scholarly journals Comparison of Frailty Index to Pneumonia Severity Measures in Older Patients With Pneumonia

2021 ◽  
Author(s):  
Chan Mi Park ◽  
Wonsock Kim ◽  
Eun Sik Lee ◽  
Hye Chang Rhim ◽  
Kyung Hwan Cho ◽  
...  
Keyword(s):  
Older Patients ◽  
Frailty Index ◽  
Pneumonia Severity

scholarly journals Study protocol: understanding the pathophysiologic mechanisms underlying delirium in older people undergoing hip fracture surgery

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
R. Gamberale ◽  
C. D’Orlando ◽  
S. Brunelli ◽  
R. Meneveri ◽  
P. Mazzola ◽  
...  
Keyword(s):  
Hip Fracture ◽  
Older People ◽  
Older Patients ◽  
Postoperative Delirium ◽  
Frailty Index ◽  
Pathophysiological Mechanisms ◽  
Hip Fracture Surgery ◽  
Endothelial Dysfunctions ◽  
Fracture Surgery ◽  
Pathophysiologic Mechanisms

Abstract Background Postoperative delirium (POD) is a common complication of older people undergoing hip fracture surgery, which negatively affects clinical- and healthcare-related outcomes. Unfortunately, POD pathophysiology is still largely unknown, despite previous studies showing that neuroinflammation, neuroendocrine dysfunction, increased reactive oxidative stress (ROS), and endothelial dysfunctions may be involved. There is also evidence that many of the pathophysiological mechanisms which are involved in delirium are involved in sarcopenia too. This article describes the protocol of a pilot study to evaluate the feasibility of a larger one that will explore the pathophysiological mechanisms correlating POD with sarcopenia. We will analyse whether various biomarkers reflecting neuroinflammation, ROS, neuroendocrine disorders, and microvasculature lesions will be simultaneously expressed in in the blood, cerebrospinal fluid (CSF), and muscles of patients developing POD. Methods Two centres will be involved in this study, each recruiting a convenient sample of ten older patients with hip fracture. All of them will undergo a baseline Comprehensive Geriatric Assessment, which will be used to construct a Rockwood-based Frailty Index (FI). Blood samples will be collected for each patient on the day of surgery and 1 day before. Additionally, CSF and muscle fragments will be taken and given to a biologist for subsequent analyses. The presence of POD will be assessed in each patient every morning until hospital discharge using the 4AT. Delirium subtypes and severity will be assessed using the Delirium Motor Subtype Scale-4 and the Delirium-O-Meter, respectively. We will also evaluate the patient’s functional status at discharge, using the Cumulated Ambulation Score. Discussion This study will be the first to correlate biomarkers of blood, CSF, and muscle in older patients with hip fracture.

scholarly journals Exploring Clinically Meaningful Changes for the Frailty Index in a Longitudinal Cohort of Hospitalized Older Patients

2020 ◽  
Vol 75 (10) ◽  
pp. 1928-1934 ◽  
Author(s):  
Olga Theou ◽  
Alexandra M van der Valk ◽  
Judith Godin ◽  
Melissa K Andrew ◽  
Janet E McElhaney ◽  
...  
Keyword(s):  
Older Adults ◽  
Older Patients ◽  
Frailty Index ◽  
Respiratory Illness ◽  
Research Network ◽  
Acute Respiratory Illness ◽  
Meaningful Change ◽  
Score Change ◽  
Surveillance Network ◽  
Clinical Frailty Scale

Abstract Background Clinically meaningful change (CMC) for frailty index (FI) scores is little studied. We estimated the CMC by associating changes in FI scores with changes in the Clinical Frailty Scale (CFS) in hospitalized patients. Methods The Serious Outcomes Surveillance Network of the Canadian Immunization Research Network enrolled older adults (65+ years) admitted to hospital with acute respiratory illness (mean age = 79.6 ± 8.4 years; 52.7% female). Patients were assigned CFS and 39-item FI scores in-person at admission and via telephone at 1-month postdischarge. Baseline frailty state was assessed at admission using health status 2 weeks before admission. We classified those whose CFS scores remained unchanged (n = 1,534) or increased (n = 4,390) from baseline to hospital admission, and whose CFS scores remained unchanged (n = 1,565) or decreased (n = 2,546) from admission to postdischarge. For each group, the CMC was represented as the FI score change value that best predicted one level CFS change, having the largest Youden J value in comparison to no change. Results From baseline to admission, 74.1% increased CFS by ≥1 level. From admission to postdischarge, 61.9% decreased CFS by ≥1 levels. A change in FI score of 0.03 best predicted both one-level CFS increase (sensitivity = 70%; specificity = 69%) and decrease (sensitivity = 66%; specificity = 61%) in comparison to no change. Of those who changed CFS by ≥1 levels, 70.9% (baseline to admission) and 72.4% (admission to postdischarge) changed their FI score by at least 0.03. Conclusions A clinically meaningful change of 0.03 in the frailty index score holds promise as a benchmark for assessing the meaningfulness of frailty interventions.

Operationalization of Frailty Index in Diabetic Older Patients

2016 ◽  
Vol 17 (3) ◽  
pp. 273-274 ◽  
Author(s):  
Jun Li ◽  
Shuangshuang Nie ◽  
Shuang Wang ◽  
Ying Li ◽  
Yupei Zou ◽  
...  
Keyword(s):  
Older Patients ◽  
Frailty Index

scholarly journals The Electronic Frailty Index is Associated with Increased Infection and All-Cause Mortality Among Older Patients with Primary Lung Cancer: A Cohort Study

2021 ◽  
Vol Volume 16 ◽  
pp. 1825-1833
Author(s):  
Yanjiao Shen ◽  
Yuting Wang ◽  
Qingyang Shi ◽  
Lisha Hou ◽  
Xiaoyan Chen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cohort Study ◽  
Older Patients ◽  
Frailty Index ◽  
Primary Lung Cancer ◽  
All Cause Mortality

Evaluating the Use of the Trauma-Specific Frailty Index in Older Patients with Hip Fractures

2020 ◽  
Vol 231 (4) ◽  
pp. e235-e236
Author(s):  
Daniel Dove ◽  
Catsim Fassassi ◽  
Juliette Hernandez ◽  
Nicolette Tedeschi ◽  
Ronald Simon
Keyword(s):  
Hip Fractures ◽  
Older Patients ◽  
Frailty Index

scholarly journals An exploration of community pharmacists’ experience, knowledge and perspectives of frailty and medicines optimisation in frail older people: A qualitative study

2021 ◽  
Vol 29 (Supplement_1) ◽  
pp. i6-i6
Author(s):  
L Faulkner ◽  
C M Hughes ◽  
H E Barry
Keyword(s):  
Older People ◽  
Older Patients ◽  
Frailty Index ◽  
External Validation ◽  
Community Pharmacists ◽  
Clinical Aspects ◽  
Frail Older People ◽  
Structured Interviews ◽  
Age Related ◽  
Medicines Optimisation

Abstract Introduction Frailty is a heightened state of vulnerability due to an accumulation of age-related defects in separate physiological systems (1). Frailty is becoming increasingly common, with up to 50% of older adults being diagnosed with mild, moderate or severe frailty (35%, 12% and 3% respectively) (2). Community pharmacists may often be the primary healthcare professional with whom frail older people have most frequent contact due to their convenience and accessibility. Therefore, it is hypothesised that community pharmacists could play a wider role in frailty identification and medicines optimisation for frail older people. Aim To explore community pharmacists’ knowledge of frailty and its assessment, their experiences and contact with frail older patients in the community pharmacy setting, and their perceptions of their role in optimising medicines for frail older people. Methods Two strategies were used to recruit community pharmacists registered in Northern Ireland (NI). Community pharmacists were recruited through the Pharmacy Forum NI bi-monthly newsletter and the School of Pharmacy Undergraduate Placement Network, followed by snowballing. The interview topic guide was developed based on the published literature, current frailty guidelines and through discussion within the research team; it was piloted with four pharmacists. Semi-structured interviews commenced in March 2020. Due to the Covid-19 pandemic, face-to-face interviews were logistically not possible, therefore telephone interviews were conducted at a time convenient to participants. All interviews were recorded, transcribed verbatim and analysed using inductive thematic analysis. Results To date, 14 interviews have been conducted, lasting between 24 and 72 minutes. Apart from one interview, all were conducted over the telephone. Participant characteristics are summarised in Table 1. Analysis of interview transcripts is ongoing. Findings to date have highlighted the key role community pharmacists feel they play in assisting frail older patients with their medicines (especially during the current pandemic). Many saw themselves as a ‘point of contact’ for frail older people and highlighted the holistic approaches they used to care of such patients: “It’s easier to get in contact with us than other healthcare professionals and we tend to be the first port of call really” [CP2]. Interviews highlighted a lack of pharmacist knowledge surrounding frailty as a condition and its assessment, with participants primarily focusing on the physical aspects of frailty (e.g. weight loss, weakness) when observing or ‘informally assessing’ patients. None of the participants reported formally assessing their patients using validated frailty tools or checklists: “It’s not something that I’ve ever thought about. We don’t have any tools readily available to us that I know of and certainly nothing that would be standardised” [CP1]. Conclusion This study has highlighted that community pharmacists felt they could contribute to optimising medicines for frail older people. However, the findings emphasise the need for more formal training for community pharmacists about the clinical aspects of frailty, frailty assessment and future interventions to address the medicines-related issues they have encountered with this patient population. References 1. Shaw RL, Gwyther H, Holland C, Bujnowska M, Kurpas D, Cano A, et al. Understanding frailty: meanings and beliefs about screening and prevention across key stakeholder groups in Europe. Ageing & Society. 2018;38(6): 1223–1252. 2. Hollinghurst J, Fry R, Akbari A, Clegg A, Lyons RA, Watkins A, et al. External validation of the electronic Frailty Index using the population of Wales within the Secure Anonymised Information Linkage Databank. Age and Ageing. 2019;48(6): 922–926.

scholarly journals Development of quantitative frailty and mortality prediction models on older patients as a palliative care needs assessment tool

2021 ◽  
Author(s):  
Vicent Blanes-Selva ◽  
Ascensión Doñate-Martínez ◽  
Gordon Linklater ◽  
Juan M. García-Gómez
Keyword(s):  
Palliative Care ◽  
Needs Assessment ◽  
Survival Time ◽  
Older Patients ◽  
Prediction Models ◽  
Frailty Index ◽  
Frailty Status ◽  
Mortality Model ◽  
One Year ◽  
The One

AbstractBackgroundPalliative care (PC) has demonstrated benefits for life-limiting illnesses. Cancer patients have mainly accessed these services, but there is growing consensus about the importance of promoting access for patients with non-malignant disease. Bad survival prognosis and patient’s frailty are usual dimensions to decide PC inclusion.ObjectivesThe main aim of this work is to design and evaluate three quantitative models based on machine learning approaches to predict frailty and mortality on older patients in the context of supporting PC decision making: one-year mortality, survival regression and one-year frailty classification.MethodsThe dataset used in this study is composed of 39,310 hospital admissions for 19,753 older patients (age >= 65) from January 1st, 2011 to December 30th, 2018. All prediction models were based on Gradient Boosting Machines. From the initial pool of variables at hospital admission, 20 were selected by a recursive feature elimination algorithm based on the random forest’s GINI importance criterion. Besides, we run an independent grid search to find the best hyperparameters in each model. The evaluation was performed by 10-fold cross-validation and area under the receiver operating characteristic curve and mean absolute error were reported. The Cox proportional-hazards model was used to compare our proposed approach with classical survival methods.ResultsThe one-year mortality model achieved an AUC ROC of 0.87 ± 0.01; the mortality regression model achieved an MAE of 329.97 ± 5.24 days. The one-year frailty classification reported an AUC ROC of 0.9 ± 0.01. The Spearman’s correlation between the admission frailty index and the survival time was –0.1, while the point-biserial correlation between one-year frailty index and survival time was –0.16.ConclusionsOne-year mortality model performance is at a state-of-the-art level. Frailty Index used in this study behaves coherently with other works in the literature. One-year frailty classifier demonstrated that frailty status within the year could be predicted accurately. To our knowledge, this is the first study predicting one-year frailty status based on a frailty index. We found mortality and frailty as two weakly correlated and complementary PC needs assessment criteria. Predictive models are available online at http://demoiapc.upv.es.

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