Validation of Risk Assessment Models of Venous Thromboembolism in Hospitalized Medical Patients

American Journal of Medicine

Volumn 129, Issue 9, 2016, Pages 1001.e9-1001.e18

  Greene, M Todd ^a,b   Spyropoulos, Alex C ^c   Chopra, Vineet ^a,b,d   Grant, Paul J ^a,b   Kaatz, Scott ^e   Bernstein, Steven J ^a,b,d   Flanders, Scott A ^a,b  

^a Michigan Hospital Medicine Safety Consortium   (United States)

^b UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

^c DONALD AND BARBARA ZUCKER SCHOOL OF MEDICINE AT HOFSTRA NORTHWELL   (United States)

^d VA ANN ARBOR HEALTHCARE SYSTEM   (United States)

^e HURLEY MEDICAL CENTER   (United States)

Author keywords

Venous thromboembolism

Indexed keywords

AGED; ARTICLE; FEMALE; HIGH RISK POPULATION; HOSPITAL PATIENT; HUMAN; INCIDENCE; MAJOR CLINICAL STUDY; MALE; OUTCOME ASSESSMENT; PRIORITY JOURNAL; RISK ASSESSMENT; RISK FACTOR; VALIDATION STUDY; VENOUS THROMBOEMBOLISM; HOSPITALIZATION; LENGTH OF STAY; MICHIGAN; PROPORTIONAL HAZARDS MODEL; REPRODUCIBILITY; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

AGED; FEMALE; HOSPITALIZATION; HUMANS; LENGTH OF STAY; MALE; MICHIGAN; MODELS, STATISTICAL; PROPORTIONAL HAZARDS MODELS; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; RISK FACTORS; VENOUS THROMBOEMBOLISM;

EID: 84971658578     PISSN: 00029343     EISSN: 15557162     Source Type: Journal    
DOI: 10.1016/j.amjmed.2016.03.031     Document Type: Article

References (21)

1. 1 Kahn, S.R., Lim, W., Dunn, A.S., et al. Prevention of VTE in nonsurgical patients: Antithrombotic Therapy and Prevention of Thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. Chest 141 (2012), e195S–e226S.
2. 2 Barbar, S., Noventa, F., Rossetto, V., et al. A risk assessment model for the identification of hospitalized medical patients at risk for venous thromboembolism: the Padua Prediction Score. J Thromb Haemost 8 (2010), 2450–2457.
3. 3 Kucher, N., Koo, S., Quiroz, R., et al. Electronic alerts to prevent venous thromboembolism among hospitalized patients. N Engl J Med 352 (2005), 969–977.
4. 4 Spyropoulos, A.C., Anderson, F.A. Jr., Fitzgerald, G., et al. Predictive and associative models to identify hospitalized medical patients at risk for VTE. Chest 140 (2011), 706–714.
5. 5 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 124 (2011), 947–954.e2.
6. 6 Huang, W., Anderson, F.A., Spencer, F.A., Gallus, A., Goldberg, R.J., Risk-assessment models for predicting venous thromboembolism among hospitalized non-surgical patients: a systematic review. J Thromb Thrombolysis 35 (2013), 67–80.
7. 7 Mahan, C.E., Liu, Y., Turpie, A.G., et al. External validation of a risk assessment model for venous thromboembolism in the hospitalised acutely-ill medical patient (VTE-VALOURR). Thromb Haemost 112 (2014), 692–699.
8. 8 Rosenberg, D., Eichorn, A., Alarcon, M., McCullagh, L., McGinn, T., Spyropoulos, A.C., External validation of the risk assessment model of the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) for medical patients in a tertiary health system. J Am Heart Assoc, 3, 2014, e001152.
9. 9 Nendaz, M., Spirk, D., Kucher, N., et al. Multicentre validation of the Geneva Risk Score for hospitalised medical patients at risk of venous thromboembolism. Explicit ASsessment of Thromboembolic RIsk and Prophylaxis for Medical PATients in SwitzErland (ESTIMATE). Thromb Haemost 111 (2014), 531–538.
10. 10 Flanders, S.A., Greene, M.T., Grant, P., et al. Hospital performance for pharmacologic venous thromboembolism prophylaxis and rate of venous thromboembolism: a cohort study. JAMA Intern Med 174 (2014), 1577–1584.
11. 11 American College of Surgeons. American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP®). 2013, American College of Surgeons, Chicago.
12. 12 Harrell, F.E. Jr., Lee, K.L., Mark, D.B., Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15 (1996), 361–387.
13. 13 Newson, R., Confidence intervals for rank statistics: Somers’ D and extensions. Stata J 6:3 (2006), 309–334.
14. 14 Spyropoulos, A.C., Upper vs. lower extremity deep vein thrombosis: outcome definitions of venous thromboembolism for clinical predictor rules or risk factor analyses in hospitalized patients. J Thromb Haemost 7 (2009), 1041–1042.
15. 15 Spyropoulos, A.C., McGinn, T., Khorana, A.A., The use of weighted and scored risk assessment models for venous thromboembolism. Thromb Haemost 108 (2012), 1072–1076.
16. 16 Cohen, A.T., Spiro, T.E., Spyropoulos, A.C., et al. D-dimer as a predictor of venous thromboembolism in acutely ill, hospitalized patients: a subanalysis of the randomized controlled MAGELLAN trial. J Thromb Haemost 12 (2014), 479–487.
17. 17 Lederle, F.A., Zylla, D., MacDonald, R., Wilt, T.J., Venous thromboembolism prophylaxis in hospitalized medical patients and those with stroke: a background review for an American College of Physicians Clinical Practice Guideline. Ann Intern Med 155 (2011), 602–615.
18. 18 Rothberg, M.B., Lindenauer, P.K., Lahti, M., Pekow, P.S., Selker, H.P., Risk factor model to predict venous thromboembolism in hospitalized medical patients. J Hosp Med 6 (2011), 202–209.
19. 19 Collins, G.S., de Groot, J.A., Dutton, S., et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Methodol, 14, 2014, 40.
20. 20 Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. J Clin Epidemiol 68:2 (2015), 134–143.
21. 21 Moons, K.G., Altman, D.G., Reitsma, J.B., et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 162 (2015), W1–W73.