title: Statistical methods to predict morbidity and mortality: self assessment techniques for burn units.
abstract: The medical records of 806 adult and paediatric burn patients were retrospectively reviewed. Patient data was summarized, coded and entered in a computer for subsequent analysis. This report describes the use of multiple regression analysis to produce equations useful for the prediction of the morbidity parameters: length of hospital stay, number of transfusions required and number of operative procedures. The multiple regression equations developed are useful as prediction tools, in patient medical audit and in assessing improvements in burn care. The classical technique of probit analysis for predicting the probability of mortality was used to develop the LA50's of burn injury. These LA50's were used as the basis for comparison of survival statistics between burned patients in this series and those registered in the National Burn Information Exchange (Feller, 1979). There is an apparent significantly improved survival in the children, young adults through 34 yrs and the older adults (60-74 yrs) in the current series of patients. This observation would support the adoption of the protocol of burn care used in treating these patients. The newer technique of discriminant analysis is also described. Discriminant analysis is a multifactorial method for discriminating between dichotomous outcomes (survivors and non-survivors). The technique of discriminant scoring proved to be 95.8 per cent accurate in predicting burn survival. Use of a burn severity scoring technique will also assist in recognition of the high risk patient.
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001803:Blood Transfusion; D002053:Burn Units; D002056:Burns / Q000401:mortality* / Q000473:pathology / Q000601:surgery / Q000628:therapy; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D007902:Length of Stay; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D012189:Retrospective Studies; D013223:Statistics as Topic