Development and Validation of a Disease-Specific Risk Adjustment System Using Automated Clinical Data

Authors

  • Ying P. Tabak,

    1. Biostatistics, Clinical Research, MedMined Services, CareFusion, 400 Nickerson Road, Marlborough, MA 01752
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    • Address correspondence to Ying P. Tabak, Ph.D., Director, Biostatistics, Clinical Research, MedMined Services, CareFusion, 400 Nickerson Road, Marlborough, MA 01752; e-mail: ying.tabak@carefusion.com. Xiaowu Sun, Ph.D., Karen G. Derby, B.A., and Richard S. Johannes, M.D., M.S., are with Clinical Research, MedMined Services, CareFusion, Marlborough, MA. Richard S. Johannes, M.D., M.S., is also with the Division of Gastroenterology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA. Stephen G. Kurtz, M.S., is with MASSPRO, Waltham, MA.

  • Xiaowu Sun,

    1. Clinical Research, MedMined Services, CareFusion, Marlborough
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  • Karen G. Derby,

    1. Clinical Research, MedMined Services, CareFusion, Marlborough
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  • Stephen G. Kurtz,

    1. MASSPRO, Waltham, MA.
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  • Richard S. Johannes

    1. Clinical Research, MedMined Services, CareFusion, Marlborough
    2. Division of Gastroenterology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
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Abstract

Objective. To develop and validate a disease-specific automated inpatient mortality risk adjustment system primarily using computerized numerical laboratory data and supplementing them with administrative data. To assess the values of additional manually abstracted data.

Methods. Using 1,271,663 discharges in 2000–2001, we derived 39 disease-specific automated clinical models with demographics, laboratory findings on admission, ICD-9 principal diagnosis subgroups, and secondary diagnosis-based chronic conditions. We then added manually abstracted clinical data to the automated clinical models (manual clinical models). We compared model discrimination, calibration, and relative contribution of each group of variables. We validated these 39 models using 1,178,561 discharges in 2004–2005.

Results. The overall mortality was 4.6 percent (n=58,300) and 4.0 percent (n=47,279) for derivation and validation cohorts, respectively. Common mortality predictors included age, albumin, blood urea nitrogen or creatinine, arterial pH, white blood counts, glucose, sodium, hemoglobin, and metastatic cancer. The average c-statistic for the automated clinical models was 0.83. Adding manually abstracted variables increased the average c-statistic to 0.85 with better calibration. Laboratory results displayed the highest relative contribution in predicting mortality.

Conclusions. A small number of numerical laboratory results and administrative data provided excellent risk adjustment for inpatient mortality for a wide range of clinical conditions.

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