Extracorporeal Life Support for Severely Burned Patients with Concurrent Inhalation Injury and Acute Respiratory Distress Syndrome: Experience from a Military Medical Burn Center.

BACKGROUND: Both inhalation injury and acute respiratory distress syndrome (ARDS) are risk factors that predict mortality in severely burned patients. Extracorporeal life support (ECLS) is widely used to rescue these patients; however, its efficacy and safety in this critical population have not been well defined. We report our experience of using ECLS for the treatment of severely burned patients with concurrent inhalation injury and ARDS. METHODS: This was a retrospective analysis of 14 patients collected from a single medical burn center from 2012 to 2019. All patients suffered from major burns with inhalation injury and ARDS, and were treated with ECLS. RESULTS: The median total body surface area of deep dermal or full thickness burns was 94.5%, ranging 47.7-99.0 %. The median revised Baux score was 122.0, ranging 90.0-155.0. All patients developed ARDS with a median partial pressure of arterial oxygen to a fraction of inspired oxygen ratio of 61.5, ranging 49.0-99.0. Indications for ECLS included sustained hypoxemia and unstable hemodynamics. The median interval for initiating ECLS was 2.5 days, ranging 1.0-156.0 days. The median duration of ECLS was 2.9 days, ranging 0.3-16.7 days. The overall survival to discharge was 42.8%. Causes of death included sepsis and multiple organ failure. ECLS-related complications included cannulation bleeding, catheter-related infection, and hemolysis. The incidence of risk factors reported in literature were higher in non-survivors, including Baux>120, albumin < 3.0 g/dL, and lactate > 8 mmol/L. CONCLUSIONS: For severely burned patients with concurrent inhalation injury and ARDS, ECLS could be a salvage treatment to improve sustained hypoxemia. However, the efficacy of hemodynamic support was limited. Identifying definite ECLS indications and rigorous patient selection would contribute to better clinical outcomes.

Artificial Intelligence-Enabled Electrocardiogram Improves the Diagnosis and Prediction of Mortality in Patients With Pulmonary Hypertension.

Background: Pulmonary hypertension is a disabling and life-threatening cardiovascular disease. Early detection of elevated pulmonary artery pressure (ePAP) is needed for prompt diagnosis and treatment to avoid detrimental consequences of pulmonary hypertension. Objectives: This study sought to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify patients with ePAP and related prognostic implications. Methods: From a hospital-based ECG database, the authors extracted the first pairs of ECG and transthoracic echocardiography taken within 2 weeks of each other from 41,097 patients to develop an AI model for detecting ePAP (PAP > 50 mm Hg by transthoracic echocardiography). The model was evaluated on independent data sets, including an external cohort of patients from Japan. Results: Tests of 10-fold cross-validation neural-network deep learning showed that the area under the receiver-operating characteristic curve of the AI model was 0.88 (sensitivity 81.0%; specificity 79.6%) for detecting ePAP. The diagnostic performance was consistent across age, sex, and various comorbidities (diagnostic odds ratio >8 for most factors examined). At 6-year follow-up, the patients predicted by the AI model to have ePAP were independently associated with higher cardiovascular mortality (HR: 3.69). Similar diagnostic performance and prediction for cardiovascular mortality could be replicated in the external cohort. Conclusions: The ECG-based AI model identified patients with ePAP and predicted their future risk for cardiovascular mortality. This model could serve as a useful clinical test to identify patients with pulmonary hypertension so that treatment can be initiated early to improve their survival prognosis.

Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model.

Electrocardiograms (ECGs) are widely used to clinically detect cardiac arrhythmias (CAs). They are also being used to develop computer-assisted methods for heart disease diagnosis. We have developed a convolution neural network model to detect and classify CAs, using a large 12-lead ECG dataset (6,877 recordings) provided by the China Physiological Signal Challenge (CPSC) 2018. Our model, which was ranked first in the challenge competition, achieved a median overall F1-score of 0.84 for the nine-type CA classification of CPSC2018's hidden test set of 2,954 ECG recordings. Further analysis showed that concurrent CAs were adequately predictive for 476 patients with multiple types of CA diagnoses in the dataset. Using only single-lead data yielded a performance that was only slightly worse than using the full 12-lead data, with leads aVR and V1 being the most prominent. We extensively consider these results in the context of their agreement with and relevance to clinical observations.

Improved survival of hospitalized patients with cardiac arrest due to coronary heart disease after implementation of post-cardiac arrest care: A population-based study.

Post-cardiac arrest care was implemented in 2010 and has been shown to improve the survival of patients with coronary heart disease (CHD). However, the findings varied for different survival conditions.We conducted a retrospective longitudinal study of records from 2007 to 2013 in the National Health Insurance Research Database. We evaluated the differences in short-term (2-day and 7-day) and long-term (30-day and survival to discharge) survival after the implementation of post-cardiac arrest care and among age subgroups. We reviewed inpatient datasets in accordance with the International Classification of Disease Clinical Modification, 9th revision codes (ICD-9-CM). Eligible participants were identified as those with simultaneous diagnoses of cardiac arrest (ICD-9-CM codes: 427.41 or 427.5) and CHD (ICD-9-CM codes: 410-414). Multiple logistic regression was applied to establish the relationship between calendar year and survival outcomes.The odds of 2-day survival from 2011 to 2013 were higher than those from 2007 to 2010 (adjusted odds ratio [aOR]: 1.15; 95% confidence interval [CI]: 1.03-1.29). Similarly, the odds of 7-day survival from 2011 to 2013 were higher than those from 2007 to 2010 (aOR: 1.11; 95% CI: 1.01-1.22). Improvements in the odds of 2-day and 7-day survival were discovered only in patients <65 years old. Our data reinforce that short-term survival improved after implementation of post-cardiac arrest care. However, older age seemed to nullify the influence of post-cardiac arrest care on survival.

Use of split-thickness plantar skin grafts in the management of leg and foot skin defects.

The basic principle of donor site selection is to take skin from areas that will heal with minimal scarring while balancing the needs of the recipient site. For skin loss from the lower legs and feet, the most common harvest site for split-thickness skin grafts is the anterior or posterior thigh; grafts from the plantar areas have been mostly used to cover the volar aspect of digits and palms. Between September 2015 and September 2017, 42 patients with areas of skin loss on the legs or feet were treated with plantar skin grafts because of their cosmetic benefits and the convenience of the surgical procedure and postoperative wound care. Our technique of harvesting a single layer of split-thickness skin graft (0.014 in. thick) from a non-weight-bearing area of the foot of the injured leg is simple and provided good functional and cosmetic outcomes at both the donor and recipient sites. All patients were very satisfied with the recovery progress and final results. Therefore, in the management of skin defects in the lower legs or feet that comprise less than 1.5% of the total body surface area, our surgical method is a reliable alternative to anterior or posterior thigh skin grafting.