PEER REVIEW HISTORY ARTICLE DETAILS TITLE (PROVISIONAL)

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1 PEER REVIEW HISTORY BMJ Open publishes all reviews undertaken for accepted manuscripts. Reviewers are asked to complete a checklist review form (see an example) and are provided with free text boxes to elaborate on their assessment. These free text comments are reproduced below. Some articles will have been accepted based in part or entirely on reviews undertaken for other BMJ Group journals. These will be reproduced where possible. TITLE (PROVISIONAL) AUTHORS REVIEWER REVIEW RETURNED GENERAL COMMENTS ARTICLE DETAILS Multi-drug and optimal heart failure therapy prescribing in older general practice populations: a clinical data linkage study Rushton, Claire; Stromberg, Anna; Jaarsma, Tiny; Kadam, Umesh VERSION 1 - REVIEW Petra Denig University Medical Center Groningen, The Netherlands. 19-Sep-2013 The question is clear, and the authors argue that data of more than a decade ago are appropriate for investigating the a priori hypothesis that multi-drug prescription influences optimal HF drug prescribing. However, in my opinion these data are outdated to answer the first question (that is, to describe multi-drug prescribing in the cardiovascular general practice population). It is to be expected that much has changed in year in the general practice population, both regarding the population itself as well as regarding their treatment. This is also problematic for answering the second question (whether multi-drug prescribing influences optimal HF drug prescribing), especially since optimal HF drug prescribing has become more complex (as the authors themselves mention, the combination treatment of RAS/betablockers in HF has increased since the new guidelines became available in 2001). One can expect that the (negative) influence of multi-drug prescribing is larger when optimal HF drug treatment involves more drugs or more complex treatment regimes. I would strongly recommend to conduct the study using more recent data. Other remarks: 1. The author mention this is a cross-sectional clinical data linkage study but in the methods it is not explained where the data came from (electronic medical records?) and how they were linked for this study. 2. In what way is this population (3 practices in North Staffordshire that are part of a research network) representative of a more general population (in the UK)? 3. Why was the reference group chosen of patients with at least 1 CVD drug prescription without a CVD diagnosis? What kind of population would this constitute (it is a substantial group!)? Are CVD diagnoses just lacking or are the drugs prescribed for non CVD diagnoses? 4. What was the underlying reason for categorising the multi-drug into four count categories: (i) 0

2 REVIEWER REVIEW RETURNED GENERAL COMMENTS (i.e. no non-cvd drugs), (ii) 1 to 3, (iii) 4 to 6 and (iv) 7 or more? The choice of these levels may very well determine whether or not (significant) differences are seen between groups. I would prefer to see and compare the data first without categorisation. 5. Is it possible to conduct a more detailed algorithm of optimal therapy (see, for example, the GAI indices in the Komaja study) taking into account levels of HF? 6. To assess the impact of comorbidity on optimal HF drug prescribing, the chosen approach using adjustments on odds ratios comparing the HF failure group with a reference group is rather difficult to interpret. This may show that the influence of a multidrug regimen on prescribing recommended HF drugs is similar for HF and non HF patients (but that was not the question). I would prefer to have a guideline adherence measure within the HF population as primary outcome and assess the influence of the multidrug regimen on this outcome to answer the second question. This prevents that the multidrug effect is discounted because it also occurs in the reference group. 7. The whole issue of comorbidity in HF (and thus multidrug therapy) and how this may affect not only HF treatment but also guideline recommendations is a hot topic at the moment. See for example, Page RL 2nd, Lindenfeld J. The comorbidity conundrum: a focus on the role of noncardiovascular chronic conditions in the heart failure patient. Curr Cardiol Rep Jun;14(3): It would be good to address / discuss this using some of such recent publications. 8. The STROBE checklist only provides a Y for all items, where a reference to a page/line number where appropriate- would be helpful. As far as I could see, some of the items were not (explicitly) addressed in the manuscript (see also some of the points above). The authors state: "This step wise adjustment was performed so that the influence of non-cvd multi-drug therapy on the observed associations could be identified." It is not clear how they actually do this. It seems that by entering first age, gender, depreviation, the 'influence' of multi-drug therapy could be underestimated. In the results it is stated: "Additional adjustment for non-cvd multidrug therapy counts did not alter the associations between HF and any of the related drug groups." From the methods, it is not clear whether/how the authors planned to test that the associations were not altered. Mette Brekke University of Oslo, Norway. department of General Practice, Institute of Health and Society 23-Sep-2013 This is an interesting and relevant study investigating the potential influence of multi-drug use on optimal treatment of heart failure, by use of rather old data (2001) from a GP database. I have some substantial questions and requests for changes to be

3 made, before I can suggest that this manuscript can be published: Introduction The opening statement that the prevalence of HF is increasing in older populations needs a reference. The use of multiple drugs is a potential problem not only among patients with HF, but also in the general elderly population. I miss a statement of this, for example with the reference to Nyborg G et al: Inappropriate prescribing for the elderly a modern epidemic? Eur J Clin Pharmacol 2012;68: You correctly say that a patient s multi-drug therapy may result in sub-optimal drug prescribing due to the clinical problem of drug interactions. This leads to an important question that should be addressed: What do you mean by sub-optimal in such a context? Is it always optimal to adhere to several guidelines at the same time? Or is optimal prescribing to omit one drug to be able to prescribe another one, even in a patient with HF? Maybe the gold standard is not always to adhere to guidelines, but to evaluate the patient s total need for prescriptions? The aims of the study are clearly described. Methods Design: OK Study population: You linked the diagnosis made by the GPs to data on drug prescriptions. A major concern is that GPs use of a diagnosis is not reliable enough to allow for valid conclusions. It is valuable to establish clinical GP databases for the purpose of research, but has the use of diagnostic codes been quality assured in relation to these databases? In the Discussion section you just state that accuracy of recording of morbidity and prescription data in GP is also well recognised (p.10) and you give a reference ref. 38. I think you are rather over-confident in your conclusion based on this systematic review of Thiru et al. They say in the Abstract:. Prescribing data were generally of better quality than diagnostic or

4 lifestyle data. And they conclude that The lack of standardised methods for assessment of quality of data in electronic patient records makes it difficult to compare results between studies. A German study investigating inter-rating reliability of the ICPC-2 in a GP setting concluded that The fair to moderate reliability on the single code level should be considered when designing studies and interpreting data ( Swiss Med Wkly Aug 22;142:w doi: /smw ) So I am not convinced that the quality of the GPs use of diagnoses is sufficiently high in your database to enable reliable results. What do you know about this? Study groups: You identified middle aged/elderly patients who had received at least one CVD drug during the study period. You selected those who had a diagnosis of HF into one group (n = 170) and those with other CVD diagnoses into another (n = 2 985). Then you present a third group (n = 1 739) which I find highly problematic: This group used CVD drugs, but had no CVD diagnosis during the study period. You call this group non-cvd reference group. Reference for what, or compared to what? Which patients are included in this group? Do they use CVD drugs they do not need? Or are their GPs prone to inaccurate use of diagnoses? I do not see why inclusion of this group should be interesting, so I suggest that you exclude these patients from your analyses. In my opinion these are patients who can not be included into the study because of uncertainty regarding their reason for taking CVD drugs. Definition of multi-drug therapy: OK. Definition of CVD drug measures: OK Statistical analysis: OK, but it should relate to two study groups, not three. You say in the last paragraph that the adjusted associations between the HF group, the non-hf CVD group and the CVD drug measures compared to the reference group are presented. As stated above, I cannot see why this is of interest and do not agree that this uncertain group of patients can be used as a reference for anything. So any comparing of the study groups of interest (the HF-group and the non-hf CVD group) to this so-called reference group should be omitted,and instead these two groups

5 should be compared to each other. Results Study population: If you omit the uncertainty group (n = 1 739) you are left with a total of 3155 patients. Out of these, 170 have a diagnosis of HF. How does this number fit in with the expected prevalence of HF in this age group? This may give some information regarding the accuracy of diagnoses in this database. Multi-drug prescribing: The word only should be omitted, as the finding that 43% in the age group y use 4 or more non-cvd drug groups in addition to their CVD-drugs, is a really high prevalence! Associations between study groups and CVD drugs: OK, apart from use of the reference group. Compare the HF-group to the non-hf CVD-group instead. Discussion The first statement should be reformulated into: In a large cardiovasculat GP population, our study showed that multi-drug prescribing was common, but it did not influence optimal prescribing in the HF-group. The second statement is inaccurate: The older HF group had the highest non-cvd multi-drug therapy, yet relatively (compared to what?) they were stillmuch more likely (compared to whom?) to receive.. Your main finding was that despite the high level of multi-drug use, as many as ca 90% of HF-patients received optimal therapy, and that this did not decrease with increasing non-cvd prescriptions. As you say, other studies have found that multi-drug use did influence optimal HF-therapy. You should include a discussion of the possibility that the high adherence to guidelines found in your study in spite of multi-drug use, could lead to problems: drug-drug interactions, side-effects, etc. (Ref. Nyborg G et al: Inappropriate prescribing for the elderly a modern epidemic? Eur J Clin Pharmacol 2012;68: , and Brekke M et al:

6 Both reviewers 1. Selection of reference groups Pharmacologically inappropriate prescriptions for elderly patients in general practice: How common? Baseline data from The Prescription Peer Academic Detailing (Rx-PAD) study. Scand J Prim Health Care. 2008;26(2):80-5. doi: / ) Strengths and limitations As stated above, I am not convinced that ref. 38 supports your statement of accuracy of morbidity data in GP. I would rather prefer a humble attitude and a discussion of the limitations of diagnostic accuracy in such settings. I think that the high number of patients in your study (n =1 739) taking a CVD drug, but without a CVD diagnosis in the study period, reflects this fact. Implications You say that. there is clearly scope for improvements Based upon your findings, which improvements do you mean? You further say that further work is still needed to investigate the impact of multi-drug therapy on optimal therapy in terms of dose and duration Should there be such an impact? What should be the gold standard? To adhere to all relevant guidelines in one single patient, or to carry out adjustments based on an overview? Tables OK, but will be changed when omitting the reference goup. Some improvements of language are needed. VERSION 1 AUTHOR RESPONSE Our construction of the reference groups was based on consideration of two core issues: a) clinical context of a database with a fixed 2-year time window - The reference was a group that were taking repeated cardiovascular drug prescriptions but did not have a CVD label in the two-year time window (for example as might be the case in hypertension or coronary heart disease that might have been recorded previous to the 2-year time-window). b) epidemiological construction of reference groups - All drugs used and recommended in heart failure are also used in other cardiovascular diseases, as

7 reviewer 1 agrees. So there is a basic probability that many of these drugs will be prescribed in the general population. In rigorous epidemiological studies, careful selection of referent groups is the basis for good interpretation. Here we had two other groups which would provide the probability of the common drugs prescribed (one without an available diagnostic category in the 2-year time window and another with CVD group of diagnosis), and against which the heart failure prescribing could be compared. This approach provided evidence that whatever the indication for commonly prescribed drugs, they were prescribed at much higher levels than the general population (the other two groups) from which they were drawn. As the reviewers will know, the following reference is one of the classic epidemiological papers on the careful selection of groups: Olli S. Miettinen. The "Case-Control" study: valid selection of subjects. Journal of Chronic Disease 1985;38: Key change - However, since both reviewers would prefer a more focused and classic comparison, we have amended all analyses to only the one reference group (Table 3, page 21), excluding the group without the CVD label in the 2-year time window, and also amended the relevant text associated with this change. Instead, we have included the 3 group comparison as a supplementary table (page 22) as we feel it provides the overall context of the general population who were on the CVD drugs that HF patient s might be on. These changes have been made to the methods and results. Reviewer: Dr Petra Denig 1. Use of more contemporary data We agree that the data used in our study is not as contemporary as it could be, but the reviewer must agree that many good studies are based on historical data and do allow the testing of a priori hypothesis. We agree with the reviewer that heart failure prescribing has changed and that this may influence the definition of optimal drug therapy. For this reason we reviewed the evidence on drug prescribing at the time of and since the time-period of our data (attached as a supplementary file to the submission). Whilst algorithms have indeed become more complex, the prescribing of ACEi and beta blocker as first line treatment has not altered. More complex algorithms that do not include these drugs (as opposed to add-on regimes) are usually due to poor tolerance and this may be related to other conditions and drugs that the patient is exposed to. We wanted to investigate the influence of multi-drug therapy on the prescribing of this first line treatment. Future work will readdress multi-drug therapy in more recent data. 2. Explaining the data linkage The data was extracted from electronic medical records that include drug prescriptions and consultation data - these were then linked by the unique NHS identification number that is allocated to each person in the UK, but we as researchers were provided with anonymised linked data sentence now added to page 4, line Representativeness of the 3 study practices We have now provided more information on the study population, but in the end these are large practices that were part of an established research network that participate in research projects such

8 as this and which provided the appropriate sample sentence added page 5, lines Construction of multi-drug count categories The categorisation was based on previous work that tested the association between multi-drug therapy and health outcomes (Kadam UT. Potential health impacts of multiple drug prescribing for older people: a case-control study. British Journal of General Practice ;583: ). The categories refer to the number of different British National Formulary non-cvd drug chapters (focused at the biological system level) that the patient is exposed to which provides an indication of the level of the patient s multimorbidity see also Kadam UT, Croft, North Staffordshire GP Consortium. Multimorbidity and physical function in older adults: a record and health status linkage study in general practice. Family Practice 2007;24(5): The reviewer asks an important question and we also analysed the multi-drug measure as a continuous variable, but we found this made little difference to the associations we have now indicated this on page 8, lines 203 and page 10, lines We have also added the mean number of multi-drug counts to Table Algorithm of optimal therapy (for example, the GAI indices) We would like to thank the reviewer for pointing out this interesting paper. The algorithm of optimal therapy used in the Komaja study is one that is based on symptoms and severity classification which is unfortunately not relevant for routine collected data but certainly provides an interesting perspective for our future studies. 7. Interpretation of adjusting for multi-drug counts The reviewer suggests that if the number of non-cvd multi-drug counts influences the odds of receiving HF drugs in BOTH groups (HF and reference comparator group) similarly, then adjusting for non-cvd drugs would result in a similar OR than before the adjustment. This would only be the case if the number of non-cvd drugs was the same in both groups and thus adjusting for them in both groups would make little difference. Our analyses clearly demonstrate that the number of patients with more non-cvd drug counts is a lot higher in the HF group than the other group and so if multi-drug counts did make a difference to optimal therapy you would expect the adjustment to make more of a difference to the HF group than the reference group. The odds ratio would therefore change in response to the adjustment but we demonstrated that this was not the case. This means that the reader can conclude that non-cvd multi-drug therapy did not influence optimal prescribing as defined in the HF group. 8. Recent publications We agree with the reviewer that HF comorbidity is a hot topic currently. For this study we wanted to focus on the context of multi-drug therapy and optimal prescribing which is less well studied. We thank the reviewer for highlighting this paper and have added this and other references to highlight the generic importance of this topic for older age groups page 4, lines and pages 11-12, lines (reference numbers 8-9, added)

9 9. Full STROBE checklist The list has been amended. 10. Step wise adjustment to the analyses We agree that our phrasing was unhelpful. The term stepwise is often framed within a statistical modelling approach, whereas we wanted to understand how adjusting for the different co-variates influenced the estimates - we have now deleted this term and rephrased the sentence page 8, line Assessing differences in adjusted associations between HF and optimal drug therapy. We agree that quantifying the level of confounding is interesting, but our statement on the lack of change in the strength of association is based on our observation of the differences in the estimate and the confidence intervals after adjustment. Reviewer: Professor Mette Brekke 1. The opening statement needs a reference Added page 4, line 83 (reference number 1 added) 2. Multiple drugs is a potential problem in the general elderly population. We agree with the reviewer about the broader implications of our study for older age groups. We have added a statement to this fact - page 4, lines (reference number 9 added). 3. What is meant by sub-optimal in such a context? The reviewer raises a very important point. For this study we empirically chose recommended first line drugs in HF to define optimal therapy but we agree that broader definitions that capture the overuse or inappropriate use of medicines are extremely relevant and we have added this to the discussion to highlight the clinical relevance of sub-optimal prescribing pages 11-12, lines and added references in discussion (reference numbers added). 4. Has the use of diagnostic codes been quality assured in relation to these databases? Whilst we agree with the reviewer that the quality of general practice data can vary, in the UK there has been concerted effort to improve the data quality recording in general practice. So we would like to respond to this comment by: a) The three general practices contributing the sample for this study were all part of an established North Staffordshire Primary Care Research Consortium and we have assessed the data quality recording in these practices (Porcheret M, Hughes R, Evans D, Jordan K, Whitehurst T, Ogden H,

10 Croft P, North Staffordshire General Practice Research Network Data quality of general practice electronic health records: the impact of a program of assessments, feedback, and training. J Am Med Inform Assoc, vol. 11(1), 78-86). b) Electronic recording of general practice consultations has been found to be high (>90%) in a systematic review of 24 studies that examined morbidity coding in UK primary care (see Jordan et al 2004 Quality of morbidity coding in general practice computerized medical records: a systematic review, Family Practice 2004; 21 (4), ). c) For the study sample we grouped patients who had received a CVD prescription at the chapter level of the READ classification. Heart failure was then selected at a sub-hierarchical level but the consultation codes were validated with the prescription data (concurrent repeat prescription of CVD drugs). Prescription recording has been found to be of higher quality in recorded data (Thiru et al review) and this approach has been used in other studies. We have adapted our original statement to better reflect more accurately this debate page 12, lines (reference numbers added). 5. Prevalence of HF The prevalence of heart failure in the general population rises from 0.9% for years to 2.9% in years and 13.1% in >75yrs. The prevalence for our study sample aged over 50 years was 5.4% within a CVD population and so fits with expected figures. Added to page 4 line Implications for improvements We refer here to the risks associated with the high levels of multi-drug prescribing found in our study and the implication that you have drawn which is all drugs with any guidelines, is not always optimal from the patient point of view. We have now clarified this page 13, lines What further recommendations could there be We refer here to further investigation of optimal therapy which takes account of dose and duration of recommended drugs. Previous studies have shown that patients receiving ACEi and beta blocker are often not taking the recommended doses. Whilst this, as the reviewer suggests, may be appropriate for the patient, there is still limited information on the influence of multi-drug therapy or comorbidity on this. This question needs further investigation to better understand what true optimal approaches should be for HF patients with comorbidity, and was outside of the methodological challenges that we faced when the question was first posed for this study. 8. Other minor edits a) only omitted page 9, line 222 b) sentence amended page 10, lines c) sentence amended page 10, lines

11 REVIEWER REVIEW RETURNED GENERAL COMMENTS VERSION 2 REVIEW Petra Denig Department of Clinical Pharmacology, University Medical Center Groningen, The Netherlands 12-Nov-2013 After reading the point-by-point response, I got completely lost in the revised manuscript. The authors say they have changed several major issues, but I cannot see this reflected in the manuscript. As such, it appears that most of the comments have not been resolved in the manuscript itself. General point: Selection of reference group As I understand now, there were 170 patients with HF and CVD prescription, 1246 patients with other CVD diagnoses and CVD prescriptions, and 1739 patients with CVD prescriptions without a CVD diagnosis. What is still not clear is whether these 1739 patients lack any linked diagnosis or have a non-cvd diagnosis linked to their CVD prescription (or both). The problem is that patients lacking any diagnosis are likely to include patients with HF, and therefore this group would be unfitting as reference group. The authors have not solved or clarified this problem adequately. Moreover, the authors claim they have changed all analysis by excluding the group without a CVD label but -as far as I can see- this was not the case. They did NOT remove the other group from tables 1 3. Also, the supplemental table 1 does NOT present the three groups in comparison. Instead, it presents overlapping groups (reference n=1739, non-hf CVD n= 2985, HF n=170; that is 4894 patients instead of 3155 patients). Point 1. Use of more contemporary data I agree and already acknowledged- that historical data can allow for the testing of a priory hypothesis but my comment that the data are outdated to answer the first question has not been resolved. There was no a priory hypothesis for describing multidrug prescribing in the cardiovascular general practice population. Point 2. Explaining the data linkage As I understand from the answer, drug prescription and diagnostic consultation data are both retrieved from electronic medical records as provided general practice network. There was not sentence added to this part to explain this better. The sentence: This study used a large clinical electronic medical database linking diagnostic data to all prescribed drugs still suggests that data were linked from different medical record sources (which is not the case, if I understand the answer correctly). Point 4. Multidrug count categories The authors refer to a previous paper from one of the authors to defend their categorization. This other paper, however, used completely different cut-points for categorization (and also did not give any substantiation for this). N.b. Adding the mean without any information on distribution or (standard) deviation in Table 2 is not very helpful. I can imagine that a median with IQR might be more meaningful. However, given point 1, the value of Table 2 is limited.

12 REVIEWER REVIEW RETURNED GENERAL COMMENTS Point 5/6. Optimal therapy algorithm It is a pity that no data on severity could be used and this is a limitation that could be better addressed. Abstract was not changed at all. Reference list contains duplicates. Point 6/7. Adjusting for multidrug counts To assess what the influence is of non-cvd multidrug use on an observed association, is a question of effect modification and not of covariate analysis. Adjusting on multidrug use in a linear model is not the correct approach to identify/assess an effect modifier. Mette Brekke Department of General Practice, Institute of Health and Society, University of Oslo, Norway 26-Oct-2013 I am generally pleased that the authors have followed my suggestions (which were partly consistent with those of Reviewer 1) in their revised manuscript. I am especially pleased that they have excluded the study group without the CDV label in the 2-year time window and now carry out thel analyses in the HF-group and the CVD-group exclusively. I strongly suggest, however, that the Supplementary Table 3, as well as the corresponding text, is omitted, as I think it is more likely to confuse readers than to enlighten them. I also do not think that it brings new and valuable information, compared to what is already given by the HF-group and the CVD-group (see my original comments). Your Ref. 9 is identical to Ref. 42. VERSION 2 AUTHOR RESPONSE 1. Professsor Mette Brekke We are pleased that the reviewer s expectations have been met. We have removed the supplementary table and the corresponding paragraph. 2. Dr Petra Denig We are slightly perplexed at this response in contrast to the first reviewer. We wonder if the combinations of drafts that were sent have somehow become mixed up. We shall try to respond to the remaining comments, which seem mainly reflective and it has spurred much reflection on our own parts. We can respond as follows: a) Selection of reference group This is a significant change that has been made to the paper, the reviewer needs to understand that the numbers she reports focus on the first paper submission which do not remain anymore. We have met reviewer 1 s expectations to have only one reference group (CVD diagnoses and related drugs prescribed) and removed the group without a diagnostic category in the 2-

13 REVIEWER year time window. However to clarify for this reviewer, there is now the heart failure group (n=170) and the reference group (n=2985). All statements to the other original reference group (without diagnostic category in the 2-year time window) have now been removed from the second revision of the paper. So the comments on this issue have been addressed. b) Use of contemporary data we agree that datasets can be used to test hypothesis as well as generate hypothesis. c) Explaining the data linkage we agree that the sentence needs to be more precise. We have added and tracked changes to the previous sentence to state Within each study general practice diagnostic data and prescribed data was extracted from the medical records and linked using individual patient NHS identifiers. The linked data was then anonymised prior to the data analysis. Page 4-5 and line d) Multi-drug count categories In this emerging field we have taken this approach to defining multi-drug counts. e) Optimal therapy algorithm In the limitations we have added and tracked changes However, the diagnostic categories do not convey the other aspect of measurement, which is severity of heart failure, which may influence levels of prescribing. Page 12 and line f) Abstract was changed as a result of reviewer 1 s comments on analysis. We should have tracked those changes and our apologies. g) Adjusting for multi-drug counts our intention was not to assess effect modification as the measurement of multi-drug is based on an overall count measure and not on specific drug categories. REVIEW RETURNED VERSION 1 - REVIEW Mette Brekke Department of general practice, Institute of health and society, University of Oslo, Norway 21-Nov-2013 BMJ Open: first published as /bmjopen on 2 January Downloaded from GENERAL COMMENTS I have no further objections to the present version of this paper - the authors have followes my suggestions. on 5 January 2019 by guest. Protected by copyright.