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Intensive Care Med ; 27(9): 1487-95, 2001 Sep.
Article in English | MEDLINE | ID: mdl-11685342

ABSTRACT

OBJECTIVE: To compare the measurements of total resistance and dynamic elastance determined by different techniques of respiratory mechanics analysis based on the time or frequency domains. DESIGN: Prospective study. SETTING: A 12-bed medical and surgical intensive care unit in a 700-bed university hospital. PATIENTS: Eighteen sedoparalyzed patients who needed controlled mechanical ventilation for acute or chronic acute respiratory failure. MEASUREMENTS: The total resistance and dynamic elastance in the time domain were determined by the occlusion technique and by multiple linear regression. The Fourier analysis was used to study the impedance of the respiratory system for elastance and resistance values in the frequency domain. RESULTS: The ANOVA analysis of the elastance variable showed no statistical differences (Ef: 41.4+/-19.0 cmH(2)O/l, Emlr: 40.8+/-17.2 cmH(2)O/l Edyn,occ: 39.5+/-14.0 cmH(2)O/l; ns) and the correlation was very good (r=0.8-0.9). The total resistances were less with multiple linear regression (13.5+/-9.3 cmH(2)O/l per s, p<0.05) than Rmax (17.0+/-11.9 cmH(2)O/l per s) or Rf (17.6+/-10.2 cmH(2)O/l per s). There were no differences between Rmax and Rf (p=0.7) and the correlation between resistances was 0.7-0.9. The agreement analysis for variables without statistical differences showed the following limits: Edyn,occ-f: -17 to 13 cmH(2)O/l; Edyn,occ-mlr: -12 to 9 cmH(2)O/l; Emlr-f: -6 to 8 cmH(2)O/l; Rmax-f: -18 to 19 cmH(2)O/l per s. This last range was related to the autoPEEP level (r=0.9). CONCLUSION: The wide agreement limits show that respiratory mechanics analysis is very dependent on the measurement technique used, particularly for resistance, perhaps due to the higher dependence on frequency.


Subject(s)
Critical Illness , Data Interpretation, Statistical , Lung Compliance , Numerical Analysis, Computer-Assisted , Respiratory Insufficiency/diagnosis , Respiratory Insufficiency/physiopathology , Respiratory Mechanics , Signal Processing, Computer-Assisted , Acute Disease , Aged , Aged, 80 and over , Analysis of Variance , Bias , Chronic Disease , Fourier Analysis , Humans , Linear Models , Middle Aged , Positive-Pressure Respiration, Intrinsic/etiology , Prospective Studies , Respiration, Artificial/adverse effects , Respiratory Insufficiency/therapy , Severity of Illness Index
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