Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data

A machine learning algorithm for peripheral artery disease prognosis using biomarker data

Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an algorithm that considers a protein panel to inform PAD prognosis may improve predictive accuracy. Biomarker-based prediction models were developed and evaluated using a model development (n = 270) and prospective validation cohort (n = 277). Plasma concentrations of 37 proteins were measured at baseline and the patients were followed for 2 years. The primary outcome was 2-year major adverse limb event (MALE; composite of vascular intervention or major amputation). Of the 37 proteins tested, 6 were differentially expressed in patients with vs. without PAD (ADAMTS13, ICAM-1, ANGPTL3, Alpha 1-microglobulin, GDF15, and endostatin). Using 10-fold cross-validation, we developed a random forest machine learning model that accurately predicts 2-year MALE in a prospective validation cohort of PAD patients using a 6-protein panel (AUROC 0.84). This algorithm can support PAD risk stratification, informing clinical decisions on further vascular evaluation and management.

Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation

This paper demonstrates the value of a framework for processing data on body acceleration as a uniquely valuable tool for diagnosing diseases that affect gait early. As a case study, we used this model to identify individuals with peripheral artery disease (PAD) and distinguish them from those without PAD. The framework uses acceleration data extracted from anatomical reflective markers placed in different body locations to train the diagnostic models and a wearable accelerometer carried at the waist for validation. Reflective marker data have been used for decades in studies evaluating and monitoring human gait. They are widely available for many body parts but are obtained in specialized laboratories. On the other hand, wearable accelerometers enable diagnostics outside lab conditions. Models trained by raw marker data at the sacrum achieve an accuracy of 92% in distinguishing PAD patients from non-PAD controls. This accuracy drops to 28% when data from a wearable accelerometer at the waist validate the model. This model was enhanced by using features extracted from the acceleration rather than the raw acceleration, with the marker model accuracy only dropping from 86 to 60% when validated by the wearable accelerometer data.

A biomechanical perspective on walking in patients with peripheral artery disease

The most common symptom of peripheral artery disease (PAD) is intermittent claudication, which consists of debilitating leg pain during walking. In clinical settings, the presence of PAD is often noninvasively evaluated using the ankle-brachial index and imaging of the arterial supply. Furthermore, various questionnaires and functional tests are commonly used to measure the severity and negative effect of PAD on quality of life. However, these evaluations only provide information on vascular insufficiency and severity of the disease, but not regarding the complex mechanisms underlying walking impairments in patients with PAD. Biomechanical analyses using motion capture and ground reaction force measurements can provide insight into the underlying mechanisms to walking impairments in PAD. This review analyzes the application of biomechanics tools to identify gait impairments and their clinical implications on rehabilitation of patients with PAD. A total of 18 published journal articles focused on gait biomechanics in patients with PAD were studied. This narriative review shows that the gait of patients with PAD is impaired from the first steps that a patient takes and deteriorates further after the onset of claudication leg pain. These results point toward impaired muscle function across the ankle, knee, and hip joints during walking. Gait analysis helps understand the mechanisms operating in PAD and could also facilitate earlier diagnosis, better treatment, and slower progression of PAD.