Inadvertent perioperative hypothermia (IPH) is one of the common complications of surgery, which can lead to a series of adverse consequences. In recent years, with the deepening development of precision medicine concepts, establishing predictive models to identify the risk of IPH early and implementing targeted interventions has become an important research direction for perioperative management. This article reviews the current research status of IPH predictive models in adults, focusing on the research design, modeling methods, selection of prediction factors, and prediction performance of different predictive models. It also explores the advantages and limitations of existing models, aiming to provide references for the selection, application, and optimization of relevant predictive models.
ObjectiveTo explore the risk factors of lymph node metastasis in patients with colorectal cancer, and construct a risk prediction model to provide reference for clinical diagnosis and treatment.MethodsThe clinicopathological data of 416 patients with colorectal cancer who underwent radical resection of colorectal cancer in the Department of Gastrointestinal Surgery of the Second Affiliated Hospital of Nanchang University from May 2018 to December 2019 were retrospectively analyzed. The correlation between lymph node metastasis and preoperative inflammatory markers, clinicopathological factors and tumor markers were analyzed. Logistic regression was used to analyze the risk factors of lymph node metastasis, and R language was used to construct nomogram model for evaluating the risk of colorectal cancer lymph node metastasis before surgery, and drew a calibration curve and compared with actual observations. The Bootstrap method was used for internal verification, and the consistency index (C-index) was calculated to evaluate the accuracy of the model.ResultsThe results of univariate analysis showed that factors such as sex, age, tumor location, smoking history, hypertension and diabetes history were not significantly related to lymph node metastasis (all P>0.05). The factors related to lymph node metastasis were tumor size, T staging, tumor differentiation level, fibrinogen, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), fibrinogen/albumin ratio (FAR), fibrinogen/prealbumin ratio (FpAR), CEA, and CA199 (all P<0.05). The results of logistic regression analysis showed the FpAR [OR=3.630, 95%CI (2.208, 5.968), P<0.001], CA199 [OR=2.058, 95%CI (1.221, 3.470), P=0.007], CEA [OR=2.335, 95%CI (1.372, 3.975), P=0.002], NLR [OR=2.532, 95%CI (1.491, 4.301), P=0.001], and T staging were independent risk factors for lymph node metastasis. The above independent risk factors were enrolled to construct regression equation and nomogram model, the area under the ROC curve of this equation was 0.803, and the sensitivity and specificity were 75.2% and 73.5%, respectively. The consistency index (C-index) of the nomogram prediction model in this study was 0.803, and the calibration curve showed that the result of predicting lymph node metastasis was highly consistent with actual observations.ConclusionsFpAR>0.018, NLR>3.631, CEA>4.620 U/mL, CA199>21.720 U/mL and T staging are independent risk factors for lymph node metastasis. The nomogram can accurately predict the risk of lymph node metastasis in patients with colorectal cancer before surgery, and provide certain assistance in the formulation of clinical diagnosis and treatment plans.
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 identify the risk factors of bone metastasis in breast cancer and construct a predictive model. MethodsThe data of breast cancer patients met inclusion and exclusion criteria from 2010 to 2015 were obtained from the SEER*Stat database. Additionally, the data of breast cancer patients diagnosed with distant metastasis in the Affiliated Hospital of Southwest Medical University from 2021 to 2023 were collected. The patients from the SEER database were randomly divided into training (70%) and validation (30%) sets using R software, and the breast cancer patients from the Affiliated Hospital of Southwest Medical University were included in the validation set. The univariate and multivariate logistic regressions were used to identify risk factors of breast cancer bone metastasis. A nomogram predictive model was then constructed based on these factors. The predictive effect of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. ResultsThe study included 8 637 breast cancer patients, with 5 998 in the training set and 2 639 (including 68 patients in the Affiliated Hospital of Southwest Medical University) in the validation set. The statistical differences in the race and N stage were observed between the training and validation sets (P<0.05). The multivariate logistic regression analysis revealed that being of white race, having a low histological grade (Ⅰ–Ⅱ), positive estrogen and progesterone receptors status, negative human epidermal growth factor receptor 2 status, and non-undergoing surgery for the primary breast cancer site increased the risk of breast cancer bone metastasis (P<0.05). The nomogram based on these risk factors showed that the AUC (95% CI) of the training and validation sets was 0.676 (0.533, 0.744) and 0.690 (0.549, 0.739), respectively. The internal calibration using 1 000 Bootstrap samples demonstrated that the calibration curves for both sets closely approximated the ideal 45-degree reference line. The decision curve analysis indicated a stronger clinical utility within a certain probability threshold range. ConclusionsThis study constructs a nomogram predictive model based on factors related to the risk of breast cancer bone metastasis, which demonstrates a good consistency between actual and predicted outcomes in both training and validation sets. The nomogram shows a stronger clinical utility, but further analysis is needed to understand the reasons of the lower differentiation of nomogram in both sets.
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.
ObjectiveTo analyze the prognostic factors of patients with bacterial bloodstream infection sepsis and to identify independent risk factors related to death, so as to potentially develop one predictive model for clinical practice. Method A non-intervention retrospective study was carried out. The relative data of adult sepsis patients with positive bacterial blood culture (including central venous catheter tip culture) within 48 hours after admission were collected from the electronic medical database of the First Affiliated Hospital of Dalian Medical University from January 1, 2018 to December 31, 2019, including demographic characters, vital signs, laboratory data, etc. The patients were divided into a survival group and a death group according to in-hospital outcome. The risk factors were analyzed and the prediction model was established by means of multi-factor logistics regression. The discriminatory ability of the model was shown by area under the receiver operating characteristic curve (AUC). The visualization of the predictive model was drawn by nomogram and the model was also verified by internal validation methods with R language. Results A total of 1189 patients were retrieved, and 563 qualified patients were included in the study, including 398 in the survival group and 165 in the death group. Except gender and pathogen type, other indicators yielded statistical differences in single factor comparison between the survival group and the death group. Independent risk factors included in the logistic regression prediction model were: age [P=0.000, 95% confidence interval (CI) 0.949 - 0.982], heart rate (P=0.000, 95%CI 0.966 - 0.987), platelet count (P=0.009, 95%CI 1.001 - 1.006), fibrinogen (P=0.036, 95%CI 1.010 - 1.325), serum potassium ion (P=0.005, 95%CI 0.426 - 0.861), serum chloride ion (P=0.054, 95%CI 0.939 - 1.001), aspartate aminotransferase (P=0.03, 95%CI 0.996 - 1.000), serum globulin (P=0.025, 95%CI 1.006 - 1.086), and mean arterial pressure (P=0.250, 95%CI 0.995 - 1.021). The AUC of the prediction model was 0.779 (95%CI 0.737 - 0.821). The prediction efficiency of the total score of the model's nomogram was good in the 210 - 320 interval, and mean absolute error was 0.011, mean squared error was 0.00018. Conclusions The basic vital signs within 48 h admitting into hospital, as well those homeostasis disordering index indicated by coagulation, liver and renal dysfunction are highly correlated with the prognosis of septic patients with bacterial bloodstream infection. Early warning should be set in order to achieve early detection and rescue patients’ lives.
ObjectiveTo explore the predictive value of a simple prediction model for patients with acute myocardial infarction.MethodsClinical data of 280 patients with acute ST-segment elevation myocardial infarction (STEMI) in the Department of Emergence Medicine, West China Hospital of Sichuan University from January 2019 to January 2020 were retrospectively analyzed. The patients were divided into a death group (n=34) and a survival group (n=246).ResultsAge, heart rate, body mass index (BMI), global registry of acute coronary events (GRACE), thrombolysis in myocardial infarction trial (TIMI) score, blood urea nitrogen, serum cystatin C and D-dimer in the survival group were less or lower than those in the death group (P<0.05). Left ventricle ejection fraction and the level of albumin, triglyceride, total cholesterol and low density lipoprotein cholesterol were higher and the incidence of Killip class≥Ⅲ was lower in the survival group compared to the death group (P<0.05). Multivariate logistic regression analysis showed that age, BMI, heart rate, diastolic blood pressure, and systolic blood pressure were independent risk factors for all-cause death in STEMI patients. Receiver operating characteristic (ROC) curve analysis showed that the area under the curve of simple prediction model for predicting death was 0.802, and similar to that of GRACE (0.816). The H-L test showed that the simple model had high accuracy in predicting death (χ2=3.77, P=0.877). Pearson correlation analysis showed that the simple prediction model was significantly correlated with the GRACE (r=0.651, P<0.001) and coronary artery stenosis score (r=0.210, P=0.001).ConclusionThe simple prediction model may be used to predict the hospitalization and long-term outcomes of STEMI patients, which is helpful to stratify high risk patients and to guide treatment.
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.
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.
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.