Muscular weakness assessment: use of normal isometric strength data. The National Isometric Muscle Strength (NIMS) Database Consortium.
OBJECTIVE
Assessment of muscle strength is vital to the management of patients with muscular weakness. Clinical interpretation of isometric strength data for individual patients has been limited because of the lack of a reference population for comparison. The purpose of this study was to develop regression equations to predict maximal isometric strength based on gender, age, height, and weight. Patients' absolute strength values may then be expressed as a percentage of their predicted values, facilitating the determination of presence and extent of weakness.


METHODS
Three separate neuromuscular research groups developed databases of normal maximal isometric strength values, using standardized testing procedures. The databases were combined into a single database, and multiple regression equations were formulated for strength prediction for the 20 muscle groups tested.


METHODS
Seven neuromuscular research units, each within the neurology department of a university-based teaching facility.


METHODS
A convenience sample of 493 volunteers who had no medical conditions that would have prohibited them from performing a maximal isometric strength test.


METHODS
Maximal isometric strength (kg) of ten muscle groups was measured bilaterally.


RESULTS
Regression equations and 95% prediction intervals are derived from the combined database. A case study demonstrates the use of the predictive equations in determining presence and extent of weakness.


CONCLUSIONS
Predictive strength equations facilitate assessment of muscular weakness.
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015992:Body Mass Index; D005260:Female; D006801:Humans; D007537:Isometric Contraction; D008297:Male; D008875:Middle Aged; D018908:Muscle Weakness / Q000175:diagnosis* / Q000503:physiopathology; D012016:Reference Values; D015203:Reproducibility of Results; D012494:Sampling Studies; D012815:Signal Processing, Computer-Assisted