- •We analysed 19 risk factors for invasive mould disease (IMD) in 1944 adult haematology patients.
- •Seven risk factors at hospital admission differentiated patients who developed IMD with 85% accuracy.
- •The risk model also identified patients with high risk of IMD despite “low risk” malignancies.
- •Further validation and development of such models can support diagnostic /antifungal stewardship.
Our objective was to develop a model that predicts a patient's risk of developing invasive mould disease (IMD) within 60 days of admission for treatment of a haematological malignancy.
We analysed 19 risk factors for IMD in a cohort of 1944 adult patients with haematological malignancies over 4127 admissions at a haematology referral centre in Northern Italy (2007-2016). We used a multivariable logistic regression to estimate the 60-day probability of developing probable or proven IMD. The model was internally validated using a bootstrap resampling procedure.
The prevalence of IMD was 3.3% (90 probable cases, 43 proven cases). Seven risk factors were retained in the final risk model: (1) uncontrolled malignancy, (2) high-risk chemotherapy regimen, (3) high-dose corticosteroids, (4) severe lymphopenia, (5) CMV reactivation or disease, (6) prolonged neutropenia, and (7) a history of previous IMD within 90 days. The model displayed good calibration and discrimination in both the derivation (aROC 0.85, 95% CI 0.84-0.86) and validation (aROC 0.83 95% CI 0.79-0.89) populations.
Our model differentiated with 85% accuracy whether or not patients developed IMD within 60-days of admission. Individualized risk assessment, aided by validated prognostic models, could assist IMD management and improve antifungal stewardship.
To read this article in full you will need to make a payment
Purchase one-time access:Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
One-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:Subscribe to Journal of Infection
Already a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
- Triggers for driving treatment of at-risk patients with invasive fungal disease.J Antimicrob Chemother. 2013; 68: iii17-iii24
- Risk stratification for invasive aspergillosis in immunocompromised patients.Ann N Y Acad Sci. 2012; 1272: 23-30
- Risk stratification for invasive fungal infections in patients with hematological malignancies: SEIFEM recommendations.Blood Rev. 2016; 31: 17-29
- Practice guidelines for the diagnosis and management of aspergillosis: 2016 Update by the Infectious Diseases Society of America.Clin Infect Dis. 2016; 63: e1-60
- Invasive aspergillosis associated with severe influenza infections.Open Forum Infect Dis. 2016; 3: ofw171
- Cytomegalovirus reactivation is associated with increased risk of late-onset invasive fungal disease after allogeneic hematopoietic stem cell transplantation: A Multicenter Study in the Current Era of Viral Load Monitoring.Biol Blood Marrow Transplant. 2017; 23: 1961-1967
- Host control of fungal infections: lessons from basic studies and human cohorts.Annu Rev Immunol. 2018; 36: 139-173
- Genetic PTX3 deficiency and aspergillosis in stem-cell transplantation.N Engl J Med. 2014; 370: 421-432
- Dectin-1 Y238X polymorphism associates with susceptibility to invasive aspergillosis in hematopoietic transplantation through impairment of both recipient- and donor-dependent mechanisms of antifungal immunity.Blood. 2010; 116: 5394-5402
- Toll-like receptor 4 polymorphisms and aspergillosis in stem-cell transplantation.N Engl J Med. 2008; 359: 1766-1777
- Serum galactomannan-based early detection of invasive aspergillosis in hematology patients receiving effective anti-mold prophylaxis.Clin Infect Dis. 2014; 59: 1696-1702
- Prognosis and prognostic research: application and impact of prognostic models in clinical practice.BMJ. 2009; 338: 1487-1490
- A risk prediction score for invasive mold disease in patients with hematological malignancies.PLoS One. 2013; 8: e75531
- Clinical manifestations of graft-versus-host-disease in human recipients if marrow form HLA matched sibling donors.Transplantation. 1974; 18: 295
- Histopathologic diagnosis of chronic graft-versus-host disease: National Institutes of Health consensus development project on criteria for clinical trials in chronic graft-versus-host disease: II. Pathology working group report.Biol Blood Marrow Transplant. 2006; 12: 31-47
- Scoring oral mucositis.Oral Oncol. 1998; 34: 63-71
- Revised definitions of invasive fungal disease from the European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (EORTC/MSG) Consensus Group.Clin Infect Dis. 2008; 46: 1813-1821
- Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration.Ann Intern Med. 2015; 162: W1-73
- Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers.Stat Med. 2014; 33: 517-535
- Assessing the performance of prediction models: a framework for traditional and novel measures.Epidemiology. 2010; 21: 128-138
- A general-purpose nomogram generator for predictive logistic regression models.Stata J. 2015; 15: 537-546
- Design and use of Candida scores at the intensive care unit.Mycoses. 2011; 54: 467-474
- Development and validation of a clinical prediction rule for candidemia in hospitalized patients with severe sepsis and septic shock.J Crit Care. 2015; 30: 715-720
- MSG-01: A Randomized, Double-Blind, Placebo-Controlled Trial of Caspofungin Prophylaxis Followed by Preemptive Therapy for Invasive Candidiasis in High-Risk Adults in the Critical Care Setting.Clin Infect Dis. 2014; 58: 1219-1226
- Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.Stat Med. 1996; 15: 361-387
- Difficulties with molecular diagnostic tests for mould and yeast infections: where do we stand?.Clin Microbiol Infect. 2014; 20: 36-41
- Clinical prediction models: a practical approach to development, validation, and updating.Springer Science & Business Media, 2008
Published online: April 08, 2019
Accepted: April 4, 2019
© 2019 The British Infection Association. Published by Elsevier Ltd. All rights reserved.