PDF(Pubmed)
Abstract：
UNASSIGNED: Predictive models incorporating relevant clinical and social features can provide meaningful insights into complex interrelated mechanisms of cardiovascular disease (CVD) risk and progression and the influence of environmental exposures on adverse outcomes. The purpose of this targeted review (2018-2019) was to examine the extent to which present-day advanced analytics, artificial intelligence, and machine learning models include relevant variables to address potential biases that inform care, treatment, resource allocation, and management of patients with CVD.
UNASSIGNED: PubMed literature was searched using the prespecified inclusion and exclusion criteria to identify and critically evaluate primary studies published in English that reported on predictive models for CVD, associated risks, progression, and outcomes in the general adult population in North America. Studies were then assessed for inclusion of relevant social variables in the model construction. Two independent reviewers screened articles for eligibility. Primary and secondary independent reviewers extracted information from each full-text article for analysis. Disagreements were resolved with a third reviewer and iterative screening rounds to establish consensus. Cohen\'s kappa was used to determine interrater reliability.
UNASSIGNED: The review yielded 533 unique records where 35 met the inclusion criteria. Studies used advanced statistical and machine learning methods to predict CVD risk (10, 29%), mortality (19, 54%), survival (7, 20%), complication (10, 29%), disease progression (6, 17%), functional outcomes (4, 11%), and disposition (2, 6%). Most studies incorporated age (34, 97%), sex (34, 97%), comorbid conditions (32, 91%), and behavioral risk factor (28, 80%) variables. Race or ethnicity (23, 66%) and social variables, such as education (3, 9%) were less frequently observed.
UNASSIGNED: Predictive models should adjust for race and social predictor variables, where relevant, to improve model accuracy and to inform more equitable interventions and decision making.
UNASSIGNED: PubMed literature was searched using the prespecified inclusion and exclusion criteria to identify and critically evaluate primary studies published in English that reported on predictive models for CVD, associated risks, progression, and outcomes in the general adult population in North America. Studies were then assessed for inclusion of relevant social variables in the model construction. Two independent reviewers screened articles for eligibility. Primary and secondary independent reviewers extracted information from each full-text article for analysis. Disagreements were resolved with a third reviewer and iterative screening rounds to establish consensus. Cohen\'s kappa was used to determine interrater reliability.
UNASSIGNED: The review yielded 533 unique records where 35 met the inclusion criteria. Studies used advanced statistical and machine learning methods to predict CVD risk (10, 29%), mortality (19, 54%), survival (7, 20%), complication (10, 29%), disease progression (6, 17%), functional outcomes (4, 11%), and disposition (2, 6%). Most studies incorporated age (34, 97%), sex (34, 97%), comorbid conditions (32, 91%), and behavioral risk factor (28, 80%) variables. Race or ethnicity (23, 66%) and social variables, such as education (3, 9%) were less frequently observed.
UNASSIGNED: Predictive models should adjust for race and social predictor variables, where relevant, to improve model accuracy and to inform more equitable interventions and decision making.
摘要：
■结合相关临床和社会特征的预测模型可以为心血管疾病（CVD）风险和进展的复杂相关机制以及环境暴露对不良结果的影响提供有意义的见解。这次有针对性的审查(2018-2019年)的目的是检查当今高级分析在多大程度上，人工智能,机器学习模型包括相关变量，以解决潜在的偏见，为护理提供信息，治疗,资源分配,和心血管疾病患者的管理。
■使用预先指定的纳入和排除标准搜索PubMed文献，以识别和批判性地评估以英文发表的关于CVD预测模型的主要研究，相关风险，programming,和结果在北美一般成年人口中。然后评估研究是否将相关社会变量纳入模型构建中。两名独立审稿人筛选了文章的资格。主要和次要独立审阅者从每篇全文文章中提取信息进行分析。与第三次审查者和反复筛选轮解决了分歧，以建立共识。科恩的卡帕被用来确定评估者间的可靠性。
■审查产生了533条独特记录，其中35条符合纳入标准。研究使用先进的统计和机器学习方法来预测CVD风险（10，29％），死亡率（19，54%），生存率（7，20%），并发症（10，29%），疾病进展（6，17%），功能结果（4，11%），和处置(2%，6%)。大多数研究纳入年龄（34，97％），性别（34，97%），合并症（32，91%），和行为风险因素(28，80%)变量。种族或民族（23，66％）和社会变量，例如教育（3，9％）的观察频率较低。
■预测模型应根据种族和社会预测变量进行调整，如果相关，提高模型的准确性，并为更公平的干预和决策提供信息。
■使用预先指定的纳入和排除标准搜索PubMed文献，以识别和批判性地评估以英文发表的关于CVD预测模型的主要研究，相关风险，programming,和结果在北美一般成年人口中。然后评估研究是否将相关社会变量纳入模型构建中。两名独立审稿人筛选了文章的资格。主要和次要独立审阅者从每篇全文文章中提取信息进行分析。与第三次审查者和反复筛选轮解决了分歧，以建立共识。科恩的卡帕被用来确定评估者间的可靠性。
■审查产生了533条独特记录，其中35条符合纳入标准。研究使用先进的统计和机器学习方法来预测CVD风险（10，29％），死亡率（19，54%），生存率（7，20%），并发症（10，29%），疾病进展（6，17%），功能结果（4，11%），和处置(2%，6%)。大多数研究纳入年龄（34，97％），性别（34，97%），合并症（32，91%），和行为风险因素(28，80%)变量。种族或民族（23，66％）和社会变量，例如教育（3，9％）的观察频率较低。
■预测模型应根据种族和社会预测变量进行调整，如果相关，提高模型的准确性，并为更公平的干预和决策提供信息。
