Respiratory resistance and reactance in adults with sickle cell anemia: Part 2-Fractional-order modeling and a clinical decision support system for the diagnosis of respiratory disorders

PLoS One. 2019 Mar 7;14(3):e0213257. doi: 10.1371/journal.pone.0213257. eCollection 2019.

Abstract

Background: A better understanding of sickle cell anemia (SCA) and improvements in drug therapy and health policy have contributed to the emergence of a large population of adults living with this disease. The mechanisms by which SCA produces adverse effects on the respiratory system of these patients are largely unknown. Fractional-order (FrOr) models have a high potential to improve pulmonary clinical science and could be useful for diagnostic purposes, offering accurate models with an improved ability to mimic nature. Part 2 of this two-part study examines the changes in respiratory mechanics in patients with SCA using the new perspective of the FrOr models. These results are compared with those obtained in traditional forced oscillation (FOT) parameters, investigated in Part 1 of the present study, complementing this first analysis.

Methodology/principal findings: The data consisted of three categories of subjects: controls (n = 23), patients with a normal spirometric exam (n = 21) and those presenting restriction (n = 24). The diagnostic accuracy was evaluated by investigating the area under the receiver operating characteristic curve (AUC). Initially, it was observed that biomechanical changes in SCA included increased values of fractional inertance, as well as damping and hysteresivity (p<0.001). The correlation analysis showed that FrOr parameters are associated with functional exercise capacity (R = -0.57), pulmonary diffusion (R = -0.71), respiratory muscle performance (R = 0.50), pulmonary flows (R = -0.62) and airway obstruction (R = 0.60). Fractional-order modeling showed high diagnostic accuracy in the detection of early respiratory abnormalities (AUC = 0.93), outperforming spirometry (p<0.03) and standard FOT analysis (p<0.01) used in Part 1 of this study. A combination of machine learning methods with fractional-order modeling further improved diagnostic accuracy (AUC = 0.97).

Conclusions: FrOr modeling improved our knowledge about the biomechanical abnormalities in adults with SCA. Changes in FrOr parameters are associated with functional exercise capacity decline, abnormal pulmonary mechanics and diffusion. FrOr modeling outperformed spirometric and traditional forced oscillation analyses, showing a high diagnostic accuracy in the diagnosis of early respiratory abnormalities that was further improved by an automatic clinical decision support system. This finding suggested the potential utility of this combination to help identify early respiratory changes in patients with SCA.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Airway Resistance*
  • Anemia, Sickle Cell / complications*
  • Decision Support Systems, Clinical*
  • Early Diagnosis*
  • Female
  • Humans
  • Male
  • Models, Theoretical*
  • ROC Curve
  • Respiration Disorders / diagnosis*
  • Respiration Disorders / etiology
  • Respiration Disorders / pathology
  • Respiratory Function Tests
  • Respiratory Mechanics*

Grants and funding

The present study was funded by the Brazilian Council of Scientific and Technological Development (Conselho Brasileiro para o Desenvolvimento Científico e Tecnológico - CNPq, grant numbers 307393/2017-8 and 304625/2016-7), the Rio de Janeiro Research Foundation (Fundação de Apoio à Pesquisa do Estado do Rio de Janeiro - FAPERJ, grant numbers E-26/010.001987/2015, E-26/202.679/2018), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES, Finance Code 001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.