Tidal volume measurements via transthoracic impedance waveform characteristics: The effect of age, body mass index and gender. A single centre interventional study.

BACKGROUND AND AIM: Measuring tidal volumes (TV) during bag-valve ventilation is challenging in the clinical setting. The ventilation waveform amplitude of the transthoracic impedance (TTI-amplitude) correlates well with TV for an individual, but poorer between patients. We hypothesized that TV to TTI-amplitude relations could be improved when adjusted for morphometric variables like body mass index (BMI), gender or age, and that TTI-amplitude cut-offs for ventilations with adequate TV (>400ml) could be established. MATERIALS AND METHODS: Twenty-one consenting adults (9 female, and 9 overall overweight) during positive pressure ventilation in anaesthesia before scheduled surgery were included. Seventeen ventilator modes were used (⩾ five breaths per mode) to adjust different TVs (150-800 ml), ventilation frequencies (10-30 min-1) and insufflation times (0.5-3.5 s). TTI from the defibrillation pads was filtered to obtain ventilation TTI-amplitudes. Linear regression models were fitted between target and explanatory variables, and compared (coefficient of determination, R2). RESULTS: The TV to TTI-amplitude slope was 1.39 Ω/l (R2=0.52), with significant differences (p<0.05) between male/female (1.04 Ω/l vs 1.84 Ω/l) and normal/overweight subjects (1.65 Ω/l vs 1.04 Ω/l). The median (interquartile range) TTI-amplitude cut-off for adequate TV was 0.51 Ω(0.14-1.20) with significant differences between males and females (0.58 Ω/0.39 Ω), and normal and overweight subjects (0.52 Ω/0.46 Ω). The TV to TTI-amplitude model improved (R2=0.66) when BMI, age and gender were included. CONCLUSIONS: TTI-amplitude to TV relations were established and cut-offs for ventilations with adequate TV determined. Patient morphometric variables related to gender, age and BMI explain part of the variability in the measurements.

Shock decision algorithm for use during load distributing band cardiopulmonary resuscitation.

AIM: Chest compressions delivered by a load distributing band (LDB) induce artefacts in the electrocardiogram. These artefacts alter shock decisions in defibrillators. The aim of this study was to demonstrate the first reliable shock decision algorithm during LDB compressions. METHODS: The study dataset comprised 5813 electrocardiogram segments from 896 cardiac arrest patients during LDB compressions. Electrocardiogram segments were annotated by consensus as shockable (1154, 303 patients) or nonshockable (4659, 841 patients). Segments during asystole were used to characterize the LDB artefact and to compare its characteristics to those of manual artefacts from other datasets. LDB artefacts were removed using adaptive filters. A machine learning algorithm was designed for the shock decision after filtering, and its performance was compared to that of a commercial defibrillator's algorithm. RESULTS: Median (90% confidence interval) compression frequencies were lower and more stable for the LDB than for the manual artefact, 80 min-1 (79.9-82.9) vs. 104.4 min-1 (48.5-114.0). The amplitude and waveform regularity (Pearson's correlation coefficient) were larger for the LDB artefact, with 5.5 mV (0.8-23.4) vs. 0.5 mV (0.1-2.2) (p < 0.001) and 0.99 (0.78-1.0) vs. 0.88 (0.55-0.98) (p < 0.001). The shock decision accuracy was significantly higher for the machine learning algorithm than for the defibrillator algorithm, with sensitivity/specificity pairs of 92.1/96.8% (machine learning) vs. 91.4/87.1% (defibrillator) (p < 0.001). CONCLUSION: Compared to other cardiopulmonary resuscitation artefacts, removing the LDB artefact was challenging due to larger amplitudes and lower compression frequencies. The machine learning algorithm achieved clinically reliable shock decisions during LDB compressions.

Filtering mechanical chest compression artefacts from out-of-hospital cardiac arrest data.

AIM: Filtering techniques to remove manual compression artefacts from the ECG have not been incorporated to defibrillators to diagnose the rhythm during cardiopulmonary resuscitation. Mechanical and manual compression artefacts may be very different. The aim of this study is to characterize the compression artefact caused by the LUCAS 2 device and to evaluate whether filtering the LUCAS 2 artefact results in an accurate rhythm analysis. METHODS: A dataset of 1045 segments were obtained from 230 out-of-hospital cardiac arrest (OHCA) patients after LUCAS 2 activation. Rhythms were 201 shockable, 270 asystole and 574 organized. Segments during asystole were used to characterize the artefact in time and frequency domains. Three filtering methods, a comb filter and two adaptive filters, were used to remove the mechanical compression artefact. The filtered ECG was then diagnosed with a shock decision algorithm from a defibrillator. RESULTS: When compared to the manual compression artefact, the LUCAS 2 artefact presented a similar amplitude (1.2 mV, p-value 0.26), fixed frequency (101.7 min(-1)), more harmonic components, smaller spectral dispersion, and a more regular waveform (p-val <3 × 10(-7)). The sensitivity (SE) and specificity (SP) before filtering the LUCAS 2 artefact were 52.8% (90% low CI, 46.0%) and 81.5% (79.0%), respectively. For the best filter, SE and SP after filtering were 97.9% (95.7%) and 84.1% (82.0%), respectively. Optimal filters require more harmonics and smaller bandwidths than for manual compressions. CONCLUSION: Filtering resulted in a large increase in SE and small increase in SP. Despite differences in artefact characteristics between manual and mechanical compressions, filtering the LUCAS 2 compression artefact results in SE/SP values comparable to those obtained for manual compression artefacts. The SP is still below the 95% recommended by the American Heart Association.

Fully automatic rhythm analysis during chest compression pauses.

AIM: Chest compression artefacts impede a reliable rhythm analysis during cardiopulmonary resuscitation (CPR). These artefacts are not present during ventilations in 30:2 CPR. The aim of this study is to prove that a fully automatic method for rhythm analysis during ventilation pauses in 30:2 CPR is reliable an accurate. METHODS: For this study 1414min of 30:2 CPR from 135 out-of-hospital cardiac arrest cases were analysed. The data contained 1942 pauses in compressions longer than 3.5s. An automatic pause detector identified the pauses using the transthoracic impedance, and a shock advice algorithm (SAA) diagnosed the rhythm during the detected pauses. The SAA analysed 3-s of the ECG during each pause for an accurate shock/no-shock decision. RESULTS: The sensitivity and PPV of the pause detector were 93.5% and 97.3%, respectively. The sensitivity and specificity of the SAA in the detected pauses were 93.8% (90% low CI, 90.0%) and 95.9% (90% low CI, 94.7%), respectively. Using the method, shocks would have been advanced in 97% of occasions. For patients in nonshockable rhythms, rhythm reassessment pauses would be avoided in 95.2% (95% CI, 91.6-98.8) of occasions, thus increasing the overall chest compression fraction (CCF). CONCLUSION: An automatic method could be used to safely analyse the rhythm during ventilation pauses. This would contribute to an early detection of refibrillation, and to increase CCF in patients with nonshockable rhythms.

A reliable method for rhythm analysis during cardiopulmonary resuscitation.

Interruptions in cardiopulmonary resuscitation (CPR) compromise defibrillation success. However, CPR must be interrupted to analyze the rhythm because although current methods for rhythm analysis during CPR have high sensitivity for shockable rhythms, the specificity for nonshockable rhythms is still too low. This paper introduces a new approach to rhythm analysis during CPR that combines two strategies: a state-of-the-art CPR artifact suppression filter and a shock advice algorithm (SAA) designed to optimally classify the filtered signal. Emphasis is on designing an algorithm with high specificity. The SAA includes a detector for low electrical activity rhythms to increase the specificity, and a shock/no-shock decision algorithm based on a support vector machine classifier using slope and frequency features. For this study, 1185 shockable and 6482 nonshockable 9-s segments corrupted by CPR artifacts were obtained from 247 patients suffering out-of-hospital cardiac arrest. The segments were split into a training and a test set. For the test set, the sensitivity and specificity for rhythm analysis during CPR were 91.0% and 96.6%, respectively. This new approach shows an important increase in specificity without compromising the sensitivity when compared to previous studies.

Automatic detection of chest compressions for the assessment of CPR-quality parameters.

AIM: Accurate chest compression detection is key to evaluate cardiopulmonary resuscitation (CPR) quality. Two automatic compression detectors were developed, for the compression depth (CD), and for the thoracic impedance (TI). The objective was to evaluate their accuracy for compression detection and for CPR quality assessment. METHODS: Compressions were manually annotated using the force and ECG in 38 out-of-hospital resuscitation episodes, comprising 869 min and 67,402 compressions. Compressions were detected using a negative peak detector for the CD. For the TI, an adaptive peak detector based on the amplitude and duration of TI fluctuations was used. Chest compression rate (CC-rate) and chest compression fraction (CCF) were calculated for the episodes and for every minute within each episode. CC-rate for rescuer feedback was calculated every 8 consecutive compressions. RESULTS: The sensitivity and positive predictive value were 98.4% and 99.8% using CD, and 94.2% and 97.4% using TI. The mean CCF and CC-rate obtained from both detectors showed no significant differences with those obtained from the annotations (P>0.6). The Bland-Altman analysis showed acceptable 95% limits of agreement between the annotations and the detectors for the per-minute CCF, per-minute CC-rate, and CC-rate for feedback. For the detector based on TI, only 3.7% of CC-rate feedbacks had an error larger than 5%. CONCLUSION: Automatic compression detectors based on the CD and TI signals are very accurate. In most cases, episode review could safely rely on these detectors without resorting to manual review. Automatic feedback on rate can be accurately done using the impedance channel.

Feasibility of automated rhythm assessment in chest compression pauses during cardiopulmonary resuscitation.

AIM: To demonstrate the feasibility of doing a reliable rhythm analysis in the chest compression pauses (e.g. pauses for two ventilations) during cardiopulmonary resuscitation (CPR). METHODS: We extracted 110 shockable and 466 nonshockable segments from 235 out-of-hospital cardiac arrest episodes. Pauses in chest compressions were already annotated in the episodes. We classified pauses as ventilation or non-ventilation pause using the transthoracic impedance. A high-temporal resolution shock advice algorithm (SAA) that gives a shock/no-shock decision in 3s was launched once for every pause longer than 3s. The sensitivity and specificity of the SAA for the analyses during the pauses were computed. RESULTS: We identified 4476 pauses, 3263 were ventilation pauses and 2183 had two ventilations. The median of the mean duration per segment of all pauses and of pauses with two ventilations were 6.1s (4.9-7.5s) and 5.1s (4.2-6.4s), respectively. A total of 91.8% of the pauses and 95.3% of the pauses with two ventilations were long enough to launch the SAA. The overall sensitivity and specificity were 95.8% (90% low one-sided CI, 94.3%) and 96.8% (CI, 96.2%), respectively. There were no significant differences between the sensitivities (P=0.84) and the specificities (P=0.18) for the ventilation and the non-ventilation pauses. CONCLUSION: Chest compression pauses are frequent and of sufficient duration to launch a high-temporal resolution SAA. During these pauses rhythm analysis was reliable. Pre-shock pauses could be minimised by analysing the rhythm during ventilation pauses when CPR is delivered at 30:2 compression:ventilation ratio.

Suppression of the cardiopulmonary resuscitation artefacts using the instantaneous chest compression rate extracted from the thoracic impedance.

AIM: To demonstrate that the instantaneous chest compression rate can be accurately estimated from the transthoracic impedance (TTI), and that this estimated rate can be used in a method to suppress cardiopulmonary resuscitation (CPR) artefacts. METHODS: A database of 372 records, 87 shockable and 285 non-shockable, from out-of-hospital cardiac arrest episodes, corrupted by CPR artefacts, was analysed. Each record contained the ECG and TTI obtained from the defibrillation pads and the compression depth (CD) obtained from a sternal CPR pad. The chest compression rates estimated using TTI and CD were compared. The CPR artefacts were then filtered using the instantaneous chest compression rates estimated from the TTI or CD signals. The filtering results were assessed in terms of the sensitivity and specificity of the shock advice algorithm of a commercial automated external defibrillator. RESULTS: The correlation between the mean chest compression rates estimated using TTI or CD was r=0.98 (95% confidence interval, 0.97-0.98). The sensitivity and specificity after filtering using CD were 95.4% (88.4-98.6%) and 87.0% (82.6-90.5%), respectively. The sensitivity and specificity after filtering using TTI were 95.4% (88.4-98.6%) and 86.3% (81.8-89.9%), respectively. CONCLUSIONS: The instantaneous chest compression rate can be accurately estimated from TTI. The sensitivity and specificity after filtering are similar to those obtained using the CD signal. Our CPR suppression method based exclusively on signals acquired through the defibrillation pads is as accurate as methods based on signals obtained from CPR feedback devices.

ECG spectral and morphological parameters reviewed and updated to detect adult and paediatric life-threatening arrhythmia.

Since the International Liaison Committee on Resuscitation approved the use of automated external defibrillators (AEDs) in children, efforts have been made to adapt AED algorithms designed for adult patients to detect paediatric ventricular arrhythmias accurately. In this study, we assess the performance of two spectral (A(2) and VFleak) and two morphological parameters (TCI and CM) for the detection of lethal ventricular arrhythmias using an American Heart Association (AHA) compliant database that includes adult and paediatric arrhythmias. Our objective was to evaluate how those parameters can be optimally adjusted to discriminate shockable from nonshockable rhythms in adult and paediatric patients. A total of 1473 records were analysed: 751 from 387 paediatric patients ( 0.01) between the adult and paediatric patients for the shockable records; the differences for nonshockable records however were significant. Still, these parameters maintained the discrimination power when paediatric rhythms were included. A single threshold could be adjusted to obtain sensitivities and specificities above the AHA goals for the complete database. The sensitivities for ventricular fibrillation (VF) and ventricular tachycardia (VT) were 91.1% and 96.6% for VFleak, and 90.3% and 99.3% for A(2). The specificities for normal sinus rhythm (NSR) and other nonshockable rhythms were 99.5% and 96.3% for VFleak, and 99.0% and 97.7% for A(2). On the other hand, the morphological parameters showed significant differences between the adult and paediatric patients, particularly for the nonshockable records, because of the faster heart rates of the paediatric rhythms. Their performance clearly degraded with paediatric rhythms. Using a single threshold, the sensitivities and specificities were below the AHA goals, particularly VT sensitivity (60.4% for TCI and 65.8% for CM) and the specificity for other nonshockable rhythms (51.7% for TCI and 34.5% for CM). The specificities, particularly for the adult case, improve when the thresholds are independently adjusted for each adult and paediatric database.