OBJECTIVE:This study aimed to estimate the clinical utility of performing multi-gene pharmacogenetic testing on patients undergoing gynecologic surgery/procedure by evaluating the prescribing rate of Clinical Pharmacogenetics Implementation Consortium (CPIC) level A medications and frequency of drug-gene interactions (DGIs). METHODS:The electronic health record was queried for 76 current procedural terminology codes to identify gynecologic surgeries/procedures that occurred between 1 January 2015 to 31 December 2020 in patients with at least one of 152 international classification of disease codes. Prescription data for CPIC level A medications was extracted. Those enrolled in the Penn Medicine Biobank were assessed for DGIs. RESULTS:The cohort consisted of 7798 female patients and 682 were in the biobank. Up to 6 years following their surgery or procedure, 80% were ordered ≥1 CPIC level A medication. Over half (54%) of these medications were ordered within 3 days after their surgery or procedure. The most common CPIC level A medications ordered were ibuprofen (57%) and ondansetron (42%). Overall, 7% of the cohort had ≥1 known or predicted DGI with medications they were prescribed. CONCLUSION:Multi-gene pharmacogenetic testing may be beneficial to gynecologic surgery/procedure patients by assisting clinicians with prescribing postoperative analgesics and future medications.

Background/Synopsis Familial hypercholesterolemia (FH) is a highly penetrant monogenic condition affecting ∼1:300 individuals. FH is associated with lifelong LDL-C elevation and increases the risk of premature ASCVD and mortality when not adequately treated. Objective/Purpose The aim of this study was to evaluate the extent to which FH is under- or mis-diagnosed in patients of a large academic medical center, using a genome-first approach. Methods Carriers of known FH-causing variants were identified among European and African Ancestry individuals participating in a large biobank. Genomic data were matched with data extrapolated from patients' electronic health records to determine the extent to which these patients were being diagnosed. Results Exome sequencing was available for 41,579 individuals. Of these, 135 were carriers of a pathogenic or likely pathogenic variant in one of the FH-causing genes. Their median (IQR) age of enrollment into the biobank was 58 years (48-65) and 50.4% were male. Only 5.2% of them were diagnosed with FH, 14.8% with hypercholesterolemia, 52.6% with other dyslipidemias, and the remaining 27.4% had no lipid-related diagnosis mentioned in their record. Carriers with a diagnosis of other dyslipidemias had a higher prevalence of diabetes (p<0.001), hypertension (p=0.043), and renal disease (p=0.005). These patients also had higher triglycerides and lower HDL-C levels than carriers diagnosed with FH or hypercholesterolemia (p<0.02 for both). Carriers of African ancestry were more likely to be more obese (p<0.001) and have higher incidence of myocardial infarction (p=0.007), diabetes (p<0.001), and renal disease (p<0.001). At their more recent encounter, only 55% of those receiving treatment were on high intensity statins and only 11% on PCSK9 inhibitors. Carriers seen by lipid specialists were significantly more likely to receive the diagnosis of FH (86%) and PCSK9 inhibitors (90%). Conclusions Our findings strongly support the statement that FH is grossly underdiagnosed and highlight the need for increased awareness and earlier universal screening for a timely diagnosis, when the presence of other risk factors masking the phenotype is minimized. These patients should be referred to lipid specialists for optimal management. Appropriate strategies are needed for the communication of actionable genetic results to biobank participants. External Funding No Familial hypercholesterolemia (FH) is a highly penetrant monogenic condition affecting ∼1:300 individuals. FH is associated with lifelong LDL-C elevation and increases the risk of premature ASCVD and mortality when not adequately treated. The aim of this study was to evaluate the extent to which FH is under- or mis-diagnosed in patients of a large academic medical center, using a genome-first approach. Carriers of known FH-causing variants were identified among European and African Ancestry individuals participating in a large biobank. Genomic data were matched with data extrapolated from patients' electronic health records to determine the extent to which these patients were being diagnosed. Exome sequencing was available for 41,579 individuals. Of these, 135 were carriers of a pathogenic or likely pathogenic variant in one of the FH-causing genes. Their median (IQR) age of enrollment into the biobank was 58 years (48-65) and 50.4% were male. Only 5.2% of them were diagnosed with FH, 14.8% with hypercholesterolemia, 52.6% with other dyslipidemias, and the remaining 27.4% had no lipid-related diagnosis mentioned in their record. Carriers with a diagnosis of other dyslipidemias had a higher prevalence of diabetes (p<0.001), hypertension (p=0.043), and renal disease (p=0.005). These patients also had higher triglycerides and lower HDL-C levels than carriers diagnosed with FH or hypercholesterolemia (p<0.02 for both). Carriers of African ancestry were more likely to be more obese (p<0.001) and have higher incidence of myocardial infarction (p=0.007), diabetes (p<0.001), and renal disease (p<0.001). At their more recent encounter, only 55% of those receiving treatment were on high intensity statins and only 11% on PCSK9 inhibitors. Carriers seen by lipid specialists were significantly more likely to receive the diagnosis of FH (86%) and PCSK9 inhibitors (90%). Our findings strongly support the statement that FH is grossly underdiagnosed and highlight the need for increased awareness and earlier universal screening for a timely diagnosis, when the presence of other risk factors masking the phenotype is minimized. These patients should be referred to lipid specialists for optimal management. Appropriate strategies are needed for the communication of actionable genetic results to biobank participants.

Background:Nonalcoholic fatty liver disease (NAFLD) is a major cause of liver-related morbidity in people with and without diabetes, but it is underdiagnosed, posing challenges for research and clinical management. Here, we determine if natural language processing (NLP) of data in the electronic health record (EHR) could identify undiagnosed patients with hepatic steatosis based on pathology and radiology reports. Methods:A rule-based NLP algorithm was built using a Linguamatics literature text mining tool to search 2.15 million pathology report and 2.7 million imaging reports in the Penn Medicine EHR from November 2014, through December 2020, for evidence of hepatic steatosis. For quality control, two independent physicians manually reviewed randomly chosen biopsy and imaging reports (n = 353, PPV 99.7%). Findings:After exclusion of individuals with other causes of hepatic steatosis, 3007 patients with biopsy-proven NAFLD and 42,083 patients with imaging-proven NAFLD were identified. Interestingly, elevated ALT was not a sensitive predictor of the presence of steatosis, and only half of the biopsied patients with steatosis ever received an ICD diagnosis code for the presence of NAFLD/NASH. There was a robust association for PNPLA3 and TM6SF2 risk alleles and steatosis identified by NLP. We identified 234 disorders that were significantly over- or underrepresented in all subjects with steatosis and identified changes in serum markers (e.g., GGT) associated with presence of steatosis. Interpretation:This study demonstrates clear feasibility of NLP-based approaches to identify patients whose steatosis was indicated in imaging and pathology reports within a large healthcare system and uncovers undercoding of NAFLD in the general population. Identification of patients at risk could link them to improved care and outcomes. Funding:The study was funded by US and German funding sources that did provide financial support only and had no influence or control over the research process.

Background: Lipoprotein(a) [Lp(a)] is a genetically driven independent risk factor for ASCVD. Aims: To determine the prevalence of Lp(a) testing in patients with CAD across the University of Pennsylvania Health Systems (UPHS) over the last 10 years. Methods: Electronic health record (EHR) data from 2012-2021 was queried to examine prevalence of Lp(a) testing in patients who presented with a diagnosis of CAD based on presence of corresponding ICD-9 or ICD-10 codes. Premature CAD was defined as CAD diagnosis in males age < 55 years or females age < 65 years. Race was self-reported. Results: Of the 173,966 patients with CAD, 2395 (1.4%) had an Lp(a) test result (mean 72.7 nmol/L), compared to 81,058 (46.6%) patients with at least one LDL-C result in the 10-year time period. About 94% of Lp(a) tests were ordered at outpatient visits, and 56.1% of the tests were ordered by Cardiology, 31.6% by Preventive Cardiology, 5.5% by Internal Medicine, and 1.2% by Neurology. Approximately 1.7% of males with CAD had an Lp(a) result (mean 65.9 nmol/L), 4.2% of males with premature CAD (mean 66.9 nmol/L), 1.6% of females with CAD (mean 84.1 nmol/L), and 3% of females with premature CAD (mean 80.9 nmol/L). The mean Lp(a) in Whites with CAD was 69.1 nmol/L compared to 104.3 nmol/L in Blacks and 54.8 nmol/L in Asians. The proportion of Whites with premature CAD who had an Lp(a) test was 3-fold higher than the proportion of Blacks with premature CAD. Conclusions: Although Lp(a) testing has increased over the past decade, it remains widely under-utilized in patients with CAD. Further, there is an apparent gap in the prevalence of testing in Black patients compared to Whites and Asians.

Abstract Background Pharmacogenomics (PGx) aims to utilize a patient’s genetic data to enable safer and more effective prescribing of medications. The Clinical Pharmacogenetics Implementation Consortium (CPIC) provides guidelines with strong evidence for 24 genes that affect 72 medications. Despite strong evidence linking PGx alleles to drug response, there is a large gap in the implementation and return of actionable pharmacogenetic findings to patients in standard clinical practice. In this study, we evaluated opportunities for genetically guided medication prescribing in a diverse health system and determined the frequencies of actionable PGx alleles in an ancestrally diverse biobank population. Methods A retrospective analysis of the Penn Medicine electronic health records (EHRs), which includes ~ 3.3 million patients between 2012 and 2020, provides a snapshot of the trends in prescriptions for drugs with genotype-based prescribing guidelines (‘CPIC level A or B’) in the Penn Medicine health system. The Penn Medicine BioBank (PMBB) consists of a diverse group of 43,359 participants whose EHRs are linked to genome-wide SNP array and whole exome sequencing (WES) data. We used the Pharmacogenomics Clinical Annotation Tool (PharmCAT), to annotate PGx alleles from PMBB variant call format (VCF) files and identify samples with actionable PGx alleles. Results We identified ~ 316.000 unique patients that were prescribed at least 2 drugs with CPIC Level A or B guidelines. Genetic analysis in PMBB identified that 98.9% of participants carry one or more PGx actionable alleles where treatment modification would be recommended. After linking the genetic data with prescription data from the EHR, 14.2% of participants (n = 6157) were prescribed medications that could be impacted by their genotype (as indicated by their PharmCAT report). For example, 856 participants received clopidogrel who carried CYP2C19 reduced function alleles, placing them at increased risk for major adverse cardiovascular events. When we stratified by genetic ancestry, we found disparities in PGx allele frequencies and clinical burden. Clopidogrel users of Asian ancestry in PMBB had significantly higher rates of CYP2C19 actionable alleles than European ancestry users of clopidrogrel (p < 0.0001, OR = 3.68). Conclusions Clinically actionable PGx alleles are highly prevalent in our health system and many patients were prescribed medications that could be affected by PGx alleles. These results illustrate the potential utility of preemptive genotyping for tailoring of medications and implementation of PGx into routine clinical care.

HomeCirculation: Cardiovascular Quality and OutcomesVol. 14, No. 6Implementation of a Machine-Learning Algorithm in the Electronic Health Record for Targeted Screening for Familial Hypercholesterolemia: A Quality Improvement Study Free AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyRedditDiggEmail Jump toFree AccessResearch ArticlePDF/EPUBImplementation of a Machine-Learning Algorithm in the Electronic Health Record for Targeted Screening for Familial Hypercholesterolemia: A Quality Improvement Study Samip Sheth, MB, Paul Lee, MS, Archna Bajaj, MD, Marina Cuchel, MD PhD, Jihane Hajj, DrNP, Daniel E. Soffer, MD, Gayley Webb, CrNP, Erik Hossain, BS, Yulia Borovskiy, MS, Marjorie Risman, MA, Kelly D. Myers, BS, Katherine A. Wilemon, BS, Daniel J. Rader, MD and Douglas Jacoby, MD Samip ShethSamip Sheth Department of Medicine (S.S., P.L., A.B., M.C., J.H., D.S., G.W., M.R., D.R., D.J.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. , Paul LeePaul Lee Department of Medicine (S.S., P.L., A.B., M.C., J.H., D.S., G.W., M.R., D.R., D.J.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. , Archna BajajArchna Bajaj Department of Medicine (S.S., P.L., A.B., M.C., J.H., D.S., G.W., M.R., D.R., D.J.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. , Marina CuchelMarina Cuchel https://orcid.org/0000-0001-6808-3824 Department of Medicine (S.S., P.L., A.B., M.C., J.H., D.S., G.W., M.R., D.R., D.J.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. , Jihane HajjJihane Hajj Department of Medicine (S.S., P.L., A.B., M.C., J.H., D.S., G.W., M.R., D.R., D.J.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. , Daniel E. SofferDaniel E. Soffer Department of Medicine (S.S., P.L., A.B., M.C., J.H., D.S., G.W., M.R., D.R., D.J.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. , Gayley WebbGayley Webb Department of Medicine (S.S., P.L., A.B., M.C., J.H., D.S., G.W., M.R., D.R., D.J.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. , Erik HossainErik Hossain Data Analytics Center (E.H., Y.B.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. , Yulia BorovskiyYulia Borovskiy Data Analytics Center (E.H., Y.B.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. , Marjorie RismanMarjorie Risman Department of Medicine (S.S., P.L., A.B., M.C., J.H., D.S., G.W., M.R., D.R., D.J.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. , Kelly D. MyersKelly D. Myers https://orcid.org/0000-0002-6142-6748 The Familial Hypercholesterolemia Foundation, Pasadena, CA (K.M., K.W.). , Katherine A. WilemonKatherine A. Wilemon https://orcid.org/0000-0003-4629-6866 The Familial Hypercholesterolemia Foundation, Pasadena, CA (K.M., K.W.). , Daniel J. RaderDaniel J. Rader https://orcid.org/0000-0002-9245-9876 Department of Medicine (S.S., P.L., A.B., M.C., J.H., D.S., G.W., M.R., D.R., D.J.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. and Douglas JacobyDouglas Jacoby Douglas Jacoby, MD, University of Pennsylvania, Pennsylvania Hospital, Farm Journal Bldg, 3rd Floor, 230 W Washington Sq Philadelphia, PA 19106. Email E-mail Address: [email protected] https://orcid.org/0000-0002-9245-9876 Department of Medicine (S.S., P.L., A.B., M.C., J.H., D.S., G.W., M.R., D.R., D.J.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Originally published10 Jun 2021https://doi.org/10.1161/CIRCOUTCOMES.120.007641Circulation: Cardiovascular Quality and Outcomes. 2021;14:e007641Goals and Vision of the ProgramFamilial hypercholesterolemia (FH) is a lipid disorder that results in elevated serum LDL (low-density lipoprotein) cholesterol and markedly increased cardiovascular risk.1,2 Classical observational data suggest that prevalence of heterozygous FH is ≈1:250, and it is estimated that only 10% of patients with FH in the United States have been diagnosed.1,2 Early and timely diagnosis of FH reduces cardiovascular risk, which heightens the need for targeted screening.2,3 To increase the detection rates of FH, several population and targeted screening strategies have been recommended and implemented. For example, mass genetic testing in the workplace and cascade genetic screening have been used in a few settings in the United States. Machine learning models trained on electronic medical record (EMR) data represent another promising approach to identify high-risk populations enriched with FH patients, but deployment of machine learning algorithms in cardiovascular medicine has been a historically challenging process.4Recently, The Familial Hypercholesterolemia Foundation developed the flag, identify, network, deliver FH (FIND FH) machine learning algorithm to identify yet-to-be diagnosed FH within millions of individual EMRs.5 FIND FH is a random forest-based algorithm trained on deidentified, structured EMR data from 939 individuals who were diagnosed with FH at specialty lipid clinics. The model selects 75 features ranging from patient demographics to prescriptions and laboratory data to predict the probability of a patient having FH. In the original study, the model demonstrated robust performance in predicting patients with higher risk of FH at both national (170 416 201 patients) and single health care system (173 733 patients from Oregon Health & Science University) levels, identifying 87% and 77% of patients in 2 independent cohorts as having a high enough suspicion of FH to warrant further evaluation and treatment (likely FH).5 At the University of Pennsylvania Healthcare System (UPHS), an internal validation of 414 patients flagged by FIND FH revealed that 29% of patients with FIND FH score >0.2 had probable or definite FH (unpublished data). However, no prior studies involving FIND FH had developed an implementation framework, complete with an outreach process, to integrate the algorithm into clinical care.The purpose of this study was to implement an observational trial of a HIPAA-compliant, IRB-approved screening and outreach program based on FIND FH as a case study of how machine learning algorithms could be deployed and utilized in a large health care system. Through this initiative, we assessed (1) the diagnostic rate of FH among clinical results from patients flagged by the algorithm, (2) the treatment of flagged patients in a preventive cardiology setting, and (3) barriers in implementation of the algorithm to inform future quality improvement initiatives.Design and Implementation of the InitiativeThe Center for Preventive Cardiology and Lipid Management at UPHS coordinated the design, implementation, and assessment of the initiative. The staff at the center include cardiologists, lipidologists, geneticists, nurse practitioners, a dietician, and nursing and administrative staff. The target population within the health care system were those patients who were identified by the algorithm to be at elevated risk for FH (FIND FH score, >0.2). To mimic a routine referral process as a part of standard clinical care, the center implemented an outreach model that involved both the prospective patients and their primary care providers (PCPs).From August 2018 to August 2019, a trained clinical research coordinator contacted the PCP for each patient flagged by the algorithm through a clinical letter or staff message sent in the EPIC EMR system (Epic Systems Corporation, Verona, WI), by phone call or by email asking the patient to be referred to the Center for Preventive Cardiology (Figure [A]). Patients >75 years of age and those without contact information were not contacted. If a provider declined permission or did not respond after one follow-up request, their patients were not further contacted. If the patient had visited a provider of the Center for Preventive Cardiology and Lipid Management in the past, they were automatically deemed eligible for contact. The initiative was approved by the Institutional Review Board of the University of Pennsylvania.Download figureDownload PowerPointFigure. Implementation of flag, identify, network, deliver familial hypercholesterolemia (FIND FH) in the University of Pennsylvania Healthcare System (UPHS).A, HIPAA-compliant recruitment model for implementation of FIND FH. B, Changes in clinical management among 92 patients who visited Penn Preventive Cardiology for the first time as a part of the initiative. C, Distribution of diagnostic method among 46 patients diagnosed with familial hypercholesterolemia at Penn Preventive Cardiology. DLCN indicates Dutch Lipid Clinic Network; DLCNS, Dutch Lipid Clinic Network Score; HIPPA, the Health Insurance Portability and Accountability Act of 1996; and MEDPED, Make Early Diagnosis to Prevent Early Death; and PCSK9 inhibitor, proprotein convertase subtilisin/kexin type 9 inhibitor.Following the provider's approval, eligible patients were contacted directly by a message sent to a patient portal (MyPennMedicine), by phone call or by email. The message included information about FH, the purpose of the quality improvement initiative, and an offer to schedule a visit with a provider in the preventive cardiology clinic. If the patient did not respond back, another follow-up message was sent 1 week after the initial message. Before each appointment, the provider was notified that a patient had been scheduled through the initiative; however, the provider was blinded to the FIND FH score as to not bias the diagnosis. The clinical evaluation by the provider followed the standard of care in our preventive cardiology program. As all the participants were deemed to be at elevated risk for FH, all participating providers agreed to offer genetic testing of the LDLR, APOB, and PCSK9 genes to all participants to reach a definitive diagnosis. While the consultation visit was billed to the patient, the genetic testing was offered free of charge and completed via next-generation sequencing with microarray confirmation at Quest Diagnostics. The Center for Preventive Cardiology has historically offered genetic testing as a part of standard clinical care, but patients typically have a copayment that depends on their insurance.After the initial visit, all patient data were recorded into the EMR, and all patients were notified of their genetic testing result by a phone call or an email from a trained clinical research coordinator. Starting September 2019, the clinical data from the initial visits were abstracted retrospectively by a clinical research coordinator. All patients were rescored following the Dutch Lipid Clinic Network criteria and the Make Early Diagnosis to Prevent Early Death criteria. Scoring was based on available clinical data and genetic test results from the initial visit.3 Any patient who was not diagnosed with FH during the clinical visit but (1) possessed a pathogenic, likely pathogenic, or variant of unknown significance in the three genes tested or (2) had probable FH based on the diagnostic criteria was asked to be reevaluated by the physician.Results of the InitiativeAmong 1 607 606 eligible patients with cardiovascular comorbidities in the UPHS EMR, 8614 were flagged as having a FIND FH score >0.2, indicating likely FH. We attempted to contact the health care providers for 5006 of these patients (442 health care providers; Figure [A]) whose individual identities were provided by LabCorp (a HIPAA covered entity) for the first phase of the implementation study defined by the scheduling capacities of the administrative staff. Identities and FIND FH scores of a randomly selected subset of 3614 patients were withheld to be provided in the second phase of the project. Of the 442 contacted PCPs, 223 (53%) responded. These 223 providers were associated with a total of 2640 patients, of whom 2167 (43% [2167 of 5006]) remained eligible to contact and 473 (9% [473 of 5006]) were ineligible to contact after a favorable or an unfavorable response by their providers, respectively. Of these 2167 patients, 1607 (32% [1607 of 5006]) were successfully contacted through phone, email, or MyPennMedicine message. Of the patients contacted, 187 (4% [187 of 5006]) expressed interest in participation, and 153 (3% [153 of 5006]) were ultimately seen in the preventive cardiology clinic.The final participant population demonstrated characteristics consistent with an at-risk population for FH. The median LDL cholesterol of participants at enrollment was 151 (interquartile range, 128–180) mg/dL, and the median total cholesterol was 234 (interquartile range, 207–265) mg/dL. Of the 153 patients, 68 (44.4%) had a first-degree relative with known premature atherosclerotic cardiovascular disease, 8 (5.2%) presented with tendinous xanthomas, and 14 (9.2%) presented with corneal arcus. Based on self-reports, 92 (60%) had never visited a clinical lipid specialist in the UPHS before the initiative, and 112 (73%) of 153 patients consented to and received genetic testing for FH for the first time as a part of the initiative.Following the initial visit, 46 patients were ultimately diagnosed with FH by (1) phenotypic clinical assessment by a physician or (2) Dutch Lipid Clinic Network/Make Early Diagnosis to Prevent Early Death criteria or (3) the presence of an FH mutation (Figure [C]). Of the 153 seen in the clinic, 31 were newly diagnosed patients who visited the specialty lipid clinic for the first time, while 15 patients had visited the clinic previously and were either already diagnosed with FH or reevaluated for FH and received intensification of care. A total of 16 patients tested positive for FH by genetic testing (14% [16 of 112]), and 42 patients received a diagnosis of FH based on clinical assessment or diagnostic criteria. Of the patients who received molecular diagnoses for FH, we observed a wide range of mutations: 7 possessed functional variants in LDLR, 3 possessed functional variants in PCSK9, and 6 possessed functional variants in APOB. Using the Dutch Lipid Clinic Network or the Make Early Diagnosis to Prevent Early Death criteria, only 23 (50% [23 of 46]) of genetically confirmed patients would have been classified as having possible FH (Figure [C]). Of the 46 diagnosed patients, the median LDL cholesterol was 196 (interquartile range, 151.25–217.5) mg/dL and the median total cholesterol was 275.5 (interquartile range, 233.25–301.25) mg/dL.We then determined the impact of the single consultation visit in improving the clinical management of the participants. While less than half of the patients were diagnosed with FH, most of the patients in the initiative saw changes in their clinical management regardless of their final diagnoses. Among the 92 patients who visited the preventive cardiology clinic for the first time, 9 (10%) had an LDL cholesterol level exceeding 190 mg/dL and 49 (53%) underwent changes in clinical management. The most common changes were intensification (39% [19 of 49]) or initiation (35% [17 of 49]) of statin regimen (Figure [B]). If the patient tested genetically positive, they received further consultation regarding cascade screening for family members.Local Challenges in ImplementationThere were several challenges in the implementation of the algorithm unique and nonspecific to this initiative. First, while we report the diagnosis rates and clinical characteristics of flagged patients who were evaluated at the clinic, based on our current cohort of patients, we cannot draw any robust conclusions about model performance in detecting FH without a control arm consisting of patients not flagged by the algorithm (FIND FH probability, <0.2). However, another prospective study at UPHS (IN TANDEM [Integrating Active Case-finding With Next-generation Sequencing for Diagnosis Through Electronic Medical Records]; https://www.clinicaltrials.gov; unique identifier: NCT03253432) validating the performance of FIND FH in predicting FH patients at different risk scores is currently in progress. Second, while provider participation was moderately high (53%), patient participation was low despite the offer of genetic testing free of charge. Patients who refused scheduling provided several explanations for doing so, including lack of awareness of FH and cardiovascular prevention, method of contact from the Preventive Cardiology office instead of PCP, long drive to visit the clinic from hospitals and clinics in other regions and states, inability to afford billed visit or parking, and wanting to directly followup with a PCP. Lastly, while patients with an International Classification of Diseases code for FH (E78.01) were excluded, the inconsistency in the usage of the code resulted in the algorithm flagging several known FH patients. Given that the International Classification of Diseases, Tenth Revision code for FH was approved by the Centers for Medicare and Medicaid Services in 2016, the limited usage of this International Classification of Diseases code was understandable.Translation to Other SettingsWe need innovation in improving response rates if the FIND FH algorithm and other EMR-based tools are to have a meaningful impact. Namely, the low yield (3% [153 of 5006]) of patients who were ultimately seen in the clinic suggests that there is high resistance in both getting the patient referred to a secondary clinic by a provider and convincing the patient to schedule a visit after the referral. In this study, the majority of the flagged patients (57%) were deemed ineligible for contact due to lack of or negative provider response. In other settings, the specialty clinic could consider prioritizing increased buy-in and coordination with super-FH providers or PCPs who see a high number of patients flagged by the FIND FH algorithm. Providing education and data about the performance of the algorithm to all PCPs in the program before outreach may also enhance awareness and subsequent care for FH in primary care settings. In addition, prioritizing providers whose flagged patients have on average higher FIND FH probabilities may increase successful outreach. Overall, we expect that the greatest opportunity for improving efficacy of outreach lies in primary care settings, and future work will focus on creating an outreach model that encourages PCPs to provide more formal referrals to the clinic instead of direct electronic messages to the patients from the clinic. One way to achieve this may be to establish staff at high-volume primary care sites to discuss the initiative with patients and providers who may be interested. An alternate EMR-based notification model where a provider receives an electronic notification during a scheduled encounter with a flagged patient could also encourage referrals. Implementations in other settings should also carefully consider how patients may perceive the value proposition of the cost and time associated with scheduling a new outpatient visit for FH workup. For example, the first phase of this study was completed before the outbreak of coronavirus disease 2019 (COVID-19) and before virtual medical visits were implemented at the Center for Preventive Cardiology; the implementation may have seen much greater uptake if virtual visitation was made available.Summary and Future DirectionsIn a large health care system, we prospectively implemented a random forest algorithm, FIND FH, through a quality improvement initiative to perform targeted screening for FH. The algorithm identified a population of patients at an elevated risk for FH, who were invited to be evaluated for FH through our outreach program. While a low percentage of eligible patients ultimately scheduled a visit to our Center for Preventive Cardiology, among those patients who were evaluated, 30% were diagnosed with FH. Most of the first-time patients who visited the clinic saw modification in lipid-lowering treatments, mostly intensification or initiation of statin therapy.In summary, our implementation of FIND FH in UPHS demonstrates well-recognized challenges in scaling and increasing the utility of machine learning algorithms for screening diseases with a range of phenotypes.4 Future directions for the implementation study include working closely with PCPs before future implementation to discuss potential concerns about FIND FH, convening focus groups of patients and physicians to understand how they would like to be approached and what would motivate them to participate in the initiative, and having the initial invitation to the patients come from both the PCP and Preventive Cardiology Center with the option for virtual or in-person visits. Our study ultimately points out the need for better dissemination and implementation studies of highly specialized machine learning tools in a rare disease prevention setting.AcknowledgmentsThis study was supported by The Familial Hypercholesterolemia Foundation that developed the FIND FH machine learning algorithm. We would like to thank William Howard, Mary McGowan, Amanda Sheldon, Dave Staszak, Cynthia Mays, Kylie Boehler, Jeff Radcliff, Carrie Castonguay, and David Wrenn for their feedback on the manuscript. We would also like to thank the Penn Data Analytics Center for their assistance in assembling the information used in this study.Sources of FundingThis quality improvement initiative was funded by The Familial Hypercholesterolemia Foundation. Funding for genetic testing was provided by Quest Diagnostics. The FIND FH program was funded in part by Amgen.Disclosures None.FootnotesDouglas Jacoby, MD, University of Pennsylvania, Pennsylvania Hospital, Farm Journal Bldg, 3rd Floor, 230 W Washington Sq Philadelphia, PA 19106. Email douglas.[email protected]upenn.eduReferences1. Abul-Husn NS, Manickam K, Jones LK, Wright EA, Hartzel DN, Gonzaga-Jauregui C, O'Dushlaine C, Leader JB, Lester Kirchner H, Lindbuchler DM, et al.. Genetic identification of familial hypercholesterolemia within a single U.S. health care system.Science. 2016; 354:aaf7000. doi: 10.1126/science.aaf7000CrossrefMedlineGoogle Scholar2. Gidding SS, Champagne MA, de Ferranti SD, Defesche J, Ito MK, Knowles JW, McCrindle B, Raal F, Rader D, Santos RD, et al.; American Heart Association Atherosclerosis, Hypertension, and Obesity in Young Committee of Council on Cardiovascular Disease in Young, Council on Cardiovascular and Stroke Nursing, Council on Functional Genomics and Translational Biology, and Council on Lifestyle and Cardiometabolic Health. The agenda for familial hypercholesterolemia: a scientific statement from the American Heart Association.Circulation. 2015; 132:2167–2192. doi: 10.1161/CIR.0000000000000297LinkGoogle Scholar3. McGowan MP, Hosseini Dehkordi SH, Moriarty PM, Duell PB. Diagnosis and treatment of heterozygous familial hypercholesterolemia.J Am Heart Assoc. 2019; 8:e013225. doi: 10.1161/JAHA.119.013225LinkGoogle Scholar4. Safarova MS, Liu H, Kullo IJ. Rapid identification of familial hypercholesterolemia from electronic health records: the SEARCH study.J Clin Lipidol. 2016; 10:1230–1239. doi: 10.1016/j.jacl.2016.08.001CrossrefMedlineGoogle Scholar5. Myers KD, Knowles JW, Staszak D, Shapiro MD, Howard W, Yadava M, Zuzick D, Williamson L, Shah NH, Banda JM, et al.. Precision screening for familial hypercholesterolaemia: a machine learning study applied to electronic health encounter data.Lancet Digital Health. 2019; 1:e393–e402. doi: 10.1016/S2589-7500(19)30150-5CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetails June 2021Vol 14, Issue 6Article InformationMetrics Download: 225 © 2021 American Heart Association, Inc.https://doi.org/10.1161/CIRCOUTCOMES.120.007641PMID: 34107738 Originally publishedJune 10, 2021 Keywordsquality improvementgenetic testinghyperlipoproteinemia type IIrisk factorsmass screeningPDF download SubjectsQuality and OutcomesLipids and CholesterolMachine Learning and Artificial IntelligencePrecision Medicine

The relationship between commonly occurring genetic variants (G1 and G2) in the APOL1 gene in African Americans and different disease traits, such as kidney disease, cardiovascular disease, and pre-eclampsia, remains the subject of controversy. Here we took a genotype-first approach, a phenome-wide association study, to define the spectrum of phenotypes associated with APOL1 high-risk variants in 1,837 African American participants of Penn Medicine Biobank and 4,742 African American participants of Vanderbilt BioVU. In the Penn Medicine Biobank, outpatient creatinine measurement-based estimated glomerular filtration rate and multivariable regression models were used to evaluate the association between high-risk APOL1 status and renal outcomes. In meta-analysis of both cohorts, the strongest phenome-wide association study associations were for the high-risk APOL1 variants and diagnoses codes were highly significant for "kidney dialysis" (odds ratio 3.75) and "end stage kidney disease" (odds ratio 3.42). A number of phenotypes were associated with APOL1 high-risk genotypes in an analysis adjusted only for demographic variables. However, no associations were detected with non-renal phenotypes after controlling for chronic/end stage kidney disease status. Using calculated estimated glomerular filtration rate -based phenotype analysis in the Penn Medicine Biobank, APOL1 high-risk status was associated with prevalent chronic/end stage kidney disease /kidney transplant (odds ratio 2.27, 95% confidence interval 1.67-3.08). In high-risk participants, the estimated glomerular filtration rate was 15.4 mL/min/1.73m2; significantly lower than in low-risk participants. Thus, although APOL1 high-risk variants are associated with a range of phenotypes, the risks for other associated phenotypes appear much lower and in our dataset are driven by a primary effect on renal disease.

Introduction: Familial hypercholesterolemia (FH) is a common underdiagnosed and undertreated condition that leads to premature cardiovascular disease. The FH Foundation developed a machine learning...

Background: Recently, guidelines have recognized elevated Lp(a) as a clinical risk factor significantly and independently associated with cardiovascular disease (CVD). Another important CVD risk factor, hypertension, has been well-described. However, few real-world studies that explore the association between lipoprotein(a) and hypertension have been conducted. Objective: To evaluate hypertension in patients with serum lipoprotein(a) [Lp(a)] at a large tertiary academic center. Methods: The University of Pennsylvania Health System was queried for all patients with an Lp(a) result between 1999-2019. The first Lp(a) result was chosen for patients with multiple Lp(a) results. In this cohort of patients, the presence of hypertension was queried by hypertension ICD-9 and ICD-10 codes. The cohort was stratified by Lp(a) tertile and the presence of hypertension was evaluated. A chi-squared analysis was performed to test for association between Lp(a) tertile and presence of hypertension. Results: The query returned a cohort of 8120 patients with a median and interquartile range of Lp(a) of 22 mg/dL and 8-64 mg/dL, respectively. The cohort had a median age of 54 years, 54% (n=4379) were men, and 21% (n=1705) were non-white. Hypertension was reported in 51% (n=4155) of patients. The tertile with low Lp(a) [median, 6 mg/dL; IQR, 4-8 mg/dL] had hypertension in 50.1% (n=1357) of patients. The tertile with moderate Lp(a) [22 mg/dL, 16-33 mg/dL] had hypertension in 50.5% (n=1367) of patients. The tertile with high Lp(a) [84 mg/dL, 64-116 mg/dL] had hypertension in 51.0% (n=1381) of patients. The association between Lp(a) tertile and presence of hypertension was insignificant ( p-value, 0.81). Conclusions: In this real-world cohort, the data suggest that Lp(a) and hypertension are not associated. New clinical trials that test Lp(a)-lowering pharmacotherapies may enable further characterization of the association between Lp(a) and hypertension in order to inform CVD prevention.

BACKGROUND:Exome sequencing is a promising tool for gene mapping in Mendelian disorders. We used this technique in an attempt to identify novel genes underlying monogenic dyslipidemias.METHODS AND RESULTS:We performed exome sequencing on 213 selected family members from 41 kindreds with suspected Mendelian inheritance of extreme levels of low-density lipoprotein cholesterol (after candidate gene sequencing excluded known genetic causes for high low-density lipoprotein cholesterol families) or high-density lipoprotein cholesterol. We used standard analytic approaches to identify candidate variants and also assigned a polygenic score to each individual to account for their burden of common genetic variants known to influence lipid levels. In 9 families, we identified likely pathogenic variants in known lipid genes (ABCA1, APOB, APOE, LDLR, LIPA, and PCSK9); however, we were unable to identify obvious genetic etiologies in the remaining 32 families, despite follow-up analyses. We identified 3 factors that limited novel gene discovery: (1) imperfect sequencing coverage across the exome hid potentially causal variants; (2) large numbers of shared rare alleles within families obfuscated causal variant identification; and (3) individuals from 15% of families carried a significant burden of common lipid-related alleles, suggesting complex inheritance can masquerade as monogenic disease.CONCLUSIONS:We identified the genetic basis of disease in 9 of 41 families; however, none of these represented novel gene discoveries. Our results highlight the promise and limitations of exome sequencing as a discovery technique in suspected monogenic dyslipidemias. Considering the confounders identified may inform the design of future exome sequencing studies.

Objective—Plasma levels of high-density lipoprotein cholesterol (HDL-C) are strongly inversely associated with coronary artery disease (CAD), and high HDL-C is generally associated with reduced risk of CAD. Extremely high HDL-C with CAD is an unusual phenotype, and we hypothesized that the HDL in such individuals may have an altered composition and reduced function when compared with controls with similarly high HDL-C and no CAD. Approach and Results—Fifty-five subjects with very high HDL-C (mean, 86 mg/dL) and onset of CAD at the age of ≈60 years with no known risk factors for CAD (cases) were identified through systematic recruitment. A total of 120 control subjects without CAD, matched for race, sex, and HDL-C level (controls), were identified. In all subjects, HDL composition was analyzed and HDL cholesterol efflux capacity was assessed. HDL phospholipid composition was significantly lower in cases (92±37 mg/dL) than in controls (109±43 mg/dL; P=0.0095). HDL cholesterol efflux capacity was significantly lower in cases (1.96±0.39) than in controls (2.11±0.43; P=0.04). Conclusions—In people with very high HDL-C, reduced HDL phospholipid content and cholesterol efflux capacity are associated with the paradoxical development of CAD.

Plasma levels of high-density lipoprotein cholesterol (HDL-C) are strongly inversely associated with coronary artery disease (CAD) in epidemiologic studies, and high HDL-C is generally associated with apparent ‘protection’ from CAD. We have been recruiting individuals with high HDL-C for about 15 years, and while most have no CAD, a minority has premature CAD, a paradoxical phenotype. We hypothesize that such individuals may have HDL with altered structure and/ or function, and are systematically comparing these individuals (cases) to older individuals with extreme high HDL-C without CAD (controls) and a healthy control group with normal HDL-C levels. We identified 60 subjects with HDL-C above the 90th percentile, premature CAD, and no other major risk factors for coronary disease. We selected 2 controls per case, each matched for age, race, gender, and HDL level. Demographic information and lipid profile (mean ± SD) of the study groups are shown below. Controls are well matched to the cases. Studies are well underway to assess HDL size distribution by NMR, HDL composition, total and ABCA1-specific cholesterol efflux capacity, lecithin-cholesterol acyltransferase (LCAT) activity, and cholesteryl ester transfer protein (CETP) activity in cases and controls. We will also compare to a group of healthy controls with normal HDL-C levels. We expect that the findings from this study will provide insight into the etiology of CAD in this paradoxical phenotype.