Risk factors for intracranial haemorrhage in patients with pulmonary embolism treated with thrombolytic therapy Development of the PE-CH Score
Received:29. Juli 2016
Accepted after major revision:19. Oktober 2016
01. Dezember 2017 (online)
Pulmonary embolism (PE) is a major cause of morbidity and mortality world-wide, and the use of thrombolytic therapy has been associated with favourable clinical outcomes in certain patient subsets. These potential benefits are counterbalanced by the risk of bleeding complications, the most devastating of which is intracranial haemorrhage (ICH). We retrospectively evaluated 9703 patients from the 2003–2012 nationwide in-patient sample database (NIS) who received thrombolytics for PE. All patients with ICH during the PE hospitalisation were identified and a clinical risk score model was developed utilizing demographics and comorbidities. The dataset was divided 1:1 into derivation and validation cohorts. During 2003–2012, 176/9705 (1.8 %) patients with PE experienced ICH after thrombolytic use. Four independent prognostic factors were identified in a backward logistic regression model, and each was assigned a number of points proportional to its regression coefficient: pre-existing Peripheral vascular disease (1 point), age greater than 65 years (Elderly) (1 point), prior Cerebrovascular accident with residual deficit (5 points), and prior myocardial infarction (Heart attack) (1 point). In the derivation cohort, scores of 0, 1, 2 and ≥ 5 points were associated with ICH risks of 1.2 %, 1.9 %, 2.4 % and 17.8 %, respectively. Rates of ICH were similar in the validation cohort. The C-statistic for the risk score was 0.65 (0.61–0.70) in the derivation cohort and 0.66 (0.60–0.72) in the validation cohort. A novel risk score, derived from simple clinical historical elements was developed to predict ICH in PE patients treated with thrombolytics.
Supplementary Material to this article is available online at www.thrombosis-online.com.
- 1 Horlander KT, Mannino DM, Leeper KV. Pulmonary embolism mortality in the United States, 1979-1998: an analysis using multiple-cause mortality data. Arch Intern Med 2003; 163: 1711-1717.
- 2 Mozaffarian D, Benjamin EJ, Go AS. et al. American Heart Association Statistics Committee and Stroke Statistics Subcommittee. American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics--2015 update: a report from the American Heart Association. Circulation 2015; 131: e29-322.
- 3 Chatterjee S, Chakraborty A, Weinberg I. et al. Thrombolysis for pulmonary embolism and risk of all-cause mortality, major bleeding, and intracranial haemorrhage: a meta-analysis. J Am Med Assoc 2014; 311: 2414-2421.
- 4 Fang MC, Go AS, Chang Y. et al. Death and disability from warfarin-associated intracranial and extracranial haemorrhages. Am J Med 2007; 120: 700-705.
- 5 Stein PD, Matta F. Thrombolytic therapy in unstable patients with acute pulmonary embolism: saves lives but underused. Am J Med 2012; 125: 465-470.
- 6 Meyer G, Vicaut E, Danays T. et al PEITHO Investigators. Fibrinolysis for patients with intermediate-risk pulmonary embolism. N Engl J Med 2014; 370: 1402-1411.
- 7 Healthcare Cost and Utilisation Project (HCUP). July 2008. Agency for Healthcare Research and Quality, Rockville, MD; 2008. HCUP Databases Available at http://www.hcup-us.ahrq.gov/nisoverview.jsp Accessed September 1, 2015.
- 8 Quan H, Sundararajan V, Halfon P. et al. Coding algorithms for defining Comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005; 43: 1130-1139.
- 9 Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992; 45: 613-619.
- 10 Andrade SE, Harrold LR, Tjia J. et al. A systematic review of validated methods for identifying cerebrovascular accident or transient ischaemic attack using administrative data. Pharmacoepidemiol Drug Saf 2012; 21 (Suppl. 01) 100-128.
- 11 Mela CF, Kopalle PK. The impact of collinearity on regression analysis: the asymmetric effect of negative and positive correlations. Applied Economics 2002; 34: 667-677.
- 12 Altman DG, Royston P. What do we mean by validating a prognostic model?. Stat Med 2000; 19: 453-473.
- 13 Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996; 15: 361-387.
- 14 Huynh T, Cox JL, Massel D. et al. FASTRAK II Network. Predictors of intracranial haemorrhage with fibrinolytic therapy in unselected community patients: a report from the FASTRAK II project. Am Heart J 2004; 148: 86-91.
- 15 Stein PD, Matta F, Steinberger DS, Keyes DC. Intracerebral haemorrhage with thrombolytic therapy for acute pulmonary embolism. Am J Med 2012; 125: 50-56.
- 16 Giele JL, Witkamp TD, Mali WP. et al. SMART Study Group. Silent brain infarcts in patients with manifest vascular disease. Stroke 2004; 35: 742-746.