OBJECTIVES: The aim of this study was to evaluate the adequacy of prophylactic dosing of enoxaparin in patients with severe obesity by performing an antifactor Xa (AFXa) assay.
SETTING: An academic medical center METHODS: In this observational study, all bariatric surgery cases at an academic center between December 2016 and April 2017 who empirically received prophylactic enoxaparin (adjusted by body mass index [BMI] threshold of 50 kg/m2) were studied. The AFXa was measured 3-5 hours after the second dose of enoxaparin.
RESULTS: A total of 105 patients were included; 85% were female with a median age of 47 years. In total, 16 patients (15.2%) had AFXa levels outside the prophylactic range: 4 (3.8%) cases were in the subprophylactic and 12 (11.4%) cases were in the supraprophylactic range. Seventy patients had a BMI <50 kg/m2 and empirically received enoxaparin 40 mg every 12 hours; AFXa was subprophylactic in 4 (5.7%) and supraprophylactic in 6 (8.6%) of these patients. Of the 35 patients with a BMI ≥50 who empirically received enoxaparin 60 mg q12h, no AFXa was subprophylactic and 6 (17.1%) were supraprophylactic. Five patients (4.8%) had major bleeding complications. One patient developed pulmonary embolism on postoperative day 35.
CONCLUSION: BMI-based thromboprophylactic dosing of enoxaparin after bariatric surgery could be suboptimal in 15% of patients with obesity. Overdosing of prophylactic enoxaparin can occur more commonly than underdosing. AFXa testing can be a practical way to measure adequacy of pharmacologic thromboprophylaxis, especially in patients who are at higher risk for venous thromboembolism or bleeding.
METHODS: A cohort study was conducted among laboratory-confirmed dengue patients aged >18 y in the central region of Peninsular Malaysia from May 2016 to November 2017. We collected demographic, clinical history, physical examination and laboratory examination information using a standardized form. Dengue severity (DS) was defined as either dengue with warning signs or severe dengue. Participants underwent daily follow-up, during which we recorded their vital signs, warning signs and full blood count results. Incidence of DS was modeled using mixed-effects logistic regression. Changes in platelet count and hematocrit were modeled using mixed-effects linear regression. The final multivariable models were adjusted for age, gender, ethnicity and previous dengue infection.
RESULTS: A total of 173 patients were enrolled and followed up. The mean body mass index (BMI) was 37.4±13.75 kg/m2. The majority of patients were Malay (65.9%), followed by Chinese (17.3%), Indian (12.7%) and other ethnic groups (4.1%). A total of 90 patients (52.0%) were male while 36 patients (20.8%) had a previous history of dengue infection. BMI was significantly associated with DS (adjusted OR=1.17; 95% CI 1.04 to 1.34) and hematocrit (%) (aβ=0.09; 95% CI 0.01 to 0.16), but not with platelet count (x103/µL) (aβ=-0.01; 95% CI -0.84 to 0.81). In the dose response analysis, we found that as BMI increases, the odds of DS, hematocrit levels and platelet levels increase during the first phase of dengue fever.
CONCLUSION: Higher BMI and higher hematocrit levels were associated with higher odds of DS. Among those with high BMI, the development of DS was observed during phase one of dengue fever instead of during phase two. These novel results could be used by clinicians to help them risk-stratify dengue patients for closer monitoring and subsequent prevention of severe dengue complications.
METHODS: This meta-analysis was performed based on the PRISMA recommendations. PubMed, Web of Science, Scopus, Embase, and Google Scholar databases were searched for all published observational studies that reported the risk of UTI based on BMI categories up to March 2020.
RESULTS: Fourteen (n = 14) articles comprising 19 studies in different populations met our inclusion criteria. The overall analysis showed a significant increased risk of UTI in subjects affected by obesity vs. individuals without obesity (RR = 1.45; 95% CI: 1.28 - 1.63; I2 = 94%), and a non-significant increased risk of UTI in subjects who were overweight (RR = 1.03; 95% CI: 0.98 - 1.10; I2 = 49.6%) and underweight (RR = 0.99; 95% CI: 0.81 - 21; I2 = 0.0%) when compared to subjects who had normal weight. In the stratified analysis, we showed that obesity increased the risk of UTI in females (RR = 1.63; 95% CI: 1.38 - 1.93) and in subjects below 60 years old (RR = 1.53; 95% CI: 1.33 - 1.75).
CONCLUSION: This systematic review and meta-analysis recognized a significant relationship between BMI and incidence of UTI in obese vs. non-obese subjects, as well as in females and in individuals below 60 years old.
METHODS: This cross-sectional study involved 1,404 school adolescents aged 12 years (46% boys and 54% girls). Socio-demographic, dietary and physical activity data were collected using questionnaires whilst body weight and height were measured and body mass index was classified based on WHO BMI-for-age Z-scores cut-off.
RESULTS: A multivariable linear regression model showed that BMI z-score was positively associated with parents' BMI (P<0.001), birth weight (P=0.003), and serving size of milk and dairy products (P=0.036) whilst inversely associated with household size (P=0.022). Overall, 13.1% of the variances in BMI Z-scores were explained by parents' BMI, birth weight, servings of milk and dairy products and household size.
CONCLUSION: This study found important determinants of body weight status among adolescents mainly associated with family and home environmental factor. This evidence could help to form the effective and tailored strategies at the earliest stage to prevent obesity in this population.
DESIGN: Cross-sectional.
SETTING: Central and eastern regions of Peninsular Malaysia.
PARTICIPANTS: A stratified random sampling was employed to select 917 secondary school-going adolescents (aged 15-17 years).
RESULTS: The prevalence of under-reporters was 17·4 %, while no over-reporters were identified. Under-reporters had higher body composition and lower dietary intakes (except for vitamin C, Cr and Fl) compared with plausible reporters (P < 0·05). Adolescents with overweight and obesity had a higher odds of under-reporting compared with under-/normal weight adolescents (P < 0·001). In model 3, the highest regression coefficient (R2 = 0·404, P < 0·001) was obtained after adjusting for reporting status.
CONCLUSIONS: Overweight and obese adolescents were more likely to under-report their food intake and consequently affect nutrient intakes estimates. Future analyses that include nutrient intake data should adjust for reporting status so that the impact of misreporting on study outcomes can be conceded and consequently improve the accuracy of dietary-related results.