Dataset Information

Linking existing instruments to develop a continuum of care measure: accuracy comparison using function-related group classification.

ABSTRACT:

Purposes

To compare measurement accuracy of test forms with varied number of items (13, 8, and 4 items) generated from the self-care physical function item bank composed of Functional Independence Measure (FIM™) and the Minimum Data Set (MDS).

Methods

Retrospective data analysis of 2499 Veterans who completed both FIM and MDS within 6 days. We compared measurement accuracy between the converted FIM (FIMc) motor score generated from the MDS and the original FIM (FIMa) motor score (13 items) at: (a) individual-level using point differences, and (b) group-level using function-related group (FRG).

Results

The differences of mean FIMa and FIMc scores were between 0.05 and 1.07 points for all test forms. Over 81% of FIMc from MDS_13 were within 15 points of the FIMa. 81-90% of FRGs generated by the FIM short forms was identical to those generated by the FIMa for stroke, lower limb amputation, knee and hip replacement; and 59.9-90.5% by all MDS test forms. All MDS test forms had above 74% agreement with same or adjacent FMGs (ICC 0.65-0.91).

Conclusions

The accuracy is dependent on the comparison level (i.e., individual or group), length of the test and which FRG is used. Our results partially support using existing instruments-without decreasing the number of the items-to generate a continuum of care measurement.

SUBMITTER: Li CY

PROVIDER: S-EPMC8489266 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Linking existing instruments to develop a continuum of care measure: accuracy comparison using function-related group classification.

Li Chih-Ying CY Romero Sergio S Simpson Kit N KN Bonilha Heather S HS Simpson Annie N AN Hong Ickpyo I Velozo Craig A CA

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation 20170524 9

<h4>Purposes</h4>To compare measurement accuracy of test forms with varied number of items (13, 8, and 4 items) generated from the self-care physical function item bank composed of Functional Independence Measure (FIM™) and the Minimum Data Set (MDS).<h4>Methods</h4>Retrospective data analysis of 2499 Veterans who completed both FIM and MDS within 6 days. We compared measurement accuracy between the converted FIM (FIMc) motor score generated from the MDS and the original FIM (FIMa) motor score ( ...[more]

PMID: 28540495

Similar Datasets

Project description:Classification approaches have been increasingly applied to differentiate patients and normal controls using resting-state functional magnetic resonance imaging data (RS-fMRI). Although most previous classification studies have reported promising accuracy within individual datasets, achieving high levels of accuracy with multiple datasets remains challenging for two main reasons: high dimensionality, and high variability across subjects. We used two independent RS-fMRI datasets (n = 31, 46, respectively) both with eyes closed (EC) and eyes open (EO) conditions. For each dataset, we first reduced the number of features to a small number of brain regions with paired t-tests, using the amplitude of low frequency fluctuation (ALFF) as a metric. Second, we employed a new method for feature extraction, named the PAIR method, examining EC and EO as paired conditions rather than independent conditions. Specifically, for each dataset, we obtained EC minus EO (EC-EO) maps of ALFF from half of subjects (n = 15 for dataset-1, n = 23 for dataset-2) and obtained EO-EC maps from the other half (n = 16 for dataset-1, n = 23 for dataset-2). A support vector machine (SVM) method was used for classification of EC RS-fMRI mapping and EO mapping. The mean classification accuracy of the PAIR method was 91.40% for dataset-1, and 92.75% for dataset-2 in the conventional frequency band of 0.01-0.08 Hz. For cross-dataset validation, we applied the classifier from dataset-1 directly to dataset-2, and vice versa. The mean accuracy of cross-dataset validation was 94.93% for dataset-1 to dataset-2 and 90.32% for dataset-2 to dataset-1 in the 0.01-0.08 Hz range. For the UNPAIR method, classification accuracy was substantially lower (mean 69.89% for dataset-1 and 82.97% for dataset-2), and was much lower for cross-dataset validation (64.69% for dataset-1 to dataset-2 and 64.98% for dataset-2 to dataset-1) in the 0.01-0.08 Hz range. In conclusion, for within-group design studies (e.g., paired conditions or follow-up studies), we recommend the PAIR method for feature extraction. In addition, dimensionality reduction with strong prior knowledge of specific brain regions should also be considered for feature selection in neuroimaging studies.

Project description:Despite the multitude of health challenges faced by adults living with HIV, we know of no HIV-specific instrument developed for the purpose of describing the health-related consequences of HIV, a concept known as disability. In a previous phase of research, adults living with HIV conceptualized disability as symptoms/impairments, difficulties carrying out day-to-day activities, challenges to social inclusion, and uncertainty that may fluctuate on a daily basis and over the course of living with HIV. In this paper, we describe the extent to which existing HIV-specific health-status instruments capture the experience of disability for adults living with HIV.We searched databases from 1980 to 2006 for English language, HIV-specific, self-reported questionnaires consisting of at least two items that were tested for reliability and validity. We then conducted a content analysis to assess how well existing questionnaires describe disability as defined by the Episodic Disability Framework, a framework that conceptualizes this experience from the perspective of adults living with HIV. We matched items of the instruments with categories of the framework to evaluate the extent to which the instruments capture major dimensions of disability in the framework.We reviewed 4274 abstracts, of which 30 instruments met the inclusion criteria and were retrieved. Of the four major dimensions of disability, symptoms/impairments were included in all 30 instruments, difficulties with day-to-day activities in 16, challenges to social inclusion in 16, and uncertainty in 9. Seven instruments contained at least 1 item from all 4 dimensions of disability (breadth) however, the comprehensiveness with which the dimensions were represented (depth) varied among the instruments.In general, symptoms/impairments and difficulties carrying out day-to-day activities were the disability dimensions characterized in greatest depth while uncertainty and challenges to social inclusion were less well represented. Although none of the instruments described the full breadth and depth of disability as conceptualized by the Episodic Disability Framework, they provide a foundation from which to build a measure of disability for adults living with HIV.

Dataset Information

Linking existing instruments to develop a continuum of care measure: accuracy comparison using function-related group classification.

Purposes

Methods

Results

Conclusions

Publications

Linking existing instruments to develop a continuum of care measure: accuracy comparison using function-related group classification.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets