Objective To explore independent risk factors for 30-day mortality in critical patients with pulmonary infection and sepsis, and build a prediction model. Methods Patients diagnosed with pulmonary infection and sepsis in the MIMIC-Ⅲ database were analyzed. The CareVue database was the training cohort (n=934), and the Metavision database was the external validation cohort (n=687). A COX proportional hazards regression model was established to screen independent risk factors and draw a nomogram. We conducted internal cross-validation and external validation of the model. Using the receiver operator characteristic (ROC) curve, Calibration chart, and decision curve analysis, we detected the discrimination, calibration, and benefit of the model respectively, comparing with the SOFA scoring model. Results Age, SOFA score, white blood cell count≤4×109/L, neutrophilic granulocyte percentage (NEU%)>85%, platelet count (PLT)≤100×109/L, PLT>300×109/L, red cell distribution width >15%, blood urea nitrogen, and lactate dehydrogenase were independent risk factors. The areas under the ROC curve of the model were 0.747 (training cohort) and 0.708 (external validation cohort), respectively, which was superior to the SOFA scoring model in terms of discrimination, calibration, and benefit. Conclusion The model established in this study can accurately and effectively predict the risk of the disease mortality, and provide a visual assessment method for early identification of high-risk patients.
Objective To scoping review the risk prediction models for sarcopenia in China was conducted, and provide reference for scientific prevention and treatment of the disease and related research. Methods We systematically searched PubMed, Web of Science, Cochrane Library, Embase, China Knowledge Network, China Biomedical Literature Database, Wanfang Database, and Weipu Database for literature related to myasthenia gravis prediction models in China, with a time frame from the construction of the database to April 30, 2024 for the search. The risk of bias and applicability of the included literature were assessed, and information on the construction of myasthenia gravis risk prediction models, model predictors, model presentation form and performance were extracted. Results A total of 25 literatures were included, the prevalence of sarcopenia ranged from 12.16% to 54.17%, and the study population mainly included the elderly, the model construction methods were categorized into two types: logistic regression model and machine learning, and age, body mass index, and nutritional status were the three predictors that appeared most frequently. Conclusion Clinical caregivers should pay attention to the high-risk factors for the occurrence of sarcopenia, construct models with accurate predictive performance and high clinical utility with the help of visual model presentation, and design prospective, multicenter internal and external validation methods to continuously improve and optimize the models to achieve the best predictive effect.
ObjectiveTo analyze the prevalence and risk factors of metabolic syndrome (MS) after adult liver transplantation (LT) recipients. MethodsThe clinicopathologic data of patients with survival time ≥1 year underwent LT in the People’s Hospital of Zhongshan City from January 1, 2015 to August 31, 2020 were analyzed retrospectively. The logistic regression model was used to analyze the risk factors affecting MS occurrence after LT, and the receiver operating characteristic (ROC) curve was used to evaluate the optimal cutoff value of the index of predicting MS occurrence and its corresponding evaluation effect. ResultsA total of 107 patients who met the inclusion criteria were collected in this study. Based on the diagnostic criteria of MS of Chinese Medical Association Diabetes Association, the occurrence rate of MS after LT was 32.7% (35/107). Multivariate logistic regression analysis showed that the increased age of the recipient [OR (95%CI)=1.106 (1.020, 1.199), P=0.014], preoperative increased body mass index [OR (95%CI)=1.439 (1.106, 1.872), P=0.007] and blood glucose level [OR (95%CI)=1.708 (1.317, 2.213), P<0.001], and with preoperative smoking history [OR (95%CI)=5.814 (1.640, 20.610), P=0.006] and drinking history [OR (95%CI)=5.390 (1.454, 19.984), P=0.012] increased the probability of MS after LT. The areas under the ROC curve (AUC) corresponding to these five indexes were 0.666, 0.669, 0.769, 0.682, and 0.612, respectively. The corresponding optimal cutoff values of three continuous variables (recipient’s age, preoperative body mass index, and blood glucose level) were 53 years old, 23.1 kg/m2, and 6.8 mmol/L, respectively. The AUC of combination of the above five indexes in predicting occurrence of MS was 0.903 [95%CI (0.831, 0.952)], and the sensitivity and specificity were 80.0% and 90.3%, respectively. ConclusionsIncidence of MS after adult LT recipient is not low. For recipients with preoperative hyperglycemia, obese, elderly, histories of drinking and smoking before LT need to pay attention to the early detection and early intervention of MS.
Objective To explore the clinical and inflammatory characteristics and risk factors of severe asthma to improve clinicians' awareness of the disease. Methods The general information of patients with asthma who visited the Department of Respiratory Medicine, the First Hospital of Shanxi Medical University from May 2018 to May 2021, as well as the diagnosis and treatment of asthma, personal history, comorbidities, auxiliary examination, asthma control test (ACT) score were collected. A total of 127 patients were included, including 40 in the severe asthma group and 87 in the mild-to-moderate asthma group. Chi-square test, independent sample t test and logistic regression were used to analyze the clinical characteristics, inflammatory markers and risk factors of severe asthma. Results Compared with the patients with mild to moderate asthma, the patients with severe asthma were more older (51.0±12.0 years vs 40.7±12.8 years, P<0.05), had more smokers (32.5% vs. 14.9%, P<0.05), and more males (67.5% vs. 40.2%, P<0.05). The patients with severe asthma got poor FEV1%pred [(56.1±23.8)% vs. (93.2±18.0)%, P<0.05] and FEV1/FVC [(56.7±13.2)% vs. (75.8±9.0)%, P<0.05)], and more exacerbations in the previous year (2.7±3.1 vs. 0.1±0.4, P<0.05), lower ACT score (14.4±3.7 vs. 18.0±5.0, P<0.05), and higher blood and induced sputum eosinophil counts [(0.54±0.44)×109/L vs. (0.27±0.32)×109/L, P<0.05; (25.9±24.2)% vs. (9.8±17.5)%, P<0.05]. There was no significant difference in the proportion of neutrophils in the induced sputum or FeNO between the two groups (P>0.05). Analysis of related risk factors showed that smoking (OR=2.740, 95%CI 1.053 - 7.130), combined with allergic rhinitis (OR=14.388, 95%CI 1.486 - 139.296) and gastroesophageal reflux (OR=2.514, 95%CI 1.105 - 5.724) were risk factors for severe asthma. Conclusions Compared with patients with mild to moderate asthma, patients with severe asthma are characterized by poor lung function, more exacerbations, and a dominant eosinophil inflammatory phenotype, which is still poorly controlled even with higher level of treatment. Risk factors include smoking, allergic rhinitis, and gastroesophageal reflux, etc.
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.
Risk stratifications are valuable aids for stratifying patients by disease severity, driving informed clinical decisions, because they allow the selection of the most appropriate strategy of treatment based on the patient's individual characteristics. The clinical algorithms help patients and their families to get a better understanding of issues relevant to treatment strategies and subsequent risks as part of the process to obtain informed consent. The current risk stratifications of coronary artery bypass grafting included the Society of Thoracic Surgeons Score, the European System for Cardiac Operative Risk Evaluation, SinoSystem for Coronary Operative Risk Evaluation. This review focuses on the progress of risk stratifications of coronary artery bypass grafting for patients undergoing cardiac surgery.
ObjectiveTo analyze the risk factors relevant retrograde type A aortic dissection (RTAD) after thoracic endovascular aortic repair (TEVAR) for Stanford type B aortic dissection and provide a reference for its prevention and management. MethodsA retrospective analysis was conducted on patients with Stanford type B aortic dissection who underwent TEVAR at the First Affiliated Hospital of Chongqing Medical University from January 2017 to June 2023. The patients met the inclusion and exclusion criteria were included in the study. The multivariate logistic regression was used to analyze the risk factors for RTAD, with a test level of α=0.05. ResultsA total of 176 patients were included, among whom 7 developed RTAD, with an occurrence rate of 3.98%. The multivariate logistic regression analysis revealed that the larger τ angle between the centerline of the aorta [OR (95%CI)=1.195 (1.032, 1.384)] and the degree of curvature of the aortic arch (the curve distance from the proximal brachiocephalic trunk to the distal left subclavian artery) [OR (95%CI)=0.756 (0.572, 0.999)], the higher probability of RTAD after TEVAR (P＜0.05). ConclusionsFrom the results of this study, it can be seen that for patients with Stanford B-type aortic dissection underwent TEVAR treatment, careful preoperative evaluation of morphological characteristics of the aortic arch (particularly the τ angle of the aorta centerline and the degree of curvature of the aortic arch (the curve distance from the proximal brachiocephalic trunk to the distal left subclavian artery) is crucial for reducing the occurrence of RTAD after TEVAR in patients with Stanford type B aortic dissection.
ObjectiveTo explore the value of geriatric nutritional risk index (GNRI) and sarcopenia on predicting postoperative complications in elderly patients with gastric cancer. MethodsAccording to the inclusion and exclusion criteria, the elderly (aged ≥60 years) patients with gastric cancer underwent radical gastrectomy in the Department of Gastrointestinal Surgery of Xuzhou Central Hospital from January 1, 2017 to December 31, 2021 were retrospectively gathered. The occurrence of postoperative complications (grade 2 or beyond by the Clavien-Dindo classification) was analyzed. The risk factors affecting postoperative complications were analyzed by univariate and multivariate logistic regression analyses to construct the prediction model, then was visualized by drawing a nomogram. The differentiation of the nomogram between the patients with postoperative complications and without postoperative complications was evaluated by the receiver operating characteristic (ROC) curve. The accuracy of the nomogram was evaluated by the calibration curve. Further, the clinical net benefit rate was analyzed by the decision curve analysis (DCA) to evaluate the clinical practicability. ResultsA total of 236 patients were gathered, 97 (41.1%) of whom had postoperative complications during hospitalization. The results of multivariate logistic regression analysis showed that the age, gender, GNRI, sarcopenia, surgical mode, and American Society of Aneshesiologists classification were the factors influencing the postoperative complications (P<0.05). The differentiation of nomogram based on the influencing factors was well, the area under the ROC curve was 0.732. The calibration curve showed that the model prediction curve was close to the ideal curve. The clinical net benefit rate by the DCA was higher when the probability of postoperative complications was 0.18 to 0.72. ConclusionsThe efficiency of nomogram based on GNRI and sarcopenia is well for predicting the occurrence of postoperative complications in elderly patients with gastric cancer. However, the nomogram needs to be further validated by prospective studies and external data.
ObjectiveTo explore the risk factors of perioperative severe complications (Clavien-Dindo grade Ⅲ and above) after laparoscopic radical resection of colorectal cancer (CRC). MethodsThe clinicopathologic data of CRC patients who met the inclusion and exclusion criteria treated in the Shaanxi Provincial People’s Hospital from January 2018 to December 2020 were retrospectively analyzed. The univariate and multivariate logistic analyses were used to explore the risk factors of perioperative severe complications after the laparoscopic radical resection of CRC. ResultsAtotal of 170 eligible patients were included in this study, and the postoperative complications occurred in 45 patients, 24 of whom were severe complications. The univariate analysis results showed that the age (P<0.001), body mass index (BMI, P=0.047), age adjusted Charlson complication index (aCCI) score (P=0.002), American Association of Anesthesiologists (ASA) classification (P<0.001), prognostic nutritional index (PNI, P=0.011), preoperative anemia (P=0.011), operation numbers of surgeon (P=0.003), and operation time (P=0.026) were related to the perioperative severe complications in the patients underwent the laparoscopic radical resection of CRC. The statistic indexes of univariate analysis (P<0.05) combined with indexes of clinical significance were included in the multivariate analysis, the results showed that the ASA classification Ⅲ– Ⅳ (OR=3.536, P=0.027), BMI ≥25 kg/m2 (OR=3.228, P=0.031), preoperative anemia (OR=2.876, P=0.049), operation numbers of surgeon <300 (OR=0.324, P=0.046), and the operation time ≥300 min (OR=3.480, P=0.020) increased the probability of perioperative severe complications in the patients underwent the laparoscopic radical resection of CRC. ConclusionsThe results of this study suggest that clinicians should pay attention to the perioperative management of patients with CRC, such as adequately evaluating the preoperative status of patients by ASA classification, PNI, and aCCI to adjust the malnutrition of patients; after operation, the patients with BMI ≥25 kg/m2 and operation time more than 300 min should be paid more attention. At the same time, the surgeon should continuously accumulate the operation numbers and improve the operation proficiency so as to reduce the occurrence of perioperative severe complications after laparoscopic radical resection of CRC.
Objective To analyze the correlation between HLA-A and B genotypes and maculopapular exanthema (MPE) caused by Carbamazepine (CBZ) and Oxcarbazepine (OXC), and to explore the genetic risk factors of MPE. Methods Patients with MPE (rash group) and patients without MPE (non-rash group) after taking CBZ or OXC were retrospectively collected from January 2016 to October 2021 in the Second Affiliated Hospital of Guangzhou Medical University. DNA was extracted from peripheral blood. HLA-A and HLA-B alleles were sequenced by high resolution sequencing, and a case-control study was conducted to analysis the correlations between MPE and HLA genotypes. Results A total of 100 patients with CBZ-MPE, 100 patients with CBZ-tolerant, 50 patients with OXC-MPE, and 50 patients with OXC-tolerant were collected. There was no significant difference in age and sex between CBZ, OXC rash groups and non-rash groups The average latency of CBZ-rash group was (11.31±11.00) days and their average dosage was (348.46±174.10) mg; the average latency of OXC-rash group was (11.67±10.34) days and their average dosage was (433.52±209.22) mg [equivalent to CBZ (289.01±139.48 mg)], showing no significant difference in latency and dosage between CBZ and OXC (P>0.05). The positive rates of HLA-A*24:02 and A*30:01 in CBZ-rash group were 28% and 6%, respectively, which were significantly higher than those in CBZ-non rash group (16% and 0%, both P=0.04). The positive rate of HLA-B*40:01 in CBZ-rash group was 18%, which was significantly lower than that in CBZ-non rash group (40%, P<0.001). No association between HLA-A or B genotype and OXC-rash was found yet. When pooled, it was still found that the positive rates of HLA-A*24:02 and A*30:01 in the rash group were higher than those in the non-rash group, while the positive rate of HLA-B*40:01 in the rash group was lower than that in the non-rash group, and the difference was statistically significant (P<0.05). Conclusions HLA-A*24:02 and A*30:01 were associated with MPE caused by CBZ, and may be common risk factors for aromatic antiepileptic drugs.