scholarly journals Effect of vocabulary mapping for conditions on phenotype cohorts

2018 ◽  
Vol 25 (12) ◽  
pp. 1618-1625 ◽  
Author(s):  
George Hripcsak ◽  
Matthew E Levine ◽  
Ning Shang ◽  
Patrick B Ryan
Keyword(s):  
Knowledge Engineering ◽  
Total Error ◽  
False Negative ◽  
International Classification Of Diseases ◽  
Error Rates ◽  
Small Error ◽  
Snomed Ct ◽  
Order Of Magnitude ◽  
Systematized Nomenclature Of Medicine ◽  
Gold Standards

Abstract Objective To study the effect on patient cohorts of mapping condition (diagnosis) codes from source billing vocabularies to a clinical vocabulary. Materials and Methods Nine International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9-CM) concept sets were extracted from eMERGE network phenotypes, translated to Systematized Nomenclature of Medicine - Clinical Terms concept sets, and applied to patient data that were mapped from source ICD9-CM and ICD10-CM codes to Systematized Nomenclature of Medicine - Clinical Terms codes using Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) vocabulary mappings. The original ICD9-CM concept set and a concept set extended to ICD10-CM were used to create patient cohorts that served as gold standards. Results Four phenotype concept sets were able to be translated to Systematized Nomenclature of Medicine - Clinical Terms without ambiguities and were able to perform perfectly with respect to the gold standards. The other 5 lost performance when 2 or more ICD9-CM or ICD10-CM codes mapped to the same Systematized Nomenclature of Medicine - Clinical Terms code. The patient cohorts had a total error (false positive and false negative) of up to 0.15% compared to querying ICD9-CM source data and up to 0.26% compared to querying ICD9-CM and ICD10-CM data. Knowledge engineering was required to produce that performance; simple automated methods to generate concept sets had errors up to 10% (one outlier at 250%). Discussion The translation of data from source vocabularies to Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) resulted in very small error rates that were an order of magnitude smaller than other error sources. Conclusion It appears possible to map diagnoses from disparate vocabularies to a single clinical vocabulary and carry out research using a single set of definitions, thus improving efficiency and transportability of research.

scholarly journals Developing and Evaluating Mappings of ICD-10 and ICD-10-CM Codes to PheCodes

2018 ◽  
Author(s):  
Patrick Wu ◽  
Aliya Gifford ◽  
Xiangrui Meng ◽  
Xue Li ◽  
Harry Campbell ◽  
...  
Keyword(s):  
Medical Center ◽  
Association Studies ◽  
International Classification Of Diseases ◽  
Snomed Ct ◽  
Unified Medical Language System ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
The Uk ◽  
Systematized Nomenclature Of Medicine

AbstractBackgroundThe PheCode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) in the electronic health record (EHR).ObjectiveHere, we present our work on the development and evaluation of maps from ICD-10 and ICD-10-CM codes to PheCodes.MethodsWe mapped ICD-10 and ICD-10-CM codes to PheCodes using a number of methods and resources, such as concept relationships and explicit mappings from the Unified Medical Language System (UMLS), Observational Health Data Sciences and Informatics (OHDSI), Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT), and National Library of Medicine (NLM). We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM→PheCode map by investigating phenotype reproducibility and conducting a PheWAS.ResultsWe mapped >75% of ICD-10-CM and ICD-10 codes to PheCodes. Of the unique codes observed in the VUMC (ICD-10-CM) and UKBB (ICD-10) cohorts, >90% were mapped to PheCodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease. A PheWAS with a lipoprotein(a) (LPA) genetic variant, rs10455872, using the ICD-9-CM and ICD-10-CM maps replicated two genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P < .001, OR = 1.60 vs. ICD-10-CM: P < .001, OR = 1.60) and with chronic ischemic heart disease (ICD-9-CM: P < .001, OR = 1.5 vs. ICD-10-CM: P < .001, OR = 1.47).ConclusionsThis study introduces the initial “beta” versions of ICD-10 and ICD-10-CM to PheCode maps that will enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for high-throughput PheWAS in the EHR.

scholarly journals Too good to be true: when overwhelming evidence fails to convince

2016 ◽  
Vol 472 (2187) ◽  
pp. 20150748 ◽  
Author(s):  
Lachlan J. Gunn ◽  
François Chapeau-Blondeau ◽  
Mark D. McDonnell ◽  
Bruce R. Davis ◽  
Andrew Allison ◽  
...  
Keyword(s):  
False Negative ◽  
False Negative Rate ◽  
Error Rates ◽  
Small Error ◽  
High Confidence ◽  
Primality Testing ◽  
Legal Evidence ◽  
Numerical Tests ◽  
Overwhelming Evidence ◽  
Made In

Is it possible for a large sequence of measurements or observations, which support a hypothesis, to counterintuitively decrease our confidence? Can unanimous support be too good to be true? The assumption of independence is often made in good faith; however, rarely is consideration given to whether a systemic failure has occurred. Taking this into account can cause certainty in a hypothesis to decrease as the evidence for it becomes apparently stronger. We perform a probabilistic Bayesian analysis of this effect with examples based on (i) archaeological evidence, (ii) weighing of legal evidence and (iii) cryptographic primality testing. In this paper, we investigate the effects of small error rates in a set of measurements or observations. We find that even with very low systemic failure rates, high confidence is surprisingly difficult to achieve; in particular, we find that certain analyses of cryptographically important numerical tests are highly optimistic, underestimating their false-negative rate by as much as a factor of 2 80 .

Design Issues of Randomized Phase II Trials and a Proposal for Phase II Screening Trials

2005 ◽  
Vol 23 (28) ◽  
pp. 7199-7206 ◽  
Author(s):  
Lawrence V. Rubinstein ◽  
Edward L. Korn ◽  
Boris Freidlin ◽  
Sally Hunsberger ◽  
S. Percy Ivy ◽  
...  
Keyword(s):  
Phase Ii ◽  
False Positive ◽  
Standard Treatment ◽  
False Negative ◽  
Error Rates ◽  
Targeted Treatment ◽  
Phase Iii ◽  
Phase Ii Trials ◽  
Treatment Control ◽  
Randomized Phase Ii

Future progress in improving cancer therapy can be expedited by better prioritization of new treatments for phase III evaluation. Historically, phase II trials have been key components in the prioritization process. There has been a long-standing interest in using phase II trials with randomization against a standard-treatment control arm or an additional experimental arm to provide greater assurance than afforded by comparison to historic controls that the new agent or regimen is promising and warrants further evaluation. Relevant trial designs that have been developed and utilized include phase II selection designs, randomized phase II designs that include a reference standard-treatment control arm, and phase II/III designs. We present our own explorations into the possibilities of developing “phase II screening trials,” in which preliminary and nondefinitive randomized comparisons of experimental regimens to standard treatments are made (preferably using an intermediate end point) by carefully adjusting the false-positive error rates (α or type I error) and false-negative error rates (β or type II error), so that the targeted treatment benefit may be appropriate while the sample size remains restricted. If the ability to conduct a definitive phase III trial can be protected, and if investigators feel that by judicious choice of false-positive probability and false-negative probability and magnitude of targeted treatment effect they can appropriately balance the conflicting demands of screening out useless regimens versus reliably detecting useful ones, the phase II screening trial design may be appropriate to apply.

scholarly journals Agreement Assessment of Tigecycline Susceptibilities Determined by the Disk Diffusion and Broth Microdilution Methods among Commonly Encountered Resistant Bacterial Isolates: Results from the TigecyclineIn VitroSurveillance in Taiwan (TIST) Study, 2008 to 2010

2011 ◽  
Vol 56 (3) ◽  
pp. 1414-1417 ◽  
Author(s):  
Jien-Wei Liu ◽  
Wen-Chien Ko ◽  
Cheng-Hua Huang ◽  
Chun-Hsing Liao ◽  
Chin-Te Lu ◽  
...  
Keyword(s):  
Susceptibility Testing ◽  
Total Error ◽  
Error Rates ◽  
Resistant Bacteria ◽  
Drug Resistant ◽  
Broth Microdilution ◽  
Content Type ◽  
E Coli ◽  
Drug Resistant Bacteria

ABSTRACTThe TigecyclineIn VitroSurveillance in Taiwan (TIST) study, initiated in 2006, is a nationwide surveillance program designed to longitudinally monitor thein vitroactivity of tigecycline against commonly encountered drug-resistant bacteria. This study compared thein vitroactivity of tigecycline against 3,014 isolates of clinically important drug-resistant bacteria using the standard broth microdilution and disk diffusion methods. Species studied included methicillin-resistantStaphylococcus aureus(MRSA;n= 759), vancomycin-resistantEnterococcus faecium(VRE;n= 191), extended-spectrum β-lactamase (ESBL)-producingEscherichia coli(n= 602), ESBL-producingKlebsiella pneumoniae(n= 736), andAcinetobacter baumannii(n= 726) that had been collected from patients treated between 2008 and 2010 at 20 hospitals in Taiwan. MICs and inhibition zone diameters were interpreted according to the currently recommended U.S. Food and Drug Administration (FDA) criteria and the European Committee on Antimicrobial Susceptibility Testing (EUCAST) criteria. The MIC90values of tigecycline against MRSA, VRE, ESBL-producingE. coli, ESBL-producingK. pneumoniae, andA. baumanniiwere 0.5, 0.125, 0.5, 2, and 8 μg/ml, respectively. The total error rates between the two methods using the FDA criteria were high: 38.4% for ESBL-producingK. pneumoniaeand 33.8% forA. baumannii. Using the EUCAST criteria, the total error rate was also high (54.6%) forA. baumanniiisolates. The total error rates between these two methods were <5% for MRSA, VRE, and ESBL-producingE. coli. For routine susceptibility testing of ESBL-producingK. pneumoniaeandA. baumanniiagainst tigecycline, the broth microdilution method should be used because of the poor correlation of results between these two methods.

scholarly journals Development of an In-House Rapid Antimicrobial Susceptibility Testing Protocol for Positive Blood Culture and Its Implementation in Routine Microbiology Laboratories

2021 ◽  
Vol 12 ◽  
Author(s):  
Min Cao ◽  
Lin Huang ◽  
Yanyan Hu ◽  
Yinfei Fang ◽  
Rong Zhang ◽  
...  
Keyword(s):  
Blood Culture ◽  
Antimicrobial Susceptibility ◽  
Susceptibility Testing ◽  
Total Error ◽  
Agar Plate ◽  
Error Rates ◽  
Antimicrobial Susceptibility Testing ◽  
Diffusion Method ◽  
High Morbidity ◽  
Clinical Strains

Bloodstream infections (BSI) are associated with high morbidity and mortality and remain a leading cause of death. Blood culture (BC) including the identification and the antimicrobial susceptibility testing of the causative microorganisms should be performed as soon as possible. In this study, we developed an in-house rapid antimicrobial susceptibility testing (rAST) protocol for positive BC. First, the rAST was performed in the simulated positive BC of standard strains (Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 25923, and Pseudomonas aeruginosa ATCC 27853) at three different times to assess the reproducibility and operability by dispensing four drops of BC broth onto a Mueller–Hinton agar plate after a positive signal. Furthermore, the rAST was performed in clinical positive BCs. The results of rAST at 4, 6, 8, and 18 h of incubation were compared with results of the standard 16- to 20-h disk diffusion method, and the preliminary breakpoints of the rAST method were established according to the inhibition diameter of sensitive strains and resistant strains. Finally, the rAST was performed in the simulated positive BC of clinical strains to evaluate the availability of the preliminary breakpoints. The rAST results of standard strains were distributed evenly at three different times. Among the 202 clinical strains used to establish the preliminary breakpoints, the number of zone diameters that could be read and interpreted (60, 87, 98, and 100%) increased with incubation time (4, 6, 8, and 18 h), and the categorical agreement was acceptable, with total error rates of 3.0, 2.3, 2.1, and 1.3% at 4, 6, 8, and 18 h of incubation, respectively. In conclusion, the in-house rAST protocol for positive BC can be implemented in routine laboratories. It provides reliable antimicrobial susceptibility testing results for BSI pathogens after 4–6 h of incubation.

scholarly journals An exploration of the properties of the CORE problem list subset and how it facilitates the implementation of SNOMED CT

2015 ◽  
Vol 22 (3) ◽  
pp. 649-658 ◽  
Author(s):  
Kin Wah Fung ◽  
Julia Xu
Keyword(s):  
Electronic Health Records ◽  
Growth Pattern ◽  
Problem List ◽  
Snomed Ct ◽  
Health Records ◽  
The Core ◽  
Core Subset ◽  
Electronic Health ◽  
Systematized Nomenclature Of Medicine ◽  
Core Problem

Abstract Objective Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is the emergent international health terminology standard for encoding clinical information in electronic health records. The CORE Problem List Subset was created to facilitate the terminology’s implementation. This study evaluates the CORE Subset’s coverage and examines its growth pattern as source datasets are being incorporated. Methods Coverage of frequently used terms and the corresponding usage of the covered terms were assessed by “leave-one-out” analysis of the eight datasets constituting the current CORE Subset. The growth pattern was studied using a retrospective experiment, growing the Subset one dataset at a time and examining the relationship between the size of the starting subset and the coverage of frequently used terms in the incoming dataset. Linear regression was used to model that relationship. Results On average, the CORE Subset covered 80.3% of the frequently used terms of the left-out dataset, and the covered terms accounted for 83.7% of term usage. There was a significant positive correlation between the CORE Subset’s size and the coverage of the frequently used terms in an incoming dataset. This implies that the CORE Subset will grow at a progressively slower pace as it gets bigger. Conclusion The CORE Problem List Subset is a useful resource for the implementation of Systematized Nomenclature of Medicine Clinical Terms in electronic health records. It offers good coverage of frequently used terms, which account for a high proportion of term usage. If future datasets are incorporated into the CORE Subset, it is likely that its size will remain small and manageable.

Error Rates in Competency Testing When Test Retaking Is Permitted

1990 ◽  
Vol 15 (1) ◽  
pp. 39-52 ◽  
Author(s):  
Huynh Huynh
Keyword(s):  
Conditional Probability ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Rasch Models ◽  
Positive Error ◽  
False Positive Error ◽  
Competency Testing ◽  
Negative Error ◽  
False Negative Error

False positive and false negative error rates are studied for competency testing where examinees are permitted to retake the test if they fail to pass. Formulae are provided for the beta-binomial and Rasch models, and estimates based on these two models are compared for several typical situations. Although Rasch estimates are expected to be more accurate than beta-binomial estimates, differences among them are found not to be substantial in a number of practical situations. Under relatively general conditions and when test retaking is permitted, the probability of making a false negative error is zero. Under the same situation, and given that an examinee is a true nonmaster, the conditional probability of making a false positive error for this examinee is one.

Evaluation of the Content Coverage of SNOMED CT Representing ICNP Seven-axis Version 1 Concepts

2011 ◽  
Vol 50 (05) ◽  
pp. 472-478 ◽  
Author(s):  
C. Lundberg ◽  
A. Coenen ◽  
D. Konicek ◽  
H. A. Park
Keyword(s):  
Coverage Rate ◽  
Snomed Ct ◽  
Validation Process ◽  
The Third ◽  
Model Version ◽  
Content Coverage ◽  
Systematized Nomenclature Of Medicine ◽  
Mapping Result

SummaryObjectives: The purpose of this study is to evaluate the ability of SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) to represent the concepts of the ICNP version 1 – the seven-axis model.Methods: The first author mapped 1658 concepts of the ICNP version 1 to SNOMED CT using CLUE browser 5.0. The second author from SNOMED Terminology Solutions – with a team of SNOMED CT experts – and the third author from the ICN with a team of ICNP experts validated the mapping result. If there was any disagreement during the validation process, the three of us convened online meetings to reach a consensus.Results: In total, SNOMED CT covered 1331 out of 1658 (80%) ICNP seven-axis model concepts ranging from a 61% coverage rate of the Actions Axis concepts to a 94% coverage rate of the Judgment axis concepts.Conclusions: SNOMED CT can represent most (80%) of the ICNP version 1 concepts. However, improvements in the ICNP version 1 in terms of concept naming and definition, and the addition of missing concepts to SNOMED CT, would lead to a greater harmonization of the ICNP seven-axis model version 1 concepts with SNOMED CT.

scholarly journals The performance of permutations and exponential random graph models when analyzing animal networks

2020 ◽  
Vol 31 (5) ◽  
pp. 1266-1276 ◽  
Author(s):  
Julian C Evans ◽  
David N Fisher ◽  
Matthew J Silk
Keyword(s):  
Social Network ◽  
Random Graph ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Network Data ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph

Abstract Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.

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