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.

An investigation of long-term outcome of rabbit anti-thymocyte globulin and cyclosporine therapy for pediatric severe aplastic anemia

“…These methods were used because they have been successfully applied to medical data sets for disease classification, and their characteristics have been previously described in detail. 8 The liblinear solver was used to optimize functions of LR. The MLP contained 2 hidden layers with 50 and 20 neurons, respectively, and each layer led to a reduced neuron number.…”

Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell Disease

“…These models utilize several features from the EHR including physiological data, clinical data, and patient comorbidities to develop a model to predict AKI as early as 48 h prior to the event. One method is to use a minimal set of streaming physiological data to develop machine-learning models to predict the early onset of sepsis [17, 18] and organ failure in SCD [14]. Our primary objective was to determine if AKI in adult patients with SCD could be predicted using bedside physiological streaming data and machine learning well before the onset of kidney injury in critically ill patients.…”

“…The electronic health record (EHR) generates data during a patient encounter, which can be used to develop predictive models for the development of AKI. Deep machine learning, a subset of the artificial intelligence universe, can leverage EHR data and potentially identify AKI in individuals with SCD association hospitalized for vaso-occlusive complications [12][13][14]. Machine-learning models have been generated to predict AKI in various patient settings including the intensive care unit (ICU), among patients after cardiac surgery or as a result of sepsis [13,15,16].…”

A systematic review of predictive models for hospital‐acquired pressure injury using machine learning

“…As a branch of artificial intelligence, machine learning (ML) has become a new statistical method that has emerged in medical practice and is increasingly being used in diagnosis (AlJame et al, 2021 ; Koga et al, 2021 ), complications (Kambakamba et al, 2020 ; Mohammed et al, 2020 ), prognosis (Akcay et al, 2020 ) and recurrence (Li et al, 2021 ) prediction. Compared to conventional statistical models, ML can actively learn the complex relationships between data, overcome the limitations of non‐linearity and maintain stability in high‐dimensional datasets (Mangold et al, 2021 ).…”