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Diagnosing delirium in older hospitalized adults with dementia: adapting the confusion assessment method to international classification of diseases, tenth revision, diagnostic criteria.

ABSTRACT: To compare performance characteristics of the Confusion Assessment Method (CAM) algorithm for screening and delirium diagnosis with criteria for delirium from the International Classification of Diseases, Tenth Revision (ICD-10) and Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) in high-risk individuals.Prospective cohort study.Academic geriatric hospital.One hundred two individuals aged 80 to 100 hospitalized for acute medical illness.Complete CAM instrument (nine items), scored using the four-item CAM diagnostic algorithm. Criterion standard classification of delirium was rated independently according to expert consensus based on DSM-IV and ICD-10 criteria for delirium.In 79 hospitalized participants, the CAM performed well for delirium screening (delirium prevalence of 24% according to DSM-IV and 14% according to ICD-10). Of all CAM features, acute onset and fluctuating course are most important for diagnosis (area under the receiver operating characteristic curve (AUC) = 0.92 in DSM-IV and 0.83 in ICD-10). The CAM diagnostic algorithm had a sensitivity of 0.74, a specificity of 1.0, and an AUC of 0.88 compared with the DSM-IV reference standard and a sensitivity of 0.82, a specificity of 0.91, and an AUC of 0.85 compared with the ICD-10. Compared with the ICD-10, adding psychomotor change to the CAM algorithm improved specificity to 97%, but sensitivity fell to 55% (AUC = 0.96). Applying psychomotor change sequentially only to the group that the CAM algorithm identified as having no delirium improved sensitivity to 91% with specificity of 85% (AUC = 0.95).Although the CAM diagnostic algorithm performed well against a DSM-IV reference standard, adding psychomotor change to the CAM algorithm improved specificity and diagnostic value against ICD-10 criteria overall in older adults with dementia and improved sensitivity and screening performance when applied sequentially in CAM-negative individuals.

SUBMITTER: Thomas C

PROVIDER: S-EPMC3422775 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Diagnosing delirium in older hospitalized adults with dementia: adapting the confusion assessment method to international classification of diseases, tenth revision, diagnostic criteria.

Thomas Christine C Kreisel Stefan H SH Oster Peter P Driessen Martin M Arolt Volker V Inouye Sharon K SK

Journal of the American Geriatrics Society 20120806 8

<h4>Objectives</h4>To compare performance characteristics of the Confusion Assessment Method (CAM) algorithm for screening and delirium diagnosis with criteria for delirium from the International Classification of Diseases, Tenth Revision (ICD-10) and Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) in high-risk individuals.<h4>Design</h4>Prospective cohort study.<h4>Setting</h4>Academic geriatric hospital.<h4>Participants</h4>One hundred two individuals aged 80 to ...[more]

PMID: 22881707

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Project description:Importance:On October 1, 2015, the US transitioned to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for recording diagnoses, symptoms, and procedures. It is unknown whether this transition was associated with changes in diagnostic category prevalence based on diagnosis classification systems commonly used for payment and quality reporting. Objective:To assess changes in diagnostic category prevalence associated with the ICD-10-CM transition. Design, Setting, and Participants:This interrupted time series analysis and cross-sectional study examined level and trend changes in diagnostic category prevalence associated with the ICD-10-CM transition and clinically reviewed a subset of diagnostic categories with changes of 20% or more. Data included insurance claim diagnoses from the IBM MarketScan Commercial Database from January 1, 2010, to December 31, 2017, for more than 18 million people aged 0 to 64 years with private insurance. Diagnoses were mapped using 3 common diagnostic classification systems: World Health Organization (WHO) disease chapters, Department of Health and Human Services Hierarchical Condition Categories (HHS-HCCs), and Agency for Healthcare Research and Quality Clinical Classification System (AHRQ-CCS). Data were analyzed from December 1, 2018, to January 21, 2020. Exposures:US implementation of ICD-10-CM. Main Outcomes and Measures:Monthly rates of individuals with at least 1 diagnosis in a diagnostic classification category per 10?000 eligible members. Results:The analytic sample contained information on 2.1 billion enrollee person-months with 3.4 billion clinically assigned diagnoses; the mean (range) monthly sample size was 22.1 (18.4 to 27.1 ) million individuals. While diagnostic category prevalence changed minimally for WHO disease chapters, the ICD-10-CM transition was associated with level changes of 20% or more among 20 of 127 HHS-HCCs (15.7%) and 46 of 282 AHRQ-CCS categories (16.3%) and with trend changes of 20% or more among 12 of 127 of HHS-HCCs (9.4%) and 27 of 282 of AHRQ-CCS categories (9.6%). For HHS-HCCs, monthly rates of individuals with any acute myocardial infarction diagnosis increased 131.5% (95% CI, 124.1% to 138.8%), primarily because HHS added non-ST-segment-elevation myocardial infarction diagnoses to this category. The HHS-HCC for diabetes with chronic complications increased by 92.4% (95% CI, 84.2% to 100.5%), primarily from including new diabetes-related hypoglycemia and hyperglycemia codes, and the rate for completed pregnancy with complications decreased by 54.5% (95% CI, -58.7% to -50.2%) partly due to removing vaginal birth after cesarean delivery as a complication. Conclusions and Relevance:These findings suggest that the ICD-10-CM transition was associated with large prevalence changes for many diagnostic categories. Diagnostic classification systems developed using ICD-9-CM may need to be refined using ICD-10-CM data to avoid unintended consequences for disease surveillance, performance assessment, and risk-adjusted payments.

Project description:BackgroundAdministrative data can be useful for stroke research but have historically lacked data on stroke severity. Hospitals increasingly report the National Institutes of Health Stroke Scale (NIHSS) score using an International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code, but this code's validity remains unclear.MethodsWe examined the concordance of ICD-10 NIHSS scores versus NIHSS scores recorded in CAESAR (Cornell Acute Stroke Academic Registry). We included all patients with acute ischemic stroke from October 1, 2015, when US hospitals transitioned to ICD-10, through 2018, the latest year in our registry. The NIHSS score (range, 0-42) recorded in our registry served as the reference gold standard. ICD-10 NIHSS scores were derived from hospital discharge diagnosis code R29.7xx, with the latter 2 digits representing the NIHSS score. Multiple logistic regression was used to explore factors associated with availability of ICD-10 NIHSS scores. We used ANOVA to examine the proportion of variation (R2) in the true (registry) NIHSS score that was explained by the ICD-10 NIHSS score.ResultsAmong 1357 patients, 395 (29.1%) had an ICD-10 NIHSS score recorded. This proportion increased from 0% in 2015 to 46.5% in 2018. In a logistic regression model, only higher registry NIHSS score (odds ratio per point, 1.05 [95% CI, 1.03-1.07]) and cardioembolic stroke (odds ratio, 1.4 [95% CI, 1.0-2.0]) were associated with availability of the ICD-10 NIHSS score. In an ANOVA model, the ICD-10 NIHSS score explained almost all the variation in the registry NIHSS score (R2=0.88). Fewer than 10% of patients had a large discordance (≥4 points) between their ICD-10 and registry NIHSS scores.ConclusionsWhen present, ICD-10 codes representing NIHSS scores had excellent agreement with NIHSS scores recorded in our stroke registry. However, ICD-10 NIHSS scores were often missing, especially in less severe strokes, limiting the reliability of these codes for risk adjustment.

Dataset Information

Diagnosing delirium in older hospitalized adults with dementia: adapting the confusion assessment method to international classification of diseases, tenth revision, diagnostic criteria.

Publications

Diagnosing delirium in older hospitalized adults with dementia: adapting the confusion assessment method to international classification of diseases, tenth revision, diagnostic criteria.

Similar Datasets

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