Respiratory Care

In chronic obstructive pulmonary disease (COPD), body mass index (BMI) is an important predictor of survival. Little is known about the prevalence of malnutrition or longitudinal changes of BMI in patients undergoing noninvasive positive-pressure ventilation (NPPV).

In a cohort study of 141 patients with COPD and severe chronic respiratory failure (mean forced expiratory volume in the first second [FEV₁] 0.80 ± 0.27 L, mean P_aCO2 55.6 ± 8.8 mm Hg), we investigated nutritional status in relation to respiratory impairment. Changes in BMI were evaluated at 6 and 12 months after initiation of NPPV.

Malnutrition, indicated by a BMI of < 20 kg/m², was found in 20.6% of the patients. BMI was significantly correlated with the severity of respiratory impairment, especially with hyperinflation (residual volume divided by total lung capacity, r = −0.55, p < 0.001). In malnourished patients (BMI < 20 kg/m²) there was a significant increase in body weight after 6 months (6.2 ± 12.5%, p < 0.05) and 12 months (12.8 ± 16.0%, p < 0.01), whereas there were no significant changes in the overall study population. Furthermore, there was no correlation between changes in BMI and changes in blood-gas values, lung function, or inspiratory muscle function, either in the entire patient group or in the subgroup of malnourished patients.

In COPD with chronic respiratory failure, malnutrition is common and strongly related to hyperinflation. After initiation of NPPV, a significant weight gain is observed in malnourished COPD patients.

To determine if the measurement of minute ventilation recovery time (V˙_ERT), a recently proposed predictor of extubation outcome, can be reproduced using a more practical, simpler method.

A case series with convenience sampling was performed in the surgical intensive care unit of a tertiary-care hospital. Nineteen patients were enrolled during weaning from mechanical ventilation, prior to the initial extubation attempt. Within-subject comparisons of V˙_ERT were performed, using 2 alternative methods for measuring baseline V˙_E and one alternative method for determining the threshold for recovery of V˙_E during the final spontaneous breathing trial prior to extubation. Comparison methods for baseline V˙_E included an 8-hour average and the last V˙_E measurement prior to the spontaneous breathing trial. The alternative threshold for defining recovery of V˙_E was 100% of the baseline value (vs 110% in the original method).

The study subjects were primarily cardiac surgery patients (63%) and were ventilated for a median of 5 days prior to extubation. V˙_ERT calculated using the 8-hour average or the last V˙_E measurement prior to the spontaneous breathing trial as baseline, and a threshold of 100% of baseline V˙_E to define recovery most closely approximated V˙_ERT obtained by the original method and similarly classified patients at high risk for reintubation (kappa statistic = 0.78 ± 0.2).

V˙_ERT can be determined using a simpler method for measuring both baseline V˙_E and the recovery threshold. These methodological modifications may increase the feasibility of measuring V˙_ERT, while reproducing the results obtained by the original method.

Pressurized metered-dose inhalers with valved holding chambers and masks are commonly used for aerosol delivery in children. Drug delivery can decrease when the dead-space volume (DSV) of the valved holding chamber is increased, but there are no published data evaluating force-dependent DSV among different masks.

Seven masks were studied. Masks were sealed at the valved holding chamber end and filled with water to measure mask volume. To measure mask DSV we used a mannequin of 2-year-old-size face and we applied the mask with forces of 1.5, 3.5, and 7 pounds. Mask seal was determined by direct observation. Intra-brand analysis was done via analysis of variance.

At 3.5 pounds of force, the DSV ranged from 29 mL to 100 mL, with 3 masks having DSV of < 50 mL. The remaining masks all had DSV > 60 mL. At 3.5 pounds of force, DSV percent of mask volume ranged from 33.7% (Aerochamber, p < 0.01 compared with other masks) to 100% (Pocket Chamber). DSV decreased with increasing force with most of the masks, and the slope of this line was inversely proportional to mask flexibility. Mask fit was 100% at 1.5 pounds of force only with the Aerochamber and Optichamber. Mask fit was poorest with the Vortex, Pocket Chamber, and BreatheRite masks.

Rigid masks with large DSV might not be not suitable for use in children, especially if discomfort from the stiff mask makes its use less acceptable to the child.

The hospital billing system is usually the source for reporting activity counts used in benchmarking efforts. Because billing is associated with a specific procedure, benchmarking data are often reported as procedure-days, procedure-shifts, or procedure-hours. Normalizing (usually to procedure-days) is required when comparing data for benchmarking purposes. For an institution that uses hourly billing, simply dividing procedure-hours by 24 (or procedure-shifts by 2 or 3) will underestimate the procedure-days reported by a daily billing system, because daily billing systems use the convention that any fractional day of service is rounded up to the next higher day. The purposes of this study were: (1) to simulate sets of data and determine the expected error with conversion by simple division, (2) to derive a more accurate procedure for normalizing benchmarking data, and (3) to compare the new normalization procedure to simple division, using simulated and actual data.

A reference population of simulated patient data was created using a spreadsheet to generate random start times paired with actual procedure durations (eg, hours of mechanical ventilation) for 5,000 patients. The spreadsheet calculated “true” billable procedure-days and procedure-shifts from the simulated procedure-hours. Next, a resampling procedure was used to simulate the effect of submitting benchmarking data based on various numbers of patients. The resulting sets of data were used to examine the association between sample size and conversion error when converting from procedure-hours to procedure-days and to generate an alternative conversion procedure that uses linear regression to estimate procedure-days from procedure-hours. An additional regression equation was generated from actual patient data, using simultaneously recorded procedure-hours and proceduredays. The set of mean conversion errors for the 2 regression equations was compared using the MannWhitney rank sum test.

In general, conversion errors (both systematic and random errors) were smaller with larger sample sizes and with longer service periods, approaching an asymptote at a sample size greater than about 20. Using division, the conversion errors for a sample size of 100 were −16% for hourly reporting, −11% for 8-hour shifts, and −8% for 12-hour shifts. The regression equations for conversion derived from simulated data were as follows. For hourly billing, procedure-days = −0.237 + (0.049) (procedure-hours). For 8-hour shifts, procedure-days = −0.205 + (0.372) (procedure-shifts). For 12-hour shifts, procedure-days = −0.114 + (0.541) (procedure-shifts). Using those regression equations, the conversion errors for a sample size of 100 were 1% for hourly reporting, −0.2% for 8-hour shifts, and −0.2% for 12-hour shifts. The regression equation (for hourly billing) derived from simulated data gave better results than did the equation derived from actual data (median error 0.39 vs −2.92, p = 0.013).