title: Evaluation of capnography using a genetic algorithm to predict PaCO2.
abstract: BACKGROUND
  Noninvasive estimates of Paco(2) are usually done by measuring exhaled carbon dioxide at end-expiration (Petco(2)). 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 Petco(2) as a surrogate for Paco(2) 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 Paco(2) prediction in comparison to Petco(2).
  
  
  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 Petco(2) and Paco(2) were 4.3 +/- 4.9 mm Hg (mean +/- SD). For the 160 breaths in the test group, Petco(2) predicted Paco(2) with bias and precision of 2.9 +/- 4.2 mm Hg. The best chromosome found by the genetic algorithm was (10 x 5 + 5 x 5 x 5)/(10 x 10) x Petco(2) - (5 x 5 x 10 + 5 x 5)/(10 x 10) x int time + 2 x 2 x 2 x 2 + (2 x 2)/10, which reduces to 0.65 x Petco(2) - 2.75 x 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 Paco(2) prediction.
authors.affiliation: Department of Anesthesiology, St. Vincent Mercy Medical Center, 2213 Cherry St, Toledo, OH 43608, USA. engoren@pol.net
authors.forename: Milo
authors.initials: M
authors.lastname: Engoren
authors.forename: Michael
authors.initials: M
authors.lastname: Plewa
authors.forename: David
authors.initials: D
authors.lastname: O'Hara
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Kline
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D019296:Capnography / Q000706:statistics & numerical data*; D002245:Carbon Dioxide / Q000097:blood*; D003198:Computer Simulation; D003327:Coronary Disease / Q000175:diagnosis / Q000503:physiopathology; D003625:Data Collection / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008171:Lung Diseases / Q000175:diagnosis / Q000503:physiopathology; D008297:Male; D008432:Mathematical Computing; D008875:Middle Aged; D008957:Models, Genetic; D010092:Oximetry / Q000706:statistics & numerical data; D029424:Pulmonary Disease, Chronic Obstructive / Q000175:diagnosis / Q000503:physiopathology; D012016:Reference Values; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012907:Smoking / Q000009:adverse effects / Q000503:physiopathology; D012984:Software
chemical_list: D002245:Carbon Dioxide