In-hospital resuscitation: opioids and other factors influencing survival

Karamarie Fecho1, Freeman Jackson1, Frances Smith1, Frank J Overdyk21Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina, USA; 2Department of Anesthesia and Perioperative Medicine, Medical University of South Carolina, Charleston, South Carolina, USAPurpose: “Code Blue” is a standard term used to alertt hospital staff that a patient requires resuscitation. This study determined rates of survival from Code Blue events and the role of opioids and other factors on survival.Methods: Data derived from medical records and the Code Blue and Pharmacy databases were analyzed for factors affecting survival.Results: During 2006, rates of survival from the code only and to discharge were 25.9% and 26.4%, respectively, for Code Blue events involving cardiopulmonary resuscitation (CPR; N = 216). Survival rates for events not ultimately requiring CPR (N = 77) were higher, with 32.5% surviving the code only and 62.3% surviving to discharge. For CPR events, rates of survival to discharge correlated inversely with time to chest compressions and defibrillation, precipitating event, need for airway management, location and age. Time of week, witnessing, postoperative status, gender and opioid use did not influence survival rates. For non-CPR events, opioid use was associated with decreased survival. Survival rates were lowest for patients receiving continuous infusions (P < 0.01) or iv boluses of opioids (P < 0.05).Conclusions: One-quarter of patients survive to discharge after a CPR Code Blue event and two-thirds survive to discharge after a non-CPR event. Opioids may influence survival from non-CPR events.Keywords: code blue, survival, opioids, cardiopulmonary resuscitation, cardiac arrest, patient safet

A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring

Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms may facilitate accurate detection of abnormal ventilation, allowing intervention before patient deterioration. The objective of this analysis was to use machine learning (ML) to classify combined waveforms of continuous capnography and pulse oximetry as normal or abnormal. We used data collected during the observational, prospective PRODIGY trial, in which patients receiving parenteral opioids underwent continuous capnography and pulse oximetry monitoring while on the general care floor [<a title="Khanna AK, Bergese SD, Jungquist CR, Morimatsu H, Uezono S, Lee S, Ti LK, Urman RD, McIntyre R, Tornero C, Dahan A, Saager L, Weingarten TN, Wittmann M, Auckley D, Brazzi L, Le Guen M, Soto R, Schramm F, Ayad S, Kaw R, Di Stefano P, Sessler DI, Uribe A, Moll V, Dempsey SJ, Buhre W, Overdyk FJ, Tanios M, Rivas E, Mejia M, Elliott K, Ali A, Fiorda-Diaz J, Carrasco-Moyano R, Mavarez-Martinez A, Gonzalez-Zacarias A, Roeth C, Kim J, Esparza-Gutierrez A, Weiss C, Chen C, Taniguchi A, Mihara Y, Ariyoshi M, Kondo I, Yamakawa K, Suga Y, Ikeda K, Takano K, Kuwabara Y, Carignan N, Rankin J, Egan K, Waters L, Sim MA, Lean LL, Liew QEL, Siu-Chun Law L, Gosnell J, Shrestha S, Okponyia C, Al-Musawi MH, Gonzalez MJP, Neumann C, Guttenthaler V, Männer O, Delis A, Winkler A, Marchand B, Schmal F, Aleskerov F, Nagori M, Shafi M, McPhee G, Newman C, Lopez E, Har SM, Asbahi M, Nordstrom McCaw K, Theunissen M, Smit-Fun V. (2020) Prediction of Opioid-Induced Respiratory Depression on Inpatient Wards Using Continuous Capnography and Oximetry: An International Prospective, Observational Trial. Anesth Analg XXX:1012–1024. https://doi.org/10.1213/ANE.0000000000004788 " href="https://link.springer.com/article/10.1007/s10877-024-01155-0#ref-CR1">1]. Abnormal ventilation segments in the data stream were reviewed by nine experts and inter-rater agreement was assessed. Abnormal segments were defined as the time series 60s before and 30s after an abnormal pattern was detected. Normal segments (90s continuous monitoring) were randomly sampled and filtered to discard sequences with missing values. Five ML models were trained on extracted features and optimized towards an Fβ score with β = 2. The results show a high inter-rater agreement (> 87%), allowing 7,858 sequences (2,944 abnormal) to be used for model development. Data were divided into 80% training and 20% test sequences. The XGBoost model had the highest Fβ score of 0.94 (with β = 2), showcasing an impressive recall of 0.98 against a precision of 0.83. This study presents a promising advancement in respiratory monitoring, focusing on reducing false alarms and enhancing accuracy of alarm systems. Our algorithm reliably distinguishes normal from abnormal waveforms. More research is needed to define patterns to distinguish abnormal ventilation from artifacts. Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

Undergraduate views of critical thinking

Clemson University\u27s Quality Enhanement Plan (QEP) is designed to assist undergraduates in the development of their critical thinking skills. Paul et al. (1997) found that professors indicate that they value critical thinking as important, explicit, and achieved in their students, but faculty also are vague and confusing in their open-ended descriptions of the conceptual and practical components of critical thinking instruction in the classroom. The purpose of the current study was to assess student views of the a typical professor\u27s views and student\u27s own personal views using modified versions of the survey items that Paul et al. (1997) employed. Students (n = 139) completed two eleven-item surveys; one framed a typical professor\u27s view and the other framed the student\u27s personal views of critical thinking. Students do not rate a typical professor as valuing critical thinking as important explicit and achieved in their students

New-onset atrial fibrillation detected by continuous capnography monitoring: a case report

Case series Patients: Male, 75-year-old Male, 72-year-oldFinal Diagnosis: Atrial fibrillationSymptoms: Apnea atrial fibrillationMedication: -Clinical Procedure: -Specialty: AnesthesiologyObjective: Unusual clinical courseBackground: Asymptomatic postoperative atrial fibrillation (AF) may go undetected. As part of a multicenter observational trial designed to develop a risk prediction score for respiratory depression, the respiratory patterns of patients admitted to standard wards were continuously assessed with capnography and pulse oximetry. The monitor measured end-tidal carbon dioxide, respiratory rate, heart rate (HR), and oxyhemoglobin saturation.Case Reports: Two men ages 75 and 72 experienced abrupt and variable postoperative changes in HR consistent with AF with rapid ventricular response, coinciding with an abnormal breathing pattern with apneic episodes. In both cases, the changes were not detected by routine clinical monitoring.Conclusions: Continuous capnography identified respiratory distress in 2 patients who experienced symptoms of AF. Continuous monitoring devices can help health care providers minimize the risk of morbidity and mortality for patients at risk of respiratory depression.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

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

Association between operational indexes and the utilization rate of a general surgery center

This is a prospective study that focused on the dynamics of operating rooms using operational indexes that measure optimization, resistance, overload and utilization of the surgical unit, and also identified the factors most associated with these indexes. A total of 1,908 surgeries were analyzed over a period of two months in 2007. The average rates of utilization, optimization and resistance indexes were 80.41%, 65.35% and 34.65% respectively. The difference between the positive and negative overload index was low (5.42%). Operating room rescheduling and delays were the variables that contributed the most to the increase in these indexes. In the linear regression statistical model, the utilization rate was found to be the first common variable selected in the overload, resistance and optimization indexes. It is essential to work on these operational indexes with a view to obtain satisfactory results in the management of the surgical center, with well-defined work processes and teamwork.Se trata de un estudio prospectivo que analizó la dinámica de las salas quirúrgicas a través de índices operacionales que miden la optimización, resistencia, sobrecarga y ocupación del centro quirúrgico, y también identificó los factores que más se asociaron a esos índices. Fueron analizadas 1.908 cirugías, durante dos meses en el año de .2007. La tasa de ocupación y los índices de optimización y resistencia promedios encontrados fueron 80,41, 65,35 y 34,65%, respectivamente. La diferencia entre el índice de sobrecarga positivo y negativo fue bajo (5,42%). El cambio de sala y el atraso, respectivamente, fueron las variables que más contribuyeron para la elevación de esos índices. En la prueba estadística de regresión linear se observó que la tasa de ocupación fue la primera variable común seleccionada tanto en los índices de sobrecarga, resistencia y optimización. Es fundamental la actuación sobre eses índices operacionales para obtener resultados satisfactorios en la administración del centro quirúrgico, con procesos bien definidos y trabajo en equipo.Estudo prospectivo que analisou a dinâmica das salas cirúrgicas através de índices operacionais que medem a otimização, resistência, sobrecarga e ocupação do centro cirúrgico, e também identificou os fatores que mais se associaram a esses índices. Foram analisadas 1908 cirurgias, durante dois meses de 2007. A taxa de ocupação e os índices de otimização e resistência médios encontrados foram 80,41, 65,35 e 34,65%, respectivamente. A diferença entre o índice de sobrecarga positivo e negativo foi baixo (5,42%). O remanejamento de sala e o atraso, respectivamente, foram as variáveis que mais contribuíram para a elevação desses índices. No teste estatístico de regressão linear observou-se que a taxa de ocupação foi a primeira variável comum selecionada tanto nos índices de sobrecarga, resistência como otimização. É fundamental a atuação sobre esses índices operacionais para se obter resultados satisfatórios no gerenciamento do centro cirúrgico, com processos bem definidos e trabalho em equipe

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

Update of the Scientific Opinion on opium alkaloids in poppy seeds

The CONTAM Panel wishes to thank the hearing experts: Pavel Cihlar, Daniel Doerge and Vaclav Lohr for the support provided to this scientific output. The CONTAM Panel acknowledges all European competent institutions and other stakeholders that provided occurrence data on opium alkaloids in food, and supported the data collection for the Comprehensive European Food Consumption Database. Adopted: 22 March 2018 Reproduction of the images listed below is prohibited and permission must be sought directly from the copyright holder:Figure A.1 in Appendix A: © Elsevier.Peer reviewedPublisher PD