Optimization of Acute Kidney Injury (AKI) Time Definitions Using the Electronic Health Record: A First Step in Automating In-Hospital AKI Detection.
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ABSTRACT:
SUBMITTER: Swan JT
PROVIDER: S-EPMC8347988 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesEvidence-based clinical scores are used frequently in clinical practice, but data collection and data entry can be time consuming and hinder their use. We investigated the programmability of 168 common clinical calculators for automation within electronic health records.MethodsWe manually reviewed and categorized variables from 168 clinical calculators as being extractable from structured data, unstructured data, or both. Advanced data retrieval methods from unstructured data sources were tabulated for diagnoses, non-laboratory test results, clinical history, and examination findings.ResultsWe identified 534 unique variables, of which 203/534 (37.8%) were extractable from structured data and 269/534 (50.4.7%) were potentially extractable using advanced techniques. Nearly half (265/534, 49.6%) of all variables were not retrievable. Only 26/168 (15.5%) of scores were completely programmable using only structured data and 43/168 (25.6%) could potentially be programmable using widely available advanced information retrieval techniques. Scores relying on clinical examination findings or clinical judgments were most often not completely programmable.ConclusionComplete automation is not possible for most clinical scores because of the high prevalence of clinical examination findings or clinical judgments - partial automation is the most that can be achieved. The effect of fully or partially automated score calculation on clinical efficiency and clinical guideline adherence requires further study.
| S-EPMC6241755 | biostudies-literature
Project description:BACKGROUND: Establishing a Case Definition (CDef) is a first step in many epidemiological, clinical, surveillance, and research activities. The application of CDefs still relies on manual steps and this is a major source of inefficiency in surveillance and research. OBJECTIVE: Describe the need and propose an approach for automating the useful representation of CDefs for medical conditions. METHODS: We translated the existing Brighton Collaboration CDef for anaphylaxis by mostly relying on the identification of synonyms for the criteria of the CDef using the NLM MetaMap tool. We also generated a CDef for the same condition using all the related PubMed abstracts, processing them with a text mining tool, and further treating the synonyms with the above strategy. The co-occurrence of the anaphylaxis and any other medical term within the same sentence of the abstracts supported the construction of a large semantic network. The 'islands' algorithm reduced the network and revealed its densest region including the nodes that were used to represent the key criteria of the CDef. We evaluated the ability of the "translated" and the "generated" CDef to classify a set of 6034 H1N1 reports for anaphylaxis using two similarity approaches and comparing them with our previous semi-automated classification approach. RESULTS: Overall classification performance across approaches to producing CDefs was similar, with the generated CDef and vector space model with cosine similarity having the highest accuracy (0.825 ± 0.003) and the semi-automated approach and vector space model with cosine similarity having the highest recall (0.809 ± 0.042). Precision was low for all approaches. CONCLUSION: The useful representation of CDefs is a complicated task but potentially offers substantial gains in efficiency to support safety and clinical surveillance.
| S-EPMC3885912 | biostudies-literature
Project description:Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated with increased mortality, morbidity, length of stay, and hospital cost. Since AKI is sometimes preventable, there is great interest in prediction. Most existing studies consider all patients and therefore restrict to features available in the first hours of hospitalization. Here, the focus is instead on rehospitalized patients, a cohort in which rich longitudinal features from prior hospitalizations can be analyzed. Our objective is to provide a risk score directly at hospital re-entry. Gradient boosting, penalized logistic regression (with and without stability selection), and a recurrent neural network are trained on two years of adult inpatient EHR data (3,387 attributes for 34,505 patients who generated 90,013 training samples with 5,618 cases and 84,395 controls). Predictions are internally evaluated with 50 iterations of 5-fold grouped cross-validation with special emphasis on calibration, an analysis of which is performed at the patient as well as hospitalization level. Error is assessed with respect to diagnosis, race, age, gender, AKI identification method, and hospital utilization. In an additional experiment, the regularization penalty is severely increased to induce parsimony and interpretability. Predictors identified for rehospitalized patients are also reported with a special analysis of medications that might be modifiable risk factors. Insights from this study might be used to construct a predictive tool for AKI in rehospitalized patients. An accurate estimate of AKI risk at hospital entry might serve as a prior for an admitting provider or another predictive algorithm.
| S-EPMC6245516 | biostudies-literature
Project description:Background Electronic medical records (EMRs) allow identification of disease-specific patient populations, but varying electronic cohort definitions could result in different populations. We compared the characteristics of an electronic medical record-derived atrial fibrillation (AF) patient population using 5 different electronic cohort definitions. Methods and Results Adult patients with at least 1 AF billing code from January 1, 2010, to December 31, 2017, were included. Based on different electronic cohort definitions, we trained 5 different logistic regression models using a labeled training data set (n=786). Each model yielded a predicted probability; patients were classified as having AF if the probability was higher than a specified cut point. Test characteristics were calculated for each model. These models were then applied to the full cohort and resulting characteristics were compared. In the training set, the comprehensive model (including demographics, billing codes, and natural language processing results) performed best, with an area under the curve of 0.89, sensitivity of 0.90, and specificity of 0.87. Among a candidate population (n=22 000), the proportion of patients identified as having AF varied from 61% in the model using diagnosis or procedure International Classification of Diseases (ICD) billing codes to 83% in the model using natural language processing of clinical notes. Among identified AF patients, the proportion of patients with a CHA2DS2-VASc score ≥2 varied from 69% to 85%; oral anticoagulant treatment rates varied from 50% to 66% depending on the model. Conclusions Different electronic cohort definitions result in substantially different AF study samples. This difference threatens the quality and reproducibility of electronic medical record-based research and quality initiatives.
| S-EPMC7335556 | biostudies-literature
Project description:ObjectivesTo describe the Children's Hospital Association's Improving Pediatric Sepsis Outcomes sepsis definitions and the identified patients; evaluate the definition using a published framework for evaluating sepsis definitions.DesignObservational cohort.SettingMulticenter quality improvement collaborative of 46 hospitals from January 2017 to December 2018, excluding neonatal ICUs.PatientsImproving Pediatric Sepsis Outcomes Sepsis was defined by electronic health record evidence of suspected infection and sepsis treatment or organ dysfunction. A more severely ill subgroup, Improving Pediatric Sepsis Outcomes Critical Sepsis, was defined, approximating septic shock.InterventionsParticipating hospitals identified patients, extracted data, and transferred de-identified data to a central data warehouse. The definitions were evaluated across domains of reliability, content validity, construct validity, criterion validity, measurement burden, and timeliness.Measurements and main resultsForty hospitals met data quality criteria across four electronic health record platforms. There were 23,976 cases of Improving Pediatric Sepsis Outcomes Sepsis, including 8,565 with Improving Pediatric Sepsis Outcomes Critical Sepsis. The median age was 5.9 years. There were 10,316 (43.0%) immunosuppressed or immunocompromised patients, 4,135 (20.3%) with central lines, and 2,352 (11.6%) chronically ventilated. Among Improving Pediatric Sepsis Outcomes Sepsis patients, 60.8% were admitted to intensive care, 26.4% had new positive-pressure ventilation, and 19.7% received vasopressors. Median hospital length of stay was 6.0 days (3.0-13.0 d). All-cause 30-day in-hospital mortality was 958 (4.0%) in Improving Pediatric Sepsis Outcomes Sepsis; 541 (6.3%) in Improving Pediatric Sepsis Outcomes Critical Sepsis. The Improving Pediatric Sepsis Outcomes Sepsis definitions demonstrated strengths in content validity, convergent construct validity, and criterion validity; weakness in reliability. Improving Pediatric Sepsis Outcomes Sepsis definitions had significant initial measurement burden (median time from case completion to submission: 15 mo [interquartile range, 13-18 mo]); timeliness improved once data capture was established (median, 26 d; interquartile range, 23-56 d).ConclusionsThe Improving Pediatric Sepsis Outcomes Sepsis definitions demonstrated feasibility for large-scale data abstraction. The patients identified provide important information about children treated for sepsis. When operationalized, these definitions enabled multicenter identification and data aggregation, indicating practical utility for quality improvement.
| S-EPMC7677030 | biostudies-literature
Project description:BackgroundText definitions for entities within bio-ontologies are a cornerstone of the effort to gain a consensus in understanding and usage of those ontologies. Writing these definitions is, however, a considerable effort and there is often a lag between specification of the main part of an ontology (logical descriptions and definitions of entities) and the development of the text-based definitions. The goal of natural language generation (NLG) from ontologies is to take the logical description of entities and generate fluent natural language. The application described here uses NLG to automatically provide text-based definitions from an ontology that has logical descriptions of its entities, so avoiding the bottleneck of authoring these definitions by hand.ResultsTo produce the descriptions, the program collects all the axioms relating to a given entity, groups them according to common structure, realises each group through an English sentence, and assembles the resulting sentences into a paragraph, to form as 'coherent' a text as possible without human intervention. Sentence generation is accomplished using a generic grammar based on logical patterns in OWL, together with a lexicon for realising atomic entities. We have tested our output for the Experimental Factor Ontology (EFO) using a simple survey strategy to explore the fluency of the generated text and how well it conveys the underlying axiomatisation. Two rounds of survey and improvement show that overall the generated English definitions are found to convey the intended meaning of the axiomatisation in a satisfactory manner. The surveys also suggested that one form of generated English will not be universally liked; that intrusion of too much 'formal ontology' was not liked; and that too much explicit exposure of OWL semantics was also not liked.ConclusionsOur prototype tools can generate reasonable paragraphs of English text that can act as definitions. The definitions were found acceptable by our survey and, as a result, the developers of EFO are sufficiently satisfied with the output that the generated definitions have been incorporated into EFO. Whilst not a substitute for hand-written textual definitions, our generated definitions are a useful starting point.AvailabilityAn on-line version of the NLG text definition tool can be found at http://swat.open.ac.uk/tools/. The questionaire and sample generated text definitions may be found at http://mcs.open.ac.uk/nlg/SWAT/bio-ontologies.html.
| S-EPMC3102894 | biostudies-literature
Project description:BACKGROUND: Despite widespread use of electronic health records (EHRs), it is unclear whether residents possess the EHR skills to perform required tasks. OBJECTIVE: We assessed first-year residents' skills on specific EHR tasks. METHODS: Incoming residents were required to participate in EHR training before starting clinical rotations. The training team developed an assessment tool for 19 EHR tasks. Senior residents used a structured template to assess interns. RESULTS: For ambulatory workflow skills, most participants (range, 70%-100%) demonstrated competence. The 3 skills for which interns needed the most assistance were (1) creating and routing a result note (17 of 68, 25%), (2) deleting a medication or changing a dose in reconciling medications (10 of 68, 15%), and (3) finding results for the past 90 days (10 of 68, 15%). For inpatient workflow skills, most interns (range, 63%-100%) demonstrated competence. The 3 skills in which interns needed the most assistance were (1) placing a referral order at discharge (23 of 68, 34%), (2) finding a temperature on a flow sheet and trending it over time (14 of 68, 21%), and (3) creating a discharge summary, having it reviewed, and forwarding it to the primary care physician (14 of 68, 21%). CONCLUSIONS: Our results should help EHR training teams at other institutions to better understand the strengths and weakness of EHR training approaches and to target training on tasks with the greatest performance deficits as well as toward underperforming individuals or groups.
| S-EPMC3886468 | biostudies-other
Project description:BACKGROUND:Defining clinical conditions from electronic health record (EHR) data underpins population health activities, clinical decision support, and analytics. In an EHR, defining a condition commonly employs a diagnosis value set or "grouper." For constructing value sets, Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) offers high clinical fidelity, a hierarchical ontology, and wide implementation in EHRs as the standard interoperability vocabulary for problems. OBJECTIVE:This article demonstrates a practical approach to defining conditions with combinations of SNOMED CT concept hierarchies, and evaluates sharing of definitions for clinical and analytic uses. METHODS:We constructed diagnosis value sets for EHR patient registries using SNOMED CT concept hierarchies combined with Boolean logic, and shared them for clinical decision support, reporting, and analytic purposes. RESULTS:A total of 125 condition-defining "standard" SNOMED CT diagnosis value sets were created within our EHR. The median number of SNOMED CT concept hierarchies needed was only 2 (25th-75th percentiles: 1-5). Each value set, when compiled as an EHR diagnosis grouper, was associated with a median of 22 International Classification of Diseases (ICD)-9 and ICD-10 codes (25th-75th percentiles: 8-85) and yielded a median of 155 clinical terms available for selection by clinicians in the EHR (25th-75th percentiles: 63-976). Sharing of standard groupers for population health, clinical decision support, and analytic uses was high, including 57 patient registries (with 362 uses of standard groupers), 132 clinical decision support records, 190 rules, 124 EHR reports, 125 diagnosis dimension slicers for self-service analytics, and 111 clinical quality measure calculations. Identical SNOMED CT definitions were created in an EHR-agnostic tool enabling application across disparate organizations and EHRs. CONCLUSION:SNOMED CT-based diagnosis value sets are simple to develop, concise, understandable to clinicians, useful in the EHR and for analytics, and shareable. Developing curated SNOMED CT hierarchy-based condition definitions for public use could accelerate cross-organizational population health efforts, "smarter" EHR feature configuration, and clinical-translational research employing EHR-derived data.
| S-EPMC6115233 | biostudies-literature
Project description:Alchemical free energy calculations hold increasing promise as an aid to drug discovery efforts. However, applications of these techniques in discovery projects have been relatively few, partly because of the difficulty of planning and setting up calculations. Here, we introduce lead optimization mapper, LOMAP, an automated algorithm to plan efficient relative free energy calculations between potential ligands within a substantial library of perhaps hundreds of compounds. In this approach, ligands are first grouped by structural similarity primarily based on the size of a (loosely defined) maximal common substructure, and then calculations are planned within and between sets of structurally related compounds. An emphasis is placed on ensuring that relative free energies can be obtained between any pair of compounds without combining the results of too many different relative free energy calculations (to avoid accumulation of error) and by providing some redundancy to allow for the possibility of error and consistency checking and provide some insight into when results can be expected to be unreliable. The algorithm is discussed in detail and a Python implementation, based on both Schrödinger's and OpenEye's APIs, has been made available freely under the BSD license.
| S-EPMC3837551 | biostudies-literature
Project description:Electronic health records (EHRs) have been successfully used in data science and machine learning projects. However, most of these data are collected for clinical use rather than for retrospective analysis. This means that researchers typically face many different issues when attempting to access and prepare the data for secondary use. We aimed to investigate how raw EHRs can be accessed and prepared in retrospective data science projects in a disciplined, effective, and efficient way. We report our experience and findings from a large-scale data science project analyzing routinely acquired retrospective data from the Kepler University Hospital in Linz, Austria. The project involved data collection from more than 150,000 patients over a period of 10 years. It included diverse data modalities, such as static demographic data, irregularly acquired laboratory test results, regularly sampled vital signs, and high-frequency physiological waveform signals. Raw medical data can be corrupted in many unexpected ways that demand thorough manual inspection and highly individualized data cleaning solutions. We present a general data preparation workflow, which was shaped in the course of our project and consists of the following 7 steps: obtain a rough overview of the available EHR data, define clinically meaningful labels for supervised learning, extract relevant data from the hospital's data warehouses, match data extracted from different sources, deidentify them, detect errors and inconsistencies therein through a careful exploratory analysis, and implement a suitable data processing pipeline in actual code. Only few of the data preparation issues encountered in our project were addressed by generic medical data preprocessing tools that have been proposed recently. Instead, highly individualized solutions for the specific data used in one's own research seem inevitable. We believe that the proposed workflow can serve as a guidance for practitioners, helping them to identify and address potential problems early and avoid some common pitfalls.
| S-EPMC9636533 | biostudies-literature