Objective To explore the potential indicators of cervical lymph node metastasis in papillary thyroid microcarcinoma (PTMC) patients and to develop a nomogram model. Methods The clinicopathologic features of PTMC patients in the SEER database from 2004 to 2015 and PTMC patients who were admitted to the Center for Thyroid and Breast Surgery of Xuanwu Hospital from 2019 to 2020 were retrospectively analyzed. The records of SEER database were divided into training set and internal verification set according to 7∶3. The patients data of Xuanwu Hospital were used as the external verification set. Logistic regression and Lasso regression were used to analyze the potential indicators for cervical lymph node metastasis. A nomogram was developed and whose predictive value was verified in the internal and external validation sets. According to the preoperative ultrasound imaging characteristics, the risk scores for PTMC patients were further calculated. The consistency between the scores based on pathologic and ultrasound imaging characteristics was verified. Results The logistic regression analysis results illustrated that male, age<55 years old, tumor size, multifocality, and extrathyroidal extension were associated with cervical lymph node metastasis in PTMC patients (P<0.001). The C index of the nomogram was 0.722, and the calibration curve exhibited to be a fairly good consistency with the perfect prediction in any set. The ROC curve of risk score based on ultrasound characteristics for predicting lymph node metastasis in PTMC patients was 0.701 [95%CI was (0.637 4, 0.765 6)], which was consistent with the risk score based on pathological characteristics (Kappa value was 0.607, P<0.001). Conclusions The nomogram model for predicting the lymph node metastasis of PTMC patients shows a good predictive value, and the risk score based on the preoperative ultrasound imaging characteristics has good consistency with the risk score based on pathological characteristics.
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 develop a radiomics nomogram based on contrast-enhanced CT (CECT) for preoperative prediction of high-risk and low-risk thymomas. Methods Clinical data of patients with thymoma who underwent surgical resection and pathological confirmation at Northern Jiangsu People's Hospital from January 2018 to February 2023 were retrospectively analyzed. Feature selection was performed using the Pearson correlation coefficient and least absolute shrinkage and selection operator (LASSO) method. An ExtraTrees classifier was used to construct the radiomics signature model and the radiomics signature. Univariate and multivariable logistic regression was applied to analyze clinical-radiological characteristics and identify variables for developing a clinical model. The radiomics nomogram model was developed by combining the radiomics signature and clinical features. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, and positive predictive value. Calibration curves and decision curves were plotted to assess model accuracy and clinical values. Results A total of 120 patients including 59 females and 61 males with an average age of 56.30±12.10 years. There were 84 patients in the training group and 36 in the validation group, 62 in the low-risk thymoma group and 58 in the high-risk thymoma group. Radiomics features (1 038 in total) were extracted from the arterial phase of CECT scans, among which 6 radiomics features were used to construct the radiomics signature. The radiomics nomogram model, combining clinical-radiological characteristics and the radiomics signature, achieved an AUC of 0.872 in the training group and 0.833 in the validation group. Decision curve analysis demonstrated better clinical efficacy of the radiomics nomogram than the radiomics signature and clinical model. Conclusion The radiomics nomogram based on CECT showed good diagnostic value in distinguishing high-risk and low-risk thymoma, which may provide a noninvasive and efficient method for clinical decision-making.
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.
Objective To construct a nomogram model for predicting delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) in emergency departments. Methods All patients with acute carbon monoxide poisoning who visited the Department of Emergency of Zigong Fourth People’s Hospital between June 1st, 2011 and May 31st, 2023 were retrospectively enrolled and randomly divided into a training set and a testing set in a 6∶4 ratio. LASSO regression was used to screen variables in the training set to establish a nomogram model for predicting DEACMP. The discrimination, calibration, and clinical practicality were compared between the nomogram and Glasgow Coma Scale (GCS) in the training and testing sets. Results A total of 475 patients with acute carbon monoxide poisoning were included, of whom 41 patients had DEACMP. Age, GCS and aspartate aminotransferase were selected as risk factors through LASSO regression, and a nomogram model was constructed based on these factors. The areas under the receiver operating characteristic curves for nomogram and GCS to predict DEACMP in the training set were 0.897 [95% confidence interval (CI) (0.829, 0.966)] and 0.877 [95%CI (0.797, 0.957)], respectively; and those for nomogram and GCS to predict DEACMP in the testing set were 0.925 [95%CI (0.865, 0.985)] and 0.858 [95%CI (0.752, 0.965)], respectively. Compared with GCS, the performance of nomogram in the training set (net reclassification index=0.495, P=0.014; integrated discrimination improvement=0.070, P=0.011) and testing set (net reclassification index=0.721, P=0.004; integrated discrimination improvement=0.138, P=0.009) were both positively improved. The calibration of nomogram in the training set and testing set was higher than that of GCS. The decision curves in the training set and testing set showed that the nomogram had better clinical net benefits than GCS. Conclusion The age, GCS and aspartate aminotransferase are risk factors for DEACMP, and the nomogram model established based on these factors has better discrimination, calibration, and clinical practicality compared to GCS.
Objective To evaluate the relationship of systemic immune inflammatory index (SII) with the clinical features and prognosis of osteosarcoma patients. Methods The clinical data of patients with osteosarcoma surgically treated in Fuzhou Second Hospital between January 2012 and December 2017 were retrospectively collected. The preoperative SII value was calculated, which was defined as platelet × neutrophil/lymphocyte count. The best critical value of SII was determined by receiver operating characteristic (ROC) curve analysis, and the relationship between SII and clinical features of patients was analyzed by χ2 test. Kaplan-Meier method and Cox proportional hazard model were used to study the effect of SII on overall survival (OS). The nomogram prediction model was established according to the independent risk factors of patients’ prognosis. Results A total of 108 patients with osteosarcoma were included in this study. Preoperative high SII was significantly correlated with tumor diameter, Enneking stage, local recurrence and metastasis (P<0.05). The median follow-up time was 62 months. The 1-, 3-, 5-year survival rates of the low SII group were significantly higher than those of the high SII group (100.0%, 96.4%, 85.1% vs. 95.4%, 73.7%, 30.7%), and the survival of the two groups were statistically different (P<0.05). Univariate Cox regression analyses showed that tumor diameter, Enneking stage, local recurrence, metastasis and SII were associated with OS (P<0.05). Multiple Cox regression analysis showed that Enneking stage (P=0.031), local recurrence (P=0.035) and SII (P=0.001) were independent risk factors of OS. The nomogram constructed according to the independent risk factors screened by the Cox regression model had good discrimination and consistency (C-index=0.774), and the calibration curve showed that the nomogram had a high consistency with the actual results. In addition, the ROC curve indicated that the nomogram had a good prediction efficiency (area under the curve=0.880). Conclusions The preoperative SII level is expected to become an important prognostic parameter for patients with osteosarcoma. The higher the SII level is, the worse the prognosis of patients will be. The nomogram prediction model built on preoperative SII level, Enneking stage and local recurrence has a good prediction efficiency, and can be used to guide the diagnosis and treatment of clinical osteosarcoma.
ObjectiveTo analyze the correlation between folate receptor-positive circulating tumor cells (FR+CTC) and the benign or malignant lesions of the lung, and to establish a malignant prediction model for pulmonary neoplasm based on clinical data, imaging and FR+CTC tests.MethodsA retrospective analysis was done on 1 277 patients admitted to the Affiliated Hospital of Qingdao University from September 2018 to December 2019, including 518 males and 759 females, with a median age of 57 (29-85) years. They underwent CTC examination of peripheral blood and had pathological results of pulmonary nodules and lung tumors. The patients were randomly divided into a trial group and a validation group. Univariate and multivariate analyses were performed on the data of the two groups. Then the nomogram prediction model was established and verified internally and externally. Receiver operating characteristic (ROC) curve was used to test the differentiation of the model and calibration curve was used to test the consistency of the model.ResultsTotally 925 patients suffered non-small cell lung cancer and 113 patients had benign diseases in the trial group; 219 patients suffered non-small cell lung cancer and 20 patients had benign diseases in the verification group. The FR+CTC in the peripheral blood of non-small cell lung cancer patients was higher than that found in the lungs of the patients who were in favorite conditions (P<0.001). Multivariate analysis showed that age≥60 years, female, FR+CTC value>8.7 FU/3 mL, positive pleural indenlation sign, nodule diameter, positive burr sign, consolidation/tumor ratio<1 were independent risk factors for benign and malignant lung tumors with a lesion diameter of ≤4 cm. Thereby, the nomogram prediction model was established. The area under the ROC curve (AUC) of the trial group was 0.918, the sensitivity was 86.36%, and the specificity was 83.19%. The AUC value of the verification group was 0.903, the sensitivity of the model was 79.45%, and the specificity was 90.00%, indicating nomogram model discrimination was efficient. The calibration curve also showed that the nomogram model calibration worked well.ConclusionFR+CTC in the peripheral blood of non-small cell lung cancer patients is higher than that found in the lungs of the patients who carry benign pulmonary diseases. The diagnostic model of clinical stage Ⅰ non-small cell lung cancer established in this study owns good accuracy and can provide a basis for clinical diagnosis.
ObjectiveTo explore the influencing factors of cancer-specific survival of patients with large hepatocellular carcinoma, and draw a nomogram to predict the cancer-specific survival rate of large hepatocellular carcinoma patients.MethodsThe clinicopathological data of patients with large hepatocellular carcinoma during the period from 1975 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) database were searched and randomly divided into training group and validation group at 1∶1. Using the training data, the Cox proportional hazard regression model was used to explore the influencing factors of cancer-specific survival and construct the nomogram; finally, the receiver operating characteristic curve (ROC curve) and the calibration curve were drawn to verify the nomogram internally and externally.ResultsThe results of the multivariate Cox proportional hazard regression model showed that the degree of liver cirrhosis, tumor differentiation, tumor diameter, T stage, M stage, surgery, and chemotherapy were independent influencing factors that affect the specific survival of patients with large hepatocellular carcinoma (P<0.05), and then these factors were enrolled into the nomogram of the prediction model. The areas under the 1, 3, and 5-year curves of the training group were 0.800, 0.827, and 0.814, respectively; the areas under the 1, 3, and 5-year curves of the validation group were 0.800, 0.824, and 0.801, respectively. The C index of the training group was 0.779, and the verification group was 0.777. The calibration curve of the training group and the verification group was close to the ideal curve of the actual situation.ConclusionThe nomogram of the prediction model drawn in this study can be used to predict the specific survival of patients with large hepatocellular carcinoma in the clinic.
Objective To develop and validate a nomogram prediction model of early knee function improvement after total knee arthroplasty (TKA). Methods One hundred and sixty-eight patients who underwent TKA at Sichuan Province Orthopedic Hospital between January 2018 and February 2021 were prospectively selected to collect factors that might influence the improvement of knee function in the early postoperative period after TKA, and the improvement of knee function was assessed using the Knee Score Scale of the Hospital for Special Surgery (HSS) at 6 months postoperatively. The patients were divided into two groups according to the postoperative knee function improvement. The preoperative, intraoperative and postoperative factors were compared between the two groups; multiple logistic regression was performed after the potential factors screened by LASSO regression; then, a nomogram predictive model was established by R 4.1.3 language and was validated internally. Results All patients were followed up at 6 months postoperatively, and the mean HSS score of the patients increased from 55.19±8.92 preoperatively to 89.27±6.18 at 6 months postoperatively (t=?40.706, P<0.001). LASSO regression screened eight influencing factors as potential factors, with which the results of multiple logistic regression analysis showed that preoperative body mass index, etiology, preoperative joint mobility, preoperative HSS scores, postoperative lower limb force line, and postoperative analgesia were independent influencing factors for the improvement of knee function in the early postoperative period after TKA (P<0.05). A nomogram model was established based on the multiple logistic regression results, and the calibration curve showed that the prediction curve basically fitted the standard curve; the receiver operating characteristic curve showed that the area under the curve of the nomogram model for the prediction of suboptimal knee function in the early postoperative period after TKA was 0.894 [95% confidence interval (0.825, 0.963)]. Conclusions There is a significant improvement in knee function in patients after TKA, and the improvement of knee function in the early postoperative period after TKA is influenced by preoperative body mass index, etiology, and preoperative joint mobility, etc. The nomogram model established accordingly can be used to predict the improvement of knee function in the early postoperative period after TKA with a high degree of differentiation and accuracy.
Objective To develop and validate a nomogram for predicting the risk of weaning failure in elderly patients with severe pneumonia undergoing mechanical ventilation. Methods A retrospective analysis was conducted on the clinical data of 330 elderly patients with severe pneumonia undergoing mechanical ventilation who were hospitalized in our hospital from July 2021 to July 2023. According to their weaning outcomes, they were divided into a successful group (n=213 ) and a failure group (n=117). Univariate analysis and multivariate non-conditional logistic regression analysis were used to explore the factors influencing the weaning failure of mechanical ventilation in elderly patients with severe pneumonia. Results Univariate analysis showed that there were significant differences in age, smoking status, chronic obstructive pulmonary disease, ventilation time, albumin, D-dimer, and oxygenation index levels between the two groups (all P<0.05). Multivariate logistic regression analysis revealed that age ≥65 years, smoking, presence of chronic obstructive pulmonary disease, ventilation time ≥7 days, D-dimer ≥2 000 μg/L, and reduced oxygenation index were risk factors for weaning failure in the elderly patients with severe pneumonia. The nomogram model constructed based on these factors had an area under ROC curve of 0.970 (95%CI 0.952 - 0.989), and the calibration curve demonstrated good agreement between predicted and observed values. Conclusions Age, smoking status, chronic obstructive pulmonary disease, ventilation time, D-dimer, and oxygenation index are influencing factors for weaning failure in elderly patients with severe pneumonia receiving mechanical ventilation. The nomogram model constructed based on these factors exhibits good discrimination and accuracy.