A Genetic "Obesity Risk Index" for Patients With Morbid Obesity

Thirlby, Richard C.; Randall, James A

doi:10.1381/096089202321144531

Cited by 22 publications

(13 citation statements)

References 11 publications

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“…All these suggest that the genetic contribution to the obesity of a patient should be evaluated before surgery is performed, as it may influence the effectiveness of the surgery. An obesity risk index, which has been described to quantify the genetic contribution to a patient's morbid obesity, could be included as one of the patient's pre-intervention variables [30].…”

Section: Discussionmentioning

confidence: 99%

Influence of Sleeve Gastrectomy on Several Experimental Models of Obesity: Metabolic and Hormonal Implications

et al. 2007

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Section: Discussionmentioning

confidence: 99%

Influence of Sleeve Gastrectomy on Several Experimental Models of Obesity: Metabolic and Hormonal Implications

et al. 2007

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“…It is possible that the factors determining caloric intake and physical activity differ across the lifespan and that BMI in children/adolescents results from a combination of factors different from adults. For example, it is known that parental overweight is a risk factor for childhood and adult obesity [24,25] . However, it is unknown whether the mechanisms for this relationship differ between children (e.g., sharing meals, similar environment) and adults (e.g., genetic contributions, activity patterns).…”

Section: Discussionmentioning

confidence: 99%

BDNF Val66Met Polymorphism Is Associated with Body Mass Index in Healthy Adults

et al. 2006

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Although recent studies suggest a possible relationship between the brain-derived neurotrophic factor Val66Met polymorphism and eating disorders, no study has examined the possibility that the Met-Met genotype is associated with a lower body mass index (BMI) in healthy individuals. We examined this possibility in 481 adults (age range 18–82 years) without significant medical or psychiatric history. After adjusting for gender, analysis of covariance showed that persons with the Met-Met genotype had a lower BMI than those with the Val-Met/Val-Val genotypes (22.28 ± 3.77 vs. 24.72 ± 4.81). A similar, though nonsignificant, trend emerged when comparing all three genotypes separately. These findings suggest a possible relationship between Val66Met polymorphism and BMI in healthy adults. Further work is needed to clarify possible mechanisms for this relationship.

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“…For instance, CCS and NYHA class may have been less consistently reported if the patients were stable and asymptomatic at the time of assessment. Family history and obesity may have been less frequently reported as they are not, or are less, amenable to treatment respectively [20,21]. With regard to serum cholesterol tests, physicians often do not copy results to other providers [22], thus leading to over or under-testing and chart inconsistency.…”

Section: Chart Completenessmentioning

confidence: 99%

Cardiologists' charting varied by risk factor, and was often discordant with patient report

Gravely–Witte

Stewart

Suskin

et al. 2008

Journal of Clinical Epidemiology

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Objective-To assess the completeness of cardiac risk factor documentation by cardiologists, and agreement with patient report.Study Design and Setting-A total of 68 Ontario cardiologists and 789 of their ambulatory cardiology patients were randomly selected. Cardiac risk factor data were systematically extracted from medical charts, and a survey was mailed to participants to assess risk factor concordance.Results-With regard to completeness of risk factor documentation, 90.4% of charts contained a report of hypertension, 87.2% of diabetes, 80.5% of dyslipidemia, 78.6% of smoking behavior, 73.0% of other comorbidities, 48.7% of family history of heart disease, and 45.9% of body mass index or obesity. Using Cohen's κ, there was a concordance of 87.7% between physician charts and patient self-report of diabetes, 69.5% for obesity, 56.8% for smoking status, 49% for hypertension and 48.4% for family history.Conclusion-Two of 4 major cardiac risk factors (hypertension and diabetes) were recorded in 90% of patient records, however arguably the most important reversible risk factors for cardiac disease (dyslipidemia and smoking) were only reported 80% of the time. The results suggest that physician chart report may not be the criterion standard for quality assessment in cardiac risk factor reporting. 1.Less than 20% of the outpatient charts completely denoted all major cardiac risk factors. Most frequently, charts were missing 1 risk factor. 2.Modifiable risk factors for cardiac disease such as dyslipidemia and smoking were reported 80% of the time. 3.Agreement between chart and patient report of cardiac risk factors ranged from substantial to moderate, while concordance for comorbid conditions was poor.What this paper adds: Neither the medical record nor patient report were necessarily the "gold standard" for risk factor documentation, and each source has distinct advantages and disadvantages for specific risk factors.Implications: Initiatives such as electronic patient records and standardized reports should be explored as avenues to improve chart reporting, and potentially patient risk factor management.Medical records are customarily used as the criterion standard to assess quality of care in the healthcare setting. Accurate and complete medical record documentation by physicians is essential to ensure appropriate treatment and optimal continuity of care. Missing information in medical charts can lead to medication errors, poorer quality patient management, and may have a negative effect on patient outcomes [1]. The poor quality of patient health records has been repeatedly documented in hospital settings [1][2][3][4]. An alternative to medical records as a quality assessment tool, patient self-report surveys are increasingly being used, and shown to be valid and accurate [5][6][7][8][9]. It is important in both clinical practice and research to identify patient treatment plans and clinical history; however, it is often difficult to obtain a complete and accurate patient profile using one data source alone ...

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A Genetic "Obesity Risk Index" for Patients With Morbid Obesity

Cited by 22 publications

References 11 publications

Influence of Sleeve Gastrectomy on Several Experimental Models of Obesity: Metabolic and Hormonal Implications

Influence of Sleeve Gastrectomy on Several Experimental Models of Obesity: Metabolic and Hormonal Implications

BDNF Val66Met Polymorphism Is Associated with Body Mass Index in Healthy Adults

Cardiologists' charting varied by risk factor, and was often discordant with patient report

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