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ScienceDirect Journal of Electrocardiology 48 (2015) 544 – 550 www.jecgonline.com
High resolution ECG-aided early prognostic model for comatose survivors of out of hospital cardiac arrest Martin Rauber, MD, a Dušan Štajer, MD, PhD, a, b Marko Noč, MD, PhD, a, b Todd T. Schlegel, MD, PhD, c Vito Starc, MD, PhD a,⁎ a University of Ljubljana, Faculty of Medicine, Ljubljana, Slovenia Centre for Intensive Internal Medicine, University Medical Centre, Ljubljana, Slovenia Department of Clinical Physiology, Karolinska Institutet, Stockholm, Sweden; and Nicollier-Schlegel Sàrl, Trélex, Switzerland b
Out of hospital cardiac arrest (OHCA) has a high mortality despite modern treatment. Reliable early prognosis in OHCA could significantly improve clinical decision making. We explored prognostic utility of advanced ECG parameters, obtained from high-resolution ECG, in combination with clinical and OHCA-related parameters during treatment with mild induced hypothermia (MIH) and after rewarming in unconscious survivors of OHCA. Ninety-two patients during MIH and 66 after rewarming were included. During MIH, a score based on initial rhythm, QRS-upslope and systolic pressure resulted in an area under curve (AUC) of 0.82 and accuracy of 80% for survival. After rewarming, a score based on admission rhythm, sum of 12 lead QRS voltages, and mean lateral ST segment level in leads I and V6 resulted in an AUC of 0.88 and accuracy of 85% for survival. ECG can assist with early prognostication in unconscious survivors of OHCA during MIH and after rewarming. © 2015 Elsevier Inc. All rights reserved.
Prediction-tool; Resuscitation; Outcome; Out-of-hospital cardiac arrest; High resolution ECG
Introduction Out of hospital cardiac arrest (OHCA) has an estimated yearly incidence of 36 to 81 events per 100,000 in developed countries . In comatose survivors of OHCA admitted to hospital, an invasive therapeutic strategy, including mild induced hypothermia (MIH) and emergency percutaneous coronary intervention (PCI), has improved survival and neurologic recovery . Still, approximately half of these patients subsequently die, mostly due to the underlying heart disease and brain injury caused by ischemia and reperfusion. Improved understanding of factors related to outcome of OHCA and a reliable prediction of outcome could help physicians optimize therapeutic efforts and better relay prognosis to patients' relatives . However, early prediction of outcome in patients with OHCA is difficult due to deep sedation and hypothermia. Assessment of neurologic recovery is only possible after sedation is discontinued and patients are rewarmed, usually 1–3 days after OHCA. Several prognostic approaches have been proposed for early prediction of outcome after OHCA with various ⁎ Corresponding author at: Institute for Physiology, Faculty of Medicine, University of Ljubljana, Slovenia. E-mail address: [email protected]
http://dx.doi.org/10.1016/j.jelectrocard.2015.04.003 0022-0736/© 2015 Elsevier Inc. All rights reserved.
reliabilities, using OHCA-related and clinical parameters [3,4]. Although ECG has been shown to reflect cardiac and brain  injury, which contributes most to the mortality following OHCA, ECG has not been used for prediction of outcome of OHCA. The aim of our study was to determine if results from conventional and advanced ECG could assist with prediction of outcome in comatose survivors of primary OHCA, both early (during MIH) and after rewarming.
Methods Patients The study was approved by the Institutional Review Board at the University Medical Centre, Ljubljana, Slovenia. Comatose Slovenian survivors of primary OHCA, older than 16 years, admitted to the University Medical Centre, Ljubljana, between August 2011 and November 2014 were included. Patients with secondary or undetermined type of cardiac arrest, paced rhythm, with no or technically inadequate high-resolution ECG were excluded. For all patients, OHCA-specific parameters, other clinical parameters at admission and during ECG recordings, high resolution ECG at admission and after rewarming, and 1-month survival were obtained.
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Treatment of patients with OHCA All study participants received treatment per a previously outlined clinical protocol . In brief, treatment included MIH, and in patients with ST-segment elevation or clinically suspected pre-arrest myocardial ischemia, emergency coronary angiography (with PCI when indicated). MIH was initiated with sedation and muscle relaxation at the discretion of the attending physician. Cooling was initiated with intravenous infusion of 0.9% saline at 0–4 °C (30 ml/kg in 30 min) and external cooling with ice packs (head, neck, axilla, and abdomen). The goal was to achieve target temperature of 32–34 °C as soon as possible. Core body temperature was measured by thermistor intravesically. MIH was maintained in the targeted range using external cooling with ice packs for 24 h, when spontaneous rewarming was initiated at a rate of 0.5°C/h. In the following 48 h, fever was prevented if necessary, using prolonged sedation and external cooling. OHCA-specific clinical parameters From the medical documentation we obtained the time to restoration of spontaneous circulation (ROSC) as well as the "shockability" of the cardiac rhythm at arrival of the emergency care team, a shockable rhythm being defined as ventricular tachycardia (VT) or ventricular fibrillation (VF) and a non-shockable rhythm as pulseless electrical activity (PEA) or asystole. Other clinical parameters We also obtained information about emergency PCI before admission to the ICU, serum arterial lactate level at admission to the ICU, and, at the time of ECG recording: core body temperature, directly measured arterial blood pressure, and information about vasoactive, inotropic or mechanical circulatory support. ECG parameters In all subjects, high-fidelity (1000 samples/s/channel) ECG systems from Cardiax/CardioSoft (Budapest, Hungary/ Houston, TX)  were utilized to acquire resting 5-min ECG recordings in the supine position to obtain a minimum of 256 waveforms acceptable for both signal averaging and variability analyses. The data were analyzed via custom software programs to calculate conventional and advanced ECG parameters [7,8]. A. Conventional ECG parameters Conventional parameters were analyzed automatically from single-lead and composite 12-lead signal templates, including RR, PR, P-wave, QRS and uncorrected and corrected QT and JT intervals; P, QRS and T-wave peak-to-peak amplitudes; frontal plane QRS and T-wave axes; and ST segment levels. Right ventricular strain was assessed by a score (RV-score) in which each of the following criteria when exceeded contributed one scoring unit: R in V1 N 0.7 mV, S in V1 b 0.2 mV, R/S in V1 N 1, R/S in V6 b 1, (R in V1 + S in V5) N 1 mV, R in I b 0.2 mV, R in V5 b 0.5 mV, R in aVR N 0.5 mV, (S in I + Q in III) N 0.15 mV and negative T in III, negative T in V2 and in V3,
QRS axis N 110°, P in II N 0.25 mV, S in V6 N 0.7 mV, qR in V1, Rsr in V1 with R N 1 mV . B. Advanced ECG parameters Signal averaging was performed using software developed by the authors [8,10,11] to generate results for parameters of: 1) Derived 3-dimensional ECG, using the Frank-lead reconstruction technique of Kors et al.  to derive vectorcardiographic parameters. These included the magnitude and orientation of the QRS and T integral vectors, defined as azimuth (horizontal vector orientation) and elevation (vertical vector orientation) , from which QRS-T angle and spatial ventricular gradient were calculated ; 2) QRS and T-waveform complexity via singular value decomposition, to derive dipolar and non-dipolar voltages of the QRS and T waveforms , among them ln DPV T, the natural logarithm of dipolar voltage of T wave , representing global T wave amplitude. 3) 12-lead high frequency (HF, 150–250 Hz) QRS ECG, from which principal vectors were determined by singular value decomposition, and the first three vectors used to calculate a peak root mean square HF QRS amplitude . Spatial velocities of the QRS complex were also analyzed as maximal QRS-upslope and downslope, represented by time-derivative of the RMS ECG signal using the method described by van Oosterom [14,15]. 4) Beat-to-beat RR and QT interval variability, evaluated via custom software programs developed by the authors as described in previous publications [7,8,10]. These included components of the frequency power spectra, obtained using the Lomb periodogram or autoregressive model, and time domain parameters, including the RMSSD of the QT interval of the dominant T wave . The “QT variability index” was also assessed by using the means and variances of the RR interval . Statistical methods The main statistical goal was to identify a limited number of predictor variables, derived from the clinical record, the ECG, or both, for use within regression equations optimized for the prediction of survival. For both the hypothermia and rewarming phases independently, statistical analyses were therefore performed via the following steps: 1. From the initial sets of OHCA-specific, other clinical and ECG variables, variables with predictive accuracy N 0.60 for survival were selected by using analysis of variance. 2. Within the selected sets, collinear variables with mutual covariance value N 0.35 were eliminated using the covariance matrix. 3. The remaining variables were used in linear discrimination analyses (LDA)  to determine regression coefficients, particularly the incremental sum of squares (IncrSS) . LDA was performed using all possible combinations in groups of 3, cumulatively collecting IncrSS for each prediction variable whenever it appeared in combinations. The rank of each prediction variable
M. Rauber et al. / Journal of Electrocardiology 48 (2015) 544–550
was presented by its median IncrSS score. Two sets of six prediction variables with highest ranks participated in the final selections of variables. 4. Variables were combined in groups with fixed numbers (2–6) of prediction variables and analyzed with LDA. For every combination in each group with a fixed number of variables, a regression equation was used to calculate the predicted set of outcome values that enabled determination of the ROC curve and prediction accuracy. For each regression equation, a validation  was performed by repetitively resampling each data set by dividing the entire group into two subgroups: one with 80% of participants and another with 20%. The large group (training set) served for determination of the regression equation that was subsequently used in the small group (test set) for prediction of the outcome and determination of the ROC curve and accuracy. By resampling 1000 times, we obtained the distribution of accuracy, necessary to derive the corresponding median value and 95% confidence intervals (upper and lower 2.5% value) for every combination in each group with a fixed number of variables. In every group with a fixed number of variables, we then finally selected that combination having the highest median accuracy. 5. From the combinations with highest median accuracies, we selected a combination with a lowest number of variables that provided ≥ 95% of AUC and accuracy of combinations with higher number of variables. 6. The two final regression equations were determined from entire populations of patients by using the selected final combinations of prediction variables. Results Ninety-two patients were enrolled in the study during MIH, with ECG recorded 19.8 ± 8.2 h after OHCA. Sixty-six patients from the initial 92 were also studied after rewarming, with the ECG recorded 60.4 ± 11.6 h after OHCA. Of the initial 92 patients included during MIH, 50 were survivors at 1 month after OHCA (Fig. 1).
Relevant demographic and clinical characteristics of patients in the two study groups are shown in Table 1. Selection of prediction variables Steps 1 and 2 from the statistical analysis described above yielded 13 and 12 prediction variables during MIH and after rewarming, respectively. Step 3 resulted in two sets of 6 prediction variables, presented in Tables 2 and 3. Prediction variables combinations The best combinations of prediction variables for each group with a fixed numbers of variables are presented in Fig. 2 (during MTH) and Fig. 3 (after rewarming). Prediction score From Figs. 2 and 3 it can be seen that a combination of 3 variables achieves most (N 95%) of AUC and prediction accuracy compared to combinations of 4, 5 or 6 variables. Using 3 variables and LDA, the following regression equations were obtained: Hypothermic score ¼ −2:80344 þ 0:97733 ISR þ0:01274 ðsystolic pressureÞ þ 0:00886 QRS‐upslope; where ISR is initial shockable rhythm and has values: 1 for shockable and 0 for unshockable rhythm; psyst is the arterial systolic pressure in mmHg; and QRS-upslope in μV/ms; Normothermic score ¼ −1:54089 þ 0:95566 ISR þ0:00014 ð12‐lead QRS voltageÞ þ0:00926 ðlateral ST segment levelÞ;
where ISR is initial shockable rhythm and has values: 1 for shockable and 0 for unshockable rhythm; 12-lead QRS voltage in μV; and lateral ST segment level in μV. Validation of prediction scores The median AUC and accuracy for prediction of 1 month survival in comatose survivors of OHCA of cardiac origin using the hypothermic score during hypothermia were 0.82
Fig. 1. Survival of the initial 92 patients enrolled in the study. OHCA: out of hospital cardiac arrest.
M. Rauber et al. / Journal of Electrocardiology 48 (2015) 544–550 Table 1 Demographic, OHCA-specific and other clinical variables in comatose survivors of primary OHCA, during mild induced hypothermia and after rewarming (Normothermia). Variable
Gender (male) Age (years) Initial shockable rhythm Time to ROSC (min) Admission lactate (mmol/L) Emergency PCI Vasopressor treatment Inotropic treatment Intraaortic balloon pump Systolic arterial pressure (mmHg) Diastolic arterial pressure (mmHg) Core body temperature (°C)
Value Hypothermia (n = 92)
Normothermia (n = 66)
70 (76%) 61.4 ± 13.8 70 (76%) 24.3 ± 15.0 3.67 ± 2.99 79 (86%) 81 (88%) 48 (52%) 18 (20%) 119 ± 19 67 ± 13 32.9 ± 0.60
51 (77%) 61.6 ± 13.5 52 (79%) 24.1 ± 15.2 3.28 ± 2.84 56 (85%) 56 (85%) 34 (52%) 12 (18%) 131 ± 28 70 ± 17 37.2 ± 0.69
Continuous variables are presented as arithmetic mean ± SD; attributive variables are presented as absolute number (percent). OHCA: out of hospital cardiac arrest, ROSC: return of spontaneous circulation; PCI: percutaneous coronary intervention.
and 80%, respectively (Fig. 2). The corresponding median AUC and accuracy using the normothermic score after rewarming were 0.88 and 85%, respectively (Fig. 3). Median values as obtained from the test sets during resampling coincided with those using the complete population. Via the logistic function, probability for survival can be assigned to a prediction score and vice versa. Values of hypothermic and normothermic scores for 10%, 50% and 90% probability of survival were − 0.64, 0.05, 0.44, and − 0.67, 0.07, 0.54, respectively.
Discussion Prediction after OHCA To our knowledge, this study is the first to show that consideration and inclusion of results from ECG can assist with prediction of outcome in OHCA. While several OHCA-specific parameters related to the severity of ischemic brain injury have previously been used for prognosis after OHCA [3,4], these parameters, used individually [19–22] or in combinations [23,24], have heretofore had limited prognostic utility, particularly during
treatment with hypothermia. There is unfortunately no reliable biochemical marker of brain injury that can currently be used for early prognosis after OHCA, while experience in evaluating the extent of ischemic brain injury by imaging techniques is also limited, unless injury is severe . During the initial 24–72 h after OHCA, patients also require sedation and relaxation, thereby further hampering neurologic assessment. Thus reliable early prediction of survival and neurologic recovery in unconscious survivors of primary OHCA remains challenging . Consequently, most patients with OHCA still enter fully aggressive therapeutic regimens while their relatives receive unreliable early prognostic information. Although the consideration and inclusion of results from ECG improved prediction of OHCA-related outcome in this study, there are also other differences between our predictive scores and those of others. First, our calculations included not only OHCA-specific parameters usually reported according to the "Utstein style" [25,26], but also other clinical parameters, in addition to the ECG parameters. Second, our prediction scores were designed for unconscious survivors of primary OHCA, not for conscious survivors of OHCA who have an excellent prognosis [1,26] and not for patients with secondary OHCA, who have a poor prognosis. Third, we constructed prognostic scores both for early (within 24 h) and late (after 48–72 h) prediction. We did this because we believe that the mechanisms of mortality, and consequently also the usefulness of one's prediction variables, most likely change with time after OHCA. Prediction parameters obtained from analysis Interestingly, no OHCA-specific parameter measured as continuous variable, including several such parameters previously confirmed in animal studies, proved to be a useful contributor in our study, possibly because measurement of time intervals during resuscitation was not accurate, or because the effect of resuscitation on perfusion of heart and brain is not entirely predictable. On the other hand, shockability of initial rhythm, an attributive OHCA-specific parameter, appeared useful. Similarly to admission arterial lactate level, shockability likely reflects ischemic burden for the heart and brain . Among other clinical parameters, systolic pressure contributed most to the prediction score during hypothermia. Low mean arterial pressure is known to be associated with
Table 2 Variables, used for prediction of survival during treatment with hypothermia (n = 92). Variable
Initial shockable rhythm Systolic pressure (mmHg) Admission lactate (mmol/L) QRS-upslope (μV/ms) DPV T (Ln μV) RV-score
Value Survivors (n = 50)
Non-survivors (n = 42)
41 (92%) 123 ± 18 2.67 ± 2.10 64.56 ± 18.88 9.04 ± 0.38 1.29 ± 1.34
25 (60%) 114 ± 20 4.23 ± 3.50 55.71 ± 26.56 8.88 ± 0.57 2.10 ± 1.54
b0.001 0.018 0.010 0.067 0.114 0.009
0.67 0.64 0.65 0.71 0.65 0.66
69% 66% 66% 68% 71% 69%
Continuous variables are presented as arithmetic mean ± SD; attributive variables are presented as absolute number (percent). QRS-upslope: maximal QRS upslope, represented by time-derivative of the RMS ECG signal; DPV T: natural logarithm of dipolar voltage of T wave, representing global T wave amplitude; RV-score: right ventricular strain score.
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Table 3 Variables, used for prediction of survival after restoration of normothermia (n = 66). Variable
Initial Shockable rhythm 12-lead QRS voltage (μV) Lateral ST segment level (μV) RMSSD QT (ms) Peak HF QRS amplitude (μV) DPV T (Ln μV)
Value Survivors (n = 44)
Non-survivors (n = 22)
40 (91%) 10003 ± 3035 − 33.23 ± 31.77 6.63 ± 8.64 4.51 ± 1.67 8.85 ± 0.43
11 (50%) 8724 ± 2879 − 55.33 ± 45.04 12.32 ± 14.08 3.91 ± 1.45 8.68 ± 0.44
b 0.001 0.042 0.009 0.024 0.066 0.057
0.68 0.67 0.65 0.60 0.62 0.62
67% 65% 66% 64% 65% 63%
Continuous variables are presented as arithmetic mean ± SD; attributive variables are presented as absolute number (percent). 12 lead QRS voltage: the sum of 12 lead QRS peak-to-peak voltages; lateral ST segment level: mean ST segment level in leads I and V6; RMSSD QT: the root-mean-square of differences between consecutive QT intervals; HF QRS: root mean square high-frequency QRS; DPV T: natural logarithm of dipolar voltage of T wave, representing global T wave amplitude.
decreased survival in patients with cardiogenic shock complicating ST-elevation myocardial infarction . In our statistical analysis (steps 1 and 2), systolic arterial pressure slightly outperformed both diastolic and mean arterial pressure, which both had predictive capability. Among clinical parameters, serum arterial lactate on admission appeared as a useful prediction variable during hypothermia, but not after rewarming; in combinations of 3 variables, lactate was outperformed by QRS-upslope and systolic pressure. Elevated admission lactate reflects ischemic injury during both cardiac arrest and during the subsequent hemodynamic instability after restoration of spontaneous circulation, possibly heralding early, but not late mortality after OHCA. We speculate that after rewarming, most hemodynamically unstable patients had already passed away. Among ECG parameters, reduced QRS-upslope appeared in all best combinations of variables from 2 to 6 during hypothermia for prediction of adverse outcome. It is known that myocardial ischemia, in its advanced phase, modifies the electrophysiological properties of the ventricular cells by reducing the upstroke slope and the amplitude of the action
Fig. 2. Median AUC and median accuracy with 95% confidence intervals for best combinations of prediction variables in groups of 2–6 variables during hypothermia. Legend: 2 variables: ISR, QRS-upslope; 3 variables: ISR, QRS-upslope, systolic pressure; 4 variables: ISR, QRS-upslope, systolic pressure, admission lactate; 5 variables: ISR, QRS-upslope, systolic pressure, RV-score, DPV T; 6 variables: ISR, QRS-upslope, systolic pressure, RV-score, admission lactate, DPV T.ISR: initial shockable rhythm; QRS-upslope: maximal QRS upslope, represented by time-derivative of the RMS ECG signal; DPV T: natural logarithm of dipolar voltage of T wave, representing global T wave amplitude; RV-score: right ventricular strain score; AUC: area under curve; ROC: receiver operating characteristic.
potential, due to an increase in the potassium level of the extracellular space . QRS slopes have been shown to be reduced according to both extent and severity of cardiac ischemia . Our observation thus confirms the relationship between reduced QRS-upslope and advanced ischemic heart injury and mortality. In our study, DPV T values in survivors were higher than in non-survivors in ECG recordings both during hypothermia and after rewarming. Previous studies have shown that highly decreased left ventricular ejection fraction (LVEF) is associated with decreased DPV T compared to mildly decreased or normal LVEF . Whether the decreased DPV T in non-survivors in this study reflexes preexisting heart disease or OHCA-related heart injury is not clear from our data. Of note is that DPV T values were also higher in hypothermia than in normothermia. This may be the result of MTH treatment, which increases QRS and consequently also T wave amplitudes by various mechanisms . There are several factors, which could be responsible for increased RV-score in non-survivors of OHCA during
Fig. 3. Median AUC and median accuracy with 95% confidence intervals for best combinations of prediction variables in groups of 2–6 variables after rewarming. Legend: 2 variables: ISR, peak HF QRS amplitude; 3 variables: ISR, 12-lead QRS voltage, lateral ST segment level; 4 variables: ISR, 12-lead QRS voltage, lateral ST segment level, RMSSD QT; 5 variables: ISR, 12-lead QRS voltage, lateral ST segment level, RMSSD QT, DPV T; 6 variables: ISR, 12-lead QRS voltage, lateral ST segment level, RMSSD QT, DPV T, peak HF QRS amplitude.ISR: initial shockable rhythm; 12 lead QRS voltage: the sum of 12 lead QRS peak-to-peak voltages; lateral ST segment level: mean ST segment level in leads I and V6; RMSSD QT: the root-mean-square of differences between consecutive QT intervals; HF QRS: root mean square high-frequency QRS; DPV T: natural logarithm of dipolar voltage of T wave, representing global T wave amplitude; AUC: area under curve; ROC: receiver operating characteristic.
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hypothermia, but not after rewarming. Left ventricular systolic dysfunction, caused by acute cardiac disease and/ or prolonged heart arrest, increases left ventricular end diastolic pressure which by transmission causes postcapillary pulmonary hypertension and increases right ventricular strain . Next, blood stasis during OHCA causes formation of micro emboli that increase pulmonary vascular resistance and right ventricular strain. Similarly, prolonged resuscitation causes contusion of the lungs, which (in combination with fluid bolus at admission) subsequently increases pulmonary vascular resistance. Patients who survived until rewarming did not have critically depressed left ventricular function, whereas pulmonary resistance was decreased by spontaneous thrombolysis, augmented by hypothermia. Peak high frequency QRS amplitude emerged as part of the best combination of 2 variables for normothermic outcome prediction. Similar to QRS-upslope, decreased high frequency QRS amplitude has previously been shown to be associated with acute myocardial ischemia, and also with acute and remote myocardial infarction . Most patients with primary OHCA have ischemic heart disease, and reduced high frequency QRS amplitude probably reflects extent of myocardial damage and is thus related to prognosis. In normothermia, the sum of 12-lead QRS voltage and ST elevation in the lateral leads also appeared in best combinations of 3–6 variables for outcome prediction. It has previously been shown that QRS amplitudes decrease with deterioration of heart function and increase with recovery , the ostensible mechanism being increase or decrease of interstitial fluid leading to decrease and increase of chest wall tissue impedance, respectively. Large fluid bolus, used for induction of MTH, could cause increased interstitial fluid volume and decreased tissue impedance; the effect could persist after rewarming in patients with poor left ventricular systolic function recovery and/or multiple organ failure. Advanced ischemic heart disease by itself decreases the amplitude of the QRS as already noted . ST depression in lateral leads after rewarming appears to predict adverse outcome of OHCA. ST depression in leads I and aVL could be related to persistent lateral wall ischemia. However, brain injury causes ECG changes as well, predominantly ST changes in lateral leads . Since the majority of our patients had undergone emergency PCI before ECGs were recorded, we speculate that ST depression in the lateral leads in non-survivors more likely reflects ischemic brain injury than myocardial ischemia. After rewarming, increased QT interval variability, represented by RMSSD QT, was associated with mortality. QT interval variability is known to be influenced by the central autonomic nervous system’s modulation of the heart with increased levels of QT variability observed in states where sympathetic discharge is increased [8,14]. In our study, higher values of RMSSD QT, observed in non-survivors, might be associated with more severe anoxic brain injury. Limitations of our study Proposed scores have important limitations for practical use. There are several mechanisms leading to an adverse
outcome after OHCA and it would be difficult for a single prognostic score to take account of all of them. Some of the ECG criteria, which we used for prognostication, are not available from a routine ECG. Though our results were validated using repetitive sampling and were provided by the test sets, the size of our population was small. Hence, our scores should be tested prospectively in a large group of patients for a definite confirmation of their reliability. Conclusions Consideration of results from ECG can assist with prediction of outcome in comatose survivors of primary OHCA. In this study, we combined parameters of both conventional and advanced ECG with OHCA-specific and other clinical variables to create improved scores for prediction of survival in such patients. A first “hypothermic" score is intended for use in OHCA patients during treatment with MIH, and second, “normothermic" score is intended for use in such patients after rewarming. Acknowledgments Supported by Grant No.: PO-510-381, Ministry for Higher Education, Science and Technology, Slovenia. References  Nichol G, Baker D. The epidemiology of sudden death. In: Paradis NA, Halperin HR, Kern KB, Wenzel V, Chamberlain DA, editors. Cardiac arrest — the science and practice of resuscitation medicine. Cambridge: Cambridge University Press; 2007. p. 26–48.  Kocjancic ST, Jazbec A, Noc M. Impact of intensified postresuscitation treatment on outcome of comatose survivors of out-of-hospital cardiac arrest according to initial rhythm. Resuscitation 2014;85:1364–9.  Sandroni C, Cariou A, Cavallaro F, Cronberg T, Friberg H, Hoedemaekers C, et al. Prognostication in comatose survivors of cardiac arrest: an advisory statement from the European Resuscitation Council and the European Society of Intensive Care Medicine. Resuscitation 2014;85:1779–89.  Sandroni C, Cavallaro F, Callaway CW, D'Arrigo S, Sanna T, Kuiper MA, et al. Predictors of poor neurological outcome in adult comatose survivors of cardiac arrest: a systematic review and meta-analysis. Part 2: patients treated with therapeutic hypothermia. Resuscitation 2013;84:1324–38.  Samuels MA. The brain–heart connection. Circulation 2007;116:77–84.  Knafelj R, Radsel P, Ploj T, Noc M. Primary percutaneous coronary intervention and mild induced hypothermia in comatose survivors of ventricular fibrillation with ST-elevation acute myocardial infarction. Resuscitation 2007;74:227–34.  Starc V, Schlegel TT. Real-time multichannel system for beat-to-beat QT interval variability. J Electrocardiol 2006;39:358–67.  Schlegel TT, Kulecz WB, Feiveson AH, Greco EC, DePalma JL, Starc V, et al. Accuracy of advanced versus strictly conventional 12-lead ECG for detection and screening of coronary artery disease, left ventricular hypertrophy and left ventricular systolic dysfunction. BMC Cardiovasc Disord 2010;10:28.  Al-Naamani K, Hijal T, Nguyen V, Andrew S, Nguyen T, Huynh T. Predictive values of the electrocardiogram in diagnosing pulmonary hypertension. Int J Cardiol 2008;127:214–8.  Batdorf BH, Feiveson AH, Schlegel TT. The effect of signal averaging on the reproducibility and reliability of measures of T-wave morphology. J Electrocardiol 2006;39:266–70.  Schlegel TT, Kulecz WB, DePalma JL, Feiveson AH, Wilson JS, Rahman MA, et al. Real-time 12-lead high frequency QRS electrocardiography for enhanced detection of myocardial ischemia and coronary artery disease. Mayo Clin Proc 2004;79:339–50.
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