Prediction of the risk of 3-year chronic kidney disease among elderly people: a community-based cohort study

OBJECTIVE

We conducted a community-based cohort study to predict the 3-year occurrence of chronic kidney disease (CKD) among population aged ≥60 years.

METHOD

Participants were selected from two communities through randomized cluster sampling in Jiading District of Shanghai, China. The two communities were randomly divided into a development cohort (n = 12012) and a validation cohort (n = 6248) with a 3-year follow-up. Logistic regression analysis was used to determine the independent predictors. A nomogram was established to predict the occurrence of CKD within 3 years. The area under the curve (AUC), the calibration curve and decision curve analysis (DCA) curve were used to evaluate the model.

RESULT

At baseline, participants in development cohort and validation cohort were with the mean age of 68.24 ± 5.87 and 67.68 ± 5.26 years old, respectively. During 3 years, 1516 (12.6%) and 544 (8.9%) new cases developed CKD in the development and validation cohorts, respectively. Nine variables (age, systolic blood pressure, body mass index, exercise, previous hypertension, triglycerides, fasting plasma glucose, glycated hemoglobin and serum creatinine) were included in the prediction model. The AUC value was 0.742 [95% confidence interval (CI), 0.728-0.756] in the development cohort and 0.881(95%CI, 0.867-0.895) in the validation cohort, respectively. The calibration curves and DCA curves demonstrate an effective predictive model.

CONCLUSION

Our nomogram model is a simple, reasonable and reliable tool for predicting the risk of 3-year CKD in community-dwelling elderly people, which is helpful for timely intervention and reducing the incidence of CKD.

A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease

BACKGROUND

The high prevalence of mild cognitive impairment (MCI) in non-dialysis individuals with chronic kidney disease (CKD) impacts their prognosis and quality of life.

OBJECTIVE

This study aims to investigate the variables associated with MCI in non-dialysis outpatient patients with CKD and to construct and verify a nomogram prediction model.

METHODS

416 participants selected from two hospitals in Chengdu, between January 2023 and June 2023. They were categorized into two groups: the MCI group (n = 210) and the non-MCI (n = 206). Univariate and multivariate binary logistic regression analyses were employed to identify independent influences (candidate predictor variables). Subsequently, regression models was constructed, and a nomogram was drawn. The restricted cubic spline diagram was drawn to further analyze the relationship between the continuous numerical variables and MCI. Internally validated using a bootstrap resampling procedure.

RESULTS

Among 416 patients, 210 (50.9%) had MCI. Logistic regression analysis revealed that age, educational level, occupational status, use of smartphones, sleep disorder, and hemoglobin were independent influencing factors of MCI (all p<.05). The model's area under the curve was 0.926,95% CI (0.902, 0.951), which was a good discriminatory measure; the Calibration curve, the Hosmer-Lemeshow test, and the Clinical Decision Curve suggested that the model had good calibration and clinical benefit. Internal validation results showed the consistency index was 0.926, 95%CI (0.925, 0.927).

CONCLUSION

The nomogram prediction model demonstrates good performance and can be used for early screening and prediction of MCI in non-dialysis patients with CKD. It provides valuable reference for medical staff to formulate corresponding intervention strategies.

Nomogram to Predict 90-Day All-Cause Mortality in Acute Ischemic Stroke Patients after Endovascular Thrombectomy

OBJECTIVE

Although Endovascular Thrombectomy (EVT) significantly improves the prognosis of Acute Ischemic Stroke (AIS) patients with large vessel occlusion, the mortality rate remains higher. This study aimed to construct and validate a nomogram for predicting 90-day all-cause mortality in AIS patients with large vessel occlusion and who have undergone EVT.

METHODS

AIS patients with large vessel occlusion in the anterior circulation who underwent EVT from May 2017 to December 2022 were included. 430 patients were randomly split into a training group (N=302) and a test group (N=128) for the construction and validation of our nomogram. In the training group, multivariate logistic regression analysis was performed to determine the predictors of 90-day all-cause mortality. The C-index, calibration plots, and decision curve analysis were applied to evaluate the nomogram performance.

RESULTS

Multivariate logistic regression analysis revealed neurological deterioration during hospitalization, age, baseline National Institutes of Health Stroke Scale (NIHSS) score, occlusive vessel location, malignant brain edema, and Neutrophil-to-lymphocyte Ratio (NLR) as the independent predictors of 90-day all-cause mortality (all p ≤ 0.039). The C-index of the training and test groups was 0.891 (95%CI 0.848-0.934) and 0.916 (95% CI: 0.865-0.937), respectively, showing the nomogram to be well distinguished. The Hosmer-Lemeshow goodness-of-fit test revealed the p-values for both the internal and external verification datasets to be greater than 0.5.

CONCLUSION

Our nomogram has incorporated relevant clinical and imaging features, including neurological deterioration, age, baseline NIHSS score, occlusive vessel location, malignant brain edema, and NLR ratio, to provide an accurate and reliable prediction of 90-day all-cause mortality in AIS patients undergoing EVT.

Comparative Analysis of Three Atherosclerotic Cardiovascular Disease Risk Prediction Models in Individuals Aged 75 and Older

Purpose

To evaluate the performance of the Framingham cardiovascular risk score (FRS)/pooled cohort equations (PCE)/China prediction for atherosclerotic cardiovascular disease (ASCVD) risk (China-PAR model) in a prospective cohort of Chinese older adults.

Patients and Methods

We assessed 717 older adults aged 75-85 years without ASCVD at the baseline from the Sichuan province of China. The participants were followed annually from 2011 to 2021. We obtained the participants' information through the medical records of physical examination and evaluated their 10-year ASCVD risk using FRS, PCE, and China-PAR. We further evaluated the predictive abilities of three assessment models.

Results

During the 10-year follow-up, 206 participants developed ASCVD, with an incidence rate of 28.73%. The FRS and China-PAR moderately underestimated the risk of ASCVD (22.1% and 12.4%, respectively), but while PCE overestimated the risk (36.1%). FRS and China-PAR were found to underestimate the risk of ASCVD (26% and 63%, respectively) for men, while PCE overestimated the risk by 8%; For women, FRS and China-PAR were found to underestimate the risk of ASCVD (14% and 35%, respectively), while PCE overestimated the risk by 88%.

Conclusion

The 10-year ASCVD risk was found to be overestimated by PCE. China-PAR had the most accurate predictions in women, while FRS was particularly well-calibrated in males. All three risk models have good discrimination, with FRS and PCE being well-calibrated in men and all three being well-calibrated in women. Therefore, accurate risk models are warranted to facilitate the prevention of ASCVD at the baseline among Chinese older adults.

Establishment and evaluation of a nomogram for in-hospital new-onset atrial fibrillation after percutaneous coronary intervention for acute myocardial infarction

Background

New-onset atrial fibrillation (NOAF) is prognostic in acute myocardial infarction (AMI). The timely identification of high-risk patients is essential for clinicians to improve patient prognosis.

Methods

A total of 333 AMI patients were collected who underwent percutaneous coronary intervention (PCI) at Zhejiang Provincial People's Hospital between October 2019 and October 2020. Least absolute shrinkage and selection operator regression (Lasso) and multivariate logistic regression analysis were applied to pick out independent risk factors. Secondly, the variables identified were utilized to establish a predicted model and then internally validated by 10-fold cross-validation. The discrimination, calibration, and clinical usefulness of the prediction model were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, Hosmer-Lemeshow test decision curve analyses, and clinical impact curve.

Result

Overall, 47 patients (14.1%) developed NOAF. Four variables, including left atrial dimension, body mass index (BMI), CHA2DS2-VASc score, and prognostic nutritional index, were selected to construct a nomogram. Its area under the curve is 0.829, and internal validation by 10-fold cross-folding indicated a mean area under the curve is 0.818. The model demonstrated good calibration according to the Hosmer-Lemeshow test (P = 0.199) and the calibration curve. It showed satisfactory clinical practicability in the decision curve analyses and clinical impact curve.

Conclusion

This study established a simple and efficient nomogram prediction model to assess the risk of NOAF in patients with AMI who underwent PCI. This model could assist clinicians in promptly identifying high-risk patients and making better clinical decisions based on risk stratification.

Prediction of vascular complications in free flap reconstruction with machine learning

OBJECTIVE

This study aims to explore the risk factors of vascular complications following free flap reconstruction and to develop a clinical auxiliary assessment tool for predicting vascular complications in patients undergoing free flap reconstruction leveraging machine learning methods.

METHODS

We reviewed the medical data of patients who underwent free flap reconstruction at the Affiliated Hospital of Zunyi Medical University retrospectively from January 1, 2019, to December 31, 2021. Statistical analysis was used to screen risk factors. A training data set was generated and augmented using the synthetic minority oversampling technique. Logistic regression, random forest and neural network, models were trained, using this dataset. The performance of these three predictive models was then evaluated and compared using a test set, with four metrics, area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

RESULTS

A total of 570 patients who underwent free flap reconstruction were included in this study, 46 of whom developed postoperative vascular complications. Among the models tested, the neural network model exhibited superior performance on the test set, achieving an AUC of 0.828. Multivariate logistic regression analysis identified that preoperative hemoglobin levels, preoperative fibrinogen levels, operation duration, smoking history, the number of anastomoses, and peripheral vascular injury as statistically significant independent risk factors for vascular complications post-free flap reconstruction. The top five predictive factors in the neural network were fibrinogen content, operation duration, donor site, body mass index (BMI), and platelet count.

CONCLUSION

Hemoglobin levels, fibrinogen levels, operation duration, smoking history, and anastomotic veins are independent risk factors for vascular complications following free flap reconstruction. These risk factors enhance the ability of machine learning models to predict the occurrence of vascular complications and identify high-risk patients. The neural network model outperformed the logistic regression and random forest models, suggesting its potential to aid clinicians in early identification of high-risk patients thereby mitigating patient suffering and improving prognosis.

Development of a Risk Prediction Model for Infection After Kidney Transplantation Transmitted from Bacterial Contaminated Preservation Solution

Background

The risk of transplant recipient infection is unknown when the preservation solution culture is positive.

Methods

We developed a prediction model to evaluate the infection in kidney transplant recipients within microbial contaminated preservation solution. Univariate logistic regression was utilized to identify risk factors for infection. Both stepwise selection with Akaike information criterion (AIC) was used to identify variables for multivariate logistic regression. Selected variables were incorporated in the nomograms to predict the probability of infection for kidney transplant recipients with microbial contaminated preservation solution.

Results

Age, preoperative creatinine, ESKAPE, PCT, hemofiltration, and sirolimus had a strongest association with infection risk, and a nomogram was established with an AUC value of 0.72 (95% confidence interval, 0.64-0.80) and Brier index 0.20 (95% confidence interval, 0.18-0.23). Finally, we found that when the infection probability was between 20% and 80%, the model oriented antibiotic strategy should have higher net benefits than the default strategy using decision curve analysis.

Conclusion

Our study developed and validated a risk prediction model for evaluating the infection of microbial contaminated preservation solutions in kidney transplant recipients and demonstrated good net benefits when the total infection probability was between 20% and 80%.

Application of deep neural survival networks to the development of risk prediction models for diabetes mellitus, hypertension, and dyslipidemia

OBJECTIVES

: Although numerous risk prediction models have been proposed, few such models have been developed using neural network-based survival analysis. We developed risk prediction models for three cardiovascular disease risk factors (diabetes mellitus, hypertension, and dyslipidemia) among a working-age population in Japan using DeepSurv, a deep feed-forward neural network.

METHODS

: Data were obtained from the Japan Epidemiology Collaboration on Occupational Health Study. A total of 51 258, 44 197, and 31 452 individuals were included in the development of risk models for diabetes mellitus, hypertension, and dyslipidemia, respectively; two-thirds of whom were used to develop prediction models, and the rest were used to validate the models. We compared the performances of DeepSurv-based models with those of prediction models based on the Cox proportional hazards model.

RESULTS

: The area under the receiver-operating characteristic curve was 0.878 [95% confidence interval (CI) = 0.864-0.892] for diabetes mellitus, 0.835 (95% CI = 0.826-0.845) for hypertension, and 0.826 (95% CI = 0.817-0.835) for dyslipidemia. Compared with the Cox proportional hazards-based models, the DeepSurv-based models had better reclassification performance [diabetes mellitus: net reclassification improvement (NRI) = 0.474, P  ≤ 0.001; hypertension: NRI = 0.194, P  ≤ 0.001; dyslipidemia: NRI = 0.397, P  ≤ 0.001] and discrimination performance [diabetes mellitus: integrated discrimination improvement (IDI) = 0.013, P  ≤ 0.001; hypertension: IDI = 0.007, P  ≤ 0.001; and dyslipidemia: IDI = 0.043, P  ≤ 0.001].

CONCLUSION

: This study suggests that DeepSurv has the potential to improve the performance of risk prediction models for cardiovascular disease risk factors.

Risk prediction models of mortality after hip fracture surgery in older individuals: a systematic review

OBJECTIVE

This study aimed to critically assess existing risk prediction models for postoperative mortality in older individuals with hip fractures, with the objective of offering substantive insights for their clinical application.

DESIGN

A comprehensive search was conducted across prominent databases, including PubMed, Embase, Cochrane Library, SinoMed, CNKI, VIP, and Wanfang, spanning original articles in both Chinese and English up until 1 December 2023. Two researchers independently extracted pertinent research characteristics, such as predictors, model performance metrics, and modeling methodologies. Additionally, the bias risk and applicability of the incorporated risk prediction models were systematically evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS

Within the purview of this investigation, a total of 21 studies were identified, constituting 21 original risk prediction models. The discriminatory capacity of the included risk prediction models, as denoted by the minimum and maximum areas under the subject operating characteristic curve, ranged from 0.710 to 0.964. Noteworthy predictors, recurrent across various models, included age, sex, comorbidities, and nutritional status. However, among the models assessed through the PROBAST framework, only one was deemed to exhibit a low risk of bias. Beyond this assessment, the principal limitations observed in risk prediction models pertain to deficiencies in data analysis, encompassing insufficient sample size and suboptimal handling of missing data.

CONCLUSION

Subsequent research endeavors should adopt more stringent experimental designs and employ advanced statistical methodologies in the construction of risk prediction models. Moreover, large-scale external validation studies are warranted to rigorously assess the generalizability and clinical utility of existing models, thereby enhancing their relevance as valuable clinical references.

Development and validation of the ASGARD risk score for safe monitoring in asymptomatic nonsevere aortic stenosis

AIMS

Current guidelines recommend serial echocardiography at minimum 1-2 year intervals for monitoring patients with nonsevere aortic valve stenosis (AS), which is costly and often clinically inconsequential.We aimed to develop and test whether the biomarker-based ASGARD risk score (Aortic Valve Stenosis Guarded by Amplified Risk Determination) can guide the timing of echocardiograms in asymptomatic patients with nonsevere AS.

METHODS

The development cohort comprised 1,093 of 1,589 (69%) asymptomatic patients with mild-to-moderate AS who remained event-free one year after inclusion into the SEAS trial. Cox regression landmark analyses with a 2-year follow-up identified the model (ASGARD) with the lowest Akaike information criterion for association to AS-related composite outcome (heart failure hospitalization, aortic valve replacement, or cardiovascular death). Fine-Gray analyses provided cumulative event rates by ASGARD score quartiles. The ASGARD score was internally validated in the remaining 496 patients (31%) from the SEAS-cohort and externally in 71 asymptomatic outpatients with nonsevere AS from six Copenhagen hospitals.

RESULTS

The ASGARD score comprises updated measurements of heart rate and age- and sex-adjusted N-terminal pro-brain natriuretic peptide upon transaortic maximal velocity (Vmax) from the previous year. The ASGARD score had high predictive accuracy across all cohorts (external validation: area under the curve: 0.74 [95% CI, 0.62-0.86]), and similar to an updated Vmax measurement. An ASGARD score ≤50% was associated with AS-related event rates ≤5% for a minimum of 15 months.

CONCLUSION

The ASGARD score could provide a personalized and safe surveillance alternative to routinely planned echocardiograms, so physicians can prioritize echocardiograms for high-risk patients.

Derivation and validation of a quantitative risk prediction model for weaning and extubation in neurocritical patients

Background

Patients with severe neurological conditions are at high risk during withdrawal and extubation, so it is important to establish a model that can quantitatively predict the risk of this procedure.

Methods

By analyzing the data of patients with traumatic brain injury and tracheal intubation in the ICU of the affiliated hospital of Hangzhou Normal University, a total of 200 patients were included, of which 140 were in the modeling group and 60 were in the validation group. Through binary logistic regression analysis, 8 independent risk factors closely related to the success of extubation were screened out, including age ≥ 65 years old, APACHE II score ≥ 15 points, combined chronic pulmonary disease, GCS score < 8 points, oxygenation index <300, cough reflex, sputum suction frequency, and swallowing function.

Results

Based on these factors, a risk prediction scoring model for extubation was constructed with a critical value of 18 points. The AUC of the model was 0.832, the overall prediction accuracy was 81.5%, the specificity was 81.6%, and the sensitivity was 84.1%. The data of the validation group showed that the AUC of the model was 0.763, the overall prediction accuracy was 79.8%, the specificity was 84.8%, and the sensitivity was 64.0%.

Conclusion

These results suggest that the extubation risk prediction model constructed through quantitative scoring has good predictive accuracy and can provide a scientific basis for clinical practice, helping to assess and predict extubation risk, thereby improving the success rate of extubation and improving patient prognosis.

Risk prediction model construction for asthma after allergic rhinitis by blood immune T effector cells

BACKGROUND

Allergic rhinitis (AR) and asthma (AS) are prevalent and frequently co-occurring respiratory diseases, with mutual influence on each other. They share similar etiology, pathogenesis, and pathological changes. Due to the anatomical continuity between the upper and lower respiratory tracts, allergic inflammation in the nasal cavity can readily propagate downwards, leading to bronchial inflammation and asthma. AR serves as a significant risk factor for AS by potentially inducing airway hyperresponsiveness in patients. Currently, there is a lack of reliable predictors for the progression from AR to AS.

METHODS

In this exhaustive investigation, we reexamined peripheral blood single cell RNA sequencing datasets from patients with AS following AR and healthy individuals. In addition, we used the bulk RNA sequencing dataset as a validation lineup, which included AS, AR, and healthy controls. Using marker genes of related cell subtype, signatures predicting the progression of AR to AS were generated.

RESULTS

We identified a subtype of immune-activating effector T cells that can distinguish patients with AS after AR. By combining specific marker genes of effector T cell subtype, we established prediction models of 16 markers. The model holds great promise for assessing AS risk in individuals with AR, providing innovative avenues for clinical diagnosis and treatment strategies.

CONCLUSION

Subcluster T effector cells may play a key role in post-AR AS. Notably, ACTR3 and HSPA8 genes were significantly upregulated in the blood of AS patients compared to healthy patients.

Development and Validation of a Nomogram for Predicting Obstructive Sleep Apnea Severity in Children

Purpose

The clinical presentation of Obstructive Sleep Apnea (OSA) in children is insidious and harmful. Early identification of children with OSA, particularly those at a higher risk for severe symptoms, is essential for making informed clinical decisions and improving long-term outcomes. Therefore, we developed and validated a risk prediction model for severity in Chinese children with OSA to effectively identify children with moderate-to-severe OSA in a clinical setting.

Patients and Methods

From June 2023 to September 2023, we retrospectively analyzed the medical records of 367 Children diagnosed with OSA through portable bedside polysomnography (PSG). Predictor variables were screened using the least absolute shrinkage and selection operator (LASSO) and logistic regression techniques to construct nomogram to predict the severity of OSA. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to determine the discrimination, calibration, and clinical usefulness of the nomogram.

Results

A total of 367 children with a median age of 84 months were included in this study. Neck circumference, ANB, gender, learning problem, and level of obstruction were identified as independent risk factors for moderate-severe OSA. The consistency indices of the nomogram in the training and validation cohorts were 0.841 and 0.75, respectively. The nomogram demonstrated a strong concordance between the predicted probabilities and the observed probabilities for children diagnosed with moderate-severe OSA. With threshold probabilities ranging from 0.1 to 1.0, the predictive model demonstrated strong predictive efficacy and yielded improved net benefit for clinical decision-making. ROC analysis was employed to classify the children into high and low-risk groups, utilizing the Optimal Cutoff value of 0.39.

Conclusion

A predictive model using LASSO regression was developed and validated for children with varying levels of OSA. This model identifies children at risk of developing OSA at an early stage.

Validation in type 2 diabetes of a metabolomic signature of all-cause mortality

CONTEXT

Mortality in type 2 diabetes is twice that of the normoglycemic population. Unravelling biomarkers that identify high-risk patients for referral to the most aggressive and costly prevention strategies is needed.

OBJECTIVE

To validate in type 2 diabetes the association with all-cause mortality of a 14-metabolite score (14-MS) previously reported in the general population and whether this score can be used to improve well-established mortality prediction models.

METHODS

This is a sub-study consisting of 600 patients from the "Sapienza University Mortality and Morbidity Event Rate" (SUMMER) study in diabetes, a prospective multicentre investigation on all-cause mortality in patients with type 2 diabetes. Metabolic biomarkers were quantified from serum samples using high-throughput proton nuclear magnetic resonance metabolomics.

RESULTS

In type 2 diabetes, the 14-MS showed a significant (p < 0.0001) association with mortality, which was lower (p < 0.0001) than that reported in the general population. This difference was mainly due to two metabolites (histidine and ratio of polyunsaturated fatty acids to total fatty acids) with an effect size that was significantly (p = 0.01) lower in diabetes than in the general population. A parsimonious 12-MS (i.e. lacking the 2 metabolites mentioned above) improved patient discrimination and classification of two well-established mortality prediction models (p < 0.0001 for all measures).

CONCLUSIONS

The metabolomic signature of mortality in the general population is only partially effective in type 2 diabetes. Prediction markers developed and validated in the general population must be revalidated if they are to be used in patients with diabetes.

EXIST: EXamining rIsk of excesS adiposiTy-Machine learning to predict obesity-related complications

Background

Obesity is associated with an increased risk of multiple conditions, ranging from heart disease to cancer. However, there are few predictive models for these outcomes that have been developed specifically for people with overweight/obesity.

Objective

To develop predictive models for obesity-related complications in patients with overweight and obesity.

Methods

Electronic health record data of adults with body mass index 25-80 kg/m2 treated in primary care practices between 2000 and 2019 were utilized to develop and evaluate predictive models for nine long-term clinical outcomes using a) Lasso-Cox models and b) a machine-learning method random survival forests (RSF). Models were trained on a training dataset and evaluated on a test dataset over 100 replicates. Parsimonious models of <10 variables were also developed using Lasso-Cox.

Results

Over a median follow-up of 5.6 years, study outcome incidence in the cohort of 433,272 patients ranged from 1.8% for knee replacement to 11.7% for atherosclerotic cardiovascular disease. Harrell C-index averaged over replicates ranged from 0.702 for liver outcomes to 0.896 for death for RSF, and from 0.694 for liver outcomes to 0.891 for death for Lasso-Cox. The Harrell C-index for parsimonious models ranged from 0.675 for liver outcomes to 0.850 for knee replacement.

Conclusions

Predictive modeling can identify patients at high risk of obesity-related complications. Interpretable Cox models achieve results close to those of machine learning methods and could be helpful for population health management and clinical treatment decisions.

A systematic review of risk prediction model of venous thromboembolism for patients with lung cancer

BACKGROUND

Venous thromboembolism (VTE) increases the risk of death or adverse outcomes in patients with lung cancer. Therefore, early identification and treatment of high-risk groups of VTE have been the research focus. In this systematic review, the risk assessment tools of VTE in patients with lung cancer were systematically analyzed and evaluated to provide a reference for VTE management.

METHODS

Relevant studies were retrieved from major English databases (The Cochrane Library, Embase, Web of Science, PubMed, Scopus, Medline) and Chinese databases (China National Knowledge Infrastructure [CNKI] and WanFang Data) until July 2023 and extracted by two researchers. This systematic review was registered at PROSPERO (no. CRD42023409748).

RESULTS

Finally, two prospective cohort studies and four retrospective cohort studies were included from 2019. There was a high risk of bias in all included studies according to the Prediction Model Risk of Bias Assessment tool (PROBAST). In the included studies, Cox and logistic regression were used to construct models. The area under the receiver operating characteristic curve (AUC) of the model ranged from 0.670 to 0.904, and the number of predictors ranged from 4 to 11. The D-dimer index was included in five studies, but significant differences existed in optimal cutoff values from 0.0005 mg/L to 2.06 mg/L. Then, three studies validated the model externally, two studies only validated the model internally, and only one study validated the model using a combination of internal and external validation.

CONCLUSION

VTE risk prediction models for patients with lung cancer have received attention for no more than 5 years. The included model shows a good predictive effect and may help identify the risk population of VTE at an early stage. In the future, it is necessary to improve data modeling and statistical analysis methods, develop predictive models with good performance and low risk of bias, and focus on external validation and recalibration of models.

Development and internal validation of a risk prediction model for stress urinary incontinence throughout pregnancy: A multicenter retrospective longitudinal study in Indonesia

BACKGROUND

This study aimed to develop a risk prediction model for stress urinary incontinence (SUI) throughout pregnancy in Indonesian women.

METHODS

We conducted a multicenter retrospective longitudinal study involving pregnant women in Indonesia, who sought care at obstetrics clinics from January 2023 to March 2023, encompassing all stages of pregnancy. We collected data on their predictive factors and SUI outcome. SUI was diagnosed based on responses to the "leaks when you are physically active/exercising" criterion in the ICIQ-UI-SF questionnaire during our investigation of the participants. The models underwent internal validation using a bootstrapping method with 1000 resampling iterations to assess discrimination and calibration.

RESULTS

A total of 660 eligible pregnant women were recruited from the two study centers, with an overall SUI prevalence of 39% (258/660). The final model incorporated three predictive factors: BMI during pregnancy, constipation, and previous delivery mode. The area under the curve (AUROC) was 0.787 (95% CI: 0.751-0.823). According to the max Youden index, the optimal cut-off point was 44.6%, with a sensitivity of 79.9% and specificity of 65.9%. A discrimination slope of 0.213 was found.

CONCLUSION

The developed risk prediction model for SUI in pregnant women offers a valuable tool for early identification and intervention among high-risk SUI populations in Indonesian pregnant women throughout their pregnancies. These findings challenge the assumption that a high BMI and multiple previous deliveries are predictors of SUI in Indonesian women. Further research is recommended to validate the model in diverse populations and settings.

Exploring KRAS-mutant pancreatic ductal adenocarcinoma: a model validation study

Introduction

Pancreatic ductal adenocarcinoma (PDAC) has the highest mortality rate among all solid tumors. Tumorigenesis is promoted by the oncogene KRAS, and KRAS mutations are prevalent in patients with PDAC. Therefore, a comprehensive understanding of the interactions between KRAS mutations and PDAC may expediate the development of therapeutic strategies for reversing the progression of malignant tumors. Our study aims at establishing and validating a prediction model of KRAS mutations in patients with PDAC based on survival analysis and mRNA expression.

Methods

A total of 184 and 412 patients with PDAC from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC), respectively, were included in the study.

Results

After tumor mutation profile and copy number variation (CNV) analyses, we established and validated a prediction model of KRAS mutations, based on survival analysis and mRNA expression, that contained seven genes: CSTF2, FAF2, KIF20B, AKR1A1, APOM, KRT6C, and CD70. We confirmed that the model has a good predictive ability for the prognosis of overall survival (OS) in patients with KRAS-mutated PDAC. Then, we analyzed differential biological pathways, especially the ferroptosis pathway, through principal component analysis, pathway enrichment analysis, Gene Ontology (GO) enrichment analysis, and gene set enrichment analysis (GSEA), with which patients were classified into low- or high-risk groups. Pathway enrichment results revealed enrichment in the cytokine-cytokine receptor interaction, metabolism of xenobiotics by cytochrome P450, and viral protein interaction with cytokine and cytokine receptor pathways. Most of the enriched pathways are metabolic pathways predominantly enriched by downregulated genes, suggesting numerous downregulated metabolic pathways in the high-risk group. Subsequent tumor immune infiltration analysis indicated that neutrophil infiltration, resting CD4 memory T cells, and resting natural killer (NK) cells correlated with the risk score. After verifying that the seven gene expression levels in different KRAS-mutated pancreatic cancer cell lines were similar to that in the model, we screened potential drugs related to the risk score.

Discussion

This study established, analyzed, and validated a model for predicting the prognosis of PDAC based on risk stratification according to KRAS mutations, and identified differential pathways and highly effective drugs.

PAGE-B incorporating moderate HBV DNA levels predicts risk of HCC among patients entering into HBeAg-positive chronic hepatitis B

BACKGROUND & AIMS

Recent studies reported that moderate HBV DNA levels are significantly associated with hepatocellular carcinoma (HCC) risk in hepatitis B e antigen (HBeAg)-positive, non-cirrhotic patients with chronic hepatitis B (CHB). We aimed to develop and validate a new risk score to predict HCC development using baseline moderate HBV DNA levels in patients entering into HBeAg-positive CHB from chronic infection.

METHODS

This multicenter cohort study recruited 3,585 HBeAg-positive, non-cirrhotic patients who started antiviral treatment with entecavir or tenofovir disoproxil fumarate at phase change into CHB from chronic infection in 23 tertiary university-affiliated hospitals of South Korea (2012-2020). A new HCC risk score (PAGED-B) was developed (training cohort, n = 2,367) based on multivariable Cox models. Internal validation using bootstrap sampling and external validation (validation cohort, n = 1,218) were performed.

RESULTS

Sixty (1.7%) patients developed HCC (median follow-up, 5.4 years). In the training cohort, age, gender, platelets, diabetes and moderate HBV DNA levels (5.00-7.99 log10 IU/ml) were independently associated with HCC development; the PAGED-B score (based on these five predictors) showed a time-dependent AUROC of 0.81 for the prediction of HCC development at 5 years. In the validation cohort, the AUROC of PAGED-B was 0.85, significantly higher than for other risk scores (PAGE-B, mPAGE-B, CAMD, and REAL-B). When stratified by the PAGED-B score, the HCC risk was significantly higher in high-risk patients than in low-risk patients (sub-distribution hazard ratio = 8.43 in the training and 11.59 in the validation cohorts, all p <0.001).

CONCLUSIONS

The newly established PAGED-B score may enable risk stratification for HCC at the time of transition into HBeAg-positive CHB.

IMPACT AND IMPLICATIONS

In this study, we developed and validated a new risk score to predict hepatocellular carcinoma (HCC) development in patients entering into hepatitis B e antigen (HBeAg)-positive chronic hepatitis B (CHB) from chronic infection. The newly established PAGED-B score, which included baseline moderate HBV DNA levels (5-8 log10 IU/ml), improved on the predictive performance of prior risk scores. Based on a patient's age, gender, diabetic status, platelet count, and moderate DNA levels (5-8 log10 IU/ml) at the phase change into CHB from chronic infection, the PAGED-B score represents a reliable and easily available risk score to predict HCC development during the first 5 years of antiviral treatment in HBeAg-positive patients entering into CHB. With a scoring range from 0 to 12 points, the PAGED-B score significantly differentiated the 5-year HCC risk: low <7 points and high ≥7 points.

Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong

INTRODUCTION/OBJECTIVES

A non-laboratory-based pre-diabetes/diabetes mellitus (pre-DM/DM) risk prediction model developed from the Hong Kong Chinese population showed good external discrimination in a primary care (PC) population, but the estimated risk level was significantly lower than the observed incidence, indicating poor calibration. This study explored whether recalibrating/updating methods could improve the model's accuracy in estimating individuals' risks in PC.

METHODS

We performed a secondary analysis on the model's predictors and blood test results of 919 Chinese adults with no prior DM diagnosis recruited from PC clinics from April 2021 to January 2022 in HK. The dataset was randomly split in half into a training set and a test set. The model was recalibrated/updated based on a seven-step methodology, including model recalibrating, revising and extending methods. The primary outcome was the calibration of the recalibrated/updated models, indicated by calibration plots. The models' discrimination, indicated by the area under the receiver operating characteristic curves (AUC-ROC), was also evaluated.

RESULTS

Recalibrating the model's regression constant, with no change to the predictors' coefficients, improved the model's accuracy (calibration plot intercept: -0.01, slope: 0.69). More extensive methods could not improve any further. All recalibrated/updated models had similar AUC-ROCs to the original model.

CONCLUSION

The simple recalibration method can adapt the HK Chinese pre-DM/DM model to PC populations with different pre-test probabilities. The recalibrated model can be used as a first-step screening tool and as a measure to monitor changes in pre-DM/DM risks over time or after interventions.

Three molecular subtypes and a five-gene signature for hepatocellular carcinoma based on m7G-related classification

BACKGROUND

The current research investigated the heterogeneity of hepatocellular carcinoma (HCC) based on the expression of N7-methylguanosine (m7G)-related genes as a classification model and developed a risk model predictive of HCC prognosis, key pathological behaviors and molecular events of HCC.

METHODS

The RNA sequencing data of HCC were extracted from The Cancer Genome Atlas (TCGA)-live cancer (LIHC) database, hepatocellular carcinoman database (HCCDB) and Gene Expression Omnibus database, respectively. According to the expression level of 29 m7G-related genes, a consensus clustering analysis was conducted. The least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression algorithm were applied to create a risk prediction model based on normalized expression of five characteristic genes weighted by coefficients. Tumor microenvironment (TME) analysis was performed using the MCP-Counter, TIMER, CIBERSORT and ESTIMATE algorithms. The Tumor Immune Dysfunction and Exclusion algorithm was applied to assess the responses to immunotherapy in different clusters and risk groups. In addition, patient sensitivity to common chemotherapeutic drugs was determined by the biochemical half-maximal inhibitory concentration using the R package pRRophetic.

RESULTS

Three molecular subtypes of HCC were defined based on the expression level of m7G-associated genes, each of which had its specific survival rate, genomic variation status, TME status and immunotherapy response. In addition, drug sensitivity analysis showed that the C1 subtype was more sensitive to a number of conventional oncolytic drugs (including paclitaxel, imatinib, CGP-082996, pyrimethamine, salubrinal and vinorelbine). The current five-gene risk prediction model accurately predicted HCC prognosis and revealed the degree of somatic mutations, immune microenvironment status and specific biological events.

CONCLUSION

In this study, three heterogeneous molecular subtypes of HCC were defined based on m7G-related genes as a classification model, and a five-gene risk prediction model was created for predicting HCC prognosis, providing a potential assessment tool for understanding the genomic variation, immune microenvironment status and key pathological mechanisms during HCC development.

Risk prediction of poor wound healing in patients with thoracoscopic lung cancer resection with drainage tube

This work established a risk prediction (RP) model for poor wound healing (PWH) in patients with thoracoscopic lung cancer (LC) resection (TLCR) after drainage tube placement to explore its application effect. 359 patients with TLCR were categorized into a good wound healing group (GWH group, 275 cases) and a poor wound healing group (PWH group, 84 cases) based on incision healing condition. The independent prediction risk factors (IPRFs) of PWH were analyzed and a RP model was constructed. 70% of the patients were classified as the model group (Mod group) and 30% were in the validation group (Val group). Resolution of the RP model was evaluated by the area under receiver operating characteristic (ROC) curve (AUC). The Hosmer-Lemeshow goodness of fit (HLGF) test was employed to evaluate the calibration of RP model. Results from the multivariate logistic regression analysis (MLRA) showed that age, preoperative albumin levels, diabetes history, dressing change frequency, and type of wound cleaning fluid were independent risk factors (IRFs) for postoperative PWH (P<0.05). In the Mod group, AUC=0.758 (P<0.05, 95% CI=0.712-0.806), and HLGF test showed P=0.493. In the Val group, AUC=0.783 (P<0.05, 95% CI=0.675-0.834), and HLGF test showed P=0.189. In conclusion, the constructed model was convenient, feasible, and demonstrates good predictive performance for postoperative incision healing issue, holding practical value and applicability.

Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model

This study analyzes the distribution of pathogenic bacteria and their antimicrobial susceptibilities in elderly patients with cardiovascular diseases to identify risk factors for pulmonary infections. A risk prediction model is established, aiming to serve as a clinical tool for early prevention and management of pulmonary infections in this vulnerable population. A total of 600 patients were categorized into infected and uninfected groups. Independent risk factors such as older age, diabetes history, hypoproteinemia, invasive procedures, high cardiac function grade, and a hospital stay of ≥10 days were identified through logistic regression. A predictive model was constructed, with a Hosmer-Lemeshow goodness of fit (P = 0.236) and an area under the receiver operating characteristic curve of 0.795, demonstrating good discriminative ability. The model had 63.40% sensitivity and 82.80% specificity, with a cut-off value of 0.13. Our findings indicate that the risk score model is valid for identifying high-risk groups for pulmonary infection among elderly cardiovascular patients. The study contributes to the early prevention and control of pulmonary infections, potentially reducing infection rates in this vulnerable population.

Prediction of Parkinson's Disease Using Machine Learning Methods

The detection of Parkinson's disease (PD) in its early stages is of great importance for its treatment and management, but consensus is lacking on what information is necessary and what models should be used to best predict PD risk. In our study, we first grouped PD-associated factors based on their cost and accessibility, and then gradually incorporated them into risk predictions, which were built using eight commonly used machine learning models to allow for comprehensive assessment. Finally, the Shapley Additive Explanations (SHAP) method was used to investigate the contributions of each factor. We found that models built with demographic variables, hospital admission examinations, clinical assessment, and polygenic risk score achieved the best prediction performance, and the inclusion of invasive biomarkers could not further enhance its accuracy. Among the eight machine learning models considered, penalized logistic regression and XGBoost were the most accurate algorithms for assessing PD risk, with penalized logistic regression achieving an area under the curve of 0.94 and a Brier score of 0.08. Olfactory function and polygenic risk scores were the most important predictors for PD risk. Our research has offered a practical framework for PD risk assessment, where necessary information and efficient machine learning tools were highlighted.

An Integrative Pancreatic Cancer Risk Prediction Model in the UK Biobank

Pancreatic cancer (PaCa) is a lethal cancer with an increasing incidence, highlighting the need for early prevention strategies. There is a lack of a comprehensive PaCa predictive model derived from large prospective cohorts. Therefore, we have developed an integrated PaCa risk prediction model for PaCa using data from the UK Biobank, incorporating lifestyle-related, genetic-related, and medical history-related variables for application in healthcare settings. We used a machine learning-based random forest approach and a traditional multivariable logistic regression method to develop a PaCa predictive model for different purposes. Additionally, we employed dynamic nomograms to visualize the probability of PaCa risk in the prediction model. The top five influential features in the random forest model were age, PRS, pancreatitis, DM, and smoking. The significant risk variables in the logistic regression model included male gender (OR = 1.17), age (OR = 1.10), non-O blood type (OR = 1.29), higher polygenic score (PRS) (Q5 vs. Q1, OR = 2.03), smoking (OR = 1.82), alcohol consumption (OR = 1.27), pancreatitis (OR = 3.99), diabetes (DM) (OR = 2.57), and gallbladder-related disease (OR = 2.07). The area under the receiver operating curve (AUC) of the logistic regression model is 0.78. Internal validation and calibration performed well in both models. Our integrative PaCa risk prediction model with the PRS effectively stratifies individuals at future risk of PaCa, aiding targeted prevention efforts and supporting community-based cancer prevention initiatives.

Minimal improvement in coronary artery disease risk prediction in Chinese population using polygenic risk scores: evidence from the China Kadoorie Biobank

BACKGROUND

Several studies have reported that polygenic risk scores (PRSs) can enhance risk prediction of coronary artery disease (CAD) in European populations. However, research on this topic is far from sufficient in non-European countries, including China. We aimed to evaluate the potential of PRS for predicting CAD for primary prevention in the Chinese population.

METHODS

Participants with genome-wide genotypic data from the China Kadoorie Biobank were divided into training ( n = 28,490) and testing sets ( n = 72,150). Ten previously developed PRSs were evaluated, and new ones were developed using clumping and thresholding or LDpred method. The PRS showing the strongest association with CAD in the training set was selected to further evaluate its effects on improving the traditional CAD risk-prediction model in the testing set. Genetic risk was computed by summing the product of the weights and allele dosages across genome-wide single-nucleotide polymorphisms. Prediction of the 10-year first CAD events was assessed using hazard ratios (HRs) and measures of model discrimination, calibration, and net reclassification improvement (NRI). Hard CAD (nonfatal I21-I23 and fatal I20-I25) and soft CAD (all fatal or nonfatal I20-I25) were analyzed separately.

RESULTS

In the testing set, 1214 hard and 7201 soft CAD cases were documented during a mean follow-up of 11.2 years. The HR per standard deviation of the optimal PRS was 1.26 (95% CI:1.19-1.33) for hard CAD. Based on a traditional CAD risk prediction model containing only non-laboratory-based information, the addition of PRS for hard CAD increased Harrell's C index by 0.001 (-0.001 to 0.003) in women and 0.003 (0.001 to 0.005) in men. Among the different high-risk thresholds ranging from 1% to 10%, the highest categorical NRI was 3.2% (95% CI: 0.4-6.0%) at a high-risk threshold of 10.0% in women. The association of the PRS with soft CAD was much weaker than with hard CAD, leading to minimal or no improvement in the soft CAD model.

CONCLUSIONS

In this Chinese population sample, the current PRSs minimally changed risk discrimination and offered little improvement in risk stratification for soft CAD. Therefore, this may not be suitable for promoting genetic screening in the general Chinese population to improve CAD risk prediction.

Significance of RNA N6-methyladenosine regulators in the diagnosis and subtype classification of coronary heart disease using the Gene Expression Omnibus database

Background

Many investigations have revealed that alterations in m6A modification levels may be linked to coronary heart disease (CHD). However, the specific link between m6A alteration and CHD warrants further investigation.

Methods

Gene expression profiles from the Gene Expression Omnibus (GEO) databases. We began by constructing a Random Forest model followed by a Nomogram model, both aimed at enhancing our predictive capabilities on specific m6A markers. We then shifted our focus to identify distinct molecular subtypes based on the key m6A regulators and to discern differentially expressed genes between the unique m6A clusters. Following this molecular exploration, we embarked on an in-depth analysis of the biological characteristics associated with each m6A cluster, revealing profound differences between them. Finally, we delved into the identification and correlation analysis of immune cell infiltration across these clusters, emphasizing the potential interplay between m6A modification and the immune system.

Results

In this research, 37 important m6Aregulators were identified by comparing non-CHD and CHD patients from the GSE20680, GSE20681, and GSE71226 datasets. To predict the risk of CHD, seven candidate m6A regulators (CBLL1, HNRNPC, YTHDC2, YTHDF1, YTHDF2, YTHDF3, ZC3H13) were screened using the logistic regression model. Based on the seven possible m6A regulators, a nomogram model was constructed. An examination of decision curves revealed that CHD patients could benefit from the nomogram model. On the basis of the selected relevant m6A regulators, patients with CHD were separated into two m6A clusters (cluster1 and cluster2) using the consensus clustering approach. The Single Sample Gene Set Enrichment Analysis (ssGSEA) and CIBERSORT methods were used to estimate the immunological characteristics of two separate m6A Gene Clusters; the results indicated a close association between seven candidate genes and immune cell composition. The drug sensitivity of seven candidate regulators was predicted, and these seven regulators appeared in numerous diseases as pharmacological targets while displaying strong drug sensitivity.

Conclusion

m6A regulators play crucial roles in the development of CHD. Our research of m6A clusters may facilitate the development of novel molecular therapies and inform future immunotherapeutic methods for CHD.

A five necroptosis-related lncRNA signature predicts the prognosis of bladder cancer and identifies hot or cold tumors

Bladder cancer (BC) is a leading cause of male cancer-related deaths globally. Immunotherapy is showing promise as a treatment option for BC. Numerous studies suggested that necroptosis and long noncoding RNAs (lncRNAs) were critical players in the development of cancers and interacting with cancer immunity. However, the prognostic value of necroptosis-related lncRNAs and their impact on immunotherapeutic response in patients with BC have yet to be well examined. Thus, this study aims to find new biomarkers for predicting prognosis and determining immune subtypes of BC to select appropriate patients from a heterogeneous population. The clinicopathology and transcriptome information from The Cancer Genome Atlas (TCGA) was downloaded, and coexpression analysis was performed to identify necroptosis-related lncRNAs. Then LASSO regression was employed to construct a prediction signature. The signature performance was evaluated by Kaplan-Meier (K-M) method, Time-dependent receiver operating characteristics (ROC). The functional enrichment, immune infiltration, immune checkpoint activation, and the half-maximal inhibitory concentration (IC50) of common drugs in risk groups were compared. The consensus clustering analysis based on lncRNAs associated with necroptosis was made to get 2 clusters to identify hot and cold tumors further. Lastly, the immune response between cold and hot tumors was discussed. In this study, a model containing 5 necroptosis-related lncRNAs was constructed. The risk score distribution of these lncRNAs was compared between low- and high-risk groups in the training, testing, and entire sets. K-M analysis showed that the low-risk patients had significantly better prognosis. The area under the ROC curve (AUC) for the 1-, 3-, and 5-year ROC curves in the entire sets were 0.690, 0.709, and 0.722, respectively. High-risk patients were enriched in lncRNAs related to tumor immunity and had better immune cell infiltration and immune checkpoint activation. Hot tumors and cold tumors were effectively distinguished by clusters 1 and cluster 2, respectively. We developed a necroptosis-related signature based on 5 prognostic lncRNAs, expected to become a new tool for evaluating the prognosis of patients with BC and classifying hot or cold tumors, thus facilitating the development of precision therapy for BC.

Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA

Background

Dilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM.

Methods

The hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells.

Results

A total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples.

Conclusion

The current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM.

Validation of a pre-endoscopy risk score for predicting the presence of gastric intestinal metaplasia in a U.S. population

BACKGROUND AND AIMS

Surveillance of gastric intestinal metaplasia (GIM) may lead to early gastric cancer detection. Our purpose was to externally validate a predictive model for endoscopic GIM previously developed in a veteran population in a second U.S.

POPULATION

METHODS

We previously developed a pre-endoscopy risk model for detection of GIM using 423 GIM cases and 1796 control subjects from the Houston Veterans Affairs Hospital. The model included sex, age, race/ethnicity, smoking, and Helicobacter pylori infection with an area under the receiver-operating characteristic curve (AUROC) of .73 for GIM and .82 for extensive GIM. We validated this model in a second cohort of patients from 6 Catholic Health Initiative (CHI)-St Luke's hospitals (Houston, Tex, USA) from January to December 2017. Cases were defined as having GIM on any gastric biopsy sample and extensive GIM as involving both the antrum and corpus. We further optimized the model by pooling both cohorts and assessing discrimination using AUROC.

RESULTS

The risk model was validated in 215 GIM cases (55 with extensive GIM) and 2469 control subjects. Cases were older than control subjects (59.8 vs 54.7 years) with more nonwhites (59.1% vs 42.0%) and H pylori infections (23.7% vs 10.9%). The model applied to the CHI-St Luke's cohort had an AUROC of .62 (95% confidence interval [CI], .57-.66) for predicting GIM and of .71 (95% CI, .63-.79) for predicting extensive GIM. When the Veterans Affairs and CHI-St Luke's cohorts were pooled, discrimination of both models improved (GIM vs extensive GIM AUROC: .74 vs .82).

CONCLUSIONS

A pre-endoscopy risk prediction model was validated and updated using a second U.S. cohort with robust discrimination for endoscopic GIM. This model should be evaluated in other U.S. populations to risk-stratify patients for endoscopic GIM screening.

A nomogram for predicting breast cancer based on hematologic and ultrasound parameters

BACKGROUND

The aim of this study was to investigate the ultrasound and hematological indicators, subsequently utilizing them to predict breast cancer and construct predictive models and columnar plots.

METHODS

The clinical data of 200 patients with breast tumors receiving ultrasound and blood tests at Henan Provincial People's Hospital from January 2020 to January 2023 were collected. Patients were divided into training and validation sets at a 6:4 ratio using R language. Variables were screened using logistic regression, and a nomogram predicting breast cancer probability was constructed based on the training set. The predictive performance of the nomogram was evaluated in the validation set through receiver operating characteristic, calibration and decision curves. Model robustness was validated by bootstrap resampling.

RESULTS

Regression analysis revealed that maximum blood flow velocity within the breast mass ≥ 16.395 m/s, perfusion index ≥ 1.505, cancer antigen 15-3 ≥ 39.620 U/m, cancer antigen 125 ≥ 42.30 U/ml, carcinoembryonic antigen ≥ 6.520 ng/ml, Adler blood flow classification II & III, breast calcification present, and diameter of the lump > 2 cm were independent risk factors for breast cancer. Based on these ultrasonic parameters and blood indicators, the developed nomogram demonstrated excellent discrimination in both the training set (AUC = 0.917) and validation set (AUC = 0.844). The calibration plot showed high consistency between the nomogram-predicted and the actual results. Decision curve analysis indicated higher net benefit of this model.

CONCLUSIONS

The nomogram developed in this study demonstrated solid predictive abilities for breast malignancy, indicating potential clinical value pending further research.

Impact of Social Support on Cardiovascular Risk Prediction Models: A Systematic Review

Cardiovascular diseases (CVD) stand as the primary causes of both mortality and morbidity on a global scale. Social factors such as low social support can increase the risk of developing heart diseases and have shown poor prognosis in cardiac patients. Resources such as PubMed and Google Scholar were searched using a boolean algorithm for articles published between 2003 and 2023. Eligible articles showed an association between social support and cardiovascular risks. A systematic review was conducted using the guidance published in the Cochrane Prognosis Method Group and the PRISMA checklist, for reviews of selected articles. A total of five studies were included in our final analysis. Overall, we found that participants with low social support developed cardiovascular events, and providing a good support system can decrease the risk of readmission in patients with a history of CVD. We also found that integrating social determinants in the cardiovascular risk prediction model showed improvement in accessing the risk. Population with good social support showed low mortality and decreased rate of readmission. There are various prediction models, but the social determinants are not primarily included while calculating the algorithms. Although it has been proven in multiple studies that including the social determinants of health (SDOH) improves the accuracy of cardiovascular risk prediction models. Hence, the inclusion of SDOH should be highly encouraged.

Can spirometry improve the performance of cardiovascular risk model in high-risk Eastern European countries?

Aims

Impaired lung function has been strongly associated with cardiovascular disease (CVD) events. We aimed to assess the additive prognostic value of spirometry indices to the risk estimation of CVD events in Eastern European populations in this study.

Methods

We randomly selected 14,061 individuals with a mean age of 59 ± 7.3 years without a previous history of cardiovascular and pulmonary diseases from population registers in the Czechia, Poland, and Lithuania. Predictive values of standardised Z-scores of forced expiratory volume measured in 1 s (FEV1), forced vital capacity (FVC), and FEV1 divided by height cubed (FEV1/ht3) were tested. Cox proportional hazards models were used to estimate hazard ratios (HRs) of CVD events of various spirometry indices over the Framingham Risk Score (FRS) model. The model performance was evaluated using Harrell's C-statistics, likelihood ratio tests, and Bayesian information criterion.

Results

All spirometry indices had a strong linear relation with the incidence of CVD events (HR ranged from 1.10 to 1.12 between indices). The model stratified by FEV1/ht3 tertiles had a stronger link with CVD events than FEV1 and FVC. The risk of CVD event for the lowest vs. highest FEV1/ht3 tertile among people with low FRS was higher (HR: 2.35; 95% confidence interval: 1.96-2.81) than among those with high FRS. The addition of spirometry indices showed a small but statistically significant improvement of the FRS model.

Conclusions

The addition of spirometry indices might improve the prediction of incident CVD events particularly in the low-risk group. FEV1/ht3 is a more sensitive predictor compared to other spirometry indices.

Establishment and validation of a prognostic nomogram for long-term low vision after diabetic vitrectomy

Purpose

We aimed to evaluate the risk factors and develop a prognostic nomogram of long-term low vision after diabetic vitrectomy.

Methods

This retrospective study included 186 patients (250 eyes) that underwent primary vitrectomy for proliferative diabetic retinopathy with a minimum follow-up period of one year. Patients were assigned to the training cohort (200 eyes) or validation cohort (50 eyes) at a 4:1 ratio randomly. Based on a cutoff value of 0.3 in best-corrected visual acuity (BCVA) measurement, the training cohort was separated into groups with or without low vision. Univariate and multivariate logistic regression analyses were performed on preoperative systemic and ocular characteristics to develop a risk prediction model and nomogram. The calibration curve and the area under the receiver operating characteristic curves (AUC) were used to evaluate the calibration and discrimination of the model. The nomogram was internally validated using the bootstrapping method, and it was further verified in an external cohort.

Results

Four independent risk factors were selected by stepwise forward regression, including tractional retinal detachment (β=1.443, OR=4.235, P<0.001), symptom duration ≥6 months (β=0.954, OR=2.595, P=0.004), preoperative BCVA measurement (β=0.540, OR=1.716, P=0.033), and hypertension (β=0.645, OR=1.905, P=0.044). AUC values of 0.764 (95% CI: 0.699-0.829) in the training cohort and 0.755 (95% CI: 0.619-0.891) in the validation cohort indicated the good predictive ability of the model.

Conclusion

The prognostic nomogram established in this study is useful for predicting long-term low vision after diabetic vitrectomy.

Performance of GAP and ILD-GAP models in predicting lung transplant or death in interstitial pneumonia with autoimmune features

OBJECTIVES

To assess the ability of two risk prediction models in interstitial lung disease (ILD) to predict death or lung transplantation in a cohort of patients with interstitial pneumonia with autoimmune features (IPAF).

METHODS

We performed a retrospective cohort study of adults with IPAF at an academic medical center. The primary outcome was a composite of lung transplantation or death. We applied the patient data to the previously described GAP and ILD-GAP models to determine the ability of these models to predict the composite outcome. Model discrimination was assessed using the c-index, and model calibration was determined by comparing the incidence ratios of observed versus expected deaths.

RESULTS

Ninety-four patients with IPAF were included. Mean (standard deviation) age was 58 (13.5) years and the majority were female (62%). The majority met serologic and morphologic criteria for IPAF (94% and 91%, respectively). The GAP model had a c-index of 0.664 (95% confidence interval [CI] 0.547-0.781), while the ILD-GAP model had a c-index of 0.569 (95% CI 0.440-0.697). In those with GAP stage 1 or GAP stage 2 disease, calibration of the GAP model was satisfactory at 2 and 3 years for the cumulative end point of lung transplantation or death.

CONCLUSION

In patients with IPAF, the GAP model performed well as a predictor of lung transplantation or death at 2 years and 3 years from ILD diagnosis in patients with GAP stage 1 and GAP stage 2 disease.

Development of a Cardiovascular Disease Risk Prediction Model: A Preliminary Retrospective Cohort Study of a Patient Sample in Saudi Arabia

Saudi Arabia has an alarmingly high incidence of cardiovascular disease (CVD) and its associated risk factors. To effectively assess CVD risk, it is essential to develop tailored models for diverse regions and ethnicities using local population variables. No CVD risk prediction model has been locally developed. This study aims to develop the first 10-year CVD risk prediction model for Saudi adults aged 18 to 75 years. The electronic health records of Saudi male and female patients aged 18 to 75 years, who were seen in primary care settings between 2002 and 2019, were reviewed retrospectively via the Integrated Clinical Information System (ICIS) database (from January 2002 to February 2019). The Cox regression model was used to identify the risk factors and develop the CVD risk prediction model. Overall, 451 patients were included in this study, with a mean follow-up of 12.05 years. Thirty-five (7.7%) patients developed a CVD event. The following risk factors were included: fasting blood sugar (FBS) and high-density lipoprotein cholesterol (HDL-c), heart failure, antihyperlipidemic therapy, antithrombotic therapy, and antihypertension therapy. The Bayesian information criterion (BIC) score was 314.4. This is the first prediction model developed in Saudi Arabia and the second in any Arab country after the Omani study. We assume that our CVD predication model will have the potential to be used widely after the validation study.

Risk Prediction Model of Early-Onset Preeclampsia Based on Risk Factors and Routine Laboratory Indicators

Background: Globally, 10-15% of maternal deaths are statistically attributable to preeclampsia. Compared with late-onset PE, the severity of early-onset PE remains more harmful with higher morbidity and mortality. Objective: To establish an early-onset preeclampsia prediction model by clinical characteristics, risk factors and routine laboratory indicators were investigated from pregnant women at 6 to 10 gestational weeks. Methods: The clinical characteristics, risk factors, and 38 routine laboratory indicators (6-10 weeks of gestation) including blood lipids, liver and kidney function, coagulation, blood count, and other indicators of 91 early-onset preeclampsia patients and 709 normal controls without early-onset preeclampsia from January 2010 to May 2021 in Peking University Third Hospital (PUTH) were retrospectively analyzed. A logistic regression, decision tree model, and support vector machine (SVM) model were applied for establishing prediction models, respectively. ROC curves were drawn; area under curve (AUCROC), sensitivity, and specificity were calculated and compared. Results: There were statistically significant differences in the rates of diabetes, antiphospholipid syndrome (APS), kidney disease, obstructive sleep apnea (OSAHS), primipara, history of preeclampsia, and assisted reproductive technology (ART) (p < 0.05). Among the 38 routine laboratory indicators, there were no significant differences in the levels of PLT/LYM, NEU/LYM, TT, D-Dimer, FDP, TBA, ALP, TP, ALB, GLB, UREA, Cr, P, Cystatin C, HDL-C, Apo-A1, and Lp(a) between the two groups (p > 0.05). The levels of the rest indicators were all statistically different between the two groups (p < 0.05). If only 12 risk factors of PE were analyzed with the logistic regression, decision tree model, and support vector machine (SVM), and the AUCROC were 0.78, 0.74, and 0.66, respectively, while 12 risk factors of PE and 38 routine laboratory indicators were analyzed with the logistic regression, decision tree model, and support vector machine (SVM), and the AUCROC were 0.86, 0.77, and 0.93, respectively. Conclusions: The efficacy of clinical risk factors alone in predicting early-onset preeclampsia is not high while the efficacy increased significantly when PE risk factors combined with routine laboratory indicators. The SVM model was better than logistic regression model and decision tree model in early prediction of early-onset preeclampsia incidence.

Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study

BACKGROUND

Stroke has multiple modifiable and nonmodifiable risk factors and represents a leading cause of death globally. Understanding the complex interplay of stroke risk factors is thus not only a scientific necessity but a critical step toward improving global health outcomes.

OBJECTIVE

We aim to assess the performance of explainable machine learning models in predicting stroke risk factors using real-world cohort data by comparing explainable machine learning models with conventional statistical methods.

METHODS

This retrospective cohort included high-risk patients from Ramathibodi Hospital in Thailand between January 2010 and December 2020. We compared the performance and explainability of logistic regression (LR), Cox proportional hazard, Bayesian network (BN), tree-augmented Naïve Bayes (TAN), extreme gradient boosting (XGBoost), and explainable boosting machine (EBM) models. We used multiple imputation by chained equations for missing data and discretized continuous variables as needed. Models were evaluated using C-statistics and F1-scores.

RESULTS

Out of 275,247 high-risk patients, 9659 (3.5%) experienced a stroke. XGBoost demonstrated the highest performance with a C-statistic of 0.89 and an F1-score of 0.80 followed by EBM and TAN with C-statistics of 0.87 and 0.83, respectively; LR and BN had similar C-statistics of 0.80. Significant factors associated with stroke included atrial fibrillation (AF), hypertension (HT), antiplatelets, HDL, and age. AF, HT, and antihypertensive medication were common significant factors across most models, with AF being the strongest factor in LR, XGBoost, BN, and TAN models.

CONCLUSIONS

Our study developed stroke prediction models to identify crucial predictive factors such as AF, HT, or systolic blood pressure or antihypertensive medication, anticoagulant medication, HDL, age, and statin use in high-risk patients. The explainable XGBoost was the best model in predicting stroke risk, followed by EBM.

Risk prediction model construction for post myocardial infarction heart failure by blood immune B cells

Background

Myocardial infarction (MI) is a common cardiac condition with a high incidence of morbidity and mortality. Despite extensive medical treatment for MI, the development and outcomes of post-MI heart failure (HF) continue to be major factors contributing to poor post-MI prognosis. Currently, there are few predictors of post-MI heart failure.

Methods

In this study, we re-examined single-cell RNA sequencing and bulk RNA sequencing datasets derived from the peripheral blood samples of patients with myocardial infarction, including patients who developed heart failure and those who did not develop heart failure after myocardial infarction. Using marker genes of the relevant cell subtypes, a signature was generated and validated using relevant bulk datasets and human blood samples.

Results

We identified a subtype of immune-activated B cells that distinguished post-MI HF patients from non-HF patients. Polymerase chain reaction was used to confirm these findings in independent cohorts. By combining the specific marker genes of B cell subtypes, we developed a prediction model of 13 markers that can predict the risk of HF in patients after myocardial infarction, providing new ideas and tools for clinical diagnosis and treatment.

Conclusion

Sub-cluster B cells may play a significant role in post-MI HF. We found that the STING1, HSPB1, CCL5, ACTN1, and ITGB2 genes in patients with post-MI HF showed the same trend of increase as those without post-MI HF.

New paradigm in the treatment of myositis-associated interstitial lung disease

INTRODUCTION

Interstitial lung disease (ILD) is the leading cause of mortality in idiopathic inflammatory myopathies or myositis. Clinical characteristics, including the course of ILD, rate of progression, radiological and pathohistological morphologies, extent and distribution of inflammation and fibrosis, responses to treatment, recurrence rate, and prognosis, are highly variable among myositis patients. A standard practice for ILD management in myositis patients has not yet been established.

AREAS COVERED

Recent studies have demonstrated the stratification of patients with myositis-associated ILD into more homogeneous groups based on the disease behavior and myositis-specific autoantibody (MSA) profile, leading to better prognoses and prevention of the burden of organ damage. This review introduces a new paradigm in the management of myositis-associated ILD based on research findings from relevant literature selected by a search of PubMed as of January 2023, as well as expert opinions.

EXPERT OPINION

Managing strategies for myositis-associated ILD are being established to stratify patients based on the severity of ILD and the prediction of prognosis based on the disease behavior and MSA profile. The development of a precision medicine treatment approach will provide benefits to all relevant communities.

Added value of patient- and drug-related factors to stratify drug-drug interaction alerts for risk of QT prolongation: Development and validation of a risk prediction model

AIMS

Many clinical decision support systems trigger warning alerts for drug-drug interactions potentially leading to QT prolongation and torsades de pointes (QT-DDIs). Unfortunately, there is overalerting and underalerting because stratification is only based on a fixed QT-DDI severity level. We aimed to improve QT-DDI alerting by developing and validating a risk prediction model considering patient- and drug-related factors.

METHODS

We fitted 31 predictor candidates to a stepwise linear regression for 1000 bootstrap samples and selected the predictors present in 95% of the 1000 models. A final linear regression model with those variables was fitted on the original development sample (350 QT-DDIs). This model was validated on an external dataset (143 QT-DDIs). Both true QTc and predicted QTc were stratified into three risk levels (low, moderate and high). Stratification of QT-DDIs could be appropriate (predicted risk = true risk), acceptable (one risk level difference) or inappropriate (two risk levels difference).

RESULTS

The final model included 11 predictors with the three most important being use of antiarrhythmics, age and baseline QTc. Comparing current practice to the prediction model, appropriate stratification increased significantly from 37% to 54% appropriate QT-DDIs (increase of 17.5% on average [95% CI +5.4% to +29.6%], p_adj  = 0.006) and inappropriate stratification decreased significantly from 13% to 1% inappropriate QT-DDIs (decrease of 11.2% on average [95% CI -17.7% to -4.7%], p_adj  ≤ 0.001).

CONCLUSION

The prediction model including patient- and drug-related factors outperformed QT alerting based on QT-DDI severity alone and therefore is a promising strategy to improve DDI alerting.

Validation of a risk prediction model for early chronic kidney disease in patients with type 2 diabetes: Data from the German/Austrian Diabetes Prospective Follow-up registry

AIM

To validate a recently proposed risk prediction model for chronic kidney disease (CKD) in type 2 diabetes (T2D).

MATERIALS AND METHODS

Subjects from the German/Austrian Diabetes Prospective Follow-up (DPV) registry with T2D, normoalbuminuria, an estimated glomerular filtration rate of 60 ml/min/1.73m² or higher and aged 39-75 years were included. Prognostic factors included age, body mass index (BMI), smoking status and HbA1c. Subjects were categorized into low, moderate, high and very high-risk groups. Outcome was CKD occurrence.

RESULTS

Subjects (n = 10 922) had a mean age of 61 years, diabetes duration of 6 years, BMI of 31.7 kg/m² , HbA1c of 6.9% (52 mmol/mol); 9.1% had diabetic retinopathy and 16.3% were smokers. After the follow-up (~59 months), 37.4% subjects developed CKD. The area under the curve (AUC; unadjusted base model) was 0.58 (95% CI 0.57-0.59). After adjustment for diabetes and follow-up duration, the AUC was 0.69 (95% CI 0.68-0.70), indicating improved discrimination. After follow-up, 15.0%, 20.1%, 27.7% and 40.2% patients in the low, moderate, high and very high-risk groups, respectively, had developed CKD. Increasing risk score correlated with increasing cumulative risk of incident CKD over a median of 4.5 years of follow-up (P < .0001).

CONCLUSIONS

The predictive model achieved moderate discrimination but good calibration in a German/Austrian T2D population, suggesting that the model may be relevant for determining CKD risk.

A Clinical Prediction Rule for Thrombosis in Critically Ill COVID-19 Patients: Step 1 Results of the Thromcco Study

The incidence of thrombosis in COVID-19 patients is exceptionally high among intensive care unit (ICU)-admitted individuals. We aimed to develop a clinical prediction rule for thrombosis in hospitalized COVID-19 patients. Data were taken from the Thromcco study (TS) database, which contains information on consecutive adults (aged ≥ 18) admitted to eight Spanish ICUs between March 2020 and October 2021. Diverse logistic regression model analysis, including demographic data, pre-existing conditions, and blood tests collected during the first 24 h of hospitalization, was performed to build a model that predicted thrombosis. Once obtained, the numeric and categorical variables considered were converted to factor variables giving them a score. Out of 2055 patients included in the TS database, 299 subjects with a median age of 62.4 years (IQR 51.5-70) (79% men) were considered in the final model (SE = 83%, SP = 62%, accuracy = 77%). Seven variables with assigned scores were delineated as age 25-40 and ≥70 = 12, age 41-70 = 13, male = 1, D-dimer ≥ 500 ng/mL = 13, leukocytes ≥ 10 × 10³/µL = 1, interleukin-6 ≥ 10 pg/mL = 1, and C-reactive protein (CRP) ≥ 50 mg/L = 1. Score values ≥28 had a sensitivity of 88% and specificity of 29% for thrombosis. This score could be helpful in recognizing patients at higher risk for thrombosis, but further research is needed.

Construction and validation of a risk prediction model for aromatase inhibitor-associated bone loss

Purpose

To establish a high-risk prediction model for aromatase inhibitor-associated bone loss (AIBL) in patients with hormone receptor-positive breast cancer.

Methods

The study included breast cancer patients who received aromatase inhibitor (AI) treatment. Univariate analysis was performed to identify risk factors associated with AIBL. The dataset was randomly divided into a training set (70%) and a test set (30%). The identified risk factors were used to construct a prediction model using the eXtreme gradient boosting (XGBoost) machine learning method. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods were used for comparison. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the model in the test dataset.

Results

A total of 113 subjects were included in the study. Duration of breast cancer, duration of aromatase inhibitor therapy, hip fracture index, major osteoporotic fracture index, prolactin (PRL), and osteocalcin (OC) were found to be independent risk factors for AIBL (p < 0.05). The XGBoost model had a higher AUC compared to the logistic model and LASSO model (0.761 vs. 0.716, 0.691).

Conclusion

The XGBoost model outperformed the logistic and LASSO models in predicting the occurrence of AIBL in patients with hormone receptor-positive breast cancer receiving aromatase inhibitors.

Predicting risk of peripartum blood transfusion during vaginal and cesarean delivery: A risk prediction model

OBJECTIVE

The objective of this study is to develop a model that will help predict the risk of blood transfusion using information available prior to delivery.

STUDY DESIGN

The study is a secondary analysis of the Consortium on Safe Labor registry. Women who had a delivery from 2002 to 2008 were included. Pre-delivery variables that had significant associations with transfusion were included in a multivariable logistic regression model predicting transfusion. The prediction model was internally validated using randomly selected samples from the same population of women.

RESULTS

Of 156,572 deliveries, 5,463 deliveries (3.5%) required transfusion. Women who had deliveries requiring transfusion were more likely to have a number of comorbidities such as preeclampsia (6.3% versus 4.1%, OR 1.21, 95% CI 1.08-1.36), placenta previa (1.8% versus 0.4%, OR 4.11, 95% CI 3.25-5.21) and anemia (10.6% versus 5.4%, OR 1.30, 95% CI 1.21-1.41). Transfusion was least likely to occur in university teaching hospitals compared to community hospitals. The c statistic was 0.71 (95% CI 0.70-0.72) in the derivation sample. The most salient predictors of transfusion included type of hospital, placenta previa, multiple gestations, diabetes mellitus, anemia, asthma, previous births, preeclampsia, type of insurance, age, gestational age, and vertex presentation. The model was well-calibrated and showed strong internal validation.

CONCLUSION

The model identified independent risk factors that can help predict the risk of transfusion prior to delivery. If externally validated in another dataset, this model can assist health care professionals counsel patients and prepare facilities/resources to reduce maternal morbidity.

Risk Prediction Models and Novel Prognostic Factors for Heart Failure with Preserved Ejection Fraction: A Systematic and Comprehensive Review

BACKGROUND

Patients with heart failure with preserved ejection fraction (HFpEF) have large individual differences, unclear risk stratification, and imperfect treatment plans. Risk prediction models are helpful for the dynamic assessment of patients' prognostic risk and early intensive therapy of high-risk patients. The purpose of this study is to systematically summarize the existing risk prediction models and novel prognostic factors for HFpEF, to provide a reference for the construction of convenient and efficient HFpEF risk prediction models.

METHODS

Studies on risk prediction models and prognostic factors for HFpEF were systematically searched in relevant databases including PubMed and Embase. The retrieval time was from inception to February 1, 2023. The Quality in Prognosis Studies (QUIPS) tool was used to assess the risk of bias in included studies. The predictive value of risk prediction models for end outcomes was evaluated by sensitivity, specificity, the area under the curve, C-statistic, C-index, etc. In the literature screening process, potential novel prognostic factors with high value were explored.

RESULTS

A total of 21 eligible HFpEF risk prediction models and 22 relevant studies were included. Except for 2 studies with a high risk of bias and 2 studies with a moderate risk of bias, other studies that proposed risk prediction models had a low risk of bias overall. Potential novel prognostic factors for HFpEF were classified and described in terms of demographic characteristics (age, sex, and race), lifestyle (physical activity, body mass index, weight change, and smoking history), laboratory tests (biomarkers), physical inspection (blood pressure, electrocardiogram, imaging examination), and comorbidities.

CONCLUSION

It is of great significance to explore the potential novel prognostic factors of HFpEF and build a more convenient and efficient risk prediction model for improving the overall prognosis of patients. This review can provide a substantial reference for further research.

Construction and validation of risk prediction model for gestational diabetes based on a nomogram

OBJECTIVE

To construct a model to predict the risk of gestational diabetes mellitus (GDM) based on a nomogram and verify it.

METHODS

Data from 182 patients with GDM treated in Xi'an International Medical Center Hospital from January 2018 to May 2021 were retrospectively analyzed. A total of 491 normal parturients who underwent physical examination in Xi'an International Medical Center Hospital during the same period were selected as controls. With a ratio of 7:3, patients with GDM were divided into a training group (n=128) and a verification (n=54) group, and 491 normal parturients were divided into a training control group (n=344) and a verification control group (n=147). Clinical data were collected, and risk factors for GDM were analyzed by logistic regression. R language was used to construct a prognostic prediction nomogram model for GDM, and a receiver operating characteristic curve was employed to evaluate the accuracy of this nomogram model in predicting the prognosis of GDM.

RESULTS

Univariate analysis revealed that age, body mass index (BMI), family history of diabetes, hemoglobin, triglycerides, serum ferritin, and fasting blood glucose in the first trimester were different between the training group and the training control group (P<0.05). Multivariate analysis revealed that age, BMI, hemoglobin, triglycerides, serum ferritin, and fasting blood glucose in the first trimester were independent risk factors for GDM (P<0.05). Based on a logistic regression equation, the risk formula was -5.971 + 1.054 * age + 1.133 * BMI + 1.763 * hemoglobin + 1.260 * triglycerides + 3.041 * serum ferritin + 1.756 * fasting blood glucose in the first trimester. The area under the curve for predicting the risk of GDM in the training group was 0.920, and that of the validation group was 0.753.

CONCLUSION

Age, BMI, hemoglobin, serum ferritin, and fasting blood glucose in the first trimester are risk factors for GDM.

Risk prediction model for major adverse cardiovascular events (MACE) during hospitalization in patients with coronary heart disease based on myocardial energy metabolic substrate

Background

The early attack of coronary heart disease (CHD) is very hidden, and clinical symptoms generally do not appear until cardiovascular events occur. Therefore, an innovative method is needed to judge the risk of cardiovascular events and guide clinical decision conveniently and sensitively. The purpose of this study is to find out the risk factors related to MACE during hospitalization. In order to develop and verify the prediction model of energy metabolism substrates, and establish a nomogram to predict the incidence of MACE during hospitalization and evaluate their performance.

Methods

The data were collected from the medical record data of Guang'anmen Hospital. This review study was collected the comprehensive clinical data of 5,935 adult patients hospitalized in the cardiovascular department from 2016 to 2021. The outcome index was the MACE during hospitalization. According to the occurrence of MACE during hospitalization, these data were divided into MACE group (n = 2,603) and non-MACE group (n = 425). Logistic regression was used to screen risk factors, and establish the nomogram to predict the risk of MACE during hospitalization. Calibration curve, C index and decision curve were used to evaluate the prediction model, and drawn ROC curve to find the best boundary value of risk factors.

Results

The logistic regression model was used to establish a risk model. Univariate logistic regression model was mainly used to screen the factors significantly related to MACE during hospitalization in the training set (each variable is put into the model in turn). According to the factors with statistical significance in univariate logistic regression, five cardiac energy metabolism risk factors, including age, albumin(ALB), free fatty acid(FFA), glucose(GLU) and apolipoprotein A1(ApoA1), were finally input into the multivariate logistic regression model as the risk model, and their nomogram were drawn. The sample size of the training set was 2,120, the sample size of the validation set was 908. The C index of the training set is 0.655 [0.621,0.689], and the C index of the validation set was 0.674 [0.623,0.724]. The calibration curve and clinical decision curve show that the model performs well. The ROC curve was used to establish the best boundary value of the five risk factors, which could quantitatively present the changes of cardiac energy metabolism substrate, and finally achieved prediction of MACE during hospitalization conveniently and sensitively.

Conclusion

Age, albumin, free fatty acid, glucose and apolipoprotein A1 are independent factors of CHD in MACE during hospitalization. The nomogram based on the above factors of myocardial energy metabolism substrate provides prognosis prediction accurately.

A Nomogram for Predicting Unplanned Intraoperative Hypothermia in Patients With Colorectal Cancer Undergoing Laparoscopic Colorectal Procedures

Unplanned intraoperative hypothermia is a complication that can lead to a variety of negative outcomes, such as cardiovascular events. We aimed to develop and validate an intraoperative hypothermia risk prediction nomogram for patients with colorectal cancer undergoing laparoscopic colorectal procedures. We conducted a prospective cohort study with 1,091 patients (ie, 765 in the training cohort, 326 in the validation cohort) from October 2020 to November 2021. We included six predictors in the nomogram model: body mass index, diabetes diagnosis, ambient temperature, ambient humidity, duration of surgery, and use of a forced-air warmer. The model performed well, and the area under the curve was 0.855. These results, together with an external validation value, mean that health care professionals can use the nomogram to calculate the intraoperative hypothermia risk for patients undergoing laparoscopic colorectal procedures and make clinical decisions based on the results.