title: Low-impact falls: demands on a system of trauma management, prediction of outcome, and influence of comorbidities.
abstract: BACKGROUND
  Falls from a low height are an extremely common source of injury, the severity of which is often underestimated. As a result, low fall patients are usually not transferred to Level I trauma centers. There are surprisingly few systematic data relating to the demands made on systems of trauma care by patients with low falls. This study addresses this issue using information from a comprehensive national trauma database. The performance of TRISS methodology, and the factors associated with prolonged hospital stay, in low fall patients is also examined.
  
  
  METHODS
  The study included 31,419 patients. Patients with low falls (< 2 m) were compared with those suffering high falls (> or = 2 m), motor vehicle crashes, assault, sports injuries, and a group with unclassified injuries. Probability of survival was estimated using TRISS, and its performance in different types of injury was assessed using measures of discrimination and calibration. The influence of coexistent medical conditions on mortality and length of stay was investigated using logistic regression.
  
  
  RESULTS
  Low falls accounted for 45.5% of all admissions, and 43.9% of the total bed days. The low fall group was older (mean age, 61.6 years), and predominantly female (62.5%) in contrast to the other groups (both p < 0.001). There were fewer severely injured patients than in all of the other groups except sports injuries. The area under the receiver operating characteristic curve for TRISS applied to low falls (0.874) was less than that for high falls (0.969), motor vehicle crashes (0.973), assaults (0.960), sports (1.000), and unclassified injuries (0.965). Also, the calibration of the TRISS model was poor for patients with low falls. A logistic regression model derived from a training set of 5,000 patients gave slightly improved discrimination and markedly improved calibration when compared with TRISS. Although there was a strong relationship between the number of coexistent medical conditions and the risk of dying after a low fall, including data on comorbidities in a predictive model did not improve performance. Prolonged stay (defined as greater than the 90th centile, 23 days) was more likely in women (p < 0.005), or with advanced age (p < 0.001) or low initial calculated probability of survival (p < 0.001). Cardiovascular and central nervous system diseases and diabetes were associated with longer hospital stay (all p < 0.001). A logistic regression model using TRISS variables and comorbidity data gave poor prediction of prolonged stay. There was considerable variation in the length of stay between institutions.
  
  
  CONCLUSIONS
  Patients with low falls make considerable demands on a system of trauma care. TRISS methodology performs less well in this group than with other types of injury. Chronic medical conditions are associated with increased mortality and more prolonged stay after a low fall. Between-institutional variation in length of stay was considerable and this, along with the poor performance of predictive models derived from routinely collected clinical data, make it unlikely that length of stay could be used as a measure of institutional performance. More robust audit measures for patients with low falls are required.
authors.affiliation: Department of Medicine, University of Sunderland, City Hospitals Sunderland, United Kingdom. lee.kennedy1@sunderland.ac.uk.
authors.forename: R L
authors.initials: RL
authors.lastname: Kennedy
authors.forename: P T
authors.initials: PT
authors.lastname: Grant
authors.forename: D
authors.initials: D
authors.lastname: Blackwell
mesh_terms: D000058:Accidental Falls / Q000401:mortality / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002908:Chronic Disease; D015897:Comorbidity; D005260:Female; D006801:Humans; D007902:Length of Stay; D016015:Logistic Models; D008297:Male; D008485:Medical Audit / Q000379:methods*; D008875:Middle Aged; D012372:ROC Curve; D012606:Scotland / Q000453:epidemiology; D016019:Survival Analysis; D014193:Trauma Centers / Q000706:statistics & numerical data*; D015599:Trauma Severity Indices; D014947:Wounds and Injuries / Q000209:etiology* / Q000401:mortality / Q000628:therapy