Predictors of mortality in adults with Sickle cell disease admitted to intensive care unit in Bahrain

Khawaja, Sana Abdulaaziz Al; Ateya, Zainab Mahdi; Hammam, Ridha Abdulla Al

doi:10.1016/j.jcrc.2017.07.032

Cited by 14 publications

(12 citation statements)

References 34 publications

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“…In other studies, ACS was the reason for ICU admission in 30% to 70% of patients 7 , 8 , 13 , 15 , 16 . This wide range can be explained by differences in types of ICUs, study populations, and varying proportions of medical and surgical admissions.…”

Section: Discussionmentioning

confidence: 75%

Nationwide retrospective study of critically ill adults with sickle cell disease in France

Agbakou

Mekontso-Dessap

Péré

et al. 2021

Sci Rep

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Little is known about patients with sickle cell disease (SCD) who require intensive care unit (ICU) admission. The goals of this study were to assess outcomes in patients admitted to the ICU for acute complications of SCD and to identify factors associated with adverse outcomes. This multicenter retrospective study included consecutive adults with SCD admitted to one of 17 participating ICUs. An adverse outcome was defined as death or a need for life-sustaining therapies (non-invasive or invasive ventilation, vasoactive drugs, renal replacement therapy, and/or extracorporeal membrane oxygenation). Factors associated with adverse outcomes were identified by mixed multivariable logistic regression. We included 488 patients admitted in 2015–2017. The main reasons for ICU admission were acute chest syndrome (47.5%) and severely painful vaso-occlusive event (21.3%). Sixteen (3.3%) patients died in the ICU, mainly of multi-organ failure following a painful vaso-occlusive event or sepsis. An adverse outcome occurred in 81 (16.6%; 95% confidence interval [95% CI], 13.3%–19.9%) patients. Independent factors associated with adverse outcomes were low mean arterial blood pressure (adjusted odds ratio [aOR], 0.98; 95% CI 0.95–0.99; p = 0.027), faster respiratory rate (aOR, 1.09; 95% CI 1.05–1.14; p < 0.0001), higher haemoglobin level (aOR, 1.22; 95% CI 1.01–1.48; p = 0.038), impaired creatinine clearance at ICU admission (aOR, 0.98; 95% CI 0.97–0.98; p < 0.0001), and red blood cell exchange before ICU admission (aOR, 5.16; 95% CI 1.16–22.94; p = 0.031). Patients with SCD have a substantial risk of adverse outcomes if they require ICU admission. Early ICU admission should be encouraged in patients who develop abnormal physiological parameters.

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Section: Discussionmentioning

confidence: 75%

Nationwide retrospective study of critically ill adults with sickle cell disease in France

Agbakou

Mekontso-Dessap

Péré

et al. 2021

Sci Rep

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“…Mortality rates for SCD patients admitted to intensive care units in Bahrain were 12.7%. This was slightly less than the overall mortality in other centers worldwide which was 17.2%[15].…”

Section: Introductionmentioning

confidence: 66%

Is sickle cell disease a risk factor for severe COVID‐19 outcomes in hospitalized patients? A multicenter national retrospective cohort study

Abdulrahman

Mohamed

AlAmmadi

et al. 2021

eJHaem

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Introduction Studies that examine the association between sickle cell disease (SCD) and COVID‐19 outcomes are lacking. This study aims to determine whether SCD is a risk factor for severe COVID‐19 infection in regard to the requirement of noninvasive ventilation/high flow nasal cannula (NIV/HFNC), mechanical ventilation (MV), or death in hospitalized patients. Methods Retrospective cohort study included COVID‐19 patients admitted to four COVID‐19 treatment facilities in Bahrain between February 24, 2020 and July 31, 2020. All SCD patients with COVID‐19 were included and compared to a randomly selected sample of non‐SCD patients with COVID‐19. Data were collected from the medical records. Multivariate logistic regression models were used to control for confounders and estimate the effect of SCD on the outcomes. Results 1792 patients with COVID‐19 were included; 38 of whom were diagnosed with SCD as well. In the SCD group, one (2.6%) patient required NIV/HFNC, one (2.6%) required MV, and one (2.6%) death occurred. In comparison, 56 (3.2%) of the non‐SCD patients required NIV/HFNC, 47 (2.7%) required MV, and death occurred in 58 (3.3%) patients. Upon adjusting for confounders, SCD had an odds ratio of 1.847 (95% CI: 0.39–8.83; p = 0.442). Conclusion Our results indicate that SCD is not a risk factor for worse COVID‐19 outcomes in hospitalized patients.

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“…The selection of relevant features involved in SCD with organ failure and control remains a challenge [34,35]. Therefore, it is important to find a subset of physiologic features that are sufficiently informative to distinguish between SCD patients at-risk of developing organ failure and those who are not.…”

Section: Discussionmentioning

confidence: 99%

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

Mohammed

Podila

Davis

et al. 2019

Preprint

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Background Sickle 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 Findings:We 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 preexisting 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 2 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). Conclusions:This 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.

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Predictors of mortality in adults with Sickle cell disease admitted to intensive care unit in Bahrain

Cited by 14 publications

References 34 publications

Nationwide retrospective study of critically ill adults with sickle cell disease in France

Nationwide retrospective study of critically ill adults with sickle cell disease in France

Is sickle cell disease a risk factor for severe COVID‐19 outcomes in hospitalized patients? A multicenter national retrospective cohort study

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

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