Nomogram Model for Screening the Risk of Type II Diabetes in Western Xinjiang, China

Prognostic Factors and Nomogram for Klebsiella pneumoniae Infections in Intensive Care Unit

Purpose

Klebsiella pneumoniae infections pose a significant threat to public health with high morbidity and mortality rates. The early identification of risk factors for mortality and accurate prognostic evaluation are important. Therefore, we aimed to identify the risk factors for mortality in patients with K. pneumoniae infections and develop a nomogram model for prognosis.

Methods

Patients diagnosed with K. pneumoniae infection were recruited from the intensive care unit of Peking University Third Hospital. The enrolled patients were categorized into survivor and non-survivor groups. Univariate and multivariate regression analyses were performed to identify independent risk factors for 30-day mortality, and a nomogram was constructed and validated.

Results

A total of 408 patients infected with K. pneumoniae at different sites were included in this study. PO2, lactate, respiratory failure, urinary tract infection, heart rate, 24h-urineoutput, neutrophil count, alkaline phosphatase, and vasoactive drug use were significant risk factors and were integrated into a nomogram to predict the risk of 7-day, 14-day, 21-day, and 28-day mortality. The nomogram demonstrated superior prognostic ability, achieving higher area under the receiver operating characteristic curve (AUC) (>0.8) and concordance index (C-index) (>0.8) values than the Pitt bacteremia, sequential organ failure assessment (SOFA), and acute physiology and chronic health evaluation (APACHE) II scores (all AUC and C-index < 0.75). Cross-validation of the nomogram confirmed its consistent performance, with both AUC and C-index values exceeding 0.75. The nomogram demonstrated a strong Hosmer-Leme-show goodness-of-fit and good calibration (p > 0.05). Additionally, decision curve analysis revealed that the nomogram provided significant clinical utility for prognostic prediction.

Conclusion

The 30-day mortality risk factors for K. pneumoniae infections were identified, and a predictive nomogram model was developed. The nomogram demonstrated good accuracy and predictive efficiency, providing a practical tool for short-term risk assessment and potentially improving clinical outcomes by providing early intervention and personalized patient management.

A Novel Body Mass Index-Based Thromboembolic Risk Score for Overweight Patients with Nonvalvular Atrial Fibrillation

BACKGROUND

A novel risk prediction model appears to be urgently required to improve the assessment of thrombotic risk in overweight patients with nonvalvular atrial fibrillation (NVAF). We developed a novel body mass index (BMI)-based thromboembolic risk score (namely AB2S score) for these patients.

METHODS

A total of 952 overweight patients with NVAF were retrospectively enrolled in this study with a 12-month follow-up. The primary endpoint was 1-year systemic thromboembolism and the time to thrombosis (TTT). The candidate risk variables identified by logistic regression analysis were included in the final nomogram model to construct AB2S score. The measures of model fit were evaluated using area under the curve (AUC), C-statistic, and calibration curve. The performance comparison of the AB2S score to the CHADS2 and CHA2DS2-VASc score was performed in terms of the AUC and decision analysis curve (DAC).

RESULTS

The AB2S score was constructed using 7 candidate risk variables, including a 3-category BMI (25 to 30, 30 to 34, or ≥35 kg/m2). It yielded a c-index of 0.885 (95% CI, 0.814-0.954) and an AUC of 0.885 (95% CI, 0.815-0.955) for predicting 1-year systemic thromboembolism in patients with NVAF. Compared to the CHADS2 score and CHA2DS2-VASc score, the AB2S score had greater AUC and DAC values in predicting the thromboembolic risk and better risk stratification in TTT (P <.0001, P =.082, respectively).

CONCLUSION

Our results highlighted the importance of a BMI-based AB2S score in determining systemic thromboembolism risk in overweight patients with NVAF, which may aid in decision-making for these patients to balance the effectiveness of anticoagulation from the underlying thrombotic risk.

Nomogram for predicting bleeding events in nonvalvular atrial fibrillation patients receiving rivaroxaban: A retrospective study

Background and Aims

To construct a bleeding events prediction model of nonvalvular atrial fibrillation (NVAF) patients receiving rivaroxaban.

Methods

We conducted a retrospective cohort study in patients with NVAF who received rivaroxaban from June 2017 to March 2019. Demographic information and clinical characteristics were obtained from the electronic medical system. Univariate analysis was used to find the primary predictive factors of bleeding events in patients receiving rivaroxaban. Multiple analysis was conducted to screen the primary independent predictive factors selected from the univariate analysis. Finally, the independent influencing factors were applied to build a prediction model by using R software; then, a nomogram was established according to the selected variables visually, and the sensitivity and specificity of the model was evaluated.

Results

Twelve primary predictive factors were selected by univariate analysis from 46 variables, and multivariate analysis showed that older age, higher prothrombin time (PT) values, history of heart failure and stroke were independent risk factors of bleeding events. The area under curve (AUC) for this novel nomogram model was 0.828 (95% CI: 0.763-0.894). The mean AUC over 10-fold stratified cross-validation was 0.787, and subgroup analysis validation also showed a satisfied AUC. In addition, the decision curve analysis showed that the PT in combination with CHA2DS2-VASc and HASBLED was more practical and accurate for predicting bleeding events than using CHA2DS2-VASc and HASBLED alone.

Conclusions

PT in combination with CHA2DS2-VASc and HASBLED could be considered as a more practical and accurate method for predicting bleeding events in patients taking rivaroxaban.

An environment-wide association study for the identification of non-invasive factors for type 2 diabetes mellitus: Analysis based on the Henan Rural Cohort study

AIM

To explore the influencing factors of Type 2 diabetes mellitus (T2DM) in the rural population of Henan Province and evaluate the predictive ability of non-invasive factors to T2DM.

METHODS

A total of 30,020 participants from the Henan Rural Cohort Study in China were included in this study. The dataset was randomly divided into a training set and a testing set with a 50:50 split for validation purposes. We used logistic regression analysis to investigate the association between 56 factors and T2DM in the training set (false discovery rate < 5 %) and significant factors were further validated in the testing set (P < 0.05). Gradient Boosting Machine (GBM) model was used to determine the ability of the non-invasive variables to classify T2DM individuals accurately and the importance ranking of these variables.

RESULTS

The overall population prevalence of T2DM was 9.10 %. After adjusting for age, sex, educational level, marital status, and body measure index (BMI), we identified 13 non-invasive variables and 6 blood biochemical indexes associated with T2DM in the training and testing dataset. The top three factors according to the GBM importance ranking were pulse pressure (PP), urine glucose (UGLU), and waist-to-hip ratio (WHR). The GBM model achieved a receiver operating characteristic (AUC) curve of 0.837 with non-invasive variables and 0.847 for the full model.

CONCLUSIONS

Our findings demonstrate that non-invasive variables that can be easily measured and quickly obtained may be used to predict T2DM risk in rural populations in Henan Province.

Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms

Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) are key indicators reflecting blood glucose control in type 2 diabetes mellitus (T2DM) patients. The purpose of this study is to establish a predictive model for blood glucose changes in T2DM patients after 3 months of treatment, achieving personalized treatment.A retrospective study was conducted on type 2 diabetes mellitus real-world medical data from 4 cities in Sichuan Province, China from January 2015 to December 2020. After data preprocessing, data inputting, data sampling, and feature screening, 16 kinds of machine learning methods were used to construct prediction models, and 5 prediction models with the best prediction performance were screened respectively. A total of 100,000 cases were included to establish the FBG model, and 2,169 cases were established to establish the HbA1c model. The best prediction model both of FBG and HbA1c finally obtained are realized by ensemble learning and modified random forest inputting, the AUC values are 0.819 and 0.970, respectively. The most important indicators of the FBG and HbA1c prediction model were FBG and HbA1c. Medication compliance, follow-up outcome, dietary habits, BMI, and waist circumference also had a greater impact on FBG levels. The prediction accuracy of the models of the two blood glucose control indicators is high and has certain clinical applicability.HbA1c and FBG are mutually important predictors, and there is a close relationship between them.

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.

A nomogram model for the risk prediction of type 2 diabetes in healthy eastern China residents: a 14-year retrospective cohort study from 15,166 participants

BACKGROUND

Risk prediction models can help identify individuals at high risk for type 2 diabetes. However, no such model has been applied to clinical practice in eastern China.

AIMS

This study aims to develop a simple model based on physical examination data that can identify high-risk groups for type 2 diabetes in eastern China for predictive, preventive, and personalized medicine.

METHODS

A 14-year retrospective cohort study of 15,166 nondiabetic patients (12-94 years; 37% females) undergoing annual physical examinations was conducted. Multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) models were constructed for univariate analysis, factor selection, and predictive model building. Calibration curves and receiver operating characteristic (ROC) curves were used to assess the calibration and prediction accuracy of the nomogram, and decision curve analysis (DCA) was used to assess its clinical validity.

RESULTS

The 14-year incidence of type 2 diabetes in this study was 4.1%. This study developed a nomogram that predicts the risk of type 2 diabetes. The calibration curve shows that the nomogram has good calibration ability, and in internal validation, the area under ROC curve (AUC) showed statistical accuracy (AUC = 0.865). Finally, DCA supports the clinical predictive value of this nomogram.

CONCLUSION

This nomogram can serve as a simple, economical, and widely scalable tool to predict individualized risk of type 2 diabetes in eastern China. Successful identification and intervention of high-risk individuals at an early stage can help to provide more effective treatment strategies from the perspectives of predictive, preventive, and personalized medicine.

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.