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Record W4200329894 · doi:10.1093/eurheartj/suab147.004

315 Cardiac surgery in the elderly: the underestimate role of frailty in the decision making

2021· article· en· W4200329894 on OpenAlexaboutno aff
Pasquale Campana, Maddalena Conte, Maria Emiliana Palaia, Laura Petraglia, Adele Ferro, Giuseppe Comentale, Emanuele Pilato, Dario Leosco, Valentina Parisi

Bibliographic record

VenueEuropean Heart Journal Supplements · 2021
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTinetti testMontreal Cognitive AssessmentDeliriumGeriatric Depression ScaleEuroSCOREPhysical therapyCardiac surgeryMini–Mental State ExaminationPopulationGerontologySurgeryCognitionCognitive impairmentIntensive care medicinePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Aims Elders represent the most common population with indication to cardiac surgery, also presenting the highest mortality/disability after interventions. Both for valve and coronary artery surgery the estimation of the surgical risk, including the frailty assessment, is recommended to guide the decision making. However, frailty results not exhaustively assessed by the commonly used surgical risk scores such as EuroSCORE I-II and score of the Society of Thoracic Surgeons and is mostly used the Kat’s Index (included in the latest European guidelines). This study aims at establishing the feasibility and the value of a Comprehensive Geriatric Assessment (CGA) in elderly undergoing cardiac surgery. Methods From June 2021we consecutively enrolled 50 elderly patients undergoing cardiac surgery (age > 65 years old). All patients underwent CGA with an expert geriatrician and the demographic, biometrics, clinical and echocardiographic data were collected. We evaluated frailty and disability (Kats index, Barthel Index and Frailty Index FI), cognitive status (Montreal Cognitive Assessment MOCA, Mini Mental State Examination MMSE and Geriatric Depression Scale), physical status (Tinetti test, Short Performance Physical Battery SPPB, Physical Activity Scale for the Elderly PASE and 6-min Walking test), delirium condition, sarcopenia and nutritional status (Mini-Nutritional Assessment MNA). A clinical, echocardiographic, and geriatric 3-month follow-up is planned. In particular, we are evaluating the impact of frailty, assessed by CGA, on peri-surgical outcome and the potential additive value of a CGA on the commonly used surgical risk-scores and Kat’s Index. Furthermore, we are assessing the impact of cardiac surgery of frail elderly at GCA. Results The CGA was feasible in all patients and lasted 1 h/patient. In our baseline data, only 23% of the enrolled patients resulted ‘frail’ according to Kat’s Index. However, in the remaining 77% of the study population, the CGA have identified 30% of patients with increased frailty index and 30% with disability, assessed by Barthel Index and physical function indexes (PASE and SPPB). In these patient, frailty and disability were associated to impaired nutritional status, assessed at MNA. Furthermore, 40% of the patients of this group resulted sarcopenic at the hand grip test. The cognitive valuation has shown a cognitive impairment in the 20% of patients at the MMSE and the 70 % at the MOCA. Of note, the 40% of the patients resulted to suffer of depression, not diagnosed before the GCA. At mid-November 2021 the follow-up will be completed. Conclusions The preliminary results of the presents study suggest that in patients undergoing cardiac surgery frailty is currently underdiagnosed. The follow-up analysis will establish if a CGA has an additive value on common surgical risk estimators. This study has a potential impact on the risk stratification of elderly patients undergoing invasive procedures and defines the need of a geriatrician in the heart team.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.354
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
Has abstractyes

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