AI Article Synopsis
- The study aimed to assess how well existing data sources can be used to identify cases of ALS and related motor neuron diseases in Georgia from 2001-2005.
- Researchers gathered data from various healthcare systems, and a neurologist confirmed ALS diagnoses by reviewing medical records.
- Results indicated that a significant number of potential ALS cases were identified, with an overall positive predictive value of 84%, which increased to 96-98% using predictive rules, showcasing the value of existing data in classifying ALS cases more efficiently.
Article Abstract
Our objective was to investigate the utility of existing data sources for identifying cases of amyotrophic lateral sclerosis (ALS) and related motor neuron diseases (MND) in the State of Georgia. Data were acquired from Medicare, Medicaid, Veterans Administration, Emory Healthcare, community neurologists, the ALS Association, and mortality records for ALS/MND patients residing in Georgia during 2001-2005. A neurologist used abstracted medical records to verify the diagnosis of ALS/MND. The positive predictive value (PPV) of an ICD code for a verified diagnosis of ALS was estimated. Simple 'rules' were developed to improve PPV. Results showed that a total of 2413 unique potential cases were identified in existing data sources. Medical records of 579 cases were available for review; the diagnosis of ALS (or a related MND) was confirmed in 486 (PPV = 84%) cases. Predictive rules, which permitted classification of ∼80% of the chart-reviewed population, improved PPV to 96-98%. In conclusion, existing data sources are useful for identifying cases of ALS/MND; most data sources contribute a substantial number of unique cases. Predictive algorithms may permit correct classification of a large proportion of cases without the need for verification based on medical record review.
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| http://dx.doi.org/10.3109/17482968.2010.515224 | DOI Listing |
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