In the Journals

Novel app may help anticipate opioid overdose

A recently developed app identified changes in breathing patterns that preceded likely opioid overdoses, as well as during an actual overdose, according to a report recently published in Science Translational Medicine.

“Existing, human-based approaches to diagnose overdose rely on medical-grade equipment or trained recognition of diagnostic signs of opioid toxicity system. ... Validating the efficacy of any opioid toxicity system requires access to patients and data while high-risk opioid use occurs, which is difficult because this can represent a medically life-threatening situation, Rajalakshmi Nandakumar, a PhD candidate at the Paul G. Allen School of Computer Science and Engineering, University of Washington, and colleagues wrote.

Researchers created an algorithm that used sonar to monitor patients’ breathing rate and identify when an opioid overdose has occurred. The app accurately identified respiratory depression, apnea and gross motor movements tied to acute opioid toxicity.

The app, named Second Chance, was then tested in 209 patients that used the legally-sanctioned supervised injection facility in Vancouver, British Columbia.

“We asked participants to prepare their drugs like they normally would, but then we monitored them for a minute pre-injection so the algorithm could get a baseline value for their breathing rate,” Nandakumar said in a press release. “After we got a baseline, we continued monitoring during the injection and then for 5 minutes afterward, because that's the window when overdose symptoms occur.”

Researchers found that Second Chance identified postinjection, opioid-induced central apnea with 96% sensitivity and 98% specificity. The app also identified respiratory depression with 87% sensitivity and 89% specificity.

Nandakumar and colleagues also tested Second Chance in 20 simulated overdose events in the operating room during routine induction of general anesthesia. The app detected 19 of the overdose events, according to researchers.

 
Researchers created an that accurately identified respiratory depression, apnea and gross motor movements tied to acute opioid toxicity.

Source:Shutterstock

“We show that a smartphone running our application placed within 1 m of a subject can monitor the subject during the postinjection period. This is the highest risk time for a fatal overdose event and the period during which a potential overdose victim would most benefit from rapid diagnosis and resuscitation,” Nandakumar and colleagues wrote.

Researchers noted though Second Chance was only tested on patients that used injectable opioids, it could be applied to all forms of opioid use. The app will be submitted for approval by the FDA. – by Janel Miller

Disclosures: The authors report being inventors on U.S. provisional patent application no. 62/675560 (submitted by the University of Washington), which is related to this work. All authors report having equity stakes in Sound Life Sciences Inc., which is related to the technology presented in this manuscript. Please see the study for all other authors’ relevant financial disclosures.

 

 

A recently developed app identified changes in breathing patterns that preceded likely opioid overdoses, as well as during an actual overdose, according to a report recently published in Science Translational Medicine.

“Existing, human-based approaches to diagnose overdose rely on medical-grade equipment or trained recognition of diagnostic signs of opioid toxicity system. ... Validating the efficacy of any opioid toxicity system requires access to patients and data while high-risk opioid use occurs, which is difficult because this can represent a medically life-threatening situation, Rajalakshmi Nandakumar, a PhD candidate at the Paul G. Allen School of Computer Science and Engineering, University of Washington, and colleagues wrote.

Researchers created an algorithm that used sonar to monitor patients’ breathing rate and identify when an opioid overdose has occurred. The app accurately identified respiratory depression, apnea and gross motor movements tied to acute opioid toxicity.

The app, named Second Chance, was then tested in 209 patients that used the legally-sanctioned supervised injection facility in Vancouver, British Columbia.

“We asked participants to prepare their drugs like they normally would, but then we monitored them for a minute pre-injection so the algorithm could get a baseline value for their breathing rate,” Nandakumar said in a press release. “After we got a baseline, we continued monitoring during the injection and then for 5 minutes afterward, because that's the window when overdose symptoms occur.”

Researchers found that Second Chance identified postinjection, opioid-induced central apnea with 96% sensitivity and 98% specificity. The app also identified respiratory depression with 87% sensitivity and 89% specificity.

Nandakumar and colleagues also tested Second Chance in 20 simulated overdose events in the operating room during routine induction of general anesthesia. The app detected 19 of the overdose events, according to researchers.

 
Researchers created an that accurately identified respiratory depression, apnea and gross motor movements tied to acute opioid toxicity.

Source:Shutterstock

“We show that a smartphone running our application placed within 1 m of a subject can monitor the subject during the postinjection period. This is the highest risk time for a fatal overdose event and the period during which a potential overdose victim would most benefit from rapid diagnosis and resuscitation,” Nandakumar and colleagues wrote.

Researchers noted though Second Chance was only tested on patients that used injectable opioids, it could be applied to all forms of opioid use. The app will be submitted for approval by the FDA. – by Janel Miller

Disclosures: The authors report being inventors on U.S. provisional patent application no. 62/675560 (submitted by the University of Washington), which is related to this work. All authors report having equity stakes in Sound Life Sciences Inc., which is related to the technology presented in this manuscript. Please see the study for all other authors’ relevant financial disclosures.

 

 

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