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Search All Research Studies
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- Adverse Drug Events (ADE) (1)
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- (-) Data (5)
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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 5 of 5 Research Studies DisplayedSangal RB, Fodeh S, Taylor A
Identification of patients with nontraumatic intracranial hemorrhage using administrative claims data.
Nontraumatic intracranial hemorrhage (ICH) is a neurological emergency of research interest; however, unlike ischemic stroke, has not been well studied in large datasets due to the lack of an established administrative claims-based definition. In this study, the investigators aimed to evaluate both explicit diagnosis codes and machine learning methods to create a claims-based definition for this clinical phenotype.
AHRQ-funded; HS023554.
Citation: Sangal RB, Fodeh S, Taylor A .
Identification of patients with nontraumatic intracranial hemorrhage using administrative claims data.
J Stroke Cerebrovasc Dis 2020 Dec;29(12):105306. doi: 10.1016/j.jstrokecerebrovasdis.2020.105306..
Keywords: Cardiovascular Conditions, Neurological Disorders, Diagnostic Safety and Quality, Data
Hsu DY, Dalal P, Sable KA
Validation of International Classification of Disease Ninth Revision codes for atopic dermatitis.
This study sought to validate the use of ICD-9-CM codes for identifying atopic dermatitis. It found that in the outpatient setting, the ICD-9-CM codes 691.8 and 692.9 alone have poor positive predictive value (PPV). Incorporation of diagnoses of asthma, hay fever, and food allergy improves PPV and specificity. In the inpatient setting, a primary discharge diagnosis of 691.8 had excellent PPV.
AHRQ-funded; HS023011.
Citation: Hsu DY, Dalal P, Sable KA .
Validation of International Classification of Disease Ninth Revision codes for atopic dermatitis.
Allergy 2017 Jul;72(7):1091-95. doi: 10.1111/all.13113.
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Keywords: Data, Diagnostic Safety and Quality, Skin Conditions
Murphy DR, Meyer AN, Bhise V
Computerized triggers of big data to detect delays in follow-up of chest imaging results.
A "trigger" algorithm was used to identify delays in follow-up of abnormal chest imaging results in a large national clinical data warehouse of electronic health record (EHR) data. In this study, the authors applied a trigger in a repository hosting EHR data from all Department of Veterans Affairs health-care facilities and analyzed data from seven facilities. The investigators concluded that application of triggers on "big" EHR data may aid in identifying patients experiencing delays in diagnostic evaluation of chest imaging results suspicious for malignancy.
Citation: Murphy DR, Meyer AN, Bhise V .
Computerized triggers of big data to detect delays in follow-up of chest imaging results.
Chest 2016 Sep;150(3):613-20. doi: 10.1016/j.chest.2016.05.001..
Keywords: Imaging, Electronic Health Records (EHRs), Health Information Technology (HIT), Data, Diagnostic Safety and Quality, Cancer
Raebel MA, Shetterly S, Lu CY
Methods for using clinical laboratory test results as baseline confounders in multi-site observational database studies when missing data are expected.
The purpose of this paper was to quantify missing baseline laboratory results, assess predictors of missingness, and examine performance of missing data methods. The researchers used the Mini-Sentinel Distributed Database to select three exposure-outcome scenarios with laboratory results as baseline confounders. They found that missing data methods performed similarly.
AHRQ-funded; HS023898.
Citation: Raebel MA, Shetterly S, Lu CY .
Methods for using clinical laboratory test results as baseline confounders in multi-site observational database studies when missing data are expected.
Pharmacoepidemiol Drug Saf 2016 Jul;25(7):798-814. doi: 10.1002/pds.4015.
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Keywords: Adverse Drug Events (ADE), Blood Thinners, Data, Diagnostic Safety and Quality, Medication
Greenberg JK, Ladner TR, Olsen MA
Validation of an International Classification of Diseases, Ninth Revision Code algorithm for identifying Chiari Malformation type 1 surgery in adults.
The purpose of this study was to validate 2 ICD-9-CM code algorithms identifying patients undergoing CM-1 decompression surgery. It concluded that the ICD-9-CM code Algorithm 2 has excellent positive predictive value and good sensitivity to identify adult CM-1 decompression surgery.
AHRQ-funded; HS019455.
Citation: Greenberg JK, Ladner TR, Olsen MA .
Validation of an International Classification of Diseases, Ninth Revision Code algorithm for identifying Chiari Malformation type 1 surgery in adults.
Neurosurgery 2015 Aug;77(2):269-73. doi: 10.1227/neu.0000000000000778..
Keywords: Data, Diagnostic Safety and Quality, Surgery