Familial Hypercholesterolaemia Quality Improvement Tool Instruction Guide

Transcription

1 Familial Hypercholesterolaemia Quality Improvement Tool Instruction Guide PRIMIS development of this tool was part supported by independent funding from Amgen. Prepared by PRIMIS January 2017 The University of Nottingham. All rights reserved.

2 Contents Introduction... 3 Aim of the Familial Hypercholesterolaemia (FH) quality improvement tool 4 Clinical audit notes and GP revalidation... 5 CHART Online... 6 Search inclusion criteria... 6 CHART Summary Sheet (classic view)... 7 Key information... 7 Breakdown of patients into risk groups... 8 Patients screened in the last 12 months... 9 Family history codes Lipid lowering drugs in the last six months View 2 - Datasheet Appendices Risk scoring Instructions for mail merge function List of columns available within the CHART datasheet References About this tool The PRIMIS Familial Hypercholesterolaemia (FH) quality improvement tool was developed in collaboration with the Applied Genetics and Ethnicity research group at The University of Nottingham. This tool is based on the FAMCAT algorithm* developed by academics in the Applied Genetics and Ethnicity research group. * Weng SF, Kai J, Neil HA, Humphries SE, Qureshi N. Improving identification of familial hypercholesterolaemia in primary care: Derivation and validation of the familial hypercholesterolaemia case ascertainment tool (FAMCAT). Atherosclerosis 2015; 238(2): doi: /j.atherosclerosis FH_Guide_V th January 2017

3 Introduction Familial Hypercholesterolaemia (FH) or genetic inherited high cholesterol is a congenital condition present from birth. It is passed from generation to generation through a faulty or abnormal gene which results in cholesterol levels between two to four times the average. It is not caused by an unhealthy diet or lifestyle. As it is an inherited condition, knowledge (and recording) of relevant family clinical history is key. The siblings or children of someone with FH have a one in two (50%) chance of having the condition. The UK prevalence of FH is currently unknown due to the lack of available data. It is estimated to be at least one in 500 people but could be as much as one in 200, as seen in other European countries. 1 Untreated patients with FH are at an increased risk of heart disease but often many are unaware that they have the condition. Around 80-90% of Familial Hypercholesterolemia cases remain undiagnosed. 1 Crucially, if left untreated, about 50 per cent of men and 30 per cent of women with FH will develop coronary heart disease by the time they are 55 (BHF). 2 Early identification and effective treatment to prevent the onset of heart disease in FH patients can help to ensure that people with FH have a normal life expectancy. 2 On average in the UK, one person a day with familial hypercholesterolemia has a heart attack. About a third of people don t survive their first heart attack, and many who do survive will have damaged hearts. Heart Matters, British Heart Foundation (2016) 2 Case finding in general practice is an effective way to identify patients with a missing diagnosis. A case finding and diagnostic intervention programme, implemented by NHS Medway CCG successfully increased FH diagnosis rates from one in 750 people, to one in 357; which is more in line with reported epidemiology rates (Heart UK). 3 Case finding can be effectively undertaken by interrogating GP clinical information systems. An algorithm for predicting FH has been developed to identify individuals in primary care with the highest probability of the condition. The familial hypercholesterolaemia case ascertainment tool (FAMCAT) is based upon coded data routinely available in general practice electronic records. 4 These patients can then be assessed and either referred for diagnosis or preventative care as appropriate. Once diagnosed, FH can be easily and effectively treated with a cholesterollowering statin. Usually, a high-intensity statin is needed to bring it down. Sometimes a different lipid-lowering drug is given as well as a statin. Lifestyle changes can also reduce the risk of heart disease. 2 FH_Guide_V1.0 Page 3 of th January 2017

4 Aim of the Familial Hypercholesterolaemia (FH) quality improvement tool The main aims of the familial hypercholesterolaemia quality improvement tool are to help identify patients who have the disease but do not have a coded diagnosis in their record, to calculate a patient s likelihood of having the disease based upon the presence of key data items and to report upon the lipid lowering treatment status for both those with, or at risk of, the disease. By undertaking a review of these patients and adding any missing diagnosis codes, practices can improve the quality of their disease register, establish a more accurate prevalence rate and ensure that patients are monitored regularly and receive appropriate management. In summary, the familial hypercholesterolaemia quality improvement tool helps practices by: identifying patients who may have the disease, but do not have a coded diagnosis and ranking them in order of likelihood establishing a more accurate prevalence rate for familial hypercholesterolaemia within their practice population highlighting patients with familial hypercholesterolaemia who are currently untreated identifying opportunities to optimise lipid lowering treatment regimes for patients with the disease highlighting recording rates of important family history information providing the facility to compare data with other practices both locally and nationally and the option to share aggregated data with their CCG via the CHART Online tool providing a mail merge function to generate patient invitation letters and an example of a validated family history questionnaire contributing to the delivery of the NHS Outcomes Framework and the Clinical Commissioning Group Outcomes Indicator Set providing data in a format that can be used for appraisal and revalidation and providing a method for GPs to reflect on their own clinical practice FH_Guide_V1.0 Page 4 of th January 2017

5 Clinical audit notes and GP revalidation This quality improvement tool has been designed to support GP revalidation. GPs can use the various displays within the CHART software to review clinical data at both patient and practice level, enabling them to maintain an overall picture of how they are managing patients at a population level but at the same time, look in detail at the care of individual patients. This is a retrospective clinical audit i.e. looking back at clinical practice that has already taken place. When conducting clinical audit for GP revalidation, GPs might choose to: Audit just their own clinical practice. Data will be included on the activity of all colleagues within the practice, but can be filtered by Usual GP. Note that the familial hypercholesterolaemia quality improvement tool reports on both patients with a coded diagnosis and with factors suggesting possible familial hypercholesterolaemia but no diagnosis. Involve fellow GPs in the clinical audit project. Several GPs who work together as a team can undertake a common audit. This is acceptable for the purpose of GP revalidation, as long as each GP can demonstrate that they have contributed fully to the clinical audit activity. Alternatively, seek their permission. Look at the care of patients with familial hypercholesterolaemia (or possible familial hypercholesterolaemia for case finder searches). This matches the following audit criteria: it is of concern for patients and has the potential to improve patient outcomes it is important and is of interest to you and your colleagues it is of clinical concern it is of local or national importance it is practically viable there is new research evidence available on the topic it is supported by good research FH_Guide_V1.0 Page 5 of th January 2017

6 CHART Before using the tool you must ensure that CHART is installed and you are familiar with how to use the software. Detailed instructions can be found on the PRIMIS website: CHART Online CHART Online is a secure web enabled tool that helps practices improve performance through comparative data analysis. By using CHART Online, practices can explore and compare the quality of their own data with anonymised data from other practices, locally or nationally, through interactive graphs. This provides a powerful tool for reducing variation across localities and may be of interest to local commissioning groups to facilitate the planning of care pathways. Aggregated summary data can be uploaded to the PRIMIS comparative analysis tool, CHART Online. There is an inbuilt security function that prevents patient identifiable data being uploaded; only aggregate data compiled from the pseudonymised responses can be transmitted. Access to the comparative views will be available online in the near future once sufficient data have been received to generate the graphs. Please upload data in the meantime to allow enough data to be received to produce the graphs. Search inclusion criteria Searches are based upon patients who are currently registered at the practice. It is recommended that the MIQUEST searches within the quality improvement tool are re-run frequently (e.g. quarterly or six monthly) to monitor standards of care and assess progress. FH_Guide_V1.0 Page 6 of th January 2017

7 CHART Summary Sheet (classic view) CHART summary sheets provide an overview of all relevant data recorded by the practice. This is the best place to start when viewing the results. Key information The first four rows of data provide some important pieces of information: an up to date count of the registered practice population the number of patients aged 16 and over the number of patients aged 16+ with a cholesterol test result recorded ever (the cohort for the summary sheet and datasheet) the number of patients aged 16+ with a cholesterol test result recorded in the last 12 months FH_Guide_V1.0 Page 7 of th January 2017

8 The tables within the summary sheet include both patients with a coded disease diagnosis and those at risk of having the disease as calculated by the FAMCAT algorithm. 4 Those patients with an existing diagnosis are clearly identified in the first column (diagnosed). Patients classified as being at risk of having the disease are then categorised by their level of risk. Risk category FAMCAT score range Diagnosed Very High Risk High Risk Population Risk n/a >1 Breakdown of patients into risk groups The first table focuses on diagnoses made in the last 12 months (for those with an existing coded diagnosis) and whether patients have been screened within the last 12 months. Evidence of screening is based upon the presence of Simon Broome or Dutch Criteria assessment codes or hyperlipidaemia screening codes. What to note about this practice The 4,730 patients identified as being aged 16 to 120 with a cholesterol test (ever) have been placed in the first row of the table. Of these 4,730 patients, eight have an existing diagnosis and 83 are deemed very high risk, 491 high risk and the remaining 4,148 are at population risk. This means their estimated risk is no greater than the background population risk. Looking at the first column; two out of the eight patients with a coded diagnosis were diagnosed within the last year. Eight out of eight patients with a coded diagnosis have no evidence of screening within the last 12 months. 81 patients have been identified as being at very high risk of developing FH and have not been screened within the last 12 months. Suggested actions Every cell within the summary tables provides a direct link to the list of patients. Simply click on the cells to access the filtered patient lists. To return to the summary sheet, click Show Summary on the toolbar. Any patients identified as being at very high risk of developing FH without screening in the last year should be reviewed. In the example data, 81 patients were identified in the red cell. The mail merge function can assist with generating invitation letters (see page 14 for instructions). Any patients with a coded diagnosis of FH who have not had screening in the last 12 months may also benefit from review. FH_Guide_V1.0 Page 8 of th January 2017

9 Patients screened in the last 12 months The second table focuses on patients who have been screened in the last 12 months and categorises them by screening method. The first row within the table is a direct copy of the screening row in the previous table (the penultimate row) and therefore the figures are the same. Reminder: click on the table cells to access the filtered patient lists. To return to the summary sheet, click Show Summary on the top toolbar. Patients can appear in more than one row within this table (e.g. Dutch criteria screening plus hyperlipidaemia screen). What to note about this practice 23 patients were identified as having been screened within the last 12 months. Of these 23 patients, two are classified as being at very high risk of developing FH and four are considered high risk. Of the two patients considered at very high risk of developing FH, one was assessed using the Simon Broome method and one had had a hyperlipidaemia screen. Of the four patients classified as being at high risk of FH (and who have been screened in the last 12 months) all have had a recent hyperlipidaemia screen and one has had a recent referral to a specialist/consultant. Suggested actions Every cell within the summary tables provides a direct link to the list of patients. Simply click on the cells to access the filtered patient lists. To return to the summary sheet, click Show Summary on the toolbar. Regularly review patients (annually) considered at very high risk or high risk of developing the disease. Ensure an accurate family history has also been recorded. Ensure accurate recording of the screening method used during assessment and any referrals to specialist clinicians. FH_Guide_V1.0 Page 9 of th January 2017

10 Family history codes The third table examines recent family history coded entries on the clinical system (recent entries are those entered after 1 st July 2016). This table aims to highlight where family history is unknown or contradictory. Note: the top row within this table contains the same figures as those within the top row of the first table. This means the entire cohort is included within this table (all patients aged 16+ with a cholesterol recording ever). Reminder: click on the table cells to access the filtered patient lists. To return to the summary sheet, click Show Summary on the top toolbar. What to note about this practice For all the patients (in this example), their FH family history is unknown. Suggested actions Patients deemed as being at very high risk of FH should be called for review (as per first table in the summary). An accurate family history can also be established as part of this review. Patients classified as being at high risk of FH who do not have a family history recorded (shown in the green cell) should have this information captured. High risk patients may well become very high risk once an accurate family history has been recorded (and this is positive). Mail merge Option 3 can assist with generating letters to send to these high risk patients. An example of a validated family history questionnaire is also included (see page 14 for instructions). Any patients identified as having contradictory family history information should have their record reviewed for further information and the patient consulted for clarification if required. The entries may be found to be correct and accurate. For example, an initial entry of no family history may later be followed by a positive family history entry that has subsequent been revealed. Patients at population level risk with an unknown family history may move into a higher risk category should a positive family history code subsequently be added. If family history codes are added to the patients records, the tool must be rerun to establish an up to date risk calculation. FH_Guide_V1.0 Page 10 of th January 2017

11 Lipid lowering drugs in the last six months The last table examines recent treatment with lipid lowering drugs in both patients with an existing diagnosis and those at risk. This table aims to highlight patients who are not receiving treatment and do not have a contraindication recorded (ever). It also shows the numbers of patients who do have recorded contraindications (ever) and categorises all patients by their treatment status. Note: the top row within this table contains the same figures as those within the top row of the first table. This means the entire cohort is included within this table (all patients aged with a cholesterol recording ever). Reminder: click on the table cells to access the filtered patient lists. To return to the summary sheet, click Show Summary on the top toolbar. What to note about this practice Three patients with an existing diagnosis are not taking a lipid lowering drug (prescribed in last six months) and do not have a contraindication recorded (ever). There are nine patients with a recorded contraindication to statins. Suggested actions Any patients with an existing diagnosis of FH who appear in the bottom row (not taking a lipid lowering drug and not contraindicated) should be considered for commencement of lipid lowering medication. FH_Guide_V1.0 Page 11 of th January 2017

12 View 2 - Datasheet The datasheet is often considered the most valuable part of the quality improvement tool. It allows practices to access the patient level data, providing all the relevant information in one place. The datasheet can be filtered as desired by the practice, to produce bespoke lists of patients. When preparing the queries to run on the clinical system, practices are asked if they want to run a pseudonymised set, which uses a patient reference number or a patient identifiable set, that will return named patient information (see example image below). The patient identifiable set is the most useful for case finding activity and also offers a mail merge function to allow practices to prepare invitation letters should they wish to call patients in for review. See the appendices for instructions. The CHART datasheet contains many columns of related data. A full list of available columns is included in the appendices of this document. You can apply your own custom filters (to generate bespoke lists of patients) by clicking on the grey drop down arrows on each column header. You can apply as many filters as you wish to further refine your list. The resulting filter can then be stored for future use. Click Store filter on the toolbar once you have created your list and name it appropriately. You can then load this filter at any time in the future (including on all future re-runs of the data). Remember to clear any applied filters before browsing the datasheet further otherwise you may not realise that you are viewing a restricted list and not the full cohort. FH_Guide_V1.0 Page 12 of th January 2017

13 Appendices 1. Risk scoring Calculation of patients level of risk is dependent upon certain codes being present within the patient s electronic record. Absence of these codes could either indicate that the patient does not have the specified risk factor or that alternative Read codes could be being used that are inaccurate or too generic. It is pertinent therefore that practices record such clinical data in as much detail as is possible and is relevant. Scores provided within this quality improvement tool should not replace clinical decision making and are only included to help inform that decision. Patients must be reviewed to confirm the accuracy of recorded information before management is decided. FH_Guide_V1.0 Page 13 of th January 2017

14 2. Instructions for mail merge function Important note: Although every care has been taken to ensure the accuracy, completeness and reliability of the FH case finder quality audit tool, we advise that a health care professional validate the output of the mail merge facility prior to any letters being sent to patients. 1. When you import the named response files into CHART, you will be given the option to create three mail merge files and save them to a location of your choice. These are: No mail merge files are created. Option 1 Option 2 This option bypasses the mail merge list creator and allows you to access the data/results. Creates a list of the patients at very high risk of FH who do not have a diagnosis code and have not been screened in the last 12 months (patients shown in the red cell of the summary table). This option allows you to send a standard letter to these patients inviting them for a review. File name: Standard very high risk mail merge source Creates a list of the patients classified as being at high risk of FH who do not have a recent (since July 2016) family history recorded (patients shown in the green cell of the summary table). Option 3 This option assists with generating letters to send to these high risk patients along with a copy of the family history questionnaire that accompanies this tool. File name: Standard high risk mail merge source Creates a list of all patients classified as being at high risk of FH (patients shown in the blue cell of the summary table). Option 4 This option may include more patients than those identified using Option 3 above, as it includes all high risk patients including those who have an existing positive family history entry. This will allow you to gain up to date information for these patients. File name: One off high risk mail merge source FH_Guide_V1.0 Page 14 of th January 2017

15 Disclaimer message 2. Upon selecting options 2, 3 or 4, a disclaimer message will appear. 3. You must select Yes otherwise the mail merge will be cancelled: 4. If you select yes you will be asked to browse to (or create) a folder where the generated patient information can be stored for later use. Choose a location that you will remember and that is secure, as these lists contain patient identifiable data. A network drive rather than a local drive is recommended (see example image to the right which shows a network drive as L:). Click OK. FH_Guide_V1.0 Page 15 of th January 2017

16 5. Once the file has been created you will see the following message: The CHART summary sheet will load as normal. If you want to create further mail merge lists, repeat steps Now open your own created template letter. If you haven t got a standard practice template letter, you must create this first and add the relevant merge fields available from the lists. Simply open a new document in MS Word and then click on the Mailings tab. Then click Start Mail Merge, Letters (see below): 7. Choose Mailings from the ribbon, then click on Select Recipients and Use Existing List : FH_Guide_V1.0 Page 16 of th January 2017

17 8. Browse to the location of the mail merge list(s) stored earlier, select the correct list and click Open : 9. Click on OK when you see the following message: 10. Type out the content of your letter inserting merge fields as needed. 11. Click Finish & Merge and Edit Individual Documents. 12. Click OK to merge to a new document. 13. Check letters and recipients before sending. FH_Guide_V1.0 Page 17 of th January 2017

18 3. List of columns available within the CHART datasheet Pseudonymised set Patient identifiable set Reference (MIQUEST pseudo ref) Usual GP Reference (system ID number) NHS number Surname Forename Both sets Age Sex Registered_Date Latest TC Ever Code Latest TC Ever Date Latest TC Ever Value1 TC in L12M? Latest LDL Ever Code Latest LDL Ever Date Latest LDL Ever Value1 LDL in L12M? Item to use Latest High Potency LLD with a TC in 3M after Date Latest Med Potency LLD with a TC in 3M after Date Latest Low Potency LLD with a TC in 3M after Date Latest Other LLD with a TC in 3M after Date Latest High Potency LLD with a LDL in 3M after Date Latest Med Potency LLD with a LDL in 3M after Date Latest Low Potency LLD with a LDL in 3M after Date Latest Other LLD with a LDL in 3M after Date Latest Family History of FH Code Latest Family History of FH Date Latest Family History of MI Code Latest Family History of MI Date Latest Family History of Raised Cholesterol Code Latest Family History of Raised Cholesterol Date RELATIVE RISK Risk Category Latest FHC Diagnosis Code Latest FHC Diagnosis Date Latest FHC Diagnosis in L12M Date Latest Secondary Cause of FHC Code Latest Secondary Cause of FHC Date FH_Guide_V1.0 Page 18 of th January 2017

19 Latest Statin Contraindication Code Latest Statin Contraindication Date Latest High Potency LLD in L6M Code Latest High Potency LLD in L6M Date Latest Med Potency LLD in L6M Code Latest Med Potency LLD in L6M Date Latest Low Potency LLD in L6M Code Latest Low Potency LLD in L6M Date Latest Other LLD in L6M Code Latest Other LLD in L6M Date LLD Category Latest Simon Broome Assessment in L12M Date Latest Dutch Criteria Assessment in L12M Date Latest Hyperlipidaemia Screening in L12M Date Patient Screened in L12M? Latest Referred to Specialist in L12M Date Latest Family History of FH Date since July 2016 Latest Family History of MI Date since July 2016 Latest Family History of Raised Cholesterol Date since July 2016 Positive Family History? Latest No Family History of CVD Date since July 2016 No. of Family History codes since July 2016 Address (patient identifiable set only for mail merge) Postcode (patient identifiable set only for mail merge) FH_Guide_V1.0 Page 19 of th January 2017

20 4. References 1. Heart UK (November 2016) What is Familial Hypercholesterolaemia? Available: Last accessed: 24 th November British Heart Foundation (November 2016) Heart Matters. Focus on: Familial Hypercholesterolaemia Available: Last accessed: 24 th November Heart UK (August 2015) Systematically Identifying Familial Hypercholesterolaemia in Primary Care. An Audit Within The Medway Clinical Commissioning Group. Available: rt_web_2015.pdf Last accessed: 24 th November Weng SF, Kai J, Neil HA, Humphries SE, Qureshi N. Improving identification of familial hypercholesterolaemia in primary care: Derivation and validation of the familial hypercholesterolaemia case ascertainment tool (FAMCAT). Atherosclerosis 2015; 238(2): doi: /j.atherosclerosis Available: Last accessed: 24 th November 2016 FH_Guide_V1.0 Page 20 of th January 2017