Predictive modeling of perioperative patient deterioration: combining unanticipated ICU admissions and mortality for improved risk prediction

OBJECTIVE

This paper presents a comprehensive analysis of perioperative patient deterioration by developing predictive models that evaluate unanticipated ICU admissions and in-hospital mortality both as distinct and combined outcomes.

MATERIALS AND METHODS

With less than 1% of cases resulting in at least one of these outcomes, we investigated 98 features to identify their role in predicting patient deterioration, using univariate analyses. Additionally, multivariate analyses were performed by employing logistic regression (LR) with LASSO regularization. We also assessed classification models, including non-linear classifiers like Support Vector Machines, Random Forest, and XGBoost.

RESULTS

During evaluation, careful attention was paid to the data imbalance therefore multiple evaluation metrics were used, which are less sensitive to imbalance. These metrics included the area under the receiver operating characteristics, precision-recall and kappa curves, and the precision, sensitivity, kappa, and F1-score. Combining unanticipated ICU admissions and mortality into a single outcome improved predictive performance overall. However, this led to reduced accuracy in predicting individual forms of deterioration, with LR showing the best performance for the combined prediction.

DISCUSSION

The study underscores the significance of specific perioperative features in predicting patient deterioration, especially revealed by univariate analysis. Importantly, interpretable models like logistic regression outperformed complex classifiers, suggesting their practicality. Especially, when combined in an ensemble model for predicting multiple forms of deterioration. These findings were mostly limited by the large imbalance in data as post-operative deterioration is a rare occurrence. Future research should therefore focus on capturing more deterioration events and possibly extending validation to multi-center studies.

CONCLUSIONS

This work demonstrates the potential for accurate prediction of perioperative patient deterioration, highlighting the importance of several perioperative features and the practicality of interpretable models like logistic regression, and ensemble models for the prediction of several outcome types. In future clinical practice these data-driven prediction models might form the basis for post-operative risk stratification by providing an evidence-based assessment of risk.

Implementation of Continuous Capnography Protocol in a Postanesthesia Care Unit for Adult Patients at High-risk of Postoperative Respiratory Depression

PURPOSE

This project aimed to implement a continuous capnography protocol in the postanesthesia care unit (PACU) for postoperative adult patients who are at high risk for respiratory failure.

DESIGN

A preintervention and postintervention quality improvement design with retrospective chart reviews evaluated patient demographics (age, weight, body mass index [BMI], perioperative fluid intake and output, use of intraoperative positive-end expiratory pressure), length of surgery, average length of PACU stay, incidence of respiratory events, and adherence to a PACU capnography protocol.

METHODS

Preimplementation data were collected from retrospective chart reviews over a 3-month period. A continuous capnography protocol was implemented for same-day surgery patients with a BMI of 35 kg/m2 or greater and who received general anesthesia. Postimplementation data were collected over 3 months in addition to adherence to the capnography protocol. This was presented using descriptive statistics.

FINDINGS

Age, length of surgery, weight, BMI, perioperative fluid intake and output, and use of positive-end expiratory pressure did not impact PACU length of stay. The average PACU length of stay decreased from 76.76 to 71.82 minutes postimplementation but was not statistically significant (P = .470). The incidence of respiratory events was 6% (n = 3). After the implementation of the continuous capnography protocol, adherence to the continuous capnography monitoring was 86% (n = 43).

CONCLUSIONS

Patients who are at high risk for postoperative respiratory failure may benefit from continuous capnography monitoring in the PACU. Capnography monitoring may decrease PACU length of stay and provide earlier detection of pending respiratory depression or failure than pulse oximetry alone.

Worry as Important "Feelers" in Clinical Anesthesia Practice: A Mixed-Methods Study

PURPOSE

Worry is an intuitive sense that goes beyond logical reasoning and is valuable in situations where patients' conditions are rapidly changing or when objective data may not fully capture the complexity of a patient's situation. Nurse anesthetists' subjective reasons for worry are quite vague as they are valued inconsistently and not accurately expressed. This study aimed to identify factors playing a role in the emergence of worry during anesthesia practice to clarify its concept.

DESIGN

Mixed-methods design consisting of quantitative online surveys followed by qualitative focus group interviews including Dutch nurse anesthetists.

METHODS

Both quantitative and qualitative thematic analyses were performed, followed by data and methodological triangulation to enhance the validity and credibility of findings and mitigate the presence of bias.

FINDINGS

Surveys (N = 102) were analyzed, and 14 nurse anesthetists participated in the focus group interviews. A total of 89% of the survey respondents reported that at least once have had the feeling of worry, of which 92% use worry during clinical anesthesia practice. Worry was mentioned to be a vital element during anesthesia practice that makes it possible to take precautionary actions to change the anesthetic care plan in a changing situation or patient deterioration.

CONCLUSIONS

While a clear definition of worry could not be given, it is a valuable element of anesthesia practice as it serves as a catalyst for critical thinking, problem-solving, clinical reasoning, and decision-making. Use of the feeling of worry alongside technological systems to make an informed decision is crucial. Technology has significantly improved the ability of health care providers to detect and respond to patient deterioration promptly, but it is crucial for nurse anesthetists to use their feeling of worry or intuition alongside technological systems and evidence-based practice to ensure quick assessments or judgments based on experience, knowledge, and observations in clinical practice.

Prediction of postoperative patient deterioration and unanticipated intensive care unit admission using perioperative factors

BACKGROUND AND OBJECTIVES

Currently, no evidence-based criteria exist for decision making in the post anesthesia care unit (PACU). This could be valuable for the allocation of postoperative patients to the appropriate level of care and beneficial for patient outcomes such as unanticipated intensive care unit (ICU) admissions. The aim is to assess whether the inclusion of intra- and postoperative factors improves the prediction of postoperative patient deterioration and unanticipated ICU admissions.

METHODS

A retrospective observational cohort study was performed between January 2013 and December 2017 in a tertiary Dutch hospital. All patients undergoing surgery in the study period were selected. Cardiothoracic surgeries, obstetric surgeries, catheterization lab procedures, electroconvulsive therapy, day care procedures, intravenous line interventions and patients under the age of 18 years were excluded. The primary outcome was unanticipated ICU admission.

RESULTS

An unanticipated ICU admission complicated the recovery of 223 (0.9%) patients. These patients had higher hospital mortality rates (13.9% versus 0.2%, p<0.001). Multivariable analysis resulted in predictors of unanticipated ICU admissions consisting of age, body mass index, general anesthesia in combination with epidural anesthesia, preoperative score, diabetes, administration of vasopressors, erythrocytes, duration of surgery and post anesthesia care unit stay, and vital parameters such as heart rate and oxygen saturation. The receiver operating characteristic curve of this model resulted in an area under the curve of 0.86 (95% CI 0.83-0.88).

CONCLUSIONS

The prediction of unanticipated ICU admissions from electronic medical record data improved when the intra- and early postoperative factors were combined with preoperative patient factors. This emphasizes the need for clinical decision support tools in post anesthesia care units with regard to postoperative patient allocation.

Triggers for medical emergency team activation after non-cardiac surgery

Deterioration after major surgery is common, with many patients experiencing a medical emergency team (MET) activation. Understanding the triggers for MET calls may help design interventions to prevent deterioration. We aimed to identify triggers for MET activation in non-cardiac surgical patients. A retrospective cohort study of adult patients who experienced a postoperative MET call at a single tertiary hospital was undertaken. The trigger and timing of each MET call and patient characteristics were collected.Four hundred and one MET calls occurred after 23,258 surgical procedures, a rate of 1.7% of all non-cardiac surgical procedures, accounting for 11.7% of all MET calls over the study period. Hypotension (41.4%) was the most common trigger, followed by tachycardia (18.5%), altered conscious state (11.0%), hypoxia (10.0%), tachypnoea (5.7%), 'other' (5.7%), clinical concern (4.0%), increased work of breathing (1.5%) and bradypnoea (0.7%). Cardiac and/or respiratory arrest triggered 1.2% of MET activations. Eighty-six percent of patients had a single MET call, 10.2% had two, 1.8% had three and one patient (0.3%) had four. The median interval between post-anaesthetic care unit (PACU) discharge and MET call was 14.7 h (95% confidence interval 4.2 to 28.9 h). MET calls resulted in intensive care unit (ICU) admission in 40 patients (10%), while 82% remained on the ward, 4% had a MET call shortly after ICU discharge and returned there, 2% returned to theatre, and 2% went to a high dependency unit.Hypotension was the most common trigger for MET calls after non-cardiac surgery. Deterioration frequently occurred within 24 h of PACU discharge. Future research should focus on prevention of hypotension and tachycardia after surgery.

Unplanned Post-Anesthesia Care Unit to ICU Transfer Following Cerebral Surgery: A Retrospective Study

Background: Unplanned transfer to intensive care unit (ICU) lead to reduced trust of patients and their families in medical staff and challenge medical staff to allocate scarce ICU resources. This study aimed to explore the incidence and risk factors of unplanned transfer to ICU during emergence from general anesthesia after cerebral surgery, and to provide guidelines for preventing unplanned transfer from post-anesthesia care unit (PACU) to ICU following cerebral surgery. Methods: This was a retrospective case-control study and included patients with unplanned transfer from PACU to ICU following cerebral surgery between January 2016 and December 2020. The control group comprised patients matched (2:1) for age (±5 years), sex, and operation date (±48 hours) as those in the case group. Stata14.0 was used for statistical analysis, and p < .05 indicated statistical significance. Results: A total of 11,807 patients following cerebral surgery operations were cared in PACU during the study period. Of the 11,807 operations, 81 unscheduled ICU transfer occurred (0.686%). Finally, 76 patients were included in the case group, and 152 in the control group. The following factors were identified as independent risk factors for unplanned ICU admission after neurosurgery: low mean blood oxygen (OR = 1.57, 95%CI: 1.20-2.04), low mean albumin (OR = 1.14, 95%CI: 1.03-1.25), slow mean heart rate (OR = 1.04, 95%CI: 1.00-1.08), blood transfusion (OR = 2.78, 95%CI: 1.02-7.58), emergency surgery (OR = 3.08, 95%CI: 1.07-8.87), lung disease (OR = 2.64, 95%CI: 1.06-6.60), and high mean blood glucose (OR = 1.71, 95%CI: 1.21-2.41). Conclusion: We identified independent risk factors for unplanned transfer from PACU to ICU after cerebral surgery based on electronic medical records. Early identification of patients who may undergo unplanned ICU transfer after cerebral surgery is important to provide guidance for accurately implementing a patient's level of care.

The association between vital signs abnormalities during postanaesthesia care unit stay and deterioration in the general ward following major abdominal cancer surgery assessed by continuous wireless monitoring

Objective: Vital signs abnormalities in the post-anaesthesia care unit (PACU) may identify patients at risk of severe postoperative complications in the general ward, but are sparsely investigated by continuous monitoring. We aimed to assess if the severity of vital signs abnormalities in the PACU was correlated to the duration of severe vital signs abnormalities and serious adverse events (SAEs) in the general ward. Design: Prospective cohort study. Primary exposure was PACU vital signs abnormalities assessed by a standardised PACU recovery score. Participants: Adult patients, aged ≥ 60 years, who underwent major abdominal cancer surgery. Main outcome measures: The duration of severe vital signs abnormalities were assessed by continuous wireless vital signs monitoring and, secondly, by any SAE within the first 96 hours in the general ward. Results: One-hundred patients were included, and 92 patients with a median of 91 hours (interquartile range, 71-95 hours) of vital signs recording were analysed. The maximum vital signs abnormalities in the PACU were not significantly correlated to overall vital signs abnormalities in the general ward (R = 0.13; P = 0.22). Severe circulatory abnormalities in the overall PACU stay and at discharge were significantly correlated to the duration of circulatory vital signs abnormalities on the ward (R = 0.32 [P = 0.00021] and R = 0.26 [P = 0.014], respectively). Seventeen patients (18%) experienced SAEs, without significant association to the PACU stay (area under the receiver operating characteristic [AUROC], 0.59; 95% CI, 0.46-0.73). Conclusion: Vital signs abnormalities in the PACU did not show a tendency towards predicting overall severe vital signs abnormalities or SAEs during the first days in the general ward. Circulatory abnormalities in the PACU showed a tendency towards predicting circulatory complications in the ward.

Development and Validation of the Medical Emergency Team-Risk Prediction Model for Clinical Deterioration in Acute Hospital Patients, at Time of an Emergency Admission

OBJECTIVES

To develop and validate a prediction model to estimate the risk of Medical Emergency Team (MET) review, within 48 hours of an emergency admission, using information routinely available at the time of hospital admission.

DESIGN

Development and validation of a multivariable risk model using prospectively collected data. Transparent Reporting of a multivariable model for Individual Prognosis Or Diagnosis recommendations were followed to develop and report the prediction model.

SETTING

A 560-bed teaching hospital, with a 22-bed ICU and 24-hour Emergency Department in Melbourne, Australia.

PATIENTS

A total of 45,170 emergency admissions of 30,064 adult patients (≥18 yr), with an inpatient length of stay greater than 24 hours, admitted under acute medical or surgical hospital services between 2015 and 2017.

MEASUREMENTS AND MAIN RESULTS

The outcome was MET review within 48 hours of emergency admission. Thirty candidate variables were selected from a routinely collected hospital dataset based on their availability to clinicians at the time of admission. The final model included nine variables: age; comorbid alcohol-related behavioral diagnosis; history of heart failure, chronic obstructive pulmonary disease (COPD), or renal disease; admitted from residential care; Charlson Comorbidity Index score 1 or 2, or 3+; at least one planned and one emergency admission in the last year; and admission diagnosis and one interaction (past history of COPD × admission diagnosis). The discrimination of the model was comparable in the training (C-statistics 0.82; 95% CI, 0.81-0.83) and the validation set (0.81; 0.80-0.83). Calibration was reasonable for training and validation sets.

CONCLUSIONS

Using only nine predictor variables available to clinicians at the time of admission, the MET-risk model can predict the risk of MET review during the first 48 hours of an emergency admission. Model utility in improving patient outcomes requires further investigation.

Preoperative Risk Assessment: A Poor Predictor of Outcome in Critically ill Elderly with Sepsis After Abdominal Surgery

Background

Postoperative outcome prediction in elderly is based on preoperative physical status but its predictive value is uncertain. The goal was to evaluate the value of risk assessment performed perioperatively in predicting outcome in case of admission to an intensive care unit (ICU).

Methods

A total of 108 postsurgical patients were retrospectively selected from a prospectively recorded database of 144 elderly septic patients (>70 years) admitted to the ICU department after elective or emergency abdominal surgery between 2012 and 2017. Perioperative risk assessment scores including Portsmouth Physiological and Operative Severity Score for the enumeration of Mortality (P-POSSUM) and American Society of Anaesthesiologists Physical Status classification (ASA) were determined. Acute Physiology and Chronic Health Evaluation IV (APACHE IV) was obtained at ICU admission.

Results

In-hospital mortality was 48.9% in elderly requiring ICU admission after elective surgery (n = 45), compared to 49.2% after emergency surgery (n = 63). APACHE IV significantly predicted in-hospital mortality after complicated elective surgery [area under the curve 0.935 (p < 0.001)] where outpatient ASA physical status and P-POSSUM did not. In contrast, P-POSSUM and APACHE IV significantly predicted in-hospital mortality when based on current physical state in elderly requiring emergency surgery (AUC 0.769 (p = 0.002) and 0.736 (p = 0.006), respectively).

Conclusions

Perioperative risk assessment reflecting premorbid physical status of elderly loses its value when complications occur requiring unplanned ICU admission. Risks in elderly should be re-assessed based on current clinical condition prior to ICU admission, because outcome prediction is more reliable then.

Rapid System Review Score-A Tool to Measure Predictive Interventions in Patients Admitted to the Postanesthesia Care Unit

PURPOSE

The Rapid System Review (RSR) score was developed to predict the number of postanesthesia care unit (PACU) interventions. We hypothesized that if RSR score was <0, no PACU interventions were expected; however as the RSR score increased, the number of PACU interventions would also increase.

DESIGN

Observational clinical study.

METHODS

The RSR score was tabulated as 0 to 3, 4 to 6, 7 to 9, 10 to 12, and 13 to 15. The corresponding number of PACU interventions was expected to be 1 to 3, 4 to 6, 7 to 9, 10 to 12, and 13 to 15.

FINDINGS

The Pearson correlation coefficient comparing RSR score and PACU interventions was 0.9 (P < 0.0001). The result was statistically significant.

CONCLUSIONS

These results suggest that as RSR score changes, the number of interventions would also alter proportionally.

A retrospective analysis of 62,571 cases of perioperative adverse events in thoracic surgery at a tertiary care teaching hospital in a developing country

Objectives

Despite a long history of concerns regarding patient safety during clinical care, some patients undergoing thoracic surgery continue to experience adverse events (AEs). AEs are a major significant source of perioperative morbidity and mortality following thoracic surgery. This study analysed the causes, treatment and prognosis of perioperative AEs to provide a reference for further surgical safety.

Methods

The authors collected a total of 62,571 thoracic surgery anaesthesia records via the Anaesthesia Information Management System (AIMS) from 14 August 2006 to 14 August 2017 and obtained 150 cases of perioperative serious AEs from the “adverse events registration” subsystem. The related hospitalization data of the 150 patients were analysed, including anaesthesia, recovery room time, ICU records and follow-up outcomes. The causes of these AEs were classified as follows: events related to the patients’ pathogenic conditions(P); surgery-related factors(S); anaesthesia-related factors(A); and interactions between pathogenic, surgical and anaesthesia factors (P&S&A). We then analysed the main clinical manifestations, causes and treatment of these events.

Results

The overall rate of perioperative AEs in thoracic surgery (n = 62,571) was 0.2%. Of these, 10.7% were.

caused by P and 23.3% by A; neither cause led to patient death. S and P&S&A accounted for 55.3 and 10.7% of AEs, respectively; together, they accounted for 66%. Twelve patients with postoperative AEs caused by S or P&S&A died within 3 days (8% of 150 cases). A total of 33%(50/150) of patients experienced sudden cardiac arrest (SCA) and recovered successfully. Surgical massive haemorrhage (22%, 33/150) was reported as a predominant mortality-related outcome in this group, and 8 of the 12 deaths were caused by massive haemorrhage.

Conclusions

The rate of perioperative AEs after thoracic surgery was 0.2%. AEs must be identified and treated immediately. An important factor in anaesthesia-related events was respiratory management. Two major clinical manifestations of surgery-related events were cardiac arrest and massive haemorrhage. Cardiac arrest was the major factor contributing to AEs, but its adverse consequences could be avoided with timely discovery and proper treatment. Massive haemorrhage is a significant cause of mortality that can be prevented with a surgeon’s early diagnosis and appropriate interventions.

Incidence, Severity, and Detection of Blood Pressure Perturbations after Abdominal Surgery

Editor’s Perspective

What We Already Know about This Topic

What This Article Tells Us That Is New

Background

Intraoperative and postoperative hypotension are associated with myocardial and kidney injury and 30-day mortality. Intraoperative blood pressure is measured frequently, but blood pressure on surgical wards is usually measured only every 4 to 6 h, leaving long intervals during which hypotension and hypertension may be undetected. This study evaluated the incidence and severity of postoperative hypotension and hypertension in adults recovering from abdominal surgery and the extent to which serious perturbations were missed by routine vital-sign assessments.

Methods

Blood pressure was recorded at 1-min intervals during the initial 48 h in adults recovering from abdominal surgery using a continuous noninvasive monitor. Caregivers were blinded to these measurements and depended on routine vital-sign assessments. Hypotension and hypertension were characterized as time under and above various mean arterial pressure thresholds.

Results

Of 502 available patients, 312 patients with high-quality records were analyzed, with a median measurement time of 48 [interquartile range: 41, 48] postoperative hours. Nearly a quarter experienced an episode of mean arterial pressure of less than 70 mm Hg lasting at least 30 min (24%; 95% CI, 20%, 29%), and 18% had an episode of mean arterial pressure of less than 65 mm Hg lasting at least 15 min. Nearly half the patients who had mean arterial pressure of less than 65 mm Hg for at least 15 min (47%; 95% CI, 34%, 61%) were undetected by routine vital-sign assessments. Episodes of mean arterial pressure greater than 110 mm Hg lasting at least 30 min were observed in 42% (95% CI, 37%, 48%) of patients; 7% had mean arterial pressure greater than 130 mm Hg for at least 30 min, 96% of which were missed by routine assessments. Episodes of mean arterial pressure less than 65 mm Hg and mean arterial pressure greater than 110 mm Hg captured by routine vital-sign assessments but not by continuous monitoring occurred in 34 and 8 patients, respectively.

Conclusions

Postoperative hypotension and hypertension were common, prolonged, profound, and largely undetected by routine vital-sign assessments in a cohort of adults recovering from abdominal surgery. Frequent or continuous blood pressure monitoring may detect hemodynamic perturbations more effectively and potentially facilitate treatment.

Are Postoperative Clinical Outcomes Influenced by Length of Stay in the Postanesthesia Care Unit?

PURPOSE

To compare clinical outcomes of patients who required a prolonged length of stay in the postanesthesia care unit (PACU) with a control group.

DESIGN

A single-center purposive-sampled retrospective medical record and database audit.

METHODS

Patients with prolonged PACU stays were compared to a group of patients whose stay was less than median for outcome measures: rapid response team (RRT) activation, cardiac arrest, unanticipated intensive care unit admissions, and survival to discharge.

FINDINGS

A total of 1,867 patients were included in the analysis (n = 931 prolonged stay and n = 933 control group). Prolonged stay in PACU was higher among patients who were older, had higher American Society of Anesthesiologist score, and were discharged to wards during the afternoon or late nursing shift. RRT activation after discharge from PACU occurred in more patients in the study group compared with the control group (7% vs 1%, respectively). There were no cardiac arrests recorded in either group within the 24 hours after PACU discharge period.

CONCLUSIONS

Prolonged stay in the PACU for 2 or more hours because of clinical reasons appears to be associated with a higher incidence of clinical deterioration in the ward setting requiring RRT intervention within 24 hours after discharge from PACU.

Effect of a newly designed observation, response and discharge chart in the Post Anaesthesia Care Unit on patient outcomes: a quasi-expermental study in Australia

OBJECTIVES

This study aimed to evaluate whether use of a discharge criteria tool for nursing assessment of patients in Post Anaesthesia Care Unit (PACU) would enhance nurses' recognition and response to patients at-risk of deterioration and improve patient outcomes.

METHODS

A prospective non-randomised pre-post intervention study was conducted in three hospitals in Australia. Participants were adults undergoing elective surgery before (n=723) and after (n=694) implementation of the Post-Anaesthetic Care Tool (PACT).

RESULTS

Nursing response to patients at-risk of deterioration was higher using PACT, with more medical consultations initiated by PACU nurses (19% vs 30%, P<0.001) and more patients with Medical Emergency Team activation criteria modified by an anaesthetist while in PACU (6.5% vs 13.8%, P<0.001). There were higher rates of analgesia administration (37.3% vs 54.2%, P=0.001), nursing assessment of pain and documentation of ongoing analgesia prior to discharge (55% vs 85%, P<0.001). More adverse events were recorded in PACU after introduction of the PACT (8.3% vs 16.7%, P<0.001). The rate of adverse events after discharge from PACU remained constant (16.5%), but the rate of cardiac events (5.1% vs 2.6%, P=0.021) and clinical deterioration (8.7% vs 4.3%, P=0.001) following PACU discharge significantly decreased, using the PACT. Despite the increased number of patients with adverse events in phase 2, healthcare costs did not increase significantly. Length of stay in PACU and length of hospital admission for those patients who had an adverse event in PACU were significantly reduced after implementation of the PACT.

CONCLUSION

This study found that using a structured discharge criteria tool, the PACT, enhanced nurses' recognition and response to patients who experienced clinical deterioration, reduced length of stay for patients who experienced an adverse event in PACU and was cost-effective.

Protocol for an observational study of delirium in the post-anaesthesia care unit (PACU) as a potential predictor of subsequent postoperative delirium

INTRODUCTION

Postoperative delirium can be a serious consequence of major surgery, associated with longer hospital stays, readmission, cognitive and functional deterioration and mortality. Delirium is an acute, reversible disorder characterised by fluctuating course, inattention, disorganised thinking and altered level of consciousness. Delirium occurring in the hours immediately following anaesthesia and delirium occurring in the postoperative period of 1-5 days have been described as distinct clinical entities. This protocol describes an observational study with the aim of determining if delirium in the first hour following tracheal tube removal is a predictor of delirium in the 5 subsequent postoperative days. Improved understanding regarding the development of postoperative delirium would improve patient care and allow more effective implementation of delirium prevention measures.

METHODS AND ANALYSIS

Patients enrolled to the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES) randomised controlled trial will be eligible for this substudy. A validated delirium assessment method, the 3-min Diagnostic Confusion Assessment Method and the Richmond Agitation and Sedation Scale will be used to assess 100 patients for delirium at 30 min and 60 min following tracheal tube removal. Patients will also be assessed for delirium over postoperative days 1-5 using three validated methods, the Confusion Assessment Method (CAM), CAM for the Intensive Care Unit and structured chart review. Logistic regression analysis will then be performed to test whether immediately postoperative delirium independently predicts subsequent postoperative delirium.

ETHICS AND DISSEMINATION

This observational substudy of ENGAGES has been approved by the ethics board of Washington University School of Medicine. Enrolment began in June 2016 and will continue until June 2017. Dissemination plans include presentations at scientific conferences and scientific publications.

TRIAL REGISTRATION NUMBER

NCT02241655.