Increasing hematocrit above 28% during early resuscitative phase is not associated with decreased mortality following severe traumatic brain injury

Background: To prevent iatrogenic damage, transfusions of red blood cells should be avoided. For this, specific and reliable transfusion triggers must be defined. To date, the optimal hematocrit during the initial operating room (OR) phase is still unclear in patients with severe traumatic brain injury (TBI). We hypothesized that hematocrit values exceeding 28%, the local hematocrit target reached by the end of the initial OR phase, resulted in more complications, increased mortality, and impaired recovery compared to patients in whom hematocrit levels did not exceed 28%. Methods: Impact of hematocrit (independent variable) reached by the end of the OR phase on mortality and morbidity determined by the extended Glasgow outcome scale (eGOS; dependent variables) was investigated retrospectively in 139 TBI patients. In addition, multiple logistic regression analysis was performed to identify additional important variables. Findings: Following severe TBI, mortality and morbidity were neither aggravated by hematocrit above 28% reached by the end of the OR phase nor worsened by the required transfusions. Upon multiple logistic regression analysis, eGOS was significantly influenced by the highest intracranial pressure and the lowest cerebral perfusion pressure values during the initial OR phase. Conclusions: Based on this retrospective observational analysis, increasing hematocrit above 28% during the initial OR phase following severe TBI was not associated with improved or worsened outcome. This questions the need for aggressive transfusion management. Prospective analysis is required to determine the lowest acceptable hematocrit value during the OR phase which neither increases mortality nor impairs recovery. For this, a larger caseload and early monitoring of cerebral metabolism and oxygenation are indispensabl

A Quality Improvement Initiative Using Peer Audit and Feedback to Improve Compliance with the Surgical Safety Checklist.

BACKGROUND The surgical safety checklist (SSC) published by the WHO in 2009 is used as standard in surgery worldwide to reduce perioperative patient mortality. However, compliance with the SSC and quality of its application is often not satisfactory. Internal audits and feedbacks seem promising for improving SSC application. The purpose of this study is to investigate whether an intervention consisting of peer observation and immediate peer feedback can be implemented with high fidelity and acceptance. METHODS Data was obtained from a national pilot programme that was initiated in Switzerland in 2018 to measure and improve compliance with the SSC using peer audit and feedback. A total of 11 hospitals with 14 sites implemented the full intervention. Each hospital formed an interprofessional project team that should perform at least 30 observations with feedback on SSC application documented in an observation tool developed specifically for this programme. Since the SSCs of the study hospitals differ greatly regarding checklist items, for each of the three SSC sections standard items were defined: four at Sign In, five at Team Time Out, two at Sign Out. Frequency analyses were performed for initiation characteristics, SSC application at item-level, feedback characteristics and programme evaluation. RESULTS The 11 hospitals documented 715 valid observations and feedback on SSC application was provided for 79% of the observations. In 61% all team members stopped their work for the SSC application and in 71% the items were read off from the checklist (instead of recalled from memory). On average, 86% of the standard items were read out by the checklist coordinator, whilst the two items at Sign Out were read out only in 60% and 74%, respectively. Additional visual checks with another source (e.g., patient wristband) took place in only 41% and verbal confirmation of the items (by someone else other than the checklist coordinator) was obtained on an average of 76% across all three checklist sections. The surgical teams reacted positively in 64% to the peer feedback. CONCLUSION Both implementation fidelity and acceptability of the intervention were high-the present intervention seems suitable for regular monitoring of the quality of SSC application with internal resources. Peer observation facilitated identifying weaknesses regarding the SSC process and application at item-level. Across all hospitals, the Sign Out section in general, visual control for item checks and lack of work interruption of all team members during SSC application showed up as the main areas of improvement

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary out- come the increase or decrease in patients’ Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort. Results: The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs . 0.69, P < 0.01 [paired t -test with 95% confidence interval]). Conclusions: The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems (CDSSs) aimed at optimizing available resources

Increasing hematocrit above 28% during early resuscitative phase is not associated with decreased mortality following severe traumatic brain injury

BACKGROUND: To prevent iatrogenic damage, transfusions of red blood cells should be avoided. For this, specific and reliable transfusion triggers must be defined. To date, the optimal hematocrit during the initial operating room (OR) phase is still unclear in patients with severe traumatic brain injury (TBI). We hypothesized that hematocrit values exceeding 28%, the local hematocrit target reached by the end of the initial OR phase, resulted in more complications, increased mortality, and impaired recovery compared to patients in whom hematocrit levels did not exceed 28%. METHODS: Impact of hematocrit (independent variable) reached by the end of the OR phase on mortality and morbidity determined by the extended Glasgow outcome scale (eGOS; dependent variables) was investigated retrospectively in 139 TBI patients. In addition, multiple logistic regression analysis was performed to identify additional important variables. FINDINGS: Following severe TBI, mortality and morbidity were neither aggravated by hematocrit above 28% reached by the end of the OR phase nor worsened by the required transfusions. Upon multiple logistic regression analysis, eGOS was significantly influenced by the highest intracranial pressure and the lowest cerebral perfusion pressure values during the initial OR phase. CONCLUSIONS: Based on this retrospective observational analysis, increasing hematocrit above 28% during the initial OR phase following severe TBI was not associated with improved or worsened outcome. This questions the need for aggressive transfusion management. Prospective analysis is required to determine the lowest acceptable hematocrit value during the OR phase which neither increases mortality nor impairs recovery. For this, a larger caseload and early monitoring of cerebral metabolism and oxygenation are indispensable

Implications of early respiratory support strategies on disease progression in critical COVID-19: a matched subanalysis of the prospective RISC-19-ICU cohort

Background: Uncertainty about the optimal respiratory support strategies in critically ill COVID-19 patients is wide‑ spread. While the risks and benefts of noninvasive techniques versus early invasive mechanical ventilation (IMV) are intensely debated, actual evidence is lacking. We sought to assess the risks and benefts of diferent respiratory sup‑ port strategies, employed in intensive care units during the frst months of the COVID-19 pandemic on intubation and intensive care unit (ICU) mortality rates. Methods: Subanalysis of a prospective, multinational registry of critically ill COVID-19 patients. Patients were subclas‑ sifed into standard oxygen therapy ≥10 L/min (SOT), high-fow oxygen therapy (HFNC), noninvasive positive-pressureBackground: Uncertainty about the optimal respiratory support strategies in critically ill COVID-19 patients is widespread. While the risks and benefits of noninvasive techniques versus early invasive mechanical ventilation (IMV) are intensely debated, actual evidence is lacking. We sought to assess the risks and benefits of different respiratory support strategies, employed in intensive care units during the first months of the COVID-19 pandemic on intubation and intensive care unit (ICU) mortality rates. Methods: Subanalysis of a prospective, multinational registry of critically ill COVID-19 patients. Patients were subclassified into standard oxygen therapy ≥10 L/min (SOT), high-flow oxygen therapy (HFNC), noninvasive positive-pressure ventilation (NIV), and early IMV, according to the respiratory support strategy employed at the day of admission to ICU. Propensity score matching was performed to ensure comparability between groups. Results: Initially, 1421 patients were assessed for possible study inclusion. Of these, 351 patients (85 SOT, 87 HFNC, 87 NIV, and 92 IMV) remained eligible for full analysis after propensity score matching. 55% of patients initially receiving noninvasive respiratory support required IMV. The intubation rate was lower in patients initially ventilated with HFNC and NIV compared to those who received SOT (SOT: 64%, HFNC: 52%, NIV: 49%, p = 0.025). Compared to the other respiratory support strategies, NIV was associated with a higher overall ICU mortality (SOT: 18%, HFNC: 20%, NIV: 37%, IMV: 25%, p = 0.016). Conclusion: In this cohort of critically ill patients with COVID-19, a trial of HFNC appeared to be the most balanced initial respiratory support strategy, given the reduced intubation rate and comparable ICU mortality rate. Nonetheless, considering the uncertainty and stress associated with the COVID-19 pandemic, SOT and early IMV represented safe initial respiratory support strategies. The presented findings, in agreement with classic ARDS literature, suggest that NIV should be avoided whenever possible due to the elevated ICU mortality risk