Determining pre-procedure fasting alert time using procedural and scheduling data.

Before a medical procedure requiring anesthesia, patients are required to not eat or drink non-clear fluids for 6 h and not drink clear fluids for 2 h. Fasting durations in standard practice far exceed these minimum thresholds due to uncertainties in procedure start time. The aim of this retrospective, observational study was to compare fasting durations arising from standard practice with different approaches for calculating the timepoint at which patients are instructed to stop eating and drinking. Scheduling data for procedures performed in the cardiac catheterization laboratory of an academic hospital in Canada (January 2020 to April 2022) were used. Four approaches utilizing machine learning (ML) and simulation were used to predict procedure start times and calculate when patients should be instructed to start fasting. Median fasting duration for standard practice was 10.08 h (IQR 3.5) for both food and clear fluids intake. The best performing alternative approach, using tree-based ML models to predict procedure start time, reduced median fasting from food/non-clear fluids to 7.7 h (IQR 2) and clear liquids fasting to 3.7 h (IQR 2.4). 97.3% met the minimum fasting duration requirements (95% CI 96.9% to 97.6%). Further studies are required to determine the effectiveness of operationalizing this approach as an automated fasting alert system.

Implementation of a Blended-Learning Perioperative Nursing Education Program in Canada.

Governmental COVID-19 mandates in Ontario, Canada, resulted in a backlog of perioperative procedures. Organization leaders were required to expand services after the pandemic; however, the ongoing nursing shortage and college-based structure of perioperative education programs complicated their response. In 2021, we developed an in-house perioperative education program using a blended-learning theory comprising online modules and videos, skills laboratory sessions, and clinical placement experiences. Nurses were required to apply for the program and remain employed at the facility for two years. Program evaluations showed that the novice nurses felt confident when beginning clinical experiences and preceptors believed the nurses were prepared for practice. Sixteen of 19 participants successfully completed the program, which helped resolve the staffing shortage. Novice nurses may benefit from a shadowing experience before applying for this type of program. Leaders in nonperioperative specialties should consider an in-house education program to help meet staffing needs in their areas.

Automating sedation state assessments using natural language processing.

INTRODUCTION: Common goals for procedural sedation are to control pain and ensure the patient is not moving to an extent that is impeding safe progress or completion of the procedure. Clinicians perform regular assessments of the adequacy of procedural sedation in accordance with these goals to inform their decision-making around sedation titration and also for documentation of the care provided. Natural language processing could be applied to real-time transcriptions of audio recordings made during procedures in order to classify sedation states that involve movement and pain, which could then be integrated into clinical documentation systems. The aim of this study was to determine whether natural language processing algorithms will work with sufficient accuracy to detect sedation states during procedural sedation. DESIGN: A prospective observational study was conducted. METHODS: Audio recordings from consenting participants undergoing elective procedures performed in the interventional radiology suite at a large academic hospital were transcribed using an automated speech recognition model. Sentences of transcribed text were used to train and evaluate several different NLP pipelines for a text classification task. The NLP pipelines we evaluated included a simple Bag-of-Words (BOW) model, an ensemble architecture combining a linear BOW model and a "token-to-vector" (Tok2Vec) component, and a transformer-based architecture using the RoBERTa pre-trained model. RESULTS: A total of 15,936 sentences from transcriptions of 82 procedures was included in the analysis. The RoBERTa model achieved the highest performance among the three models with an area under the ROC curve (AUC-ROC) of 0.97, an F1 score of 0.87, a precision of 0.86, and a recall of 0.89. The Ensemble model showed a similarly high AUC-ROC of 0.96, but lower F1 score of 0.79, precision of 0.83, and recall of 0.77. The BOW approach achieved an AUC-ROC of 0.97 and the F1 score was 0.7, precision was 0.83 and recall was 0.66. CONCLUSION: The transformer-based architecture using the RoBERTa pre-trained model achieved the best classification performance. Further research is required to confirm the that this natural language processing pipeline can accurately perform text classifications with real-time audio data to allow for automated sedation state assessments. CLINICAL RELEVANCE: Automating sedation state assessments using natural language processing pipelines would allow for more timely documentation of the care received by sedated patients, and, at the same time, decrease documentation burden for clinicians. Downstream applications can also be generated from the classifications, including for example real-time visualizations of sedation state, which may facilitate improved communication of the adequacy of the sedation between clinicians, who may be performing supervision remotely. Also, accumulation of sedation state assessments from multiple procedures may reveal insights into the efficacy of particular sedative medications or identify procedures where the current approach for sedation and analgesia is not optimal (i.e. a significant amount of time spent in "pain" or "movement" sedation states).

An Introductory Guide to Artificial Intelligence in Interventional Radiology: Part 1 Foundational Knowledge.

Artificial intelligence (AI) is rapidly evolving and has transformative potential for interventional radiology (IR) clinical practice. However, formal training in AI may be limited for many clinicians and therefore presents a challenge for initial implementation and trust in AI. An understanding of the foundational concepts in AI may help familiarize the interventional radiologist with the field of AI, thus facilitating understanding and participation in the development and deployment of AI. A pragmatic classification system of AI based on the complexity of the model may guide clinicians in the assessment of AI. Finally, the current state of AI in IR and the patterns of implementation are explored (pre-procedural, intra-procedural, and post-procedural).

Metabolic syndrome and surgical complications: a systematic review and meta-analysis of 13 million individuals.

BACKGROUND: Metabolic syndrome (MetS) is characterised by the presence of at least three of the five following components: insulin resistance, obesity, chronic hypertension, elevated serum triglycerides, and decreased high-density lipoprotein cholesterol concentrations. It is estimated to affect 1 in 3 people around the globe and is reported to affect 46% of surgical patients. For people with MetS who undergo surgery, an emerging body of literature points to significantly poorer postoperative outcomes compared with nonaffected populations. The aim of this study is to review the current evidence on the risks of surgical complications in patients with MetS compared to those without MetS. METHODS: Systematic review and meta-analysis using PRISMA and AMSTAR reporting guidelines. RESULTS: The meta-analysis included 63 studies involving 1 919 347 patients with MetS and 11 248 114 patients without MetS. Compared to individuals without the condition, individuals with MetS were at an increased risk of mortality (OR 1.75 95% CI: 1.36-2.24; P <0.01); all surgical site infection types as well as dehiscence (OR 1.64 95% CI: 1.52-1.77; P <0.01); cardiovascular complications (OR 1.56 95% CI: 1.41-1.73; P <0.01) including myocardial infarction, stroke, cardiac arrest, cardiac arrythmias and deep vein thrombosis; increased length of hospital stay (MD 0.65 95% CI: 0.39-0.9; P <0.01); and hospital readmission (OR 1.55 95% CI: 1.41-1.71; P <0.01). CONCLUSION: MetS is associated with a significantly increased risk of surgical complications including mortality, surgical site infection, cardiovascular complications, increased length of stay, and hospital readmission. Despite these risks and the high prevalence of MetS in surgical populations there is a lack of evidence on interventions for reducing surgical complications in patients with MetS. The authors suggest prioritising interventions across the surgical continuum that include (1) preoperative screening for MetS; (2) surgical prehabilitation; (3) intraoperative monitoring and management; and (4) postoperative rehabilitation and follow-up.

Predicting Blood Pressure After Nitroglycerin Infusion Dose Titration in Critical Care Units: A Multicenter Retrospective Study.

Critical care nurses use physiological indicators, such as blood pressure, to guide their decision-making regarding the titration of nitroglycerin infusions. A retrospective study was conducted to determine the accuracy of systolic blood pressure predictions during nitroglycerin infusions. Data were extracted from the publicly accessible eICU program database. The accuracy of a linear model, least absolute shrinkage and selection operator, ridge regression, and a stacked ensemble model trained using the AutoGluon-Tabular framework were investigated. A persistence model, where the future value in a time series is predicted as equal to its preceding value, was used as the baseline comparison for model accuracy. Internal-external validation was used to examine if heterogeneity among hospitals could contribute to model performance. The sample consisted of 827 patients and 2541 nitroglycerin dose titrations with corresponding systolic blood pressure measurements. The root-mean-square error on the test set for the stacked ensemble model developed using the AutoGluon-Tabular framework was the lowest of all models at 15.3 mm Hg, equating to a 22% improvement against the baseline. Internal-external validation revealed consistent accuracy across hospitals. Further studies are needed to determine the impact of using systolic blood pressure predictions to inform nurses' clinical decision-making regarding nitroglycerin infusion titration in critical care.

Nursing Informatics' Contribution to One Health.

OBJECTIVES: To summarise contemporary knowledge in nursing informatics related to education, practice, governance and research in advancing One Health. METHODS: This descriptive study combined a theoretical and an empirical approach. Published literature on recent advancements and areas of interest in nursing informatics was explored. In addition, empirical data from International Medical Informatics Association (IMIA) Nursing Informatics (NI) society reports were extracted and categorised into key areas regarding needs, established activities, issues under development and items not current. RESULTS: A total of 1,772 references were identified through bibliographic database searches. After screening and assessment for eligibility, 146 articles were included in the review. Three topics were identified for each key area: 1) education: "building basic nursing informatics competence", "interdisciplinary and interprofessional competence" and "supporting educators competence"; 2) practice: "digital nursing and patient care", "evidence for timely issues in practice" and "patient-centred safe care"; 3) governance: "information systems in healthcare", "standardised documentation in clinical context" and "concepts and interoperability", and 4) research: "informatics literacy and competence", "leadership and management", and "electronic documentation of care". 17 reports from society members were included. The data showed overlap with the literature, but also highlighted needs for further work, including more strategies, methods and competence in nursing informatics to support One Health. CONCLUSIONS: Considering the results of this study, from the literature nursing informatics would appear to have a significant contribution to make to One Health across settings. Future work is needed for international guidelines on roles and policies as well as knowledge sharing.

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.

Integrated Pulmonary Index during nurse-administered procedural sedation: Study protocol for a cluster-randomized trial.

AIM: To determine if smart alarm-guided treatment of respiratory depression using the Integrated Pulmonary Index is an effective way to implement capnography during nurse-administered sedation. DESIGN: Parallel cluster-randomized trial. METHODS: Nurses will be randomized to use capnography with or without the Integrated Pulmonary Index enabled. Capnography alarm performance will be compared between nurses using capnography alone or with the Integrated Pulmonary Index enabled. The target sample size is 400 adult patients scheduled for elective procedures with nurse-administered sedation. The primary outcome is the number of seconds in an alert condition state without an intervention being applied. Secondary outcomes are alarm burden, number of appropriate alarms, number of inappropriate alarms, total duration of alert conditions, choice of alarm settings and adverse sedation events. This study has been funded since April 2021. DISCUSSION: Implementing capnography into practice for respiratory monitoring during nurse-administered sedation is considered a high priority. The Integrated Pulmonary Index shows promise as a strategy to optimize the implementation of capnography for respiratory monitoring during nurse-administered sedation. If it is found in this study that using the Integrated Pulmonary Index improves the nursing management of physiologically abnormal states during nurse-administered sedation, it would provide the high-level evidence needed to support broader use of this 'smart alarm' strategy for respiratory monitoring in practice. IMPACT: With advances in medical technology continuing to expand the indications for minimally invasive surgical techniques, the use of nurse-administered sedation during medical procedures is likely to expand in the future. The findings may be applied to other populations receiving nurse-administered sedation during medical procedures. Results from this study will help translate the usage of smart alarm-guided treatment of respiratory depression during procedural sedation. TRIAL REGISTRATION: NCT05068700.

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.

Accuracy and precision of continuous non-invasive arterial pressure monitoring in critical care: A systematic review and meta-analysis.

OBJECTIVE: To summarize the evidence regarding the accuracy of continuous non-invasive arterial pressure measurements in adult critical care patients. RESEARCH METHODOLOGY: Medline, EMBASE, and CINAHL were searched for studies that included adult critical care patients reporting the agreement between continuous non-invasive and invasive arterial pressure measurements. The studies were selected and assessed for risk of bias using the Revised Quality Assessment of Diagnostic Accuracy Studies tool by two independent reviewers. The Grading of Recommendations, Assessment, Development and Evaluations approach was used. Pooled estimates of the mean bias and limits of agreement with outer 95% confidence intervals (termed population limits of agreement) were calculated. RESULTS: Population limits of agreement for systolic blood pressure were wide, spanning from -36.13 mmHg to 28.28 mmHg (18 studies; 785 participants). Accuracy of diastolic blood pressure measurements was highly inconsistent across studies, resulting in imprecise estimates for the population limits of agreement. Population limits of agreement for mean arterial pressure spanned from -39.96 mmHg to 44.36 mmHg (17 studies; 765 participants). The evidence was rated as very low-quality due to very serious concerns about heterogeneity and imprecision. CONCLUSION: Substantial differences in blood pressure were identified between measurements taken from continuous non-invasive and invasive monitoring devices. Clinicians should consider this broad range of uncertainty if using these devices to inform clinical decision-making in critical care.

Midazolam for sedation before procedures in adults and children: a systematic review update.

BACKGROUND: Midazolam is used for sedation before diagnostic and therapeutic medical procedures by several routes including oral, intravenous, intranasal and intramuscular. This is an update of a Cochrane review published in 2016, which aimed to determine the evidence on the effectiveness of midazolam for sedation when administered before a diagnostic or therapeutic procedure in adults and children. METHODS: We searched CENTRAL, MEDLINE, Embase and two trials registers up to May 2020 together with reference checking to identify additional studies. We imposed no language restrictions. Randomized controlled trials of midazolam in comparison with placebo or other medications used for sedation were included. Two authors independently extracted data and assessed risk of bias for each included study. RESULTS: Eight new trials were included in this update, which resulted in changed conclusions for the intravenous midazolam versus placebo, oral midazolam versus chloral hydrate and oral midazolam versus placebo comparisons. Effect estimates for all outcomes within the intravenous midazolam versus placebo (7 trials; 633 adults and 32 children) are uncertain due to concerns about imprecision and risk of bias. Midazolam resulted in a higher level of sedation than placebo (mean difference (MD) 1.05; 95% confidence interval (95% CI) 0.69 to 1.41; 1 study; 100 adults). There was no difference in anxiety (RR 0.43, 95% CI 0.09 to 1.99; I2 = 75%; 2 studies; 123 adults). Risk of difficulty performing procedures was lower in the midazolam group (RR 0.5; 95% CI 0.29 to 0.86; I2 = 45%; 3 studies; 191 adults and 32 children). There was no difference in discomfort (RR 0.51; 95% CI 0.25 to 1.04; I2 = 0%; 2 studies; 190 adults). Five trials with 336 children were included in the oral midazolam versus chloral hydrate comparison. Midazolam was less likely to result in moderate sedation (RR 0.30, 95% CI 0.11 to 0.82; I2 = 64%; 2 studies, 228 participants). This effect estimate is highly uncertain due to concerns about the risk of bias, imprecision and inconsistency. There was no difference in ratings of anxiety (SMD - 0.26; 95% CI - 0.75 to 0.23; I2 = 0%; 2 studies; 68 participants). Midazolam increased risk of incomplete procedures (RR 4.01; 95% CI 1.92 to 8.40; I2 = 0%; 4 studies, 268 participants). This effect estimate is uncertain due to concerns about the risk of bias. There were four trials with 359 adults and 77 children included in the oral midazolam versus placebo comparison. Midazolam reduced ratings of anxiety (SMD - 1.01; 95% CI - 1.86 to - 0.16; I2 = 92%; 4 studies; 436 participants). It is unclear if midazolam has an effect on difficulty performing procedures. Meta-analysis was not performed because there was only one incomplete procedure in the midazolam group in one of the trials. Midazolam reduced pain in one study with 99 adults (MD - 2; 95% CI - 2.5 to - 1.6; moderate quality). The effect estimate is uncertain due to concerns about the risk of bias. CONCLUSION: The additional evidence arising from inclusion of new studies in this updated review has not produced sufficient high-quality evidence to determine whether midazolam produces more effective sedation than other medications or placebo in any specific population included in this review. For adults, there was low-quality evidence that intravenous midazolam did not reduce the risk of anxiety or discomfort/pain in comparison to placebo, but the sedation level was higher. By combining results from adults and children, there was low-quality evidence of a large reduction in the risk of procedures being difficult to perform with midazolam in comparison to placebo. The effect estimates for this comparison are uncertain because there was concern about risk of bias and imprecision. There is moderate-quality evidence suggesting that oral midazolam produces less-effective sedation than chloral hydrate for completion of procedures for children undergoing non-invasive diagnostic procedures. Ratings of anxiety were not different between oral midazolam and chloral hydrate. The extent to which giving oral midazolam to adults or children decreases anxiety during procedures compared with placebo is uncertain due to concerns about risk of bias and imprecision. There was moderate-quality evidence from one study that oral midazolam reduced the severity of discomfort/pain for adults during a brief diagnostic procedure in comparison with placebo.

Development and validation of the nursing confidence in managing sedation complications scale.

AIM: To develop the Nursing Confidence in Managing Sedation Complications Scale. DESIGN: A multi-phased approach was used. METHODS: An initial bank of items was created based on the authors' experience and clinical practice guidelines. An expert panel assessed content validity. Exploratory factor analysis was used for item reduction and regression was used to explore construct validity. Responsiveness was evaluated using a pre-test post-test design. RESULTS: Criteria for content validity was met for 34 items. An 18-item, three-factor solution was identified from exploratory factor analysis performed using Nursing Confidence in Managing Sedation Complications Scale scores from 228 nurses. Subscales accounted for 66% of the variance. Cronbach's alpha for the scale (0.95) and subscales was high (>0.85). There were differences (p < .001) in Nursing Confidence in Managing Sedation Complications Scale scores relative to years of experience and work environment. NC-MSCS scores increased significantly from before to after sedation training (mean difference = 31.8; 95% CI = 24.4-39; N = 31).

High flow nasal oxygen during procedural sedation for cardiac implantable electronic device procedures: A randomised controlled trial.

BACKGROUND: High flow nasal oxygen may better support the vulnerable respiratory state of patients during procedural sedation. OBJECTIVE: The objective of this study was to investigate the effects of high flow nasal oxygen in comparison to facemask oxygen on ventilation during cardiac implantable electronic device procedures performed with procedural sedation. DESIGN: A randomised controlled trial. SETTING: The study was conducted at one academic hospital in Canada. PARTICIPANTS: Adults undergoing elective cardiac implantable electronic device procedures with sedation administered by an anaesthesia assistant, supervised by an anaesthesiologist from August 2019 to March 2020. INTERVENTIONS: Participants were randomised 1 : 1 to facemask (≥ 8 l ·â€Šmin-1) or high flow nasal oxygen (50 l ·â€Šmin-1 and a 50 : 50 oxygen to air ratio). MAIN OUTCOME MEASURES: The primary outcome was peak transcutaneous carbon dioxide. Outcomes were analysed using Bayesian statistics. RESULTS: The 129 participants who were randomised and received sedation were included. The difference in peak transcutaneous carbon dioxide was 0.0 kPa (95% CI -0.17 to 0.18). Minor adverse sedation events were 6.4 times more likely to occur in the high flow nasal oxygen group. This estimate is imprecise (95% CI 1.34 to 42.99). The odds ratio for oxygen desaturation for the high flow nasal oxygen group compared with the facemask group was 1.2 (95% CI 0.37 to 3.75). The difference in satisfaction with sedation scores between groups was 0.0 (95% CI -0.33 to 0.23). CONCLUSIONS: Ventilation, as measured by TcCO2, is highly unlikely to differ by a clinically important amount between high flow nasal oxygen at 50 l min-1 or facemask oxygen at 8 l min-1. Further research with a larger sample size would be required to determine the optimal oxygen:air ratio when using high flow nasal oxygen during cardiac implantable electronic device procedures performed with sedation. TRIAL REGISTRATION NUMBER: NCT03858257.

Accuracy and precision of zero-heat-flux temperature measurements with the 3M™ Bair Hugger™ Temperature Monitoring System: a systematic review and meta-analysis.

Zero-heat-flux thermometers provide clinicians with the ability to continuously and non-invasively monitor body temperature. These devices are increasingly being used to substitute for more invasive core temperature measurements during surgery and in critical care. The aim of this review was to determine the accuracy and precision of zero-heat-flux temperature measurements from the 3M™ Bair Hugger™ Temperature Monitoring System. Medline and EMBASE were searched for studies that reported on a measurement of core or peripheral temperature that coincided with a measurement from the zero-heat-flux device. Study selection and quality assessment was performed independently using the Revised Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2). The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach was used to summarize the strength of the evidence. Pooled estimates of the mean bias and limits of agreement with outer 95% confidence intervals (population limits of agreement) were calculated. Sixteen studies were included. The primary meta-analysis of zero-heat-flux versus core temperature consisted of 22 comparisons from 16 individual studies. Data from 952 participants with 314,137 paired measurements were included. The pooled estimate for the mean bias was 0.03 °C. Population limits of agreement, which take into consideration the between-study heterogeneity and sampling error, were wide, spanning from - 0.93 to 0.98 °C. The GRADE evidence quality rating was downgraded to moderate due to concerns about study limitations. Population limits of agreement for the sensitivity analysis restricted to studies rated as having low risk of bias across all the domains of the QUADAS-2 were similar to the primary analysis. The range of uncertainty in the accuracy of a thermometer should be taken into account when using this device to inform clinical decision-making. Clinicians should therefore consider the potential that a temperature measurement from a 3M™ Bair Hugger™ Temperature Monitoring System could be as much as 1 °C higher or lower than core temperature. Use of this device may not be appropriate in situations where a difference in temperature of less than 1 °C is important to detect.

Effectiveness of nurse-led clinics in the early discharge period after percutaneous coronary intervention: A systematic review.

BACKGROUND: Readmission after percutaneous coronary intervention is common in the early postdischarge period, often linked to limited opportunity for education and preparation for self-care. Attending a nurse-led clinic within 30 d after discharge has the potential to enhance health outcomes. OBJECTIVE: The aim of the study was to synthesise the available literature on the effectiveness of nurse-led clinics, during early discharge (up to 30 d), for patients who have undergone percutaneous coronary intervention. REVIEW METHOD USED: A systematic review of randomised and quasi-randomised controlled trials was undertaken. DATA SOURCES: The databases included PubMed, OVID, CINAHL, EMBASE, the Cochrane Library, SCOPUS, and ProQuest. REVIEW METHODS: Databases were searched up to November 2018. Two independent reviewers assessed studies using the Cochrane risk-of-bias tool. RESULTS: Of 2970 articles screened, only four studies, representing 244 participants, met the review inclusion criteria. Three of these studies had low to moderate risk of bias, with the other study unclear. Interventions comprised physical assessments and individualised education. Reported outcomes included quality of life, medication adherence, cardiac rehabilitation attendance, and psychological symptoms. Statistical pooling was not feasible owing to heterogeneity across interventions, outcome measures, and study reporting. Small improvements in quality of life and some self-management behaviours were reported, but these changes were not sustained over time. CONCLUSIONS: This review has identified an important gap in the research examining the effectiveness of early postdischarge nurse-led support after percutaneous coronary intervention on outcomes for patients and health services. More robust research with sufficiently powered sample sizes and clearly defined interventions, comparison groups, and outcomes is recommended to determine effectiveness of nurse-led clinics in the early discharge period.

Accuracy of the PHQ-2 Alone and in Combination With the PHQ-9 for Screening to Detect Major Depression: Systematic Review and Meta-analysis.

Importance: The Patient Health Questionnaire depression module (PHQ-9) is a 9-item self-administered instrument used for detecting depression and assessing severity of depression. The Patient Health Questionnaire-2 (PHQ-2) consists of the first 2 items of the PHQ-9 (which assess the frequency of depressed mood and anhedonia) and can be used as a first step to identify patients for evaluation with the full PHQ-9. Objective: To estimate PHQ-2 accuracy alone and combined with the PHQ-9 for detecting major depression. Data Sources: MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, PsycINFO, and Web of Science (January 2000-May 2018). Study Selection: Eligible data sets compared PHQ-2 scores with major depression diagnoses from a validated diagnostic interview. Data Extraction and Synthesis: Individual participant data were synthesized with bivariate random-effects meta-analysis to estimate pooled sensitivity and specificity of the PHQ-2 alone among studies using semistructured, fully structured, or Mini International Neuropsychiatric Interview (MINI) diagnostic interviews separately and in combination with the PHQ-9 vs the PHQ-9 alone for studies that used semistructured interviews. The PHQ-2 score ranges from 0 to 6, and the PHQ-9 score ranges from 0 to 27. Results: Individual participant data were obtained from 100 of 136 eligible studies (44 318 participants; 4572 with major depression [10%]; mean [SD] age, 49 [17] years; 59% female). Among studies that used semistructured interviews, PHQ-2 sensitivity and specificity (95% CI) were 0.91 (0.88-0.94) and 0.67 (0.64-0.71) for cutoff scores of 2 or greater and 0.72 (0.67-0.77) and 0.85 (0.83-0.87) for cutoff scores of 3 or greater. Sensitivity was significantly greater for semistructured vs fully structured interviews. Specificity was not significantly different across the types of interviews. The area under the receiver operating characteristic curve was 0.88 (0.86-0.89) for semistructured interviews, 0.82 (0.81-0.84) for fully structured interviews, and 0.87 (0.85-0.88) for the MINI. There were no significant subgroup differences. For semistructured interviews, sensitivity for PHQ-2 scores of 2 or greater followed by PHQ-9 scores of 10 or greater (0.82 [0.76-0.86]) was not significantly different than PHQ-9 scores of 10 or greater alone (0.86 [0.80-0.90]); specificity for the combination was significantly but minimally higher (0.87 [0.84-0.89] vs 0.85 [0.82-0.87]). The area under the curve was 0.90 (0.89-0.91). The combination was estimated to reduce the number of participants needing to complete the full PHQ-9 by 57% (56%-58%). Conclusions and Relevance: In an individual participant data meta-analysis of studies that compared PHQ scores with major depression diagnoses, the combination of PHQ-2 (with cutoff ≥2) followed by PHQ-9 (with cutoff ≥10) had similar sensitivity but higher specificity compared with PHQ-9 cutoff scores of 10 or greater alone. Further research is needed to understand the clinical and research value of this combined approach to screening.

Avoiding and Managing Error in Interventional Radiology Practice: Tips and Tools.

While there are limited data on error in interventional radiology (IR), the literature so far indicates that many errors in IR are potentially preventable. Yet, understanding the sources for error and implementing effective countermeasures can be challenging. Traditional methods for reducing error such as increased vigilance and new policies may be effective but can also contribute to an "error cycle." A hierarchy of effectiveness for patient safety interventions is outlined, and the characteristics of "high-reliability" organizations in other "high-risk" industries are examined for clues that could be implemented in IR. The evidence behind team error reduction strategies such as checklists is considered along with individual approaches such as "slowing down when you should." However, error in medicine is inevitable, and this article also seeks to outline an evidence-based approach to managing the psychological impact of being involved in medical error as a physician.

Pre-apneic capnography waveform abnormalities during procedural sedation and analgesia.

Capnography monitoring is recommended for use during procedural sedation. This study examined associations between capnography waveform abnormalities and the onset of apnea. Capnography waveforms from a sample of 102 participants undergoing moderate procedural sedation with bolus doses of midazolam and fentanyl were analyzed using a mixed effects Cox model. Patients were at increased risk of apnea (classified as end-tidal carbon dioxide concentration of zero) while demonstrating a capnography waveform abnormality classified as hypopnea (more than 10% increase or decrease from baseline end-tidal carbon dioxide concentration) (Hazard Ratio 2.14; 95% CI 1.75 to 2.62). Risk of apnea was not increased during capnography waveform abnormalities classified as bradypnea (capnography-derived respiratory rate less than 8 breaths/min) (Hazard Ratio 0.64; 95% CI 0.33 to 1.25). These estimates were similar when apneic episodes were defined as only those that lasted more than 20 s duration. Deciphering which capnography waveform abnormalities should promote intervention (and therefore alarms to signal the event to clinicians) from those that do not is an essential step towards successful implementation of this technology into practice. Our results indicate that using information about the history of previous capnography waveform abnormalities may be a promising solution to assist prediction of apneic episodes.