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Date: September 18, 2024

Reference: Dillon et al. Naloxone and Patient Outcomes in Out-of-Hospital Cardiac Arrests in California. JAMA Network Open. August 20, 2024

Guest Skeptic: Dr. Chris Root is an emergency medicine and emergency medicine service (EMS) physician at the University of New Mexico, Albuquerque. Before attending medical school, he was a New York City Paramedic. Chris completed his emergency medicine residency and EMS fellowship at UNM. He currently practices emergency medicine in New Mexico in the ED, in the field with EMS and with the UNM Lifeguard Air Emergency Services.

Case: You are working as a paramedic, and you respond to a cardiac arrest. On arrival, you find a 35-year-old male, pulseless and apneic with cardio-pulmonary resuscitation (CPR) in progress by a bystander. There is drug paraphernalia scattered around the room. You and your partner initiate high-quality CPR, place a supraglottic airway, establish intra-osseous (IO) access and administer epinephrine. Your partner asks if you want to administer naloxone as well.

Background: We’ve discussed out-of-hospital cardiac arrest (OHCA) at least once or twice on the SGEM (see long list at end of blog). Today’s study looks at the role of naloxone in OHCA.

Naloxone is a well-established medication used primarily for reversing opioid overdoses. As a competitive opioid antagonist, naloxone binds to opioid receptors in the central nervous system, effectively displacing opioids and reversing their effects, particularly respiratory depression. This makes naloxone an essential tool for emergency responders dealing with opioid-related incidents. Typically administered via intravenous (IV), intramuscular (IM), or intranasal (IN) routes, naloxone acts rapidly, often restoring normal breathing within minutes. Its safety profile is well-tolerated, with the primary adverse effects related to the abrupt reversal of opioid effects, such as acute withdrawal symptoms.

Traditionally, naloxone has been used in cases of suspected opioid overdose where patients exhibit signs of severe respiratory depression or loss of consciousness (LOC). However, its role in broader emergency care contexts, such as OHCA, is evolving. Opioid-associated OHCA has become increasingly common due to the ongoing opioid crisis, with opioids contributing to a significant proportion of cardiac arrests [1-4]. In these scenarios, the pathophysiology involves opioid-induced respiratory depression leading to hypoxia, hypotension, and eventually cardiac arrest. Given this progression, naloxone’s ability to counteract opioid effects offers a potential intervention point, even in cardiac arrest scenarios.

Current guidelines from organizations like the American Heart Association (AHA) suggest considering naloxone in suspected opioid-associated OHCA cases [5]. However, the efficacy of naloxone in improving outcomes in such cardiac arrests remains a topic of ongoing research and debate. While naloxone is not traditionally viewed as a standard treatment in cardiac arrest care, its potential to address underlying opioid toxicity provides a rationale for its use in selected patients. This has led to variability in EMS protocols, with some agencies including naloxone in their cardiac arrest protocols while others do not specifically recommend it, highlighting a gap in definitive guidance [6].

As the landscape of OHCA continues to evolve, understanding the role of naloxone in these critical situations is vital for EMS providers. This discussion sets the stage for exploring naloxone’s place in the management of cardiac arrest, particularly as new evidence emerges regarding its impact on outcomes such as return of spontaneous circulation (ROSC) and survival to hospital discharge.

Clinical Question: Is naloxone administration in undifferentiated OHCA associated with survival to hospital discharge?

Reference: Dillon et al. Naloxone and Patient Outcomes in Out-of-Hospital Cardiac Arrests in California. JAMA Network Open. August 20, 2024

  • Population: Adult patients (aged 18 or older) who received EMS treatment for nontraumatic OHCA in three Northern California counties between 2015 and 2023.
    • Excluded: Patients under 18 and missing data regarding medication administration
  • Exposure: Naloxone administration during resuscitation.
  • Comparison: No naloxone administration during resuscitation.
  • Outcome:
    • Primary Outcome: Survival to hospital discharge
    • Secondary Outcomes: Sustained ROSC (detectable pulse for at least 20 minutes or at the end of EMS care)
  • Type of Study: Retrospective cohort study

Authors’ Conclusions: “In this retrospective cohort of patients with OHCA, EMS-administered naloxone was associated with clinically significant improvements in ROSC and survival to hospital discharge.”

Quality Checklist for Observational Study:

  1. Did the study address a clearly focused issue? Yes
  2. Did the authors use an appropriate method to answer their question? Yes
  3. Was the cohort recruited in an acceptable way? Yes
  4. Was the exposure accurately measured to minimize bias? Yes
  5. Was the outcome accurately measured to minimize bias? Yes
  6. Have the authors identified all-important confounding factors? Unsure
  7. Was the follow-up of subjects complete enough? Yes
  8. How precise are the results? Precise
  9. Do you believe the results? No
  10. Can the results be applied to the local population? Unsure
  11. Do the results of this study fit with other available evidence? No
  12. Funding of the Study: One author reported a grant from the National Heart Lung and Blood Institute. One author reported grants from the Substance Abuse and Mental Health Services Administration outside of this work.

Results: There were 8,195 people identified in this study with OHCAs. Of these patients, 1,165 received naloxone (14%) while 7,030 did not receive naloxone (86%). The median age was 65 years and 68% were male. Nine percent were drug related while 91% were not drug related.

Key Result: Naloxone administration was associated with increased ROSC and increased survival to hospital discharge compared to those who did not receive naloxone.

  • Primary Outcome: Survival to hospital discharge was 15.9% in the naloxone group vs 9.7% in the non-naloxone group. This gives an absolute risk difference (ARD) of 6.2% (95% CI; 2.3%-10.0%) P<0.001
  • Secondary Outcomes: Sustained ROSC was 34.5% in the naloxone group vs 22.9% in the non-naloxone group. This gives an ARD of 15.2% (95% CI; 9.9%-20.6%) P<0.001

Interestingly, they found higher ROSC with naloxone in both “drug-related” and “non-drug related” arrests.

1. Confounders and Neighbors: The exposed and unexposed groups were very different. Every p-value in Table 1 is significant, which is not what you typically see. The exposed group was more likely to be younger, to have an unwitnessed arrest, less likely to have ventricular fibrillation, etc. The authors generated propensity score models based on age, sex, initial cardiac rhythm, comorbid conditions, whether the OHCA was witnessed, and whether the cause of arrest was drug-related.

Rosenbaum and Rubin defined propensity score matching in 1983 as “the probability of treatment assignment conditional on observed baseline covariates” [7].  It represents an attempt to balance two groups conditionally on the distribution of measured baseline covariates. Propensity score matching is a statistical attempt to decrease bias in an observational study. For those who want to take a deeper dive into this topic here are a few references [8-10].

They performed two types of regression analysis, first, an inverse probability weighted regression adjustment. For this analysis, they calculate the likelihood that an individual receives a given treatment (naloxone) and then, based on the characteristics above, a propensity score, and then they assign weights based on the inverse of the propensity score creating a “pseudo population” wherein the confounders are equally distributed amongst the treatment groups. Then they did nearest neighbor propensity score matching. They used the same propensity scores generated for the inverse probability weighted analysis and then paired every patient who received naloxone to a patient whose propensity score was most similar. These are robust statistical methods but they’re no substitute for a prospective randomized trial.

2. Unmeasured Confounders: Chart extraction can only capture so much. Whether or not an arrest was “drug-related” was based on the documentation in the EMS chart, yet 60% of patients who received naloxone had “non-drug related” 13% of patients in the no naloxone group also didn’t receive any epinephrine. There are always factors that determine why a clinician makes a certain decision that we’ll never be able to measure, which is why prospective RCTs are so important for treatment-oriented questions.

3. Patient-Oriented Outcome (POO): Discharge from the hospital was the primary outcome, which is an important patient-oriented outcome or POO, however, there is no data on patient status at the time of discharge. Especially with a disease process as potentially debilitating as cardiac arrest, it’s important to know not just that the patient left the hospital, but how they were when they left. Awake and talking or bed-bound with a trach and a PEG tube? An example of this would be the PARAMEDIC 2 trial that we covered on SGEM#238.

4. Calculating an NNT from Observational Data: This is something that epidemiologists and biostatisticians have been talking about for decades [11]. To remind everyone, the NNT estimates the average number of patients who need to be treated to positively impact one person with therapeutic benefit. As with any summary statistics, the NNT can imply a sense of certainty that is not justified. A major strength of the NNT is its simplicity, making complex research easier to understand. A weakness, however, is also simplicity, hiding the complexity of research, ignoring confidence intervals, and obscuring biases. For most topics, these details are more important than any individual number, like the NNT. Here is a link to a YouTube video on the topic.

Calculating an NNT from an observational study adds another layer of uncertainty. This is because causality cannot be established in this type of study design. There is a large risk of bias due to measured and unmeasured confounders. Sophisticated statistical adjustments like propensity score matching can be done but cannot eliminate all biases. These and other things inherent to this summary statistic should make us much more cautious in our interpretation of an NNT.

5. Narcan: How much Narcan? When Narcan? The data lacks specifics about the naloxone administration, we don’t know the route of administration, or if any naloxone was given by bystanders or law enforcement before EMS arrival. We also don’t know when the naloxone was given, during the resuscitation which the authors remind us of can introduce immortal person-time bias, which is a very cool name for a band.

Comment on Authors’ Conclusion Compared to SGEM Conclusion: We commend the authors for using a robust statistical analysis to investigate this important question. We are skeptical of their findings, and we agree that prospective RCTs are needed to better understand the role of naloxone in OHCA resuscitation.

SGEM Bottom Line: The focus in OHCA needs to be high-quality CPR and early defibrillation of shockable rhythms. In an optimized resuscitation with suspicion of opiate overdose, it may be reasonable to consider naloxone administration.

Case Resolution: You administer naloxone via the IO and after two additional rounds of CPR the patient attains ROSC. He is transported to the emergency department and admitted to the intensive care unit (ICU). Unfortunately, he remains comatose, and imaging is consistent with anoxic brain injury. He is declared brain dead and his family elects to pursue organ donation, his organs going on to save eight lives.

Dr. Chris Root

Clinical Application: If you suspect you are treating an opioid-associated OCHA, once the treatments with robust evidence are in progress, meaning you have prioritized high-quality CPR and prompt defibrillation of shockable rhythms, then it is reasonable to consider giving naloxone.   

What Do I Tell the Patient’s Family? You’re loved one has stopped breathing and this has caused his heart to stop beating. We think this happened because of an overdose of the drugs he was using. We’re doing everything we can to try and restart his heart.

Keener Kontest: Last week’s winner was Dr. Kay Dingwell from PEI. She knew Félix d’Herelle was the researcher who discovered phages as a therapy for bacterial infections.

Listen to the SGEM podcast on iTunes to hear the new keener question. If you know the answer send an email to TheSGEM@gmail.com with “keener” in the subject line. The first correct answer will receive a shoutout on the next SGEM episode.

Other FOAMed on this Study and SGEM Episodes on OHCA:

  • EM Lighthouse Project: Episode 88 Naloxone in Cardiac Arrest
  • SGEM#50: Under Pressure Journal Club: Vasopressin, Steroids and Epinephrine in Cardiac Arrest
  • SGEM#54: Baby It’s Cold Outside: Pre-hospital Therapeutic Hypothermia in Out-of-Hospital Cardiac Arrest
  • SGEM#59: Can I Get a Witness: Family Members Present During CPR
  • SGEM#64: Classic EM Paper: OPALS Study
  • SGEM#107: Can’t Touch This: Hands-on Defibrillation
  • SGEM#136: CPR – Man or Machine?
  • SGEM#143: Call Me Maybe for Bystander CPR
  • SGEM#152: Movin’ on Up – Higher Floors, Lower Survival for OHCA
  • SGEM#162: Not Stayin’ Alive More Often with Amiodarone or Lidocaine in OHCA
  • SGEM#189: Bring Me to Life in OHCA
  • SGEM#238: The Epi Don’t Work for OHCA
  • SGEM#247: Supraglottic Airways Gonna Save You for an OHCA?
  • SGEM#275: 10th Avenue Freeze-Out – Therapeutic Hypothermia after Non-Shockable Cardiac Arrest
  • SGEM#306: Fire Brigade and the Staying Alive APP for OHCAs in Paris
  • SGEM#314: OHCA – Should you Take ‘em on the Run Baby if you Don’t get ROSC?
  • SGEM#329: Will Corticosteroids Help if…I Will Survive a Cardiac Arrest?
  • SGEM#336: You Can’t Always Get What You Want – TTM2 Trial
  • SGEM#344: We Will…We Will Cath You – But Should We After an OHCA Without ST Elevations?
  • SGEM#353: At the COCA, COCA for OHCA
  • SGEM#380: OHCAs Happen and you’re head over heels – Head elevated during CPR?
  • SGEM#396: And iGel Myself I’m Over You, Cus I’m the King (tube) of Wishful Thinking

Remember to be skeptical of anything you learn, even if you heard it on the Skeptics’ Guide to Emergency Medicine.

References:

  1. Chen N, Callaway CW, Guyette FX, et al; Pittsburgh Post-Cardiac Arrest Service. Arrestetiology among patients resuscitated from cardiac arrest. Resuscitation. 2018;130:33-40. doi:10.1016/j.resuscitation.2018.06.024
  2. Smith G, Beger S, Vadeboncoeur T, et al. Trends in overdose-related out-of-hospital cardiac arrest in Arizona. Resuscitation. 2019;134:122-126. doi:10.1016/j.resuscitation.2018.10.019
  3. Elmer J, Lynch MJ, Kristan J, et al; Pittsburgh Post-Cardiac Arrest Service. Recreational drug overdose-related cardiac arrests: break on through to the other side. Resuscitation. 2015;89:177-181. doi:10.1016/j. resuscitation. 2015.01.028
  4. Hess EP, Campbell RL, White RD. Epidemiology, trends, and outcome of out-of-hospital cardiac arrest of non-cardiac origin. Resuscitation. 2007;72(2):200-206. doi:10.1016/j.resuscitation.2006.06.040
  5. Dezfulian C, Orkin AM, Maron BA, et al; American Heart Association Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Cardiovascular and Stroke Nursing; Council on Quality of Care and Outcomes Research; and Council on Clinical Cardiology. Opioid-associated out-of-hospital cardiac arrest: distinctive clinical features and implications for health care and public responses: a scientific statement from the American Heart Association. Circulation. 2021;143(16): e836-e870. doi:10.1161/CIR.0000000000000958
  6. Dillon DG, Porto GD, Eswaran V, Shay C, and Montoy JCC. Identification and treatment of opioid-associated out-of- hospital cardiac arrest in emergency medical service protocols. JAMA Netw Open. 2022;5(5): e2214351. doi:10. 1001/jamanetworkopen.2022.14351
  7. Rosenbaum, Paul R., and Donald B. Rubin. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika, vol. 70, no. 1, 1983, pp. 41–55. JSTOR, https://doi.org/10.2307/2335942. Accessed 23 Jan. 2023.
  8. Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res. 2011 May;46(3):399-424. doi: 10.1080/00273171.2011.568786. Epub 2011 Jun 8. PMID: 21818162; PMCID: PMC3144483.
  9. “Propensity Score Matching – Beginner’s guide to causal inference from observational data”https://towardsdatascience.com/propensity-score-matching-a0d373863eec
  10. Propensity Score Matching: Definition & Overview” https://www.statisticshowto.com/propensity-score-matching/
  11. Laupacis A, Sackett DL, Roberts RS. An assessment of clinically useful measures of the consequences of treatment. N Engl J Med. 1988 Jun 30;318(26):1728-33. doi: 10.1056/NEJM198806303182605. PMID: 3374545.