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      We congratulate Spyropoulos et al on their recent publication of the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) registry findings
      • Spyropoulos A.C.
      • Anderson Jr, F.A.
      • Fitzgerald G.
      • et al.
      Predictive and associative models to identify hospitalized medical patients at risk for VTE.
      and are grateful for their thoughtful comments. We agree that our research using International Classification of Diseases, Ninth Revision (ICD-9) codes for outcomes is inherently limited by the accuracy of ICD-9 coding and the retrospective nature of data extraction and analysis. In our case, coding limitations may have led to a portion of patients that experienced upper-extremity deep venous thrombosis, superficial thrombophlebitis, or chronic deep venous thrombosis being included among the group classified as having deep venous thrombosis, increasing our observed rate of venous thromboembolism. In our erratum published last month
      • Woller S.C.
      • Stevens S.M.
      • Jones J.P.
      • et al.
      Derivation and validation of a simple model to identify venous thromboembolism risk in medical patients.
      we correct that the risk factors of: 1) prior venous thromboembolism, 2) an order in the chart for bed rest, 3) peripherally inserted central venous catheter line, and 4) cancer diagnosis, persisted as most predictive of venous thromboembolism, however, with an area under the receiver operating characteristic curve equal to 0.74, not 0.84. We identified this inaccuracy as a function of retrospective electronic data extraction, a limitation of studies such as ours.
      The rate of venous thromboembolism we observed (3.6% in the derivation cohort and 4.5% in the validation cohort) is higher than that reported in some studies,
      • Leizorovicz A.
      • Cohen A.T.
      • Turpie A.G.
      • Olsson C.G.
      • Vaitkus P.T.
      • Goldhaber S.Z.
      Randomized, placebo-controlled trial of dalteparin for the prevention of venous thromboembolism in acutely ill medical patients.
      but does not differ from recently reported rates in others.
      • Fanikos J.
      • Rao A.
      • Seger A.C.
      • et al.
      Venous thromboembolism prophylaxis for medical service—mostly cancer—patients at hospital discharge.
      These events were identified by ICD-9 code, also with the ascribed limitations as noted above. The size and duration of our study (143,975 admissions from January 1, 2000 through December 31, 2006 in the derivation cohort and 46,856 admissions between January 1, 2008 and December 31, 2009 in the validation cohort) were, we believe, adequate to mitigate most limitations attributable to infrequent ICD-9 classification errors.
      We believe that our ability to identify and consider risk factors for venous thromboembolism that presented during hospitalization was a key study strength. The emergence of automated electronic medical record interrogation performed daily among hospitalized patients to assess venous thromboembolism risk in “real time” as exists at our institution
      • Evans R.S.
      • Lloyd J.F.
      • Aston V.T.
      • et al.
      Computer surveillance of patients at high risk for and with venous thromboembolism.
      permits consideration of venous thromboembolism risk factors that may be not only associative but also predictive. Our inclusion of risk factors that occur during hospital stay is meaningful considering that individual patient risk for venous thromboembolism often increases during hospitalization, and therefore a patient observed to be low risk upon hospitalization may become high risk for a great majority of the hospital course. It is for this reason that we performed our assessment of the predictive ability for each risk factor discretely present: 1) before hospitalization, 2) during hospitalization, and 3) any time in the electronic medical record up until the time of hospital discharge. We agree that venous thromboembolism risk is a continuous variable, as highlighted on the included Figure. We ultimately acknowledge that the decision to provide prophylaxis is binary (“yes or no”).We agree that our risk assessment model as well as those proposed by Spyropoulos et al require prospective validation.
      Figure thumbnail gr1
      FigureRisk assessment model (RAM) performance in validation cohort. AUC is area under the receiver operating characteristic (ROC) curve. We observed the AUC attributable to the retained 4 risk factors as superior to the AUC attributable to the Kucher Score.

      References

        • Spyropoulos A.C.
        • Anderson Jr, F.A.
        • Fitzgerald G.
        • et al.
        Predictive and associative models to identify hospitalized medical patients at risk for VTE.
        Chest. 2011; 140: 706-714
        • Woller S.C.
        • Stevens S.M.
        • Jones J.P.
        • et al.
        Derivation and validation of a simple model to identify venous thromboembolism risk in medical patients.
        Am J Med. 2012; 125: e27
        • Leizorovicz A.
        • Cohen A.T.
        • Turpie A.G.
        • Olsson C.G.
        • Vaitkus P.T.
        • Goldhaber S.Z.
        Randomized, placebo-controlled trial of dalteparin for the prevention of venous thromboembolism in acutely ill medical patients.
        Circulation. 2004; 110: 874-879
        • Fanikos J.
        • Rao A.
        • Seger A.C.
        • et al.
        Venous thromboembolism prophylaxis for medical service—mostly cancer—patients at hospital discharge.
        Am J Med. 2011; 124: 1143-1150
        • Evans R.S.
        • Lloyd J.F.
        • Aston V.T.
        • et al.
        Computer surveillance of patients at high risk for and with venous thromboembolism.
        AMIA Annu Symp Proc. 2010; 2010: 217-221

      Linked Article

      • The “Risk” of Risk Assessment Models for Venous Thromboembolism in Medical Patients
        The American Journal of MedicineVol. 125Issue 11
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          We read with interest the derivation and validation of a simple risk assessment model (RAM) by Woller et al1 for identifying at-risk hospitalized medical patients for venous thromboembolism. As the authors point out, previous RAMs for this group have been complex, with incomplete scoring systems and inadequate validation. The authors derived and validated their 4-factor RAM (which included previous venous thromboembolism, bed rest, cancer, and peripherally inserted central catheter lines) from data using an electronic medical record and query of International Classification of Diseases (ICD)-9 codes attributable to hospitalization, with an excellent receiver operating curve of >0.80.
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