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Concordance between administrative data and clinical review for mortality in the randomized on/off bypass follow-up study (ROOBY-FS).

Quin JA, Hattler B, Shroyer ALW, Kemp D, Almassi GH, Bakaeen FG, Carr BM, Bishawi M, Collins JF, Grover FL, Wagner TH, Department of Veteran Affairs (CSP#517-FS) ROOBY Follow-up Study's Endpoints Committee. Concordance between administrative data and clinical review for mortality in the randomized on/off bypass follow-up study (ROOBY-FS). Journal of Cardiac Surgery. 2017 Dec 1; 32(12):751-756.

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Abstract:

BACKGROUND: The optimal methodology to identify cardiac versus non-cardiac cause of death following cardiac surgery has not been determined. METHODS: The Randomized On/Off Bypass Trial was a multicenter, randomized, controlled clinical trial of 2203 patients (February 2002-May 2008) comparing 1-year cardiac outcomes between off-pump and on-pump bypass surgery. In 2013, the Veterans Affairs (VA) Cooperative Studies Program funded a follow-up study to assess 5-year outcomes including mortality. Deaths were identified and confirmed using the National Death Index (NDI), VA Vital Status file, and medical records. An Endpoints Committee (EC) reviewed patient medical records and classified each cause of death as cardiac, non-cardiac, or unknown. Using pre-determined ICD-10 codes, NDI death certificates were independently used to classify deaths as cardiac or non-cardiac. Cause of death was compared between the NDI and EC classifications and concordance measured, using Kappa statistics. RESULTS: Of the 297 5-year deaths identified by the NDI and/or VA vital status file and confirmed by the EC, 219 had adequate patient records for EC cause of death determination. The EC adjudicated 141 of these deaths as non-cardiac and 78 as cardiac, while the NDI classified 150 as non-cardiac and 69 as cardiac; agreement was 77.6% (kappa 0.500; P? < 0.001). CONCLUSIONS: Since concordance between EC and NDI cause of death classifications was only moderate, caution should be exercised in relying exclusively on NDI data to determine cause of death. A hybrid approach, integrating multiple information sources, may provide the most accurate approach to classifying cause of death.





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