Email updates

Keep up to date with the latest news and content from BMC Pulmonary Medicine and BioMed Central.

Open Access Research article

A predictive tool for an effective use of 18F-FDG PET in assessing activity of sarcoidosis

Rémy LM Mostard1, Sander MJ Van Kuijk2, Johny A Verschakelen3, Marinus JPG van Kroonenburgh4, Patty J Nelemans2, Petal AHM Wijnen5 and Marjolein Drent67*

Author Affiliations

1 Department of Respiratory Medicine, Atrium Medical Centre, Heerlen, The Netherlands

2 Department of Epidemiology, University Maastricht, Maastricht, The Netherlands

3 Department of Radiology, University Hospital Gasthuisberg, Leuven, Belgium

4 Department of Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands

5 Department of Clinical Chemistry, Maastricht University Medical Centre, Maastricht, The Netherlands

6 Faculty of Health, Medicine and Life Sciences, University Maastricht, The Netherlands and Department of interstitial lung diseases, Hospital Gelderse Valley, Ede, The Netherlands

7 Faculty of Health, Medicine and Life Sciences; UNS 40 room 4.550, University Maastricht The Netherlands, PO Box 3100, 6202 NC, Maastricht, The Netherlands

For all author emails, please log on.

BMC Pulmonary Medicine 2012, 12:57  doi:10.1186/1471-2466-12-57

Published: 14 September 2012

Abstract

Background

18F-FDG PET/CT (PET) is useful in assessing inflammatory activity in sarcoidosis. However, no appropriate indications are available. The aim of this study was to develop a prediction rule that can be used to identify symptomatic sarcoidosis patients who have a high probability of PET-positivity.

Methods

We retrospectively analyzed a cohort of sarcoidosis patients with non organ specific persistent disabling symptoms (n = 95). Results of soluble interleukin-2 receptor (sIL-2R) assessment and high-resolution computed tomography (HRCT) were included in the predefined model. HRCT scans were classified using a semi-quantitative scoring system and PET findings as positive or negative, respectively. A prediction model was derived based on logistic regression analysis. We quantified the model’s performance using measures of discrimination and calibration. Finally, we constructed a prediction rule that should be easily applicable in clinical practice.

Results

The prediction rule showed good calibration and good overall performance (goodness-of-fit test, p = 0.78, Brier score 20.1%) and discriminated between patients with positive and negative PET findings (area under the receiver-operating characteristic curve, 0.83). If a positive predictive value for the presence of inflammatory activity of ≥90% is considered acceptable for clinical decision-making without referral to PET, PET would be indicated in only 29.5% of the patients. Using a positive predictive value of 98%, about half of the patients (46.3%) would require referral to PET.

Conclusions

The derived and internally validated clinical prediction rule, based on sIL-2R levels and HRCT scoring results, appeared to be useful to identify sarcoidosis patients with a high probability of inflammatory activity. Using this rule may enable a more effective use of PET scan for assessment of inflammatory activity in sarcoidosis.

Keywords:
Clinical prediction rule; High-resolution computed tomography; Soluble interleukin-2 receptor; PET; Sarcoidosis