An Electronic Health Record-Compatible Model to Predict Personalized Treatment Effects From the Diabetes Prevention Program: A Cross-Evidence Synthesis Approach Using Clinical Trial and Real-World Data.

OBJECTIVE: To develop an electronic health record (EHR)-based risk tool that provides point-of-care estimates of diabetes risk to support targeting interventions to patients most likely to benefit. PATIENTS AND METHODS: A risk prediction model was developed and validated in a large observational database of patients with an index visit date between January 1, 2012, and December 31, 2016, with treatment effect estimates from risk-based reanalysis of clinical trial data. The risk model development cohort included 1.1 million patients with prediabetes from the OptumLabs Data Warehouse (OLDW); the validation cohort included a distinct sample of 1.1 million patients in OLDW. The randomly assigned clinical trial cohort included 3081 people from the Diabetes Prevention Program (DPP) study. RESULTS: Eleven variables reliably obtainable from the EHR were used to predict diabetes risk. This model validated well in the OLDW (C statistic = 0.76; observed 3-year diabetes rate was 1.8% (95% confidence interval [CI], 1.7 to 1.9) in the lowest-risk quarter and 19.6% (19.4 to 19.8) in the highest-risk quarter). In the DPP, the hazard ratio (HR) for lifestyle modification was constant across all levels of risk (HR, 0.43; 95% CI, 0.35 to 0.53), whereas the HR for metformin was highly risk dependent (HR, 1.1; 95% CI, 0.61 to 2.0 in the lowest-risk quarter vs HR, 0.45; 95% CI, 0.35 to 0.59 in the highest-risk quarter). Fifty-three percent of the benefits of population-wide dissemination of the DPP lifestyle modification and 73% of the benefits of population-wide metformin therapy can be obtained by targeting the highest-risk quarter of patients. CONCLUSION: The Tufts-Predictive Analytics and Comparative Effectiveness DPP Risk model is an EHR-compatible tool that might support targeted diabetes prevention to more efficiently realize the benefits of the DPP interventions.

Clinical evaluation of a portable near-infrared device for detection of traumatic intracranial hematomas.

The purpose of this multicenter observational clinical study was to evaluate the performance of a near-infrared (NIR)-based, non-invasive, portable device to screen for traumatic intracranial hematomas. Five trauma centers collected data using the portable NIR device at the time a computed tomography (CT) scan was performed to evaluate a suspected traumatic brain injury (TBI). The CT scans were read by an independent neuroradiologist who was blinded to the NIR measurements. Of 431 patients enrolled, 365 patients were included in the per-protocol population analyzed. Of the 365 patients, 96 were determined by CT scan to have intracranial hemorrhages of various sizes, depths, and anatomical locations. The NIR device demonstrated sensitivity of 88% (95% confidence interval [CI] 74.9,95.0%), and specificity of 90.7% (95% CI 86.4,93.7%), in detecting the 50 intracranial hematomas that were large enough to be clinically important (larger than 3.5 mL in volume), and that were less than 2.5 cm from the surface of the brain. For all 96 cases with intracranial hemorrhage, regardless of size and type of hemorrhage, the sensitivity was 68.7% (CI 58.3,77.6%), and specificity was 90.7% (CI 86.4,93.7%). These results confirm the results of previous studies that indicate that a NIR-based portable device can reliably screen for intracranial hematomas that are superficial and of a size likely to be of clinical importance. The NIR device cannot replace CT scanning in the diagnosis of TBI, but the device might be useful to supplement clinical information used to triage TBI patients, and in situations in which CT scanning is not readily available.