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Use of ICD-9 coding for estimating the occurrence of cerebrovascular malformations
Mitchell F Berman1, Christian Stapf, Robert R Sciacca
1Department of Anesthesiology, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA.
Insights
Accurate epidemiologic data on cerebrovascular malformations is limited. State discharge registries are unreliable for detailed epidemiologic studies due to high false-positive coding rates.
Area of Science:
- Neurology
- Public Health
- Medical Informatics
Background:
- Epidemiologic data for cerebrovascular malformations are scarce.
- Accurate disease detection rates are needed for public health initiatives.
Purpose of the Study:
- Determine the distribution of International Classification of Diseases, Ninth Revision, (ICD-9) codes for cerebrovascular malformations.
- Evaluate the utility of state discharge registries for estimating cerebrovascular malformation detection rates.
Main Methods:
- Reviewed patient records with ICD-9 code 747.81 (cerebrovascular anomaly) from 1992-1999.
- Calculated hospital admission rates using 1995-1999 California and New York state discharge databases.
Main Results:
- 88% of patients with the ICD-9 code had a confirmed cerebrovascular malformation.
- Arteriovenous malformations (AVMs) and cavernous malformations were the most common lesions.
- The sensitivity of ICD-9 coding was 94%, with a false-positive rate of 1.7 per 100,000 person-years.
Conclusions:
- State discharge databases yield admission rates consistent with disease detection (approx. 1 per 100,000 person-years).
- High false-positive coding rates compromise the accuracy of these registries for detailed epidemiologic data.
- Current state discharge registries are not suitable for comprehensive epidemiologic studies of cerebrovascular malformations.
Background And Purpose:
Accurate epidemiologic data concerning cerebrovascular malformations are scarce. Our goals were to determine the distribution of lesions in the International Classification of Diseases, Ninth Revision, (ICD-9) code for cerebrovascular malformations and to evaluate the use of state discharge registries for estimating their detection rate.
Methods:
We reviewed records of all patients discharged from our center between January 1, 1992, and June 30, 1999, whose diagnoses included the ICD-9 code for cerebrovascular anomaly (code 747.81) to determine the accuracy of the coding. Hospital admission rates for cerebrovascular anomaly were calculated by using the 1995-1999 state discharge databases of California and New York.
Results:
Of 804 patients with this code, 706 (88%) had a lesion consistent with the diagnosis. Five lesions accounted for 99% of the diagnoses; the two most common were AVM (66%) and cavernous malformation (13%). The ratio of AVMs to all cerebrovascular anomalies was similar to that in a prior population-based study. The sensitivity of identifying a patient with cerebrovascular malformation by using ICD-9 coding was 94%; the false-positive rate was 1.7 cases per 100,000 person-years. For California and New York, rates of first hospital admission for cerebrovascular malformation were 1.5 and 1.8 cases per 100,000 person-years, respectively.
Conclusion:
Rates of admission for cerebrovascular malformations calculated from state discharge databases are consistent with disease detection rates in the range of 1 case per 100,000 person-years. However, the false-positive rate for coding is in the same range as the disease detection rate. Thus, current state discharge registries cannot serve as sources of detailed epidemiologic data.
