Ventilation during cardiopulmonary resuscitation with mechanical chest compressions: how often are two insufflations being given during the 3-second ventilation pauses?

BACKGROUND: Mechanical chest compression devices in 30:2 mode provide 3-second pauses to allow for two insufflations. We aimed to determine how often two insufflations are provided in these ventilation pauses, in order to assess if prehospital providers are able to ventilate out-of-hospital cardiac arrest (OHCA) patients successfully during mechanical chest compressions. METHODS: Data from OHCA cases of the regional ambulance service of Utrecht, The Netherlands, were prospectively collected in the UTrecht studygroup for OPtimal registry of cardIAc arrest database (UTOPIA). Compression pauses and insufflations were visualized on thoracic impedance and waveform capnography signals recorded by manual defibrillators. Ventilation pauses were analyzed for number of insufflations, duration of the subintervals of the ventilation cycles, and ratio of successfully providing two insufflations over the course of the resuscitation. Generalized linear mixed effects models were used to accurately estimate proportions and means. RESULTS: In 250 cases, 8473 ventilation pauses were identified, of which 4305 (51%) included two insufflations. When corrected for non-independence of the data across repeated measures within the same subjects with a mixed effects analysis, two insufflations were successfully provided in 45% of ventilation pauses (95% CI: 40-50%). In 19% (95% CI: 16-22%) none were given. CONCLUSION: Providing two insufflations during pauses in mechanical chest compressions is mostly unsuccessful. We recommend developing strategies to improve giving insufflations when using mechanical chest compression devices. Increasing the pause duration might help to improve insufflation success.

Classification of trauma-related preventable death; protocol of a Delphi procedure.

BACKGROUND: Trauma-related (preventable) death is used to evaluate the management and quality of trauma care worldwide. Therefore, it is necessary to identify fatalities in the trauma care population and assess them on preventability. However, the definition on trauma-related preventable death lacks validity due to differences in terminology and classifications. This study aims to reach consensus on the definition of trauma-related preventable death by performing a Delphi procedure, thereby, improving the assessment of trauma-related preventable death and thereby enhancing the quality of trauma care. METHODS: Based on the results of a recently performed systematic review Hakkenbrak (2021). The definitions used to describe trauma-related preventable death could be divided into four categories: 1) Clinical definition based on panel review or expert opinion, 2) Trauma prediction algorithm, 3) Clinical definition with an additional trauma prediction algorithm and 4) Others (e.g., errors in care or detailed clinical definition). A three round, electronic Delphi study will be performed in the Netherlands to reach consensus. Experts from the department of Trauma surgery, Neurosurgery, Forensic medicine, Anaesthesiology and Emergency medicine, of the designated Level 1 trauma centres in the Netherlands, will be invited to participate. In the first round the panel will comment on the composed categories and trauma prediction algorithms. In the second and third round a feedback report will be presented and the questions with disagreement will be retested. DISCUSSION: The identification and assessment of trauma-related preventable death is necessary to evaluate and improve trauma care. Therefore, a valid, fair, and applicable definition of trauma-related preventable death is required. The Delphi technique is utilized to reach group consensus to obtain a scientifically valid definition of trauma-related preventable death.

Effect of Daytime versus Nighttime on Prehospital Care and Outcomes after Severe Traumatic Brain Injury.

Background/Objectives: Severe traumatic brain injury (TBI) is a frequent cause of morbidity and mortality worldwide. In the Netherlands, suspected TBI is a criterion for the dispatch of the physician-staffed helicopter emergency medical services (HEMS) which are operational 24 h per day. It is unknown if patient outcome is influenced by the time of day during which the incident occurs. Therefore, we investigated the association between the time of day of the prehospital treatment of severe TBI and 30-day mortality. Methods: A retrospective analysis of prospectively collected data from the BRAIN-PROTECT study was performed. Patients with severe TBI treated by one of the four Dutch helicopter emergency medical services were included and followed up to one year. The association between prehospital treatment during day- versus nighttime, according to the universal daylight period, and 30-day mortality was analyzed with multivariable logistic regression. A planned subgroup analysis was performed in patients with TBI with or without any other injury. Results: A total of 1794 patients were included in the analysis, of which 1142 (63.7%) were categorized as daytime and 652 (36.3%) as nighttime. Univariable analysis showed a lower 30-day mortality in patients with severe TBI treated during nighttime (OR 0.74, 95% CI 0.60-0.91, p = 0.004); this association was no longer present in the multivariable model (OR 0.82, 95% CI 0.59-1.16, p = 0.262). In a subgroup analysis, no association was found between mortality rates and the time of prehospital treatment in patients with combined injuries (TBI and any other injury). Patients with isolated TBI had a lower mortality rate when treated during nighttime than when treated during daytime (OR 0.51, 95% CI 0.34-0.76, p = 0.001). Within the whole cohort, daytime versus nighttime treatments were not associated with differences in functional outcome defined by the Glasgow Outcome Scale. Conclusions: In the overall study population, no difference was found in 30-day mortality between patients with severe TBI treated during day or night in the multivariable model. Patients with isolated severe TBI had lower mortality rates at 30 days when treated at nighttime.

Decision Curve Analysis of In-Hospital Mortality Prediction Models: The Relative Value of Pre- and Intraoperative Data For Decision-Making.

BACKGROUND: Clinical prediction modeling plays a pivotal part in modern clinical care, particularly in predicting the risk of in-hospital mortality. Recent modeling efforts have focused on leveraging intraoperative data sources to improve model performance. However, the individual and collective benefit of pre- and intraoperative data for clinical decision-making remains unknown. We hypothesized that pre- and intraoperative predictors contribute equally to the net benefit in a decision curve analysis (DCA) of in-hospital mortality prediction models that include pre- and intraoperative predictors. METHODS: Data from the VitalDB database featuring a subcohort of 6043 patients were used. A total of 141 predictors for in-hospital mortality were grouped into preoperative (demographics, intervention characteristics, and laboratory measurements) and intraoperative (laboratory and monitor data, drugs, and fluids) data. Prediction models using either preoperative, intraoperative, or all data were developed with multiple methods (logistic regression, neural network, random forest, gradient boosting machine, and a stacked learner). Predictive performance was evaluated by the area under the receiver-operating characteristic curve (AUROC) and under the precision-recall curve (AUPRC). Clinical utility was examined with a DCA in the predefined risk preference range (denoted by so-called treatment threshold probabilities) between 0% and 20%. RESULTS: AUROC performance of the prediction models ranged from 0.53 to 0.78. AUPRC values ranged from 0.02 to 0.25 (compared to the incidence of 0.09 in our dataset) and high AUPRC values resulted from prediction models based on preoperative laboratory values. A DCA of pre- and intraoperative prediction models highlighted that preoperative data provide the largest overall benefit for decision-making, whereas intraoperative values provide only limited benefit for decision-making compared to preoperative data. While preoperative demographics, comorbidities, and surgery-related data provide the largest benefit for low treatment thresholds up to 5% to 10%, preoperative laboratory measurements become the dominant source for decision support for higher thresholds. CONCLUSIONS: When it comes to predicting in-hospital mortality and subsequent decision-making, preoperative demographics, comorbidities, and surgery-related data provide the largest benefit for clinicians with risk-averse preferences, whereas preoperative laboratory values provide the largest benefit for decision-makers with more moderate risk preferences. Our decision-analytic investigation of different predictor categories moves beyond the question of whether certain predictors provide a benefit in traditional performance metrics (eg, AUROC). It offers a nuanced perspective on for whom these predictors might be beneficial in clinical decision-making. Follow-up studies requiring larger datasets and dedicated deep-learning models to handle continuous intraoperative data are essential to examine the robustness of our results.

Etomidate versus Ketamine as Prehospital Induction Agent in Patients with Suspected Severe Traumatic Brain Injury.

BACKGROUND: Severe traumatic brain injury is a leading cause of morbidity and mortality among young people around the world. Prehospital care focuses on the prevention and treatment of secondary brain injury and commonly includes tracheal intubation after induction of general anesthesia. The choice of induction agent in this setting is controversial. This study therefore investigated the association between the chosen induction medication etomidate versus S(+)-ketamine and the 30-day mortality in patients with severe traumatic brain injury who received prehospital airway management in the Netherlands. METHODS: This study is a retrospective analysis of the prospectively collected observational data of the Brain Injury: Prehospital Registry of Outcomes, Treatments and Epidemiology of Cerebral Trauma (BRAIN-PROTECT) cohort study. Patients with suspected severe traumatic brain injury who were transported to a participating trauma center and who received etomidate or S(+)-ketamine for prehospital induction of anesthesia for advanced airway management were included. Statistical analyses were performed with multivariable logistic regression and inverse probability of treatment weighting analysis. RESULTS: In total, 1,457 patients were eligible for analysis. No significant association between the administered induction medication and 30-day mortality was observed in unadjusted analyses (32.9% mortality for etomidate versus 33.8% mortality for S(+)-ketamine; P = 0.716; odds ratio, 1.04; 95% CI, 0.83 to 1.32; P = 0.711), as well as after adjustment for potential confounders (odds ratio, 1.08; 95% CI, 0.67 to 1.73; P = 0.765; and risk difference 0.017; 95% CI, -0.051 to 0.084; P = 0.686). Likewise, in planned subgroup analyses for patients with confirmed traumatic brain injury and patients with isolated traumatic brain injury, no significant differences were found. Consistent results were found after multiple imputations of missing data. CONCLUSIONS: The analysis found no evidence for an association between the use of etomidate or S(+)-ketamine as an anesthetic agent for intubation in patients with traumatic brain injury and mortality after 30 days in the prehospital setting, suggesting that the choice of induction agent may not influence the patient mortality rate in this population.

Traumatic Cardiac Arrest-A Narrative Review.

A paradigm shift in traumatic cardiac arrest (TCA) perception switched the traditional belief of futility of TCA resuscitation to a more optimistic perspective, at least in selected cases. The goal of TCA resuscitation is to rapidly and aggressively treat the common potentially reversible causes of TCA. Advances in diagnostics and therapy in TCA are ongoing; however, they are not always translating into improved outcomes. Further research is needed to improve outcome in this often young and previously healthy patient population.

Is assessment of skeletal muscle mass useful to predict time-to-awake in awake craniotomies?

Awake craniotomy is the gold standard for the resection of brain lesions near eloquent areas. For the commonly used asleep-awake-asleep technique, the patient must be awake and fully cooperative as soon as possible after discontinuation of anesthetics. A shorter emergence time is essential to decrease the likelihood of adverse events. Previous research found no relationship between body mass index (BMI) and time-to-awake for intravenous anesthesia with propofol, which is a lipophilic agent. As BMI cannot differentiate between fat and muscle tissue, we hypothesize that skeletal muscle mass, particularly when combined with BMI, may better predict time-to-awake from propofol sedation. We aimed to evaluate the relationship between skeletal muscle mass and the time-to-awake in patients undergoing awake craniotomy, as well as the interaction between skeletal muscle mass and BMI. In 260 patients undergoing an awake craniotomy, we used preoperative magnetic resonance imaging to assess temporalis muscle and cross-sectional skeletal muscle area of the masseter muscles and at level of the third cervical vertebra. Time-to-awake was dichotomized as ≤20 and >20 minutes. No association between various measures of skeletal muscle mass and time-to-awake was observed, and no interaction between skeletal muscle mass and BMI was found (all P > .05). Likewise, patients with a high BMI and low skeletal muscle mass (indicating an increased proportion of fat tissue) did not have a prolonged time-to-awake. Skeletal muscle mass did not predict time-to-awake in patients undergoing awake craniotomy, neither in isolation nor in combination with a high BMI.

Early hyperbaric oxygen therapy is associated with favorable outcome in patients with iatrogenic cerebral arterial gas embolism: systematic review and individual patient data meta-analysis of observational studies.

BACKGROUND: Iatrogenic cerebral arterial gas embolism (CAGE) caused by invasive medical procedures may be treated with hyperbaric oxygen therapy (HBOT). Previous studies suggested that initiation of HBOT within 6-8 h is associated with higher probability of favorable outcome, when compared to time-to-HBOT beyond 8 h. We performed a group level and individual patient level meta-analysis of observational studies, to evaluate the relationship between time-to-HBOT and outcome after iatrogenic CAGE. METHODS: We systematically searched for studies reporting on time-to-HBOT and outcome in patients with iatrogenic CAGE. On group level, we meta-analyzed the differences between median time-to-HBOT in patients with favorable versus unfavorable outcome. On individual patient level, we analyzed the relationship between time-to-HBOT and probability of favorable outcome in a generalized linear mixed effects model. RESULTS: Group level meta-analysis (ten studies, 263 patients) shows that patients with favorable outcome were treated with HBOT 2.4 h (95% CI 0.6-9.7) earlier than patients with unfavorable outcome. The generalized linear mixed effects model (eight studies, 126 patients) shows a significant relationship between time-to-HBOT and probability of favorable outcome (p = 0.013) that remains significant after correcting for severity of manifestations (p = 0.041). Probability of favorable outcome decreases from approximately 65% when HBOT is started immediately, to 30% when HBOT is delayed for 15 h. CONCLUSIONS: Increased time-to-HBOT is associated with decreased probability of favorable outcome in iatrogenic CAGE. This suggests that early initiation of HBOT in iatrogenic CAGE is of vital importance.

Association between prehospital end-tidal carbon dioxide levels and mortality in patients with suspected severe traumatic brain injury.

PURPOSE: Severe traumatic brain injury is a leading cause of mortality and morbidity, and these patients are frequently intubated in the prehospital setting. Cerebral perfusion and intracranial pressure are influenced by the arterial partial pressure of CO2 and derangements might induce further brain damage. We investigated which lower and upper limits of prehospital end-tidal CO2 levels are associated with increased mortality in patients with severe traumatic brain injury. METHODS: The BRAIN-PROTECT study is an observational multicenter study. Patients with severe traumatic brain injury, treated by Dutch Helicopter Emergency Medical Services between February 2012 and December 2017, were included. Follow-up continued for 1 year after inclusion. End-tidal CO2 levels were measured during prehospital care and their association with 30-day mortality was analyzed with multivariable logistic regression. RESULTS: A total of 1776 patients were eligible for analysis. An L-shaped association between end-tidal CO2 levels and 30-day mortality was observed (p = 0.01), with a sharp increase in mortality with values below 35 mmHg. End-tidal CO2 values between 35 and 45 mmHg were associated with better survival rates compared to < 35 mmHg. No association between hypercapnia and mortality was observed. The odds ratio for the association between hypocapnia (< 35 mmHg) and mortality was 1.89 (95% CI 1.53-2.34, p < 0.001) and for hypercapnia (≥ 45 mmHg) 0.83 (0.62-1.11, p = 0.212). CONCLUSION: A safe zone of 35-45 mmHg for end-tidal CO2 guidance seems reasonable during prehospital care. Particularly, end-tidal partial pressures of less than 35 mmHg were associated with a significantly increased mortality.

Patients with infective endocarditis undergoing cardiac surgery have distinct ROTEM profiles and more bleeding complications compared to patients without infective endocarditis.

BACKGROUND: The coagulation system is crucial in the pathogenesis of infective endocarditis and undergoes significant changes during course of the disease. However, little is known about the implications of those changes in the perioperative period. Aim of the present study was to delineate the specific coagulation patterns and their clinical consequence in patients undergoing cardiac surgery due to infective endocarditis. METHODS: In this single-centre, exploratory, prospective observational study, we investigated the incidence and degree of coagulopathy in patients with (n = 31) and without infective endocarditis (n = 39) undergoing cardiac valve surgery. The primary outcome was the differences between these two groups in rotational thromboelastometry (ROTEM) results before, during and after surgery. The secondary outcomes were the differences between the groups in heparin sensitivity, bleeding complications, and transfusion requirements. RESULTS: Most ROTEM parameters in EXTEM, INTEM and FIBTEM assays were significantly altered in patients with infective endocarditis. Clotting time in the EXTEM assay was significantly prolonged in the endocarditis group at all time-points, while all clot firmness parameters (A5, A10 and MCF) were significantly increased. The heparin sensitivity index was significantly lower in the endocarditis group (median index 0.99 vs 1.17s. IU-1.kg-1, p = .008), indicating increased heparin resistance. Patients with infective endocarditis had more bleeding complications as assessed by the universal definition of perioperative bleeding score (OR 3.0, p = .018), and more patients with endocarditis underwent early re-exploration (p = .018). CONCLUSIONS: The findings of this exploratory investigation show significantly altered coagulation profiles in patients with infective endocarditis, with concomitant hyper- and hypocoagulability. Furthermore, the incidence of bleeding complications and transfusion requirements were increased in patients with endocarditis. These results show the potential of ROTEM to detect coagulation abnormalities in patients with infective endocarditis. Existing point-of-care coagulation testing guided algorithms for optimizing perioperative coagulation management possibly need to be adjusted for these high-risk patients undergoing cardiac surgery.

Decision curve analysis confirms higher clinical utility of multi-domain versus single-domain prediction models in patients with open abdomen treatment for peritonitis.

BACKGROUND: Prediction modelling increasingly becomes an important risk assessment tool in perioperative systems approaches, e.g. in complex patients with open abdomen treatment for peritonitis. In this population, combining predictors from multiple medical domains (i.e. demographical, physiological and surgical variables) outperforms the prediction capabilities of single-domain prediction models. However, the benefit of these prediction models for clinical decision-making remains to be investigated. We therefore examined the clinical utility of mortality prediction models in patients suffering from peritonitis with a decision curve analysis. METHODS: In this secondary analysis of a large dataset, a traditional logistic regression approach, three machine learning methods and a stacked ensemble were employed to examine the predictive capability of demographic, physiological and surgical variables in predicting mortality under open abdomen treatment for peritonitis. Calibration was examined with calibration belts and predictive performance was assessed with the area both under the receiver operating characteristic curve (AUROC) and under the precision recall curve (AUPRC) and with the Brier Score. Clinical utility of the prediction models was examined by means of a decision curve analysis (DCA) within a treatment threshold range of interest of 0-30%, where threshold probabilities are traditionally defined as the minimum probability of disease at which further intervention would be warranted. RESULTS: Machine learning methods supported available evidence of a higher prediction performance of a multi- versus single-domain prediction models. Interestingly, their prediction performance was similar to a logistic regression model. The DCA demonstrated that the overall net benefit is largest for a multi-domain prediction model and that this benefit is larger compared to the default "treat all" strategy only for treatment threshold probabilities above about 10%. Importantly, the net benefit for low threshold probabilities is dominated by physiological predictors: surgical and demographics predictors provide only secondary decision-analytic benefit. CONCLUSIONS: DCA provides a valuable tool to compare single-domain and multi-domain prediction models and demonstrates overall higher decision-analytic value of the latter. Importantly, DCA provides a means to clinically differentiate the risks associated with each of these domains in more depth than with traditional performance metrics and highlighted the importance of physiological predictors for conservative intervention strategies for low treatment thresholds. Further, machine learning methods did not add significant benefit either in prediction performance or decision-analytic utility compared to logistic regression in these data.

Prehospital Intubation of Patients with Severe Traumatic Brain Injury: A Dutch Nationwide Trauma Registry Analysis.

OBJECTIVE: Patients with severe traumatic brain injury (TBI) are commonly intubated during prehospital treatment despite a lack of evidence that this is beneficial. Accumulating evidence even suggests that prehospital intubation may be hazardous, in particular when performed by inexperienced EMS clinicians. To expand the limited knowledge base, we studied the relationship between prehospital intubation and hospital mortality in patients with severe TBI in a large Dutch trauma database. We specifically hypothesized that the relationship differs depending on whether a physician-based emergency medical service (EMS) was involved in the treatment, as opposed to intubation by paramedics. METHODS: A retrospective analysis was performed using the Dutch Nationwide Trauma Registry that includes all trauma patients in the Netherlands who are admitted to any hospital with an emergency department. All patients treated for severe TBI (Head Abbreviated Injury Scale score ≥4) between January 2015 and December 2019 were selected. Multivariable logistic regression was used to assess the relationship between prehospital intubation and mortality while adjusting for potential confounders. An interaction term between prehospital intubation and the involvement of physician-based EMS was added to the model. Complete case analysis as well as multiple imputation were performed. RESULTS: 8946 patients (62% male, median age 63 years) were analyzed. The hospital mortality was 26.4%. Overall, a relationship between prehospital intubation and higher mortality was observed (complete case: OR 1.86, 95% CI 1.35-2.57, p < 0.001; multiple imputation: OR 1.92, 95% CI 1.56-2.36, p < 0.001). Adding the interaction revealed that the relationship of prehospital intubation may depend on whether physician-based EMS is involved in the treatment (complete case: p = 0.044; multiple imputation: p = 0.062). Physician-based EMS involvement attenuated but did not completely remove the detrimental association between prehospital intubation and mortality. CONCLUSION: The data do not support the common practice of prehospital intubation. The effect of prehospital intubation on mortality might depend on EMS clinician experience, and it seems prudent to involve prehospital personnel well proficient in prehospital intubation whenever intubation is potentially required. The decision to perform prehospital intubation should not merely be based on the largely unsupported dogma that it is generally needed in severe TBI, but should rather individually weigh potential benefits and harms.

Smartwatch based automatic detection of out-of-hospital cardiac arrest: Study rationale and protocol of the HEART-SAFE project.

Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality. Immediate detection and treatment are of paramount importance for survival and good quality of life. The first link in the 'chain of survival' after OHCA - the early recognition and alerting of emergency medical services - is at the same time the weakest link as it entirely depends on witnesses. About one half of OHCA cases are unwitnessed, and victims of unwitnessed OHCA have virtually no chance of survival with good neurologic outcome. Also in case of a witnessed cardiac arrest, alerting of emergency medical services is often delayed for several minutes. Therefore, a technological solution to automatically detect cardiac arrests and to instantly trigger an emergency response has the potential to save thousands of lives per year and to greatly improve neurologic recovery and quality of life in survivors. The HEART-SAFE consortium, consisting of two academic centres and three companies in the Netherlands, collaborates to develop and implement a technical solution to reliably detect OHCA based on sensor signals derived from commercially available smartwatches using artificial intelligence. In this manuscript, we describe the rationale, the envisioned solution, as well as a protocol outline of the work packages involved in the development of the technology.

Evaluation of electronic screening in the preoperative process.

STUDY OBJECTIVE: Rising patient numbers, with increasing complexity, challenge the sustainability of the current preoperative process. We evaluated whether an electronic screening application can distinguish patients that need a preoperative consultation from low-risk patients that can be first seen on the day of surgery. DESIGN: Prospective cohort study. SETTING: Preoperative clinic of a tertiary academic hospital. PATIENTS: 1395 adult patients scheduled for surgery or procedural sedation. INTERVENTIONS: We assessed a novel electronic preoperative screening application which consists of a questionnaire with a maximum of 185 questions regarding the patient's medical history and current state of health. The application provides an extensive health report, including an American Society of Anesthesiologists physical status (ASA-PS) classification and a recommendation for either consultation by an anesthesiologist at the preoperative clinic or approval for screening on the day of surgery. MEASUREMENTS: The recommendation of the electronic screening system was compared with the regular preoperative assessment using measures of diagnostic accuracy and agreement. Secondary outcomes included ASA-PS classification, patient satisfaction, and the anesthesiologists' opinion on the completeness and quality of the screening report. RESULTS: Sensitivity to detect patients who needed additional consultation was 97.5% (95%CI 91.2-99.7) and the negative likelihood ratio was 0.08 (95%CI 0.02-0.32). 407 (29.2%) patients were approved for surgery by both electronic screening and anesthesiologist. In 909 (65.2%) cases, the electronic screening system recommended further consultation while the anesthesiologist approved the patient (specificity 30.9% (95%CI 28.4-33.5); poor level of agreement (ĸ = 0.04)). Agreement regarding ASA-PS classification scores was weak (ĸ = 0.48). The majority of patients (78.0%) felt positive about electronic screening replacing the regular preoperative assessment. CONCLUSIONS: Electronic screening can reliably identify patients who can have their first contact with an anesthesiologist on the day of surgery, potentially allowing a major proportion of patients to safely bypass the preoperative clinic.