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Methods Inf Med 1976; 15(02): 87-90
DOI: 10.1055/s-0038-1635725
Original Article
Schattauer GmbH

Development of a Decision Guide — Optimal Discriminators for Meningitis as Determined by Statistical Analysis

ENTWICKLUNG VON ENTSCHEIDUNGSKRITERIEN — DURCH STATISTISCHE ANALYSE ERMITTELTE OPTIMALE TRENNKRITERIEN FÜR MENINGITIS
S. Levi
1   Departments of Biometry and Pediatrics, Medical University of South Carolina, Charleston, S.C., USA
,
J. Rebecca Grant
1   Departments of Biometry and Pediatrics, Medical University of South Carolina, Charleston, S.C., USA
,
M. C. Westphal
1   Departments of Biometry and Pediatrics, Medical University of South Carolina, Charleston, S.C., USA
,
Dan Lurie
1   Departments of Biometry and Pediatrics, Medical University of South Carolina, Charleston, S.C., USA
› Author Affiliations
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Publication History

Publication Date:
19 February 2018 (online)

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Recently physician extenders (pediatric nurse practitioners, corpsmen) have been assuming increased clinical responsibilities in primary health care delivery. To assure that certain medical standards are being met, decision guides (clinical algorithms) offer one acceptable mechanism. In developing a decision guide for meningitis in children, 193 cases were reviewed and statistically analyzed to determine optimal clinical discriminators for this disease. A statistical technique was used to assign numerical weights to various signs and symptoms so that the sum of the weights for present symptoms produces a discriminant equation for the diagnosis of meningitis. Optimal clinical discriminators as determined through the statistical techniques reveal a close association with presently known signs and symptoms indicative of meningitis in children. The optimal clinical discriminators were : nuchal rigidity, bulging fontanel, altered sensorium, seizure, and fever plus Kernig/Brudzinski sign. It is reasoned that this statistical technique has applicability for developing optimal clinical discriminators for diseases and that this technique will also lead to the development of more reliable decision guides.

Seit einiger Zeit übernehmen die Arzthelfer (pädiatrische Krankenschwestern, Sanitäter) immer mehr klinische Pflichten in der Patientenbetreuung. Um sicherzugehen, daß bestimmte Anforderungsstandards erfüllt werden, hat man Verfahreinsanweisungen (klinische Algorithmen) eingeführt. Als Pilot-Studie für die Entwicklung einer solchem Anleitung für die Meningitis bei Kindern wurden 193 Fälle untersucht und statistisch analysiert, um optimale klinische Diskriminanzkriterien für diese Krankheit festzulegen. Mittels eines statistischen Verfahrens wurden den verschiedenen Zeichen und Symptomen numerische Gewichte so zugeordnet, daß die Summe dieser Werte der vorhandenen Symptome eine diskriminierende Gleichung für die Diagnose »Meningitis« darstellt. Die durch diese statistische Technik bestimmten optimalen klinischen Kriterien zeigen enge Beziehungen zu den bekannten Zeichen und Symptomen der Meningitis bei Kindern. Die optimalen klinischen Trennkriterien waren: Nackensteifigkeit, hervortretende Fontanelle, Benommenheit, Krämpfe und Fieber sowie das Zeichen von Kernig und Brudzinski. Die verwendete statistische Technik sollte sich generell für die Entwicklung optimaler klinischer Kriterien anwenden lassen und auch zur Bildung verläßlicher diagnostischer Anleitungen führen.

Key-Words

Decision Guides - Statistical Discriminators - Meningitis

Schlüssel-Wörter

Entscheidungshilfen - statistische Trennkriterien - Meningitis
 

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