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 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.
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 a novel prediction model based on cerebrospinal fluid (CSF) lactate for early identification of high-risk central nervous system (CNS) infection patients in the emergency setting. Methods Patients diagnosed with CNS infections admitted to the Department of Emergency Medicine of West China Hospital, Sichuan University between January 1, 2020 and December 31, 2023 were retrospectively selected. Patients were classified into a survival group and a death group according to their 28-day survival status, and clinical characteristics were compared between groups. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of 28-day mortality, which were subsequently used to construct a nomogram. Results A total of 173 patients were included, comprising 135 in the survival group and 38 in the death group. Multivariate analysis identified the Acute Physiology and Chronic Health Evaluation Ⅳ (APACHE Ⅳ) score [odds ratio (OR)=1.027, 95% confidence interval (CI) (1.002, 1.055), P=0.034], CSF lactate [OR=1.147, 95%CI (1.025, 1.286), P=0.018], and interleukin-6 [OR=1.002, 95%CI (1.001, 1.004), P=0.002] as independent predictors of 28-day mortality. The integrated model combining APACHE Ⅳ score, CSF lactate, and interleukin-6, demonstrated superior predictive performance compared with the APACHE Ⅳ score alone (P=0.020), and showed good calibration (Hosmer-Lemeshow P=0.50). Conclusions This tool may provide a useful instrument for emergency physicians to assess the 28-day mortality risk in patients with CNS infections, potentially facilitating early and targeted interventions for high-risk individuals. However, as the findings of this study are derived from a single-center retrospective dataset, the clinical applicability of this model requires further external validation through large-scale, prospective, multicenter studies to evaluate its generalizability.
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.
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.
ObjectiveTo discover the indicators and develop a model for predicting protracted hypoparathyroidism (HPT) after thyroid cancer surgery in order to guide the early therapy for patients with HPT.MethodsThe clinical and postoperative pathological data of patients with thyroid cancer who received surgical treatment in the Xuanwu Hospital and Beijing Pinggu Hospital from January 2019 to December 2020 were retrospectively analyzed. The potential indicators of postoperative HPT and protracted HPT were analyzed by logistic and LASSO regression analysis. A nomogram for predicting protracted HPT was constructed in the training set, and the discrimination and consistency of the nomogram were verified in the training set and the validation set respectively.ResultsAccording to the inclusion and exclusion criteria, a total of 464 patients diagnosed with thyroid cancer were finally included in the study. Among the 100 patients with postoperative HPT (except 1 case of incomplete data), 62 patients showed short-term HPT and 37 patients developed protracted HPT. Multivariate logistic regression analysis showed that the preoperative intact parathyroid hormone (iPTH) level [OR=0.953, 95%CI (0.931, 0.976), P<0.001], lobectomy with contralateral partial lobectomy [OR=3.247, 95%CI (1.112, 9.485), P=0.031], and total thyroidectomy [OR=11.096, 95%CI (5.432, 22.664), P<0.001] were related to postoperative HPT. The multivariant logistic regression analysis revealed that postoperative iPTH level was a predictive factor for protracted HPT [OR=0.719, 95%CI (0.588, 0.879), P=0.001]. The area under receiver operating characteristic curve (AUC) value of postoperative iPTH level in predicting protracted HPT was 0.848 [95%CI (0.755, 0.942)]; The cut-off value was 9.405 ng/L, and its specificity and sensitivity were 0.659 and 0.944, respectively. Moreover, the AUC value of the nomogram model including postoperative iPTH level and other clinicopathologic features (extraglandular invasion, cumulative maximum tumor diameter, and central lymph node dissection) for predicting protracted HPT was 0.900 [95%CI (0.817, 0.982)]; The cut-off score was 118.891, and its specificity and sensitivity were 0.772 and 0.944, respectively; The Hosmer-Lemeshow goodness of fit test indicated good fit of nomogram (χ2=8.605, P=0.377). The AUC value of the nomogram was 0.640 [95%CI (0.455, 0.826)] in the validation set (Pinggu Hospital data). The Hosmer-Lemeshow goodness of fit test also indicated good fit of nomogram (χ2=12.266, P=0.140).ConclusionsThe postoperative iPTH level is an important influencing factor of protracted HPT. The nomogram prediction model based on postoperative iPTH level and other clinicopathologic features has a favorable predictive value for protracted HPT.
ObjectiveTo retrospectively analyze the causes and risk factors of unplanned extubation (UE) in cancer patients during peripherally inserted central catheter (PICC) retention, so as to provide references for effectively predicting the occurrence of UE. Methods27 998 cancer patients who underwent PICC insertion, maintenance and removal in the vascular access nursing center of our hospital from January 2016 to June 2023 were retrospectively analyzed. General information, catheterization information, and maintenance information were collected. The Chi-squared test was used for univariate analysis, multivariate analysis was used by binary unconditional logistic regression. They were randomly divided into modeling group and internal validation group according to the ratio of 7∶3. The related nomogram prediction model and internal validation were established. ResultsThe incidence of UE during PICC retention in tumor patients was 2.80% (784/27 998 cases). Univariate analysis showed that age, gender, diagnosis, catheter retention time, catheter slipping, catheter related infection, catheter related thrombosis, secondary catheter misplacement, dermatitis, and catheter blockage had an impact on UE (P<0.05). Age, diagnosis, catheter retention time, catheter slipping, catheter related infection, catheter related thrombosis, secondary catheter misplacement, and catheter blockage are independent risk factors for UE (P<0.05). Based on the above 8 independent risk factors, a nomogram model was established to predict the risk of UE during PICC retention in tumor patients. The ROC area under the predicted nomogram was 0.90 (95%CI 0.89 to 0.92) in the modeling group, and the calibration curve showed good predictive consistency. Internal validation showed that the area under the ROC curve of the prediction model was 0.91 (95%CI 0.89 to 0.94), and the trend of the prediction curve was close to the standard curve. ConclusionPatients aged ≥60 years, non chest tumor patients, catheter retention time (≤6 months), catheter slipping, catheter related infections, catheter related thrombosis, secondary catheter misplacement, and catheter blockage increase the risk of UE. The nomogram model established in this study has good predictive ability and discrimination, which is beneficial for clinical screening of patients with different degrees of risk, in order to timely implement targeted prevention and effective treatment measures, and ultimately reduce the occurrence of UE.
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.
Objective To construct, validate and evaluate a nomogram prediction model based on triglyceride-glucose index for predicting the risk of type 2 diabetes mellitus (T2DM) in patients with obstructive sleep apnea (OSA). Methods A total of 414 patients diagnosed with OSA who were hospitalized in the Second Affiliated Hospital of Kunming Medical University from July 2013 to July 2023 were retrospectively analyzed. They were randomly divided into training set (n=289) and validation set (n=125) at a ratio of 7:3 using R software. In the training set, univariate logistic regression, best subsets regression (BSR) and multivariate Logistic regression were used to determine the independent predictors of OSA combined with T2DM and construct a nomogram. The area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow goodness of fit test, decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the discrimination, calibration and clinical applicability of the nomogram prediction model. Finally, the internal validation of the nomogram prediction model was carried out on the validation set. Results In the training set, the results of univariate logistic regression, BSR and multivariate logistic regression analysis showed that hypertension (OR=2.413, 95%CI 1.276-4.563, P=0.007), apnea hypopnea index (OR=1.034, 95%CI 1.014-1.053, P=0.001), triglyceride-glucose index( OR=12.065, 95%CI 5.735-25.379, P<0.001), triglyceride/high density lipoprotein cholesterol (OR=0.736, 95%CI 0.634-0.855, P<0.001) were independent predictors of T2DM in OSA patients. A nomogram prediction model was constructed based on the above four predictors. In the training set and validation set, the AUC, sensitivity, and specificity of the nomogram prediction model for predicting the risk of T2DM in OSA patients were 0.820 (95%CI 0.771-0.869), 75.7%, 75.9% and 0.778 (95%CI 0.696-0.861), 74.5%, 73.0%, respectively, indicating that the nomogram had good discrimination. The calibration curve showed that the nomogram had a good calibration for predicting T2DM in OSA patients. DCA and CIC also showed that the nomogram prediction model had certain clinical utility. Conclusions A simple, fast and effective nomogram prediction model with good discrimination, calibration and clinical applicability was successfully constructed, validated and evaluated. It can be used to predict the risk of T2DM in OSA patients and help clinicians to identify patients with high risk of T2DM in OSA patients.
