ArticleCureus2024
Risk Categories for Discharge Planning Using AM-PAC "6-Clicks" Basic Mobility Scores in Non-Surgical Hospitalized Adults.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Machine Learning in Discharge Planning for Stroke Patients: A Review of Feature Representation and Aggregation Strategies With Suggestions for Improvement.Journal of clinical nursing · 2026Article
- Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly discharge planning is important for safe, cost-effective, and timely hospital discharges. Patients with deconditioning are at risk for prolonged lengths of stay related to discharge needs. Functional mobility outcome measures are associated with discharge disposition. The purpose of this study is to examine the clinical usefulness of risk categories based on the Activity Measure for Post-Acute Care (AM-PAC) "6-clicks" Basic Mobility (6cBM) scores on predicting discharge destination.
methodsA retrospective cohort study of 3739 adults admitted to general medical units at an urban, academic hospital between January 1, 2018 and February 29, 2020 who received at least two physical therapy visits and had an AM-PAC 6cBM recorded within 48 hours of admission and before discharge. The outcome variable was discharge destination dichotomized to post-acute care facilities (PACF); inpatient rehabilitation, skilled nursing facility, or subacute rehabilitation) or home (with or without home care services). The predictor variables were 6cBM near admission and discharge. Logistic regression was used to estimate the odds of being discharged to PACF compared to home, based on the Three-level risk categorization system: (a) low (6cBM score > 20), (b) moderate (6cBM score 15-19), or (c) high (6cBM score < 14) risk.
resultsAnalysis indicated important differences between the three risk categories in both time periods. Based on 6cBM at admission, patients in the high-risk category were nine times more likely to be discharged to PACF than those in the low-risk category. At discharge, those in the high-risk category were 29 times more likely to go to PACF than those in the low-risk category. Other characteristics differentiating patients who went to PACF were sex (males), age (older) and longer hospitalization.
conclusionsPredicting risk for discharge to a PACF using risk categories based on AM-PAC 6cBM can be useful for early discharge planning.
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