Development of empirically based time-to-death curves for combat casualty deaths in Iraq and Afghanistan

Abstract

The United States Department of Defense medical planners need survival-time estimates for anticipated patient streams associated with projected combat scenarios. Survival-time estimates should be grounded in empirical observations. Unfortunately, research in this domain has been limited to a single paper describing the development of died-of-wounds curves for combat casualties with life-threatening injuries. The curves developed from that research were based on a small dataset (n = 160, with 26 deaths and 134 survivors) of forward surgical (Role II) casualties and subject matter experts’ judgments. This paper reports the first empirically based time-to-death curves for combat casualties based on a large sample. The results indicate that survival time varied across roles of care at which casualties died but was at most weakly associated with injury severity. Time-to-death curves were, therefore, developed for the overall study population of valid times to death and for Role I, Role II and Role III care. The log-logistic probability distribution provided the best representation of the survival times for the overall study population, while the log-normal distribution was the best choice for Role I, Role II and Role III care. The proposed time-to-death curves should refine the survival-time estimates used in combat medical logistics planning.

Keywords

Time to death combat mortality statistical modeling log-normal probability distribution

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References

Office of the Assistant Secretary of Defense, Health Affairs, Force Health Protection and Readiness. Joint force health protection concept of operations. Falls Church, VA: Department of Defense; 2007, pp.23–26.

Mitchell

Parker

Galarneau

. Statistical modeling of combat mortality events by using subject matter expert opinions and Operation Iraqi Freedom empirical results from the Navy-Marine Corps Combat Trauma Registry. J Defense Model Simulat 2010; 7: 145–155.

Mitchell

Galarneau

Hancock

. Modeling dynamic casualty mortality curves in the tactical medical logistics (TML+) planning tool. Technical Report No. 04–31, 2004. San Diego, CA: Naval Health Research Center.

Galarneau

Hancock

Konoske

. The Navy-Marine Corps Combat Trauma Registry. Mil Med 2006; 171: 691–697.

Gennarelli

Wodzin

. AIS 2005: a contemporary injury scale. Injury 2006; 37: 1083–1091.

Committee on Medical Aspects of Automotive Safety. Rating the severity of tissue damage. I. The abbreviated injury scale. J Am Med Assoc 1971; 215(2): 277–280.

Gennarelli

Wodzin

Kilgo

Osler

Meredith

. The worst injury predicts mortality outcome the best: rethinking the role of multiple injuries in trauma outcome scoring. J Trauma 2003; 55: 599–607.

Baker

O’Neill

. The injury severity score: an update. J Trauma 1976; 16: 882–885.

10.

Baker

O’Neill

Haddon

Jr . The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma 1974; 14: 187–196.

11.

Champion

. Trauma scoring. Scand J Surg 2002; 91: 12–22.

12.

Copes

Champion

Sacco

. Progress in characterizing anatomic injury. J Trauma 1990; 30: 1200–1207.

13.

Meredith

Evans

Kilgo

. A comparison of the abilities of nine scoring algorithms in predicting mortality. J Trauma 2002; 53: 621–629.

14.

Osler

Baker

Long

. A modification of the injury severity score that both improves accuracy and simplifies scoring. J Trauma 1997; 43: 922–926.

15.

Osler

Rutledge

Deis

. ICISS: an international classification of disease-9 based injury severity score. J Trauma 1996; 41: 380–388.

16.

Eastridge

Mabry

Seguin

. Death on the battlefield (2001–2011): implications for the future of combat casualty care. J Trauma 2012; 73: 431–437.

17.

Kuehl

. Design of experiments: statistical principles of research design and analysis. 2nd ed. Independence, KY: Duxbury Press, 2000, pp.107–111.

18.

Allison

. Survival analysis using SAS: a practical guide. 2nd ed.Cary, NC: SAS Institute, 2010, pp.29–124.

19.

Connock

Hyde

Moore

. Cautions regarding the fitting and interpretation of survival curves: examples from NICE single technology appraisals of drugs for cancer. Pharmacoeconomics 2011; 29: 827–837.

20.

Akaike

. A new look at the statistical model identification. IEEE Trans Automat Contr 1974; 19: 716–723.

21.

Tohira

Jacobs

Mountain

. Systematic review of predictive performance of injury severity scoring tools. Scand J Trauma Resusc Emerg Med 2012; 20: 63.

22.

Eastridge

Owsley

Sebesta

. Admission physiology criteria after injury on the battlefield predict medical resource utilization and patient mortality. J Trauma 2006; 61: 820–823.