Interhospital ECMO transport during Covid-19 pandemic: A clinical experince

Objectives: During COVID pandemic, ECMO support for the patients with ARDS have saved many lives. Although its an important and effective treatment modality, management of ECMO could be done in a few specialized centers. In this study, we share our in- and out-of-city ECMO transport experience of the patients with COVID-ARDS. Method(s): A total of 75 patients (57% male- 43 %female) were included in this study. The decision ECMO support, initiation at referral hospital, and transport process of all of the patients to our centre were performed by our mobile ECMO team. All transports were done by land ambulance Results: Mean age of the patients was 43.4+/-11.5 years. Mean intubation period before ECMO support was 8.5 +/-8.3 days. We transferred 14 patients from the centers within the city and 12 patients from the centers outside the city to our hospital. Mean distance between our center and the referral center was 36,2 kms (max 269- min 1). We did not experience any major complication during transport. A total of 30 patients (38,6 %) were weaned from ECMO and discharged from hospital. Conclusion(s): ECMO support is an advanced treatment modality for pulmonary failure patients. The decision of initiation, cannulation, transport and management should be performed by experienced centers to achive acceptable results.

Prediction of treatment cost by artificial neural network of patients with COVID-19 in intensive care unit

Objective: Artificial neural networks (ANNs) are computer systems that are inspired by the biological neural networks that make up mammalian brains. An ANN is built from a network of linked units or nodes known as artificial neurons, which are roughly modeled after the neurons in the human brain. Each link, like synapses in a human brain, has the ability to send a signal to other neurons. The connections are referred to as edges. Neurons and edges usually have a weight that changes as learning progresses. The weight changes the intensity of the signal at a connection. Artificial neural networks have found applications in a wide range of fields due to their capacity to recreate and simulate nonlinear phenomena. System identification and control, medical diagnostics, data mining, visualization, machine translation, distinguishing highly invasive cancer cell lines from less invasive lines using simply cell shape information, and many more domains are examples of application areas. In this study, ANN analysis was utilized by us to forecast the total cost of therapy or the prognosis of severe COVID-19 the patients in the intensive care unit (ICU). Methods: The parameters such as ages, and the other biochemical parameters that affect the staying periods (days) of COVID-19 infected patients in ICU were evaluated by using an ANN analysis. For this a computer program, Pythia®, was used to develop ANN models. Real data was used for that selected patients in this study. Results: The real data obtained from the ICU and gave to the computer as initial parameters. The computer program gave 15 neurons for the first level, one neurons for the second level as the most suitable model for the prediction (SSD = 0.000995). This program predicts a total cost 144.930,94 Turkish Lira (27.300 USD) where the real cost 142.234,06 Turkish Lira (26.792 USD) for the real patient in 2019. This relation was found to be good to predict the possible affected parameters on staying times. Conclusion: The ANN model developed and released in this research does not necessitate any experimental parameters. Besides, ANN has the ability to deliver helpful and exact prediction or information regarding the expense of COVID-19 patients in ICU. © 2022. All Rights Reserved.