[Construction and evaluation of a model for predicting ischemic stroke risk in patients with sudden sensorineural hearing loss]

Objective:To explore the related factors of sudden sensorineural hearing loss complicated with ischemic stroke, construct the risk prediction model, and verify the prediction effect of the model. Methods:A retrospective analysis was performed on 901 sudden sensorineural hearing loss patients hospitalized from January 2017 to December 2020, The patients were divided into the ischemic stroke group（100 cases） and the sudden deafness group（801 cases） according to whether they were complicated with ischemic stroke, The independent correlation factors of sudden deafness complicated with ischemic stroke were screened by univariate analysis and multivariate Logistic regression model, and the risk prediction model and internal verification were established. The original data were randomly divided into the modeling group（631 cases） and the validation group（270 cases） at a 7∶3 ratio. Hosmer-Lemeshow and receiver operating characteristic curve were used to test the goodness of fit and predictive effect of the model, and 270 patients were included again in the application research of the model and to test the prediction effect of the model. Results:The results of single factor analysis showed that age, NEUR, NC, NLR, PLR, TC, HDL-C, BUN, TC-HDL-C, TG/HDL-C, LDL-C/HDL-C, Hcy, FIB and cervical vascular plaque were related factors of sudden sensorineural hearing loss complicated with ischemic stroke（P<0.05）. Age（OR=2.816）, NEUR（OR=2.707）, Hcy（OR=88.833）, FIB（OR=1.389）, TC-HDL-C（OR=1.613）, cervical vascular plaque（OR=2.862） are the independent risk factors of SNHL complicated with ischemic stroke. These 6 factors are used to construct a prediction model. Hosmer-lemeshow test results, the area under the ROC curve of the modeling group was 0.846, P=0.555, Youden index was 0.564, sensitivity was 0.820, and specificity was 0.744. In the validation group, the area under ROC curve was 0.847, P=0.288, Youden index was 0.432, sensitivity was 0.783, and specificity was 0.649. Conclusion:The risk prediction model constructed in this study shows good prediction efficiency. which can provide references for the clinical screening of ischemic stroke risks in patients with sudden sensorineural hearing loss and early interventions in early stage.

Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention

Genome-wide association studies (GWASs) have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers. The genetic variants associated with a cancer can be combined into a polygenic risk score (PRS), which captures part of an individual's genetic susceptibility to cancer. Recently, PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer, which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk. In this context, we provide an overview of the major discoveries from cancer GWASs. We then review the methodologies used for PRS construction, and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors. Potential utility of PRSs in cancer risk prediction, screening, and precision prevention are illustrated. Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.

Performance of early risk assessment tools to predict the later development of gestational diabetes

BACKGROUND

Several prognostic models for gestational diabetes mellitus (GDM) are provided in the literature; however, their clinical significance has not been thoroughly evaluated, especially with regard to application at early gestation and in accordance with the most recent diagnostic criteria. This external validation study aimed to assess the predictive accuracy of published risk estimation models for the later development of GDM at early pregnancy.

METHODS

In this cohort study, we prospectively included 1132 pregnant women. Risk evaluation was performed before 16 + 0 weeks of gestation including a routine laboratory examination. Study participants were followed-up until delivery to assess GDM status according to the IADPSG 2010 diagnostic criteria. Fifteen clinical prediction models were calculated according to the published literature.

RESULTS

Gestational diabetes mellitus was diagnosed in 239 women, that is 21.1% of the study participants. Discrimination was assessed by the area under the ROC curve and ranged between 60.7% and 76.9%, corresponding to an acceptable accuracy. With some exceptions, calibration performance was poor as most models were developed based on older diagnostic criteria with lower prevalence and therefore tended to underestimate the risk of GDM. The highest variable importance scores were observed for history of GDM and routine laboratory parameters.

CONCLUSIONS

Most prediction models showed acceptable accuracy in terms of discrimination but lacked in calibration, which was strongly dependent on study settings. Simple biochemical variables such as fasting glucose, HbA1c and triglycerides can improve risk prediction. One model consisting of clinical and laboratory parameters showed satisfactory accuracy and could be used for further investigations.

Inaccurate Risk Assessment by the ACS NSQIP Risk Calculator in Aortic Surgery

OBJECTIVES

The aim of this retrospective study was to assess the predictive performance of the American College of Surgeons (ACS) risk calculator for aortic aneurysm repair for the patient population of a Dutch tertiary referral hospital.

METHODS

This retrospective study included all patients who underwent elective endovascular or open aortic aneurysm repair at our institution between the years 2013 and 2019. Preoperative patient demographics and postoperative complication data were collected, and individual risk assessments were generated using five different current procedural terminology (CPT) codes. Receiver operating characteristic (ROC) curves, calibration plots, Brier scores, and Index of Prediction Accuracy (IPA) values were generated to evaluate the predictive performance of the ACS risk calculator in terms of discrimination and calibration.

RESULTS

Two hundred thirty-four patients who underwent elective endovascular or open aortic aneurysm repair were identified. Only five out of thirteen risk predictions were found to be sufficiently discriminative. Furthermore, the ACS risk calculator showed a structurally insufficient calibration. Most Brier scores were close to 0; however, comparison to a null model though IPA-scores showed the predictions generated by the ACS risk calculator to be inaccurate. Overall, the ACS risk calculator showed a consistent underestimation of the risk of complications.

CONCLUSIONS

The ACS risk calculator proved to be inaccurate within the framework of endovascular and open aortic aneurysm repair in our medical center. To minimize the effects of patient selection and cultural differences, multicenter collaboration is necessary to assess the performance of the ACS risk calculator in aortic surgery.

Risk Prediction Models for Inadvertent Intraoperative Hypothermia: A Systematic Review

PURPOSES

Inadvertent intraoperative hypothermia (core temperature <36°C) is a common surgical complication with several adverse events. Hypothermia prediction models can be a tool for providing the healthcare staff with information on the risk of inadvertent hypothermia. Our systematic review aimed to identify, demonstrate, and evaluate the available intraoperative hypothermia risk prediction models in surgical populations.

DESIGN

This study is a systematic review of literature.

METHODS

We systematically searched multiple databases (Ovid MEDLINE, Web of Science, Embase, and Cochrane Center Register of Controlled Trials). Two reviewers independently examined abstracts and the full text for eligibility. Data collection was guided by the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS checklist), and methodological quality and applicability were assessed by the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

FINDINGS

A total of 3,672 references were screened, of which eight articles were included in this study. All the models had a high risk of bias since most of them lacked model validation. Also, they failed to report the model performance and final model presentations, which restricted their clinical application.

CONCLUSIONS

The researchers should present models in a more standard way and improve the existing models to increase their predictive values for clinical application.

Evaluation of Feature Selection Techniques for Breast Cancer Risk Prediction

This study evaluates several feature ranking techniques together with some classifiers based on machine learning to identify relevant factors regarding the probability of contracting breast cancer and improve the performance of risk prediction models for breast cancer in a healthy population. The dataset with 919 cases and 946 controls comes from the MCC-Spain study and includes only environmental and genetic features. Breast cancer is a major public health problem. Our aim is to analyze which factors in the cancer risk prediction model are the most important for breast cancer prediction. Likewise, quantifying the stability of feature selection methods becomes essential before trying to gain insight into the data. This paper assesses several feature selection algorithms in terms of performance for a set of predictive models. Furthermore, their robustness is quantified to analyze both the similarity between the feature selection rankings and their own stability. The ranking provided by the SVM-RFE approach leads to the best performance in terms of the area under the ROC curve (AUC) metric. Top-47 ranked features obtained with this approach fed to the Logistic Regression classifier achieve an AUC = 0.616. This means an improvement of 5.8% in comparison with the full feature set. Furthermore, the SVM-RFE ranking technique turned out to be highly stable (as well as Random Forest), whereas relief and the wrapper approaches are quite unstable. This study demonstrates that the stability and performance of the model should be studied together as Random Forest and SVM-RFE turned out to be the most stable algorithms, but in terms of model performance SVM-RFE outperforms Random Forest.

Development and Validation of a Diagnostic Model to Predict the Risk of Ischemic Liver Injury After Stanford A Aortic Dissection Surgery

Objective: To define the risk factors of ischemic liver injury (ILI) following Stanford A aortic dissection surgery and to propose a diagnostic model for individual risk prediction. Methods: We reviewed the clinical parameters of ILI patients who underwent cardiac surgery from Beijing Anzhen Hospital, Capital Medical University between January 1, 2015 and October 30, 2020. The data was analyzed by the use of univariable and multivariable logistic regression analysis. A risk prediction model was established and validated, which showed a favorable discriminating ability and might contribute to clinical decision-making for ILI after Stanford A aortic dissection (AAD) surgery. The discriminative ability and calibration of the diagnostic model to predict ILI were tested using C statistics, calibration plots, and clinical usefulness. Results: In total, 1,343 patients who underwent AAD surgery were included in the study. After univariable and multivariable logistic regression analysis, the following variables were incorporated in the prediction of ILI: pre-operative serum creatinine, pre-operative RBC count <3.31 T/L, aortic cross-clamp time >140 min, intraoperative lactic acid level, the transfusion of WRBC, atrial fibrillation within post-operative 24 h. The risk model was validated by internal sets. The model showed a robust discrimination, with an area under the receiver operating characteristic (ROC) curve of 0.718. The calibration plots for the probability of perioperative ischemic liver injury showed coherence between the predictive probability and the actual probability (Hosmer-Lemeshow test, P = 0.637). In the validation cohort, the nomogram still revealed good discrimination (C statistic = 0.727) and good calibration (Hosmer-Lemeshow test, P = 0.872). The 10-fold cross-validation of the nomogram showed that the average misdiagnosis rate was 9.95% and the lowest misdiagnosis rate was 9.81%. Conclusion: Our risk model can be used to predict the probability of ILI after AAD surgery and have the potential to assist clinicians in making treatment recommendations.

Risk prediction models for atherosclerotic cardiovascular disease: A systematic assessment with particular reference to Qatar

Background: Atherosclerotic cardiovascular disease (ASCVD) is a common disease in the State of Qatar and results in considerable morbidity, impairment of quality of life and mortality. The American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) is currently used in Qatar to identify those at high risk of ASCVD. However, it is unclear if this is the optimal ASCVD risk prediction model for use in Qatar's ethnically diverse population.

Aims: This systematic review aimed to identify, assess the methodological quality of and compare the properties of established ASCVD risk prediction models for the Qatari population.

Methods: Two reviewers performed head-to-head comparisons of established ASCVD risk calculators systematically. Studies were independently screened according to predefined eligibility criteria and critically appraised using Prediction Model Risk Of Bias Assessment Tool. Data were descriptively summarized and narratively synthesized with reporting of key statistical properties of the models.

Results: We identified 20,487 studies, of which 41 studies met our eligibility criteria. We identified 16 unique risk prediction models. Overall, 50% (n = 8) of the risk prediction models were judged to be at low risk of bias. Only 13% of the studies (n = 2) were judged at low risk of bias for applicability, namely, PREDICT and QRISK3.Only the PREDICT risk calculator scored low risk in both domains.

Conclusions: There is no existing ASCVD risk calculator particularly well suited for use in Qatar's ethnically diverse population. Of the available models, PREDICT and QRISK3 appear most appropriate because of their inclusion of ethnicity. In the absence of a locally derived ASCVD for Qatar, there is merit in a formal head-to-head comparison between PCE, which is currently in use, and PREDICT and QRISK3.

Development and verification of a risk prediction model for bronchopulmonary dysplasia in very low birth weight infants

BACKGROUND

To analyze the risk factors of bronchopulmonary dysplasia (BPD) of very low birth weight infants (VLBWIs), and to develop and verify a risk prediction model of BPD.

METHODS

The data of 611 VLBWIs from the neonatal intensive care unit (NICU) of a tertiary grade A hospital in Suzhou from January 2017 to September 2019 were collected. The data was randomly divided into the modeling set (451 cases) and the validation set (160 cases). Binary logistic regression was used to analyze the data, and the model was examined by a receiver operating characteristic (ROC) curve. The grouped data was used to verify the sensitivity and specificity of the model.

RESULTS

The study found that neonatal asphyxia, the positive rate of sputum culture, neonatal sepsis, neonatal respiratory distress syndrome (NRDS), blood transfusions (≥3), patent ductus arteriosus (PDA), the time of invasive mechanical ventilation, the duration of oxygen therapy, and the time of parenteral nutrition were the independent risk factors of BPD, while 1 min Apgar score was a protective factor. The model formula was Z=neonatal asphyxia * 1.229 + the positive rate of sputum culture * 1.265 + neonatal sepsis * 1.677 + NRDS * 1.848 + blood transfusions (≥3) * 1.455 + PDA * 1.835 - 1 min Apgar score * 0.25 + the time of invasive mechanical ventilation * 0.123 + the duration of oxygen therapy * 0.09 + the time of parenteral nutrition * 0.057 - 8.077. The area under the ROC curve of this model was 0.965 (95% CI: 0.946-0.983), with a sensitivity of 93.7% and a specificity of 91.3%. Verification of this prediction model showed a sensitivity of 92.9% and a specificity of 76%, demonstrating that the effects of this model were satisfactory.

CONCLUSIONS

The risk prediction model had a good predictive effect for the risk of BPD in VLBWIs, and can provide a reference for preventive treatment and nursing intervention.

Development and validation of the dialysis dementia risk score: A retrospective, population-based, nested case-control study

Background

Dementia is prevalent and underdiagnosed in the dialysis population. We aimed to develop and validate a simple dialysis dementia scoring system to facilitate identification of individuals who are at high risk for dementia.

Methods

We applied a retrospective, nested case‐control study design using a national dialysis cohort derived from the National Health Insurance Research Database in Taiwan. Patients aged between 40 and 80 years were included and 2940 patients with incident dementia were matched to 29,248 non‐dementia controls. All subjects were randomly divided into the derivation and validation sets with a ratio of 4:1. Conditional logistic regression models were used to identify factors contributing to the risk score. The cutoff value of the risk score was determined by Youden's J statistic and the graphic method.

Results

The dialysis dementia risk score (DDRS) finally included age and 10 comorbidities as risk predictors. The C‐statistic of the model was 0.71 (95% confidence interval [CI] 0.70–0.72). Calibration revealed a strong linear relationship between predicted and observed dementia risk (R² = 0.99). At a cutoff value of 50 points, the high‐risk patients had an approximately three‐fold increased risk of having dementia compared to those with low risk (odds ratio [OR] 3.03, 95% CI 2.78–3.31). The DDRS performance, including discrimination (C‐statistic 0.71, 95% CI 0.69–0.73) and calibration (p value of Hosmer−Lemeshow test for goodness of fit = 0.18), was acceptable during validation. The OR value (2.82, 95% CI 2.37–3.35) was similar to those in the derivation set.

Conclusion

The DDRS system has the potential to serve as an easily accessible screening tool to determine the high‐risk groups who deserve subsequent neurological evaluation in daily clinical practice.

Construction of a Risk Prediction Model for Hospital-Acquired Pulmonary Embolism in Hospitalized Patients

The purpose of this study is to establish a novel pulmonary embolism (PE) risk prediction model based on machine learning (ML) methods and to evaluate the predictive performance of the model and the contribution of variables to the predictive performance. We conducted a retrospective study at the Shanghai Tenth People's Hospital and collected the clinical data of in-patients that received pulmonary computed tomography imaging between January 1, 2014 and December 31, 2018. We trained several ML models, including logistic regression (LR), support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), compared the models with representative baseline algorithms, and investigated their predictability and feature interpretation. A total of 3619 patients were included in the study. We discovered that the GBDT model demonstrated the best prediction with an area under the curve value of 0.799, whereas those of the RF, LR, and SVM models were 0.791, 0.716, and 0.743, respectively. The sensibilities of the GBDT, LR, RF, and SVM models were 63.9%, 68.1%, 71.5%, and 75%, respectively; the specificities were 81.1%, 66.1, 72.7%, and 65.1%, respectively; and the accuracies were 77.8%, 66.5%, 72.5%, and 67%, respectively. We discovered that the maximum D-dimer level contributed the most to the outcome prediction, followed by the extreme growth rate of the plasma fibrinogen level, in-hospital duration, and extreme growth rate of the D-dimer level. The study demonstrates the superiority of the GBDT model in predicting the risk of PE in hospitalized patients. However, in order to be applied in clinical practice and provide support for clinical decision-making, the predictive performance of the model needs to be prospectively verified.

Development and Validation of a Risk Prediction Model for 1-Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction

Background

Readmission over the first year following hospitalization for acute myocardial infarction (AMI) is common among younger adults (≤55 years). Our aim was to develop/validate a risk prediction model that considered a broad range of factors for readmission within 1 year.

Methods and Results

We used data from the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study, which enrolled young adults aged 18 to 55 years hospitalized with AMI across 103 US hospitals (N=2979). The primary outcome was ≥1 all‐cause readmissions within 1 year of hospital discharge. Bayesian model averaging was used to select the risk model. The mean age of participants was 47.1 years, 67.4% were women, and 23.2% were Black. Within 1 year of discharge for AMI, 905 (30.4%) of participants were readmitted and were more likely to be female, Black, and nonmarried. The final risk model consisted of 10 predictors: depressive symptoms (odds ratio [OR], 1.03; 95% CI, 1.01–1.05), better physical health (OR, 0.98; 95% CI, 0.97–0.99), in‐hospital complication of heart failure (OR, 1.44; 95% CI, 0.99–2.08), chronic obstructive pulmomary disease (OR, 1.29; 95% CI, 0.96–1.74), diabetes mellitus (OR, 1.23; 95% CI, 1.00–1.52), female sex (OR, 1.31; 95% CI, 1.05–1.65), low income (OR, 1.13; 95% CI, 0.89–1.42), prior AMI (OR, 1.47; 95% CI, 1.15–1.87), in‐hospital length of stay (OR, 1.13; 95% CI, 1.04–1.23), and being employed (OR, 0.88; 95% CI, 0.69–1.12). The model had excellent calibration and modest discrimination (C statistic=0.67 in development/validation cohorts).

Conclusions

Women and those with a prior AMI, increased depressive symptoms, longer inpatient length of stay and diabetes may be more likely to be readmitted. Notably, several predictors of readmission were psychosocial characteristics rather than markers of AMI severity. This finding may inform the development of interventions to reduce readmissions in young patients with AMI.

Risk prediction model for deep surgical site infection (DSSI) following open reduction and internal fixation of displaced intra-articular calcaneal fracture

Deep surgical site infection (DSSI) is a serious complication affecting the surgical outcome of displaced intra‐articular calcaneal fracture, and a risk prediction model based on the identifiable risk factors will provide great clinical value in prevention and prompt interventions. This study retrospectively identified patients operated for calcaneal fracture between January 2014 and December 2019, with a follow‐up ≥1 year. The data were extracted from electronic medical records, with regard to demographics, comorbidities, injury, surgery and laboratory biomarkers at admission. Univariate and multivariate logistics regression analyses were used to identify the independent factors for DSSI, thereby the risk prediction model was developed. Among 900 patients included, 2.7% developed a DSSI. The multivariate analyses identified five factors independently associated with DSSI, including current smoking (OR, 2.8; 95% confidence interval [CI], 1.3‐6.4; P = .021), BMI ≥ 26.4 kg/m² (OR, 3.1; 95% CI, 1.6‐8.4; P = .003), ASA ≥II (OR, 1.3; 95% CI, 1.0‐5.1; P = .043), incision level of II (OR, 3.8; 95% CI, 1.3‐12.6; P = .018) and NLR ≥6.4 (OR, 3.2; 95% CI, 1.3‐7.5; P = .008). A score of 14 as the optimal cut‐off value was corresponding to sensitivity of 0.542 and specificity of 0.872 (area, 0.766; P < .001); ≥14 was associated with 8.1‐times increased risk of DSSI; a score of 7 was corresponding sensitivity of 100% and 10 corresponding to sensitivity of 0.875. The risk prediction model exhibited excellent performance in distinguishing the risk of DSSI and could be considered in practice for improvement of wound management, but its validity requires to be verified by better‐design studies.

Development of a dementia prediction model for primary care: The Hisayama Study

INTRODUCTION

We aimed to develop a risk prediction model for incident dementia using predictors that are available in primary-care settings.

METHODS

A total of 795 subjects aged 65 years or over were prospectively followed-up from 1988 to 2012. A Cox proportional-hazards regression was used to develop a multivariable prediction model. The developed model was translated into a simplified scoring system based on the beta-coefficient. The discrimination of the model was assessed by Harrell's C statistic, and the calibration was assessed by a calibration plot.

RESULTS

During the follow-up period, 364 subjects developed dementia. In the multivariable model, age, female sex, low education, leanness, hypertension, diabetes, history of stroke, current smoking, and sedentariness were selected as predictors. The developed model and simplified score showed good discrimination and calibration.

DISCUSSION

The developed risk prediction model is feasible and practically useful in primary-care settings to identify individuals at high risk for future dementia.

Predicting Readmission to Intensive Care After Cardiac Surgery Within Index Hospitalization: A Systematic Review

Readmission to the cardiac intensive care unit after cardiac surgery has significant implications for both patients and healthcare providers. Identifying patients at risk of readmission potentially could improve outcomes. The objective of this systematic review was to identify risk factors and clinical prediction models for readmission within a single hospitalization to intensive care after cardiac surgery. PubMed, MEDLINE, and EMBASE databases were searched to identify candidate articles. Only studies that used multivariate analyses to identify independent predictors were included. There were 25 studies and five risk prediction models identified. The overall rate of readmission pooled across the included studies was 4.9%. In all 25 studies, in-hospital mortality and duration of hospital stay were higher in patients who experienced readmission. Recurring predictors for readmission were preoperative renal failure, age >70, diabetes, chronic obstructive pulmonary disease, preoperative left ventricular ejection fraction <30%, type and urgency of surgery, prolonged cardiopulmonary bypass time, prolonged postoperative ventilation, postoperative anemia, and neurologic dysfunction. The majority of readmissions occurred due to respiratory and cardiac complications. Four models were identified for predicting readmission, with one external validation study. As all models developed to date had limitations, further work on larger datasets is required to develop clinically useful models to identify patients at risk of readmission to the cardiac intensive care unit after cardiac surgery.

Development and validation of a risk prediction model and nomogram for colon adenocarcinoma based on methylation-driven genes

Evidence suggests that abnormal DNA methylation patterns play a crucial role in the etiology and pathogenesis of colon adenocarcinoma (COAD). In this study, we identified a total of 97 methylation-driven genes (MDGs) through a comprehensive analysis of the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate Cox regression analysis identified four MDGs (CBLN2, RBM47, SLCO4C1, and TMEM220) associated with overall survival (OS) in COAD patients. A risk prediction model was then developed based on these four MDGs to predict the prognosis of COAD patients. We also created a nomogram that incorporated risk scores, age, and TNM stage to promote a personalized prediction of OS in COAD patients. Compared with the traditional TNM staging system, our new nomogram was better at predicting the OS of COAD patients. In cell experiments, we confirmed that the mRNA expression levels of CLBN2 and TMEM220 were regulated by the methylation of their promoter regions. Moreover, immunohistochemistry showed that CBLN2 and TMEM220 were potential prognostic biomarkers for COAD patients. In summary, we have established a risk prediction model and nomogram that might be effectively utilized to promote the prediction of OS in COAD patients.

A Model for Risk Prediction of Cerebrovascular Disease Prevalence-Based on Community Residents Aged 40 and above in a City in China

Cerebrovascular disease (CVD) is the leading cause of death in many countries including China. Early diagnosis and risk assessment represent one of effective approaches to reduce the CVD-related mortality. The purpose of this study was to understand the prevalence and influencing factors of cerebrovascular disease among community residents in Qingyunpu District, Nanchang City, Jiangxi Province, and to construct a model of cerebrovascular disease risk index suitable for local community residents. A stratified cluster sampling method was used to sample 2147 community residents aged 40 and above, and the prevalence of cerebrovascular diseases and possible risk factors were investigated. It was found that the prevalence of cerebrovascular disease among local residents was 4.5%. Poisson regression analysis found that old age, lack of exercise, hypertension, diabetes, smoking, and family history of cerebrovascular disease are the main risk factors for local cerebrovascular disease. The relative risk ORs were 3.284, 2.306, 2.510, 3.194, 1.949, 2.315, respectively. For these six selected risk factors, a cerebrovascular disease risk prediction model was established using the Harvard Cancer Index method. The R value of the risk prediction model was 1.80 (sensitivity 81.8%, specificity 47.0%), which was able to well predict the risk of cerebrovascular disease among local residents. This provides a scientific basis for the further development of local cerebrovascular disease prevention and control work.

Prediction and clinical utility of a liver cancer risk model in Chinese adults: A prospective cohort study of 0.5 million people

China has made rapid progress in reducing the incidence of HBV infection in the past three decades, along with a rapidly changing lifestyle and aging population. We aimed to develop and validate an up-to-date liver cancer risk prediction model with routinely available predictors and evaluate its applicability for screening guidance. Using data from the China Kadoorie Biobank, we included 486 285 participants in this analysis. Fifteen risk factors were included in the model. Flexible parametric survival models were used to estimate the 10-year absolute risk of liver cancer. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. A total of 2706 participants occurred liver cancer over the 4 814 320 person-years of follow-up. Excellent discrimination of the model was observed in both development and validation datasets, with c-statistics (95% CI) of 0.80 (0.79-0.81) and 0.80 (0.78-0.82) respectively, as well as excellent calibration of observed and predicted risks. Decision curve analysis revealed that use of the model in selecting participants for screening improved benefit at a threshold of 2% 10-year risk, compared to current guideline of screening all HBsAg carriers. Our model was more sensitive than current guideline for cancer screening (28.17% vs 25.96%). We developed and validated a CKB-PLR (Prediction for Liver cancer Risk Based on the China Kadoorie Biobank Study) model to predict the absolute risk of liver cancer for both HBsAg seropositive and seronegative populations. Application of the model is beneficial for precisely identifying the high-risk groups among the general population.

Development and validation of a nomogram to better predict hypertension based on a 10-year retrospective cohort study in China

Background

Hypertension is a highly prevalent disorder. A nomogram to estimate the risk of hypertension in Chinese individuals is not available.

Methods

6201 subjects were enrolled in the study and randomly divided into training set and validation set at a ratio of 2:1. The LASSO regression technique was used to select the optimal predictive features, and multivariate logistic regression to construct the nomograms. The performance of the nomograms was assessed and validated by AUC, C-index, calibration curves, DCA, clinical impact curves, NRI, and IDI.

Results

The nomogram^140/90 was developed with the parameters of family history of hypertension, age, SBP, DBP, BMI, MCHC, MPV, TBIL, and TG. AUCs of nomogram^140/90 were 0.750 in the training set and 0.772 in the validation set. C-index of nomogram^140/90 were 0.750 in the training set and 0.772 in the validation set. The nomogram^130/80 was developed with the parameters of family history of hypertension, age, SBP, DBP, RDWSD, and TBIL. AUCs of nomogram^130/80 were 0.705 in the training set and 0.697 in the validation set. C-index of nomogram^130/80 were 0.705 in the training set and 0.697 in the validation set. Both nomograms demonstrated favorable clinical consistency. NRI and IDI showed that the nomogram^140/90 exhibited superior performance than the nomogram^130/80. Therefore, the web-based calculator of nomogram^140/90 was built online.

Conclusions

We have constructed a nomogram that can be effectively used in the preliminary and in-depth risk prediction of hypertension in a Chinese population based on a 10-year retrospective cohort study.

Funding

This study was supported by the Hebei Science and Technology Department Program (no. H2018206110).

Development of a risk prediction model of pneumothorax in percutaneous computed tomography guided transthoracic needle lung biopsy

INTRODUCTION

To retrospectively evaluate the incidence of and the risk factors for pneumothorax and intercostal catheter insertion (ICC) after CT-guided lung biopsy and to generate a risk prediction model for developing a pneumothorax and requiring an ICC.

METHODS

255 CT-guided lung biopsies performed for 249 lesions in 249 patients from August 2014 to August 2019 were retrospectively analysed using multivariate logistic regression analysis. Risk prediction models were established using backward stepwise variable selection and likelihood ratio tests and were internally validated using split-sample methods.

RESULTS

The overall incidence of pneumothorax was 30.2% (77/255). ICC insertion was required for 8.32% (21/255) of all procedures. The significant independent risk factors for pneumothorax were lesions not in contact with pleura (P < 0.001), a shorter skin-to-pleura distance (P = 0.01), the needle crossing a fissure (P = 0.004) and emphysema (P = 0.01); those for ICC insertion for pneumothorax were a needle through emphysema (P < 0.001) and lesions in the upper lobe (P = 0.017). AUC of the predictive models for pneumothorax and ICC insertion were 0.800 (95% CI: 0.745-0.856) and 0.859 (95% CI: 0.779-0.939) respectively. Upon internal validation, AUC of the testing sets of pneumothorax and ICC insertion were 0.769 and 0.822 on average respectively.

CONCLUSION

The complication rates of pneumothorax and ICC insertion after CT-guided lung biopsy at our institution are comparable to results from previously reported studies. This study provides highly accurate risk prediction models of pneumothorax and ICC insertion for patients undergoing CT-guided lung biopsies.

Gingival Crevicular Placental Alkaline Phosphatase Is an Early Pregnancy Biomarker for Pre-Eclampsia

Early and innovative diagnostic strategies are required to predict the risk of developing pre-eclampsia (PE). The purpose of this study was to evaluate the performance of gingival crevicular fluid (GCF) placental alkaline phosphatase (PLAP) concentrations to correctly classify women at risk of PE. A prospectively collected, retrospectively stratified cohort study was conducted, with 412 pregnant women recruited at 11–14 weeks of gestation. Physical, obstetrical, and periodontal data were recorded. GCF and blood samples were collected for PLAP determination by ELISA assay. A multiple logistic regression classification model was developed, and the classification efficiency of the model was established. Within the study cohort, 4.3% of pregnancies developed PE. GCF-PLAP concentration was 3- to 6-fold higher than in plasma samples. GCF-PLAP concentrations and systolic blood pressure were greater in women who developed PE (p = 0.015 and p < 0.001, respectively). The performance of the multiparametric model that combines GCF-PLAP concentration and the levels of systolic blood pressure (at 11–14 weeks gestation) showed an association of systolic blood pressure and GCF-PLAP concentrations with the likelihood of developing PE (OR:1.07; 95% CI 1.01–1.11; p = 0.004 and OR:1.008, 95% CI 1.000–1.015; p = 0.034, respectively). The model had a sensitivity of 83%, a specificity of 72%, and positive and negative predictive values of 12% and 99%, respectively. The area under the receiver operating characteristic (AUC-ROC) curve was 0.77 and correctly classified 72% of PE pregnancies. In conclusion, the multivariate classification model developed may be of utility as an aid in identifying pre-symptomatic women who subsequently develop PE.

Impact of antiviral therapy on risk prediction model for hepatocellular carcinoma development in patients with chronic hepatitis B

AIM

Risk prediction models for hepatocellular carcinoma (HCC) development are available. However, the influence of antiviral therapy (AVT) on these models in patients with chronic hepatitis B is unknown.

METHODS

The dynamic changes in risk prediction models during AVT and the association between risk prediction model and the risk of chronic hepatitis B-related HCC development were investigated. Between 2005 and 2017, 4917 patients with chronic hepatitis B (3361 noncirrhotic, 1556 cirrhotic) were recruited.

RESULTS

The mean age of the study population was 49.3 years and 60.6% (n = 2980) of the patients were male. The mean Chinese University-HCC (CU-HCC) score was 12.7 at baseline in the overall study population, and decreased significantly (mean, 8.7) after 1 year of AVT (p < 0.001). The score was maintained throughout 5 years of AVT (mean, 8.4-8.8; p > 0.05). The proportion of high-risk patients (CU-HCC score ≥ 20) was 28.9% at baseline, and decreased significantly after 1 year of AVT (5.0%; p < 0.001), and remained stable through 5 years of AVT (2.2%-3.6%; p > 0.05). In addition to the score at baseline, the CU-HCC score at 1 year of AVT independently predicted the risk of HCC development (hazard ratio = 1.072; p < 0.001), together with male gender and platelet count (all p < 0.05).

CONCLUSIONS

The CU-HCC score significantly decreased at 1 year of AVT and was maintained thereafter. The CU-HCC score after 1 year of AVT independently predicted the risk of HCC development in patients with chronic hepatitis B.

Prediction models and nomograms for 10-year risk of end-stage renal disease in Chinese type 2 diabetes mellitus patients in primary care

AIMS

To develop and validate 10-year risk prediction models, nomograms and charts for end-stage renal disease (ESRD) in Chinese patients with type 2 diabetes mellitus (T2DM) in primary care, in order to guide individualized treatment.

MATERIALS AND METHODS

This was a 10-year population-based observational cohort study. A total of 141 516 Chinese T2DM patients without history of cardiovascular disease or ESRD who were managed in public primary care clinics in 2008 were included and followed up until December 2017. Two-thirds of these patients were randomly selected to develop sex-specific ESRD risk prediction models using Cox regressions. The validity and accuracy of the models were tested on the remaining third of patients using Harrell's C-index. We selected variables based on their clinical and statistical importance to construct the nomograms and charts.

RESULTS

The median follow-up period was 9.75 years. The cumulative incidence of ESRD was 6.0% (men: 6.1%, women: 5.9%). Age, diabetes duration, systolic blood pressure (SBP), SBP variability, diastolic blood pressure, triglycerides, glycated haemoglobin (HbA1c), HbA1c variability, urine albumin to creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR) were significant predictors for both sexes. Smoking and total cholesterol to HDL cholesterol ratio were additional significant predictors for men and women, respectively. The models showed Harrell's C-statistics of 0.889/0.889 (women/men). Age, eGFR, UACR, SBP and HbA1c were selected for both sexes to develop nomograms and charts.

CONCLUSIONS

Using routinely available variables, the 10-year ESRD risk of Chinese T2DM patients in primary care can be predicted with approximately 90% accuracy. We have developed different tools to facilitate routine ESRD risk prediction in primary care, so that individualized care can be provided to prevent or delay ESRD in T2DM patients.

Risk stratification and corresponding postoperative treatment strategies for occult contralateral lymph node metastasis in pyriform sinus squamous cell carcinoma patients with ipsilateral node-positive necks

Background

To quantitatively predict the probability of occult contralateral lymph node metastasis (cLNM) for pyriform sinus squamous cell carcinoma (PSSC) patients with ipsilateral node-positive necks to guide postoperative adjuvant treatment.

Methods

Two hundred and twenty-seven PSSC patients with ipsilateral lymph node metastasis (iLNM) were retrospectively analyzed.

Results

Multivariate logistic analyses showed that five factors including maximum tumor diameter (MTD) of more than 4.0 cm, existence of tumor extension across the midline (EAM), internal jugular vein adhesion (IJVA), lymphovascular invasion (LVI), and lymph nodal fusion (LNF) were independent risk factors for cLNM. A predictive nomogram was created based on these factors. The accuracy and validity of our model were verified by concordance index (C-index) 0.862 [95% confidence interval (CI): 0.810–0.914] in development cohort and 0.860 (95% CI: 0.820–0.900) after 1,000 bootstrapping. The calibration curve also showed a relatively favorable agreement. We then stratified patients into three groups based on their cLNM risk scores. Possible cLNM rates for low-risk, moderate-risk, and relatively high-risk subgroups were 3.6%, 21.8%, and 60.7%, respectively.

Conclusions

A new postoperative adjuvant radiotherapy (PART) strategy selection flow chart was created for PSSC patients based on our newly built nomogram which can effectively predict the individualized possibility of cLNM. For patients in high-risk subgroup, therapeutic-dose PART is highly recommended even for those with contralateral clinical N0 neck disease. For those in moderate-risk subgroup, prophylactic-dose PART is recommended. However, for patients in low-risk subgroup, regular follow-up is sufficient given the extremely low occult cLNM rate.

A Panel of 6 Biomarkers Significantly Improves the Prediction of Type 2 Diabetes in the MONICA/KORA Study Population

CONTEXT

Improved strategies to identify persons at high risk of type 2 diabetes are important to target costly preventive efforts to those who will benefit most.

OBJECTIVE

This work aimed to assess whether novel biomarkers improve the prediction of type 2 diabetes beyond noninvasive standard clinical risk factors alone or in combination with glycated hemoglobin A1c (HbA1c).

METHODS

We used a population-based case-cohort study for discovery (689 incident cases and 1850 noncases) and an independent cohort study (262 incident cases, 2549 noncases) for validation. An L1-penalized (lasso) Cox model was used to select the most predictive set among 47 serum biomarkers from multiple etiological pathways. All variables available from the noninvasive German Diabetes Risk Score (GDRSadapted) were forced into the models. The C index and the category-free net reclassification index (cfNRI) were used to evaluate the predictive performance of the selected biomarkers beyond the GDRSadapted model (plus HbA1c).

RESULTS

Interleukin-1 receptor antagonist, insulin-like growth factor binding protein 2, soluble E-selectin, decorin, adiponectin, and high-density lipoprotein cholesterol were selected as the most relevant biomarkers. The simultaneous addition of these 6 biomarkers significantly improved the predictive performance both in the discovery (C index [95% CI], 0.053 [0.039-0.066]; cfNRI [95% CI], 67.4% [57.3%-79.5%]) and the validation study (0.034 [0.019-0.053]; 48.4% [35.6%-60.8%]). Significant improvements by these biomarkers were also seen on top of the GDRSadapted model plus HbA1c in both studies.

CONCLUSION

The addition of 6 biomarkers significantly improved the prediction of type 2 diabetes when added to a noninvasive clinical model or to a clinical model plus HbA1c.