Saskia F van Vugt1, Berna Dl Broekhuizen2, Nicolaas Pa Zuithoff2, Gerrit A van Essen2, Mark H Ebell3, Samuel Coenen4, Margareta Ieven5, Christine Lammens5, Herman Goossens5, Chris C Butler6, Kerenza Hood7, Paul Little8, Theo Jm Verheij2. 1. University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands, s.f.vanvugt@umcutrecht.nl. 2. University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands. 3. Department of Epidemiology and Biostatistics, University of Georgia, College of Public Health, Athens, GA, USA. 4. Centre for General Practice, University of Antwerp and Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute (VAXINFECTIO), Antwerp, Belgium. 5. Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute (VAXINFECTIO), Antwerp, Belgium. 6. Institute of Primary Care and Public Health, Cardiff University, School of Medicine, and. 7. Institute of Translation, Innovation, Methodology and Engagement, Cardiff, UK, and. 8. University of Southampton, FMedSci FRCGP Primary Care and Population Sciences Unit, Faculty of Medicine, Southampton, UK.
Abstract
BACKGROUND: Valid clinical predictors of influenza in patients presenting with lower respiratory tract infection (LRTI) symptoms would provide adequate patient information and reassurance. AIM: Assessing the validity of an existing diagnostic model (Flu Score) to detect influenza in LRTI patients. DESIGN AND SETTING: A European diagnostic study recruited 1801 adult primary care patients with LRTI-like symptoms existing ≤7 days between October and April 2007-2010. METHOD: History and physical examination findings were recorded and nasopharyngeal swabs taken. Polymerase chain reaction (PCR) for influenza A/B was performed as reference test. Diagnostic accuracy of the Flu Score (1× onset <48 hours + 2× myalgia + 1× chills or sweats + 2× fever and cough) was expressed as area under the curve (AUC), calibration slopes and likelihood ratios (LRs). RESULTS: A total of 273 patients (15%) had influenza on PCR. The AUC of the Flu Score during winter months was 0.66 [95% CI (95% confidence internal) 0.63-0.70]. During peak influenza season, both influenza prevalence (24%) and AUC were higher [0.71 (95% CI 0.66-0.76], but calibration remained poor. The Flu Score assigned 64% of the patients as 'low-risk' (10% had influenza, LR - 0.6). About 12% were classified as 'high risk' of whom 32% had influenza (LR + 2.7). During peak influenza season, 60% and 14% of patients were classified as low and high risk, respectively, with influenza prevalences being 14% (LR - 0.5) and 50% (LR + 3.2). CONCLUSION: The Flu-Score attributes a small subgroup of patients with a high influenza risk (prevalence 32%). However, clinical usefulness is limited because this group is small and the association between predicted and observed risks is poor. Considerable diagnostic imprecision remains when it comes to differentiating those with influenza on clinical grounds from the many other causes of LRTI in primary care. New point of care tests are required that accurately, rapidly and cost effectively detect influenza in patients with respiratory tract symptoms in primary care.
BACKGROUND: Valid clinical predictors of influenza in patients presenting with lower respiratory tract infection (LRTI) symptoms would provide adequate patient information and reassurance. AIM: Assessing the validity of an existing diagnostic model (Flu Score) to detect influenza in LRTI patients. DESIGN AND SETTING: A European diagnostic study recruited 1801 adult primary care patients with LRTI-like symptoms existing ≤7 days between October and April 2007-2010. METHOD: History and physical examination findings were recorded and nasopharyngeal swabs taken. Polymerase chain reaction (PCR) for influenza A/B was performed as reference test. Diagnostic accuracy of the Flu Score (1× onset <48 hours + 2× myalgia + 1× chills or sweats + 2× fever and cough) was expressed as area under the curve (AUC), calibration slopes and likelihood ratios (LRs). RESULTS: A total of 273 patients (15%) had influenza on PCR. The AUC of the Flu Score during winter months was 0.66 [95% CI (95% confidence internal) 0.63-0.70]. During peak influenza season, both influenza prevalence (24%) and AUC were higher [0.71 (95% CI 0.66-0.76], but calibration remained poor. The Flu Score assigned 64% of the patients as 'low-risk' (10% had influenza, LR - 0.6). About 12% were classified as 'high risk' of whom 32% had influenza (LR + 2.7). During peak influenza season, 60% and 14% of patients were classified as low and high risk, respectively, with influenza prevalences being 14% (LR - 0.5) and 50% (LR + 3.2). CONCLUSION: The Flu-Score attributes a small subgroup of patients with a high influenza risk (prevalence 32%). However, clinical usefulness is limited because this group is small and the association between predicted and observed risks is poor. Considerable diagnostic imprecision remains when it comes to differentiating those with influenza on clinical grounds from the many other causes of LRTI in primary care. New point of care tests are required that accurately, rapidly and cost effectively detect influenza in patients with respiratory tract symptoms in primary care.
Authors: Alison Callahan; Ethan Steinberg; Jason A Fries; Saurabh Gombar; Birju Patel; Conor K Corbin; Nigam H Shah Journal: NPJ Digit Med Date: 2020-07-13
Authors: Michael Moore; Beth Stuart; Mark Lown; Ann Van den Bruel; Sue Smith; Kyle Knox; Matthew J Thompson; Paul Little Journal: Ann Fam Med Date: 2019-05 Impact factor: 5.166
Authors: Alison Callahan; Ethan Steinberg; Jason A Fries; Saurabh Gombar; Birju Patel; Conor K Corbin; Nigam H Shah Journal: NPJ Digit Med Date: 2020-07-13