Leveraging the Electronic Health Record to Create an Automated Real-Time Prognostic Tool for Peripheral Arterial Disease

Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review

Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.

Artificial intelligence of arterial Doppler waveforms to predict major adverse outcomes among patients with diabetes mellitus

OBJECTIVE

Patients with diabetes mellitus (DM) are at increased risk for peripheral artery disease (PAD) and its complications. Arterial calcification and non-compressibility may limit test interpretation in this population. Developing tools capable of identifying PAD and predicting major adverse cardiac event (MACE) and limb event (MALE) outcomes among patients with DM would be clinically useful. Deep neural network analysis of resting Doppler arterial waveforms was used to detect PAD among patients with DM and to identify those at greatest risk for major adverse outcome events.

METHODS

Consecutive patients with DM undergoing lower limb arterial testing (April 1, 2015-December 30, 2020) were randomly allocated to training, validation, and testing subsets (60%, 20%, and 20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict all-cause mortality, MACE, and MALE at 5 years using quartiles based on the distribution of the prediction score.

RESULTS

Among 11,384 total patients, 4211 patients with DM met study criteria (mean age, 68.6 ± 11.9 years; 32.0% female). After allocating the training and validation subsets, the final test subset included 856 patients. During follow-up, there were 262 deaths, 319 MACE, and 99 MALE. Patients in the upper quartile of prediction based on deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 3.58; 95% confidence interval [CI], 2.31-5.56), MACE (HR, 2.06; 95% CI, 1.49-2.91), and MALE (HR, 13.50; 95% CI, 5.83-31.27).

CONCLUSIONS

An artificial intelligence enabled analysis of a resting Doppler arterial waveform permits identification of major adverse outcomes including all-cause mortality, MACE, and MALE among patients with DM.

Artificial Intelligence of Arterial Doppler Waveforms to Predict Major Adverse Outcomes Among Patients Evaluated for Peripheral Artery Disease

BACKGROUND

Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all-cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer-assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events.

METHODS AND RESULTS

Consecutive patients (April 1, 2015, to December 31, 2020) undergoing ankle-brachial index testing were included. Patients were randomly allocated to training, validation, and testing subsets (60%/20%/20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict major adverse cardiac events, major adverse limb events, and all-cause death at 5 years. Patients were then analyzed in groups based on the quartiles of each prediction score in the training set. Among 11 384 total patients, 10 437 patients met study inclusion criteria (mean age, 65.8±14.8 years; 40.6% women). The test subset included 2084 patients. During 5 years of follow-up, there were 447 deaths, 585 major adverse cardiac events, and 161 MALE events. After adjusting for age, sex, and Charlson comorbidity index, deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 2.44 [95% CI, 1.78-3.34]), major adverse cardiac events (HR, 1.97 [95% CI, 1.49-2.61]), and major adverse limb events (HR, 11.03 [95% CI, 5.43-22.39]) at 5 years.

CONCLUSIONS

An artificial intelligence-enabled analysis of Doppler arterial waveforms enables identification of major adverse outcomes among patients with peripheral artery disease, which may promote early adoption and adherence of risk factor modification.

Using Polygenic Risk Scores for Prioritizing Individuals at Greatest Need of a Cardiovascular Disease Risk Assessment

Background The aim of this study was to provide quantitative evidence of the use of polygenic risk scores for systematically identifying individuals for invitation for full formal cardiovascular disease (CVD) risk assessment. Methods and Results A total of 108 685 participants aged 40 to 69 years, with measured biomarkers, linked primary care records, and genetic data in UK Biobank were used for model derivation and population health modeling. Prioritization tools using age, polygenic risk scores for coronary artery disease and stroke, and conventional risk factors for CVD available within longitudinal primary care records were derived using sex-specific Cox models. We modeled the implications of initiating guideline-recommended statin therapy after prioritizing individuals for invitation to a formal CVD risk assessment. If primary care records were used to prioritize individuals for formal risk assessment using age- and sex-specific thresholds corresponding to 5% false-negative rates, then the numbers of men and women needed to be screened to prevent 1 CVD event are 149 and 280, respectively. In contrast, adding polygenic risk scores to both prioritization and formal assessments, and selecting thresholds to capture the same number of events, resulted in a number needed to screen of 116 for men and 180 for women. Conclusions Using both polygenic risk scores and primary care records to prioritize individuals at highest risk of a CVD event for a formal CVD risk assessment can efficiently prioritize those who need interventions the most than using primary care records alone. This could lead to better allocation of resources by reducing the number of risk assessments in primary care while still preventing the same number of CVD events.

Applications of artificial intelligence for patients with peripheral artery disease

OBJECTIVE

Applications of artificial intelligence (AI) have been reported in several cardiovascular diseases but its interest in patients with peripheral artery disease (PAD) has been so far less reported. The aim of this review was to summarize current knowledge on applications of AI in patients with PAD, to discuss current limits, and highlight perspectives in the field.

METHODS

We performed a narrative review based on studies reporting applications of AI in patients with PAD. The MEDLINE database was independently searched by two authors using a combination of keywords to identify studies published between January 1995 and December 2021. Three main fields of AI were investigated including natural language processing (NLP), computer vision and machine learning (ML).

RESULTS

NLP and ML brought new tools to improve the screening, the diagnosis and classification of the severity of PAD. ML was also used to develop predictive models to better assess the prognosis of patients and develop real-time prediction models to support clinical decision-making. Studies related to computer vision mainly aimed at creating automatic detection and characterization of arterial lesions based on Doppler ultrasound examination or computed tomography angiography. Such tools could help to improve screening programs, enhance diagnosis, facilitate presurgical planning, and improve clinical workflow.

CONCLUSIONS

AI offers various applications to support and likely improve the management of patients with PAD. Further research efforts are needed to validate such applications and investigate their accuracy and safety in large multinational cohorts before their implementation in daily clinical practice.

Evaluating Social Determinants of Health Variables in Advanced Analytic and Artificial Intelligence Models for Cardiovascular Disease Risk and Outcomes: A Targeted Review

Introduction/Purpose

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.

Methods

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.

Results

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.

Conclusions

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.

Artificial intelligence for the evaluation of peripheral artery disease using arterial Doppler waveforms to predict abnormal ankle-brachial index

BACKGROUND

Patients with peripheral artery disease (PAD) are at increased risk for major adverse limb and cardiac events including mortality. Developing screening tools capable of accurate PAD identification is a necessary first step for strategies of adverse outcome prevention. This study aimed to determine whether machine analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with PAD.

METHODS

Consecutive patients (4/1/2015 - 12/31/2020) undergoing rest and postexercise ankle-brachial index (ABI) testing were included. Patients were randomly allocated to training, validation, and testing subsets (70%/15%/15%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict normal (> 0.9) or PAD (⩽ 0.9) using rest and postexercise ABI. A separate dataset of 151 patients who underwent testing during a period after the model had been created and validated (1/1/2021 - 3/31/2021) was used for secondary validation. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate test performance.

RESULTS

Among 11,748 total patients, 3432 patients met study criteria: 1941 with PAD (mean age 69 ± 12 years) and 1491 without PAD (64 ± 14 years). The predictive model with highest performance identified PAD with an AUC 0.94 (CI = 0.92-0.96), sensitivity 0.83, specificity 0.88, accuracy 0.85, and positive predictive value (PPV) 0.90. Results were similar for the validation dataset: AUC 0.94 (CI = 0.91-0.98), sensitivity 0.91, specificity 0.85, accuracy 0.89, and PPV 0.89 (postexercise ABI comparison).

CONCLUSION

An artificial intelligence-enabled analysis of a resting Doppler arterial waveform permits identification of PAD at a clinically relevant performance level.

Comparison of Electronic Frailty Metrics for Prediction of Adverse Outcomes of Abdominal Surgery

Importance

Electronic frailty metrics have been developed for automated frailty assessment and include the Hospital Frailty Risk Score (HFRS), the Electronic Frailty Index (eFI), the 5-Factor Modified Frailty Index (mFI-5), and the Risk Analysis Index (RAI). Despite substantial differences in their construction, these 4 electronic frailty metrics have not been rigorously compared within a surgical population.

Objective

To characterize the associations between 4 electronic frailty metrics and to measure their predictive value for adverse surgical outcomes.

Design, Setting, and Participants

This retrospective cohort study used electronic health record data from patients who underwent abdominal surgery from January 1, 2010, to December 31, 2020, at 20 medical centers within Kaiser Permanente Northern California (KPNC). Participants included adults older than 50 years who underwent abdominal surgical procedures at KPNC from 2010 to 2020 that were sampled for reporting to the National Surgical Quality Improvement Program.

Main Outcomes and Measures

Pearson correlation coefficients between electronic frailty metrics and area under the receiver operating characteristic curve (AUROC) of univariate models and multivariate preoperative risk models for 30-day mortality, readmission, and morbidity, which was defined as a composite of mortality and major postoperative complications.

Results

Within the cohort of 37 186 patients, mean (SD) age, 67.9 (female, 19 127 [51.4%]), correlations between pairs of metrics ranged from 0.19 (95% CI, 0.18- 0.20) for mFI-5 and RAI 0.69 (95% CI, 0.68-0.70). Only 1085 of 37 186 (2.9%) were classified as frail based on all 4 metrics. In univariate models for morbidity, HFRS demonstrated higher predictive discrimination (AUROC, 0.71; 95% CI, 0.70-0.72) than eFI (AUROC, 0.64; 95% CI, 0.63-0.65), mFI-5 (AUROC, 0.58; 95% CI, 0.57-0.59), and RAI (AUROC, 0.57; 95% CI, 0.57-0.58). The predictive discrimination of multivariate models with age, sex, comorbidity burden, and procedure characteristics for all 3 adverse surgical outcomes improved by including HFRS into the models.

Conclusions and Relevance

In this cohort study, the 4 electronic frailty metrics demonstrated heterogeneous correlation and classified distinct groups of surgical patients as frail. However, HFRS demonstrated the highest predictive value for adverse surgical outcomes.

Artificial Intelligence for Education, Proctoring, and Credentialing in Cardiovascular Medicine

Artificial intelligence and machine learning are rapidly gaining popularity in every aspect of cardiovascular medicine. This review discusses the past, present, and future of artificial intelligence in education, remote proctoring, credentialing, research, and publication as they pertain to cardiovascular procedures. This review describes the benefits and limitations of artificial intelligence and machine learning and the exciting potential of integrating advanced simulation, holography, virtual reality, and extended reality into disease diagnosis and patient care, as well as their roles in cardiovascular research and education. Nonetheless, much of the available data resides in electronic medical records or within industry-sponsored proprietary programs that are not compatible or standardized for current clinical application. Many areas in cardiovascular medicine would benefit from the introduction or increased use of artificial intelligence. Web-based artificial intelligence applications could be used to address unmet needs for education, on-demand procedural proctoring, credentialing, and recredentialing for interventionists and physicians in remote locations. Further progress in artificial intelligence will require further collaboration among computer scientists and researchers in order to identify and correct the most relevant problems and to implement the best data-based approach to achieving this goal. The future success of artificial intelligence in cardiovascular medicine will depend on the degree of collaboration between all pertinent experts in this field. This will undoubtedly be a prolonged, stepwise process.

Predictive Accuracy of a Clinical and Genetic Risk Model for Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) risk estimation using clinical factors with or without genetic information may identify AF screening candidates more accurately than the guideline-based age threshold of ≥65 years.

METHODS

We analyzed 4 samples across the United States and Europe (derivation: UK Biobank; validation: FINRISK, Geisinger MyCode Initiative, and Framingham Heart Study). We estimated AF risk using the CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology AF) score and a combination of CHARGE-AF and a 1168-variant polygenic score (Predict-AF). We compared the utility of age, CHARGE-AF, and Predict-AF for predicting 5-year AF by quantifying discrimination and calibration.

RESULTS

Among 543 093 individuals, 8940 developed AF within 5 years. In the validation sets, CHARGE-AF (C index range, 0.720-0.824) and Predict-AF (0.749-0.831) had largely comparable discrimination, both favorable to continuous age (0.675-0.801). Calibration was similar using CHARGE-AF (slope range, 0.67-0.87) and Predict-AF (0.65-0.83). Net reclassification improvement using Predict-AF versus CHARGE-AF was modest (net reclassification improvement range, 0.024-0.057) but more favorable among individuals aged <65 years (0.062-0.11). Using Predict-AF among 99 530 individuals aged ≥65 years across each sample, 70 849 had AF risk <5%, of whom 69 067 (97.5%) did not develop AF, whereas 28 681 had AF risk ≥5%, of whom 2264 (7.9%) developed AF. Of 11 379 individuals aged <65 years with AF risk ≥5%, 435 (3.8%) developed AF before age 65 years, with roughly half (46.9%) meeting anticoagulation criteria.

CONCLUSIONS

AF risk estimation using clinical factors may prioritize individuals for AF screening more precisely than the age threshold endorsed in current guidelines. The additional value of genetic predisposition is modest but greatest among younger individuals.

Reducing Nontraumatic Lower-Extremity Amputations by 20% by 2030: Time to Get to Our Feet: A Policy Statement From the American Heart Association

Nontraumatic lower-extremity amputation is a devastating complication of peripheral artery disease (PAD) with a high mortality and medical expenditure. There are ≈150 000 nontraumatic leg amputations every year in the United States, and most cases occur in patients with diabetes. Among patients with diabetes, after an ≈40% decline between 2000 and 2009, the amputation rate increased by 50% from 2009 to 2015. A number of evidence-based diagnostic and therapeutic approaches for PAD can reduce amputation risk. However, their implementation and adherence are suboptimal. Some racial/ethnic groups have an elevated risk of PAD but less access to high-quality vascular care, leading to increased rates of amputation. To stop, and indeed reverse, the increasing trends of amputation, actionable policies that will reduce the incidence of critical limb ischemia and enhance delivery of optimal care are needed. This statement describes the impact of amputation on patients and society, summarizes medical approaches to identify PAD and prevent its progression, and proposes policy solutions to prevent limb amputation. Among the actions recommended are improving public awareness of PAD and greater use of effective PAD management strategies (eg, smoking cessation, use of statins, and foot monitoring/care in patients with diabetes). To facilitate the implementation of these recommendations, we propose several regulatory/legislative and organizational/institutional policies such as adoption of quality measures for PAD care; affordable prevention, diagnosis, and management; regulation of tobacco products; clinical decision support for PAD care; professional education; and dedicated funding opportunities to support PAD research. If these recommendations and proposed policies are implemented, we should be able to achieve the goal of reducing the rate of nontraumatic lower-extremity amputations by 20% by 2030.

Usability of a Digital Registry to Promote Secondary Prevention for Peripheral Artery Disease Patients

Objective

To evaluate usability of a quality improvement tool that promotes guideline-based care for patients with peripheral arterial disease (PAD).

Patients and Methods

The study was conducted from July 19, 2018, to August 21, 2019. We compared the usability of a PAD cohort knowledge solution (CKS) with standard management supported by an electronic health record (EHR). Two scenarios were developed for usability evaluation; the first for the PAD-CKS while the second evaluated standard EHR workflow. Providers were asked to provide opinions about the PAD-CKS tool and to generate a System Usability Scale (SUS) score. Metrics analyzed included time required, number of mouse clicks, and number of keystrokes.

Results

Usability evaluations were completed by 11 providers. SUS for the PAD-CKS was excellent at 89.6. Time required to complete 21 tasks in the CKS was 4 minutes compared with 12 minutes for standard EHR workflow (median, P = .002). Completion of CKS tasks required 34 clicks compared with 148 clicks for the EHR (median, P = .002). Keystrokes for CKS task completion was 8 compared with 72 for EHR (median, P = .004). Providers indicated that overall they found the tool easy to use and the PAD mortality risk score useful.

Conclusions

Usability evaluation of the PAD-CKS tool demonstrated time savings, a high SUS score, and a reduction of mouse clicks and keystrokes for task completion compared to standard workflow using the EHR. Provider feedback regarding the strengths and weaknesses also created opportunities for iterative improvement of the PAD-CKS tool.

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review

Background

Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.

Objective

This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.

Methods

A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.

Results

A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.

Conclusions

Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Detection and Severity Assessment of Peripheral Occlusive Artery Disease via Deep Learning Analysis of Arterial Pulse Waveforms: Proof-of-Concept and Potential Challenges

Toward the ultimate goal of affordable and non-invasive screening of peripheral occlusive artery disease (PAD), the objective of this work is to investigate the potential of deep learning-based arterial pulse waveform analysis in detecting and assessing the severity of PAD. Using an established transmission line model of arterial hemodynamics, a large number of virtual patients associated with PAD of a wide range of severity and the corresponding arterial pulse waveform data were created. A deep convolutional neural network capable of detecting and assessing the severity of PAD based on the analysis of brachial and ankle arterial pulse waveforms was constructed, evaluated for efficacy, and compared with the state-of-the-art ankle-brachial index (ABI) using the virtual patients. The results suggested that deep learning may diagnose PAD more accurately and robustly than ABI. In sum, this work demonstrates the initial proof-of-concept of deep learning-based arterial pulse waveform analysis for affordable and convenient PAD screening as well as presents challenges that must be addressed for real-world clinical applications.

Natural Language Processing for the Evaluation of Methodological Standards and Best Practices of EHR-based Clinical Research

The effective use of EHR data for clinical research is challenged by the lack of methodologic standards, transparency, and reproducibility. For example, our empirical analysis on clinical research ontologies and reporting standards found little-to-no informatics-related standards. To address these issues, our study aims to leverage natural language processing techniques to discover the reporting patterns and data abstraction methodologies for EHR-based clinical research. We conducted a case study using a collection of full articles of EHR-based population studies published using the Rochester Epidemiology Project infrastructure. Our investigation discovered an upward trend of reporting EHR-related research methodologies, good practice, and the use of informatics related methods. For example, among 1279 articles, 24.0% reported training for data abstraction, 6% reported the abstractors were blinded, 4.5% tested the inter-observer agreement, 5% reported the use of a screening/data collection protocol, 1.5% reported that team meetings were organized for consensus building, and 0.8% mentioned supervision activities by senior researchers. Despite that, the overall ratio of reporting/adoption of methodologic standards was still low. There was also a high variation regarding clinical research reporting. Thus, continuously developing process frameworks, ontologies, and reporting guidelines for promoting good data practice in EHR-based clinical research are recommended.

Artificial Intelligence in Cardiology: Present and Future

Artificial intelligence (AI) is a nontechnical, popular term that refers to machine learning of various types but most often to deep neural networks. Cardiology is at the forefront of AI in medicine. For this review, we searched PubMed and MEDLINE databases with no date restriction using search terms related to AI and cardiology. Articles were selected for inclusion on the basis of relevance. We highlight the major achievements in recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take center stage in the field. Artificial intelligence requires a close collaboration among computer scientists, clinical investigators, clinicians, and other users in order to identify the most relevant problems to be solved. Best practices in the generation and implementation of AI include the selection of ideal data sources, taking into account common challenges during the interpretation, validation, and generalizability of findings, and addressing safety and ethical concerns before final implementation. The future of AI in cardiology and in medicine in general is bright as the collaboration between investigators and clinicians continues to excel.

Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data

BACKGROUND

Patients with peripheral artery disease (PAD) are at risk of major adverse cardiac and cerebrovascular events. There are no readily available risk scores that can accurately identify which patients are most likely to sustain an event, making it difficult to identify those who might benefit from more aggressive intervention. Thus, we aimed to develop a novel predictive model-using machine learning methods on electronic health record data-to identify which PAD patients are most likely to develop major adverse cardiac and cerebrovascular events.

METHODS AND RESULTS

Data were derived from patients diagnosed with PAD at 2 tertiary care institutions. Predictive models were built using a common data model that allowed for utilization of both structured (coded) and unstructured (text) data. Only data from time of entry into the health system up to PAD diagnosis were used for modeling. Models were developed and tested using nested cross-validation. A total of 7686 patients were included in learning our predictive models. Utilizing almost 1000 variables, our best predictive model accurately determined which PAD patients would go on to develop major adverse cardiac and cerebrovascular events with an area under the curve of 0.81 (95% CI, 0.80-0.83).

CONCLUSIONS

Machine learning algorithms applied to data in the electronic health record can learn models that accurately identify PAD patients at risk of future major adverse cardiac and cerebrovascular events, highlighting the great potential of electronic health records to provide automated risk stratification for cardiovascular diseases. Common data models that can enable cross-institution research and technology development could potentially be an important aspect of widespread adoption of newer risk-stratification models.

Clinical Decision Support for Peripheral Artery Disease: Answering the Call

See Article by Arruda-Olson et al.

Leveraging the Electronic Health Record to Create an Automated Real-Time Prognostic Tool for Peripheral Arterial Disease

Background Automated individualized risk prediction tools linked to electronic health records ( EHR s) are not available for management of patients with peripheral arterial disease. The goal of this study was to create a prognostic tool for patients with peripheral arterial disease using data elements automatically extracted from an EHR to enable real-time and individualized risk prediction at the point of care. Methods and Results A previously validated phenotyping algorithm was deployed to an EHR linked to the Rochester Epidemiology Project to identify peripheral arterial disease cases from Olmsted County, MN, for the years 1998 to 2011. The study cohort was composed of 1676 patients: 593 patients died over 5-year follow-up. The c-statistic for survival in the overall data set was 0.76 (95% confidence interval [CI], 0.74-0.78), and the c-statistic across 10 cross-validation data sets was 0.75 (95% CI, 0.73-0.77). Stratification of cases demonstrated increasing mortality risk by subgroup (low: hazard ratio, 0.35 [95% CI, 0.21-0.58]; intermediate-high: hazard ratio, 2.98 [95% CI, 2.37-3.74]; high: hazard ratio, 8.44 [95% CI, 6.66-10.70], all P<0.0001 versus the reference subgroup). An equation for risk calculation was derived from Cox model parameters and β estimates. Big data infrastructure enabled deployment of the real-time risk calculator to the point of care via the EHR . Conclusions This study demonstrates that electronic tools can be deployed to EHR s to create automated real-time risk calculators to predict survival of patients with peripheral arterial disease. Moreover, the prognostic model developed may be translated to patient care as an automated and individualized real-time risk calculator deployed at the point of care.