Algorithm for Automatic Forced Spirometry Quality Assessment: Technological Developments

<div><p>We hypothesized that the implementation of automatic real-time assessment of quality of forced spirometry (FS) may significantly enhance the potential for extensive deployment of a FS program in the community. Recent studies have demonstrated that the application of quality criteria defined by the ATS/ERS (American Thoracic Society/European Respiratory Society) in commercially available equipment with automatic quality assessment can be markedly improved. To this end, an algorithm for assessing quality of FS automatically was reported. The current research describes the mathematical developments of the algorithm. An innovative analysis of the shape of the spirometric curve, adding 23 new metrics to the traditional 4 recommended by ATS/ERS, was done. The algorithm was created through a two-step iterative process including: (1) an initial version using the standard FS curves recommended by the ATS; and, (2) a refined version using curves from patients. In each of these steps the results were assessed against one expert's opinion. Finally, an independent set of FS curves from 291 patients was used for validation purposes. The novel mathematical approach to characterize the FS curves led to appropriate FS classification with high specificity (95%) and sensitivity (96%). The results constitute the basis for a successful transfer of FS testing to non-specialized professionals in the community.</p></div

Importance of normalizing both actual to mitochondrial and actual mitochondrial <i>Po</i>₂ () to mitochondrial <i>P</i>₅₀ when and/or <i>P</i>₅₀ may vary within or between muscles.

<p>Panel 1: Example of two muscles (A & B) with the same but different <i>P</i>₅₀ that happen to have the same absolute (closed circles). Although is lower in A than B, normalization of both axes (Panel 2) shows that in this case, relative to <i>P</i>₅₀ is the same, and this means that ROS generation will be the same for A and B. Panel 3: Example of two muscles (A & B) with the same <i>P</i>₅₀ but different that again happen to have the same absolute (closed circles). is again lower in A than B, but normalization of both axes (Panel 4) shows that relative to <i>P</i>₅₀ is lower in A than B, and this means that ROS generation will be high for A and normal for B.</p

Zone Z3 analysis.

<p>Examples of FV curves that present irregularity in the descent from PEF.</p

Spirometer metrics.

<p>Metrics involved in the traditional criteria: (A) <i>FVC</i> and <i>FEV1</i>, (B) <i>PEFT</i>.</p

Mitochondrial <i>Po</i>₂ () as a function of regional ratios of metabolic capacity () to blood flow () at four altitudes.

<p>The lower (supply) is in relation to (demand), the lower is at any altitude; also, at any ratio falls with increasing altitude. Vertical dashed lines mark the normal range of . Both panels show the same data, but the lower panel expands the y-axis in its lower range to show when ROS generation is high (i.e., when ). Below 17,000ft, ROS generation remains low, but above this altitude, regions of normal muscle with high ratio generate high ROS levels, until at the Everest summit, almost the entire muscle generates high ROS levels.</p

Reduction of maximal and mitochondrial <i>Po</i>₂ in a <i>homogeneous</i> muscle as a function of altitude, (solid line, computed from the O₂ pathway model) and degree of ROS generation as a function of mitochondrial <i>Po</i>₂ (computed from the mitochondrial respiration model).

<p>Below 24,000 ft, ROS production is low (open circles), but above 24,000 ft, ROS production abruptly increases (closed circles).</p

Zone Z4 analysis.

<p>Example of FT curve that present irregularity in the final part.</p

Zone Z5 analysis.

<p>Examples of FV curve with peak and valley.</p

Zone Z2 analysis.

<p>Examples of FV curves that present (A) bimodal peak; (B) flat peak and (C) slow peak.</p

Computed sensitivity (Sen) and specificity (Spe) using the current automatic classification algorithm and using only the four traditional ATS/ERS quality criteria applied to P2.

<p>Computed sensitivity (Sen) and specificity (Spe) using the current automatic classification algorithm and using only the four traditional ATS/ERS quality criteria applied to P2.</p