Complementing chronic frailty assessment at hospital admission with an electronic frailty index (FI-Laboratory) comprising routine blood test results

What is the additive value of nutritional deficiency to VA-FI in the risk assessment for heart failure patients?

OBJECTIVES

To assess the impact of adding the Prognostic Nutritional Index (PNI) to the U.S. Veterans Health Administration frailty index (VA-FI) for the prediction of time-to-death and other clinical outcomes in Veterans hospitalized with Heart Failure.

METHODS

A retrospective cohort study of veterans hospitalized for heart failure (HF) from October 2015 to October 2018. Veterans ≥50 years with albumin and lymphocyte counts, needed to calculate the PNI, in the year prior to hospitalization were included. We defined malnutrition as PNI ≤43.6, based on the Youden index. VA-FI was calculated from the year prior to the hospitalization and identified three groups: robust (≤0.1), prefrail (0.1-0.2), and frail (>0.2). Malnutrition was added to the VA-FI (VA-FI-Nutrition) as a 32nd deficit with the total number of deficits divided by 32. Frailty levels used the same cut-offs as the VA-FI. We compared categories based on VA-FI to those based on VA-FI-Nutrition and estimated the hazard ratio (HR) for post-discharge all-cause mortality over the study period as the primary outcome and other adverse events as secondary outcomes among patients with reduced or preserved ejection fraction in each VA-FI and VA-FI-Nutrition frailty groups.

RESULTS

We identified 37,601 Veterans hospitalized for HF (mean age: 73.4 ± 10.3 years, BMI: 31.3 ± 7.4 kg/m2). In general, VA-FI-Nutrition reclassified 1959 (18.6%) Veterans to a higher frailty level. The VA-FI identified 1,880 (5%) as robust, 8,644 (23%) as prefrail, and 27,077 (72%) as frail. The VA-FI-Nutrition reclassified 382 (20.3%) from robust to prefrail and 1577 (18.2%) from prefrail to frail creating the modified-prefrail and modified-frail categories based on the VA-FI-Nutrition. We observed shorter time-to-death among Veterans reclassified to a higher frailty status vs. those who remained in their original group (Median of 2.8 years (IQR:0.5,6.8) in modified-prefrail vs. 6.3 (IQR:1.8,6.8) years in robust, and 2.2 (IQR:0.7,5.7) years in modified-frail vs. 3.9 (IQR:1.4,6.8) years in prefrail). The adjusted HR in the reclassified groups was also significantly higher in the VA-FI-Nutrition frailty categories with a 38% increase in overall all-cause mortality among modified-prefrail and a 50% increase among modified-frails. Similar trends of increasing adverse events were also observed among reclassified groups for other clinical outcomes.

CONCLUSION

Adding PNI to VA-FI provides a more accurate and comprehensive assessment among Veterans hospitalized for HF. Clinicians should consider adding a specific nutrition algorithm to automated frailty tools to improve the validity of risk prediction in patients hospitalized with HF.

Effect of canakinumab on frailty: A post hoc analysis of the CANTOS trial

Although inflammation is strongly associated with frailty, whether medications that lower inflammation decrease frailty is unclear and randomized trial evidence is scant. We sought to test whether canakinumab, a therapeutic monoclonal antibody that inhibits IL-1β and reduces C-reactive protein (CRP), can lower frailty risk. This was a post hoc analysis of the Canakinumab ANti-inflammatory Thrombosis Outcome Study (CANTOS), a randomized double-blind placebo-controlled trial of 10,061 stable postmyocardial infarction patients randomized to subcutaneous canakinumab once every 3 months. Incident frailty was measured using a 34-item cumulative-deficit Frailty Index (FI). Time-to-event analysis using intent to treat. A total of 9942 CANTOS participants had data to calculate a baseline FI. Median age was 61 (IQR 54-68); 74% were male, 12% Asian, 3% Black, 80% White, and 16% Hispanic/Latino. At baseline, mean FI score was 0.12 and 13% were frail using a cutoff of 0.2. Over 5 years, 1080 participants (12.5%) became frail and mean FI scores increased to 0.14. There was no effect on frailty incidence according to randomization to any canakinumab dose versus placebo over time, HR 1.03 (0.91-1.17), p = 0.63. Results were similar using phenotypic frailty. Additionally, the primary findings of CANTOS in terms of canakinumab-associated cardiovascular event reduction were unchanged in analyses stratified by baseline frailty. In conclusion, among stable adult patients with atherosclerosis, random allocation to interleukin-1b inhibition with canakinumab versus placebo did not lower risk of incident frailty over 5 years. More randomized data are needed to understand the role of targeted anti-inflammatory medications for frailty prevention in older adults.

Comparing frailty prevalence between countries: validation of the Global Burden of Disease study Frailty Index (GBD-FI) in the survey of health, ageing and retirement in Europe

BACKGROUND

Accurate comparable prevalence proportions are required to better understand the epidemiology of frailty. Estimates in many countries are missing or incomparable. The Global Burden of Disease Frailty Index (GBD-FI) applies the deficit accumulation model to generate frailty scores from items available in the Global Burden of Disease study.

OBJECTIVE

To externally validate the GBD-FI.

METHODS

Data were obtained from the Survey of Health Ageing and Retirement in Europe (SHARE). A 20-item modified GBD-FI was compared with established frailty measures: a 70-item frailty index (FI-70), the Clinical Frailty Scale (CFS), Frailty Phenotype (FP) and SHARE-FI. Area under receiver operating characteristic curves (AUC) were fitted to examine diagnostic accuracy for frailty and predictive validity for 2-year mortality.

RESULTS

In total, 31,624 participants aged ≥50 years from 15 countries were included. Frailty prevalence was 22% using the GBD-FI (ranging from 8% in Switzerland to 41% in Poland). The GBD-FI had good to excellent diagnostic accuracy for frailty, irrespective of approach; the AUC ranged from 0.86 (95% confidence interval: 0.85-0.87) measuring frailty using the CFS to 0.94 (0.93-0.94) with the FI-70. The GBD-FI had similar accuracy for 2-year mortality (AUC 0.71, 0.69-0.74) compared with the CFS (0.73; P = 0.186), FP (0.73; P = 0.392) and SHARE-FI (0.70; P = 0.255) but lower than the FI-70 (0.76; P < 0.001).

CONCLUSION

The GBD-FI demonstrated concurrent and predictive validity, suggesting it is a valid measure of frailty. It has the potential to be an efficient, replicable and consistent approach to comparing frailty between countries and regions across time using GBD data.

Combined use of the Clinical Frailty Scale and laboratory tests in acutely hospitalized older patients

AIMS

To evaluate the Clinical Frailty Scale (CFS) and a Frailty Index based on laboratory tests (FI-lab) in terms of what each assesses about frailty and to determine the appropriateness of combined use of these two frailty scales.

METHODS

This was a prospective observational cohort study in an acute geriatric ward of a university hospital. The FI-lab is the proportion of laboratory parameters that yield abnormal results from a total of 23. The FI-lab and CFS were assessed at admission. Data on activities of daily living (ADL), cognition, geriatric syndromes, and comorbidities were also collected. Main outcomes were in-hospital mortality and 90-day mortality after admission.

RESULTS

In total, 378 inpatients (mean age 85.2 ± 5.8 years, 59.3% female) were enrolled. ADL and cognition correlated strongly with the CFS (Spearman's |r|> 0.60) but weakly with the FI-lab (|r|< 0.30). Both the CFS and FI-lab correlated weakly with geriatric syndromes and comorbidities (|r|< 0.40). The correlation between the CFS and FI-lab was also weak (r = 0.28). The CFS and FI-lab were independently associated with in-hospital mortality and 90-day mortality after admission. The Akaike information criterion was lower for models using both the CFS and FI-lab than for models using either tool alone.

CONCLUSIONS

The CFS and FI-lab each reflected only some of the aspects of frailty in acutely hospitalized older patients. The model fit was better when the two frailty scales were used together to assess the mortality risk than when either was used alone.

Prognostic significance of frailty in hospitalized elderly patients with community-acquired pneumonia: a retrospective cohort study

BACKGROUND

Frailty is associated with poor prognosis in a wide range of illnesses. However, its prognostic implications for older patients with community-acquired pneumonia (CAP) are not adequately addressed.

METHODS

In this study, patients were classified into 3 groups according to the frailty index based on standard laboratory tests (FI-Lab) score: robust (FI-Lab < 0.2), pre-frail (FI-Lab 0.2-0.35), and frail (FI-Lab ≥ 0.35). The relationships between frailty and all-cause mortality and short-term clinical outcomes (length of stay, duration of antibiotic therapy, in-hospital mortality) were examined.

RESULTS

Finally, 1164 patients were included, the median age was 75 years (interquartile range: 69, 82), and 438 patients (37.6%) were women. According to FI-Lab, 261(22.4%), 395(33.9%), and 508(43.6%) were robust, pre-frail, and frail. After adjustment for confounding variables, frailty was independently associated with prolonged antibiotic treatment (p = 0.037); pre-frailty and frailty were independently associated with longer inpatient days (p < 0.05 for both). The risk of in-hospital mortality was independently increased in frail patients (HR = 5.01, 95% CI = 1.51-16.57, p = 0.008) but not pre-frail patients (HR = 2.87, 95% CI = 0.86-9.63, p = 0.088) compared to robust patients. During a median follow-up of 33.9 months (interquartile range: 32.8 to 35.1 months), 408 (35.1%) patients died, of whom 29 (7.1%) were robust, 112 (27.5%) were pre-frail, and 267 (65.9%) were frail. Compared to robust patients, frail and pre-frail were significantly associated with increased risk for all-cause death (HR = 4.29, 95%CI: 1.78-10.35 and HR = 2.42 95%CI: 1.01-5.82, respectively).

CONCLUSIONS

Frailty is common among older patients with CAP and is strongly associated with increased mortality, longer length of stay, and duration of antibiotics. A routine frail assessment at the admission of elderly patients with CAP is necessary as the first step for appropriate multidisciplinary interventions.

Development, validation and performance of laboratory frailty indices: A scoping review

INTRODUCTION

Frailty is a syndrome characterised by decline in functional ability and increasing vulnerability to disease and associated with adverse outcomes. Several established methods exist for assessing frailty. This scoping review aims to characterise the development and validation of frailty indices based on laboratory test results (FI-Lab) and to assess their utility.

METHODS

Studies were included in the review if they included data concerning the development and/or testing an FI-Lab using the deficit accumulation method. Studies were identified using PubMed/MEDLINE, Embase (Elsevier), OpenGrey and Google Scholar from 2010 to 2021. Two reviewers independently screened all abstracts, and those that met the inclusion criteria were reviewed in detail. Data extracted included details about the study characteristics, number, type and coding of laboratory variables included, validation, and outcomes. A narrative synthesis of the available evidence was adopted.

RESULTS

The search yielded 915 articles, of which 29 studies were included. In general, 89% of studies were conducted after 2016 and 51% in a hospital-based setting. The number of variables included in FI-Labs ranged from 13 to 77, and 51% included some non-laboratory variables in their indices, with pulse and blood pressure being the most frequent. The validity of FI-Lab was demonstrated through change with age, correlation with established frailty indices and association with adverse health outcomes. The most frequent outcome studied was mortality (79% of the studies), with FI-Lab associated with increased mortality in all but one. Other outcomes studied included self-reported health, institutionalisation, and activities of daily living. The effect of combining the FI-Lab with a non-laboratory-based FI was assessed in 7 studies with a marginal increase in predictive ability.

CONCLUSION

Frailty indices constructed based on the assessment of laboratory variables, appear to be a valid measure of frailty and robust to the choice of variables included.

Using EHR Data to Identify Patient Frailty and Risk for ICU Transfer

The predictive properties of four definitions of a frailty risk score (FRS) constructed using combinations of nursing flowsheet data, laboratory tests, and ICD-10 codes were examined for time to first intensive care unit (ICU) transfer in medical-surgical inpatients ≥50 years of age. Cox regression modeled time to first ICU transfer and Schemper-Henderson explained variance summarized predictive accuracy of FRS combinations. Modeling by age group and controlling for sex, all FRS measures significantly predicted time to first ICU transfer. Further multivariable modeling controlling for clinical characteristics substantially improved predictive accuracy. The effect of frailty on time to first ICU transfer depended on age, with highest risk in 50 to <60 years and ≥80 years age groups. Frailty prevalence ranged from 25.1% to 56.4%. Findings indicate that FRS-based frailty is a risk factor for time to first ICU transfer and should be considered in assessment and care-planning to address frailty in high-risk patients.Frailty prevalence was highest med-surg pts 60 to <70 years (56%); highest risk for time to first ICU transfer was in younger (50 to <60 years) and older (≥80 years) groups.

Personalised Assessment and Rapid Intervention in Frail Patients with Lung Cancer: The Impact of an Outpatient Occupational Therapy Service

INTRODUCTION

Lung cancer and its treatments cause or accelerate frailty, detrimentally affecting function and quality of life. Occupational therapists (OTs) provide global assessments and interventions, but services are often available for inpatients. The impact of holistic assessment and early intervention in the outpatient setting is unknown.

MATERIALS AND METHODS

A tertiary cancer center in North East England piloted a Lung Cancer Outpatient OT Service for patients with thoracic malignancy and a Clinical Frailty Scale Score (CFS) ≥ 5. Service effectiveness was evaluated through calculation of admission avoidance, hospital length-of-stay reduction, completion/impact of advance care planning and patient/family feedback. Demographics, frailty level, required interventions, and onward referrals were recorded.

RESULTS

A total of 153 patients (median age of 71 (range 46-90) received OT assessment and intervention. 48% were in the lowest socioeconomic quintile. Median CFS score was 5 (mildly frail (range: 4-7)) at initial assessment. Total of 918 interventions were delivered (median: 5, IQR: 3-7, Range 0-22). 48% of patients engaged in future planning (national average: 10%-15% P < .0001) and 78.5% achieved preferred place of death (national average 30%). An estimated 37 hospital admissions were avoided. In admitted patients, average inpatient stay when known to the service was 6.5 days less than other frail lung cancer patients in our unit (95% CI 4-9.1 days P < .0001). Higher CFS was associated with poor survival (P < .05).

CONCLUSION

Outpatient OT services can avoid and shorten hospital admissions through advance care planning, management of functional disruption, onward referral to other allied health professionals and palliative care. A comprehensive multidisciplinary outpatient service may benefit patients further and should be the focus of future research.

The Association Between the Clinical Frailty Scale and Adverse Health Outcomes in Older Adults in Acute Clinical Settings - A Systematic Review of the Literature

Background

Frail older adults experience higher rates of adverse health outcomes. Therefore, assessing pre-hospital frailty early in the course of care is essential to identify the most vulnerable patients and determine their risk of deterioration. The Clinical Frailty Scale (CFS) is a frailty assessment tool that evaluates pre-hospital mobility, energy, physical activity, and function to generate a score that ranges from very fit to terminally ill.

Purpose

To synthesize the evidence of the association between the CFS degree and all-cause mortality, all-cause readmission, length of hospital stay, adverse discharge destination, and functional decline in patients >65 years in acute clinical settings.

Design

Systematic review with narrative synthesis.

Methods

Electronic databases (PubMed, EMBASE, CINAHL, Scopus) were searched for prospective or retrospective studies reporting a relationship between pre-hospital frailty according to the CFS and the outcomes of interest from database inception to April 2020.

Results

Our search yielded 756 articles, of which 29 studies were included in this review (15 were at moderate risk and 14 at low risk of bias). The included studies represented 26 cohorts from 25 countries (N = 44166) published between 2011 and 2020. All included studies showed that pre-hospital frailty according to the CFS is an independent predictor of all adverse health outcomes included in the review.

Conclusion

A primary purpose of the CFS is to grade clinically increased risk (i.e. risk stratification). Our results report the accumulated knowledge on the risk-predictive performance of the CFS and highlight the importance of routinely including frailty assessments, such as the CFS, to estimate biological age, improve risk assessments, and assist clinical decision-making in older adults in acute care. Further research into the potential of the CFS and whether implementing the CFS in routine practice will improve care and patients' quality of life is warranted.

The frailty index based on laboratory test data as a tool to investigate the impact of frailty on health outcomes: a systematic review and meta-analysis

The frailty index (FI) quantifies frailty as deficit accumulation. It has been adapted to employ laboratory test data (FI-Lab). Our objective was to systematically review and meta-analyse the FI-Lab's ability to predict mortality. Secondary objectives were to review the FI-Lab's association with adverse health outcomes and whether FI-Lab scores differed between the sexes. A systematic literature search was carried out using six online databases to identify studies that measured the FI-Lab in humans. Hazard ratios (HRs) were combined in a meta-analysis to create a pooled risk estimate for mortality. Of the 1,201 papers identified, spanning January 2010 until 11 July 2022, 38 were included. FI-Lab scores per 0.01 unit increase predicted mortality overall (HR = 1.04; 95% confidence interval (CI) = 1.03-1.05) and for studies with a mean age of 81+ years (HR = 1.04; 95% CI = 1.03-1.05). The quality of evidence for these meta-analyses are moderate and high, respectively. Further, higher FI-Lab scores were associated with more frequent adverse health outcomes. Sex differences in FI-Lab scores varied, with no consistent indication of a sex effect. The FI-Lab is associated with mortality and with a variety of adverse health outcomes. No consistent sex differences in FI-Lab scores were observed, with several studies in disagreement. Notably, these conclusions were most relevant to older (65+ years old) individuals; further evidence in younger people is needed in both clinical and population representative studies.

Frailty Index was Associated With Adverse Outcomes in Admitted Elderly Patients With Type 2 Diabetes Mellitus

We aimed to investigate the role of the laboratory frailty index (LFI) in diabetic complications and incident disability in admitted older patients with type 2 diabetes mellitus (T2DM). We retrospectively collected the clinical data of older patients with T2DM from December 2018 to May 2020. Frailty was quantified using the LFI, which considers the accumulation of 27 items of abnormal laboratory outcomes. Univariate and multivariate analyses were performed to evaluate the relationship between LFI and diabetes-related adverse outcomes. In total, 293 consecutive older patients with T2DM were recruited for this study. According to the predefined LFI criteria, 110 (37.5%) participants were non-frail, 131 (44.7%) were prefrail, and 52 (17.8%) were frail. Univariate and multivariate analysis revealed that LFI was associated with the diabetic microangiopathy complications (odds ratio for prefrail [ORprefrail] 1.760, 95% confidence interval for prefrail [CIprefrail] 1.019-3.041, P = .043; ORfrail 4.667, 95% CIfrail 2.012-10.826, P < .001) and activities of daily living (ADL) disability (ORprefrail 2.323, 95% CIprefrail 1.209-4.463, P = .011; ORfrail 9.367, 95% CIfrail 4.030-21.775, P < .001), but not with the diabetic macroangiopathy complications and diabetic peripheral neuropathy. Frailty, as determined by the LFI, was proven to be an effective tool for the prediction of diabetic microangiopathy complications and ADL disability.

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.

The Clinical Frailty Scale can indicate prognosis and care requirements on discharge in oncology and haemato-oncology inpatients: A cohort study

OBJECTIVES

Routinely used performance status scales, assessing patients' suitability for cancer treatment, have limited ability to account for multimorbidity, frailty and cognition. The Clinical Frailty Scale (CFS) is a suggested alternative, but research detailing its use in oncology is limited. This study aims to evaluate if CFS is associated with prognosis and care needs on discharge in oncology inpatients.

METHODS

We evaluated a large, single-centre cohort study in this research. CFS was recorded for adult inpatients at a Regional Cancer Centre. The associations between CFS, age, tumour type, discharge destination and care requirements and survival were evaluated.

RESULTS AND CONCLUSIONS

A total of 676 patients were included in the study. Levels of frailty were high (Median CFS 6, 81.8% scored ≥5) and CFS correlated with performance status (R = 0.13: P = 0.047). Patients who were frail (CFS ≥ 5) were less likely to be discharged home (62.9%) compared with those who were not classed as frail (86.1%) (OR 3.6 [95%CI 2.1 to 6.3]: P < 0.001). Higher CFS was significantly associated with poorer prognosis in all ages. Solid organ malignancy (hazard ratio [HR] 2.60 [95%CI 2.05-3.32]) and CFS (HR 1.43 [95%CI 1.29-1.59]; P < 0.001) were independently associated with poorer survival. This study demonstrated that CFS may help predict prognosis in adult oncology inpatients of any age. This may aid informed shared decision-making in this setting. Future work should establish if routine CFS measurement can aid the appropriate prescription of systemic therapy and enable early conversations about discharge planning.

The association of a frailty index from laboratory tests and vital signs with clinical outcomes in hospitalized older adults

BACKGROUND

Frailty, a state of vulnerability to stressors resulting from loss of physiological reserve due to multisystemic dysfunction, is common among hospitalized older adults. Hospital clinicians need objective and practical instruments that identify older adults with frailty. The FI-LAB is based on laboratory values and vital signs and may capture biological changes of frailty that predispose hospitalized older adults to complications. The study's aim was to assess the association of the FI-LAB versus VA-FI with hospital and post-hospital clinical outcomes in older adults.

METHODS

Retrospective cohort study was conducted on Veterans aged ≥60 admitted to a VA hospital. We identified acute hospitalizations January 2011-December-2014 with 1-year follow-up. A 31-item FI-LAB was created from blood laboratory tests and vital signs collected within the first 48 h of admission and scores were categorized as low (<0.25), moderate (0.25-0.40), and high (>0.40). For each FI-LAB group, we obtained odds ratio (OR) and confidence intervals (CI) for hospital and post-hospital outcomes using multivariate binomial logistic regression. Additionally, we calculated hazard ratios (HR) and CI for all-cause in-hospital mortality comparing the high and moderate FI-LAB group with the low group.

RESULTS

Patients were 1407 Veterans, mean age 72.7 (SD = 9.0), 67.8% Caucasian, 96.1% males, 47.0% (n = 661), 41.0% (n = 577), and 12.0% (n = 169) were in the low, moderate, and high FI-LAB groups, respectively. Moderate and high scores were associated with prolonged LOS, OR:1.62 (95% CI:1.29-2.03); and 3.36 (95% CI:2.27-4.99), ICU admission, OR:1.40 (95% CI:1.03-1.90); and OR:2.00 (95% CI:1.33-3.02), nursing home placement OR:2.36 (95% CI:1.26-4.44); and 5.99 (95% CI:2.83-12.70), 30-day readmissions OR:1.74 (95% CI:1.20-2.52); and 2.20 (95% CI:1.30-3.74), 30-day mortality OR: 2.51 (95% CI:1.01-6.23); and 8.97 (95% CI:3.42-23.53), 6-month mortality OR:3.00 (95% CI:1.90-4.74); and 6.16 (95% CI:3.55-10.71), and 1-year mortality OR: 2.66 (95% CI:1.87-3.79); and 4.76 (95% CI:3.00-7.54) respectively. The high FI-LAB group showed higher risk of in-hospital mortality, HR:18.17 (95% CI:4.01-80.52) with an area-under-the-curve of 0.843 (95% CI:0.75-0.93).

CONCLUSIONS

High and moderate FI-LAB scores were associated with worse in-hospital and post-hospital outcomes. The FI-LAB may identify hospitalized older patients with frailty at higher risk and assist clinicians in implementing strategies to improve outcomes.

Impact of preoperative laboratory frailty index on mortality and clinical outcomes in older surgical patients with cancer

Frailty in older patients is associated with poor postoperative outcomes. The use of uncomplicated frailty measurement tools is preferred in busy clinical settings. Therefore, we validated the frailty index using routine laboratory data and the surgical outcomes of older patients with cancer who underwent cancer resection. We retrospectively analyzed 9015 patients aged 65 years and older who underwent cancer resection at a single tertiary hospital. Based on electronic-medical-record data regarding preoperative blood test results and vital signs, Laboratory Frailty Index (FI-Lab) scores were generated to measure preoperative frailty. The associations of FI-Lab with postoperative length of stay (LOS), readmission within 30 days, intensive care unit (ICU) admission within 30 days, and mortality were evaluated. The mean FI-Lab score of the 9015 patients was 0.20 ± 0.10. Increased FI-Lab scores (0.25–0.4; > 0.4) were associated with longer LOS, increased readmission within 30 days of surgery, ICU admission, and increased mortality, compared with FI-Lab scores < 0.25. The FI-Lab score, as a frailty indicator, was able to predict the risk of poor postoperative outcomes. Therefore, the FI-Lab is a potentially useful tool for assessing preoperative frailty in older patients with cancer in acute clinical setting.

Preoperative frailty based on laboratory data and postoperative health outcomes in patients undergoing coronary artery bypass graft surgery

BACKGROUND

Frailty is associated with adverse surgical outcomes. Patients with cardiovascular diseases have many risk factors of frailty; thus, preoperative frailty evaluation is necessary to predict adverse outcomes after coronary artery bypass graft (CABG) surgery. Laboratory data based-frailty assessments are objective and not time-consuming, addressing the need for an accurate but simple frailty screening for patients awaiting CABG surgery.

OBJECTIVES

This retrospective study aimed to determine the association between laboratory based-frailty and patient health outcomes after CABG surgery.

METHODS

We evaluated 896 patients who underwent on-pump or off-pump CABG surgery between August 1, 2015 and July 31, 2020 at a tertiary hospital. The frailty index-laboratory (FI-LAB), which comprises 32 laboratory parameters and vital signs, was used for frailty assessment.

RESULTS

The patients were divided into three groups according to their preoperative FI-LAB level as low (FI-LAB <0.25, 23.0%), moderate (FI-LAB ≥0.25 to ≤0.4, 54.9%), and high (FI-LAB>0.4, 22.1%) frailty groups. In the confounder-adjusted analysis, the lengths of hospital stay and intensive care unit stay were longer by 2.20 days (p=.023) and by 0.89 days (p=.009), respectively, in the high frailty group than those in the low frailty group. The odds ratio for 30-day readmission was also 2.58 times higher in the high frailty group than that in the low frailty group.

CONCLUSION

A high preoperative FI-LAB score indicates increasing risks of adverse postoperative outcomes among CABG surgery patients. FI-LAB has potential strengths to capture the need for a more thorough frailty assessment for cardiac surgery patients.

Paracetamol dosing in hospital and on discharge for older people who are frail or have low body weight

Aims

To describe paracetamol dosing and liver function test (LFT) monitoring in older hospital inpatients who are frail or have low body weight.

Methods

Retrospective observational study, at a 790‐bed metropolitan public health service in Australia. Patients aged ≥70 years, with body weight <50 kg or frailty index based on laboratory data (FI‐Lab) score ≥0.3, who were administered paracetamol during an admission with length‐of‐stay >72 hours, were included. Data were extracted from electronic medical records. Paracetamol doses administered in hospital, and doses prescribed on discharge, were compared against consensus guidelines that recommended ≤60 mg/kg/d for older people weighing <50 kg, and ≤3000 mg/d for frail older people.

Results

In total, 240 admissions (n = 229 patients, mean age 84.7 years) were analysed. During 150 (62.5%) admissions, higher than recommended paracetamol doses were prescribed. On 138 (57.5%) occasions, patients were prescribed paracetamol on discharge, and 112/138 (81.2%) doses were higher than recommended. Most discharge prescriptions (97/138, 70.3%) were for regular administration. The median daily dose on discharge for patients <50 kg was 83.7 mg/kg (interquartile range 73.6–90.9 mg/kg). For frail patients ≥50 kg, the median daily discharge dose was 3990 mg (interquartile range 3000–4000 mg). LFTs were measured in hospital for 151/200 (75.5%) and 93/166 (56.0%) patients who received paracetamol for >48 hours and >5 days, respectively.

Conclusion

Majority of paracetamol doses prescribed for frail or low‐weight older patients in hospital and on discharge were higher than recommended in consensus guidelines. LFTs were not measured for 44% patients who received paracetamol regularly for >5 days. Further studies are needed to explore long‐term outcomes of this practice.

Accuracy, feasibility and predictive ability of different frailty instruments in an acute geriatric setting

Aim

To investigate the feasibility and accuracy of four frailty instruments: FRAIL, Clinical Frailty Scale (CFS), hand grip strength (HGS) and the Spanish Frailty-VIG; and to evaluate their ability to predict adverse outcomes in an acute care setting (ACS).

Findings

The four instruments had high feasibility but provided variable prevalence of frailty. FRAIL and CFS predicted well for three-month mortality, and FRAIL also for length of stay. However, none of the instruments predicted for the other outcomes.

Message

The FRAIL and CFS may be of value in diagnostic and therapeutic decision-making in an acute geriatric setting, given their prognostic ability and feasibility. Further studies are needed to identify the best frailty instrument in an ACS.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41999-022-00645-1.

Purpose

To analyze the feasibility, accuracy and the ability of different frailty instruments to predict adverse outcomes.

Methods

A prospective cohort study was conducted in patients ≥ 70 years admitted to the acute care setting (ACS). Feasibility and prevalence of frailty were assessed by FRAIL, Clinical Frailty Scale (CFS), hand grip strength (HGS) and the Spanish Frailty-VIG. Receiver operator characteristic (ROC) curves and area under the curve (AUC) were performed to identify frailty according to each instrument, setting VIG as the reference. For each instrument, multiple logistic regressions were used to examine the effect of frailty on primary outcome (i.e., three-month mortality) and secondary outcomes (i.e., in-hospital mortality, length of stay, institutionalization, functional decline and 30-day readmission).

Results

A total of 185 patients were included, with a median age of 89 years. The feasibility of the instruments was 100%, except for HGS (67%). The prevalence of frailty varied from 65.2% (FRAIL) to 86.7% (VIG). AUCs against VIG ranged from 0.69 (95% confidence interval [CI] 0.57–0.81: FRAIL) to 0.77 (95% CI 63.5–90.2: CFS). Frail patients defined by FRAIL were 2.7times more likely to have a prolonged length of stay than non-frail patients (95% CI 1.385–5.416). Three-month mortality occurred more among frail patients, either defined by FRAIL (OR 2.5; 95% CI 1.072–5.881) or CFS (OR 3.7; 95% CI 1.255–10.812), than in non-frail patients.

Conclusion

The four instruments had high feasibility providing variable prevalence of frailty. FRAIL and CFS predicted well for three-month mortality, and FRAIL also for length of stay. However, none of the instruments predicted for the other secondary outcomes of the study.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41999-022-00645-1.

Association of a modified laboratory frailty index with adverse outcomes in geriatric rehabilitation inpatients: RESORT

A higher number of laboratory measurements is associated with mortality in patients admitted to hospital, but is not part of the frailty index based on laboratory tests (FILab). This study aimed to modify the FI-Lab (mFI-Lab) by accounting for the number of laboratory measurements and compare its validity to predict institutionalization and mortality at three-month post-discharge with the clinical frailty scale (CFS) in geriatric rehabilitation inpatients. In 1819 patients (median age 83.3 [77.6-88.3], 56.6% female), a higher FI-Lab was not associated with institutionalization but a higher risk of mortality. A higher mFI-Lab was associated with lower odds of institutionalization but a higher risk of mortality. A higher CFS was associated with institutionalization and higher mortality. The Akaike information criterion value was lowest for the CFS, followed by the mFI-Lab and the FI-Lab. The CFS is better than the mFI-Lab predicting short-term adverse outcomes in geriatric rehabilitation inpatients. When using laboratory data to estimate frailty, the mFI-Lab rather than the FI-Lab should be used.

The relative impact of socioeconomic position and frailty varies by population setting

Introduction

Frailty and socioeconomic position (SEP) are well-established determinants of health. However, we know less about the contributions of frailty and SEP in older adults, especially in acute settings. We set out to answer how frailty and SEP might influence health outcomes in older people, comparing a population sample and patients managed by a speciality acute frailty service.

Methods

We used the Delirium and Population Health Informatics Cohort, a population sample of 1510 individuals aged ≥70 years from the London Borough of Camden and 1750 acute frailty patients. SEP was determined using the Index of Multiple Deprivation. Linear and Cox proportional hazard regression models were conducted to assess SEP on frailty, readmission, and mortality outcomes.

Results

In the population sample, SEP was significantly associated with frailty and mortality with successive increases in rate of death for each IMD quintile (HR = 1.28, 95% CI 1.11 to 1.49, P < 0.005). Increasing SEP, age, and admission status among hospitalized individuals were associated with greater frailty. For individuals seen by the speciality frailty service, SEP was not associated with frailty, mortality, or readmission.

Discussion

When older people experience acute illness severe enough to require secondary care, particularly specialist services, this overcomes any prior advantages conferred by a higher SEP.

Comparison of the modified Frailty-Index based on laboratory tests and the Clinical Frailty Scale in predicting mortality among geriatric rehabilitation inpatients: RESORT

OBJECTIVE

To compare the associations of the FI-lab, modified (m)FI-lab and Clinical Frailty Scale (CFS) with one-year mortality.

STUDY DESIGN

An observational longitudinal inception cohort of inpatients admitted to the geriatric rehabilitation wards in the Royal Melbourne Hospital, Victoria, Australia.

MAIN OUTCOME MEASURES

The measured ratio was defined as the proportion of measured laboratory tests to the total number of tests (n = 77). The FI-lab is the proportion of abnormal results to the total measured laboratory tests. The mFI-lab was calculated by dividing the FI-lab by the measured ratio. The measured ratio of laboratory tests, FI-lab, mFI-lab and CFS were assessed at admission to geriatric rehabilitation. Patients' mortality data were obtained from the Registry of Births, Deaths and Marriages Victoria and medical records.

RESULTS

The total of 1819 inpatients had a median age of 83.3 [77.5-88.3] years and 56.5% were female. The median measured ratio, FI-lab, mFI-lab and CFS scores were 0.58 [0.47-0.70], 0.31 [0.23-0.38], 0.51 [0.38-0.69] and 6 (Abbasi et al., 2018Gill, Gahbauer, Allore & Han, 2006; Howlett et al., 2014;) respectively. The one-year mortality rate was 17.1%. The measured ratio was not associated with one-year mortality. Higher FI-lab (hazard ratio (HR)=1.180, 95%CI: 1.037-1.343), mFI-lab (HR=1.074, 95%CI: 1.030-1.119) and CFS scores (HR=1.350, 95%CI: 1.191-1.530) were associated with higher risk of one-year mortality. The area under the curve (AUC) of FI-lab, mFI-lab and CFS with one-year mortality were 0.581, 0.587 and 0.612 respectively.

CONCLUSION

The FI-lab, mFI-lab and CFS poorly predict mortality in geriatric rehabilitation inpatients despite the statistically significant associations shown.

Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare

Healthcare costs due to unplanned readmissions are high and negatively affect health and wellness of patients. Hospital readmission is an undesirable outcome for elderly patients. Here, we present readmission risk prediction using five machine learning approaches for predicting 30-day unplanned readmission for elderly patients (age ≥ 50 years). We use a comprehensive and curated set of variables that include frailty, comorbidities, high-risk medications, demographics, hospital, and insurance utilization to build these models. We conduct a large-scale study with electronic health record (her) data with over 145,000 observations from 76,000 patients. Findings indicate that the category boost (CatBoost) model outperforms other models with a mean area under the curve (AUC) of 0.79. We find that prior readmissions, discharge to a rehabilitation facility, length of stay, comorbidities, and frailty indicators were all strong predictors of 30-day readmission. We present in-depth insights using Shapley additive explanations (SHAP), the state of the art in machine learning explainability.

Frailty-aware care: giving value to frailty assessment across different healthcare settings

Healthcare systems need to adapt to better serve an aging population with complex presentations. Frailty assessments are a potential means to address this heterogeneity in aging to identify individuals at increased risk for adverse health outcomes. Furthermore, frailty assessments offer an opportunity to optimize patient care in various healthcare settings. While the vast number of frailty assessment tools available can be a source of confusion for clinicians, each tool has features adaptable to the constraints and goals of different healthcare settings. This review discusses and compares barriers, facilitators, and the application of frailty assessments in primary care, the emergency department/intensive care unit and surgical care to cover a breadth of settings with different frailty assessment considerations. The implementation of frailty-aware care across healthcare settings potentiates better healthcare outcomes for older adults.

Frailty and In-Hospital Mortality Risk Using EHR Nursing Data

PurposeThe purpose of this study was to evaluate four definitions of a Frailty Risk Score (FRS) derived from EHR data that includes combinations of biopsychosocial risk factors using nursing flowsheet data or International Classification of Disease, 10th revision (ICD-10) codes and blood biomarkers and its predictive properties for in-hospital mortality in adults ≥50 years admitted to medical-surgical units. Methods In this retrospective observational study and secondary analysis of an EHR dataset, survival analysis and Cox regression models were performed with sociodemographic and clinical covariates. Integrated area under the ROC curve (iAUC) across follow-up time based on Cox modeling was estimated. Results The 46,645 patients averaged 1.5 hospitalizations (SD = 1.1) over the study period and 63.3% were emergent admissions. The average age was 70.4 years (SD = 11.4), 55.3% were female, 73.0% were non-Hispanic White (73.0%), mean comorbidity score was 3.9 (SD = 2.9), 80.5% were taking 1.5 high risk medications, and 42% recorded polypharmacy. The best performing FRS-NF-26-LABS included nursing flowsheet data and blood biomarkers (Adj. HR = 1.30, 95% CI [1.28, 1.33]), with good accuracy (iAUC = .794); the reduced model with age, sex, and FRS only demonstrated similar accuracy. The poorest performance was the ICD-10 code-based FRS. Conclusion The FRS captures information about the patient that increases risk for in-hospital mortality not accounted for by other factors. Identification of frailty enables providers to enhance various aspects of care, including increased monitoring, applying more intensive, individualized resources, and initiating more informed discussions about treatments and discharge planning.

Frailty assessment using routine clinical data: An integrative review

BACKGROUND

Frailty is a common but complex problem in older adults. Frailty assessment using routine clinical data has been suggested as a pragmatic approach based on electronic health records from primary care center or hospital settings.

PURPOSE

We aimed to explore the tools and outcome variables used in the published studies on frailty assessment using routine clinical data.

METHODS

An integrative literature review was conducted using the method of Whittemore and Knafl. A literature search was conducted in PubMed, EMBASE, and CINAHL from January 2010 to October 2021.

RESULTS

A total of 45 studies and thirteen frailty assessment tools were analyzed. The assessment items were generally biased toward frailty's risk factors rather than the mechanisms or phenotypes of frailty. Similar to using conventional tools, routine clinical data-based frailty was associated with adverse health outcomes.

CONCLUSIONS

Frailty assessment based on routine clinical data could efficiently evaluate frailty using electronic health records from primary care centers or hospitals. However, they need refinement to consider the risk factors, mechanisms, and frailty phenotypes.

Respiratory Symptoms Are Associated With Frailty in Older Adults With Normal Spirometry, Independent of Smoking, in the Canadian Longitudinal Study of Aging

BACKGROUND

Recent studies have demonstrated that even in the absence of lung impairment as determined by spirometry, smoking and respiratory symptoms are associated with poor overall health and well-being. However, this relationship is not well defined; and it remains unclear the degree to which symptoms are related to poor health, independent of smoking. This is of particular importance to older adults, as they are more likely to exhibit respiratory symptoms and are, therefore, at risk of not receiving appropriate treatment if they have never smoked and have normal spirometry.

METHODS

We performed a cross-sectional analysis of data from the Canadian Longitudinal Study on Aging to delineate the associations of respiratory symptoms and smoking on the health of participants age 45-86 who exhibited normal spirometry. Participant health was estimated using a frailty index, a multidimensional measure of vulnerability to adverse outcomes that has been validated in numerous health settings.

RESULTS

Of the 21,293 participants included in our analysis, 87% exhibited a normal FEV₁, FVC, and FEV₁/FVC; of those, 45% reported at least one respiratory symptom, and 50% were former or current smokers. Both respiratory symptoms and smoking were independently associated with frailty (median interquartile range [IQR] = 0.11 [0.07-0.15]), the most substantial associations observed for those having at least one respiratory symptom (adjusted β 0.023, 95% CI 0.022-0.025) and current smokers with > 10 pack-year exposure (adjusted β 0.014, 95% CI [0.010-0.019). Not only was the association between symptoms and frailty evident in never smokers, a significant proportion of the total effect of smoking on frailty was observed to be mediated by symptoms.

CONCLUSIONS

Our data show that respiratory symptoms, regardless of smoking history, were a significant correlate of frailty in older adults with normal spirometry. Hence, they should not be simply regarded as a benign by-product of aging.

Development and validation of a prediction model for actionable aspects of frailty in the text of clinicians' encounter notes

OBJECTIVE

Frailty is a prevalent risk factor for adverse outcomes among patients with chronic lung disease. However, identifying frail patients who may benefit from interventions is challenging using standard data sources. We therefore sought to identify phrases in clinical notes in the electronic health record (EHR) that describe actionable frailty syndromes.

MATERIALS AND METHODS

We used an active learning strategy to select notes from the EHR and annotated each sentence for 4 actionable aspects of frailty: respiratory impairment, musculoskeletal problems, fall risk, and nutritional deficiencies. We compared the performance of regression, tree-based, and neural network models to predict the labels for each sentence. We evaluated performance with the scaled Brier score (SBS), where 1 is perfect and 0 is uninformative, and the positive predictive value (PPV).

RESULTS

We manually annotated 155 952 sentences from 326 patients. Elastic net regression had the best performance across all 4 frailty aspects (SBS 0.52, 95% confidence interval [CI] 0.49-0.54) followed by random forests (SBS 0.49, 95% CI 0.47-0.51), and multi-task neural networks (SBS 0.39, 95% CI 0.37-0.42). For the elastic net model, the PPV for identifying the presence of respiratory impairment was 54.8% (95% CI 53.3%-56.6%) at a sensitivity of 80%.

DISCUSSION

Classification models using EHR notes can effectively identify actionable aspects of frailty among patients living with chronic lung disease. Regression performed better than random forest and neural network models.

CONCLUSIONS

NLP-based models offer promising support to population health management programs that seek to identify and refer community-dwelling patients with frailty for evidence-based interventions.

Clinical frailty as a key characteristic of the patient population of the NHS Nightingale North West COVID-19 temporary emergency field hospital: cohort study April to June 2020

Objectives

COVID-19 temporary emergency ‘field’ hospitals have been established in the UK to support the surge capacity of the National Health Service while protecting the community from onward infection. We described the population of one such hospital and investigated the impact of frailty on clinical outcomes.

Design

Cohort study.

Setting

NHS Nightingale Hospital North West, April–June 2020.

Participants

All in-patients with COVID-19.

Main Outcome Measures

Mortality and duration of admission.

Methods

We analysed factors associated with mortality using logistic regression and admission duration using Cox's regression, and described trends in frailty prevalence over time using linear regression.

Results

A total of 104 COVID-19 patients were admitted, 74% with moderate-to-severe frailty (clinical frailty score, CFS > 5). A total of 84 were discharged, 14 transferred to other hospitals, and six died on site. High C-reactive protein (CRP) > 50 mg/dL predicted 30-day mortality (adjusted odds ratio 11.9, 95%CI 3.2–51.5, p < 0.001). Patients with CFS > 5 had a 10-day median admission, versus 7-day for CFS ≤ 5 and half the likelihood of discharge on a given day (adjusted hazard ratio 0.51, 95%CI 0.29–0.92, p  =  0.024). CRP > 50 mg/dL and hospital-associated COVID-19 also predicted admission duration. As more frail patients had a lower rate of discharge, prevalence of CFS > 5 increased from 64% initially to 90% in the final week (non-zero slope p < 0.001). Conclusions: The NNW population was characterized by high levels of frailty, which increased over the course of the hospital's operation, with subsequent operational implications. Identifying and responding to the needs of this population, and acknowledging the risks of this unusual clinical context, helped the hospital to keep patients safe.

Clinical frailty score as an independent predictor of outcome in COVID-19 hospitalised patients

PURPOSE OF THE STUDY

We explored potential predictive variables associated with outcomes using baseline clinical parameters of 500 hospitalised patients with COVID -19 in a single centre, UK.

METHODS

Retrospective study collecting demographic and clinical characteristics of patients admitted at Southend University Hospital from 20th February to 7th May 2020.

RESULTS

The mean age of the cohort admitted to hospital with Covid-19 was 69.4 and 58% were over 70. Comorbidities were more frequently observed in non-survivors, whose mean Clinical Frailty Scale was significantly higher (5 vs 3) than survivors, p < 0.001. In addition, mean C-reactive protein was significantly higher.

CONCLUSION

Older and frailer patients with high inflammatory markers were at risk of poor outcomes. Integrated frailty and age-based risk stratification is essential, in addition to monitoring saturation /FiO2 ratio (SFR) and inflammatory markers throughout the disease course to allow for early intervention to improve patient outcomes. A frailty-based risk-stratification approach, rather than age may prove more valuable when considering interventions in patients with multiple comorbidities.

Association between clinical frailty, illness severity and post-discharge survival: a prospective cohort study of older medical inpatients in Norway

Aim

To assess impact of frailty screening and two markers of illness severity on survival following discharge from the hospital.

Findings

Independently of age, ward (acute geriatric and general medical) and comorbidity, both higher degree of frailty and illness severity associated with reduced survival probability following discharge. The impact of frailty on survival was higher in those experiencing high clinical and laboratory illness severity.

Message

The prognostic value of frailty screening increased when performed in conjunction with two markers of illness severity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41999-021-00555-8.

Purpose

Study associations between frailty, illness severity and post-discharge survival in older adults admitted to medical wards with acute clinical conditions.

Methods

Prospective cohort study of 195 individuals (mean age 86; 63% females) admitted to two medical wards with acute illness, followed up for all-cause mortality for 20 months after discharge. Ward physicians screened for frailty and quantified its degree from one to eight using Clinical Frailty Scale (CFS), while clinical illness severity was estimated by New Early Warning Score 2 (NEWS2) and laboratory illness severity was calculated by a frailty index (FI-lab) using routine blood tests.

Results

CFS, NEWS2 and FI-lab scores were independently associated with post-discharge survival in an adjusted Cox proportional hazards model with age, ward category (acute geriatric and general medical) and comorbidity as covariates. Adjusted hazard ratios and 95% confidence intervals were 1.54 (1.24–1.91) for CFS, 1.12 (1.03–1.23) for NEWS2, and 1.02 (1.00–1.05) for FI-lab. A frailty × illness severity category interaction effect (p = 0.003), suggested that the impact of frailty on survival was greater in those experiencing higher levels of illness severity. Among patients with at least moderate frailty (CFS six to eight) and high illness severity according to both NEWS2 and FI-lab, two (13%) were alive at follow-up.

Conclusion

Frailty screening aided prognostication of survival following discharge in older acutely ill persons admitted to medical wards. The prognostic value of frailty increased when combined with readily available illness severity markers acquired during admission.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41999-021-00555-8.

The degree of frailty as a translational measure of health in aging

Frailty is a multiply determined, age-related state of increased risk for adverse health outcomes. We review how the degree of frailty conditions the development of late-life diseases and modifies their expression. The risks for frailty range from subcellular damage to social determinants. These risks are often synergistic-circumstances that favor damage also make repair less likely. We explore how age-related damage and decline in repair result in cellular and molecular deficits that scale up to tissue, organ and system levels, where they are jointly expressed as frailty. The degree of frailty can help to explain the distinction between carrying damage and expressing its usual clinical manifestations. Studying people-and animals-who live with frailty, including them in clinical trials and measuring the impact of the degree of frailty are ways to better understand the diseases of old age and to establish best practices for the care of older adults.

Do specific delirium aetiologies have different associations with death? A longitudinal cohort of hospitalised patients

Aim

To investigate aetiology-specific associations with mortality among older patients with delirium.

Findings

Delirium predicted mortality, as did inflammatory and metabolic disorders. However, there was no evidence for any interactions between these factors.

Message

Mortality from delirium is consistent regardless of underlying aetiology, suggesting that no aetiology carries better or worse prognosis than another.

Purpose

To describe aetiology-specific associations with mortality among older hospital patients with delirium.

Methods

Over 21 months, a cohort of 1702 patients with 2471 acute hospital admissions (median age 85, IQR 80–90, 56% women) were assessed for delirium, categorised with inflammatory and metabolic aetiologies based on available laboratory results, and followed up for all-cause mortality. Interactions between aetiology and delirium were tested.

Results

The total mortality for the cohort was 35.2%. While inflammation, metabolic disturbance, and delirium at time of admission all demonstrated independent associations with mortality, there was no evidence for any interactions between delirium and these laboratory-measured aetiologies.

Conclusions

Delirium remains an important predictor of death in older hospital patients, irrespective of underlying aetiology.

A practical laboratory index to predict institutionalization and mortality - an 18-year population-based follow-up study

Background

Previously, several indexes based on a large number of clinical and laboratory tests to predict mortality and frailty have been produced. However, there is still a need for an easily applicable screening tool for every-day clinical practice.

Methods

A prospective study with 10- and 18-year follow-ups. Fourteen common laboratory tests were combined to an index. Cox regression model was used to analyse the association of the laboratory index with institutionalization and mortality.

Results

The mean age of the participants (n = 1153) was 73.6 (SD 6.8, range 64.0–100.0) years. Altogether, 151 (14.8%) and 305 (29.9%) subjects were institutionalized and 422 (36.6%) and 806 (69.9%) subjects deceased during the 10- and 18-year follow-ups, respectively. Higher LI (laboratory index) scores predicted increased mortality. Mortality rates increased as LI scores increased both in unadjusted and in age- and gender-adjusted models during both follow-ups. The LI did not significantly predict institutionalization either during the 10- or 18-year follow-ups.

Conclusions

A practical index based on routine laboratory tests can be used to predict mortality among older people. An LI could be automatically counted from routine laboratory results and thus an easily applicable screening instrument in clinical settings.

Rationing care in COVID-19: if we must do it, can we do better?

The COVID-19 pandemic has seen a proposal for frailty to be used as a rationing criterion. This commentary suggests circumstances under which that is defensible: in the face of lack of capacity to treat everyone, and as an alternative to age in stratifying risk. How best to stratify risk is likely to evolve and may include information about illness severity and dynamic measures. Current research must focus on mobilizing better, COVID-19-specific prognostic information, with a goal of best discriminating which lives are most and least likely to be saved should scarcity of resources dictate that not everyone can receive critical care.

Clinical Characteristics, Frailty, and Mortality of Residents With COVID-19 in Nursing Homes of a Region of Madrid

Objectives

To describe the clinical characteristics, 30-day mortality, and associated factors of patients living in nursing homes (NH) with COVID-19, from March 20 to June 1, 2020.

Design

This is a retrospective study. A geriatric hospital-based team acted as a consultant and coordinated the care of older people living in NHs from the hospital.

Setting and Participants

A total of 630 patients aged 70 and older with Coronavirus Disease 2019 COVID-19 living in 55 NHs.

Methods

A logistic regression was performed to analyze the factors associated with mortality. In addition, Kaplan-Meier curves were applied according to mortality and its associated factors using the log-rank Mantel-Cox test.

Results

The diagnosis of COVID-19 was mainly made by clinical compatibility (N = 430). Median age was 87 years, 64.6% were women and 45.9% were transferred to be cared for at the hospital. A total of 282 patients died (44.7%) within the 30 days of first attention by the team. A severe form of COVID-19 occurred in 473 patients, and the most frequent symptoms were dyspnea (n = 332) and altered level of consciousness (n = 301). According to multiple logistic regression, male sex (P = .019), the Clinical Frailty Score (CFS) ≥6 (P = .004), dementia (P = .012), dyspnea (P < .001), and having a severe form of COVID-19 (P = .001), were associated with mortality, whereas age and care setting were not.

Conclusions and Implications

Mortality of the residents living in NHs with COVID-19 was almost 45%. The altered level of consciousness as an atypical presentation of COVID-19 should be considered in this population. A severe form of the disease, present in more than three-quarters of patients, was associated with mortality, apart from the male sex, CFS ≥6, dementia, and dyspnea, whereas age and care setting were not. These findings may also help to recognize patients in which the Advance Care Planning process is especially urgent to assist in the decisions about their care.

The role of illness acuity on the association between frailty and mortality in emergency department patients referred to internal medicine

BACKGROUND

we investigated whether two frailty tools predicted mortality among emergency department (ED) patients referred to internal medicine and how the level of illness acuity influenced any association between frailty and mortality.

METHODS

two tools, embedded in a Comprehensive Geriatric Assessment (CGA), were the clinical frailty scale (CFS) and a 57-item deficit accumulation frailty index (FI-CGA). Illness acuity was assessed using the Canadian Triage and Acuity Scale (CTAS). We examined all-cause 30-day and 6-month mortality and time to death.

RESULTS

in 808 ED patients (mean age ± SD 80.8 ± 8.8, 54.4% female), the mean FI-CGA score was 0.44 ± 0.14, and the CFS was 5.6 ± 1.6. A minority (307; 38%) were classified as having high acuity (CTAS: 1-2). The 30-day mortality rate was 17%; this increased to 34% at 6 months. Compared to well patients with low acuity, the risk of 30-day mortality was 22.5 times (95% CI: 9.35-62.12) higher for severely frail patients with high acuity; 53% of people with very severe frailty (CFS = 8) and high acuity died within 30 days. When acuity was low, the risk for 30-day mortality was significantly higher only among those with very high levels of frailty (CFS 7-9, FI-CGA > 0.5). When acuity was high, even lower levels of frailty (CFS 5-6, FI-CGA 0.4-0.5) were associated with higher 30-day mortality.

CONCLUSIONS

across levels of frailty, higher acuity increased mortality risk. When acuity was low, the risk was significant only when the degree of frailty was high, whereas when acuity was high, even lower levels of frailty were associated with greater mortality risk.

Comorbidities in Older Persons with Controlled HIV Infection: Correlations with Frailty Index Subtypes

Frailty is prevalent in persons with human immunodeficiency virus (PWH), but factors predisposing older PWH to frailty remain uncertain. We examined factors associated with frailty and determined whether there were multiple frailty subtypes in older adults with controlled HIV infection. This was a cross-sectional outpatient study in an urban HIV clinic. Twenty-nine clinical indicators were extracted from medical records to compute a Frailty Index (FI) for 389 older (age 50+) PWH (range = 50-93; mean = 61.1, standard deviation = 7.2; 85% men) receiving HIV treatment in Calgary, Canada. We used regressions to identify factors associated with FI values. Latent class analysis was used to identify FI subtypes. Age, employment status, and duration of known HIV infection were the strongest predictors of FI (p's < 0.05). Four FI subtypes were identified. Subtype 1 (severe metabolic dysfunction+polypharmacy) had the highest mean FI (0.30). Subtype 2 (less severe metabolic dysfunction+polypharmacy) and Subtype 3 (lung and liver dysfunction+polypharmacy) had lower but equivalent mean FIs (0.20 for each). Subtype 4 (least severe metabolic dysfunction) had the lowest mean FI (0.13; p's < 0.001). Sociodemographic and behavioral characteristics differed among the subtypes. Individuals with Subtype 1 were older and more frequently unemployed/retired, whereas those with Subtype 3 were more likely to smoke, use crack/cocaine, have heavy alcohol use, and live in temporary/unstable housing. The clinical presentation of frailty in older PWH is heterogeneous. The metabolic syndrome, hepatitis C virus coinfection, cirrhosis, lung disease, and polypharmacy were associated with frailty as were unemployment/retirement, unstable housing, and substance use.

Frailty and Mortality in Hospitalized Older Adults With COVID-19: Retrospective Observational Study

Objectives

To determine the association between frailty and short-term mortality in older adults hospitalized for coronavirus disease 2019 (COVID-19).

Design

Retrospective single-center observational study.

Setting and participants

Eighty-one patients with COVID-19 confirmed by reverse-transcriptase polymerase chain reaction (RT-PCR), at the Geriatrics department of a general hospital in Belgium.

Measurements

Frailty was graded according to the Rockwood Clinical Frailty Scale (CFS). Demographic, biochemical, and radiologic variables, comorbidities, symptoms, and treatment were extracted from electronic medical records.

Results

Participants (N = 48 women, 59%) had a median age of 85 years (range 65-97 years) and a median CFS score of 7 (range 2-9); 42 (52%) were long-term care residents. Within 6 weeks, 18 patients died. Mortality was significantly but weakly associated with age (Spearman r = 0.241, P = .03) and CFS score (r = 0.282, P = .011), baseline lactate dehydrogenase (LDH; r = 0.301, P = .009), lymphocyte count (r = −0.262, P = .02), and RT-PCR cycle threshold (Ct, r = −0.285, P = .015). Mortality was not associated with long-term care residence, dementia, delirium, or polypharmacy. In multivariable logistic regression analyses, CFS, LDH, and RT-PCR Ct (but not age) remained independently associated with mortality. Both age and frailty had poor specificity to predict survival. A multivariable model combining age, CFS, LDH, and viral load significantly predicted survival.

Conclusions and Implications

Although their prognosis is worse, even the oldest and most severely frail patients may benefit from hospitalization for COVID-19, if sufficient resources are available.