We described the use of the HFRS to predict adverse outcomes in a large nationally representative US cohort of older adults receiving mechanical ventilation. Among this cohort, we found that the intermediate and high risk for frailty groups, as categorized by the HFRS, were associated with increased risks of prolonged hospitalization and 30-day emergency hospital readmissions, compared to the low risk for frailty group. However, they were inversely associated with in-hospital mortality when compared to the low risk for frailty group. Overall, the HFRS had moderate discrimination and accuracy in predicting these outcomes. The use of the HFRS either as a continuous variable or with splines did not provide additional value over using the HFRS subcategories of low, intermediate, and high risk.
Comparison with previous studies
Prior studies of the HFRS focused on validating its use in older adults admitted in hospitalized settings, demonstrating good calibration and discrimination.26–28, 43–46 Recently, there has been interest in validating the HFRS in the use of administrative databases of critically ill patients.30,47,48 A prior study of 1,498 patients in a tertiary German ICU evaluated the use of the HFRS for a combined endpoint of mortality and risk of readmission, and they found no association after adjustment for severity of illness scoring.30 In a large population study of patients with pneumonia in Wales, Szakmany et al found that the HFRS had only moderate ability for predicting inpatient, 6-month, and 1-year mortality in hospital and ICU patients.48 Conversely, a study of 12,854 patients from the Medical Information Mart for Intensive Care (MIMIC-III) database found that higher HFRS was associated with an increased risk of 28-day mortality.47,49
In our study, we found that >40% of our cohort died in-hospital. Prior studies of critically ill patients have associated frailty with increased risks of mortality.10,11 While the HFRS performed well on a patient population of all older adults independent of mechanical ventilation status, it did not perform as well in a population of older adults receiving mechanical ventilation. Counterintuitively, we found that the HFRS was inversely associated with mortality in the NRD (i.e., lower HFRS was associated with the highest risks of in-hospital mortality). There may be some explanations for this phenomenon, including potential selection bias of patients admitted to ICUs (i.e., frail patients with higher severity of illness may choose not to undergo mechanical ventilation) and/or coding biases.
Unlike prior studies, we evaluated the validity of the HFRS to predict prolonged hospitalization and 30-day emergency hospital readmissions. Patient hospitalizations with higher HFRS were associated with prolonged hospitalizations and higher risks of 30-day readmissions; however, the HFRS only had moderate discrimination and accuracy to predict these outcomes. Our findings and other prior studies would suggest that the HFRS should be used with caution in administrative datasets of critically ill patients until better models or prediction scores of frailty can be developed specifically for use in this patient population.
Strengths and Limitations
Our study had several strengths including the use of a large dataset, comprising close to 650,000 weighted hospitalizations of older adult patients receiving mechanical ventilation. To our knowledge, our study represents the first study examining the use of the HFRS in a large representative study, allowing for generalizability to all older critically ill patients receiving mechanical ventilation. Additionally, we assessed both model discrimination and calibration, allowing for confidence in the results presented.
However, our study has some limitations. Coding biases may affect the relative prevalence of admission comorbidities, diagnoses, and treatments. The HFRS itself is derived from a composite of ICD-10-CM codes, which may be prone to coding biases. Patients that had prolonged hospitalization and/or survived their hospitalization may appear to be more “frail,” if they have more ICD-10-CM secondary diagnoses. These coding biases could potentially explain the results seen for in-hospital mortality. Other codes, such as dementia in Alzheimer’s disease (F00) or use of vasopressors, may be undercoded, similar to other US database studies including the Centers for Medicare & Medicaid Services and National Inpatient Sample databases.43,44,50 In addition, the NRD does not collect severity of illness information, such as the sequential organ failure assessment (SOFA) or Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) scores. We may not be able to adequately control for selection bias that may occur if clinicians admit frail patients that have lower severity of illness. Furthermore, the NRD is unable to capture information of patients that may have died outside of hospital (i.e., at home, hospice).
Clinical implications and future directions
Accurate predictions of prognosis and outcomes of frail critically ill patients is important for intensivists to aid in shared decision-making, goals of care discussion, and end-of-life planning, with patients and their families. Our study highlights a need to develop and validate intuitive and easy to use frailty scores that can be applied to critically ill patients both at the bedside and in large clinical administrative databases. The quick identification of frail critically ill patient can assist in identifying patients that would benefit from early geriatric medicine and/or palliative care referral. This may have important implications in preventing unnecessary hospital readmissions and ensuring goal-concordant care.
While the HFRS may have utility in administrative databases, the HFRS is difficult to apply and calculate at the bedside, and it may not intuitively classify the risk of in-hospital mortality in all patient populations. The mFI is a promising alternative; however, the mFI still needs further development and validation for use with ICD-10-CM codes.24,51 Furthermore, it has become increasingly recognized that frailty may exist in younger critically ill patients.8,9 Future research should be directed at developing frailty prediction scores that can be applied to a broad population of critically ill patients.