Circulating biomarker- and magnetic resonance-based nomogram predicting long-term outcomes in dilated cardiomyopathy / 中华医学杂志(英文版)

BACKGROUND@#Dilated cardiomyopathy (DCM) has a high mortality rate and is the most common indication for heart transplantation. Our study sought to develop a multiparametric nomogram to assess individualized all-cause mortality or heart transplantation (ACM/HTx) risk in DCM patients.@*METHODS@#The present study is a retrospective cohort study. The demographic, clinical, blood test, and cardiac magnetic resonance imaging (CMRI) data of DCM patients in the tertiary center (Fuwai Hospital) were collected. The primary endpoint was ACM/HTx. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied for variable selection. Multivariable Cox regression was used to develop a nomogram. The concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.@*RESULTS@#A total of 218 patients were included in the present study. They were randomly divided into a training cohort and a validation cohort. The nomogram was established based on eight variables, including mid-wall late gadolinium enhancement, systolic blood pressure, diastolic blood pressure, left ventricular ejection fraction, left ventricular end-diastolic diameter, left ventricular end-diastolic volume index, free triiodothyronine, and N-terminal pro-B type natriuretic peptide. The AUCs regarding 1-year, 3-year, and 5-year ACM/HTx events were 0.859, 0.831, and 0.840 in the training cohort and 0.770, 0.789, and 0.819 in the validation cohort, respectively. The calibration curve and DCA showed good accuracy and clinical utility of the nomogram.@*CONCLUSIONS@#We established and validated a circulating biomarker- and CMRI-based nomogram that could provide a personalized prediction of ACM/HTx for DCM patients, which might help risk stratification and decision-making in clinical practice.

A diagnostic prediction model for hypertension in Han and Yugur population from the China National Health Survey (CNHS) / 中华医学杂志(英文版)

BACKGROUND@#The prevalence of hypertension is high among Chinese adults, thus, identifying non-hypertensive individuals at high risk for intervention will help to improve the efficiency of primary prevention strategies.@*METHODS@#The cross-sectional data on 9699 participants aged 20 to 80 years were collected from the China National Health Survey in Gansu and Hebei provinces in 2016 to 2017, and they were nonrandomly split into the training set and validation set based on location. Multivariable logistic regression analysis was performed to develop the diagnostic prediction model, which was presented as a nomogram and a website with risk classification. Predictive performances of the model were evaluated using discrimination and calibration, and were further compared with a previously published model. Decision curve analysis was used to calculate the standardized net benefit for assessing the clinical usefulness of the model.@*RESULTS@#The Lasso regression analysis identified the significant predictors of hypertension in the training set, and a diagnostic model was developed using logistic regression. A nomogram with risk classification was constructed to visualize the model, and a website ( https://chris-yu.shinyapps.io/hypertension_risk_prediction/ ) was developed to calculate the exact probabilities of hypertension. The model showed good discrimination and calibration, with the C-index of 0.789 (95% confidence interval [CI]: 0.768, 0.810) through internal validation and 0.829 (95% CI: 0.816, 0.842) through external validation. Decision curve analysis demonstrated that the model was clinically useful. The model had a higher area under receiver operating characteristic curves in training and validation sets compared with a previously published diagnostic model based on Northern China population.@*CONCLUSION@#This study developed and validated a diagnostic model for hypertension prediction in Gansu Province. A nomogram and a website were developed to make the model conveniently used to facilitate the individualized prediction of hypertension in the general population of Han and Yugur.

Predicting survival and prognosis of postoperative breast cancer brain metastasis: a population-based retrospective analysis / 中华医学杂志(英文版)

BACKGROUND@#Breast cancer is one of the most common cancer in women and a proportion of patients experiences brain metastases with poor prognosis. The study aimed to construct a novel predictive clinical model to evaluate the overall survival (OS) of patients with postoperative brain metastasis of breast cancer (BCBM) and validate its effectiveness.@*METHODS@#From 2010 to 2020, a total of 310 female patients with BCBM were diagnosed in The Affiliated Cancer Hospital of Xinjiang Medical University, and they were randomly assigned to the training cohort and the validation cohort. Data of another 173 BCBM patients were collected from the Surveillance, Epidemiology, and End Results Program (SEER) database as an external validation cohort. In the training cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to determine the fundamental clinical predictive indicators and the nomogram was constructed to predict OS. The model capability was assessed using receiver operating characteristic, C-index, and calibration curves. Kaplan-Meier survival analysis was performed to evaluate clinical effectiveness of the risk stratification system in the model. The accuracy and prediction capability of the model were verified using the validation and SEER cohorts.@*RESULTS@#LASSO Cox regression analysis revealed that lymph node metastasis, molecular subtype, tumor size, chemotherapy, radiotherapy, and lung metastasis were statistically significantly correlated with BCBM. The C-indexes of the survival nomogram in the training, validation, and SEER cohorts were 0.714, 0.710, and 0.670, respectively, which showed good prediction capability. The calibration curves demonstrated that the nomogram had great forecast precision, and a dynamic diagram was drawn to increase the maneuverability of the results. The Risk Stratification System showed that the OS of low-risk patients was considerably better than that of high-risk patients ( P < 0.001).@*CONCLUSION@#The nomogram prediction model constructed in this study has a good predictive value, which can effectively evaluate the survival rate of patients with postoperative BCBM.

Nomogram-Based Prediction of Risk of Cervical Lymph Node Metastasis in Differentiated Thyroid Carcinoma / 中国医学科学院学报

Objective To establish a nomogram for predicting the risk of cervical lymph node metastasis in differentiated thyroid carcinoma (DTC). Methods The patients with complete clinical data of DTC and cervical lymph node ultrasound and diagnosed based on pathological evidence from January 2019 to December 2021 were assigned into a training group (n=444) and a validation group (n=125).Lasso regression was performed to screen the data with differences between groups,and multivariate Logistic regression to establish a prediction model with the factors screened out by Lasso regression.C-index and calibration chart were employed to evaluate the prediction performance of the established model. Results The predictive factors for establishing the model were lymph node short diameter≥0.5 cm,long-to-short-axis ratio<2,disappearance of lymph node hilum,cystic transformation,hyperechogenicity,calcification,and abnormal blood flow (all P<0.001).The established model demonstrated a good discriminative ability,with the C index of 0.938 (95%CI=0.926-0.961) in the training group. Conclusion The nomogram established based on the ultrasound image features of cervical lymph nodes in DTC can accurately predict the risk of cervical lymph node metastasis in DTC.

Establishment and validation of a preoperative nomogram model for predicting the risk of hepatocellular carcinoma with microvascular invasion / 中华外科杂志

Objective: To establish and validate a nomogram model for predicting the risk of microvascular invasion(MVI) in hepatocellular carcinoma. Methods: The clinical data of 210 patients with hepatocellular carcinoma who underwent hepatectomy at Department of Hepatobiliary and Pancreatic Surgery,the Affiliated Hospital of Qingdao University from January 2013 to October 2021 were retrospectively analyzed. There were 169 males and 41 females, aged(M(IQR)) 57(12)years(range:30 to 80 years). The patients were divided into model group(the first 170 cases) and validation group(the last 40 cases) according to visit time. Based on the clinical data of the model group,rank-sum test and multivariate Logistic regression analysis were used to screen out the independent related factors of MVI. R software was used to establish a nomogram model to predict the preoperative MVI risk of hepatocellular carcinoma,and the validation group data were used for external validation. Results: Based on the modeling group data,the receiver operating characteristic curve was used to determine that cut-off value of DeRitis ratio,γ-glutamyltransferase(GGT) concentration,the inverse number of activated peripheral blood T cell ratio (-aPBTLR) and the maximum tumor diameter for predicting MVI, which was 0.95((area under curve, AUC)=0.634, 95%CI: 0.549 to 0.719), 38.2 U/L(AUC=0.604, 95%CI: 0.518 to 0.689),-6.05%(AUC=0.660, 95%CI: 0.578 to 0.742),4 cm(AUC=0.618, 95%CI: 0.533 to 0.703), respectively. Univariate and multivariate Logistic regression analysis showed that DeRitis≥0.95,GGT concentration ≥38.2 U/L,-aPBTLR>-6.05% and the maximum tumor diameter ≥4 cm were independent related factors for MVI in hepatocellular carcinoma patients(all P<0.05). The nomogram prediction model based on the above four factors established by R software has good prediction efficiency. The C-index was 0.758 and 0.751 in the model group and the validation group,respectively. Decision curve analysis and clinical impact curve showed that the nomogram model had good clinical benefits. Conclusions: DeRitis ratio,serum GGT concentration,-aPBTLR and the maximum tumor diameter are valuable factors for preoperative prediction of hepatocellular carcinoma with MVI. A relatively reliable nomogram prediction model could be established on them.

Study on risk factors of abnormal pulmonary function among dust-exposed workers and prediction model / 中华劳动卫生职业病杂志

Objective: To explore the influencing factors of abnormal pulmonary function in dust-exposed workers and establish the risk prediction model of abnormal pulmonary function. Methods: In April 2021, a total of 4255 dust exposed workers from 47 enterprises in 2020 were included in the study. logistic regression was used to analyze the influencing factors of abnormal pulmonary function in dust-exposed workers, and the corresponding nomogram prediction model was established. The model was evaluated by ROC curve, Calibrationpolt and decision analysis curve. Results: logistic regression analysis showed that age (OR=1.03, 95%CI=1.02~1.05, P<0.001) , physical examination type (OR=4.52, 95%CI=1.69~12.10, P=0.003) , dust type (Comparison with coal dust, Cement dust, OR=3.45, 95%CI=1.45~8.18, P=0.005, Silica dust (OR=2.25, 95%CI=1.01~5.03, P=0.049) , blood pressure (OR=1.63, 95%CI=1.22~2.18, P=0.001) , creatinine (OR=0.08, 95%CI=0.05~0.12, P<0.001) , daily exposure time (OR=1.06, 95%CI=1.10~1.12, P=0.034) and total dust concentration (OR=1.29, 95%CI=1.08~1.54, P=0.005) were the influencing factors of abnormal pulmonary function. The area under the ROC curve of risk prediction nomogram model was 0.764. The results of decision analysis curve showed that the nomogram model had reference value in the prevention and intervention of abnormal pulmonary function when the threshold probability exceeded 0.05. Conclusion: The accuracy ofthe nomogram model constructed by logistic regression werewell in predicting the risk of abnormal lung function of dust-exposed workers.

New model of PIRADS and adjusted prostatespecific antigen density of peripheral zone improves the detection rate of initial prostate biopsy: a diagnostic study / 亚洲男科学杂志(英文版)

This study explored a new model of Prostate Imaging Reporting and Data System (PIRADS) and adjusted prostate-specific antigen density of peripheral zone (aPSADPZ) for predicting the occurrence of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). The demographic and clinical characteristics of 853 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), PSAD of peripheral zone (PSADPZ), aPSADPZ, and peripheral zone volume ratio (PZ-ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with the calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves. The AUCs of PSA, PSAD, PSADPZ, aPSADPZ, and PZ-ratio were 0.669, 0.762, 0.659, 0.812, and 0.748 for PCa diagnosis, while 0.713, 0.788, 0.694, 0.828, and 0.735 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of PCa or csPCa. The new model significantly improved the diagnostic accuracy of PCa (0.945 vs 0.830, P < 0.01) and csPCa (0.937 vs 0.845, P < 0.01) compared with the base model. In addition, the number of patients with PCa and csPCa predicted by the new model was in good agreement with the actual number of patients with PCa and csPCa in high-risk threshold. This study demonstrates that aPSADPZ has a higher predictive accuracy for PCa diagnosis than the conventional indicators. Combining aPSADPZ with PIRADS can improve PCa diagnosis and avoid unnecessary biopsies.

Establishment and validation of a nomogram for predicting prognosis of gastric neuroendocrine neoplasms based on data from 490 cases in a single center / 南方医科大学学报

OBJECTIVE@#To develop and validate a nomogram for predicting outcomes of patients with gastric neuroendocrine neoplasms (G-NENs).@*METHODS@#We retrospectively collected the clinical data from 490 patients with the diagnosis of G-NEN at our medical center from 2000 to 2021. Log-rank test was used to analyze the overall survival (OS) of the patients. The independent risk factors affecting the prognosis of G-NEN were identified by Cox regression analysis to construct the prognostic nomogram, whose performance was evaluated using the C-index, receiver-operating characteristic (ROC) curve, area under the ROC curve (AUC), calibration curve, DCA, and AUDC.@*RESULTS@#Among the 490 G-NEN patients (mean age of 58.6±10.92 years, including 346 male and 144 female patients), 130 (26.5%) had NET G1, 54 (11.0%) had NET G2, 206 (42.0%) had NEC, and 100 (20.5%) had MiNEN. None of the patients had NET G3. The numbers of patients in stage Ⅰ-Ⅳ were 222 (45.3%), 75 (15.3%), 130 (26.5%), and 63 (12.9%), respectively. Univariate and multivariate analyses identified age, pathological grade, tumor location, depth of invasion, lymph node metastasis, distant metastasis, and F-NLR as independent risk factors affecting the survival of the patients (P < 0.05). The C-index of the prognostic nomogram was 0.829 (95% CI: 0.800-0.858), and its AUC for predicting 1-, 3- and 5-year OS were 0.883, 0.895 and 0.944, respectively. The calibration curve confirmed a good consistency between the model prediction results and the actual observations. For predicting 1-year, 3-year and 5-year OS, the TNM staging system and the nomogram had AUC of 0.033 vs 0.0218, 0.191 vs 0.148, and 0.248 vs 0.197, respectively, suggesting higher net benefit and better clinical utility of the nomogram.@*CONCLUSION@#The prognostic nomogram established in this study has good predictive performance and clinical value to facilitate prognostic evaluation of individual patients with G-NEN.

Establishment and validation of a nomogram-based predictive model for idiopathic aldosteronism / 中华内科杂志

Objective: To establish and validate a nomogram-based predictive model for idiopathic hyperaldosteronism (IHA). Methods: This cross-sectional study was conducted with the collected clinical and biochemical data of patients with primary aldosteronism (PA) including 249 patients with unilateral primary aldosteronism (UPA) and 107 patients with IHA, who were treated at the Department of Endocrinology of the First Affiliated Hospital of Chongqing Medical University from November 2013 to November 2022. Plasma aldosterone concentration (PAC) and plasma renin concentration (PRC) were measured by chemiluminescence. Stepwise regression analysis was applied to select the key predictors of IHA, and a nomogram-based scoring model was developed. The model was validated in another external independent cohort of patients with PA including 62 patients with UPA and 43 patients with IHA, who were diagnosed at the Department of Endocrinology, First Affiliated Hospital of Zhengzhou University. An independent-sample t test, Mann-Whitney U test, and χ2 test were used for statistical analysis. Results: In the training cohort, in comparison with the UPA group, the IHA group showed a higher serum potassium level [M(Q1, Q3), 3.4 (3.1, 3.8) mmol/L vs. 2.7 (2.1, 3.1) mmol/L] and higher PRC [4.0 (2.1, 8.2) mU/L vs. 1.5 (0.6, 3.4) mU/L] and a lower PAC post-saline infusion test (SIT) [305 (222, 416) pmol/L vs. 720 (443, 1 136) pmol/L] and a lower rate of unilateral adrenal nodules [33.6% (36/107) vs. 81.1% (202/249)]; the intergroup differences in these measurements were statistically significant (all P<0.001). Serum potassium level, PRC, PAC post-SIT, and the rate of unilateral adrenal nodules showed similar performance in the IHA group in the validation cohort. After stepwise regression analysis for all significant variables in the training cohort, a scoring model based on a nomogram was constructed, and the predictive parameters included the rate of unilateral adrenal nodules, serum potassium concentration, PAC post-SIT, and PRC in the standing position. When the total score was ≥14, the model showed a sensitivity of 0.65 and specificity of 0.90 in the training cohort and a sensitivity of 0.56 and specificity of 1.00 in the validation cohort. Conclusion: The nomogram was used to successfully develop a model for prediction of IHA that could facilitate selection of patients with IHA who required medication directly.

Development and validation of a prognostic prediction model for patients with stage Ⅰ to Ⅲ colon cancer incorporating high-risk pathological features / 中华外科杂志

Objective: To examine a predictive model that incorporating high risk pathological factors for the prognosis of stage Ⅰ to Ⅲ colon cancer. Methods: This study retrospectively collected clinicopathological information and survival outcomes of stage Ⅰ~Ⅲ colon cancer patients who underwent curative surgery in 7 tertiary hospitals in China from January 1, 2016 to December 31, 2017. A total of 1 650 patients were enrolled, aged (M(IQR)) 62 (18) years (range: 14 to 100). There were 963 males and 687 females. The median follow-up period was 51 months. The Cox proportional hazardous regression model was utilized to select high-risk pathological factors, establish the nomogram and scoring system. The Bootstrap resampling method was utilized for internal validation of the model, the concordance index (C-index) was used to assess discrimination and calibration curves were presented to assess model calibration. The Kaplan-Meier method was used to plot survival curves after risk grouping, and Cox regression was used to compare disease-free survival between subgroups. Results: Age (HR=1.020, 95%CI: 1.008 to 1.033, P=0.001), T stage (T3:HR=1.995,95%CI:1.062 to 3.750,P=0.032;T4:HR=4.196, 95%CI: 2.188 to 8.045, P<0.01), N stage (N1: HR=1.834, 95%CI: 1.307 to 2.574, P<0.01; N2: HR=3.970, 95%CI: 2.724 to 5.787, P<0.01) and number of lymph nodes examined (≥36: HR=0.438, 95%CI: 0.242 to 0.790, P=0.006) were independently associated with disease-free survival. The C-index of the scoring model (model 1) based on age, T stage, N stage, and dichotomous variables of the lymph nodes examined (<12 and ≥12) was 0.723, and the C-index of the scoring model (model 2) based on age, T stage, N stage, and multi-categorical variables of the lymph nodes examined (<12, 12 to <24, 24 to <36, and ≥36) was 0.726. A scoring system was established based on age, T stage, N stage, and multi-categorical variables of lymph nodes examined, the 3-year DFS of the low-risk (≤1), middle-risk (2 to 4) and high-risk (≥5) group were 96.3% (n=711), 89.0% (n=626) and 71.4% (n=313), respectively. Statistically significant difference was observed among groups (P<0.01). Conclusions: The number of lymph nodes examined was an independent prognostic factor for disease-free survival after curative surgery in patients with stage Ⅰ to Ⅲ colon cancer. Incorporating the number of lymph nodes examined as a multi-categorical variable into the T and N staging system could improve prognostic predictive validity.

Risk modeling based on HER-2 related genes for bladder cancer survival prognosis assessment / 北京大学学报(医学版)

OBJECTIVE@#To investigate the correlation between the human epidermal growth factor receptor-2-related genes (HRGs) and survival prognosis of bladder cancer and to construct a predictive model for survival prognosis of bladder cancer patients based on HRGs.@*METHODS@#HRGs in bladder cancer were found by downloading bladder tumor tissue mRNA sequencing data and clinical data from the cancer genome atlas (TCGA), downloading HER-2 related genes from the molecular signatures database (MsigDB), and crossing the two databases. Further identifying HRGs associated with bladder cancer survival (P < 0.05) by using single and multi-factor Cox regression analysis and constructing HRGs risk score model (HRSM), the bladder cancer patients were categorized into high-risk and low-risk groups accor-ding to the median risk score. Survival analysis of the patients in high- and low-risk groups was conducted using R language and correlation of HRGs with clinical characteristics. A multi-factor Cox regression analysis was used to verify the independent factors affecting the prognosis of the patients with bladder cancer. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of HRSM was calculated, and a nomogram was constructed for survival prediction of the bladder cancer patients. Analysis of HRSM and patient immune cell infiltration correlation was made using the TIMER database.@*RESULTS@#A total of 13 HRGs associated with patient survival were identified in this study. Five genes (BTC, CDC37, EGF, PTPRR and EREG) were selected for HRSM by multi-factor Cox regression analysis. The 5-year survival rate of the bladder cancer patients in the high-risk group was significantly lower than that of the patients in the low-risk group. High expression of PTPRR was found to be significantly and negatively correlated with tumor grade and stage by clinical correlation analysis, while EREG was found to be the opposite; Increased expression of EGF was associated with high grade, however, the high expression ofCDC37showed the opposite result. And no significant correlation was found between BTC expression and clinical features. Correlation analysis of HRSM with immune cells revealed a positive correlation between risk score and infiltration of dendritic cells, CD8+T cells, CD4+T cells, neutrophils and macrophages.@*CONCLUSION@#HRGs have an important role in the prognosis of bladder cancer patients and may serve as new predictive biomarkers and potential targets for treatment.

Establishment and validation of a novel nomogram to predict overall survival after radical nephrectomy / 中华肿瘤杂志

Objective: To establish a nomogram prognostic model for predicting the 5-, 10-, and 15-year overall survival (OS) of non-metastatic renal cell carcinoma patients managed with radical nephrectomy (RN), compare the modelled results with the results of pure pathologic staging, the Karakiewicz nomogram and the Mayo Clinic Stage, Size, Grade, and Necrosis (SSIGN) score commonly used in foreign countries, and stratify the patients into different prognostic risk subgroups. Methods: A total of 1 246 non-metastatic renal cell carcinoma patients managed with RN in Sun Yat-sen University Cancer Center (SYSUCC) from 1999 to 2020 were retrospectively analyzed. Multivariate Cox regression analysis was used to screen the variables that influence the prognosis for nomogram establishment, and the bootstrap random sampling was used for internal validation. The time-receiver operating characteristic curve (ROC), the calibration curve and the clinical decision curve analysis (DCA) were applied to evaluate the nomogram. The prediction efficacy of the nomogram and that of the pure pathologic staging, the Karakiewicz nomogram and the SSIGN score was compared through the area under the curve (AUC). Finally, patients were stratified into different risk subgroups according to our nomogram scores. Results: A total of 1 246 patients managed with RN were enrolled in this study. Multivariate Cox regression analysis showed that age, smoking history, pathological nuclear grade, sarcomatoid differentiation, tumor necrosis and pathological T and N stages were independent prognostic factors for RN patients (all P<0.05). A nomogram model named SYSUCC based on these factors was built to predict the 5-, 10-, and 15-year survival rate of the participating patients. In the bootstrap random sampling with 1 000 iterations, all these factors occurred for more than 800 times as independent predictors. The Harrell's concordance index (C-index) of SYSUCC was higher compared with pure pathological staging [0.770 (95% CI: 0.716-0.823) vs 0.674 (95% CI: 0.621-0.728)]. The calibration curve showed that the survival rate as predicted by the SYSUCC model simulated the actual rate, while the clinical DCA showed that the SYSUCC nomogram has a benefit in certain probability ranges. In the ROC analysis that included 857 patients with detailed pathological nuclear stages, the nomogram had a larger AUC (5-/10-year AUC: 0.823/0.804) and better discriminating ability than pure pathological staging (5-/10-year AUC: 0.701/0.658), Karakiewicz nomogram (5-/10-year AUC: 0.772/0.734) and SSIGN score (5-/10-year AUC: 0.792/0.750) in predicting the 5-/10-year OS of RN patients (all P<0.05). In addition, the AUC of the SYSUCC nomogram for predicting the 15-year OS (0.820) was larger than that of the SSIGN score (0.709), and there was no statistical difference (P<0.05) between the SYSUCC nomogram, pure pathological staging (0.773) and the Karakiewicz nomogram (0.826). The calibration curve was close to the standard curve, which indicated that the model has good predictive performance. Finally, patients were stratified into low-, intermediate-, and high-risk subgroups (738, 379 and 129, respectively) according to the SYSUCC nomogram scores, among whom patients in intermediate- and high-risk subgroups had a worse OS than patients in the low-risk subgroup (intermediate-risk group vs. low-risk group: HR=4.33, 95% CI: 3.22-5.81, P<0.001; high-risk group vs low-risk group: HR=11.95, 95% CI: 8.29-17.24, P<0.001), and the high-risk subgroup had a worse OS than the intermediate-risk group (HR=2.63, 95% CI: 1.88-3.68, P<0.001). Conclusions: Age, smoking history, pathological nuclear grade, sarcomatoid differentiation, tumor necrosis and pathological stage were independent prognostic factors for non-metastasis renal cell carcinoma patients after RN. The SYSUCC nomogram based on these independent prognostic factors can better predict the 5-, 10-, and 15-year OS than pure pathological staging, the Karakiewicz nomogram and the SSIGN score of patients after RN. In addition, the SYSUCC nomogram has good discrimination, agreement, risk stratification and clinical application potential.

Nomogram prediction model of cervical anastomotic leakage after esophageal cancer surgery / 中华肿瘤杂志

Objective: To retrospectively analyze the risk factors of anastomotic leakage in the neck after esophageal cancer and establish a nomogram prediction model that can accurately predict the occurrence of anastomotic leakage in the neck of the patient. Methods: The study retrospectively analyzed 702 patients who underwent radical esophageal cancer surgery between January 2010 and May 2015 at Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. A multivariate logistic regression model was used to determine the risk factors for neck anastomotic leak, and a nomogram model was constructed, internal validation methods were used to evaluate and verify the predictive effectiveness of the nomogram. Results: There were 702 patients in the whole group, 492 in the training group and 210 in the validation group. The incidence of postoperative cervical anastomotic leak was 16.1% (79/492) in 492 patients with esophageal cancer in the training group. Multifactorial analysis revealed calcification of the descending aorta (OR=2.12, 95% CI: 1.14, 3.94, P=0.018), calcification of the celiac artery (OR=2.29, 95% CI: 1.13, 4.64, P=0.022), peripheral vascular disease (OR=5.50, 95% CI: 1.64, 18.40, P=0.006), postoperative ventilator-assisted breathing (OR=5.33, 95% CI: 1.83, 15.56, P=0.002), pleural effusion or septic chest (OR=3.08, 95% CI: 1.11, 8.55, P=0.031), incisional fat liquefaction and infection (OR=3.49, 95% CI: 1.68, 7.27, P=0.001) were independent risk factors for the development of cervical anastomotic leak after esophageal cancer surgery. The results of the nomogram prediction model showed that the consistency indices of the training and external validation groups were 0.73 and 0.74, respectively (P<0.001), suggesting that the prediction model has good predictive efficacy. Conclusion: The nomogram prediction model can intuitively predict the incidence of postoperative cervical anastomotic leakage in patients with high prediction accuracy, which can help provide a clinical basis for preventing cervical anastomotic leak and individualized treatment of patients.

Establishment and validation of a novel nomogram to predict overall survival after radical nephrectomy / 中华肿瘤杂志

Objective: To establish a nomogram prognostic model for predicting the 5-, 10-, and 15-year overall survival (OS) of non-metastatic renal cell carcinoma patients managed with radical nephrectomy (RN), compare the modelled results with the results of pure pathologic staging, the Karakiewicz nomogram and the Mayo Clinic Stage, Size, Grade, and Necrosis (SSIGN) score commonly used in foreign countries, and stratify the patients into different prognostic risk subgroups. Methods: A total of 1 246 non-metastatic renal cell carcinoma patients managed with RN in Sun Yat-sen University Cancer Center (SYSUCC) from 1999 to 2020 were retrospectively analyzed. Multivariate Cox regression analysis was used to screen the variables that influence the prognosis for nomogram establishment, and the bootstrap random sampling was used for internal validation. The time-receiver operating characteristic curve (ROC), the calibration curve and the clinical decision curve analysis (DCA) were applied to evaluate the nomogram. The prediction efficacy of the nomogram and that of the pure pathologic staging, the Karakiewicz nomogram and the SSIGN score was compared through the area under the curve (AUC). Finally, patients were stratified into different risk subgroups according to our nomogram scores. Results: A total of 1 246 patients managed with RN were enrolled in this study. Multivariate Cox regression analysis showed that age, smoking history, pathological nuclear grade, sarcomatoid differentiation, tumor necrosis and pathological T and N stages were independent prognostic factors for RN patients (all P<0.05). A nomogram model named SYSUCC based on these factors was built to predict the 5-, 10-, and 15-year survival rate of the participating patients. In the bootstrap random sampling with 1 000 iterations, all these factors occurred for more than 800 times as independent predictors. The Harrell's concordance index (C-index) of SYSUCC was higher compared with pure pathological staging [0.770 (95% CI: 0.716-0.823) vs 0.674 (95% CI: 0.621-0.728)]. The calibration curve showed that the survival rate as predicted by the SYSUCC model simulated the actual rate, while the clinical DCA showed that the SYSUCC nomogram has a benefit in certain probability ranges. In the ROC analysis that included 857 patients with detailed pathological nuclear stages, the nomogram had a larger AUC (5-/10-year AUC: 0.823/0.804) and better discriminating ability than pure pathological staging (5-/10-year AUC: 0.701/0.658), Karakiewicz nomogram (5-/10-year AUC: 0.772/0.734) and SSIGN score (5-/10-year AUC: 0.792/0.750) in predicting the 5-/10-year OS of RN patients (all P<0.05). In addition, the AUC of the SYSUCC nomogram for predicting the 15-year OS (0.820) was larger than that of the SSIGN score (0.709), and there was no statistical difference (P<0.05) between the SYSUCC nomogram, pure pathological staging (0.773) and the Karakiewicz nomogram (0.826). The calibration curve was close to the standard curve, which indicated that the model has good predictive performance. Finally, patients were stratified into low-, intermediate-, and high-risk subgroups (738, 379 and 129, respectively) according to the SYSUCC nomogram scores, among whom patients in intermediate- and high-risk subgroups had a worse OS than patients in the low-risk subgroup (intermediate-risk group vs. low-risk group: HR=4.33, 95% CI: 3.22-5.81, P<0.001; high-risk group vs low-risk group: HR=11.95, 95% CI: 8.29-17.24, P<0.001), and the high-risk subgroup had a worse OS than the intermediate-risk group (HR=2.63, 95% CI: 1.88-3.68, P<0.001). Conclusions: Age, smoking history, pathological nuclear grade, sarcomatoid differentiation, tumor necrosis and pathological stage were independent prognostic factors for non-metastasis renal cell carcinoma patients after RN. The SYSUCC nomogram based on these independent prognostic factors can better predict the 5-, 10-, and 15-year OS than pure pathological staging, the Karakiewicz nomogram and the SSIGN score of patients after RN. In addition, the SYSUCC nomogram has good discrimination, agreement, risk stratification and clinical application potential.

Nomogram prediction model of cervical anastomotic leakage after esophageal cancer surgery / 中华肿瘤杂志

Objective: To retrospectively analyze the risk factors of anastomotic leakage in the neck after esophageal cancer and establish a nomogram prediction model that can accurately predict the occurrence of anastomotic leakage in the neck of the patient. Methods: The study retrospectively analyzed 702 patients who underwent radical esophageal cancer surgery between January 2010 and May 2015 at Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. A multivariate logistic regression model was used to determine the risk factors for neck anastomotic leak, and a nomogram model was constructed, internal validation methods were used to evaluate and verify the predictive effectiveness of the nomogram. Results: There were 702 patients in the whole group, 492 in the training group and 210 in the validation group. The incidence of postoperative cervical anastomotic leak was 16.1% (79/492) in 492 patients with esophageal cancer in the training group. Multifactorial analysis revealed calcification of the descending aorta (OR=2.12, 95% CI: 1.14, 3.94, P=0.018), calcification of the celiac artery (OR=2.29, 95% CI: 1.13, 4.64, P=0.022), peripheral vascular disease (OR=5.50, 95% CI: 1.64, 18.40, P=0.006), postoperative ventilator-assisted breathing (OR=5.33, 95% CI: 1.83, 15.56, P=0.002), pleural effusion or septic chest (OR=3.08, 95% CI: 1.11, 8.55, P=0.031), incisional fat liquefaction and infection (OR=3.49, 95% CI: 1.68, 7.27, P=0.001) were independent risk factors for the development of cervical anastomotic leak after esophageal cancer surgery. The results of the nomogram prediction model showed that the consistency indices of the training and external validation groups were 0.73 and 0.74, respectively (P<0.001), suggesting that the prediction model has good predictive efficacy. Conclusion: The nomogram prediction model can intuitively predict the incidence of postoperative cervical anastomotic leakage in patients with high prediction accuracy, which can help provide a clinical basis for preventing cervical anastomotic leak and individualized treatment of patients.

Development and validation of prognostic nomogram for malignant pleural mesothelioma / 中华肿瘤杂志

Objective: To development the prognostic nomogram for malignant pleural mesothelioma (MPM). Methods: Two hundred and ten patients pathologically confirmed as MPM were enrolled in this retrospective study from 2007 to 2020 in the People's Hospital of Chuxiong Yi Autonomous Prefecture, the First and Third Affiliated Hospital of Kunming Medical University, and divided into training (n=112) and test (n=98) sets according to the admission time. The observation factors included demography, symptoms, history, clinical score and stage, blood cell and biochemistry, tumor markers, pathology and treatment. The Cox proportional risk model was used to analyze the prognostic factors of 112 patients in the training set. According to the results of multivariate Cox regression analysis, the prognostic prediction nomogram was established. C-Index and calibration curve were used to evaluate the model's discrimination and consistency in raining and test sets, respectively. Patients were stratified according to the median risk score of nomogram in the training set. Log rank test was performed to compare the survival differences between the high and low risk groups in the two sets. Results: The median overall survival (OS) of 210 MPM patients was 384 days (IQR=472 days), and the 6-month, 1-year, 2-year, and 3-year survival rates were 75.7%, 52.6%, 19.7%, and 13.0%, respectively. Cox multivariate regression analysis showed that residence (HR=2.127, 95% CI: 1.154-3.920), serum albumin (HR=1.583, 95% CI: 1.017-2.464), clinical stage (stage Ⅳ: HR=3.073, 95% CI: 1.366-6.910) and the chemotherapy (HR=0.476, 95% CI: 0.292-0.777) were independent prognostic factors for MPM patients. The C-index of the nomogram established based on the results of Cox multivariate regression analysis in the training and test sets were 0.662 and 0.613, respectively. Calibration curves for both the training and test sets showed moderate consistency between the predicted and actual survival probabilities of MPM patients at 6 months, 1 year, and 2 years. The low-risk group had better outcomes than the high-risk group in both training (P=0.001) and test (P=0.003) sets. Conclusion: The survival prediction nomogram established based on routine clinical indicators of MPM patients provides a reliable tool for prognostic prediction and risk stratification.

Risk factors analysis and prediction model construction of submucosal deep infiltration of early colorectal tumor / 中华肿瘤杂志

Objective: To investigate the risk factors for the development of deep infiltration in early colorectal tumors (ECT) and to construct a prediction model to predict the development of deep infiltration in patients with ECT. Methods: The clinicopathological data of ECT patients who underwent endoscopic treatment or surgical treatment at the Cancer Hospital, Chinese Academy of Medical Sciences from August 2010 to December 2020 were retrospectively analyzed. The independent risk factors were analyzed by multifactorial regression analysis, and the prediction models were constructed and validated by nomogram. Results: Among the 717 ECT patients, 590 patients were divided in the within superficial infiltration 1 (SM1) group (infiltration depth within SM1) and 127 patients in the exceeding SM1 group (infiltration depth more than SM1). There were no statistically significant differences in gender, age, and lesion location between the two groups (P>0.05). The statistically significant differences were observed in tumor morphological staging, preoperative endoscopic assessment performance, vascular tumor emboli and nerve infiltration, and degree of tumor differentiation (P<0.05). Multivariate regression analysis showed that only erosion or rupture (OR=4.028, 95% CI: 1.468, 11.050, P=0.007), localized depression (OR=3.105, 95% CI: 1.584, 6.088, P=0.001), infiltrative JNET staging (OR=5.622, 95% CI: 3.029, 10.434, P<0.001), and infiltrative Pit pattern (OR=2.722, 95% CI: 1.347, 5.702, P=0.006) were independent risk factors for the development of deep submucosal infiltration in ECT. Nomogram was constructed with the included independent risk factors, and the nomogram was well distinguished and calibrated in predicting the occurrence of deep submucosal infiltration in ECT, with a C-index and area under the curve of 0.920 (95% CI: 0.811, 0.929). Conclusion: The nomogram prediction model constructed based on only erosion or rupture, local depression, infiltrative JNET typing, and infiltrative Pit pattern has a good predictive efficacy in the occurrence of deep submucosal infiltration in ECT.

Construction and evaluation of a nomogram prediction model for periprosthetic fractures after total hip arthroplasty / 中国骨伤

OBJECTIVE@#To construct and evaluate nomogram prediction model for periprosthetic fractures in patients undergoing total hip arthroplasty (THA).@*METHODS@#A total of 538 patients who underwent THA from April 2013 to February 2019 were selected as the research subjects, including 318 males and 220 females, aged 40 to 60 years old with an average age of (50.79±6.37) years old. All patients with THA were divided into non-fracture group (506 patients) and fracture group (32 pathents) according to the 3-year follow-up results. Univariate and multivariate Logistic regression analyses were performed to analyze the influencing factors of postoperative periprosthetic fractures in patients with THA. A nomogram prediction model for periprosthetic fractures in patients undergoing THA was constructed, and the validity and discrimination of the prediction model were evaluated.@*RESULTS@#The proportion of patients with osteoporosis, trauma history, and hip revision in the fracture group were higher than those in the non-fracture group(P<0.05), and the proportion of bone cement prosthesis was lower than that in the non-fracture group(P<0.05). The osteoporosis status[OR=4.177, 95%CI(1.815, 9.617), P<0.05], trauma history[OR=7.481, 95%CI(3.104, 18.031), P<0.05], and hip revision[OR=11.371, 95%CI(3.220, 40.153, P<0.05] were independent risk factors for postoperative periprosthetic fractures in patients undergoing THA, cemented prosthesis [OR=0.067, 95%CI(0.019, 0.236), P<0.05] was an independent protective factor for postoperative periprosthetic fractures in patients undergoing THA(P<0.05). Hosmer-Lemeshow goodness of fit test showed that χ2=7.864, P=0.325;the area under the curve (AUC) for periprosthetic fractures in patients undergoing THA was 0.892 with a sensitivity of 87.5% and a specificity of 77.7% by receiver operating characteristic(ROC) curve.@*CONCLUSION@#The nomogram prediction model for periprosthetic fractures after THA constructed in this study has good discrimination, which is beneficial to clinical prediction of periprosthetic fractures in patients undergoing THA, and facilitates individualized fracture prevention.

Construction and evaluation of a nomogram for predicting the prognosis of patients with colorectal cancer with peritoneal carcinomatosis treated with cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy / 中华胃肠外科杂志

Objectives: To construct a nomogram incorporating important prognostic factors for predicting the overall survival of patients with colorectal cancer with peritoneal metastases treated with cytoreductive surgery (CRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC), the aim being to accurately predict such patients' survival rates. Methods: This was a retrospective observational study. Relevant clinical and follow-up data of patients with colorectal cancer with peritoneal metastases treated by CRS + HIPEC in the Department of Peritoneal Cancer Surgery, Beijing Shijitan Hospital, Capital Medical University from 2007 January to 2020 December were collected and subjected to Cox proportional regression analysis. All included patients had been diagnosed with peritoneal metastases from colorectal cancer and had no detectable distant metastases to other sites. Patients who had undergone emergency surgery because of obstruction or bleeding, or had other malignant diseases, or could not tolerate treatment because of severe comorbidities of the heart, lungs, liver or kidneys, or had been lost to follow-up, were excluded. Factors studied included: (1) basic clinicopathological characteristics; (2) details of CRS+HIPEC procedures; (3) overall survival rates; and (4) independent factors that influenced overall survival; the aim being to identify independent prognostic factors and use them to construct and validate a nomogram. The evaluation criteria used in this study were as follows. (1) Karnofsky Performance Scale (KPS) scores were used to quantitatively assess the quality of life of the study patients. The lower the score, the worse the patient's condition. (2) A peritoneal cancer index (PCI) was calculated by dividing the abdominal cavity into 13 regions, the highest score for each region being three points. The lower the score, the greater is the value of treatment. (3) Completeness of cytoreduction score (CC), where CC-0 and CC-1 denote complete eradication of tumor cells and CC-2 and CC-3 incomplete reduction of tumor cells. (4) To validate and evaluate the nomogram model, the internal validation cohort was bootstrapped 1000 times from the original data. The accuracy of prediction of the nomogram was evaluated with the consistency coefficient (C-index), and a C-index of 0.70-0.90 suggest that prediction by the model was accurate. Calibration curves were constructed to assess the conformity of predictions: the closer the predicted risk to the standard curve, the better the conformity. Results: The study cohort comprised 240 patients with peritoneal metastases from colorectal cancer who had undergone CRS+HIPEC. There were 104 women and 136 men of median age 52 years (10-79 years) and with a median preoperative KPS score of 90 points. There were 116 patients (48.3%) with PCI≤20 and 124 (51.7%) with PCI>20. Preoperative tumor markers were abnormal in 175 patients (72.9%) and normal in 38 (15.8%). HIPEC lasted 30 minutes in seven patients (2.9%), 60 minutes in 190 (79.2%), 90 minutes in 37 (15.4%), and 120 minutes in six (2.5%). There were 142 patients (59.2%) with CC scores 0-1 and 98 (40.8%) with CC scores 2-3. The incidence of Grade III to V adverse events was 21.7% (52/240). The median follow-up time is 15.3 (0.4-128.7) months. The median overall survival was 18.7 months, and the 1-, 3- and 5-year overall survival rates were 65.8%, 37.2% and 25.7%, respectively. Multivariate analysis showed that KPS score, preoperative tumor markers, CC score, and duration of HIPEC were independent prognostic factors. In the nomogram constructed with the above four variables, the predicted and actual values in the calibration curves for 1, 2 and 3-year survival rates were in good agreement, the C-index being 0.70 (95% CI: 0.65-0.75). Conclusions: Our nomogram, which was constructed with KPS score, preoperative tumor markers, CC score, and duration of HIPEC, accurately predicts the survival probability of patients with peritoneal metastases from colorectal cancer treated with cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy.

Assessment of risk factors for bronchopulmonary dysplasia with pulmonary hypertension and construction of a prediction nomogram model / 中华儿科杂志

Objective: To explore the risk factors of pulmonary hypertension (PH) in premature infants with bronchopulmonary dysplasia (BPD), and to establish a prediction model for early PH. Methods: This was a retrospective cohort study. Data of 777 BPD preterm infants with the gestational age of <32 weeks were collected from 7 collaborative units of the Su Xinyun Neonatal Perinatal Collaboration Network platform in Jiangsu Province from January 2019 to December 2022. The subjects were randomly divided into a training cohort and a validation cohort at a ratio of 8∶2 by computer, and non-parametric test or χ2 test was used to examine the differences between the two retrospective cohorts. Univariate Logistic regression and multivariate logistic regression analyses were used in the training cohort to screen the risk factors affecting the PH associated with BPD. A nomogram model was constructed based on the severity of BPD and its risk factors,which was internally validated by the Bootstrap method. Finally, the differential, calibration and clinical applicability of the prediction model were evaluated using the training and verification queues. Results: A total of 130 among the 777 preterm infants with BPD had PH, with an incidence of 16.7%, and the gestational age was 28.7 (27.7, 30.0) weeks, including 454 males (58.4%) and 323 females (41.6%). There were 622 preterm infants in the training cohort, including 105 preterm infants in the PH group. A total of 155 patients were enrolled in the verification cohort, including 25 patients in the PH group. Multivariate Logistic regression analysis revealed that low 5 min Apgar score (OR=0.87, 95%CI 0.76-0.99), cesarean section (OR=1.97, 95%CI 1.13-3.43), small for gestational age (OR=9.30, 95%CI 4.30-20.13), hemodynamically significant patent ductus arteriosus (hsPDA) (OR=4.49, 95%CI 2.58-7.80), late-onset sepsis (LOS) (OR=3.52, 95%CI 1.94-6.38), and ventilator-associated pneumonia (VAP) (OR=8.67, 95%CI 3.98-18.91) were all independent risk factors for PH (all P<0.05). The independent risk factors and the severity of BPD were combined to construct a nomogram map model. The area under the receiver operating characteristic (ROC) curve of the nomogram model in the training cohort and the validation cohort were 0.83 (95%CI 0.79-0.88) and 0.87 (95%CI 0.79-0.95), respectively, and the calibration curve was close to the ideal diagonal. Conclusions: Risk of PH with BPD increases in preterm infants with low 5 minute Apgar score, cesarean section, small for gestational age, hamodynamically significant patent ductus arteriosus, late-onset sepsis, and ventilator-associated pneumonia. This nomogram model serves as a useful tool for predicting the risk of PH with BPD in premature infants, which may facilitate individualized early intervention.