Objective The management of pulmonary nodules is a common clinical problem, and this study constructed a nomogram model based on FUT7 methylation combined with CT imaging features to predict the risk of adenocarcinoma in patients with pulmonary nodules. Methods The clinical data of 219 patients with pulmonary nodules diagnosed by histopathology at the First Affiliated Hospital of Zhengzhou University from 2021 to 2022 were retrospectively analyzed. The FUT7 methylation level in peripheral blood were detected, and the patients were randomly divided into training set (n=154) and validation set (n=65) according to proportion of 7:3. They were divided into a lung adenocarcinoma group and a benign nodule group according to pathological results. Single-factor analysis and multi-factor logistic regression analysis were used to construct a prediction model in the training set and verified in the validation set. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model, the calibration curve was used to evaluate the consistency of the model, and the clinical decision curve analysis (DCA) was used to evaluate the clinical application value of the model. The applicability of the model was further evaluated in the subgroup of high-risk CT signs (located in the upper lobe, vascular sign, and pleural sign). Results Multivariate logistic regression analysis showed that female, age, FUT7_CpG_4, FUT7_CpG_6, sub-solid nodules, lobular sign and burr sign were independent risk factors for lung adenocarcinoma (P<0.05). A column-line graph prediction model was constructed based on the results of the multifactorial analysis, and the area under the ROC curve was 0.925 (95%CI 0.877 - 0.972 ), and the maximum approximate entry index corresponded to a critical value of 0.562, at which time the sensitivity was 89.25%, the specificity was 86.89%, the positive predictive value was 91.21%, and the negative predictive value was 84.13%. The calibration plot predicted the risk of adenocarcinoma of pulmonary nodules was highly consistent with the risk of actual occurrence. The DCA curve showed a good clinical net benefit value when the threshold probability of the model was 0.02 - 0.80, which showed a good clinical net benefit value. In the upper lobe, vascular sign and pleural sign groups, the area under the ROC curve was 0.903 (95%CI 0.847 - 0.959), 0.897 (95%CI 0.848 - 0.945), and 0.894 (95%CI 0.831 - 0.956). Conclusions This study developed a nomogram model to predict the risk of lung adenocarcinoma in patients with pulmonary nodules. The nomogram has high predictive performance and clinical application value, and can provide a theoretical basis for the diagnosis and subsequent clinical management of pulmonary nodules.
ObjectiveTo explore the predictive value of a simplified signs scoring system for the severity and prognosis of patients with coronavirus disease 2019 (COVID-19). Methods Clinical data of 1 605 confirmed patients with COVID-19 from January to May 2020 in 45 hospitals of Sichuan and Hubei Provinces were retrospectively analyzed. The patients were divided into a mild group (n=1150, 508 males, average age of 51.32±16.26 years) and a severe group (n=455, 248 males, average age of 57.63±16.16 years). ResultsAge, male proportion, respiratory rate, systolic blood pressure and mean arterial pressure in the severe group were higher than those in the mild group (P<0.05). Peripheral oxygen saturation (SpO2) and Glasgow coma scale (GCS) were lower than those in the mild group (P<0.05). Multivariate logistic regression analysis showed that age, respiratory rate, SpO2, and GCS were independent risk factors for severe patients with COVID-19. Based on the above indicators, the receiver operating characteristic (ROC) curve analysis showed that the area under the curve of the simplified signs scoring system for predicting severe patients was 0.822, which was higher than that of the quick sequential organ failure assessment (qSOFA) score and modified early warning score (MEWS, 0.629 and 0.631, P<0.001). The ROC analysis showed that the area under the curve of the simplified signs scoring system for predicting death was 0.796, higher than that of qSOFA score and MEWS score (0.710 and 0.706, P<0.001). ConclusionAge, respiratory rate, SpO2 and GCS are independent risk factors for severe patients with COVID-19. The simplified signs scoring system based on these four indicators may be used to predict patient's risk of severe illness or early death.
As the volume of medical research using large language models (LLM) surges, the need for standardized and transparent reporting standards becomes increasingly critical. In January 2025, Nature Medicine published statement titled by TRIPOD-LLM reporting guideline for studies using large language models. This represents the first comprehensive reporting framework specifically tailored for studies that develop prediction models based on LLM. It comprises a checklist with 19 main items (encompassing 50 sub-items), a flowchart, and an abstract checklist (containing 12 items). This article provides an interpretation of TRIPOD-LLM’s development methods, primary content, scope, and the specific details of its items. The goal is to help researchers, clinicians, editors, and healthcare decision-makers to deeply understand and correctly apply TRIPOD-LLM, thereby improving the quality and transparency of LLM medical research reporting and promoting the standardized and ethical integration of LLM into healthcare.
With the increasing availability of clinical and biomedical big data, machine learning is being widely used in scientific research and academic papers. It integrates various types of information to predict individual health outcomes. However, deficiencies in reporting key information have gradually emerged. These include issues like data bias, model fairness across different groups, and problems with data quality and applicability. Maintaining predictive accuracy and interpretability in real-world clinical settings is also a challenge. This increases the complexity of safely and effectively applying predictive models to clinical practice. To address these problems, TRIPOD+AI (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis+artificial intelligence) introduces a reporting standard for machine learning models. It is based on TRIPOD and aims to improve transparency, reproducibility, and health equity. These improvements enhance the quality of machine learning model applications. Currently, research on prediction models based on machine learning is rapidly increasing. To help domestic readers better understand and apply TRIPOD+AI, we provide examples and interpretations. We hope this will support researchers in improving the quality of their reports.
Objective To identify and screen sensitive predictors associated with subscapularis (SSC) tendon tear and develop a web-based dynamic nomogram to assist clinicians in early identification and intervention of SSC tendon tear. Methods Between July 2016 and December 2021, 528 consecutive cases of patients who underwent shoulder arthroscopic surgery with completely MRI and clinical data were retrospectively analyzed. Patients admitted between July 2016 and July 2019 were included in the training cohort, and patients admitted between August 2019 and December 2021 were included in the validation cohort. According to the diagnosis of arthroscopy, the patients were divided into SSC tear group and non-SSC tear group. Univariate analysis, least absolute shrinkage and selection operator (LASSO) method, and 10-fold cross-validation method were used to screen for reliable predictors highly associated with SSC tendon tear in a training set cohort, and R language was used to build a nomogram model for internal and external validation. The prediction performance of the nomogram was evaluated by concordance index (C-index) and calibration curve with 1 000 Bootstrap. Receiver operating curves were drawn to evaluate the diagnostic performance (sensitivity, specificity, predictive value, likelihood ratio) of the predictive model and MRI (based on direct signs), respectively. Decision curve analysis (DCA) was used to evaluate the clinical implications of predictive models and MRI. Results The nomogram model showed good discrimination in predicting the risk of SSC tendon tear in patients [C-index=0.878; 95%CI (0.839, 0.918)], and the calibration curve showed that the predicted results were basically consistent with the actual results. The research identified 6 predictors highly associated with SSC tendon tears, including coracohumeral distance (oblique sagittal) reduction, effusion sign (Y-plane), subcoracoid effusion sign, biceps long head tendon displacement (dislocation/subluxation), multiple posterosuperior rotator cuff tears (≥2, supra/infraspinatus), and MRI suspected SSC tear (based on direct sign). Compared with MRI diagnosis based on direct signs of SSC tendon tear, the predictive model had superior sensitivity (80.2% vs. 57.0%), positive predictive value (53.9% vs. 53.3%), negative predictive value (92.7% vs. 86.3%), positive likelihood ratio (3.75 vs. 3.66), and negative likelihood ratio (0.25 vs. 0.51). DCA suggested that the predictive model could produce higher clinical benefit when the risk threshold probability was between 3% and 93%. ConclusionThe nomogram model can reliably predict the risk of SSC tendon tear and can be used as an important tool for auxiliary diagnosis.
ObjectiveTo establish and internally validate a predictive model for poorly differentiated adenocarcinoma based on CT imaging and tumor marker results. MethodsPatients with solid and partially solid lung nodules who underwent lung nodule surgery at the Department of Thoracic Surgery, the Affiliated Brain Hospital of Nanjing Medical University in 2023 were selected and randomly divided into a training set and a validation set at a ratio of 7:3. Patients' CT features, including average density value, maximum diameter, pleural indentation sign, and bronchial inflation sign, as well as patient tumor marker results, were collected. Based on postoperative pathological results, patients were divided into a poorly differentiated adenocarcinoma group and a non-poorly differentiated adenocarcinoma group. Univariate analysis and logistic regression analysis were performed on the training set to establish the predictive model. The receiver operating characteristic (ROC) curve was used to evaluate the model's discriminability, the calibration curve to assess the model's consistency, and the decision curve to evaluate the clinical value of the model, which was then validated in the validation set. ResultsA total of 299 patients were included, with 103 males and 196 females, with a median age of 57.00 (51.00, 67.25) years. There were 211 patients in the training set and 88 patients in the validation set. Multivariate analysis showed that carcinoembryonic antigen (CEA) value [OR=1.476, 95%CI (1.184, 1.983), P=0.002], cytokeratin 19 fragment antigen (CYFRA21-1) value [OR=1.388, 95%CI (1.084, 1.993), P=0.035], maximum tumor diameter [OR=6.233, 95%CI (1.069, 15.415), P=0.017], and average density [OR=1.083, 95%CI (1.020, 1.194), P=0.040] were independent risk factors for solid and partially solid lung nodules as poorly differentiated adenocarcinoma. Based on this, a predictive model was constructed with an area under the ROC curve of 0.896 [95%CI (0.810, 0.982)], a maximum Youden index corresponding cut-off value of 0.103, sensitivity of 0.750, and specificity of 0.936. Using the Bootstrap method for 1000 samplings, the calibration curve predicted probability was consistent with actual risk. Decision curve analysis indicated positive benefits across all prediction probabilities, demonstrating good clinical value. ConclusionFor patients with solid and partially solid lung nodules, preoperative use of CT to measure tumor average density value and maximum diameter, combined with tumor markers CEA and CYFRA21-1 values, can effectively predict whether it is poorly differentiated adenocarcinoma, allowing for early intervention.
Objective To evaluate the clinical application value of four inflammatory indices [monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR)] in predicting postoperative mortality risk in patients with Siewert type Ⅱ esophagogastric junction adenocarcinoma, and to explore the predictive performance of four inflammatory indices. Methods This retrospective study collected clinical data from 310 patients with Siewert typeⅡ esophagogastric junction adenocarcinoma who were admitted to the Second Hospital of Lanzhou University between October 2016 and March 2023, and met the inclusion and exclusion criteria. Univariate analysis was used to initially screen variables related to postoperative mortality risk. The variance inflation factor (VIF) analysis was performed to assess multicollinearity issues, and multivariate regression analysis was used to further reveal the independent effects of key variables on postoperative mortality risk. The performance of the predictive models was evaluated using receive operatior characteristic curves and Kaplan-Meier survival analysis, and the effects of different inflammatory indices on patient survival were explored. Finally, machine learning methods such as Light GBM, random forest, support vector machine (SVM), and XGBoost were used to evaluate the predictive performance of the four inflammatory indices. Results The four inflammatory indices were significantly associated with postoperative mortality risk in patients with Siewert type Ⅱ esophagogastric junction adenocarcinoma (MLR: HR=2.6884, 95% CI 1.4559 to 4.9642, P=0.002; PLR: HR=1.0022, 95% CI1.0001 to 1.0043, P=0.041; SII: HR=1.0003, 95% CI1.0001 to 1.0006, P=0.002; NLR: HR=1.0697, 95% CI 1.0277 to 1.1134, P=0.001). Machine learning model results showed that NLR had the best performance in the random forest model, with an AUC of 0.863 in the training set and an AUC of 0.834 in the test set. Conclusion Preoperative clinical indicators, especially the NLR inflammatory factor, are of significant importance in predicting the postoperative mortality risk of patients with Siewert typeⅡ esophagogastric junction adenocarcinoma.
ObjectiveTo construct a new model for predicting the overall survival rate of gastric cancer and to guide the clinical work.MethodsThe clinical information and gene expression information of patients with gastric cancer were downloaded through The Cancer Genome Atlas (TCGA) database. The clinicopathologic characteristics and gene expression information affecting the overall survival rate of gastric cancer patients were screened by univariate COX regression and Lasson regression, then the predictive model was constructed by multiple COX regression model, and the predictive model was tested by receiver operating characteristic curve, calibration curve and decision curve analysis curve. The effect of genes included in the predictive model on the overall survival rate of patients with gastric cancer was discussed, and the predictive model diagram was drawn.ResultsThrough repeated screening and comparison of the model, the patient’s age, T stage, N stage, M stage and 12 genes (INCENP, IGHD3-16, ITFG1-AS1, NEK5, MATN3, YWHABP2, SYT12, LINC01210, ZNF385C, LINC01980, CYMP-AS1 and FAT3) were included in the predictive model. The prediction ability of this model was close to or more than 80%, which was significantly higher than that of the traditional TNM staging prediction system. All the indexes included in the model were significantly different by univariate and multivariate COX regression analysis（P<0.05）, and the 12 genes included were the risk factors affecting the overall survival rate of gastric cancer.ConclusionThe gastric cancer prediction model constructed by combining clinical characteristics and genomics has good predictive ability and can guide clinical work.
Objective To explore the independent risk factors for hospital infections in tertiary hospitals in Gansu Province, and establish and validate a prediction model. Methods A total of 690 patients hospitalized with hospital infections in Gansu Provincial Hospital between January and December 2021 were selected as the infection group; matched with admission department and age at a 1∶1 ratio, 690 patients who were hospitalized during the same period without hospital infections were selected as the control group. The information including underlying diseases, endoscopic operations, blood transfusion and immunosuppressant use of the two groups were compared, the factors influencing hospital infections in hospitalized patients were analyzed through multiple logistic regression, and the logistic prediction model was established. Eighty percent of the data from Gansu Provincial Hospital were used as the training set of the model, and the remaining 20% were used as the test set for internal validation. Case data from other three hospitals in Gansu Province were used for external validation. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were used to evaluate the model effectiveness. Results Multiple logistic regression analysis showed that endoscopic therapeutic manipulation [odds ratio (OR)=3.360, 95% confidence interval (CI) (2.496, 4.523)], indwelling catheter [OR=3.100, 95%CI (2.352, 4.085)], organ transplantation/artifact implantation [OR=3.133, 95%CI (1.780, 5.516)], blood or blood product transfusions [OR=3.412, 95%CI (2.626, 4.434)], glucocorticoids [OR=2.253, 95%CI (1.608, 3.157)], the number of underlying diseases [OR=1.197, 95%CI (1.068, 1.342)], and the number of surgical procedures performed during hospitalization [OR=1.221, 95%CI (1.096, 1.361)] were risk factors for hospital infections. The regression equation of the prediction model was: logit(P)=–2.208+1.212×endoscopic therapeutic operations+1.131×indwelling urinary catheters+1.142×organ transplantation/artifact implantation+1.227×transfusion of blood or blood products+0.812×glucocorticosteroids+0.180×number of underlying diseases+0.200×number of surgical procedures performed during the hospitalization. The internal validation set model had a sensitivity of 72.857%, a specificity of 77.206%, an accuracy of 76.692%, and an AUC value of 0.817. The external validation model had a sensitivity of 63.705%, a specificity of 70.934%, an accuracy of 68.669%, and an AUC value of 0.726. Conclusions Endoscopic treatment operation, indwelling catheter, organ transplantation/artifact implantation, blood or blood product transfusion, glucocorticoid, number of underlying diseases, and number of surgical cases during hospitalization are influencing factors of hospital infections. The model can effectively predict the occurrence of hospital infections and guide the clinic to take preventive measures to reduce the occurrence of hospital infections.
ObjectiveTo investigate the prognosis and satisfaction of the R2 intervention procedure and develop related predictive models. Methods The clinical data of 64 patients with primary craniofacial hyperhidrosis who underwent R2 intervention surgery at the First Affiliated Hospital of Fujian Medical University from November 2018 to October 2022 were retrospectively analyzed. By statistically analyzing the risk factors for compensatory hyperhidrosis (CH) and satisfaction, and conducting feature screening, a relevant prediction model was established. ResultsFinally, 51 patients were collected, including 43 (84.3%) males and 8 (15.7%) females, with an average age of (30.27±7.22) years. Overall postoperative satisfaction was high, with only 5.9% of patients expressing regret about the surgery. However, 92.2% of patients experienced CH. The onset of postoperative CH was most prominent within the first 3 months postoperatively, with the incidence rate stabilizing thereafter. Preoperative heart rate and R2 sympathetic nerve clipping were identified as independent risk factors for severe CH. The preoperative body mass index, the degree of sweating in the chest and abdomen, are significantly correlated with postoperative satisfaction. Conclusion The R2 intervention surgery effectively alleviates the symptoms of primary craniofacial hyperhidrosis, and patient satisfaction is high.