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Methodological Challenges in Studies Comparing Prehospital Advanced Life Support with Basic Life Support

Published online by Cambridge University Press:  03 April 2017

Timmy Li*
Affiliation:
Department of Emergency Medicine, University of Rochester School of Medicine & Dentistry, Rochester, New YorkUSA Department of Public Health Sciences, University of Rochester School of Medicine & Dentistry, Rochester, New YorkUSA
Courtney M. C. Jones
Affiliation:
Department of Emergency Medicine, University of Rochester School of Medicine & Dentistry, Rochester, New YorkUSA Department of Public Health Sciences, University of Rochester School of Medicine & Dentistry, Rochester, New YorkUSA
Manish N. Shah
Affiliation:
Department of Emergency Medicine, University of Rochester School of Medicine & Dentistry, Rochester, New YorkUSA Department of Public Health Sciences, University of Rochester School of Medicine & Dentistry, Rochester, New YorkUSA Department of Medicine, Division of Geriatrics/Aging, University of Rochester School of Medicine & Dentistry, Rochester, New YorkUSA
Jeremy T. Cushman
Affiliation:
Department of Emergency Medicine, University of Rochester School of Medicine & Dentistry, Rochester, New YorkUSA Department of Public Health Sciences, University of Rochester School of Medicine & Dentistry, Rochester, New YorkUSA
Todd A. Jusko
Affiliation:
Department of Public Health Sciences, University of Rochester School of Medicine & Dentistry, Rochester, New YorkUSA Department of Environmental Medicine, University of Rochester School of Medicine & Dentistry, Rochester, New YorkUSA
*
Correspondence: Timmy Li, BA, EMT-B University of Rochester School of Medicine & Dentistry Department of Emergency Medicine 265 Crittenden Blvd, Box 655C Rochester, New York 14642 USA E-mail: [email protected]

Abstract

Determining the most appropriate level of care for patients in the prehospital setting during medical emergencies is essential. A large body of literature suggests that, compared with Basic Life Support (BLS) care, Advanced Life Support (ALS) care is not associated with increased patient survival or decreased mortality. The purpose of this special report is to synthesize the literature to identify common study design and analytic challenges in research studies that examine the effect of ALS, compared to BLS, on patient outcomes. The challenges discussed in this report include: (1) choice of outcome measure; (2) logistic regression modeling of common outcomes; (3) baseline differences between study groups (confounding); (4) inappropriate statistical adjustment; and (5) inclusion of patients who are no longer at risk for the outcome. These challenges may affect the results of studies, and thus, conclusions of studies regarding the effect of level of prehospital care on patient outcomes should require cautious interpretation. Specific alternatives for avoiding these challenges are presented.

LiT, JonesCMC, ShahMN, CushmanJT, JuskoTA. Methodological Challenges in Studies Comparing Prehospital Advanced Life Support with Basic Life Support. Prehosp Disaster Med. 2017;32(4):444–450.

Type
Special Reports
Copyright
© World Association for Disaster and Emergency Medicine 2017 

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Footnotes

Conflicts of interest: none

References

1. Centers for Disease Control and Prevention. National Hospital Ambulatory Medical Care Survey: 2010 Emergency Department Summary Tables. http://www.cdc.gov/nchs/data/ahcd/ nhamcs_emergency/2010_ed_web_tables.pdf. Accessed November 9, 2013.Google Scholar
2. Centers for Disease Control and Prevention. Leading Causes of Death. http://www.cdc.gov/nchs/fastats/lcod.htm. Accessed November 24, 2013.Google Scholar
3. Ryynanen, OP, Iirola, T, Reitala, J, Palve, H, Malmivaara, A. Is Advanced Life Support better than Basic Life Support in prehospital care? A systematic review. Scand J Trauma Resusc Emerg Med. 2010;18:62.CrossRefGoogle ScholarPubMed
4. Liberman, M, Mulder, D, Lavoie, A, Denis, R, Sampalis, JS. Multi-center Canadian study of prehospital trauma care. Ann Surg. 2003;237(2):153-160.CrossRefGoogle Scholar
5. National Highway Traffic Safety Administration. National EMS Scope of Practice Model. Washington, DC USA: United States Department of Transportation; 2007.Google Scholar
6. Gulli, B, Chatelain, L, Stratford, C. Emergency Care and Transportation of the Sick and Injured. 9th ed. Sudbury, Massachusetts USA: Jones and Bartlett Publishers; 2005.Google Scholar
7. Mistovich, J, Karren, K. Prehospital Emergency Care. 8th ed. Upper Saddle River, New Jersey USA: Pearson Prentice Hall; 2008.Google Scholar
8. Bailey, ED, O’Connor, RE, Ross, RW. The use of emergency medical dispatch protocols to reduce the number of inappropriate scene responses made by Advanced Life Support personnel. Prehosp Emerg Care. 2000;4(2):186-189.CrossRefGoogle ScholarPubMed
9. Clawson, J, Dernocoeur, K, Rose, B. Principles of Emergency Medical Dispatch. Salt Lake City, Utah USA: Priority Press; 2008.Google Scholar
10. American Society for Testing and Materials. Standard Practice for Emergency Medical Dispatch. West Conshohocken, Pennsylvania USA: Annual Book of ASTM Standards; 1990.Google Scholar
11. Culley, LL, Henwood, DK, Clark, JJ, Eisenberg, MS, Horton, C. Increasing the efficiency of Emergency Medical Services by using criteria based dispatch. Ann Emerg Med. 1994;24(5):867-872.CrossRefGoogle ScholarPubMed
12. Shah, MN, Bishop, P, Lerner, EB, Czapranski, T, Davis, EA. Derivation of Emergency Medical Services dispatch codes associated with low-acuity patients. Prehosp Emerg Care. 2003;7(4):434-439.CrossRefGoogle ScholarPubMed
13. Shah, MN, Bishop, P, Lerner, EB, Fairbanks, RJ, Davis, EA. Validation of using EMS dispatch codes to identify low-acuity patients. Prehosp Emerg Care. 2005;9(1):24-31.CrossRefGoogle ScholarPubMed
14. Stout, J, Pepe, PE, Mosesso, VN Jr. All-Advanced Life Support vs tiered-response ambulance systems. Prehosp Emerg Care. 2000;4(1):1-6.CrossRefGoogle ScholarPubMed
15. Lewis, RJ. Prehospital care of the multiply injured patient: the challenge of figuring out what works. JAMA. 2004;291(11):1382-1384.CrossRefGoogle ScholarPubMed
16. Liberman, M, Mulder, D, Sampalis, J. Advanced or Basic Life Support for trauma: meta-analysis and critical review of the literature. J Trauma. 2000;49(4):584-599.CrossRefGoogle ScholarPubMed
17. Bakalos, G, Mamali, M, Komninos, C, et al. Advanced Life Support versus Basic Life Support in the prehospital setting: a meta-analysis. Resuscitation. 2011;82(9):1130-1137.CrossRefGoogle ScholarPubMed
18. Sanghavi, P, Jena, AB, Newhouse, JP, Zaslavsky, AM. Outcomes of Basic versus Advanced Life Support for out-of-hospital medical emergencies. Ann Intern Med. 2015;163(9):681-690.CrossRefGoogle ScholarPubMed
19. Sanghavi, P, Jena, AB, Newhouse, JP, Zaslavsky, AM. Outcomes after out-of-hospital cardiac arrest treated by Basic vs Advanced Life Support. JAMA Intern Med. 2015;175(2):196-204.CrossRefGoogle ScholarPubMed
20. Seamon, MJ, Doane, SM, Gaughan, JP, et al. Prehospital interventions for penetrating trauma victims: a prospective comparison between Advanced Life Support and Basic Life Support. Injury. 2013;44(5):634-638.CrossRefGoogle ScholarPubMed
21. Stiell, IG, Wells, GA, Field, B, et al. Advanced Cardiac Life Support in out-of-hospital cardiac arrest. N Engl J Med. 2004;351(7):647-656.CrossRefGoogle ScholarPubMed
22. Stiell, IG, Spaite, DW, Field, B, et al. Advanced Life Support for out-of-hospital respiratory distress. N Engl J Med. 2007;356(21):2156-2164.CrossRefGoogle ScholarPubMed
23. Ma, MH, Chiang, WC, Ko, PC, et al. Outcomes from out-of-hospital cardiac arrest in Metropolitan Taipei: does an Advanced Life Support service make a difference? Resuscitation. 2007;74(3):461-469.CrossRefGoogle Scholar
24. Spaite, DW, Criss, EA, Valenzuela, TD, Meislin, HW. Prehospital Advanced Life Support for major trauma: critical need for clinical trials. Ann Emerg Med. 1998;32(4):480-489.CrossRefGoogle ScholarPubMed
25. Goel, K, Lennon, RJ, Tilbury, RT, Squires, RW, Thomas, RJ. Impact of cardiac rehabilitation on mortality and cardiovascular events after percutaneous coronary intervention in the community. Circulation. 2011;123(21):2344-2352.CrossRefGoogle ScholarPubMed
26. Rodgers, A, Walker, N, Schug, S, et al. Reduction of postoperative mortality and morbidity with epidural or spinal anesthesia: results from overview of randomized trials. BMJ. 2000;321(7275):1493.CrossRefGoogle ScholarPubMed
27. McNutt, LA, Wu, C, Xue, X, Hafner, JP. Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol. 2003;157(10):940-943.CrossRefGoogle ScholarPubMed
28. Zhang, J, Yu, KF. What’s the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA. 1998;280(19):1690-1691.CrossRefGoogle ScholarPubMed
29. Cummings, P. The relative merits of risk ratios and odds ratios. Arch Pediatr Adolesc Med. 2009;163(5):438-445.CrossRefGoogle ScholarPubMed
30. Lee, J, Tan, CS, Chia, KS. A practical guide for multivariate analysis of dichotomous outcomes. Ann Acad Med Singapore. 2009;38(8):714-719.CrossRefGoogle ScholarPubMed
31. Knol, MJ, Le Cessie, S, Algra, A, Vandenbroucke, JP, Groenwold, RH. Overestimation of risk ratios by odds ratios in trials and cohort studies: alternatives to logistic regression. CMAJ. 2012;184(8):895-899.CrossRefGoogle ScholarPubMed
32. Nemes, S, Jonasson, JM, Genell, A, Steineck, G. Bias in odds ratios by logistic regression modelling and sample size. BMC Med Res Methodol. 2009;9:56.CrossRefGoogle ScholarPubMed
33. Spiegelman, D, Hertzmark, E. Easy SAS calculations for risk or prevalence ratios and differences. Am J Epidemiol. 2005;162(3):199-200.CrossRefGoogle ScholarPubMed
34. Potter, D, Goldstein, G, Fung, SC, Selig, M. A controlled trial of prehospital Advanced Life Support in trauma. Ann Emerg Med. 1988;17(6):582-588.CrossRefGoogle ScholarPubMed
35. Stiell, IG, Nesbitt, LP, Pickett, W, et al. The OPALS Major Trauma Study: impact of Advanced Life Support on survival and morbidity. CMAJ. 2008;178(9):1141-1152.CrossRefGoogle ScholarPubMed
36. Nurminen, M. To use or not to use the odds ratio in epidemiologic analyses? Eur J Epidemiol. 1995;11(4):365-371.CrossRefGoogle ScholarPubMed
37. Kurth, T, Sonis, J. Assessment and control of confounding in trauma research. J Trauma Stress. 2007;20(5):807-820.CrossRefGoogle ScholarPubMed
38. Psaty, BM, Koepsell, TD, Lin, D, et al. Assessment and control for confounding by indication in observational studies. J Am Geriatr Soc. 1999;47(6):749-754.CrossRefGoogle ScholarPubMed
39. Salas, M, Hofman, A, Stricker, BH. Confounding by indication: an example of variation in the use of epidemiologic terminology. Am J Epidemiol. 1999;149(11):981-983.CrossRefGoogle ScholarPubMed
40. Ringdal, KG, Skaga, NO, Hestnes, M, et al. Abbreviated injury scale: not a reliable basis for summation of injury severity in trauma facilities? Injury. 2013;44(5):691-699.CrossRefGoogle Scholar
41. Brenner, H, Blettner, M. Controlling for continuous confounders in epidemiologic research. Epidemiology. 1997;8(4):429-434.CrossRefGoogle ScholarPubMed
42. Royston, P, Altman, DG, Sauerbrei, W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006;25(1):127-141.CrossRefGoogle ScholarPubMed
43. Gill, MR, Reiley, DG, Green, SM. Interrater reliability of Glasgow Coma Scale scores in the emergency department. Ann Emerg Med. 2004;43(2):215-223.CrossRefGoogle ScholarPubMed
44. Fewell, Z, Davey Smith, G, Sterne, JA. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am J Epidemiol. 2007;166(6):646-655.CrossRefGoogle ScholarPubMed
45. Austin, PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399-424.CrossRefGoogle ScholarPubMed
46. Wunsch, H, Linde-Zwirble, WT, Angus, DC. Methods to adjust for bias and confounding in critical care health services research involving observational data. J Crit Care. 2006;21(1):1-7.CrossRefGoogle ScholarPubMed
47. Martens, EP, Pestman, WR, de Boer, A, Belitser, SV, Klungel, OH. Instrumental variables: application and limitations. Epidemiology. 2006;17(3):260-267.CrossRefGoogle ScholarPubMed
48. Greenland, S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2000;29(4):722-729.CrossRefGoogle ScholarPubMed
49. Morshed, S, Tornetta, P 3rd, Bhandari, M. Analysis of observational studies: a guide to understanding statistical methods. J Bone Joint Surg Am. 2009;91(Suppl 3):50-60.CrossRefGoogle ScholarPubMed
50. Brookhart, MA, Rassen, JA, Schneeweiss, S. Instrumental variable methods in comparative safety and effectiveness research. Pharmacoepidemiol Drug Saf. 2010;19(6):537-554.CrossRefGoogle ScholarPubMed
51. McConnell, KJ, Newgard, CD, Mullins, RJ, Arthur, M, Hedges, JR. Mortality benefit of transfer to Level I versus Level II trauma centers for head-injured patients. Health Serv Res. 2005;40(2):435-457.CrossRefGoogle Scholar
52. Rassen, JA, Schneeweiss, S, Glynn, RJ, Mittleman, MA, Brookhart, MA. Instrumental variable analysis for estimation of treatment effects with dichotomous outcomes. Am J Epidemiol. 2009;169(3):273-284.CrossRefGoogle ScholarPubMed
54. Rothman, K, Greenland, S, Lash, T. Modern Epidemiology. 3rd ed. Philadelphia, Pennsylvania USA: Lippincott Willams & Wilkins; 2008.Google Scholar
55. Elwert, F, Winship, C. Endogenous selection bias: the problem of conditioning on a collider variable. Annual Review of Sociology. 2014;40(1):31-53.CrossRefGoogle ScholarPubMed
56. Del Junco, DJ, Bulger, EM, Fox, EE, et al. Collider bias in trauma comparative effectiveness research: the stratification blues for systematic reviews. Injury. 2015;46(5):775-780.CrossRefGoogle ScholarPubMed
57. Liu, W, Brookhart, MA, Schneeweiss, S, Mi, X, Setoguchi, S. Implications of M bias in epidemiologic studies: a simulation study. Am J Epidemiol. 2012;176(10):938-948.CrossRefGoogle ScholarPubMed
58. McNamee, R. Confounding and confounders. Occupational and Environmental Medicine. 2003;60(3):227-234.CrossRefGoogle ScholarPubMed
59. Cole, SR, Platt, RW, Schisterman, EF, et al. Illustrating bias due to conditioning on a collider. Int J Epidemiol. 2010;39(2):417-420.CrossRefGoogle ScholarPubMed
60. Schisterman, EF, Cole, SR, Platt, RW. Over-adjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology. 2009;20(4):488-495.CrossRefGoogle Scholar
61. Whitcomb, BW, Schisterman, EF, Perkins, NJ, Platt, RW. Quantification of collider-stratification bias and the birthweight paradox. Paediatr Perinat Epidemiol. 2009;23(5):394-402.CrossRefGoogle ScholarPubMed
62. Greenland, S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology. 2003;14(3):300-306.CrossRefGoogle ScholarPubMed
63. Cole, SR, Hernan, MA. Fallibility in estimating direct effects. Int J Epidemiol. 2002;31(1):163-165.CrossRefGoogle ScholarPubMed
64. Meaney, PA, Nadkarni, VM, Kern, KB, Indik, JH, Halperin, HR, Berg, RA. Rhythms and outcomes of adult in-hospital cardiac arrest. Crit Care Med. 2010;38(1):101-108.CrossRefGoogle ScholarPubMed
65. Nadkarni, VM, Larkin, GL, Peberdy, MA, et al. First documented rhythm and clinical outcome from in-hospital cardiac arrest among children and adults. JAMA. 2006;295(1):50-57.CrossRefGoogle ScholarPubMed