Correlating eligibility criteria generalizability and adverse events using Big Data for patients and clinical trials

Annals of the New York Academy of Sciences

Volumn 1387, Issue 1, 2017, Pages 34-43

  Sen, Anando ^a   Ryan, Patrick B ^a,b   Goldstein, Andrew ^a,c   Chakrabarti, Shreya ^a   Wang, Shuang ^a   Koski, Eileen ^d   Weng, Chunhua ^a  

^a Columbia University ^*  (United States)

^b JANSSEN RESEARCH AND DEVELOPMENT   (United States)

^c NEW YORK UNIVERSITY   (United States)

^d IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

adverse events; clinical trials; eligibility criteria; generalizability; population representativeness; trait dependencies

Indexed keywords

BILIRUBIN; LACTIC ACID; ANTIINFECTIVE AGENT;

ADOLESCENT; ADULT; ADVERSE OUTCOME; AGE; AGED; ARTICLE; BILIRUBIN BLOOD LEVEL; BREATHING RATE; CORRELATION COEFFICIENT; ELECTRONIC HEALTH RECORD; GENERALIZABILITY INDEX FOR STUDY TRAITS 2.0; HEART RATE; HUMAN; INTERNATIONAL NORMALIZED RATIO; LACTATE BLOOD LEVEL; MAJOR CLINICAL STUDY; MATHEMATICAL ANALYSIS; MEAN ARTERIAL PRESSURE; METHODOLOGY; MULTIPLE ORGAN FAILURE; PARTIAL THROMBOPLASTIN TIME; RANDOMIZED CONTROLLED TRIAL (TOPIC); SEPSIS; SYSTEMIC INFLAMMATORY RESPONSE SYNDROME; SYSTOLIC BLOOD PRESSURE; TEMPERATURE; THROMBOCYTE COUNT; YOUNG ADULT; BIOLOGY; CLINICAL TRIAL (TOPIC); DATA MINING; IMMUNOLOGY; MICROBIOLOGY; PATHOPHYSIOLOGY; PATIENT SELECTION; PROCEDURES; SOFTWARE; TRANSLATIONAL RESEARCH;

ADULT; ANTI-INFECTIVE AGENTS; CLINICAL TRIALS AS TOPIC; COMPUTATIONAL BIOLOGY; DATA MINING; ELECTRONIC HEALTH RECORDS; HUMANS; PATIENT SELECTION; SEPSIS; SOFTWARE; SYSTEMIC INFLAMMATORY RESPONSE SYNDROME; TRANSLATIONAL MEDICAL RESEARCH;

EID: 84987975581     PISSN: 00778923     EISSN: 17496632     Source Type: Journal    
DOI: 10.1111/nyas.13195     Document Type: Article

References (41)

1. Fernandez-Hermida, J.R., A. Calafat, E. Becoña, et al. 2012. Assessment of generalizability, applicability and predictability (GAP) for evaluating external validity in studies of universal family-based prevention of alcohol misuse in young people: systematic methodological review of randomized controlled trials. Addiction 107: 1570–1579.
2. Neely, A.S. & L. Backman. 1995. Effects of multifactorial memory training in old age: generalizability across tasks and individuals. J. Gerontol. Ser. B Psychol. Sci. Soc. Sci. 50B: P134–P140.
3. Weiss, N.S. 2007. Generalizability of cancer clinical trial results. Cancer 109: 341.
4. Pearl, J. 2015. Generalizing experimental findings. J. Causal Inference 3: 259–266.
5. Slack, M.K. & J.R. Draugalis. 2001. Establishing the internal and external validity of experimental studies. Am. J. Health Syst. Pharm. 58: 2173–2181; quiz 2182–2183.
6. Bonell, C., A. Oakley, J. Hargreaves, et al. 2006. Assessment of generalisability in trials of health interventions: suggested framework and systematic review. BMJ 333: 346–349.
7. Kukull, W.A. & M. Ganguli. 2012. Generalizability: the trees, the forest, and the low-hanging fruit. Neurology 78: 1886–1891.
8. Bijker, N., J.L. Peterse, I.S. Fentiman, et al. 2003. Effects of patient selection on the applicability of results from a randomised clinical trial (EORTC 10853) investigating breast-conserving therapy for DCIS. Br. J. Cancer 87: 615–620.
9. Schoenmaker, N. & W.A. Van Gool. 2004. The age gap between patients in clinical studies and in the general population: a pitfall for dementia research. Lancet Neurol. 3: 627–630.
10. Masoudi, F.A., E.P. Havranek, P. Wolfe, et al. 2003. Most hospitalized older persons do not meet the enrollment criteria for clinical trials in heart failure. Am. Heart J. 146: 250–257.
11. Ma, H. & C. Weng. 2016. Identification of questionable exclusion criteria in mental disorder clinical trials using a medical encyclopedia. Pac. Symp. Biocomput. 21: 219–230.
12. Musen, M.A., J.A. Rohn, L.M. Fagan & E.H. Shortliffe. 1987. Knowledge engineering for a clinical trial advice system: uncovering errors in protocol specification. Bull. Cancer 74: 291–296.
13. Ross, J., S. Tu, S. Carini, & I. Sim. 2010. Analysis of eligibility criteria complexity in clinical trials. AMIA Jt. Summits Transl. Sci. Proc. 2010: 46–50.
14. Hao, T., A. Rusanov, M.R. Boland & C. Weng. 2014. Clustering clinical trials with similar eligibility criteria features. J. Biomed. Inform. 52: 112–120.
15. Rubin, D.L., J. Gennari & M.A. Musen. 2000. Knowledge representation and tool support for critiquing clinical trial protocols. Proc. AMIA Symp. 2000: 724–728.
16. Weng, C. 2015. Optimizing clinical research participant selection with informatics. Trends Pharmacol. Sci. 36: 706–709.
17. Weng, C., Y. Li, P. Ryan, et al. 2014. A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records. Appl. Clin. Inform. 5: 463–479.
18. Johnson, S.B. 1996. Generic data modeling for clinical repositories. J. Am. Med. Inform. Assoc. 3: 328–339.
19. Katzan, I., M. Speck, C. Dopler, et al. 2011. The Knowledge Program: an innovative, comprehensive electronic data capture system and warehouse. AMIA Annu. Symp. Proc. 2011: 683–692.
20. Tai, M., L. Vu & J.R. Bostwick. 2014. Antidepressant treatment concerns and recommendations for avoiding adverse events. U.S. Pharm. 39: 34–36.
21. Pretorius, R.W., G. Gataric, S.K. Swedlund & J.R. Miller. 2013. Reducing the risk of adverse drug events in older adults. Am. Fam. Physician 87: 331–336.
22. Karson, A.S. & D.W. Bates. 1999. Screening for adverse events. J. Eval. Clin. Pract. 5: 23–32.
23. Smits, M., M. Zegers, P.P. Groenewegen, et al. 2010. Exploring the causes of adverse events in hospitals and potential prevention strategies. Qual. Saf. Health Care 19: e5.
24. Ory, M., B. Resnick, P.J. Jordan, et al. 2005. Screening, safety, and adverse events in physical activity interventions: collaborative experiences from the behavior change consortium. Ann. Behav. Med. 29(Suppl): 20–28.
25. International Council for Harmonisation. 1996. Guidance for industry—good clinical practice: consolidated guidance. Geneva: International Council for Harmonisation.
26. Moore, T.J., M.R. Cohen & C.D. Furberg. 2007. Serious adverse drug events reported to the Food and Drug Administration, 1998–2005. Arch. Intern. Med. 167: 1752–1759.
27. Pressler, T.R. & E.E. Kaizar. 2013. The use of propensity scores and observational data to estimate randomized controlled trial generalizability bias. Stat. Med. 32: 3552–3568.
28. Greenhouse, J.B., E.E. Kaizar, K. Kelleher, et al. 2008. Generalizing from clinical trial data: a case study. The risk of suicidality among pediatric antidepressant users. Stat. Med. 27: 1801–1813.
29. Buchanan, A., M. Hudgens, S. Cole, et al. 2015. Generalizing evidence from randomized trials using inverse probability of sampling weights. Technical Report Series. Department of Biostatistics, University of North Carolina, Chapel Hill, NC.
30. Wang, T.S., A.S. Hellkamp, C.B. Patel, et al. 2014. Representativeness of RELAX-AHF clinical trial population in acute heart failure. Circ. Cardiovasc. Qual. Outcomes 7: 259–268.
31. Susukida, R., R.M. Crum, E.A. Stuart, et al. 2016. Assessing sample representativeness in randomized control trials: application to the National Institute of Drug Abuse Clinical Trials Network. Addiction 111: 1226–1234.
32. Bleeker, S.E., H.A. Moll, E.W. Steyerberg, et al. 2003. External validation is necessary in prediction research: a clinical example. J. Clin. Epidemiol. 56: 826–832.
33. He, Z., P. Ryan, J. Hoxha, et al. 2016. Multivariate analysis of the population representativeness of related clinical studies. J. Biomed. Inform. 60: 66–76.
34. Marik, P.E. & J. Lipman. 2007. The definition of septic shock: implications for treatment. Crit. Care Resusc. 9: 101–103.
35. Stony Brook Medicine. 2013. Severe sepsis/septic shock: recognition and treatment protocols. August 9, 2013. http://www.survivingsepsis.org/sitecollectiondocuments/protocols-sepsis-treatment-stony-brook.pdf
36. Wolfram MathWorld. 2016. Hypersurface. Accessed July 1, 2016. http://mathworld.wolfram.com/Hypersurface.html.
37. Wikipedia. 2016. Weighted correlation coefficients. Accessed July 1, 2016. https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient#Weighted_correlation_coefficient.
38. Sikaris, K. 2009. The correlation of hemoglobin A1c to blood glucose. J. Diabetes Sci. Technol. 3: 429–438.
39. Weiskopf, N.G., G. Hripcsak, S. Swaminathan & C. Weng. 2013. Defining and measuring completeness of electronic health records for secondary use. J. Biomed. Inform. 46: 830–836.
40. Weiskopf, N.G., A. Rusanov & C. Weng. 2013. Sick patients have more data: the non-random completeness of electronic health records. AMIA Annu. Symp. Proc. 2013: 1472–1477.
41. Wilson, J. & A. Bock. 2012. The benefit of using both claims data and electronic medical record data in health care analysis. Optum Insight 1–4.