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Record W2622299066 · doi:10.1186/s12874-017-0361-1

Limitations of pulmonary embolism ICD-10 codes in emergency department administrative data: let the buyer beware

2017· article· en· W2622299066 on OpenAlexafffund
K. Burles, Grant Innes, Kevin Senior, Eddy Lang, Andrew D. McRae

Bibliographic record

VenueBMC Medical Research Methodology · 2017
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsFoothills Medical CentreAlberta Health ServicesAlberta HealthUniversity of Calgary
FundersAlberta InnovatesAlberta Innovates - Health Solutions
KeywordsMedical diagnosisMedicineDiagnosis codePulmonary embolismCoding (social sciences)ICD-10Emergency departmentMedical recordFalse positive paradoxCurrent Procedural TerminologyAmbulatoryAmbulatory careHealth careEmergency medicineMedical emergencyStatisticsComputer sciencePopulationSurgeryPsychiatryArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Administrative data is a useful tool for research and quality improvement; however, validity of research findings based on these data depends on their reliability. Diagnoses assigned by physicians are subsequently converted by nosologists to ICD-10 codes (International Statistical Classification of Diseases and Related Health Problems, 10th Revision). Several groups have reported ICD-9 coding errors in inpatient data that have implications for research, quality improvement, and policymaking, but few have assessed ICD-10 code validity in ambulatory care databases. Our objective was to evaluate pulmonary embolism (PE) ICD-10 code accuracy in our large, integrated hospital system, and the validity of using these codes for operational and health services research using ED ambulatory care databases. METHODS: Ambulatory care data for patients (age ≥ 18 years) with a PE ICD-10 code (I26.0 and I26.9) were obtained from the records of four urban EDs between July 2013 to January 2015. PE diagnoses were confirmed by reviewing medical records and imaging reports. In cases where chart diagnosis and ICD-10 code were discrepant, chart review was considered correct. Physicians' written discharge diagnoses were also searched using 'pulmonary embolism' and 'PE', and patients who were diagnosed with PE but not coded as PE were identified. Coding discrepancies were quantified and described. RESULTS: One thousand, four hundred and fifty-three ED patients had a PE ICD-10 code. Of these, 257 (17.7%) were false positive, with an incorrectly assigned PE code. Among the 257 false positives, 193 cases had ambiguous ED diagnoses such as 'rule out PE' or 'query PE', while 64 cases should have had non-PE codes. An additional 117 patients (8.90%) with a PE discharge diagnosis were incorrectly assigned a non-PE ICD-10 code (false negative group). The sensitivity of PE ICD-10 codes in this dataset was 91.1% (95%CI, 89.4-92.6) with a specificity of 99.9% (95%CI, 99.9-99.9). The positive and negative predictive values were 82.3% (95%CI, 80.3-84.2) and 99.9% (95%CI, 99.9-99.9), respectively. CONCLUSIONS: Ambulatory care data, like inpatient data, are subject to coding errors. This confirms the importance of ICD-10 code validation prior to use. The largest proportion of coding errors arises from ambiguous physician documentation; therefore, physicians and data custodians must ensure that quality improvement processes are in place to promote ICD-10 coding accuracy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.308
metaresearch head score (Gemma)0.654
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.692
Threshold uncertainty score0.854

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3080.654
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.010
Science and technology studies0.0020.008
Scholarly communication0.0090.014
Open science0.0070.006
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.762
GPT teacher head0.582
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations103
Published2017
Admission routes2
Has abstractyes

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