Hein Janssens (1), Joshua Steinberg (2), Hans Postema (3), Matthijs Janssen (4)
Affiliation(s):
1 Primary Health Care Centre Lobede, Lobith-Tolkamer (NL),
2. Radboud University Medical Centre, Nijmegen (NL),
3. UHS Wilson Family Practice Residency Binghamton, NY (USA),
4. Family Physicians Praxis Het Doktershuis, Ridderkerk (NL)
Background: The optimal, gold standard for the diagnosis of gout requires polarized microscope visualization of monosodium urate (MSU) crystals in synovial fluid aspirated from an inflamed painful joint. However, about 90% of patients with gout are diagnosed by physicians who hardly have the tools or experience to make an optimal diagnosis. This implies the vast majority of cases being diagnosed clinically based upon patient characteristics and clinical symptoms. A clinical diagnosis carries the risk being inaccurate in about 30% of all cases leading to incorrect, unnecessary or delayed therapy. In patients presenting with mono-arthritis the 2010 Gout Diagnostic Decision Rule improves clinical diagnostic performance according to internal and external validations studies in the primary and secondary care setting using the gold standard as reference test (1-4). The disclosure of a valid decision rule alone does not improve diagnostic accuracy in daily practice, e.g., if physicians do not have it at their fingertips during patient care.
Objective: To present an electronic application (App) of the 2010 Gout Diagnostic Decision Rule for physicians’ use in daily medical practice (e.g. smartphone).
Methods: The algorithm behind the 2010 Gout Diagnostic Decision Rule (see addendum) was digitalized for electronic device installation (iOS and Android operating system) aiming to weight seven necessary patient characteristics (after checking by the user) for contribution to the diagnosis, to calculate a final gout diagnosis score, and to guide the user how to manage the patient depending on the score. In addition, educational background information (about gout, gout diagnosis, and the 2010 Gout Diagnosis Decision Rule itself) and direct internet links to the referenced scientific articles were provided.
Results: A validated gout diagnosis App for worldwide use by physicians in medical practice, when microscopic synovial fluid analysis is not available, that is freely available the Apple iTunes App Store for iPhone and iPad users, and in the Google Play Store for users of Android devices. Search terms are ‘gout diagnosis’, ‘gout diagnosis calculator’, or ‘gout calculator’. The App is multilingual, including English, Dutch, German, French, and Spanish.
Conclusion: Although the identification by polarized microscopy of MSU crystals in synovial fluid aspirated from an inflamed painful joint is the gold standard for gout diagnosing, facilities for this are very often not available, e.g. in the primary care setting, where 90% of all patients with gout are diagnosed and treated. Accurate clinical diagnosing is relevant in that case, because it prevents incorrect, unnecessary or delayed therapy. A diagnostic decision rule was developed and validated for that. We present digital versions of this rule for smartphone and tablet devices which are easy to use, freely available and widely distributed to put it at the fingertips of physicians in their busy daily medical practice, and to optimize gout care for the many patients all over the world.
Addendum: Points: male 2; similar previous attack 2 pts; onset<1day 0.5 pt; joint redness 1 pt; MTP1 2.5 pts; hypertension or cardiovascular disease 1.5 pts; SUA >0.35mmol/L 3.5 pts. Total score ≤ 4: gout very unlikely: consider other arthritis diagnoses; > 4 AND < 8: diagnosis is indeterminate; consider synovial fluid analysis; ≥ 8: make diagnosis gout and start specific treatment.