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 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.
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 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.
Objective To study the risk factors of developing progressive pulmonary fibrosis (PPF) within one year in patients diagnosed with rheumatoid arthritis-associated interstitial lung disease (RA-ILD), and develop a nomogram. Methods A retrospective study was conducted in 145 cases of RA-ILD patients diagnosed and followed up in the Affiliated Hospital of Qingdao University from January 2010 to October 2022. Among them, 106 patients and 39 patients were randomly assigned to a training group and a verification group. The independent predictors of PPF in patients with RA-ILD within one year were determined by univariate and multivariate logistic regression analysis. Then a nomogram is established through these independent predictive variables. Calibration curve, Hosmer-Lemeshow test, receiver operating characteristic (ROC) curve and area under ROC curve (AUC) and clinical decision curve were used to evaluate the predictive efficiency of the nomogram model for PPF in RA-ILD patients within one year. Finally, internal validation was used to test the stability of the model. Results Of the 145 patients with RA-ILD, 62 (42.76%) developed PPF within one year, including 40 (37.7%) in the training group and 22 (56.41%) in the verification group. The PPF patients had higher proportion of subpleural abnormalities, higher visual score of fibrosis and shorter duration of RA. Logistic regression analysis showed that the duration of rheumatoid arthritis (RA), visual score of fibrosis and subpleural abnormality were independent risk factors for the occurrence of PPF within one year after diagnosis of RA-ILD. A nomogram was constructed based on these independent risk factors. The AUC values of the training group and the verification group were 0.798 (95%CI 0.713 - 0.882) and 0.822 (95%CI 0.678 - 0.967) respectively, indicating that the model had a good ability to distinguish. The clinical decision curve showed that the clinical benefit of PPF risk prediction model was greater when the risk threshold was between 0.06 and 0.71. Conclusion According to the duration of RA, the visual score of fibrosis and the presence of subpleural abnormalities, the predictive model of PPF was drawn to provide reference for the clinical prediction of PPF in patients with RA-ILD within one year after diagnosis.
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 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 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.
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.
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.
