Frailty assessed by administrative tools and mortality in patients with pneumonia admitted to the hospital and ICU in Wales

Abstract

The ideal method of identifying frailty is uncertain, and data on long-term outcomes is relatively limited. We examined frailty indices derived from population-scale linked data on Intensive Care Unit (ICU) and hospitalised non-ICU patients with pneumonia to elucidate the influence of frailty on mortality. Longitudinal cohort study between 2010–2018 using population-scale anonymised data linkage of healthcare records for adults admitted to hospital with pneumonia in Wales. Primary outcome was in-patient mortality. Odds Ratios (ORs [95% confidence interval]) for age, hospital frailty risk score (HFRS), electronic frailty index (eFI), Charlson comorbidity index (CCI), and social deprivation index were estimated using multivariate logistic regression models. The area under the receiver operating characteristic curve (AUC) was estimated to determine the best fitting models. Of the 107,188 patients, mean (SD) age was 72.6 (16.6) years, 50% were men. The models adjusted for the two frailty indices and the comorbidity index had an increased odds of in-patient mortality for individuals with an ICU admission (ORs for ICU admission in the eFI model 2.67 [2.55, 2.79], HFRS model 2.30 [2.20, 2.41], CCI model 2.62 [2.51, 2.75]). Models indicated advancing age, increased frailty and comorbidity were also associated with an increased odds of in-patient mortality (eFI, baseline fit, ORs: mild 1.09 [1.04, 1.13], moderate 1.13 [1.08, 1.18], severe 1.17 [1.10, 1.23]. HFRS, baseline low, ORs: intermediate 2.65 [2.55, 2.75], high 3.31 [3.17, 3.45]). CCI, baseline < 1, ORs: ‘1–10′ 1.15 [1.11, 1.20], > 10 2.50 [2.41, 2.60]). For predicting inpatient deaths, the CCI and HFRS based models were similar, however for longer term outcomes the CCI based model was superior. Frailty and comorbidity are significant risk factors for patients admitted to hospital with pneumonia. Frailty and comorbidity scores based on administrative data have only moderate ability to predict outcome.

Multifactor Logistic Analysis to Explore the Risk Factors of Safety Risks in the Transport of Critically Ill Patients with ICU and the Improvement of Nursing Strategies

“…With the sudden change of the surrounding environment, the patient's condition will change unpredictably, which poses a great threat to the patient's life [ 20 ]. The side effects during transport mainly included hypoxemia, airway obstruction, displacement or prolapse of endotracheal catheter, shedding of arteriovenous lines, severe fluctuation of hemodynamics, increased intracranial pressure, hemorrhage, and even cardiorespiratory arrest, sudden death, and other serious adverse consequences [ 21 ]. Risk factors usually include two aspects: patient factors and medical factors; the former includes patient condition factors and posture factors; the latter includes escort personnel factors, equipment and drug factors, transport tool factors, and communication and coordination factors.…”

External validation of the hospital frailty risk score among older adults receiving mechanical ventilation

“…A German ICU study of 1,498 patients evaluated the HFRS to predict a combined endpoint of mortality and risk of readmission and found no association after adjustment for severity of illness 21 . In a large Wales population study, the HFRS had only moderate ability for predicting inpatient, 6-month, and 1-year mortality in hospital and ICU patients 41 . Conversely, a US study of 12,854 patients, using the single-center Medical Information Mart for Intensive Care (MIMIC-III) database, found that higher HFRS was associated with an increased risk of 28-day mortality 40 , 42 .…”

“…Prior HFRS studies focused on validating it in general hospitalizations, including non-ICU and ICU patients 17 – 19 , 36 – 39 . Recently, there has been interest in externally validating the HFRS in ICU administrative databases, as interest in big data frailty research increases 21 , 40 , 41 . A German ICU study of 1,498 patients evaluated the HFRS to predict a combined endpoint of mortality and risk of readmission and found no association after adjustment for severity of illness 21 .…”

Identifying Frail Patients by Using Electronic Health Records in Primary Care: Current Status and Future Directions

“…Furthermore, adapted eFI scores >0.32 may identify patients most likely to benefit from in-home pharmacist medication reviews, as shown in a small sample study ( 71 ). At last, the eFI has also been attempted to predict in-patient mortality after hospitalization or ICU admission for critically ill community-dwelling patients, but maybe less predictive value than the hospital frailty risk score (HRFS, constructed from hospital data, mainly ICD codes) ( 72 , 73 ). Similarly, building a new frailty tool using hospital discharge diagnostic data (ICD-10 codes) may address quick frailty assessment in patients returning from hospital to the community ( 74 ).…”