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OBJECTIVES - Hospitalisations for serious infections are common among middle age and older adults and frequently used as study outcomes. Yet, few studies have evaluated the performance of diagnosis codes to identify serious infections in this population. We sought to determine the positive predictive value (PPV) of diagnosis codes for identifying hospitalisations due to serious infections among middle age and older adults.
SETTING AND PARTICIPANTS - We identified hospitalisations for possible infection among adults >=50 years enrolled in the Tennessee Medicaid healthcare programme (2008-2012) using International Classifications of Diseases, Ninth Revision diagnosis codes for pneumonia, meningitis/encephalitis, bacteraemia/sepsis, cellulitis/soft-tissue infections, endocarditis, pyelonephritis and septic arthritis/osteomyelitis.
DESIGN - Medical records were systematically obtained from hospitals randomly selected from a stratified sampling framework based on geographical region and hospital discharge volume.
MEASURES - Two trained clinical reviewers used a standardised extraction form to abstract information from medical records. Predefined algorithms served as reference to adjudicate confirmed infection-specific hospitalisations. We calculated the PPV of diagnosis codes using confirmed hospitalisations as reference. Sensitivity analyses determined the robustness of the PPV to definitions that required radiological or microbiological confirmation. We also determined inter-rater reliability between reviewers.
RESULTS - The PPV of diagnosis codes for hospitalisations for infection (n=716) was 90.2% (95% CI 87.8% to 92.2%). The PPV was highest for pneumonia (96.5% (95% CI 93.9% to 98.0%)) and cellulitis (91.1% (95% CI 84.7% to 94.9%)), and lowest for meningitis/encephalitis (50.0% (95% CI 23.7% to 76.3%)). The adjudication reliability was excellent (92.7% agreement; first agreement coefficient: 0.91). The overall PPV was lower when requiring microbiological confirmation (45%) and when requiring radiological confirmation for pneumonia (79%).
CONCLUSIONS - Discharge diagnosis codes have a high PPV for identifying hospitalisations for common, serious infections among middle age and older adults. PPV estimates for rare infections were imprecise.
© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
Importance - Clinical guidelines recommend that clinicians estimate the probability of malignancy for patients with indeterminate pulmonary nodules (IPNs) larger than 8 mm. Adherence to these guidelines is unknown.
Objectives - To determine whether clinicians document the probability of malignancy in high-risk IPNs and to compare these quantitative or qualitative predictions with the validated Mayo Clinic Model.
Design, Setting, and Participants - Single-institution, retrospective cohort study of patients from a tertiary care Department of Veterans Affairs hospital from January 1, 2003, through December 31, 2015. Cohort 1 included 291 veterans undergoing surgical resection of known or suspected lung cancer from January 1, 2003, through December 31, 2015. Cohort 2 included a random sample of 239 veterans undergoing inpatient or outpatient pulmonary evaluation of IPNs at the hospital from January 1, 2003, through December 31, 2012.
Exposures - Clinician documentation of the quantitative or qualitative probability of malignancy.
Main Outcomes and Measures - Documentation from pulmonary and/or thoracic surgery clinicians as well as information from multidisciplinary tumor board presentations was reviewed. Any documented quantitative or qualitative predictions of malignancy were extracted and summarized using descriptive statistics. Clinicians' predictions were compared with risk estimates from the Mayo Clinic Model.
Results - Of 291 patients in cohort 1, 282 (96.9%) were men; mean (SD) age was 64.6 (9.0) years. Of 239 patients in cohort 2, 233 (97.5%) were men; mean (SD) age was 65.5 (10.8) years. Cancer prevalence was 258 of 291 cases (88.7%) in cohort 1 and 110 of 225 patients with a definitive diagnosis (48.9%) in cohort 2. Only 13 patients (4.5%) in cohort 1 and 3 (1.3%) in cohort 2 had a documented quantitative prediction of malignancy prior to tissue diagnosis. Of the remaining patients, 217 of 278 (78.1%) in cohort 1 and 149 of 236 (63.1%) in cohort 2 had qualitative statements of cancer risk. In cohort 2, 23 of 79 patients (29.1%) without any documented malignancy risk statements had a final diagnosis of cancer. Qualitative risk statements were distributed among 32 broad categories. The most frequently used statements aligned well with Mayo Clinic Model predictions for cohort 1 compared with cohort 2. The median Mayo Clinic Model-predicted probability of cancer was 68.7% (range, 2.4%-100.0%). Qualitative risk statements roughly aligned with Mayo predictions.
Conclusions and Relevance - Clinicians rarely provide quantitative documentation of cancer probability for high-risk IPNs, even among patients drawn from a broad range of cancer probabilities. Qualitative statements of cancer risk in current practice are imprecise and highly variable. A standard scale that correlates with predicted cancer risk for IPNs should be used to communicate with patients and other clinicians.
Electronic health records (EHRs) have increasingly emerged as a powerful source of clinical data that can be leveraged for reuse in research and in modular health apps that integrate into diverse health information technologies. A key challenge to these use cases is representing the knowledge contained within data from different EHR systems in a uniform fashion. We reviewed several recent studies covering the knowledge representation in the common data models for the Observational Medical Outcomes Partnership (OMOP) and its Observational Health Data Sciences and Informatics program, and the United States Patient Centered Outcomes Research Network (PCORNet). We also reviewed the Health Level 7 Fast Healthcare Interoperability Resource standard supporting app-like programs that can be used across multiple EHR and research systems. There has been a recent growth in high-impact efforts to support quality-assured and standardized clinical data sharing across different institutions and EHR systems. We focused on three major efforts as part of a larger landscape moving towards shareable, transportable, and computable clinical data. The growth in approaches to developing common data models to support interoperable knowledge representation portends an increasing availability of high-quality clinical data in support of research. Building on these efforts will allow a future whereby significant portions of the populations in the world may be able to share their data for research.
Georg Thieme Verlag KG Stuttgart.
Patient portal research has focused on medical outpatient settings, with little known about portal use during hospitalizations or by surgical patients. We measured portal adoption among patients admitted to surgical services over two years. Surgical services managed 37,025 admissions of 31,310 unique patients. One-fourth of admissions (9,362, 25.3%) involved patients registered for the portal. Registration rates were highest for admissions to laparoscopic/gastrointestinal (55%) and oncology/endocrine (50%) services. Portal use occurred during 1,486 surgical admissions, 4% of all and 16% of those registered at admission. Inpatient portal use was associated with patients who were white, male, and had longer lengths of stay (p < 0.01). Viewing health record data and secure messaging were the most commonly used functions, accessed in 4,836 (72.9%) and 1,626 (24.5%) user sessions. Without specific encouragement, hospitalized surgical patients are using our patient portal. The surgical inpatient setting may provide opportunities for patient engagement using patient portals.
AIMS - This study harnessed the electronic medical record to assess pancreas volume in patients with type 1 diabetes (T1D) and matched controls to determine whether pancreas volume is altered in T1D and identify covariates that influence pancreas volume.
METHODS - This study included 25 patients with T1D and 25 age-, sex-, and weight-matched controls from the Vanderbilt University Medical Center enterprise data warehouse. Measurements of pancreas volume were made from medical imaging studies using magnetic resonance imaging (MRI) or computed tomography (CT).
RESULTS - Patients with T1D had a pancreas volume 47% smaller than matched controls (41.16 ml vs. 77.77 ml, P < 0.0001) as well as pancreas volume normalized by subject body weight, body mass index, or body surface area (all P < 0.0001). Pancreatic volume was smaller with a longer duration of T1D across the patient population (N = 25, P = 0.04). Additionally, four individual patients receiving multiple imaging scans displayed progressive declines in pancreas volume over time (~ 6% of volume/year), whereas five controls scanned a year apart did not exhibit a decline in pancreas size (P = 0.03). The pancreas was uniformly smaller on the right and left side of the abdomen.
CONCLUSIONS - Pancreas volume declines with disease duration in patients with T1D, suggesting a protracted pathological process that may include the exocrine pancreas.
OBJECTIVE - To evaluate the phenotyping performance of three major electronic health record (EHR) components: International Classification of Disease (ICD) diagnosis codes, primary notes, and specific medications.
MATERIALS AND METHODS - We conducted the evaluation using de-identified Vanderbilt EHR data. We preselected ten diseases: atrial fibrillation, Alzheimer's disease, breast cancer, gout, human immunodeficiency virus infection, multiple sclerosis, Parkinson's disease, rheumatoid arthritis, and types 1 and 2 diabetes mellitus. For each disease, patients were classified into seven categories based on the presence of evidence in diagnosis codes, primary notes, and specific medications. Twenty-five patients per disease category (a total number of 175 patients for each disease, 1750 patients for all ten diseases) were randomly selected for manual chart review. Review results were used to estimate the positive predictive value (PPV), sensitivity, andF-score for each EHR component alone and in combination.
RESULTS - The PPVs of single components were inconsistent and inadequate for accurately phenotyping (0.06-0.71). Using two or more ICD codes improved the average PPV to 0.84. We observed a more stable and higher accuracy when using at least two components (mean ± standard deviation: 0.91 ± 0.08). Primary notes offered the best sensitivity (0.77). The sensitivity of ICD codes was 0.67. Again, two or more components provided a reasonably high and stable sensitivity (0.59 ± 0.16). Overall, the best performance (Fscore: 0.70 ± 0.12) was achieved by using two or more components. Although the overall performance of using ICD codes (0.67 ± 0.14) was only slightly lower than using two or more components, its PPV (0.71 ± 0.13) is substantially worse (0.91 ± 0.08).
CONCLUSION - Multiple EHR components provide a more consistent and higher performance than a single one for the selected phenotypes. We suggest considering multiple EHR components for future phenotyping design in order to obtain an ideal result.
© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: email@example.com.
SUMMARY Fluoroquinolone use before tuberculosis (TB) diagnosis delays the time to diagnosis and treatment, and increases the risk of fluoroquinolone-resistant TB and death. Ascertainment of fluoroquinolone exposure could identify such high-risk patients. We compared four methods of ascertaining fluoroquinolone exposure in the 6 months prior to TB diagnosis in culture-confirmed TB patients in Tennessee from January 2007 to December 2009. The four methods included a simple questionnaire administered to all TB suspects by health department personnel (FQ-Form), an in-home interview conducted by research staff, outpatient and inpatient medical record review, and TennCare pharmacy database review. Of 177 TB patients included, 72 (41%) received fluoroquinolones during the 6 months before TB diagnosis. Fluoroquinolone exposure determined by review of inpatient and outpatient medical records was considered the gold standard for comparison. The FQ-Form had 61% [95% confidence interval (CI) 48-73] sensitivity and 93% (95% CI 85-98) specificity (agreement 79%, kappa = 0.56) while the in-home interview had 28% (95% CI 18-40) sensitivity and 99% (94-100%) specificity (agreement 68%, kappa = 0.29). A simple questionnaire administered by health department personnel identified fluoroquinolone exposure before TB diagnosis with moderate reliability.
The use of electronic medical record data linked to biological specimens in health care settings is expected to enable cost-effective and rapid genomic analyses. Here, we present a model that highlights potential advantages for genomic discovery and describe the operational infrastructure that facilitated multiple simultaneous discovery efforts.
Scrubbing identifying information from narrative clinical documents is a critical first step to preparing the data for secondary use purposes, such as translational research. Evidence suggests that the differential distribution of protected health information (PHI) in clinical documents could be used as additional features to improve the performance of automated de-identification algorithms or toolkits. However, there has been little investigation into the extent to which such phenomena transpires in practice. To empirically assess this issue, we identified the location of PHI in 140,000 clinical notes from an electronic health record system and characterized the distribution as a function of location in a document. In addition, we calculated the 'word proximity' of nearby PHI elements to determine their co-occurrence rates. The PHI elements were found to have non-random distribution patterns. Location within a document and proximity between PHI elements might therefore be used to help de-identification systems better label PHI.