External validation of six COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting.

OBJECTIVES: To systematically evaluate the performance of COVID-19 prognostic models and scores for mortality risk in older populations across three health-care settings: hospitals, primary care, and nursing homes. STUDY DESIGN AND SETTING: This retrospective external validation study included 14,092 older individuals of ≥70 years of age with a clinical or polymerase chain reaction-confirmed COVID-19 diagnosis from March 2020 to December 2020. The six validation cohorts include three hospital-based (CliniCo, COVID-OLD, COVID-PREDICT), two primary care-based (Julius General Practitioners Network/Academisch network huisartsgeneeskunde/Network of Academic general Practitioners, PHARMO), and one nursing home cohort (YSIS) in the Netherlands. Based on a living systematic review of COVID-19 prediction models using Prediction model Risk Of Bias ASsessment Tool for quality and risk of bias assessment and considering predictor availability in validation cohorts, we selected six prognostic models predicting mortality risk in adults with COVID-19 infection (GAL-COVID-19 mortality, 4C Mortality Score, National Early Warning Score 2-extended model, Xie model, Wang clinical model, and CURB65 score). All six prognostic models were validated in the hospital cohorts and the GAL-COVID-19 mortality model was validated in all three healthcare settings. The primary outcome was in-hospital mortality for hospitals and 28-day mortality for primary care and nursing home settings. Model performance was evaluated in each validation cohort separately in terms of discrimination, calibration, and decision curves. An intercept update was performed in models indicating miscalibration followed by predictive performance re-evaluation. MAIN OUTCOME MEASURE: In-hospital mortality for hospitals and 28-day mortality for primary care and nursing home setting. RESULTS: All six prognostic models performed poorly and showed miscalibration in the older population cohorts. In the hospital settings, model performance ranged from calibration-in-the-large -1.45 to 7.46, calibration slopes 0.24-0.81, and C-statistic 0.55-0.71 with 4C Mortality Score performing as the most discriminative and well-calibrated model. Performance across health-care settings was similar for the GAL-COVID-19 model, with a calibration-in-the-large in the range of -2.35 to -0.15 indicating overestimation, calibration slopes of 0.24-0.81 indicating signs of overfitting, and C-statistic of 0.55-0.71. CONCLUSION: Our results show that most prognostic models for predicting mortality risk performed poorly in the older population with COVID-19, in each health-care setting: hospital, primary care, and nursing home settings. Insights into factors influencing predictive model performance in the older population are needed for pandemic preparedness and reliable prognostication of health-related outcomes in this demographic.

Limited incremental predictive value of the frailty index and other vulnerability measures from routine care data for mortality risk prediction in older patients with COVID-19 in primary care.

BACKGROUND: During the COVID-19 pandemic, older patients in primary care were triaged based on their frailty or assumed vulnerability for poor outcomes, while evidence on the prognostic value of vulnerability measures in COVID-19 patients in primary care was lacking. Still, knowledge on the role of vulnerability is pivotal in understanding the resilience of older people during acute illness, and hence important for future pandemic preparedness. Therefore, we assessed the predictive value of different routine care-based vulnerability measures in addition to age and sex for 28-day mortality in an older primary care population of patients with COVID-19. METHODS: From primary care medical records using three routinely collected Dutch primary care databases, we included all patients aged 70 years or older with a COVID-19 diagnosis registration in 2020 and 2021. All-cause mortality was predicted using logistic regression based on age and sex only (basic model), and separately adding six vulnerability measures: renal function, cognitive impairment, number of chronic drugs, Charlson Comorbidity Index, Chronic Comorbidity Score, and a Frailty Index. Predictive performance of the basic model and the six vulnerability models was compared in terms of area under the receiver operator characteristic curve (AUC), index of prediction accuracy and the distribution of predicted risks. RESULTS: Of the 4,065 included patients, 9% died within 28 days after COVID-19 diagnosis. Predicted mortality risk ranged between 7-26% for the basic model including age and sex, changing to 4-41% by addition of comorbidity-based vulnerability measures (Charlson Comorbidity Index, Chronic Comorbidity Score), more reflecting impaired organ functioning. Similarly, the AUC of the basic model slightly increased from 0.69 (95%CI 0.66 - 0.72) to 0.74 (95%CI 0.71 - 0.76) by addition of either of these comorbidity scores. Addition of a Frailty Index, renal function, the number of chronic drugs or cognitive impairment yielded no substantial change in predictions. CONCLUSION: In our dataset of older COVID-19 patients in primary care, the 28-day mortality fraction was substantial at 9%. Six different vulnerability measures had little incremental predictive value in addition to age and sex in predicting short-term mortality.

Delirium in older patients with COVID-19: Prevalence, risk factors and clinical outcomes across the first three waves of the pandemic.

OBJECTIVES: Delirium is a serious condition, which poses treatment challenges during hospitalisation for COVID-19. Improvements in testing, vaccination and treatment might have changed patient characteristics and outcomes through the pandemic. We evaluated whether the prevalence and risk factors for delirium, and the association of delirium with in-hospital mortality changed through the pandemic. METHODS: This study was part of the COVID-OLD study in 19 Dutch hospitals including patients ≥70 years in the first (spring 2020), second (autumn 2020) and third wave (autumn 2021). Multivariable logistic regression models were used to study risk factors for delirium, and in-hospital mortality. Differences in effect sizes between waves were studied by including interaction terms between wave and risk factor in logistic regression models. RESULTS: 1540, 884 and 370 patients were included in the first, second and third wave, respectively. Prevalence of delirium in the third wave (12.7%) was significantly lower compared to the first (22.5%) and second wave (23.5%). In multivariable-adjusted analyses, pre-existing memory problems was a consistent risk factor for delirium across waves. Previous delirium was a risk factor for delirium in the first wave (OR 4.02), but not in the second (OR 1.61) and third wave (OR 2.59, p-value interaction-term 0.028). In multivariable-adjusted analyses, delirium was not associated with in-hospital mortality in all waves. CONCLUSION: Delirium prevalence declined in the third wave, which might be the result of vaccination and improved treatment strategies. Risk factors for delirium remained consistent across waves, although some attenuation was seen in the second wave.

Cardiovascular Risk Management for All Older Patients?

Current guidelines on cardiovascular risk management are extrapolated to all older adults. It is, however, highly debatable whether recommendations also apply for patients with dementia since previous studies have not included this specific population. Time to benefit as well as higher risk of adverse events play a crucial role in the decision process of prescribing or deprescribing. Regular monitoring is needed in older patients with dementia, in order to make individual-based treatment strategies. Cardiovascular risk management in older patients with dementia should focus on quality of life, preventing cognitive and functional deterioration, and maintaining independence.

Frailty in Non-geriatric Patients With Head and Neck cancer.

OBJECTIVE: Patients with head and neck cancer (HNC) are characterized by a poor lifestyle and comorbidity. The Geriatric 8 (G8) is an established screening tool to identify frail older patients with cancer. However, studies evaluating frailty in younger HNC patients are lacking. The aim of this study is to evaluate if the G8 can identify frailty and if it is related to mortality in younger HNC patients. STUDY DESIGN: Case-control study design. SETTING: Tertiary cancer center. METHODS: We studied patients <70 years with HNC. Patients with G8 ≤ 14 were considered frail. Patients were matched to nonfrail (G8 > 14) control patients. Patients were matched according to sex, age, smoking, tumor location, and period of first consultation. Baseline health characteristics were compared between frail patients and nonfrail controls. Second, the treatment plan and adverse outcomes were compared. RESULTS: Forty-five patients with G8 ≤ 14 were included and matched to 90 nonfrail controls. The median follow-up time was 357 days. Frail patients had a significantly lower body mass index and level of education, a worse World Health Organization performance status, and reported lower experienced overall health. 28.9% of the frail patients died after 1 year versus 10% of the nonfrail control patients (hazard ratio: 3.87 [95% confidence interval: 1.32-11.36], p = 0.014). CONCLUSION: The G8 is a valid screening tool to identify frail patients in younger HNC patients.

Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk.

Biological age captures a person's age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers. These measures are often studied in isolation, but here we present a large-scale study comparing them. In 2 prospective cohorts (n = 3 222), we compared epigenetic (DNAm Horvath, DNAm Hannum, DNAm Lin, DNAm epiTOC, DNAm PhenoAge, DNAm DunedinPoAm, DNAm GrimAge, and DNAm Zhang) and metabolomic-based (MetaboAge and MetaboHealth) biomarkers in reflection of biological age, as represented by 5 frailty measures and overall mortality. Biomarkers trained on outcomes with biophysiological and/or mortality information outperformed age-trained biomarkers in frailty reflection and mortality prediction. DNAm GrimAge and MetaboHealth, trained on mortality, showed the strongest association with these outcomes. The associations of DNAm GrimAge and MetaboHealth with frailty and mortality were independent of each other and of the frailty score mimicking clinical geriatric assessment. Epigenetic, metabolomic, and clinical biological age markers seem to capture different aspects of aging. These findings suggest that mortality-trained molecular markers may provide novel phenotype reflecting biological age and strengthen current clinical geriatric health and well-being assessment.

A study protocol of external validation of eight COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting.

BACKGROUND: The COVID-19 pandemic has a large impact worldwide and is known to particularly affect the older population. This paper outlines the protocol for external validation of prognostic models predicting mortality risk after presentation with COVID-19 in the older population. These prognostic models were originally developed in an adult population and will be validated in an older population (≥ 70 years of age) in three healthcare settings: the hospital setting, the primary care setting, and the nursing home setting. METHODS: Based on a living systematic review of COVID-19 prediction models, we identified eight prognostic models predicting the risk of mortality in adults with a COVID-19 infection (five COVID-19 specific models: GAL-COVID-19 mortality, 4C Mortality Score, NEWS2 + model, Xie model, and Wang clinical model and three pre-existing prognostic scores: APACHE-II, CURB65, SOFA). These eight models will be validated in six different cohorts of the Dutch older population (three hospital cohorts, two primary care cohorts, and a nursing home cohort). All prognostic models will be validated in a hospital setting while the GAL-COVID-19 mortality model will be validated in hospital, primary care, and nursing home settings. The study will include individuals ≥ 70 years of age with a highly suspected or PCR-confirmed COVID-19 infection from March 2020 to December 2020 (and up to December 2021 in a sensitivity analysis). The predictive performance will be evaluated in terms of discrimination, calibration, and decision curves for each of the prognostic models in each cohort individually. For prognostic models with indications of miscalibration, an intercept update will be performed after which predictive performance will be re-evaluated. DISCUSSION: Insight into the performance of existing prognostic models in one of the most vulnerable populations clarifies the extent to which tailoring of COVID-19 prognostic models is needed when models are applied to the older population. Such insight will be important for possible future waves of the COVID-19 pandemic or future pandemics.

Atypical presentation of COVID-19 in older patients is associated with frailty but not with adverse outcomes.

PURPOSE: Older patients with COVID-19 can present with atypical complaints, such as falls or delirium. In other diseases, such an atypical presentation is associated with worse clinical outcomes. However, it is not known whether this extends to COVID-19. We aimed to study the association between atypical presentation of COVID-19, frailty and adverse outcomes, as well as the incidence of atypical presentation. METHODS: We conducted a retrospective observational multi-center cohort study in eight hospitals in the Netherlands. We included patients aged ≥ 70 years hospitalized with COVID-19 between February 2020 until May 2020. Atypical presentation of COVID-19 was defined as presentation without fever, cough and/or dyspnea. We collected data concerning symptoms on admission, demographics and frailty parameters [e.g., Charlson Comorbidity Index (CCI) and Clinical Frailty Scale (CFS)]. Outcome data included Intensive Care Unit (ICU) admission, discharge destination and 30-day mortality. RESULTS: We included 780 patients, 9.5% (n = 74) of those patients had an atypical presentation. Patients with an atypical presentation were older (80 years, IQR 76-86 years; versus 79 years, IQR 74-84, p = 0.044) and were more often classified as severely frail (CFS 6-9) compared to patients with a typical presentation (47.6% vs 28.7%, p = 0.004). Overall, there was no significant difference in 30-day mortality between the two groups in univariate analysis (32.4% vs 41.5%; p = 0.173) or in multivariate analysis [OR 0.59 (95% CI 0.34-1.0); p = 0.058]. CONCLUSIONS: In this study, patients with an atypical presentation of COVID-19 were more frail compared to patients with a typical presentation. Contrary to our expectations, an atypical presentation was not associated with worse outcomes.

A prospective, multicentre study to assess frailty in elderly patients with leg ulcers (GERAS study).

BACKGROUND: Although leg ulcers are a burdensome disease most common in those aged 65 years and older, frailty in this population has not yet been well established. OBJECTIVES: The aim of this study was to prospectively explore and compare the presence of frailty in elderly patients with chronic leg or foot ulcers by applying different validated frailty screening methods in three healthcare settings and to assess the feasibility of frailty screening. METHODS: We compared frailty of leg ulcer patients referred to an academic hospital with a non-academic hospital, leg ulcer patients receiving (primary) homecare, and a dermato-oncology patient population (control group). Frailty and quality of life were assessed using four validated questionnaires: the Groninger Frailty Indicator, Geriatric-8, Mini-Cog and Wound Quality of Life. To analyse data multiple (non)-parametric tests were performed. RESULTS: Fifty of 60 included leg ulcer patients (83%) scored "frail" on at least one frailty questionnaire (GFI, G8 or Mini-Cog). The number of patients scoring "frail" on two or three out of three applied frailty questionnaires were significantly higher in the academic and homecare ulcer population compared with the non-academic ulcer population and control group (p = 0.002). In the academic ulcer population mean Wound Quality of Life scores were 30.2 (SD 17.6), compared with 17.7 (SD 13.1) in the non-academic and 15.0 (SD 10.4) in the homecare ulcer population (p = 0.002). CONCLUSION: The majority of patients suffering from leg ulcers in this study was frail. The highest frailty prevalence was observed in the academic and homecare ulcer populations. The largest impaired quality of life was reported in the academic ulcer population. In dermatology practice, implementing frailty screening and initiating appropriate (paramedical) supportive care should be considered to improve patient outcomes.

Inflammaging and Blood Pressure Profiles in Late Life: The Screening for CKD among Older People across Europe (SCOPE) Study.

The neutrophil-to-lymphocyte ratio (NLR) is a marker for systemic inflammation. Since inflammation plays a relevant role in vascular aging, the aim of this study was to investigate whether NLR is associated with blood pressure profiles in older adults. This study was performed within the framework of the SCOPE study including 2461 outpatients aged 75 years and over. Mean blood pressure values, namely systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP) were investigated across tertiles of NLR. Change in blood pressure levels in 2 years of follow-up were compared across categories of baseline NLR. Data of 2397 individuals were used, of which 1854 individuals had hypertension. Mean values of blood pressure did not differ across categories of baseline NLR in individuals without hypertension. Individuals with hypertension with a high-range NLR had lower SBP and PP when compared to those in low-range NLR (mean difference SBP -2.94 mmHg, p = 0.032 and PP -2.55 mmHg, p = 0.030). Mean change in blood pressure in 2 years did only slightly differ in non-clinically relevant ranges, when compared across tertiles of baseline NLR. NLR as a marker of inflammaging was not associated with unfavorable blood pressure profiles in older individuals with or without hypertension.

Delirium in older COVID-19 patients: Evaluating risk factors and outcomes.

OBJECTIVES: A high incidence of delirium has been reported in older patients with Coronavirus disease 2019 (COVID-19). We aimed to identify determinants of delirium, including the Clinical Frailty Scale, in hospitalized older patients with COVID-19. Furthermore, we aimed to study the association of delirium independent of frailty with in-hospital outcomes in older COVID-19 patients. METHODS: This study was performed within the framework of the multi-center COVID-OLD cohort study and included patients aged ≥60 years who were admitted to the general ward because of COVID-19 in the Netherlands between February and May 2020. Data were collected on demographics, co-morbidity, disease severity, and geriatric parameters. Prevalence of delirium during hospital admission was recorded based on delirium screening using the Delirium Observation Screening Scale (DOSS) which was scored three times daily. A DOSS score ≥3 was followed by a delirium assessment by the ward physician In-hospital outcomes included length of stay, discharge destination, and mortality. RESULTS: A total of 412 patients were included (median age 76, 58% male). Delirium was present in 82 patients. In multivariable analysis, previous episode of delirium (Odds ratio [OR] 8.9 [95% CI 2.3-33.6] p = 0.001), and pre-existent memory problems (OR 7.6 [95% CI 3.1-22.5] p < 0.001) were associated with increased delirium risk. Clinical Frailty Scale was associated with increased delirium risk (OR 1.63 [95%CI 1.40-1.90] p < 0.001) in univariable analysis, but not in multivariable analysis. Patients who developed delirium had a shorter symptom duration and lower levels of C-reactive protein upon presentation, whereas vital parameters did not differ. Patients who developed a delirium had a longer hospital stay and were more often discharged to a nursing home. Delirium was associated with mortality (OR 2.84 [95% CI1.71-4.72] p < 0.001), but not in multivariable analyses. CONCLUSIONS: A previous delirium and pre-existent memory problems were associated with delirium risk in COVID-19. Delirium was not an independent predictor of mortality after adjustment for frailty.

The Multidimensional Prognostic Index Predicts Mortality in Older Outpatients with Cognitive Decline.

Since the heterogeneity of the growing group of older outpatients with cognitive decline, it is challenging to evaluate survival rates in clinical shared decision making. The primary outcome was to determine whether the Multidimensional Prognostic Index (MPI) predicts mortality, whilst assessing the MPI distribution was considered secondary. This retrospective chart review included 311 outpatients aged ≥65 years and diagnosed with dementia or mild cognitive impairment (MCI). The MPI includes several domains of the comprehensive geriatric assessment (CGA). All characteristics and data to calculate the risk score and mortality data were extracted from administrative information in the database of the Alzheimer's Center and medical records. The study population (mean age 76.8 years, men = 51.4%) was divided as follows: 34.1% belonged to MPI category 1, 52.1% to MPI category 2 and 13.8% to MPI category 3. Patients with dementia have a higher mean MPI risk score than patients with MCI (0.47 vs. 0.32; p < 0.001). The HRs and corresponding 95% CIs for mortality in patients in MPI categories 2 and 3 were 1.67 (0.81−3.45) and 3.80 (1.56−9.24) compared with MPI category 1, respectively. This study shows that the MPI predicts mortality in outpatients with cognitive decline.

Muscle mass and estimates of renal function: a longitudinal cohort study.

BACKGROUND: Creatinine is the most widely used test to estimate the glomerular filtration rate (GFR), but muscle mass as key determinant of creatinine next to renal function may confound such estimates. We explored effects of 24-h height-indexed creatinine excretion rate (CER index) on GFR estimated with creatinine (eGFRCr ), muscle mass-independent cystatin C (eGFRCys ), and the combination of creatinine and cystatin C (eGFRCr-Cys ) and predicted probabilities of discordant classification given age, sex, and CER index. METHODS: We included 8076 adults enrolled in the PREVEND study. Discordant classification was defined as not having eGFRCr  <60 mL/min per 1.73 m2 when eGFRCys was <60 mL/min/1.73 m2 . Baseline effects of age and sex on CER index were quantified with linear models using generalized least squares. Baseline effects of CER index on eGFR were quantified with quantile regression and logistic regression. Effects of annual changes in CER index on trajectories of eGFR were quantified with linear mixed-effects models. Missing observations in covariates were multiply imputed. RESULTS: Mean (SD) CER index was 8.0 (1.7) and 6.1 (1.3) mmol/24 h per meter in male and female participants, respectively (Pdifference  < 0.001). In male participants, baseline CER index increased until 45 years of age followed by a gradual decrease, whereas a gradual decrease across the entire range of age was observed in female participants. For a 70-year-old male participant with low muscle mass (CER index of 2 mmol/24 h per meter), predicted baseline eGFRCr and eGFRCys disagreed by 24.7 mL/min/1.73 m2 (and 30.1 mL/min/1.73 m2 when creatinine was not corrected for race). Percentages (95% CI) of discordant classification in male and female participants aged 60 years and older with low muscle mass were 18.5% (14.8-22.1%) and 15.2% (11.4-18.5%), respectively. For a 70-year-old male participant who lost muscle during follow-up, eGFRCr and eGFRCys disagreed by 1.5, 5.0, 8.5, and 12.0 mL/min/1.73 m2 (and 6.7, 10.7, 13.5, and 15.9 mL/min/1.73 m2 when creatinine was not corrected for race) at baseline, 5 years, 10 years, and 15 years of follow-up, respectively. CONCLUSIONS: Low muscle mass may cause considerable overestimation of single measurements of eGFRCr . Muscle wasting may cause spurious overestimation of repeatedly measured eGFRCr . Implementing muscle mass-independent markers for estimating renal function, like cystatin C as superior alternative to creatinine, is crucial to accurately assess renal function in settings of low muscle mass or muscle wasting. This would also eliminate the negative consequences of current race-based approaches.

Characteristics and outcomes of older patients hospitalised for COVID-19 in the first and second wave of the pandemic in The Netherlands: the COVID-OLD study.

BACKGROUND: as the coronavirus disease of 2019 (COVID-19) pandemic progressed diagnostics and treatment changed. OBJECTIVE: to investigate differences in characteristics, disease presentation and outcomes of older hospitalised COVID-19 patients between the first and second pandemic wave in The Netherlands. METHODS: this was a multicentre retrospective cohort study in 16 hospitals in The Netherlands including patients aged ≥ 70 years, hospitalised for COVID-19 in Spring 2020 (first wave) and Autumn 2020 (second wave). Data included Charlson comorbidity index (CCI), disease severity and Clinical Frailty Scale (CFS). Main outcome was in-hospital mortality. RESULTS: a total of 1,376 patients in the first wave (median age 78 years, 60% male) and 946 patients in the second wave (median age 79 years, 61% male) were included. There was no relevant difference in presence of comorbidity (median CCI 2) or frailty (median CFS 4). Patients in the second wave were admitted earlier in the disease course (median 6 versus 7 symptomatic days; P < 0.001). In-hospital mortality was lower in the second wave (38.1% first wave versus 27.0% second wave; P < 0.001). Mortality risk was 40% lower in the second wave compared with the first wave (95% confidence interval: 28-51%) after adjustment for differences in patient characteristics, comorbidity, symptomatic days until admission, disease severity and frailty. CONCLUSIONS: compared with older patients hospitalised in the first COVID-19 wave, patients in the second wave had lower in-hospital mortality, independent of risk factors for mortality.The better prognosis likely reflects earlier diagnosis, the effect of improvement in treatment and is relevant for future guidelines and treatment decisions.

Health outcome priorities in older patients with head and neck cancer.

OBJECTIVES: Older patients with head and neck cancer often have comorbidity, have reduced life-expectancy and await intensive treatment. For the decision-making process, knowledge of a patient's health outcome prioritization is of paramount importance. We aim to study the health outcome priorities of older patients with head and neck cancer, and to evaluate whether general health, markers of physical, cognitive, and social functioning, and quality of life are associated with health outcome prioritization. MATERIALS AND METHODS: Patients aged ≥70 years with head and neck cancer received a Comprehensive Geriatric Assessment and their priorities were assessed using the Outcome Prioritization Tool (OPT). Distribution of first priority, and associations with general health, markers of physical, cognitive, and social functioning, and quality of life were evaluated using ANOVA or chi-square. RESULTS: Of the 201 included patients, the OPT was available in 170 patients. The majority prioritized maintaining independence (n = 91, 53.3%), followed by extending life (n = 58, 34.1%), reducing pain (n = 14, 8.2%), and reducing other symptoms (n = 7, 4.1%). Housing situation, Body Mass Index, presence of musculoskeletal diseases, and quality of life were significantly related to prioritization of health outcomes. Reducing pain or other symptoms was more often prioritized by patients who lived alone, had a history of musculoskeletal problems, or had poor perceived quality of life. Age, sex, comorbidity, and markers of physical and cognitive functioning were not associated with health prioritization. CONCLUSION: Maintaining independence is most often prioritized by older patients with head and neck cancer. In addition, we found that health outcome priorities of older patients are only limited based on general and specific health characteristics. We suggest to systematically discuss patients' priorities in order to facilitate complex treatment decisions in older patients with cancer.

The Multidimensional Prognostic Index as a Measure of Frailty in Elderly Patients with Head and Neck Cancer.

PURPOSE: The multidimensional prognostic index (MPI) is a prognostic model derived from the comprehensive geriatric assessment (CGA) which can predict 1-year mortality risk in elderly individuals. We hypothesized that the MPI also reflects the degree of frailty and thus will correlate with established measures of frailty. Therefore, the aim of this study is to explore whether the MPI-score is a measure of frailty in older head and neck cancer patients and is associated with several physical functioning measurements. PATIENTS AND METHODS: From November 2019 to July 2020, a prospective cohort study enrolled patients with head and neck cancer aged ≥70 years, and patients <70 years with an abnormal G8 score. The MPI-score ranged from 0 to 1 and was categorized in MPI-stage 1 (≤0.33, non-frail); MPI-stage 2 (0.34-0.66, mildly frail), and MPI-stage 3 (≥0.67, severe frail). Pearson's correlation coefficient and multivariable linear regression were used to study the association between MPI-score and the physical functioning measurements handgrip strength, gait speed, and the timed up and go test (TUGT). RESULTS: A total of 163 patients were included. One hundred four (63.8%) patients were categorized as non-frail according MPI-stage 1, and 59 (36.2%) patients as mildly or severe frail (n=55 MPI-stage 2; n=4 MPI-stage 3, respectively). A higher MPI-score was significantly associated with lower hand grip strength (B -0.49 [95% CI -0.71; -0.28] p<0.001), lower gait speed (B -0.41 [95% CI -0.55; -0.25] p<0.001), and a slower TUGT (B 0.53 [95% CI 0.66; 0.85] p<0.001). CONCLUSION: Almost one-third of the included patients with head and neck cancer was mild or severe frail. A higher MPI-score, indicating higher degree of frailty, was associated with worse physical performance by lower handgrip strength, gait speed, and a slower TUGT. Thus, the MPI reflects the degree of frailty.

Multidimensional Prognostic Index and Outcomes in Older Patients Undergoing Transcatheter Aortic Valve Implantation: Survival of the Fittest.

Selecting patients with a high chance of endured benefit from transcatheter aortic valve implantation (TAVI) is becoming relevant with changing indications and increasing number of TAVI being performed. The aim of our study was to investigate the association of the multidimensional prognostic index (MPI) based on a comprehensive geriatric assessment (CGA) on survival. The TAVI Care & Cure program is a prospective, observational registry of patients referred for TAVI at the Erasmus MC University Medical Center. Consecutive patients who underwent a complete CGA and TAVI were included. CGA components were used to calculate the MPI score. The impact of the MPI score on survival was evaluated using Cox regression. Furthermore, 376 patients were included, 143 (38.0%) patients belonged to the MPI-1 group and 233 (61.9%) patients to the MPI-2-3 group. After 3 years, 14.9% of the patients in the MPI-1 group and 30.5% of the patients in the MPI-2-3 group died (p = 0.001). Patients in MPI-1 had increased chances of overall survival in comparison with patients in MPI group 2-3 Hazard Ratio (HR) 0.57, (95% Confidence Interval (CI) 0.33-0.98)). In this study we found that the MPI tool could be useful to assess frailty and to predict which patient will have a higher chance of enduring benefit from a TAVI procedure.

Frailty is associated with in-hospital mortality in older hospitalised COVID-19 patients in the Netherlands: the COVID-OLD study.

BACKGROUND: During the first wave of the coronavirus disease 2019 (COVID-19) pandemic, older patients had an increased risk of hospitalisation and death. Reports on the association of frailty with poor outcome have been conflicting. OBJECTIVE: The aim of the present study was to investigate the independent association between frailty and in-hospital mortality in older hospitalised COVID-19 patients in the Netherlands. METHODS: This was a multicentre retrospective cohort study in 15 hospitals in the Netherlands, including all patients aged ≥70 years, who were hospitalised with clinically confirmed COVID-19 between February and May 2020. Data were collected on demographics, co-morbidity, disease severity and Clinical Frailty Scale (CFS). Primary outcome was in-hospital mortality. RESULTS: A total of 1,376 patients were included (median age 78 years (interquartile range 74-84), 60% male). In total, 499 (38%) patients died during hospital admission. Parameters indicating presence of frailty (CFS 6-9) were associated with more co-morbidities, shorter symptom duration upon presentation (median 4 versus 7 days), lower oxygen demand and lower levels of C-reactive protein. In multivariable analyses, the CFS was independently associated with in-hospital mortality: compared with patients with CFS 1-3, patients with CFS 4-5 had a two times higher risk (odds ratio (OR) 2.0 (95% confidence interval (CI) 1.3-3.0)) and patients with CFS 6-9 had a three times higher risk of in-hospital mortality (OR 2.8 (95% CI 1.8-4.3)). CONCLUSIONS: The in-hospital mortality of older hospitalised COVID-19 patients in the Netherlands was 38%. Frailty was independently associated with higher in-hospital mortality, even though COVID-19 patients with frailty presented earlier to the hospital with less severe symptoms.

Association between Clinical Frailty Scale score and hospital mortality in adult patients with COVID-19 (COMET): an international, multicentre, retrospective, observational cohort study.

BACKGROUND: During the COVID-19 pandemic, the scarcity of resources has necessitated triage of critical care for patients with the disease. In patients aged 65 years and older, triage decisions are regularly based on degree of frailty measured by the Clinical Frailty Scale (CFS). However, the CFS could also be useful in patients younger than 65 years. We aimed to examine the association between CFS score and hospital mortality and between CFS score and admission to intensive care in adult patients of all ages with COVID-19 across Europe. METHODS: This analysis was part of the COVID Medication (COMET) study, an international, multicentre, retrospective observational cohort study in 63 hospitals in 11 countries in Europe. Eligible patients were aged 18 years and older, had been admitted to hospital, and either tested positive by PCR for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or were judged to have a high clinical likelihood of having SARS-CoV-2 infection by the local COVID-19 expert team. CFS was used to assess level of frailty: fit (CFS1-3), mildly frail (CFS4-5), or frail (CFS6-9). The primary outcome was hospital mortality. The secondary outcome was admission to intensive care. Data were analysed using a multivariable binary logistic regression model adjusted for covariates (age, sex, number of drugs prescribed, and type of drug class as a proxy for comorbidities). FINDINGS: Between March 30 and July 15, 2020, 2434 patients (median age 68 years [IQR 55-77]; 1480 [61%] men, 954 [30%] women) had CFS scores available and were included in the analyses. In the total sample and in patients aged 65 years and older, frail patients and mildly frail patients had a significantly higher risk of hospital mortality than fit patients (total sample: CFS6-9 vs CFS1-3 odds ratio [OR] 2·71 [95% CI 2·04-3·60], p<0·0001 and CFS4-5 vs CFS1-3 OR 1·54 [1·16-2·06], p=0·0030; age ≥65 years: CFS6-9 vs CFS1-3 OR 2·90 [2·12-3·97], p<0·0001 and CFS4-5 vs CFS1-3 OR 1·64 [1·20-2·25], p=0·0020). In patients younger than 65 years, an increased hospital mortality risk was only observed in frail patients (CFS6-9 vs CFS1-3 OR 2·22 [1·08-4·57], p=0·030; CFS4-5 vs CFS1-3 OR 1·08 [0·48-2·39], p=0·86). Frail patients had a higher incidence of admission to intensive care than fit patients (CFS6-9 vs CFS1-3 OR 1·54 [1·21-1·97], p=0·0010), whereas mildly frail patients had a lower incidence than fit patients (CFS4-5 vs CFS1-3 OR 0·71 [0·55-0·92], p=0·0090). Among patients younger than 65 years, frail patients had an increased incidence of admission to intensive care (CFS6-9 vs CFS1-3 OR 2·96 [1·98-4·43], p<0·0001), whereas mildly frail patients had no significant difference in incidence compared with fit patients (CFS4-5 vs CFS1-3 OR 0·93 [0·63-1·38], p=0·72). Among patients aged 65 years and older, frail patients had no significant difference in the incidence of admission to intensive care compared with fit patients (CFS6-9 vs CFS1-3 OR 1·27 [0·92-1·75], p=0·14), whereas mildly frail patients had a lower incidence than fit patients (CFS4-5 vs CFS1-3 OR 0·66 [0·47-0·93], p=0·018). INTERPRETATION: The results of this study suggest that CFS score is a suitable risk marker for hospital mortality in adult patients with COVID-19. However, treatment decisions based on the CFS in patients younger than 65 years should be made with caution. FUNDING: LOEY Foundation.