Comparison of scales for the evaluation of aneurysmal subarachnoid haemorrhage: a retrospective cohort study.

BACKGROUND/OBJECTIVES: Aneurysmal subarachnoid haemorrhage (aSAH) is a life-threatening event with major complications. Delayed cerebral infarct (DCI) occurs most frequently 7 days after aSAH and can last for a prolonged period. To determine the most predictive radiological scales in grading subarachnoid or ventricular haemorrhage or both for functional outcome at 3 months in a large aSAH population, we conducted a single-centre retrospective study. METHODS: A 3-year single-centre retrospective cohort study of 230 patients hospitalised for aSAH was analysed. Initial computed tomography (CT) scans in patients hospitalised for aSAH were blindly assessed using eight grading systems: the Fisher grade, modified Fisher grade, Barrow Neurological Institute scale, Hijdra scale, Intraventricular Haemorrhage (IVH) score, Graeb score and LeRoux score. RESULTS: Of 200 patients with aSAH who survived to day 7 and were included for DCI analysis, 39% of cases were complicated with DCI. The Hijdra scale was the best predictor for DCI, with a receiver operating characteristic area under the curve (ROCAUC) of 0.80 (95% confidence interval (CI), 0.74-0.85). The IVH score was the most effective grading system for predicting acute hydrocephalus, with a ROCAUC of 0.85 (95% CI, 0.79-0.89). In multivariate analysis, the Hijdra scale was the best predictor of the occurrence of DCI (hazard ratio, 1.18; 95% CI, 1.10-1.25). CONCLUSIONS: Although these results have yet to be prospectively confirmed, our findings suggest that the Hijdra scale may be a good predictor of DCI and could be useful in daily clinical practice. CLINICAL RELEVANCE STATEMENT: Better assessment of subarachnoid haemorrhage patients would allow for better prognostication and management of expectations, as well as referral for appropriate services and helping to appropriate use limited critical care resources. KEY POINTS: Aneurysmal subarachnoid haemorrhage is a life-threatening event that causes severe disability and leads to major complications such as delayed cerebral infarction. Accurate assessment of the amount of blood in the subarachnoid spaces on computed tomography with the Hijdra scale can better predict the risk of delayed cerebral infarct. The Hijdra scale could be a good triage tool for subarachnoid haemorrhage patients.

Predictive Modeling of COVID-19 Intensive Care Unit Patient Flows and Nursing Complexity: A Monte Carlo Simulation Study.

This study aimed to develop a Monte Carlo simulation model to forecast the number of ICU beds needed for COVID-19 patients and the subsequent nursing complexity in a French teaching hospital during the first and second pandemic outbreaks. The model used patient data from March 2020 to September 2021, including age, sex, ICU length of stay, and number of patients on mechanical ventilation or extracorporeal membrane oxygenation. Nursing complexity was assessed using a simple scale with three levels based on patient status. The simulation was performed 1000 times to generate a scenario, and the mean outcome was compared with the observed outcome. The model also allowed for a 7-day forecast of ICU occupancy. The simulation output had a good fit with the actual data, with an R2 of 0.998 and a root mean square error of 0.22. The study demonstrated the usefulness of the Monte Carlo simulation model for predicting the demand for ICU beds and could help optimize resource allocation during a pandemic. The model's extrinsic validity was confirmed using open data from the French Public Health Authority. This study provides a valuable tool for healthcare systems to anticipate and manage surges in ICU demand during pandemics.

Prognostic factors after acute mesenteric ischemia: which patients require specific management?

PURPOSE: Diagnosis and treatment of AMI are a real issue for implicating physicians. In the literature, only one AMI stroke center has reported its results so far, with increasing survival rates. Our aim was to analyze acute mesenteric ischemia (AMI) related mortality and predictive factors, in a single academic center, before creating a dedicated intestinal stroke center. METHODS: All the patients with an AMI, between January 2015 and December 2020, were retrospectively included. They were divided into 2 groups according to the early mortality: death during the first 30 days and alive. The 2 groups were compared. RESULTS: 173 patients (57% of men), were included, with a mean age of 68 ± 16 years. Overall mortality rate was 61%. Mortality occurred within the first 30 days in 78% of dead cases. Dead patients were significantly older, more frequently admitted from intensive care, with more serious clinical, laboratory and radiological characteristics. We have identified 3 protective factors - history of abdominal surgery (Odd Ratio = 0.1; 95%CI = 0.01-0.8, p = 0.03), medical management with curative anticoagulation (OR = 0.09; 95%CI = 0.02-0.5, p = 0.004) and/or antiplatelets (OR = 0.04; 95%CI = 0.006-0.3, p = 0.001)-, and 2 predictive factors of mortality - age > 70 years (OR = 7; 95%CI = 1.4-37, p = 0.02) and previous history of coronaropathy (OR = 13; 95%CI = 1.7-93, p = 0.01). CONCLUSIONS: AMI is a severe disease with high morbidity and mortality rates. Even if its diagnosis is still difficult because of non-specific presentation, its therapeutic management needs to be changed in order to improve survival rates, particularly in patients older than 70 years with history of coronaropathy. Developing a dedicated organization would improve the diagnosis and the management of patients with AMI.

Comparison of kinetic changes during helicopter medical evacuations: civilian versus military flights.

BACKGROUND: Helicopter evacuation is crucial for providing medical care to casualties. Previous civilian studies have demonstrated that air transport can enhance survival rates compared with ground transport. However, there has been limited research on specific accelerations during helicopter flights, particularly in military flights. This study aims to analyse and compare the accelerations endured during civilian and military helicopter evacuations. METHODS: Accelerations were recorded during evacuation flights from the site of injury to the first medical responders in civilian helicopter EC135 T1, and military Puma SA.330 and Caiman NH90 TTH helicopters. The research investigated global acceleration and compared acceleration distributions along the vertical, lateral and longitudinal axes. A specific comparative study of the take-off phases was also performed. RESULTS: The analysis showed that vertical loads caused the most extreme accelerations for all types of helicopter but these extreme accelerations were rare and lasted for less than 1 s. Military flights show similar acceleration intensities to civilian flights, but accelerations are higher during short periods of the take-off phase. CONCLUSIONS: The findings suggest that helicopter evacuations during military operations are as safe as civilian evacuations and highlight the importance of patient positioning in the aircraft. However, further research should investigate the haemodynamic response to accelerations experienced during actual evacuation flights.

Lung-Dependent Areas Collapse, Monitored by Electrical Impedance Tomography, May Predict the Oxygenation Response to Prone Ventilation in COVID-19 Acute Respiratory Distress Syndrome.

OBJECTIVES: ICUs have had to deal with a large number of patients with acute respiratory distress syndrome COVID-19, a significant number of whom received prone ventilation, which is a substantial consumer of care time. The selection of patients that we have to ventilate in prone position seems interesting. We evaluate the correlation between the percentage of collapsed dependent lung areas in the supine position, monitoring by electrical impedance tomography and the oxygenation response (change in Pao2/Fio2 ratio) to prone position. DESIGN: An observational prospective study. SETTING: From October 21, 2020, to 30 March 30, 2021. At the Sainte Anne military teaching Hospital and the Timone University Hospital. PATIENTS: Fifty consecutive patients admitted in our ICUs, with COVID-19 acute respiratory distress syndrome and required mechanical, were included. Twenty-four (48%) received prone ventilation. Fifty-eight prone sessions were investigated. INTERVENTIONS: An electrical impedance tomography recording was made in supine position, daily and repeated just before and just after the prone session. The daily dependent area collapse was calculated in relation to the previous electrical impedance tomography recording. Prone ventilation response was defined as a Pao2/Fio2 ratio improvement greater than 20%. MEASUREMENT AND MAIN RESULTS: The main outcome was the correlation between dependent area collapse and the oxygenation response to prone ventilation. Dependent area collapse was correlated with oxygenation response to prone ventilation (R2 = 0.49) and had a satisfactory prediction accuracy of prone response with an area under the curve of 0.94 (95% CI, 0.87-1.00; p < 0.001). Best Youden index was obtained for a dependent area collapse greater than 13.5 %. Sensitivity of 92% (95% CI, 78-97), a specificity of 91% (95% CI, 72-97), a positive predictive value of 94% (95% CI, 88-100), a negative predictive value of 87% (95% CI, 78-96), and a diagnostic accuracy of 91% (95% CI, 84-98). CONCLUSIONS: Dependent lung areas collapse (> 13.5%), monitored by electrical impedance tomography, has an excellent positive predictive value (94%) of improved oxygenation during prone ventilation.

In Vivo Tumour Mapping Using Electrocorticography Alterations During Awake Brain Surgery: A Pilot Study.

During awake brain surgery for tumour resection, in situ EEG recording (ECoG) is used to identify eloquent areas surrounding the tumour. We used the ECoG setup to record the electrical activity of cortical and subcortical tumours and then performed frequency and connectivity analyses in order to identify ECoG impairments and map tumours. We selected 16 patients with cortical (8) and subcortical (8) tumours undergoing awake brain surgery. For each patient, we computed the spectral content of tumoural and healthy areas in each frequency band. We computed connectivity of each electrode using connectivity markers (linear and non-linear correlations, phase-locking and coherence). We performed comparisons between healthy and tumour electrodes. The ECoG alterations were used to implement automated classification of the electrodes using clustering or neural network algorithms. ECoG alterations were used to image cortical tumours.Cortical tumours were found to profoundly alter all frequency contents (normalized and absolute power), with an increase in the δ activity and a decreases for the other bands (P < 0.05). Cortical tumour electrodes showed high level of connectivity compared to surrounding electrodes (all markers, P < 0.05). For subcortical tumours, a relative decrease in the γ1 band and in the alpha band in absolute amplitude (P < 0.05) were the only abnormalities. The neural network algorithm classification had a good performance: 93.6 % of the electrodes were classified adequately on a test subject. We found significant spectral and connectivity ECoG changes for cortical tumours, which allowed tumour recognition. Artificial neural algorithm pattern recognition seems promising for electrode classification in awake tumour surgery.

Motion analysis of cardiopulmonary resuscitation.

OBJECTIVE: Some cardiopulmonary resuscitation (CPR) monitoring devices were released in recent years. Some of them are motion sensors. There are no guidelines were to position future or present sensors during CPR. We evaluate the possible influence of the location of motion sensors by a high-speed camera during a CPR on a manikin. MATERIAL AND METHODS: We performed a motion analysis by a high-speed camera during chest compression (CC) on a manikin to quantify chest inhomogeneous displacements and rescuer motion. RESULTS: Midline chest was found to have an inhomogeneous depth during CC (19 mm for the upper sternum, 27 mm for the middle of the sternum, and 47 mm for the xiphoid). Rescuer anatomy has a complex motion. CONCLUSION: The direct application of the sensor under the hand performing CC seems to be the more accurate solution if the device allows it.

Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence.

BACKGROUND: In intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive clinical data and blood samples. This study sought to develop an artificial intelligence (AI) model utilizing key hemodynamic parameters to predict ICU mortality within the first 24 h and assess its performance relative to SAPS-2. METHODS: We conducted an analysis of select hemodynamic parameters and the structure of heart rate curves to identify potential predictors of ICU mortality. A machine-learning model was subsequently trained and validated on distinct patient cohorts. The AI algorithm's performance was then compared to the SAPS-2, focusing on classification accuracy, calibration, and generalizability. MEASUREMENTS AND MAIN RESULTS: The study included 1298 ICU admissions from March 27th, 2015, to March 27th, 2017. An additional cohort from 2022 to 2023 comprised 590 patients, resulting in a total dataset of 1888 patients. The observed mortality rate stood at 24.0%. Key determinants of mortality were the Glasgow Coma Scale score, heart rate complexity, patient age, duration of diastolic blood pressure below 50 mmHg, heart rate variability, and specific mean and systolic blood pressure thresholds. The AI model, informed by these determinants, exhibited a performance profile in predicting mortality that was comparable, if not superior, to the SAPS-2. CONCLUSIONS: The AI model, which integrates heart rate and blood pressure curve analyses with basic clinical parameters, provides a methodological approach to predict in-hospital mortality in ICU patients. This model offers an alternative to existing tools that depend on extensive clinical data and laboratory inputs. Its potential integration into ICU monitoring systems may facilitate more streamlined mortality prediction processes.

Resuscitation and Forensic Factors Influencing Outcome in Penetrating Cardiac Injury.

BACKGROUND: Cardiac injury caused by a sharp object is a medical and surgical therapeutic challenge. Mortality risk factors have been identified but there are major discrepancies in the literature. The aim of this study was to analyse the management of victims of penetrating cardiac injuries before and after admission to hospital and the anatomical characteristics of these injuries in order to facilitate diagnosis of the most critical patients. METHODS: To carry out this study, we conducted a retrospective analytical study with epidemiological data on victims of penetrating cardiac injuries. We included two types of patients, with those who underwent autopsy in our institution after death from sharp injury to the heart or great vessels and those who survived with treatment in the emergency department or intensive care unit between January 2015 and February 2022. RESULTS: We included 30 autopsied patients and 12 survivors aged between 18 and 73 years. Higher mortality was associated with prehospital or in-hospital cardiorespiratory arrest (OR = 4, CI [1.71-9.35]), preoperative mechanical ventilation (OR = 10, CI [1.53-65.41]), preoperative catecholamines (OR = 7, CI [1.12-6.29]), preoperative and perioperative adrenaline (OR = 13, CI [1.98-85.46] and [1.98-85.46]), penetrating cardiac injury (OR = 14, CI [2.10-93.22]), multiple cardiac injuries (OR = 1.5, CI [1.05-2.22]) and an Organ Injury Scaling of the American Association for the Surgery of Trauma (AAST-OIS) score of 5 (OR = 2.9, CI [1.04-8.54]; p = 0.0329) with an AUC-ROC curve value of 0.708 (CI [0.543-0.841]). CONCLUSIONS: This study identified risk mortality factors in penetrating cardiac injury patients. These findings can help improve the diagnosis and management of these patients. The AAST-OIS score may be a good tool to diagnose critical patients.

Herpetic encephalitis: which treatment for which body weight?

BACKGROUND: Prognosis of herpetic encephalitis remains severe, with a high proportion of deaths and sequelae. Its treatment is based on acyclovir, but the precise and most effective modalities of this treatment are not established. The objective of this study was to determine them. METHODS: For this, we carried out a descriptive, retrospective, monocentric study, using the current coding database at Marseille University Hospitals. Cohort was intended to be exhaustive for the disease, from January 2000 to June 2019, including patients hospitalized in intensive care and conventional hospitalization sector. Patients (n = 76) included were at least 16 years of age and had a clinical presentation, cerebral Magnetic Resonance Imaging, and/or electroencephalogram abnormalities consistent with herpetic encephalitis confirmed by a positive HSV-PCR in the CSF. Clinical data and treatment, including the doses actually administered to the patient, were compared according to patient's outcome. RESULTS: The mortality rate was 12%, whereas 49% had complete recovery and 39% sequelae impeding independence. Poor outcome was statistically associated with persistence of confusion, aphasia, and impaired consciousness lasting more than 5 days, superinfection, status epilepticus, and length of stay in intensive care unit. A statistical decision tree, constructed using the Classification And Regression Tree model, to prioritize treatment management, showed two main factors that influence the outcome: the patient's weight, and the average daily acyclovir dose actually administered. CONCLUSION: These results suggest to modify acyclovir management in herpetic encephalitis, for low-weight patients (< 79 kg) with a minimum dosage of 2550 mg/day (850 mg/ 8 h), when possible.

Triage and monitoring of COVID-19 patients in intensive care using unsupervised machine learning.

BACKGROUND: We designed an algorithm to assess COVID-19 patients severity and dynamic intubation needs and predict their length of stay using the breathing frequency (BF) and oxygen saturation (SpO2) signals. METHODS: We recorded the BF and SpO2 signals for confirmed COVID-19 patients admitted to the ICU of a teaching hospital during both the first and subsequent outbreaks of the pandemic in France. An unsupervised machine-learning algorithm (the Gaussian mixture model) was applied to the patients' data for clustering. The algorithm's robustness was ensured by comparing its results against actual intubation rates. We predicted intubation rates using the algorithm every hour, thus conducting a severity evaluation. We designed a S24 severity score that represented the patient's severity over the previous 24 h; the validity of MS24, the maximum S24 score, was checked against rates of intubation risk and prolonged ICU stay. RESULTS: Our sample included 279 patients. . The unsupervised clustering had an accuracy rate of 87.8% for intubation recognition (AUC = 0.94, True Positive Rate 86.5%, true Negative Rate 90.9%). The S24 score of intubated patients was significantly higher than that of non-intubated patients at 48 h before intubation. The MS24 score allowed for the distinguishing between three severity levels with an increased risk of intubation: green (3.4%), orange (37%), and red (77%). A MS24 score over 40 was highly predictive of an ICU stay greater than 5 days at an accuracy rate of 81.0% (AUC = 0.87). CONCLUSIONS: Our algorithm uses simple signals and seems to efficiently visualize the patients' respiratory situations, meaning that it has the potential to assist staffs' in decision-making. Additionally, real-time computation is easy to implement.

Beta-lactam allergy labeling in intensive care units: An observational, retrospective study.

ABSTRACT: This retrospective study aimed to describe the association between the "ß-lactam allergy" labeling (BLAL) and the outcomes of a cohort of intensive care unit (ICU) patients.Retrospective cohort study.Seven ICU of the Aix Marseille University Hospitals from Marseille in France.We collected the uses of the label "ß-lactam allergy" in the electronic medical files of patients aged 18 years or more who required more than 48 hours in the ICU with mechanical ventilation and/or vasopressors admitted to 7 ICUs of a single institution.We retrospectively compared the patients with this labeling (BLAL group) with those without this labeling (control group).The primary outcome was the duration of ICU stay. Among the 7146 patients included in the analysis, 440 and 6706 patients were classified in the BLAL group and the control group, respectively. The prevalence of BLAL was 6.2%. In univariate and multivariate analyses, BLAL was weakly or not associated with the duration of ICU and hospital stays (respectively, 6 [3-14] vs 6 [3-14] days, standardized beta -0.09, P = .046; and 18 [10-29] vs 15 [8-28] days, standardized beta -0.09, P = .344). In multivariate analysis, the ICU and 28-day mortality rates were both lower in the BLAL group than in the control group (aOR 0.79 95% CI [0.64-0.98] P = .032 and 0.79 [0.63-0.99] P = .042). Antibiotic use differed between the 2 groups, but the outcomes were similar in the subgroups of septic patients in the BLAL group and the control group.In our cohort, the labeling of a ß-lactam allergy was not associated with prolonged ICU and hospital stays. An association was found between the labeling of a ß-lactam allergy and lower ICU and 28-day mortality rates.Trial registration: Retrospectively registered.

Factors Associated with 90-Day Mortality in Invasively Ventilated Patients with COVID-19 in Marseille, France.

OBJECTIVES: To describe clinical characteristics and management of intensive care units (ICU) patients with laboratory-confirmed COVID-19 and to determine 90-day mortality after ICU admission and associated risk factors. METHODS: This observational retrospective study was conducted in six intensive care units (ICUs) in three university hospitals in Marseille, France. Between 10 March and 10 May 2020, all adult patients admitted in ICU with laboratory-confirmed SARS-CoV-2 and respiratory failure were eligible for inclusion. The statistical analysis was focused on the mechanically ventilated patients. The primary outcome was the 90-day mortality after ICU admission. RESULTS: Included in the study were 172 patients with COVID-19 related respiratory failure, 117 of whom (67%) received invasive mechanical ventilation. 90-day mortality of the invasively ventilated patients was 27.4%. Median duration of ventilation and median length of stay in ICU for these patients were 20 (9-33) days and 29 (17-46) days. Mortality increased with the severity of ARDS at ICU admission. After multivariable analysis was carried out, risk factors associated with 90-day mortality were age, elevated Charlson comorbidity index, chronic statins intake and occurrence of an arterial thrombosis. CONCLUSION: In this cohort, age and number of comorbidities were the main predictors of mortality in invasively ventilated patients. The only modifiable factor associated with mortality in multivariate analysis was arterial thrombosis.

Value of assessment of multivoxel proton chemical shift imaging to predict long term outcome in patients after out-of-hospital cardiac arrest: A preliminary prospective observational study.

BACKGROUND: Existing methods to predict recovery after out-of-hospital cardiac arrest (OHCA) lack of accuracy. The aim of this study was to determine whether quantitative proton chemical shift imaging (1H-CSI) during the subacute stage of OHCA can predict neurological outcome of such patients. METHODS: This monocentric prospective observational study was conducted in a Intensive Care Unit of a teaching hospital. Forty consecutive patients with OHCA were enrolled between January 1st 2011-December 31st 2013. Multivoxel 1H-CSI values were compared to structural magnetic resonance imaging (MRI) sequences (fluid-attenuated inversion recovery and diffusion-weighted imaging). Ratios of N-acetyl-aspartate (NAA) to creatine (Cr) and choline compounds were analyzed using region of interest in bilateral lenticular cores and thalami. The outcome evaluated was the Cerebral Performance Category (CPC) at 6 months, dichotomized as favorable (CPC 1-2) and unfavorable outcome (CPC 3-5). The performance was compared by area under the receiver operating characteristic (ROCAUC) curves analysis. RESULTS: Twenty nine OHCA had an interpretable MRI. Eight patients (28%) had favorable outcome at 6 months. The worst NAA/Cr in lenticular cores was the best 1H-CSI marker, with 80% sensitivity (95% confidence interval (CI), 57-94) and a 100% specificity (95% CI, 63-100) with a positive predictive value of 100%. Prognostic accuracy, as quantified by the ROCAUC, was higher with the worst NAA/Cr in lenticular cores (ROCAUC 0.88; 95% CI, 0.70-0.97) than with the structural MRI sequences. CONCLUSION: In this preliminary study we found that multivoxel 1H-CSI in lenticular cores was highly predictive of unfavorable outcome at 6 months.

Rapid sequence induction traceability in an ICU dedicated patient data management system: a multicentric retrospective study.

PURPOSE: Patient data management systems (PDMS) are widely used in intensive care units (ICUs) to improve care traceability. Verbal orders are still used for prescriptions requiring immediate execution but should be subsequently recorded in the system. We assessed the rapid sequence induction (RSI) traceability for endotracheal intubation in an ICU dedicated PDMS. MATERIALS AND METHODS: A retrospective study was conducted on anonymous databases in 21 ICUs. Endotracheal tube insertions performed during one year were compared to the number of RSI registered in the PDMS. RESULTS: We listed 5516 endotracheal tube insertions. A suxamethonium injection was registered in 829 cases and a rocuronium administration in 909 cases. The RSI traceability rate in the overall cohort was 31.5% and was greater in the units where nurses were allowed to record a drug administration before the computerized physician order entry. CONCLUSIONS: PDMS are supposed to improve prescription completeness and traceability, but our study suggests an opposite result. A co-responsibility policy between physicians and nurses should be promoted to improve care traceability. PDMS ergonomic improvements and enhanced integration in clinical workflow might also result in better compliance with documentation requirements. In each centre, indicators of PDMS correct use should be defined and periodically monitored.

Data on prognostication models comparison for neurological recovery after cardiac arrest using proton chemical shift imaging (1H-CSI).

We report in this data article the statistical comparison of three models for neurological prognostication 6 months after cardiac arrest: M1 associated SAPS II and coma Glasgow score at MRI, M2 associated SAPS II, coma Glasgow score, and FLAIR-DWI "deep gray nuclei"score, M3 associated SAPS II, coma Glasgow score, FLAIR-DWI "deep gray nuclei"score, and Lenticular cores NAA/Cr ratio. These data are related to "Value of assessment of multivoxel proton chemical shift imaging to predict long term outcome in patients after out-of-hospital cardiac arrest: A preliminary prospective observational study" (Quintard et al., 2018) [1].