Oral administration of liposome-encapsulated thymol could alleviate the inflammatory parameters in serum and hippocampus in a rat model of Alzheimer's disease.

BACKGROUND: Neuroinflammation is closely related to Alzheimer's Disease (AD) pathology, hence supplements with anti-inflammatory property could help attenuate the progression of AD. This study was conducted to evaluate the potential anti-inflammatory effects of liposome encapsulated thymol (LET), administered orally, in prevention of Alzheimer in a rat model by anti-inflammatory mechanisms. METHODS: The rats were grouped into six groups (n = 10 animals per group), including Control healthy (Con), Alzheimer's disease (AD) model, AD model treated with free thymol in 40 and 80 mg/kg body weight (TH40 and TH80), AD model treated with LET in 40 and 80 mg/kg of body weight (LET40 and LET80). The behavioral response of step through latency (Passive Avoidance Test), concentrations of interleukin-1ß (IL-1ß), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) and cyclooxygenase-2 (COX-2) and brain-derived neurotrophic factor (BDNF) were assessed in serum and hippocampus. RESULTS: The results showed that significant increase in concentrations of IL-1ß (P = 0.001), IL-6 (P = 0.001), TNF-α (P = 0.001) and COX-2 (P = 0.001) in AD group compared with healthy control rats. AD induction significantly reduced step through latency and revealed deficits in passive avoidance performance. The results also showed the treatment with free thymol especially in higher concentrations and also LTE could decrease serum concentrations of IL-1ß (P < 0.05), IL-6 (P < 0.05), TNF-α (P < 0.05), and COX-2 (P < 0.05) and increase BDNF (P < 0.05) compared with control Alzheimer rats in hippocampus and serum. There were also significant correlations between serum and hippocampus concentrations of IL-1ß (r2 = 0.369, P = 0.001), IL-6 (r2 = 0.386, P = 0.001), TNF-α (r2 = 0.412, P = 0.001), and COX-2 (r2 = 0.357, P = 0.001). It means a closed and positive relation between serum and hippocampus concentrations of IL-1ß, IL-6, TNF-α, and COX-2. CONCLUSIONS: LET demonstrates its ability to attenuate neuroinflammatory reaction in AD model through suppression of IL-1ß, IL-6, and TNF-α and COX-2 indicators. Hence, it can ameliorate AD pathogenesis by declining inflammatory reaction.

Predicting Acute Brain Injury in Venoarterial Extracorporeal Membrane Oxygenation Patients with Tree-Based Machine Learning: Analysis of the Extracorporeal Life Support Organization Registry.

Objective: To determine if machine learning (ML) can predict acute brain injury (ABI) and identify modifiable risk factors for ABI in venoarterial extracorporeal membrane oxygenation (VA-ECMO) patients. Design: Retrospective cohort study of the Extracorporeal Life Support Organization (ELSO) Registry (2009-2021). Setting: International, multicenter registry study of 676 ECMO centers. Patients: Adults (≥18 years) supported with VA-ECMO or extracorporeal cardiopulmonary resuscitation (ECPR). Interventions: None. Measurements and Main Results: Our primary outcome was ABI: central nervous system (CNS) ischemia, intracranial hemorrhage (ICH), brain death, and seizures. We utilized Random Forest, CatBoost, LightGBM and XGBoost ML algorithms (10-fold leave-one-out cross-validation) to predict and identify features most important for ABI. We extracted 65 total features: demographics, pre-ECMO/on-ECMO laboratory values, and pre-ECMO/on-ECMO settings.Of 35,855 VA-ECMO (non-ECPR) patients (median age=57.8 years, 66% male), 7.7% (n=2,769) experienced ABI. In VA-ECMO (non-ECPR), the area under the receiver-operator characteristics curves (AUC-ROC) to predict ABI, CNS ischemia, and ICH was 0.67, 0.67, and 0.62, respectively. The true positive, true negative, false positive, false negative, positive, and negative predictive values were 33%, 88%, 12%, 67%, 18%, and 94%, respectively for ABI. Longer ECMO duration, higher 24h ECMO pump flow, and higher on-ECMO PaO2 were associated with ABI.Of 10,775 ECPR patients (median age=57.1 years, 68% male), 16.5% (n=1,787) experienced ABI. The AUC-ROC for ABI, CNS ischemia, and ICH was 0.72, 0.73, and 0.69, respectively. The true positive, true negative, false positive, false negative, positive, and negative predictive values were 61%, 70%, 30%, 39%, 29% and 90%, respectively, for ABI. Longer ECMO duration, younger age, and higher 24h ECMO pump flow were associated with ABI. Conclusions: This is the largest study predicting neurological complications on sufficiently powered international ECMO cohorts. Longer ECMO duration and higher 24h pump flow were associated with ABI in both non-ECPR and ECPR VA-ECMO.

Adult Highlights From the Extracorporeal Life Support Organization Registry: 2017-2022.

The Extracorporeal Life Support Organization (ELSO) registry captures clinical data and outcomes on patients receiving extracorporeal membrane oxygenation (ECMO) support across the globe at participating centers. It provides a very unique opportunity to benchmark outcomes and analyze the clinical course to help identify ways of improving patient outcomes. In this review, we summarize select adult ECMO articles published using the ELSO registry over the past 5 years. These articles highlight innovative utilization of the registry data in generating hypotheses for future clinical trials. Members of the ELSO Scientific Oversight Committee can be found here: https://www.elso.org/registry/socmembers.aspx .

Interpretable machine learning-based predictive modeling of patient outcomes following cardiac surgery.

BACKGROUND: The clinical applicability of machine learning predictions of patient outcomes following cardiac surgery remains unclear. We applied machine learning to predict patient outcomes associated with high morbidity and mortality after cardiac surgery and identified the importance of variables to the derived model's performance. METHODS: We applied machine learning to the Society of Thoracic Surgeons Adult Cardiac Surgery Database to predict postoperative hemorrhage requiring reoperation, venous thromboembolism (VTE), and stroke. We used permutation feature importance to identify variables important to model performance and a misclassification analysis to study the limitations of the model. RESULTS: The study dataset included 662,772 subjects who underwent cardiac surgery between 2015 and 2017 and 240 variables. Hemorrhage requiring reoperation, VTE, and stroke occurred in 2.9%, 1.2%, and 2.0% of subjects, respectively. The model performed remarkably well at predicting all 3 complications (area under the receiver operating characteristic curve, 0.92-0.97). Preoperative and intraoperative variables were not important to model performance; instead, performance for the prediction of all 3 outcomes was driven primarily by several postoperative variables, including known risk factors for the complications, such as mechanical ventilation and new onset of postoperative arrhythmias. Many of the postoperative variables important to model performance also increased the risk of subject misclassification, indicating internal validity. CONCLUSIONS: A machine learning model accurately and reliably predicts patient outcomes following cardiac surgery. Postoperative, as opposed to preoperative or intraoperative variables, are important to model performance. Interventions targeting this period, including minimizing the duration of mechanical ventilation and early treatment of new-onset postoperative arrhythmias, may help lower the risk of these complications.

Utilizing Machine Learning to Predict Neurological Injury in Venovenous Extracorporeal Membrane Oxygenation Patients: An Extracorporeal Life Support Organization Registry Analysis.

Background: Venovenous extracorporeal membrane oxygenation (VV-ECMO) is associated with acute brain injury (ABI), including central nervous system (CNS) ischemia (defined as ischemic stroke or hypoxic-ischemic brain injury) and intracranial hemorrhage (ICH). There is limited data on prediction models for ABI and neurological outcomes in VV-ECMO. Research Question: Can machine learning (ML) accurately predict ABI and identify modifiable factors of ABI in VV-ECMO? Study Design and Methods: We analyzed adult (≥18 years) VV-ECMO patients in the Extracorporeal Life Support Organization Registry (2009-2021) from 676 centers. ABI was defined as CNS ischemia, ICH, brain death, and seizures. Overall, 65 total variables were extracted including clinical characteristics and pre-ECMO and on-ECMO variables. Random Forest, CatBoost, LightGBM, and XGBoost ML algorithms (10-fold leave-one-out cross-validation) were used to predict ABI. Feature Importance Scores were used to pinpoint variables most important for predicting ABI. Results: Of 37,473 VV-ECMO patients (median age=48.1 years, 63% male), 2,644 (7.1%) experienced ABI: 610 (2%) and 1,591 (4%) experienced CNS ischemia and ICH, respectively. The median ECMO duration was 10 days (interquartile range=5-20 days). The area under the receiver-operating characteristics curves to predict ABI, CNS ischemia, and ICH were 0.67, 0.63, and 0.70, respectively. The accuracy, positive predictive, and negative predictive values for ABI were 79%, 15%, and 95%, respectively. ML identified pre-ECMO cardiac arrest as the most important risk factor for ABI while ECMO duration and bridge to transplantation as an indication for ECMO were associated with lower risk of ABI. Interpretation: This is the first study to use machine learning to predict ABI in a large cohort of VV-ECMO patients. Performance was sub-optimal due to the low reported prevalence of ABI with lack of standardization of neuromonitoring/imaging protocols and data granularity in the ELSO Registry. Standardized neurological monitoring and imaging protocols may improve machine learning performance to predict ABI.

Benchmarking Modern Named Entity Recognition Techniques for Free-text Health Record Deidentification.

Electronic Health Records (EHRs) have become the primary form of medical data-keeping across the United States. Federal law restricts the sharing of any EHR data that contains protected health information (PHI). De-identification, the process of identifying and removing all PHI, is crucial for making EHR data publicly available for scientific research. This project explores several deep learning-based named entity recognition (NER) methods to determine which method(s) perform better on the de-identification task. We trained and tested our models on the i2b2 training dataset, and qualitatively assessed their performance using EHR data collected from a local hospital. We found that 1) Bi-LSTM-CRF represents the best-performing encoder/decoder combination, 2) character-embeddings tend to improve precision at the price of recall, and 3) transformers alone under-perform as context encoders. Future work focused on structuring medical text may improve the extraction of semantic and syntactic information for the purposes of EHR deidentification.

Pneumonia initiates a tauopathy.

Pneumonia causes short- and long-term cognitive dysfunction in a high proportion of patients, although the mechanism(s) responsible for this effect are unknown. Here, we tested the hypothesis that pneumonia-elicited cytotoxic amyloid and tau variants: (1) are present in the circulation during infection; (2) lead to impairment of long-term potentiation; and, (3) inhibit long-term potentiation dependent upon tau. Cytotoxic amyloid and tau species were recovered from the blood and the hippocampus following pneumonia, and they were present in the extracorporeal membrane oxygenation oxygenators of patients with pneumonia, especially in those who died. Introduction of immunopurified blood-borne amyloid and tau into either the airways or the blood of uninfected animals acutely and chronically impaired hippocampal information processing. In contrast, the infection did not impair long-term potentiation in tau knockout mice and the amyloid- and tau-dependent disruption in hippocampal signaling was less severe in tau knockout mice. Moreover, the infection did not elicit cytotoxic amyloid and tau variants in tau knockout mice. Therefore, pneumonia initiates a tauopathy that contributes to cognitive dysfunction.

Automated multi-class brain tumor types detection by extracting RICA based features and employing machine learning techniques.

Among the other cancer types, the brain tumor is one the leading cause of cancer across globe. If the tumor is properly identified at an earlier stage, then the chances of the survival can be increased. To categorize the brain tumor there are several factors including texture, type and location of brain tumor. We proposed a novel reconstruction independent component analysis (RICA) feature extraction method to detect multi-class brain tumor types (pituitary, meningioma, and glioma). We then employed the robust machine learning techniques as support vector machine (SVM) with quadratic and linear kernels and linear discriminant analysis (LDA). For training and testing of the data validation, a 10-fold cross validation was employed. For the multi-class classification, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and AUC were, respectively, 97.78%, 100%, 100%, 99.07, 99.34% and 0.9892 to detect pituitary using SVM Cubic followed by meningioma with accuracy (96.96%0, AUC (0.9348) and glioma with accuracy (95.88%), AUC (0.9635). The findings indicates that RICA feature based proposed methodology has more potential to detect the multiclass brain tumor types for improving diagnostic efficiency and can further improve the prediction accuracy to achieve the clinical outcomes.

Development of a Deep Learning Network to Classify Inferior Vena Cava Collapse to Predict Fluid Responsiveness.

OBJECTIVES: To create a deep learning algorithm capable of video classification, using a long short-term memory (LSTM) network, to analyze collapsibility of the inferior vena cava (IVC) to predict fluid responsiveness in critically ill patients. METHODS: We used a data set of IVC ultrasound (US) videos to train the LSTM network. The data set was created from IVC US videos of spontaneously breathing critically ill patients undergoing intravenous fluid resuscitation as part of 2 prior prospective studies. We randomly selected 90% of the IVC videos to train the LSTM network and 10% of the videos to test the LSTM network's ability to predict fluid responsiveness. Fluid responsiveness was defined as a greater than 10% increase in the cardiac index after a 500-mL fluid bolus, as measured by bioreactance. RESULTS: We analyzed 211 videos from 175 critically ill patients: 191 to train the LSTM network and 20 to test it. Using standard data augmentation techniques, we increased our sample size from 191 to 3820 videos. Of the 175 patients, 91 (52%) were fluid responders. The LSTM network was able to predict fluid responsiveness moderately well, with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval [CI], 0.43-1.00), a positive likelihood ratio of infinity, and a negative likelihood ratio of 0.3 (95% CI, 0.12-0.77). In comparison, point-of-care US experts using video review offline and manual diameter measurement via software caliper tools achieved an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.83-0.99). CONCLUSIONS: We demonstrated that an LSTM network can be trained by using videos of IVC US to classify IVC collapse to predict fluid responsiveness. Our LSTM network performed moderately well given the small training cohort but worse than point-of-care US experts. Further training and testing of the LSTM network with a larger data sets is warranted.

Respiratory Variation in Carotid Artery Peak Systolic Velocity Is Unable to Predict Fluid Responsiveness in Spontaneously Breathing Critically Ill Patients When Assessed by Novice Physician Sonologists.

BACKGROUND: Respiratory variation in carotid artery peak systolic velocity (ΔVpeak) assessed by point-of-care ultrasound (POCUS) has been proposed as a noninvasive means to predict fluid responsiveness. We aimed to evaluate the ability of carotid ΔVpeak as assessed by novice physician sonologists to predict fluid responsiveness. METHODS: This study was conducted in 2 intensive care units. Spontaneously breathing, nonintubated patients with signs of volume depletion were included. Patients with atrial fibrillation/flutter, cardiogenic, obstructive or neurogenic shock, or those for whom further intravenous (IV) fluid administration would be harmful were excluded. Three novice physician sonologists were trained in POCUS assessment of carotid ΔVpeak. They assessed the carotid ΔVpeak in study participants prior to the administration of a 500 mL IV fluid bolus. Fluid responsiveness was defined as a ≥10% increase in cardiac index as measured using bioreactance. RESULTS: Eighty-six participants were enrolled, 50 (58.1%) were fluid responders. Carotid ΔVpeak performed poorly at predicting fluid responsiveness. Test characteristics for the optimum carotid ΔVpeak of 8.0% were: area under the receiver operating curve = 0.61 (95% CI: 0.48-0.73), sensitivity = 72.0% (95% CI: 58.3-82.56), specificity = 50.0% (95% CI: 34.5-65.5). CONCLUSIONS: Novice physician sonologists using POCUS are unable to predict fluid responsiveness using carotid ΔVpeak. Until further research identifies key limiting factors, clinicians should use caution directing IV fluid resuscitation using carotid ΔVpeak.

Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection.

BACKGROUND: The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. PURPOSE: The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs. MATERIALS AND METHODS: Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis. RESULTS: For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively. CONCLUSION: AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs.

Change in Carotid Blood Flow and Carotid Corrected Flow Time Assessed by Novice Sonologists Fails to Determine Fluid Responsiveness in Spontaneously Breathing Intensive Care Unit Patients.

Measurement of carotid blood flow (CBF) and corrected carotid flow time (ccFT) has been proposed as a non-invasive means of determining fluid responsiveness. We evaluated the ability of CBF and ccFT as assessed by novice sonologists to determine fluid responsiveness in intensive care unit patients. Three novice physician sonologists performed carotid ultrasounds before and after a fluid bolus and calculated changes in CBF and ccFT. Fluid responsiveness was defined as a ≥10% increase in cardiac index as measured using bioreactance. Of 112 participants, 56 (50%) were fluid responders. Changes in CBF and ccFT performed poorly at determining fluid responsiveness: 19 mL/min (area under the receiver operating characteristic curve: 0.58, 95% confidence interval: 0.47-0.68) and 6 ms (0.59, 0.46-0.65) respectively. Novice physician sonologists are unable to determine fluid responsiveness using CBF or ccFT. Further research is needed to identify the key limiting factors in using carotid ultrasound to determine fluid responsiveness.

Detecting prostate cancer using deep learning convolution neural network with transfer learning approach.

Prostate Cancer in men has become one of the most diagnosed cancer and also one of the leading causes of death in United States of America. Radiologists cannot detect prostate cancer properly because of complexity in masses. In recent past, many prostate cancer detection techniques were developed but these could not diagnose cancer efficiently. In this research work, robust deep learning convolutional neural network (CNN) is employed, using transfer learning approach. Results are compared with various machine learning strategies (Decision Tree, SVM different kernels, Bayes). Cancer MRI database are used to train GoogleNet model and to train Machine Learning classifiers, various features such as Morphological, Entropy based, Texture, SIFT (Scale Invariant Feature Transform), and Elliptic Fourier Descriptors are extracted. For the purpose of performance evaluation, various performance measures such as specificity, sensitivity, Positive predictive value, negative predictive value, false positive rate and receive operating curve are calculated. The maximum performance was found with CNN model (GoogleNet), using Transfer learning approach. We have obtained reasonably good results with various Machine Learning Classifiers such as Decision Tree, Support Vector Machine RBF kernel and Bayes, however outstanding results were obtained by using deep learning technique.

Multiscale entropy analysis to quantify the dynamics of motor movement signals with fist or feet movement using topographic maps.

BACKGROUND: Brain neural activity is measured using electroencephalography (EEG) recording from the scalp. The EEG motor/imagery tasks help disabled people to communicate with the external environment. OBJECTIVE: In this paper, robust multiscale sample entropy (MSE) and wavelet entropy measures are employed using topographic maps' analysis and tabulated form to quantify the dynamics of EEG motor movements tasks with actual and imagery opening and closing of fist or feet movements. METHODS: To distinguish these conditions, we used the topographic maps which visually show the significance level of the brain regions and probes for dominant activities. The paired t-test and Posthoc Tukey test are used to find the significance levels. RESULTS: The topographic maps results obtained using MSE reveal that maximum electrodes show the significance in frontpolar, frontal, and few frontal and parietal brain regions at temporal scales 3, 4, 6 and 7. Moreover, it was also observed that the distribution of significance is from frontoparietal brain regions. Using wavelet entropy, the significant results are obtained at frontpolar, frontal, and few electrodes in right hemisphere. The highest significance is obtained at frontpolar electrodes followed by frontal and few central and parietal electrodes.

Performance of a 25% Inferior Vena Cava Collapsibility in Detecting Fluid Responsiveness When Assessed by Novice Versus Expert Physician Sonologists.

OBJECTIVES: Inferior vena cava collapsibility (cIVC) measured by point-of-care ultrasound (POCUS) has been proposed as a noninvasive means of assessing fluid responsiveness. We aimed to prospectively evaluate the performance of a 25% cIVC cutoff value to detect fluid responsiveness among spontaneously breathing intensive care unit (ICU) patients when assessed with POCUS by novice versus expert physician sonologists. METHODS: Prospective observational study of spontaneously breathing ICU patients. Fluid responsiveness was defined as a >10% increase in cardiac index following a 500 mL fluid bolus, measured by bioreactance. Novice sonologist measured cIVC with POCUS. Their measurements were later compared to an expert physician sonologist who independently reviewed the POCUS images and assessed cIVCs. RESULTS: Of the 85 participants, 44 (52%) were fluid responders. A 25% cIVC cutoff value performed better when assessed by expert sonologists than novice physician sonologists (receiver-operator characteristic curve, ROC = 0.82 [0.74-0.88] vs ROC = 0.69 [0.60-0.77]). CONCLUSIONS: A 25% cIVC cutoff value measured by POCUS detects fluid responsiveness. However, the experience of the physician sonologist affects test performance and should be considered when interpreting and clinically using cIVC to direct intravenous fluid resuscitation.

Extracorporeal Life Support in Adults with Acute Respiratory Failure: Current Evidence-Based Practices.

There has been rapid adoption of extracorporeal life support (ECLS) in adult patients with severe acute respiratory failure. Extracorporeal membrane oxygenation (ECMO) is used to rescue patients with severe hypoxemic and hypercapnic respiratory failure refractory to optimal therapy and extracorporeal carbon dioxide removal (ECCO2R) supports hypercapnic respiratory failure and allows very low tidal volume ventilation to minimize the risk of ventilator-induced lung injury. Currently over 3,000 cases of ECLS (ECMO and ECCO2R) in adults with respiratory failure are reported annually to the Extracorporeal Life Support Organization registry. Advances in the care of patients with acute respiratory distress syndrome, technological innovations in extracorporeal circuitry, and insights from modern clinical trials of ECLS have led to favorable outcomes and a renewed interest in the use of this technology. Significant gaps in knowledge about best practices remain, however. This review will summarize indications for respiratory support in adults, current evidence available from clinical trials and our institution's experience with adult respiratory ECLS.

The Restrictive IV Fluid Trial in Severe Sepsis and Septic Shock (RIFTS): A Randomized Pilot Study.

OBJECTIVES: It is unclear if a low- or high-volume IV fluid resuscitation strategy is better for patients with severe sepsis and septic shock. DESIGN: Prospective randomized controlled trial. SETTING: Two adult acute care hospitals within a single academic system. PATIENTS: Patients with severe sepsis and septic shock admitted from the emergency department to the ICU from November 2016 to February 2018. INTERVENTIONS: Patients were randomly assigned to a restrictive IV fluid resuscitation strategy (≤ 60 mL/kg of IV fluid) or usual care for the first 72 hours of care. MEASUREMENTS AND MAIN RESULTS: We enrolled 109 patients, of whom 55 were assigned to the restrictive resuscitation group and 54 to the usual care group. The restrictive group received significantly less resuscitative IV fluid than the usual care group (47.1 vs 61.1 mL/kg; p = 0.01) over 72 hours. By 30 days, there were 12 deaths (21.8%) in the restrictive group and 12 deaths (22.2%) in the usual care group (odds ratio, 1.02; 95% CI, 0.41-2.53). There were no differences between groups in the rate of new organ failure, hospital or ICU length of stay, or serious adverse events. CONCLUSIONS: This pilot study demonstrates that a restrictive resuscitation strategy can successfully reduce the amount of IV fluid administered to patients with severe sepsis and septic shock compared with usual care. Although limited by the sample size, we observed no increase in mortality, organ failure, or adverse events. These findings further support that a restrictive IV fluid strategy should be explored in a larger multicenter trial.

Examining the role of extracorporeal membrane oxygenation in patients following suspected or confirmed suicide attempts: A case series.

The decision to offer extracorporeal membrane oxygenation (ECMO) is based on a risk/benefit assessment and the likelihood of a treatable underlying condition or the feasibility of destination therapy (durable mechanical support or thoracic organ transplantation) should heart-lung function fail to improve. Patients who present following suspected suicide attempts who fail medical therapy may pose a dilemma for clinicians. An assessment to determine if a patient has a high likelihood of psychiatric recovery such that bridging with ECMO or ultimately destination therapy could or should be offered is not always feasible in the setting of critical illness. This case series reviews our institution's experience with ECMO in the management of five patients who presented following suspected or confirmed suicide attempts. All five patients survived to hospital discharge. Two had subsequent psychiatric admissions, one following a repeat suicide attempt. A discussion of these cases demonstrates the effectiveness of ECMO in supporting this group of patients in the short-term. The self-limited natural history of many psychiatric episodes, poisonings and traumatic injuries makes the use of ECMO a potentially reasonable support strategy. However, careful consideration must be given to psychiatric history and follow-up given the substantial commitment of resources, potential for complications and for stranding patients on extracorporeal therapy without definitive destination therapy.