Dataset Information

Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.

ABSTRACT: 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 early identification and treatment and potentially reduce mortality. OBJECTIVE:The aim of this study was to test the hypothesis that machine learning physiomarkers can predict the development of organ dysfunction in a sample of adult patients with SCD admitted to intensive care units (ICUs). METHODS:We applied diverse machine learning methods, statistical methods, and data visualization techniques to develop classification models to distinguish SCD from controls. RESULTS:We studied 63 sequential SCD patients admitted to ICUs with 163 patient encounters (mean age 30.7 years, SD 9.8 years). A subset of these patient encounters, 22.7% (37/163), met the sequential organ failure assessment criteria. The other 126 SCD patient encounters served as controls. A set of signal processing features (such as fast Fourier transform, energy, and continuous wavelet transform) derived from heart rate, blood pressure, and respiratory rate was identified to distinguish patients with SCD who developed acute physiological deterioration leading to organ failure from patients with SCD who did not meet the criteria. A multilayer perceptron model accurately predicted organ failure up to 6 hours before onset, with an average sensitivity and specificity of 96% and 98%, respectively. CONCLUSIONS:This retrospective study demonstrated the viability of using machine learning to predict acute 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.

SUBMITTER: Mohammed A

PROVIDER: S-EPMC7254279 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.

Mohammed Akram A Podila Pradeep S B PSB Davis Robert L RL Ataga Kenneth I KI Hankins Jane S JS Kamaleswaran Rishikesan R

Journal of medical Internet research 20200513 5

<h4>Background</h4>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 ena ...[more]

PMID: 32401216

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Project description:BackgroundAlthough it has been reported that patients with pneumococcal pneumonia may develop meningitis, lumbar puncture is not systematically recommended in these patients, even in patients with associated bacteremia or invasive pneumococcal disease. The aim of this study was to determine the characteristics and outcomes of patients admitted to intensive care unit (ICU) for pneumococcal community-acquired pneumonia who developed meningitis.MethodsWe retrospectively included all consecutive patients admitted to our ICU from January 2006 to December 2020 for severe pneumococcal community-acquired pneumonia according to American Thoracic Society criteria. Meningitis was defined as pleocytosis > 5 cells/mm3 or a positive culture of cerebrospinal fluid for Streptococcus pneumoniae in lumbar puncture. The primary endpoint was the proportion of patients with meningitis during their ICU stay.ResultsOverall, 262 patients [64(52-75) years old] were included: 154(59%) were male, 80(30%) had chronic respiratory disease, 105(39%) were immunocompromised and 6(2%) were vaccinated against S. pneumoniae. A lumbar puncture was performed in 88(34%) patients with a delay from ICU admission to puncture lumbar of 10.5 (2.8-24.1) h and after the initiation of pneumococcal antibiotherapy in 81(92%) patients. Meningitis was diagnosed in 14 patients: 16% of patients with lumbar puncture and 5% of patients in the whole population. Patients with meningitis had more frequently human immunodeficiency virus positive status (29 vs. 5%, p = 0.02), neurological deficits on ICU admission (43 vs. 16%, p = 0.03) and pneumococcal bacteremia (71 vs. 30%, p < 0.01) than those without. The ICU mortality rate (14 vs. 13%, p = 0.73) and the mortality rate at Day-90 (21 vs. 15%, p = 0.83) did not differ between patients with and without meningitis. The proportion of patients with neurological disorders at ICU discharge was higher in patients with meningitis (64 vs. 23%, p < 0.001) than in those without. The other outcomes did not differ at ICU discharge, Day-30 and Day-90 between the two groups of patients.ConclusionMeningitis was diagnosed in 16% of patients with severe pneumococcal community-acquired pneumonia in whom a lumbar puncture was performed, was more frequent in patients with pneumococcal bacteremia and was associated with more frequent neurological disorders at ICU discharge. Further studies are needed to confirm these results.

Dataset Information

Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.

Publications

Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.

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