A prediction nomogram for the 3-year risk of incident diabetes among Chinese adults

Prediction of the 10-year incidence of type 2 diabetes mellitus based on advanced anthropometric indices using machine learning methods in the Iranian population

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a growing chronic disease that can lead to disability and early death. This study aimed to establish a predictive model for the 10-year incidence of T2DM based on novel anthropometric indices.

METHODS

This was a prospective cohort study comparing people with (n = 1256) and without (n = 5193) diabetes mellitus in phase II of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study. The association of several anthropometric indices in phase I, including Body Mass Index (BMI), Body Adiposity Index (BAI), Abdominal Volume Index (AVI), Visceral Adiposity Index (VAI), Weight-Adjusted-Waist Index (WWI), Body Roundness Index (BRI), Body Surface Area (BSA), Conicity Index (C-Index) and Lipid Accumulation Product (LAP) with T2DM incidence (in phase II) were examined; using Logistic Regression (LR) and Decision Tree (DT) analysis.

RESULTS

BMI followed by VAI and LAP were the best predictors of T2DM incidence. Participants with BMI < 21.25 kg/m2 and VAI ≤ 5.9 had a lower chance of diabetes than those with higher BMI and VAI levels (0.033 vs. 0.967 incident rate). For BMI > 25 kg/m2, the chance of diabetes rapidly increased (OR = 2.27).

CONCLUSIONS

BMI, VAI, and LAP were the best predictors of T2DM incidence.

Inhibition of YIPF2 Improves the Vulnerability of Oligodendrocytes to Human Islet Amyloid Polypeptide

Excessive secretion of human islet amyloid polypeptide (hIAPP) is an important pathological basis of diabetic encephalopathy (DE). In this study, we aimed to investigate the potential implications of hIAPP in DE pathogenesis. Brain magnetic resonance imaging and cognitive scales were applied to evaluate white matter damage and cognitive function. We found that the concentration of serum hIAPP was positively correlated with white matter damage but negatively correlated with cognitive scores in patients with type 2 diabetes mellitus. In vitro assays revealed that oligodendrocytes, compared with neurons, were more prone to acidosis under exogenous hIAPP stimulation. Moreover, western blotting and co-immunoprecipitation indicated that hIAPP interfered with the binding process of monocarboxylate transporter (MCT)1 to its accessory protein CD147 but had no effect on the binding of MCT2 to its accessory protein gp70. Proteomic differential analysis of proteins co-immunoprecipitated with CD147 in oligodendrocytes revealed Yeast Rab GTPase-Interacting protein 2 (YIPF2, which modulates the transfer of CD147 to the cell membrane) as a significant target. Furthermore, YIPF2 inhibition significantly improved hIAPP-induced acidosis in oligodendrocytes and alleviated cognitive dysfunction in DE model mice. These findings suggest that increased CD147 translocation by inhibition of YIPF2 optimizes MCT1 and CD147 binding, potentially ameliorating hIAPP-induced acidosis and the consequent DE-related demyelination.

A machine learning screening model for identifying the risk of high-frequency hearing impairment in a general population

BACKGROUND

Hearing impairment (HI) has become a major public health issue in China. Currently, due to the limitations of primary health care, the gold standard for HI diagnosis (pure-tone hearing test) is not suitable for large-scale use in community settings. Therefore, the purpose of this study was to develop a cost-effective HI screening model for the general population using machine learning (ML) methods and data gathered from community-based scenarios, aiming to help improve the hearing-related health outcomes of community residents.

METHODS

This study recruited 3371 community residents from 7 health centres in Zhejiang, China. Sixty-eight indicators derived from questionnaire surveys and routine haematological tests were delivered and used for modelling. Seven commonly used ML models (the naive Bayes (NB), K-nearest neighbours (KNN), support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), boosting, and least absolute shrinkage and selection operator (LASSO regression)) were adopted and compared to develop the final high-frequency hearing impairment (HFHI) screening model for community residents. The model was constructed with a nomogram to obtain the risk score of the probability of individuals suffering from HFHI. According to the risk score, the population was divided into three risk stratifications (low, medium and high) and the risk factor characteristics of each dimension under different risk stratifications were identified.

RESULTS

Among all the algorithms used, the LASSO-based model achieved the best performance on the validation set by attaining an area under the curve (AUC) of 0.868 (95% confidence interval (CI): 0.847-0.889) and reaching precision, specificity and F-score values all greater than 80%. Five demographic indicators, 7 disease-related features, 5 behavioural factors, 2 environmental exposures, 2 hearing cognitive factors, and 13 blood test indicators were identified in the final screening model. A total of 91.42% (1235/1129) of the subjects in the high-risk group were confirmed to have HI by audiometry, which was 3.99 times greater than that in the low-risk group (22.91%, 301/1314). The high-risk population was mainly characterized as older, low-income and low-educated males, especially those with multiple chronic conditions, noise exposure, poor lifestyle, abnormal blood indices (e.g., red cell distribution width (RDW) and platelet distribution width (PDW)) and liver function indicators (e.g., triglyceride (TG), indirect bilirubin (IBIL), aspartate aminotransferase (AST) and low-density lipoprotein (LDL)). An HFHI nomogram was further generated to improve the operability of the screening model for community applications.

CONCLUSIONS

The HFHI risk screening model developed based on ML algorithms can more accurately identify residents with HFHI by categorizing them into the high-risk groups, which can further help to identify modifiable and immutable risk factors for residents at high risk of HI and promote their personalized HI prevention or intervention.

A nomogram model for predicting 5-year risk of prediabetes in Chinese adults

Early identification is crucial to effectively intervene in individuals at high risk of developing pre-diabetes. This study aimed to create a personalized nomogram to determine the 5-year risk of pre-diabetes among Chinese adults. This retrospective cohort study included 184,188 participants without prediabetes at baseline. Training cohorts (92,177) and validation cohorts (92,011) were randomly assigned (92,011). We compared five prediction models on the training cohorts: full cox proportional hazards model, stepwise cox proportional hazards model, multivariable fractional polynomials (MFP), machine learning, and least absolute shrinkage and selection operator (LASSO) models. At the same time, we validated the above five models on the validation set. And we chose the LASSO model as the final risk prediction model for prediabetes. We presented the model with a nomogram. The model's performance was evaluated in terms of its discriminative ability, clinical utility, and calibration using the area under the receiver operating characteristic (ROC) curve, decision curve analysis, and calibration analysis on the training cohorts. Simultaneously, we also evaluated the above nomogram on the validation set. The 5-year incidence of prediabetes was 10.70% and 10.69% in the training and validation cohort, respectively. We developed a simple nomogram that predicted the risk of prediabetes by using the parameters of age, body mass index (BMI), fasting plasma glucose (FBG), triglycerides (TG), systolic blood pressure (SBP), and serum creatinine (Scr). The nomogram's area under the receiver operating characteristic curve (AUC) was 0.7341 (95% CI 0.7290-0.7392) for the training cohort and 0.7336 (95% CI 0.7285-0.7387) for the validation cohort, indicating good discriminative ability. The calibration curve showed a perfect fit between the predicted prediabetes risk and the observed prediabetes risk. An analysis of the decision curve presented the clinical application of the nomogram, with alternative threshold probability spectrums being presented as well. A personalized prediabetes prediction nomogram was developed and validated among Chinese adults, identifying high-risk individuals. Doctors and others can easily and efficiently use our prediabetes prediction model when assessing prediabetes risk.

Machine learning for predicting hepatitis B or C virus infection in diabetic patients

Highly prevalent hepatitis B and hepatitis C virus (HBV and HCV) infections have been reported among individuals with diabetes. Given the frequently asymptomatic nature of hepatitis and the challenges associated with screening in some vulnerable populations such as diabetes patients, we conducted an investigation into the performance of various machine learning models for the identification of hepatitis in diabetic patients while also evaluating the significance of features. Analyzing NHANES data from 2013 to 2018, machine learning models were evaluated; random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO) along with stacked ensemble model. We performed hyperparameter tuning to improve the performance of the model, and selected important predictors using the best performance model. LASSO showed the highest predictive performance (AUC-ROC = 0.810) rather than other models. Illicit drug use, poverty, and race were highly ranked as predictive factors for developing hepatitis in diabetes patients. Our study demonstrated that a machine-learning-based model performed optimally in the detection of hepatitis among diabetes patients, achieving high performance. Furthermore, models and predictors evaluated from the current study, we expect, could be supportive information for developing screening or treatment methods for hepatitis care in diabetes patients.

Progress of the application clinical prediction model in polycystic ovary syndrome

Clinical prediction models play an important role in the field of medicine. These can help predict the probability of an individual suffering from disease, complications, and treatment outcomes by applying specific methodologies. Polycystic ovary syndrome (PCOS) is a common disease with a high incidence rate, huge heterogeneity, short- and long-term complications, and complex treatments. In this systematic review study, we reviewed the progress of clinical prediction models in PCOS patients, including diagnosis and prediction models for PCOS complications and treatment outcomes. We aimed to provide ideas for medical researchers and clues for the management of PCOS. In the future, models with poor accuracy can be greatly improved by adding well-known parameters and validations, which will further expand our understanding of PCOS in terms of precision medicine. By developing a series of predictive models, we can make the definition of PCOS more accurate, which can improve the diagnosis of PCOS and reduce the likelihood of false positives and false negatives. It will also help discover complications earlier and treatment outcomes being known earlier, which can result in better outcomes for women with PCOS.

Establishment of a nomogram for predicting functional constipation among children in China: Using the Rome IV criteria

BACKGROUND AND STUDY AIMS

Childhood functional constipation (FC) is gradually becoming an emerging public health problem. This study aimed to develop a personalized nomogram for the prediction of incident FC among Chinese children, and the diagnosis of FC was based on the Rome IV criteria.

PATIENTS AND METHODS

This cross-sectional study was conducted from Nov. 2020 to Jan. 2021 among children residing in Anhui province, China. An electronic questionnaire regarding the general demographic and clinical characteristics of all children was completed by their primary caregivers. The multivariate logistic regression analysis was applied to identify risk factors for FC. Moreover, a nomogram was constructed for FC based on the risk factors identified from the multivariate analysis.

RESULTS

In this study, a total of 901 electronic questionnaires were collected, of which 832 (92.3%) questionnaires were properly completed and included in the final analysis. The prevalence of FC among Chinese children was 11.3% based on the Rome IV criteria. After controlling for potential confounding factors, the multivariate logistic regression analysis showed that inadequate sleep, picky eating, and positive family history of FC were identified as key risk factors of FC. The area under the receiver operating characteristic curve of the nomogram was 0.694 (95 %CI: 0.6412-0.7459). Further, a calibration curve drawn illustrated that the predicted probabilities reasonably approximately the actual prevalence of FC in this population.

CONCLUSION

Inadequate sleep, picky eating, and positive family history of FC were identified as risk factors of FC. An easy-to-use nomogram was constructed based on these three significant factors. Besides, this nomogram was validated to have acceptable discrimination and calibration capabilities. Hence, this nomogram may enable clinical professionals to predict the risk of FC among Chinese children and further provide optimized disease prevention and intervention for this population.

Effect of extracurricular tutoring on adolescent students cognitive ability: A propensity score matching analysis

In recent years, there has been a substantial increase in the number of adolescent students attending extracurricular tutoring. However, extracurricular tutoring, being a distinct form of education, may have varying effects on cognitive capabilities compared to conventional education. Accordingly, the purpose of this paper is to conduct a thorough examination of the effects of extracurricular tutoring on cognitive abilities among adolescent students. The study utilized national data from the China Family Panel Study 2018 to explore the relationship between involvement in extracurricular tutoring among students and cognitive abilities. The study included 2567 valid respondents. A binary logistic regression model was built to determine the factors associated with tutoring involvement while controlling for 19 individual, family, and school factors. Furthermore, a propensity score-matched analysis was conducted in order to mitigate potential bias by using confounding variables identified in the previous step. The study results show that participation in extracurricular tutoring can significantly increase the level of cognitive ability of adolescent students, with adjustments made for age, gender, ethnicity, number of family members, net family income per capita, education and training expenditure in the past years, change of residence for enrollment, change of domicile address for enrollment, locality of the current school, class size, hold a position as a class cadre, average daily study time on weekdays, average study time per day during weekends. The findings imply that the government should provide tutorial subsidies to disadvantaged groups of adolescent students, allocate educational resources equitably, and invest more in education resources in less developed regions to foster fair and healthy development of education and improve the cognitive abilities of young students in the long-term.

Influencing factors of prognosis in children with pulmonary atresia with intact ventricle septum after transthoracic balloon dilation of pulmonary valve and construction of a nomograph prediction model

This study aimed to identify factors that affect the prognosis of children with pulmonary valve atresia and intact ventricular septum treated with transthoracic balloon dilation of the pulmonary valve. The study included 148 participants who were followed up for 5 years. Of these, 10 died, while 138 survived. Independent sample t-test and χ2 test were used to analyze clinical data of children in the death and survival groups. It was found that height, weight, body surface area, arterial oxygen saturation, degree of tricuspid regurgitation, pulmonary valve cross valve pressure difference, ICU length of stay, length of stay, reoperation intervention, and complications were statistically significant (P<0.05). ROC curve analysis of the measurement indicators with statistically significant differences showed that height, weight, body surface area, arterial oxygen saturation, ICU length of stay, and length of stay had AUCs ranging from 0.723 to 0.870. Logistic regression analysis revealed that the degree of tricuspid regurgitation, pulmonary valve cross valvular pressure difference, ICU length of stay, reoperation intervention, and complications were independent risk factors that affect the prognosis of patients with PA/IVS undergoing transthoracic balloon dilation of pulmonary valve. The study proposed a nomogram prediction model using R language software 4.0 "rms" package, which was validated using calibration curve and decision curve. The model had a C-index of 0.667 (95% CI: 0.643-0.786) and high degree of fit. This study provides clinicians with a prediction model to identify children with poor prognosis after treatment with transpulmonary valve balloon dilatation.           .

A Nomogram for Predicting Prognosis of Advanced Schistosomiasis japonica in Dongzhi County-A Case Study

BACKGROUNDS

Advanced schistosomiasis is the late stage of schistosomiasis, seriously jeopardizing the quality of life or lifetime of infected people. This study aimed to develop a nomogram for predicting mortality of patients with advanced schistosomiasis japonica, taking Dongzhi County of China as a case study.

METHOD

Data of patients with advanced schistosomiasis japonica were collected from Dongzhi Schistosomiasis Hospital from January 2019 to July 2022. Data of patients were randomly divided into a training set and validation set with a ratio of 7:3. Candidate variables, including survival outcomes, demographics, clinical features, laboratory examinations, and ultrasound examinations, were analyzed and selected by LASSO logistic regression for the nomogram. The performance of the nomogram was assessed by concordance index (C-index), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). The calibration of the nomogram was evaluated by the calibration plots, while clinical benefit was evaluated by decision curve and clinical impact curve analysis.

RESULTS

A total of 628 patients were included in the final analysis. Atrophy of the right liver, creatinine, ascites level III, N-terminal procollagen III peptide, and high-density lipoprotein were selected as parameters for the nomogram model. The C-index, sensitivity, specificity, PPV, and NPV of the nomogram were 0.97 (95% [CI]: [0.95-0.99]), 0.78 (95% [CI]: [0.64-0.87]), 0.97 (95% [CI]: [0.94-0.98]), 0.78 (95% [CI]: [0.64-0.87]), 0.97 (95% [CI]: [0.94-0.98]) in the training set; and 0.98 (95% [CI]: [0.94-0.99]), 0.86 (95% [CI]: [0.64-0.96]), 0.97 (95% [CI]: [0.93-0.99]), 0.79 (95% [CI]: [0.57-0.92]), 0.98 (95% [CI]: [0.94-0.99]) in the validation set, respectively. The calibration curves showed that the model fitted well between the prediction and actual observation in both the training set and validation set. The decision and the clinical impact curves showed that the nomogram had good clinical use for discriminating patients with high risk of death.

CONCLUSIONS

A nomogram was developed to predict prognosis of advanced schistosomiasis. It could guide clinical staff or policy makers to formulate intervention strategies or efficiently allocate resources against advanced schistosomiasis.

The nonlinear correlation between the cardiometabolic index and the risk of diabetes: A retrospective Japanese cohort study

BACKGROUND

The cardiometabolic index (CMI) has been proposed as a novel indicator of cardiometabolic status. However, evidence on the relationship between CMI and diabetes mellitus (DM) risk was limited. Our study aimed to explore the relationship between CMI and DM risk among a large cohort of Japanese adults.

METHODS

This retrospective cohort study recruited 15453 Japanese adults without diabetes at baseline who underwent physical examinations at the Murakami Memorial Hospital between 2004 and 2015. Cox proportional-hazards regression was applied to evaluate the independent relationship between CMI and diabetes. Our study performed a generalized smooth curve fitting (penalized spline technique) and an additive model (GAM) to determine the non-linear relationship between CMI and DM risk. In addition, a set of sensitivity analyses and subgroup analyses were employed to evaluate the relationship between CMI and incident DM.

RESULTS

After adjusting for confounding covariates, CMI was positively related to the DM risk in Japanese adults (HR: 1.65, 95%CI: 1.43-1.90, P<0.0001). A series of sensitivity analyses were also employed in this study to guarantee the reliability of the findings. In addition, our study discovered a non-linear association between CMI and diabetes risk. CMI's inflection point was 1.01. A strong positive association between CMI and diabetes incidence was also discovered to the left of the inflection point (HR: 2.96, 95%CI: 1.96-4.46, P<<0.0001). However, their association was not significant when CMI was higher than 1.01 (HR: 1.27, 95%CI: 0.98-1.64, P=0.0702). Interaction analysis showed that gender, BMI, habit of exercise, and smoking status interacted with CMI.

CONCLUSION

Increased CMI level at baseline is associated with incident DM. The association between CMI and incident DM is also non-linear. A high CMI level is associated with an increased risk for DM when CMI is below 1.01.

Association Between Hypertension and New-Onset Non-Alcoholic Fatty Liver Disease in Chinese Non-Obese People: A Longitudinal Cohort Study

BACKGROUND

Quantification of the relationship between hypertension and non-alcoholic fatty liver disease (NAFLD) risk is limited and controversial. This study aimed to investigate the relationship between hypertension and NAFLD in non-obese Chinese and to use different methods to demonstrate that hypertension is an independent risk factor for NAFLD.

METHODS

On 16,153 nonobese individuals, a retrospective cohort study was conducted in China to examine the impact of hypertension on incident NAFLD. We compared five methods: multivariable Cox proportional-hazards regression, propensity score-matched (PSM) analysis, propensity score adjustment method (considering the propensity score as a covariate in a multivariable Cox proportional-hazard regression), and two propensity score-based weighted methods-The first one estimated the hypertension effect in the overall study population-inverse probability of treatment weights (IPTW), the other in the hypertensive population-standardized mortality ratio (SMR) weights. We also used a genetic matching (GenMatch) algorithm to match the participants for sensitive analysis.

RESULTS

Between 2010 and 2014, 16,153 participants met our inclusion criteria, including 2427 (15.03%) with hypertension. A total of 2321 (14.37%) participants developed NAFLD during the median follow-up of 2.98 years. The crude hazard ratio (HR) between hypertension and incident NAFLD was 2.05 (95% confidence interval (CI): 1.87, 2.25). The adjusted HR depended on the different methods, ranging from 1.09 (95% CI: 0.77, 1.23) for the PSM method to 2.24 (95% CI: 2.05, 2.44) for the SMR weighted analysis. Hypertensive participants with high propensity scores had a higher risk of developing NAFLD in the future. Excluding participants with propensity scores <8% yielded comparable hazard ratios with a narrower range, from 1.04 to 1.80. After adjusting for the confounding variables, the relationship also existed in the GenMatch cohort as a sensitivity analysis (HR=1.06, 95% CI 1.01-1.13).

CONCLUSION

Hypertension is a significant cause of NAFLD in Chinese adults in non-obese Chinese adults, with the hazard ratio ranging from 1.09 to 2.24.

Development of nomogram to predict in-hospital death for patients with intracerebral hemorrhage: A retrospective cohort study

Aim

This study aimed to investigate the association between systemic immune-inflammation (SII) and the risk of in-hospital death for patients with intracerebral hemorrhage (ICH) in the intensive care units (ICU) and to further develop a prediction model related to SII in predicting the risk of in-hospital death for patients with ICH.

Methods

In this retrospective cohort study, we included 1,176 patients with ICH from the Medical Information Mart for Intensive Care III (MIMIC-III) database. All patients were randomly assigned to the training group for the construction of the nomogram and the testing group for the validation of the nomogram based on a ratio of 8:2. Predictors were screened by the least absolute shrinkage and selection operator (LASSO) regression analysis. A multivariate Cox regression analysis was used to investigate the association between SII and in-hospital death for patients with ICH in the ICU and develop a model for predicting the in-hospital death risk for ICU patients with ICH. The receiver operator characteristic curve was used to assess the predicting performance of the constructed nomogram.

Results

In the training group, 232 patients with ICH died while 708 survived. LASSO regression showed some predictors, including white blood cell count, glucose, blood urea nitrogen, SII, the Glasgow Coma Scale, age, heart rate, mean artery pressure, red blood cell, bicarbonate, red blood cell distribution width, liver cirrhosis, respiratory failure, renal failure, malignant cancer, vasopressor, and mechanical ventilation. A prediction model integrating these predictors was established. The area under the curve (AUC) of the nomogram was 0.810 in the training group and 0.822 in the testing group, indicating that this nomogram might have a good performance.

Conclusion

Systemic immune-inflammation was associated with an increased in-hospital death risk for patients with ICH in the ICU. A nomogram for in-hospital death risk for patients with ICH in the ICU was developed and validated.

Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study

Background

People with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures.

Methods

A systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer–Lemeshow test).

Results

Forty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52–0.60, 0.50–0.59, and 0.50–0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54–0.73, 0.52–0.67, and 0.59–0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05).

Conclusions

In Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01622-5.

Development and validation of a prediction model of catheter-related thrombosis in patients with cancer undergoing chemotherapy based on ultrasonography results and clinical information

Central venous catheters can be used conveniently to deliver medications and improve comfort in patients with cancer. However, they can cause major complications. The current study aimed to develop and validate an individualized nomogram for early prediction of the risk of catheter-related thrombosis (CRT) in patients with cancer receiving chemotherapy. In total, 647 patients were included in the analysis. They were randomly assigned to the training (n = 431) and validation (n = 216) cohorts. A nomogram for predicting the risk of CRT in the training cohort was developed based on logistic regression analysis results. The accuracy and discriminatory ability of the model were determined using area under the receiver operating characteristic curve (AUROC) values and calibration plots. Multivariate logistic regression analysis showed that body mass index, risk of cancer-related thrombosis, D-dimer level, and blood flow velocity were independent risk factors of CRT. The calibration plot showed an acceptable agreement between the predicted and actual probabilities of CRT. The AUROC values of the nomogram were 0.757 (95% confidence interval: 0.717-0.809) and 0.761 (95% confidence interval: 0.701-0.821) for the training and validation cohorts, respectively. Our model presents a novel, user-friendly tool for predicting the risk of CRT in patients with cancer receiving chemotherapy. Moreover, it can contribute to clinical decision-making.

Development and validation of a carotid atherosclerosis risk prediction model based on a Chinese population

Purpose

This study aimed to identify independent risk factors for carotid atherosclerosis (CAS) and construct and validate a CAS risk prediction model based on the Chinese population.

Methods

This retrospective study included 4,570 Chinese adults who underwent health checkups (including carotid ultrasound) at the Zhenhai Lianhua Hospital, Ningbo, China, in 2020. All the participants were randomly assigned to the training and validation sets at a ratio of 7:3. Independent risk factors associated with CAS were identified using multivariate logistic regression analysis. The least absolute shrinkage and selection operator combined with 10-fold cross-validation were screened for characteristic variables, and nomograms were plotted to demonstrate the risk prediction model. C-index and receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA) were used to evaluate the risk model’s discrimination, calibration, and clinical applicability.

Results

Age, body mass index, diastolic blood pressure, white blood cell count, mean platelet volume, alanine transaminase, aspartate transaminase, and gamma-glutamyl transferase were identified as independent risk factors for CAS. In the training, internal validation, and external validation sets, the risk model showed good discriminatory power with C-indices of 0.961 (0.953–0.969), 0.953 (0.939–0.967), and 0.930 (0.920–0.940), respectively, and excellent calibration. The results of DCA showed that the prediction model could be beneficial when the risk threshold probabilities were 1–100% in all sets. Finally, a network computer (dynamic nomogram) was developed to facilitate the physicians’ clinical operations. The website is https://nbuhgq.shinyapps.io/DynNomapp/.

Conclusion

The development of risk models contributes to the early identification and prevention of CAS, which is important for preventing and reducing adverse cardiovascular and cerebrovascular events.

Development and validation of a nomogram based on the hospital information system for quantitative assessment of the risk of cardiocerebrovascular complications of diabetes

Background

Although the prevention and treatment of the cardiocerebrovascular complications (CCVCs) of diabetes have been clarified, their incidence is still high. This is largely due to the lack of predictive models to objectively assess the risk of CCVC in patients with type 2 diabetes mellitus (T2DM), reducing their treatment adherence. Despite the fact that the risk factors of CCVC in T2DM patients have been identified, no prediction model for identifying T2DM patients with the risk of CCVC is available. Therefore, the aim of this study is to establish a nomogram based on hospital information system data to quantitatively assess the risk of CCVCs in T2DM patients. This model is contributed to individualized therapeutic treatments and motivating T2DM patients to adhere to lifestyle interventions.

Methods

The medical records of 1,556 T2DM patients, comprising 1,145 cases in the training cohort and 411 in the validation cohort were retrospectively analyzed. CCVCs of diabetes, including coronary heart disease, cerebral ischemia, and intracerebral hemorrhage, were extracted from the medical records. Univariate and multivariate logistic regression analyses were performed to screen the independent correlates of CCVCs from the demographic information and laboratory test data, which were utilized to establish a nomogram for predicting the risk of CCVCs in these patients. We used internal and external validation based on the training and validation cohorts to evaluate the model performance.

Results

The incidence of CCVCs in the training cohort (26.99%) was similar to the validation cohort (25.79%). Disease duration, body mass index (BMI), systolic blood pressure (SBP), glycosylated hemoglobin (HbA1c), and uric acid (UA) levels were finally included in the established nomogram. In both the internal and external validation, the nomogram showed good discrimination [area under the curve (AUC) =0.850 and 0.825, respectively] and calibration (P=0.127 and P=0.096, respectively). Decision curve analysis showed that the nomogram produced a net benefit in both the training and validation cohorts.

Conclusions

The nomogram developed for predicting the risk of CCVC in T2DM patients may help improve treatment adherence. Further multi-center prospective investigations are required to predict the timing of CVCC in T2DM patients.

Construction of a ceRNA Network and Analysis of Tumor Immune Infiltration in Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is characterized by high malignancy, frequent metastasis, and recurrence with an unfavorable prognosis. This study is aimed at constructing a prognostic model for tumor-infiltrating immune cells and a competing endogenous RNA (ceRNA) network in PAAD and analyzing susceptibilities of chemotherapy and immunotherapy of PAAD. Gene expression profiles and clinical information of PAAD were downloaded from The Cancer Genome Atlas (TCGA) database and divided into the tumor group and the normal group. A total of five PAAD survival-related key genes in the ceRNA network and three survival-related immune infiltrating cells were uncovered, and two survival risk models and nomograms were constructed. The efficiency and performance of the two models were verified using multi-index area under the curve analysis at different time points, decision curve analysis, and calibration curves. Co-expression analysis showed that LRRC1, MIR600HG, and RNF166 in the ceRNA network and tumor-infiltrating immune cells including CD8 T cells and M1 macrophages were likely related to the PAAD prognosis, and the expression of key ceRNA-related genes was experimently validated in tissues and cell lines by RT-qPCR. Patients with low risk scores for key genes in the ceRNA network displayed a positive response to anti-programmed death-1 (PD-1) treatment and greater sensitivity to chemotherapeutic drugs such as docetaxel, lapatinib, and paclitaxel. More importantly, our results suggested that the IC50 values of gemcitabine in PAAD were not significantly different between the high and low risk groups. The expression levels of immune checkpoints were significantly different in the high-risk and low-risk groups. The prognostic model, nomogram, and drug analysis may provide an essential reference for PAAD patient management in the clinic.

A novel nomogram based on a retrospective study of 346 patients to predict the recurrence risk of condyloma acuminatum after 5-aminolevulinic acid photodynamic therapy

Condyloma acuminatum (CA) is a sexually transmitted disease caused by human papillomavirus (HPV) often with high recurrence rate after treatment. This study aimed to construct and evaluate a nomogram model containing three clinical parameters to predict the recurrence risk of CA after 5-aminolevulinic acid photodynamic therapy (ALA-PDT). A predictive model was established based on a training cohort of 346 CA patients treated with ALA-PDT between January 2013 and July 2018. A validation cohort of 123 CA patients was recruited from August 2018 to December 2019. The least absolute shrinkage and selection operator (LASSO) regression model was used to optimize the clinical feature selection. A nomogram predicting the recurrence of CA after ALA-PDT was constructed based on the predictors identified by LASSO regression. C-index and area under the curve (AUC) values were used to evaluate the discrimination. Calibration was evaluated with a calibration curve. The net benefit was performed via decision curve analysis (DCA). In the training cohort, 55 (15.89%) patients experienced recurrences after ALA-PDT. Predictors selected by LASSO regression were concomitant human immunodeficiency virus (HIV) infection [Hazard ratio (HR) = 4.4; 95% confidence interval (CI), 2.5-7.9; p < 0.001], skin and mucosa as affected area (HR = 1.7; 95% CI, 0.9-3.1; p = 0.109), and more than one time of CO₂ laser therapy (HR = 6.3; 95% CI, 2.8-13.9; p < 0.001). The nomogram showed a good performance in predicting recurrence as the C-indexes were 0.843 (95% CI, 0.799-0.887) in the training cohort, and 0.831 (95% CI, 0.727-0.934) in the validation cohort. The AUCs of the nomogram were 0.85 in training and 0.8 in validation. DCA confirmed the nomogram was clinically useful when the intervention was determined at the non-adherence possibility threshold of 5%. This nomogram can provide individualized prediction for the recurrence risk of CA in patients treated by ALA-PDT.

A simple nomogram for identifying individuals at high risk of undiagnosed diabetes in rural population

AIMS

To sought for an easily applicable nomogram for detecting individuals at high risk of undiagnosed type 2 diabetes.

METHODS

The development cohort included 2542 participants recruited randomly from a rural population in 2011.The glycemic status of subjects was determined using the fasting plasma glucose test and the oral glucose tolerance test. The Bayesian Model Average approach was used to search for a parsimonious model with minimum number of predictor and maximum discriminatory power. The corresponding prediction nomograms were constructed and checked for discrimination, calibration, clinical usefulness, and generalizability in nationwide population in 2012.

RESULTS

The non-lab nomogram including waist circumference and systolic blood pressure was the most parsimonious with the area under receiver operating characteristic curve (AUC) of 0.71 (95 %CI = 0.64-0.76). Adding low-density lipoprotein cholesterol in the non-lab nomogram generated the lab-based nomogram with significantly improved AUC of 0.83 (0.78-0.87, P < 0.001). The nomograms had a positive net benefit at threshold probability between 0.01 and 0.15. Applying the non-lab nomogram to the national population yielded the AUC of 0.66 (0.63-0.70) and 0.68 (0.65-0.71) in the cohorts aged 40-64 and 30-69 years, respectively.

CONCLUSIONS

The novel nomograms could help promote the early detection of undiagnosed diabetes in rural Vietnamese population.

Establishment and validation of a nomogram model for predicting the survival probability of differentiated thyroid carcinoma patients: a comparison with the eighth edition AJCC cancer staging system

PURPOSE

This study aimed to develop a clinically predictive nomogram model to predict the survival probability of differentiated thyroid carcinoma patients and compare the value of this model with that of the eighth edition AJCC cancer staging system.

METHODS

We selected 59,876 differentiated thyroid carcinoma patients diagnosed between 2004 and 2015 from the SEER database and separated those patients into a training set (70%) and a validation set (30%) randomly. We used Cox regression analysis to build the nomogram model (model 1) and the eighth edition AJCC cancer staging model (model 2). Then we compared the predictive accuracy, discrimination, and clinical usage of both models by calculating AUC (Area under the curve), C-index, as well as analyzing DCA (Decision Curve Analysis) performance respectively.

RESULTS

AUCs of all predicted time points (12-month, 36-month, 60-month, and 120-month) of model 1 were 0.933, 0.913, 0.879, and 0.868 for the training set; 0.933, 0.926, 0.916, and 0.894 for the validation set. As for model 2, data were 0.938, 0.906, 0.866, and 0.847 for the training set; 0.924, 0.925, 0.912, and 0.867 for the validation set. C-indices of model 1 were higher than those of model 2 (0.923 vs. 0.918 for the training set, 0.938 vs. 0.930 for the validation set). DCA comparison showed that the net benefit of model 1 was bigger when comparing with that of model 2.

CONCLUSIONS

Model 1 provided with both better predictive accuracy and clinical usage compared with those of model 2 and might be able to predict the survival probability of differentiated thyroid carcinoma patients visually and accurately with a higher net benefit.

Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches

Diabetes mellitus is one of the most common human diseases worldwide and may cause several health-related complications. It is responsible for considerable morbidity, mortality, and economic loss. A timely diagnosis and prediction of this disease could provide patients with an opportunity to take the appropriate preventive and treatment strategies. To improve the understanding of risk factors, we predict type 2 diabetes for Pima Indian women utilizing a logistic regression model and decision tree—a machine learning algorithm. Our analysis finds five main predictors of type 2 diabetes: glucose, pregnancy, body mass index (BMI), diabetes pedigree function, and age. We further explore a classification tree to complement and validate our analysis. The six-fold classification tree indicates glucose, BMI, and age are important factors, while the ten-node tree implies glucose, BMI, pregnancy, diabetes pedigree function, and age as the significant predictors. Our preferred specification yields a prediction accuracy of 78.26% and a cross-validation error rate of 21.74%. We argue that our model can be applied to make a reasonable prediction of type 2 diabetes, and could potentially be used to complement existing preventive measures to curb the incidence of diabetes and reduce associated costs.

Association of hypertension and incident diabetes in Chinese adults: a retrospective cohort study using propensity-score matching

BACKGROUND

Reliable quantification of the relationship between hypertension and diabetes risk is limited, especially among Chinese people. We aimed to investigate the association between hypertension and the risk of diabetes in a large cohort of the Chinese population.

METHODS

This was a retrospective propensity score-matched cohort study among 211,809 Chinese adults without diabetes at baseline between 2010 and 2016. The target independent and dependent variable were hypertension at baseline and incident diabetes during follow-up respectively. The propensity score matching using a non-parsimonious multivariable logistic regression was conducted to balance the confounders between 28,711 hypertensive patients and 28,711 non-hypertensive participants. The doubly robust estimation method was used to investigate the association between hypertension and diabetes.

RESULTS

In the propensity-score matching cohort, diabetes risk increased by 11.0% among hypertensive patients (HR = 1.110, 95% confidence interval (CI): 1.031-1.195, P = 0.00539). And diabetes risk dropped to 8.3% among hypertensive subjects after adjusting for the propensity score (HR = 1.083, 95%CI: 1.006-1.166, P = 0.03367). Compared to non-hypertensive participants with low propensity score, the risk of incident diabetes increased by 2.646 times among hypertensive patients with high propensity score (HR = 3.646, 95%CI: 2.635-5.045, P < 0.0001).

CONCLUSION

Hypertension was associated with an 11.0% increase in the risk of developing diabetes in Chinese adults. And the figure dropped to 8.3% after adjusting the propensity score. Additionally, compared to non-hypertensive participants with low propensity scores, the risk of incident diabetes increased by 2.646 times among hypertensive patients with high propensity scores.

Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study

PURPOSE

The aim of this study was to derivate and validate a nomogram based on independent predictors to better evaluate the 5-year risk of T2D in non-obese adults.

PATIENTS AND METHODS

This is a historical cohort study from a collection of databases that included 12,940 non-obese participants without diabetes at baseline. All participants were randomised to a derivation cohort (n = 9651) and a validation cohort (n = 3289). In the derivation cohort, the least absolute shrinkage and selection operator (LASSO) regression model was used to determine the optimal risk factors for T2D. Multivariate Cox regression analysis was used to establish the nomogram of T2D prediction. The receiver operating characteristic (ROC) curve, C-index, calibration curve, and decision curve analysis were performed by 1000 bootstrap resamplings to evaluate the discrimination ability, calibration, and clinical practicability of the nomogram.

RESULTS

After LASSO regression analysis of the derivation cohort, it was found that age, fatty liver, γ-glutamyltranspeptidase, triglycerides, glycosylated hemoglobin A1c and fasting plasma glucose were risk predictors, which were integrated into the nomogram. The C-index of derivation cohort and validation cohort were 0.906 [95% confidence interval (CI), 0.878-0.934] and 0.837 (95% CI, 0.760-0.914), respectively. The AUC of 5-year T2D risk in the derivation cohort and validation cohort was 0.916 (95% CI, 0.889-0.943) and 0.829 (95% CI, 0.753-0.905), respectively. The calibration curve indicated that the predicted probability of nomogram is in good agreement with the actual probability. The decision curve analysis demonstrated that the predicted nomogram was clinically useful.

CONCLUSION

Our nomogram can be used as a reasonable, affordable, simple, and widely implemented tool to predict the 5-year risk of T2D in non-obese adults. With this model, early identification of high-risk individuals is helpful to timely intervene and reduce the risk of T2D in non-obese adults.