A comparison of human and machine-based predictions of successful weaning from mechanical ventilation

Medical Decision Making

Volumn 20, Issue 2, 2000, Pages 160-169

  Gottschalk, Allan ^a,c   Hyzer, M Chris ^b   Geer, Ralph T ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c HOSPITAL OF THE UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Decision analysis; Mechanical ventilation; Neural networks; Respiratory failure; Weaning

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; ARTIFICIAL VENTILATION; CLINICAL ARTICLE; COMPUTER PROGRAM; DATA ANALYSIS; HUMAN; MATHEMATICAL PARAMETERS; MEDICAL DECISION MAKING; RESPIRATORY FAILURE;

EID: 0034037252     PISSN: 0272989X     EISSN: None     Source Type: Journal    
DOI: 10.1177/0272989X0002000202     Document Type: Article

References (48)

1. Ely EW, Baker AM, Dunagan DP, et al. Effect on the duration of mechanical ventilation of identifying patients capable of breathing spontaneously. N Engl J Med. 1996;335:1864-9.
2. Cohen IL, Booth FY. Cost containment and mechanical ventilation in the United States. New Horizons. 1994,2:283-90.
3. Groeger JS, Guntupalli KK, Strosberg M, et al. Descriptive analysis of critical care units in the United States: patient characteristics and intensive care unit utilization. Crit Care Med. 1993;21:279-91.
4. Higgins TL. Pro: early endotracheal extubation is preferable to late extubation in patients following coronary artery surgery. J Cardiothorac Vasc Anesth. 1992;6:488-93.
5. Siliciano D. Con: early extubation is not preferable to late extubation in patients undergoing coronary artery surgery. J Cardiothorac Vase Anesth. 1992;6:494-8.
6. Milic-Emili J. Is weaning an art or a science? Am Rev Respir Dis. 1986;134:1107-8.
7. Weinberger SE, Weiss JW. Weaning from ventilatory support. N Engl J Med. 1995;332:388-9.
8. Manthous CA, Schmidt GA, Hall JB. Liberation from mechanical ventilation a decade of progress. Chest. 1998;114: 886-901.
9. Mador MJ. Weaning from mechanical ventilation: what have we learned and what do we still need to know? Chest. 1998; 114:672-4.
10. Lecky JH, Ominsky AJ. Postoperative respiratory management. Chest. 1972;62:50S-57S.
11. Morganroth ML, Morganroth JL, Nett LM, Petty TL. Criteria for weaning from prolonged mechanical ventilation. Arch Intern Med. 1984;144:1012-6.
12. Yang KL, Tobin MJ. A prospective study of indexes predicting the outcome of trials of weaning from mechanical ventilation. N Engl J Med. 1991;324:1445-50.
13. Listello D, Sessler CN. Unplanned extubation. Clinical predictors for reintubation. Chest. 1994;105:1496-503.
14. Scheinhorn DJ, Hassenpfug M, Artinian BM, LaBree L, Catlin JL. Predictors of weaning after 6 weeks of mechanical ventilation. Chest. 1995;107:500-5.
15. Khan N, Brown A, Venkataraman ST. Predictors of extubation success and failure in mechanically ventilated infants and children. Crit Care Med. 1996;24:1568-79.
16. Proctor HJ, Woolson R. Prediction of respiratory muscle fatigue by measurements of the work of breathing. Surg Gynecol Obstet. 1973;136:367-70.
17. Sassoon CS, Te TT, Mahutte CK, Light RW. Airway occlusion pressure, an important indicator for successful weaning in patients with chronic obstructive pulmonary disease. Am Rev Respir Dis. 1987;135:107-13.
18. Sahn SA, Lakshminarayan S. Bedside criteria for discontinuation of mechanical ventilation. Chest. 1973;63:1002-5.
19. Mohsenifar Z, Hay A, Hay J, Lewis MI, Koerner SK. Gastric intramural pH as a predictor of success or failure in weaning patients from mechanical ventilation. Ann Intern Med. 1993; 119:794-8.
20. Yang KL. Inspiratory pressure/maximal inspiratory pressure ratio: a predictive index of weaning outcome. Intens Care Med. 1993;19:204-8.
21. Epstein SK. Etiology of extubation failure and the predictive value of the rapid shallow breathing index. Am J Respir Crit Care Med. 1995;152:545-9.
22. Capdevila XJ, Perrigault PF, Perey PJ, Roustan JPA, d'Athis F. Occlusion pressure and its ratio to maximum inspiratory pressure are useful predictors for successful extubation following T-piece weaning trial. Chest. 1995;108:482-9.
23. Levy MM, Miyasaki A, Langston D. Work of breathing as a weaning parameter in mechanically ventilated patients. Chest. 1995;108:1018-20.
24. Jubran A, Tobin MJ. Passive mechanics of lung and chest wall in patients who failed or succeeded in trials of weaning. Am J Respir Crit Care Med. 1997;155:916-21.
25. Vassilakopoulos T, Zakynthinos S, Roussos C. The tension-time index and the frequency/tidal volume ratio are the major pathophysiologic determinants of weaning failure and success. Am J Respir Crit Care Med. 1998;158:378-85.
26. Engoren M. Approximate entropy of respiratory rate and tidal volume during weaning from mechanical ventilation. Crit Care Med. 1998;26:1817-23.
27. Albert DA. Decision theory in medicine: a review and critique. Health and Society. 1978;56:362-400.
28. Berner ES, Webster GD, Shugerman AA, et al. Performance of four computer-based diagnostic systems. N Engl J Med. 1994;330:1792-6.
29. Kassirer JP. A report card on computer-assisted diagnosis -the grade: C. N Engl J Med. 1994;330:1824-5.
30. Poor HV. An Introduction to Signal Detection and Estimation. New York: Springer-Verlag, 1994.
31. Haykin S. Neural Networks: A Comprehensive Foundation. New York: Macmillan, 1994.
32. Baxt WG. Application of artificial neural networks to clinical medicine. Lancet. 1995;346:1135-8.
33. Penny W, Frost D. Neural networks in clinical medicine. Med Decis Making. 1996;16:386-98.
34. Somoza E, Somoza JR. A neural-network approach to predicting admission decisions in a psychiatric emergency room. Med Decis Making. 1993;13:273-80.
35. Baxt WG, Skora J. Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet. 1996;347:12-5.
36. Dybowski R, Weller P, Chang R, Grant V. Prediction of outcome in critically ill patients using artificial neural network synthesized by genetic algorithm. Lancet. 1996;347:1146-50.
37. Brickley MR, Shepherd JP. Performance of a neural network trained to make third-molar treatment-planning decisions. Med Decis Making. 1996;16:153-60.
38. Lapuerta P, L'Italien GJ, Paul S, et al. Neural network assessment of perioperative cardiac risk in vascular surgery patients. Med Decis Making. 1998;18:70-5.
39. Ashutosh K, Lee H, Mohan CK, Ranka S, Mehrotra K, Alexander C. Prediction criteria for successful weaning from respiratory support: Statistical and connectionist analyses. Crit Care Med. 1992;20:1295-301.
40. Ashutosh K. Prediction criteria for weaning: correction. Crit Care Med. 1994;22:183.
41. Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Networks. 1994;5:989-93.
42. Efron B, Gong G. A leisurely look at the bootstrap, the jackknife, and cross-validation. Am Statistician. 1983;37:36-48.
43. Tourassi GD, Floyd CE. The effect of data sampling on the performance evaluation of artificial neural networks in medical diagnosis. Med Decis Making. 1997;17:186-92.
44. Stone M. Cross-validatory choice and assessment of statistical predictions. J R Statist Soc B. 1974;36:111-47.
45. Lee KH, Hui KP, Chan TB, Tan WC, Lim TK. Rapid shallow breathing (frequency-tidal volume ratio) did not predict extubation outcome. Chest. 1994;105:540-3.
46. Hilberman M, Kamm B, Lamy M, Dietrich HP, Martz K, Osborn JJ. An analysis of potential physiological predictors of respiratory adequacy following cardiac surgery. J Thorac Cardiovasc Surg. 1976;71:711-20.
47. Multz AS, Aldrich TK, Prezant DJ, Karpel JP, Hendler JM. Maximal inspiratory pressure is not a reliable test of inspiratory muscle strength in mechanically ventilated patients. Am Rev Respir Dis. 1990;142:529-32.
48. Esteban A, Frutos F, Tobin MJ, et al. A comparison of four methods of weaning patients from mechanical ventilation. Spanish Lung Failure Collaborative Group. N Engl J Med. 1995;332:345-50.