value was analyzed by drawing receiver operating characteristic (ROC) curves.ResultsA total of 400 patients underwent lung cancer surgery, and 38 patients (9.5%) developed ALI after operation. Among them, 2 cases progressed to acute respiratory distress syndrome and were treated in intensive care unit. There were no deaths. The predictive scores of the patients in the ALI group were higher than those in the non-ALI group, and the difference was statistically significant (all P<0.001). There was a good correlation between the three scoring methods (allP<0.001). The three scoring methods had better diagnostic value for early prediction of high risk ALI patients after lung cancer surgery and their area under ROC curve (AUC) were larger than 0.8. LIPS score performed better than others, with an AUC of 0.833, 95%CI (0.79, 0.87).ConclusionThree predictive scoring methods may be applied to early prediction of high risk ALI patients after lung cancer surgery, in which LIPS performs better than others.
Objective To evaluate the correlation between pelvic incidence (PI) angle, hip deflection angle (HDA), combined deflection angle (CDA) and osteonecrosis of the femoral head (ONFH) after femoral neck fracture, in order to explore early predictive indicators for ONFH occurrence after femoral neck fracture. Methods A study was conducted on patients with femoral neck fractures who underwent cannulated screw internal fixation between December 2018 and December 2020. Among them, 208 patients met the selection criteria and were included in the study. According to the occurrence of ONFH, the patients were allocated into ONFH group and non-NOFH group. PI, HDA, and CDA were measured based on the anteroposterior X-ray films of pelvis and axial X-ray films of the affected hip joint before operation, and the differences between the two groups were compared. The receiver operating characteristic curve (ROC) was used to evaluate the value of the above imaging indicators in predicting the occurrence of ONFH. ResultsAmong the 208 patients included in the study, 84 patients experienced ONFH during follow-up (ONFH group) and 124 patients did not experience ONFH (non-ONFH group). In the non-ONFH group, there were 59 males and 65 females, the age was 18-86 years (mean, 53.9 years), and the follow-up time was 18-50 months (mean, 33.2 months). In the ONFH group, there were 37 males and 47 females, the age was 18-76 years (mean, 51.6 years), and the follow-up time was 8-45 months (mean, 22.1 months). The PI, HDA, and CDA were significantly larger in the ONFH group than in the non-ONFH group (P<0.05). ROC curve analysis showed that the critical value of PI was 19.82° (sensitivity of 40.5%, specificity of 86.3%, P<0.05); the critical value of HDA was 20.94° (sensitivity of 77.4%, specificity of 75.8%, P<0.05); and the critical value of CDA was 39.16° (sensitivity of 89.3%, specificity of 83.1%, P<0.05). Conclusion There is a correlation between PI, HDA, CDA and the occurrence of ONFH after femoral neck fracture, in which CDA can be used as an important reference indicator. Patients with CDA≥39.16° have a higher risk of ONFH after femoral neck fracture.
ObjectiveTo investigate the factors associated with unplanned readmission within 30 days after discharge in adult patients who underwent coronary artery bypass grafting (CABG) and to develop and validate a risk prediction model. MethodsA retrospective analysis was conducted on the clinical data of patients who underwent isolated CABG at the Nanjing First Hospital between January 2020 and June 2024. Data from January 2020 to August 2023 were used as a training set, and data from September 2023 to June 2024 were used as a validation set. In the training set, patients were divided into a readmission group and a non-readmission group based on whether they had unplanned readmission within 30 days post-discharge. Clinical data between the two groups were compared, and logistic regression was performed to identify independent risk factors for unplanned readmission. A risk prediction model and a nomogram were constructed, and internal validation was performed to assess the model’s performance. The validation set was used for validation. ResultsA total of 2 460 patients were included, comprising 1 787 males and 673 females, with a median age of 70 (34, 89) years. The training set included 1 932 patients, and the validation set included 528 patients. In the training set, there were statistically significant differences between the readmission group (79 patients) and the non-readmission group (1 853 patients) in terms of gender, age, carotid artery stenosis, history of myocardial infarction, preoperative anemia, and heart failure classification (P<0.05). The main causes of readmission were poor wound healing, postoperative pulmonary infections, and new-onset atrial fibrillation. Multivariable logistic regression analysis revealed that females [OR=1.659, 95%CI (1.022, 2.692), P=0.041], age [OR=1.042, 95%CI (1.011, 1.075), P=0.008], carotid artery stenosis [OR=1.680, 95%CI (1.130, 2.496), P=0.010], duration of first ICU stay [OR=1.359, 95%CI (1.195, 1.545), P<0.001], and the second ICU admission [OR=4.142, 95%CI (1.507, 11.383), P=0.006] were independent risk factors for unplanned readmission. In the internal validation, the area under the curve (AUC) was 0.806, and the net benefit rate of the clinical decision curve analysis (DCA) was >3%. In the validation set, the AUC was 0.732, and the DCA net benefit rate ranged from 3% to 48%. ConclusionFemales, age, carotid artery stenosis, duration of first ICU stay, and second ICU admission are independent risk factors for unplanned readmission within 30 days after isolated CABG. The constructed nomogram demonstrates good predictive power.
Cardiovascular diseases are the leading cause of death and their diagnosis and treatment rely heavily on the variety of clinical data. With the advent of the era of medical big data, artificial intelligence (AI) has been widely applied in many aspects such as imaging, diagnosis and prognosis prediction in cardiovascular medicine, providing a new method for accurate diagnosis and treatment. This paper reviews the application of AI in cardiovascular medicine.
The clinical performance and failure issues are significantly influenced by prosthetic malposition in unicompartmental knee arthroplasty (UKA). Uncertainty exists about the impact of the prosthetic joint line height in UKA on tibial insert wear. In this study, we combined the UKA musculoskeletal multibody dynamics model, finite element model and wear model to investigate the effects of seven joint line height cases of fixed UKA implant on postoperative insert contact mechanics, cumulative sliding distance, linear wear depth and volumetric wear. As the elevation of the joint line height in UKA, the medial contact force and the joint anterior-posterior translation during swing phase were increased, and further the maximum von Mises stress, contact stress, linear wear depth, cumulative sliding distance, and the volumetric wear also were increased. Furthermore, the wear area of the insert gradually shifted from the middle region to the rear. Compared to 0 mm joint line height, the maximum linear wear depth and volumetric wear were decreased by 7.9% and 6.8% at –2 mm joint line height, and by 23.7% and 20.6% at –6 mm joint line height, the maximum linear wear depth and volumetric wear increased by 10.7% and 5.9% at +2 mm joint line height, and by 24.1% and 35.7% at +6 mm joint line height, respectively. UKA prosthetic joint line installation errors can significantly affect the wear life of the polyethylene inserted articular surfaces. Therefore, it is conservatively recommended that clinicians limit intraoperative UKA joint line height errors to –2−+2 mm.
ObjectiveTo investigate the risk factors for anastomotic leakage after McKeown esophagectomy, and to establish a risk prediction model for early clinical intervention.MethodsWe selected 469 patients including 379 males and 90 females, with a median age of 67 (42-91) years, who underwent McKeown esophagectomy in our department from 2018 to 2019. The clinical data of the patients were analyzed.ResultsAmong the 469 patients, 7.0% (33/469) patients had anastomotic leakage after McKeown esophagectomy. Logistic analysis showed that the risk factors for anastomotic leakage were operation time >4.5 h, postoperative low albumin and postoperative hypoxemia (P<0.05). A prognostic nomogram model was established based on these factors with the area under the receiver operator characteristic curve of 0.769 (95%CI 0.677-0.861), indicating a good predictive value.ConclusionOperation time >4.5 h, postoperative low albumin and postoperative hypoxemia are the independent risk factors for anastomotic leakage after McKeown esophagectomy. Through the nomogram prediction model, early detection and intervention can be achieved, and the hospital stay can be shortened.
Electroencephalogram (EEG) has been an important tool for scientists to study epilepsy and evaluate the treatment of epilepsy for half a century, since epilepsy seizures are caused by the diffusion of excessive discharge of brain neurons. This paper reviews the clinical application of scalp EEG in the treatment of intractable epilepsy with vagus nerve stimulation (VNS) in the past 30 years. It mainly introduces the prediction of the therapeutic effect of VNS on intractable epilepsy based on EEG characteristics and the effect of VNS on EEG of patients with intractable epilepsy, and expounds some therapeutic mechanisms of VNS. For predicting the efficacy of VNS based on EEG characteristics, EEG characteristics such as epileptiform discharge, polarity of slow cortical potential changes, changes of EEG symmetry level and changes of EEG power spectrum are described. In view of the influence of VNS treatment on patients’ EEG characteristics, the change of epileptiform discharge, power spectrum, synchrony, brain network and amplitude of event-related potential P300 are described. Although no representative EEG markers have been identified for clinical promotion, this review paves the way for prospective studies of larger patient populations in the future to better apply EEG to the clinical treatment of VNS, and provides ideas for predicting VNS efficacy, assessing VNS efficacy, and understanding VNS treatment mechanisms, with broad medical and scientific implications.
In intensive care units (ICU), the occurrence of acute hypotensive episodes (AHE) is the key problem for the clinical research and it is meaningful for clinical care if we can use appropriate computational technologies to predict the AHE. In this study, based on the records of patients in ICU from the MIMICⅡclinical data, the chaos signal analysis method was applied to the time series of mean artery pressure, and then the patient's Lyapunov exponent curve was drawn ultimately. The research showed that a curve mutation appeared before AHE symptoms took place. This is powerful and clear basis for AHE determination. It is also expected that this study may offer a reference to research of AHE theory and clinical application.