Clement J McDonald

National Institutes of Health, Maryland, United States

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Publications (202)1048.97 Total impact

  • JAMA Internal Medicine 09/2014; · 13.25 Impact Factor
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    ABSTRACT: Recent research has suggested that high vitamin B12 levels may be associated with increased mortality after ICU admission. However, it is known that impaired liver function may lead to elevated B12 since B12 is metabolized through the liver, and therefore high B12 levels may serve as a proxy for poor liver function. The aim of this study is to assess the impact that liver function and liver disease have on the relationship between high vitamin B12 levels and mortality in the ICU. We performed an observational cohort study using ICU data that were collected from patients admitted to four ICU types (medical, surgical, cardiac care and cardiac surgery recovery) in one large urban hospital from 2001 to 2008. We analyzed the medical records of 1,684 adult patients (age ≥ 18 years) who had vitamin B12 and liver function measurements up to 14 days prior to ICU admission or within 24 hours after admission. While we found an association between high B12 and mortality when we did not control for any potential confounders, after we adjusted for liver function and liver disease, no significant association existed between B12 and mortality using multivariable logistic regression (30-day mortality: OR=1.18, 95% CI 0.81 to 1.72, p=0.3890; 90-day mortality: OR=1.20, 95% CI 0.84 to 1.71, p=0.3077). Elevated B12 levels are not a significant predictor of mortality after ICU admission when liver function is controlled for, and may instead be a proxy for poor liver function.
    e-SPEN journal. 04/2014; 9(2):e76-e83.
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    ABSTRACT: To develop a generalizable method for identifying patient cohorts from electronic health record (EHR) data-in this case, patients having dialysis-that uses simple information retrieval (IR) tools. We used the coded data and clinical notes from the 24 506 adult patients in the Multiparameter Intelligent Monitoring in Intensive Care database to identify patients who had dialysis. We used SQL queries to search the procedure, diagnosis, and coded nursing observations tables based on ICD-9 and local codes. We used a domain-specific search engine to find clinical notes containing terms related to dialysis. We manually validated the available records for a 10% random sample of patients who potentially had dialysis and a random sample of 200 patients who were not identified as having dialysis based on any of the sources. We identified 1844 patients that potentially had dialysis: 1481 from the three coded sources and 1624 from the clinical notes. Precision for identifying dialysis patients based on available data was estimated to be 78.4% (95% CI 71.9% to 84.2%) and recall was 100% (95% CI 86% to 100%). Combining structured EHR data with information from clinical notes using simple queries increases the utility of both types of data for cohort identification. Patients identified by more than one source are more likely to meet the inclusion criteria; however, including patients found in any of the sources increases recall. This method is attractive because it is available to researchers with access to EHR data and off-the-shelf IR tools.
    Journal of the American Medical Informatics Association 01/2014; · 3.57 Impact Factor
  • Clement J McDonald
    JAMA Internal Medicine 10/2013; 173(18):1755-1756. · 13.25 Impact Factor
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    ABSTRACT: To understand the factors that influence success in scrubbing personal names from narrative text. We developed a scrubber, the NLM Name Scrubber (NLM-NS), to redact personal names from narrative clinical reports, hand tagged words in a set of gold standard narrative reports as personal names or not, and measured the scrubbing success of NLM-NS and that of four other scrubbing/name recognition tools (MIST, MITdeid, LingPipe, and ANNIE/GATE) against the gold standard reports. We ran three comparisons which used increasingly larger name lists. The test reports contained more than 1 million words, of which 2388 were patient and 20 160 were provider name tokens. NLM-NS failed to scrub only 2 of the 2388 instances of patient name tokens. Its sensitivity was 0.999 on both patient and provider name tokens and missed fewer instances of patient name tokens in all comparisons with other scrubbers. MIST produced the best all token specificity and F-measure for name instances in our most relevant study (study 2), with values of 0.997 and 0.938, respectively. In that same comparison, NLM-NS was second best, with values of 0.986 and 0.748, respectively, and MITdeid was a close third, with values of 0.985 and 0.796 respectively. With the addition of the Clinical Center name list to their native name lists, Ling Pipe, MITdeid, MIST, and ANNIE/GATE all improved substantially. MITdeid and Ling Pipe gained the most-reaching patient name sensitivity of 0.995 (F-measure=0.705) and 0.989 (F-measure=0.386), respectively. The privacy risk due to two name tokens missed by NLM-NS was statistically negligible, since neither individual could be distinguished among more than 150 000 people listed in the US Social Security Registry. The nature and size of name lists have substantial influences on scrubbing success. The use of very large name lists with frequency statistics accounts for much of NLM-NS scrubbing success.
    Journal of the American Medical Informatics Association 09/2013; · 3.57 Impact Factor
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    ABSTRACT: STUDY OBJECTIVE: Medication history is an essential part of patient assessment in emergency care. Patient-reported medication history can be incomplete. We study whether an electronic pharmacy-sourced prescription record can supplement the patient-reported history. METHODS: In a community hospital, we compared the patient-reported history obtained by triage nurses to a proprietary electronic pharmacy record in all emergency department (ED) patients during 3 months. RESULTS: Of 9,426 triaged patients, 5,001 (53%) had at least 1 (mean 7.7) prescription medication in the full-year electronic pharmacy record. Counting only recent prescription medications (supply lasting to at least 7 days before the ED visit), 3,688 patients (39%) had at least 1 (mean 4.0) recent medication. After adjustment for possible false-positive results, recent electronic prescription medication record enriched the patient-reported history by 28% (adding 1.1 drugs per patient). However, only 60% of patients with any active prescription medications from either source had any recent prescription medications in their electronic pharmacy record. CONCLUSION: The electronic pharmacy prescription record augments the manually collected history.
    Annals of emergency medicine 05/2013; · 4.33 Impact Factor
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    05/2013;
  • Swapna Abhyankar, Clement J McDonald
    JAMA The Journal of the American Medical Association 04/2013; 309(16):1680-1. · 29.98 Impact Factor
  • Clement J McDonald, Daniel J Vreeman, Swapna Abhyankar
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    ABSTRACT: The same code standards should be used in both research and clinical care to facilitate data integration across domains.
    Science translational medicine 04/2013; 5(179):179le1. · 10.76 Impact Factor
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    ABSTRACT: INTRODUCTION: Two-thirds of U.S. adults are overweight or obese, which puts them at higher risk of developing chronic diseases and of death compared to normal weight individuals. However, recent studies have found that overweight and obesity by themselves may be protective in some contexts, such as hospitalization in an intensive care unit (ICU). Our objective was to determine the relationship between body mass index (BMI) and mortality 30 days and one year after ICU admission. METHODS: We performed a cohort analysis of 16,812 adult patients from MIMIC-II, a large database of ICU patients at a tertiary care hospital in Boston, Massachusetts. The data were originally collected during the course of clinical care, and we subsequently extracted our dataset independently of the study outcome. RESULTS: Compared to normal weight patients, obese patients had 26% and 43% lower mortality risk at 30 days and one year after ICU admission, respectively (OR 0.74 [95% CI, 0.64-0.86] and 0.57 [95% CI, 0.49-0.67]); overweight patients had nearly 20% and 30% lower mortality risk (OR 0.81 [95% CI, 0.70-0.93] and 0.68 [95% CI, 0.59-0.79]). Severely obese patients (BMI [greater than or equal to]40 kg/m2) did not have a significant survival advantage at 30 days (OR 0.94 [95% CI, 0.74-1.20]), but did have 30% lower mortality risk at one year (OR 0.70 [95% CI, 0.54-0.90]). There was no significant difference in admission acuity or ICU and hospital length of stay across BMI categories. CONCLUSION: Our study supports the hypothesis that patients who are overweight or obese have improved survival both 30 days and one year after ICU admission.
    Critical care (London, England) 12/2012; 16(6):R235. · 4.72 Impact Factor
  • Clement J McDonald, William M Tierney
    Health Affairs 06/2012; 31(6):1365; atuhor reply 1366. · 4.64 Impact Factor
  • Swapna Abhyankar, Dina Demner-Fushman, Clement J McDonald
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    ABSTRACT: Clinical databases provide a rich source of data for answering clinical research questions. However, the variables recorded in clinical data systems are often identified by local, idiosyncratic, and sometimes redundant and/or ambiguous names (or codes) rather than unique, well-organized codes from standard code systems. This reality discourages research use of such databases, because researchers must invest considerable time in cleaning up the data before they can ask their first research question. Researchers at MIT developed MIMIC-II, a nearly complete collection of clinical data about intensive care patients. Because its data are drawn from existing clinical systems, it has many of the problems described above. In collaboration with the MIT researchers, we have begun a process of cleaning up the data and mapping the variable names and codes to LOINC codes. Our first step, which we describe here, was to map all of the laboratory test observations to LOINC codes. We were able to map 87% of the unique laboratory tests that cover 94% of the total number of laboratory tests results. Of the 13% of tests that we could not map, nearly 60% were due to test names whose real meaning could not be discerned and 29% represented tests that were not yet included in the LOINC table. These results suggest that LOINC codes cover most of laboratory tests used in critical care. We have delivered this work to the MIMIC-II researchers, who have included it in their standard MIMIC-II database release so that researchers who use this database in the future will not have to do this work.
    Journal of Biomedical Informatics 05/2012; 45(4):642-50. · 2.13 Impact Factor
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    ABSTRACT: The PhenX Toolkit provides researchers with recommended, well-established, low-burden measures suitable for human subject research. The database of Genotypes and Phenotypes (dbGaP) is the data repository for a variety of studies funded by the National Institutes of Health, including genome-wide association studies. The dbGaP requires that investigators provide a data dictionary of study variables as part of the data submission process. Thus, dbGaP is a unique resource that can help investigators identify studies that share the same or similar variables. As a proof of concept, variables from 16 studies deposited in dbGaP were mapped to PhenX measures. Soon, investigators will be able to search dbGaP using PhenX variable identifiers and find comparable and related variables in these 16 studies. To enhance effective data exchange, PhenX measures, protocols, and variables were modeled in Logical Observation Identifiers Names and Codes (LOINC® ). PhenX domains and measures are also represented in the Cancer Data Standards Registry and Repository (caDSR). Associating PhenX measures with existing standards (LOINC® and caDSR) and mapping to dbGaP study variables extends the utility of these measures by revealing new opportunities for cross-study analysis.
    Human Mutation 03/2012; 33(5):849-57. · 5.21 Impact Factor
  • Clement J McDonald, Michael H McDonald
    Archives of internal medicine 02/2012; 172(3):285-7. · 11.46 Impact Factor
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    M C Lin, D J Vreeman, Clement J McDonald, S M Huff
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    ABSTRACT: We wanted to develop a method for evaluating the consistency and usefulness of LOINC code use across different institutions, and to evaluate the degree of interoperability that can be attained when using LOINC codes for laboratory data exchange. Our specific goals were to: (1) Determine if any contradictory knowledge exists in LOINC. (2) Determine how many LOINC codes were used in a truly interoperable fashion between systems. (3) Provide suggestions for improving the semantic interoperability of LOINC. We collected Extensional Definitions (EDs) of LOINC usage from three institutions. The version space approach was used to divide LOINC codes into small sets, which made auditing of LOINC use across the institutions feasible. We then compared pairings of LOINC codes from the three institutions for consistency and usefulness. The number of LOINC codes evaluated were 1917, 1267 and 1693 as obtained from ARUP, Intermountain and Regenstrief respectively. There were 2022, 2030, and 2301 version spaces among ARUP and Intermountain, Intermountain and Regenstrief and ARUP and Regenstrief respectively. Using the EDs as the gold standard, there were 104, 109 and 112 pairs containing contradictory knowledge and there were 1165, 765 and 1121 semantically interoperable pairs. The interoperable pairs were classified into three levels: (1) Level I - No loss of meaning, complete information was exchanged by identical codes. (2) Level II - No loss of meaning, but processing of data was needed to make the data completely comparable. (3) Level III - Some loss of meaning. For example, tests with a specific 'method' could be rolled-up with tests that were 'methodless'. There are variations in the way LOINC is used for data exchange that result in some data not being truly interoperable across different enterprises. To improve its semantic interoperability, we need to detect and correct any contradictory knowledge within LOINC and add computable relationships that can be used for making reliable inferences about the data. The LOINC committee should also provide detailed guidance on best practices for mapping from local codes to LOINC codes and for using LOINC codes in data exchange.
    Journal of Biomedical Informatics 01/2012; 45(4):658-66. · 2.13 Impact Factor
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    ABSTRACT: Interoperable health information exchange depends on adoption of terminology standards, but international use of such standards can be challenging because of language differences between local concept names and the standard terminology. To address this important barrier, we describe the evolution of an efficient process for constructing translations of LOINC terms names, the foreign language functions in RELMA, and the current state of translations in LOINC. We also present the development of the Italian translation to illustrate how translation is enabling adoption in international contexts. We built a tool that finds the unique list of LOINC Parts that make up a given set of LOINC terms. This list enables translation of smaller pieces like the core component "hepatitis c virus" separately from all the suffixes that could appear with it, such "Ab.IgG", "DNA", and "RNA". We built another tool that generates a translation of a full LOINC name from all of these atomic pieces. As of version 2.36 (June 2011), LOINC terms have been translated into nine languages from 15 linguistic variants other than its native English. The five largest linguistic variants have all used the Part-based translation mechanism. However, even with efficient tools and processes, translation of standard terminology is a complex undertaking. Two of the prominent linguistic challenges that translators have faced include: the approach to handling acronyms and abbreviations, and the differences in linguistic syntax (e.g. word order) between languages. LOINC's open and customizable approach has enabled many different groups to create translations that met their needs and matched their resources. Distributing the standard and its many language translations at no cost worldwide accelerates LOINC adoption globally, and is an important enabler of interoperable health information exchange.
    Journal of Biomedical Informatics 01/2012; 45(4):667-73. · 2.13 Impact Factor
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    ABSTRACT: Background: US public health programs screen four million newborns every year to identify serious but potentially treatable disorders before symptoms appear. A positive newborn screening (NBS) result must be transmitted rapidly to allow follow-up, diagnosis and intervention before the baby suffers significant morbidity or mortality. Because conditions detectable by NBS are rare, aggregate NBS results and confirmed case information across all NBS programs are needed to support quality research into improved testing and treatment; however, there are many barriers to aggregating results, including variations in condition names, screening methods and how results are reported. The Health Resources and Services Administration and the National Library of Medicine developed a panel of standard NBS names and codes to use in HL7 messages for standardizing electronic results reporting. However, screening for lysosomal storage disorders (LSDs) is a new effort and currently, there is no consensus on naming and reporting these conditions. For example, each disorder can have multiple names based on researchers' names, related genes, and affected enzymes. Objective: To develop standard condition and test names and codes to standardize electronic reporting for the LSDs detectable by NBS. Methods: Organized a Newborn Screening Translational Research Network workgroup of LSD experts, including pediatricians, geneticists, biochemists and patient advocates. Analyzed variations in naming LSDs and the tests used for screening. Created an algorithm for assigning standard codes to these tests and conditions. Results: Developed a hierarchy of LOINC codes with SNOMED CT coded answer lists for LSDs detectable by NBS. Updated the annotated example HL7 message to include guidance for reporting LSD screening results. Conclusions: Standard codes and names will enable researchers, clinicians and public health surveillance efforts to aggregate NBS results from all of the states screening for LSDs. These data are essential for creating case definitions and providing effective follow-up care.
    139st APHA Annual Meeting and Exposition 2011; 11/2011
  • Daniel Vreeman, Clement McDonald, Kathy Mercer
    Public Health Informatics Conference 2011 Centers for Disease Control and Prevention; 08/2011
  • Public Health Informatics Conference 2011 Centers for Disease Control and Prevention; 08/2011

Publication Stats

6k Citations
1,048.97 Total Impact Points

Institutions

  • 2009–2014
    • National Institutes of Health
      • National Center for Biotechnology Information
      Maryland, United States
  • 1975–2012
    • Regenstrief Institute, Inc.
      Indianapolis, Indiana, United States
  • 2010
    • National Library of Medicine
      Maryland, United States
    • University of Utah
      • Department of Biomedical Informatics
      Salt Lake City, UT, United States
    • Washington DC VA Medical Center
      Washington, Washington, D.C., United States
  • 1982–2007
    • Indiana University-Purdue University Indianapolis
      • • Department of Medicine
      • • Department of Pediatrics
      Indianapolis, Indiana, United States
  • 2003–2004
    • Indiana University-Purdue University School of Medicine
      • Department of Medicine
      Indianapolis, IN, United States
  • 1991–1996
    • Richard L. Roudebush VA Medical Center
      Indianapolis, Indiana, United States
  • 1990
    • Purdue University
      • Department of Pharmacy Practice
      West Lafayette, IN, United States