• Daniela Grimaldi, Francesco Nonino, Sabina Cevoli, Alberto Vandelli, Roberto D'Amico, and Pietro Cortelli.
• Alma Mater Studiorum-Università di Bologna, Dipartimento di Scienze Neurologiche, Via Ugo Foscolo, 7, 40123 Bologna, Italy. daniela.grimaldi@unibo.it
• J. Neurol. 2009 Jan 1;256(1):51-7.

ObjectiveTo determine the diagnostic accuracy of an algorithm structured in four clinical scenarios to discriminate benign primary headaches from serious secondary non-traumatic headaches (NTH) in the emergency department (ED).BackgroundNTH is usually a benign symptom but can occasionally result in serious outcome making the disposition of patients with NTH difficult in the ED.Design And MethodsConsecutive adults patients referring to 8 EDs of the Emilia-Romagna region in Italy for NTH as the chief complaint were recruited in the study for a 30-day period. ED physicians attributed to each patient one of the four clinical scenarios (1, 2 and 3 identifying serious secondary headaches and scenario 4 identifying benign primary headaches) or an undetermined scenario when none of the four scenarios applied. Reference standards of the study were the head CT scan and a follow-up telephone interview after three months by the ED admission.ResultsThe test was administered to 256 out of 302 (85%) eligible patients. The analysis (scenario 1,2,3 vs scenario 4) was based on 180 patients who completed the follow-up showing a sensitivity of 100% (95% confidence interval, 81% to 100%) and a specificity of 64% (56% to 71%). The likelihood ratio for a positive test was 2.67 (2.15 to 3.31) and the likelihood ratio for a negative test was 0.04 (0.003 to 0.64).ConclusionsAn algorithm based on four clinical scenarios can be administered to the majority of patients presenting to the ED with the chief complaint of NTH. The algorithm showed a good accuracy in identifying patients with non-life threatening causes of headache and could be used as a risk stratification tool to improve clinical decision- making. Further studies are required to validate this diagnostic algorithm.

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