Introduction: Noninvasive estimates of Paco2 are usually done by measuring exhaled carbon dioxide at end-expiration (Petco2). While commonly used in studies involving healthy patients, it is less useful in sicker patients. Conditions that affect the terminal dead space and hence the accuracy of Petco2 as a surrogate for Paco2 may also affect other components of the capnogram. A genetic algorithm is a computer technique for discovering relationships between variables. The purpose of this study was to use a genetic algorithm to improve the precision of Paco2 prediction in comparison to Petco2.
Methods: Inspiratory and expiratory volumes were measured and analyzed by the computerized capnogram. Data were recorded for 2 min. Within 5 min of recording the capnograms, arterial blood gases were obtained. After excluding artifact and incomplete capnograms, five of the remaining breaths from each patient were selected. A genetic algorithm, constructed in postfix notation, consisted of 1,000 chromosomes with genes randomly selected from the 11 capnographic data fields and mathematical operators. The algorithm was constructed on 400 breaths from 83 randomly selected patients (construction group) and tested on 160 breaths from the remaining 32 patients (test group).
Results: For the construction group, the bias and precision between Petco2 and Paco2 were 4.3 ± 4.9 mm Hg (mean ± SD). For the 160 breaths in the test group, Petco2 predicted Paco2 with bias and precision of 2.9 ± 4.2 mm Hg. The best chromosome found by the genetic algorithm was (10 × 5 + 5 × 5 × 5)/(10 × 10) × Petco2 – (5 × 5 × 10 + 5 × 5)/(10 × 10) × int time + 2 × 2 × 2 × 2 + (2 × 2)/10, which reduces to 0.65 × Petco2 – 2.75 × int time + 16.4. This produced a bias and precision of 0.9 ± 4.1 mm Hg in the construction group and 0 ± 3.7 mm Hg in the test group (p < 0.01).
Conclusions: In this study of nonintubated emergency department patients, a genetic algorithm produced an improvement in bias and precision of Paco2 prediction.