Strategies for screening for familial hypercholesterolaemia in primary care and other community settings (Review)

BACKGROUND
Familial hypercholesterolaemia is a common inherited condition that is associated with premature cardiovascular disease. The increased cardiovascular morbidity and mortality, resulting from high levels of cholesterol since birth, can be prevented by starting lipid-lowering therapy. However, the majority of patients in the UK and worldwide remain undiagnosed. Established diagnostic criteria in current clinical practice are the Simon-Broome and Dutch Lipid Clinical network criteria and patients are classified as having probable, possible or definite familial hypercholesterolaemia.


OBJECTIVES
To assess the effectiveness of healthcare interventions strategies to systematically improve identification of familial hypercholesterolaemia in primary care and other community settings compared to usual care (incidental approaches to identify familial hypercholesterolaemia in primary care and other community settings).


SEARCH METHODS
We searched the Cochrane Inborn Errors of Metabolism Trials Register. Date of last search: 13 September 2021. We also searched databases (Cochrane Central Register of Controlled Trials (CENTRAL), Ovid MEDLINE, PubMed, Embase, CINAHL, Web of Science, and SCOPUS) as well as handsearching relevant conference proceedings, reference lists of included articles, and the grey literature. Date of last searches: 05 March 2020.  SELECTION CRITERIA: As per the Effective Practice and Organisation of Care (EPOC) Group guidelines, we planned to include randomised controlled trials (RCTs), cluster-RCTs and non-randomised studies of interventions (NRSI). Eligible NRSI were non-randomised controlled trials, prospective cohort studies, controlled before-and-after studies, and interrupted-time-series studies. We planned to selected studies with healthcare interventions strategies that aimed to systematically identify people with possible or definite clinical familial hypercholesterolaemia, in primary care and other community settings. These strategies would be compared with usual care or no intervention. We considered participants of any age from the general population who access primary care and other community settings.


DATA COLLECTION AND ANALYSIS
Two authors planned to independently select studies according to the inclusion criteria, to extract data and assess for risk of bias and the certainty of the evidence (according to the GRADE criteria). We contacted corresponding study authors in order to obtain further information for all the studies considered in the review.


MAIN RESULTS
No eligible RCTs or NRSIs were identified for inclusion, however, we excluded 28 studies.


AUTHORS' CONCLUSIONS
Currently, there are no RCTs or controlled NRSI evidence to determine the most appropriate healthcare strategy to systematically identify possible or definite clinical familial hypercholesterolaemia in primary care or other community settings. Uncontrolled before-and-after studies were identified, but were not eligible for inclusion. Further studies assessing healthcare strategies of systematic identification of familial hypercholesterolaemia need to be conducted with diagnosis confirmed by genetic testing or validated through clinical phenotype (or both).


Objectives
To assess the e ectiveness of healthcare interventions strategies to systematically improve identification of familial hypercholesterolaemia in primary care and other community settings compared to usual care (incidental approaches to identify familial hypercholesterolaemia in primary care and other community settings).

Search methods
We searched the Cochrane Inborn Errors of Metabolism Trials Register.Date of last search: 13 September 2021.
We also searched databases (Cochrane Central Register of Controlled Trials (CENTRAL), Ovid MEDLINE, PubMed, Embase, CINAHL, Web of Science, and SCOPUS) as well as handsearching relevant conference proceedings, reference lists of included articles, and the grey literature.Date of last searches: 05 March 2020.

Selection criteria
As per the E ective Practice and Organisation of Care (EPOC) Group guidelines, we planned to include randomised controlled trials (RCTs), cluster-RCTs and non-randomised studies of interventions (NRSI).Eligible NRSI were non-randomised controlled trials, prospective cohort studies, controlled before-and-a er studies, and interrupted-time-series studies.
We planned to selected studies with healthcare interventions strategies that aimed to systematically identify people with possible or definite clinical familial hypercholesterolaemia, in primary care and other community settings.These strategies would be compared with usual care or no intervention.
We considered participants of any age from the general population who access primary care and other community settings.

Main results
No eligible RCTs or NRSIs were identified for inclusion, however, we excluded 28 studies.

Authors' conclusions
Currently, there are no RCTs or controlled NRSI evidence to determine the most appropriate healthcare strategy to systematically identify possible or definite clinical familial hypercholesterolaemia in primary care or other community settings.Uncontrolled before-and-a er studies were identified, but were not eligible for inclusion.Further studies assessing healthcare strategies of systematic identification of familial hypercholesterolaemia need to be conducted with diagnosis confirmed by genetic testing or validated through clinical phenotype (or both).

P L A I N L A N G U A G E S U M M A R Y
Healthcare strategies for identifying possible or definite clinical familial hypercholesterolaemia in primary care and other community settings

Background
One of the most common inherited conditions is familial hypercholesterolaemia, people with this condition have raised cholesterol from birth.This condition can result in the arteries being narrowed by excess cholesterol sticking to their walls and can lead to heart disease at an early age.However, treatment with cholesterol-lowering tablets markedly reduces this risk.
As well as raised cholesterol in the blood, family history of heart disease and the presence of fatty lumps under the skin could indicate familial hypercholesterolaemia.
It is important that community-based health professionals, such as general practitioners and community pharmacists, can identify those at risk of possible or probable familial hypercholesterolaemia and refer them to a specialist.Specialists can confirm a diagnosis of familial hypercholesterolaemia through examination and a genetic test.This review explores the impact of these healthcare strategies in primary care and other community settings to systematically identify people with possible and definite clinical familial hypercholesterolaemia.

Study characteristics
We did not find any studies that we could include in this review.

Key results
There were no studies eligible for inclusion in the review.

Quality of evidence
There were no studies included in the review.

Conclusions
Currently, there is a lack of evidence regarding the most appropriate healthcare strategy to identify possible or definite clinical familial hypercholesterolaemia in primary care and other community settings.Better-designed studies, with diagnosis of definite familial hypercholesterolaemia confirmed by genetic tests, are needed to clearly answer this question.

B A C K G R O U N D Description of the condition
Familial hypercholesterolaemia (FH) is an autosomal-dominant disease and has long been recognised as a cause of premature coronary heart disease (CHD) (Nordestgaard 2013).In the majority of people with FH, the disorder is caused by a mutation of the low-density lipoprotein (LDL) receptor gene which impairs the proper function of the receptor, thus resulting in very high levels of plasma cholesterol.This leads to early onset atherosclerosis, causing excess morbidity and mortality from CHD (Goldstein 1995).
It has been reported that the majority of people with FH have the heterozygous form, with an estimated one in 500 people a ected (Baumer 2009;Foody 2014).However, more contemporary data suggest that prevalence may be as high as one in 200, with over 30 million individuals a ected worldwide (Benn 2012;Hu 2020;Nordestgaard 2012;Nordestgaard 2013;Weigman 2015).Based on predicted prevalence and the number of people currently diagnosed, it is reported that the majority of a ected individuals remain undiagnosed (Demott 2008;Nordestgaard 2013).The importance of early identification is to allow treatment prior to the appearance of CHD symptoms, since a ected individuals have an estimated 100-fold increase in CHD mortality compared to una ected adults (Demott 2008;Nordestgaard 2013;Simon Broome Register Group 1991).It is estimated that half of the men with heterozygote FH will have developed CHD by 55 years of age and one third of women by 60 years of age (Marks 2003).
The criteria identify individuals with possible or definite FH as adults with total cholesterol > 7.5 mmol/L (LDL > 4.9 mmol/L) or children (less than 16 years of age) with total cholesterol over 6.7 mmol/L (LDL > 4.0 mmol/L), combined with a family history of premature heart disease or raised cholesterol or presence of tendinous xanthomata (or a combination of these) (NICE 2008;Qureshi 2009).This has been adopted in England and Wales following recommendations by the National Institute for Health and Care Excellence (NICE), following a review of nonrandomised studies (NICE 2017).(Bell 2012;Bell 2013;Troeung 2016).

Description of the intervention
In primary care and other community settings, the usual care most o en involves incidental identification of those who may be at risk of FH, which may include the following strategies: • assessment of FH opportunistically during an unrelated clinical consultation; • assessment of FH as part of a routine health check or health screen; • assessment of FH when an individual raises concerns about their cholesterol or family history of heart disease.An alert could be added to the medical records of individuals at risk to remind their doctor to check their cholesterol level.These reminders are an example of changing clinician behaviour using antecedent cues (Michie 2004).Agreement could be reached with local pathology laboratories contacting the primary care physician by telephone when a very high cholesterol result is processed (Bell 2014a).

Why it is important to do this review
The WHO recognises the need to prevent and control cardiovascular disease (CVD), and that managing raised lipids is a key modifiable risk factor (WHO 2011).Internationally, it is recognised that a universal targeted approach to identify and manage heterozygote FH is a key priority to prevent CVD (Benn 2012;Nordestgaard 2013;Reiner 2015;Robinson 2013;Watts 2015).
For successful identification of FH, case finding needs to extend beyond the specialist lipid clinic to primary care (NICE 2008) and other community non-health settings, such as occupational health services (Kirke 2015).
For people who are a ected by FH, there is strong evidence for the benefits of early identification and treatment (Demott 2008;Marks 2003;NICE 2017).High-intensity lipid-lowering treatment is very e ective with a 44% reduction in CHD mortality (Besseling 2016).However, evidence-based approaches to support guideline implementation are underdeveloped (Grimshaw 1993;Michie 2004).Improving the current low detection rate of FH is urgently needed.
Moreover, identification of index cases will lead to the detection of a ected but asymptomatic relatives, especially those at a younger age, resulting in early initiation of statin treatment to lower cholesterol with the recognised reduction in premature mortality, and long-term morbidity.As 50% of first degree relatives of people with confirmed FH will also have the condition, cascade screening by specialists has been shown to be a cost-e ective approach (Marks 2003;Nherera 2011).This has also improved quality of life in those family members identified (van Maarle 2003).
To overcome existing gaps in care and reduce the preventable global burden of disease arising from FH, dissemination of current evidence to healthcare providers and policy makers is needed.This can inform the development of the most e ective evidencebased guidelines to deliver optimal care for people with FH, thus enabling clinicians to expedite diagnosis and initiate e ective treatment.Evidence on which strategies are e ective for improving the identification of FH is needed in order to prioritise primary healthcare resources and target those individuals at greater risk of developing premature CHD.
The review may also provide an exemplar for improving identification of other common monogenic disorders in the primary care setting.Furthermore, the evidence may provide generic findings relevant to developing other pertinent interventions in this context.

O B J E C T I V E S
To assess the e ectiveness of healthcare interventions strategies to systematically improve identification of familial hypercholesterolaemia in primary care and other community settings compared to usual care (incidental approaches to identify familial hypercholesterolaemia in primary care and other community settings).

M E T H O D S
Criteria for considering studies for this review Due to the complex nature of the intervention and setting, we have included NRSIs as we anticipated potential limitations in the availability of RCTs.

Types of participants
Eligible participants of any age from the general population who access primary care or other community healthcare settings.We excluded participants who were selected from specialist settings with expertise in lipid disorders or those with a previous diagnosis of FH or other inherited lipid disorders.If the study contained both eligible and ineligible participants, we would have included the study if the data on eligible participants could be extracted (where at least 70% of the participants were eligible for our review).

Types of interventions
Intervention strategies that aimed to systematically identify people with possible or definite FH, in primary care and other community healthcare settings.Interventions which involved specialists delivering the interventions in these settings (e.g.FH nurse specialists performing case findings) were also appropriate for inclusion.
A systematic intervention strategy for identifying FH is defined as: • prospective general population screening for FH using diagnostic criteria; • retrospective electronic and manual health records search for participants who might have FH (i.e. based on elevated cholesterol levels, relevant family history, clinical characteristics, or combination of these factors); Cochrane Database of Systematic Reviews individuals; pathology laboratories reporting back high cholesterol levels).
We planned to compare these systematic strategies to identify individuals with possible or definite clinical FH with usual care, where participants continued to receive their standard or current medical care (Reeves 2016).In this situation, usual care for FH identification in primary care and other community healthcare practice could include no activity to identify people with FH, or incidental and non-systematic strategies during routine consultations.An example of usual care could be noting a raised cholesterol during consultation with individuals presenting with concerns about their personal or family history.However, we excluded studies that used a comparator based on routinely available or historical data due to inherent biases associated with using this non-directly comparative design.
Where possible, we planned to compare interventions to each other if the usual care healthcare strategies were similar between or within the studies.

Primary outcomes
1. Diagnosis of definite FH at the end of study follow-up, defined by a positive genetic mutation test or clinical phenotype typical of FH (as defined by diagnostic criteria) 2. Diagnosis of possible and probable FH (as defined by diagnostic criteria) at the end of study follow-up 3. Adverse events at the end of study follow-up (e.g.drug adverse events, hospitalisations, all-cause mortality) Definition of the terms definite FH and possible FH can be found in an appendix (Appendix 1).

Secondary outcomes
1. Cholesterol levels in participants diagnosed with FH (total cholesterol, LDL cholesterol) at the end of study follow-up, and from the date of FH diagnosis 2. Cardiovascular mortality and morbidity of FH participants at end of study follow-up (minimum of one year follow-up) 3. Lipid-lowering treatment prescribed to people with FH (including stratification of statin prescribing by high, medium, low potency at end of study) 4. Referral of FH participants, at end of study follow-up, to a specialist service 5. Adverse self-reported psychological e ects at end of study follow-up (e.g.worry, anxiety, depression with a validated instrument) 6. Management errors (e.g.misdiagnosis, inappropriate statin prescribing, inappropriate referrals to specialist) Where multiple measurements of the same outcome are reported at di erent follow-up times, we would report all measurements as per the categories below: 1. short-term (outcome reported closest to three months of end of study follow-up (range can include one to four months)); 2. medium-term (outcome reported closest to six months of end of study follow-up (range can include five to nine months)); 3. long-term (outcome reported closest to 12 months of end of study follow-up (range can include over 10 months)).

Search methods for identification of studies
We searched for all relevant published and unpublished trials without restrictions on language, year or publication status.

Electronic searches
We

Reference lists
We checked the bibliographies of identified studies and any relevant systematic reviews for further references to relevant studies.We contacted the lead authors of identified studies to identify any available unpublished material and missing data or information regarding ongoing studies.

Handsearching
We have conducted handsearching for the relevant resources up to 05 March 2020.Search strategies for handsearching the following journals can be found in the appendices (Appendix 3).
• We complemented the searches by making contact with leaders and researchers known to be active in the field in order to identify additional trials, including unpublished and ongoing studies.

Selection of studies
We selected studies according to chapter 7 of the Cochrane Handbook for Systematic Reviews of Interventions (Lefebvre 2021).
We saved the results from the searches in Endnote reference managing so ware (EndNote X8).Two authors (JT*, MLDS) independently screened the titles and abstracts of the identified studies to check for eligibility.We then retrieved the full texts (where available) of the potentially eligible studies and two authors (JT, MLDS) independently screened these.We resolved any disagreements through discussion, or where necessary, with the assistance of a third author (NQ or JLB).
To guard against potential duplicate publication, we linked multiple reports using the same study participants.We reached agreement in all cases.We recorded details of studies excluded at the full-text stage together with reasons for exclusion based on the inclusion criteria for the review.

Data extraction and management
Two review authors (MLDS, HA-H) would have independently extracted and recorded data from included studies following guidance in chapter 5 of the Cochrane Handbook for Systematic Reviews of Interventions (Li 2021).We would have used standard data extraction forms, based on the checklist from the Cystic Fibrosis and Genetics Disorders Review Group, which we would have modified to allow relevant data to be captured from all the study designs which were eligible for inclusion in this review.
The data extraction form would have included study characteristics such as study methodology, participant characteristics (including ethnic or cultural characteristics, geographical location), sample size, strategies and characteristics (including process and duration of intervention), primary and secondary outcome measures, FH diagnostic criteria and definitions of other outcomes, and analysis performed in the original trials.
For NRSIs, we would have extracted additional data on confounding factors, the comparability of groups on confounding factors, methods used to control for confounding and on multiple e ect estimates (both unadjusted and adjusted estimates) (Reeves 2021).Confounding factors need to be associated with both the intervention and the outcomes, thus such factors would be those assessed at a population-or service-level.Therefore, studies which have adjusted for confounders at participant-level, e.g.gender, should not have been adjusted for in the analyses.Population-or service-level factors may include, but are not limited to: • type of health professional(s) delivering the intervention; • size of the population being assessed; • workload of the health professional(s) • introduction of clinical nurse specialists • introduction of direct pathway to access genetic testing in primary care or other community settings For future versions of the review, if studies are included, we plan to resolve any discrepancies that arise in data extraction through discussion with a third author (JLB).

Assessment of risk of bias in included studies
If studies are included in future updates of the review, two review authors (NQ and SW) will independently assess the risk of bias; if there is a disagreement, a third author (JLB) will check each assessment and we will discuss the outcome until consensus is achieved.
If we include RCTs in future updates of the review, we will assess the risk of bias using the criteria outlined in the For controlled ITS designs, we will use the EPOC Risk of Bias tool which is based on assessing seven standard domains: • intervention independent of other changes; • shape of the intervention pre-specified; • intervention unlikely to a ect data collection; • knowledge of the allocated interventions adequately prevented during the study; • incomplete outcome data adequately reported; • selective outcome reporting; and • other risk of bias.
We will assess each domain as low, unclear or high risk of bias.

Measures of treatment e ect
If we include studies in future updates of this review, where possible, we will report dichotomous outcomes using risk ratios (RR) together with 95% confidence intervals (95% CIs).For studies which have used randomisation, we will extract 2x2 data and estimate crude RRs; however, for studies without randomisation, we will extract RRs which would have been adjusted for baseline di erences or the ratio of the RR post-intervention compared to the RR for pre-intervention.Where studies report other adjusted measures of e ect, e.g.odds ratios, we will extract these and report them separately.
Where possible, we will report continuous outcomes using mean di erences (MD) together with 95% CIs.For studies which have used randomisation, we will extract raw data and estimate crude MDs; however, for studies without randomisation, we will extract MDs for the absolute change or the relative change, which will have been adjusted for baseline di erences.
For studies with a non-randomised study design, we will consider additional analysis on adjusting for baseline group di erences, with appropriate regression analysis based on the form of the outcome variables (continuous or binary).
For studies which have used a controlled ITS design, we will extract quantitative results from either a regression analysis with time trends before and a er the intervention, adjusted for autocorrelation and any periodic changes, or from an ARIMA analysis.We will present the results for the outcomes as the change in level (immediate e ect of the intervention) and the change in slope (longer-term e ect of the intervention).If the results from a study using a controlled ITS design are only reported as data points in a scanable graph or in a table, we will attempt to re-analyse the data by contacting study authors for permission to use their raw study data (Ramsay 2003).

Unit of analysis issues
For future updates, if we include studies, we will consider whether any unit of analysis errors were made in the reported analysis for each study.For cluster-randomised studies, where the analysis may not adjust for the e ect of clustering, if we identify a unit-of-analysis issue, we will attempt to correct the analysis by re-analysing the presented data.Or if there is insu icient information presented, we will contact the authors to obtain the necessary information or raw data.If we are not able to re-analyse the data taking into account clustering, we will report the uncorrected estimates without a measure of uncertainty (e.g.95% CI) and analyse the results separately from non-clustered studies.For future updates, if we include studies with multiple-arm groups, we will only include intervention and comparator groups which are relevant to the systematic review.Since we anticipate that studies will include di erent comparator groups, where it is appropriate, we will combine di erent comparator groups together and compare this to the active intervention group in the meta-analysis.For studies which include multiple intervention groups, we will combine these together and compare this to the comparator group in the metaanalysis.For further unit of analysis issues, we will analyse these following the recommendation in chapter 23 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021).
For studies that have accounted for repeated measures we aim to report their study measures.

Dealing with missing data
For future updates of this review, if we include studies and where important data are missing (e.g. standard deviations), we will contact the authors of studies published less than 10 years ago to request additional data.If we are unable to retrieve missing data, we will conduct a sensitivity analysis to compare study outcomes grouped by the amount of missing data: large amount (more than 30%); moderate amount (10% to 30%); low amount (less than 10%).We will then discuss the potential impact of the missing data on the findings of the review.

Assessment of heterogeneity
For future updates of this review, if we include studies, we plan to assess any heterogeneity we identify as follows.

Clinical heterogeneity
We will consider clinical heterogeneity which can result from di erences between studies in the characteristics of the populations, interventions and outcomes.We will fully discuss the influence of clinical heterogeneity on the observed e ects.

Methodological heterogeneity
It is likely that we will identify heterogeneity as a result of bias from the di erent study designs included in the review.We will fully discuss the influence of methodological heterogeneity on the observed e ects.

Statistical heterogeneity
We will quantify the proportion of variation in the meta-analyses due to clinical and methodological heterogeneity using I (Higgins 2002).We will also visually examine the inconsistency of the 95% CIs within each meta-analysis.We will describe the proportion of variability due to heterogeneity using the following values described in chapter 10 of the Cochrane Handbook for Systematic Reviews of Interventions (Deeks 2021): • 0% to 40%: might not be important; • 30% to 60% may represent substantial heterogeneity; • 75% to 100% considerable heterogeneity.
Due to the di erent designs of any included studies, we anticipate that we will identify moderate to high levels of heterogeneity within each meta-analysis; therefore, we will conduct a thorough examination of the heterogeneity to try to describe reasons for its presence.

Assessment of reporting biases
Where any future meta-analysis contains more than 10 studies, we plan to use funnel plots to assess the potential e ects of reporting biases.Where funnel plot asymmetry is apparent, we will consider possible sources of asymmetry other than publication bias, e.g. based on domains of methodological quality, and conduct appropriate sensitivity analyses.

Data synthesis
If studies are included in future updates of this review, we plan to initially summarise all included studies using narrative synthesis methods.This will involve the use of narrative text and tables to summarise data based on the type of intervention and according to setting, we will consider outcomes in the light of di erences in study designs and address potential sources of bias, potential confounding factors and any further methodological limitations for each of the studies being reviewed, including how these may have impacted on the study findings.We plan then to provide a synthesised summary of the studies, including the range and size of any reported associations and important study characteristics.
We will conduct separate meta-analyses for RCTs and NRSIs.We will use random-e ects models to conduct meta-analyses due to anticipated di erences in the e ectiveness of the intervention by type of intervention, comparator, setting, and populations.We will include all studies in the primary meta-analyses, irrespective of the risk of bias scores given for each domain.
We will not include the quantitative findings from studies deemed to have a critical risk of bias in the synthesis (Reeves 2021).

Subgroup analysis and investigation of heterogeneity
If we include studies in future updates of this review, for each meta-analysis which includes at least five studies, we will perform subgroup analyses based on study-level variables and report a P value relating to the statistical test for di erences between subgroups, where appropriate.We will consider the following characteristics: • mode of diagnosis for FH (genetic or clinical); • age of participant (under 10 years of age or 10 years of age and above); • type of systematic intervention (prospective population screen, retrospective computer search, proactive computer reminders, population-based case finding); • type of comparator; • type of setting (primary medical care, employer-based clinics, community pharmacists).
We will undertake subgroup analyses using RevMan (RevMan 2020) or Stata version 16 (STATA 2019).We anticipate that due to the complexity of the intervention that most studies would include complex interventions and therefore we grouped studies primarily based on similarities between outcome measures.

Sensitivity analysis
If studies are included in future updates of this review, we plan to assess the impact of methodological quality on the results of the meta-analyses.We will compare the pooled e ect size from this sensitivity analysis to the pooled e ect size from the original analyses.For RCTs, the sensitivity analysis would be based on only those studies with an overall low risk of bias; for NRSIs (including controlled ITS designs), the sensitivity analysis would be based on only those with an overall low or moderate risk of bias.We will report sensitivity analyses in table format within this review.

Summary of findings and assessment of the certainty of the evidence
If we include studies in future updates of this review, we will create summary of findings tables for the primary outcomes, following the GRADE approach suggested in chapter 14 of the Cochrane Handbook for Systematic Review of Interventions (Schünemann 2021).This will state the participant population setting, intervention, comparison, and main outcome measures.In addition, the tables would have presented the quality rating of the evidence as 'high', 'moderate', 'low', or 'very low' using the following five GRADE considerations: • risk of bias (serious or very serious); • inconsistency (serious or very serious); • indirectness (serious or very serious); • imprecision (serious or very serious); • publication bias (likely or very likely).
For NRSIs we will also consider the following factors: Cochrane Database of Systematic Reviews • size of e ect (large or very large); • confounding (either reduces the demonstrated e ect or increases the e ect if no e ect was observed (yes or no)).
In GRADE, we will rate NRSIs initially as low quality and downgrade or upgrade according to GRADE guidelines, if appropriate.We will present outcomes for these studies in separate tables from outcomes for the results of RCTs.

Results of the search
Please refer to Figure 1.

Cochrane Database of Systematic Reviews
We found 4671 citations using the search strategy run on 05 March 2020 and 13 September 2021, from which we identified 32 citations (to 29 studies) as potentially relevant.Following review of the full-text articles, none of the studies were eligible for inclusion in this review.We excluded 28 studies (31 citations) for not fulfilling the inclusion criteria, for further information please refer to the relevant table (Characteristics of excluded studies).We assessed one study as ongoing (Arnold-Reed 2017).

Included studies
We found no studies that were eligible for inclusion in this review.

Excluded studies
We excluded 28 studies from the review, as described in 'Characteristics of excluded studies'

Ongoing studies
We identified one study which is still ongoing (Arnold-Reed 2017).This study will use a pragmatic cluster pre-post intervention design to assess the e ectiveness of a primary care-based model of care on detection and management of FH in adults in Australia.

Risk of bias in included studies
There were no studies included in this review.

Other potential sources of bias
There were no studies included in this review.

E ects of interventions
There were no studies included in this review.

Summary of main results
Identifying individuals with FH and starting lipid-lowering treatment will reduce the risk of premature CHD.However, there is no evidence from RCTs or eligible NRSIs for healthcare strategies to identify FH in primary care and other community settings (NRSIs as defined by EPOC guideline (non-RCTs, prospective cohort studies, controlled before-and-a er studies, and controlled ITS studies)).
No studies were identified that fulfilled the inclusion criteria.However, we did identify three studies which would have been eligible for inclusion except for their study design.In these studies, before-and-a er designs without a control group were used.Such designs are usually not eligible for informing the e ectiveness of an intervention due to the inherent biases within them as a result of not having a control group (EPOC 2017).Apparent di erences identified between the two periods could be due to reasons not related to the intervention.

Overall completeness and applicability of evidence
There were no studies included in this review.

Quality of the evidence
There were no studies included in this review.

Potential biases in the review process
We followed the guidance of the Cochrane Handbook for Systematic Reviews of Interventions with two review authors independently selecting studies and extracting data.We performed a thorough search of literature.It is possible that studies published in journals that were outside our search strategy may have been missed.

Agreements and disagreements with other studies or reviews
Three of the excluded studies were only excluded on study design, but participants and interventions were relevant to this review.All three studies were undertaken in General (Family) Practice; with two studies carried out in the UK (Green 2016;Weng 2018) and the remaining study in Australia (Bell 2013).All three studies used di erent electronic health records reminders in individuals with raised cholesterol, one with postal invitation to participants (Weng 2018) and one with FH specialist nurse assessment (Green 2016).The Green study showed modest absolute improvement in detection of definite FH using initially Simon-Broome, then both Simon-Broome and the DLCN criteria (Green 2016).In the other two studies there was no evidence of an improvement in diagnosis, which could be due to small sample sizes (Bell 2013;Weng 2018).Further, all three studies had short follow-up periods.Although these studies could not be included in the systematic review and were of poor quality, if the findings are confirmed in more robustly designed studies, this could suggest that interrogating the primary care electronic health records of individuals with raised cholesterol levels, may lead to an improvement in identifying people with FH.
There have been no previous published systematic reviews on this topic.However, there have been several narrative reviews on the detection of FH in primary care.For example, a paper by Lan outlines di erent screening methods for FH, which also included search of primary care electronic health records (Lan 2019).As part of their suggestions for future research in this area, the authors recommend clinical trials of screening protocols and testing interventions.
The NICE guidelines on identifying FH were updated following a structured review of the evidence and suggested that clinicians should systematically search primary care electronic healthcare records for individuals at risk of FH.Further, the condition should be suspected in individuals with total cholesterol greater than 7.5 mmol/L or personal or family history of premature coronary heart disease (or both) (NICE 2017).The guidelines recommend that, a er excluding possible secondary causes for raised cholesterol, healthcare professionals should use either the Simon-Broome or DLCN criteria to make a clinical diagnosis of FH and refer individuals on to specialist care for genetic testing.Evidence from one of the (uncontrolled before-and-a er) studies excluded from the current review informed recommendations in the NICE clinical guidelines (Green 2016).Other studies listed in the updated NICE guidelines as evidence for the use of primary care electronic health records, were also identified in our search results.However, we excluded Cochrane Database of Systematic Reviews these as they did not provide a control group, which was one of our inclusion criteria (Bell 2014b;Gray 2008;Kirke 2015;Qureshi 2016;Troeung 2016).A more recent narrative review for the European Atherosclerosis Society Consensus Statement on identifying and managing FH (Nordestgaard 2013) cited one of the excluded papers (Benn 2012), but did not specifically consider the role of primary care or other community health professionals in identifying FH.

Implications for practice
The review provided no evidence from randomised controlled trials or controlled non-randomised studies to inform the most e ective healthcare strategy for identifying familial hypercholesterolaemia (FH) in primary care and other community settings.However, there is a potential role for searching primary care electronic health records.

Implications for research
A pragmatic trial design should be adopted to answer this research question using di erent diagnostic assessment criteria in primary care electronic health records.Genetic confirmation of FH should be included, as well as a detailed description of processes and outcome measures, particularly referral to specialists and both surrogate and disease outcome measures of cardiovascular disease.To identify later results for clinical outcome measures, at least one year study follow-up would be required.Considering the current evidence of the benefits of identifying FH, a study design using a true control group with no active identification maybe ethically unacceptable.
Internationally, considering the primary outcome measure proposed in this systematic review, the most commonly used diagnostic assessment criteria for FH is the Dutch Lipid Clinic Network (DLCN) (Defesche 2004; Reiner 2011; Watts 2011).In the UK, Simon-Broome criteria is the most frequently used, as recommended by the original 2008 NICE guidelines on FH and the updated version in 2017 (NICE 2008;NICE 2017).Other assessment criteria have also been used worldwide, such as MedPed (Williams 1993), the Japanese criteria (Harada-Shiba 2012) and the Canadian criteria (Ruel 2018).It would be challenging to combine studies using di erent diagnostic assessment criteria.
Although several of the criteria include genetic testing, such as DLCN and Simon-Broome criteria, currently genetic testing is not routinely performed in primary care (Green 2016;Qureshi 2016).Hence the primary outcome measure directly collated from primary care will be based on clinical phenotype.When geneticallyconfirmed FH is collated, this is currently based on the diagnosis of those people referred from primary to specialist care.

A C K N O W L E D G E M E N T S
The authors would like to thank Jennifer Tranter for her contribution to the protocol and dra versions of this review.(1937 to present) S1 (MH "familial hypercholesterolemia" OR MH "hypertriglyceridemia" OR MH "hyperlipidemia" OR MH "hyperlipoproteinemia") S2 (TI "clinic" OR TI "clinical practice" OR TI "general practice" OR TI "gp" OR TI "general practitioner" OR TI "community" OR TI"primary care" OR TI "primary health care" OR TI "primary healthcare") S3 (TI "detection" OR TI "database" OR TI "laboratory" OR TI "audit" OR TI "screening" OR TI "mass screening" OR TI "records" OR TI "identification" OR TI "identity") Strategiesfor screening for familial hypercholesterolaemia in primary care and other community settings (Review) Copyright © 2021 The Cochrane Collaboration.Published by John Wiley & Sons, Ltd.
Strategiesfor screening for familial hypercholesterolaemia in primary care and other community settings (Review) Copyright © 2021 The Cochrane Collaboration.Published by John Wiley & Sons, Ltd.

S4
hypertriglyceridemia) OR ti(hypercholesteremia) OR ti(hyperlipidemia) OR ti(familial combined hyperlipidemia) AND ti(mass screening) OR ti(audit) OR ti(diagnosis) OR ti(identifying) OR ti(identify risk) OR ti(database) OR ti(detection) AND ti(general practice) OR ti(general practitioners) OR ti(clinical practice) OR ti(primary care) OR ti(primary health care) OR ti(community) (familial hypercholesterolaemia) OR TS=(familial hypercholesterolemia) OR TS=(hyperlipidaemia) OR TS=(hyperlipidemia) OR TS=(familial combined hyperlipidaemia) OR TS=(familial combined hyperlipidemia) OR TS=(hyperlipoproteinaemia) OR TS=(hyperlipoproteinemia) OR TS=(familial hypertriglyceridemia) OR TS=(familial hypertriglyceridaemia) #2 TI=(screen) OR TI=(mass screen) OR TI=(audit) OR TI=(detect*) OR TI=(identify) OR TI=(identification) OR TI=(record) OR TI=(diagnose) OR TI=(diagnosis) #3 TI=(GP) OR TI=(general practice) OR TI =(general practitioner) OR TI=(primary health) OR TI=(primary health care) OR TI=(primary healthcare) OR TI =(laboratory) OR TI=(community) OR hyperlipidemia OR hyperlipidaemia OR hyperlipoproteinemia OR hyperlipoproteinaemia OR hypertriglycerolaemia OR hypercholesterolaemia OR hypercholesterolemia AND mass PRE/1 screening OR diagnosis OR audit OR detect OR record OR database OR identify OR identification AND general practice OR general practitioner OR GP OR primary health care OR primary healthcare OR community OR laboratory OR clinic OR clinical practice ISRCTN registry (www.isrctn.com/)(All years) (hypercholesterolaemia) OR (hypercholesterolemia) OR (hyperlipidemia) OR (hyperlipidaemia) OR (hyperlipoproteinaemia) OR (hyperlipoproteinemia) OR (hypertryglycerolaemia) within Condition: (familial hypercholesterolaemia) OR (familial hypercholesterolemia) (Continued) Strategies for screening for familial hypercholesterolaemia in primary care and other community settings (Review) Copyright © 2021 The Cochrane Collaboration.Published by John Wiley & Sons, Ltd.
The Inborn Errors of Metabolism Trials Register is compiled from electronic searches of the Cochrane Central Register of Controlled Trials (CENTRAL) (updated with each new issue of the Cochrane Library), and weekly searches of MEDLINE.Unpublished work is identified by searching the abstract books of the Society for the Study of Inborn Errors of Metabolism conference and the SHS Inborn Error Review Series.For full details of all searching activities for the register, please see the relevant section of the Cochrane Cystic Fibrosis and Genetic Disorders Group's website.
Date of the most recent search of the Cochrane Cystic Fibrosis and Genetic Disorders Group's Inborn Errors of Metabolism Trials Register: 13 September 2021.In addition to the above, we have conducted a search of the following databases combining free-text terms and controlled vocabulary where applicable.For details of our search strategies, please see Appendix 2. Platform (ICTRP) (www.who.int/ictrp.en;all years: searched to 05 March 2020).Strategies for screening for familial hypercholesterolaemia in primary care and other community settings (Review) Copyright © 2021 The Cochrane Collaboration.Published by John Wiley & Sons, Ltd.
CochraneHandbook for Systematic Reviews of Interventions according to the following domains: random sequence generation; allocation concealment; blinding of participants and personnel; blinding of outcome assessment; incomplete outcome data; selective outcome reporting; and other sources of bias (Higgins 2017).We will assess each domain as low, unclear or high risk of bias.If we include NRSIs in future updates of the review, we will assess the risk of bias for each outcome of interest in each study using the ROBINS-I tool developed by the Cochrane Non-Randomised Studies Methods Group (Sterne 2016).The tool considers seven domains: two domains of bias pre-intervention (bias due to confounding at population-or service-level (examples are described in 'Data extraction and management section') and bias in selection of participants into the study); one domain of bias at intervention (bias in the measurement of interventions); and four domains of bias post-intervention (bias due to departures from intended interventions, bias due to missing data, bias in measurement of outcomes and bias in selection of the reported result.We will assess each domain as low, moderate, serious, or critical risk of bias or no information.

Strategies for screening for familial hypercholesterolaemia in primary care and other community settings (Review)
Copyright © 2021 The Cochrane Collaboration.Published by John Wiley & Sons, Ltd.
The authors would like to thank the Nottingham City Clinical Commissioning Group for commissioning and funding this review as part of a programme grant development award.The authors would also like to thank Dr Christopher Respinger for his contribution to dra ing the 'Why it is important to do this review' section, our patient representative Mr William Rowland for cowriting the lay summary.This project was supported by the National Institute for Health Research, via Cochrane Infrastructure funding to the Cochrane Cystic Fibrosis and Genetic Disorders Group.The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the Systematic Reviews Programme, NIHR, NHS or the Department of Health.Steyn K, Fourie JM, Shepherd J. Detection and measurement of hypercholesterolaemia in South Africans attending general practitioners in private practice -the cholesterol monitor.South African Medical Journal 1998;88(12):1569-74.Troeung L, Arnold-Reed D, Ping-Delfos WC, Watts GF, Pang J, Lugonja M, et al.A new electronic screening tool for identifying risk of familial hypercholesterolaemia in general practice.Heart 2016;102(11):855-61.Ryan J, Pang J, Watts GF, Vickery A. Using general practice electronic databases to increase the identification of familial hypercholesterolaemia in primary care.Atherosclerosis 2017;263:e61.Vickery AW, Ryan J, Pang, J, Garton-Smith J, Watts GF.Increasing the detection of familial hypercholesterolaemia using general practice electronic databases.Heart, Lung and Circulation 2017;26(5):450-4.Zamora A, Masan L, Comas-Cufi M, Vila A, Plana N, Garcia-Gil M, et al.Familial hypercholesterolaemia in a European Mediterranean population -prevalence and clinical data from 2.5 million primary care patients.Journal of Clinical Lipidology 2017;11:1013-22.