scholarly journals Outcome of children with sickle cell disease admitted to intensive care - a single institution experience

2010 ◽  
Vol 150 (5) ◽  
pp. 614-617 ◽  
Author(s):  
Jack L. Bartram ◽  
Swee L. Thein ◽  
Kate Gardner ◽  
Yaya Egberongbe ◽  
Pam D’Silva ◽  
...  
Keyword(s):  
Intensive Care ◽  
Sickle Cell Disease ◽  
Sickle Cell ◽  
Cell Disease ◽  
Single Institution

scholarly journals Characteristics and outcomes of patients with sickle cell disease admitted to pediatric intensive care: A retrospective review

2020 ◽  
Vol 11 (2) ◽  
pp. 68
Author(s):  
Ehab Hanafy ◽  
Mustafa Altoonisi ◽  
Al Jawharah Alghuraydh ◽  
Areej Alatawi ◽  
Badriah Alsabah ◽  
...  
Keyword(s):  
Intensive Care ◽  
Sickle Cell Disease ◽  
Sickle Cell ◽  
Retrospective Review ◽  
Pediatric Intensive Care ◽  
Cell Disease

scholarly journals Patients with sickle cell disease and suspected COVID‐19 in a paediatric intensive care unit

2020 ◽  
Vol 190 (1) ◽  
Author(s):  
Claire Heilbronner ◽  
Laureline Berteloot ◽  
Pierre Tremolieres ◽  
Laurent Dupic ◽  
Laure Saint Blanquat ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Sickle Cell Disease ◽  
Sickle Cell ◽  
Paediatric Intensive Care Unit ◽  
Paediatric Intensive Care ◽  
Cell Disease

scholarly journals Outcome of adults with sickle cell disease admitted to critical care - experience of a single institution in the UK

2010 ◽  
Vol 150 (5) ◽  
pp. 610-613 ◽  
Author(s):  
Kate Gardner ◽  
Cathy Bell ◽  
Jack L. Bartram ◽  
Marlene Allman ◽  
Moji Awogbade ◽  
...  
Keyword(s):  
Critical Care ◽  
Sickle Cell Disease ◽  
Sickle Cell ◽  
Cell Disease ◽  
Single Institution ◽  
Care Experience ◽  
The Uk

scholarly journals Machine learning predicts early-onset acute organ failure in critically ill patients with sickle cell disease

2019 ◽  
Author(s):  
Akram Mohammed ◽  
Pradeep S. B. Podila ◽  
Robert L. Davis ◽  
Kenneth I. Ataga ◽  
Jane S. Hankins ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Sickle Cell Disease ◽  
Kidney Disease ◽  
Organ Failure ◽  
Organ Dysfunction ◽  
Sickle Cell ◽  
Machine Learning Techniques ◽  
Cell Disease ◽  
Learning Techniques

AbstractBackgroundSickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications including pain episodes, stroke, and kidney disease. Patients with SCD develop chronic organ dysfunction, which may progress to organ failure during disease exacerbations. Early detection of acute physiological deterioration leading to organ failure is not always attainable. Machine learning techniques that allow for prediction of organ failure may enable earlier identification and treatment, and potentially reduce mortality. We tested the hypothesis that machine learning physiomarkers could predict the development of organ dysfunction in an adult sample of patients with SCD admitted to intensive care units.Methods and FindingsWe studied 63 sequential SCD patients with 163 patient encounters, mean age 33.0±11.0 years, admitted to intensive care units, some of whom (6.7%) had pre-existing cardiovascular or kidney disease. A subset of these patient encounters (37; 23%) met sequential organ failure assessment (SOFA) criteria. The site of organ failure included: central nervous system (32), cardiovascular (11), renal (10), liver (7), respiratory (5) and coagulation (2) systems. Most (81.5%) of the patient encounters who experienced organ failure had single organ failure. The other 126 SCD patient encounters served as controls. A set of signal processing features (such as fast fourier transform, energy, continuous wavelet transform, etc.) derived from heart rate, blood pressure, and respiratory rate were identified to distinguish patients with SCD who developed acute physiological deterioration leading to organ failure, from SCD patients who did not meet the criteria. A random forest model accurately predicted organ failure up to six hours prior to onset, with a five-fold cross-validation accuracy of 94.57% (average sensitivity and specificity of 90.24% and 98.9% respectively).ConclusionsThis study demonstrates the viability of using machine learning to predict acute physiological deterioration heralded organ failure among hospitalized adults with SCD. The discovery of salient physiomarkers through machine learning techniques has the potential to further accelerate the development and implementation of innovative care delivery protocols and strategies for medically vulnerable patients.

scholarly journals Reduced-Intensity Allogeneic Stem Cell Transplant for Sickle Cell Disease in Pediatrics: A Single Institution Experience

2018 ◽  
Vol 24 (3) ◽  
pp. S434-S435
Author(s):  
Michael J. Eckrich ◽  
Lisa Madden ◽  
Candace Taylor ◽  
Christina Matheny ◽  
Erica Frausto-Garcia ◽  
...  
Keyword(s):  
Stem Cell ◽  
Sickle Cell Disease ◽  
Sickle Cell ◽  
Stem Cell Transplant ◽  
Cell Transplant ◽  
Allogeneic Stem Cell Transplant ◽  
Cell Disease ◽  
Single Institution ◽  
Reduced Intensity

Blood transfusion usage among adults with sickle cell disease - a single institution experience over ten years

2011 ◽  
Vol 152 (6) ◽  
pp. 766-770 ◽  
Author(s):  
Emma Drasar ◽  
Norris Igbineweka ◽  
Nisha Vasavda ◽  
Matthew Free ◽  
Moji Awogbade ◽  
...  
Keyword(s):  
Sickle Cell Disease ◽  
Blood Transfusion ◽  
Sickle Cell ◽  
Cell Disease ◽  
Single Institution

scholarly journals Adult Sickle Cell Disease: A Five-Year Experience of Intensive Care Management in a University Hospital in Oman

2012 ◽  
Vol 12 (2) ◽  
pp. 177-183 ◽  
Author(s):  
Qutaiba Amir Tawfic ◽  
Rajini Kausalya ◽  
Dhuha M. Alwan ◽  
Jyoti Burad ◽  
Ahmed K Mohammed ◽  
...  
Keyword(s):  
Intensive Care ◽  
Sickle Cell Disease ◽  
Sickle Cell ◽  
Care Management ◽  
University Hospital ◽  
Cell Disease ◽  
Intensive Care Management
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