Abstract
Aim of study:
In hospital cardiac arrests occur at a rate of 1 to 5 per 1,000 admissions and are associated with significant morbidity and mortality. We aimed to investigate the association between deviations from ACLS protocol and patient outcomes.
Methods:
This retrospective review was conducted at a single academic medical center. Data was collected on patients who suffered cardiac arrest from December 2015-November 2019. Our primary endpoint was return of spontaneous circulation. Secondary endpoints included survival to discharge and discharge with favorable neurological outcomes.
Results:
108 patients were included, 74 obtained return of spontaneous circulation, and 23 survived to discharge. The median number of deviations from the ACLS protocol per event in ROSC group was 1 (IQR 0–3) compared to 6.5 (IQR 4–12) in non-ROSC group (p<.0001). The probability of obtaining ROSC was 96% with 0–2 deviations per event, 59% with 2–5 deviations per event, and 11% with greater than 6 deviations per event (p<.0001). The median deviation per event in patients who survived to discharge was 0 (IQR 0–1) vs. 3 (IQR 1–6, p<.0001) in those who did not. Lastly, survival to discharge with a favorable neurological outcome may be associated we less deviations per event (p <0.006).
Conclusion:
Our findings highlight the importance of adherence to the ACLS protocol. We found that deviations from the algorithm are associated with decreased rates of ROSC and survival to discharge. Additionally, higher rates of protocol deviations may be associated with higher rates of neurological impairments after cardiac arrest.
Keywords: Cardiopulmonary Resuscitation, Cardiac Arrest, Sudden, Advanced Cardiac Life Support, In-Hospital Cardiac Arrest, Guideline, Adrenaline
Introduction:
In-hospital cardiac arrests (IHCA) are common, with 1 to 5 events occurring per 1,000 hospital admissions, and are associated with significant morbidity and mortality (1). Substantial variations in survival rates from IHCA exist, with rates between 8.3%−31.4% reported (2,3). While trends in survival over time have increased, low survival to discharge rates continue to exist across multiple studies (2,3,4,5,6). Furthermore, only a fraction of patients who survival to discharge have a favorable neurological outcome with around 80% of patients who survive having favorable neurological outcomes (7).
The American Heart Association’s Advanced Cardiac Life Support Guidelines (ACLS) can support clinicians in treatment decisions for patients in cardiac arrest based on the rhythm the patient is in (i.e ventricular tachycardia, ventricular fibrillation, pulseless electrical activity, or asystole)(8). The guidelines are updated periodically with the most recent evidence. However, compliance with the ACLS Algorithm has been reported as low across multiple studies and may be associated with worse outcomes from in-hospital cardiac arrests (9,10,11,12).
To the best of our knowledge, no previous report has been published the impact of ACLS protocol deviations to the 2015 update of the algorithm. Our goal was to investigate this effect and we hypothesized that higher rates of ACLS protocol deviations would be associated with decreased rates of ROSC, survival to discharge, and survival to discharge with a favorable neurological outcome.
Materials and Methods:
Design, Setting, and Population:
We conducted a retrospective review of IHCAs at a single academic medical center in Cambridge, MA. All adult patients (>18yrs old) who suffered from an IHCA at our facility during December 2015 to November 2019 were considered for inclusion. We chose December 2015 as our cut off point for enrollment as the 2015 update to the ACLS guidelines was published in November 2015 and expect providers would need an adaptation period (8). Patients were excluded from the analysis if the ACLS algorithm was intentionally not followed (i.e patient status post cardiac surgery), there was poor documentation of the arrest record, cardiopulmonary resuscitation (CPR) was less than 2 minutes, there was no cardiopulmonary arrest, or the patient’s advanced directive was not in favor of resuscitation.
The resuscitation team at our institution is composed of an intensivist, residents, two nurses, a respiratory therapist, an anesthesia provider (to aide in airway management), and a nursing supervisor. The nursing supervisor is responsible for documentation of the resuscitation record and this is their only role. All IHCAs which occur at our institution require completion of an internal quality and safety report. Due to this, we are able to ensure all events were collected during the enrollment period.
Formal institutional review board approval was obtained for medical record review (IRB number 023–2018). A waiver of informed consent from patients was obtained as this was retrospective chart review and assessed as low risk.
Data collection:
Patient’s medical records were reviewed to collect demographics including the length of the event, initial rhythm, and location where the event occurred (i.e. critical care unit, telemetry unit, non-telemetry unit, step down unit, or others.) We reviewed medical records to obtain the suspected cause of the cardiac arrest. The suspected cause was determined by reading notes to understand what led to the cardiac arrest and what the overseeing provider documented as the suspected caused the event. Our institution utilizes a standard proforma during each event with documentation for each minute of the resuscitation effort. On this record there is a separate free text section which allows for comments where deviations may be documented. This resuscitation records was reviewed to collect deviations from the 2015 update to the ACLS protocol. No data was collected from defibrillators or review software as we do not have any at our institution.
Deviations from the ACLS algorithm were classified by a collection tool previously used and validated by McEvoy and Honarmand (10,11). We updated to the previously used collection tool to better classify deviations in accordance to our event documentation. The classifications of deviations include, delays in CPR, Pulseless ventricular tachycardia(VT)/ventricular fibrillation (VF) deviations, pulseless electrical activity (PEA)/asystole deviations, and other deviations. Deviations included delays or absence of CPR, delays in administration of medication or indicated defibrillation, medications or defibrillation administered at the incorrect time interval, administration of defibrillation when not indicated, incorrect administration of medication, or airway management was indicated but not performed (table 1).
Table 1:
Methods for which deviations were classified during data collection. This tool was p previously used and validated by McEvoy (11) and Honarmand (10).
| Delays in CPR in Pulseless Cardiac Arrest | • CPR not started within the same minute of recognizing pulselessness • CPR delayed > 1 minute at pulse/rhythm check • CPR delayed for any reason (i.e placement of endotracheal tube or mechanical CPR device) |
| Pulseless VT/ VF Algorithm Deviations | • Any indicated ACLS drug given before shock # 2 • Incorrect dose of any indicated drug • Incorrect sequence of any indicated drug • Incorrect ACLS drug given (or given at incorrect time) for pulseless VT/VF • Incorrect time for pulse/rhythm check • Drug omission or excess (i.e., no adrenaline given when indicated; incorrect interval for adrenaline other than the recommended 3–5 minutes) • Delay > 1 min between rhythm recognition and shock delivery • Shock delivered at incorrect energy level or no shock delivered at appropriate interval • Failure to resume CPR immediately after shock delivery |
| PEA/ Asystole Algorithm Deviations | • Incorrect dose of any indicated drug • Incorrect sequence of any indicated drug • Incorrect ACLS drug given ( or given at the incorrect time) for PEA/ asystole • Incorrect time interval for pulse/rhythm check • Delays in administering indicated drug • Drug omission or excess (i.e., no adrenaline given when indicated; incorrect interval for adrenaline other than the recommended 3–5 minutes) • Administered shock in a patient with PEA or asystolic rhythm |
| Other Deviations | • Airway management indicated but not done |
Measures and outcomes:
We analyzed ACLS algorithm adherence during the study period recording deviations based on the categories listed in Table 1. Each deviation from the algorithm was classified as one deviation. However, deviations in which multiple intervals passed with missed actions were considered more than one deviation. For example, if adrenaline (epinephrine) was administered every 9 minutes rather than every 3–5 minutes based on the ACLS protocol two deviations would be recorded.
Our primary outcome was return of spontaneous circulation, which was classified as a palpable pulse for at least 2 minutes. We chose 2 minutes as the cut off point for ROSC as we found it challenging to determine if the patient continued to have a pulse at the 20-minute mark (which is the Utstein definition of a survived event) as documentation on the resuscitation record stopped in most cases once ROSC was achieved. Secondary outcomes included survival to discharge and survival to discharge with a favorable neurological outcome. Neurological outcomes were assessed using the Cerebral Performance Category (CPC) scales. CPC scores of 1 were considered a favorable outcome.
Statistical analysis:
Data were summarized using simple descriptive statistics including means with standard deviations, or medians with interquartile ranges, as appropriate. Wilcoxon rank sum test was used to test the difference between median values. For the primary outcome of total deviations, we treated the outcome variable as a continuously distributed variable. We assessed distribution of each variable of interest with visual inspection of histograms. The histogram of total deviations had a left skew due to the high number of events with lower amounts of deviations, so we created groups to define the total amount of deviations per event to offset the abnormal distribution. We chose to categorize deviations in clinically meaningful thresholds as the following: low (0–2 deviations per event), medium (3–5 deviations per event), and high (>6 deviations per event).
We performed a Chi square / fisher for univariate test of data to evaluate the association between ROSC and category of deviations during IHCAs. We developed multivariable logistic regression models to assess the influence of potential confounders on our primary exposure. For the multivariable models we generated models with category of deviation as the independent variable of interest with reference group of 0 – 2 deviations. The primary and secondary outcome variables were the dependent variables. We manually evaluated relevant confounders for multivariable adjustment and variables which demonstrated univariate association (p < 0.2) with the outcome of interest were carried forward for multivariable modeling. Once entered into the multivariable model, if the confounder variable is no longer associated with outcome, it was removed from the model. The candidate variables assessed were selected initially for their potential clinical relevance for outcomes of interest and included deviation categories, age, location at time of the arrest, initial rhythm, suspected cause of the arrest, length of the event, and presence of the following past medical history; coronary artery disease, chronic kidney disease, congestive heart failure, diabetes mellitus, atrial fibrillation, hypertension, chronic obstructive pulmonary disease, hyperlipidemia, cancer, venous thromboembolism, cerebrovascular attack, pulmonary fibrosis, or cardiomyopathy (the results of univariate analyses of candidate variables can be found in supplemental table #1). The confounding variables that were included in all multivariate analyses were length of the event and deviation categories.
With any analysis of intra-arrest interventions, the possibility of resuscitation time bias exists where duration of the arrest, which is a strong predictor of outcome, is also significantly associated with an exposure of interest (13). In our current study, the probability of deviation increases with duration of arrest. Therefore, we categorized duration of arrests as follows: 0–10 minutes, 10–20 minutes, greater than 20 minutes and we chose a priori to include length of arrest in multivariable models. In our analysis of our primary outcome, ROSC, we performed a logistic regression including age, length, and total deviations.
We performed a single post hoc analysis in which we calculated the amount of deviations per minute for all events and assessed differences in deviations-per-minute for each of the outcomes of interest using Wilcoxon rank sum test for the outcomes of ROSC and survival to discharge. This was done to help reduce the possibility that events that had high amounts of deviations from the ACLS protocol were not because the events were longer.
All statistical tests of the data were performed using SAS Software version 9.4 (SAS institute, Cary, NC, USA) and were assumed to be two-sided with an alpha of 0.05.
Results:
Between the inclusion period of December 2015 and November 2019 a total of 115 patient records were reviewed. However, 7 patients were excluded from analysis; two patients were excluded as they were status post cardiothoracic surgery and the ACLS algorithm was intentionally not followed, two patients were excluded for poor documentation, two patients were excluded as there was no loss of pulse, and one was excluded as the patient’s advanced directive was not in favor of resuscitation.
A total of 108 patients were included in analysis. There was a high portion of arrests within the intensive care unit (74 patients, 68%), followed by telemetry (13 patients, 12%), non-telemetry (4 patients, 4%), and other areas of the hospital including dialysis, cardiac catheterization lab, electrophysiology lab, and post anesthesia care unit (17 patients, 15%, table 2). The initial rhythm was PEA or asystole in 81.5% of patients. The most common suspected cause of the cardiac arrest was respiratory failure, followed by an unknown cause, sepsis, or other causes including prolonged QT interval, electrolyte abnormality, seizure, heart block, hemorrhage, pulmonary embolism, or myocardial infarction (table 2). Return of spontaneous circulation was obtained in 74 (68.5%) patients and only 23 (21.3%) patients survived to discharge.
Table 2:
Patient characteristics including age, sex, location of arrest, initial rhythm, suspected cause of the arrest, survival to discharge, and survival with a favorable neurological outcome.
| Patient Characteristics | ||
|---|---|---|
| ROSC (n = 74) | No ROSC (n = 34) | |
| Age – median (IQR) | 73 (67–82) | 74 (68–82) |
| Male Sex – n (%) | 45 (63.4%) | 22 (64.7%) |
| Length—median (IQR) | 8 mins (4–12) | 22 mins (15–36) |
| Location – n (%) | ||
| ICU | 51 (71.8%) | 23 (67.6%) |
| Telemetry | 9 (12.7%) | 4 (11.8%) |
| Non-telemetry | 2 (2.8) | 2 (5.9%) |
| Other location (EP, dialysis, etc) | 12 (16.2%) | 5 (14.7%) |
| Initial Rhythm—n (%) | ||
| VT/VF | 12 (16.9%) | 5 (14.7%) |
| PEA/asystole | 59 (83.1%) | 29 (85.3%) |
| Suspected Cause—n (%) | ||
| Respiratory Failure | 26 (35.1%) | 8 (23.5%) |
| Unknown | 10 (13.5%) | 10 (29.4%) |
| Sepsis | 4 (5.4%) | 5 (14.7%) |
| Hypotension | 4 (5.4%) | 3 (8.8%) |
| Other (PE, MI, etc.) | 30 (40.5%) | 8 (23.6%) |
| Survival to discharge—n (%) | 23 (31%) | 0 |
| Survival with Favorable Neurological Outcome—n (%) | 20 (27%) | 0 |
The median deviation per event in the ROSC group was 1 (IQR 0–3) was lower than the non-ROSC group 6.5 (IQR 4–12, p<.0001, figure 1).There was a significant association between the category of deviations and probability of ROSC, where greater deviation categories had a lower probability of ROSC. The probability of ROSC per deviation category were as follows; 96% with 0–2 deviations, 59% with 2–5 deviations, and 11% with greater that 6 deviations (p < 0.001). Of patients who obtained ROSC, 68% (50) had 0–2 deviations per event. In multivariate analysis category of protocol deviations had significant association with ROSC (0–2 deviations ref., 3–5 deviations OR 0.081 (95% CI 0.01–0.48), >6 deviations OR 0.003 (95% CI<0.001–0.105, p<0.001, table 3).
Figure 1:
Total deviations per event by ROSC. The median deviation per event in the ROSC group was 1 (IQR 0–3) compared to 6.5 (IQR 4–12) in the non-ROSC group (p<.0001).
Table 3:
Results of multivariate analysis of protocol deviations by deviation categories with odds ratios and 95% confidence intervals in patients who obtained ROSC and survived to discharge.
| ROSC | Survive to Discharge | |||
|---|---|---|---|---|
| Deviations per Event | Unadjusted OR (95% CI) | Adjusted OR (95% CI) | Unadjusted OR (95% CI) | Adjusted OR (95% CI) |
| 0–2 | Ref. | Ref. | Ref. | Ref. |
| 3–5 | 0.06 (0.01–0.28) | 0.08 (0.01–0.48) | 0.16 (0.05–0.56) | 0.19 (0.05–0.68) |
| >6 | 0.005 (<0.001–0.04) | 0.003 (<0.001–0.11) | 0.04 (0.002–0.77) | 0.03 (0.002–0.69) |
The median deviation per event in patients who survived was 0 (IQR 0–1) vs. patients who did not survive 3 (IQR 1–6, p<.0001, figure 2). For the secondary outcome of survival to discharge we found that higher rates of protocol deviations were associated with a decrease in likelihood of survival to discharge. The probability of surviving to discharge per deviation category were as follows: 87% had 0–2 deviations, 13% had 3–5 deviations, and no patients who had greater than 6 deviations survived to discharge (p<0.05). In a multivariate analysis, deviation categories had a significant impact on survival to discharge (0–2 deviations ref., 3–5 deviations OR 0.16 (95% CI 0.5–0.56), and >6 deviations OR 0.04 (95% CI 0.94–1.01, p <0.05, table 3).
Figure 2:
Total deviations per event compared to patient survival to discharge. The median deviation per event in patients who survived was 0 (IQR 0–1) vs. 3 (IQR 1–6) in patients who did not survive (p<.0001).
For the secondary outcome of survival with a favorable neurological outcome we performed the same analysis of deviation categories compared to survival to discharge with a favorable neurological outcome. A total of 23 patient survived to discharge and 3 patients had neurological impairment. We found that all 20 patients who survived to discharge with a favorable neurological outcome had 0–2 deviations per event, whereas the 3 patients who had neurological impairment had greater than 3 deviations per event (p<0.006).
In the post-hoc sensitivity analysis to assess deviations per minute, the median deviations per minute in the ROSC group was 0.15 (IQR 0–0.33) compared to the non-ROSC group median of 0.31 (IQR 0.23–0.44, p<0.001). The median deviations per minute in patients who survived to discharge was 0 (IQR 0–0.17) compared to patients who did not survive median of 0.25 (IQR 0.13–0.38, p<0.001).
Discussion:
We report the results of a single center retrospective study investigating the effect of ACLS protocol deviations. We found significant association between protocol deviations and outcomes. Specifically, increasing number of protocol deviations were associated with decreased probability of obtaining ROSC even when controlling for relevant confounders. Secondarily, there was a significant association between protocol deviations and survival to hospital discharge. Our study is not the first to evaluate protocol deviations with ROSC and survival to discharge, however, to the best of our knowledge it is the first to evaluate protocol compliance and survival to discharge with a favorable neurological outcome.
Our study was designed to assess the relationship between the 2015 ACLS protocol deviations and outcomes from in-hospital cardiac arrest. Previous research has been conducted investigating the effect of compliance to the 2005 and 2010 versions of the ACLS guidelines on ROSC and survival to discharge(10,11,12). However, to the best of our knowledge no previous work has investigated the effect of compliance to the 2015 guidelines. The primary hypothesis for this analysis was that an increased frequency of protocol deviation is associated with worse probability of success from the code. ACLS guidelines summarize the best available evidence for resuscitation efforts yet only few studies have assessed whether compliance to the guidelines improves outcomes. Cline et al initially identified that compliance to the ACLS algorithm was low and deviations did not vary between trained ACLS providers and non-ACLS trained providers (9). McEvoy and colleagues performed a retrospective analysis investigating the effect of protocol deviations on ROSC but failed to include to survival to discharge as an outcome (11). Additionally, McEvoy and colleagues study consisted of randomly selected patients from a larger sample which may have impacted their results (11). Similarly, Ornato et al. performed a retrospective review of the National Registry of Cardiopulmonary Resuscitation and found that resuscitation errors were associated with decreased rates of ROSC and survival to discharge (12).
Most recently, Honarmand and colleagues performed a retrospective review of 160 patients investigating the effect of ACLS protocol deviations on ROSC and survival to discharge (10). They found that higher rates of protocol deviations were associated with lower rates of ROSC and that lower rates of protocol deviations may be associated with better of survival to discharge. However, the authors state their study was not powered for such an analysis and suggested further studies to confirm their findings (10). Lastly, Honarmand et al. did not include patients who were in critical care units at the time of their cardiac arrest and thus we chose to include these patients to evaluate if all patients would benefit from lower protocol deviations (10).
Our results are similar, as we found that higher rates of deviations are associated with a decreased likelihood of ROSC. We were able to perform multivariate analysis investigating survival to discharge and were able to examine the association between a favorable neurological recovery and rates of protocol deviations. One possible reason our analysis of survival to discharge was different than that of Honarmand et al(10) was that we observed lower amounts deviation in events where ROSC was obtained and higher amounts deviations where ROSC was not obtained (figure 1). Another possible reason for the difference in our results is our decision to the include patients who suffered cardiac arrest in critical care units. However, these patients are vital to include as most IHCA’s occur within critical care units and this had not been fully investigated in other papers (3).
This is a single center observational study of in-hospital cardiac arrest in which we have attempted to limit the possibility of confounding with multivariable modeling. After multivariable adjustment, the relationship between deviation and outcomes were strong with very high strength of association in a dose-dependent fashion. ACLS guidelines undergo revision on an approximate 5-year cycle and we restricted our analysis to include resuscitation efforts performed exclusively during the most recent ACLS revision. It is possible that not all ACLS algorithm deviations have the same impact on outcomes and thus future studies could attempt to replicate these findings with subsequent iterations of resuscitation guidelines and to determine which deviations are the largest culprits for effects on outcomes. The results of our study are impacted by the traditional limitations of retrospective research. Specifically, our study was largely based on reviewing resuscitation records which were documented during a potentially chaotic event which could lead to poor documentation. Due to this, it is possible that deviations could be related to poor documentation rather than true deviations. However, our resuscitation teams are comprised of multiple members, one of whom is assigned to solely document the event. Additionally, our study is limited by its single center design and small sample size. Multiple studies have previously investigated possible methods for increasing compliance to the ACLS algorithm, but few have demonstrated meaningful clinical improvements (14,15,16). Nonetheless, the results of this review are only suggesting increased compliance to a previously established guideline.
Conclusion:
The findings of our review strongly highlight the importance of adherence to the ACLS protocol. We found that deviations from the algorithm are associated with decreased rates of ROSC and survival to discharge. Additionally, higher rates of protocol deviations may be associated with higher rates of neurological impairments after cardiac arrest. These results suggest that further education of the ACLS protocol may be beneficial and methods of increasing compliance may improve outcomes.
Supplementary Material
Footnotes
Conflicts of interest: All authors of this manuscript have no relevant financial or non-financial relationships to disclose.
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