Association of Insomnia and Obstructive Sleep Apnea with Worse Oral Mucositis and Quality of Life in Head and Neck Cancer Patients Undergoing Radiation Therapy.

BACKGROUND: Patients with head and neck cancer (HNC) undergoing radiation therapy (RT) often experience sleep disturbances that may contribute to oral mucositis (OM) and quality of life (QOL). METHODS: Patients with HNC treated with RT at a single institution were examined. Sleep questionnaires were given on the first day of RT to assess for insomnia and obstructive sleep apnea (OSA). Patient-reported QOL and oral mucositis were assessed during RT. Associations between insomnia and OSA with QOL were assessed using the Mann-Whitney U test. Linear mixed models assessed associations with OM. RESULTS: Among 87 patients, 34 patients (39%) had subthreshold or greater insomnia and 47 patients (54%) screened positive for OSA. Upon RT completion, patients with subthreshold or greater insomnia had worse physical function (p = 0.005), fatigue (p = 0.01), insomnia (p < 0.001), and sticky saliva (p = 0.002). Patients screening positive for OSA had worse physical function (p = 0.01), sticky saliva (p = 0.02), fatigue (p = 0.007), insomnia (p = 0.009), and pain (p = 0.005). Upon linear mixed model evaluation, subthreshold or greater insomnia (p = 0.01) and positive OSA screen (p = 0.002) were associated with worse OM. CONCLUSION: Insomnia and OSA are highly prevalent in patients with HNC undergoing RT. These sleep disturbances are associated with worse QOL and OM during treatment.

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.

Electronic Health Record Knowledge, Skills, and Attitudes Among Newly Graduated Nurses: A Scoping Review.

Background This scoping review aimed to identify key characteristics related to electronic health record (EHR) end-user system optimization levels among graduating/newly graduated nurses. Method Using a systematic approach to conducting a scoping review, 11 articles were identified from three databases. Articles were reviewed for nursing level status, type of EHR used for research, type of assessment, and which EHR knowledge, skills, or attitudes were studied. Results The majority of the studies used a reported by evaluator approach that examined the accuracy of EHR documentation (e.g., skills). EHR knowledge was examined in two studies. No study examined EHR attitudes. Studies that reflected self-reported data mainly focused on EHR skills related to navigating, finding, utilizing, and showing and EHR knowledge gains. Conclusion Graduating/newly graduated nurses have gaps in EHR skills, knowledge, and attitudes. Simulation and EHR education are viable pedagogical approaches to building EHR end-user system optimization. [J Contin Educ Nurs. 2022;53(11):505-512.].

An Educational Intervention to Improve Comfort with Applying and Interpreting Transcutaneous CO2 and End-tidal CO2 Monitoring in the PACU.

PURPOSE: The purpose of this study was to assess the effectiveness of an educational program about measuring ventilation using devices that assess carbon dioxide levels in patients recovering from a surgical procedure. DESIGN: A pre-post survey of knowledge attainment from an educational intervention about measuring ventilation using end-tidal carbon dioxide (EtCO2) and transcutaneous carbon dioxide (tcPCO2) devices in the postanesthesia care unit (PACU) was distributed to current members of the American Society of PeriAnesthesia Nurses. METHODS: Participants received a 12-question pre-intervention (five were related to demographics) and a five-question post-intervention survey. Non-demographic survey questions used a one to five Likert scale to assess comfortability or confidence. The intervention created was a voice-over presentation designed to improve PACU RN's comfort and confidence with using and interpreting tcPCO2 or EtCO2 in the PACU. FINDINGS: PACU RNs (N = 108) reported they 'never' or 'rarely' used EtCO2 (n = 57, 52.7%) monitoring or tcPCO2 (n = 93, 86.1%) monitoring in the PACU. A paired t test revealed statistically significant differences in the PACU RN's pre-survey and posttest comfortability of applying and interpreting EtCO2 or tcPCO2 monitors (P < .05). CONCLUSIONS: Capnography monitoring should be considered a standard of care for PACU patients. Education of registered nurses working in the PACU is critical before implementing EtCO2 or tcPCO2 monitoring.

Nurses' Experience and Perception of Technology Use in Practice: A Qualitative Study Using an Extended Technology Acceptance Model.

The purposes of this study are to provide insight into the factors identified as benefits and drawbacks of technology use by nurses and obtain suggestions on improving technology, based on challenges identified to improve patient outcomes. Holden's extended technology acceptance model was used to describe nurses' perception of technology use in practice. A descriptive design and thematic analysis were used to evaluate participants' logged reflections of their experiences and suggestions for improvements of health information technology. Findings indicate that nurses' experiences and perceptions regarding the use of various types of technology (electronic health records, large databases, crowdsourcing, bio-surveillance) were positive. New subdomains emerged for job performance and efficiency, usefulness for public health, contribution to nursing science, and better communication. Identified challenges were insufficient training and support, lack of interoperability, risk of low-quality care from reliance on technology, increased cost, glitches, and alarm fatigue. Understanding nurses' perceptions of technology is anticipated to enhance care quality and possibly lead to improved outcomes. Nurses' perceptions of the use and acceptance of technology are important as their view is associated with performance and may directly influence clinical outcomes and patient satisfaction. Thematic findings include recommendations from participants to improve patient outcomes and patient care.

Patient Deterioration on General Care Units: A Concept Analysis.

Patient deterioration is a phenomenon that occurs from the inability to recognize it or respond to a change in condition. Despite the published reports on recognizing a deteriorating patient on general care floors, a gap remains in the ability of nurses to describe the concept, affecting patient outcomes. Walker and Avant's approach was applied to analyze patient deterioration. The aim of this article was to explore and clarify the meaning of patient deterioration and identify attributes, antecedents, and consequences. The defining attributes were compared to early warning scores. An operational definition was developed and its value to nurses established.

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

BACKGROUND: Capnography 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. OBJECTIVE: To 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). METHODS: A 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). RESULTS: A 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. CONCLUSIONS: Decision 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.

Modeling the Cost Savings of Continuous Pulse Oximetry and Capnography Monitoring of United States General Care Floor Patients Receiving Opioids Based on the PRODIGY Trial.

INTRODUCTION: Despite the high incidence of respiratory depression on the general care floor and evidence that continuous monitoring improves patient outcomes, the cost-benefit of continuous pulse oximetry and capnography monitoring of general care floor patients remains unknown. This study modeled the cost and length of stay savings, investment break-even point, and likelihood of cost savings for continuous pulse oximetry and capnography monitoring of general care floor patients at risk for respiratory depression. METHODS: A decision tree model was created to compare intermittent pulse oximetry versus continuous pulse oximetry and capnography monitoring. The model utilized costs and outcomes from the PRediction of Opioid-induced respiratory Depression In patients monitored by capnoGraphY (PRODIGY) trial, and was applied to a modeled cohort of 2447 patients receiving opioids per median-sized United States general care floor annually. RESULTS: Continuous pulse oximetry and capnography monitoring of high-risk patients is projected to reduce annual hospital cost by $535,531 and cumulative patient length of stay by 103 days. A 1.5% reduction in respiratory depression would achieve a break-even investment point and justify the investment cost. The probability of cost saving is ≥ 80% if respiratory depression is decreased by ≥ 17%. Expansion of continuous monitoring to high- and intermediate-risk patients, or to all patients, is projected to reach a break-even point when respiratory depression is reduced by 2.5% and 3.5%, respectively, with a ≥ 80% probability of cost savings when respiratory depression decreases by ≥ 27% and ≥ 31%, respectively. CONCLUSION: Compared to intermittent pulse oximetry, continuous pulse oximetry and capnography monitoring of general care floor patients receiving opioids has a high chance of being cost-effective. TRIAL REGISTRATION: www.clinicaltrials.gov , Registration ID: NCT02811302.

Opioid-induced respiratory depression increases hospital costs and length of stay in patients recovering on the general care floor.

BACKGROUND: Opioid-induced respiratory depression is common on the general care floor. However, the clinical and economic burden of respiratory depression is not well-described. The PRediction of Opioid-induced respiratory Depression In patients monitored by capnoGraphY (PRODIGY) trial created a prediction tool to identify patients at risk of respiratory depression. The purpose of this retrospective sub-analysis was to examine healthcare utilization and hospital cost associated with respiratory depression. METHODS: One thousand three hundred thirty-five patients (N = 769 United States patients) enrolled in the PRODIGY trial received parenteral opioids and underwent continuous capnography and pulse oximetry monitoring. Cost data was retrospectively collected for 420 United States patients. Differences in healthcare utilization and costs between patients with and without ≥1 respiratory depression episode were determined. The impact of respiratory depression on hospital cost per patient was evaluated using a propensity weighted generalized linear model. RESULTS: Patients with ≥1 respiratory depression episode had a longer length of stay (6.4 ± 7.8 days vs 5.0 ± 4.3 days, p = 0.009) and higher hospital cost ($21,892 ± $11,540 vs $18,206 ± $10,864, p = 0.002) compared to patients without respiratory depression. Patients at high risk for respiratory depression, determined using the PRODIGY risk prediction tool, who had ≥1 respiratory depression episode had higher hospital costs compared to high risk patients without respiratory depression ($21,948 ± $9128 vs $18,474 ± $9767, p = 0.0495). Propensity weighted analysis identified 17% higher costs for patients with ≥1 respiratory depression episode (p = 0.007). Length of stay significantly increased total cost, with cost increasing exponentially for patients with ≥1 respiratory depression episode as length of stay increased. CONCLUSIONS: Respiratory depression on the general care floor is associated with a significantly longer length of stay and increased hospital costs. Early identification of patients at risk for respiratory depression, along with early proactive intervention, may reduce the incidence of respiratory depression and its associated clinical and economic burden. TRIAL REGISTRATION: ClinicalTrials.gov , NCT02811302 .

Healthcare Shift Workers' Temporal Habits for Eating, Sleeping, and Light Exposure: A Multi-Instrument Pilot Study.

BACKGROUND: Circadian misalignment can impair healthcare shift workers' physical and mental health, resulting in sleep deprivation, obesity, and chronic disease. This multidisciplinary research team assessed eating patterns and sleep/physical activity of healthcare workers on three different shifts (day, night, and rotating-shift). To date, no study of real-world shift workers' daily eating and sleep has utilized a largely-objective measurement. METHOD: During this fourteen-day observational study, participants wore two devices (Actiwatch and Bite Technologies counter) to measure physical activity, sleep, light exposure, and eating time. Participants also reported food intake via food diaries on personal mobile devices. RESULTS: In fourteen (5 day-, 5 night-, and 4 rotating-shift) participants, no baseline difference in BMI was observed. Overall, rotating-shift workers consumed fewer calories and had less activity and sleep than day- and night-shift workers. For eating patterns, compared to night- and rotating-shift, day-shift workers ate more frequently during work days. Night workers, however, consumed more calories at work relative to day and rotating workers. For physical activity and sleep, night-shift workers had the highest activity and least sleep on work days. CONCLUSION: This pilot study utilized primarily objective measurement to examine shift workers' habits outside the laboratory. Although no association between BMI and eating patterns/activity/sleep was observed across groups, a small, homogeneous sample may have influenced this. Overall, shift work was associated with 1) increased calorie intake and higher-fat and -carbohydrate diets and 2) sleep deprivation. A larger, more diverse sample can participate in future studies that objectively measure shift workers' real-world habits.

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 model development of women's daily asthma control using fitness tracker sleep disruption.

BACKGROUND: Night-time wakening with asthma symptoms is an important indicator of disease control and severity, with no gold-standard objective measurement. OBJECTIVE: The study objective was to use fitness tracker sleep data to develop predictive models of daily disease control-related asthma-specific wakening and FEV1 in working-aged women with poorly controlled asthma. METHODS: A repeated measures panel design included data from 43 women with poorly controlled asthma. Two components of asthma control were the primary outcomes, measured daily as (1) self-reported asthma-specific wakening and (2) self-administered spirometry to measure FEV1. Data were analyzed using generalized linear mixed models. RESULTS: Our models demonstrated predictive value (AUC=0.77) for asthma-specific night-time wakening and good predictive value (AUC=0.83) for daily FEV1. CONCLUSIONS: Fitness tracker sleep efficiency and wake counts demonstrate clinical utility as predictive of asthma-specific night-time wakening and daily FEV1. Fitness tracker sleep data demonstrated predictive capability for daily asthma outcomes.

Opioid-Induced Sedation and Respiratory Depression: Are Sedation Scales Enough to Prevent Adverse Drug Events Postoperatively?

OBJECTIVES: Nurses who care for hospitalized patients are responsible for ensuring adequate pain management is provided in a safe manner. The clinical challenge is balancing the effective control of the patient's pain with the side effects of administering opioids. The aim of this literature review is to explore the evidence on how nurses assess for opioid-induced sedation and advancing respiratory depression and how they integrate those data in their critical thinking skills when deciding to administer opioids for pain. DESIGN: A matrix method was used to guide the review and synthesis of the evidence. Tables with column headings (citation, purpose of study, design/measurements, outcomes, and results) were constructed to record data extracted from each study. DATA SOURCES: Primary source research articles were examined using the MESH terms sedation, sedation scale, respiratory depression, opioid, pain, pain assessment, adverse events, naloxone and postoperative. REVIEW/ANALYSIS METHODS: Original studies such as retrospective case-control studies and descriptive studies were included. The final studies that met the inclusion criteria and were independently reviewed by the authors. The two main areas of interest were the evidence for how nurses assess for advancing sedation and excessive respiratory depression and how nurses integrate their assessment data in their critical thinking skills when deciding to administer opioids for pain. RESULTS: Results indicated a lack of evidence examining the relationships among sedation, respiratory depression, and adverse events and the overall impact of managing these variables on patients' pain. CONCLUSIONS: This review revealed a lack of evidence between how nurses assess for opioid induced advancing sedation and excessive respiratory depression, and the impact, including the adverse events associate with acute pain management.

American Society for Pain Management Nursing Guidelines on Monitoring for Opioid-Induced Advancing Sedation and Respiratory Depression: Revisions.

OBJECTIVES: This report presents up-to-date evidence and expert consensus-based revisions to the ASPMN 2011 guidelines that inform interprofessional clinical decision-making for hospitalized adults receiving opioid analgesics. DESIGN: Systematic review of the literature. METHODS: A 14-member expert panel was charged with reviewing and grading the strength of scientific evidence published in peer reviewed journals and revising the ASPMN 2011 existing guidelines. Panel members formulated recommendations based on the strength of evidence and reached consensus through discussion, reappraisal of evidence, and voting by majority when necessary. The American Society of Anesthesiologists evidence categories for grading and classifying the strength of the evidence were used. Recommendations were subjected to a critical review by ASPMN members as well as external reviews. RESULTS: The 2011 guidelines were found to still be relevant to clinical practice, but new evidence substantiated refinement and more specific recommendations for electronic monitoring. The revised guidelines present risk factors divided into three categories: patient-specific, treatment-related, and environment of care. Specific recommendations for the use of electronic monitoring are delineated. CONCLUSIONS: All hospitalized patients that are administered opioids for acute pain are at risk of opioid induced advancing sedation and respiratory depression, but some patients are at high risk and require extra vigilance to prevent adverse events. All patients must be assessed for level of risk. Adaptations to the plan of care and monitoring strategies should be driven by iterative re-assessments according to level of risk. NURSING PRACTICE IMPLICATIONS: Opioid medications continue to be a major component in the management of acute pain. Clinicians have the primary responsibility for safe and effective pain management. Evidence based monitoring strategies can improve patient safety with opioids.

Nurse-Delivered Brief Behavioral Treatment for Insomnia in Lung Cancer Survivors: A Pilot RCT.

Objective/Background: Insomnia occurs in 50 to 80% of lung cancer survivors. Cognitive behavioral therapy is the standard treatment for insomnia (CBTI); however, treatment length and lack of psychologists trained in CBTI limits access. Brief Behavioral Treatment for Insomnia (BBTI), a nurse-delivered modified CBTI, is proposed. This feasibility pilot study sought to compare the BBTI intervention to attention control Healthy Eating Program (HEP) for insomnia in lung cancer survivors. Participants: The participants comprised adults, 21 years of age or older with insomnia and stage I/II non-small cell lung cancer, more than 6 weeks from surgery and living in Western NY. Methods: Participants (n = 40) were randomly assigned to an experimental (BBTI) or attention control condition (Healthy Eating Program). Thirty participants completed the study. Results: Participants were 66 years of age (± 7.6; range 53-82), 40% (n = 16) male, 87.5% (n = 35) Caucasian, 50% (n = 20) married, BMI 27.7 (± 5.8), and 12% (n = 5) never smokers. Baseline sleep diary sleep efficiency, ISI and other baseline covariates were balanced between the groups. Sleep efficiency improved ≥85% in BBTI group (p = .02), but not in HEP control group (p = 1.00). Mean ISI for BBTI and attention control were 6.40 ± 4.98 and 14.10 ± 4.48 (p = .001) respectively. In addition, BBTI group mean total FACT-L score improved by 6.66 points from baseline while HEP group score worsened (p = .049). Conclusions: BBTI is a practical, evidence-based, clinically relevant intervention that improved sleep and quality of life in lung cancer survivors with insomnia. Additional research to evaluate efficacy, duration, and implementation strategies are essential.

Efficacy of Precise Foot Massage Therapy on Pain and Anxiety Following Cardiac Surgery: Pilot Study.

BACKGROUND: Pain is the most pervasive distressing symptom following cardiac surgery. Forty percent of postoperative cardiac patients report inadequate pain management. Undertreated acute pain results in increased anxiety, delayed wound healing, and increased chance of persistent chronic pain. Foot massage is a safe, visible complementary approach to manage acute pain following surgery. AIM: The aim of this study is to evaluate the efficacy of integrating foot massage therapy for managing postcardiac pain. METHOD: A randomized placebo controlled single blinded trial comparing foot massage to placebo was conducted at a large hospital in Saudi Arabia. Thirty-one patients who had undergone cardiac surgery (16 in experimental and 15 in placebo group) participated in the study. Ten-minute foot massage was delivered to the experimental group by a nurse researcher, twice during one day, within 30 minutes after receiving an opioid pain medication. RESULTS: The findings of this study indicate that foot massage significantly (p < .05) decreases pain intensity and anxiety in patients who have undergone cardiac surgery compared with a placebo control group. CONCLUSION: Providing non-pharmacologic interventions for pain is the responsibility of the nursing staff. Foot massage is within the scope of nursing practice and is a safe and effective manner of improving patient care. Foot massage in conjunction with pharmacological interventions is effective in improving pain and anxiety. Future studies should consider focusing on frequency, dose, feasibility, acceptability, and participants' satisfaction.

Identifying Patients Experiencing Opioid-Induced Respiratory Depression During Recovery From Anesthesia: The Application of Electronic Monitoring Devices.

BACKGROUND: Postsurgical patients experiencing opioid-related adverse drug events have 55% longer hospital stays, 47% higher costs associated with their care, 36% increased risk of 30-day readmission, and 3.4 times higher risk of inpatient mortality compared to those with no opioid-related adverse drug events. Most of the adverse events are preventable. GENERAL AIM: This study explored three types of electronic monitoring devices (pulse oximetry, capnography, and minute ventilation [MV]) to determine which were more effective at identifying the patient experiencing respiratory compromise and, further, to determine whether algorithms could be developed from the electronic monitoring data to aid in earlier detection of respiratory depression. MATERIALS AND METHODS: A study was performed in the postanesthesia care unit (PACU) in an inner city. Sixty patients were recruited in the preoperative admissions department on the day of their surgery. Forty-eight of the 60 patients wore three types of electronic monitoring devices while they were recovering from back, neck, hip, or knee surgery. Machine learning models were used for the analysis. RESULTS: Twenty-four of the 48 patients exhibited sustained signs of opioid-induced respiratory depression (OIRD). Although the SpO2 values did not change, end-tidal CO2 levels increased, and MV decreased, representing hypoventilation. A machine learning model was able to predict an OIRD event 10 min before the actual event occurred with 80% accuracy. LINKING EVIDENCE TO ACTION: Electronic monitoring devices are currently used as a tool to assess respiratory status using thresholds to distinguish when respiratory depression has occurred. This study introduces a potential paradigm shift from a reactive approach to a proactive approach that would identify a patient at high risk for OIRD. Capnography and MV were found to be effective tools in detecting respiratory compromise in the PACU.

The common meanings and shared practices of sedation assessment in the context of managing patients with an opioid: A phenomenological study.

AIMS AND OBJECTIVES: To examine the common meanings of opioid-induced sedation and shared practices in the context of post-operative pain management in expert Post-Anesthesia Care Unit nurses during patient's pain management with opioids. BACKGROUND: Within the clinical setting, linear pain and sedation scales are not enough to support clinical judgement with acute pain management. Because sedation measurement rests along a fluctuating continuum, it is possible for a patient to be sedated and then shift to increasing alertness, and then to drift back to a sedated state. This potential for acute clinical transition can be challenging to nurses of all levels, even for expert nurses. DESIGN: Interpretive phenomenology. METHODS: Twenty expert Post-Anesthetic Care Unit nurses, with more than 7 years of nursing experience, participated in qualitative interviews regarding their lived experiences. Interviews were analysed using a modified seven-stage process for interpretation by Diekelmann, Allen and Tanner. The manuscript was developed utilising the COREQ guidelines for reporting qualitative studies. RESULTS: Four themes identified through the participant's stories were recognising every patient is different, engaging in iterative knowing, walking a fine line, and looking beyond and anticipating. This study identified a constitutive pattern of interpreting sedation by integrating practical understanding and anticipating beyond. CONCLUSIONS: This study indicates a deeper complexity in the way opioid-induced sedation is assessed and balanced with pain management by nurses in the Post-Anesthetic Care Unit. RELEVANCE TO PRACTICE: Nurses in the study adapted their practices around pain management with opioids, in response to their patient's level of sedation; incorporating practices such as giving small, incremental doses and changing the drug. Nurses valued the importance of having "eyeballs on everybody" and being ready to meet the needs of their patient. They appreciate the time to watch and wait for their patient to respond, to better judge the result of their interventions.

Validation of fitness tracker for sleep measures in women with asthma.

OBJECTIVE: Nighttime wakening with asthma symptoms is a key to assessment and therapy decisions, with no gold standard objective measure. The study aims were to (1) determine the feasibility, (2) explore equivalence, and (3) test concordance of a consumer-based accelerometer with standard actigraphy for measurement of sleep patterns in women with asthma as an adjunct to self-report. METHODS: Panel study design of women with poorly controlled asthma from a university-affiliated primary care clinic system was used. We assessed sensitivity and specificity, equivalence and concordance of sleep time, sleep efficiency, and wake counts between the consumer-based accelerometer Fitbit Charge™ and Actigraph wGT3X+. We linked data between devices for comparison both automatically by 24-hour period and manually by sleep segment. RESULTS: Analysis included 424 938 minutes, 738 nights, and 833 unique sleep segments from 47 women. The fitness tracker demonstrated 97% sensitivity and 40% specificity to identify sleep. Between device equivalence for total sleep time (15 and 42-minute threshold) was demonstrated by sleep segment. Concordance improved for wake counts and sleep efficiency when adjusting for a linear trend. CONCLUSIONS: There were important differences in total sleep time, efficiency, and wake count measures when comparing individual sleep segments versus 24-hour measures of sleep. Fitbit overestimates sleep efficiency and underestimates wake counts in this population compared to actigraphy. Low levels of systematic bias indicate the potential for raw measurements from the devices to achieve equivalence and concordance with additional processing, algorithm modification, and modeling. Fitness trackers offer an accessible and inexpensive method to quantify sleep patterns in the home environment as an adjunct to subjective reports, and require further informatics development.