The use of prediction equations has been recommended for calculating energy expenditure. We evaluated two equations that predict energy expenditure, each of which were corrected for two different stress factors, and compared the values obtained with those calculated by indirect calorimetry. The subjects were 55 critically ill children on mechanical ventilation. Basal metabolic rates were calculated with the Harris-Benedict and Talbot methods. Measured resting energy expenditure was 4.72 +/- 2.53 MJ/d. The average difference between measured resting energy expenditure and the Harris-Benedict prediction with a stress factor of 1.5 was -0.98 MJ/d, with an SD delta of 1.56 MJ/d and limits of agreement from -4.12 to 2.15; for a stress factor of 1.3 the average difference was -0.22 MJ/d, with an SD delta of 1.57 MJ/d and limits of agreement from -3.37 to 2.93. The average difference between measured resting energy expenditure and the Talbot prediction with a stress factor of 1.5 was -0.23 MJ/d, with an SD delta of 1.36 MJ/d and limits of agreement from -2.95 to 2.48; for a stress factor of 1.3, it was 0.42 MJ/d, with an SD delta of 1.24 MJ/d and limits of agreement from -2.04 to 2.92. These limits of agreement indicate large differences in energy expenditure between the measured value and the prediction estimated for some patients. Therefore, neither the Harris-Benedict nor the Talbot method will predict resting energy expenditure with acceptable precision for clinical use. Indirect calorimetry appears to be the only useful way of determining resting energy expenditure in these patients.

Purpose To develop a national online survey to be administered by the American College of Clinical Engineers Healthcare Technology Foundation to hospitals and healthcare workers to determine the problems associated with alarms in hospitals. Methods An online survey was developed by a 16-member task force representing professionals from clinical engineering, nursing, and technology to evaluate the reasons health-care workers do not respond to clinical alarms. Results A total of 1327 persons responded to the survey; most (94%) worked in acute care hospitals. About half of the respondents were registered nurses (51%), and one-third of respondents (31%) worked in a critical care unit. Most respondents (>90%) agreed or strongly agreed with the statements covering the purpose of clinical alarms and the need for prioritized and easily differentiated audible and visual alarms. Likewise, many respondents identified nuisance alarms as problematic; most agreed or strongly agreed that the alarms occur frequently (81%), disrupt patient care (77%), and can reduce trust in alarms and cause caregivers to disable them (78%). Conclusions Effective clinical alarm management relies on (1) equipment designs that promote appropriate use, (2) clinicians who take an active role in learning how to use equipment safely over its full range of capabilities, and (3) hospitals that recognize the complexities of managing clinical alarms and devote the necessary resources to develop effective management schemes.

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