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Prediction of “Mostly Dead” vs “All Dead” After In-hospital Cardiac Arrest: Comment on “A Validated Prediction Tool for Initial Survivors of In-Hospital Cardiac Arrest”

Prediction of “Mostly Dead” vs “All Dead” After In-hospital Cardiac Arrest: Comment on “A... In-hospital cardiac arrest1 and out-of-hospital cardiac arrest2 have similar prevalence and outcome. Efforts are warranted to improve care for either condition since out-of-hospital cardiac arrest is the third-leading cause of death in the United States.2 If patients who are resuscitated from cardiac arrest survive to discharge, they usually have good functional capacity or health-related quality of life.3 Family members and care providers of patients with cardiac arrest need guidance about whether patients are likely to survive to discharge without severe neurologic deficits so as to guide treatment decisions after the initial resuscitation period. Thus predicting prognosis after cardiac arrest is a focus of interest of health care providers and policy makers as well as the media. In this issue, Chan et al4 have attempted to provide such guidance by developing and validating a clinical prediction rule for survival to discharge without severe neurologic deficits after initial resuscitation from in-hospital cardiac arrest.5 Their Cardiac Arrest Survival Postresuscitation In-hospital (CASPRI) rule meets most criteria for a high-quality clinical prediction rule.6 The authors used appropriate methods for building a predictive model and accounting for clustering of patients within hospitals. But their model validation using “data-splitting” is associated with decreased predictive accuracy, as well as a less precise estimate of accuracy. Instead, bootstrap methods are a more efficient validation procedure.5 There are some limitations to the approach described. First, there is a risk of selection bias because patients had to survive the initial resuscitation to be included in the analysis. Initial resuscitation efforts are likely to have been influenced in part by clinicians' intra-arrest assessment of prognosis. Second, although prearrest cerebral performance category was a key variable in the final clinical prediction rule, post hoc assessment of prearrest neurologic status has never been validated. As well, the analysis does not incorporate any information about the timing of prognosis assessment and withdrawal of care, which others have suggested commonly occurs sooner than recommended.7 It is important to note that the sole modifiable factor included in the rule was whether the patient was monitored prior to arrest. Some have concluded that early identification of patients at increased risk of in-hospital cardiac arrest has limited effect.8 However, novel technologies are becoming available that could reduce barriers to monitoring and early intervention at comparatively little cost (eg, ViSi, Sotera Wireless Inc). We encourage ongoing efforts to improve resuscitation by evaluating such new interventions rather than efforts to predict poor prognosis after its onset. We note that the easiest way to reduce the large regional variation in outcome after the onset of cardiac arrest are to not attempt resuscitation of any patient or to withdraw care from all patients who seemingly have a poor prognosis. But that strategy would obviously be unacceptable to most of the public and health care providers. Given the limitations described herein, we urge caution to those who consider applying the rule prospectively to guide clinical practice. As Miracle Max noted in Rob Reiner's film, The Princess Bride (1987): “There's a big difference between mostly dead and all dead. Mostly dead is slightly alive.” Most members of the public would want health care providers to persevere in caring for a patient who is slightly alive. Back to top Article Information Correspondence: Dr Nichol, University of Washington–Harborview Center for Prehospital Emergency Care, 325 Ninth Ave, PO Box 359727, Seattle, WA 98040 (nichol@uw.edu). Published Online: May 28, 2012. doi:10.1001/archinternmed.2012.2279 Financial Disclosure: Dr Nichol is a board member of the Medic One Foundation and has received travel reimbursement from the American Heart Association and the Resuscitation Outcomes Consortium Study Meetings. Dr Nichol's university has a contract with Sotera Wireless. References 1. Merchant RM, Yang L, Becker LB, et al; American Heart Association Get With the Guidelines–Resuscitation Investigators. Incidence of treated cardiac arrest in hospitalized patients in the United States. Crit Care Med. 2011;39(11):2401-240621705896PubMedGoogle ScholarCrossref 2. Taniguchi D, Baernstein A, Nichol G. Cardiac arrest: a public health perspective. Emerg Med Clin North Am. 2012;30(1):1-1222107970PubMedGoogle ScholarCrossref 3. Stiell IG, Nesbitt LP, Nichol G, et al; OPALS Study Group. Comparison of the cerebral performance category score and the Health Utilities Index for survivors of cardiac arrest. Ann Emerg Med. 2009;53(2):241-24818450329PubMedGoogle ScholarCrossref 4. Chan PS, Spertus JA, Krumholz HM, et al; Get With the Guidelines–Resuscitation Registry Investigators. A validated prediction tool for the initial survivors of in-hospital cardiac arrest [published online May 28, 2012]. Arch Intern Med. 2012;172(12):947-953Google Scholar 5. Breiman L. The little bootstrap and other methods for density selection in regression: X-fixed prediction error. JASA. 1992;87:738-754Google Scholar 6. Laupacis A, Sekar N, Stiell IG. Clinical prediction rules: a review and suggested modifications of methodological standards. JAMA. 1997;277(6):488-4949020274PubMedGoogle ScholarCrossref 7. Mccarty K, Nichol G, Chikani V, et al. Early withdrawal of post-arrest care after therapeutic hypothermia in victims of out-of-hospital cardiac arrest. Circulation. 2010;122:A232Google Scholar 8. Chan PS, Jain R, Nallmothu BK, Berg RA, Sasson C. Rapid response teams: a systematic review and meta-analysis. Arch Intern Med. 2010;170(1):18-2620065195PubMedGoogle ScholarCrossref http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Internal Medicine American Medical Association

Prediction of “Mostly Dead” vs “All Dead” After In-hospital Cardiac Arrest: Comment on “A Validated Prediction Tool for Initial Survivors of In-Hospital Cardiac Arrest”

Archives of Internal Medicine , Volume 172 (12) – Jun 25, 2012

Prediction of “Mostly Dead” vs “All Dead” After In-hospital Cardiac Arrest: Comment on “A Validated Prediction Tool for Initial Survivors of In-Hospital Cardiac Arrest”

Abstract

In-hospital cardiac arrest1 and out-of-hospital cardiac arrest2 have similar prevalence and outcome. Efforts are warranted to improve care for either condition since out-of-hospital cardiac arrest is the third-leading cause of death in the United States.2 If patients who are resuscitated from cardiac arrest survive to discharge, they usually have good functional capacity or health-related quality of life.3 Family members and care providers of patients with cardiac arrest need guidance about...
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Publisher
American Medical Association
Copyright
Copyright © 2012 American Medical Association. All Rights Reserved.
ISSN
0003-9926
eISSN
1538-3679
DOI
10.1001/archinternmed.2012.2279
Publisher site
See Article on Publisher Site

Abstract

In-hospital cardiac arrest1 and out-of-hospital cardiac arrest2 have similar prevalence and outcome. Efforts are warranted to improve care for either condition since out-of-hospital cardiac arrest is the third-leading cause of death in the United States.2 If patients who are resuscitated from cardiac arrest survive to discharge, they usually have good functional capacity or health-related quality of life.3 Family members and care providers of patients with cardiac arrest need guidance about whether patients are likely to survive to discharge without severe neurologic deficits so as to guide treatment decisions after the initial resuscitation period. Thus predicting prognosis after cardiac arrest is a focus of interest of health care providers and policy makers as well as the media. In this issue, Chan et al4 have attempted to provide such guidance by developing and validating a clinical prediction rule for survival to discharge without severe neurologic deficits after initial resuscitation from in-hospital cardiac arrest.5 Their Cardiac Arrest Survival Postresuscitation In-hospital (CASPRI) rule meets most criteria for a high-quality clinical prediction rule.6 The authors used appropriate methods for building a predictive model and accounting for clustering of patients within hospitals. But their model validation using “data-splitting” is associated with decreased predictive accuracy, as well as a less precise estimate of accuracy. Instead, bootstrap methods are a more efficient validation procedure.5 There are some limitations to the approach described. First, there is a risk of selection bias because patients had to survive the initial resuscitation to be included in the analysis. Initial resuscitation efforts are likely to have been influenced in part by clinicians' intra-arrest assessment of prognosis. Second, although prearrest cerebral performance category was a key variable in the final clinical prediction rule, post hoc assessment of prearrest neurologic status has never been validated. As well, the analysis does not incorporate any information about the timing of prognosis assessment and withdrawal of care, which others have suggested commonly occurs sooner than recommended.7 It is important to note that the sole modifiable factor included in the rule was whether the patient was monitored prior to arrest. Some have concluded that early identification of patients at increased risk of in-hospital cardiac arrest has limited effect.8 However, novel technologies are becoming available that could reduce barriers to monitoring and early intervention at comparatively little cost (eg, ViSi, Sotera Wireless Inc). We encourage ongoing efforts to improve resuscitation by evaluating such new interventions rather than efforts to predict poor prognosis after its onset. We note that the easiest way to reduce the large regional variation in outcome after the onset of cardiac arrest are to not attempt resuscitation of any patient or to withdraw care from all patients who seemingly have a poor prognosis. But that strategy would obviously be unacceptable to most of the public and health care providers. Given the limitations described herein, we urge caution to those who consider applying the rule prospectively to guide clinical practice. As Miracle Max noted in Rob Reiner's film, The Princess Bride (1987): “There's a big difference between mostly dead and all dead. Mostly dead is slightly alive.” Most members of the public would want health care providers to persevere in caring for a patient who is slightly alive. Back to top Article Information Correspondence: Dr Nichol, University of Washington–Harborview Center for Prehospital Emergency Care, 325 Ninth Ave, PO Box 359727, Seattle, WA 98040 (nichol@uw.edu). Published Online: May 28, 2012. doi:10.1001/archinternmed.2012.2279 Financial Disclosure: Dr Nichol is a board member of the Medic One Foundation and has received travel reimbursement from the American Heart Association and the Resuscitation Outcomes Consortium Study Meetings. Dr Nichol's university has a contract with Sotera Wireless. References 1. Merchant RM, Yang L, Becker LB, et al; American Heart Association Get With the Guidelines–Resuscitation Investigators. Incidence of treated cardiac arrest in hospitalized patients in the United States. Crit Care Med. 2011;39(11):2401-240621705896PubMedGoogle ScholarCrossref 2. Taniguchi D, Baernstein A, Nichol G. Cardiac arrest: a public health perspective. Emerg Med Clin North Am. 2012;30(1):1-1222107970PubMedGoogle ScholarCrossref 3. Stiell IG, Nesbitt LP, Nichol G, et al; OPALS Study Group. Comparison of the cerebral performance category score and the Health Utilities Index for survivors of cardiac arrest. Ann Emerg Med. 2009;53(2):241-24818450329PubMedGoogle ScholarCrossref 4. Chan PS, Spertus JA, Krumholz HM, et al; Get With the Guidelines–Resuscitation Registry Investigators. A validated prediction tool for the initial survivors of in-hospital cardiac arrest [published online May 28, 2012]. Arch Intern Med. 2012;172(12):947-953Google Scholar 5. Breiman L. The little bootstrap and other methods for density selection in regression: X-fixed prediction error. JASA. 1992;87:738-754Google Scholar 6. Laupacis A, Sekar N, Stiell IG. Clinical prediction rules: a review and suggested modifications of methodological standards. JAMA. 1997;277(6):488-4949020274PubMedGoogle ScholarCrossref 7. Mccarty K, Nichol G, Chikani V, et al. Early withdrawal of post-arrest care after therapeutic hypothermia in victims of out-of-hospital cardiac arrest. Circulation. 2010;122:A232Google Scholar 8. Chan PS, Jain R, Nallmothu BK, Berg RA, Sasson C. Rapid response teams: a systematic review and meta-analysis. Arch Intern Med. 2010;170(1):18-2620065195PubMedGoogle ScholarCrossref

Journal

Archives of Internal MedicineAmerican Medical Association

Published: Jun 25, 2012

Keywords: cardiac arrest,survivors

References