Resumption of Cardiac Activity after Withdrawal of Life-Sustaining Measures.

BACKGROUND: The minimum duration of pulselessness required before organ donation after circulatory determination of death has not been well studied. METHODS: We conducted a prospective observational study of the incidence and timing of resumption of cardiac electrical and pulsatile activity in adults who died after planned withdrawal of life-sustaining measures in 20 intensive care units in three countries. Patients were intended to be monitored for 30 minutes after determination of death. Clinicians at the bedside reported resumption of cardiac activity prospectively. Continuous blood-pressure and electrocardiographic (ECG) waveforms were recorded and reviewed retrospectively to confirm bedside observations and to determine whether there were additional instances of resumption of cardiac activity. RESULTS: A total of 1999 patients were screened, and 631 were included in the study. Clinically reported resumption of cardiac activity, respiratory movement, or both that was confirmed by waveform analysis occurred in 5 patients (1%). Retrospective analysis of ECG and blood-pressure waveforms from 480 patients identified 67 instances (14%) with resumption of cardiac activity after a period of pulselessness, including the 5 reported by bedside clinicians. The longest duration after pulselessness before resumption of cardiac activity was 4 minutes 20 seconds. The last QRS complex coincided with the last arterial pulse in 19% of the patients. CONCLUSIONS: After withdrawal of life-sustaining measures, transient resumption of at least one cycle of cardiac activity after pulselessness occurred in 14% of patients according to retrospective analysis of waveforms; only 1% of such resumptions were identified at the bedside. These events occurred within 4 minutes 20 seconds after a period of pulselessness. (Funded by the Canadian Institutes for Health Research and others.).

Improving Safety Recommendations Before Implementation: A Simulation-Based Event Analysis to Optimize Interventions Designed to Prevent Recurrence of Adverse Events.

INTRODUCTION: Pediatric inpatients are at high risk of adverse events (AE). Traditionally, root cause analysis was used to analyze AEs and identify recommendations for change. Simulation-based event analysis (SBEA) is a protocol that systematically reviews AEs by recreating them using in situ simulated patients, to understand clinician decision making, improve error discovery, and, through guided sequential debriefing, recommend interventions for error prevention. Studies suggest that these interventions are rarely tested before dissemination. This study investigates the use of simulation to optimize recommendations generated from SBEA before implementation. METHODS: Recommendations and interventions developed through SBEA of 2 hospital-based AEs (event A: error of commission; event B: error of detection) were tested using in situ simulation. Each scenario was repeated 8 times. Interventions were modified based on participant feedback until the error stopped occurring and data saturation was reached. RESULTS: Data saturation was reached after 6 simulations for both scenarios. For scenario A, a critical error was repeated during the first 2 scenarios using the initial interventions. After modifications, errors were corrected or mitigated in the remaining 6 scenarios. For scenario B, 1 intervention, the nursing checklist, had the highest impact, decreasing average time to error detection to 6 minutes. Based on feedback from participants, changes were made to all but one of the original proposed interventions. CONCLUSIONS: Even interventions developed through improved analysis techniques, like SBEA, require testing and modification. Simulation optimizes interventions and provides opportunity to assess efficacy in real-life settings with clinicians before widespread implementation.

Predicting Time to Death After Withdrawal of Life-Sustaining Measures Using Vital Sign Variability: Derivation and Validation.

To develop a predictive model using vital sign (heart rate and arterial blood pressure) variability to predict time to death after withdrawal of life-supporting measures. DESIGN: Retrospective analysis of observational data prospectively collected as part of the Death Prediction and Physiology after Removal of Therapy study between May 1, 2014, and May 1, 2018. SETTING: Adult ICU. PATIENTS: Adult patients in the ICU with a planned withdrawal of life-supporting measures and an expectation of imminent death. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Vital sign waveforms and clinical data were prospectively collected from 429 patients enrolled from 20 ICUs across Canada, the Czech Republic, and the Netherlands. Vital sign variability metrics were calculated during the hour prior to withdrawal. Patients were randomly assigned to the derivation cohort (288 patients) or the validation cohort (141 patients), of which 103 and 54, respectively, were eligible for organ donation after circulatory death. Random survival forest models were developed to predict the probability of death within 30, 60, and 120 minutes following withdrawal using variability metrics, features from existing clinical models, and/or the physician's prediction of rapid death. A model employing variability metrics alone performed similarly to a model employing clinical features, whereas the combination of variability, clinical features, and physician's prediction achieved the highest area under the receiver operating characteristics curve of all models at 0.78 (0.7-0.86), 0.79 (0.71-0.87), and 0.8 (0.72-0.88) for 30-, 60- and 120-minute predictions, respectively. CONCLUSIONS: Machine learning models of vital sign variability data before withdrawal of life-sustaining measures, combined with clinical features and the physician's prediction, are useful to predict time to death. The impact of providing this information for decision support for organ donation merits further investigation.

Simulation-Based Event Analysis Improves Error Discovery and Generates Improved Strategies for Error Prevention.

INTRODUCTION: An adverse event (AE) is a negative consequence of health care that results in unintended injury or illness. The study investigates whether simulation-based event analysis is different from traditional event analysis in uncovering root causes and generating recommendations when analyzing AEs in hospitalized children. METHODS: Two simulation scenarios were created based on real-life AEs identified through the hospital's Safety Reporting System. Scenario A involved an error of commission (inpatient drug error) and scenario B involved detecting an error that already occurred (drug infusion error). Each scenario was repeated 5 times with different, voluntary clinicians. Content analysis, using deductive and inductive approaches to coding, was used to analyze debriefing data. Causes and recommendations were compiled and compared with the traditional event analysis. RESULTS: Errors were reproduced in 60% (3/5) of scenario A. In scenario B, participants identified the error in 100% (5/5) of simulations (average time to error detection = 15 minutes). Debriefings identified reasons for errors including product labeling, memory aid interpretation, and lack of standard work for patient handover. To prevent error, participants suggested improved drug labeling, specialized drug kits, alert signs, and handoff checklists. Compared with traditional event analysis, simulation-based event analysis revealed unique causes for error and new recommendations. CONCLUSIONS: Using simulation to analyze AEs increased unique error discovery and generated new recommendations. This method is different from traditional event analysis because of the immediate clinician debriefings in the clinical environment. Hospitals should consider simulation-based event analysis as an important addition to the traditional process.