Implementation of a Machine-Learning Algorithm in the Electronic Health Record for Targeted Screening for Familial Hypercholesterolemia: A Quality Improvement Study.
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SUBMITTER: Sheth S
PROVIDER: S-EPMC8204766 | biostudies-literature | 2021 Jun
REPOSITORIES: biostudies-literature
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Implementation of a Machine-Learning Algorithm in the Electronic Health Record for Targeted Screening for Familial Hypercholesterolemia: A Quality Improvement Study.
Sheth Samip S Lee Paul P Bajaj Archna A Cuchel Marina M Hajj Jihane J Soffer Daniel E DE Webb Gayley G Hossain Erik E Borovskiy Yulia Y Risman Marjorie M Myers Kelly D KD Wilemon Katherine A KA Rader Daniel J DJ Jacoby Douglas D
Circulation. Cardiovascular quality and outcomes 20210610 6
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Project description:ObjectiveTo assess the impact of a multi-pronged educational approach on the knowledge, attitudes, and behaviors regarding Familial Hypercholesterolemia (FH) management at a large academic medical center with the aim of empowering primary care clinicians (PCC) to diagnose and treat FH.MethodsA comprehensive educational program for PCCs on FH management was developed and piloted from July 2022 to March 2024. Components of our intervention included: 1. Implementation of a novel clinical decision support tool in the electronic medical record for FH management, 2. Development and dissemination of an interactive educational website focused on FH and its management, 3. Delivery of virtual instructional sessions to increase awareness of the tool, provide education on its use, and obtain support from institutional leadership, and 4. Direct outreach to a pilot subset of PCCs whose patients had been detected using the validated FIND FH® machine learning algorithm. Participating clinicians were surveyed at baseline before the intervention and after the educational session.Results70 PCC consented to participate in the study with a survey completion rate of 79 % (n = 55) and 42 % (n = 23) for the baseline and follow-up surveys, respectively. Objective PCC knowledge scores improved from 40 to 65 % of responders correctly responding to at least 2/3rds of survey questions. Despite the fact that 87 % identified PCC's as most effective for early detection of FH, 100 % of PCCs who received direct outreach chose to defer care to an outpatient cardiologist over pursuing workup in the primary care setting.ConclusionEmpowering PCCs in management of FH serves as a key strategy in addressing this underdiagnosed and undertreated potentially life-threatening condition. A systems-based approach to addressing these aims may include leveraging EMR-based clinical decision support models and cross-disciplinary educational partnerships with medical specialists.
| S-EPMC11338952 | biostudies-literature
Project description:BackgroundFamilial hypercholesterolemia (FH) is a condition characterized by high cholesterol levels and increased risk for coronary heart disease (CHD) that often goes undiagnosed. The Dutch Lipid Network Criteria (DLNC) are used to identify FH in clinical settings via physical examination, personal and family history of CHD, in addition to the presence of deleterious mutations of the LDLR, ApoB, and PCSK9 genes. Agreement between clinical and genetic diagnosis of FH varies. While an ICD diagnosis code was not available for coding FH until 2016, Systematized Nomenclature of Medicine (SNOMED) clinical concept codes, including genetic diagnoses, for FH have been utilized in electronic health records (EHRs).ObjectiveTo evaluate the concordance of identifying FH via SNOMED and ICD-10 CM codes vs the DLNC in an EHR database.MethodsUsing the Practice Fusion EHR database, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value were calculated comparing an FH cohort identified via SNOMED and ICD-10 CM codes to one identified via the DLNC.ResultsAmong 907,616 patients with hypercholesterolemia, 2,180 were identified as FH via SNOMED code (zero were identified via ICD-10 CM), 259 had a DLNC score 6-8 (probable FH), and 45 had a DLNC score >8 (definite FH). Compared to DLNC score >8, the sensitivity, specificity, and PPV of the FH SNOMED code were 84.4%, 99.4%, and 6.4%, respectively. Compared to DLNC score ≥6, the sensitivity was 36.8% and the specificity was 99.5% with a PPV of 18.7%.ConclusionCompared to the clinical criteria for FH, identification of FH patients via SNOMED diagnosis codes had high sensitivity and specificity, but low PPV. The discordance of these two techniques in identifying FH patients speaks to the challenges in identifying FH patients in large electronic databases such as administrative claims and EHR.
| S-EPMC6241698 | biostudies-literature
Project description:BackgroundFamilial hypercholesterolemia (FH) is a genetic disorder characterized by a high level of low-density lipoprotein cholesterol (LDL-C) and is an important cause for premature cardiovascular disease. Because of underdiagnoses, an acute event is often the first clinical manifestation of FH. There are limited data on the prevalence and treatment of FH among adults admitted for treatment of acute cardiovascular events in Bulgaria. Our objective was to assess the proportion and management of FH patients from those admitted to hospital for treatment of acute symptomatic acute atherosclerotic cardiovascular events (ASCVD), the achievement of LDL-C targets of European Society of Cardiology/European Atherosclerosis Society guidelines and related public healthcare resources.ObjectiveDigitalized healthcare records for patients admitted for treatment of symptomatic ASCVD acute events between August 2018 and August 2019 were used for the analysis. Five cardiology hospitals provided data for hospitalizations, laboratory tests, and ambulatory follow-ups up to February 2020. Patients' hospital and ambulatory records were linked, and medical histories were extracted via a specifically developed algorithm, and analyzed. Outcomes included the proportion of patients classified as FH as defined by the Dutch Lipid Network Criteria (DLNC), use of lipid-lowering therapy, LDL-C achieved by 1, 3, 6, and 12 months post-index event, and public resources spent on hospital and ambulatory treatment.ResultsWe reviewed 11,090 hospital records of patients admitted for treatment of acute events in the period August 2018-August 2019 with ICD codes for ASCVD (Supplementary Table S3). FH was identified in 731 (6.6%) patients, with DLNC score ≥ 3, (682 with coronary artery disease, 32 with cerebrovascular disease, and 17 with peripheral artery disease). We did not find the criteria for FH in 5797 patients. The remaining 4562 records were inconclusive due to lack of data in the hospital dossier. Less than half of FH patients (274/731, 37%) were discharged on high-intensity statin therapy prescribed (34/731, 5%) with combination therapy. The vast majority (96.2% with LDL-C ≥ 1.8 mmol/l) had poorly controlled LDL-C during the first year after discharge. Patients with a probable/definite DLNC score ≥ 6 points and those with recurrent events contributed to the higher cost paid both by the healthcare system and the patients themselves.ConclusionThese findings reinforce the need for more aggressive lipid-lowering therapy, and underline the efficiency of using an electronic medical records search tool to support physicians in improving early FH diagnosis, aiming to minimize residual and future ASCVD events among FH patients and their family members. Supplementary file1 (MP4 21838 KB).
| S-EPMC8107160 | biostudies-literature
Project description:Background Familial hypercholesterolemia ( FH ), is a historically underdiagnosed, undertreated, high-risk condition that is associated with a high burden of cardiovascular morbidity and mortality. In this study, we use a population-based approach using electronic health record ( EHR )-based algorithms to identify FH . We report the major adverse cardiovascular events, mortality, and cost of medical care associated with this diagnosis. Methods and Results In our 1.18 million EHR- eligible cohort, International Classification of Diseases, Ninth Revision ( ICD -9) code-defined hyperlipidemia was categorized into FH and non- FH groups using an EHR algorithm designed using the modified Dutch Lipid Clinic Network criteria. Major adverse cardiovascular events, mortality, and cost of medical care were analyzed. A priori associated variables/confounders were used for multivariate analyses using binary logistic regression and linear regression with propensity score-based weighted methods as appropriate. EHR FH was identified in 32 613 individuals, which was 2.7% of the 1.18 million EHR cohort and 13.7% of 237 903 patients with hyperlipidemia. FH had higher rates of myocardial infarction (14.77% versus 8.33%; P<0.0001), heart failure (11.82% versus 10.50%; P<0.0001), and, after adjusting for traditional risk factors, significantly correlated to a composite major adverse cardiovascular events variable (odds ratio, 4.02; 95% CI, 3.88-4.16; P<0.0001), mortality (odds ratio, 1.20; CI, 1.15-1.26; P<0.0001), and higher total revenue per-year (incidence rate ratio, 1.30; 95% CI, 1.28-1.33; P<0.0001). Conclusions EHR -based algorithms discovered a disproportionately high prevalence of FH in our medical cohort, which was associated with worse outcomes and higher costs of medical care. This data-driven approach allows for a more precise method to identify traditionally high-risk groups within large populations allowing for targeted prevention and therapeutic strategies.
| S-EPMC6662375 | biostudies-literature
Project description:Familial hypercholesterolemia (FH) is an autosomal dominant disorder characterized by abnormally high concentrations of low-density lipoprotein (LDL) cholesterol in the blood, which predisposes affected persons to premature coronary heart disease (CHD) and death. FH is one of the most common inherited disorders and the most common one known to cause premature CHD in people of European descent. The vast majority of people with FH have inherited a single mutation from one parent in either the LDL receptor (LDLR), apolipoprotein B (APOB), or proprotein convertase subtilisin/kexin type 9 (PCSK9) genes. Despite their greatly elevated risk of coronary heart disease, most individuals with FH remain undiagnosed, untreated, or inadequately treated. Cascade screening is a mechanism for identifying people at risk for a genetic condition by a process of systematic family tracing. The National Institute for Health and Clinical Excellence in the United Kingdom recommends cascade screening of close biological relatives of people with a clinical diagnosis of FH in order to effectively identify additional FH patients. The ultimate goal of this testing is to reduce morbidity and mortality from heart disease in persons with FH through early diagnosis and effective disease management. The goal of this article is to outline the available evidence on the clinical validity and utility of cascade screening for FH, while emphasizing the availability, usefulness, and recommendation for including DNA testing (if the disease-causing mutation has been identified).
| S-EPMC3102597 | biostudies-literature
Project description:Familial hypercholesterolemia (FH) is a common hereditary cholesterol metabolic disease that usually leads to an increase in the level of low-density lipoprotein cholesterol in plasma and an increase in the risk of cardiovascular disease. The lack of disease screening and diagnosis often results in FH patients being unable to receive early intervention and treatment, which may mean early occurrence of cardiovascular disease. Thus, more requirements for FH identification and management have been proposed. Recently, machine learning (ML) has made great progress in the field of medicine, including many innovative applications in cardiovascular medicine. In this review, we discussed how ML can be used for FH screening, diagnosis and risk assessment based on different data sources, such as electronic health records, plasma lipid profiles and corneal radian images. In the future, research aimed at developing ML models with better performance and accuracy will continue to overcome the limitations of ML, provide better prediction, diagnosis and management tools for FH, and ultimately achieve the goal of early diagnosis and treatment of FH.
| S-EPMC10562581 | biostudies-literature
Project description:Background and objectivesFamilial hypercholesterolemia (FH) increases the risk of premature cardiovascular disease through disrupted low-density lipoprotein cholesterol (LDL-C) metabolism. Although FH is a severe condition, it remains widely underdiagnosed, which can be attributed to barriers in genetic testing and a lack of awareness. This study aims to propose and evaluate a targeted screening program for FH in South Korea by integrating the General Health Screening Program (GHSP) with cascade genetic screening.MethodsThe study included individuals with LDL-C levels ≥190 mg/dL identified during the 2021 GHSP (primary participants). Data on demographics, lifestyle, medical history, and family history were collected through questionnaires. Targeted next-generation sequencing was used to identify pathogenic mutations in the PCSK9, APOB, LDLRAP1, and LDLR genes associated with FH. Pathogenic mutations found in primary participants were confirmed in their relatives (secondary participants) using Sanger sequencing. Participant characteristics were analyzed based on the presence of pathogenic mutations.ResultsAmong 83 individuals with severe hypercholesterolemia identified through the GHSP, 7 primary participants (8.4%) carried pathogenic mutations in the LDLR and PCSK9 genes. In secondary participants, pathogenic mutations were identified in 61.1% of the relatives of 4 patients with pathogenic mutations. The prevalence of pathogenic mutations was significantly higher in primary participants compared to secondary participants.ConclusionsIntegrating community resources with FH screening can enhance the early detection and treatment of FH. By utilizing GHSP data and adding genetic screening, the proposed model provides a strategy to reduce the cardiovascular risks associated with FH, supporting its wider adoption at the national level.
| S-EPMC11569943 | biostudies-literature
Project description:BackgroundA major problem in treating acute kidney injury (AKI) is that clinical criteria for recognition are markers of established kidney damage or impaired function; treatment before such damage manifests is desirable. Clinicians could intervene during what may be a crucial stage for preventing permanent kidney injury if patients with incipient AKI and those at high risk of developing AKI could be identified.ObjectiveIn this study, we evaluate a machine learning algorithm for early detection and prediction of AKI.DesignWe used a machine learning technique, boosted ensembles of decision trees, to train an AKI prediction tool on retrospective data taken from more than 300 000 inpatient encounters.SettingData were collected from inpatient wards at Stanford Medical Center and intensive care unit patients at Beth Israel Deaconess Medical Center.PatientsPatients older than the age of 18 whose hospital stays lasted between 5 and 1000 hours and who had at least one documented measurement of heart rate, respiratory rate, temperature, serum creatinine (SCr), and Glasgow Coma Scale (GCS).MeasurementsWe tested the algorithm's ability to detect AKI at onset and to predict AKI 12, 24, 48, and 72 hours before onset.MethodsWe tested AKI detection and prediction using the National Health Service (NHS) England AKI Algorithm as a gold standard. We additionally tested the algorithm's ability to detect AKI as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. We compared the algorithm's 3-fold cross-validation performance to the Sequential Organ Failure Assessment (SOFA) score for AKI identification in terms of area under the receiver operating characteristic (AUROC).ResultsThe algorithm demonstrated high AUROC for detecting and predicting NHS-defined AKI at all tested time points. The algorithm achieves AUROC of 0.872 (95% confidence interval [CI], 0.867-0.878) for AKI detection at time of onset. For prediction 12 hours before onset, the algorithm achieves an AUROC of 0.800 (95% CI, 0.792-0.809). For 24-hour predictions, the algorithm achieves AUROC of 0.795 (95% CI, 0.785-0.804). For 48-hour and 72-hour predictions, the algorithm achieves AUROC values of 0.761 (95% CI, 0.753-0.768) and 0.728 (95% CI, 0.719-0.737), respectively.LimitationsBecause of the retrospective nature of this study, we cannot draw any conclusions about the impact the algorithm's predictions will have on patient outcomes in a clinical setting.ConclusionsThe results of these experiments suggest that a machine learning-based AKI prediction tool may offer important prognostic capabilities for determining which patients are likely to suffer AKI, potentially allowing clinicians to intervene before kidney damage manifests.
| S-EPMC6080076 | biostudies-literature
Project description:Familial hypercholesterolemia (FH) is a common inherited disorder that results in premature atherosclerosis. Diagnosis of FH is suspected on the basis of clinical criteria, but confirmation requires genetic testing. In the era of statins, early diagnosis and initiation of treatment can modify disease progression and outcomes. Therefore, cascade screening with a combination of lipid concentration measurements and DNA testing should be used to identify relatives of index cases with a clinical diagnosis of FH. Autosomal dominant FH is related to mutations in the low-density lipoprotein receptor (LDLR), apolipoprotein B-100 (APOB), or proprotein convertase subtilisin/kexin type 9 (PCSK9) genes. Genetic screening of the LDLR gene is challenging to achieve at a feasible cost, especially in people who do not have a founder effect. Nucleotide sequencing of all exons and flanking splicing regions in combination with multiplex ligation probe amplification to detect large insertions or deletions is considered the gold-standard approach to screen for LDLR mutations. Alternatively, the cDNA can be sequenced; however, this procedure is not suitable for use in large populations, because of the need of RNA extraction. Multiplex analysis can be appropriate for population with founder effects or a low number of different mutations. Finally, there are many techniques for a mutation scanning approach, which have some benefits over sequencing, and also with the potential for detecting known and novel mutations. Familial defective Apo B is amenable to genetic diagnosis by screening for a few mutations. Recently, gain-of-function mutations in PCSK9 gene have been demonstrated to cause FH phenotype. Strategies for population screening, cost-effectiveness of genetic screening, ethical aspects, and insurance policies are discussed and need implementation worldwide.
| S-EPMC3681171 | biostudies-literature
Project description:BackgroundWe designed the Collaborative Approach to Reach Everyone with Familial Hypercholesterolemia (CARE-FH) clinical trial to improve FH screening in primary care and facilitate guideline-based care.ObjectiveThe goal was to incorporate perspectives from end-users (healthcare system leaders, primary care clinicians, cardiologists, genetic counselors, nurses, and clinic staff) and improve translation of screening guidance into practice.MethodsWe partnered with end-users to sequentially define the current state of FH screening, assess acceptability, feasibility, and appropriateness of implementing an FH screening program, and select clinically actionable strategies at the patient-, clinician-, and system-level to be deployed as a package in the CARE-FH clinical trial. Methods informed by implementation science and human centered design included: contextual inquiries, surveys, and deliberative engagement sessions.ResultsScreening for FH occurred rarely in primary care, and then only after a cardiovascular event or sometimes due to a family history of high cholesterol or early heart attack. Surveys suggested FH screening in primary care was acceptable, appropriate, and feasible. Reported and observed barriers to screening include insufficient time at patient encounters to screen, cost and convenience of testing for patients, and knowledge regarding causes of dyslipidemia. Facilitators included clear guidance on screening criteria and new therapies to treat FH. These results led to the development of multilevel strategies that were presented to end-users, modified, and then pilot tested in one primary care clinic.ConclusionsA refined implementation strategy package for FH screening was created with a goal of improving FH awareness, identification, and initiation of guideline-based care.Clinical trial registrationhttps://clinicaltrials.gov/study/NCT05284513?id=NCT05284513&rank=1 Unique Identifier: NCT05284513.
| S-EPMC11069448 | biostudies-literature