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

Noninvasive monitoring technologies to identify discomfort and distressing symptoms in persons with limited communication at the end of life: a scoping review

BACKGROUND

Discomfort and distressing symptoms are common at the end of life, while people in this stage are often no longer able to express themselves. Technologies may aid clinicians in detecting and treating these symptoms to improve end-of-life care. This review provides an overview of noninvasive monitoring technologies that may be applied to persons with limited communication at the end of life to identify discomfort.

METHODS

A systematic search was performed in nine databases, and experts were consulted. Manuscripts were included if they were written in English, Dutch, German, French, Japanese or Chinese, if the monitoring technology measured discomfort or distressing symptoms, was noninvasive, could be continuously administered for 4 hours and was potentially applicable for bed-ridden people. The screening was performed by two researchers independently. Information about the technology, its clinimetrics (validity, reliability, sensitivity, specificity, responsiveness), acceptability, and feasibility were extracted.

RESULTS

Of the 3,414 identified manuscripts, 229 met the eligibility criteria. A variety of monitoring technologies were identified, including actigraphy, brain activity monitoring, electrocardiography, electrodermal activity monitoring, surface electromyography, incontinence sensors, multimodal systems, and noncontact monitoring systems. The main indicators of discomfort monitored by these technologies were sleep, level of consciousness, risk of pressure ulcers, urinary incontinence, agitation, and pain. For the end-of-life phase, brain activity monitors could be helpful and acceptable to monitor the level of consciousness during palliative sedation. However, no manuscripts have reported on the clinimetrics, feasibility, and acceptability of the other technologies for the end-of-life phase.

CONCLUSIONS

Noninvasive monitoring technologies are available to measure common symptoms at the end of life. Future research should evaluate the quality of evidence provided by existing studies and investigate the feasibility, acceptability, and usefulness of these technologies in the end-of-life setting. Guidelines for studies on healthcare technologies should be better implemented and further developed.

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

BACKGROUND

The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation.

METHODS

A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies.

RESULTS

A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications.

CONCLUSIONS

The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice.

EDITOR’S PERSPECTIVE

Machine learning-based risk prediction of hypoxemia for outpatients undergoing sedation colonoscopy: a practical clinical tool

OBJECTIVES

Hypoxemia as a common complication in colonoscopy under sedation and may result in serious consequences. Unfortunately, a hypoxemia prediction model for outpatient colonoscopy has not been developed. Consequently, the objective of our study was to develop a practical and accurate model to predict the risk of hypoxemia in outpatient colonoscopy under sedation.

METHODS

In this study, we included patients who received colonoscopy with anesthesia in Nanjing First Hospital from July to September 2021. Risk factors were selected through the least absolute shrinkage and selection operator (LASSO). Prediction models based on logistic regression (LR), random forest classifier (RFC), extreme gradient boosting (XGBoost), support vector machine (SVM), and stacking classifier (SCLF) model were implemented and assessed by standard metrics such as the area under the receiver operating characteristic curve (AUROC), sensitivity and specificity. Then choose the best model to develop an online tool for clinical use.

RESULTS

We ultimately included 839 patients. After LASSO, body mass index (BMI) (coefficient = 0.36), obstructive sleep apnea-hypopnea syndrome (OSAHS) (coefficient = 1.32), basal oxygen saturation (coefficient = -0.14), and remifentanil dosage (coefficient = 0.04) were independent risk factors for hypoxemia. The XGBoost model with an AUROC of 0.913 showed the best performance among the five models.

CONCLUSION

Our study selected the XGBoost as the first model especially for colonoscopy, with over 95% accuracy and excellent specificity. The XGBoost includes four variables that can be quickly obtained. Moreover, an online prediction practical tool has been provided, which helps screen high-risk outpatients with hypoxemia swiftly and conveniently.

Improvement in Nurses' Knowledge of Subcutaneous Catheter Use for Pain Management

BACKGROUND

Nurses often have insufficient knowledge of subcutaneous catheter use for pain management. This quality improvement project evaluated implementation of an evidence-based subcutaneous catheter nursing policy with education to improve pain management for hospitalized patients.

METHOD

A convenience sample of nurses (N = 515) completed a posttest after online training on effective subcutaneous pain management. Patient pain ratings were assessed to evaluate whether they changed after nurses' training.

RESULTS

Posttest scores showed the online learning module effectively contributed to nurses' knowledge of subcutaneous catheter pain management. A statistically significant reduction occurred in patient pain ratings (p < .001) postintervention. The number of patients experiencing moderate or severe pain decreased by 58%, for a significant reduction in pain.

CONCLUSION

An online learning module was successful in educating nurses on pain medication administration through an indwelling subcutaneous catheter. [J Contin Educ Nurs. 2024;55(1):13-20.].

Identifying Early Opioid-Induced Respiratory Depression and Rapid Response Team Activation

BACKGROUND

Opioids can cause respiratory depression, which could lead to patient harm. The project site noted a gap in identifying and monitoring postsurgical thoracic patients at risk for opioid-induced respiratory depression (OIRD), so an evidence-based solution was sought.

AIMS

The purpose of this quality improvement project was to determine if translating the research by Khanna et al. (2020) on implementing the prediction of opioid-induced respiratory depression in patients monitored by capnography (PRODIGY) risk prediction tool would affect rapid response team (RRT) activation among postsurgical thoracic patients in a cardiovascular and thoracic care unit (CVTCU) at John Muir Medical Center, Concord Campus over four weeks.

METHODS

The four-week quantitative quasi-experimental project had a total sample size of 29 participants. Pulse oximetry was used to identify OIRD in the comparison group (n = 12). The implementation group consisted of patients identified as at-risk for OIRD by the PRODIGY risk prediction tool and were monitored with pulse oximetry and capnography (n = 17).

RESULTS

A χ2 analysis showed χ2 (1, n = 29) = .73, p = .393 for activation of the RRT using the PRODIGY risk prediction tool, which was not statistically significant. However, clinical significance was supported by a 5.9% increase in RRT activations.

CONCLUSION

Based on the results, implementing the PRODIGY risk prediction tool and capnography monitoring on at-risk patients may affect RRT activation in this population.

Nocturnal Hypoventilation in the Patients Submitted to Thoracic Surgery

Introduction

Nocturnal hypoventilation may occur due to obesity, concomitant chronic obstructive pulmonary disease (COPD), obstructive sleep apnea, and/or the use of narcotic analgesics. The aim of the study was to evaluate the risk and severity of nocturnal hypoventilation as assessed by transcutaneous continuous capnography in the patients submitted to thoracic surgery.

Materials and Methods

The material of the study consisted of 45 obese (BMI 34.8 ± 3.7 kg/m2) and 23 nonobese (25.5 ± 3.6 kg/m2) patients, who underwent thoracic surgery because of malignant (57 patients) and nonmalignant tumors. All the patients received routine analgesic treatment after surgery including intravenous morphine sulfate. Overnight transcutaneous measurements of CO2 partial pressure (tcpCO2) were performed before and after surgery in search of nocturnal hypoventilation, i.e., the periods lasting at least 10 minutes with tcpCO2 above 55 mmHg.

Results

Nocturnal hypoventilation during the first night after thoracic surgery was detected in 10 patients (15%), all obese, three of them with COPD, four with high suspicion of moderate-to-severe OSA syndrome, and one with chronic daytime hypercapnia. In the patients with nocturnal hypoventilation, the mean tcpCO2 was 53.4 ± 6.1 mmHg, maximal tcpCO2 was 59.9 ± 8.4 mmHg, and minimal tcpCO2 was 46.4 ± 6.7 mmHg during the first night after surgery. In these patients, there were higher values of minimal, mean, and maximal tcpCO2 in the preoperative period. Nocturnal hypoventilation in the postoperative period did not influence the duration of hospitalization. Among 12 patients with primary lung cancer who died during the first two years of observation, there were 11 patients without nocturnal hypoventilation in the early postoperative period.

Conclusion

Nocturnal hypoventilation may occur in the patients after thoracic surgery, especially in obese patients with bronchial obstruction, obstructive sleep apnea, or chronic daytime hypercapnia, and does not influence the duration of hospitalization.

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.

Machine learning in perioperative medicine: a systematic review

Background

Risk stratification plays a central role in anesthetic evaluation. The use of Big Data and machine learning (ML) offers considerable advantages for collection and evaluation of large amounts of complex health-care data. We conducted a systematic review to understand the role of ML in the development of predictive post-surgical outcome models and risk stratification.

Methods

Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, we selected the period of the research for studies from 1 January 2015 up to 30 March 2021. A systematic search in Scopus, CINAHL, the Cochrane Library, PubMed, and MeSH databases was performed; the strings of research included different combinations of keywords: “risk prediction,” “surgery,” “machine learning,” “intensive care unit (ICU),” and “anesthesia” “perioperative.” We identified 36 eligible studies. This study evaluates the quality of reporting of prediction models using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist.

Results

The most considered outcomes were mortality risk, systemic complications (pulmonary, cardiovascular, acute kidney injury (AKI), etc.), ICU admission, anesthesiologic risk and prolonged length of hospital stay. Not all the study completely followed the TRIPOD checklist, but the quality was overall acceptable with 75% of studies (Rev #2, comm #minor issue) showing an adherence rate to TRIPOD more than 60%. The most frequently used algorithms were gradient boosting (n = 13), random forest (n = 10), logistic regression (LR; n = 7), artificial neural networks (ANNs; n = 6), and support vector machines (SVM; n = 6). Models with best performance were random forest and gradient boosting, with AUC > 0.90.

Conclusions

The application of ML in medicine appears to have a great potential. From our analysis, depending on the input features considered and on the specific prediction task, ML algorithms seem effective in outcomes prediction more accurately than validated prognostic scores and traditional statistics. Thus, our review encourages the healthcare domain and artificial intelligence (AI) developers to adopt an interdisciplinary and systemic approach to evaluate the overall impact of AI on perioperative risk assessment and on further health care settings as well.

The Effectiveness of Capnography Versus Pulse Oximetry in Detecting Respiratory Adverse Events in the Postanesthesia Care Unit (PACU): A Narrative Review and Synthesis

PURPOSE

The objective of this review was to evaluate the effectiveness of capnography monitoring versus standard monitoring of pulse oximetry in detecting respiratory adverse events in nonintubated pediatric and adult postanesthesia care unit (PACU) patients.

DESIGN

Experimental, quasi-experimental, and observational studies examining pulse oximetry and capnography in adult and pediatric patients in the PACU were included in this systematic review.

METHODS

An initial search of MEDLINE and CINAHL, PubMed, Web of Science, Prospero, Google Scholar, and Cochrane was undertaken to identify articles on the topic. The text words contained in the titles and abstracts of relevant articles, and the index terms used to describe the articles were used to develop a full search strategy in July 2019. Reference lists of studies included at critical appraisal stage were hand-searched. Studies published in English from 1978 onward were included.

FINDINGS

Meta-analysis was not possible due to variation in outcome measurements; therefore, results are presented in narrative form. Four studies were included in the review: 1 randomized controlled trial (RCT) and 3 observational cross-sectional studies. The RCT was considered of moderate to high quality, and the observational cross-sectional studies were of high quality. The main findings of this review suggest that there is limited high-quality evidence that capnography improves detection of respiratory adverse events in the PACU versus pulse oximetry.

CONCLUSIONS

The lack of RCTs and varied outcomes measures in the 4 studies reviewed meant that meta-analysis was not possible. Early detection of respiratory adverse events afforded by the addition of PETCO₂ to SpO₂ in the PACU was seen in these studies. More research is needed to determine if widespread implementation of capnography in addition to pulse oximetry would reduce severity of respiratory related adverse events in the PACU through more timely identification.

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.

Evaluation of Noninvasive Respiratory Volume Monitoring in the PACU of a Low Resource Kenyan Hospital

This research aims to evaluate the use of the noninvasive respiratory volume monitor (RVM) compared to the standard of care (SOC) in the Post-Anesthesia Care Unit (PACU) of Kijabe Hospital, Kenya. The RVM provides real-time measurements for quantitative monitoring of non-intubated patients. Our evaluation was focused on the incidence of postoperative opioid-induced respiratory depression (OIRD). The RVM cohort (N = 50) received quantitative OIRD assessment via the RVM, which included respiratory rate, minute ventilation, and tidal volume. The SOC cohort (N = 46) received qualitative OIRD assessment via patient monitoring with oxygenation measurements (SpO₂) and physical examination. All diagnosed cases of OIRD were in the RVM cohort (9/50). In the RVM cohort, participants stayed longer in the PACU and required more frequent airway maneuvers and supplemental oxygen, compared to SOC (all p < 0.05). The SOC cohort may have had fewer diagnoses of OIRD due to the challenging task of distinguishing hypoventilation versus OIRD in the absence of quantitative data. To account for the higher OIRD risk with general anesthesia (GA), a subgroup analysis was performed for only participants who underwent GA, which showed similar results. The use of RVM for respiratory monitoring of OIRD may allow for more proactive care.

Opioid-Induced In-Hospital Deaths: A 10-Year Review of Australian Coroners' Cases Exploring Similarities and Lessons Learnt

Although opioids are the cornerstone of moderate-to-severe acute pain management they are appropriately recognised as high-risk medicines. Patient and health service delivery factors can contribute to an increased risk of death associated with excessive sedation and respiratory impairment. Despite increasing awareness of opioid-induced ventilation impairment (OIVI), no reliable method consistently identifies individual characteristics and factors that increase mortality risk due to respiratory depression events. This study assessed similarities in available coronial inquest cases reviewing opioid-related deaths in Australian hospitals from 2010 to 2020. Cases included for review were in-hospital deaths that identified patient factors, clinical errors and service delivery factors that resulted in opioid therapy contributing to the death. Of the 2879 coroner’s inquest reports reviewed across six Australian states, 15 met the criteria for inclusion. Coroner’s inquest reports were analysed qualitatively to identify common themes, contributing patient and service delivery factors and recommendations. Descriptive statistics were used to summarise shared features between cases. All cases included had at least one, but often more, service delivery factors contributing to the death, including insufficient observations, prescribing/administration error, poor escalation and reduced communication. Wider awareness of the individual characteristics that pose increased risk of OIVI, greater uptake of formal, evidence-based pain management guidelines and improved documentation and observations may reduce OIVI mortality rates.

Postoperative respiratory state assessment using the Integrated Pulmonary Index (IPI) and resultant nurse interventions in the post-anesthesia care unit: a randomized controlled trial

Although postoperative adverse respiratory events, defined by a decrease in respiratory rate (RR) and/or a drop in oxygen saturation (SpO₂), occur frequently, many of such events are missed. The purpose of the current study was to assess whether continuous monitoring of the integrated pulmonary index (IPI), a composite index of SpO₂, RR, end-tidal PCO₂ and heart rate, alters our ability to identify and prevent adverse respiratory events in postoperative patients. Eighty postoperative patients were subjected to continuous respiratory monitoring during the first postoperative night using RR and pulse oximetry and the IPI monitor. Patients were randomized to receive intervention based on standard care (observational) or based on the IPI monitor (interventional). Nurses were asked to respond to adverse respiratory events with an intervention to improve the patient’s respiratory condition. There was no difference in the number of patients that experienced at least one adverse respiratory event: 21 and 16 in observational and interventional group, respectively (p = 0.218). Compared to the observational group, the use of the IPI monitor led to an increase in the number of interventions performed by nurses to improve the respiratory status of the patient (average 13 versus 39 interventions, p < 0.001). This difference was associated with a significant reduction of the median number of events per patient (2.5 versus 6, p < 0.05) and a shorter median duration of events (62 s versus 75 s, p < 0.001). The use of the IPI monitor in postoperative patients did not result in a reduction of the number of patients experiencing adverse respiratory events, compared to standard clinical care. However, it did lead to an increased number of nurse interventions and a decreased number and duration of respiratory events in patients that experienced postoperative adverse respiratory events.