Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data
Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada. Medical Care
(Impact Factor: 3.23).
Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms.
ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms.
Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm.
These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
Available from: Hassan Assareh
- "Discrepancies in the four chronic conditions of diabetes, hepatitis , HIV and hypertension as well as smoking status were investigated. These five are among most frequently captured conditions in risk-adjustment models232425. Their effect on care and treatment make their recording required or more likely.Record linked APDC includes a unique patient identifier that enables the identification and linkage of patient-specific admissions. Each record is assigned with up to 55 codes for morbidities (principal diagnosis and comorbidities) based on the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) Seventh Edition. "
[Show abstract] [Hide abstract]
ABSTRACT: Diagnostic data routinely collected for hospital admitted patients and used for case-mix adjustment in care provider comparisons and reimbursement are prone to biases. We aim to measure discrepancies, variations and associated factors in recorded chronic morbidities for hospital admitted patients in New South Wales (NSW), Australia. Of all admissions between July 2010 and June 2014 in all NSW public and private acute hospitals, admissions with over 24 hours stay and one or more of the chronic conditions of diabetes, smoking, hepatitis, HIV, and hypertension were included. The incidence of a non-recorded chronic condition in an admission occurring after the first admission with a recorded chronic condition (index admission) was considered as a discrepancy. Poisson models were employed to (i) derive adjusted discrepancy incidence rates (IR) and rate ratios (IRR) accounting for patient, admission, comorbidity and hospital characteristics and (ii) quantify variation in rates among hospitals. The discrepancy incidence rate was highest for hypertension (51% of 262,664 admissions), followed by hepatitis (37% of 12,107), smoking (33% of 548,965), HIV (27% of 1500) and diabetes (19% of 228,687). Adjusted rates for all conditions declined over the four-year period; with the sharpest drop of over 80% for diabetes (47.7% in 2010 vs. 7.3% in 2014), and 20% to 55% for the other conditions. Discrepancies were more common in private hospitals and smaller public hospitals. Inter-hospital differences were responsible for 1% (HIV) to 9.4% (smoking) of variation in adjusted discrepancy incidences, with an increasing trend for diabetes and HIV. Chronic conditions are recorded inconsistently in hospital administrative datasets, and hospitals contribute to the discrepancies. Adjustment for patterns and stratification in risk adjustments; and furthermore longitudinal accumulation of clinical data at patient level, refinement of clinical coding systems and standardisation of comorbidity recording across hospitals would enhance accuracy of datasets and validity of case-mix adjustment.
Available from: Alex Bottle
- "We defined multi-morbidity as the simultaneous presence of at least 2 conditions from a list of 20 major conditions including chronic illnesses (Table A in S1 File). This list comprised conditions with a prevalence of at least 1% and defined in the Charlson and Elixhauser sets of comorbidity groups232425, which assigns ICD-10 clinical diagnoses codes to each medical condition. For descriptive statistics, categorical variables (i.e. "
[Show abstract] [Hide abstract]
No formal definition for the "complex elderly" exists; moreover, these older patients with high levels of multi-morbidity are not readily identified as such at point of hospitalisation, thus missing a valuable opportunity to manage the older patient appropriately within the hospital setting.
To empirically identify the complex elderly patient based on degree of multi-morbidity.
Retrospective observational study using administrative data.
English hospitals during the financial year 2012-13.
All admitted patients aged 65 years and over.
By using exploratory analysis (correspondence analysis) we identify multi-morbidity groups based on 20 target conditions whose hospital prevalence was ≥ 1%.
We examined a total of 2788900 hospital admissions. Multi-morbidity was highly prevalent, 62.8% had 2 or more of the targeted conditions while 4.7% had six or more. Multi-morbidity increased with age from 56% (65-69yr age-groups) up to 67% (80-84yr age-group). The average multi-morbidity was 3.2±1.2 (SD). Correspondence analysis revealed 3 distinct groups of older patients. Group 1 (multi-morbidity ≤2), associated with cancer and/or metastasis; Group 2 (multi-morbidity of 3, 4 or 5), associated with chronic pulmonary disease, lung disease, rheumatism and osteoporosis; finally Group 3 with the highest level of multi-morbidity (≥6) and associated with heart failure, cerebrovascular accident, diabetes, hypertension and myocardial infarction.
By using widely available hospital administrative data, we propose patients in Groups 2 and 3 to be identified as the complex elderly. Identification of multi-morbidity patterns can help to predict the needs of the older patient and improve resource provision.
Available from: sciencedirect.com
- "Comorbidities contributing to the Charlson Comorbidity Index (CCI) were identified using the ICD-10 coding algorithm developed by Quan  which has been validated for use with older populations . A 12 month lookback period was used to identify comorbidities. "
[Show abstract] [Hide abstract]
Injury is the most common reason for admission to hospital in people with dementia in Australia. However relatively little is known about the temporal trends and the hospital experience of people with dementia hospitalised for an injury. This population-based data linkage study compared the causes, temporal trends and health outcomes for injury-related hospitalisations in people with and without dementia.
Hospitalisation and death data for 235,612 individuals aged 65 years and over admitted to hospital for an injury over the ten year period (2003-2012) in New South Wales, Australia were probabilistically linked. Descriptive statistics including chi square tests, observed and age-standardised admission rates and rate ratios (RRs) were calculated. Trends over time were analysed using negative binomial regression.
There were 331,432 injury-related hospitalisations over the study period. Both the observed (RR 3.16; 95% CI 3.13-3.19) and age-standardised admission rate ratios (RR 1.78; 95% CI 1.77-1.79) were higher for people with dementia. Age-standardised rates increased by 3.5% (95% CI 3.1-3.9) per annum over the study period for people without dementia. In contrast, for people with dementia, rates increased by 2.4% (95% CI 1.8-3.1) per annum until 2007 and then decreased by 3.1% (95% CI -4.4 to -1.7) per annum from 2007 onwards. Compared to people without dementia, a higher proportion of people with dementia were hospitalised as a result of a fall (90.9% vs 75.2%, p<0.0001), sustained a fracture (57.2% vs 52.1%, p<0.0001), notably hip fracture (30.7% vs 14.7%, p<0.0001), had longer mean hospital lengths of stay (LOS) (16.5 vs 13.6 days), and higher 30-day mortality (8.7% vs 3.6% p<0.0001), although this pattern was not consistent across all injury types.
People with dementia are disproportionately represented in injury-related hospitalisations, experience longer hospital LOS and have poorer outcomes. Ninety percent of hospitalisations for people with dementia were as a result of a fall, highlighting the importance of developing and implementing effective fall-related preventive strategies in this high risk population.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.