Ahmed Hasanin

BACKGROUND: Super-obesity is a serious disorder which requires bariatric surgery. The association of super-obesity and difficult intubation was not adequately established.

OBJECTIVES: To determine if super-obesity and super-super-obesity are associated with difficult intubation or not.

SETTING: University Hospital.

METHODS: A cohort of obese patients scheduled for bariatric surgery was prospectively recruited. Super-obesity and super-super-obesity were defined as body mass index ≥50 kg/m and 60 kg/m, respectively. Intubation difficulty was assessed by 2 methods: (1) intubation difficulty scale; (2) number of intubation attempts. Risk factors for difficult intubation were recorded. Univariate and multivariate analysis for risk factors for difficult intubation and difficult mask ventilation were performed.

RESULTS: A total of 658 patients were enrolled in the study including 205 (31%) super-obese and 52 (8%) super-super-obese patients. Ninety-nine (15%) patients required more than 1 intubation attempt, while 215 (33%) patients had intubation difficulty scale ≥5. Ninety-four (14.4%) patients had mask ventilation of moderate difficulty, while only 2 (.3%) patients needed 2-person ventilation. The independent risk factors for difficult intubation using the two stated definitions were STOP-Bang and Mallampati score values. The independent risk factors for mask ventilation of moderate difficulty were STOP-Bang score, Mallampati score, and limited neck extension.

CONCLUSION: Within obese patients, neither super-obesity nor super-super-obesity was associated with difficult intubation or difficult mask ventilation. High STOP-Bang and Mallampati score are the independent factors for difficult intubation.

BACKGROUND: In obese patients, non-invasive blood pressure monitoring in the arm is difficult due to the arm size and morphology. We compared the non-invasive oscillometric wrist blood pressure measurement with the arm and forearm in obese patients monitored with invasive radial blood pressure (reference standard).

METHODS: This prospective observational study included adult obese patients scheduled for bariatric surgery. Non-invasive blood pressure was measured at the arm, upper forearm and wrist of one upper extremity, while invasive blood pressure was simultaneously measured through a radial arterial catheter in the contralateral upper extremity. The accuracy of non-invasive blood pressure reading at each site was evaluated for absolute and trending values using the Bland-Altman analysis and Spearman's correlation coefficient.

RESULTS: In 40 patients, 262, 259, and 263 pairs of non-invasive blood pressure readings were obtained from the arm, forearm, and wrist sites, respectively. As primary outcome, the correlation coefficient for systolic blood pressure was higher for the wrist (0.92, 95% confidence interval (CI) [0.9-0.94]) than for the arm (0.74, 95% CI [0.68-0.79]) and the forearm (0.71, 95% CI [0.64-0.76]) (P<0.05). The non-invasive systolic wrist blood pressure showed the least mean bias and the narrowest limits of agreement (-0.3±7.6mmHg) when compared with forearm (4.3±16) and arm measurements (14.2±13.6) (P<0.05). For trending values, the correlation coefficient was the highest at the wrist.

CONCLUSION: In obese patients undergoing bariatric surgery, non-invasive blood pressure measured at the wrist showed the highest accuracy in comparison with the arm and forearm.

PURPOSE: To evaluate the accuracy of noninvasive blood pressure (NIBP) measurement at the dependent- and nondependent arms in the lateral position, using invasive blood pressure (IBP) as reference.

METHODS: This prospective observational study included 42 adult patients undergoing surgery in the lateral position. Paired readings of IBP and NIBP were obtained at either arm. The accuracy of both arms in detecting mean arterial pressure (MAP) <70 mmHg was evaluated using the area under the receiver operating characteristic curve (AUC). The agreement between the IBP and NIBP was evaluated using the Bland-Altman and error grid analyses.

RESULTS: We analyzed 350 and 347 paired readings at the dependent- and nondependent arms. The AUC for detecting hypotension was comparable in both arms. The negative and positive predictive values (95% confidence interval) were 100% (99-100%) and 24% (14-34%), respectively for the dependent arm at cutoff value MAP ≤86 mmHg; and were 99% (96-100%) and 21% (13-30%), respectively for the nondependent arm at cutoff value MAP ≤75 mmHg. The mean bias for MAP was -6.0 ± 9.1 and 6.3 ± 10.1 mmHg; and for systolic blood pressure was 0.3 ± 11.6 and 13.2 ± 12.6 mmHg, in the dependent- and nondependent arm, respectively. Error grid analysis showed that the proportions of paired MAP readings in risk zone A were 71 and 82% in the dependent- and the nondependent arms, respectively.

CONCLUSION: In the lateral position, the NIBP readings at both arms are not interchangeable with the corresponding IBP readings. However, NIBP measurement at both arms can be used to accurately rule out hypotension.

BACKGROUND: Studies of the accuracy of different airway tests are lacking in elderly. We evaluated and compared the accuracy of thyromental height in predicting difficult intubation in relation to the other traditional airway tests in elderly.

METHODS: We included 120 patients aged ≥ 65 years scheduled for general anesthesia with tracheal intubation. Thyromental height, modified Mallampati test, thyromental distance and sternomental distance were evaluated. Cormack-Lehane grade > 2 was considered difficult laryngoscopy. Difficult tracheal intubation was considered if successful intubation required more than 2 attempts. The accuracy of different tests in predicting difficult intubation and difficult laryngoscopy were evaluated through area under receiver operating characteristic (AUROC) curves. Univariate and multivariate analyses were conducted to identify risk factors for difficult intubation and difficult laryngoscopy.

RESULTS: Our cohort had a mean age of 71(7) years. We encountered difficult laryngoscopy in 15/120 (12%) patients, difficult intubation in 20/120 (17%) patients, and failed laryngoscopy requiring alternative methods for securing the airway in 3/120 (3%) patients. For predicting difficult intubation, thyromental height and modified Mallampati test showed the highest accuracy AUROC (95% confidence interval): 0.9 (0.83-0.95), cut-off value ≤ 5.9 cm, and AUROC (95% confidence interval): 0.89 (0.82-0.94), cut-off value > 2, respectively. Low thyromental height and high modified Mallampati test were the only independent risk factors for difficult laryngoscopy and difficult intubation.

CONCLUSION: In elderly scheduled for elective procedure, both thyromental height and modified Mallampati tests showed good accuracy in predicting difficult intubation and difficult laryngoscopy, whilst thyromental distance and sternomental distance were poor predictors.

BACKGROUND: This study aimed to evaluate the accuracy of pulse oximetry-derived oxygen saturation (SpO) on room air, determined at hospital admission, as a predictor for the need for mechanical ventilatory support in patients with Coronavirus Disease-2019 (COVID-19).

METHODS: In this retrospective observational study, demographic and clinical details of the patients were obtained during ICU admission. SpO and respiratory rate (RR) on room air were determined within the first 6 h of hospital admission. As all measurements were obtained on room air, we calculated the simplified respiratory rate‑oxygenation (ROX) index by dividing the SpO by the RR. Based on the use of any assistance of mechanical ventilator (invasive or noninvasive), patients were divided into mechanical ventilation (MV) group and oxygen therapy group. The accuracy of the SpO, CT score, and ROX index to predict the need to MV were determined using the Area under receiver operating curve (AUC).

RESULTS: We included 72 critically ill patients who tested COVID-19-positive. SpO on the room air could predict any MV requirement (AUC [95% confidence interval]: 0.9 [0.8-0.96], sensitivity: 70%, specificity 100%, cut-off value ≤78%, P < 0.001). Within the MV group, the use of noninvasive ventilation (NIV) was successful in 37 (74%) patients, whereas 13 patients (26%) required endotracheal intubation. The cut-off ROX value for predicting early NIV failure was ≤1.4, with a sensitivity of 85%, a specificity of 86%, and an AUC of 0.86 (95% confidence interval of 0.73-0.94, P < 0.0001).

CONCLUSIONS: A baseline SpO ≤78% is an excellent predictor of MV requirement with a positive predictive value of 100%. Moreover, the ROX index measured within the first 6 h of hospital admission is a good indicator of early NIV failure.

OBJECTIVES: Accurate estimation of a critically ill patient's caloric requirements is essential for a proper nutritional plan. This study aimed to evaluate the use of point-of-care ultrasound (US) to predict the resting energy expenditure (REE) in critically ill patients.

METHODS: In 69 critically ill patients, we measured the REE using indirect calorimetry (REE_IC), muscle layer thicknesses (MLTs), and cardiac output (CO). Muscle thickness was measured at the biceps and the quadriceps muscles. Patients were randomly split into a model development group (n = 46) and a cross-validation group (n = 23). In the model development group, a multiple regression analysis was applied to generate REE using US (REE_US) values. In the cross-validation group, REE was calculated by the REE_US and the resting energy expenditure using the Harris-Benedict equation (REE_HB), and both were compared to the REE_IC.

RESULTS: In the model development group, the REE_US was predicted by the following formula: predicted REE_US (kcal/d) = 206 + 173.5 × CO (L/min) + 137 × MLT (cm) - 230 × (women = 1; men = 0) (R  = 0.8; P < .0001). In the cross-validated group, the REE_IC and REE_US values were comparable (mean difference, -66 [-3.3%] kcal/d; P = .14). However, the difference between the mean REE_IC and the mean REE_HB was 455.8 (26%) kcal/d (P < .001). According to a Bland-Altman analysis, the REE_US agreed well with the REE_IC, whereas the REE_HB did not.

CONCLUSIONS: Resting energy expenditure could be estimated from US measurements of MLTs and CO. Our point-of-care US model explains 80% of the change in the REE in critically ill patients.