Cardiovascular Risk Assessment Using Artificial Intelligence-Enabled Event Adjudication and Hematologic Predictors

Linear and non-linear relationships between red blood cell indices and cardiovascular risk factors: findings from the China National Health Survey

INTRODUCTION

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Red blood cell indices (RBIs) are associated with CVD risk factors (CRFs) and easy to test, making them useful as a screening tool for early identification of individuals at high risk for CVDs.

METHODS

Data from 31,781 participants in the China National Health Survey conducted from 2012 to 2017 were analyzed. Linear and non-linear relationships between RBIs and CRFs (hyperuricemia, diabetes, dyslipidemia) were assessed using restricted cubic splines. Propensity score weighting was used to balance confounders between RBI groups in the multivariable logistic regression models.

RESULTS

Hemoglobin concentration, red blood cell count and hematocrit all showed a significant linear dose-response association with all CRFs (p values < 0.001). Higher RBIs levels were associated with increased risk of hyperuricemia, diabetes, high LDL, high triglycerides, and high total cholesterol, but decreased HDL. For example, compared to the lowest quantile of HGB, the highest quantile had a 26% (13-40%) higher risk for hyperuricemia, a 43% (25-63%) higher risk of diabetes, 87% (61%-1.18 fold) higher risk of high LDL, and 68% (52-85%) higher risk of high triglycerides. Non-linear relationships were revealed between RBIs and most CRFs except uric acid and glucose. Sex differences were observed, with stronger associations between RBIs and hyperuricemia in women but stronger links with high LDL in men.

CONCLUSIONS

Elevated RBIs indicated higher risk of multiple CRFs. These findings suggest incorporating RBIs into CVD screening strategies to facilitate early prevention efforts, with consideration of sex differences.

Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism

Background

Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients.

Methods

The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019-2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019-2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan-Meier survival curves.

Findings

Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95-0.99) for internal validation. External validation further confirmed the RF model's effectiveness, yielding an AUC of 0.97 (95% CI 0.94-0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27 ± 3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients.

Interpretation

This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies.

Funding

This work is supported by the National Natural Science Foundation of China (NO.81974315); Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).

Development and Validation of the CANHEART Population-Based Laboratory Prediction Models for Atherosclerotic Cardiovascular Disease

BACKGROUND

Prediction of atherosclerotic cardiovascular disease (ASCVD) in primary prevention assessments exclusively with laboratory results may facilitate automated risk reporting and improve uptake of preventive therapies.

OBJECTIVE

To develop and validate sex-specific prediction models for ASCVD using age and routine laboratory tests and compare their performance with that of the pooled cohort equations (PCEs).

DESIGN

Derivation and validation of the CANHEART (Cardiovascular Health in Ambulatory Care Research Team) Lab Models.

SETTING

Population-based cohort study in Ontario, Canada.

PARTICIPANTS

A derivation and internal validation cohort of adults aged 40 to 75 years without cardiovascular disease from April 2009 to December 2015; an external validation cohort of primary care patients from January 2010 to December 2014.

MEASUREMENTS

Age and laboratory predictors measured in the outpatient setting included serum total cholesterol, high-density lipoprotein cholesterol, triglycerides, hemoglobin, mean corpuscular volume, platelets, leukocytes, estimated glomerular filtration rate, and glucose. The ASCVD outcomes were defined as myocardial infarction, stroke, and death from ischemic heart or cerebrovascular disease within 5 years.

RESULTS

Sex-specific models were developed and internally validated in 2 160 497 women and 1 833 147 men. They were well calibrated, with relative differences less than 1% between mean predicted and observed risk for both sexes. The c-statistic was 0.77 in women and 0.71 in men. External validation in 31 697 primary care patients showed a relative difference less than 14% and an absolute difference less than 0.3 percentage points in mean predicted and observed risks for both sexes. The c-statistics for the laboratory models were 0.72 for both sexes and were not statistically significantly different from those for the PCEs in women (change in c-statistic, -0.01 [95% CI, -0.03 to 0.01]) or men (change in c-statistic, -0.01 [CI, -0.04 to 0.02]).

LIMITATION

Medication use was not available at the population level.

CONCLUSION

The CANHEART Lab Models predict ASCVD with similar accuracy to more complex models, such as the PCEs.

PRIMARY FUNDING SOURCE

Canadian Institutes of Health Research.

The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare

INTRODUCTION

Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice.

AREAS COVERED

In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP.

EXPERT OPINION

The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.

Old and Novel Predictors for Cardiovascular Risk in Diabetic Foot Syndrome—A Narrative Review

Diabetic foot syndrome (DFS) is a complication associated with diabetes that has a strong negative impact, both medically and socio-economically. Recent epidemiological data show that one in six patients with diabetes will develop an ulcer in their lifetime. Vascular complications associated with diabetic foot have multiple prognostic implications in addition to limiting functional status and leading to decreased quality of life for these patients. We searched the electronic databases of PubMed, MEDLINE and EMBASE for studies that evaluated the role of DFS as a cardiovascular risk factor through the pathophysiological mechanisms involved, in particular the inflammatory ones and the associated metabolic changes. In the era of evidence-based medicine, the management of these cases in multidisciplinary teams of “cardio-diabetologists” prevents the occurrence of long-term disabling complications and has prognostic value for cardiovascular morbidity and mortality among diabetic patients. Identifying artificial-intelligence-based cardiovascular risk prediction models or conducting extensive clinical trials on gene therapy or potential therapeutic targets promoted by in vitro studies represent future research directions with a modulating role on the risk of morbidity and mortality in patients with DFS.