Detection of familial hypercholesterolaemia: external validation of the FAMCAT clinical case-finding algorithm to identify patients in primary care

Clinical and genetic diagnosis of familial hypercholesterolaemia in patients undergoing coronary angiography: the Ludwigshafen Risk and Cardiovascular Health Study

AIMS

To investigate the prevalence of familial hypercholesterolaemia (FH) and compare the performance of clinical criteria and genetic testing in patients undergoing coronary angiography.

METHODS AND RESULTS

The prevalence of FH was determined with the Dutch Lipid Clinical Network (DLCN), US 'Make Early Diagnosis to Prevent Early Death' (US-MEDPED), Simon Broome (SB) criteria, the 'familial hypercholesterolaemia case ascertainment tool' (FAMCAT), and a clinical algorithm. Genetic screening was conducted with a custom array from Affymetrix (CARRENAL array) harbouring 944 FH mutations.The study cohort consisted of 3267 patients [78.6% with coronary artery disease (CAD)]. FH was diagnosed in 2.8%, 2.2%, 3.9%, and 7.9% using the DLCN, US-MEDPED, SB criteria, and the FAMCAT. The clinical algorithm identified the same patients as the SB criteria. Pathogenic FH mutations were found in 1.2% (1.2% in patients with CAD, 1.0% in patients without CAD). FH was more frequently diagnosed in younger patients. With genetic testing as reference, the clinical criteria achieved areas under the ROC curve [area under the curves (AUCs)] in the range of 0.56-0.68. Using only low-density lipoprotein cholesterol (LDL-C) corrected for statin intake, an AUC of 0.68 was achieved.

CONCLUSION

FH is up to four-fold more prevalent in patients undergoing coronary angiography than in contemporary cohorts representing the general population. Different clinical criteria yield substantially different diagnosis rates, overestimating the prevalence of FH compared with genetic testing. LDL-C testing alone may be sufficient to raise the suspicion of FH, which then needs to be corroborated by genetic testing.

LAY SUMMARY

In this study, we investigated the frequency of familial hypercholesterolaemia-a common genetic condition leading to markedly elevated low-density lipoprotein (LDL) cholesterol and increased risk of atherosclerosis-in 3267 patients undergoing coronary angiography according to commonly used diagnostic scoring systems and genetic testing.

A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department

This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision-recall curve (AUCPR), Hosmer-Lemeshow (H-L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital's ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802-0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787-0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality.

A case report of heterozygous familial hypercholesterolaemia with LDLR gene mutation complicated by premature coronary artery disease detected in primary care

Background

Familial hypercholesterolaemia (FH) is an autosomal dominant genetic condition predominantly caused by the low-density lipoprotein receptor (LDLR) gene mutation.

Case summary

This is the case of a 54-year-old Malay woman with genetically confirmed FH complicated by premature coronary artery disease (PCAD). She was clinically diagnosed in primary care at 52 years old, fulfilling the Simon Broome Criteria (possible FH), Dutch Lipid Clinic Criteria (score of 8: probable FH), and Familial Hypercholesterolaemia Case Ascertainment Tool (relative risk score of 9.51). Subsequently, she was confirmed to have a heterozygous LDLR c.190+4A>T intron 2 pathogenic variant at the age of 53 years. She was known to have hypercholesterolaemia and was treated with statin since the age of 25. However, the lipid-lowering agent was not intensified to achieve the recommended treatment target. The delayed FH diagnosis has caused this patient to have PCAD and percutaneous coronary intervention (PCI) at the age of 29 years and a second PCI at the age of 49 years. She also has a very strong family history of hypercholesterolaemia and PCAD, where seven out of eight of her siblings were affected. Despite this, FH was not diagnosed early, and cascade screening of family members was not conducted, resulting in a missed opportunity to prevent PCAD.

Discussion

Familial hypercholesterolaemia can be clinically diagnosed in primary care to identify those who may require genetic testing. Multidisciplinary care focuses on improving identification, cascade screening, and management of FH, which is vital to improving prognosis and ultimately preventing PCAD.

Generation and validation of a classification model to diagnose familial hypercholesterolaemia in adults

BACKGROUND AND AIMS

The early diagnosis of familial hypercholesterolaemia is associated with a significant reduction in cardiovascular disease (CVD) risk. While the recent use of statistical and machine learning algorithms has shown promising results in comparison with traditional clinical criteria, when applied to screening of potential FH cases in large cohorts, most studies in this field are developed using a single cohort of patients, which may hamper the application of such algorithms to other populations. In the current study, a logistic regression (LR) based algorithm was developed combining observations from three different national FH cohorts, from Portugal, Brazil and Sweden. Independent samples from these cohorts were then used to test the model, as well as an external dataset from Italy.

METHODS

The area under the receiver operating characteristics (AUROC) and precision-recall (AUPRC) curves was used to assess the discriminatory ability among the different samples. Comparisons between the LR model and Dutch Lipid Clinic Network (DLCN) clinical criteria were performed by means of McNemar tests, and by the calculation of several operating characteristics.

RESULTS

AUROC and AUPRC values were generally higher for all testing sets when compared to the training set. Compared with DLCN criteria, a significantly higher number of correctly classified observations were identified for the Brazilian (p < 0.01), Swedish (p < 0.01), and Italian testing sets (p < 0.01). Higher accuracy (Acc), G mean and F1 score values were also observed for all testing sets.

CONCLUSIONS

Compared to DLCN criteria, the LR model revealed improved ability to correctly classify observations, and was able to retain a similar number of FH cases, with less false positive retention. Generalization of the LR model was very good across all testing samples, suggesting it can be an effective screening tool if applied to different populations.

Subtyping Severe Hypercholesterolemia by Genetic Determinant to Stratify Risk of Coronary Artery Disease

BACKGROUND

Severe hypercholesterolemia, defined as LDL (low-density lipoprotein) cholesterol (LDL-C) measurement ≥190 mg/dL, is associated with increased risk for coronary artery disease (CAD). Causes of severe hypercholesterolemia include monogenic familial hypercholesterolemia, polygenic hypercholesterolemia, elevated lipoprotein(a) [Lp(a)] hypercholesteremia, polygenic hypercholesterolemia with elevated Lp(a) (two-hit), or nongenetic hypercholesterolemia. The added value of using a genetics approach to stratifying risk of incident CAD among those with severe hypercholesterolemia versus using LDL-C levels alone for risk stratification is not known.

METHODS

To determine whether risk stratification by genetic cause provided better 10-year incident CAD risk stratification than LDL-C level, a retrospective cohort study comparing incident CAD risk among severe hypercholesterolemia subtypes (genetic and nongenetic causes) was performed among 130 091 UK Biobank participants. Analyses were limited to unrelated, White British or Irish participants with available exome sequencing data. Participants with cardiovascular disease at baseline were excluded from analyses of incident CAD.

RESULTS

Of 130 091 individuals, 68 416 (52.6%) were women, and the mean (SD) age was 56.7 (8.0) years. Of the cohort, 9.0% met severe hypercholesterolemia criteria. Participants with LDL-C between 210 and 229 mg/dL and LDL-C ≥230 mg/dL showed modest increases in incident CAD risk relative to those with LDL-C between 190 and 209 mg/dL (210-229 mg/dL: hazard ratio [HR], 1.3 [95% CI, 1.1-1.7]; ≥230 mg/dL: HR, 1.3 [95% CI, 1.0-1.7]). In contrast, when risk was stratified by genetic subtype, monogenic familial hypercholesterolemia, elevated Lp(a), and two-hit hypercholesterolemia subtypes had increased rates of incident CAD relative to the nongenetic hypercholesterolemia subtype (monogenic familial hypercholesterolemia: HR, 2.3 [95% CI, 1.4-4.0]; elevated Lp(a): HR, 1.5 [95% CI, 1.2-2.0]; two-hit: HR, 1.9 [95% CI, 1.4-2.6]), while polygenic hypercholesterolemia did not.

CONCLUSIONS

Genetics-based subtyping for monogenic familial hypercholesterolemia and Lp(a) in those with severe hypercholesterolemia provided better stratification of 10-year incident CAD risk than LDL-C-based stratification.

Reducing Premature Coronary Artery Disease in Malaysia by Early Identification of Familial Hypercholesterolemia Using the Familial Hypercholesterolemia Case Ascertainment Tool (FAMCAT): Protocol for a Mixed Methods Evaluation Study

BACKGROUND

Familial hypercholesterolemia (FH) is predominantly caused by mutations in the 4 FH candidate genes (FHCGs), namely, low-density lipoprotein receptor (LDLR), apolipoprotein B-100 (APOB-100), proprotein convertase subtilisin/kexin type 9 (PCSK9), and the LDL receptor adaptor protein 1 (LDLRAP1). It is characterized by elevated low-density lipoprotein cholesterol (LDL-c) levels leading to premature coronary artery disease. FH can be clinically diagnosed using established clinical criteria, namely, Simon Broome (SB) and Dutch Lipid Clinic Criteria (DLCC), and can be identified using the Familial Hypercholesterolemia Case Ascertainment Tool (FAMCAT), a primary care screening tool.

OBJECTIVE

This study aims to (1) compare the detection rate of genetically confirmed FH and diagnostic accuracy between the FAMCAT, SB, and DLCC in the Malaysian primary care setting; (2) identify the genetic mutation profiles, including novel variants, in individuals with suspected FH in primary care; (3) explore the experience, concern, and expectation of individuals with suspected FH who have undergone genetic testing in primary care; and (4) evaluate the clinical utility of a web-based FH Identification Tool that includes the FAMCAT, SB, and DLCC in the Malaysian primary care setting.

METHODS

This is a mixed methods evaluation study conducted in 11 Ministry of Health primary care clinics located at the central administrative region of Malaysia. In Work stream 1, the diagnostic accuracy study design is used to compare the detection rate and diagnostic accuracy of the FAMCAT, SB, and DLCC against molecular diagnosis as the gold standard. In Work stream 2, the targeted next-generation sequencing of the 4 FHCGs is used to identify the genetic mutation profiles among individuals with suspected FH. In Work stream 3a, a qualitative semistructured interview methodology is used to explore the experience, concern, and expectation of individuals with suspected FH who have undergone genetic testing. Lastly, in Work stream 3b, a qualitative real-time observation of primary care physicians using the "think-aloud" methodology is applied to evaluate the clinical utility of a web-based FH Identification Tool.

RESULTS

The recruitment for Work stream 1, and blood sampling and genetic analysis for Work stream 2 were completed in February 2023. Data collection for Work stream 3 was completed in March 2023. Data analysis for Work streams 1, 2, 3a, and 3b is projected to be completed by June 2023, with the results of this study anticipated to be published by December 2023.

CONCLUSIONS

This study will provide evidence on which clinical diagnostic criterion is the best to detect FH in the Malaysian primary care setting. The full spectrum of genetic mutations in the FHCGs including novel pathogenic variants will be identified. Patients' perspectives while undergoing genetic testing and the primary care physicians experience in utilizing the web-based tool will be established. These findings will have tremendous impact on the management of patients with FH in primary care and subsequently reduce their risk of premature coronary artery disease.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/47911.

Electronic health record-based facilitation of familial hypercholesterolaemia detection sensitivity of different algorithms in genetically confirmed patients

Aims

Familial hypercholesterolaemia (FH) is a disorder of LDL cholesterol clearance, resulting in increased risk of cardiovascular disease. Recently, we developed a Dutch Lipid Clinic Network (DLCN) criteria-based algorithm to facilitate FH detection in electronic health records (EHRs). In this study, we investigated the sensitivity of this and other algorithms in a genetically confirmed FH population.

Methods and results

All patients with a healthcare insurance-related coded diagnosis of 'primary dyslipidaemia' between 2018 and 2020 were assessed for genetically confirmed FH. Data were extracted at the time of genetic confirmation of FH (T1) and during the first visit in 2018-2020 (T2). We assessed the sensitivity of algorithms on T1 and T2 for DLCN ≥ 6 and compared with other algorithms [familial hypercholesterolaemia case ascertainment tool (FAMCAT), Make Early Diagnoses to Prevent Early Death (MEDPED), and Simon Broome (SB)] using EHR-coded data and using all available data (i.e. including non-coded free text). 208 patients with genetically confirmed FH were included. The sensitivity (95% CI) on T1 and T2 with EHR-coded data for DLCN ≥ 6 was 19% (14-25%) and 22% (17-28%), respectively. When using all available data, the sensitivity for DLCN ≥ 6 was 26% (20-32%) on T1 and 28% (22-34%) on T2. For FAMCAT, the sensitivity with EHR-coded data on T1 was 74% (67-79%) and 32% (26-39%) on T2, whilst sensitivity with all available data was 81% on T1 (75-86%) and 45% (39-52%) on T2. For Make Early Diagnoses to Prevent Early Death MEDPED and SB, using all available data, the sensitivity on T1 was 31% (25-37%) and 17% (13-23%), respectively.

Conclusions

The FAMCAT algorithm had significantly better sensitivity than DLCN, MEDPED, and SB. FAMCAT has the best potential for FH case-finding using EHRs.

Developing a Hybrid Risk Assessment Tool for Familial Hypercholesterolemia: A Machine Learning Study of Chinese Arteriosclerotic Cardiovascular Disease Patients

Background

Familial hypercholesterolemia (FH) is an autosomal-dominant genetic disorder with a high risk of premature arteriosclerotic cardiovascular disease (ASCVD). There are many alternative risk assessment tools, for example, DLCN, although their sensitivity and specificity vary among specific populations. We aimed to assess the risk discovery performance of a hybrid model consisting of existing FH risk assessment tools and machine learning (ML) methods, based on the Chinese patients with ASCVD.

Materials and Methods

In total, 5,597 primary patients with ASCVD were assessed for FH risk using 11 tools. The three best performing tools were hybridized through a voting strategy. ML models were set according to hybrid results to create a hybrid FH risk assessment tool (HFHRAT). PDP and ICE were adopted to interpret black box features.

Results

After hybridizing the mDLCN, Taiwan criteria, and DLCN, the HFHRAT was taken as a stacking ensemble method (AUC_class[94.85 ± 0.47], AUC_prob[98.66 ± 0.27]). The interpretation of HFHRAT suggests that patients aged <75 years with LDL-c >4 mmol/L were more likely to be at risk of developing FH.

Conclusion

The HFHRAT has provided a median of the three tools, which could reduce the false-negative rate associated with existing tools and prevent the development of atherosclerosis. The hybrid tool could satisfy the need for a risk assessment tool for specific populations.

Introducing genetic testing with case finding for familial hypercholesterolaemia in primary care: qualitative study of patient and health professional experience

BACKGROUND

Familial hypercholesterolaemia (FH) is a common inherited condition causing elevated cholesterol, premature heart disease, and early death. Although FH can be effectively treated, over 80% of people with FH remain undetected.

AIM

To explore patient and health professional experiences of introducing genetic testing with case finding for FH in primary care.

DESIGN AND SETTING

Qualitative study in UK general practice.

METHOD

Semi-structured interviews with a purposeful sample of 41 participants (24 patients and 17 health professionals) from eight practices, using an electronic case-finding tool (FAMCAT) to identify patients with higher likelihood of having FH and who were then offered diagnostic genetic testing in primary care. Data were analysed thematically.

RESULTS

While prior awareness of FH was low, patients were unsurprised to be identified as being at risk, and positive about being offered genetic testing by their practice. Patients not found to have FH were relieved, although some felt frustrated that their high cholesterol lacked a clear cause. Those confirmed to have FH largely expected and accepted this outcome. Practitioners saw detection of FH as an important new opportunity for preventive care. They found the case-finding tool easy to apply and noted patients' high uptake of genetic testing. While they were comfortable referring appropriate patients for further specialist management, GPs sought clearer definition about responsibility for identification and long- term care of FH in future care pathways.

CONCLUSION

Introducing genetic testing with electronic case finding for FH in primary care was positively experienced by patients and practitioners. Further development of this approach could help improve detection of FH in the general population.

Comparative study on the performance of different classification algorithms, combined with pre- and post-processing techniques to handle imbalanced data, in the diagnosis of adult patients with familial hypercholesterolemia

Familial Hypercholesterolemia (FH) is an inherited disorder of cholesterol metabolism. Current criteria for FH diagnosis, like Simon Broome (SB) criteria, lead to high false positive rates. The aim of this work was to explore alternative classification procedures for FH diagnosis, based on different biological and biochemical indicators. For this purpose, logistic regression (LR), naive Bayes classifier (NB), random forest (RF) and extreme gradient boosting (XGB) algorithms were combined with Synthetic Minority Oversampling Technique (SMOTE), or threshold adjustment by maximizing Youden index (YI), and compared. Data was tested through a 10 × 10 repeated k-fold cross validation design. The LR model presented an overall better performance, as assessed by the areas under the receiver operating characteristics (AUROC) and precision-recall (AUPRC) curves, and several operating characteristics (OC), regardless of the strategy to cope with class imbalance. When adopting either data processing technique, significantly higher accuracy (Acc), G-mean and F₁ score values were found for all classification algorithms, compared to SB criteria (p < 0.01), revealing a more balanced predictive ability for both classes, and higher effectiveness in classifying FH patients. Adjustment of the cut-off values through pre or post-processing methods revealed a considerable gain in sensitivity (Sens) values (p < 0.01). Although the performance of pre and post-processing strategies was similar, SMOTE does not cause model’s parameters to loose interpretability. These results suggest a LR model combined with SMOTE can be an optimal approach to be used as a widespread screening tool.

Clinical decision support for familial hypercholesterolemia (CDS-FH): Rationale and design of a cluster randomized trial in primary care

BACKGROUND

Familial hypercholesterolemia (FH) is an underdiagnosed and undertreated genetic disorder with high risk of premature atherosclerotic cardiovascular disease and death. Clinical decision support (CDS) systems have the potential to aid in the identification and management of patients with FH. Prior studies using computer-based systems to screen patients for FH have shown promising results, but there has been no randomized controlled trial conducted. The aim of the current cluster randomized study is to evaluate if a CDS can increase the identification of FH.

METHODS

We have developed a CDS integrated in the electronic health records that will be activated in patients with elevated cholesterol levels (total cholesterol >8 mmol/L or low-density lipoprotein-cholesterol >5.5 mmol/L, adjusted for age, ongoing lipid lowering therapy and presence of premature coronary artery disease) at increased risk for FH. When activated, the CDS will urge the physician to send an automatically generated referral to the local lipid clinic for further evaluation. To evaluate the effects of the CDS, all primary care clinics will be cluster randomized 1:1 to either CDS intervention or standard care in a Swedish region with almost 500,000 inhabitants. The primary endpoint will be the number of patients diagnosed with FH at 30 months. Resource use and long-term health consequences will be estimated to assess the cost-effectiveness of the intervention.

CONCLUSION

Despite increasing awareness of FH, the condition remains underdiagnosed and undertreated. The present study will investigate whether a CDS can increase the number of patients being diagnosed with FH.

Cost-Effectiveness of Screening Algorithms for Familial Hypercholesterolaemia in Primary Care

Although familial hypercholesterolemia (FH) screening within primary care is considered cost-effective, which screening approach is cost-effective has not been established. This study determines the cost-effectiveness of six case-finding strategies for screening of electronic health records to identify index patients who have genetically confirmed monogenic FH in English primary care. A decision tree was constructed to represent pathways of care for each approach (FH Case Identification Tool (FAMCAT) versions 1 and 2, cholesterol screening, Dutch Lipid Clinic Network (DLCN), Simon Broome criteria, no active screening). Clinical effectiveness was measured as the number of monogenic FH cases identified. Healthcare costs for each algorithm were evaluated from an NHS England perspective over a 12 week time horizon. The primary outcome was the incremental cost per additional monogenic FH case identified (ICER). FAMCAT2 was found to dominate (cheaper and more effective) cholesterol and FAMCAT1 algorithms, and extendedly dominate DLCN. The ICER for FAMCAT2 vs. no active screening was 8111 GBP (95% CI: 4088 to 14,865), and for Simon Broome vs. FAMCAT2 was 74,059 GBP (95% CI: -1,113,172 to 1,697,142). Simon Broome found the largest number of FH cases yet required 102 genetic tests to identify one FH patient. FAMCAT2 identified fewer, but only required 23 genetic tests.

Role of an automated screening tool for familial hypercholesterolemia in patients with premature coronary artery disease

Background and aims

To validate an automated screening tool for patients with premature coronary artery disease (CAD) and high total cholesterol or LDL-C levels and assess if it would provide clinicians with additional support in identifying patients with Familial Hypercholesterolemia (FH).

Methods

An IT-based automated screening tool based on coronary angiography data recorded in the KARDIO registry and laboratory values was validated among patients undergone coronary angiography in the Heart Hospital at Tampere University Hospital between 2007 and 2017 fulfilling the criteria of premature CAD (men <55 years and women <60 years) and history of high total cholesterol (>8 mmol/l) or LDL-cholesterol (>5 mmol/l) levels. Electronic health records were retrospectively analyzed to determine if these patients had been diagnosed with FH based on clinical features and whether genetic testing had been conducted.

Results

The automated screening tool identified 0.7% (211/28295) of all patients undergone coronary angiography and revealed history of high cholesterol in 8% (211/2678) of patients with premature CAD during the study period. Fifty-one percent (107/211) of these patients fulfilled the clinical criteria for probable/definite FH based on the Dutch Lipid Clinic Network (DLCN) criteria.None of the patients had been diagnosed with FH based on clinical criteria before or after diagnosis of CAD. Thirteen percent of patients (n = 14) with probable/definite FH had been tested for genetic mutations of FH before or after CAD, five (36%) of them having a pathogenic FH variant. Two patients were referred to cascade screening.

Conclusions

FH was underdiagnosed among the population studied. An automated screening tool in cardiac care could provide additional support for clinicians in diagnosing patients potentially having FH.

Case-finding and genetic testing for familial hypercholesterolaemia in primary care

OBJECTIVE

Familial hypercholesterolaemia (FH) is a common inherited disorder that remains mostly undetected in the general population. Through FH case-finding and direct access to genetic testing in primary care, this intervention study described the genetic and lipid profile of patients found at increased risk of FH and the outcomes in those with positive genetic test results.

METHODS

In 14 Central England general practices, a novel case-finding tool (Familial Hypercholetserolaemia Case Ascertainment Tool, FAMCAT1) was applied to the electronic health records of 86 219 patients with cholesterol readings (44.5% of total practices' population), identifying 3375 at increased risk of FH. Of these, a cohort of 336 consenting to completing Family History Questionnaire and detailed review of their clinical data, were offered FH genetic testing in primary care.

RESULTS

Genetic testing was completed by 283 patients, newly identifying 16 with genetically confirmed FH and 10 with variants of unknown significance. All 26 (9%) were recommended for referral and 19 attended specialist assessment. In a further 153 (54%) patients, the test suggested polygenic hypercholesterolaemia who were managed in primary care. Total cholesterol and low-density lipoprotein-cholesterol levels were higher in those patients with FH-causing variants than those with other genetic test results (p=0.010 and p=0.002).

CONCLUSION

Electronic case-finding and genetic testing in primary care could improve identification of FH; and the better targeting of patients for specialist assessment. A significant proportion of patients identified at risk of FH are likely to have polygenic hypercholesterolaemia. There needs to be a clearer management plan for these individuals in primary care.

TRIAL REGISTRATION NUMBER

NCT03934320.

Comparing the performance of the novel FAMCAT algorithms and established case-finding criteria for familial hypercholesterolaemia in primary care

OBJECTIVE

Familial hypercholesterolaemia (FH) is a common inherited disorder causing premature coronary heart disease (CHD) and death. We have developed the novel Familial Hypercholesterolaemia Case Ascertainment Tool (FAMCAT 1) case-finding algorithm for application in primary care, to improve detection of FH. The performance of this algorithm was further improved by including personal history of premature CHD (FAMCAT 2 algorithm). This study has evaluated their performance, at 95% specificity, to detect genetically confirmed FH in the general population. We also compared these algorithms to established clinical case-finding criteria.

METHODS

Prospective validation study, in 14 general practices, recruiting participants from the general adult population with cholesterol documented. For 260 participants with available health records, we determined possible FH cases based on FAMCAT thresholds, Dutch Lipid Clinic Network (DLCN) score, Simon-Broome criteria and recommended cholesterol thresholds (total cholesterol >9.0 mmol/L if ≥30 years or >7.5 mmol/L if <30 years), using clinical data from electronic and manual extraction of patient records and family history questionnaires. The reference standard was genetic testing. We examined detection rate (DR), sensitivity and specificity for each case-finding criteria.

RESULTS

At 95% specificity, FAMCAT 1 had a DR of 27.8% (95% CI 12.5% to 50.9%) with sensitivity of 31.2% (95% CI 11.0% to 58.7%); while FAMCAT 2 had a DR of 45.8% (95% CI 27.9% to 64.9%) with sensitivity of 68.8% (95% CI 41.3% to 89.0%). DLCN score ≥6 points yielded a DR of 35.3% (95% CI 17.3% to 58.7%) and sensitivity of 37.5% (95% CI 15.2% to 64.6%). Using recommended cholesterol thresholds resulted in DR of 28.0% (95% CI 14.3% to 47.6%) with sensitivity of 43.8% (95% CI 19.8% to 70.1%). Simon-Broome criteria had lower DR 11.3% (95% CI 6.0% to 20.0%) and specificity 70.9% (95% CI 64.8% to 76.5%) but higher sensitivity of 56.3% (95% CI 29.9% to 80.2%).

CONCLUSIONS

In primary care, in patients with cholesterol documented, FAMCAT 2 performs better than other case-finding criteria for detecting genetically confirmed FH, with no prior clinical review required for case finding.

TRIAL REGISTRATION NUMBER

NCT03934320.

Beyond the Usual Suspects: Expanding on Mutations and Detection for Familial Hypercholesterolemia

Introduction: Familial hypercholesterolemia (FH) is a highly prevalent condition, predisposing individuals to premature cardiovascular disease and with a genetic basis more complex than initially thought. Advances in molecular technologies have provided novel insights into the role of next-generation-sequencing, the assessment and classification of newly found variants, the complex genotype-phenotype correlation, and the position of FH in the context of other dyslipidaemias.Areas covered: Understanding the scope of genetic determinants of FH has expanded substantially. This article reviews the current literature on the complexity that comes with this incremental knowledge and highlights the added value of genetic testing as an addition to phenotypic diagnosis of FH. Moreover, we discuss the broad genetic basis of FH, with a focus on the three main FH genes, but we also pay attention to polygenic hypercholesterolemia as well as minor and modulator genes involved in FH.Expert opinion: Both the availability and the need for genetic analysis of FH are on the rise as costs of sequencing continue to drop and new therapies require a genetic diagnosis for reimbursement. However, greater use of genetic testing requires more education of healthcare professionals, since molecular technologies will allow for rapid and accurate evaluation of large numbers of detected variants.

Application of a risk stratification tool for familial hypercholesterolaemia in primary care: an observational cross-sectional study in an unselected urban population

OBJECTIVE

The Familial Hypercholesterolaemia Case Ascertainment Tool (FAMCAT) has been proposed to enhance case finding in primary care. In this study, we test application of the FAMCAT algorithm to describe risks of familial hypercholesterolaemia (FH) in a large unselected and ethnically diverse primary care cohort.

METHOD

We studied patients aged 18-65 years from three contiguous areas in inner London. We retrospectively applied the FAMCAT algorithm to routine primary care data and estimated the numbers of possible cases of FH and the potential service implications of subsequent investigation and management.

RESULTS

Of the 777 128 patients studied, the FAMCAT score estimated between 11 736 and 23 798 (1.5%-3.1%) individuals were likely to have FH, depending on an assumed FH prevalence of 1 in 250 or 1 in 500, respectively. There was over-representation of individuals of South Asian ethnicity among those likely to have FH, with this cohort making up 41.9%-45.1% of the total estimated cases, a proportion which significantly exceeded their 26% representation in the study population.

CONCLUSIONS

We have demonstrated feasibility of application of the FAMCAT as an aid to case finding for FH using routinely recorded primary care data. Further research is needed on validity of the tool in different ethnic groups and more refined consideration of family history should be explored. While FAMCAT may aid case finding, implementation requires information on the cost-effectiveness of additional health services to investigate, diagnose and manage case ascertainment in those identified as likely to have FH.

Improving detection and management of familial hypercholesterolaemia in Australian general practice

OBJECTIVE

Familial hypercholesterolaemia (FH) is characterised by elevated low-density lipoprotein (LDL)-cholesterol and increased risk of cardiovascular disease. However, FH remains substantially underdiagnosed and undertreated. We employed a two-stage pragmatic approach to identify and manage patients with FH in primary healthcare.

METHODS

Medical records for 232 139 patients who attended 15 general practices at least once in the previous 2 years across five Australian States were first screened for potential risk of FH using an electronic tool (TARB-Ex) and confirmed by general practitioner (GP) clinical assessment based on phenotypic Dutch Lipid Clinic Network Criteria (DLCNC) score. Follow-up GP consultation and management was provided for patients with phenotypic FH.

RESULTS

A total of 1843 patients were identified by TARB-Ex as at potential risk of FH (DLCNC score ≥5). After GP medical record review, 900 of these patients (49%) were confirmed with DLCNC score ≥5 and classified as high-risk of FH. From 556 patients subsequently clinically assessed by GPs, 147 (26%) were diagnosed with phenotypic FH (DLCNC score >6). Follow-up GP consultation and management for 77 patients resulted in a significant reduction in LDL-cholesterol (-16%, p<0.01). A higher proportion of these patients attained the treatment target of 50% reduction in LDL-cholesterol (74% vs 62%, p<0.001) and absolute levels of LDL-cholesterol goals compared with baseline (26% vs 12%, p<0.05).

CONCLUSIONS

A pragmatic approach integrating electronic medical record tools and clinical GP follow-up consultation is a feasible method to identify and better manage patients with FH in the primary healthcare setting.

TRIAL REGISTRATION NUMBER

12616000630415.

Therapeutic inertia in the management of dyslipidaemia and hypertension in incident type 2 diabetes and the resulting risk factor burden: Real-world evidence from primary care

OBJECTIVE

To investigate trends in the prevalence of hypertension and dyslipidaemia in incident type 2 diabetes (T2DM), time to antihypertensive (AHT) and lipid-lowering therapy (LLT), and the association with systolic blood pressure (SBP) and lipid control.

RESEARCH DESIGN AND METHODS

Using The Health Improvement Network UK primary care database, 254 925 people with incident T2DM and existing dyslipidaemia or hypertension were identified. Among those without atherosclerotic cardiovascular disease (ASCVD) history and not on AHT or LLT at diagnosis, the adjusted median months to initiating an AHT or an LLT, and the probabilities of high SBP or lipid levels over 2 years in people initiating therapy within or after 1 year were evaluated according to high and low ASCVD risk status.

RESULTS

At diabetes diagnosis, 66% and 66% had dyslipidaemia and hypertension, respectively. During 2005 to 2016, dyslipidaemia prevalence increased by 10% in people aged <60 years, while hypertension prevalence remained stable in all age groups. Among those with high ASCVD risk status in the age groups 18 to 39, 40 to 49, and 50 to 59 years, the median number of months to initiation of therapy were 20.4 (95% confidence interval [CI] 20.3-20.5), 10.9 (95% CI 10.8-11.0), and 9.5 (95% CI 9.4-9.6) in the dyslipidaemia subcohort, and 28.1 (95% CI 28.0-28.2), 19.2 (95% CI 19.1-19.3), and 19.9 (95% CI 19.8-20.0) in the hypertension subcohort. Among people with high and low ASCVD risk status, respectively, compared to early LLT initiators, those who initiated LLT after 1 year had a 65.3% to 85.3% and a 65.0% to 85.3% significantly higher probability of failing lipid control at 2 years of follow-up, while late AHT initiators had a 46.5% to 57.9% and a 40.0% to 58.7% significantly higher probability of failing SBP control.

CONCLUSIONS

Significant delay in initiating cardioprotective therapies was observed, and time to first prescription was similar in the primary prevention setting, irrespective of ASCVD risk status across all T2DM diagnosis age groups, resulting in poor risk factor control at 2 years of follow-up.

Improvement of Definite Diagnosis of Familial Hypercholesterolemia Using an Expanding Genetic Analysis

BACKGROUND

The deeper understanding of the complex hereditary basis of familial hypercholesterolemia (FH) has raised the rationale of genetic testing, which has been underutilized in clinical practice.

OBJECTIVES

The present study aimed to explore the variant spectrum of FH in an expanding manner and compare its diagnostic performance.

METHODS

A total of 169 Chinese individuals (124 index cases and 45 relatives) with clinical definite/probable FH were consecutively enrolled. Next-generation sequencing was performed for genetic analysis of 9 genes associated with hypercholesterolemia (major genes: LDLR, APOB, and PCSK9; minor genes: LDLRAP1, LIPA, STAP1, APOE, ABCG5, and ABCG8) including the evaluations of small-scale variants and large-scale copy number variants (CNVs).

RESULTS

Among the 169 clinical FH patients included, 98 (58.0%) were men. A total of 85 (68.5%) index cases carried FH-associated variants. The proportion of FH caused by small-scale variants in LDLR, APOB, and PCSK9 genes was 62.1% and then increased by 6.5% when other genes and CNVs were further included. Furthermore, the variants in LDLR, APOB, and PCSK9 genes occupied 75% of all FH-associated variants. Of note, there were 8 non-LDLR CNVs detected in the present study.

CONCLUSIONS

LDLR, APOB, and PCSK9 genes should be tested in the initial genetic screening, although variants in minor genes also could explain phenotypic FH, suggesting that an expanding genetic testing may be considered to further explain phenotypic FH.

Systematic Identification of Familial Hypercholesterolaemia in Primary Care-A Systematic Review

Familial hypercholesterolaemia (FH) is a common inherited cause of premature cardiovascular disease, but the majority of patients remain undiagnosed. The aim of this systematic review was to assess the effectiveness of interventions to systematically identify FH in primary care. No randomised, controlled studies were identified; however, three non-randomised intervention studies were eligible for inclusion. All three studies systematically identified FH using reminders (on-screen prompts) in electronic health records. There was insufficient evidence that providing comments on laboratory test results increased the identification of FH using the Dutch Lipid Clinic Network (DLCN) criteria. Similarly, using prompts combined with postal invitation demonstrated no significant increase in definite FH identification using Simon-Broome (SB) criteria; however, the identification of possible FH increased by 25.4% (CI 17.75 to 33.97%). Using on-screen prompts alone demonstrated a small increase of 0.05% (95% CI 0.03 to 0.07%) in identifying definite FH using SB criteria; however, when the intervention was combined with an outreach FH nurse assessment, the result was no significant increase in FH identification using a combination of SB and DLCN criteria. None of the included studies reported adverse effects associated with the interventions. Currently, there is insufficient evidence to determine which is the most effective method of systematically identifying FH in non-specialist settings.

Screening for potential familial hypercholesterolaemia in general practice: an observational study on prevalence and management

BACKGROUND

Familial hypercholesterolaemia (FH) is a common genetic disorder causing premature cardiovascular disease (CVD). The estimated prevalence of probable or definite FH is 1:200-250 individuals, according to the Dutch Lipid Clinic Network (DLCN) criteria for FH. In Denmark approximately 12% of cases are identified.

AIM

To provide knowledge of the prevalence and management of FH in general practice.

DESIGN & SETTING

A collaboration between six general practice clinics and the department of cardiology at Bispebjerg hospital in Denmark.

METHOD

A total of 9652 patient records were screened for hypercholesterolaemia. All patients with a low-density lipoprotein cholesterol (LDL-C) ≥5.0 mmol/l were included in the study population and their records were investigated in order to perform a diagnostic score according to the DLCN criteria.

RESULTS

It was found that 2382 individuals had a lipid measurement available, and 236 of those had an LDL-C ≥5.0 mmol/l. In total, 34 individuals were found to have probable or definite FH (DLCN score ≥5). Only three individuals had been diagnosed and treated with lipid-lowering therapy. Of 236 individuals with high LDL-C, only 25 individuals met their treatment target. By excluding patients with signs of secondary hypercholesterolaemia, a subgroup of 115 individuals with potential primary hypercholesterolaemia was established. Among those, 21 individuals were found to have probable or definite FH (1:114 individuals).

CONCLUSION

The study shows that there is a massive lack of recognition of FH in general practice. Despite a measured high LDL-C, the diagnosis is rarely made and only a few patients are treated accordingly. Of the patients undergoing treatment, only a few reached their treatment target.

Massive data screening is a second opportunity to improve the management of patients with familial hypercholesterolemia phenotype

INTRODUCTION

Familial Hypercholesterolemia (FH) is an autosomal dominant disease with an estimated prevalence between 1/200-250. It is under-treated and underdiagnosed. Massive data screening can increase the detection of patients with FH.

METHODS

Study population: Residents in the health coverage area (N: 195.000 inhabitants) and with at least one determination of cholesterol linked to low-density lipoproteins (LDL-C) carried out between January 1, 2010 and December 30, 2019. The highest LDL-C values were selected.

EXCLUSION CRITERIA

nephrotic syndrome, hypothyroidism, Hypothyroid treatment or triglycerides> 400 mg / dL. Seven algorithms suggestive of Familial Hypercholesterolemia Phenotype (HF-P) were analyzed, selecting the most efficient algorithm that could easily be translated into clinical practice.

RESULTS

Based on 6.264.877 assistances and 288.475 patients, after applying the inclusion-exclusion criteria, 504.316 tests were included, corresponding to 106.382 adults and 10.509 <18 years. The selected algorithm presented a prevalence of 0.62%. 840 patients with HF-P were detected, 55.8% being women and 178 <18 years old, 9.3% had a history of cardiovascular disease (CVD) and 16.4% had died. 65% of the patients in primary prevention had LDL-C values> 130 mg / dL and 83% in secondary prevention values> 70mg / dL. A ratio of 7.64 (1-18) patients with HF-P per analytical requesting physician was obtained.

CONCLUSIONS

Massive data screening and patient profiling are effective tools and easily applicable in clinical practice for the detection of patients with FH.

Advances, gaps and opportunities in the detection of familial hypercholesterolemia: overview of current and future screening and detection methods

PURPOSE OF REVIEW

Studies reaffirm that familial hypercholesterolemia is more prevalent than initially considered, with a population frequency of approximately one in 300. The majority of patients remains unidentified. This warrants critical evaluation of existing screening methods and exploration of novel methods of detection.

RECENT FINDINGS

New public policy recommendations on the detection of familial hypercholesterolemia have been made by a global community of experts and advocates. Phenotypic tools for diagnosing index cases remain inaccurate. Genetic testing is the gold standard for familial hypercholesterolemia and a new international position statement has been published. Correction of LDL cholesterol (LDL-C) for the cholesterol content of lipoprotein(a) [Lp(a)] may increase the precision of the phenotypic diagnosis of familial hypercholesterolemia. Cascade cotesting for familial hypercholesterolemia and elevated Lp(a) levels provides a new opportunity to stratify risk in families. Digital technology and machine learning methods, coupled with clinical alert and decision support systems, lead the way in more efficient approaches for detecting and managing index cases. Universal screening of children, combined with child-parent cascade testing, appears to be the most effective method for underpinning a population strategy for maximizing the detection of familial hypercholesterolemia.

SUMMARY

Detection of familial hypercholesterolemia can be enhanced by optimizing current diagnostic algorithms, probing electronic health records with novel information technologies and integrating universal screening of children with cascade testing of parents and other relatives.

Evaluating a clinical tool (FAMCAT) for identifying familial hypercholesterolaemia in primary care: a retrospective cohort study

Background

Familial hypercholesterolaemia (FH) is an inherited lipid disorder causing premature heart disease, which is severely underdiagnosed. Improving the identification of people with FH in primary care settings would help to reduce avoidable heart attacks and early deaths.

Aim

To evaluate the accuracy of the familial hypercholesterolaemia case ascertainment identifcation tool (FAMCAT) for identifying FH in primary care.

Design & setting

A retrospective cohort study of 1 030 183 patients was undertaken. Data were extracted from the UK Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) database. Patient were aged >16 years.

Method

The FAMCAT algorithm was compared with methods of FH detection recommended by national guidelines (Simon Broome diagnostic criteria, Dutch Lipid Clinic Network [DLCN] Score, and cholesterol levels >99^th centile). Discrimination and calibration were assessed by area under the receiver operating curve (AUC) and by comparing observed versus predicted cases.

Results

A total of 1707 patients had a diagnosis of FH. FAMCAT showed a high level of discrimination (AUC = 0.844, 95% confidence interval [CI] = 0.834 to 0.854), performing significantly better than Simon Broome criteria (AUC = 0.730, 95% CI = 0.719 to 0.741), DLCN Score (AUC = 0.766, 95% CI = 0.755 to 0.778), and screening cholesterols >99 th centile (AUC = 0.579, 95% CI = 0.571 to 0.588). Inclusion of premature myocardial infarction (MI) and fitting cholesterol as a continuous variable improved the accuracy of FAMCAT (AUC = 0.894, 95% CI = 0.885 to 0.903).

Conclusion

Better performance of the FAMCAT algorithm, compared with other approaches for case finding of FH in primary care, such as Simon Broome criteria, DLCN criteria or very high cholesterol levels, has been confirmed in a large population cohort.

Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care

Familial hypercholesterolaemia (FH) is a common inherited disorder, causing lifelong elevated low-density lipoprotein cholesterol (LDL-C). Most individuals with FH remain undiagnosed, precluding opportunities to prevent premature heart disease and death. Some machine-learning approaches improve detection of FH in electronic health records, though clinical impact is under-explored. We assessed performance of an array of machine-learning approaches for enhancing detection of FH, and their clinical utility, within a large primary care population. A retrospective cohort study was done using routine primary care clinical records of 4,027,775 individuals from the United Kingdom with total cholesterol measured from 1 January 1999 to 25 June 2019. Predictive accuracy of five common machine-learning algorithms (logistic regression, random forest, gradient boosting machines, neural networks and ensemble learning) were assessed for detecting FH. Predictive accuracy was assessed by area under the receiver operating curves (AUC) and expected vs observed calibration slope; with clinical utility assessed by expected case-review workload and likelihood ratios. There were 7928 incident diagnoses of FH. In addition to known clinical features of FH (raised total cholesterol or LDL-C and family history of premature coronary heart disease), machine-learning (ML) algorithms identified features such as raised triglycerides which reduced the likelihood of FH. Apart from logistic regression (AUC, 0.81), all four other ML approaches had similarly high predictive accuracy (AUC > 0.89). Calibration slope ranged from 0.997 for gradient boosting machines to 1.857 for logistic regression. Among those screened, high probability cases requiring clinical review varied from 0.73% using ensemble learning to 10.16% using deep learning, but with positive predictive values of 15.5% and 2.8% respectively. Ensemble learning exhibited a dominant positive likelihood ratio (45.5) compared to all other ML models (7.0-14.4). Machine-learning models show similar high accuracy in detecting FH, offering opportunities to increase diagnosis. However, the clinical case-finding workload required for yield of cases will differ substantially between models.

Non-HDL or LDL cholesterol in heterozygous familial hypercholesterolaemia: findings of the Simon Broome Register

PURPOSE OF REVIEW

The role of non-HDL-C in the identification and management of lipid disorders is not clearly defined, although UK guidelines recommend its wider use in assessing the need for lipid-lowering therapy and as a treatment target.

RECENT FINDINGS

We examined the implications of the use of non-HDL-C as opposed to LDL-C in 253 people with hypercholesterolaemia before treatment and 573 after treatment in whom fasting total serum cholesterol, HDL-C and LDL-C had been recorded and the diagnosis of heterozygous familial hypercholesterolemia (heFH) was investigated by genetic testing. The difference and the limits of agreement between non-HDL-C and LDL-C calculated using the Friedewald formula were assessed in those with and without heFH-causing mutations.

SUMMARY

There were 147 mutation-positive and 106 mutation-negative pretreatment participants and 395 mutation-positive and 178 mutation-negative patients receiving treatment. The difference between non-HDL-C and LDL-C pretreatment in mutation-positive people (mean LDL-C 7.73 mmol/l) was 0.67 mmol/l (95% CI 0.62-0.73) and posttreatment (mean LDL-C 4.71 mmol/l) was 0.62 mmol/l (95% CI 0.59-0.65) with wide limits of agreement of -0.02 to 1.37 and 0.07-1.18 mmol/l, respectively. Among patients with heterozygous familial hypercholesterolaemia, use of estimated LDL-C derived from non-HDL-C in place of calculated LDL-C may result in diagnostic misclassification and difficulty in assessing the true reduction in LDL-C with treatment, because of the wide inter-individual limits of agreement around the mean difference between non-HDL-C and LDL-C.

Familial Hypercholesterolaemia in 2020: A Leading Tier 1 Genomic Application

Familial hypercholesterolaemia (FH) is caused by a major genetic defect in the low-density lipoprotein (LDL) clearance pathway. Characterised by LDL-cholesterol elevation from birth, FH confers a significant risk for premature coronary artery disease (CAD) if overlooked and untreated. With risk exposure beginning at birth, early detection and intervention is crucial for the prevention of CAD. Lowering LDL-cholesterol with lifestyle and statin therapy can reduce the risk of CAD. However, most individuals with FH will not reach guideline recommended LDL-cholesterol targets. FH has an estimated prevalence of approximately 1:250 in the community. Multiple strategies are required for screening, diagnosing and treating FH. Recent publications on FH provide new data for developing models of care, including new therapies. This review provides an overview of FH and outlines some recent advances in the care of FH for the prevention of CAD in affected families. The future care of FH in Australia should be developed within the context of the National Health Genomics Policy Framework.