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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.
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Introduction: During COVID-19, renal impairment is associated with poor prognosis in intensive care unit (ICU). We aimed to assess the existence and incidence of early renal dysfunction and its prognostic value in patients with COVID-19-related acute respiratory distress syndrome (ARDS). Methods: In this prospective multicenter study, patients aged over 18 years with invasive mechanical ventilation (MV) for ARDS were enrolled in 3 ICUs. Precise evaluation of renal dysfunction markers, including urinary protein electrophoresis (UPE) and quantification, was performed within 24 hours after MV onset. Results: From March 2020 to December 2021, 135 patients were enrolled as follows: 100 with COVID-19 ARDS and 35 with non-COVID-19 ARDS. UPE found more tubular dysfunction in patients with COVID-19 (68% vs. 21.4%, P < 0.0001) and more normal profiles in patients without COVID-19 (65.0% vs. 11.2%, P = 0.0003). Patients with COVID-19 significantly displayed early urinary leakage of tubular proteins such as beta-2-microglobulin (ß2m) and free light chains, tended to display acute kidney injury (AKI) more frequently (51.0% vs. 34.3%, P = 0.088), had longer MV (20 vs. 9 days, P < 0.0001) and longer ICU stay (26 vs. 15 days, P < 0.0001). In COVID-19 ARDS, leakage of free lambda light chain was associated with the onset of Kidney Disease: Improving Global Outcomes (KDIGO) ≥2 AKI (odds ratio [OR]: 1.014, 95% confidence interval [CI] 1.003-1.025, P = 0.011). Conclusion: Patients with COVID-19-related ARDS display a proximal tubular dysfunction before the onset of AKI, which predicts AKI. Proximal tubular damage seems an important mechanism of COVID-19-induced nephropathy. Analysis of urinary proteins is a reliable noninvasive tool to assess proximal tubular dysfunction in the ICU.
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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.
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Identification of Traumatic axonal injury (TAI) is critical in clinical practice, particularly in terms of long-term prognosis, but also for medico-legal issues, to verify whether the death or the after-effects were attributable to trauma. Multidisciplinary approaches are an undeniable asset when it comes to solving these problems. The aim of this work is therefore to list the different techniques needed to identify axonal lesions and to understand the lesion mechanisms involved in their formation. Imaging can be used to assess the consequences of trauma, to identify indirect signs of TAI, to explain the patient's initial symptoms and even to assess the patient's prognosis. Three-dimensional reconstructions of the skull can highlight fractures suggestive of trauma. Microscopic and immunohistochemical techniques are currently considered as the most reliable tools for the early identification of TAI following trauma. Finite element models use mechanical equations to predict biomechanical parameters, such as tissue stresses and strains in the brain, when subjected to external forces, such as violent impacts to the head. These parameters, which are difficult to measure experimentally, are then used to predict the risk of injury. The integration of imaging data with finite element models allows researchers to create realistic and personalized computational models by incorporating actual geometry and properties obtained from imaging techniques. The personalization of these models makes their forensic approach particularly interesting.
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Diffuse Axonal Injury , Humans , Biomechanical Phenomena , Diffuse Axonal Injury/diagnostic imaging , Diffuse Axonal Injury/physiopathology , Diffuse Axonal Injury/pathology , Finite Element Analysis , Imaging, Three-Dimensional , Forensic Pathology/methods , Axons/pathology , Axons/physiology , Forensic Medicine/methodsSubject(s)
Anesthesia, Conduction , Patient Satisfaction , Virtual Reality , Humans , Anesthesia, Conduction/methods , Male , Hypnosis , Female , Middle AgedABSTRACT
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.
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Artificial Intelligence , Intensive Care Units , Humans , Pilot Projects , Heart Rate , Hospital Mortality , ROC CurveABSTRACT
BACKGROUND: The aims of this study were to describe pharmacokinetic/pharmacodynamic target attainment in intensive care unit (ICU) patients treated with continuously infused ß-lactam antibiotics, their associated covariates, and the impact of dosage adjustment. METHODS: This prospective, observational, cohort study was performed in three ICUs. Four ß-lactams were continuously infused, and therapeutic drug monitoring (TDM) was performed at days 1, 4, and 7. The primary pharmacokinetic/pharmacodynamic target was an unbound ß-lactam plasma concentration four times above the bacteria's minimal inhibitory concentration during the whole dosing interval. The demographic and clinical covariates associated with target attainment were evaluated. RESULTS: A total of 170 patients were included (426 blood samples). The percentages of empirical ß-lactam underdosing at D1 were 66% for cefepime, 43% for cefotaxime, 47% for ceftazidime, and 14% for meropenem. Indexed creatinine clearance was independently associated with treatment underdose if increased (adjusted odds ratio per unit, 1.01; 95% CI, 1.00 to 1.01; p = 0.014) or overdose if decreased (adjusted odds ratio per unit, 0.95; 95% CI, 0.94 to 0.97; p < 0.001). Pharmacokinetic/pharmacodynamic target attainment was significantly increased after ß-lactam dosage adjustment between day 1 and day 4 vs. no adjustment (53.1% vs. 26.2%; p = 0.018). CONCLUSIONS: This study increases our knowledge on the optimization of ß-lactam therapy in ICU patients. A large inter- and intra-patient variability in plasmatic concentrations was observed, leading to inadequate exposure. A combined indexed creatinine clearance and TDM approach enables adequate dosing for better pharmacokinetic/pharmacodynamic target attainment.
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BACKGROUND: Burnout syndrome (BOS) impacts health workers and has become a real public health issue. The primary objective of this observational study was to re-evaluate the incidence of BOS among anesthesiologists and intensivists (AI) in France, ten years after the SESMAT study, a French Physician Health Survey carried out among burnout salaried AI. The secondary objective was to investigate risks factors. METHODS: The REPAR survey is an observational study carried in France among AI, residents, and seniors, whatever their main mode of practice, in the framework of a self-questionnaire distributed on the Internet from 11 April 2018 to 1 July 2018. BOS was assessed using the Copenhagen Burnout Inventory (CBI). A score above 50% on two of the dimensions (personal burnout and work-related burnout) indicated BOS, as a main criterion. In order to investigate risks factors, questions were about sociodemographic characteristics, professional and extraprofessional environments, personality and mood using the Major Depression Inventory (MDI). A significance threshold of p < 0.05 was retained. RESULTS: Among 1519 questionnaires received, 1500 completed questionnaires were analyzed. There were 775 men (52%) and 721 women (48%), aged 23 to 74 years. Among those, 24% suffered from BOS, 9% showed depressive symptoms (MDI > 25) and 35% were considering changing jobs or stopping their studies. There was no significant difference with the SESMAT study for the general BOS score. After multivariate analysis, 12 variables were significantly associated with the main criterion. CONCLUSIONS: Ten years after the SESMAT study, the incidence of BOS in AI has not decreased in the largest cohort of AI studied to date in France.
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Burnout, Professional , Physicians , Male , Humans , Female , Anesthesiologists , Incidence , Burnout, Professional/epidemiology , Surveys and Questionnaires , France/epidemiologyABSTRACT
Background: Acute kidney injury (AKI) is the second most frequent condition after acute respiratory distress syndrome (ARDS) in critically ill patients with severe COVID-19 and is strongly associated with mortality. The aim of this multicentric study was to assess the impact of the specific treatments of COVID-19 and ARDS on the risk of severe AKI in critically ill COVID-19 patients. Methods: In this cohort study, data from consecutive patients older than 18 years admitted to 6 ICUs for COVID-19-related ARDS requiring invasive mechanical ventilation were included. The incidence and severity of AKI, defined according to the 2012 KDIGO definition, were monitored during the entire ICU stay until day 90. Patients older than 18 years admitted to the ICU for COVID-19-related ARDS requiring invasive mechanical ventilation were included. Results: 164 patients were included in the final analysis; 97 (59.1%) displayed AKI, of which 39 (23.8%) had severe stage 3 AKI, and 21 (12.8%) required renal replacement therapy (RRT). In univariate analysis, severe AKI was associated with angiotensin-converting enzyme inhibitors (ACEI) exposure (p = 0.016), arterial hypertension (p = 0.029), APACHE-II score (p = 0.004) and mortality at D28 (p = 0.008), D60 (p < 0.001) and D90 (p < 0.001). In multivariate analysis, the factors associated with the onset of stage 3 AKI were: exposure to ACEI (OR: 4.238 (1.307−13.736), p = 0.016), APACHE II score (without age) (OR: 1.138 (1.044−1.241), p = 0.003) and iNO (OR: 5.694 (1.953−16.606), p = 0.001). Prone positioning (OR: 0.234 (0.057−0.967), p = 0.045) and dexamethasone (OR: 0.194 (0.053−0.713), p = 0.014) were associated with a decreased risk of severe AKI. Conclusions: Dexamethasone was associated with the prevention of the risk of severe AKI and RRT, and iNO was associated with severe AKI and RRT in critically ill patients with COVID-19. iNO should be used with caution in COVID-19-related ARDS.
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BACKGROUND: LUS is a validated tool for the management of COVID-19 pneumonia. Chair positioning (CP) may have beneficial effects on oxygenation and lung aeration, and may be an easier alternative to PP. This study assessed the effects of a CP session on oxygenation and lung aeration (LA) changes in non-intubated COVID-19 patients. METHODS: A retrospective multicenter study was conducted in an ICU. We analyzed data from LUS exams and SpO2:FiO2 performed before/after a CP session in non-intubated COVID-19 patients. Patients were divided into groups of responders or non-responders in terms of oxygenation or LA. RESULTS: Thirty-three patients were included in the study; fourteen (44%) were oxygenation non-responders and eighteen (56%) were oxygenation responders, while thirteen (40.6%) and nineteen (59.4%) patients were classified as LA non-responders and responders, respectively. Changes in oxygenation and LA before/after a CP session were not correlated (r = -0.19, p = 0.3, 95% CI: -0.5-0.17). The reaeration scores did not differ between oxygenation responders and non-responders (1 (-0.75-3.75) vs. 4 (-1-6), p = 0.41). The LUS score was significantly correlated with SpO2:FiO2 before a CP session (r = 0.37, p = 0.04, 95% CI: 0.03-0.64) but not after (r = 0.17, p = 0.35, 95% CI: -0.19-0.50). CONCLUSION: A CP session was associated with improved oxygenation and LA in more than half of the non-intubated COVID-19 patients.
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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.
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COVID-19 , Triage , Critical Care , Humans , Retrospective Studies , SARS-CoV-2 , Unsupervised Machine LearningABSTRACT
BACKGROUND: Traumatic brain injury (TBI) lesions are known to evolve over time, but the duration and consequences of cerebral remodelling are unclear. Degenerative mechanisms occurring in the chronic phase after TBI could constitute "tertiary" lesions related to the neurological outcome. OBJECTIVE: The objective of this prospective study of severe TBI was to longitudinally evaluate the volume of white and grey matter structures and white matter integrity with 2 time-point multimodal MRI. METHODS: Longitudinal MRI follow-up was obtained for 11 healthy controls (HCs) and 22 individuals with TBI (mean [SD] 60 [15] months after injury) along with neuropsychological assessments. TBI individuals were classified in the "favourable" recovery group (Glasgow Outcome Scale Extended [GOSE] 6-8) and "unfavourable" recovery group (GOSE 3-5) at 5 years. Variation in brain volumes (3D T1-weighted image) and white matter integrity (diffusion tensor imaging [DTI]) were quantitatively assessed over time and used to predict neurological outcome. RESULTS: TBI individuals showed a marked decrease in volumes of whole white matter (median -11.4% [interquartile range -5.8; -14.6]; p < 0.001) and deep grey nuclear structures (-17.1% [-10.6; -20.5]; p < 0.001). HCs did not show any significant change over the same time period. Median volumetric loss in several brain regions was higher with GOSE 3-5 than 6-8. These lesions were associated with lower fractional anisotropy and higher mean diffusivity at baseline. Volumetric variations were positively correlated with normalized fractional anisotropy and negatively with normalized mean diffusivity at baseline and follow-up. A computed predictive model with baseline DTI showed good accuracy to predict neurological outcome (area under the receiver operating characteristic curve 0.82 [95% confidence interval 0.81-0.83]) CONCLUSIONS: We characterised the striking atrophy of deep brain structures after severe TBI. DTI imaging in the subacute phase can predict the occurrence and localization of these tertiary lesions as well as long-term neurological outcome. TRIAL REGISTRATION: ClinicalTrials.gov: NCT00577954. Registered on October 2006.
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Brain Injuries, Traumatic , Diffusion Tensor Imaging , Humans , Brain/pathology , Brain Injuries, Traumatic/complications , Brain Injuries, Traumatic/diagnostic imaging , Brain Injuries, Traumatic/pathology , Follow-Up Studies , Magnetic Resonance Imaging , Prospective Studies , Case-Control StudiesABSTRACT
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.
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BACKGROUND: The COVID-19 crisis has strained world health care systems. This study aimed to develop an innovative prediction score using clinical and biological parameters (PREDICT score) to anticipate the need of intensive care of COVID-19 patients already hospitalized in standard medical units. METHODS: PREDICT score was based on a training cohort and a validation cohort retrospectively recruited in 2020 in the Marseille University Hospital. Multivariate analyses were performed, including clinical, and biological parameters, comparing a baseline group composed of COVID-19 patients exclusively treated in standard medical units to COVID-19 patients that needed intensive care during their hospitalization. RESULTS: Independent variables included in the PREDICT score were: age, Body Mass Index, Respiratory Rate, oxygen saturation, C-reactive protein, neutrophil-lymphocyte ratio and lactate dehydrogenase. The PREDICT score was able to correctly identify more than 83% of patients that needed intensive care after at least 1 day of standard medical hospitalization. CONCLUSIONS: The PREDICT score is a powerful tool for anticipating the intensive care need for COVID-19 patients already hospitalized in a standard medical unit. It shows limitations for patients who immediately need intensive care, but it draws attention to patients who have an important risk of needing intensive care after at least one day of hospitalization.
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The application of Enhanced Recovery After Surgery (ERAS) in neurosurgical practice is a relatively new concept. A limited number of studies involving ERAS protocols within neurosurgery, specifically for elective craniotomy, have been published, contrary to the ERAS spine surgery pathways that are now promoted by numerous national and international dedicated surgical societies and hospitals. In this review, we want to present the patient surgical journey from an anaesthesia perspective through the key components that can be included in the ERAS pathways for neurosurgical procedures, both craniotomies and major spine surgery.