Patients with suspected opioid overdose frequently require naloxone treatment. Despite recommendations to observe such patients for 4 to 24 hours after naloxone, earlier discharge is becoming more common. This prospective, observational study of patients with presumed opioid overdose examines the safety of early disposition decisions and the accuracy of outcome prediction by physicians 1 hour after the administration of naloxone.

The study was carried out at St. Paul’s Hospital, an inner city teaching centre that cares for most of the injection drug users in Vancouver, BC. Patients were formally assessed 1 hour after receiving naloxone for presumed opioid overdose. Demographics, medical history and physical examination were documented on specific data forms, and physicians recorded their comfort with early discharge. Patients were followed up, and those who required a critical intervention or suffered a pre-defined adverse event (AE) within 24 hours of their 1-hour assessment were identified.

Of 573 patients, 48% were discharged in less than 2 hours, 23% in 2–4 hours and 29% in >4 hours. 94 patients who were held in the emergency department (ED) or admitted required a critical intervention, including supplemental oxygen for hypoxia (74), repeat naloxone (52), antibiotics administered intravenously (IV) (14), assisted ventilations (13), fluid bolus for hypotension (12), charcoal for associated life-threatening overdose (6), IV inotropic agents (2), antiarrhythmics for sustained tachycardia >130 beats/min (1), and administration of bicarbonate for arterial [HCO3] <5 or venous CO2 <5 (1). Physicians predicted adverse events with 94% sensitivity and 59% specificity. No discharged patients suffered a serious AE within 24 hours of ED discharge.

Emergency physicians can clinically identify patients at risk of deterioration after naloxone reversal of suspected opioid overdose. Prolonged observation or hospital admission is not usually required. Selective early discharge of patients with presumed opioid overdose is feasible and appears safe. A clinical prediction rule may be useful in identifying patients eligible for early discharge.

Our goals were to determine whether selection bias occurred in a prehospital study comparing an esophageal detector device (EDD) to a disposable capnometer for detecting esophageal intubation, and to determine whether such a bias would have changed the study’s conclusions about EDD effectiveness.

In a study of patients requiring prehospital intubation, we determined the sensitivity, specificity and predictive values of the EDD for detecting esophageal intubation. We then compared intubation success rate in patients who were enrolled in the study (n = 129) to that in eligible patients who were excluded from it (n = 107). After finding that the incidence of failed intubation was higher in the “excluded” group, we used sensitivity and specificity parameters derived from the study population to assess whether EDD test characteristics would differ in studied vs. excluded patients.

The first intubation attempt was successful in 125 of 129 study patients and 76 of 107 excluded patients (97% vs. 71%, p = 0.03), confirming the presence of selection bias. The negative predictive value of the EDD for esophageal intubation was 98% in the study cohort and would have been 77% in patients like those excluded (i.e., difficult intubation cases).

Conclusion: The high “first attempt” intubation success rate seen in this study was due to selective exclusion of failed intubations. This selection bias led to a clinically important overestimation of the EDD’s negative predictive value. Bias may substantially alter the estimations of test accuracy reported in scientific studies. To reduce the chance of unrecognized selection bias in studies of diagnostic tests, investigators must determine whether recruited subjects resemble patients in whom the test will ultimately be used.