Identification of Novel Clinical Factors Associated with Hepatic Fat Accumulation in Extreme Obesity

Objectives. The accumulation of lipids stored as excess triglycerides in the liver (steatosis) is highly prevalent in obesity and has been associated with several clinical characteristics, but most studies have been based on relatively small sample sizes using a limited set of variables. We sought to identify clinical factors associated with liver fat accumulation in a large cohort of patients with extreme obesity. Methods. We analyzed 2929 patients undergoing intraoperative liver biopsy during a primary bariatric surgery. Univariate and multivariate regression modeling was used to identify associations with over 200 clinical variables with the presence of any fat in the liver and with moderate to severe versus mild fat accumulation. Results. A total of 19 data elements were associated with the presence of liver fat and 11 with severity of liver fat including ALT and AST, plasma lipid, glucose, and iron metabolism variables, several medications and laboratory measures, and sleep apnea. The accuracy of a multiple logistic regression model for presence of liver fat was 81% and for severity of liver fat accumulation was 77%. Conclusions. A limited set of clinical factors can be used to model hepatic fat accumulation with moderate accuracy and may provide potential mechanistic insights in the setting of extreme obesity

Identification of Novel Clinical Factors Associated with Hepatic Fat Accumulation in Extreme Obesity

Objectives. The accumulation of lipids stored as excess triglycerides in the liver (steatosis) is highly prevalent in obesity and has been associated with several clinical characteristics, but most studies have been based on relatively small sample sizes using a limited set of variables. We sought to identify clinical factors associated with liver fat accumulation in a large cohort of patients with extreme obesity. Methods. We analyzed 2929 patients undergoing intraoperative liver biopsy during a primary bariatric surgery. Univariate and multivariate regression modeling was used to identify associations with over 200 clinical variables with the presence of any fat in the liver and with moderate to severe versus mild fat accumulation. Results. A total of 19 data elements were associated with the presence of liver fat and 11 with severity of liver fat including ALT and AST, plasma lipid, glucose, and iron metabolism variables, several medications and laboratory measures, and sleep apnea. The accuracy of a multiple logistic regression model for presence of liver fat was 81% and for severity of liver fat accumulation was 77%. Conclusions. A limited set of clinical factors can be used to model hepatic fat accumulation with moderate accuracy and may provide potential mechanistic insights in the setting of extreme obesity

Completeness and accuracy of anthropometric measurements in electronic medical records for children attending primary care

Background: Electronic medical records (EMRs) from primary care may be a feasible source of height and weight data. However the use of EMRs in research has been impeded by lack of standardization of EMRs systems, data access and concerns about the quality of the data. Objectives: The study objectives were to determine the data completeness and accuracy of child heights and weights collected in primary care EMRs, and to identify factors associated with these data quality attributes. Methods: A cross-sectional study examining height and weight data for children <19 years from EMRs through the Electronic Medical Records Administrative data Linked Database (EMRALD), a network of family practices across the province of Ontario. Body mass index z-scores were calculated using the WHO Growth Standards and Reference. Results: A total of 54,964 children were identified from EMRALD. Overall, 93% had at least 1 complete set of growth measurements to calculate a BMI z-score. 66.2% of all primary care visits had complete BMI z-score data. After stratifying by visit type 89.9% of well-child visits and 33.9% of sick visits had complete BMI z-score data; incomplete BMI z-score was mainly due to missing height measurements. Only 2.7% of BMI z-score data were excluded due to implausible values. Conclusions: Data completeness at well-child visits and overall data accuracy were greater than 90%. EMRs may be a valid source of data to provide estimates of obesity in children who attend primary care

Completeness and accuracy of anthropometric measurements in electronic medical records for children attending primary care

Background: Electronic medical records (EMRs) from primary care may be a feasible source of height and weight data. However the use of EMRs in research has been impeded by lack of standardization of EMRs systems, data access and concerns about the quality of the data.Objectives: The study objectives were to determine the data completeness and accuracy of child heights and weights collected in primary care EMRs, and to identify factors associated with these data quality attributes.Methods: A cross-sectional study examining height and weight data for children <19 years from EMRs through the Electronic Medical Records Administrative data Linked Database (EMRALD), a network of family practices across the province of Ontario. Body mass index z-scores were calculated using the WHO Growth Standards and Reference.Results: A total of 54,964 children were identified from EMRALD. Overall, 93% had at least 1 complete set of growth measurements to calculate a BMI z-score. 66.2% of all primary care visits had complete BMI z-score data. After stratifying by visit type 89.9% of well-child visits and 33.9% of sick visits had complete BMI z-score data; incomplete BMI z-score was mainly due to missing height measurements. Only 2.7% of BMI z-score data were excluded due to implausible values.Conclusions: Data completeness at well-child visits and overall data accuracy were greater than 90%. EMRs may be a valid source of data to provide estimates of obesity in children who attend primary care

Desenvolvimento de uma ferramenta para a otimização do registo de saúde eletrónico na abordagem multidisciplinar no tratamento da obesidade

Os sistemas de informação em saúde são muito importantes, permitindo a gestão de todas as informações médicas e administrativas para melhorar a qualidade dos cuidados de saúde, melhorando assim todo o procedimento de gestão e seguimento do doente. A obesidade é uma patologia que necessita de acompanhamento multidisciplinar, o registo de saúde eletrónico adaptado ao tipo de patologia e centrada do doente, permite aumentar a eficiência e a eficácia dos clínicos e também um melhor seguimento do doente. Este trabalho teve por objetivo elaborar uma ferramenta computacional, mais concretamente um registo de saúde eletrónico na área da obesidade. Todos os objetivos foram alcançados, resultando assim uma ferramenta funcional e multidisciplinar. No entanto, não foi possível validar a ferramenta em ambiente hospitalar, neste caso o CHSJ, devido a pandemia (SARS-COV2). No futuro, a ferramenta poderá evoluir através da criação de novas funcionalidades e se possível, ser testada em ambiente hospitalar permitindo avaliar a usabilidade e funcionalidade da mesma.Health information systems are very important, allowing the management of all medical and administrative information to improve the quality of health care, thus improving the entire management and patient monitoring procedure. Obesity is a pathology that needs multidisciplinary monitoring, the electronic health record adapted to the type of pathology and centered on the patient, allows to increase the efficiency and effectiveness of clinicians and also a better follow-up of the patient. This work aimed to develop a computational tool, more specifically an electronic health record in the area of obesity. All objectives were achieved, thus resulting in a functional and multidisciplinary tool. However, it was not possible to validate the tool in a hospital environment, in this case CHSJ, due to the pandemic (SARS-COV2). In the future, the tool may evolve through the creation of new functionalities and, if possible, be tested in a hospital environment allowing to evaluate its usability and functionality

An electronic health record-enabled obesity database

<p>Abstract</p> <p>Background</p> <p>The effectiveness of weight loss therapies is commonly measured using body mass index and other obesity-related variables. Although these data are often stored in electronic health records (EHRs) and potentially very accessible, few studies on obesity and weight loss have used data derived from EHRs. We developed processes for obtaining data from the EHR in order to construct a database on patients undergoing Roux-en-Y gastric bypass (RYGB) surgery.</p> <p>Methods</p> <p>Clinical data obtained as part of standard of care in a bariatric surgery program at an integrated health delivery system were extracted from the EHR and deposited into a data warehouse. Data files were extracted, cleaned, and stored in research datasets. To illustrate the utility of the data, Kaplan-Meier analysis was used to estimate length of post-operative follow-up.</p> <p>Results</p> <p>Demographic, laboratory, medication, co-morbidity, and survey data were obtained from 2028 patients who had undergone RYGB at the same institution since 2004. Pre-and post-operative diagnostic and prescribing information were available on all patients, while survey laboratory data were available on a majority of patients. The number of patients with post-operative laboratory test results varied by test. Based on Kaplan-Meier estimates, over 74% of patients had post-operative weight data available at 4 years.</p> <p>Conclusion</p> <p>A variety of EHR-derived data related to obesity can be efficiently obtained and used to study important outcomes following RYGB.</p