Assessing functional motor changes and their relationship to discharge needs in the pediatric intensive care unit (PICU) population is difficult given challenges quantifying small functional gains with current tools. Therefore, we compared the Physical Abilities and Mobility Scale (PAMS) to the Functional Status Scale (FSS) in PICU patients to assess correlation and differences and association with discharge needs. This study was a retrospective chart review of all patients (2-18 years old) admitted to the PICU and cardiac PICU for over 9 months who received early mobility services, including PAMS and FSS scoring. Correlation between scales, relationship of scores to disposition, and logistic regression model of changes in PAMS in relation to disposition were determined. Data were obtained for 122 patients. PAMS and FSS scores strongly negatively correlated (Spearman's ρ = - 0.85), but with a nonlinear relationship, as the PAMS more readily differentiated among patients with higher functional status. The median FSS at discharge was 12.5 for those recommended an inpatient rehabilitation facility (IRF) ( n = 24), versus 9 for those recommended discharge home ( n = 83, Δ 3.5, 95% confidence interval [CI]: 1-6, around one-tenth of FSS scale). The corresponding median PAMS were 42 and 66 (Δ 24, 95% CI: 10-30, one-fourth of PAMS scale). Although not statistically significant, a logistic regression model was consistent with patients who showed modest change in PAMS across hospitalization but persistent deficits (PAMS < 60) were more likely to be recommended an IRF. The PAMS correlates to the FSS, but appears more sensitive to small functional changes, especially in higher functioning patients. It may be useful in prognosticating discharge needs.
Keyphrases
- end stage renal disease
- intensive care unit
- ejection fraction
- newly diagnosed
- chronic kidney disease
- prognostic factors
- mental health
- peritoneal dialysis
- primary care
- traumatic brain injury
- physical activity
- heart failure
- patient reported outcomes
- dendritic cells
- immune response
- palliative care
- big data
- data analysis