Using a Patient-Centered Composite Endpoint in a Secondary Analysis of the Control of Hypertension in Pregnancy Study (CHIPS) Trial

Using a Patient-Centered Composite Endpoint in a Secondary Analysis of the Control of Hypertension in Pregnancy Study (CHIPS) Trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Using a Patient-Centered Composite Endpoint in a Secondary Analysis of the Control of Hypertension in Pregnancy Study (CHIPS) Trial Rebecca K Metcalfe, Mark Harrison, Joel Singer, Mary Lewisch, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1909786/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Feb, 2023 Read the published version in Trials → Version 1 posted 5 You are reading this latest preprint version Abstract Background Clinical trials commonly use multiple endpoints to measure the impact of an intervention. While this improves the comprehensiveness of outcomes, it can make trial results difficult to interpret. We examined the impact of integrating patient weights into a composite endpoint on interpretation of CHIPS (Control of Hypertension in Pregnancy Study) trial results. Methods Outcome weights were extracted from a previous patient preferences study in pregnancy hypertension (N=183 women) which identified: (i) seven outcomes most important to women (taking medication, severe hypertension, pre-eclampsia, blood transfusion, Caesarean, delivery <34 weeks, and baby born smaller-than-expected), and (ii) three preference subgroup (1) ‘equal prioritizers’, 62%; (2) ‘early delivery avoiders’, 23%; and (3) ‘medication minimizers’, 14%. Outcome weights from the preference subgroups were integrated with CHIPS data for the seven outcomes identified in the preference study. A weighted composite score was derived for each participant by multiplying the preference weight for each outcome by the binary outcome if it occurred. Analyses considered equal weights and those from the preference subgroups. Mean composite scores were compared between trial arms (t-tests). Results Composite scores were similar between trial arms with use of equal weights or those of Subgroup (1) (95% confidence intervals [CIs]: -0.03, 0.02; and p >0.50 for each). ‘Tight’ control was superior when using Subgroup (2) weights (95% CIs: 0.002, 0.07; p =0.03), and ‘less-tight’ control superior when using Subgroup (3) weights (95% CIs: -0.11, -0.04; p <0.01). Conclusions Evidence-based recommendations for ‘tight’ control are consistent with most women’s preferences, but for a sixth of women, ‘less-tight’ control is more preference consistent. Depending on patient preferences, a single trial may support different interventions. Future trials should specify component weights to improve interpretation. Trial Registration: NCT01192412 Patient-centered Randomized controlled trial Composite endpoints Pregnancy hypertension Perinatal Figures Figure 1 Introduction Clinical trials in cardiovascular medicine routinely use primary, secondary and other endpoints to capture the breadth of an intervention’s effects. However, this can make interpretation of trial results challenging, as an intervention’s effects can vary by outcome, including benefits and harms. 1 Composite endpoints are often used to overcome these challenges, particularly in pregnancy, “…to circumvent a contrived prioritization of one-half of the mother–infant pair and acknowledge the interconnectedness of mothers and babies at the time of childbirth.” 2 Composites are typically dichotomous, and treat outcomes as equal, which may not be the case. To apply trial results in practice, clinicians and patients must consider which endpoints are important to them and to what degree. 3 The international CHIPS (Control of Hypertension in Pregnancy Study; ClinicalTrials.gov NCT01192412) 4 randomized controlled trial (RCT) compared ‘less-tight’ with ‘tight’ control of blood pressure (BP) for management of chronic or gestational hypertension; women who progressed to preeclampsia remained in their allocated group. ‘Less-tight’ control aimed to minimize antihypertensive therapy (target diastolic BP of 100 mmHg), while ‘tight’ control aimed to normalize BP (target diastolic BP of 85 mmHg). While ‘tight’ (vs. ‘less-tight’) control did not change the incidence of the primary fetal/newborn and secondary maternal composite outcomes (with equally-valued components), 4 ‘tight’ control has been recommended by many guidelines based on a decrease in severe maternal hypertension and some preeclampsia-related complications. 5 – 8 These findings that were recently replicated in a separate trial. 9 However, recommendations did not integrate women’s preferences or concerns, like taking medications during pregnancy. 10 In a secondary analysis of CHIPS trial data, we explored whether weighting outcomes to reflect patient preferences would change the interpretation of trial results. Methods We integrated pregnant women’s preferences for management of pregnancy hypertension 11 with individual event data from the CHIPS trial. 4 Outcome data from the 981 women enrolled in CHIPS were included (Table 1 ). Inclusion criteria were: 14 + 0 -33 + 6 weeks’ gestation, nonproteinuric chronic or gestational hypertension, office diastolic BP of 90-105mmHg (or 85-105mmHg if the women were taking antihypertensive medication), and a live fetus. 4 On average, participants were ≈ 34 years of age and enrolled at ≈ 24 weeks’. Most (75%) women had chronic hypertension. Roughly half were taking antihypertensives. Preferences were obtained from a separate study, 11 in which 183 pregnant women in Canada prioritized CHIPS trial outcomes, including the primary perinatal (pregnancy loss and/or neonatal care unit admission > 48hr) and secondary maternal outcomes (serious maternal complications). Participants identified five maternal and two fetal/newborn outcomes as important and sufficiently different between treatment arms to influence their preferred BP control (Table 1 ). 4 Preference subgroup weights were derived from a best-worst scaling task 12 that quantified the relative value of composite outcome components (where each component’s relative importance was expressed as a proportion, and all components summed to 100%). 11 Latent class analysis identified three preference subgroups (Table 1 ): (1) ‘ equal prioritizers’ (62%) who placed fairly equal weight on each outcome; (2) ‘ early delivery avoiders’ (23%) who prioritized avoiding delivery before 34 weeks (weight of 42%); and (3) ‘ medication minimizers’ (14%) who prioritized avoiding antihypertensive medication (weight of 58%). Notably, severe hypertension (11–20% weight) and pre-eclampsia (10–16% weight) were prioritized in all subgroups. Table 1 CHIPS trial event rates of the seven outcomes women prioritized, overall and by trial arm a Outcome data from CHIPS trial 4 Weights from preference study 11 Outcome Overall (N = 981) Trial Arm Subgroups based on patient preference weights b 'Less tight' control (N = 493) ‘Tight' control (N = 488) Equal weights (1) (N = 114) (2) (N = 44) (3) (N = 25) Antihypertensives 837 (85.3%) 379 (76.9%) c 458 (93.9%) c 14% 14% 2% 58% Severe hypertension 334 (34.0%) 200 (40.6%) c 134 (27.5%) c 14% 11% 20% 20% Pre-eclampsia 464 (47.3%) 241 (48.9%) 223 (45.7%) 14% 15% 16% 10% Blood transfusion 24 (2.4%) 16 (3.2%) 8 (1.6%) 14% 20% 2% 0% Caesarean 481 (49.0%) 231 (47.0%) 250 (51.4%) 14% 13% 2% 4% Delivery < 34 wks 138 (14.1%) 77 (15.7%) 61 (12.6%) 14% 18% 42% 2% BW < 10th % ile 175 (17.8%) 79 (16.1%) 96 (19.8%) 14% 8% 16% 5% BW (birthweight), wks (weeks) a Of the 987 women randomized in CHIPS, outcomes were available for 981 following 6 withdrawals and losses to follow-up, with the exception of antihypertensive medication for which data were available for 986 women. 4 b Subgroup (1) was ‘equal prioritizers’, (2) ‘early delivery avoiders’, and (3) ‘medication minimizers’ 11 . c The difference between groups was statistically significant at the p < 0.001 level. We considered equal weights (as assumed in conventional analysis) and the three preference subgroup weights. For each approach, a composite score was derived for each CHIPS trial participant by multiplying the patient preference weight for each outcome by the binary outcome of its occurrence. 13 Thus, higher composite scores indicated worse outcomes (more highly-weighted events occurred). Mean composite scores between interventions were compared using t -tests. A threshold analysis for preference subgroups that supported ‘less-tight’ over ‘tight’ control was conducted to determine the extent to which preferences would need to shift to yield a finding congruent with current clinical guidance. (See supplemental materials for detailed methods.) This study was reviewed and approved by the Behavioural Research Ethics Board (H17-01194) at the University of British Columbia. Results Table 2 shows that using equal weights in the composite score produced no difference in score between treatment arms; the significantly higher frequency of antihypertensive medication use in ‘tight’ control was offset by the significantly higher frequency of severe hypertension in ‘less-tight’ control. Similar results were found using Subgroup (1) weights (‘ equal prioritizers’ ). Table 2 Mean weighted composite outcome score a by blood pressure control, and t - scores for each analysis ‘Less tight’ control ‘Tight’ control 95% CI t -test results Lower Upper t p Equal Weights 0.35 0.36 -0.03 0.02 -0.43 0.67 Subgroup (1) ‘Equal prioritizers’ 0.3 0.34 -0.03 0.02 -0.34 0.73 Subgroup (2) ‘Early delivery avoiders’ 0.28 0.24 0.002 0.07 2.12 0.03 Subgroup (3) ‘Medication minimizers’ 0.61 0.68 -0.11 b -0.04 b -4.14 < 0.01 a Lower scores indicate fewer highly weighted events occurred. b Favours ‘less tight’ control. Using Subgroup (2) weights (‘ early delivery avoiders’ ), the apparently lower rate of early delivery (and significantly lower incidence of severe hypertension) in the ‘tight’ control arm resulted in a lower (better) composite outcome score for ‘tight’ (vs ‘less-tight’) control. The use of significantly more antihypertensive therapy in ‘less-tight’ control contributed little given the low weighting of this outcome (Table 2 ). Using Subgroup (3) weights (‘ medication minimizers’) , the significantly lower frequency of antihypertensive medication use in ‘less-tight’ (vs. ‘tight’) control, combined with a high weighting (58%) resulted in a significantly lower (better) composite outcome score, despite significantly more severe hypertension (20% weight) (Table 2 ). The threshold analysis conducted for Subgroup (3) showed that once the weight applied to avoiding antihypertensive medication was reduced to 0.41 (from 0.58), ‘less-tight’ control was no longer the preferred treatment (Fig. 1 ). Discussion This re-analysis of CHIPS trial outcomes incorporated patient views and demonstrated that integrating patient preferences for outcomes and their associated weights into trial analyses is feasible and can identify different management approaches based on the results of a single trial. Our findings suggest that while almost two-thirds of women prioritize adverse outcomes equally, as assumed in the primary CHIPS analyses, about one quarter prioritize very preterm birth that clearly favours ‘tight’ control. A distinct minority prioritize minimizing antihypertensive medication above other adverse outcomes, making ‘less-tight’ control the most value-congruent BP management for them. Recent clinical practice guidelines have recommended ‘tight’ control of pregnancy hypertension, 5 – 8 based on findings of a significant reduction in development of severe hypertension and some preeclampsia-related complications, without an increase in perinatal risk, from CHIPS 4 and other RCTs. 14 As severe hypertension is an important outcome to women and is prioritized across preference groups, 11 our findings suggest that ‘tight’ control is appropriate for the vast majority (≈ 85%) of pregnant women. While integrating preferences into composites has been considered in cardiology 3 , 13 and other fields, 15 this is the first study to integrate patient weights with individual event data from a high-quality RCT in pregnancy. Our findings show that specifying outcome weights may change interpretation of trial results when applied to individual women. Importantly, our methods are easily adapted to other trial and non-trial approaches, and can be used with other statistical methods that accommodate confounders and covariates (e.g., linear regression; ANCOVA). Limitations of our work include use of preference weights that reflect women’s values in Canada; despite its multiethnic population, values may differ elsewhere. Preferences were identified after CHIPS was completed; consequently, different composite components may have been identified a priori. However, CHIPS evaluated standard obstetric outcomes that cover most of the subsequently-published relevant core outcome set. These results are statistically significant at the group level, but clinical significance likely depends on individual preferences. Finally, our approach presents challenges for statistical power (e.g., power calculations), although these come with the benefit of improved interpretability. Conclusions This study illustrates that integrating patient values into trial analyses can change the interpretation of trials results for clinical decision-making. Future trials with composite or multiple outcomes should seek patient preference weights to improve the interpretation of trial results and support patient-centered care. Abbreviations ANCOVA Analysis of covariance BP Blood pressure CHIPS Control of hypertension in pregnancy study RCT Randomized controlled trial Declarations Ethical approval and consent to participate This study was reviewed and approved by the Behavioural Research Ethics Board (H17-01194) at the University of British Columbia. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are not publicly available as participant consent was not obtained for open distribution of data. Aggregate data are available as part of the Supplementary Appendix of initial publication of the CHIPS trial results (10.1056/NEJMoa1404595). Data may be available from the corresponding author upon reasonable request. Competing Interests The authors declare that they have no competing interests. Funding This study was funded by peer-reviewed grants from two government entities: the Canadian Institutes of Health Research (MCT 87522) and the BC SUPPORT Unit (RWCT-001). Funders had no involvement in the design of the study, the collection, analysis and interpretation of data, or the presentation of findings. Author’s contributions All authors contributed to the conceptualization and design of the study, have approved the submitted version and have agreed to be accountable for their own contributions and the work as a whole. In addition: RKM contributed to the acquisition, analysis and interpretation of data, and drafted the initial manuscript; MH JS & TL contributed to the analysis and interpretation of data, and provided feedback on manuscript drafts; ML contributed to the acquisition and interpretation of data; PvD & LAM contributed to the aquation and interpretation of data and provided feedback on manuscript drafts; NB contributed to the acquisition, analysis and interpretation of data, and contributed to the initial draft manuscript. Acknowledgements We would like to acknowledge the time and contributions of the 981 participants in the CHIPS trial and the 183 participants in the preferences study that made this work possible. As well as the members of the CHIPS Study Group. References Cordoba G, Schwartz L, Woloshin S, Bae H, Gøtzsche PC. Definition, reporting, and interpretation of composite outcomes in clinical trials: systematic review. BMJ. 2010;341:c3920. Panariello N, Jurczak A, Spector J, Kumar V, Semrau K. Coherence in measurement and programming in maternal and newborn health: experience from the BetterBirth trial. J Clin Epidemiol. 2019;113:83–5. Stolker JM, et al. Rethinking composite end points in clinical trials: insights from patients and trialists. Circulation. 2014;130:1254–61. Magee LA, et al. Less-Tight versus Tight Control of Hypertension in Pregnancy. N Engl J Med. 2015;372:407–17. Butalia S, et al. Hypertension Canada’s 2018 Guidelines for the Management of Hypertension in Pregnancy. Can J Cardiol. 2018;34:526–31. World Health Organization. WHO recommendations on drug treatment for non-severe hypertension in pregnancy. . (2020). National Institute for Health and Care Excellence. Hypertension in pregnancy: diagnosis and management . 55 https://www.nice.org.uk/guidance/ng133 (2019). Magee LA, et al. The Hypertensive Disorders of Pregnancy: The 2021 International Society for the Study of Hypertesion in Pregnancy Classification, Diagnosis & Management Recommendations for International Practice. Pregnancy Hypertens. 2021. doi: 10.1016/j.preghy.2021.09.008 . Tita AT, et al. Treatment for Mild Chronic Hypertension during Pregnancy. N Engl J Med. 2022;386:1781–92. Sinclair M, Lagan BM, Dolk H, McCullough JE. M. An assessment of pregnant women’s knowledge and use of the Internet for medication safety information and purchase. J Adv Nurs. 2018;74:137–47. Metcalfe RK, et al. Patient Preferences and Decisional Needs When Choosing a Treatment Approach for Pregnancy Hypertension: A Stated Preference Study. Can J Cardiol. 2020;36:775–9. Mühlbacher AC, Zweifel P, Kaczynski A, Johnson FR. Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues. Health Economics Review 6, (2016). Ahmad Y, et al. A new method of applying randomised control study data to the individual patient: A novel quantitative patient-centred approach to interpreting composite end points. Int J Cardiol. 2015;195:216–24. Abalos E, Duley L, Steyn DW, Gialdini C. Antihypertensive drug therapy for mild to moderate hypertension during pregnancy. Cochrane Database of Systematic Reviews. 2018. doi: 10.1002/14651858.CD002252.pub4 . Udogwu UN, et al. A patient-centered composite endpoint weighting technique for orthopaedic trauma research. BMC Med Res Methodol. 2019;19:242. Supplementary Files PatientInfographicCHIPSAnalysis.pdf SupplementaryMethods.docx SupplementaryMatCHIPSStudyGroup.docx Cite Share Download PDF Status: Published Journal Publication published 07 Feb, 2023 Read the published version in Trials → Version 1 posted Editorial decision: Minor revision 16 Oct, 2022 Reviewers agreed at journal 09 Aug, 2022 Reviewers invited by journal 06 Aug, 2022 Editor assigned by journal 04 Aug, 2022 First submitted to journal 29 Jul, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1909786","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":126952388,"identity":"22d1d390-d537-417a-b7fa-6091ea191e8b","order_by":0,"name":"Rebecca K Metcalfe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYPACC34G9gY2CFuCsHLGBqAyyQaeAyRrkUggUov5tMPHH/yokZDgl3z87MGHijoGg9vNDxh+1ODWInM7LbGx55iEhOTsNHPDGWcOMxjcOWbA2HMMtxYJ6RzDZgY2iTqD2zls0rxtBxgMbuQwMDOw4dOS/7GZ4Z+EhP3NM2zSf9vqoFr+4bWFsZmxTULCQIKHTZqxjRmihbENn5Y0w5m9fRISEmfSzCR7zhzmkQT65WBvHz4tyQ8+/PhmI8HffviZxI+KOjm+280PH/z4hlsLBuABEQdI0DAKRsEoGAWjAAsAALv6Sxgj4BjoAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-5583-5679","institution":"The University of British Columbia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"K","lastName":"Metcalfe","suffix":""},{"id":126952389,"identity":"10ae0a44-1e2e-43c5-b81a-fd29080469b9","order_by":1,"name":"Mark Harrison","email":"","orcid":"","institution":"The University of British Columbia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Harrison","suffix":""},{"id":126952390,"identity":"ece3ec74-8a63-48be-be5a-5416128d01a6","order_by":2,"name":"Joel Singer","email":"","orcid":"","institution":"The University of British Columbia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Joel","middleName":"","lastName":"Singer","suffix":""},{"id":126952391,"identity":"0a7c2d51-2414-4356-97b6-3244d1b7ea98","order_by":3,"name":"Mary Lewisch","email":"","orcid":"","institution":"BC SUPPORT Unit","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mary","middleName":"","lastName":"Lewisch","suffix":""},{"id":126952392,"identity":"8b757b0a-97f9-4f3e-8299-bef83ed67827","order_by":4,"name":"Terry Lee","email":"","orcid":"","institution":"The University of British Columbia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Terry","middleName":"","lastName":"Lee","suffix":""},{"id":126952393,"identity":"5a9113ff-f9de-47d9-a1d4-3a5cb7435027","order_by":5,"name":"Peter von Dadelszen","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"von Dadelszen","suffix":""},{"id":126952394,"identity":"7e14444b-d10b-48cd-a8e3-9446c3efeb65","order_by":6,"name":"Laura A. Magee","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Laura","middleName":"A.","lastName":"Magee","suffix":""},{"id":126952395,"identity":"502781e6-0e75-4167-99e5-6650fe7ce832","order_by":7,"name":"Nick Bansback","email":"","orcid":"https://orcid.org/0000-0002-1510-3462","institution":"The University of British Columbia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nick","middleName":"","lastName":"Bansback","suffix":""}],"badges":[],"createdAt":"2022-07-29 15:34:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1909786/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1909786/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13063-023-07118-1","type":"published","date":"2023-02-07T18:45:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":24997518,"identity":"69194a7c-2d34-4b5c-97b1-64f9450fb3b1","added_by":"auto","created_at":"2022-08-09 17:21:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73048,"visible":true,"origin":"","legend":"\u003cp\u003eThreshold analysis results for Subgroup (3) ‘\u003cem\u003emedication minimizers\u003c/em\u003e’\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1909786/v1/fbee9086fbf76d585569ef91.png"},{"id":44719418,"identity":"249c2e9e-3c75-42ca-8a2a-35b88c8e85aa","added_by":"auto","created_at":"2023-10-16 18:54:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":393773,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1909786/v1/03026c04-0c12-42d3-80ce-cb997d65a1e3.pdf"},{"id":24997519,"identity":"80865e9a-ea0d-4d40-b333-eb5fdc74da3b","added_by":"auto","created_at":"2022-08-09 17:21:06","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":193623,"visible":true,"origin":"","legend":"","description":"","filename":"PatientInfographicCHIPSAnalysis.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1909786/v1/9167c2de2be1390f7d13f434.pdf"},{"id":24996657,"identity":"1afd0a62-3560-4350-b394-d312a4956641","added_by":"auto","created_at":"2022-08-09 17:16:06","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":17056,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-1909786/v1/cefb6f63b58dec163a271e7c.docx"},{"id":24996656,"identity":"6a9fbd6d-8511-4052-a5ff-fa2b41d1273f","added_by":"auto","created_at":"2022-08-09 17:16:06","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":28425,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMatCHIPSStudyGroup.docx","url":"https://assets-eu.researchsquare.com/files/rs-1909786/v1/fa1c149cf98baf75c74f5754.docx"}],"financialInterests":"","formattedTitle":"Using a Patient-Centered Composite Endpoint in a Secondary Analysis of the Control of Hypertension in Pregnancy Study (CHIPS) Trial","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClinical trials in cardiovascular medicine routinely use primary, secondary and other endpoints to capture the breadth of an intervention\u0026rsquo;s effects. However, this can make interpretation of trial results challenging, as an intervention\u0026rsquo;s effects can vary by outcome, including benefits and harms.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Composite endpoints are often used to overcome these challenges, particularly in pregnancy, \u0026ldquo;\u0026hellip;to circumvent a contrived prioritization of one-half of the mother\u0026ndash;infant pair and acknowledge the interconnectedness of mothers and babies at the time of childbirth.\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Composites are typically dichotomous, and treat outcomes as equal, which may not be the case. To apply trial results in practice, clinicians and patients must consider which endpoints are important to them and to what degree.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe international CHIPS (Control of Hypertension in Pregnancy Study; ClinicalTrials.gov NCT01192412)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e randomized controlled trial (RCT) compared \u0026lsquo;less-tight\u0026rsquo; with \u0026lsquo;tight\u0026rsquo; control of blood pressure (BP) for management of chronic or gestational hypertension; women who progressed to preeclampsia remained in their allocated group. \u0026lsquo;Less-tight\u0026rsquo; control aimed to minimize antihypertensive therapy (target diastolic BP of 100 mmHg), while \u0026lsquo;tight\u0026rsquo; control aimed to normalize BP (target diastolic BP of 85 mmHg). While \u0026lsquo;tight\u0026rsquo; (vs. \u0026lsquo;less-tight\u0026rsquo;) control did not change the incidence of the primary fetal/newborn and secondary maternal composite outcomes (with equally-valued components),\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e \u0026lsquo;tight\u0026rsquo; control has been recommended by many guidelines based on a decrease in severe maternal hypertension and some preeclampsia-related complications.\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e These findings that were recently replicated in a separate trial.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e However, recommendations did not integrate women\u0026rsquo;s preferences or concerns, like taking medications during pregnancy.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn a secondary analysis of CHIPS trial data, we explored whether weighting outcomes to reflect patient preferences would change the interpretation of trial results.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe integrated pregnant women\u0026rsquo;s preferences for management of pregnancy hypertension\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e with individual event data from the CHIPS trial.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOutcome data from the 981 women enrolled in CHIPS were included (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Inclusion criteria were: 14\u003csup\u003e+\u0026thinsp;0\u003c/sup\u003e-33\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e weeks\u0026rsquo; gestation, nonproteinuric chronic or gestational hypertension, office diastolic BP of 90-105mmHg (or 85-105mmHg if the women were taking antihypertensive medication), and a live fetus.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e On average, participants were \u0026asymp;\u0026thinsp;34 years of age and enrolled at \u0026asymp;\u0026thinsp;24 weeks\u0026rsquo;. Most (75%) women had chronic hypertension. Roughly half were taking antihypertensives.\u003c/p\u003e \u003cp\u003ePreferences were obtained from a separate study,\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e in which 183 pregnant women in Canada prioritized CHIPS trial outcomes, including the primary perinatal (pregnancy loss and/or neonatal care unit admission\u0026thinsp;\u0026gt;\u0026thinsp;48hr) and secondary maternal outcomes (serious maternal complications). Participants identified five maternal and two fetal/newborn outcomes as important and sufficiently different between treatment arms to influence their preferred BP control (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Preference subgroup weights were derived from a best-worst scaling task\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e that quantified the relative value of composite outcome components (where each component\u0026rsquo;s relative importance was expressed as a proportion, and all components summed to 100%).\u003csup\u003e11\u003c/sup\u003e Latent class analysis identified three preference subgroups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): (1) \u0026lsquo;\u003cem\u003eequal prioritizers\u0026rsquo;\u003c/em\u003e (62%) who placed fairly equal weight on each outcome; (2) \u0026lsquo;\u003cem\u003eearly delivery avoiders\u0026rsquo;\u003c/em\u003e (23%) who prioritized avoiding delivery before 34 weeks (weight of 42%); and (3) \u0026lsquo;\u003cem\u003emedication minimizers\u0026rsquo;\u003c/em\u003e (14%) who prioritized avoiding antihypertensive medication (weight of 58%). Notably, severe hypertension (11\u0026ndash;20% weight) and pre-eclampsia (10\u0026ndash;16% weight) were prioritized in all subgroups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCHIPS trial event rates of the seven outcomes women prioritized, overall and by trial arm\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eOutcome data from CHIPS trial\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eWeights from preference study\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;981)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTrial Arm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSubgroups based on patient preference weights\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e'Less tight' control\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;493)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lsquo;Tight' control\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;488)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEqual weights\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e(1)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;114)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e(2)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;44)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e(3)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;25)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e837 (85.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e379 (76.9%)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e458 (93.9%)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334 (34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200 (40.6%)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134 (27.5%)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-eclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e464 (47.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241 (48.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e223 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood transfusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaesarean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e481 (49.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e231 (47.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250 (51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelivery\u0026thinsp;\u0026lt;\u0026thinsp;34 wks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW\u0026thinsp;\u0026lt;\u0026thinsp;10th % ile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cp\u003eBW (birthweight), wks (weeks)\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e \u003cem\u003eOf the 987 women randomized in CHIPS, outcomes were available for 981 following 6 withdrawals and losses to follow-up, with the exception of antihypertensive medication for which data were available for 986 women.\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003eb\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eSubgroup (1) was \u0026lsquo;equal prioritizers\u0026rsquo;, (2) \u0026lsquo;early delivery avoiders\u0026rsquo;, and (3) \u0026lsquo;medication minimizers\u0026rsquo;\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003csup\u003ec\u003c/sup\u003e \u003cem\u003eThe difference between groups was statistically significant at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 level.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eWe considered equal weights (as assumed in conventional analysis) and the three preference subgroup weights. For each approach, a composite score was derived for each CHIPS trial participant by multiplying the patient preference weight for each outcome by the binary outcome of its occurrence.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Thus, higher composite scores indicated worse outcomes (more highly-weighted events occurred). Mean composite scores between interventions were compared using \u003cem\u003et\u003c/em\u003e-tests. A threshold analysis for preference subgroups that supported \u0026lsquo;less-tight\u0026rsquo; over \u0026lsquo;tight\u0026rsquo; control was conducted to determine the extent to which preferences would need to shift to yield a finding congruent with current clinical guidance. (See supplemental materials for detailed methods.)\u003c/p\u003e \u003cp\u003eThis study was reviewed and approved by the Behavioural Research Ethics Board (H17-01194) at the University of British Columbia.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that using equal weights in the composite score produced no difference in score between treatment arms; the significantly higher frequency of antihypertensive medication use in \u0026lsquo;tight\u0026rsquo; control was offset by the significantly higher frequency of severe hypertension in \u0026lsquo;less-tight\u0026rsquo; control. Similar results were found using Subgroup (1) weights (\u0026lsquo;\u003cem\u003eequal prioritizers\u0026rsquo;\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean weighted composite outcome score\u003csup\u003ea\u003c/sup\u003e by blood pressure control, and \u003cem\u003et\u003c/em\u003e-\u003cem\u003escores\u003c/em\u003e for each analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lsquo;Less tight\u0026rsquo; control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lsquo;Tight\u0026rsquo; control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e-test results\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEqual Weights\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubgroup (1)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u0026lsquo;Equal prioritizers\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubgroup (2)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u0026lsquo;Early delivery avoiders\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubgroup (3)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u0026lsquo;Medication minimizers\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.04\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eLower scores indicate fewer highly weighted events occurred.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eFavours \u0026lsquo;less tight\u0026rsquo; control.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing Subgroup (2) weights (\u0026lsquo;\u003cem\u003eearly delivery avoiders\u0026rsquo;\u003c/em\u003e), the apparently lower rate of early delivery (and significantly lower incidence of severe hypertension) in the \u0026lsquo;tight\u0026rsquo; control arm resulted in a lower (better) composite outcome score for \u0026lsquo;tight\u0026rsquo; (vs \u0026lsquo;less-tight\u0026rsquo;) control. The use of significantly more antihypertensive therapy in \u0026lsquo;less-tight\u0026rsquo; control contributed little given the low weighting of this outcome (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing Subgroup (3) weights (\u0026lsquo;\u003cem\u003emedication minimizers\u0026rsquo;)\u003c/em\u003e, the significantly lower frequency of antihypertensive medication use in \u0026lsquo;less-tight\u0026rsquo; (vs. \u0026lsquo;tight\u0026rsquo;) control, combined with a high weighting (58%) resulted in a significantly lower (better) composite outcome score, despite significantly more severe hypertension (20% weight) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe threshold analysis conducted for Subgroup (3) showed that once the weight applied to avoiding antihypertensive medication was reduced to 0.41 (from 0.58), \u0026lsquo;less-tight\u0026rsquo; control was no longer the preferred treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis re-analysis of CHIPS trial outcomes incorporated patient views and demonstrated that integrating patient preferences for outcomes and their associated weights into trial analyses is feasible and can identify different management approaches based on the results of a single trial. Our findings suggest that while almost two-thirds of women prioritize adverse outcomes equally, as assumed in the primary CHIPS analyses, about one quarter prioritize very preterm birth that clearly favours \u0026lsquo;tight\u0026rsquo; control. A distinct minority prioritize minimizing antihypertensive medication above other adverse outcomes, making \u0026lsquo;less-tight\u0026rsquo; control the most value-congruent BP management for them.\u003c/p\u003e \u003cp\u003eRecent clinical practice guidelines have recommended \u0026lsquo;tight\u0026rsquo; control of pregnancy hypertension,\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e based on findings of a significant reduction in development of severe hypertension and some preeclampsia-related complications, without an increase in perinatal risk, from CHIPS\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and other RCTs.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e As severe hypertension is an important outcome to women and is prioritized across preference groups,\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e our findings suggest that \u0026lsquo;tight\u0026rsquo; control is appropriate for the vast majority (\u0026asymp;\u0026thinsp;85%) of pregnant women.\u003c/p\u003e \u003cp\u003eWhile integrating preferences into composites has been considered in cardiology\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and other fields,\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e this is the first study to integrate patient weights with individual event data from a high-quality RCT in pregnancy. Our findings show that specifying outcome weights may change interpretation of trial results when applied to individual women. Importantly, our methods are easily adapted to other trial and non-trial approaches, and can be used with other statistical methods that accommodate confounders and covariates (e.g., linear regression; ANCOVA).\u003c/p\u003e \u003cp\u003eLimitations of our work include use of preference weights that reflect women\u0026rsquo;s values in Canada; despite its multiethnic population, values may differ elsewhere. Preferences were identified after CHIPS was completed; consequently, different composite components may have been identified \u003cem\u003ea\u003c/em\u003e priori. However, CHIPS evaluated standard obstetric outcomes that cover most of the subsequently-published relevant core outcome set. These results are statistically significant at the group level, but clinical significance likely depends on individual preferences. Finally, our approach presents challenges for statistical power (e.g., power calculations), although these come with the benefit of improved interpretability.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study illustrates that integrating patient values into trial analyses can change the interpretation of trials results for clinical decision-making. Future trials with composite or multiple outcomes should seek patient preference weights to improve the interpretation of trial results and support patient-centered care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eANCOVA\u003c/span\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of covariance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eBP\u003c/span\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eCHIPS\u003c/span\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eControl of hypertension in pregnancy study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eRCT\u003c/span\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandomized controlled trial\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Behavioural Research Ethics Board (H17-01194) at the University of British Columbia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are not publicly available as participant consent was not obtained for open distribution of data. Aggregate data are available as part of the Supplementary Appendix of initial publication of the CHIPS trial results (10.1056/NEJMoa1404595). Data may be available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by peer-reviewed grants from two government entities: the Canadian Institutes of Health Research (MCT 87522) and the BC SUPPORT Unit (RWCT-001). Funders had no involvement in the design of the study, the collection, analysis and interpretation of data, or the presentation of findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the conceptualization and design of the study, have approved the submitted version and have agreed to be accountable for their own contributions and the work as a whole.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition: \u003cstrong\u003eRKM\u003c/strong\u003e contributed to the acquisition, analysis and interpretation of data, and drafted the initial manuscript; \u003cstrong\u003eMH JS \u0026amp; TL\u003c/strong\u003e contributed to the analysis and interpretation of data, and provided feedback on manuscript drafts; \u003cstrong\u003eML\u003c/strong\u003e contributed to the acquisition and interpretation of data; \u003cstrong\u003ePvD \u0026amp; LAM\u003c/strong\u003e contributed to the aquation and interpretation of data and provided feedback on manuscript drafts; \u003cstrong\u003eNB\u003c/strong\u003e contributed to the acquisition, analysis and interpretation of data, and contributed to the initial draft manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the time and contributions of the 981 participants in the CHIPS trial and the 183 participants in the preferences study that made this work possible. As well as the members of the CHIPS Study Group.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCordoba G, Schwartz L, Woloshin S, Bae H, G\u0026oslash;tzsche PC. Definition, reporting, and interpretation of composite outcomes in clinical trials: systematic review. BMJ. 2010;341:c3920.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanariello N, Jurczak A, Spector J, Kumar V, Semrau K. Coherence in measurement and programming in maternal and newborn health: experience from the BetterBirth trial. J Clin Epidemiol. 2019;113:83\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStolker JM, et al. Rethinking composite end points in clinical trials: insights from patients and trialists. Circulation. 2014;130:1254\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagee LA, et al. Less-Tight versus Tight Control of Hypertension in Pregnancy. N Engl J Med. 2015;372:407\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eButalia S, et al. Hypertension Canada\u0026rsquo;s 2018 Guidelines for the Management of Hypertension in Pregnancy. Can J Cardiol. 2018;34:526\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. \u003cem\u003eWHO recommendations on drug treatment for non-severe hypertension in pregnancy.\u003c/em\u003e. (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Institute for Health and Care Excellence. \u003cem\u003eHypertension in pregnancy: diagnosis and management\u003c/em\u003e. 55 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nice.org.uk/guidance/ng133\u003c/span\u003e\u003cspan address=\"https://www.nice.org.uk/guidance/ng133\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagee LA, et al. The Hypertensive Disorders of Pregnancy: The 2021 International Society for the Study of Hypertesion in Pregnancy Classification, Diagnosis \u0026amp; Management Recommendations for International Practice. 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Can J Cardiol. 2020;36:775\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026uuml;hlbacher AC, Zweifel P, Kaczynski A, Johnson FR. Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues. \u003cem\u003eHealth Economics Review\u003c/em\u003e 6, (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad Y, et al. A new method of applying randomised control study data to the individual patient: A novel quantitative patient-centred approach to interpreting composite end points. Int J Cardiol. 2015;195:216\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbalos E, Duley L, Steyn DW, Gialdini C. Antihypertensive drug therapy for mild to moderate hypertension during pregnancy. Cochrane Database of Systematic Reviews. 2018. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/14651858.CD002252.pub4\u003c/span\u003e\u003cspan address=\"10.1002/14651858.CD002252.pub4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUdogwu UN, et al. A patient-centered composite endpoint weighting technique for orthopaedic trauma research. BMC Med Res Methodol. 2019;19:242.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"trials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trls","sideBox":"Learn more about [Trials](http://trialsjournal.biomedcentral.com/)","snPcode":"13063","submissionUrl":"https://www.editorialmanager.com/trls","title":"Trials","twitterHandle":"MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Patient-centered, Randomized controlled trial, Composite endpoints, Pregnancy hypertension, Perinatal","lastPublishedDoi":"10.21203/rs.3.rs-1909786/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1909786/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eClinical trials commonly use multiple endpoints to measure the impact of an intervention. While this improves the comprehensiveness of outcomes, it can make trial results difficult to interpret. We examined the impact of integrating patient weights into a composite endpoint on interpretation of CHIPS (Control of Hypertension in Pregnancy Study) trial results. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOutcome weights were extracted from a previous patient preferences study in pregnancy hypertension (N=183 women) which identified: (i) seven outcomes most important to women (taking medication, severe hypertension, pre-eclampsia, blood transfusion, Caesarean, delivery \u0026lt;34 weeks, and baby born smaller-than-expected), and (ii) three preference subgroup (1) ‘equal prioritizers’, 62%; (2) ‘early delivery avoiders’, 23%; and (3) ‘medication minimizers’, 14%. \u003c/p\u003e\u003cp\u003eOutcome weights from the preference subgroups were integrated with CHIPS data for the seven outcomes identified in the preference study. A weighted composite score was derived for each participant by multiplying the preference weight for each outcome by the binary outcome if it occurred. Analyses considered equal weights and those from the preference subgroups. Mean composite scores were compared between trial arms (t-tests). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eComposite scores were similar between trial arms with use of equal weights or those of Subgroup (1) (95% confidence intervals [CIs]: -0.03, 0.02; and \u003cem\u003ep\u003c/em\u003e\u0026gt;0.50 for each). ‘Tight’ control was superior when using Subgroup (2) weights (95% CIs: 0.002, 0.07; \u003cem\u003ep\u003c/em\u003e=0.03), and ‘less-tight’ control superior when using Subgroup (3) weights (95% CIs: -0.11, -0.04; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eEvidence-based recommendations for ‘tight’ control are consistent with most women’s preferences, but for a sixth of women, ‘less-tight’ control is more preference consistent. Depending on patient preferences, a single trial may support different interventions. Future trials should specify component weights to improve interpretation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTrial Registration: \u003c/strong\u003eNCT01192412\u003c/p\u003e","manuscriptTitle":"Using a Patient-Centered Composite Endpoint in a Secondary Analysis of the Control of Hypertension in Pregnancy Study (CHIPS) Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-09 17:16:04","doi":"10.21203/rs.3.rs-1909786/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2022-10-16T15:34:13+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-08-09T11:26:10+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-08-06T14:29:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-08-04T13:41:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Trials","date":"2022-07-29T11:33:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"trials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trls","sideBox":"Learn more about [Trials](http://trialsjournal.biomedcentral.com/)","snPcode":"13063","submissionUrl":"https://www.editorialmanager.com/trls","title":"Trials","twitterHandle":"MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"45c970b0-6551-4fdb-b0e8-ab1303439c5e","owner":[],"postedDate":"August 9th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T18:52:41+00:00","versionOfRecord":{"articleIdentity":"rs-1909786","link":"https://doi.org/10.1186/s13063-023-07118-1","journal":{"identity":"trials","isVorOnly":false,"title":"Trials"},"publishedOn":"2023-02-07 18:45:57","publishedOnDateReadable":"February 7th, 2023"},"versionCreatedAt":"2022-08-09 17:16:04","video":"","vorDoi":"10.1186/s13063-023-07118-1","vorDoiUrl":"https://doi.org/10.1186/s13063-023-07118-1","workflowStages":[]},"version":"v1","identity":"rs-1909786","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1909786","identity":"rs-1909786","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}