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Evaluating early-life asthma definitions as a marker for subsequent asthma in an electronic medical record setting.

ABSTRACT: Case definitions for asthma incidence in early life vary between studies using medical records to define disease. This study assessed the impact of different approaches to using medical records on estimates of asthma incidence by age 3 and determined the validity of early-life asthma case definitions in predicting school-age asthma.Asthma diagnoses and medications by age 3 were used to classify 7103 children enrolled in Kaiser Permanente Georgia according to 14 definitions of asthma. School-age asthma was defined as an asthma diagnosis between ages 5 and 8. Sensitivity (probability of asthma by age 3 given school-age asthma), specificity (probability of no asthma by age 3 given no school-age asthma), positive and negative predictive values (probability of (no) school-age asthma given (no) asthma by age 3), and likelihood ratios (combining sensitivity and specificity) were used to determine predictive ability.9.0-35.2% of children were classified as asthmatic by age 3 depending on asthma case definition. Early-life asthma classifications were more specific than sensitive and were better at identifying children who would not have school-age asthma (negative predictive values: 80.7-86.6%) than at predicting children who would have school-age asthma (positive predictive values: 43.5-71.5%).Choice of case definition had a large impact on the estimate of asthma incidence. While ability to predict school-age asthma was limited, several case definitions performed similarly to clinical asthma prediction tools used in previous asthma research (e.g., the Asthma Predictive Index).

SUBMITTER: Pennington AF

PROVIDER: S-EPMC4995117 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Evaluating early-life asthma definitions as a marker for subsequent asthma in an electronic medical record setting.

Pennington Audrey Flak AF Strickland Matthew J MJ Freedle Karen A KA Klein Mitchel M Drews-Botsch Carolyn C Hansen Craig C Darrow Lyndsey A LA

Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology 20160530 6

<h4>Background</h4>Case definitions for asthma incidence in early life vary between studies using medical records to define disease. This study assessed the impact of different approaches to using medical records on estimates of asthma incidence by age 3 and determined the validity of early-life asthma case definitions in predicting school-age asthma.<h4>Methods</h4>Asthma diagnoses and medications by age 3 were used to classify 7103 children enrolled in Kaiser Permanente Georgia according to 14 ...[more]

PMID: 27116587

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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.

Project description:BackgroundAsthma exacerbation leading to emergency department (ED) visit is prevalent, an indicator of poor control of asthma, and is a potentially preventable clinical outcome.ObjectiveWe propose to utilize multiple data elements available in electronic medical records (EMRs) and claims database to create separate algorithms with high validity for clinical and research purposes to identify asthma exacerbation-related ED visit among the general population.MethodsWe performed a retrospective study with inclusion criteria of patients aged 4 to 40 years, a visit to Geisinger ED from January 1, 2006, to October 28, 2013, with asthma on their problem list. Different electronic data elements including chief complaints, vitals, season, smoking, medication use, and discharge diagnoses were obtained to create the algorithm. A stratified random sample was generated to select the charts for review. Chart review was performed to classify patients with asthma-related ED visit, that is, the gold standard. Two reviewers performed the chart review and validation was done on a small subset.ResultsThere were 966 eligible ED visits in the EMR sample and 731 in the claims sample. Agreement between reviewers was 95.45% and kappa statistic was 0.91. Mean age of the EMR sample was 22 years, and mostly white (93%). Multiple models conventionally used in studies were evaluated and the final model chosen included principal diagnosis, bronchodilator, and steroid use for both algorithms, chief complaints for EMR, and secondary diagnosis for claims. Area under the curve was 0.93 (95% confidence interval: 0.91-0.94) and 0.94 (0.93-0.96), respectively, for EMR and claims data, with positive predictive value of > 94%. The algorithms are visually presented using nomograms.ConclusionWe were able to develop two separate algorithms for EMR and claims to identify asthma exacerbation-related ED visit with excellent diagnostic ability and varying discrimination threshold for clinical and research purposes.

Project description:BACKGROUND:Many health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in EHR notes can confuse patients, which may interfere with potential benefits of patient access to EHR notes. OBJECTIVE:The aim of this study was to develop and evaluate the usability and content quality of NoteAid, a Web-based natural language processing system that links medical terms in EHR notes to lay definitions, that is, definitions easily understood by lay people. METHODS:NoteAid incorporates two core components: CoDeMed, a lexical resource of lay definitions for medical terms, and MedLink, a computational unit that links medical terms to lay definitions. We developed innovative computational methods, including an adapted distant supervision algorithm to prioritize medical terms important for EHR comprehension to facilitate the effort of building CoDeMed. Ten physician domain experts evaluated the user interface and content quality of NoteAid. The evaluation protocol included a cognitive walkthrough session and a postsession questionnaire. Physician feedback sessions were audio-recorded. We used standard content analysis methods to analyze qualitative data from these sessions. RESULTS:Physician feedback was mixed. Positive feedback on NoteAid included (1) Easy to use, (2) Good visual display, (3) Satisfactory system speed, and (4) Adequate lay definitions. Opportunities for improvement arising from evaluation sessions and feedback included (1) improving the display of definitions for partially matched terms, (2) including more medical terms in CoDeMed, (3) improving the handling of terms whose definitions vary depending on different contexts, and (4) standardizing the scope of definitions for medicines. On the basis of these results, we have improved NoteAid's user interface and a number of definitions, and added 4502 more definitions in CoDeMed. CONCLUSIONS:Physician evaluation yielded useful feedback for content validation and refinement of this innovative tool that has the potential to improve patient EHR comprehension and experience using patient portals. Future ongoing work will develop algorithms to handle ambiguous medical terms and test and evaluate NoteAid with patients.

Dataset Information

Evaluating early-life asthma definitions as a marker for subsequent asthma in an electronic medical record setting.

Publications

Evaluating early-life asthma definitions as a marker for subsequent asthma in an electronic medical record setting.

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