A comparative study of the use of four fall risk assessment tools on acute medical wards.
OBJECTIVE
To compare the effectiveness of four falls risk assessment tools (STRATIFY, Downton, Tullamore, and Tinetti) by using them simultaneously in the same environment.


METHODS
Prospective, open, observational study.


METHODS
Two acute medical wards admitting predominantly older patients.


METHODS
One hundred thirty-five patients, 86 female, mean age+/-standard deviation 83.8+/-8.01 (range 56-100).


METHODS
A single clinician prospectively completed the four falls risk assessment tools. The extent of completion and time to complete each tool was recorded. Patients were followed until discharge, noting the occurrence of falls. The sensitivity, specificity, negative predictive accuracy, positive predictive accuracy, and total predictive accuracy were calculated.


RESULTS
The number of patients that the STRATIFY correctly identified (n=90) was significantly higher than the Downton (n=46; P<.001), Tullamore (n=66; P=.005), or Tinetti (n=52; P<.001) tools, but the STRATIFY had the poorest sensitivity (68.2%). The STRATIFY was also the only tool that could be fully completed in all patients (n=135), compared with the Downton (n=130; P=.06), Tullamore (n=130; P=.06), and Tinetti (n=17; P<.001). The time required to complete the STRATIFY tool (average 3.85 minutes) was significantly less than for the Downton (6.34 minutes; P<.001), Tinetti (7.4 minutes; P<.001), and Tullamore (6.25 minutes; P<.001). The Kaplan-Meier test showed that the STRATIFY (log rank P=.001) and Tullamore tools (log rank P<.001) were effective at predicting falls over the first week of admission. The Downton (log rank P=.46) and Tinetti tools (log rank P=.41) did not demonstrate this characteristic.


CONCLUSIONS
Significant differences were identified in the performance and complexity between the four risk assessment tools studied. The STRATIFY tool was the shortest and easiest to complete and had the highest predictive value but the lowest sensitivity.
Vassallo
Michael
M
Kings Mill Hospital, Sutton in Ashfield, United Kingdom. michael.vassallo@rbch-tr.swest.nhs.uk
Stockdale
Rachel
R
Sharma
Jagdish C
JC
Briggs
Roger
R
Allen
Stephen
S
D000058:Accidental Falls / Q000517:prevention & control*; D000208:Acute Disease / Q000628:therapy*; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D010362:Patients' Rooms / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D011446:Prospective Studies; D018570:Risk Assessment / Q000379:methods; D006113:United Kingdom