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 explore the association between the ZJU index and obstructive sleep apnea hypopnea syndrome (OSAHS) and to develop a prediction model based on ZJU index. MethodsClinical data of patients diagnosed by polysomnography were retrospectively collected from January 2021 to July 2024. Participants were categorized into OSAHS and non-OSAHS groups, and the general data of the two groups were compared. Regression analysis was performed to analyze the influencing factors of OSAHS, a prediction model of OSAHS was constructed based on the ZJU index, and the diagnostic efficacy was evaluated by using the subject's work characteristics (ROC) curve and calibration curve. Rusults A total of 211 patients were included in this study, including 165 in the OSAHS group and 46 in the non-OSAHS group. The multifactorial results showed that ZJU index and gender were the influencing factors for the occurrence of OSAHS (P<0.05), and a prediction model was constructed by combining the ZJU index with gender, and the area under the ROC curve (AUC) was 0.786 (95%CI: 0.717-0.85). The sensitivity was 51.5% and the specificity was 91.3%. The calibration curve showed good agreement between predicted and actual results. ConclusionZJU index is associated with OSAHS, and the prediction model constructed by ZJU index combined with gender could be well used to predict the occurrence of OSAHS.
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 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 correlation between spread through air space (STAS) of sub-centimeter non-small cell lung cancer and clinical characteristics and radiological features, constructing a nomogram risk prediction model for STAS to provide a reference for the preoperative planning of sub-centimeter non-small cell lung cancer patients. MethodsThe data of patients with sub-centimeter non-small cell lung cancer who underwent surgical treatment in Nanjing Drum Tower Hospital from January 2022 to October 2023 were retrospectively collected. According to the pathological diagnosis of whether the tumor was accompanied with STAS, they were divided into a STAS positive group and a STAS negative group. The clinical and radiological data of the two groups were collected for univariate logistic regression analysis, and the variables with statistical differences were included in the multivariate analysis. Finally, independent risk factors for STAS were screened out and a nomogram model was constructed. The sensitivity and specificity were calculated based on the Youden index, and area under the curve (AUC), calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the model. ResultsA total of 112 patients were collected, which included 17 patients in the STAS positive group, consisting of 11 males and 6 females, with a mean age of (59.0±10.3) years. The STAS negative group included 95 patients, with 30 males and 65 females, and a mean age of (56.8±10.3) years. Univariate logistic regression analysis showed that male, anti-GAGE7 antibody positive, mean CT value and spiculation were associated with the occurrence of STAS (P＜0.05). Multivariate regression analysis showed that associations between STAS and male (OR=5.974, 95%CI 1.495 to 23.872), anti-GAGE7 antibody positive (OR=11.760, 95%CI 1.619 to 85.408) and mean CT value (OR=1.008, 95%CI 1.004 to 1.013) were still significant (P＜0.05), while the association between STAS and spiculation was not significant anymore (P=0.438). Based on the above three independent predictors, a nomogram model of STAS in sub-centimeter non-small cell lung cancer was constructed. The AUC value of the model was 0.890, the sensitivity was 76.5%, and the specificity was 91.6%. The calibration curve was well fitted, suggesting that the model had a good prediction efficiency for STAS. The DCA plot showed that the model had a good clinically utility. ConclusionMale, anti-GAGE7 antibody positive and mean CT value are independent predictors of STAS positivity of sub-centimeter non-small cell lung cancer, and the nomogram model established in this study has a good predictive value and provides reference for preoperative planning of patients.
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.
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 study the differential expression of minichromosome maintenance protein (MCM) gene family in hepatocellular carcinoma (HCC) and to explore its survival predictive value.MethodsTranscriptome data, clinical data, and survival information of patients with HCC were extracted from The Cancer Genome Atlas (TCGA), and the differential expression of MCM gene was analyzed. The prognostic value of differentially expressed of MCM gene was studied by Cox proportional hazards regression model, the prognostic model and risk score system were constructed. On the basis of risk score, a number of indicators were included to construct a nomogram to predict the3- and 5-year survival probability of HCC patients, and to verify and evaluate their predictive ability and accuracy.ResultsThe expressions of MCM2, MCM3, MCM4, MCM5, MCM6, MCM7, MCM8, and MCM10 in HCC tissues were higher than those of normal liver tissues (P<0.05), and univariate analysis showed that they were all related to prognosis (P<0.05). Multivariate analysis showed that MCM6 and MCM10 were independent factors affecting survival of HCC patients (P<0.05). Through multivariate analysis, a prognostic model consisting of MCM6, MCM8, and MCM10 was constructed, and a risk scoring system was established. It had been verified that this risk score was an independent risk factor affecting the prognosis of patients with HCC, and the prognosis of patients with high scores were worse than those of patients with low scores (P<0.001). We used TNM stage, T stage, and risk score to construct a nomogram with a consistency index (C index) of 0.723 and draw a time-dependent receiver operating characteristic curve, the results showed that area under the curve of 3- and 5-year were 0.731 and 0.704, respectively.ConclusionsMCM6,MCM8, and MCM10 in the MCM gene family have important prognostic value in HCC. The nomogram constructed in this study can better predict the survival probability of HCC patients.
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 investigate the value of CT-based radiomics and clinical data in predicting the efficacy of non-vascularized bone grafting (NVBG) in hip preservation, and to construct a visual, quantifiable, and effective method for decision-making of hip preservation. Methods Between June 2009 and June 2019, 153 patients (182 hips) with osteonecrosis of the femoral head (ONFH) who underwent NVBG for hip preservation were included, and the training and testing sets were divided in a 7∶3 ratio to define hip preservation success or failure according to the 3-year postoperative follow-up. The radiomic features of the region of interest in the CT images were extracted, and the radiomics-scores were calculated by the linear weighting and coefficients of the radiomic features after dimensionality reduction. The clinical predictors were screened using univariate and multivariate Cox regression analysis. The radiomics model, clinical model, and clinical-radiomics (C-R) model were constructed respectively. Their predictive performance for the efficacy of hip preservation was compared in the training and testing sets, with evaluation indexes including area under the curve, C-Index, sensitivity, specificity, and calibration curve, etc. The best model was visualised using nomogram, and its clinical utility was assessed by decision curves. ResultsAt the 3-year postoperative follow-up, the cumulative survival rate of hip preservation was 70.33%. Continued exposure to risk factors postoperative and Japanese Investigation Committee (JIC) staging were clinical predictors of the efficacy of hip preservation, and 13 radiomic features derived from least absolute shrinkage and selection operator downscaling were used to calculate Rad-scores. The C-R model outperformed both the clinical and radiomics models in predicting the efficacy of hip preservation 1, 2, 3 years postoperative in both the training and testing sets (P<0.05), with good agreement between the predicted and observed values. A nomogram constructed based on the C-R model showed that patients with lower Rad-scores, no further postoperative exposure to risk factors, and B or C1 types of JIC staging had a higher probability of femoral survival at 1, 2, 3 years postoperatively. The decision curve analysis showed that the C-R model had a higher total net benefit than both the clinical and radiomics models with a single predictor, and it could bring more net benefit to patients within a larger probability threshold. Conclusion The prediction model and nomogram constructed by CT-based radiomics combined with clinical data is a visual, quantifiable, and effective method for decision-making of hip preservation, which can predict the efficacy of NVBG before surgery and has a high value of clinical application.