Operationalizing a frailty index using routine blood and urine tests

Association between laboratory data-based frailty index and clinical health outcomes in critically ill older patients: A retrospective correlational study

BACKGROUND

Although frailty assessment is crucial for understanding critically ill patients' prognosis, traditional frailty measures require substantial efforts and time from health care professionals. To address this limitation, the laboratory frailty index (FI-LAB) based on laboratory clinical data was developed. However, knowledge regarding its correlation with health outcomes among critically ill older patients is limited.

AIM

To identify the association between the FI-LAB and acute, mid- and long-term outcomes among critically ill older adults.

STUDY DESIGN

This retrospective correlational study used electronic health records of 2106 older patients who were admitted to the intensive care unit at a tertiary hospital in Seoul, Korea. Acute and mid-term outcomes included occurrence of delirium and in-hospital mortality, and the long-term outcome included 1-year mortality. Logistic regression was used to explore the relationships across FI-LAB, delirium, and in-hospital mortality, while Cox proportional hazard regression was used to analyse the relationship between FI-LAB and 1-year mortality.

RESULTS

Frailty assessed by FI-LAB was significantly associated with increased risk of delirium (odds ratio [OR] = 6.21, 95% confidence interval [CI] = 2.31-25.39, p = .009), in-hospital mortality (OR = 2.38, 95% CI = 1.15-5.79, p = .014), and 1-year mortality (hazard ratio = 2.47, 95% CI = 1.16-5.25, p = .019) after controlling for covariates.

CONCLUSIONS

The study highlighted the importance of using FI-LAB for screening frailty in critically ill older adults. Health care providers can improve patients' acute, mid- and long-term outcomes to develop more individualised management plans based on FI-LAB scores.

RELEVANCE TO CLINICAL PRACTICE

The FI-LAB score calculated from routine laboratory data can be used by nurses as a screening tool to identify frail older adults in critical care. Early detection of frailty would allow for closer monitoring and the implementation of interventions to reduce delirium and mortality.

Prognostic value of electronic health records-based frailty measures for all-cause mortality in older patients with non-small cell lung cancer

INTRODUCTION

Frailty screening is important to guide treatment decisions for older patients with non-small cell lung cancer (NSCLC). However, the performance of frailty measures (FMs) remains unclear. This study aimed to evaluate the prognostic value of FMs based on electronic health records (EHR) data in clinical settings for all-cause mortality in older patients with NSCLC.

MATERIALS AND METHODS

We retrospectively analyzed 4253 patients aged ≥65 years, newly diagnosed with NSCLC (2007-2018) using EHR data from the National Cancer Center, Korea. Frailty was measured by either laboratory tests (frailty index based on routine laboratory tests [FI-Lab]), comorbidities and performance status (electronic Frailty index [eFI]), or both (combined frailty index [FI-combined]). Patients were categorized as frail or non-frail. Cox proportional hazards models and C-index were used to estimate the predictive ability of FMs for all-cause mortality in 1 year, 3 years, and 5 years post-diagnosis, adjusting for age, sex, and SEER stage.

RESULTS

EHR-based FMs could enhance the prognostic ability to predict the survival of older patients with NSCLC. In the total population, FI-Lab showed the largest predictive value, especially for 1-year mortality with an adjusted hazard ratio for frail vs. non-frail groups of 2.25 (95 % CI 2.02-2.51) and C-index of 0.74 compared to 0.72 in the base model (p-value<0.001). FI-Lab could improve the prognostic ability for 1-year mortality in patients with regional and distant SEER stages and those receiving systemic therapy, whereas FI-combined could improve the prediction of 3-year and 5-year mortality in patients with localized disease and receiving surgery.

DISCUSSION

Easy-to-use FMs derived from EHR data can enhance the prediction of all-cause mortality in older patients with NSCLC. Oncologists can utilize comprehensive FMs comprising comorbidities, functional status, and subclinical tests or FI-Lab, depending on the patient's medical condition, to facilitate shared cancer care planning.

A Frailty Index to Predict Mortality, Resource Utilization and Costs in Patients Undergoing Coronary Artery Bypass Graft Surgery in Ontario

Background

People living with frailty are vulnerable to poor outcomes and incur higher health care costs after coronary artery bypass graft (CABG) surgery. Frailty-defining instruments for population-level research in the CABG setting have not been established. The objectives of the study were to develop a preoperative frailty index for CABG (pFI-C) surgery using Ontario administrative data; assess pFI-C suitability in predicting clinical and economic outcomes; and compare pFI-C predictive capabilities with other indices.

Methods

A retrospective cohort study was conducted using health administrative data of 50,682 CABG patients. The pFI-C comprised 27 frailty-related health deficits. Associations between index scores and mortality, resource use and health care costs (2022 Canadian dollars [CAD]) were assessed using multivariable regression models. Capabilities of the pFI-C in predicting mortality were evaluated using concordance statistics; goodness of fit of the models was assessed using Akakie Information Criterion.

Results

As assessed by the pFI-C, 22% of the cohort lived with frailty. The pFI-C score was strongly associated with mortality per 10% increase (odds ratio [OR], 3.04; 95% confidence interval [CI], [2.83,3.27]), and was significantly associated with resource utilization and costs. The predictive performances of the pFI-C, Charlson, and Elixhauser indices and Johns Hopkins Aggregated Diagnostic Groups were similar, and mortality models containing the pFI-C had a concordance (C)-statistic of 0.784. Cost models containing the pFI-C showed the best fit.

Conclusions

The pFI-C is predictive of mortality and associated with resource utilization and costs during the year following CABG. This index could aid in identifying a subgroup of high-risk CABG patients who could benefit from targeted perioperative health care interventions.

[Treatment response and complications of older patients with ANCA(antineutrophil cytoplasmatic antibody)-associated vasculitis]

BACKGROUND

ANCA (antineutrophil cytoplasmatic antibody)-associated vasculitis (AAV) mainly affects elderley people but adjusted therapy concepts for this patient group are lacking.

AIM

The aim of this study was therefore to analyze differences in course and outcome of patients with AAV with respect to age.

MATERIALS AND METHODS

62 patients were analyzed for treatment response, of whom 53 (85%) experienced adverse events (AE and SAE) that could be evaluated. Older (> 65 yrs.) versus younger (< 65 yrs.) patients were compared. Treatment response was assessed at 6 months, complications were assessed over 18 months.

RESULTS

Treatment response was not seen to differ by age groups. In multiple logistic regression, pulmonary involvement (OR = 6,9; CI = 1,7-27,8, p < 0,01) and ΔGFR [ml/min] (OR = 0,93; CI = 0,89-0,97, p < 0,01) were predictors of SAE. 14 patients had more than 1 SAE. Again, pulmonary involvement (28,2% vs. 78,6%, p < 0,01) was a risk factor and older patients (78,6% vs. 43,6%, p = 0,025) were more frequently affected. Patients with multiple SAEs received glucocorticoids of more than 5 mg/d for longer periods of time (171 ± 65 days vs. 120 ± 70 days, p = 0,03).

DISCUSSION

No differences were found between older and younger patients with regard to treatment response. Multiple SAEs occurred more frequently in elderly patients. There was a correlation between pulmonary manifestation and duration of glucocorticoid therapy with a complicated course. The most frequent SAEs were infections requiring hospitalisation.

CONCLUSION

Therapy for elderly patients should be individualized with the goal of a fast reduction of glucocorticoids. Special monitoring is indicated for elderly patients, especially those with pulmonary involvement.

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.

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.

A novel easy-to-use index to predict institutionalization and death in older population - a 10-year population-based follow-up study

BACKGROUND

Various indexes have been developed to estimate the risk for mortality, institutionalization, and other adverse outcomes for older people. Most indexes are based on a large number of clinical or laboratory parameters. An index based on only a few parameters would be more practical to use in every-day clinical practice. Our aim was to create an index to predict the risk for mortality and institutionalization with as few parameters as possible without compromising their predictive ability.

METHODS

A prospective study with a 10-year follow-up period. Thirty-six clinical and fourteen laboratory parameters were combined to form an index. Cox regression model was used to analyze the association of the index with institutionalization and mortality. A backward statistical method was used to reduce the number of parameters to form an easy-to-use index for predicting institutionalization and mortality.

RESULTS

The mean age of the participants (n = 1172) was 73.1 (SD 6.6, range 64‒97) years. Altogether, 149 (14%) subjects were institutionalized, and 413 (35%) subjects deceased during the follow-up. Institutionalization and mortality rates increased as index scores increased both for the large 50-parameter combined index and for the reduced indexes. After a backward variable selection in the Cox regression model, three clinical parameters remained in the index to predict institutionalization and six clinical and three laboratory parameters in the index to predict mortality. The reduced indexes showed a slightly better predictive value for both institutionalization and mortality compared to the full index.

CONCLUSIONS

A large index with fifty parameters included many unimportant parameters that did not increase its predictive value, and therefore could be replaced with a reduced index with only a few carefully chosen parameters, that were individually associated with institutionalization or death.

Prognostic significance of frailty status in patients with primary lung cancer

Lung cancer has one of the highest morbidity and mortality rates in the world. Frailty is common in many countries and is a major cause of premature functional decline and premature death in older adults, and may affect the treatment and prognosis of lung cancer patients. To investigate the predictive value of frailty at diagnosis on all-cause mortality in lung cancer patients, this study retrospectively collected and analysed clinical information on lung cancer patients from 2015-2018. A total of 1667 patients with primary lung cancer were finally included in this study. The median follow-up time of patients was 650 (493, 1001.5) days. A total of 297(17.8%) patients had FI-LAB(the frailty index based on laboratory test) status of frail at the moment of diagnosis and the all-cause mortality rate for all patients was 61.1% (1018/1667). In a univariate model, we found a higher total all-cause mortality risk in frail patients (frail vs. robust, HR(hazard ratio) = 1.616, 95% CI(confidence interval) = 1.349,1.936), after balancing other variables combined into model 1 to model 6. The results were analyzed visually using ROC(Receiver operating characteristic) curves with nomogram and the AUC values ranged from 0.866-0.874. The final inclusion of age, TNM stage, CCI(Charlson comorbidity index) score, surgery history and chemotherapy into a multifactorial model balanced the predictive power of frailty grading on all-cause mortality. The study showed that for lung cancer patients, the higher the level of frailty at diagnosis, the higher the risk of all-cause mortality. In the context of widespread electronic medical records in hospitals, it is convenient and feasible to use FI-LAB to assess the prognosis of lung cancer patients.

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.

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.

Frailty index based on laboratory tests improves prediction of short-and long-term mortality in patients with critical acute myocardial infarction

Background

Previous studies have shown that the frailty index based on laboratory tests (FI-Lab) can identify older adults at increased risk of adverse health outcomes. This study aimed to determine whether the FI-Lab is associated with mortality risk and can provide incremental improvements in risk stratification of patients with critical acute myocardial infarction (AMI).

Materials and methods

We conducted a secondary analysis of data from the Medical Information Mart for Intensive Care (MIMIC)-IV database. A 33-item FI-Lab was constructed. Outcomes of interest were in-hospital and 1-year mortality. Logistic regression models were used to investigate the association between the FI-Lab and outcomes. For the assessment of the incremental predictive value, the FI-Lab was added to several risk stratification scoring systems for critically ill patients, and the following indices were calculated: Δ C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI).

Results

Out of 2,159 patients, 477 died in hospital (22.1%), and 898 died during the 1-year follow-up period. After adjustment for confounders, the FI-Lab was associated with increased in-hospital mortality [odds ratio (OR) = 1.06, 95% confidence interval (CI): 1.05-1.07] and 1-year mortality (OR = 1.05, 95% CI: 1.04-1.06) when assessed as a continuous variable (per 0.01-score increase). When assessed as a categorical variable, the FI-Lab was associated with in-hospital mortality (2nd Quartile: OR = 1.89, 95% CI: 1.18-3.03; 3rd Quartile: OR = 3.46, 95% CI: 2.20-5.46; and 4th Quartile: OR = 5.79, 95% CI: 3.61-9.28 compared to 1st Quartile) as well as 1-year mortality (2nd Quartile: OR = 1.66, 95% CI: 1.23-2.24; 3rd Quartile: OR = 2.40, 95% CI: 1.76-3.26; and 4th Quartile: OR = 3.76, 95% CI: 2.66-5.30 compared to 1st Quartile) after adjustment for confounders. The addition of the FI-Lab to all disease severity scores improved discrimination and significantly reclassified in-hospital and 1-year mortality risk.

Conclusion

The FI-Lab was a strong predictor of short- and long-term mortality in patients with critical AMI. The FI-Lab improved the ability to predict mortality in patients with critical AMI and therefore might be useful in the clinical decision-making process.

A frailty index based on routine laboratory data predicts increased risk of mortality in Chinese community-dwelling adults aged over 55 years: a five-year prospective study

Background

Frailty can be operationalized based on the accumulation of deficits using a frailty index (FI) and is associated with an increased risk of adverse health outcomes. Here, we aim to compare validity of a FI from laboratory data with that of the common clinical FI for prediction of mortality in adults aged 55 + years, also examine whether combined FI could improve identification of adults aged 55 + years at increased risk of death.

Methods

Data for this analysis were obtained from the Beijing Longitudinal Study of Aging that involved 1,257 community-dwelling Chinese people, aged 55 + years at baseline. The main outcome measure was 5-year mortality. An FI-self-report based on 30 self-reported health-related data was constructed. An FI-lab was developed using laboratory data, in addition to pulse, systolic and diastolic blood pressure, pulse pressure, body mass index (BMI) and waist. A combined FI comprised all items from each FI. Kaplan–Meier survival curve and Cox proportional hazards models were performed to evaluate the risk of each FI on death. The area under receiver operating characteristic(ROC) curves were used to compare the discriminative performance of each FI.

Results

Of 1257 participants, 155 died and 156 lost at the end of the 5-year follow-up. The mean FI-self-report score was 0.11 ± 0.10, the FI-lab score was 0.33 ± 0.14 and FI-combined score was 0.19 ± 0.09. Higher frailty level defined by each FI was associated with higher risk of death. After adjustment for age and sex, Cox proportional hazards models showed that the higher scores of frailty were associated with a higher risk of mortality for each FI, the hazard ratios for the FI-self-report and FI-lab and FI-combined were 1.04 (1.03 to 1.05) and 1.02 (1.01 to 1.03) and 1.05 (1.04 to 1.07), respectively. The areas under the ROC curve were 0.79 (0.77–0.82) for the FI-self-report, 0.77(0.75–0.80) for the FI-lab and 0.81(0.78–0.82) for FI-combined.

Conclusions

A FI from laboratory data can stratify older adults at increased risk of death alone and in combination with FI based on self-report data. Assessment in clinical settings of creating an FI using routine collected laboratory data needs to be further developed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03374-z.

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.

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.

Frailty indices based on self-report, blood-based biomarkers and examination-based data in the Canadian Longitudinal Study on Aging

BACKGROUND

Frailty can be operationalised using the deficit accumulation approach, which considers health deficits across multiple domains. We aimed to develop, validate and compare three different frailty indices (FI) constructed from self-reported health measures (FI-Self Report), blood-based biomarkers (FI-Blood) and examination-based assessments (FI-Examination).

METHODS

Up to 30,027 participants aged 45-85 years from the baseline (2011-2015) comprehensive cohort of the Canadian Longitudinal Study on Aging were included in the analyses. Following standard criteria, three FIs were created: a 48-item FI-Self Report, a 23-item FI-Blood and a 47-item FI-Examination. In addition a 118-item FI-Combined was constructed. Mortality status was ascertained in July 2019.

RESULTS

FI-Blood and FI-Examination demonstrated broader distributions than FI-Self Report. FI-Self Report and FI-Blood scores were higher in females, whereas FI-Examination scores were higher in males. All FI scores increased nonlinearly with age and were highest at lower education levels. In sex and age-adjusted models, a 0.01 increase in FI score was associated with a 1.08 [95% confidence interval (CI): 1.07,1.10], 1.05 (1.04,1.06), 1.07 (1.05,1.08) and a 1.13 (1.11,1.16) increased odds of mortality for FI-Self Report, FI-Blood, FI-Examination and FI-Combined, respectively. Inclusion of the three distinct FI types in a single model yielded the best prognostic accuracy and model fit, even compared to the FI-Combined, with all FIs remaining independently associated with mortality.

CONCLUSION

Characteristics of all FIs were largely consistent with previously established FIs. To adequately capture frailty levels and to improve our understanding of the heterogeneity of ageing, FIs should consider multiple types of deficits including self-reported, blood and examination-based measures.

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.

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.

A Simple Risk Score Based on Routine Clinical Parameters Can Predict Frailty in Hospitalized Heart Failure Patients

Frailty syndrome (FS) has recently attracted attention as one of the major predictors of heart failure (HF) course severity. We aimed to develop a simple tool for predicting frailty in hospitalized HF patients using routine clinical parameters. A total of 153 hospitalized patients diagnosed with heart failure with reduced ejection fraction (HFrEF) were included in the study. Presence of FS was assessed with the SHARE-FI questionnaire. Clinical and biochemical parameters were collected. Using ROC curves and logistic regression analysis, a model predicting FS presence was developed and tested. Proposed model includes five variables with following cut-off values (1 point for each variable): age > 50 years, systolic pressure on admission < 110 mmHg, total cholesterol < 4.85 mmol/L, bilirubin ≥ 15.5 mmol/L, and alanine aminotransferase ≤ 34 U/L. Receiving 5 points was considered a high risk of FS with positive and negative predictive values (NPV), 83% and 72%, respectively, and specificity of 97%. Awarding 2 points or less ruled out FS in the studied group with negative predictive value 94%. The presented novel, simple score predicts FS in HFrEF patients with routine clinical parameters and has good positive and negative predictive values.

Haemodynamic frailty - A risk factor for acute kidney injury in the elderly

Clinical frailty in the elderly is defined by a composite measure of functional psychomotor decline. Herein, we develop the concept of haemodynamic frailty (HDF), a state of increased predisposition to disease prevalent in the elderly and characterised by impairment of the network of compensatory responses governing the defence of circulatory volume and adaptive haemodynamic function. We review the factors predisposing the elderly to HDF, with a focus on the impaired capacity to sustain total body water balance. As a component of HDF, dehydration generates vulnerability to diseases caused by tissue hypoperfusion, including acute kidney injury. We provide a detailed mechanistic explanation of how dehydration and depletion of the intravascular volume impacts on renal blood flow to become an important element of the heightened risk of acute kidney injury (AKI) in the elderly. We bring these mechanistic considerations into the clinical context with reference to examples of how pre-renal (haemodynamic) and intrinsic (involving renal parenchymal damage) AKI risk is elevated in the setting of dehydration. Finally, we present HDF as a state of opportunity to prevent disease, for which diagnostic and interventional standards need to be refined. Further prospective studies are warranted to help clarify the clinical utility of assessing and managing HDF with regard to the mitigation of AKI risk in the elderly.

Prevalence of frailty and prediction of mortality in Chinese cancer patients using a frailty index-based clinical algorithm-A multicentre study

Purpose

To investigate the frailty status in Chinese cancer patients through establishing a novel prediction algorithm.

Methods

The percentage of frailty in various age groups, locations, and tumor types in Chinese cancer patients was investigated. The prediction capacity of frailty on mortality of Chinese cancer patients was analysed by the frailty index composing of routine laboratory data (FI‐LAB) accessible from a blood test and calculated as the ratio of abnormal factors to 22 total variables. The establishment of a novel algorithm, MCP (mortality of cancer patients), to predict the 5‐year mortality in Chinese cancer patients was accomplished and the algorithm's prediction capacity was tested in the training and validation sets using receiver operating characteristic (ROC) analysis.

Results

We found that the risk of death in cancer patients can be successfully identified through FI‐LAB. The univariable and multivariable Cox regression were used to evaluate the effect of frailty on death. In the 5‐year follow‐up, 20.6% of the 2959 participants (age = 55.8 ± 11.7 years; 43.5% female) died, while the mean FI‐LAB score in baseline was 0.23 (standard deviation = 0.13; range = 0–0.73). Frailty (after adjusting for gender, age, and other confounders) directly correlated with an increased risk of death, hazard ratio of 12.67 (95% confidence interval [CI]: 7.19, 22.31), compared to those without frailty. In addition, the MCP algorithm (MCP) = 3.678 × FI‐LAB + 1.575 × sex + 1.779 × first tumor node metastasis staging, presented an area under the ROC (AUC) of 0.691 (95% CI: 0.656–0.726) and 0.648 (95% CI: 0.613–0.684) in the training and validation sets, respectively.

Conclusion

Frailty as defined by FI‐LAB was common and indicated a significant death risk in cancer patients. Our novel developed algorithm MCP had a passable prediction capacity on 5‐year MCP.

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.

Early Outcomes After Surgical Management of Geriatric Patella Fractures

Objectives:

To report the incidence and risk factors for prolonged hospitalization, discharge to a facility, and postoperative complications in geriatric patients who underwent surgery for patella fracture.

Design:

Retrospective database review.

Setting:

The American College of Surgeons—National Surgical Quality Improvement Program (NSQIP) collects data from 600 hospitals across the United States.

Patients/Participants:

NSQIP patients over 65 years of age with patella fractures.

Intervention:

Surgical fixation of patella fracture including extensor mechanism repair.

Main Outcome Measurements:

Prolonged hospitalization, discharge to a facility, and 30-day post-operative complications.

Results:

1721 patients were included in the study. The average age was 74.9 years. 358 (20.8%) patients were male. 122 (7.1%) patients had a length of stay greater than 7 days. Factors associated with prolonged length of stay include pre-existing renal failure, need for emergent surgery, and time to surgery greater than 24 hours from admission. 640 patients (37.2%) of patients were discharged to a facility after surgery. Discharge to facility was associated with age >77 years, obesity, anemia, thrombocytopenia, pre-operative SIRS, and CCI > 0.5. Admission from home decreased the odds of discharge to a facility. The most common postoperative complications in this population were unplanned readmission (3.4%), unplanned reoperation (2.7%), surgical site infection (1.1%), mortality (1.0%), venous thromboembolism (0.8%), and wound dehiscence (0.2%). Complication rates increased with anemia and ASA class IV-V.

Conclusions:

Geriatric patients undergoing operative intervention for patella fractures are at high risk for prolonged hospitalization, discharge to facility, unplanned readmission or reoperation, and surgical site complications in the first 30 days following surgery. This study highlights modifiable and non-modifiable risk factors associated with adverse events. Early recognition of these factors can allow for close monitoring and multidisciplinary intervention in the perioperative period to improve outcomes.

Level of Evidence:

Prognostic level III.

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

BACKGROUND

Acutely ill and frail older adults have complex social and health care needs. It is important to understand how this complexity affects acute outcomes for admission to hospital. We validated a frailty index using routine admission laboratory tests with outcomes after patients were admitted to hospital.

METHODS

In a prospective cohort of older adults admitted to a large tertiary hospital in the United Kingdom, we created a frailty index from routine admission laboratory investigations (FI-Laboratory) linked to data comprising hospital outcomes. We evaluated the association between the FI-Laboratory and total days spent in hospital, discharge to a higher level of care, readmission and mortality.

RESULTS

Of 2552 admissions among 1750 older adults, we were able to generate FI-Laboratory values for 2254 admissions (88.3% of the cohort). More than half of admitted patients were women (55.3%) and the mean age was 84.6 (SD 14.0) years. We found that the FI-Laboratory correlated weakly with the Clinical Frailty Scale (CFS; r ² = 0.09). An increase in the CFS and the equivalent of 3 additional abnormal laboratory test results in the FI-Laboratory, respectively, were associated with an increased proportion of inpatient days (rate ratios [RRs] 1.43, 95% confidence interval [CI] 1.35-1.52; and 1.47, 95% CI 1.41-1.54), discharge to a higher level of care (odd ratios [ORs] 1.39, 95% CI 1.27-1.52; and 1.30, 95% CI 1.16-1.47) and increased readmission rate (hazard ratios [HRs] 1.26, 95% CI 1.17-1.37; and 1.18, 95% CI 1.11-1.26). Increases in the CFS and FI-Laboratory were associated with increased mortality HRs of 1.39 (95% CI 1.28-1.51) and 1.45 (95% CI 1.37-1.54), respectively.

INTERPRETATION

We determined that FI-Laboratory, distinct from baseline frailty, could be used to predict risk of many adverse outcomes. The score is therefore a useful way to quantify the degree of acute illness in frail older adults.

Frail Older Individuals Maintaining a Steady Standing Position: Associations Between Sway Measurements with Frailty Status Across Four Different Frailty Instruments

Objective

An analysis of the relationships between static equilibrium parameters and frailty status and/or severity across four different frailty measures.

Design

Cross-sectional analysis.

Setting

Geriatric wards of a general hospital.

Participants

One hundred twenty-three geriatric inpatients comprising 70 women (56.5%) and 53 men (42.7%) with an age range of 68–95 years.

Methods

The variation in the center of pressure (CoP), ie, the length of sway, the area of sway, and the mean speed, was assessed for different positions/tasks: 1) wide standing with eyes open (WSEO); 2) wide standing with eyes closed (WSEC); 3) narrow standing with eyes open (NSEO) and 4) narrow standing with eyes closed (NSEC), using a force plate. Frailty status and/or frailty severity were evaluated using the frailty phenotype (FP), the clinical frailty scale (CFS), the 14-item frailty index based on a comprehensive geriatric assessment (FI-CGA), and a 47-item frailty index (FI).

Results

WSEO length of sway (FP, CFS, FI-CGA, FI), WSEO area of sway (FP, CFS, FI-CGA, FI), and WSEO mean speed (FP, CFS, FI-CGA, FI), WSEC length of sway (FP, FI-CGA, FI), WSEC area of sway (FP, FI-CGA, FI) and WSEC mean speed (FI-CGA, FI), NSEO length of sway (FP, FI-CGA, FI), NSEO area of sway (FP, CFS, FI-CGA, FI), and NSEO mean speed (FP, CFS, FI-CGA, FI), NSEC length of sway (FI-CGA, FI), NSEC area of sway (FI-CGA, FI) and NSEC mean speed (FI-CGA, FI) were associated with the frailty status and/or severity across the four different frailty instruments (all p < 0.05, respectively).

Conclusion

Greater fluctuations in CoP with increasing frailty status and/or severity were a uniform finding across various major frailty instruments.

Clinical guidelines on frailty

The paper represents clinical guidelines on frailty, developed by Russian Association of gerontologists and geriatricians. The guidelines are dedicated to geriatricians, internal medicine specialists, general practitioners, family doctors and other specialists, dealing with adult patients. The paper includes screening and diagnostic tools and treatment options for patients with frailty, along with prevention, rehabilitation and medical care organization in this cohort of patients.

Development and Validation of the Nursing Home Minimum Data Set 3.0 Mortality Risk Score (MRS3)

Background

To develop a score to predict mortality using the Minimum Data Set 3.0 (MDS 3.0) that can be readily calculated from items collected during nursing home (NH) residents' admission assessments.

Participants

We developed a training cohort of Medicare beneficiaries newly admitted to United States NHs during 2012 (N = 1,426,815) and a testing cohort from 2013 (N = 1,160,964).

Methods

Data came from the MDS 3.0 assessments linked to the Medicare Beneficiary Summary File. Using the training dataset, we developed a composite MDS 3.0 Mortality Risk Score (MRS3) consisting of 17 clinical items and patients' age groups based on their relation to 30-day mortality. We assessed the calibration and discrimination of the MRS3 in predicting 30- and 60-day mortality and compared its performance to the Charlson Comorbidity Index and the clinician's assessment of 6-month prognosis measured at admission.

Results

The 30- and 60-day mortality rates for the testing population were 2.8% and 5.6%, respectively. Results from logistic regression models suggest that the MRS3 performed well in predicting death within 30 and 60 days (C-Statistics of 0.744 [95% confidence limit (CL) = 0.741, 0.747] and 0.709 [95% CL = 0.706, 0.711], respectively). The MRS3 was a superior predictor of mortality compared to the Charlson Comorbidity Index (C-statistics of 0.611 [95% CL = 0.607, 0.615] and 0.608 [95% CL = 0.605, 0.610]) and the clinicians' assessments of patients' 6-month prognoses (C-statistics of 0.543 [95% CL = 0.542, 0.545] and 0.528 [95% CL = 0.527, 0.529]).

Conclusions

The MRS3 is a good predictor of mortality and can be useful in guiding decision-making, informing plans of care, and adjusting for patients' risk of mortality.

Prediction of chemotherapy adverse reactions and mortality in older patients with primary lung cancer through frailty index based on routine laboratory data

Objectives

To assess the role of a pre-chemotherapy frailty index based on routine laboratory data in predicting mortality and chemotherapy adverse reactions among older patients with primary lung cancer.

Design

Retrospective cohort study.

Setting

West China Hospital, Chengdu, China.

Participants

We included patients aged ≥60 years with primary lung cancer receiving the first course of chemotherapy.

Measurements

Data were collected from medical records, local government death databases or telephone interviews. Outcomes included chemotherapy adverse reactions and all-cause mortality. We constructed a frailty index based on 44 laboratory variables (FI-LAB) before chemotherapy, and chose the following cutoff points: robust (0.0-0.2), pre-frail (0.2-0.35) and frail (≥0.35).

Results

We included 1,020 patients (71.4% male; median age: 65 years old). Both pre-frailty and frailty was associated with any chemotherapy adverse reactions and infections during chemotherapy (OR=3.48, 95%CI: 1.77-6.87; OR=3.58, 95%CI: 1.55-8.26, respectively). Frail patients had a shorter median overall survival rate compared to robust patients (18.05 months vs 38.89 months, log-rank p<0.001). After adjusting for some potential confounding variables, the risk of all-cause mortality was dramatically increased in frail patients (HR:2.13, 95% CI:1.51-3.00) with an average follow-up of 3.9 years. Each 0.01 or per standard deviation (SD) increase in the FI-LAB value significantly increased the HR of death by 2.0% (HR:1.02, 95% CI: 1.01-1.03) and 23.0% (HR: 1.23, 95% CI: 1.13-1.34), respectively.

Conclusions

Frailty assessed by routine laboratory data indicates increased risks of chemotherapy adverse reactions and death in older patients with primary lung cancer receiving the first course of chemotherapy.

Bed rest and accelerated aging in relation to the musculoskeletal and cardiovascular systems and frailty biomarkers: A review

Prolonged bed rest and lifelong physical inactivity cause deleterious effects to multiple physiological systems that appear to hasten aging processes. Many such changes are similar to those seen with microgravity in space, but at a much faster rate. Head down tilt bed rest models are used to study whole-body changes that occur with spaceflight. We propose that bed rest can be used to quantify accelerated human aging in relation to frailty. In particular, frailty as a measure of the accumulation of deficits estimates the variability in aging across systems, and moves away from the traditional single-system approach. Here, we provide an overview of the impact of bed rest on the musculoskeletal and cardiovascular systems as well as frailty-related biological markers and inflammatory cytokines. We also propose future inquiries to study the accumulation of deficits with head down bed rest and bed rest in the clinical setting, specifically to understand how unrepaired and unremoved subclinical and subcellular damage give rise to clinically observable health problems.

Predictability of a modified Mini- Nutritional- Assessment version on six-month and one-year mortality in hospitalized geriatric patients: a comparative analysis

Recently we introduced a modified Mini Nutritional Assessment (MNA) Short Form (MNA-SF) and Long Form (MNA-SF) with operationalization of the ‘mobility’ and ‘neuropsychological problems’ items of the MNA using scores on Barthel Index mobility item and Mini Mental State Examination and Geriatric Depression Scale scores. We have now evaluated the abilities of this modified MNA-SF and MNA-LF to predict mortality in comparison with the standard MNA-SF and MNA-LF and the Nutritional Risk Screening 2002 (NRS 2002) and the Malnutrition Universal Screening Tool (MUST). A prospective analysis was performed in 240 hospitalised geriatric patients aged ≥ 65 years. Malnutrition and/or malnutrition risk were assessed using the modified MNA-SF and MNA-LF, the standard MNA-SF and MNA-LF, and the NRS 2002 and MUST. The modified MNA-SF and MNA-LF and the standard MNA-SF and MNA-LF assessments (all p < 0.05), but not NRS 2002 or MUST (all p ≥ 0.05), predicted six-month and/or one-year mortality. Prediction of six-month and/or one-year mortality by the modified MNA-SF was comparable with predictions by the standard MNA-SF and MNA-LF (all p ≥ 0.05). The modified MNA-LF showed better prediction of six-month and one-year mortality than the standard MNA-SF and MNA-LF (all p < 0.05). The modified MNA-LF (all adjusted p < 0.05), but none of the other instruments (all adjusted p ≥ 0.05), predicted six-month and one-year mortality independently of age, sex, frailty, comorbidity and ADL disability burden. The modified MNA-SF and MNA-LF emerged as potentially valuable tools for predicting mortality in patients hospitalised on geriatric wards.

Surgical Risk Assessment and Prevention in Elderly Spinal Deformity Patients

Introduction:

Prevalence of adult deformity surgery in the elderly individuals continues to increase. These patients have additional considerations for the spine surgeon during surgical planning. We perform an informative review of the spinal and geriatric literature to assess preoperative and intraoperative factors that impact surgical complication occurrences in this population.

Significance:

There is a need to understand surgical risk assessment and prevention in geriatric patients who undergo thoracolumbar adult deformity surgery in order to prevent complications.

Methods:

Searches of relevant biomedical databases were conducted by a medical librarian. Databases searched included MEDLINE, Web of Science, CINAHL, IPA, Cochrane, PQ Health and Medical, SocINDEX, and WHO’s Global Health Library. Search strategies utilized Medical Subject Headings plus text words for extensive coverage of scoliosis and surgical technique concepts.

Results:

Degenerative scoliosis affects 68% of the geriatric population, and the rate of surgical interventions for this pathology continues to increase. Complications following spinal deformity surgery in this patient population range from 37% to 62%. Factors that impact outcomes include age, comorbidities, blood loss, and bone quality. Using these data, we summarize multimodal risk prevention strategies that can be easily implemented by spine surgeons.

Conclusions:

After evaluation of the latest literature on the complications associated with adult deformity surgery in geriatric patients, comprehensive perioperative management is necessary for improved outcomes. Preoperative strategies include assessing physiological age via frailty score, nutritional status, bone quality, dementia/delirium risk, and social activity support. Intraoperative strategies include methods to reduce blood loss and procedural time. Postoperatively, development of a multidisciplinary team approach that encourages early ambulation, decreases opiate use, and ensures supportive discharge planning is imperative for better outcomes for this patient population.

Associations between a laboratory frailty index and adverse health outcomes across age and sex

OBJECTIVE

Early frailty may be captured by a frailty index (FI) based entirely on vital signs and laboratory tests. Our aim was to examine associations between a laboratory-based FI (FI-Lab) and adverse health outcomes, and investigate how this changed with age.

METHODS

Up to 8988 individuals aged 20+ years from the 2003-2004 and 2005-2006 National Health and Nutrition Examination Survey cohorts were included. Characteristics of the FI-Lab were compared to those of a self-reported clinical FI. Associations between each FI and health care use, self-reported health, and disability were examined in the full sample and across age groups.

RESULTS

Laboratory-based FI scores increased with age but did not demonstrate expected sex differences. Women aged 20-39 years had higher FI scores than men; this pattern reversed after age 60 years. FI-Lab scores were associated with poor self-reported health (odds ratio[95% confidence interval]: 1.46[1.39-1.54]), high health care use (1.35[1.29-1.42]), and high disability (1.41[1.32-1.50]), even among those aged 20-39 years.

CONCLUSION

Higher FI-Lab scores were associated with poor health outcomes at all ages. Associations in the youngest group support the notion that deficit accumulation occurs across the lifespan. FI-Lab scores could be utilized as an early screening tool to identify deficit accumulation at the cellular and molecular level before they become clinically visible.

Changes of a frailty index based on common blood and urine tests during a hospital stay on geriatric wards predict 6-month and 1-year mortality in older people

Background

We aimed to evaluate the abilities of a 21-item frailty index based on laboratory blood and urine tests (FI-Lab21) assessed at different points in time, ie, at admission to hospital (FI-Lab21_admission) and before discharge from hospital (FI-Lab21_discharge), and the change of the FI-Lab21 during the hospital stay to predict 6-month and 1-year mortality in hospitalized geriatric patients.

Methods

Five hundred hospitalized geriatric patients aged ≥65 years were included in this analysis. Follow-up data were acquired after a period of 6 months and 1 year.

Results

The FI-Lab21_admission and FI-Lab21_discharge scores were 0.33±0.15 and 0.31±0.14, respectively (P<0.001). The FI-Lab21_admission and FI-Lab21_discharge both predicted 6-month and 1-year mortality (areas under the receiver operating characteristic curves: 0.72, 0.72, 0.77, and 0.75, respectively, all P<0.001). The predictive abilities for 6-month and 1-year mortality of the FI-Lab21_admission were inferior compared with those of the FI-Lab21_discharge (all P<0.05). Patients with a reduction in or stable FI-Lab21 score during the hospital stay revealed lower 6-month and 1-year mortality rates compared with the persons whose FI-Lab21 score increased during the hospital stay (all P<0.05). After adjustment for age, sex, and FI-Lab21_admission, each 1% decrease in the FI-Lab21 during the hospital stay was associated with a decrease in 6-month and 1-year mortality of 5.9% and 5.3% (both P<0.001), respectively.

Conclusion

The FI-Lab21 assessed at admission or discharge and the changes of the FI-Lab21 during the hospital stay emerged as interesting and feasible approaches to stratify mortality risk in hospitalized geriatric patients.

Prediction of mortality in Chinese very old people through the frailty index based on routine laboratory data

The increased risk of death in older adults can be successfully identified through frailty index (FI), based on comprehensive geriatric assessment data and self-reported data from the accumulated deficit, although the method depending on routine laboratory data (FI-LAB) remains uncertain. In the current study, the capacity of FI-LAB in evaluating the risk of mortality in a very old Chinese community cohort was analyzed. The 90-year- and above old individuals from a Dujiangyan community in Sichuan Province, China, who had completed a health assessment at baseline (in 2005) and whose laboratory data were analyzed (n = 736) from cumulative data from the Project of Longevity and Aging. The FI-LAB data was constructed from routine laboratory data and calculated as the ratio of abnormal factors in 22 variables (including red blood cells, white blood cells, and alanine transaminase) that can be assessed through blood tests. The multivariable Cox regression was used to evaluate the effect of frailty on death. In the four-year follow-up, 53.5% of the 736 participants (age = 93.6 ± 3.4 years; 67.5% women), were reported dead. The FI-LAB mean baseline value was 0.21 (standard deviation = 0.10; range = 0 to 0.55). Frailty (after adjusting for gender, age, and other confounders) could be directly correlated with increased death risk, with a hazard ratio of 1.31 (95% confidence interval (CI): 1.07–1.61) in comparison with those without frailty among the individuals. Frailty as defined by FI-LAB, established only on routine laboratory data, indicates a significant death risk in the very old people.

Predictive validity of two frailty tools for mortality in Chinese nursing home residents: frailty index based on common laboratory tests (FI-Lab) versus FRAIL-NH

BACKGROUND

Little is known about frailty in Chinese nursing home residents.

AIMS

(1) To evaluate the prevalence of frailty in nursing home residents according to the FI-Lab or FRAIL-NH; and (2) to compare the predictive validity of these two tools for mortality.

METHODS

We conducted a prospective study in four nursing homes in China. Frailty was assessed using the fatigue, resistance, ambulation, illness, loss of weight, nutrition, and help with dressing questionnaire (FRAIL-NH) and frailty index based on common laboratory tests (FI-Lab), respectively. The survival status was collected via medical records or telephone interviews. Receiver-operating characteristic (ROC) curves were calculated to estimate the area under the ROC curves (AUCs) for FI-Lab and FRAIL-NH in relation to mortality. Cox proportional hazard models were applied to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality by FRAIL-NH and FI-Lab, separately.

RESULTS

We included 329 participants. The FI-Lab score was significantly and strongly associated with the FRAIL-NH score (r = 0.799, p < 0.001). Frailty was defined as the FI-Lab score ≥ 0.3 or the FRAIL-NH score ≥ 6, and the prevalence of frailty was 56.2% and 58.7%, respectively. Seventy-three participants (22.7%) died during the 1-year follow-up. The FI-Lab (AUC 0.700, 95% CI 0.647-0.750) was slightly better than the FRAIL-NH (AUC 0.676, 95% CI 0.622-0.727) for predicting mortality (p = 0.025). After adjusted for age and gender, the increment of the FI-Lab score was associated with mortality (adjusted HR per 0.01 increment in score 1.07, 95% CI 1.05-1.09), the increment of the FRAIL-NH score was also associated with mortality (adjusted HR per 1 increment in score 1.28, 95% CI 1.19-1.46).

CONCLUSION

The FI-Lab and FRAIL-NH are valuable for predicting mortality in Chinese nursing home residents.

Association between a frailty index based on common laboratory tests and QTc prolongation in older adults: the Rugao Longevity and Ageing Study

BACKGROUND

Risk factors for heart rate-corrected QT interval (QTc) proglongation should be explored to stratify high-risk individuals to aid the prevention of incident cardiovascular events and mortality. The diversity of risk factors for QTc prolongation suggests that use of the frailty index (FI), indicating general health deficits, may be an effective approach, especially in the elderly, to identify the risk of QTc prolongation.

METHODS

We used the data of 1,780 individuals aged 70-87 years from the Rugao Longevity and Ageing Study (RuLAS), a community-based longitudinal study. The FI was constructed using 20 routine laboratory tests, plus the body mass index and measures of systolic and diastolic blood pressures (FI-Lab).

RESULTS

The mean FI-Lab value was 0.24±0.09. The mean heart rate-corrected QT interval (QTc) was 407±38 ms. The prevalence of QTc prolongation was 5.2% in elderly community populations aged 70-87 years. A higher FI-Lab value was associated with a higher risk for QTc prolongation. Each 10% increase in the FI-Lab value increased the odds ratio (OR) by 33% (OR: 1.33; 95% CI: 1.07-1.64). Compared with the lowest quartile, the top quartile FI-Lab score was associated with a 2.50-fold QTc prolongation risk in elderly individuals (95% CI: 1.21-5.19).

CONCLUSION

An FI based on routine laboratory data can identify older adults at increased risk for QTc prolongation. The FI approach may therefore be useful for the risk stratification of QTc prolongation.

Development of a Physiological Frailty Index for the World Trade Center General Responder Cohort

Responders to the 9/11/2001 WTC attacks were exposed to multiple toxic pollutants. Since 2002, the health of the responder cohort has been continuously tracked by the WTC Health Monitoring Program. However, no assessments have been made of frailty, an important health metric given the current average age of the WTC responder cohort (55 years). In this study, we use laboratory test results and other physiological parameters to construct a physiological frailty index (FI-Lab) for this cohort. The study sample comprised responders aged 40 years or older who completed a health monitoring visit at Mount Sinai Center within the past 5 years. For each subject, FI-Lab was computed as the proportion of 20 physiological parameters (lab tests, pulmonary function, and blood pressure) on which the subject had abnormal values. Using negative binomial regression models, we tested FI-Lab's association with the SF-12 wellbeing score and various demographic characteristics. FI-Lab showed strong associations with the physical and mental components of the SF-12 as well as age, race, and smoking status. Using a cutoff of 0.25 to define presence of physiological/preclinical frailty, we found frailty prevalence in the study sample to be approximately 12%. This study demonstrates the feasibility of assessing preclinical frailty in the WTC responder cohort.