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Thromb Haemost 2022; 122(07): 1231-1238
DOI: 10.1055/a-1698-6506
Stroke, Systemic or Venous Thromboembolism

Derivation and Validation of a Risk Factor Model to Identify Medical Inpatients at Risk for Venous Thromboembolism

Michael B. Rothberg
1   Center for Value-Based Care Research, Cleveland Clinic, Cleveland, Ohio, United States
2   Department of Internal Medicine, Cleveland Clinic, Cleveland, Ohio, United States
,
Aaron C. Hamilton
3   Department of Hospital Medicine, Cleveland Clinic, Cleveland, Ohio, United States
,
M. Todd Greene
4   The Michigan Hospital Medicine Safety Consortium Data Coordinating Center, Ann Arbor, Michigan, United States
5   Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States
6   Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, United States
,
Jacqueline Fox
1   Center for Value-Based Care Research, Cleveland Clinic, Cleveland, Ohio, United States
,
Oleg Lisheba
7   Enterprise Analytics eResearch Department, Cleveland Clinic, Cleveland, Ohio, United States
,
Alex Milinovich
8   Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, United States
,
Thomas N. Gautier IV
9   Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States
,
Priscilla Kim
9   Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States
,
Scott Kaatz
4   The Michigan Hospital Medicine Safety Consortium Data Coordinating Center, Ann Arbor, Michigan, United States
10   Division of Hospital Medicine, Henry Ford Hospital, Detroit, Michigan, United States
,
Bo Hu
8   Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, United States
› Author Affiliations Funding This study was sponsored by U.S. Department of Health and Human Services, Agency for Healthcare Research and Quality (R01HS022883). The sponsor had no role in the design, conduct, interpretation, writing, or publication of the study.
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Abstract

Background Venous thromboembolism (VTE) prophylaxis is recommended for hospitalized medical patients at high risk for VTE. Multiple risk assessment models exist, but few have been compared in large datasets.

Methods We constructed a derivation cohort using 6 years of data from 12 hospitals to identify risk factors associated with developing VTE within 14 days of admission. VTE was identified using a complex algorithm combining administrative codes and clinical data. We developed a multivariable prediction model and applied it to three validation cohorts: a temporal cohort, including two additional years, a cross-validation, in which we refit the model excluding one hospital each time, applying the refitted model to the holdout hospital, and an external cohort. Performance was evaluated using the C-statistic.

Results The derivation cohort included 155,026 patients with a 14-day VTE rate of 0.68%. The final multivariable model contained 13 patient risk factors. The model had an optimism corrected C-statistic of 0.79 and good calibration. The temporal validation cohort included 53,210 patients, with a VTE rate of 0.64%; the external cohort had 23,413 patients and a rate of 0.49%. Based on the C-statistic, the Cleveland Clinic Model (CCM) outperformed both the Padua (0.76 vs. 0.72, p = 0.002) and IMPROVE (0.68, p < 0.001) models in the temporal cohort. C-statistics for the CCM at individual hospitals ranged from 0.68 to 0.78. In the external cohort, the CCM C-statistic was similar to Padua (0.70 vs. 0.66, p = 0.17) and outperformed IMPROVE (0.59, p < 0.001).

Conclusion A new VTE risk assessment model outperformed recommended models.

Keywords

VTE - risk models - validation

Note

This study was presented at the Society of General Internal Medicine Annual Meeting in Denver, Colorado, United States, April 13, 2018.


Supplementary Material



Publication History

Received: 11 May 2021

Accepted: 13 November 2021

Accepted Manuscript online:
16 November 2021

Article published online:
29 December 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Lederle FA, Zylla D, MacDonald R, Wilt TJ. 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 2011; 155 (09) 602-615
    CrossrefPubMedGoogle Scholar
  • 2 Dentali F, Douketis JD, Gianni M, Lim W, Crowther MA. Meta-analysis: anticoagulant prophylaxis to prevent symptomatic venous thromboembolism in hospitalized medical patients. Ann Intern Med 2007; 146 (04) 278-288
    CrossrefPubMedGoogle Scholar
  • 3 Wein L, Wein S, Haas SJ, Shaw J, Krum H. Pharmacological venous thromboembolism prophylaxis in hospitalized medical patients: a meta-analysis of randomized controlled trials. Arch Intern Med 2007; 167 (14) 1476-1486
    CrossrefPubMedGoogle Scholar
  • 4 Kahn SR, Lim W, Dunn AS. 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 2012; 141 (2, Suppl): e195S-e226S
    CrossrefPubMedGoogle Scholar
  • 5 Greene MT, Spyropoulos AC, Chopra V. et al. Validation of risk assessment models of venous thromboembolism in hospitalized medical patients. Am J Med 2016; 129 (09) 1001.e9-1001.e18
    CrossrefPubMedGoogle Scholar
  • 6 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 2010; 8 (11) 2450-2457
    CrossrefPubMedGoogle Scholar
  • 7 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 2014; 111 (03) 531-538
    Article in Thieme ConnectPubMedGoogle Scholar
  • 8 Schünemann HJ, Cushman M, Burnett AE. et al. American Society of Hematology 2018 guidelines for management of venous thromboembolism: prophylaxis for hospitalized and nonhospitalized medical patients. Blood Adv 2018; 2 (22) 3198-3225
    CrossrefPubMedGoogle Scholar
  • 9 Spyropoulos AC, Anderson Jr FA, FitzGerald G. et al; IMPROVE Investigators. Predictive and associative models to identify hospitalized medical patients at risk for VTE. Chest 2011; 140 (03) 706-714
    CrossrefPubMedGoogle Scholar
  • 10 Fang MC, Fan D, Sung SH. et al. Validity of using inpatient and outpatient administrative codes to identify acute venous thromboembolism: the CVRN VTE study. Med Care 2017; 55 (12) e137-e143
    CrossrefPubMedGoogle Scholar
  • 11 Flanders SA, Greene MT, Grant P. et al. Hospital performance for pharmacologic venous thromboembolism prophylaxis and rate of venous thromboembolism : a cohort study. JAMA Intern Med 2014; 174 (10) 1577-1584
    CrossrefPubMedGoogle Scholar
  • 12 Barbar S, Prandoni P. Scoring systems for estimating risk of venous thromboembolism in hospitalized medical patients. Semin Thromb Hemost 2017; 43 (05) 460-468
    Article in Thieme ConnectPubMedGoogle Scholar
  • 13 Samama MM, Cohen AT, Darmon JY. et al; Prophylaxis in Medical Patients with Enoxaparin Study Group. A comparison of enoxaparin with placebo for the prevention of venous thromboembolism in acutely ill medical patients. N Engl J Med 1999; 341 (11) 793-800
    CrossrefPubMedGoogle Scholar
  • 14 Leizorovicz A, Cohen AT, Turpie AG, Olsson CG, Vaitkus PT, Goldhaber SZ. PREVENT Medical Thromboprophylaxis Study Group. Randomized, placebo-controlled trial of dalteparin for the prevention of venous thromboembolism in acutely ill medical patients. Circulation 2004; 110 (07) 874-879
    CrossrefPubMedGoogle Scholar
  • 15 Alotaibi GS, Wu C, Senthilselvan A, McMurtry MS. The validity of ICD codes coupled with imaging procedure codes for identifying acute venous thromboembolism using administrative data. Vasc Med 2015; 20 (04) 364-368
    CrossrefPubMedGoogle Scholar
  • 16 Sanfilippo KM, Wang T-F, Gage BF, Liu W, Carson KR. Improving accuracy of International Classification of Diseases codes for venous thromboembolism in administrative data. Thromb Res 2015; 135 (04) 616-620
    CrossrefPubMedGoogle Scholar
  • 17 Stuck AK, Spirk D, Schaudt J, Kucher N. Risk assessment models for venous thromboembolism in acutely ill medical patients. A systematic review. Thromb Haemost 2017; 117 (04) 801-808
    Article in Thieme ConnectPubMedGoogle Scholar
  • 18 Kucher N, Koo S, Quiroz R. et al. Electronic alerts to prevent venous thromboembolism among hospitalized patients. N Engl J Med 2005; 352 (10) 969-977
    CrossrefPubMedGoogle Scholar
  • 19 Woller SC, Stevens SM, Jones JP. et al. Derivation and validation of a simple model to identify venous thromboembolism risk in medical patients. Am J Med 2011; 124 (10) 947-954
    CrossrefPubMedGoogle Scholar
  • 20 White RH, Garcia M, Sadeghi B. et al. Evaluation of the predictive value of ICD-9-CM coded administrative data for venous thromboembolism in the United States. Thromb Res 2010; 126 (01) 61-67
    CrossrefPubMedGoogle Scholar
  • 21 Haut ER, Lau BD, Kraenzlin FS. et al. Improved prophylaxis and decreased rates of preventable harm with the use of a mandatory computerized clinical decision support tool for prophylaxis for venous thromboembolism in trauma. Arch Surg 2012; 147 (10) 901-907
    CrossrefPubMedGoogle Scholar
  • 22 Khanna R, Vittinghoff E, Maselli J, Auerbach A. Unintended consequences of a standard admission order set on venous thromboembolism prophylaxis and patient outcomes. J Gen Intern Med 2012; 27 (03) 318-324
    CrossrefPubMedGoogle Scholar
  • 23 Rosenberg D, Eichorn A, Alarcon M, McCullagh L, McGinn T, Spyropoulos AC. 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 2014; 3 (06) e001152
    CrossrefPubMedGoogle Scholar
  • 24 Spyropoulos AC. 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 2009; 7 (06) 1041-1042
    CrossrefPubMedGoogle Scholar
  • 25 Akl EA, Ramly EP, Kahale LA. et al. Anticoagulation for people with cancer and central venous catheters. Cochrane Database Syst Rev 2014; (10) CD006468
    PubMedGoogle Scholar