Description

Gedde-Dahl et al developed a simple score based on findings on admission to identify patients with severe meningococcal disease. This can help identify patients who may benefit from more aggressive therapy or the use of experimental modalities. The authors are from several hospitals in Norway.


 

NOTE: The authors studied 14 other severity models covering 23 variables.

 

Spectrum of meningococcal disease: meningitis, septicemia, and mixed meningitis-sepsis, with both mild and severe infections.

 

Parameters on admission or presentation:

(1) blood pressure

(2) cyanosis

(3) ecchymosis

(4) diarrhea

(5) temperature of extremities

(6) nuchal or back rigidity

(7) temperature

 

Parameter

Finding

Points

blood pressure

systolic blood pressure or adjusted blood pressure >= 100 mm Hg

0

 

systolic blood pressure and adjusted blood pressure < 100 mm Hg

1

cyanosis

absent

0

 

present

1

ecchymosis

absent

0

 

present

1

diarrhea

absent

0

 

present

1

extremities

normal or warm

0

 

cold

1

nuchal or back rigidity

present

0

 

absent

1

temperature

< 40°C

0

 

>= 40°C

1

 

The adjusted blood pressure was derived from published reports in the literature and was intended to standardize the systolic blood pressures to compensate for age-related change.

 

Age of the Patient

Adjusted Blood Pressure

0 to 13 years of age

= (systolic blood pressure) + (1.75 * (14 - (age)))

14 to 19 years of age

= systolic blood pressure in mm Hg

>= 20 years of age

= (systolic blood pressure) – (0.27 * ((age) – 19))

 

bedside score =

= SUM(points for all 7 parameters)

 

MOC score =

= (bedside score) / 7 * 100

 

where:

• If fewer than 7 parameters were reported, then the denominator was adjusted to be the same as the number of parameters reported.

 

Interpretation:

• minimum score: 0

• maximum bedside score: 7; maximum MOC score 100

• A cutoff for the MOC score > 54 (bedside score >= 4) was used to identify severe disease.

 

Performance:

• A score > 54 was 100% sensitive and 95% specific for identifying severe disease. 95.6% of patients were correctly classified.

 


To read more or access our algorithms and calculators, please log in or register.