An APTA Neurology Section Sponsored Clinical Practice Guideline on a Core Set of Outcome Measures for Neurologic Physical Therapy Practice: An Update
Authors/Institutions: Kirsten Potter, PT, DPT, MS,1 Jane Sullivan PT, DHS, MS,2 Jennifer Moore PT, DHS, NCS,3-4 Linda O'Dwyer, MA, MSLIS,2 Chih-Hung Chang, PhD,2,3 Kathleen Blankshain, BA, SPT,2 Sandra Kaplan, PT, PhD5
Rockhurst University,1 Northwestern University,2 Rehabilitation Institute of Chicago,3 Sunnaas Hospital,4 Rutgers - The State University of New Jersey5
Rockhurst University,1 Northwestern University,2 Rehabilitation Institute of Chicago,3 Sunnaas Hospital,4 Rutgers - The State University of New Jersey5
Purpose: The Institute of Medicine defines Clinical Practice Guidelines (CPGs) as statements that “include recommendations intended to optimize patient care that are informed by a systematic review of evidence and an assessment of the benefits and harms of alternative care options.” Supported by the Neurology Section (NS) and funded by APTA, a taskforce is developing a CPG on a core set of outcome measures for neurologic physical therapy practice. This effort will be completed by December 2016. This presentation outlines efforts to date.
Methods: A diverse expert panel of clinicians, researchers, and consumers of neurologic physical therapy (CNPT) was invited to help inform the CPG scope and process. To identify the purpose and scope, online surveys were distributed to CNPTs and NS members. 303 NS members and 215 CNPTs responded. The majority (59% CNPTs; 65% NS members) agreed it is very important or essential to use standardized outcome measures. Both CNPTs and NS members agreed that gait and balance are two key constructs to include. Other constructs including transfers and patient-stated goals were recommended by NS members. Using the survey results and expert panel feedback, the CPG team selected OMs that assess gait, balance, transfers and patient-stated goals from the list of 242 tools reviewed by the NS-sponsored Evaluation Database to Guide Effectiveness (EDGE) groups and 64 additional OMs identified by the CPG workgroup. The CPG team first eliminated measures that could not be used across conditions as well as those that were not recommended by the EDGE groups. Measures that captured constructs outside the CPG scope of balance, gait, transfers, and patient-stated goals were eliminated, as were measures that could not be used to assess change over time or did not have published responsiveness data. Further analysis of the items in each measure was done to ensure that at least 75% of the items address the CPG constructs. Finally, the measures were assessed for clinical utility. A reference librarian conducted a literature search on the remaining measures. To date, 15,935 references have been screened for inclusion in the systematic review. Currently article reviewers are examining 81 full-text articles for methodological quality and strength of the data related to reliability, internal consistency and responsiveness using a modified version of the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN). The next steps for the CPG will be to complete data extraction; rate the methodologic quality of articles and strength of psychometrics of each OM, and select OMs for the core set. The CPG is expected to be published in late 2016.
Conclusions: Implementation of a core set of outcome measures will facilitate comparisons across patients, clinicians, facilities, and diagnoses; improve standardization of practice to avoid unwanted variability; and, ultimately, enable clinicians and researchers to better evaluate the efficacy of interventions. Each benefit is important to the development of best practice guidelines and advancement of the physical therapy profession.
Funding Source: This project is supported by a grant from the American Physical Therapy Association.
Methods: A diverse expert panel of clinicians, researchers, and consumers of neurologic physical therapy (CNPT) was invited to help inform the CPG scope and process. To identify the purpose and scope, online surveys were distributed to CNPTs and NS members. 303 NS members and 215 CNPTs responded. The majority (59% CNPTs; 65% NS members) agreed it is very important or essential to use standardized outcome measures. Both CNPTs and NS members agreed that gait and balance are two key constructs to include. Other constructs including transfers and patient-stated goals were recommended by NS members. Using the survey results and expert panel feedback, the CPG team selected OMs that assess gait, balance, transfers and patient-stated goals from the list of 242 tools reviewed by the NS-sponsored Evaluation Database to Guide Effectiveness (EDGE) groups and 64 additional OMs identified by the CPG workgroup. The CPG team first eliminated measures that could not be used across conditions as well as those that were not recommended by the EDGE groups. Measures that captured constructs outside the CPG scope of balance, gait, transfers, and patient-stated goals were eliminated, as were measures that could not be used to assess change over time or did not have published responsiveness data. Further analysis of the items in each measure was done to ensure that at least 75% of the items address the CPG constructs. Finally, the measures were assessed for clinical utility. A reference librarian conducted a literature search on the remaining measures. To date, 15,935 references have been screened for inclusion in the systematic review. Currently article reviewers are examining 81 full-text articles for methodological quality and strength of the data related to reliability, internal consistency and responsiveness using a modified version of the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN). The next steps for the CPG will be to complete data extraction; rate the methodologic quality of articles and strength of psychometrics of each OM, and select OMs for the core set. The CPG is expected to be published in late 2016.
Conclusions: Implementation of a core set of outcome measures will facilitate comparisons across patients, clinicians, facilities, and diagnoses; improve standardization of practice to avoid unwanted variability; and, ultimately, enable clinicians and researchers to better evaluate the efficacy of interventions. Each benefit is important to the development of best practice guidelines and advancement of the physical therapy profession.
Funding Source: This project is supported by a grant from the American Physical Therapy Association.
Factors Predicting Passage of the National Physical Therapy Examination in a Private Midwestern University
KELLY M. MEINERS PT, MPT, PhD, ATC
Background: Various studies have examined variables indicating success on the National Physical Therapist Examination. Studies on students with Bachelor’s level PT degrees found a correlation between preadmission GPA and performance in the program.1,2,3 Other studies show that preadmission GPA is predictor of PT program GPA4. Both first-year PT GPA5 and overall GPA in the PT program are strong predictor of NPTE success.4,6-8 Overall, the research literature evidence indicates a positive correlation between undergraduate GPA and NPTE scores, as well as between undergraduate GPA and PT program GPA
Purpose: The purpose of this study was to determine which variables predict success for first-time NPTE passage and NPTE score.
Methods: This quantitative study used a retrospective design. Two separate data analyses were conducted. Data was collected from DPT graduates 2012-2014 at a private liberal arts university with a systems based curriculum model. A hierarchical multiple regression analysis and a hierarchical logistics regression analysis were performed to determine which variables were most predictive of first-time NPTE score and first time NPTE passage. Clinical performance as measured by the CPI: Version 2006 average score, pre-admission variables of undergraduate grade point average (UGPA), quantitative and verbal reasoning Graduate Record Exam (GRE) scores and first-year program PT GPA were independent variables.
Results: With all seven independent variables entered into the equation, the hierarchical multiple regression model could predict 39% of the variance seen in NPTE scores. Analysis of individual variables, showed that first year PT program GPA was the strongest predictor, as 24% of NPTE score variance could be predicted by first year GPA (beta= .572, p=.000). The hierarchical logistic regression analysis model with inclusion of all variables was able to predict 23% to 53% of the variability in NPTE passage with a positive predictive value of 95.58.
Conclusion: Courses later in the curriculum build on the knowledge attained during the first year of the curriculum. Those students who do not attain the necessary knowledge during that first year may continue to have difficulty as coursework becomes more complex, leading to failure on the NPTE. Further research is needed to investigate specific curricular content knowledge that has implications on NPTE passage. Study limitations include lack of generalizability to other PT programs as only one professional physical therapy program was investigated in this study.
Select References
1. Balogun JA. Predictors of academic and clinical performance in a baccalaureate physical therapy program. Physical Therapy 1988;68(2):238-242.
2. Gross MT. Relative value of multiple physical therapy admissions criteria in predicting didactic, clinical, and licensure performance. Journal of Physical Therapy Education 1989;3(1):7-14.
3. Roehrig SM. Prediction of licensing examination scores in physical therapy graduates. Physical Therapy 1988;68:694-698.
4. Thieman TJ, Weddle M, Moore MA. (2003). Predicting academic, clinical, and licensure examination performance in a professional (entry-level) master’s degree program in physical therapy. Journal of Physical Therapy Education 2003;17(2):32-37.
5. Dockter M. (2001). An analysis of physical therapy preadmission factors on academic success and success on the national licensing examination. Journal of Physical Therapy Education 2001;15(1):60-64.
6. Adams CL, Glavin K, Hutchins K, Lee T, Zimmerman C. An evaluation of the internal reliability, construct validity, and predictive validity of the Physical Therapist Clinical Performance Instrument (PT CPI). Journal of Physical Therapy Education, 2008;22(2):42-50.
7. Dillon LS, Tomaka J. (2010). Predictors of the NPTE in a Hispanic-serving institution’s physical therapy education program. Journal of Physical Therapy Education 2010;24(2): 14.
8. Utzman R, Riddle D, Jewell D. Use of demographic and quantitative admissions data to predict academic difficulty among professional physical therapy students. Physical Therapy 2007;87:1164-1180
Purpose: The purpose of this study was to determine which variables predict success for first-time NPTE passage and NPTE score.
Methods: This quantitative study used a retrospective design. Two separate data analyses were conducted. Data was collected from DPT graduates 2012-2014 at a private liberal arts university with a systems based curriculum model. A hierarchical multiple regression analysis and a hierarchical logistics regression analysis were performed to determine which variables were most predictive of first-time NPTE score and first time NPTE passage. Clinical performance as measured by the CPI: Version 2006 average score, pre-admission variables of undergraduate grade point average (UGPA), quantitative and verbal reasoning Graduate Record Exam (GRE) scores and first-year program PT GPA were independent variables.
Results: With all seven independent variables entered into the equation, the hierarchical multiple regression model could predict 39% of the variance seen in NPTE scores. Analysis of individual variables, showed that first year PT program GPA was the strongest predictor, as 24% of NPTE score variance could be predicted by first year GPA (beta= .572, p=.000). The hierarchical logistic regression analysis model with inclusion of all variables was able to predict 23% to 53% of the variability in NPTE passage with a positive predictive value of 95.58.
Conclusion: Courses later in the curriculum build on the knowledge attained during the first year of the curriculum. Those students who do not attain the necessary knowledge during that first year may continue to have difficulty as coursework becomes more complex, leading to failure on the NPTE. Further research is needed to investigate specific curricular content knowledge that has implications on NPTE passage. Study limitations include lack of generalizability to other PT programs as only one professional physical therapy program was investigated in this study.
Select References
1. Balogun JA. Predictors of academic and clinical performance in a baccalaureate physical therapy program. Physical Therapy 1988;68(2):238-242.
2. Gross MT. Relative value of multiple physical therapy admissions criteria in predicting didactic, clinical, and licensure performance. Journal of Physical Therapy Education 1989;3(1):7-14.
3. Roehrig SM. Prediction of licensing examination scores in physical therapy graduates. Physical Therapy 1988;68:694-698.
4. Thieman TJ, Weddle M, Moore MA. (2003). Predicting academic, clinical, and licensure examination performance in a professional (entry-level) master’s degree program in physical therapy. Journal of Physical Therapy Education 2003;17(2):32-37.
5. Dockter M. (2001). An analysis of physical therapy preadmission factors on academic success and success on the national licensing examination. Journal of Physical Therapy Education 2001;15(1):60-64.
6. Adams CL, Glavin K, Hutchins K, Lee T, Zimmerman C. An evaluation of the internal reliability, construct validity, and predictive validity of the Physical Therapist Clinical Performance Instrument (PT CPI). Journal of Physical Therapy Education, 2008;22(2):42-50.
7. Dillon LS, Tomaka J. (2010). Predictors of the NPTE in a Hispanic-serving institution’s physical therapy education program. Journal of Physical Therapy Education 2010;24(2): 14.
8. Utzman R, Riddle D, Jewell D. Use of demographic and quantitative admissions data to predict academic difficulty among professional physical therapy students. Physical Therapy 2007;87:1164-1180