No Difference in the Phenotypic Expression of Frailty among Elderly Patients Recently Diagnosed with Cancer Vs Cancer Free Patients

Is Cancer Associated With Frailty? Results From a Longitudinal Study of Old Danish Twins

BACKGROUND

Although age is an important risk factor for both cancer and frailty, it is unclear whether cancer itself increases the risk of frailty. We aimed to assess the association between cancer and frailty in a longitudinal cohort of older Danish twins, taking familial effect into account.

METHODS

Using the Danish Cancer Registry, cancer cases were identified among participants aged 70 and over in the Longitudinal Study of Aging Danish Twins (LSADT). Frailty was evaluated over 10 years of follow-up using the frailty index (FI) and defined as FI > 0.21. Stratified Cox regressions were performed on discordant twin pairs (pairs where one twin had incident cancer and the other was cancer-free) and on all LSADT individual twins (twin pairs and singletons) with no history of cancer.

RESULTS

Among the 72 discordant pairs (n = 144, median age at inclusion = 75) included in the study, the median FI at inclusion was 0.08 for both cancer twins and cancer-free co-twins. From the stratified Cox regression, twins with cancer had an increased hazard of developing frailty (hazard ratio [HR] = 3.67, 95% confidence interval [CI] = 1.02-13.14) compared with their cancer-free co-twins. The analyses on individual twins (n = 4 027) provided similar results, showing an increased hazard of frailty in individuals with cancer (HR = 2.57, 95% CI = 1.77-3.74) compared with those without cancer.

CONCLUSIONS

We showed a higher risk of becoming frail following a cancer diagnosis in both discordant twin pairs and individual twins. These findings support the importance of monitoring frailty in older adults with cancer through geriatric assessments and the implementation of frailty interventions.

Frailty in chronic myeloid leukemia: evidence from 2016-2018 Nationwide Inpatient Sample of the US

BACKGROUND

Frailty is a marker of poor prognosis in older adults with hematologic malignancies and contributes to the severe vulnerability of the aging population to adverse health outcomes. This study aimed to determine the association between frailty and outcomes in hospitalized patients with chronic myeloid leukemia (CML).

METHODS

The International Classification of Diseases (ICD-10) identified data on hospitalized patients 20 years or older admitted with CML between 2016 and 2018 in the US National Inpatient Sample (NIS) database. The cohort was further divided into groups of patients with or without frailty. Logistic regression analysis was performed to determine associations between study variables and clinical outcomes. A stratified analysis of the association between frailty and in-hospital mortality by age group was also performed.

RESULTS

A total of 13,849 hospitalized patients with CML were included, 49.6% of whom had frailty. The mean age of the patients was 65.1 years, and 7,619 (56.2%) of them were male. Frailty was associated with nearly 4 times the risk of in-hospital mortality, 3 times the risk of unfavorable discharge, 3 times the risk of prolonged LOS,, and significantly more in total hospital costs. In addition, frailty was associated with a significantly increased risk of in-hospital mortality in all age subgroups (< 40 years, 40-59 years, and > 60 years) compared with no frailty.

CONCLUSIONS

Frailty strongly predicts poor clinical outcomes in US patients with CML.

Cardiac Comorbidity Risk Score: Zero-Burden Machine Learning to Improve Prediction of Postoperative Major Adverse Cardiac Events in Hip and Knee Arthroplasty

Background In this retrospective, observational study we introduce the Cardiac Comorbidity Risk Score, predicting perioperative major adverse cardiac events (MACE) after elective hip and knee arthroplasty. MACE is a rare but important driver of mortality, and existing tools, eg, the Revised Cardiac Risk Index demonstrate only modest accuracy. We demonstrate an artificial intelligence-based approach to identify patients at high risk of MACE within 4 weeks (primary outcome) of arthroplasty, that imposes zero additional burden of cost/resources. Methods and Results Cardiac Comorbidity Risk Score calculation uses novel machine learning to estimate MACE risk from patient electronic health records, without requiring blood work or access to any demographic data beyond that of sex and age, and accounts for variable/missing/incomplete information across patient records. Validated on a deidentified cohort (age >45 years, n=445 391), performance was evaluated using the area under the receiver operator characteristics curve (AUROC), sensitivity/specificity, positive predictive value, and positive/negative likelihood ratios. In our cohort (age 63.5±10.5 years, 58.2% women, 34.2%/65.8% hip/knee procedures), 0.19% (882) experienced the primary outcome. Cardiac Comorbidity Risk Score achieved area under the receiver operator characteristics curve=80.0±0.4% (95% CI) for women and 80.1±0.5% (95% CI) for males, with 36.4% and 35.1% sensitivities, respectively, at 95% specificity, significantly outperforming Revised Cardiac Risk Index across all studied age-, sex-, risk-, and comorbidity-based subgroups. Conclusions Cardiac Comorbidity Risk Score, a novel artificial intelligence-based screening tool using known and unknown comorbidity patterns, outperforms state-of-the-art in predicting MACE within 4 weeks postarthroplasty, and can identify patients at high risk that do not demonstrate traditional risk factors.

Neuropsychiatric Disorders and Frailty in Older Adults over the Spectrum of Cancer: A Narrative Review

BACKGROUND

The interplay between different neuropsychiatric conditions, beyond dementia, in the presence of a diagnosis of cancer in older adults may mediate patients' fitness and cancer-related outcomes. Here, we aimed to investigate the presence of depression, sleep disturbances, anxiety, attitude, motivation, and support in older adults receiving a diagnosis of cancer and the dimension of frailty in order to understand the magnitude of the problem.

METHODS

This review provides an update of the state of the art based on references from searches of PubMed between 2000 and June 2021.

RESULTS

The evidence obtained underscored the tight association between frailty and unfavorable clinical outcomes in older adults with cancer. Given the intrinsic correlation of neuropsychiatric disorders with frailty in the realm of cancer survivorship, the evidence showed they might have a correlation with unfavorable clinical outcomes, late-life geriatric syndromes and higher degree of frailty.

CONCLUSIONS

The identification of common vulnerabilities among neuropsychiatric disorders, frailty, and cancer may hold promise to unmask similar shared pathways, potentially intercepting targeted new interventions over the spectrum of cancer with the delivery of better pathways of care for older adults with cancer.

Can We Distinguish Age-Related Frailty from Frailty Related to Diseases ? Data from the MAPT Study

BACKGROUND

No study has tried to distinguish subjects that become frail due to diseases (frailty related to diseases) or in the absence of specific medical events; in this latter case, it is possible that aging process would act as the main frailty driver (age-related frailty).

OBJECTIVES

To classify subjects according to the origin of physical frailty: age-related frailty, frailty related to diseases, frailty of uncertain origin, and to compare their clinical characteristics.

MATERIALS AND METHODS

We performed a secondary analysis of the Multidomain Alzheimer Preventive Trial (MAPT), including 195 subjects ≥70 years non-frail at baseline who became frail during a 5-year follow-up (mean age 77.8 years ± 4.7; 70% female). Physical frailty was defined as presenting ≥3 of the 5 Fried criteria: weight loss, exhaustion, weakness, slowness, low physical activity. Clinical files were independently reviewed by two different clinicians using a standardized assessment method in order to classify subjects as: "age-related frailty", "frailty related to diseases" or "frailty of uncertain origin". Inconsistencies among the two raters and cases of uncertain frailty were further assessed by two other experienced clinicians.

RESULTS

From the 195 included subjects, 82 (42%) were classified as age-related frailty, 53 (27%) as frailty related to diseases, and 60 (31%) as frailty of uncertain origin. Patients who became frail due to diseases did not differ from the others groups in terms of functional, cognitive, psychological status and age at baseline, however they presented a higher burden of comorbidity as measured by the Cumulative Illness Rating Scale (CIRS) (8.20 ± 2.69; vs 6.22 ± 2.02 frailty of uncertain origin; vs. 3.25 ± 1.65 age-related frailty). Time to incident frailty (23.4 months ± 12.1 vs. 39.2 ± 19.3 months) and time spent in a pre-frailty condition (17.1 ± 11.4 vs 26.6 ± 16.6 months) were shorter in the group of frailty related to diseases compared to age-related frailty. Orthopedic diseases (n=14, 26%) were the most common pathologies leading to frailty related to diseases, followed by cardiovascular diseases (n=9, 17%) and neurological diseases (n = 8, 15%).

CONCLUSION

People classified as age-related frailty and frailty related to diseases presented different frailty-associated indicators. Future research should target the underlying biological cascades leading to these two frailty classifications, since they could ask for distinct strategies of prevention and management.