Prevalence of frailty and associated factors in coronary artery bypass graft surgery patients
Bibliographic record
Abstract
Abstract Background Frail patients are at increased risk for postoperative delirium, complications, delayed recovery, prolonged hospital and intensive care unit stay, morbidity and mortality. The number of studies examining frailty in cardiovascular surgery patients is limited (1,2). In this context, it is important to determine the prevalence of frailty in cardiac surgery patients (3,4). Purpose This research was conducted to examine the prevalence of frailty in coronary artery bypass graft surgery patients and associated factors with frailty. Methods The research was descriptive cross-sectional type. It was conducted between 18.02.2021 and 18.02.2022 at the cardiovascular surgery department of a training and research hospital in Turkey. A total of 96 patients who had undergone coronary artery bypass graft (CABG) were included. The "Sociodemographic and Clinical Data Form" which was prepared in accordance with the literature, the "Modified Fried Frailty Index", the "Mini Nutritional Assessment Test-Short Form", the "Barthel Daily Living Activities Index", the "Charlson Comorbidity Index", and the "Montreal Cognitive Assessment Scale" were used to collect data. The data were analyzed using the SPSS 23.0 package program. Descriptive statistics, Kolmogorov-Smirnov test, Chi-square test, One Way ANOVA, Kruskal Wallis test and multinomial logistic regression analysis were used in the evaluation of the data. Results As a result of the study, the mean age of the patients was 65.65±8.72 years (min:40-max:85), 62.5% consisted of males. The majority of patients (94.8%) had comorbid diseases, most of whom had hypertension (66.7%) and diabetes mellitus (63.5%). The mean ejection fraction of the patients was normal (54.59±8.70) according to the European Society of Cardiology and the majority (80.2%) were in the preserved LVEF group. The mean EuroSCORE was intermediate (4.79±2.40) and 49% of the patients were at intermediate risk for mortality. The majority of the patients (94.8%) were in the ASA III class according to the American Society of Anaesthesiologists. Of the patients. 14.6% were determined as ‘non-frail’, 45.8% as ‘pre-frail’ and 39.6% as ‘frail’. Age, educational status, income level, beta-blocker use, nutritional status, daily living activities, cognitive status, number of comorbidities, and level of potassium, procalcitonin and creatinine were found to be associated with frailty (p<0.05). Malnutrition, decreased daily living activity, cognitive impairment and creatinine elevation were found to be predictors of frailty (p<0.05). Sociodemographic and Clinical Characteristics by Frailty Status was presented in Table 1. Conclusions More than a third of CABG patients were frail. Malnutrition, decreased daily living activity, impaired cognitive status and high creatinine levels increased the risk. It is important to assesment frail cardiac surgery patients and provide them with appropriate nursing management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".