1248-P: Estimating Diabetes Duration from Electronic Medical Records (EMRs) with an Intrinsic Diabetes Risk Prediction Model: From Clinical Trial to Real-World Clinical Practice

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1248-P ◽  
Author(s):  
HUI SHAO ◽  
SHUANG YANG ◽  
CHARLES STOECKER ◽  
VIVIAN FONSECA ◽  
XIANG CHENG ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Practice ◽  
Prediction Model ◽  
Electronic Medical Records ◽  
Risk Prediction ◽  
Real World ◽  
Medical Records ◽  
Diabetes Risk ◽  
Risk Prediction Model ◽  
Diabetes Duration

Fall Ascertainment and Development of a Risk Prediction Model Using Electronic Medical Records

2019 ◽  
Vol 67 (7) ◽  
pp. 1417-1422 ◽  
Author(s):  
Caryn E. S. Oshiro ◽  
Timothy B. Frankland ◽  
A. Gabriela Rosales ◽  
Nancy A. Perrin ◽  
Christina L. Bell ◽  
...  
Keyword(s):  
Prediction Model ◽  
Electronic Medical Records ◽  
Risk Prediction ◽  
Medical Records ◽  
Risk Prediction Model

Diabetes risk prediction model for non-obese Asian Indians residing in North India using cut-off values for pancreatic and intra-abdominal fat volume and liver span

2016 ◽  
Vol 8 (5) ◽  
pp. 729-731 ◽  
Author(s):  
Shajith Anoop ◽  
Anoop Misra ◽  
Kalaivani Mani ◽  
Ravindra Mohan Pandey ◽  
Seema Gulati
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Asian Indians ◽  
Abdominal Fat ◽  
Diabetes Risk ◽  
Risk Prediction Model ◽  
North India ◽  
Fat Volume

Abstract 17956: External Validation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Matthew J Kolek ◽  
Amy J Graves ◽  
Aihua Bian ◽  
Pedro L Teixeira ◽  
Moore B Shoemaker ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Prediction Model ◽  
Electronic Medical Records ◽  
Risk Prediction ◽  
Medical Records ◽  
Risk Model ◽  
External Validation ◽  
Left Ventricular ◽  
Cox Proportional Hazards Model ◽  
Ambulatory Setting

Background: Atrial fibrillation (AF) contributes to substantial morbidity, mortality, and healthcare costs. Accurate prediction of incident AF might enhance patient care and improve outcomes. We aimed to externally validate the AF risk model developed by the CHARGE-AF investigators utilizing a large repository of electronic medical records (EMR). Methods: Using a database of de-identified EMRs, we conducted a retrospective cohort study of subjects serially followed in internal medicine clinics at our institution (minimum 3 visits in a 24 month window). Subjects were followed for incident AF from 2005 until 2010. We applied the published CHARGE-AF Cox proportional hazards model beta coefficients to our cohort. Predictors included age, race, height, weight, systolic and diastolic blood pressure, treatment for hypertension, smoking status, diabetes, heart failure, history of myocardial infarction, left ventricular hypertrophy, and PR interval. Calibration and discrimination were assessed by generating calibration plots and calculating C-statistics. Results: The study included 33,494 subjects with median age 57 years (25th to 75th percentile: 49 - 67), 57% women, 86% whites, and 14% African Americans. During the mean follow-up period of 4.8 ± 0.85 years, 2455 (7.3%) subjects developed AF. After correcting for baseline hazard, the CHARGE-AF model over-predicted AF at the highest risk deciles but was otherwise well-calibrated (Figure) and showed good discrimination, with a C-statistic of 0.746 (95% confidence interval: 0.738 to 0.754). Conclusion: From clinical factors readily accessible in a large de-identified EMR repository, we externally validated the CHARGE-AF risk prediction model to identify individuals at risk for developing AF in an ambulatory setting. These data not only provide strong validation for the CHARGE-AF risk prediction tool, but also indicate that the tool, and thus primary prevention strategies, can be implemented in an EMR context.

scholarly journals Development of a Risk Score for QT Prolongation in the Intensive Care Unit Using Time-Series Electrocardiogram Data and Electronic Medical Records

2021 ◽  
Vol 27 (3) ◽  
pp. 182-188
Author(s):  
Tae Young Kim ◽  
Byung Jin Choi ◽  
Yeryung Koo ◽  
Sukhoon Lee ◽  
Dukyong Yoon
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Clinical Practice ◽  
Electronic Medical Records ◽  
Real World ◽  
Medical Records ◽  
Qt Prolongation ◽  
Icu Patients ◽  
Scoring Model ◽  
Risk Scoring

Objectives: Drug-induced QT prolongation can lead to life-threatening arrhythmia. In the intensive care unit (ICU), various drugs are administered concurrently, which can increase the risk of QT prolongation. However, no well-validated method to evaluate the risk of QT prolongation in real-world clinical practice has been established. We developed a risk scoring model to continuously evaluate the quantitative risk of QT prolongation in real-world clinical practice in the ICU.Methods: Continuous electrocardiogram (ECG) signals measured by patient monitoring devices and Electronic Medical Records data were collected for ICU patients. QT and RR intervals were measured from raw ECG data, and a corrected QT interval (QTc) was calculated by Bazett’s formula. A case-crossover study design was adopted. A case was defined as an occurrence of QT prolongation ≥12 hours after any previous QT prolongation. The patients served as their own controls. Conditional logistic regression was conducted to analyze prescription, surgical history, and laboratory test data. Based on the regression analysis, a QTc prolongation risk scoring model was established.Results: In total, 811 ICU patients who experienced QT prolongation were included in this study. Prescription information for 13 drugs was included in the risk scoring model. In the validation dataset, the high-risk group showed a higher rate of QT prolongation than the low-and low moderate-risk groups.Conclusions: Our proposed model may facilitate risk stratification for QT prolongation during ICU care as well as the selection of appropriate drugs to prevent QT prolongation.

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