Dataset Information

Validation of clinic weights from electronic health records against standardized weight measurements in weight loss trials.

ABSTRACT:

Objective

To validate clinic weights in electronic health records against researcher-measured weights for outcome assessment in weight loss trials.

Methods

Clinic and researcher-measured weights from a published trial (BE WELL) were compared using Lin's concordance correlation coefficient, Bland and Altman's limits of agreement, and polynomial regression model. Changes in clinic and researcher-measured weights in BE WELL and another trial, E-LITE, were analyzed using growth curve modeling.

Results

Among BE WELL (n = 330) and E-LITE (n = 241) participants, 96% and 90% had clinic weights (mean [SD] of 5.8 [6.1] and 3.7 [3.9] records) over 12 and 15 months of follow-up, respectively. The concordance correlation coefficient was 0.99, and limits of agreement plots showed no pattern between or within treatment groups, suggesting overall good agreement between researcher-measured and nearest-in-time clinic weights up to 3 months. The 95% confidence intervals for predicted percent differences fell within ±3% for clinic weights within 3 months of the researcher-measured weights. Furthermore, the growth curve slopes for clinic and researcher-measured weights by treatment group did not differ significantly, suggesting similar inferences about treatment effects over time, in both trials.

Conclusions

Compared with researcher-measured weights, close-in-time clinic weights showed high agreement and inference validity. Clinic weights could be a valid pragmatic outcome measure in weight loss studies.

SUBMITTER: Xiao L

PROVIDER: S-EPMC5269438 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Publications

Validation of clinic weights from electronic health records against standardized weight measurements in weight loss trials.

Xiao Lan L Lv Nan N Rosas Lisa G LG Au David D Ma Jun J

Obesity (Silver Spring, Md.) 20170106 2

<h4>Objective</h4>To validate clinic weights in electronic health records against researcher-measured weights for outcome assessment in weight loss trials.<h4>Methods</h4>Clinic and researcher-measured weights from a published trial (BE WELL) were compared using Lin's concordance correlation coefficient, Bland and Altman's limits of agreement, and polynomial regression model. Changes in clinic and researcher-measured weights in BE WELL and another trial, E-LITE, were analyzed using growth curve ...[more]

PMID: 28059466

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Project description:Background/objectivesPragmatic trials are increasingly used to study the implementation of weight loss interventions in real-world settings. This study compared researcher-measured body weights versus electronic medical record (EMR)-derived body weights from a pragmatic trial conducted in an underserved patient population.Subjects/methodsThe PROPEL trial randomly allocated 18 clinics to usual care (UC) or to an intensive lifestyle intervention (ILI) designed to promote weight loss. Weight was measured by trained technicians at baseline and at 6, 12, 18, and 24 months. A total of 11 clinics (6 UC/5 ILI) with 577 enrolled patients also provided EMR data (n = 561), which included available body weights over the period of the trial.ResultsThe total number of assessments were 2638 and 2048 for the researcher-measured and EMR-derived body weight values, respectively. The correlation between researcher-measured and EMR-derived body weights was 0.988 (n = 1 939; p < 0.0001). The mean difference between the EMR and researcher weights (EMR-researcher) was 0.63 (2.65 SD) kg, and a Bland-Altman graph showed good agreement between the two data collection methods; the upper and lower boundaries of the 95% limits of agreement are -4.65 kg and +5.91 kg, and 71 (3.7%) of the values were outside the limits of agreement. However, at 6 months, percent weight loss in the ILI compared to the UC group was 7.3% using researcher-measured data versus 5.5% using EMR-derived data. At 24 months, the weight loss maintenance was 4.6% using the technician-measured data versus 3.5% using EMR-derived data.ConclusionAt the group level, body weight data derived from researcher assessments and an EMR showed good agreement; however, the weight loss difference between ILI and UC was blunted when using EMR data. This suggests that weight loss studies that rely on EMR data may require larger sample sizes to detect significant effects.Clinical trial registrationClinicalTrials.gov number NCT02561221.

Project description:ObjectiveTo create an electronic frailty index (eFRAGICAP) using electronic health records (EHR) in Catalunya (Spain) and assess its predictive validity with a two-year follow-up of the outcomes: homecare need, institutionalization and mortality in the elderly. Additionally, to assess its concurrent validity compared to other standardized measures: the Clinical Frailty Scale (CFS) and the Risk Instrument for Screening in the Community (RISC).MethodsThe eFRAGICAP was based on the electronic frailty index (eFI) developed in United Kingdom, and includes 36 deficits identified through clinical diagnoses, prescriptions, physical examinations, and questionnaires registered in the EHR of primary health care centres (PHC). All subjects > 65 assigned to a PHC in Barcelona on 1st January, 2016 were included. Subjects were classified according to their eFRAGICAP index as: fit, mild, moderate or severe frailty. Predictive validity was assessed comparing results with the following outcomes: institutionalization, homecare need, and mortality at 24 months. Concurrent validation of the eFRAGICAP was performed with a sample of subjects (n = 333) drawn from the global cohort and the CFS and RISC. Discrimination and calibration measures for the outcomes of institutionalization, homecare need, and mortality and frailty scales were calculated.Results253,684 subjects had their eFRAGICAP index calculated. Mean age was 76.3 years (59.5% women). Of these, 41.1% were classified as fit, and 32.2% as presenting mild, 18.7% moderate, and 7.9% severe frailty. The mean age of the subjects included in the validation subsample (n = 333) was 79.9 years (57.7% women). Of these, 12.6% were classified as fit, and 31.5% presented mild, 39.6% moderate, and 16.2% severe frailty. Regarding the outcome analyses, the eFRAGICAP was good in the detection of subjects who were institutionalized, required homecare assistance, or died at 24 months (c-statistic of 0.841, 0.853, and 0.803, respectively). eFRAGICAP was also good in the detection of frail subjects compared to the CFS (AUC 0.821) and the RISC (AUC 0.848).ConclusionThe eFRAGICAP has a good discriminative capacity to identify frail subjects compared to other frailty scales and predictive outcomes.

Dataset Information

Validation of clinic weights from electronic health records against standardized weight measurements in weight loss trials.

Objective

Methods

Results

Conclusions

Publications

Validation of clinic weights from electronic health records against standardized weight measurements in weight loss trials.

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