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 ≤5 breaths/min (bpm), oxygen saturation ≤85%, or end-tidal carbon dioxide ≤15 or ≥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.

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There are at least 9 studies that provide evidence that insomnia is a significant risk factor for recurrent and new onset major depressive disorder (MDD), two of which suggest that this association also exists specifically for the elderly. In this study, archival data from a community sample of healthy elderly participants were used to assess the extent to which insomnia predicts future illness in this age cohort. Out of the 147 participants with no prior history of mental illness, 66 participants were classified as having no insomnia, 47 had indeterminate insomnia, and 34 had persistent insomnia. Twelve participants developed MDD during the 1-year follow-up period. Two had no insomnia, 4 had indeterminate insomnia, and 6 had persistent insomnia. Persistent insomnia with onset of depression occurred only in female participants and was significantly associated with middle insomnia. These data suggest that elderly participants with persistent insomnia are at greater risk for the development of new onset depression.

Introduction Chronic pain is difficult to treat and often precedes or exacerbates sleep disturbances such as insomnia. Insomnia, in turn, can amplify the pain experience. Both conditions are associated with inflammatory processes, which may be involved in the bidirectional relationship between pain and sleep. Cognitive behavioral therapy (CBT) for pain and CBT for insomnia are evidence based interventions for, respectively, chronic pain and insomnia. The study objectives were to determine the feasibility of combining CBT for pain and for insomnia and to assess the effects of the combined intervention and the stand alone interventions on pain, sleep, and mood outcomes compared to a control condition. Methods Twenty-one adults with co-occurring chronic pain and chronic insomnia were randomized to either CBT for pain, CBT for insomnia, combined CBT for pain and insomnia, or a wait-list control condition. Results The combined CBT intervention was feasible to deliver and produced significant improvements in sleep, disability from pain, depression and fatigue compared to the control condition. Overall, the combined intervention appeared to have a strong advantage over CBT for pain on most outcomes, modest advantage over both CBT for insomnia in reducing insomnia severity in chronic pain patients. Discussion CBT for pain and CBT for insomnia may be combined with good results for patients with co-occurring chronic pain and insomnia.