ObjectiveTo systematically evaluate the risk prediction model of anastomotic fistula after radical resection of esophageal cancer, and to provide objective basis for selecting a suitable model. MethodsA comprehensive search was conducted on Chinese and English databases including CNKI, Wanfang, VIP, CBM, PubMed, EMbase, Web of Science, The Cochrane Library for relevant studies on the risk prediction model of anastomotic fistula after radical resection of esophageal cancer from inception to April 30, 2023. Two researchers independently screened literatures and extracted data information. PROBAST tool was used to assess the risk of bias and applicability of included literatures. Meta-analysis was performed on the predictive value of common predictors in the model with RevMan 5.3 software. ResultsA total of 18 studies were included, including 11 Chinese literatures and 7 English literatures. The area under the curve (AUC) of the prediction models ranged from 0.68 to 0.954, and the AUC of 10 models was >0.8, indicating that the prediction performance was good, but the risk of bias in the included studies was high, mainly in the field of research design and data analysis. The results of the meta-analysis on common predictors showed that age, history of hypertension, history of diabetes, C-reactive protein, history of preoperative chemotherapy, hypoproteinemia, peripheral vascular disease, pulmonary infection, and calcification of gastric omental vascular branches are effective predictors for the occurrence of anastomotic leakage after radical surgery for esophageal cancer (P<0.05). ConclusionThe study on the risk prediction model of anastomotic fistula after radical resection of esophageal cancer is still in the development stage. Future studies can refer to the common predictors summarized by this study, and select appropriate methods to develop and verify the anastomotic fistula prediction model in combination with clinical practice, so as to provide targeted preventive measures for patients with high-risk anastomotic fistula as soon as possible.
ObjectiveTo investigate the predictive value of volatile organic compounds (VOCs) on pulmonary nodules in people aged less than 50 years.MethodsThe 147 patients with pulmonary nodules and aged less than 50 years who were treated in the Department of Thoracic Surgery of Sichuan Cancer Hospital from August 1, 2019 to January 15, 2020 were divided into a lung cancer group and a lung benign disease group. The lung cancer group included 36 males and 68 females, with the age of 27-49 (43.54±5.73) years. The benign lung disease group included 23 males and 20 females, with the age of 22-49 (42.49±6.83) years. Clinical data and exhaled breath samples were collected prospectively from the two groups. Exhaled breath VOCs were analyzed by gas chromatography mass spectrometry. Binary logistic regression analysis was used to select variables and establish a prediction model. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve of the prediction model were calculated.ResultsThere were statistically significant differences in sex (P=0.034), smoking history (P=0.047), cyclopentane (P=0.002), 3-methyl pentane (P=0.043) and ethylbenzene (P=0.009) between the two groups. The sensitivity, specificity and area under the ROC curve of the prediction model with gender, cyclopentane, 3-methyl pentane, ethylbenzene and N,N-dimethylformamide as variables were 80.8%, 60.5% and 0.781, respectively.ConclusionThe combination of VOCs and clinical characteristics has a certain predictive value for the benign and malignant pulmonary nodules in people aged less than 50 years.
Objective To investigate the risk factors for Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections, and construct a clinical model for predicting the risk of CRKP infections. Methods A retrospective analysis was performed on Klebsiella pneumoniae infection patients hospitalized in the Third Hospital of Hebei Medical University from May 2020 to May 2021. The patients were divided into a CRKP group (117 cases) and a Carbapenem-sensitive Klebsiella pneumoniae (CSKP) group (191 cases). The predictors were screened by full subset regression using R software (version 4.3.1). The truncation values of continuous data were determined by Youden index. Nomogram and score table model for CRKP infections risk prediction was constructed based on binary logistic regression. The receiver operator characteristic (ROC) curve and area under curve (AUC) were used to evaluate the accuracy of models. Calibration curve and decision curve were used to evaluate the performance of models. Results308 patients with Klebsiella pneumoniae infections were included. A total of 8 predictors were selected by using full subset regression and truncation values were determined according to Youden index: intensive care unit (ICU) stay at time of infection>2 days, male, acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) score>15 points, hospitalization stay at time of infection>10 days, any history of Gram-negative bacteria infection in the last 6 months, heart disease, lung infection, antibiotic exposure history in the last 6 months. The AUC of CRKP prediction risk curve model was 0.811 (95%CI 0.761 - 0.860). When the optimal cut-off value of the constructed CRKP prediction risk rating table was 6 points, the AUC was 0.723 (95%CI 0.672 - 0.774). The Bootstrap method was used for internal repeated sampling for 1000 times for verification. The model calibration curve and Hosmer-Lemeshow test (P=0.618) showed that these models have good calibration degree. The decision curve showed that these models have good clinical effectiveness. Conclusion The prediction model of CRKP infections based on the above 8 risk factors can be used as a risk prediction tool for clinical identification of CRKP infections.
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.
ObjectiveTo investigate relationship of long non-coding RNA FoxP4-AS1 expression with lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC).MethodsReal time fluorescent quantitative polymerase chain reaction was used to detect the expression level of FoxP4-AS1 in 52 cases of PTC tissues and corresponding adjacent tissues, PTC cells (TPC-1, B-CPAP, K1), and normal thyroid follicular epithelial cells (Nthy-ori3-1). Univariate and multivariate analysis were used to identify the influencing factors of LNM in PTC. Receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of influencing factors of LNM in PTC.ResultsThe expression level of FoxP4-AS1 in the PTC tissues was significantly decreased as compared with the corresponding adjacent tissues (t=7.898, P<0.001), which in the different cells had statistical difference (F=29.866, P<0.001): expression levels in the TPC-1 and K1 cells were lower than Nthy-ori3-1 cells (P<0.05) and in the B-CPAP cells and Nthy-ori3-1 cells had no statistical difference (P>0.05) by multiple comparisons. Univariate analysis showed that the extraglandular invasion (χ2=4.205, P=0.040)and low expression of FoxP4-AS1 (χ2=7.144, P=0.008) were the influencing factors of LNM in PTC. Binary logistic regression analysis showed that extraglandular invasion [OR=9.455, 95%CI (1.120, 79.835), P=0.039] and low expression ofFoxP4-AS1[OR=5.437, 95%CI (1.488, 19.873), P=0.010] were risk factors for LNM of PTC. The area under the ROC curve ofFoxP4-AS1,extraglandular invasion alone, and combination of the two were 0.679, 0.656, and 0.785, respectively.ConclusionsFoxP4-AS1 is down-regulated in PTC. Low level of FoxP4-AS1 is a risk factor for LNM of PTC. Combined detection of expression level of FoxP4-AS1 and extraglandular invasion has a high predictive value for LNM of PTC.
Objective To analyze the value of serum levels of miR-141-3p, miR-130a, miR-29a-3p, and miR-210 in predicting early neurological deterioration (END) in non-traumatic intracerebral hemorrhage. Methods The patients with non-traumatic cerebral hemorrhage who met the selection criteria and were admitted to Chengde Central Hospital between February 2021 and October 2022 were prospectively selected by convenience sampling method. The serum miR-141-3p, miR-130a, miR-29a-3p, and miR-210 levels upon admission and the occurrence of neurological deterioration within 24 h were collected, and the patients were divided into a deterioration group and a non-deterioration group according to whether neurological deterioration occurred. The correlation of serum miR-141-3p, miR-130a, miR-29a-3p, and miR-210 levels with the END of non-traumatic intracerebral hemorrhage and their predictive value to the END of non-traumatic intracerebral hemorrhage were analyzed. Results A total of 235 patient were enrolled. Of the 235 patients, 45 (19.1%) showed neurological deterioration and 190 (80.9%) showed no neurological deterioration. The levels of miR-141-3p and miR-29a-3p in the deteriorating group were significantly lower than those in the non-deteriorating group [(1.11±0.32) vs. (1.76±0.51) ng/mL, P<0.001; (1.19±0.31) vs. (1.71±0.51) ng/mL, P<0.001], and the levels of miR-130a and miR-210 were significantly higher than those in the non-deteriorating group [(5.13±1.11) vs. (3.82±1.03) ng/mL, P<0.001; (3.96±0.76) vs. (2.78±0.50) ng/mL, P<0.001]. Multivariate logistic regression analysis showed that serum miR-141-3p and miR-29a-3p levels were protective factors for the occurrence of END in non-traumatic intracerebral hemorrhage patients [odds ratio (OR)=0.513, 95% confidence interval (CI) (0.330, 0.798), P=0.003; OR=0.582, 95%CI (0.380, 0.893), P=0.013], and serum miR-130a and miR-210 levels were independent risk factors for that [OR=2.046, 95%CI (1.222, 3.426), P=0.007; OR=2.377, 95%CI (1.219, 4.638), P=0.011]. The area under the receiver operating characteristic curve was 0.857 [95%CI (0.760, 0.954)] in predicting the END of non-traumatic intracerebral hemorrhage by the combined probability of the serum miR-141-3p, miR-130a, miR-29a-3p, and miR-210 levels obtained by logistic regression, and the sensitivity was 86.7%, the specificity was 94.7%, the positive predictive value was 79.6%, and the negative predictive value was 96.8% according to the cut-off value of the prediction probability of the combined test. Conclusion The combined detection of serum miR-141-3p, miR-130a, miR-29a-3p, and miR-210 has a high predictive value in the occurrence of END in non-traumatic intracerebral hemorrhage patients.
Objective To explore the risk factors of female’s breast cancer in secondary cities of the west and establish a risk prediction model to identify high-risk groups， and provide the basis for the primary and secondary preve-ntion of breast cancer. Methods Random sampling （method of random digits table） 1 700 women in secondary cities of the west （including 1 020 outpatient cases and 680 physical examination cases） were routinely accept the questionnaire survey. Sixty-two patients were confirmed breast cancer with pathologically. Based on the X-image of the mammary gland patients and questionnaire survey to put mammographic density which classificated into high- and low-density groups. The relationships between the mammographic density， age， body mass index （BMI）， family history of breast cancer， socio-economic status （SES）， lifestyle， reproductive fertility situation， and breast cancer were analyzed， then a risk prediction model of breast cancer which fitting related risk factors was established. Results Univariate analysis showed that risk factors for breast cancer were age （P=0.006）， BMI （P=0.007）， age at menarche （P=0.039）， occupation （P=0.001）， domicile place （P=0.000）， educational level （P=0.001）， health status compared to the previous year （P=0.046）， age at first birth （P=0.014）， whether menopause （P=0.003）， and age at menopause （P=0.006）. The unconditional logistic regr-ession analysis showed that the significant risk factors were age （P=0.003）， age at first birth （P=0.000）， occupation （P=0.010）， and domicile place （P=0.000）， and the protective factor was age at menarche （P=0.000）. The initially established risk prediction model in the region which fitting related risk factors was y=-5.557＋0.042x1-0.375x2＋1.206x3＋0.509x4＋2.135x5. The fitting coefficient （R square）=0.170， it could reflect 17% of the actual situation. Conclusions The breast cancer risk prediction model which established by using related risk factors analysis and epidemiological investigation could guide the future clinical work，but there is still need the validation studies of large populations for the model.
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 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.
Objective To investigate the factors influencing the occurrence of postoperative pulmonary complications (PPCs) in liver transplant recipients and to construct Nomogram model to identify high-risk patients. Methods The clinical data of 189 recipients who underwent liver transplantation at the General Hospital of Eastern Theater Command from November 1, 2019 to November 1, 2022 were retrospective collected, and divided into PPCs group (n=61) and non-PPCs group (n=128) based on the occurrence of PPCs. Univariate and multivariate logistic regression analyses were used to determine the risk factors for PPCs, and the predictive effect of the Nomogram model was evaluated by receiver operator characteristic curve (ROC) and calibration curve. Results Sixty-one of 189 liver transplant patients developed PPCs, with an incidence of 32.28%. Univariate analysis results showed that PPCs were significantly associated with age, smoking, Child-Pugh score, combined chronic obstructive pulmonary disease (COPD), combined diabetes mellitus, prognostic nutritional index (PNI), time to surgery, amount of bleeding during surgery, and whether or not to diuretic intraoperatively (P<0.05). Multivariate logistic regression analysis showed that age [OR=1.092, 95%CI (1.034, 1.153), P=0.002], Child-Pugh score [OR=1.575, 95%CI (1.215, 2.041), P=0.001], combined COPD [OR=4.578, 95%CI (1.832, 11.442), P=0.001], combined diabetes mellitus [OR=2.548, 95%CI (1.024, 6.342), P=0.044], preoperative platelet count (PLT) [OR=1.076, 95%CI (1.017, 1.138), P=0.011], and operative time [OR=1.061, 95%CI (1.012, 1.113), P=0.014] were independent risk factors for PPCs. The prediction model for PPCs which constructed by using the above six independent risk factors in Nomogram had an area under the ROC curve of 0.806. Hosmer and Lemeshow goodness of fit test (P=0.129), calibration curve, and decision curve analysis showed good agreement with Nomogram model. Conclusion The Nomogram model constructed based on age, Child-Pugh score, combined COPD, combined diabetes mellitus, preoperative PLT, and time of surgery can better identify patients at high risk of developing PPCs after liver transplantation.