A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Hypopharyngeal Squamous Cell Carcinomas: A Population-Based Study

Background: Hypopharyngeal squamous cell carcinomas (HPSCC) is one of the causes of death in elderly patients, an accurate prediction of survival can effectively improve the prognosis of patients. However, there is no accurate assessment of the survival prognosis of elderly patients with HPSCC. The purpose of this study is to establish a nomogram to predict the cancer-specific survival (CSS) of elderly patients with HPSCC.Methods: The clinicopathological data of all patients from 2004 to 2018 were downloaded from the SEER database. These patients were randomly divided into a training set (70%) and a validation set (30%). The univariate and multivariate Cox regression analysis confirmed independent risk factors for the prognosis of elderly patients with HPSCC. A new nomogram was constructed to predict 1-, 3-, and 5-year CSS in elderly patients with HPSCC. Then used the consistency index (C-index), the calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to assess the clinical value of the model.Results: A total of 3,172 patients were included in the study, and they were randomly divided into a training set (N = 2,219) and a validation set (N = 953). Univariate and multivariate analysis suggested that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage were independent risk factors for patient prognosis. These nine variables are included in the nomogram to predict the CSS of patients. The C-index for the training set and validation was 0.713 (95% CI, 0.697–0.729) and 0.703 (95% CI, 0.678–0.729), respectively. The AUC results of the training and validation set indicate that this nomogram has good accuracy. The calibration curve indicates that the observed and predicted values are highly consistent. DCA indicated that the nomogram has a better clinical application value than the traditional TNM staging system.Conclusion: This study identified risk factors for survival in elderly patients with HPSCC. We found that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage are independent prognostic factors. A new nomogram for predicting the CSS of elderly HPSCC patients was established. This model has good clinical application value and can help patients and doctors make clinical decisions.

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A Web-Based Prediction Model for Overall Survival of Elderly Patients With Malignant Bone Tumors: A Population-Based Study

Frontiers in Public Health ◽

10.3389/fpubh.2021.812395 ◽

2022 ◽

Vol 9 ◽

Author(s):

Jie Tang ◽

JinKui Wang ◽

Xiudan Pan

Keyword(s):

Risk Factors ◽

Overall Survival ◽

Elderly Patients ◽

Prediction Model ◽

Bone Tumors ◽

Receiver Operating Curve ◽

Training Set ◽

Malignant Bone Tumors ◽

Independent Risk Factors ◽

Validation Set

Background: Malignant bone tumors (MBT) are one of the causes of death in elderly patients. The purpose of our study is to establish a nomogram to predict the overall survival (OS) of elderly patients with MBT.Methods: The clinicopathological data of all elderly patients with MBT from 2004 to 2018 were downloaded from the SEER database. They were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate Cox regression analysis was used to identify independent risk factors for elderly patients with MBT. A nomogram was built based on these risk factors to predict the 1-, 3-, and 5-year OS of elderly patients with MBT. Then, used the consistency index (C-index), calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model was. Decision curve analysis (DCA) was used to assess the clinical potential application value of the nomogram. Based on the scores on the nomogram, patients were divided into high- and low-risk groups. The Kaplan-Meier (K-M) curve was used to test the difference in survival between the two patients.Results: A total of 1,641 patients were included, and they were randomly assigned to the training set (N = 1,156) and the validation set (N = 485). The univariate and multivariate analysis of the training set suggested that age, sex, race, primary site, histologic type, grade, stage, M stage, surgery, and tumor size were independent risk factors for elderly patients with MBT. The C-index of the training set and the validation set were 0.779 [0.759–0.799] and 0.801 [0.772–0.830], respectively. The AUC of the training and validation sets also showed similar results. The calibration curves of the training and validation sets indicated that the observed and predicted values were highly consistent. DCA suggested that the nomogram had potential clinical value compared with traditional TNM staging.Conclusion: We had established a new nomogram to predict the 1-, 3-, 5-year OS of elderly patients with MBT. This predictive model can help doctors and patients develop treatment plans and follow-up strategies.

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Score for the Risk and Overall Survival of Lung Metastasis in Patients First Diagnosed With Soft Tissue Sarcoma: A Novel Nomogram-Based Risk Assessment System

Technology in Cancer Research & Treatment ◽

10.1177/15330338211066240 ◽

2022 ◽

Vol 21 ◽

pp. 153303382110662

Author(s):

Zhiyi Fan ◽

Changxing Chi ◽

Yuexin Tong ◽

Zhangheng Huang ◽

Youxin Song ◽

...

Keyword(s):

Risk Factors ◽

Overall Survival ◽

Prognostic Factors ◽

Soft Tissue ◽

Soft Tissue Sarcoma ◽

Tumor Size ◽

Training Set ◽

N Stage ◽

Independent Risk Factors ◽

Testing Set

Background: Metastatic soft tissue sarcoma (STS) patients have a poor prognosis with a 3-year survival rate of 25%. About 30% of them present lung metastases (LM). This study aimed to construct 2 nomograms to predict the risk of LM and overall survival of STS patients with LM. Materials and Methods: The data of patients were derived from the Surveillance, Epidemiology, and End Results database during the period of 2010 to 2015. Logistic and Cox analysis was performed to determine the independent risk factors and prognostic factors of STS patients with LM, respectively. Afterward, 2 nomograms were, respectively, established based on these factors. The performance of the developed nomogram was evaluated with receiver operating characteristic curves, area under the curve (AUC) calibration curves, and decision curve analysis (DCA). Results: A total of 7643 patients with STS were included in this study. The independent predictors of LM in first-diagnosed STS patients were N stage, grade, histologic type, and tumor size. The independent prognostic factors for STS patients with LM were age, N stage, surgery, and chemotherapy. The AUCs of the diagnostic nomogram were 0.806 in the training set and 0.799 in the testing set. For the prognostic nomogram, the time-dependent AUC values of the training and testing set suggested a favorable performance and discrimination of the nomogram. The 1-, 2-, and 3-year AUC values were 0.698, 0.718, and 0.715 in the training set, and 0.669, 0.612, and 0717 in the testing set, respectively. Furthermore, for the 2 nomograms, calibration curves indicated satisfactory agreement between prediction and actual survival, and DCA indicated its clinical usefulness. Conclusion: In this study, grade, histology, N stage, and tumor size were identified as independent risk factors of LM in STS patients, age, chemotherapy surgery, and N stage were identified as independent prognostic factors of STS patients with LM, these developed nomograms may be an effective tool for accurately predicting the risk and prognosis of newly diagnosed patients with LM.

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A Web-based Prediction Model for Overall Survival of Elderly Patients With Malignant Bone Tumors: a Population-based Study

10.21203/rs.3.rs-962779/v1 ◽

2021 ◽

Author(s):

Jie Tang ◽

Jinkui Wang ◽

Xiudan Pan

Keyword(s):

Risk Factors ◽

Overall Survival ◽

Elderly Patients ◽

Prediction Model ◽

Bone Tumors ◽

Receiver Operating Curve ◽

Training Set ◽

Malignant Bone Tumors ◽

Independent Risk Factors ◽

Validation Set

Abstract Background: Malignant bone tumors(MBT) are one of the causes of death in elderly patients. The purpose of our study is to establish a nomogram to predict the overall survival(OS) of elderly patients with MBT.Methods: The clinicopathological data of all elderly patients with MBT from 2004 to 2018 were downloaded from the SEER database. They were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate Cox regression analysis was used to identify independent risk factors for elderly patients with MBT. A nomagram was built based on these risk factors to predict the 1-, 3-, and 5-year OS of elderly patients with MBT. Then, used the consistency index (C-index), calibration curve, and the area under the receiver operating curve(AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis(DCA) was used to evaluate the clinical potential application value of nomogram. Based on the scores on the nomogram, patients were divided into high- and low-risk groups. Kaplan-Meier (K-M) curve was used to test the difference in survival between the two groups of patients.Results: A total of 1641 patients were included, and they were randomly assigned to the training set (N=1156) and the validation set (N=485). The univariate and multivariate analysis of the training set suggested that age, sex, race, primary site, histologic type, grade, stage, M stage, surgery, and tumor size were independent risk factors for elderly patients with MBT. The C-index of the training set and the validation set were 0.779[0.759-0.799] and 0.801[0.772-0.830], respectively. The AUC of the training set and the validation set also showed similar results. The calibration curves of the training set and the validation set both showed that the observed value and the predicted value were highly consistent. DCA suggested that the nomogram had potential clinical value compared with traditional TNM staging.Conclusion: We had established a new nomogram to predict the 1-, 3-, 5-year OS of elderly patients with MBT. This predictive model can help doctors and patients develop treatment plans and follow-up strategies.

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A Nomogram for Predicting Lymphovascular Invasion in Superficial Esophageal Squamous Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.663802 ◽

2021 ◽

Vol 11 ◽

Author(s):

Rongwei Ruan ◽

Shengsen Chen ◽

Yali Tao ◽

Jiangping Yu ◽

Danping Zhou ◽

...

Keyword(s):

Risk Factors ◽

Squamous Cell Carcinoma ◽

Esophageal Squamous Cell Carcinoma ◽

Tumor Size ◽

Lymphovascular Invasion ◽

Depth Of Invasion ◽

Tumor Differentiation ◽

Lasso Regression ◽

Training Set ◽

Validation Set

The lymphovascular invasion (LVI) status facilitates the determination of the optimal therapeutic strategy for superficial esophageal squamous cell carcinoma (SESCC), but in clinical practice, LVI must be confirmed by postoperative pathology. However, studies of the risk factors for LVI in SESCC are limited. Consequently, this study aimed to identify the risk factors for LVI and use these factors to establish a prediction model. The data of 516 patients who underwent radical esophagectomy between January 2007 and September 2019 were retrospectively collected (training set, n=361, January 2007 to May 2015; validation set, n=155, June 2015 to September 2019). In the training set, least absolute shrinkage and selection operator (LASSO) regression and multivariate analyses were utilized to identify predictive factors for LVI in patients with SESCC. A nomogram was then developed using these predictors. The area under the curve (AUC), calibration curve, and decision curve were used to evaluate the efficiency, accuracy, and clinical utility of the model. LASSO regression indicated that the tumor size, depth of invasion, tumor differentiation, lymph node metastasis (LNM), sex, circumferential extension, the presence of multiple lesions, and the resection margin were correlated with LVI. However, multivariate analysis revealed that only the tumor size, depth of invasion, tumor differentiation, and LNM were independent risk factors for LVI. Incorporating these four variables, model 1 achieved an AUC of 0.817 in predicting LVI. Adding circumferential extension to model 1 did not appreciably change the AUC and integrated discrimination improvement, but led to a significant increase in the net reclassification improvement (p=0.011). A final nomogram was constructed by incorporating tumor size, depth of invasion, tumor differentiation, LNM, and circumferential extension and showed good discrimination (training set, AUC=0.833; validation set, AUC=0.819) and good calibration in the training and validation sets. Decision curve analysis demonstrated that the nomogram was clinically useful in both sets. Thus, it is possible to predict the status of LVI using this nomogram scoring system, which can aid the selection of an appropriate treatment plan.

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Nomogram for predicting reoperation following internal fixation of nondisplaced femoral neck fractures in elderly patients

Journal of Orthopaedic Surgery and Research ◽

10.1186/s13018-021-02697-8 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Jian Zhu ◽

Hongzhi Hu ◽

Xiangtian Deng ◽

Yiran Zhang ◽

Xiaodong Cheng ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Femoral Neck ◽

Elderly Patients ◽

Internal Fixation ◽

Calibration Curve ◽

Weight Bearing ◽

Femoral Neck Fractures ◽

Multivariate Logistic Regression ◽

Independent Risk Factors

Abstract Objective We aimed to evaluate risk factors and develop a nomogram for reoperation after internal fixation of nondisplaced femoral neck fractures (FNFs) in elderly patients. Methods We conducted a retrospective study involving a total of 255 elderly patients who underwent closed reduction and internal fixation with cannulated screw system for nondisplaced FNFs between January 2016 and January 2019. We collected data on demographics, preoperative radiological parameters, surgery, serum biochemical markers, and postoperative rehabilitation. In addition, we performed univariate and multivariate logistic regression analyses to determine independent risk factors for reoperation, and then developed a nomogram to assess the risks of reoperation. Besides, discriminative ability, calibration, and clinical usefulness of the nomogram were evaluated using the concordance index (C-index), the receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA), respectively. We employed bootstrap method to validate the performance of the developed nomogram. Results Our analysis showed that among the 255 patients, 28 (11.0%) underwent reoperation due to osteonecrosis of the femoral head (14 cases), mechanical failure (8 cases) or nonunion (6 cases). All of the 28 patients underwent conversion surgery to arthroplasty. The multivariate logistic regression analysis demonstrated that preoperative posterior tilt angle ≥ 20°, Pauwel’s III type, younger patients, preoperative elevated levels of alkaline phosphatase (ALP), preoperative hypoalbuminemia, and early postoperative weight-bearing were independent risk factors for reoperation. In addition, the C-index and the bootstrap value of the developed nomogram was 0.850 (95% CI = 0.803–0.913) and 0.811, respectively. Besides, the calibration curve showed good consistency between the actual diagnosed reoperation and the predicted probability, while the DCA indicated that the nomogram was clinically valuable. Conclusions Our analysis showed we successfully developed and validated a nomogram for personalized prediction of reoperation after internal fixation of nondisplaced FNFs in elderly patients. This model would help in individualized evaluation of the need for reoperation and inform strategies aimed at eliminating the need for the reoperation.

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Development and validation of a nomogram for predicting carbapenem-resistant organism infection and prognostic risk factors analysis in elderly hospitalized patients

10.21203/rs.3.rs-36090/v1 ◽

2020 ◽

Author(s):

Yao Wang ◽

Huiling Li ◽

Ying Chen ◽

Yanjun Fu ◽

Jianfeng Xi ◽

...

Keyword(s):

Risk Factors ◽

Nosocomial Infection ◽

Elderly Patients ◽

Resistant Organism ◽

Urinary Catheters ◽

Training Set ◽

Good Discrimination ◽

Carbapenem Resistant ◽

Influence Curve ◽

Validation Set

Abstract Background: With the extensive use of carbapenem-resistant organism (CRO), CRO infection is constantly detected clinically which has limited available drugs. There have many mechanisms of CRO resistance and rapid horizontal transmission, while the elderly with low resistance is more likely to acquire nosocomial infection. We aimed to screen elderly patients for nosocomial CRO infection and potential risk factors for prognosis. Methods: A total of 177 patients with CRO and carbapenem-sensitive organism (CSO) infection were included in this study. A least absolute shrinkage and selection operator (LASSO) analysis was used to select variables. The nomogram was constructed with multivariable logistic regression. The performance of the model was assessed by the receiver operating characteristic(ROC) curve, calibration, decision curve , and clinical influence curve. Using X-tile to stratify the prognosis risk, Kaplan-Meier curve for risk assessment. Results: Respiratory diseases, mechanical ventilation, indwelling urinary catheters , and APACHE II over 20 were selected as predictors of the CRO infection model. The model showed good discrimination and consistency in the training set and the validation set. The area under the ROC was 0.840 (95% CI: 0.773-0.900)in the training set and 0.822 (95% CI: 0.678-0.936) in the validation set. Decision analysis and influence curve showed that the model was clinically useful. Hepatobiliary diseases, indwelling urinary catheters, and hospital stays longer than 20 days were used as prognostic predictors. After analysis, the prognostic model demonstrated good discrimination of 0.817 (95% CI: 0.729-0.893) and consistency. Risk stratification showed the high-risk group had a poorer prognosis. Conclusion: Predicting clinically relevant risk factors for CRO nosocomial infection and prognosis in elderly patients. This may help the treatment of clinical drug-resistant infections.

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Development and Validation of a Nomogram Based on Noninvasive Liver Reserve and Fibrosis (PALBI and FIB-4) Model to Predict Posthepatectomy Liver Failure Grade B-C in Patients with Hepatocellular Carcinoma

Journal of Oncology ◽

10.1155/2021/6665267 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Wenhui Zhong ◽

Feng Zhang ◽

Kaijun Huang ◽

Yiping Zou ◽

Yubin Liu

Keyword(s):

Risk Factors ◽

Hepatocellular Carcinoma ◽

Logistic Regression ◽

Liver Failure ◽

Roc Curves ◽

Postoperative Liver Failure ◽

Multivariate Logistic Regression ◽

Training Set ◽

Independent Risk Factors ◽

Validation Set

Hepatectomy is currently one of the most effective treatments for hepatocellular carcinoma (HCC). However, postoperative liver failure (PHLF) is a serious complication and the leading cause of mortality in patients with HCC after hepatectomy. This study attempted to develop a novel nomogram based on noninvasive liver reserve and fibrosis models, platelet-albumin-bilirubin grade (PALBI) and fibrosis-4 index (FIB-4), able to predict PHLF grade B-C. This was a single-centre retrospective study of 574 patients with HCC undergoing hepatectomy between 2014 and 2018. The independent risk factors of PHLF were screened using univariate and multivariate logistic regression analyses. Multivariate logistic regression was performed using the training set, and the nomogram was developed and visualised. The utility of the model was evaluated in a validation set using the receiver operating characteristic (ROC) curve. A total of 574 HCC patients were included (383 in the training set and 191 for the validation set) and included PHLF grade B-C complications of 14.8, 15.4, and 13.6%, respectively. Overall, cirrhosis ( P < 0.026 , OR = 2.296, 95% confidence interval (CI) 1.1.02–4.786), major hepatectomy ( P = 0.031 , OR = 2.211, 95% CI 1.077–4.542), ascites ( P = 0.014 , OR = 3.588, 95% 1.299–9.913), intraoperative blood loss ( P < 0.001 , OR = 4.683, 95% CI 2.281–9.616), PALBI score >−2.53 (, OR = 3.609, 95% CI 1.486–8.764), and FIB-4 score ≥1.45 ( P < 0.001 , OR = 5.267, 95% CI 2.077–13.351) were identified as independent risk factors associated with PHLF grade B-C in the training set. The areas under the ROC curves for the nomogram model in predicting PHLF grade B-C were significant for both the training and validation sets (0.832 vs 0.803). The proposed nomogram predicted PHLF grade B-C among patients with HCC with a better prognostic accuracy than other currently available fibrosis and noninvasive liver reserve models.

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A New Preoperative Risk Score for Predicting Postoperative Complications in Elderly Patients Undergoing Hepatectomy

World Journal of Surgery ◽

10.1007/s00268-021-05985-w ◽

2021 ◽

Author(s):

Koichi Tomita ◽

Itsuki Koganezawa ◽

Masashi Nakagawa ◽

Shigeto Ochiai ◽

Takahiro Gunji ◽

...

Keyword(s):

Risk Factors ◽

Skeletal Muscle ◽

Postoperative Complications ◽

Elderly Patients ◽

Risk Score ◽

Surgical Procedure ◽

Evaluation Method ◽

Preoperative Risk ◽

Preoperative Serum ◽

Independent Risk Factors

Abstract Background Postoperative complications are not rare in the elderly population after hepatectomy. However, predicting postoperative risk in elderly patients undergoing hepatectomy is not easy. We aimed to develop a new preoperative evaluation method to predict postoperative complications in patients above 65 years of age using biological impedance analysis (BIA). Methods Clinical data of 59 consecutive patients (aged 65 years or older) who underwent hepatectomy at our institution between 2017 and 2020 were retrospectively analyzed. Risk factors for postoperative complications (Clavien-Dindo ≥ III) were evaluated using multivariate regression analysis. Additionally, a new preoperative risk score was developed for predicting postoperative complications. Results Fifteen patients (25.4%) had postoperative complications, with biliary fistula being the most common complication. Abnormal skeletal muscle mass index from BIA and type of surgical procedure were found to be independent risk factors in the multivariate analysis. These two variables and preoperative serum albumin levels were used for developing the risk score. The postoperative complication rate was 0.0% with a risk score of ≤ 1 and 57.1% with a risk score of ≥ 4. The area under the receiver operating characteristic curve of the risk score was 0.810 (p = 0.001), which was better than that of other known surgical risk indexes. Conclusion Decreased skeletal muscle and the type of surgical procedure for hepatectomy were independent risk factors for postoperative complications after elective hepatectomy in elderly patients. The new preoperative risk score is simple, easy to perform, and will help in the detection of high-risk elderly patients undergoing elective hepatectomy.

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A Prognostic Model for Patients With Gastric Signet Ring Cell Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211027912 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110279

Author(s):

Qinping Guo ◽

Yinquan Wang ◽

Jie An ◽

Siben Wang ◽

Xiushan Dong ◽

...

Keyword(s):

Risk Factors ◽

Regression Analysis ◽

Cox Regression ◽

Signet Ring Cell Carcinoma ◽

Signet Ring Cell ◽

Training Set ◽

Cox Regression Analysis ◽

Signet Ring ◽

Ring Cell ◽

Independent Risk Factors

Background: The aim of our study was to develop a nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRC). Methods: GSRC patients from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and validation sets. Multivariate Cox regression analyses screened for OS and CSS independent risk factors and nomograms were constructed. Results: A total of 7,149 eligible GSRC patients were identified, including 4,766 in the training set and 2,383 in the validation set. Multivariate Cox regression analysis showed that gender, marital status, race, AJCC stage, TNM stage, surgery and chemotherapy were independent risk factors for both OS and CSS. Based on the results of the multivariate Cox regression analysis, prognostic nomograms were constructed for OS and CSS. In the training set, the C-index was 0.754 (95% CI = 0.746-0.762) for the OS nomogram and 0.762 (95% CI: 0.753-0.771) for the CSS nomogram. In the internal validation, the C-index for the OS nomogram was 0.758 (95% CI: 0.746-0.770), while the C-index for the CSS nomogram was 0.762 (95% CI: 0.749-0.775). Compared with TNM stage and SEER stage, the nomogram had better predictive ability. In addition, the calibration curves also showed good consistency between the predicted and actual 3-year and 5-year OS and CSS. Conclusion: The nomogram can effectively predict OS and CSS in patients with GSRC, which may help clinicians to personalize prognostic assessments and clinical decisions.

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Development and Validation of a Nomogram to Predict Type 2 Diabetes Mellitus in Overweight and Obese Adults: A Prospective Cohort Study from 82938 Adults in China

International Journal of Endocrinology ◽

10.1155/2020/8899556 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Qingqing Liu ◽

Jie Yuan ◽

Maerjiaen Bakeyi ◽

Jie Li ◽

Zilong Zhang ◽

...

Keyword(s):

Diabetes Mellitus ◽

Risk Factors ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Cohort Study ◽

Prospective Cohort ◽

Training Set ◽

Obese Adults ◽

Validation Set

Background. The twin epidemic of overweight/obesity and type 2 diabetes mellitus (T2DM) is a major public health problem globally, especially in China. Overweight/obese adults commonly coexist with T2DM, which is closely related to adverse health outcomes. Therefore, this study aimed to develop risk nomogram of T2DM in Chinese adults with overweight/obesity. Methods. We used prospective cohort study data for 82938 individuals aged ≥20 years free of T2DM collected between 2010 and 2016 and divided them into a training (n = 58056) and a validation set (n = 24882). Using the least absolute shrinkage and selection operator (LASSO) regression model in training set, we identified optimized risk factors of T2DM, followed by the establishment of T2DM prediction nomogram. The discriminative ability, calibration, and clinical usefulness of nomogram were assessed. The results were assessed by internal validation in validation set. Results. Six independent risk factors of T2DM were identified and entered into the nomogram including age, body mass index, fasting plasma glucose, total cholesterol, triglycerides, and family history. The nomogram incorporating these six risk factors showed good discrimination regarding the training set, with a Harrell’s concordance index (C-index) of 0.859 [95% confidence interval (CI): 0.850–0.868] and an area under the receiver operating characteristic curve of 0.862 (95% CI: 0.853–0.871). The calibration curves indicated well agreement between the probability as predicted by the nomogram and the actual probability. Decision curve analysis demonstrated that the prediction nomogram was clinically useful. The consistent of findings was confirmed using the validation set. Conclusions. The nomogram showed accurate prediction for T2DM among Chinese population with overweight and obese and might aid in assessment risk of T2DM.

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