A predictive model for fall risk in hospitalized adults: A case-control study.

Area of Science:

• Nursing
• Patient Safety
• Clinical Risk Management

Background:

• Understanding patient and environmental factors is crucial for managing fall risk.
• Care delivery processes also play a role in patient falls.
• Predictive factors for falls require identification for effective management.

Purpose of the Study:

• To develop and validate a predictive model for falls in hospitalized adult patients.
• To assess both intrinsic (patient-related) and extrinsic (care process-related) factors.
• To create a tool for identifying and managing fall risk in clinical and surgical settings.

Main Methods:

• A matched case-control study design was employed.
• Data were collected from 536 adult patients in a Brazilian teaching hospital (2013-2014).
• Conditional logistic regression was used for data analysis.

Main Results:

• The SAK Fall Scale (Severo-Almeida-Kuchenbecker) was developed and validated.
• The scale comprises seven variables: disorientation, frequent urination, walking limitations, lack of caregiver, postoperative status, previous falls, and recent medication administration.
• The SAK Fall Scale demonstrated acceptable predictive accuracy for falls.

Conclusions:

• The SAK Fall Scale incorporates five intrinsic and two extrinsic variables, distinguishing it from other fall prediction scales.
• This easily applicable scale can be utilized globally by nurses to improve patient safety.
• Implementing this fall risk model supports preventive interventions and advances nursing practice.