Antiretroviral therapy has changed human immunodeficiency virus (HIV) infection from a near-certainly fatal illness to one that can be managed chronically. More patients are taking antiretroviral drugs (ARVs) for longer periods of time, which naturally results in more observed toxicity. Overdose with ARVs is not commonly reported. The most serious overdose outcomes have been reported in neonates who were inadvertently administered supratherapeutic doses of HIV prophylaxis medications. Typical ARV regimens include a "backbone" of two nucleoside reverse transcriptase inhibitors (NRTI) and a "base" of either a protease inhibitor (PI) or nonnucleoside reverse transcriptase inhibitor. New classes of drugs called entry inhibitors and integrase inhibitors have also emerged. Older NRTIs were associated with mitochondrial toxicity, but this is less common in the newer drugs, emtricitabine, lamivudine, and tenofovir. Mitochondrial toxicity results from NRTI inhibition of a mitochondrial DNA polymerase. Mitochondrial toxicity manifests as myopathy, neuropathy, hepatic failure, and lactic acidosis. Routine lactate assessment in asymptomatic patients is not indicated. Lactate concentration should be obtained in patients taking NRTIs who have fatigue, nausea, vomiting, or vague abdominal pain. Mitochondrial toxicity can be fatal and is treated by supportive care and discontinuing NRTIs. Metabolic cofactors like thiamine, carnitine, and riboflavin may be helpful in managing mitochondrial toxicity. Lipodystrophy describes changes in fat distribution and lipid metabolism that have been attributed to both PIs and NRTIs. Lipodystrophy consists of loss of fat around the face (lipoatrophy), increase in truncal fat, and hypertriglyceridemia. There is no specific treatment of lipodystrophy. Clinicians should be able to recognize effects of chronic toxicity of ARVs, especially mitochondrial toxicity.

Background: Increasing naloxone access has been identified as a primary strategy to reduce opioid overdose deaths. To supplement community naloxone training and distribution access points, EMS systems have instituted public safety-based naloxone leave behind (NLB) programs that allow emergency medical responders to distribute "leave behind" naloxone kits on the scene of an overdose. This model presents an opportunity to expand naloxone access for individuals at high risk for future overdoses. Objectives: To evaluate the preliminary outcomes of a novel EMS-based NLB program in Howard County, Maryland. Methods: This exploratory study involved analysis of data from the Howard County NLB Program. Basic statistical analysis of program performance metrics and participant demographic characteristics were performed. Results: From June 2018 to June 2019, Howard County Department of Fire and Rescue Services responded to 239 overdose calls and distributed 120 naloxone kits to individuals on the scene of an overdose, a 50.21% distribution rate. The HCNLB program connected 143 patients (59.83%) to peer recovery A c c e p t e d M a n u s c r i p tspecialists. Among the 143 patients linked to peer recovery support specialist services, 87 (60.84%) had accepted an NLB kit from EMS. The fully adjusted logistic regression model revealed that those whose kit was left with a family member on the scene were 5.16 times more likely to be connected to peer support specialists (OR = 5.16, p=0.000) while those whose kit was left with a friend or given directly to the patient were 3.69 times (OR=3.69, CI= 1.13 -12.06, p<0.05) and 2.37 times (OR=2.37, CI= 1.10 -5.14, p<0.05) more likely, respectively, to be connected to follow up services as compared to those who did not accept a kit, controlling for other variables in the model. Conclusion: This study highlights the importance of engaging an individual's family and social network when offering connections to treatment and recovery resources. NLB initiatives can potentially augment existing community-based naloxone training structures, thus widening the scope of the life-saving drug and reaching those most at risk of dying from an opioid overdose.

These findings call attention to the need for the development of a versatile and accurate model that can more accurately predict the number of patient encounters and transports associated with mass-gathering events so that medical needs can be anticipated and sufficient resources can be provided.