Detection of subclinical hemorrhage using electrical impedance: a porcine study.

Objective.Analyze the performance of electrical impedance tomography (EIT) in an innovative porcine model of subclinical hemorrhage and investigate associations between EIT and hemodynamic trends.Approach. Twenty-five swine were bled at slow rates to create an extended period of subclinical hemorrhage during which the animal's heart rate (HR) and blood pressure (BP) remained stable from before hemodynamic deterioration, where stable was defined as <15% decrease in BP and <20% increase in HR-i.e.hemorrhages were hidden from standard vital signs of HR and BP. Continuous vital signs, photo-plethysmography, and continuous non-invasive EIT data were recorded and analyzed with the objective of developing an improved means of detecting subclinical hemorrhage-ideally as early as possible.Main results. Best area-under-the-curve (AUC) values from comparing bleed to no-bleed epochs were 0.96 at a 80 ml bleed (∼15.4 min) using an EIT-data-based metric and 0.79 at a 120 ml bleed (∼23.1 min) from invasively measured BP-i.e.the EIT-data-based metric achieved higher AUCs at earlier points compared to standard clinical metrics without requiring image reconstructions.Significance.In this clinically relevant porcine model of subclinical hemorrhage, EIT appears to be superior to standard clinical metrics in early detection of hemorrhage.

A Representativeness-informed Model for Research Record Selection from Electronic Medical Record Systems.

Scientific and clinical studies have a long history of bias in recruitment of underprivileged and minority populations. This underrepresentation leads to inaccurate, inapplicable, and non-generalizable results. Electronic medical record (EMR) systems, which now drive much research, often poorly represent these groups. We introduce a method for quantifying representativeness using information theoretic measures and an algorithmic approach to select a more representative record cohort than random selection when resource limitations preclude researchers from reviewing every record in the database. We apply this method to select cohorts of 2,000-20,000 records from a large (2M+ records) EMR database at the Vanderbilt University Medical Center and assess representativeness based on age, ethnicity, race, and gender. Compared to random selection - which will on average mirror the EMR database demographics - we find that a representativeness-informed approach can compose a cohort of records that is approximately 5.8 times more representative.

An updated, computable MEDication-Indication resource for biomedical research.

The MEDication-Indication (MEDI) knowledgebase has been utilized in research with electronic health records (EHRs) since its publication in 2013. To account for new drugs and terminology updates, we rebuilt MEDI to overhaul the knowledgebase for modern EHRs. Indications for prescribable medications were extracted using natural language processing and ontology relationships from six publicly available resources: RxNorm, Side Effect Resource 4.1, Mayo Clinic, WebMD, MedlinePlus, and Wikipedia. We compared the estimated precision and recall between the previous MEDI (MEDI-1) and the updated version (MEDI-2) with manual review. MEDI-2 contains 3031 medications and 186,064 indications. The MEDI-2 high precision subset (HPS) includes indications found within RxNorm or at least three other resources. MEDI-2 and MEDI-2 HPS contain 13% more medications and over triple the indications compared to MEDI-1 and MEDI-1 HPS, respectively. Manual review showed MEDI-2 achieves the same precision (0.60) with better recall (0.89 vs. 0.79) compared to MEDI-1. Likewise, MEDI-2 HPS had the same precision (0.92) and improved recall (0.65 vs. 0.55) than MEDI-1 HPS. The combination of MEDI-1 and MEDI-2 achieved a recall of 0.95. In updating MEDI, we present a more comprehensive medication-indication knowledgebase that can continue to facilitate applications and research with EHRs.