Embedding Measurement within Existing Computerized Data Systems: Scaling Clinical Laboratory and Medical Records Heart Failure Data to Predict ICU Admission

摘要

This study employs existing data sources to develop a new measure of intensive care unit (ICU) admission risk for heart failure patients. Outcome measures were constructed from laboratory, accounting, and medical record data for 973 adult inpatients with primary or secondary heart failure. Several scoring interpretations of the laboratory indicators were evaluated relative to their measurement and predictive properties. Cases were restricted to tests within first lab draw that included at least 15 indicators. After optimizing the original clinical observations, a satisfactory heart failure severity scale was calibrated on a 0-1000 continuum. Patients with unadjusted CHF severity measures of 550 or less were 2.7 times more likely to be admitted to the ICU than those with higher measures. Patients with low HF severity measures (<550) adjusted for demographic and diagnostic risk factors are about six times more likely to be admitted to the ICU than those with higher adjusted measures. A nomogram facilitates routine clinical application. Existing computerized data systems could be programmed to automatically structure clinical laboratory reports using the results of studies like this one to reduce data volume with no loss of information, make laboratory results more meaningful to clinical end users, improve the quality of care, reduce errors and unneeded tests, prevent unnecessary ICU admissions, lower costs, and improve patient satisfaction. Existing data typically examined piecemeal form a coherent scale measuring heart failure severity sensitive to increased likelihood of ICU admission. Marked improvements in ROC curves were found for the aggregate measures relative to individual clinical indicators.