Prediction of Opioid-Induced Respiratory Depression on Inpatient Wards Using Continuous Capnography and Oximetry: An International Prospective, Observational Trial

Ashish K. Khanna*, Sergio D. Bergese, Carla R. Jungquist, Hiroshi Morimatsu, Shoichi Uezono, Simon Lee, Lian Kah Ti, Richard D. Urman, Robert McIntyre, Carlos Tornero, Albert Dahan, Leif Saager, Toby N. Weingarten, Maria Wittmann, Dennis Auckley, Luca Brazzi, Morgan Le Guen, Roy Soto, Frank Schramm, Sabry AyadRoop Kaw, Paola Di Stefano, Daniel I. Sessler, Alberto Uribe, Vanessa Moll, Susan J. Dempsey, Wolfgang Buhre, Frank J. Overdyk, PRediction of Opioid-induced respiratory Depression In patients monitored by capnoGraphY (PRODIGY) Group Collaborators

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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BACKGROUND: Opioid-related adverse events are a serious problem in hospitalized patients. Little is known about patients who are likely to experience opioid-induced respiratory depression events on the general care floor and may benefit from improved monitoring and early intervention. The trial objective was to derive and validate a risk prediction tool for respiratory depression in patients receiving opioids, as detected by continuous pulse oximetry and capnography monitoring. METHODS: PRediction of Opioid-induced respiratory Depression In patients monitored by capnoGraphY (PRODIGY) was a prospective, observational trial of blinded continuous capnography and oximetry conducted at 16 sites in the United States, Europe, and Asia. Vital signs were intermittently monitored per standard of care. A total of 1335 patients receiving parenteral opioids and continuously monitored on the general care floor were included in the analysis. A respiratory depression episode was defined as respiratory rate = 60 mm Hg for >= 3 minutes; apnea episode lasting >30 seconds; or any respiratory opioid-related adverse event. A risk prediction tool was derived using a multivariable logistic regression model of 46 a priori defined risk factors with stepwise selection and was internally validated by bootstrapping. RESULTS: One or more respiratory depression episodes were detected in 614 (46%) of 1335 general care floor patients (43% male; mean age, 58 +/- 14 years) continuously monitored for a median of 24 hours (interquartile range [IQR], 17-26). A multivariable respiratory depression prediction model with area under the curve of 0.740 was developed using 5 independent variables: age >= 60 (in decades), sex, opioid naivety, sleep disorders, and chronic heart failure. The PRODIGY risk prediction tool showed significant separation between patients with and without respiratory depression (P<.001) and an odds ratio of 6.07 (95% confidence interval [CI], 4.44-8.30;P<.001) between the high- and low-risk groups. Compared to patients without respiratory depression episodes, mean hospital length of stay was 3 days longer in patients with >= 1 respiratory depression episode (10.5 +/- 10.8 vs 7.7 +/- 7.8 days;P<.0001) identified using continuous oximetry and capnography monitoring. CONCLUSIONS: A PRODIGY risk prediction model, derived from continuous oximetry and capnography, accurately predicts respiratory depression episodes in patients receiving opioids on the general care floor. Implementation of the PRODIGY score to determine the need for continuous monitoring may be a first step to reduce the incidence and consequences of respiratory compromise in patients receiving opioids on the general care floor.

Original languageEnglish
Pages (from-to)1012-1024
Number of pages13
JournalAnesthesia and Analgesia
Issue number4
Publication statusPublished - Oct 2020


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