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. 2014 Dec 10;2(3):176-182.
doi: 10.1002/ams2.96. eCollection 2015 Jul.

Development of a prompt model for predicting neurological outcomes in patients with return of spontaneous circulation from out-of-hospital cardiac arrest

Kazumi Kumagai  1 , Yasutaka Oda  2 , Chiyomi Oshima  1 , Tadashi Kaneko  1 , Kotaro Kaneda  1 , Yoshikatsu Kawamura  1 , Yasuaki Ogino  1 , Susumu Yamashita  3 , Kiyoshi Ichihara  4 , Tsuyoshi Maekawa  1   2 , Ryosuke Tsuruta  1   2
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Development of a prompt model for predicting neurological outcomes in patients with return of spontaneous circulation from out-of-hospital cardiac arrest

Kazumi Kumagai et al. Acute Med Surg. .

Abstract

Aim: Early prediction of the neurological outcomes of patients with out-of-hospital cardiac arrest is important to select the optimal clinical management. We hypothesized that clinical data recorded at the site of cardiopulmonary resuscitation would be clinically useful.

Methods: This retrospective cohort study included patients with return of spontaneous circulation after cardiopulmonary resuscitation who were admitted to our university hospital between January 2000 and November 2013 or two affiliated hospitals between January 2006 and November 2013. Clinical parameters recorded on arrival included age (A), arterial blood pH (B), time from cardiopulmonary resuscitation to return of spontaneous circulation (C), pupil diameter (D), and initial rhythm (E). Glasgow Outcome Scale was recorded at 6 months and a favorable neurological outcome was defined as a score of 4-5 on the Glasgow Outcome Scale. Multiple logistic regression analysis was carried out to derive a formula to predict neurological outcomes based on basic clinical parameters.

Results: The regression equation was derived using a teaching dataset (total, n = 477; favourable outcome, n = 55): EP = 1/(1 + e-x ), where EP is the estimated probability of having a favorable outcome, and x = (-0.023 × A) + (3.296 × B) - (0.070 × C) - (1.006 × D) + (2.426 × E) - 19.489. The sensitivity, specificity, and accuracy were 80%, 92%, and 90%, respectively, for the validation dataset (total, n = 201; favourable outcome, n = 25).

Conclusion: The 6-month neurological outcomes can be predicted in patients resuscitated from out-of-hospital cardiac arrest using clinical parameters that can be easily recorded at the site of cardiopulmonary resuscitation.

Keywords: Cardiopulmonary arrest; logistic regression; neurological outcomes; prediction; return of spontaneous circulation.

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Figures

Figure 1
Figure 1
Patients with return of spontaneous circulation (ROSC) after cardiopulmonary resuscitation who were admitted to our university hospital between January 2000 and November 2013 (derivation dataset) or two affiliated hospitals between January 2006 and November 2013 (validation dataset). ICU, intensive care unit.
Figure 2
Figure 2
Receiver‐operating characteristic curve for the probability of favorable neurological outcomes based on the prediction model derived from the derivation dataset. The diagnostic efficacies of four individual parameters (pH, time, age, and pupil size), except for ventricular fibrillation or pulseless ventricular tachycardia (Vf) which was recorded as binary (yes = 1; no = 0), were compared with those of the estimated probability of favorable outcomes computed using the prediction model. Time denotes time from starting cardiopulmonary resuscitation to the return of spontaneous circulation.
Figure 3
Figure 3
Receiver‐operating characteristic curve for the probability of favorable neurological outcomes in the validation dataset of patients with return of spontaneous circulation after cardiopulmonary resuscitation.
See this image and copyright information in PMC

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