Acute lung injury is one of the common and serious complications of acute aortic dissection, and it greatly affects the recovery of patients. Old age, overweight, hypoxemia, smoking history, hypotension, extensive involvement of dissection and pleural effusion are possible risk factors for the acute lung injury before operation. In addition, deep hypothermia circulatory arrest and blood product infusion can further aggravate the acute lung injury during operation. In this paper, researches on risk factors, prediction model, prevention and treatment of acute aortic dissection with acute lung injury were reviewed, in order to provide assistance for clinical diagnosis and treatment.
Objective To predict the patients who can benefit from local surgery for bone-only metastatic breast cancer (bMBC). Methods Patients newly diagnosed with bMBC between 2010 and 2019 in SEER database were randomly divided into a training set and a validation set at a ratio of 7∶3. The Cox proportional hazards model was used to analyze the independent prognostic factors of overall survival in the training set, and the variables were screened and the prognostic prediction model was constructed. The concordance index (C-index), time-dependent clinical receiver operating characteristic curve and area under the curve (AUC), calibration curve and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical applicability of the model in the training set and validation set, respectively. The model was used to calculate the patient risk score and classify the patients into low-, medium- and high-risk groups. Survival analysis was used to compare the survival difference between surgical and non-surgical patients in different risk groups. Results A total of 2057 patients were enrolled with a median age of 45 years (interquartile range 47-62 years) and a median follow-up of 32 months (interquartile range 16-53 months). Totally 865 patients (42.1%) died. Multivariate Cox proportional hazards model analysis showed that the overall survival of patients with surgery was better than that of patients without surgery [hazard ratio=0.51, 95% confidence interval (0.43, 0.60), P<0.001]. Chemotherapy, marital status, molecular subtype, age, pathological type and histological grade were independent prognostic factors for overall survival (P<0.05), and a prognostic prediction model was constructed based on the independent prognostic factors. The C-index was 0.702 in the training set and 0.703 in the validation set. The 1-, 3-, and 5-year AUCs of the training set and validation set were 0.734, 0.727, 0.731 and 0.755, 0.737, 0.708, respectively. The calibration curve showed that the predicted survival rates of 1, 3, and 5 years in the training set and the validation set were highly consistent with the actual survival rates. DCA showed that the prediction model had certain clinical applicability in the training set and the validation set. Patients were divided into low-, medium- and high-risk subgroups according to their risk scores. The results of log-rank test showed that local surgery improved overall survival in the low-risk group (training set: P=0.013; validation set: P=0.024), but local surgery did not improve overall survival in the medium-risk group (training set: P=0.45; validation set: P=0.77) or high-risk group (training set: P=0.56; validation set: P=0.94). Conclusions Local surgery can improve the overall survival of some patients with newly diagnosed bMBC. The prognostic stratification model based on clinicopathological features can evaluate the benefit of local surgery in patients with newly diagnosed bMBC.
ObjectiveWhen using multi-center data to construct clinical prediction models, the independence assumption of data will be violated, and there is an obvious clustering effect among research objects. In order to fully consider the clustering effect, this study intends to compare the model performance of the random intercept logistic regression model (RI) and the fixed effects model (FEM) considering the clustering effect with the standard logistic regression model (SLR) and the random forest algorithm (RF) without considering the clustering effect under different scenarios. MethodsIn the process of forecasting model establishment, the prediction performance of different models at the center level was simulated when there were different degrees of clustering effects, including the difference of discrimination and calibration in different scenarios, and the change trend of this difference at different event rates was compared. ResultsAt the center level, different models, except RF, showed little difference in the discrimination of different scenarios under the clustering effect, and the mean of their C-index changed very little. When using multi-center highly clustered data for forecasting, the marginal forecasts (M.RI, SLR and RF) had calibrated intercepts slightly less than 0 compared with the conditional forecasts, which overestimated the average probability of prediction. RF performed well in intercept calibration under the condition of multi-center and large samples, which also reflected the advantage of machine learning algorithm for processing large sample data. When there were few multiple patients in the center, the FEM made conditional predictions, the calibrated intercept was greater than 0, and the predicted mean probability was underestimated. In addition, when the multi-center large sample data were used to develop the prediction model, the slopes of the three conditional forecasts (FEM, A.RI, C.RI) were well calibrated, while the calibrated slopes of the marginal forecasts (M.RI and SLR) were greater than 1, which led to the problem of underfitting, and the underfitting problem became more prominent with the increase in the central aggregation effect. In particular, when there were few centers and few patients, overfitting of the data could mask the difference in calibration performance between marginal and conditional forecasts. Finally, the lower the event rate the central clustering effect at the central level had a more pronounced impact on the forecasting performance of the different models. ConclusionThe highly clustered multi-center data are used to construct the model and apply it to the prediction in a specific environment. RI and FEM can be selected for conditional prediction when the number of centers is small or the difference between centers is large due to different incidence rates. When the number of hearts is large and the sample size is large, RI can be selected for conditional prediction or RF for edge prediction.
Objective To determine the risk factors of anastomotic leakage after elective colectomy in elderly patients with colon cancer, and to establish a model for predicting the risk of postoperative anastomotic leakage based on these factors. Methods The clinical data of 122 over 65 years old elderly patients who underwent colon cancer surgery in the First Hospital of Lanzhou University from January 2018 to December 2021 were analyzed retrospectively. Single factor analysis and multivariate logistic regression were used to analyze the potential risk factors for anastomotic leakage. A nomogram predictive model was established based on the determined independent risk factors, and the predictive performance of the model was evaluated by the receiver operating characteristic curve. Results Among the 122 patients included in this study, 10 had postoperative anastomotic leakage and 112 had no anastomotic leakage. Single factor analysis results showed that the occurrence of anastomotic leakage was associated with body mass index, smoking, combined diabetes, age-adjusted Charlson comorbidity index, intraoperative and postoperative blood transfusion within 2 days, preoperative hemoglobin, preoperative albumin, and preoperative prognostic nutritional index (P＜0.05). The results of multivariate logistic regression analysis showed that smoking [OR=15.529, 95%CI (1.529, 157.690), P=0.020], age-adjusted Charlson comorbidity index [OR=1.742, 95%CI (1.024, 2.966), P=0.041], and intraoperative and postoperative blood transfusion within 2 days [OR=82.223, 95%CI (1.265, 5 343.025), P=0.038] were independent risk factors for anastomotic leakage. A nomogram predictive model was established based on three independent risk factors. The area under the receiver operating characteristic curve of the model was 0.897 [95%CI (0.804, 0.990)], and its corrected C-index value was 0.881, indicating that the model had good predictive ability for the risk of anastomotic leakage. Conclusions Smoking, higher age-adjusted Charlson comorbidity index, and intraoperative and postoperative blood transfusion within 2 days are important risk factors for anastomotic leak in elderly patients undergoing elective colon cancer resection. This nomogram predictive model based on the combination of the three factors is helpful for surgeons to optimize treatment decisions and postoperative monitoring.
Objective To explore the risk factors of chronic postoperative inguinal pain (CPIP) after transabdominal preperitoneal hernia repair (TAPP), establish and verify the risk prediction model, and then evaluate the prediction effectiveness of the model. Methods The clinical data of 362 patients who received TAPP surgery was retrospectively analyzed and divided into model group (n=300) and validation group (n=62). The risk factors of CPIP in the model group were screened by univariate analysis and multivariate logistic regression analysis, and the risk prediction model was established and tested. Results The incidence of CPIP at 6 months after operation was 27.9% (101/362). Univariate analysis showed that gender (χ2= 12.055, P=0.001), age (t=–4.566, P<0.01), preoperative pain (χ2=44.686, P<0.01) and early pain at 1 week after operation (χ2=150.795, P<0.01) were related to CPIP. Multivariate logistic regression analysis showed that gender, age, preoperative pain, early pain at 1 week after operation, and history of lower abdominal surgery were independent risk predictors of CPIP. The area under curve (AUC) of the receiver operating characteristic (ROC) of the risk prediction model was calculated to be 0.933 [95%CI (0.898, 0.967)], and the optimal cut-off value was 0.129, while corresponding specificity and sensitivity were 87.6% and 91.5% respectively. The prediction accuracy, specificity and sensitivity of the model were 91.9% (57/62), 90.7% and 94.7%, respectively when the validation group data were substituted into the prediction model. Conclusion Female, age≤64 years old, preoperative pain, early pain at 1 week after operation and without history of lower abdominal surgery are independent risk factors for the incidence of CPIP after TAPP, and the risk prediction model established on this basis has good predictive efficacy, which can further guide the clinical practice.
ObjectiveTo explore the risk factors of lymph node metastasis (LNM) in patients with early gastric cancer (EGC), and try to establish a risk prediction model for LNM of EGC.MethodsThe clinicopathologic data of EGC patients who underwent radical gastrectomy and lymph node dissection from January 1, 2015 to December 31, 2019 in this hospital were retrospectively analyzed. Univariate analysis and logistic regression analysis were used to determine the risk factors for LNM of EGC, and the risk prediction model for LNM of EGC was established based on the multivariate results.ResultsA total of 311 cases of EGC were included in this study, and 60 (19.3%) cases had LNM. Univariate and multivariate analysis showed that age (younger), depth of tumor invasion (submucosa), vascular invasion, and undifferentiated carcinoma were the risk factors for LNM of EGC (P<0.05). The optimal threshold for predicting LNM of EGC was 0.158 (area under the receiver operating characteristic curve was 0.864), the sensitivity was 80.0%, and the specificity was 79.3%.ConclusionsFrom results of this study, risk factors for LNM of EGC have age, depth of invasion, vascular invasion, and differentiation degree. Risk prediction model for LNM of EGC established on this results has high sensitivity and specificity, which could provide some references for treatment strategy of EGC.
ObjectiveTo construct a model for predicting prognosis risk in patients with pancreatic malignancy (PM).MethodsThe clinicopathological data of 8 763 patients with PM undergone resection between 2010 and 2015 were collected and analyzed by SEER*Stat (v8.3.5) and R software, respectively. The univariate and multivariate Cox proportional hazard regression analysis were used to analyze the factors for predicting prognosis outcome risk and constructed the nomograms of patients with PM, respectively. Kaplan-Meier method was used to evaluate the survival of patients according to relevant factors and the high risk group and low risk group of patients with PM. The discriminative ability and calibration of the nomograms to predict overall survival were tested by using C-index, area under ROC curve (AUC) and calibration plots.ResultsThe multivariate Cox proportional hazard regression analysis showed that age, T staging, N staging, M staging, histological type, the differentiation, number of regional lymph node dissection, chemotherapy, and radiotherapy were independent factors for predicting the prognosis of patients with PM (P<0.05). Based on regression analysis of patients with PM, a nomograms model for predicting the risk of patients with PM was established, including age, T staging, N staging, M staging, histological type, the differentiation, tumor location, type of surgery, number of regional lymph node dissection, chemotherapy, and radiotherapy. The discriminative ability and calibration of the nomograms revealed good predictive ability as indicated by the C-index (0.747 for modeling group and 0.734 for verification group). The 3- and 5-year survival AUC values of the modeling group were 0.766 and 0.781, and the validation group were 0.758 and 0.783, respectively. The calibration plots showed that predictive value of the 3- and 5-year survival were close to the actual values in both modeling group and the verification group. ConclusionsIndependent predictors of survival risk after curative-intent surgery for PM were selected to create nomograms for predicting overall survival. The nomograms provide a basis for judging the prognosis of PM patients.
The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.
Objective To analyze the correlation among the clinicopathologic features, ultrasound imaging features, and axillary lymph node metastasis in breast cancer patients with negative clinical evaluation of axillary lymph nodes (cN0), and to establish a logistic regression model to predict axillary lymph node metastasis, so as to provide a reference for more accurate evaluation of axillary lymph node status in cN0 breast cancer patients. Methods The data of 501 female patients with cN0 breast cancer who were hospitalized and operated in the Affiliated Hospital of Wuhan University of Science and Technology (Xiaogan Central Hospital) from December 2013 to October 2020 were collected. Among them, 376 patients from December 2013 to December 2019 were selected to establish a prediction model for axillary lymph node metastasis of cN0 breast cancer. In the modeling group, the basic information, clinical pathological characteristics, and ultrasound imaging features of patients were analyzed by single factor analysis. The factors with statistical significance were included in the multivariate logistic regression analysis, and the logistic regression prediction model was established. The model was evaluated by the correction curve and Hosmer-Lemeshow test goodness of fit. The model was validated in the validation group (125 patients from January to October 2020), and the receiver operation characteristic (ROC) curve was drawn. Results The probability of positive axillary lymph nodes in 501 patients with cN0 breast cancer was 28.14% (141/501). The univariate analysis results of the modeling group showed that the histological grade, vascular invasion, progesterone receptor (PR), Ki-67, age, molecular typing, ultrasound breast imaging-reporting and data system (BI-RADS) grade were associated with axillary lymph node metastasis. Multivariate logistic regression analysis showed that the vascular infiltration, positive estrogen receptor (ER) , ultrasound BI-RADS grade 4C and Ki-67≥14% increased the probability of axillary lymph node metastasis (P<0.05). Using the above prediction factors to establish the prediction nomogram, the area under the ROC curve (AUC) of the modeling group was 0.72 [95%CI (0.66, 0.78)], the cut-off value was 0.30, the sensitivity was 61.00%, and the specificity was 71.20%. The newly established axillary lymph node transfer logistic regression model was applied to the validation group (n=125), and the AUC was 0.72 [95%CI (0.53, 0.76)]. The truncation value was 0.40, and the total coincidence rate was 69.60% (87/125), positive predictive value was 47.37% (18/38), and negative predictive value was 91.95% (80/87). Conclusions Vascular invasion, positive ER , ultrasound BI-RADS grade 4C, and Ki-67≥14% are risk predictors of axillary lymph node metastasis in cN0 breast cancer patients. The negative predictive value of the model is 91.95%, which has a higher value in predicting axillary lymph node metastasis in early breast cancer patients, and can provide a reference for screening exempt sentinel lymph node biopsy population.
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.