ObjectiveTo examine the impact of increased body mass index (BMI) on (1) tracheotomy timing and (2) short‐term surgical complications requiring a return to the operating room and 30‐day mortality utilizing data from the Multi‐Institutional Study on Tracheotomy (MIST). MethodsA retrospective analysis of patients from the MIST database who underwent surgical or percutaneous tracheotomy between 2013 and 2016 at eight institutions was completed. Unadjusted and adjusted logistic regression analyses were used to assess the impact of obesity on tracheotomy timing and complications. ResultsAmong the 3369 patients who underwent tracheotomy, 41.0% were obese and 21.6% were morbidly obese. BMI was associated with higher rates of prolonged intubation prior to tracheotomy accounting for comorbidities, indication for tracheotomy, institution, and type of tracheostomy (p = 0.001). Morbidly obese patients (BMI ≥35 kg/m2) experienced a longer duration of intubation compared with patients with a normal BMI (median days intubated [IQR 25%–75%]: 11.0 days [7–17 days] versus 9.0 days [5–14 days];p < 0.001) but did not have statistically higher rates of return to the operating room within 30 days (p = 0.12) or mortality (p = 0.90) on multivariable analysis. This same finding of prolonged intubation was not seen in overweight, nonobese patients when compared with normal BMI patients (median days intubated [IQR 25%–75%]: 10.0 days [6–15 days] versus 10.0 days [6–15 days];p = 0.36). ConclusionBMI was associated with increased duration of intubation prior to tracheotomy. Although morbidly obese patients had a longer duration of intubation, there were no differences in return to the operating room or mortality within 30 days. Level of Evidence3Laryngoscope, 134:4674–4681, 2024
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Detecting Body Mass Index from a Facial Photograph in Lifestyle Intervention
This study aimed to identify whether a research participant’s body-mass index (BMI) can be correctly identified from their facial image (photograph) in order to improve data capturing in dissemination and implementation research. Facial BMI (fBMI) was measured using an algorithm formulated to identify points on each enrolled participant’s face from a photograph. Once facial landmarks were detected, distances and ratios between them were computed to characterize facial fatness. A regression function was then used to represent the relationship between facial measures and BMI values to then calculate fBMI from each photo image. Simultaneously, BMI was physically measured (mBMI) by trained researchers, calculated as weight in kilograms divided by height in meters squared (adult BMI). Correlation analysis of fBMI to mBMI (n = 1210) showed significant correlation between fBMI and BMIs in normal and overweight categories (p < 0.0001). Further analysis indicated fBMI to be less accurate in underweight and obese participants. Matched pair data for each individual indicated that fBMI identified participant BMI an average of 0.4212 less than mBMI (p < 0.0007). Contingency table analysis found 109 participants in the ‘obese’ category of mBMI were positioned into a lower category for fBMI. Facial imagery is a viable measure for dissemination of human research; however, further testing to sensitize fBMI measures for underweight and obese individuals are necessary.
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- Award ID(s):
- 1650474
- PAR ID:
- 10091256
- Date Published:
- Journal Name:
- Technologies
- Volume:
- 6
- Issue:
- 3
- ISSN:
- 2227-7080
- Page Range / eLocation ID:
- 83
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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