Clinical Frailty Score vs Hospital Frailty Risk Score for predicting mortality and other adverse outcome in hospitalised patients with COVID-19: Spanish case series

Frailty among Stroke Patients and its Effects on Hospital Outcomes: Frailty among Stroke

BACKGROUND

Although assessment of frailty is increasingly being included in routine practice, its effects on hospital outcomes is not well studied. In this study, we used a national database to estimate the effects of frailty on hospital outcomes among stroke patients.

METHODS

This study was a retrospective analysis of data from Nationwide Inpatient Sample (NIS) database collected during the years 2016 to 2019. Adult patients 45 years and older with a primary diagnosis of stroke were included for the analysis. Primary outcome was frequency of frailty among stroke patients. Secondary outcomes were in-hospital mortality, prolonged length of stay, mechanical ventilation, and acute renal failure. Frailty levels were assessed by using the Hospital Frailty Risk Score (HFRS).

RESULTS

Among 2,031,085 stroke hospitalizations, 362,140 (17.8%) were non-frail, 1,333,000 (65.6%) were pre-frail, and 335,945 (16.6%) were frail. Regression analysis showed that the odds of mortality were significantly higher among frail (aOR, 2.82, 95% CI: 2.63-3.04) and pre-frail (aOR, 1.62, 95% CI: 1.53-1.73) patients, compared to non-frail patients. Similarly, the odds of mechanical ventilation were significantly higher among frail (aOR, 9.72, 95% CI: 8.84-10.69) and pre-frail (aOR, 3.41, 95% CI: 3.12-3.73) patients. The odds of acute renal failure were significantly higher among frail (aOR, 6.96, 95% CI: 6.62-7.33) and pre-frail (aOR, 2.94, 95% CI: 2.80-3.08) patients.

CONCLUSION

Collaborative efforts by neurologists, neurosurgeons, and physiatrists towards identifying frailty and incorporating it in risk estimation measures could help improve management strategies, resource utilization, and optimization of patient outcomes among frail stroke patients.

COVID-19-Associated Outcomes of Critical Illness in Patients with Frailty: a Cohort Study

Background

Pre-admission frailty has been associated with higher hospital mortality in patients with critical illness. We aimed to measure the prevalence of frailty and its associated outcomes in patients with COVID-19 critical illness.

Methods

A historical cohort study of all adults admitted to ICU with a pneumonia diagnosis in Alberta, Canada between May 1, 2020, and October 31, 2020. At ICU admission patients were routinely assessed for frailty using the Clinical Frailty Scale (CFS). Frailty was defined as a CFS score ≥5. Primary outcomes were pre-admission frailty prevalence and hospital mortality.

Results

The cohort (n=521) prevalence of frailty was 34.2% (n=178), mean (SD) age was 58.8 (14.9) years, APACHE II 22.8 (8.0), and 39.5% (n=206) were female. COVID-19 pneumonia was diagnosed in (19.0%; n=99) admissions; pre-admission frailty was present in 20.2% (n=20) vs. 79.8% (n=79) non-frail (p<.001). Among ICU patients admitted with COVID-19, hospital mortality in frail patients was 35.4% (n=63) vs. 14.0% (n=48) in non-frail (p<.001).

Conclusion

Pre-admission frailty was present in 20.2% of COVID-19 ICU admissions and was associated with higher risk of hospital mortality. Frailty assessment may yield valuable prognostic information when considering COVID-19 ICU admission; however, further study is needed to identify effect on patient-centred outcomes in this heterogeneous population.

Prevalence of frailty according to the Hospital Frailty Risk Score and related factors in older patients with acute coronary syndromes in Vietnam

OBJECTIVES

(1) To investigate the prevalence of frailty defined by the Hospital Frailty Risk Score (HFRS), a new scale for assessing frailty, in older patients with acute coronary syndrome (ACS); (2) To identify associations between frailty and the prescriptions of cardiovascular medications, percutaneous coronary intervention (PCI) and in-hospital adverse outcomes.

METHODS

An observational study was conducted in patients aged older than 60 years with ACS at Thong Nhat Hospital from August to December 2022. The Hospital Frailty Risk Score is retrospectively calculated for all participants based on ICD-10 codes, and those with HFRS scores ≥5 were defined as frail. Logistic regression models were applied to examine the relationship between frailty and the study outcomes.

RESULTS

There were 511 participants in the study. The median age was 72.7, 60% were male and 29% were frail. Frailty was associated with lower odds of beta-blocker use at admission (OR .49 95% CI .25-.94), treatment with PCI during hospitalisation (OR .48, 95% CI .30-.75), but did not show an association with prescriptions of cardiovascular drugs at discharge. Frailty was significantly associated with increased odds of adverse outcomes, including major bleeding (OR 4.07, 95% CI1.73-9.54), hospital-acquired pneumonia (OR 2.55, 95% CI 1.20-5.42), all-cause in-hospital mortality (OR 3.14, 95% CI 1.37-7.20) and non-cardiovascular in-hospital mortality (OR 10.73, 95% CI 1.93-59.55).

CONCLUSIONS

The HFRS was an effective tool for stratifying frailty and predicting adverse health outcomes in older patients with ACS. Further research is needed to compare the HFRS with other frailty assessment tools in this population.

Validation of the Hospital Frailty Risk Score in older adults hospitalized with community-acquired pneumonia

AIM

Frailty results from age-associated declines in physiological reserve and function and is prevalent in older people. Our aim is to examine the association of the Hospital Frailty Risk Score (HFRS) with adverse events in older patients hospitalized with community-acquired pneumonia (CAP) and hypothesise that frailty is a comparable predictor of outcomes in CAP versus traditional severity indices such as CURB-65.

METHODS

Retrospective review of electronic medical records in patients ≥65 years with CAP admitted to a tertiary hospital from 1 January to 30 April 2021. Patients were identified using ICD codes for CAP and categorized as high risk (>15), intermediate risk (5-15) and low risk (<5) of frailty using the HFRS.

RESULTS

Of 429 patients with CAP, 53.8% male, mean age of 82.9 years, older patients (85 vs. 79.7 years, P < 0.001) were at higher risk of frailty. Using the HFRS, 47.6% were deemed at high risk, 35.9% at intermediate risk, and 16.6% at low risk of frailty. Multivariate logistic regression shows that HFRS was more strongly associated (≥7 days, OR 1.042, CI 1.017-1.069) than CURB-65 (OR 0.995, CI 0.810-1.222) with long hospital length of stay (LOS), while CURB-65 (Confusion, Urea >7mmol/L, Respiratory rate >30, Blood pressure, age => 65 years old) was more strongly associated with mortality at 30, 90 and 365 days, compared with the HFRS. Comparing the values for the area under the receiver operator characteristic curve, the HFRS was found to be a better predictor of long LOS, while CURB-65 remains a better predictor of mortality.

CONCLUSIONS

Patients with high risk of frailty have higher healthcare utilization and HFRS is a better predictor of long LOS than CURB-65 but CURB-65 was a better predictor of mortality. Geriatr Gerontol Int 2024; 24: 135-141.

Frailty is Associated With Increased Complication, Readmission, and Hospitalization Costs Following Primary Total Knee Arthroplasty

BACKGROUND

Frailty has been associated with poor postoperative outcomes in various medical conditions and surgical procedures. However, the relationship between frailty and outcomes after primary total knee arthroplasty (TKA) has not been well-described. This study investigated the association of the Hospital Frailty Risk Score (HFRS) with postoperative events and hospitalization costs after primary TKA.

METHODS

Using a nationwide readmissions database, we identified 884,479 patients discharged after primary TKA for osteoarthritis between January 2017 and November 2019. HFRS was calculated for each patient to determine frailty status. We used multivariate logistic regressions to evaluate the association of frailty with 30-readmission rate and negative binomial regressions to evaluate lengths of hospital stay and hospitalization costs. The 30-day reoperation and complication rates were compared using chi-square tests.

RESULTS

Frailty was associated with increased odds of 30-day readmissions (odds ratio [OR]: 1.89, 95% confidence interval [CI]: 1.82-1.96), longer lengths of stay (OR: 1.43, 95% CI: 1.43-1.44), and higher hospitalization costs (OR: 1.16, 95% CI: 1.16-1.17). Frail patients also had significantly higher rates of 30-day reoperations (0.6 versus 0.4%), surgical complications (0.6 versus 0.4%), medical complications (3.4 versus 1.3%), and other complications (0.9 versus 0.5%) (P < .01).

CONCLUSIONS

Frailty, as measured using HFRS, was associated with increased adverse events and health care burdens in patients undergoing TKA. The HFRS could be used to swiftly identify high-risk patients undergoing TKA and to potentially help optimize patients prior to elective TKA.

TYPE OF STUDY

Level III retrospective cohort study.

Higher Hospital Frailty Risk Score is associated with increased complications and healthcare resource utilization after endovascular treatment of ruptured intracranial aneurysms

AIM

To use the Hospital Frailty Risk Score (HFRS) to investigate the impact of frailty on complication rates and healthcare resource utilization in patients who underwent endovascular treatment of ruptured intracranial aneurysms (IAs).

METHODS

A retrospective cohort study was performed using the 2016-2019 National Inpatient Sample database. All adult patients (≥18 years) undergoing endovascular treatment for IAs after subarachnoid hemorrhage were identified using ICD-10-CM codes. Patients were categorized into frailty cohorts: low (HFRS <5), intermediate (HFRS 5-15) and high (HFRS >15). Patient demographics, adverse events, length of stay (LOS), discharge disposition, and total cost of admission were assessed. Multivariate logistic regression analysis was used to identify independent predictors of prolonged LOS, increased cost, and non-routine discharge.

RESULTS

Of the 33 840 patients identified, 7940 (23.5%) were found to be low, 20 075 (59.3%) intermediate and 5825 (17.2%) high frailty by HFRS criteria. The rate of encountering any adverse event was significantly greater in the higher frailty cohorts (low: 59.9%; intermediate: 92.4%; high: 99.2%, p<0.001). There was a stepwise increase in mean LOS (low: 11.7±8.2 days; intermediate: 18.7±14.1 days; high: 26.6±20.1 days, p<0.001), mean total hospital cost (low: $62 888±37 757; intermediate: $99 670±63 446; high: $134 937±80 331, p<0.001), and non-routine discharge (low: 17.3%; intermediate: 44.4%; high: 69.4%, p<0.001) with increasing frailty. On multivariate regression analysis, a similar stepwise impact was found in prolonged LOS (intermediate: OR 2.38, p<0.001; high: OR 4.49, p<0.001)], total hospital cost (intermediate: OR 2.15, p<0.001; high: OR 3.62, p<0.001), and non-routine discharge (intermediate: OR 2.13, p<0.001; high: OR 4.17, p<0.001).

CONCLUSIONS

Our study found that greater frailty as defined by the HFRS was associated with increased complications, LOS, total costs, and non-routine discharge.

External validation of the hospital frailty risk score among hospitalised home care clients in Canada: a retrospective cohort study

BACKGROUND

The Hospital Frailty Risk Score (HFRS) is scored using ICD-10 diagnostic codes in administrative hospital records. Home care clients in Canada are routinely assessed with Resident Assessment Instrument-Home Care (RAI-HC) which can calculate the Clinical Frailty Scale (CFS) and the Frailty Index (FI).

OBJECTIVE

Measure the correlation between the HFRS, CFS and FI and compare prognostic utility for frailty-related outcomes.

DESIGN

Retrospective cohort study.

SETTING

Alberta, British Columbia and Ontario, Canada.

SUBJECTS

Home care clients aged 65+ admitted to hospital within 180 days (median 65 days) of a RAI-HC assessment (n = 167,316).

METHODS

Correlation between the HFRS, CFS and FI was measured using the Spearman correlation coefficient. Prognostic utility of each measure was assessed by comparing measures of association, discrimination and calibration for mortality (30 days), prolonged hospital stay (10+ days), unplanned hospital readmission (30 days) and long-term care admission (1 year).

RESULTS

The HFRS was weakly correlated with the FI (ρ 0.21) and CFS (ρ 0.28). Unlike the FI and CFS, the HFRS was unable to discriminate for 30-day mortality (area under the receiver operator characteristic curve (AUC) 0.506; confidence interval (CI) 0.502-0.511). It was the only measure that could discriminate for prolonged hospital stay (AUC 0.666; CI 0.661-0.673). The HFRS operated like the FI and CFI when predicting unplanned readmission (AUC 0.530 CI 0.526-0.536) and long-term care admission (AUC 0.600; CI 0.593-0.606).

CONCLUSIONS

The HFRS identifies a different subset of older adult home care clients as frail than the CFS and FI. It has prognostic utility for several frailty-related outcomes in this population, except short-term mortality.

Association between Clinical Frailty Scale (CFS) and clinical presentation and outcomes in older inpatients with COVID-19

BACKGROUND

Frailty is a physiological condition characterized by a decreased reserve to stressors. In patients with COVID-19, frailty is a risk factor for in-hospital mortality. The aim of this study was to assess the relationship between clinical presentation, analytical and radiological parameters at admission, and clinical outcomes according to frailty, as defined by the Clinical Frailty Scale (CFS), in old people hospitalized with COVID-19.

MATERIALS AND METHODS

This retrospective cohort study included people aged 65 years and older and admitted with community-acquired COVID-19 from 3 March 2020 to 31 April 2021. Patients were categorized using the CFS. Primary outcomes were symptoms of COVID-19 prior to admission, mortality, readmission, admission in intensive care unit (ICU), and need for invasive mechanical ventilation. Analysis of clinical symptoms, clinical outcomes, and CFS was performed using multivariable logistic regression, and results were expressed as odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

Of the 785 included patients, 326 (41.5%, 95% CI 38.1%-45.0%) were defined as frail (CFS ≥ 5 points): 208 (26.5%, 95% CI 23.5%-29.7%) presented mild-moderate frailty (CFS 5-6 points) and 118 (15.0%, 95% CI 12.7%-17.7%), severe frailty (7-9 points). After adjusting for epidemiological variables (age, gender, residence in a nursing home, and Charlson comorbidity index), frail patients were significantly less likely to present dry cough (OR 0.58, 95% CI 0.40-0.83), myalgia-arthralgia (OR 0.46, 95% CI 0.29-0.75), and anosmia-dysgeusia (OR 0.46, 95% CI 0.23-0.94). Confusion was more common in severely frail patients (OR 3.14; 95% CI 1.64-5.97). After adjusting for epidemiological variables, the risk of in-hospital mortality was higher in frail patients (OR 2.79, 95% CI 1.79-4.25), including both those with mild-moderate frailty (OR 1.98, 95% CI 1.23-3.19) and severe frailty (OR 5.44, 95% CI 3.14-9.42). Readmission was higher in frail patients (OR 2.11, 95% CI 1.07-4.16), but only in mild-moderate frailty (OR 2.35, 95% CI 1.17-4.75)..

CONCLUSION

Frail patients presented atypical symptoms (less dry cough, myalgia-arthralgia, and anosmia-dysgeusia, and more confusion). Frailty was an independent predictor for death, regardless of severity, and mild-moderate frailty was associated with readmission.

Mortality and survival in nonagenarians during the COVID-19 pandemic: Unstable equilibrium of aging

Introduction

Aging puts the human body under an immense stress and makes it extremely susceptible to many diseases, often leading to poor outcomes and even death. Long-living individuals represent a unique group of people who withstood the stress of time and offer an abundance of information on the body's ability to endure the pressure of aging. In this study, we sought to identify predictors of overall one-year mortality in 1641 long-living individuals. Additionally, we analyzed risk factors for COVID-19-related morality, since statistics demonstrated an extreme vulnerability of older adults.

Methods

We conducted a two-stage evaluation, including a comprehensive geriatric assessment for major aging-associated: frailty, cognitive impairment, frontal lobe dysfunction, chronic pain, anxiety, risk of falls, sensory deficit, depression, sarcopenia, risk of malnutrition, fecal and urinary incontinence, dependence in Activities of Daily Living, dependence in Instrumental Activities of Daily Living, polypragmasia, and orthostatic hypotension; extensive blood testing, a survey, and a one-year follow-up interview.

Results

The most reliable predictors of overall mortality were cognitive impairment, malnutrition, frailty, aging-associated diseases and blood markers indicating malnutrition-induced metabolic dysfunctions (decreased levels of protein fractions, iron, 25-hydroxyvitamin D, and HDL), and aging biomarkers, such as IGF-1 and N-terminal pro b-type natriuretic peptide. In post-COVID 19 participants, the most significant mortality predictors among geriatric syndromes were depression, frontal lobe dysfunction and frailty, and similar to overall mortality blood biomarkers - 25-hydroxyvitamin D, IGF-1, HDL as well as high white blood cell, neutrophils counts and proinflammatory markers. Based on the results, we built a predictive model of overall mortality in the long-living individuals with f-score=0.76.

Conclusion

The most sensitive and reliable predictors of mortality were modifiable. This is another evidence of the critical importance of proper geriatric care and support for individuals in their "golden years". These results could facilitate geriatric institutions in their pursuit for providing improved care and could aid physicians in detecting early signs of potentially deadly outcomes. Additionally, our findings could be used in developing day-to-day care guidelines, which would greatly improve prevention statistics.

Association between a Frailty Index from Common Laboratory Values and Vital Signs (FI-LAB) and Hospital and Post-Hospital Outcomes in Veterans with COVID-19 Infection

OBJECTIVES

Determine the association of higher FI-LAB scores, derived from common laboratory values and vital signs, with hospital and post-hospital outcomes in Veterans hospitalized with COVID-19 infection.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective, multicenter, cohort study of 7 Veterans Health Administration (VHA) medical centers in Florida and Puerto Rico. Patients aged 18 years and older hospitalized with COVID-19 and followed for up to 1 year post discharge or until death. Clinical Frailty Measure: FI-LAB.

MAIN OUTCOMES AND MEASURES

Hospital and post-hospital outcomes.

RESULTS

Of the 671 eligible patients, 615 (91.5%) patients were included (mean [SD] age, 66.1 [14.8] years; 577 men [93.8%]; median stay, 8 days [IQR:3-15]. There were sixty-one in-hospital deaths. Veterans in the moderate and high FI-LAB groups had a higher proportion of inpatient mortality (13.3% and 20.6%, respectively) than the low group (4.1%), p <0.001. Moderate and high FI-LAB scores were associated with greater inpatient mortality when compared to the low group, OR:3.22 (95%CI:1.59-6.54), p=.001 and 6.05 (95%CI:2.48-14.74), p<0.001, respectively. Compared with low FI-LAB scores, moderate and high scores were also associated with prolonged length of stay, intensive care unit (ICU) admission, and transfer.

CONCLUSIONS AND RELEVANCE

In this study of patients admitted to 7 VHA Hospitals during the first surge of the pandemic, higher FI-LAB scores were associated with higher in-hospital mortality and other in-hospital outcomes; FI-LAB can serve as a validated, rapid, feasible, and objective frailty tool in hospitalized adults with COVID-19 that can aid clinical care.

Predictors of COVID-19 outcomes: Interplay of frailty, comorbidity, and age in COVID-19 prognosis

Prior research has identified frailty, comorbidity, and age as predictors of outcomes for patients with coronavirus disease 2019 (COVID-19), including mortality. However, it remains unclear how these factors play different roles in COVID-19 prognosis. This study focused on correlations between frailty, comorbidity and age, and their correlations to discharge outcome and length-of-stay in hospitalized patients with COVID-19. Clinical data was collected from 56 patients who were ≥50 years old and admitted from March 2020 to June 2020 primarily for COVID-19. Frailty Risk Score (FRS) and the Charlson Comorbidity Index (CCI) were used for assessment of frailty and comorbidity burden, respectively. Age had significant positive correlation with FRS and CCI (P < .001, P < .001, respectively). There was also significant positive correlation between FRS and CCI (P < .001). For mortality, patients who died during their hospitalization had significantly higher FRS and CCI (P = .01 and P < .001, respectively) but were not significantly older than patients who did not. FRS, CCI, and age were all significantly associated when looking at overall adverse discharge outcome (transfer to other facility or death) (P < .001, P = .005, and P = .009, respectively). However, none of the 3 variables were significantly correlated with length-of-stay. Multivariate analysis showed FRS (P = .007) but not patient age (P = .967) was significantly associated with death. We find that frailty is associated with adverse outcomes from COVID-19 and supplants age in multivariable analysis. Frailty should be part of risk assessment of older adults with COVID-19.

Association between frailty and short- and long-term mortality in patients with critical acute myocardial infarction: Results from MIMIC-IV

Background

Frailty has been recognized as an important prognostic indicator in patients with acute myocardial infarction (AMI). However, no study has focused on critical AMI patients. We aimed to determine the impact of frailty on short- and long-term mortality risk in critical AMI patients.

Methods

Data from the Medical Information Mart for Intensive Care (MIMIC)-IV database was used. Frailty was assessed using the Hospital Frailty Risk Score (HFRS). Outcomes were in-hospital mortality and 1-year mortality. Logistic regression and Cox proportional-hazards models were used to investigate the association between frailty and outcomes.

Results

Among 5,003 critical AMI patients, 2,176 were non-frail (43.5%), 2,355 were pre-frail (47.1%), and 472 were frail (9.4%). The in-hospital mortality rate was 13.8%, and the 1-year mortality rate was 29.5%. In our multivariable model, frailty was significantly associated with in-hospital mortality [odds ratio (OR) = 1.30, 95% confidence interval (CI): 1.20-1.41] and 1-year mortality [hazard ratio (HR) = 1.29, 95% CI: 1.24-1.35] as a continuous variable (per five-score increase). When assessed as categorical variables, pre-frailty and frailty were both associated with in-hospital mortality (OR = 2.80, 95% CI: 2.19-3.59 and OR = 2.69, 95% CI: 1.93-3.73, respectively) and 1-year mortality (HR = 2.32, 95% CI: 2.00-2.69 and HR = 2.81, 95% CI: 2.33-3.39, respectively) after adjustment for confounders. Subgroup analysis showed that frailty was only associated with in-hospital mortality in critically ill patients with non-ST-segment elevation myocardial infarction (STEMI) but not STEMI (p for interaction = 0.012). In addition, frailty was associated with 1-year mortality in both STEMI and non-STEMI patients (p for interaction = 0.447). The addition of frailty produced the incremental value over the initial model generated by baseline characteristics for both in-hospital and 1-year mortality.

Conclusion

Frailty, as assessed by the HFRS, was associated with both in-hospital and 1-year mortality in critical AMI patients. Frailty improves the prediction of short- and long-term mortality in critical AMI patients and may have potential clinical applications.

Frailty, Comorbidity, and Associations With In-Hospital Mortality in Older COVID-19 Patients: Exploratory Study of Administrative Data

BACKGROUND

Older adults have worse outcomes following hospitalization with COVID-19, but within this group there is substantial variation. Although frailty and comorbidity are key determinants of mortality, it is less clear which specific manifestations of frailty and comorbidity are associated with the worst outcomes.

OBJECTIVE

We aimed to identify the key comorbidities and domains of frailty that were associated with in-hospital mortality in older patients with COVID-19 using models developed for machine learning algorithms.

METHODS

This was a retrospective study that used the Hospital Episode Statistics administrative data set from March 1, 2020, to February 28, 2021, for hospitalized patients in England aged 65 years or older. The data set was split into separate training (70%), test (15%), and validation (15%) data sets during model development. Global frailty was assessed using the Hospital Frailty Risk Score (HFRS) and specific domains of frailty were identified using the Global Frailty Scale (GFS). Comorbidity was assessed using the Charlson Comorbidity Index (CCI). Additional features employed in the random forest algorithms included age, sex, deprivation, ethnicity, discharge month and year, geographical region, hospital trust, disease severity, and International Statistical Classification of Disease, 10th Edition codes recorded during the admission. Features were selected, preprocessed, and input into a series of random forest classification algorithms developed to identify factors strongly associated with in-hospital mortality. Two models were developed; the first model included the demographic, hospital-related, and disease-related items described above, as well as individual GFS domains and CCI items. The second model was similar to the first but replaced the GFS domains and CCI items with the HFRS as a global measure of frailty. Model performance was assessed using the area under the receiver operating characteristic (AUROC) curve and measures of model accuracy.

RESULTS

In total, 215,831 patients were included. The model using the individual GFS domains and CCI items had an AUROC curve for in-hospital mortality of 90% and a predictive accuracy of 83%. The model using the HFRS had similar performance (AUROC curve 90%, predictive accuracy 82%). The most important frailty items in the GFS were dementia/delirium, falls/fractures, and pressure ulcers/weight loss. The most important comorbidity items in the CCI were cancer, heart failure, and renal disease.

CONCLUSIONS

The physical manifestations of frailty and comorbidity, particularly a history of cognitive impairment and falls, may be useful in identification of patients who need additional support during hospitalization with COVID-19.

Two Years with COVID-19: The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study

INTRODUCTION

Frailty, a measure of biological aging, has been linked to worse COVID-19 outcomes. However, as the mortality differs across the COVID-19 waves, it is less clear whether a medical record-based electronic frailty index (eFI) that we have previously developed for older adults could be used for risk stratification in hospitalized COVID-19 patients.

OBJECTIVES

The aim of the study was to examine the association of frailty with mortality, readmission, and length of stay in older COVID-19 patients and to compare the predictive accuracy of the eFI to other frailty and comorbidity measures.

METHODS

This was a retrospective cohort study using electronic health records (EHRs) from nine geriatric clinics in Stockholm, Sweden, comprising 3,980 COVID-19 patients (mean age 81.6 years) admitted between March 2020 and March 2022. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the Clinical Frailty Scale, and the Hospital Frailty Risk Score. Comorbidity was measured using the Charlson Comorbidity Index. We analyzed in-hospital mortality and 30-day readmission using logistic regression, 30-day and 6-month mortality using Cox regression, and the length of stay using linear regression. Predictive accuracy of the logistic regression and Cox models was evaluated by area under the receiver operating characteristic curve (AUC) and Harrell's C-statistic, respectively.

RESULTS

Across the study period, the in-hospital mortality rate decreased from 13.9% in the first wave to 3.6% in the latest (Omicron) wave. Controlling for age and sex, a 10% increment in the eFI was significantly associated with higher risks of in-hospital mortality (odds ratio = 2.95; 95% confidence interval = 2.42-3.62), 30-day mortality (hazard ratio [HR] = 2.39; 2.08-2.74), 6-month mortality (HR = 2.29; 2.04-2.56), and a longer length of stay (β-coefficient = 2.00; 1.65-2.34) but not with 30-day readmission. The association between the eFI and in-hospital mortality remained robust across the waves, even after the vaccination rollout. Among all measures, the eFI had the best discrimination for in-hospital (AUC = 0.780), 30-day (Harrell's C = 0.733), and 6-month mortality (Harrell's C = 0.719).

CONCLUSION

An eFI based on routinely collected EHRs can be applied in identifying high-risk older COVID-19 patients during the continuing pandemic.

Frailty in the context of COVID-19 pandemic: A life-threatening condition

The pandemic outbreak of coronavirus disease 2019 (COVID-19) has caused emerging challenges for healthcare systems regarding the assistance to the older adult population which, added to the increased life expectancy, may be exposing frail older adults to an increased risk of unfavorable health outcomes. Frailty has a pathogenesis of multifactorial etiology and is defined as a condition characterized by progressive decline in physiological function, weakness, decreased strength, and reduced resilience to stressors, leading to vulnerability and an increased risk of fractures, falls, institutionalization, and death. In the context of COVID-19, frail older adults accounted for approximately 51% of hospitalized patients with confirmed cases and elevated risk of mortality in-hospital. In addition, frailty may be associated with recent “excess mortality” reported by the World Health Organization (WHO) in terms of the full death toll associated directly (due to the disease) or indirectly (due to the pandemic's impact on health systems and society) to COVID-19. Therefore, this mini review aimed to provide a summarized discussion from meta-analyses data regarding the impact of frailty in community-dwelling older adults hospitalized with COVID-19 on short-term mortality risk.

The association of frailty with clinical and economic outcomes among hospitalized older adults with hip fracture surgery

Frailty is a common condition among older adults with hip fracture. In our study analyzing National Inpatient Sample data, frailty was found to be associated with up to six times increase in in-patient mortality, 55% increased length of hospital stay, and 29% increase in hospital cost.

INTRODUCTION

Hip fracture is a significant public health issue posing adverse health outcomes and substantial economic burden to patients and society. Frailty is a prevalent geriatric condition associated with poor clinical outcome among older adults. The association between hip fracture and frailty on both clinical and economic outcomes at the national level has not been estimated. We aimed to determine the association between frailty and in-hospital mortality, length of hospital stay (LOS), and total hospital cost among older patients aged ≥ 65 years who underwent surgery for hip fracture.

METHODS

We did an analysis of administrative data using the National Inpatient Sample (NIS) data from 2016 and 2017. Our analysis included data on 29,735 hospitalizations. We first conducted a descriptive analysis of the patient characteristics (demographics and clinical) and hospital-related factors. Three multivariable regression analysis models were then used to determine independent associations between frailty and in-hospital mortality, LOS, and total hospital cost. All three models were adjusted for patients' demographic and clinical characteristics and hospital-related factors.

RESULTS

Moderate and high frailty risk were associated with higher odds of death (OR = 2.94 and 95% CI 1.91-4.51 and OR = 5.99 and 95% CI 3.79-9.47), increased LOS (17% and 55%, p < 0.0001), and higher total hospital cost (7% and 29%, p < 0.0001) respectively compared to low frailty risk.

CONCLUSION

Frailty was associated with mortality, LOS, and hospital cost after adjusting for patient demographic, clinical, and hospital-related factors. Further research is needed to explore what pre-surgical measures can be assessed to mitigate in-hospital mortality and hospital cost in frail older patients hospitalized for hip fracture surgery.

Hospital Frailty Risk Score Predicts Outcomes in Chronic Obstructive Pulmonary Disease Exacerbations

INTRODUCTION

Patients with chronic obstructive pulmonary disease (COPD) are at high risk for frailty and prone to complications after admission for an acute exacerbation. We aim to investigate the association between frailty risk and functional outcomes in patients with acute exacerbations of COPD, using a nationwide database.

METHODS

This retrospective cohort study included patients with acute exacerbations of COPD who were admitted by ambulance. We assessed frailty using the Hospital Frailty Risk Score (HFRS) and compared the outcomes between low frailty risk (HFRS < 5) and frailty at risk (HFRS ≥ 5) groups. The primary outcome was prolonged hospitalization (≥30 days). The secondary outcomes were in-hospital mortality, readmission (≤90 days), poor activities of daily living (ADL) at discharge, and difficulty in returning home.

RESULTS

There were 3,396 eligible patients (mean age, 75.9 ± 11.2 years; 20.4% female). The rate of frailty at risk patients was 14.0%. Frailty at risk patients were significantly higher rates of prolonged hospitalization (32.9% vs. 17.5%), more in-hospital mortality (16.4% vs. 12.5%), more difficulty in returning home (34.6% vs. 22.9%), and poorer ADL at discharge (8.7% vs. 12.4%) than those of low frailty risk. Multivariate analysis with adjusted covariates showed that HFRS was independently associated with prolonged hospitalization (odds ratio, 2.0; 95% confidence interval, 1.4-2.9).

CONCLUSIONS

HFRS can be used to predict the outcome of patients with acute exacerbations of COPD. This finding supports the validity of using the HFRS in clinical practice with patients with acute exacerbations of COPD.

Association of the Intermountain Risk Score with major adverse health events in patients positive for COVID-19: an observational evaluation of a US cohort

OBJECTIVES

The Intermountain Risk Score (IMRS), composed using published sex-specific weightings of parameters in the complete blood count (CBC) and basic metabolic profile (BMP), is a validated predictor of mortality. We hypothesised that IMRS calculated from prepandemic CBC and BMP predicts COVID-19 outcomes and that IMRS using laboratory results tested at COVID-19 diagnosis is also predictive.

DESIGN

Prospective observational cohort study.

SETTING

Primary, secondary, urgent and emergent care, and drive-through testing locations across Utah and in sections of adjacent US states. Viral RNA testing for SARS-CoV-2 was conducted from 3 March to 2 November 2020.

PARTICIPANTS

Patients aged ≥18 years were evaluated if they had CBC and BMP measured in 2019 and tested positive for COVID-19 in 2020.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was a composite of hospitalisation or mortality, with secondary outcomes being hospitalisation and mortality separately.

RESULTS

Among 3883 patients, 8.2% were hospitalised and 1.6% died. Subjects with low, mild, moderate and high-risk IMRS had the composite endpoint in 3.5% (52/1502), 8.6% (108/1256), 15.5% (152/979) and 28.1% (41/146) of patients, respectively. Compared with low-risk, subjects in mild-risk, moderate-risk and high-risk groups had HR=2.33 (95% CI 1.67 to 3.24), HR=4.01 (95% CI 2.93 to 5.50) and HR=8.34 (95% CI 5.54 to 12.57), respectively. Subjects aged <60 years had HR=3.06 (95% CI 2.01 to 4.65) and HR=7.38 (95% CI 3.14 to 17.34) for moderate and high risks versus low risk, respectively; those ≥60 years had HR=1.95 (95% CI 0.99 to 3.86) and HR=3.40 (95% CI 1.63 to 7.07). In multivariable analyses, IMRS was independently predictive and was shown to capture substantial risk variation of comorbidities.

CONCLUSIONS

IMRS, a simple risk score using very basic laboratory results, predicted COVID-19 hospitalisation and mortality. This included important abilities to identify risk in younger adults with few diagnosed comorbidities and to predict risk prior to SARS-CoV-2 infection.

Predictive value of frailty in the mortality of hospitalized patients with COVID-19: a systematic review and meta-analysis

Background

The present study aimed to analyze the impact of frailty on mortality risk among hospitalized patients with coronavirus disease 2019 (COVID-19).

Methods

Literature searches were conducted using the MEDLINE, Embase, and Cochrane databases for articles reporting the association between frailty and mortality in hospitalized patients with COVID-19. The quality of the included studies was assessed using the Newcastle-Ottawa scale (NOS). A random-effects meta-analysis was performed to calculate the pooled effects.

Results

A total of 21 studies with 26,652 hospitalized patients were included. Sixteen studies used the Clinical Frailty Score (CFS), and five used other frailty assessment tools. The pooled estimates of frailty in hospitalized patients with COVID-19 were 51.4% [95% confidence interval (CI): 39.9–62.9%]. In the CFS group, frail patients experienced a higher rate of short-term mortality than non-frail patients [odds ratio (OR) =3.0; 95% CI: 2.3–3.9; I²=72.7%; P<0.001]. In the other tools group, frail patients had a significantly increased short-term mortality risk compared with non-frail patients (OR =2.4; 95% CI: 1.4–4.1; P=0.001). Overall, a higher short-term mortality risk was observed for frail patients than non-frail patients (OR =2.8; 95% CI: 2.3–3.5; P<0.001). In older adults, frail patients had a higher rate of short-term mortality than non-frail patients (OR =2.3; 95% CI: 1.8–2.9; P<0.001).

Conclusions

Compared to non-frail hospitalized patients with COVID-19, frail patients suffered a higher risk of all-cause mortality, and this result was also found in the older adult group.

Association between Prehospital Hypoxemia and Admission to Intensive Care Unit during the COVID-19 Pandemic: A Retrospective Cohort Study

Background and Objectives: The aim of this study was to assess the association between prehospital peripheral oxygen saturation (SpO₂) and intensive care unit (ICU) admission in confirmed or suspected coronavirus disease 19 (COVID-19) patients. Materials and Methods: We carried out a retrospective cohort study on patients requiring prehospital intervention between 11 March 2020 and 4 May 2020. All adult patients in whom a diagnosis of COVID-19 pneumonia was suspected by the prehospital physician were included. Patients who presented a prehospital confounding respiratory diagnosis and those who were not eligible for ICU admission were excluded. The main exposure was “Low SpO₂” defined as a value < 90%. The primary outcome was 48-h ICU admission. Secondary outcomes were 48-h mortality and 30-day mortality. We analyzed the association between low SpO₂ and ICU admission or mortality with univariable and multivariable regression models. Results: A total of 145 patients were included. A total of 41 (28.3%) patients had a low prehospital SpO₂ and 21 (14.5%) patients were admitted to the ICU during the first 48 h. Low SpO₂ was associated with an increase in ICU admission (OR = 3.4, 95% CI = 1.2–10.0), which remained significant after adjusting for sex and age (aOR = 5.2, 95% CI = 1.8–15.4). Mortality was higher in low SpO₂ patients at 48 h (OR = 7.1 95% CI 1.3–38.3) and at 30 days (OR = 3.9, 95% CI 1.4–10.7). Conclusions: In our physician-staffed prehospital system, first low prehospital SpO₂ values were associated with a higher risk of ICU admission during the COVID-19 pandemic.

Clinical Frailty Score vs Hospital Frailty Risk Score for predicting mortality and other adverse outcome in hospitalised patients with COVID-19: Spanish case series

OBJECTIVES

Frailty can be used as a predictor of adverse outcomes in people with coronavirus disease 2019 (COVID-19). The aim of the study was to analyse the prognostic value of two different frailty scores in patients hospitalised for COVID-19.

MATERIAL AND METHODS

This retrospective cohort study included adult (≥18 years) inpatients with COVID-19 and took place from 3 March to 2 May 2020. Patients were categorised by Clinical Frailty Score (CFS) and Hospital Frailty Risk Score (HFRS). The primary outcome was in-hospital mortality, and secondary outcomes were tocilizumab treatment, length of hospital stay, admission in intensive care unit (ICU) and need for invasive mechanical ventilation. Results were analysed by multivariable logistic regression and expressed as odds ratios (ORs), adjusting for age, sex, kidney function and comorbidity.

RESULTS

Of the 290 included patients, 54 were frail according to the CFS (≥5 points; prevalence 18.6%, 95% confidence interval [CI]: 14.4-23.7) vs 65 by HFRS (≥5 points; prevalence: 22.4%, 95% CI 17.8-27.7). Prevalence of frailty increased with age according to both measures: 50-64 years, CFS 1.9% vs HFRS 12.3%; 65-79 years, CFS 31.5% vs HFRS 40.0%; and ≥80 years, CFS 66.7% vs HFRS 40.0% (P < .001). CFS-defined frailty was independently associated with risk of death (OR 3.67, 95% CI 1.49-9.04) and less treatment with tocilizumab (OR 0.28, 95% CI 0.08-0.93). HFRS-defined frailty was independently associated with length of hospital stay over 10 days (OR 2.89, 95% CI 1.53-5.44), ICU admission (OR 4.18, 95% CI 1.84-9.52) and invasive mechanical ventilation (OR 5.93, 95% CI 2.33-15.10).

CONCLUSION

In the spring 2020 wave of the COVID-19 pandemic in Spain, CFS-defined frailty was an independent predictor for death, while frailty as measured by the HFRS was associated with length of hospital stay over 10 days, ICU admission and use of invasive mechanical ventilation.