Influence of intercostal muscles contraction on sonographic evaluation of lung sliding: a physiological study on healthy subjects.

OBJECTIVES: To investigate the following: (a) effects of intercostal muscle contraction on sonographic assessment of lung sliding and (b) inter-rater and intra-observer agreement on sonographic detection of lung sliding and lung pulse. METHODS: We used Valsalva and Muller maneuvers as experimental models in which closed glottis and clipped nose prevent air from entering the lungs, despite sustained intercostal muscles contraction. Twenty-one healthy volunteers underwent bilateral lung ultrasound during tidal breathing, apnea, hyperventilation, and Muller and Valsalva maneuvers. The same expert recorded 420 B-mode clips and 420 M-mode images, independently evaluated for the presence or absence of lung sliding and lung pulse by three raters unaware of the respiratory activity corresponding to each imaging. RESULTS: During Muller and Valsalva maneuvers, lung sliding was certainly recognized in up to 73.0% and up to 68.7% of imaging, respectively, with a slight to fair inter-rater agreement for Muller maneuver and slight to moderate for Valsalva. Lung sliding was unrecognized in up to 42.0% of tidal breathing imaging, and up to 12.5% of hyperventilation imaging, with a slight to fair inter-rater agreement for both. During apnea, interpretation errors for sliding were irrelevant and inter-rater agreement moderate to perfect. Even if intra-observer agreement varied among raters and throughout respiratory patterns, we found it to be higher than inter-rater reliability. CONCLUSIONS: Intercostal muscles contraction produces sonographic artifacts that may simulate lung sliding. Clinical studies are needed to confirm this hypothesis. We found slight to moderate inter-rater agreement and globally moderate to almost perfect intra-observer agreement for lung sliding and lung pulse. TRIAL REGISTRATION: ClinicalTrials.gov registration number. NCT02386696.

Good outcome prediction after out-of-hospital cardiac arrest: A prospective multicenter observational study in Korea (the KORHN-PRO registry).

AIM: To assess the ability of clinical examination, biomarkers, electrophysiology and brain imaging, individually or in combination to predict good neurological outcomes at 6 months after CA. METHODS: This was a retrospective analysis of the Korean Hypothermia Network Prospective Registry 1.0, which included adult out-of-hospital cardiac arrest (OHCA) patients (≥18 years). Good outcome predictors were defined as both pupillary light reflex (PLR) and corneal reflex (CR) at admission, Glasgow Coma Scale Motor score (GCS-M) >3 at admission, neuron-specific enolase (NSE) <17 µg/L at 24-72 h, a median nerve somatosensory evoked potential (SSEP) N20/P25 amplitude >4 µV, continuous background without discharges on electroencephalogram (EEG), and absence of anoxic injury on brain CT and diffusion-weighted imaging (DWI). RESULTS: A total of 1327 subjects were included in the final analysis, and their median age was 59 years; among them, 412 subjects had a good neurological outcome at 6 months. GCS-M >3 at admission had the highest specificity of 96.7% (95% CI 95.3-97.8), and normal brain DWI had the highest sensitivity of 96.3% (95% CI 92.9-98.4). When the two predictors were combined, the sensitivities tended to decrease (ranging from 2.7-81.1%), and the specificities tended to increase, ranging from81.3-100%. Through the explorative variation of the 2021 European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) prognostication strategy algorithms, good outcomes were predicted, with a specificity of 83.2% and a sensitivity of 83.5% in patients by the algorithm. CONCLUSIONS: Clinical examination, biomarker, electrophysiology, and brain imaging predicted good outcomes at 6 months after CA. When the two predictors were combined, the specificity further improved. With the 2021 ERC/ESICM guidelines, the number of indeterminate patients and the uncertainty of prognostication can be reduced by using a good outcome prediction algorithm.

The predictive value of highly malignant EEG patterns after cardiac arrest: evaluation of the ERC-ESICM recommendations.

PURPOSE: The 2021 guidelines endorsed by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) recommend using highly malignant electroencephalogram (EEG) patterns (HMEP; suppression or burst-suppression) at > 24 h after cardiac arrest (CA) in combination with at least one other concordant predictor to prognosticate poor neurological outcome. We evaluated the prognostic accuracy of HMEP in a large multicentre cohort and investigated the added value of absent EEG reactivity. METHODS: This is a pre-planned prognostic substudy of the Targeted Temperature Management trial 2. The presence of HMEP and background reactivity to external stimuli on EEG recorded > 24 h after CA was prospectively reported. Poor outcome was measured at 6 months and defined as a modified Rankin Scale score of 4-6. Prognostication was multimodal, and withdrawal of life-sustaining therapy (WLST) was not allowed before 96 h after CA. RESULTS: 845 patients at 59 sites were included. Of these, 579 (69%) had poor outcome, including 304 (36%) with WLST due to poor neurological prognosis. EEG was recorded at a median of 71 h (interquartile range [IQR] 52-93) after CA. HMEP at > 24 h from CA had 50% [95% confidence interval [CI] 46-54] sensitivity and 93% [90-96] specificity to predict poor outcome. Specificity was similar (93%) in 541 patients without WLST. When HMEP were unreactive, specificity improved to 97% [94-99] (p = 0.008). CONCLUSION: The specificity of the ERC-ESICM-recommended EEG patterns for predicting poor outcome after CA exceeds 90% but is lower than in previous studies, suggesting that large-scale implementation may reduce their accuracy. Combining HMEP with an unreactive EEG background significantly improved specificity. As in other prognostication studies, a self-fulfilling prophecy bias may have contributed to observed results.

Mastering the brain in critical conditions: an update.

Acute brain injuries, such as traumatic brain injury and ischemic and hemorragic stroke, are a leading cause of death and disability worldwide. While characterized by clearly distict primary events-vascular damage in strokes and biomechanical damage in traumatic brain injuries-they share common secondary injury mechanisms influencing long-term outcomes. Growing evidence suggests that a more personalized approach to optimize energy substrate delivery to the injured brain and prognosticate towards families could be beneficial. In this context, continuous invasive and/or non-invasive neuromonitoring, together with clinical evaluation and neuroimaging to support strategies that optimize cerebral blood flow and metabolic delivery, as well as approaches to neuroprognostication are gaining interest. Recently, the European Society of Intensive Care Medicine organized a 2-day course focused on a practical case-based clinical approach of acute brain-injured patients in different scenarios and on future perspectives to advance the management of this population. The aim of this manuscript is to update clinicians dealing with acute brain injured patients in the intensive care unit, describing current knowledge and clinical practice based on the insights presented during this course.

Performance of the ERC/ESICM-recommendations for neuroprognostication after cardiac arrest: insights from a prospective multicenter cohort.

Brief abstract: In a multicentre network of 28 ICUs in France and Belgium, all comatose patients who fulfilled the 2021 ERC-ESICM criteria for poor outcome after cardiac arrest died or survived with severe neurological disability, even after excluding patients with active WLST to limit self-fulfilling prophecy bias. However, in almost half of the patients, these criteria were not fulfilled, resulting in an indeterminate outcome; in these patients, normal NSE levels and benign EEG predicted neurological recovery, helping reduce prognostic uncertainty. AIM: To investigate the performance of the 2021 ERC/ESICM-recommended algorithm for predicting poor outcome after cardiac arrest (CA) and potential tools for predicting neurological recovery in patients with indeterminate outcome. METHODS: Prospective, multicenter study on out-of-hospital CA survivors from 28 ICUs of the AfterROSC network. In patients comatose with a Glasgow Coma Scale motor score ≤3 at ≥72 hours after resuscitation, we measured: 1) the accuracy of neurological examination, biomarkers (neuron-specific enolase, NSE), electrophysiology (EEG and SSEP) and neuroimaging (brain CT and MRI) for predicting poor outcome (modified Rankin scale score≥4 at 90 days), and 2) the ability of low or decreasing NSE levels and benign EEG to predict good outcome in patients whose prognosis remained indeterminate. RESULTS: Among 337 included patients, the ERC-ESICM algorithm predicted poor neurological outcome in 175 patients, of whom 106 (60%) had withdrawal of life-sustaining treatment (WLST). Among the 69 patients without active WLST, the positive predictive value for an unfavourable outcome was 100% [95-100]%. The specificity of individual predictors ranged from 90% for EEG to 100% for clinical examination and SSEP. Among the remaining 162 patients with indeterminate outcome, a combination of 2 favourable signs predicted good outcome with 99[96-100]% specificity and 23[11-38%]% sensitivity. Conclusion All comatose resuscitated patients not undergoing WLST who fulfilled the ERC-ESICM criteria for poor outcome after CA had poor outcome at three months, even if a self-fulfilling prophecy cannot be completely excluded. In patients with indeterminate outcome (half of the population), favourable signs predicted neurological recovery, reducing prognostic uncertainty.