Understanding the Hospital Frailty Risk Score
The Hospital Frailty Risk Score (HFRS) is a data-driven tool designed to systematically screen for frailty in hospital patients, particularly older adults. It automatically calculates a patient's frailty risk using routinely collected administrative data, specifically diagnostic codes from the International Classification of Diseases, 10th Revision (ICD-10). The HFRS analyzes a list of 109 specific ICD-10 codes associated with frailty and assigns weighted points. The total score helps identify patients at greater risk of adverse outcomes, providing a cost-effective and scalable method for population-level health planning.
How the HFRS is Calculated and Categorized
The HFRS calculation, based on methodology from Gilbert et al., uses diagnostic information from current and prior hospital admissions, typically over a two-year period. Each of the 109 relevant ICD-10 codes has a specific weight, and these weights are summed for a total score. For practical use, the continuous score is often categorized into risk groups:
- Low Risk: A score below 5 points.
- Intermediate Risk: A score between 5 and 15 points.
- High Risk: A score above 15 points.
This categorization helps identify patients with a higher likelihood of longer hospital stays, increased mortality, and higher readmission rates.
The Clinical Importance of the HFRS
Predicting adverse outcomes is particularly beneficial in acute care settings. The HFRS promotes a frailty-attuned approach, encouraging a broader assessment of patient vulnerability. It helps identify high-risk older patients who might benefit from a Comprehensive Geriatric Assessment (CGA) and guides discussions about goals of care, especially concerning ICU admissions, as high-risk patients have higher mortality rates in the ICU. Furthermore, by identifying patients likely to have prolonged hospital stays and increased readmissions, the HFRS aids in better resource allocation and intervention targeting.
HFRS Comparison Table
| Feature | Hospital Frailty Risk Score (HFRS) | Clinical Frailty Scale (CFS) |
|---|---|---|
| Data Source | Routinely collected administrative ICD-10 codes from electronic health records. | Manual, direct clinical assessment based on a 9-point scale of functional capacity. |
| Automation | Fully automated, which minimizes cost, time, and inter-rater variability. | Requires a trained clinician to perform a face-to-face evaluation. |
| Assessment Scope | Focuses on a pre-defined set of 109 comorbidities associated with frailty. | A comprehensive tool that includes a wider range of functional, cognitive, and social factors. |
| Prediction | Strong predictor of prolonged hospital stay and re-admissions, though sometimes limited for short-term mortality. | Considered a strong predictor of short-, mid-, and long-term mortality and hospitalization. |
| Sensitivity | May have poor sensitivity for detecting frailty compared to manual tools, potentially misclassifying frail patients as non-frail. | High sensitivity as it involves a direct and holistic patient assessment. |
| Use Case | Ideal for population-level screening and health system planning due to its automated, low-cost nature. | Best used for individual patient assessment in clinical practice where a detailed, holistic view is needed. |
Limitations and Future Directions
Despite its benefits, the HFRS has limitations due to its reliance on coded administrative data, which may not fully capture all aspects of frailty, and its accuracy is dependent on coding quality. It can have poor sensitivity compared to manual scales like the Clinical Frailty Scale (CFS), potentially misclassifying frail patients. Future efforts aim to integrate additional clinical biomarkers and refine cut-offs to improve the HFRS's predictive accuracy. The goal is to combine the score with broader clinical information for a more holistic assessment, helping to identify and effectively intervene for vulnerable older adults.
Conclusion
The Hospital Frailty Risk Score (HFRS) is a significant tool for identifying and managing frailty in hospitals. By using existing administrative data, it provides a scalable, cost-effective way to screen patients at high risk of adverse outcomes like longer stays, readmissions, and mortality. While it has limitations in data specificity and sensitivity compared to manual tools, the HFRS is valuable for population-level risk stratification. Ongoing research aims to refine its application and integrate it with clinical data to further enhance its role in improving geriatric care.
References
Additional information on the development, validation, and application of the HFRS in various patient populations and settings can be found in the following references: {Link: NCBI https://pmc.ncbi.nlm.nih.gov/articles/PMC11940347/}.
