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

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

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

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 \u3e30 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 \u3c .001) and an odds ratio of 6.07 (95% confidence interval [CI], 4.44-8.30; P \u3c .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 \u3c .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

The Relationship among Chronic Pain, Opiates, and Sleep

Thesis (Ph.D.)--University of Rochester. School of Nursing. Dept. of Nursing, 2008.The overall aim of this study was to examine the relationships among chronic pain, opiates, respiration, and sleep in a sample of subjects referred for assessment of sleep disorders. This study assessed: (a) whether increasing dosages of opiate predict severity of sleep disordered breathing, sleep architecture, sleep continuity abnormalities, and/or excessive daytime sleepiness; (b) whether the study groups ([no pain vs. pain] and [pain minus opiate treatment vs. pain plus opiate treatment]) differed with respect to severity of sleep disordered breathing, sleep architecture, sleep continuity abnormalities; (c) whether the known risk factors for sleep disordered breathing differed for persons with and without chronic pain, and (d) whether intensity of pain predicted severity of sleep disordered breathing. Methods A descriptive cross sectional study was conducted. There were two types of independent variables, (a) risk factors for sleep disordered breathing (BMI, age, gender, number of systems affected by co-morbid diseases, and presence of anatomical abnormalities typical of obstructive sleep apnea), and (b) those that were directly related to the investigational hypothesis (pain incidence and intensity and/or opiate use and dose). Dependent Variables included: measures of sleep disordered breathing (e.g., frequency of central and obstructive events), sleep architecture (e.g., percent of stages 1-4 and REM), and sleep continuity measures (e.g., Sleep Latency, Number of Awakenings, and Total Sleep Time). After orthogonally coding for group membership, regression models were used for statistical analysis. Pain, Opiates and Sleep Results Data was collected on a total of 419 subjects (no pain [n = 171], pain –opiate Tx [n = 187], and pain +opiate Tx [n = 61]). Sample demographic (mean +/- SD) was as follows: age 50 yr + 12.; 51% male; BMI 33.8 + 7; Epworth Sleepiness Scale 10.3 + 5; pain intensity 3.8 + 2 (0-10 scale); morphine equivalent dose 152 + 195 mg; and 98% of subjects with pain had non-malignant chronic pain. Per study hypotheses (a) there was a positive dose response relationship between amount of opiate and frequency of central apneic events and percent of stage 3/4 sleep; (b) the [no pain vs. pain] group comparison revealed that subjects with pain had a lower percent of stage 1 sleep, and the [pain minus opiate vs. pain plus opiate]) group comparison revealed that subjects treated with opiates had significantly more central apneic events, more stage 2 sleep and less REM sleep; (c) the known risk factors for sleep disordered breathing differ in persons with and without chronic pain (chronic pain subjects were older, female and suffered from more comorbidity); (d) there was a relationship between pain intensity and frequency of central apneic events and obstructive apneic events. Greater pain intensity was associated with more frequent central apneic events and fewer obstructive apneic events. Conclusion These data suggest that the management of chronic pain with opiates is not likely to exacerbate obstructive sleep apnea at stable opiate doses; however; central sleep apnea may be worsened. The magnitude of the effect is modest, and the clinical relevance of the effect is unknown. Thus, the potential for marginal respiratory disturbance (an increase of 2.8 central events for every 100 mg. morphine equivalent opiate dose) must be weighed against the therapeutic value of pain management with opiates

Introducing the Sleep Disorders Symptom Checklist-25: A Primary Care Friendly and Comprehensive Screener for Sleep Disorders

Background and Objective With sleep disorders highly prevalent and associated with poor health outcomes, screening for sleep disorders in primary care could reduce the burden of chronic diseases and costs of health care. Currently, a brief comprehensive primary-care-friendly multiple-sleep-disorders screening instrument is not available. The Sleep Disorders Symptom Checklist (SDS-CL)-17, a single-page instrument, was developed to screen for six sleep disorders (insomnia, obstructive sleep apnea, restless legs syndrome/periodic limb movement disorder, circadian rhythm sleep-wake disorders, narcolepsy, and parasomnias) and evaluated psychometrically. SDS-CL-17 psychometrics are reported. The resulting development of a more comprehensive single-page 25-item instrument, the SDS-CL-25, based on validation study results is described. Approaches for clinical use of the SDS-CL-25 are recommended. Methods A cross-sectional study using nested data from two previous research studies (n = 395 sleep clinic referrals and n = 299 community volunteers) was used. SDS-CL-17 subscale scores and physician diagnoses were analysed using receiver operator characteristic curves. Resulting cut-point scores determined sensitivities/specificities. Study subject interview data were used to assess patient-friendliness of the instrument. Results Sensitivities/specificities for the diagnosed sleep disorders ranged from 0.64 to 0.88. Interviewees endorsed the instrument as user-friendly. Conclusions While the SDS-CL-17 is useful, the SDS-CL-25 assesses for a much larger number of sleep disorders yet retains brevity and is therefore recommended for ongoing clinical use. Psychometric evaluation of the SDS-CL-25 continues

Validation of Capturing Sleep Diary Data via a Wrist-Worn Device

Paper sleep diaries are the gold standard for assessment of sleep continuity variables in clinical practice as well as research. Unfortunately, paper diaries can be filled out weekly instead of daily, lost, illegible or destroyed; and are considered out of date according to the newer technology savvy generations. In this study, we assessed the reliability and validity of using a wrist-worn electronic sleep diary. Design. A prospective design was used to compare capturing 14 days of sleep continuity data via paper to a wrist-worn electronic device that also captured actigraphy data. Results. Thirty-five healthy community dwelling adults with mean (sd) age of 36 (15), 80% Caucasians, and 74% females were enrolled. All sleep continuity variables via electronic and paper diary capture methods were significantly correlated with moderate, positive relationships. Assessment of validity revealed that electronic data capture had a significant relationship with objective measure of sleep continuity variables as measured by actigraphy. Paper diary variables were not significantly associated with objective measures. Conclusions. The use of a wrist-worn device to capture daily sleep diary data is as accurate as and for some variables more accurate than using paper diaries

Deep learning classification of capnography waveforms: secondary analysis of the PRODIGY study

Capnography monitors trigger high priority ‘no breath’ alarms when CO2 measurements do not exceed a given threshold over a specified time-period. False alarms occur when the underlying breathing pattern is stable, but the alarm is triggered when the CO2 value reduces even slightly below the threshold. True ‘no breath’ events can be falsely classified as breathing if waveform artifact causes an aberrant spike in CO2 values above the threshold. The aim of this study was to determine the accuracy of a deep learning approach to classifying segments of capnography waveforms as either ‘breath’ or ‘no breath’. A post hoc secondary analysis of data from 9 North American sites included in the PRediction of Opioid-induced Respiratory Depression In Patients Monitored by capnoGraphY (PRODIGY) study was conducted. We used a convolutional neural network to classify 15 s capnography waveform segments drawn from a random sample of 400 participants. Loss was calculated over batches of 32 using the binary cross-entropy loss function with weights updated using the Adam optimizer. Internal-external validation was performed by iteratively fitting the model using data from all but one hospital and then assessing its performance in the remaining hospital. The labelled dataset consisted of 10,391 capnography waveform segments. The neural network’s accuracy was 0.97, precision was 0.97 and recall was 0.96. Performance was consistent across hospitals in internal-external validation. The neural network could reduce false capnography alarms. Further research is needed to compare the frequency of alarms derived from the neural network with the standard approach.</p

Predicting Prolonged Apnea During Nurse-Administered Procedural Sedation: Machine Learning Study

BackgroundCapnography is commonly used for nurse-administered procedural sedation. Distinguishing between capnography waveform abnormalities that signal the need for clinical intervention for an event and those that do not indicate the need for intervention is essential for the successful implementation of this technology into practice. It is possible that capnography alarm management may be improved by using machine learning to create a “smart alarm” that can alert clinicians to apneic events that are predicted to be prolonged. ObjectiveTo determine the accuracy of machine learning models for predicting at the 15-second time point if apnea will be prolonged (ie, apnea that persists for >30 seconds). MethodsA secondary analysis of an observational study was conducted. We selected several candidate models to evaluate, including a random forest model, generalized linear model (logistic regression), least absolute shrinkage and selection operator regression, ridge regression, and the XGBoost model. Out-of-sample accuracy of the models was calculated using 10-fold cross-validation. The net benefit decision analytic measure was used to assist with deciding whether using the models in practice would lead to better outcomes on average than using the current default capnography alarm management strategies. The default strategies are the aggressive approach, in which an alarm is triggered after brief periods of apnea (typically 15 seconds) and the conservative approach, in which an alarm is triggered for only prolonged periods of apnea (typically >30 seconds). ResultsA total of 384 apneic events longer than 15 seconds were observed in 61 of the 102 patients (59.8%) who participated in the observational study. Nearly half of the apneic events (180/384, 46.9%) were prolonged. The random forest model performed the best in terms of discrimination (area under the receiver operating characteristic curve 0.66) and calibration. The net benefit associated with the random forest model exceeded that associated with the aggressive strategy but was lower than that associated with the conservative strategy. ConclusionsDecision curve analysis indicated that using a random forest model would lead to a better outcome for capnography alarm management than using an aggressive strategy in which alarms are triggered after 15 seconds of apnea. The model would not be superior to the conservative strategy in which alarms are only triggered after 30 seconds