Optimizing the clinical functioning information tool (ClinFIT) in routine clinical practice: development of functional staging cutoff scores for rehabilitation provision and intensity
DOI:
https://doi.org/10.2340/jrm.v57.44170Keywords:
functioning, outcome measures, rehabilitation, ClinFITAbstract
Objective: To develop data-driven functional staging cutoff scores for the Clinical Functioning Information Tool (ClinFIT) total raw score to stratify patients according to rehabilitation provision and intensity.
Methods: This observational study included adult inpatients (n = 270) admitted to a tertiary rehabilitation unit. ClinFIT total scores at admission were analysed alongside the Therapy Disciplines domain of the Rehabilitation Complexity Scale to represent rehabilitation intensity. Receiver Operating Characteristic analysis was used to identify optimal cutoff points distinguishing between levels of rehabilitation intensity. Subgroup analyses were conducted by age, sex, and diagnosis.
Results: Participants were predominantly male (54.1%), with a mean age of 62.9 ± 14.3 years. ClinFIT total raw scores improved significantly across all health conditions at discharge compared with admission (p < 0.001), reflecting substantial functional recovery during inpatient rehabilitation. Two ClinFIT total score cutoffs were identified: 135 (light vs moderate) and 192 (moderate vs high intensity), with acceptable discriminatory performance (AUCs: 0.720, 0.748, respectively). Subgroup analyses supported the robustness of this 3-level staging system across demographic and diagnostic groups.
Conclusion: This study provides evidence-based cutoff scores for ClinFIT, supporting its clinical use for stratifying rehabilitation provision and intensity. These findings may enhance clinical decision-making, optimize resource allocation, and promote wider adoption of the ClinFIT. Further validation in external and diverse populations is warranted.
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Copyright (c) 2025 Bhasker Amatya, Adrian Martinez de la Torre , Masahiko Mukaino , Krystal Song, Melissa Selb, Gerold Stucki , Khan Fary
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