Subscribe to RSS
DOI: 10.1055/s-0040-1701483
Derivation and Validation of a Prediction Model for Venous Thromboembolism in Primary Care
Funding This study was funded by grants from AlfaSigma, which were not directly involved in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; or the preparation, review, or approval of the manuscript.Publication History
19 April 2019
24 December 2019
Publication Date:
14 April 2020 (online)
Abstract
Background Most episodes of venous thromboembolism (VTE) occurred in primary care. To date, no score potentially able to identify those patients who may deserve an antithrombotic prophylaxis has been developed.
Aim The objective of this study is to develop and validate a prediction model for VTE in primary care.
Methods Using the Health Search Database, we identified a cohort of 1,359,880 adult patients between 2002 and 2013. The date of the first General Practitioner's (GP) visit was the cohort entry date. All VTE cases (index date) observed up to December 2014 were identified. The cohort was randomly divided in a development and a validation cohort. According to nested case-cohort analysis, up to five controls were matched to their respective cases on month and year of cohort entry and duration of follow-up.
The score was evaluated according to explained variance (pseudo R2) as a performance measure, ratio of predicted to observed cases as model calibration and area under the curve (AUC) as discrimination measure.
Results The score was able to explain 27.9% of the variation for VTE occurrence. The calibration measure revealed a margin of error lower than 10% in 70% of the population. In terms of discrimination, AUC was 0.82 (95% confidence interval: 0.82–0.83). Results of sensitivity analyses substantially confirmed these findings.
Conclusion The present score demonstrated a very good accuracy in predicting the risk of VTE in primary care. This score may be therefore implemented in clinical practice so aiding GPs in making decision on patients potentially at risk of VTE.
Keywords
venous thromboembolism - prediction model - score - primary health care - antithrombotic prophylaxisAuthors' Contributions
All persons that contributed to this manuscript met the criteria for authorship and are listed as authors.
-
References
- 1 Goldhaber SZ. Venous thromboembolism: epidemiology and magnitude of the problem. Best Pract Res Clin Haematol 2012; 25 (03) 235-242
- 2 Leizorovicz A, Cohen AT, Turpie AGG, 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
- 3 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
- 4 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
- 5 Gould MK, Garcia DA, Wren SM. , et al. Prevention of VTE in nonorthopedic surgical patients: Antithrombotic Therapy and Prevention of Thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. Chest 2012; 141 (2, Suppl) e227S-e277S
- 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
- 7 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
- 8 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
- 9 Caprini JA, Arcelus JI, Reyna JJ. Effective risk stratification of surgical and nonsurgical patients for venous thromboembolic disease. Semin Hematol 2001; 38 (02) (Suppl. 05) 12
- 10 Anderson Jr FA, Wheeler HB, Goldberg RJ. , et al. A population-based perspective of the hospital incidence and case-fatality rates of deep vein thrombosis and pulmonary embolism. The Worcester DVT Study. Arch Intern Med 1991; 151 (05) 933-938
- 11 Spencer FA, Lessard D, Emery C, Reed G, Goldberg RJ. Venous thromboembolism in the outpatient setting. Arch Intern Med 2007; 167 (14) 1471-1475
- 12 Gussoni G, Campanini M, Silingardi M. , et al; GEMINI Study Group. In-hospital symptomatic venous thromboembolism and antithrombotic prophylaxis in Internal Medicine. Findings from a multicenter, prospective study. Thromb Haemost 2009; 101 (05) 893-901
- 13 X Report Health Search. Available at: https://report.healthsearch.it/ . Accessed June 2, 2018
- 14 Coloma PM, Schuemie MJ, Trifirò G. , et al; EU-ADR consortium. Drug-induced acute myocardial infarction: identifying ‘prime suspects’ from electronic healthcare records-based surveillance system. PLoS ONE 2013; 8 (08) e72148
- 15 Lapi F, Simonetti M, Michieli R. , et al. Assessing 5-year incidence rates and determinants of osteoporotic fractures in primary care. Bone 2012; 50 (01) 85-90
- 16 Sterrantino C, Trifirò G, Lapi F. , et al. Burden of community-acquired pneumonia in Italian general practice. Eur Respir J 2013; 42 (06) 1739-1742
- 17 Filippi A, Vanuzzo D, Bignamini AA, Sessa E, Brignoli O, Mazzaglia G. Computerized general practice databases provide quick and cost-effective information on the prevalence of angina pectoris. Ital Heart J 2005; 6 (01) 49-51
- 18 Mille in Rete. Available at: http://www.svemg.it/?page_id=1783 . Published 2016. Accessed June 2, 2018
- 19 Cimminiello C, Filippi A, Mazzaglia G, Pecchioli S, Arpaia G, Cricelli C. Venous thromboembolism in medical patients treated in the setting of primary care: a nationwide case-control study in Italy. Thromb Res 2010; 126 (05) 367-372
- 20 Samama MM. An epidemiologic study of risk factors for deep vein thrombosis in medical outpatients: the Sirius study. Arch Intern Med 2000; 160 (22) 3415-3420
- 21 Spencer FA, Gurwitz JH, Schulman S. , et al. Venous thromboembolism in older adults: A community-based study. Am J Med 2014; 127 (06) 530-7.e3
- 22 Steyerberg EW, Vickers AJ, Cook NR. , et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010; 21 (01) 128-138
- 23 Ganna A, Reilly M, de Faire U, Pedersen N, Magnusson P, Ingelsson E. Risk prediction measures for case-cohort and nested case-control designs: an application to cardiovascular disease. Am J Epidemiol 2012; 175 (07) 715-724
- 24 Van Hoorde K, Van Huffel S, Timmerman D, Bourne T, Van Calster B. A spline-based tool to assess and visualize the calibration of multiclass risk predictions. J Biomed Inform 2015; 54: 283-293
- 25 Van Calster B, Nieboer D, Vergouwe Y, De Cock B, Pencina MJ, Steyerberg EW. A calibration hierarchy for risk models was defined: from utopia to empirical data. J Clin Epidemiol 2016; 74: 167-176
- 26 Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006; 26 (06) 565-574
- 27 Van Calster B, Wynants L, Verbeek JFM. , et al. Reporting and interpreting decision curve analysis: a guide for investigators. Eur Urol 2018; 74 (06) 796-804
- 28 Cohen AT, Davidson BL, Gallus AS. , et al; ARTEMIS Investigators. Efficacy and safety of fondaparinux for the prevention of venous thromboembolism in older acute medical patients: randomised placebo controlled trial. BMJ 2006; 332 (7537): 325-329
- 29 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
- 30 Cox DR. Note on Grouping. J Am Stat Assoc 1957; 52: 543-547
- 31 Salim A, Delcoigne B, Villaflores K. , et al. Comparisons of risk prediction methods using nested case-control data. Stat Med 2017; 36 (03) 455-465
- 32 Ray JG. Dyslipidemia, statins, and venous thromboembolism: a potential risk factor and a potential treatment. Curr Opin Pulm Med 2003; 9 (05) 378-384
- 33 Wolkewitz M, Cooper BS, Palomar-Martinez M, Olaechea-Astigarraga P, Alvarez-Lerma F, Schumacher M. Nested case-control studies in cohorts with competing events. Epidemiology 2014; 25 (01) 122-125
- 34 Royston P, Altman DG. External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol 2013; 13 (01) 33
- 35 Dentali F, Mumoli N, Fontanella A, Di Minno MND. Efficacy and safety of extended antithrombotic prophylaxis in elderly medically ill patients. Eur Respir J 2017; 49 (01) 1601887
- 36 Dentali F, Mumoli N, Prisco D, Fontanella A, Di Minno MND. Efficacy and safety of extended thromboprophylaxis for medically ill patients. A meta-analysis of randomised controlled trials. Thromb Haemost 2017; 117 (03) 606-617
- 37 Cohen AT, Harrington RA, Goldhaber SZ. , et al; APEX Investigators. Extended thromboprophylaxis with betrixaban in acutely Ill medical patients. N Engl J Med 2016; 375 (06) 534-544
- 38 Bosson JL, Pouchain D, Bergmann JF. ; ETAPE Study Group. A prospective observational study of a cohort of outpatients with an acute medical event and reduced mobility: incidence of symptomatic thromboembolism and description of thromboprophylaxis practices. J Intern Med 2006; 260 (02) 168-176
- 39 Crowther MA, Warkentin TE. Bleeding risk and the management of bleeding complications in patients undergoing anticoagulant therapy: focus on new anticoagulant agents. Blood 2008; 111 (10) 4871-4879
- 40 Djulbegovic B, Hozo I. When should potentially false research findings be considered acceptable?. PLoS Med 2007; 4 (02) e26
- 41 Fitzgerald M, Saville BR, Lewis RJ. Decision curve analysis. JAMA 2015; 313 (04) 409-410
- 42 Kerr KF, Brown MD, Zhu K, Janes H. Assessing the clinical impact of risk prediction models with decision curves: guidance for correct interpretation and appropriate use. J Clin Oncol 2016; 34 (21) 2534-2540
- 43 Rousson V, Zumbrunn T. Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies. BMC Med Inform Decis Mak 2011; 11 (01) 45
- 44 Localio AR, Goodman S. Beyond the usual prediction accuracy metrics: reporting results for clinical decision making. Ann Intern Med 2012; 157 (04) 294-295
- 45 Vickers AJ, Van Calster B, Steyerberg EW. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ 2016; 352: i6
- 46 Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999; 94 (446) 496-509