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      Activity Measure for Post-Acute Care “6-Clicks” Basic Mobility Scores Predict Discharge Destination After Acute Care Hospitalization in Select Patient Groups: A Retrospective, Observational Study

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          HIGHLIGHTS

          • A standardized Basic Mobility score of 42.9 predicts home vs institution discharge.

          • Orthopedic diagnoses may have a cutoff score of 41.5 to predict home discharge.

          • Cutoff scores vary by diagnostic group and discharge destination.

          • Cutoff scores vary by time of assessment relative to admission for some diagnoses.

          Abstract

          Objectives

          To establish cutoff scores for the Activity Measure for Post-Acute Care “6-Clicks” standardized Basic Mobility scores (sBMSs) for predicting discharge destination after acute care hospitalization for diagnostic subgroups within an acute care population and to evaluate the need for a second score to improve predictive ability.

          Design

          Retrospective, observational design.

          Setting

          Major medical center in metropolitan area.

          Participants

          Electronic medical records of 1696 adult patients (>18 years) admitted to acute care from January to October 2018. Records were stratified by orthopedic, cardiac, pulmonary, stroke, and other neurological diagnoses (N=1696). Interventions: None

          Main Outcome Measure

          Physical therapists scored patients’ sBMSs after referral for physical therapy and prior to discharge. Receiver operating characteristic curves delineated sBMS cutoff scores distinguishing various pairings of home, home with services, inpatient rehabilitation, or skilled nursing facility discharges. First and second sBMSs were compared with percentage change of the area under the curve and inferential statistics.

          Results

          Home vs institution cutoff score was 42.88 for combined sample, pulmonary and neurological cases. The cutoff score for orthopedic diagnoses score was 41.46. Cardiac and stroke model quality invalidated cutoff scores. Home without services vs skilled nursing discharges and home with services vs skilled nursing discharges were predicted with varying cutoff scores per diagnosis. sBMS cutoff scores collected closer to discharge were either the same or higher than first cutoffs, with varying effects on predictive ability .

          Conclusions

          sBMSs can help decide institution vs home discharge and finer distinctions among discharge settings for some diagnostic groups. A single sBMS may provide sufficient assistance with discharge destination decisions but timing of scoring and diagnostic group may influence cutoff score selection.

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          Most cited references30

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          Receiver operating characteristic curve in diagnostic test assessment.

          The performance of a diagnostic test in the case of a binary predictor can be evaluated using the measures of sensitivity and specificity. However, in many instances, we encounter predictors that are measured on a continuous or ordinal scale. In such cases, it is desirable to assess performance of a diagnostic test over the range of possible cutpoints for the predictor variable. This is achieved by a receiver operating characteristic (ROC) curve that includes all the possible decision thresholds from a diagnostic test result. In this brief report, we discuss the salient features of the ROC curve, as well as discuss and interpret the area under the ROC curve, and its utility in comparing two different tests or predictor variables of interest.
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            Improved comorbidity adjustment for predicting mortality in Medicare populations.

            To define and improve the performance of existing comorbidity scores in predicting mortality in Medicare enrollees. Study participants were two Medicare populations who had complete drug coverage either through Medicaid or a statewide pharmacy assistance program: New Jersey Medicare enrollees (NNJ, = 235,881) and Pennsylvania Medicare enrollees (NPA = 230,913). Frequently used comorbidity scores were computed for all subjects during the baseline year (January 1, 1994, to December 31, 1994, and one year later in Pennsylvania). The study outcome was one-year mortality during the following year. Performance of scores was measured with the c-statistic derived from multivariate logistic regression models. Empirical weights were derived in the New Jersey population and the performance of scores with new weights was validated in the Pennsylvania population. A score based on ICD-9-diagnoses (Romano) performed 60 percent better than one based on patterns of medication use (Chronic Disease Score, or CDS-1) (c = 0.771 vs. c = 0.703). The performance of the Romano score was further improved slightly by inclusion of the number of different prescription drugs used during the past year. Modeling the 17 conditions included in the Romano score as separate binary indicators increased its performance by 8 percent (c = 0.781). We derived elderly-specific weights for these scores in the New Jersey sample, including negative weights for the use of some drugs, for example, lipid lowering drugs. Applying these weights, the performance of Romano and CDS-1 scores improved in an independent validation sample of Pennsylvania Medicare enrollees by 8.3 percent and 43 percent compared to the scores with the original weights. When we added an indicator of nursing home residency, age, and gender, the Romano score reached a performance of c = 0.80. We conclude that in epidemiologic studies of the elderly, a modified diagnosis-based score using empirically derived weights provides improved adjustment for comorbidity and enhances the validity of findings.
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              Applied Logistic Regression

              From the reviews of the First Edition.<br> "An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references."-Choice<br> "Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent."-Contemporary Sociology<br> "An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical."-The Statistician<br> In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet.
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                Author and article information

                Contributors
                Journal
                Arch Rehabil Res Clin Transl
                Arch Rehabil Res Clin Transl
                Archives of Rehabilitation Research and Clinical Translation
                Elsevier
                2590-1095
                16 July 2022
                September 2022
                16 July 2022
                : 4
                : 3
                : 100204
                Affiliations
                [a ]Post Acute Services, Burke Rehabilitation Hospital, White Plains, NY
                [b ]Department of Physical Therapy, Hunter College, The City University of New York, New York, NY
                Author notes
                [* ]Corresponding author Suzanne Babyar, PT, PhD, Department of Physical Therapy, Hunter College, The City University of New York, 425 East 25th Street, New York, NY 10010. sbabyar@ 123456hunter.cuny.edu
                Article
                S2590-1095(22)00028-3 100204
                10.1016/j.arrct.2022.100204
                9482026
                6900d059-73af-4b6b-bdeb-339f92e57a6e
                © 2022 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Categories
                Original Research

                outcome assessment, health care 2,continuity of patient care,hospitalization,patient discharge,patient transfer,rehabilitation,am-pac, activity measure for post-acute care,auc, area under curve,irf, inpatient rehabilitation facility,roc, receiver operating characteristic,sbms, am-pac “6-clicks” standardized basic mobility scores,snf, skilled nursing facility

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