ObjectiveProlonged mechanical ventilation (PMV) is a prognostic marker for short-term adverse outcomes in patients after lung transplantation.The risk of prolonged mechanical ventilation after lung transplantation is still not clear. The study to identify the risk factors of prolonged mechanical ventilation (PMV) after lung transplantation.Methods This retrospective observational study recruited patients who underwent lung transplantation in Wuxi People’s Hospital from January 2020 to December 2022. Relevant information was collected from patients and donors, including recipient data (gender, age, BMI, blood type, comorbidities), donor data (age, BMI, time of endotracheal intubation, oxygenation index, history of smoking, and any comorbidity with multidrug-resistant bacterial infections), and surgical data (surgical mode, incision type, operation time, cold ischemia time of the donor lung, intraoperative bleeding, and ECMO support), and postoperative data (multi-resistant bacterial lung infection, multi-resistant bacterial bloodstream infection, and mean arterial pressure on postoperative admission to the monitoring unit). Patients with a duration of mechanical ventilation ≤72 hours were allocated to the non-prolonged mechanical ventilation group, and patients with a duration of mechanical ventilation>72 hours were allocated to the prolonged mechanical ventilation group. LASSO regression analysis was applied to screen risk factors., and a clinical prediction model for the risk of prolonged mechanical ventilation after lung.ResultsPatients who met the inclusion criteria were divided into the training set and the validation set. There were 307 cases in the training set group and 138 cases in the validation set group. The basic characteristics of the training set and the validation set were compared. There were statistically significant differences in the recipient’s BMI, donor’s gender, CRKP of the donor lung swab, whether the recipient had pulmonary infection before the operation, the type of transplantation, the cold ischemia time of the donor lung, whether ECMO was used during the operation, the duration of ECMO assistance, CRKP of sputum, and the CRE index of the recipient's anal test (P<0.05). 2. The results of the multivariate logistic regression model showed that female recipients, preoperative mechanical ventilation in recipients, preoperative pulmonary infection in recipients, intraoperative application of ECMO, and the detection of multi-drug resistant Acinetobacter baumannii, multi-drug resistant Klebsiella pneumoniae and maltoclomonas aeruginosa in postoperative sputum were independent risk factors for prolonged mechanical ventilation after lung transplantation. The AUC of the clinical prediction model in the training set and the validation set was 0.838 and 0.828 respectively, suggesting that the prediction model has good discrimination. In the decision curves of the training set and the validation set, the threshold probabilities of the curves in the range of 0.05-0.98 and 0.02-0.85 were higher than the two extreme lines, indicating that the model has certain clinical validity.ConclusionsFemale patients, Preoperative pulmonary infection, preoperative mechanical ventilation,blood type B, blood type O, application of ECMO assistance, multi-resistant Acinetobacter baumannii infection, multi-resistant Klebsiella pneumoniae infection, and multi-resistant Stenotrophomonas maltophilia infection are independent risk factors for PMV (prolonged mechanical ventilation) after lung transplantation.
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.
Objective To explore the risk factors for long-term death of patients with acute myocardial infarction (AMI) and reduced left ventricular ejection fraction (LVEF), and develop and validate a prediction model for long-term death. Methods This retrospective cohort study included 1013 patients diagnosed with AMI and reduced LVEF in West China Hospital of Sichuan University between January 2010 and June 2019. Using the RAND function of Excel software, patients were randomly divided into three groups, two of which were combined for the purpose of establishing the model, and the third group was used for validation of the model. The endpoint of the study was all-cause mortality, and the follow-up was until January 20th, 2021. Cox proportional hazard model was used to evaluate the risk factors affecting the long-term death, and then a prediction model based on those risk factors was established and validated. Results During a median follow-up of 1377 days, 296 patients died. Multivariate Cox regression analysis showed that age≥65 years [hazard ratio (HR)=1.842, 95% confidence interval (CI) (1.067, 3.179), P=0.028], Killip class≥Ⅲ[HR=1.941, 95%CI (1.188, 3.170), P=0.008], N-terminal pro-brain natriuretic peptide≥5598 pg/mL [HR=2.122, 95%CI (1.228, 3.665), P=0.007], no percutaneous coronary intervention [HR=2.181, 95%CI (1.351, 3.524), P=0.001], no use of statins [HR=2.441, 95%CI (1.338, 4.454), P=0.004], and no use of β-blockers [HR=1.671, 95%CI (1.026, 2.720), P=0.039] were independent risk factors for long-term death. The prediction model was established and patients were divided into three risk groups according to the total score, namely low-risk group (0-2), medium-risk group (4-6), and high-risk group (8-12). The results of receiver operating characteristic curve [area under curve (AUC)=0.724, 95%CI (0.680, 0.767), P<0.001], Hosmer-Lemeshow test (P=0.108), and Kaplan-Meier survival curve (P<0.001) showed that the prediction model had an efficient prediction ability, and a strong ability in discriminating different groups. The model was also shown to be valid in the validation group [AUC=0.758, 95%CI (0.703, 0.813), P<0.001]. Conclusions In patients with AMI and reduced LVEF, age≥65 years, Killip class≥Ⅲ, N-terminal pro-brain natriuretic peptide≥5598 pg/mL, no percutaneous coronary intervention, no use of statins, and no use of β-blockers are independent risk factors for long-term death. The developed risk prediction model based on these risk factors has a strong prediction ability.
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 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.
Acute kidney injury (AKI) is a complication with high morbidity and mortality after cardiac surgery. In order to predict the incidence of AKI after cardiac surgery, many risk prediction models have been established worldwide. We made a detailed introduction to the composing features, clinical application and predictive capability of 14 commonly used models. Among the 14 risk prediction models, age, congestive heart failure, hypertension, left ventricular ejection fraction, diabetes, cardiac valve surgery, coronary artery bypass grafting (CABG) combined with cardiac valve surgery, emergency surgery, preoperative creatinine, preoperative estimated glomerular filtration rate (eGFR), preoperative New York Heart Association (NYHA) score>Ⅱ, previous cardiac surgery, cadiopulmonary bypass (CPB) time and low cardiac output syndrome (LCOS) are included in many risks prediction models (>3 times). In comparison to Mehta and SRI models, Cleveland risk prediction model shows the best discrimination for the prediction of renal replacement therapy (RRT)-AKI and AKI in the European. However, in Chinese population, the predictive ability of the above three risk prediction models for RRT-AKI and AKI is poor.
ObjectiveTo systematically evaluate the risk prediction models for postoperative delirium in adults with cardiac surgery. MethodsThe SinoMed, CNKI, Wanfang, VIP, PubMed, EMbase, Web of Science, and Cochrane Library databases were searched to collect studies on risk prediction models for postoperative delirium in cardiac surgery published up to January 29, 2025. Two researchers screened the literature according to inclusion and exclusion criteria, used the PROBAST bias tool to assess the quality of the literature, and conducted a meta-analysis of common predictors in the model using Stata 17.0 software. ResultsA total of 21 articles were included, establishing 45 models with 28733 patients. Age, cardiopulmonary bypass time, history of diabetes, history of cerebrovascular disease, and gender were the top five common predictors. The area under the curve (AUC) of the 45 models ranged from 0.6 to 0.926. Fourteen out of the 21 studies had good applicability, while the applicability of the remaining seven was unclear; 20 studies had a high risk of bias. Meta-analysis showed that the incidence of postoperative delirium in adults with cardiac surgery was 18.6% [95%CI (15.7%, 21.6%)], and age [OR=1.04 (1.04, 1.05), P<0.001], history of cerebrovascular disease [OR=1.76 (1.46, 2.06), P<0.001], gender [OR=1.73 (1.43, 2.03), P<0.001], minimum mental state examination score [OR=1.00 (0.82, 1.17), P<0.001], and length of ICU stay [OR=5.59 (4.29, 6.88), P<0.001] weer independent influencing factors of postoperative delirium after cardiac surgery. ConclusionThe risk prediction models for postoperative delirium after cardiac surgery have good predictive performance, but there is a high overall risk of bias. In the future, large-sample, multicenter, high-quality prospective clinical studies should be conducted to construct the optimal risk prediction model for postoperative delirium in adults with cardiac surgery, aiming to identify and prevent the occurrence of postoperative delirium as early as possible.
Objective To identify risk factors for fibrosis progression and develop a predictive model in patients with usual interstitial pneumonia (UIP) pattern on CT. Methods We retrospectively enrolled 453 patients with CT-defined UIP or probable UIP, followed for one year. The study endpoint was either meeting progressive pulmonary fibrosis (PPF) criteria or completing one-year follow-up. Clinical features, pulmonary function, and laboratory data were collected. Independent risk factors were identified using logistic regression. Patients were randomly divided into training and validation cohorts at a 7:3 ratio. A nomogram was constructed in the training cohort using R and its performance and clinical utility were evaluated in the validation cohort. Results During one-year follow-up, 160 patients (35.3%) met PPF criteria. Multivariate analysis showed that higher baseline levels of CA19-9 and CA125, as well as the presence of pulmonary hypertension, were independent risk factors for pulmonary fibrosis progression, while a higher percentage of predicted forced vital capacity (FVC) and the presence of emphysema were protective factors. A nomogram model was constructed using these five variables, with the area under the curve (AUC) for predicting fibrosis progression being 0.854 in the training set and 0.817 in the validation set. Clinical decision curve analysis indicated that the model provided the greatest clinical benefit when the threshold probability was between 0.12 and 0.93. Conclusion A nomogram incorporating baseline CA19-9, CA125, FVC % predicted, pulmonary hypertension, and emphysema shows potential for predicting one-year fibrosis progression in UIP patients.
ObjectiveTo analyze the influencing factors of preoperative frailty in elderly esophageal cancer patients, and to construct and evaluate a nomogram model. MethodsElderly esophageal cancer patients hospitalized in the First Affiliated Hospital of Navy Medical University from January 2022 to August 2024 were selected. General information of the patients was collected, and the patients were divided into a frailty group and a non-frailty group according to the frailty score. Single-factor and multi-factor logistic regression analysis were used to screen the independent influencing factors of preoperative frailty in elderly esophageal cancer patients, and the nomogram model was constructed and evaluated accordingly. ResultsA total of 332 elderly esophageal cancer patients were included, including 256 males and 76 females, with an average age of (69.71±5.95) years. The incidence of preoperative frailty was 45.48%. Multivariate logistic regression analysis showed that age≥70 years [OR=2.986, 95%CI (1.796, 4.965), P<0.001], basic diseases≥2 types [OR=3.129, 95%CI (1.794, 5.457), P=0.012], self-care ability [OR=2.599, 95%CI (1.512, 4.467), P<0.001], and depression [OR=3.784, 95%CI (2.135, 6.706), P=0.005] were independent risk factors for preoperative frailty in elderly esophageal cancer patients. The nomogram model was constructed as follows: Z=?2.038+1.094×(age≥70 years)+1.141×(basic diseases≥2 types)+0.955×self-care ability (non-independent)+1.331×depression, with an area under the receiver operating characteristic curve of 0.802 [95%CI (0.756, 0.847)], and the sensitivity and specificity were 78.8% and 65.2%, respectively. The goodness of fit test showed that the model had good discrimination and calibration (χ2=6.64, P=0.355). ConclusionAge≥70 years, basic diseases≥2 types, self-care ability (non-independent), and depression are independent risk factors for preoperative frailty in elderly esophageal cancer patients. The constructed nomogram model shows good predictive performance and can identify elderly esophageal cancer patients with preoperative frailty, providing a reference for the formulation of corresponding intervention measures.
ObjectiveTo construct a demand model for electronic medical record (EMR) data quality in regards to the lifecycle in machine learning (ML)-based disease risk prediction, to guide the implementation of EMR data quality assessment. MethodsReferring to the lifecycle in ML-based predictive model, we explored the demand for EMR data quality. First, we summarized the key data activities involved in each task on predicting disease risk with ML through a literature review. Second, we mapped the data activities in each task to the associated requirements. Finally, we clustered those requirements into four dimensions. ResultsWe constructed a three-layer structured ring to represent the demand model for EMR data quality in ML-based disease risk prediction research. The inner layer shows the seven main tasks in ML-based predictive models: data collection, data preprocessing, feature representation, feature selection and extraction, model training, model evaluation and optimization, and model deployment. The middle layer is the key data activities in each task; and the outer layer represents four dimensions of data quality requirements: operability, completeness, accuracy, and timeliness. ConclusionThe proposed model can guide real-world EMR data governance, improve its quality management, and promote the generation of real-world evidence.
