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 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.
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.
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.
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 analyze the relevant risk factors affecting postoperative relapse-free survival (RFS) in the primary gastrointestinal stromal tumors (GIST) and develop a Nomogram predictive model of postoperative RFS for the GIST patients. MethodsThe patients diagnosed with GIST by postoperative pathology from January 2011 to December 2020 at the First Hospital of Lanzhou University and Gansu Provincial People’s Hospital were collected, and then were randomly divided into a training set and a validation set at a ratio of 7∶3 using R software function. The univariate and multivariate Cox regression analysis were used to identify the risk factors affecting the RFS for the GIST patients after surgery, and then based on this, the Nomogram predictive model was constructed to predict the probability of RFS at 3- and 5-year after surgery for the patients with GIST. The effectiveness of the Nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), consistency index (C-index), and calibration curve, and the clinical utility of the Nomogram and the modified National Institutes of Health (M-NIH) classification standard was evaluated using the decision curve analysis (DCA). ResultsA total of 454 patients were included, including 317 in the training set and 137 in the validation set. The results of multivariate Cox regression analysis showed that the tumor location, tumor size, differentiation degree, American Joint Committee onCancer TNM stage, mitotic rate, CD34 expression, treatment method, number of lymph node detection, and targeted drug treatment time were the influencing factors of postoperative RFS for the GIST patients (P<0.05). The Nomogram predictive model was constructed based on the influencing factors. The C-index of the Nomogram in the training set and validation set were 0.731 [95%CI (0.679, 0.783)] and 0.685 [95%CI (0.647, 0.722)], respectively. The AUC (95%CI) of distinguishing the RFS at 3- and 5-year after surgery were 0.764 (0.681, 0.846) and 0.724 (0.661, 0.787) in the training set and 0.749 (0.625, 0.872) and 0.739 (0.647, 0.832) in the validation set, respectively. The calibration curve results showed that a good consistency of the 3-year and 5-year recurrence free survival rates between the predicted results and the actual results in the training set, while which was slightly poor in the validation set. There was a higher net benefit for the 3-year recurrence free survival rate after GIST surgery when the threshold probability range was 0.19 to 0.57. When the threshold probability range was 0.44 to 0.83, there was a higher net benefit for the 5-year recurrence free survival rate after GIST surgery. And within the threshold probability ranges, the net benefit of the Nomogram was better than the M-NIH classification system at the corresponding threshold probability. ConclusionsThe results of this study suggest that the patients with GIST located in the other sites (mainly including the esophagus, duodenum, and retroperitoneum), with tumor size greater than 5 cm, poor or undifferentiated differentiation, mitotic rate lower than 5/50 HPF, negative CD34 expression, ablation treatment, number of lymph nodes detected more than 4, and targeted drug treatment time less than 3 months need to closely pay attentions to the postoperative recurrence. The discrimination and clinical applicability of the Nomogram predictive model are good.
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.
Objective To clarify the specific clinical predictive efficacy of CT and serological indicators for the progression of connective tissue disease-associated interstitial lung disease (CTD-ILD) to progressive pulmonary fibrosis (PPF). Methods Patients who were diagnosed with CTD-ILD in Chest Hospital of Zhengzhou University Between January 2020 and December 2021 were recruited in the study. Clinical data and high-resolution CT results of the patients were collected. The patients were divided into a stable group and a progressive group (PPF group) based on whether PPF occurred during follow-up. COX proportional hazards regression was used to identify risk factors affecting the progression of CTD-ILD to PPF, and a risk prediction model was established based on the results of the COX regression model. The predictive efficacy of the model was evaluated through internal cross-validation. Results A total of 194 patients diagnosed with CTD-ILD were enrolled based on the inclusion and exclusion criteria. Among them, 34 patients progressed to PPF during treatment, and 160 patients did not progress. The variables obtained at lambda$1se in LASSO regression were ANCA associated vasculitis, lymphocytes, albumin, erythrocyte sedimentation rate, and serum ferritin. Multivariate COX regression analysis showed that the extent of fibrosis, serum ferritin, albumin, and age were independent risk factors for the progression of CTD-ILD to PPF (all P<0.05). A prediction model was established based on the results of the multivariate COX regression analysis. The area under the receiver operator characteristic curve at 6 months, 9 months, and 12 months was 0.989, 0.931, and 0.797, respectively, indicating that the model has good discrimination and sensitivity, and good predictive efficacy. The calibration curve showed a good overlap between predicted and actual values. Conclusions The extent of fibrosis, serum ferritin, albumin, and age are independent risk factors for the progression of CTD-ILD to PPF. The model established based on this and externally validated shows good predictive efficacy.
Objective To analyze the influencing factors for postoperative anastomotic leak (AL) in carcinoma of the esophagus and gastroesophageal junction and construct a nomogram predictive model. Methods The patients who underwent radical esophagectomy at Jinling Hospital Affiliated to Nanjing University School of Medicine from January 2018 to June 2020 were included in this study. Relevant variables were screened using univariate and multivariate logistic regression analyses. A nomogram was then developed to predict the risk factors associated with postoperative AL. The predictive performance of the nomogram was validated using the receiver operating characteristic (ROC) curve. Results A total of 468 patients with carcinoma of the esophagus and gastroesophageal junction were included in the study, comprising 354 males and 114 females, with a mean age of (62.8±7.2) years. The tumors were predominantly located in the middle or lower esophagus, and 51 (10.90%) patients experienced postoperative AL. Univariate logistic regression analysis indicated that age, body mass index (BMI), tumor location, preoperative albumin levels, diabetes mellitus, anastomosis technique, anastomosis site, and C-reactive protein (CRP) levels were potentially associated with AL (P<0.05). Multivariate logistic regression analysis identified age, BMI, tumor location, diabetes mellitus, anastomosis technique, and CRP levels as independent risk factors for AL (P<0.05). A nomogram was developed based on the findings from the multivariate logistic regression analysis. The area under the receiver operating characteristic (ROC) curve was 0.803, indicating a strong concordance between the actual observations and the predicted outcomes. Furthermore, decision curve analysis demonstrated that the newly established nomogram holds significant value for clinical decision-making. Conclusion The predictive model for postoperative AL in patients with carcinoma of the esophagus and gastroesophageal junction demonstrates strong predictive validity and is essential for guiding clinical monitoring, early detection, and preventive strategies.
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.