Identifying Elevated Risk for Future Pain Crises in Sickle-Cell Disease Using Photoplethysmogram Patterns Measured During Sleep: A Machine Learning Approach

Ji, Yunhua; Chalacheva, Patjanaporn; Rosen, Carol L.; DeBaun, Michael R.; Coates, Thomas D.; Khoo, Michael C. K.

doi:10.3389/fdgth.2021.714741

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…They found that worse sleep efficiency was associated with the next-day pain and more severe pain (26). In another study by Ji et al, the authors used finger photoplethysmogram and heart rate measured overnight to successfully build a machine learning model that was able to predict future VOCs by correlating peripheral vasoconstriction to experiencing future VOCs in patients with SCD (27).…”

Section: Related Workmentioning

confidence: 99%

Use of consumer wearables to monitor and predict pain in patients with sickle cell disease

Vuong,

Utkarsh,

Stojancic

et al. 2023

Front. Digit. Health

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BackgroundIn sickle cell disease (SCD), unpredictable episodes of acute severe pain, known as vaso-occlusive crises (VOC), disrupt school, work activities and family life and ultimately lead to multiple hospitalizations. The ability to predict VOCs would allow a timely and adequate intervention. The first step towards this ultimate goal is to use patient-friendly and accessible technology to collect relevant data that helps infer a patient's pain experience during VOC. This study aims to: (1) determine the feasibility of remotely monitoring with a consumer wearable during hospitalization for VOC and up to 30 days after discharge, and (2) evaluate the accuracy of pain prediction using machine learning models based on physiological parameters measured by a consumer wearable.MethodsPatients with SCD (≥18 years) who were admitted for a vaso-occlusive crisis were enrolled at a single academic center. Participants were instructed to report daily pain scores (0–10) in a mobile app (Nanbar) and to continuously wear an Apple Watch up to 30 days after discharge. Data included heart rate (in rest, average and variability) and step count. Demographics, SCD genotype, and details of hospitalization including pain scores reported to nurses, were extracted from electronic medical records. Physiological data from the wearable were associated with pain scores to fit 3 different machine learning classification models. The performance of the machine learning models was evaluated using: accuracy, F1, root-mean-square error and area under the receiver-operating curve.ResultsBetween April and June 2022, 19 patients (74% HbSS genotype) were included in this study and followed for a median time of 28 days [IQR 22–34], yielding a dataset of 2,395 pain data points. Ten participants were enrolled while hospitalized for VOC. The metrics of the best performing model, the random forest model, were micro-averaged accuracy of 92%, micro-averaged F1-score of 0.63, root-mean-square error of 1.1, and area under the receiving operating characteristic curve of 0.9.ConclusionOur random forest model accurately predicts high pain scores during admission for VOC and after discharge. The Apple Watch was a feasible method to collect physiologic data and provided accuracy in prediction of pain scores.

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Section: Related Workmentioning

confidence: 99%

Use of consumer wearables to monitor and predict pain in patients with sickle cell disease

Vuong,

Utkarsh,

Stojancic

et al. 2023

Front. Digit. Health

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“…11 There is however a growing body of evidence that suggests that additional clinical indicators that determine elevated risk for a sickle cell crisis may be bene cial. 12 In light of the need for better preventative care and predictive tools, this paper explores using quantitative phase imaging (QPI) and machine learning to develop an algorithm that can identify patients at elevated risk for a sickle cell crisis. This could enable earlier interventions and improved outcomes.…”

Section: Introductionmentioning

confidence: 99%

Computing Sickle Erythrocyte Health Index Based on Quantitative Phase Imaging and Machine Learning

Ansong-Ansongton,

Adamson

2023

Preprint

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Sickle cell disease (SCD) is a genetic disorder characterized by abnormal hemoglobin and deformation of red blood cells (RBCs), leading to complications and reduced life expectancy. This study developed an in-vitro assessment, the Sickle Erythrocyte Health Index, using quantitative phase imaging (QPI) and machine learning to model the health of RBCs in people with SCD. The Health Index combines assessment of cell deformation, sickle-shaped classification, and membrane flexibility to evaluate erythrocyte health. Using QPI and image processing, the percentage of sickle-shaped cells and membrane flexibility were quantified. Statistically significant differences were observed between individuals with and without SCD, indicating the impact of underlying pathophysiology on erythrocyte health. Additionally, sodium metabisulfite led to an increase in sickle-shaped cells and a decrease in flexibility for the sickle cell blood samples. Based on these findings, two approaches were used to calculate the Index: one using hand-crafted features and one using learned features from deep learning models. Both indices showed significant differences between non-SCD and SCD groups and sensitivity to changes induced by sodium metabisulfite. The Sickle Erythrocyte Health Index has important clinical implications for SCD management and could be used by providers when making treatment decisions. Further research is warranted to evaluate the clinical utility and applicability of the Index in diverse patient populations.

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“…Machine learning is the usage of data and analytics to predict outcomes, allowing computers to execute operations without explicit instructions. Machine learning models have been applied to SCD and non-SCD pain-related research to visualize how pain indicators relate to subjective pain [10][11][12]. Health care studies involving machine learning have evaluated patients suffering from chronic and postoperative pain using heart rate variability (HRV), brain activity, and clinical data to create complex multivariable models that attempt to predict pain levels [10].…”

Section: Introductionmentioning

confidence: 99%

Predicting Pain in People With Sickle Cell Disease in the Day Hospital Using the Commercial Wearable Apple Watch: Feasibility Study (Preprint)

Stojancic¹,

Subramaniam²,

Vuong³

et al. 2022

Preprint

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BACKGROUND Sickle cell disease (SCD) is a genetic red blood cell disorder associated with severe complications including chronic anemia, stroke, and recurrent episodes of pain called vaso-occlusive crises (VOCs). VOCs are unpredictable, difficult to treat, and the leading cause of hospitalization. Recent efforts have focused on the use of mobile health technology (mHealth) to develop algorithms to predict pain in SCD. Combining the data collection abilities of a consumer wearable, such as the Apple Watch, and machine learning techniques for patients living with SCD, may help us better understand the pain experience and find trends to predict pain from VOCs. OBJECTIVE The aim of this study is to: (1) evaluate machine learning models for accuracy for people with SCD with VOCs admitted to the Day Hospital, and (2) predict the pain scores of VOCs with the Apple Watch using machine learning in people with SCD. METHODS Following approval of the institutional review board, patients with SCD, over 18 years of age, and admitted to the Duke University SCD Day Hospital for a VOC between July 2021 to September 2021 were approached to participate in the study. Participants were provided with an Apple Watch Series 3, worn for the duration of their visit. Data collected from the Apple Watch included heart rate, heart rate variability (calculated), and calories. Pain scores, vital signs, and analgesics were collected from the Electronic Medical Record. Data was analyzed using 3 different machine learning models (Multinomial Logistic Regression Model, the Gradient Boosting Model, and the Random Forest Model), to assess the accuracy of pain scores. RESULTS We enrolled 20 patients, all of which identified as Black or African American, and consisted of 12 females (60%) and 8 males (40%). All participants had confirmed diagnoses of SCD, with 14 individuals diagnosed with HbSS (70%), 5 with HbSC (25%), and 1 with HbSOArab (5%). The median age of the population was 35.5 years. The median time each individual spent wearing the Apple Watch was 2 hours and 17 minutes and a total of 15,683 data points were collected across the population. The best-performing model was the Random Forest model, which was able to predict the pain scores with an accuracy of 84.5%, and a root-mean-square error (RMSE) of 0.84. CONCLUSIONS The high level of accuracy from this model validates the ability to utilize a non-invasive device, the Apple Watch, to predict pain scores during VOCs. It is a novel approach and presents a low-cost method that could benefit clinicians and individuals with SCD in the treatment of VOCs.

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Identifying Elevated Risk for Future Pain Crises in Sickle-Cell Disease Using Photoplethysmogram Patterns Measured During Sleep: A Machine Learning Approach

Cited by 5 publications

References 39 publications

Use of consumer wearables to monitor and predict pain in patients with sickle cell disease

Use of consumer wearables to monitor and predict pain in patients with sickle cell disease

Computing Sickle Erythrocyte Health Index Based on Quantitative Phase Imaging and Machine Learning

Predicting Pain in People With Sickle Cell Disease in the Day Hospital Using the Commercial Wearable Apple Watch: Feasibility Study (Preprint)

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