OBJECTIVES: To compare the effectiveness of four falls risk assessment tools (STRATIFY, Downton, Tullamore, and Tinetti) by using them simultaneously in the same environment. DESIGN: Prospective, open, observational study. SETTING: Two acute medical wards admitting predominantly older patients. PARTICIPANTS: One hundred thirty-five patients, 86 female, mean age+/-standard deviation 83.8+/-8.01 (range 56-100). MEASUREMENTS: 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. CONCLUSION: 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.
OBJECTIVES: To compare the effectiveness of four falls risk assessment tools (STRATIFY, Downton, Tullamore, and Tinetti) by using them simultaneously in the same environment. DESIGN: Prospective, open, observational study. SETTING: Two acute medical wards admitting predominantly older patients. PARTICIPANTS: One hundred thirty-five patients, 86 female, mean age+/-standard deviation 83.8+/-8.01 (range 56-100). MEASUREMENTS: 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. CONCLUSION: 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.
Authors: Christopher R Carpenter; Michael S Avidan; Tanya Wildes; Susan Stark; Susan A Fowler; Alexander X Lo Journal: Acad Emerg Med Date: 2014-10-07 Impact factor: 3.451
Authors: Louis H Poppler; Andrew P Groves; Gina Sacks; Anchal Bansal; Kristen M Davidge; Jenifer A Sledge; Heidi Tymkew; Yan Yan; Jessica M Hasak; Patricia Potter; Susan E Mackinnon Journal: Ann Fam Med Date: 2016-11 Impact factor: 5.166
Authors: Marta Aranda-Gallardo; Jose M Morales-Asencio; Jose C Canca-Sanchez; Silvia Barrero-Sojo; Claudia Perez-Jimenez; Angeles Morales-Fernandez; Margarita Enriquez de Luna-Rodriguez; Ana B Moya-Suarez; Ana M Mora-Banderas Journal: BMC Health Serv Res Date: 2013-04-02 Impact factor: 2.655
Authors: Michel H C Bleijlevens; Marike R C Hendriks; Jolanda C M van Haastregt; Erik van Rossum; Gertrudis I J M Kempen; Joseph P M Diederiks; Harry F J M Crebolder; Jacques Th M van Eijk Journal: BMC Public Health Date: 2008-09-24 Impact factor: 3.295