Pain Intensity Assessment in Sickle Cell Disease Patients Using Vital Signs During Hospital Visits

Padhee, Swati; Alambo, Amanuel; Banerjee, Tanvi; Subramaniam, Arvind; Abrams, Daniel M.; Nave, Gary K; Shah, Nirmish

doi:10.1007/978-3-030-68790-8_7

Cited by 2 publications

(5 citation statements)

References 9 publications

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“…Our study demonstrates that although there are complications associated with using medication data for prediction at the same time as pain measurements, ML models can be used for dynamic pain and medication measurements. Our findings indicate the importance of medication information (achieving an accuracy of 82.3%) and demonstrate that a larger cohort of patient data with deep representational learning improves model performance (by 17.5% as compared with Padhee et al [6] and by 24.6% as compared with Yang et al [4]). Furthermore, from our unsupervised analysis, we distinguished unique patient profiles (Table 4) that can help isolate different patient profiles to further understand the role of physiology and medication in pain response.…”

Section: Overviewsupporting

confidence: 54%

“…Ultimately, as pain is inherently subjective, medical providers and patients have difficulty in determining the ideal treatment and management strategies for pain. As a result, there has been an increasing focus on developing and implementing pain prediction models from objective measures over the past several years [3,4,[6][7][8]. However, in addition to the slow development of these models, the difficulty also lies in understanding the severity of a patient's pain level and their response to pain management strategies.…”

Section: Introductionmentioning

confidence: 99%

“…Alambo et al [8] examined 424 clinical notes from the same cohort of 40 patients to predict the prevalence of pain and whether pain increased, decreased, or remained constant. Padhee et al [6], using 6 objective vital signs and the nature of hospital visit information from 59,728 records of a different cohort of 47 patients over 5 consecutive years, demonstrated that with more data for each patient, the accuracy for pain prediction improved (accuracy of 65.3%). In this study, we demonstrated that with more data from a larger cohort of patients and medication information, the accuracy improved by 17.5% compared with Padhee et al [6] and by 24.6% compared with Yang et al [4].…”

Section: Introductionmentioning

confidence: 99%

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Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study

Padhee¹,

Nave²,

Banerjee³

et al. 2022

JMIR Form Res

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Background Sickle cell disease (SCD) is the most common inherited blood disorder affecting millions of people worldwide. Most patients with SCD experience repeated, unpredictable episodes of severe pain. These pain episodes are the leading cause of emergency department visits among patients with SCD and may last for several weeks. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting a patient’s pain intensity level. Objective This study aims to learn deep feature representations of subjective pain trajectories using objective physiological signals collected from electronic health records. Methods This study used electronic health record data collected from 496 Duke University Medical Center participants over 5 consecutive years. Each record contained measures for 6 vital signs and the patient’s self-reported pain score, with an ordinal range from 0 (no pain) to 10 (severe and unbearable pain). We also extracted 3 features related to medication: medication type, medication status (given or applied, or missed or removed or due), and total medication dosage (mg/mL). We used variational autoencoders for representation learning and designed machine learning classification algorithms to build pain prediction models. We evaluated our results using an accuracy and confusion matrix and visualized the qualitative data representations. Results We designed a classification model using raw data and deep representational learning to predict subjective pain scores with average accuracies of 82.8%, 70.6%, 49.3%, and 47.4% for 2-point, 4-point, 6-point, and 11-point pain ratings, respectively. We observed that random forest classification models trained on deep represented features outperformed models trained on unrepresented data for all pain rating scales. We observed that at varying Likert scales, our models performed better when provided with medication data along with vital signs data. We visualized the data representations to understand the underlying latent representations, indicating neighboring representations for similar pain scores with a higher resolution of pain ratings. Conclusions Our results demonstrate that medication information (the type of medication, total medication dosage, and whether the medication was given or missed) can significantly improve subjective pain prediction modeling compared with modeling with only vital signs. This study shows promise in data-driven estimated pain scores that will help clinicians with additional information about the patient’s condition, in addition to the patient’s self-reported pain scores.

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Section: Overviewsupporting

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study

Padhee¹,

Nave²,

Banerjee³

et al. 2022

JMIR Form Res

Self Cite

View full text Add to dashboard Cite

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“…Many biomedical measurements, including pain, are taken at irregular intervals. Within a hospital environment, for example, patient vital signs are measured based on hospital staff schedule and patient availability [1]. For self-reported data collected through a mobile app [2], the irregularity may be even greater.…”

Section: Introductionmentioning

confidence: 99%

“…In the present study, we investigate whether it is possible to classify patients' experiences with pain over time based only on their reported pain scores. This may be particularly helpful in the future, as promising studies have shown the potential to infer subjective pain from physiological data from wearable devices and electronic health records [1], [16].…”

Section: Introductionmentioning

confidence: 99%

Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples

Nave,

Padhee,

Alambo

et al. 2021

Preprint

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Irregularly sampled time series data are common in a variety of fields. Many typical methods for drawing insight from data fail in this case. Here we attempt to generalize methods for clustering trajectories to irregularly and sparsely sampled data. We first construct synthetic data sets, then propose and assess four methods of data alignment to allow for application of spectral clustering. We also repeat the same process for real data drawn from medical records of patients with sickle cell diseasepatients whose subjective experiences of pain were tracked for several months via a mobile app.We find that different methods for aligning irregularly sampled sparse data sets can lead to different optimal numbers of clusters, even for synthetic data with known properties. For the case of sickle cell disease, we find that three clusters is a reasonable choice, and these appear to correspond to (1) a low pain group with occasionally acute pain, (2) a group which experiences moderate mean pain that fluctuates often from low to high, and (3) a group that experiences persistent high levels of pain.Our results may help physicians and patients better understand and manage patients' pain levels over time, and we expect that the methods we develop will apply to a wide range of other data sources in medicine and beyond.

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Pain Intensity Assessment in Sickle Cell Disease Patients Using Vital Signs During Hospital Visits

Cited by 2 publications

References 9 publications

Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study

Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study

Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples

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