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.
ObjectiveTo investigate the risk factors for central lymph node metastasis (CLNM) in patients with clinically negative lymph node (cN0 stage) papillary thyroid carcinoma (PTC).MethodsThe clinicopathological data of 250 patients with cN0 PTC who underwent thyroidectomy and central lymph node dissection (CLND) in Department of General Surgery of Xuzhou Central Hospital from June 2016 to June 2019 were retrospectively analyzed. The influencing factors of CLNM in patients with cN0 PTC were analyzed by univariate analysis and binary logistic regression, and then R software was used to establish a nomogram prediction model, receiver operating characteristic curve was used to evaluate the differentiation degree of the model, and Bootstrap method was used for internal verification to evaluate the calibration degree of the model.ResultsCLNM occurred in 147 of 250 patients with cN0 PTC, with an incidence of 58.8%. Univariate analysis showed that multifocal, bilateral, tumor diameter, and age were correlated with CLNM (P<0.01). The results of binary logistic regression analysis showed that multifocal, bilateral tumors, age≥45 years old, and tumor diameter>1 cm were independent risk factors for CLNM in patients with cN0 PTC (P<0.05). The area under the curve (AUC) of the nomogram prediction model established on this basis was 0.738, and the calibration prediction curve in the calibration diagram fitted well with the ideal curve.ConclusionsCLNM is more likely to occur in PTC. The nomogram model constructed in this study can be used as an auxiliary means to predict CLNM in clinical practice.
ObjectiveTo analyze the risk factors and develop a nomagram predictive model for early recurrence after curative resection for hepatocellular carcinoma (HCC). MethodsThe clinicopathologic data of the patients with HCC who underwent radical hepatectomy at the First Affiliated Hospital of Xinjiang Medical University from August 2017 to August 2021 were retrospectively collected. The univariate and multivariate logistic regression analysis were used to screen for the risk factors of early recurrence for HCC after radical hepatectomy, and a nomogram predictive model was established based on the risk factors. The receiver operating characteristic (ROC) curve and calibration curve were used to validate the predictive performance of the model, and the decision curve analysis (DCA) curve was used to evaluate its clinical practicality. ResultsA total of 302 patients were included based on the inclusion and exclusion criteria, and 145 (48.01%) of whom experienced early recurrence. The results of multivariate logistic regression model analysis showed that the preoperative neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), γ-glutamate transferase (GGT), alpha fetoprotein (AFP), tumor size, and microvascular invasion (MVI) were the influencing factors of early recurrence for HCC after radical resection (P<0.05). The nomogram was established based on the risk factors. The area under the ROC curve of the nomogram was 0.858 [95%CI (0.816, 0.899)], and the Brier index of the calibration curve of the nomogram was 0.152. The predicted result of the nomogram was relatively close to the true result (Hosmer-Lemeshow test, P=0.913). The DCA result showed that the clinical net benefit of intervention based on the predicted probability of the model was higher than that of non-intervening in all HCC patients and intervening in all HCC patients when the threshold probability was in the range of 0.1 to 0.8. ConclusionsThe results of this study suggest that for the patients with the risk factors such as preoperative NLR greater than 2.13, PLR greater than 108.15, GGT greater than 46.0 U/L, AFP higher than 18.96 μg/L, tumor size greater than 4.9 cm, and presence of preoperative MVI need to closely pay attention to the postoperative early recurrence. The nomogram predictive model constructed based on these risk factors in this study has a good discrimination and accuracy, and it could obtain clinical net benefit when the threshold probability is 0.1 to 0.8.
ObjectiveTo analyze the current status and risk factors of postoperative complications in patients with retroperitoneal tumor (RPT) and to establish a nomogram for predicting the occurrence of postoperative complications. MethodsThe clinicopathologic data of patients with RPT who met the inclusion criteria in the West China Hospital of Sichuan University from June 2019 to May 2022 were retrospectively collected. The risk factors of postoperative complications were analyzed by using univariate and multivariate analyses, and the nomogram was constructed based on the risk factors and validated. ResultsA total of 205 patients were collected in this study, 70 (34.1%) of whom had postoperative complications. The multivariate analysis results of logistic regression showed that the preoperative serum albumin ＜35 g/L [OR=2.355, 95%CI (1.256, 4.416), P=0.008], tumor sarcoma [OR=2.498, 95%CI (1.219, 5.120), P=0.012], and visceral resection [OR=2.008, 95%CI (1.042, 3.868), P=0.037] increased the probability of postoperative complications for the patients with RPT. The area under the receiver operating characteristic curve of the nomogram based on the risk factors in predicting the occurrence of postoperative complications was 0.704 [95%CI (0.626, 0.781), P<0.001]. The consistency index of the nomogram by internal verification was 0.704 [95%CI (0.628, 0.779)]. The calibration curve of the nomogram showed that the predicted value was basically consistent with the actual value, the Hosmer-Lemeshow goodness-of-fit test model had a good goodness-of-fit (χ2=3.407, P=0.906). ConclusionsFrom the results of this study, the tumor sarcoma, lower preoperative serum albumin, and visceral resection are associated with postoperative complications for patients with RPT. The nomogram based on risk factors has a good predictive value for postoperative complications.
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.
ObjectiveTo explore the risk factors affecting occurrence of arteriosclerosis obliterans (ASO) for patients with type 2 diabetes mellitus (T2DM) and to develop a nomogram predictive model using these risk factors. MethodsA case-control study was conducted. The patients with T2DM accompanied with ASO and those with T2DM alone, admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2017 to December 2022, were retrospectively collected according to the inclusion and exclusion criteria. The basic characteristics, blood, thyroid hormones, and other relevant indicators of the paitents in two groups were compared. The multivariate logistic regression analysis was used to identify the risk factors for the occurrence of ASO in the patients with T2DM, and then a nomogram predictive model was developed. ResultsThere were 119 patients with T2DM alone and 114 patients with T2DM accompanied with lower extremity ASO in this study. The significant differences were observed between the two groups in terms of smoking history, white blood cell count, neutrophil count, lymphocyte count, platelet count, systemic immune-inflammation index, systemic inflammatory response index (SIRI), high-density lipoprotein cholesterol, apolipoprotein A1 (ApoA1), apolipoprotein α (Apoα), serum cystatin C, free-triiodothyronine (FT3), total triiodothyronine, FT3/total triiodothyronine ratio, fibrinogen (Fib), fibrinogen degradation products, and plasma D-dimer (P<0.05). Further the results of the multivariate logistic regression analysis revealed that the history of smoking, increased Fib level and SIRI value increased the probabilities of ASO occurrence in the patients with T2DM [OR (95%CI)=2.921 (1.023, 4.227), P=0.003; OR (95%CI)=2.641 (1.810, 4.327), P<0.001; OR (95%CI)=1.020 (1.004, 1.044), P=0.018], whereas higher levels of ApoA1 and FT3 were associated with reduced probabilities of ASO occurrence in the patients with T2DM [OR (95%CI)=0.231 (0.054, 0.782), P=0.021; OR (95%CI)=0.503 (0.352, 0.809), P=0.002]. The nomogram predictive model based on these factors demonstrated a good discrimination for predicting the ASO occurrence in the T2DM patients [area under the receiver operating characteristic curve (95%CI)=0.788 (0.730, 0.846)]. The predicted curve closely matched the ideal curve (Hosmer-Lemeshow goodness-of-fit test, χ2=5.952, P=0.653). The clinical decision analysis curve showed that the clinical net benefit of intervention based on the nomogram model was higher within a threshold probability range of 0.18 to 0.80 compared to no intervention or universal intervention. ConclusionsThe analysis results indicate that T2DM patients with a smoking history, elevated Fib level and SIRI value, as well as decreased ApoA1 and FT3 levels should be closely monitored for ASO risk. The nomogram predictive model based on these features has a good discriminatory power for ASO occurrence in T2DM patients, though its value warrants further investigation.
ObjectiveTo explore the risk factors affecting the prognosis of patients with metastatic breast cancer (MBC) and construct a nomogram survival prediction model.MethodsThe patients with MBC from 2010 to 2013 were collected from surveillance, epidemiology, and end results (SEER) database, then were randomly divided into training group and validation group by R software. SPSS software was used to compare the survival and prognosis of MBC patients with different metastatic sites in the training group by log-rank method and construct the Kaplan-Meier survival curve. The Cox proportional hazards model was used to analyze the factors of 3-year overall survival, then construct a nomogram survival prediction model by the independent prognostic factors. The C-index was used to evaluate its predictive value and the calibration curve was used to verify the nomogram survival prediction model by internal and external calibration graph.ResultsA total of 3 288 patients with MBC were collected, including 2 304 cases in the training group and 984 cases in the validation group. The data of the two groups were comparable. The median follow-up time of training group and validation group was 34 months and 34 months, respectively. In the training group, the results of Cox proportional hazards model showed that the older, black race, higher histological grading, without operation, ER (–), PR (–), HER-2 (–), and metastases of bone, brain, liver and lung were the risk factors of survival prognosis (P<0.05) and constructed the nomogram survival prediction model with these independent prognostic factors. The nomogram survival prediction showed a good accuracy with C-index of 0.704 [95%CI (0.691, 0.717)] in internal validation (training group) and C-index of 0.691 [95%CI (0.671, 0.711)] in external validation (validation group) in predicting 3-year overall survival. All calibration curves showed excellent consistency.ConclusionNomogram for predicting 3-year overall survival of patients with MBC in this study has a good predictive capability, and it is conducive to development of individualized clinical treatment.
ObjectiveTo explore the relation between preoperative serum gamma-glutamyl transpeptidase to platelet ratio (GPR) and overall survival (OS) of patients with hepatitis B virus-associated hepatocellular carcinoma (Abbreviated as “patients with HCC”), and to establish a nomogram for predicting OS. MethodsAccording to the inclusion and exclusion criteria, the clinicopathologic data of patients with HCC who underwent radical resection in the Department of Hepatobiliary Surgery of Xianyang Central Hospital, from January 15, 2012 to December 15, 2018, were retrospectively analyzed. The optimal critical value of GPR was determined by receiver operating characteristic curve, then the patients were divided into a low GPR group (GPR was optimal critical value or less ) and high GPR group (GPR was more optimal critical value). The Kaplan-Meier method was used to draw the survival curve and analyze the OS of patients. The univariate and multivariate Cox proportional hazards regression model were used to analyze the factors influencing prognosis in the patients with HCC. According to the risk factors of OS for patients with HCC, a nomogram was established. The consistency index and calibration curve in predicting the 3-year and 5-year accumulative OS rates of patients with HCC were evaluated. ResultsA total of 213 patients were gathered. The optimal critical value of GPR was 0.906. There were 114 patients in the low GPR group and 99 patients in the high GPR group. The Kaplan-Meier survival curve analysis showed that the 1-, 3- and 5-year accumulative OS rates were 99.1%, 81.8%, 60.6% in the low GPR group, respectively, which were 74.2%, 49.1%, 35.7% in the low GPR group, respectively. The OS curve of the low GPR group was better than that of the high GPR group (χ2=25.893, P<0.001). The multivariate analysis results showed that the microvascular invasion, incomplete capsule, intraoperative bleeding ＞1 000 mL, postoperative complications, GPR >0.906, low tumor differentiation, and late TNM stage did not contribute to accumulative OS in the patients with HCC (P<0.05). The consistency index (95%CI) of the nomogram in predicting accumulative OS rates at 3- and 5-year for patients with HCC were 0.761 (0.739, 0.783) and 0.735 (0.702, 0.838), respectively. The calibration curves of 3- and 5-year accumulative OS rates of the nomogram were in good agreement with the actual results. ConclusionsPreoperative GPR is associated with OS, and patients with higher GPR have worse prognosis. The nomogram based on GPR has a good accuracy and differentiation.
ObjectiveTo explore the value of geriatric nutritional risk index (GNRI) and sarcopenia on predicting postoperative complications in elderly patients with gastric cancer. MethodsAccording to the inclusion and exclusion criteria, the elderly (aged ≥60 years) patients with gastric cancer underwent radical gastrectomy in the Department of Gastrointestinal Surgery of Xuzhou Central Hospital from January 1, 2017 to December 31, 2021 were retrospectively gathered. The occurrence of postoperative complications (grade 2 or beyond by the Clavien-Dindo classification) was analyzed. The risk factors affecting postoperative complications were analyzed by univariate and multivariate logistic regression analyses to construct the prediction model, then was visualized by drawing a nomogram. The differentiation of the nomogram between the patients with postoperative complications and without postoperative complications was evaluated by the receiver operating characteristic (ROC) curve. The accuracy of the nomogram was evaluated by the calibration curve. Further, the clinical net benefit rate was analyzed by the decision curve analysis (DCA) to evaluate the clinical practicability. ResultsA total of 236 patients were gathered, 97 (41.1%) of whom had postoperative complications during hospitalization. The results of multivariate logistic regression analysis showed that the age, gender, GNRI, sarcopenia, surgical mode, and American Society of Aneshesiologists classification were the factors influencing the postoperative complications (P<0.05). The differentiation of nomogram based on the influencing factors was well, the area under the ROC curve was 0.732. The calibration curve showed that the model prediction curve was close to the ideal curve. The clinical net benefit rate by the DCA was higher when the probability of postoperative complications was 0.18 to 0.72. ConclusionsThe efficiency of nomogram based on GNRI and sarcopenia is well for predicting the occurrence of postoperative complications in elderly patients with gastric cancer. However, the nomogram needs to be further validated by prospective studies and external data.
Objective To investigate the risk factors for secondary pulmonary fungal infection in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). And a visual tool using nomogram was developed and validated to assist in the clinical prediction of the probability of pulmonary fungal infection occurrence in AECOPD patients. Methods A retrospective cohort study method was used to collect AECOPD patients hospitalized in the Department of Respiratory, The First Affiliated Hospital of Chengdu Medical College from January 2021 to December 2021 as a training set. And AECOPD patients between January 2020 and December 2020 were collected as a validation set. Independent risk factors were determined through univariate, Lasso regression analyses. and multivariable logistic, A nomogram prediction model was constructed with these independent risk factors, and the nomogram was evaluated by receiver operating characteristic area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Results The use of glucocorticoid, combined use of antibiotics, duration of antibiotic use and hypoalbuminemia were independent risk factors for secondary pulmonary fungal infection in AECOPD patients (all P<0.05). The training set and validation set of the constructed prediction model had an AUC value of 0.915 [95%CI: 0.891 - 0.940] and 0.830 [95%CI: 0.790 - 0.871], respectively. The calibration curve showed that the predicted probability was in good agreement with the actual observed probability of pulmonary fungal infection in AECOPD patients. The corresponding decision curve analysis (DCA) indicated the nomogram had relatively ideal clinical utility. Conclusions The result showed that the use of glucocorticoid, combined use of antibiotics, prolonged antibiotic therapy and hypoalbuminemia was independent risk factors for pulmonary fungal infection in AECOPD patients. The clinical prediction model for secondary pulmonary fungal infection in AECOPD patients constructed in this study has strong predictive power and clinical practicability.