Detection of Flares by Decrease in Physical Activity, Collected Using Wearable Activity Trackers in Rheumatoid Arthritis or Axial Spondyloarthritis: An Application of Machine Learning Analyses in Rheumatology.

OBJECTIVE: Flares in rheumatoid arthritis (RA) and axial spondyloarthritis (SpA) may influence physical activity. The aim of this study was to assess longitudinally the association between patient-reported flares and activity-tracker-provided steps per minute, using machine learning. METHODS: This prospective observational study (ActConnect) included patients with definite RA or axial SpA. For a 3-month time period, physical activity was assessed continuously by number of steps/minute, using a consumer grade activity tracker, and flares were self-assessed weekly. Machine-learning techniques were applied to the data set. After intrapatient normalization of the physical activity data, multiclass Bayesian methods were used to calculate sensitivities, specificities, and predictive values of the machine-generated models of physical activity in order to predict patient-reported flares. RESULTS: Overall, 155 patients (1,339 weekly flare assessments and 224,952 hours of physical activity assessments) were analyzed. The mean ± SD age for patients with RA (n = 82) was 48.9 ± 12.6 years and was 41.2 ± 10.3 years for those with axial SpA (n = 73). The mean ± SD disease duration was 10.5 ± 8.8 years for patients with RA and 10.8 ± 9.1 years for those with axial SpA. Fourteen patients with RA (17.1%) and 41 patients with axial SpA (56.2%) were male. Disease was well-controlled (Disease Activity Score in 28 joints mean ± SD 2.2 ± 1.2; Bath Ankylosing Spondylitis Disease Activity Index score mean ± SD 3.1 ± 2.0), but flares were frequent (22.7% of all weekly assessments). The model generated by machine learning performed well against patient-reported flares (mean sensitivity 96% [95% confidence interval (95% CI) 94-97%], mean specificity 97% [95% CI 96-97%], mean positive predictive value 91% [95% CI 88-96%], and negative predictive value 99% [95% CI 98-100%]). Sensitivity analyses were confirmatory. CONCLUSION: Although these pilot findings will have to be confirmed, the correct detection of flares by machine-learning processing of activity tracker data provides a framework for future studies of remote-control monitoring of disease activity, with great precision and minimal patient burden.

Reabilitaçäo em doentes amputados por doença arterial periférica

Cinquenta e um pacientes com amputaçöes de membro inferior decorrentes de doença arterial periférica foram avaliados quanto a condiçöes e tempo de reabilitaçäo e acompanhados no seguimento tardio por um período médio de 24 meses. Trinta e três pacientes (64%) eram diabéticos. Sucesso imediato de reabilitaçäo foi considerado como a recuperaçäo da marcha bi-pedal com prótese. Quarenta e cinco pacientes (88%) atingiram esta condiçäo. O tempo médio de reabilitaçäo para amputados ao nível da perna foi de três meses e para amputados ao nível da coxa de cinco meses. No seguimento tardio a avaliaçäo foi feita quanto `a persistência no uso da prótese e grau de independência de vida. A mortalidade foi estudada pelo método atuarial. Todos os pacientes amputados na perna (100%) usavam a prótese para locomoçäo e tinham vida independente no último seguimento, enquanto 75% dos amputados em coxa estavam em uso da prótese e apenas 25% tinham vida totalmente independente. A mortalidade estudada pelo método atuarial foi analisada para pacientes diabéticos e näo diabéticos. Todas as mortes exceto uma ocorreram nos diabéticos, resultando neste grupo sobrevida cumulativa de 31% em 4 anos