Critically ill patients with COVID-19 with ECMO and artificial liver plasma exchange: A retrospective study.

Medicine (Baltimore)

Department of Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China.

Published: June 2020

COVID-19 is an emerging infectious disease capable of causing severe pneumonia. We aimed to characterize a group of critically ill patients in a single-center study.This was a retrospective case series of 23 patients with confirmed COVID-19-related critical illness in the intensive care unit (ICU) of a hospital in Hangzhou Zhejiang Province between January 22 and March 20, 2020.Of the 23 critically ill patients, the median age was 66 years (interquartile range [IQR] 59-80 years). The median time from disease onset to ICU admission was 10 days (IQR 6-11 days), to mechanical ventilation (MV) was 11 days (IQR 7.75-13 days), to artificial liver plasma exchange was 12 days (IQR 9.75-14.75 days), and to extracorporeal membrane oxygenation (ECMO) was 22 days (IQR 17.5-30 days). Nine patients required high flow oxygen. Fourteen patients received MV. Six required ECMO. Nine received artificial liver plasma exchange. Mortality was 0 at day 28.Mortality was 0 at day 28 in our single-center study. Extracorporeal membrane oxygenation reduced the requirements for ventilator support. Artificial liver plasma exchange significantly reduced inflammatory cytokine levels. These supportive therapies helped to extend the patients' survival times and increase the chance of follow-up treatments.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328989PMC
http://dx.doi.org/10.1097/MD.0000000000021012DOI Listing

Publication Analysis

Top Keywords

artificial liver
16
liver plasma
16
plasma exchange
16
days iqr
16
critically ill
12
ill patients
12
days
8
extracorporeal membrane
8
membrane oxygenation
8
patients
6

Similar Publications

Machine learning applications in healthcare clinical practice and research.

World J Clin Cases

January 2025

Department of Gastroenterology, Laiko General Hospital, National and Kapodistrian University of Athens, Athens 11527, Greece.

Machine learning (ML) is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis, thus creating machines that can complete tasks otherwise requiring human intelligence. Among its various applications, it has proven groundbreaking in healthcare as well, both in clinical practice and research. In this editorial, we succinctly introduce ML applications and present a study, featured in the latest issue of the .

View Article and Find Full Text PDF

Surface-enhanced Raman spectroscopy (SERS) is widely recognized as a powerful analytical technique, offering molecular identification by amplifying characteristic vibrational signals, even at the single-molecule level. While SERS has been successfully applied for a wide range of targets including pesticides, dyes, bacteria, and pharmaceuticals, it has struggled with the detection of molecules with inherently low Raman scattering cross-sections. Urea, a key nitrogen-containing biomolecule and the diamide of carbonic acid, is a prime example of such a challenging target.

View Article and Find Full Text PDF

Objective: To identify the early predictors of a self-reported persistence of long COVID syndrome (LCS) at 12 months after hospitalisation and to propose the prognostic model of its development.

Design: A combined cross-sectional and prospective observational study.

Setting: A tertiary care hospital.

View Article and Find Full Text PDF

Liver cancer remains one of the most formidable challenges in modern medicine, characterized by its high incidence and mortality rate. Emerging evidence underscores the critical roles of the immune microenvironment in tumor initiation, development, prognosis, and therapeutic responsiveness. However, the composition of the immune microenvironment of liver cancer (LC-IME) and its association with clinicopathological significance remain unelucidated.

View Article and Find Full Text PDF

Artificial intelligence model predicts M2 macrophage levels and HCC prognosis with only globally labeled pathological images.

Front Oncol

December 2024

Department of Hepatobiliary and Pancreatic Surgery, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, Henan, China.

Background And Aims: The levels of M2 macrophages are significantly associated with the prognosis of hepatocellular carcinoma (HCC), however, current detection methods in clinical settings remain challenging. Our study aims to develop a weakly supervised artificial intelligence model using globally labeled histological images, to predict M2 macrophage levels and forecast the prognosis of HCC patients by integrating clinical features.

Methods: CIBERSORTx was used to calculate M2 macrophage abundance.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!