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 investigate the prognostic factors of primary gastric squamous cell carcinoma (SCC) and develop a nomogram for predicting the survival of gastric SCC.MethodsData of 199 cases of primary gastric SCC from 2004 to 2015 were collected in the National Cancer Institute SEER database by SEER Stat 8.3.5 software. X-tile software was used to determine the best cut-off value of the age, SPSS 25.0 software was used to analyze the prognostic factors of gastric SCC and draw a Kaplan-Meier curve, and then the Cox proportional hazard regression model analysis was performed to obtain independent prognostic factors of gastric SCC. We used R studio software to visualize the model and draw a nomogram. C-index was used to evaluate the prediction effect of the nomogram. Bootstrap analyses with 1 000 resamples were applied to complete the internal verification of the nomogram.ResultsAmong the 199 patients, survival rates for 1-, 3-, and 5-year were 40.7%, 22.4%, and 15.4%, respectively. Age (χ2=6.886, P=0.009), primary site (χ2=14.918, P=0.037), race (χ2=7.668, P=0.022), surgery (χ2=16.523, P<0.001), histologic type (χ2=9.372, P=0.009), T stage (χ2=11.639, P=0.009), and M stage (χ2=31.091, P<0.001) had a significant correlation with survival time of patients. The results of the Cox proportional hazard regression model showed that, age [HR=1.831, 95%CI was (1.289, 2.601)], primary site [HR=1.105, 95%CI was (1.019, 1.199)], M stage [HR=2.222, 95%CI was (1.552, 3.179)], and surgery [HR=0.561, 95%CI was (0.377, 0.835)] were independent prognostic factors affecting the survival of gastric SCC. Four independent prognostic factors contributed to constructing a nomogram with a C-index of 0.700.ConclusionIn this research, a reliable predictive model is constructed and drawn into a nomogram, which can be used for clinical reference.
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.
Objective To identify and analyze risk factors for acute renal failure (ARF) following lung transplantation and to develop a predictive model. Methods Data for this study were obtained from the United Network for Organ Sharing (UNOS) database, encompassing patients who underwent unilateral or bilateral lung transplantation between 2015 and 2022. We analyzed both preoperative and postoperative clinical characteristics of the patients. A combined approach utilizing random forest and least absolute shrinkage and selection operator (LASSO) regression was employed to identify key factors associated with the incidence of ARF post-transplantation, based on which a nomogram model was developed. The predictive performance of the constructed model was evaluated in both training and validation sets, using receiver operating characteristic (ROC) curves and area under the curve (AUC) metrics to verify and compare model effectiveness. ResultsA total of 15 110 lung transplantation patients were included in the study, consisting of6 041 males and 9 069 females, with a median age of 62.00 years (interquartile range: 54.00 to 67.00). The analysis revealed statistically significant differences between postoperative renal dialysis and non-dialysis patients regarding preoperative lung diagnosis, estimated glomerular filtration rate (eGFR), mechanical ventilation, preoperative ICU treatment, extracorporeal membrane oxygenation (ECMO) support, infections occurring within two weeks prior to transplantation, Karnofsky Performance Status (KPS) score, waitlist duration, double-lung transplantation, and ischemia time (P<0.05). Five key variables associated with ARF after lung transplantation were identified through random forest and LASSO regression: recipients’ eGFR, preoperative ICU treatment, ECMO support, bilateral lung transplantation, and ischemia time. A nomogram model was subsequently established. Model evaluation demonstrated that the constructed predictive model achieved high accuracy in both training and validation sets, with favorable AUC values, confirming its validity and reliability. ConclusionThis study identifies common risk factors for ARF following lung transplantation and introduces an effective predictive model with potential clinical applications.
ObjectiveTo analyze the risk factors influencing major postoperative complications (MPC) after minimally invasive radical gastrectomy for gastric cancer following neoadjuvant chemotherapy (NACT), and to construct a nomogram for accurately predicting MPC risk factors, and provide a reference for clinical decision-making. MethodsThe gastric cancer patients who underwent minimally invasive radical gastrectomy in the Department of General Surgery of the First Medical Center of the Chinese PLA General Hospital from February 2012 to December 2022 and met the inclusion criteria of this study were retrospectively collected. The univariate and multivariate logistic regression model were used to evaluate the risk factors influencing MPC and a nomogram model was constructed. The MPC were defined as Clavien-Dindo classification grade Ⅱ and beyond. The area under the receiver operating characteristic curve (AUC) and the calibration curve were used to evaluate the discrimination and accuracy of the nomogram model. ResultsA total of 362 patients were included in this study, among whom 65 cases (18.0%) experienced MPC. The multivariate logistic regression analysis showed that the age ≥58 years old, body mass index (BMI) ≥25 kg/m2, tumor long diameter ≥30 mm, operative time ≥300 min, and preoperative neutrophil-to-lymphocyte ratio (NLR) ≥3.7 were the risk factors influencing MPC. The nomogram model constructed using the above variables showed that the AUC (95%CI) was 0.731 (0.662, 0.801) in predicting the risk of MPC. The calibration curves showed that the prediction curve of the nomogram in predicting the MPC was agree well with the actual MPC (Hosmer-Lemeshow test: χ2=9.293, P=0.056). ConclusionFrom the results of this study, nomogram model constructed by combining age, BMI, tumor long diameter, operative time, and preoperative NLR can distinguish between patients with and without MPC after minimally invasive radical gastrectomy for gastric cancer following NACT, and has a better accuracy.
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.
ObjectiveTo investigate the predictive value of preoperative red blood cell distribution width to platelet count ratio (RPR) and platelet-albumin-bilirubin (PALBI) scoring for postoperative complications after radical resection of hepatic alveolar echinococcosis (HAE). MethodsAccording to the inclusion and exclusion criteria, the clinicopathologic data of patients diagnosed with HAE and underwent radical hepatectomy in the Affiliated Hospital of Qinghai University from January 2018 to October 2022 were retrospectively collected. The risk factors affecting postoperative complications after radical hepatectomy for HAE were analyzed by univariate and multivariate unconditional logistic regression analysis, which were used to construct the nomogram. The receiver operating characteristic curve was used to evaluate the value in predicting postoperative complications by nomogram model. The discrimination of the nomogram was evaluated using Bootstrap internal 1 000 resampling and evaluated using a consistency index. The predicted postoperative complications probability by nomogram and actual postoperative complications probability were calculated by Kaplan-Meier method, and the calibration curve was drawn. The calibration ability of the nomogram model was evaluated by Hosmer-Lemeshow goodness-of-fit test. The decision curve analysis was used to evaluate clinical benefit of the nomogram model. ResultsA total of 160 patients with HAE radical hepatectomy were included, of which 105 had no postoperative complications and 55 had postoperative complications. The multivariate unconditional logistic regression analysis showed that the operation time ≥207 min, intraoperative bleeding ≥650 mL, and albumin ＜38 g/L, RPR ≥0.054, and higher PALBI grading (3 levels) were the risk factors affecting postoperative complications after HAE radical hepatectomy (OR＞1, P<0.05). Based on the risk factors, the nomogram was constructed. The area under the receiver operating characteristic curve (95%CI) predicted by the nomogram for the postoperative complications was 0.873 (0.808, 0.937), with an optimal cutoff value of 0.499. The consistency index was 0.855 for discriminating postoperative complications after HAE radical hepatectomy. The calibration curve was tested by Hosmer-Limeshow and showed a good fit between the predicted curve by the nomogram and actual curve (χ2=3.193, P=0.367), indicating that the nomogram had a good calibration ability. The decision curve analysis showed that there was a good clinical applicability within the range of 11% to 93% of the threshold probability. ConclusionsThe preoperative RPR and PALBI scoring are risk factors affecting postoperative complications after radical hepatectomy for HAE. The nomogram constructed with risk factors including RPR and PALBI has a good predictive value for postoperative complications after radical hepatectomy for HAE.
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.
