Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials
Using data from two large clinical trials that showed heterogeneity in blood pressure treatment effects, Sanjay Basu and colleagues investigate how risks of treatment benefit and harm vary across individuals.
Vyšlo v časopise:
Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLoS Med 14(10): e32767. doi:10.1371/journal.pmed.1002410
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pmed.1002410
Souhrn
Using data from two large clinical trials that showed heterogeneity in blood pressure treatment effects, Sanjay Basu and colleagues investigate how risks of treatment benefit and harm vary across individuals.
Zdroje
1. Bromfield S, Muntner P. High blood pressure: the leading global burden of disease risk factor and the need for worldwide prevention programs. Curr Hypertens Rep. 2013;15:134–6. doi: 10.1007/s11906-013-0340-9 23536128
2. Forouzanfar MH, Liu P, Roth GA, Ng M, Biryukov S, Marczak L, et al. Global burden of hypertension and systolic blood pressure of at least 110 to 115 mm Hg, 1990–2015. JAMA. 2017;317:165–82. doi: 10.1001/jama.2016.19043 28097354
3. SPRINT Research Group. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med. 2015;2015:2103–16.
4. ACCORD Study Group. Effects of intensive blood-pressure control in type 2 diabetes mellitus. N Engl J Med. 2010;2010:1575–85.
5. Xie X, Atkins E, Lv J, Bennett A, Neal B, Ninomiya T, et al. Effects of intensive blood pressure lowering on cardiovascular and renal outcomes: updated systematic review and meta-analysis. Lancet. 2016;387:435–43. doi: 10.1016/S0140-6736(15)00805-3 26559744
6. Hayward RA, Kent DM, Vijan S, Hofer TP. Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis. BMC Med Res Methodol. 2006;6:18. doi: 10.1186/1471-2288-6-18 16613605
7. Burke JF, Hayward RA, Nelson JP, Kent DM. Using internally developed risk models to assess heterogeneity in treatment effects in clinical trials. Circ Cardiovasc Qual Outcomes. 2014;7:163–9. doi: 10.1161/CIRCOUTCOMES.113.000497 24425710
8. Kent DM, Rothwell PM, Ioannidis JP, Altman DG, Hayward RA. Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal. Trials. 2010;11:85. doi: 10.1186/1745-6215-11-85 20704705
9. Dorresteijn JA, Visseren FL, Ridker PM, Wassink AM, Paynter NP, Steyerberg EW, et al. Estimating treatment effects for individual patients based on the results of randomised clinical trials. BMJ. 2011;343:d5888. doi: 10.1136/bmj.d5888 21968126
10. Patel KK, Arnold SV, Chan PS, Tang Y, Pokharel Y, Jones PG, et al. Personalizing the intensity of blood pressure control. Circ Cardiovasc Qual Outcomes. 2017;10:e003624. doi: 10.1161/CIRCOUTCOMES.117.003624 28373269
11. Blood Pressure Lowering Treatment Trialists’ Collaboration, Sundström J, Arima H, Woodward M, Jackson R, Karmali K, et al. Blood pressure-lowering treatment based on cardiovascular risk: a meta-analysis of individual patient data. Lancet. 2014;384:591–8. doi: 10.1016/S0140-6736(14)61212-5 25131978
12. New England Journal of Medicine. SPRINT data analysis challenge. Waltham (Massachusetts): Massachusetts Medical Society; 2017 [cited 2017 Apr 17]. Available: https://challenge.nejm.org/pages/home.
13. Tibshirani R, Bien J, Friedman J, Hastie T, Simon N, Taylor J, et al. Strong rules for discarding predictors in lasso‐type problems. J R Stat Soc Series B Stat Methodol. 2012;74:245–66. doi: 10.1111/j.1467-9868.2011.01004.x 25506256
14. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMC Med. 2015;13:1. doi: 10.1186/s12916-014-0241-z 25563062
15. Brown EG, Wood L, Wood S. The medical dictionary for regulatory activities (MedDRA). Drug Saf. 1999;20:109–17. 10082069
16. Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for Cox’s proportional hazards model via coordinate descent. J Stat Softw. 2011;39:1.
17. Akaike H. Information theory and an extension of the maximum likelihood principle. In: Parzen E, Tanabe K, Kitagawa G, editors. Selected papers of Hirotugu Akaike. New York: Springer; 1998. pp. 199–213.
18. D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117:743–53. doi: 10.1161/CIRCULATIONAHA.107.699579 18212285
19. Basu S, Sussman J, Rigdon J, Steimle L, Denton B, Hayward R. Risk calculator for benefit and harm from intensive blood pressure treatment. Palo Alto: Stanford University; 2017 [cited 2017 Sep 26]. Available: http://sanjaybasu.shinyapps.io/intbp.
20. Moore DF. Applied survival analysis using R. New York: Springer; 2016.
21. Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27:861–74.
22. Metz CE. Basic principles of ROC analysis. Semin Nucl Med. 1978;8:283–98. doi: 10.1016/S0001-2998(78)80014-2 112681
23. Bavishi C, Bangalore S, Messerli FH. Outcomes of intensive blood pressure lowering in older hypertensive patients. J Am Coll Cardiol. 2017;69:486–93. doi: 10.1016/j.jacc.2016.10.077 28153104
24. Perkovic V, Rodgers A. Redefining blood-pressure targets—SPRINT starts the marathon. N Engl J Med. 2015;373:2175–8. doi: 10.1056/NEJMe1513301 26551394
25. VanderWeele TJ, Knol MJ. Interpretation of subgroup analyses in randomized trials: heterogeneity versus secondary interventions. Ann Intern Med. 2011;154:680–3. doi: 10.7326/0003-4819-154-10-201105170-00008 21576536
26. Wallach JD, Sullivan PG, Trepanowski JF, Sainani KL, Steyerberg EW, Ioannidis JPA. Evaluation of evidence of statistical support and corroboration of subgroup claims in randomized clinical trials. JAMA Intern Med. 2017;177:554–60. doi: 10.1001/jamainternmed.2016.9125 28192563
27. Basu S, Sussman JB, Hayward RA. Detecting heterogeneous treatment effects to guide personalized blood pressure treatment: a modeling study of randomized clinical trials. Ann Intern Med. 2017;154:680–3. doi: 10.7326/M16-1756 28055048
28. Chobanian AV. Hypertension in 2017—what is the right target? JAMA. 2017;317:579–80. doi: 10.1001/jama.2017.0105 28135357
29. Yeh RW, Secemsky EA, Kereiakes DJ, Normand S- LT, Gershlick AH, Cohen DJ, et al. Development and validation of a prediction rule for benefit and harm of dual antiplatelet therapy beyond 1 year after percutaneous coronary intervention. JAMA. 2016;315:1735–49. doi: 10.1001/jama.2016.3775 27022822
30. Agarwal R. Implications of blood pressure measurement technique for implementation of systolic blood pressure intervention trial (SPRINT). J Am Heart Assoc. 2017;6:e004536. doi: 10.1161/JAHA.116.004536 28159816
31. GBD 2013 DALYs and HALE Collaborators, Murray CJL, Barber RM, Foreman KJ, Abbasoglu Ozgoren A, Abd-Allah F, et al. Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition. Lancet. 2015;386:2145–91. doi: 10.1016/S0140-6736(15)61340-X 26321261
Štítky
Interné lekárstvoČlánok vyšiel v časopise
PLOS Medicine
2017 Číslo 10
- Statinová intolerance
- Očkování proti virové hemoragické horečce Ebola experimentální vakcínou rVSVDG-ZEBOV-GP
- Parazitičtí červi v terapii Crohnovy choroby a dalších zánětlivých autoimunitních onemocnění
- Metamizol v liečbe pooperačnej bolesti u detí do 6 rokov veku
- Co dělat při intoleranci statinů?
Najčítanejšie v tomto čísle
- Assessing the neuroprotective benefits for babies of antenatal magnesium sulphate: An individual participant data meta-analysis
- A combination of plasma phospholipid fatty acids and its association with incidence of type 2 diabetes: The EPIC-InterAct case-cohort study
- Quantifying underreporting of law-enforcement-related deaths in United States vital statistics and news-media-based data sources: A capture–recapture analysis
- Intergenerational diabetes and obesity—A cycle to break?