A six-minute walk test (6MWT) is an analytical exercise used to assess functional exercise capacity and monitor the progression of illnesses such as COPD and emphysema. During this test, the patient’s peripheral oxygen saturation levels and perceived breathlessness/fatigue are monitored while they are instructed to conduct a brisk walk for six minutes followed by a monitored recovery period of three minutes. The results are then marked against a predicted value for someone of their demographic, age and stature. These tests are useful in a clinical setting but a number of factors can limit the efficiency and viability of the test such as weather, comorbidities and conditions that limit the patient’s ability to walk.
A research team based at the Universitat Politecnica de Catalunya, Spain developed a novel system that may be used to predict a patient’s six-minute walk test outcome without needing the patient to conduct the physical test. This model used respiratory (FEV1, FVC & predicted values) and cardiopulmonary (ECG, SpO2) data and a Bayesian network to predict a patient’s achieved walking distance (6MWD), maximum heart rate (HRmax) and recovery index during the three minutes post-test (HRR3).
The study utilised a group of 46 Belgian individuals aged 60 – 69 years with varying comorbidities and lifestyles. The Bayesian network worked to factor severity of disease based on data from six-minute walk tests of the population. The classification of severity of disease proved to be 78% accurate using three severity groups. A strong correlation was identified between predicted HRmax and practical HRmax values, a moderate correlation was identified between predicted 6MWD and practical 6MWD values.
“Our model provides a dual-function tool – firstly, the trained model allows the prediction of the 6MWT outcomes and thus, the evaluation of the functional exercise capacity of the patients. And secondly, it can assess the disease severity and progression by inferring the predefined FEV1 [percent predicted] groups, and how disease severity might progress (ie, improved or worsened) by modifying the available patient data. Both capabilities enable the proposed model to be used for more personalized monitoring of COPD patients in their home environment” – a statement from the research team.
This method did not include a control group, limiting the viability of the data, some other limitations include the lower than needed sample size for an accurate representation of a population, only one 6MWT attempt was obtained from each individual – the data was highly dependent on subject effort and participation. This prediction method does not account for underlying conditions or unaccounted for co-morbidities.
The publication concluded by recognising the need for further studies to refine and validate the model, further studies would require larger populations and more diverse co-morbidities. The researchers highlighted the potential of this method as an approximation tool to monitor a patient’s condition without the need to perform physical exercise. If refined and verified this tool may prove incredibly useful in situations wherein an individual could not perform such a test.
Read an article on the paper here:
https://www.infectiousdiseaseadvisor.com/home/topics/respiratory/copd-monitoring-model-offers-potential-alternative-to-6-minute-walk-test/
Romero D, Blanco-Almazán D, Groenendaal W, et al. Predicting 6-minute walking test outcomes in patients with chronic obstructive pulmonary disease without physical performance measures. Comput Methods Programs Biomed. Published online July 11, 2022. doi:10.1016/j.cmpb.2022.107020
Read the original publication here:
https://www.sciencedirect.com/science/article/pii/S0169260722004023?via%3Dihub
