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András Szabó Doctoral School of Theoretical and Translational Medicine, Semmelweis University, Budapest, Hungary

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Dominika Szabó Doctoral School of Theoretical and Translational Medicine, Semmelweis University, Budapest, Hungary
Heart and Vascular Center, Semmelweis University, Budapest, Hungary

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Krisztina Tóth Doctoral School of Theoretical and Translational Medicine, Semmelweis University, Budapest, Hungary

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Balázs Szécsi Doctoral School of Theoretical and Translational Medicine, Semmelweis University, Budapest, Hungary

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Rita Szentgróti Doctoral School of Theoretical and Translational Medicine, Semmelweis University, Budapest, Hungary

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Ádám Nagy Gottsegen National Cardiovascular Center, Budapest, Hungary

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Csaba Eke Doctoral School of Theoretical and Translational Medicine, Semmelweis University, Budapest, Hungary

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Ágnes Sándor Doctoral School of Theoretical and Translational Medicine, Semmelweis University, Budapest, Hungary
Department of Anesthesiology and Intensive Therapy, Semmelweis University, Budapest, Hungary

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Kálmán Benke Heart and Vascular Center, Semmelweis University, Budapest, Hungary

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Béla Merkely Heart and Vascular Center, Semmelweis University, Budapest, Hungary

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János Gál Department of Anesthesiology and Intensive Therapy, Semmelweis University, Budapest, Hungary

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Andrea Székely Doctoral School of Theoretical and Translational Medicine, Semmelweis University, Budapest, Hungary
Heart and Vascular Center, Semmelweis University, Budapest, Hungary

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Abstract

Purpose

The frailty concept has become a fundamental part of daily clinical practice. In this study our purpose was to create a risk estimation method with a comprehensive aspect of patients' preoperative frailty.

Patients and methods

In our prospective, observational study, patients were enrolled between September 2014 and August 2017 in the Department of Cardiac Surgery and Department of Vascular Surgery at Semmelweis University, Budapest, Hungary. A comprehensive frailty score was built from four main domains: biological, functional-nutritional, cognitive-psychological and sociological. Each domain contained numerous indicators. In addition, the EUROSCORE for cardiac patients and the Vascular POSSUM for vascular patients were calculated and adjusted for mortality.

Results

Data from 228 participants were included for statistical analysis. A total of 161 patients underwent vascular surgery, and 67 underwent cardiac surgery. The preoperatively estimated mortality was not significantly different (median: 2.700, IQR (interquartile range): 2.000–4.900 vs. 3.000, IQR: 1.140–6.000, P = 0.266). The comprehensive frailty index was significantly different (0.400 (0.358–0.467) vs. 0.348 (0.303–0.460), P = 0.001). In deceased patients had elevated comprehensive frailty index (0.371 (0.316–0.445) vs. 0.423 (0.365–0.500), P < 0.001). In the multivariate Cox model an increased risk for mortality in quartiles 2, 3 and 4 compared with quartile 1 as a reference was found (AHR (95% CI): 1.974 (0.982–3.969), 2.306 (1.155–4.603), and 3.058 (1.556–6.010), respectively).

Conclusion

The comprehensive frailty index developed in this study could be an important predictor of long-term mortality after vascular or cardiac surgery. Accurate frailty estimation could make the traditional risk scoring systems more accurate and reliable.

Abstract

Purpose

The frailty concept has become a fundamental part of daily clinical practice. In this study our purpose was to create a risk estimation method with a comprehensive aspect of patients' preoperative frailty.

Patients and methods

In our prospective, observational study, patients were enrolled between September 2014 and August 2017 in the Department of Cardiac Surgery and Department of Vascular Surgery at Semmelweis University, Budapest, Hungary. A comprehensive frailty score was built from four main domains: biological, functional-nutritional, cognitive-psychological and sociological. Each domain contained numerous indicators. In addition, the EUROSCORE for cardiac patients and the Vascular POSSUM for vascular patients were calculated and adjusted for mortality.

Results

Data from 228 participants were included for statistical analysis. A total of 161 patients underwent vascular surgery, and 67 underwent cardiac surgery. The preoperatively estimated mortality was not significantly different (median: 2.700, IQR (interquartile range): 2.000–4.900 vs. 3.000, IQR: 1.140–6.000, P = 0.266). The comprehensive frailty index was significantly different (0.400 (0.358–0.467) vs. 0.348 (0.303–0.460), P = 0.001). In deceased patients had elevated comprehensive frailty index (0.371 (0.316–0.445) vs. 0.423 (0.365–0.500), P < 0.001). In the multivariate Cox model an increased risk for mortality in quartiles 2, 3 and 4 compared with quartile 1 as a reference was found (AHR (95% CI): 1.974 (0.982–3.969), 2.306 (1.155–4.603), and 3.058 (1.556–6.010), respectively).

Conclusion

The comprehensive frailty index developed in this study could be an important predictor of long-term mortality after vascular or cardiac surgery. Accurate frailty estimation could make the traditional risk scoring systems more accurate and reliable.

Introduction

Background/rationale

More accurate and reliable risk estimation methods are required for daily practice in medicine. In the surgical field, invasive and non-invasive treatment could have entirely different risks, outcomes and burdens. In elderly patients, aortic valve replacement by a surgical or transcatheter approach is a good example. The choice needs to be guided by multidisciplinary risk estimation to select the best treatment for the patient, which could be challenging and requires multifactorial decisions. To support the decision-making process, different risk calculation methods are used with very different accuracies and predictive values. Traditional risk scores are calculated using basic biological variables and surgical (operative) load/risk but not by taking into account the patients' frailty, which is well proven to be an independent risk factor for postoperative mortality and morbidity [1–4]. A gold standard definition of frailty is missing, but the commonly accepted definition of frailty is a clinical state where patients have a decreased ability to react to physical stress because of reduced physiological reserve and capacity [5]. According to modern frailty conception this clinical syndrome is developing by accumulation of different deficits, including physical, clinical, cognitive, psychological and social problems. An up-to-date review clearly summarizes the evolution of the frailty concept which emphasizes that frailty syndrome is how many ways more complex than e.g. decreased muscle strength. Furthermore, this work from Wleklik et al., highlights the fact that frailty syndrome could be reversible which could directly lead clinicians toward the need for adequate prehabilitation [6]. In this manner correct frailty estimation could be useful selection for prehabilitation and help to make and prove – sometimes difficult – clinical questions. In the literature increasing numbers of evidence and recommendation can be found about the improvement of frailty and the importance of prehabilitation [7, 8].

The general frailty concept is increasingly being accepted and has become a part of daily clinical routine. Currently, we still do not have an exact, comprehensive frailty assessment method for mapping, describing and understanding patients' frailty status. An increasing number of studies have emphasized that frailty status is superior to special surgical risk scores. Using only one or a few parameters from the frailty tools could be misleading [9]. Furthermore, it seems to be the case that frailty is not a good predictor for early postoperative problems but instead has a specific effect on mid- and long-term mortality and morbidity.

When conducting an overview of the literature, the effect of frailty on negative outcomes is clearly identifiable. According to a meta-analysis, the prevalence of frailty is approximately 19–62% in the surgical and intensive care population. The odds ratios for mortality of frail patients ranges from 1.76 to 3.09, and the length of stay (LOS), discharge to other types of care and surgical complications were also elevated. However, the additional benefits of frailty estimation in this field are obvious, and better standardization of the different frailty scores is highly recommended according to the final conclusions of this meta-analysis [10].

The effect of frailty syndrome on mortality and mortality is important to investigate in contrast of currently used risk estimation methods. These methods built up mostly with physiological conditions and each medical discipline has its own routine processes. In cardiac surgery the EuroScore II, in vascular surgery the Vascular POSSUM (V-POSSUM) are the one of the most frequently used risk estimation methods [3, 4].

Objectives

Our aim was to investigate patients' preoperative frailty using a multidomain assessment and modelling of its effect on postoperative mortality. Behind the overall influence, the effect of distinct frailty aspects was also analysed. Comparing and adjusting traditionally used risk estimation methods were also important parts of our work to evaluate the summarized accuracy of both types of scores.

Patients and methods

Study design, setting, participants

In our prospective, observational study, patients were enrolled between September 2014 and August 2017 in the Department of Cardiac Surgery and Department of Vascular Surgery at Semmelweis University, Budapest, Hungary. This study was approved by the local scientific ethics committee (TuKEB 250/2013) and was registered on clinicaltrials.gov (NCT02224222). This current report is a sub-analysis of the database created for the original aim, which was to investigate patients' preoperative risk factors before cardiac and vascular surgery. In cardiac surgical group surgical valve replacement (aortic and mitral valve), coronary artery bypass grafting or ascending aorta reconstruction was performed. In vascular surgical group procedures on arterial system were performed (operations on abdominal aorta, iliac and femoral artery system or carotid artery endarterectomies).

Definitions and measurements (variables and data sources and grouping), study size

The inclusion criteria were age over 18 years and elective surgery. Exclusion criteria were pregnancy and patients with a legal incapacity or considered to have a limited capability of understanding the study procedures and providing informed consent. All enrolled participants were capable of making their own decisions regarding their participation in this study, and accordingly, written consent was obtained. A study nurse, a medical student or a postdoctoral fellow invited patients to participate in this study during their outpatient anaesthesiology visit. All staff members were trained by a psychologist to perform the cognitive mapping and assessments. The baseline questionnaires were completed 30 days before surgery.

Basic anthropometric data, such as height, weight, age, and sex, were collected. Place of residence, education, marital and working status were also recorded. The previous medical history (including past illness and surgeries) was collected, and the frailty indicators were also mapped. In cardiac surgical patients EuroScore II, in vascular surgical patients V-POSSUM was calculated and transformed into predicted mortality percentage.

EuroScore II contains 3 basic domains: 1. the patient related factors (age, sex, renal impairment or insufficiency, respiratory disease, atherosclerosis, poor mobility, previous cardiac surgery, endocarditis, preoperative critical state, diabetes mellitus treated with insulin), 2. cardiac related factors (congestive heart failure, angina severity, current myocardial infarction, left ventricular ejection fraction, pulmonary hypertension) and 3., operation-related factors (urgency, weight of procedures (e.g. valve replacement with coronary artery bypass grafting) and the involvement of thoracic aorta) [4]. The V-POSSUM contains two domains: 1., physiology parameters (age, cardiac failure, respiratory disease, heart rhythm, systolic blood pressure, pulse rate, haemoglobin level, white blood cells, blood urea nitrogen, serum potassium and sodium level, Glasgow Coma Scale level) and 2., operative parameters (operation type, number of procedures, planned blood loss, peritoneal contamination, coexisting malignancy, urgency) [3].

Building a comprehensive frailty score

The comprehensive frailty score was built from four main domains. Each domain contained numerous indicators. All the indicators had values between 0 and 1. For binomial indicators (e.g. atrial fibrillation or diabetes), the presence of the illness scored 1 point. In the case of continuous variables (e.g. self-rated scales), the original score was calculated to obtain a value between 0 and 1. The biological frailty domain was composed of cardiovascular risk factors (congestive heart failure, atrial fibrillation, chronic coronary syndrome, hypertension, previous myocardial infarction or stroke, diabetes) and non-cardiovascular diseases (asthma or chronic obstructive pulmonary disease (COPD), arthritis, degenerative spinal disorders, chronic renal insufficiency, neoplasia). The chronic administration of medications was also considered, and regularly taking more than 5 drugs was identified as a potential risk factor. The functional domain included functional indicators (being able to move heavy objects, engage in sports and housework) and nutritional parameters (body mass index (BMI) lower than 20, serum albumin level lower than 35 g L−1 and unintended weight loss (more than 10% during the last 6 months)). The main cognitive and psychological domains were cognitive dysfunction, depression, anxiety and self-reported happiness and satisfaction. The sociological frailty domain included education, living alone, Caldwell Social Support Dimension Scale and self-reported financial problems. The education indicator was clustered into low (elementary and high school) and high levels (college and higher education).

Self-reported physical function tests

In the functional domain, there were self-reported indicators for physical status. Our indicators were derived from the activities of daily living questionnaire, such as transferring heavy objects and performing housework independently. Regarding sports, engaging in more than one exercise session per week was accepted as regular sports activity. Its limitation is that some patients were not able to do any exercises because of their medical conditions (severe lower limb artery stenosis, etc.).

Mini mental state examination

To measure the patients' cognitive performance, the Mini-Mental State Examination (MMSE) was used. However, the MMSE is designed for detecting dementia; it has a high specificity for cognitive impairment, and its clinical relevance has been proven in numerous studies [11]. The test contains questions that map to cognitive function, including language skills, short-term memory and computing abilities. In the current setting, the MMSE was scored as 0, 0.3, 0.7 and 1 according to the original score of 27–30, 24–26, 21–23 and below 21, respectively [12].

Beck depression inventory

The Beck Depression Inventory (BDI) contains 21 multiple-choice questions created by Aaron T. Beck in 1961 [13]. The inventory went through numerous modifications; currently, the BDI-II is used, a version made in 1996. It also has modified cut-off values: 14–19 points are associated with mild depression, 20–28 points with moderate depression and over 29 points with severe depression [14]. In the current study, the definition of depression was 13 points and above on the BDI.

State-Trait Anxiety Inventory

Anxiety was measured by the State-Trait Anxiety Inventory (STAI). It has two axes, trait and state anxiety, which both include 20, 4-point Likert-scale questions [15]. In this study, the trait axis was mapped, and general anxiety was determined as a score of at least 40 points on the STAI-T [16].

Caldwell Social Support Dimension Scale

The Caldwell Social Support Dimension Scale (CSSDS) is a self-report inventory to map patients' social support and social network. It contains aspects about support by family and nonfamily persons [17]. In the current study, the overall social support dimension scale was used.

Other self-reported indicator scales

In the functional, psychological and social domains, simple self-rated questions were used to map happiness, satisfaction, current health status and daily financial problems. The patients were able to choose values on a continuous scale between 1 and 10. The power of the predictive ability for mortality and morbidity of these simple questions was proven in earlier studies [18, 19]. The indicator was calculated as follows: (1-original value/10) (e.g., patients with a self-rated score of 7/10 received 0.3 points, patients with a self-rated score 4/10 received 0.6 points, etc.).

Living alone is a well-proven risk factor for mortality, especially in elderly individuals, and it was used as an indicator in the social frailty main domain [20, 21].

Preoperative surgical risk

For traditional surgical risk estimation in the case of vascular surgical patients, the Vascular POSSUM was used, while for cardiac surgical patients, Euroscore II was used [1, 2, 22]. The original score was translated for estimated mortality in percentages. In the Cox regression model, adjustment of the comprehensive frailty index for estimated mortality was performed.

Outcomes

The primary outcome was mortality during the follow-up. Secondary outcomes were short- and mid-term mortality (at 1, 2 and 4 years of follow-up).

Statistical analysis

Normality was tested with the Kolmogorov–Smirnov test. Normal distributions are described with means and standard deviations. Skewed distributions are described with medians and interquartile ranges (IQR 25–75) and were compared with the Mann–Whitney U test and the Wilcoxon rank-sum test. Categorical data are presented as quantities and percentages and were assessed with the chi-square test and Fischer's exact test. A two-sided alpha level of 0.05 was applied.

Multivariable Cox regression models were used as the primary analysis to discover independent risk factors for mortality with adjustment for the Euroscore II and Vascular POSSUM scores. For the comparability of the mortality risk calculation scores, the estimated mortality in percentages was calculated, and this value was used in the adjustment methods. Multivariable two-sided tests with an alpha level of less than 0.05 were considered statistically significant. We used backwards variable elimination to create a model for predicting mortality. We performed our statistical analyses with IBM-SPSS 25.0 software (International Business Machines Corporation, Armonk, New York, United States of America) and jamovi for statistical and graphical tools. Jamovi extensions ClinicoPathDescriptives, deathwatch, felxplot, jjstatsplot, jsurvival, medmod and scatr were used [23].

Results

Participants, descriptive data

Data from 228 participants were included for statistical analysis. A total of 161 patients underwent vascular surgery, and 67 underwent cardiac surgery. The median age of the whole cohort was 68.00 years, and the interquartile range was 60.50–73.00 years. A total of 64.07% were male, and the median BMI was 27.44 (IQR 24.30–29.75). The median follow-up time was 2012 days, IQR 1471–2413 days. Regarding the described parameters, a significant difference was not verified. During the follow-up, 95 patients died (41.667%). The one-, two-, three- and four-year mortality rates were 6.140% (14), 10.088% (23), 18.421% (42) and 23.246% (53), respectively. The incidence of different indicators of the comprehensive frailty score are shown in Table 1.

Table 1.

The incidence of different indicators of the comprehensive frailty score among the whole population (a: inverted score, BDI: Beck Depression Inventory, BMI: body mass index, CCS: chronic coronary syndrome, COPD: chronic obstructive pulmonary disease, CSSDS:Caldwell Social Support Dimension Scale, STAI: State-Trait Anxiety Inventory, TIA: transient ischemic attack)

Count%MedianInterquartile range
Biological variablesAtrial fibrillation2510.96%
Congestive heart failure2310.09%
CCS8336.40%
Diabetes mellitus9039.47%
Hypertension20690.35%
Myocardial infarction4117.98%
Stroke (or TIA)6126.75%
Arthritis12856.14%
Asthma62.63%
Neoplasia in last 5 years146.14%
Renal disease4220.79%
COPD8035.09%
Degenerative spinal disease3515.35%
More than 5 regularly used medications13760.09%
Biological domain subindex0.2860.214–0.385
Functional and nutritional variablesBMI (≤20 or ≥30)2611.40%
Unintended weight loss2210.05%
Current pain/chronic pain9844.95%
Self-rated health statusa0.4000.400–0.400
Low albumin level (≤35 g L−1)4623.71%
Lack of sport activities8741.23%
Unable to doing heavy work around the house11550.66%
Unable to do housecleaning and home maintenance9642.86%
Functional frailty domain subindex0.3000.200–0.425
Cognitive and psychological variablesCognitive impairment5222.81%
Self-rated happinessa0.3000.10–0.50
Self-rated satisfactiona0.3000.20–0.50
STAI (≥40 points)11251.61%
BDI (≥13 points)3718.50%
Cognitive and psychological frailty domain subindex0.2450.10–0.40
Social variablesCSSDS10043.86%
Living alone5021.93%
Lower education level11148.68%
Self-rated financial problems2210.00%
Social frailty domain subindex0.2500.25–0.50
Comprehensive frailty index0.3930.33–0.46
RatiosBiological frailty domain24.950%18.44–34.79%
Functional frailty domain26.759%19.22–34.51%
Cognitive and psychological frailty domain20.703%11.95–31.13%
Social frailty domain23.730%14.53–32.51%

Outcome data regarding the type of surgery

The mortality during the follow-up time was significantly higher in the vascular surgical group (47.826% vs. 26.866%, P = 0.003). The preoperatively estimated mortality was not significantly different (median: 2.700, IQR: 2.000–4.900 vs. 3.000, IQR: 1.140–6.000, P = 0.266). The comprehensive frailty index showed significant, remarkable differences (0.400, IQR: 0.358–0.467 vs. 0.348, IQR: 0.303–0.460, P = 0.001). The indicators are summarized in Table 2 according to the type of surgery. There were significant differences between the two groups in the biological (0.357, IQR: 0.214–0.429 vs. 0.357, IQR: 0.214–0.429, P = 0.001) and functional domains (0.325, IQR: 0.200–0.425 vs. 0.325, IQR: 0.200–0.450, P = 0.011).

Table 2.

The observed indicators in different frailty domains between surgical groups (a: inverted score, BDI: Beck Depression Inventory, BMI: body mass index, CCS: chronic coronary syndrome, COPD: chronic obstructive pulmonary disease, CSSDS: Caldwell Social Support Dimension Scale, STAI: State-Trait Anxiety Inventory, TIA: transient ischemic attack)

Vascular surgical patientsCardiac surgical patients
Count%MedianInterquartile rangeCount%MedianInterquartile rangeP-value
Biological variablesAtrial fibrillation159.320%1014.930%0.217
Congestive heart failure1710.560%68.960%0.714
CCS5534.160%2841.790%0.275
Diabetes mellitus6540.370%2537.310%0.667
Hypertension14489.440%6292.540%0.471
Myocardial infarction3521.740%68.960%0.056
Stroke (or TIA)5735.400%45.970%0.001
Arthritis10867.080%2029.850%0.001
Asthma42.480%22.990%0.830
Neoplasia in last 5 years116.830%34.480%0.500
Renal disease2719.850%1522.730%0.637
COPD5836.020%2232.840%0.646
Degenerative spinal disease159.320%2029.850%0.001
More than 5 regular used medicine10867.080%2943.280%0.001
Biological domain subindex0.3570.214–0.4290.2140.214–0.3570.001
Functional and nutritional variablesBMI (≤20 or ≥30)4326.710%1522.390%0.306
Unintended weight loss1811.840%45.970%0.183
Current pain/chronic pain8552.800%1322.810%0.001
Self-rated health statusa0.4000.400–0.4000.4000.400–0.6000.577
Low albumin level (≤35 g L−1)32.360%4364.180%0.001
Lack of sport activities6741.880%2039.220%0.737
Unable to doing heavy work around the house9760.250%1827.270%0.001
Unable to do housecleaning and home maintenance6843.040%2842.420%0.933
Functional frailty domain subindex0.3250.200–0.4500.2750.175–0.4250.011
Cognitive and psychological variablesCognitive impairment4024.845%1217.910%0.299
Self-rated happinessa0.3000.100–0.5000.2000.100–0.5000.666
Self-rated satisfactiona0.3000.200–0.5000.3000.200–0.5000.126
STAI (≥40 points)8250.930%3053.570%0.733
BDI (≥13 points)2717.760%1020.830%0.633
Cognitive and psychological frailty domain subindex0.2600.120–0.4000.2000.080–0.4000.098
Social variablesCSSDS6741.610%3349.250%0.290
Living alone3320.500%1725.370%0.418
Lower education level8351.550%2841.790%0.179
Self-rated financial problems2012.420%23.390%0.048
Social frailty domain subindex0.2500.250–0.5000.2500.000–0.5000.807
Comprehensive frailty index0.4000.358–0.4670.3480.303–0.4600.001
RatiosBiological frailty domain25.231%19.582–34.924%24.829%17.575–33.944%0.651
Functional frailty domain27.526%20.000–33.796%24.623%17.339–35.233%0.607
Cognitive and psychological frailty domain20.741%12.516–31.818%20.664%8.7363–30.270%0.348
Social frailty domain23.529%15.953–31.028%24.87%0.000–40.698%0.599
Estimated mortality2.7002.000–4.9003.0001.140–6.0000.266

Main results – long-term mortality regarding differences in preoperative indicators

Patients who died during the follow-up time had significantly higher biological, functional and sociological domain subindex scores. The comprehensive frailty index was also increased (0.371, IQR: 0.316–0.445 vs. 0.423, IQR: 0.365–0.500, P < 0.001). However, the cognitive and psychological domain subindex did not differ significantly, and cognitive impairment (16.541% vs. 31.579%, P = 0.029) and self-rated happiness (0.200, IQR: 0.000–0.500 vs. 0.300, IQR: 0.100–0.500, P = 0.045) were worse in the non-survivor cohort (Table 3).

Table 3.

The observed indicators in different frailty domains according to long-term mortality (a: inverted score, BDI: Beck Depression Inventory, BMI: body mass index, CCS: chronic coronary syndrome, COPD: chronic obstructive pulmonary disease, CSSDS: Caldwell Social Support Dimension Scale, STAI: State-Trait Anxiety Inventory, TIA: transient ischemic attack)

Survivor (n = 133)Non-survivor (n = 95)
Count%MedianInterquartile rangeCount%MedianInterquartile rangeP-value
Vascular surgical patients8457.174%7742.826%0.003
Cardiac surgical patients4973.134%1826.866%
Biological variablesAtrial fibrillation129.023%1313.684%0.267
Congestive heart failure86.015%1515.789%0.016
CCS5138.346%3233.684%0.471
Diabetes mellitus4433.083%4648.421%0.019
Hypertension12291.729%8488.421%0.404
Myocardial infarction2216.541%2021.153%0.385
Stroke (or TIA)2921.805%3233.684%0.046
Arthritis6750.376%6164.211%0.038
Asthma43.008%22.105%0.675
Neoplasia in last 5 years118.271%33.158%0.113
Renal disease2117.500%2125.610%0.163
COPD4030.075%4042.105%0.061
Degenerative spinal disease2015.038%1515.789%0.877
More than 5 regularly used medications7657.143%6164.211%0.283
Biological domain subindex0.2860.214–0.3570.3570.231–0.4290.002
Functional and nutritional variablesBMI (≤20 or ≥30)129.023%1414.737%0.181
Unintended weight loss118.594%1112.088%0.397
Current pain/chronic pain5140.157%4751.648%0.093
Self-rated health statusa0.4000.200–0.4000.4000.400–0.4000.572
Low albumin level (≤35 g L−1)3126.496%1519.481%0.261
Lack of sport activities4235.000%4549.451%0.035
Unable to doing heavy work around the house6246.970%5355.789%0.190
Unable to do housecleaning and home maintenance5541.985%4144.086%0.754
Functional frailty domain subindex0.3000.175–0.4250.3430.233–0.4500.018
Cognitive and psychological variablesCognitive impairment2216.541%3031.579%0.029
Self-rated happinessa0.2000.000–0.5000.3000.100–0.5000.045
Self-rated satisfactiona0.3000.200–0.5000.3000.200–0.5000.142
STAI (≥40 points)6551.587%4751.648%0.993
BDI (≥13 points)1714.912%2023.256%0.132
Cognitive and psychological frailty domain subindex0.2400.100–0.3750.2600.120–0.4800.152
Social variablesCSSDS5239.098%4850.526%0.086
Living alone2619.549%2425.263%0.304
Lower education level5843.609%5355.789%0.070
Self-rated financial problems129.375%1010.870%0.715
Social frailty domain subindex0.2500.000–0.3330.2500.250–0.5000.007
Comprehensive frailty index0.3710.316–0.4450.4230.365–0.500<0.001
RatiosBiological frailty domain24.829%18.132–34.924%25.025%19.017–34.1670.828
Functional frailty domain28.020%18.503–35.484%25.607%19.958–32.3000.351
Cognitive and pychological frailty domain20.741%11.523–31.542%20.108%12.160–30.8940.827
Social frailty domain22.846%0.000–32.169%25.253%15.709–33.3970.415
Estimated mortality2.4001.700–4.0003.2002.300–5.700<0.001

Comprehensive frailty score and prediction of long-term mortality

For the examination of mortality risk, four subgroups were created according to the comprehensive frailty index quartiles. In univariate Cox regression, an odds ratio of 1.449 (95% CI: 1.199–1.751, P < 0.001) was found. After adjusting for traditional surgical risk using the estimated mortality OR = 1.384 (95% CI: 1.140–1.680, P = 0.001) was calculated. The adjusted odds ratios calculated according to the comprehensive frailty index quartiles in the multivariate Cox regression are shown in Fig. 1.

Fig. 1.
Fig. 1.

The adjusted odds ratios for morality according to the comprehensive frailty index quartiles in the multivariate Cox regression model

Citation: Physiology International 110, 2; 10.1556/2060.2023.00195

Kaplan-Meier analysis according to the comprehensive frailty index quartiles showed a significant difference in mortality, as presented in Fig. 2 (Mantel–Cox log-rank test, P = 0.001).

Fig. 2.
Fig. 2.

Kaplan-Meier analysis according to the comprehensive frailty score quartiles

Citation: Physiology International 110, 2; 10.1556/2060.2023.00195

Relationship between comprehensive frailty index and surgical risk estimation methods

Between comprehensive frailty index and routinely used surgical risk estimation methods (EuroScore II and V-POSSUM) positive correlation was observed. The predicted mortality was calculated with each specific method and it was analysed in aspect of frailty. The result is showed in Fig. 3.

Fig. 3.
Fig. 3.

Scatter plot shows the relationship between comprehensive frailty index and estimated mortality. Trend line was fitted used polynomial method, grey strip represents 95% confidence interval (Pearson r = 0.262, P < 0.001)

Citation: Physiology International 110, 2; 10.1556/2060.2023.00195

Discussion

Key findings

The current study found that the comprehensive frailty index is an important, independent and reliable indicator for long-term mortality of vascular and cardiac surgical patients. Even a moderate elevation in the patient's frailty index could have consequences. The current frailty index was constructed from biological, functional, sociological, cognitive and psychological elements, and these variables were clustered into 4 main frailty domains.

In conclusion, our current study found a more than 3-fold risk for mortality in the most frail patient population compared to the least frail cohort.

In the studied clinical setting, no evidence for any influence of the comprehensive frailty index on short-term mortality was detected. However, positive correlation was found between comprehensive frailty index and estimated postoperative mortality calculated by using Euroscore II and V-POSSUM.

Relationship to previous studies

Each year, an increasing number of original articles investigate frailty in special subgroups, for example, in vascular surgical and cardiac surgical populations. The basic mechanisms of the general frailty concept are supposed to be widely applicable. In our current study, a comprehensive frailty index was developed based on a comprehensive geriatric assessment [24]. A recently published meta-analysis emphasized the importance of frailty and the preoperative conditions and illnesses that we included in our frailty index [25]. That article clearly showed that frailty significantly increased the risk of mortality among patients undergoing transcatheter aortic valve implantation (TAVI or TAVR) (HR: 2.16, 95% CI: 1.57–3.00). Our current findings confirmed the elevated mortality risk (AHR = 1.384, 95% CI: 1.140–1.680, P = 0.001) in our vascular and cardiac surgical population.

Afilalo et al. published an article which studied similar cohort as our current work. In this study authors were compared 7 different frailty tools. The prevalence of frailty was 26%–68% in the cohort that included 1,020 patients underwent transcatheter aortic valve replacement (TAVR) or surgical aortic valve replacement (SAVR) procedure. Among tools the strongest one was the Essential Frailty Tool (EFT) which is similar, multidimensional but simplified method like ours. It was showed heavy influence on one year mortality (adjusted odds ratio [OR]: 3.72; 95% confidence interval [CI]: 2.54–5.45) with a C-statistic improvement of 0.071 (P < 0.001) and integrated discrimination improvement of 0.067 (P < 0.001) [26].

It is supposed that the mortality prediction of the comprehensive frailty index becomes stronger as time passes after surgery. As the recently published article described, frailty parameters did not show any association with short- and mid-term mortality after endovascular techniques for aortic repair [27]. Short-term mortality is highly dependent on physical and surgical conditions, being strongly associated with the type of surgery and the perioperative risk factors and postoperative complications.

Shi et al. investigated frailty and the Lee score for their predictive value for mortality and functional decline with severe symptoms among patients who underwent artificial aortic valve implantation [12]. Their frailty index predicted twelve-month mortality in a cohort of patients with transcatheter intervention but did not accurately predict mortality in the surgical group. However, the Lee score had a more accurate predictive value in the surgical population. Furthermore, they reported a slightly higher adjusted hazard ratio for poor outcomes AHR (95% CI) in quartiles 2, 3 and 4 compared to quartile 1 as a reference: 2.7 (0.8–9.5), 2.8 (0.8–10.5), 6.0 (1.5–23.3), P = 0.010 compared to our findings AHR (95% CI) in quartiles 2, 3 and 4 compared to quartile 1 as a reference: 1.974 (0.982–3.969), 2.306 (1.155–4.603), and 3.058 (1.556–6.010), respectively.

Among patients with severe aortic stenosis who underwent TAVR or valve replacement surgery, there was no difference in 30-day mortality or complications, but there was in the length of hospital stay and the 1-year all-cause mortality. In this study, clustering for the frail and fit groups was performed, and the frail group had an adjusted hazard ratio of 3.51 (95% CI 1.4–8.5, P = 0.007) for mortality. These findings are consistent with our results, especially in our most frail group (the fourth quarter of the comprehensive frailty score) [28].

A similar single-centre prospective cohort study demonstrated similar findings (OR: 3.68 [95% CI 1.21–11.19], P = 0.02) using their own comprehensive frailty assessment based on cognitive, psychological, and functional tests in TAVR patients. In addition, they verified a strongly increased risk for 30-day mortality and major adverse cardiovascular and cerebral events (MACCEs) and 1-year MACCEs [29].

Regarding reliability, our models are consistent with findings in the literature. As the accuracy of our unadjusted and adjusted models were checked by receiver operating characteristic (ROC) curves, the c-statistic was found to be between 0.632–0.654. A previous retrospective cohort study with 24,499 patients found almost the same reliability in a general population admitted to the intensive care unit (ICU). They compared different frailty scoring systems regarding 30-, 90- and one-year mortality prediction and found nearly the same accuracy. They compared the Clinical Frailty Score, the Frailty Index – Acute Care and the Changes in Health, End-Stage Disease, Signs, and Symptoms Scale (CHESS) performance using the c-statistic. Their findings were more accurate among ICU patients without a need for mechanical ventilation (c- stat: approx. 0.64) and slightly weaker in the mechanically ventilated group (s- stat: approx. 0.62) [30].

In this study, clustering according to the comprehensive frailty index quartiles was an artificial step. In the literature, exact cut-off values for distinct categories cannot induce the different content of the described indices. However, the categorization helps to describe and understand the differences between the patient's frailty status. On the other hand, we cannot exclude national differences in health care, psychosocial status, etc. Therefore, using quartiles instead of cut-off values in our multidimensional frailty approach can be justified.

Significance of the study findings and what this study adds to our knowledge

Determining patients' frailty is becoming a routine part of risk estimation. Current literature statements and findings suggest that not only are more risk estimation methods being developed but also that clinicians are more often facing a significantly frail population during daily work. Indeed, “eyeball” frailty testing has also been used with great success. As the general population is ageing, the increasing incidence of sarcopenic obesity, diabetes mellitus and cardiovascular diseases is increasing the need for a comprehensive risk estimation method [31]. Complex invasive interventions in the elderly population are also more frequently being performed, and these facts emphasize the importance of our work.

Strengths of this study

The conception of frailty and comprehension of preoperative risk management have increasing relevance in our daily practice. In our current work, the importance of different unconventional risk factors was emphasized and proven in aspects of long-term mortality, and irrespective of the type of cardiovascular surgery.

Limitations of this study

As limitations of this study, its single-centre design and the rather small size of the enrolled population size should be mentioned. Further limitation can be the time-consuming comprehensive frailty estimation process. A presented comprehensive method could take much time depending on each patient's capability, disabilities, and current health status. Further investigation should be conducted to assign the most effective, but enough comprehensive frailty estimation method.

Conclusion

A comprehensive frailty index could be a useful and reliable method for estimating long-term mortality among vascular and cardiac surgery patients. An extensive approach to frailty is obligatory for correctly describing patients' frailty status. Using a comprehensive frailty score in parallel with traditional risk estimation methods could be more accurate for calculating the patients' preoperative risk and prognosis, especially their risk of long-term mortality.

Funding source

Project no. RRF-2.3.1-21-2022-00003 has been implemented with the support provided by the European Union.

References

  • 1.

    Roques F, Nashef SA, Michel P, Gauducheau E, de Vincentiis C, Baudet E, et al. Risk factors and outcome in European cardiac surgery: analysis of the EuroSCORE multinational database of 19030 patients. Eur J Cardiothorac Surg 1999; 15(6): 816822. https://doi.org/10.1016/s1010-7940(99)00106-2.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Mosquera D, Chiang N, Gibberd R. Evaluation of surgical performance using V-POSSUM risk-adjusted mortality rates. ANZ J Surg 2008; 78(7): 535539. https://doi.org/10.1111/j.1445-2197.2008.04567.x.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Midwinter MJ, Tytherleigh M, Ashley S. Estimation of mortality and morbidity risk in vascular surgery using POSSUM and the Portsmouth predictor equation. Br J Surg 1999; 86(4): 471474. https://doi.org/10.1046/j.1365-2168.1999.01112.x.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Nashef SAM, Roques F, Sharples LD, Nilsson J, Smith C, Goldstone AR, et al. EuroSCORE II dagger. Eur J Cardiothorac Surg 2012; 41(4): 734745. https://doi.org/10.1093/ejcts/ezs043.

    • Search Google Scholar
    • Export Citation
  • 5.

    Xue QL. The frailty syndrome: definition and natural history. Clin Geriatr Med 2011; 27(1): 115. https://doi.org/10.1016/j.cger.2010.08.009.

  • 6.

    Wleklik M, Uchmanowicz I, Jankowska EA, Vitale C, Lisiak M, Drozd M, et al. Multidimensional approach to frailty. Front Psychol 2020; 11: 564. https://doi.org/10.3389/fpsyg.2020.00564.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Tamuleviciute-Prasciene EA-O, Drulyte K, Jurenaite G, Kubilius R, Bjarnason-Wehrens B. Frailty and exercise training: how to provide best care after cardiac surgery or intervention for elder patients with Valvular heart disease. Biomed Res Int 2018; 2018: 9849475. https://doi.org/10.1155/2018/9849475.

    • Search Google Scholar
    • Export Citation
  • 8.

    McCann M, Stamp N, Ngui A, Litton E. Cardiac prehabilitation. J Cardiothorac Vasc Anesth 2019; 33(8): 22552265. https://doi.org/10.1053/j.jvca.2019.01.023.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.

    Yamamoto M, Hayashida K, Watanabe Y, Mouillet G, Hovasse T, Chevalier B, et al. Effect of body mass index <20 kg/m(2) on events in patients who underwent transcatheter aortic valve replacement. Am J Cardiol 2015; 115(2): 227233. https://doi.org/10.1016/j.amjcard.2014.10.026.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Darvall JN, Gregorevic KJ, Story DA, Hubbard RE, Lim WK. Frailty indexes in perioperative and critical care: a systematic review. Arch Gerontol Geriatr 2018; 79: 8896. https://doi.org/10.1016/j.archger.2018.08.006.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12(3): 189198. https://doi.org/10.1016/0022-3956(75)90026-6.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Shi S, Festa N, Afilalo J, Popma JJ, Khabbaz KR, Laham RJ, et al. Comparative utility of frailty to a general prognostic score in identifying patients at risk for poor outcomes after aortic valve replacement. BMC Geriatr 2020; 20(1): 38. https://doi.org/10.1186/s12877-020-1440-4.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatr 1961; 4: 561571. https://doi.org/10.1001/archpsyc.1961.01710120031004.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Beck AT, Steer RA, Ball R, Ranieri W. Comparison of Beck depression inventories -IA and -II in psychiatric outpatients. J Pers Assess 1996; 67(3): 588597. https://doi.org/10.1207/s15327752jpa6703_13.

    • Search Google Scholar
    • Export Citation
  • 15.

    Kopp MS. Psychophysiological characteristics of anxiety patients and controls. Psychother Psychosom 1989; 52(1–3): 7479. https://doi.org/10.1159/000288302.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Szekely A, Balog P, Benko E, Breuer T, Szekely J, Kertai MD, et al. Anxiety predicts mortality and morbidity after coronary artery and valve surgery–a 4-year follow-up study. Psychosom Med 2007; 69(7): 625631. https://doi.org/10.1097/PSY.0b013e31814b8c0f.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Caldwell RA, Pearson JL, Chin RJ. Stress-moderating effects: social support in the context of gender and locus of control. Pers Soc Psychol Bull 1987; 13(1): 517. https://doi.org/10.1177/0146167287131001.

    • Search Google Scholar
    • Export Citation
  • 18.

    Wuorela M, Lavonius S, Salminen M, Vahlberg T, Viitanen M, Viikari L. Self-rated health and objective health status as predictors of all-cause mortality among older people: a prospective study with a 5-, 10-, and 27-year follow-up. BMC Geriatr 2020; 20(1): 120. https://doi.org/10.1186/s12877-020-01516-9.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Siahpush M, Spittal M, Singh GK. Happiness and life satisfaction prospectively predict self-rated health, physical health, and the presence of limiting, long-term health conditions. Am J Health Promot 2008; 23(1): 1826. https://doi.org/10.4278/ajhp.061023137.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    OʼSúilleabháin PS, Gallagher S, Steptoe A. Loneliness, living alone, and all-cause mortality: the role of emotional and social loneliness in the elderly during 19 Years of follow-up. Psychosom Med 2019; 81(6): 521526. https://doi.org/10.1097/PSY.0000000000000710.

    • Search Google Scholar
    • Export Citation
  • 21.

    Gopinath B, Rochtchina E, Anstey KJ, Mitchell P. Living alone and risk of mortality in older, community-dwelling adults. JAMA Intern Med 2013; 173(4): 320321. https://doi.org/10.1001/jamainternmed.2013.1597.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Sohail I, Jonker L, Stanton A, Walker M, Joseph T. Physiological POSSUM as an indicator for long-term survival in vascular surgery. Eur J Vasc Endovasc Surg 2013; 46(2): 223226. https://doi.org/10.1016/j.ejvs.2013.05.018.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    The jamovi project [Computer Software]. jamovi; 2021 [cited 2023 February 6]. Version 1.6:[open statistical software for the desktop and cloud]. Available from: https://www.jamovi.org.

  • 24.

    Lee H, Lee E, Jang IY. Frailty and comprehensive geriatric assessment. J Korean Med Sci 2020; 35(3): e16. https://doi.org/10.3346/jkms.2020.35.e16.

  • 25.

    van Mourik MS, Velu JF, Lanting VR, Limpens J, Bouma BJ, Piek JJ, et al. Preoperative frailty parameters as predictors for outcomes after transcatheter aortic valve implantation: a systematic review and meta-analysis. Neth Heart J 2020; 28(5): 280292. https://doi.org/10.1007/s12471-020-01379-0.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Afilalo J, Lauck S, Kim DH, Lefèvre T, Piazza N, Lachapelle K, et al. Frailty in older adults undergoing aortic valve replacement: the FRAILTY-AVR study. J Am Coll Cardiol 2017; 70(6): 689700. https://doi.org/10.1016/j.jacc.2017.06.024.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Kishimoto Y, Yoshikawa Y, Morimoto K, Onohara T, Horie H, Kumagai K, et al. Impact of frailty on early and mid-term outcomes of hybrid aortic arch repair. Surg Today 2022; 52(8): 11941201. https://doi.org/10.1007/s00595-021-02443-x.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28.

    Green P, Woglom AE, Genereux P, Daneault B, Paradis JM, Schnell S, et al. The impact of frailty status on survival after transcatheter aortic valve replacement in older adults with severe aortic stenosis: a single-center experience. JACC Cardiovasc Interv 2012; 5(9): 974981. https://doi.org/10.1016/j.jcin.2012.06.011.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29.

    Stortecky S, Schoenenberger AW, Moser A, Kalesan B, Jüni P, Carrel T, et al. Evaluation of multidimensional geriatric assessment as a predictor of mortality and cardiovascular events after transcatheter aortic valve implantation. JACC Cardiovasc Interv 2012; 5(5): 489496. https://doi.org/10.1016/j.jcin.2012.02.012.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.

    Turcotte LA, Zalucky AA, Stall NM, Downar J, Rockwood K, Theou O, et al. Baseline frailty as a predictor of survival after critical care: a retrospective cohort study of older adults receiving home care in Ontario, Canada. Chest 2021; 160(6): 21012111. https://doi.org/10.1016/j.chest.2021.06.009.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    McIsaac DI, MacDonald DB, Aucoin SD. Frailty for perioperative clinicians: a narrative review. Anesth Analg 2020; 130(6): 14501460. https://doi.org/10.1213/ane.0000000000004602.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 1.

    Roques F, Nashef SA, Michel P, Gauducheau E, de Vincentiis C, Baudet E, et al. Risk factors and outcome in European cardiac surgery: analysis of the EuroSCORE multinational database of 19030 patients. Eur J Cardiothorac Surg 1999; 15(6): 816822. https://doi.org/10.1016/s1010-7940(99)00106-2.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Mosquera D, Chiang N, Gibberd R. Evaluation of surgical performance using V-POSSUM risk-adjusted mortality rates. ANZ J Surg 2008; 78(7): 535539. https://doi.org/10.1111/j.1445-2197.2008.04567.x.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Midwinter MJ, Tytherleigh M, Ashley S. Estimation of mortality and morbidity risk in vascular surgery using POSSUM and the Portsmouth predictor equation. Br J Surg 1999; 86(4): 471474. https://doi.org/10.1046/j.1365-2168.1999.01112.x.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Nashef SAM, Roques F, Sharples LD, Nilsson J, Smith C, Goldstone AR, et al. EuroSCORE II dagger. Eur J Cardiothorac Surg 2012; 41(4): 734745. https://doi.org/10.1093/ejcts/ezs043.

    • Search Google Scholar
    • Export Citation
  • 5.

    Xue QL. The frailty syndrome: definition and natural history. Clin Geriatr Med 2011; 27(1): 115. https://doi.org/10.1016/j.cger.2010.08.009.

  • 6.

    Wleklik M, Uchmanowicz I, Jankowska EA, Vitale C, Lisiak M, Drozd M, et al. Multidimensional approach to frailty. Front Psychol 2020; 11: 564. https://doi.org/10.3389/fpsyg.2020.00564.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Tamuleviciute-Prasciene EA-O, Drulyte K, Jurenaite G, Kubilius R, Bjarnason-Wehrens B. Frailty and exercise training: how to provide best care after cardiac surgery or intervention for elder patients with Valvular heart disease. Biomed Res Int 2018; 2018: 9849475. https://doi.org/10.1155/2018/9849475.

    • Search Google Scholar
    • Export Citation
  • 8.

    McCann M, Stamp N, Ngui A, Litton E. Cardiac prehabilitation. J Cardiothorac Vasc Anesth 2019; 33(8): 22552265. https://doi.org/10.1053/j.jvca.2019.01.023.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.

    Yamamoto M, Hayashida K, Watanabe Y, Mouillet G, Hovasse T, Chevalier B, et al. Effect of body mass index <20 kg/m(2) on events in patients who underwent transcatheter aortic valve replacement. Am J Cardiol 2015; 115(2): 227233. https://doi.org/10.1016/j.amjcard.2014.10.026.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Darvall JN, Gregorevic KJ, Story DA, Hubbard RE, Lim WK. Frailty indexes in perioperative and critical care: a systematic review. Arch Gerontol Geriatr 2018; 79: 8896. https://doi.org/10.1016/j.archger.2018.08.006.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12(3): 189198. https://doi.org/10.1016/0022-3956(75)90026-6.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Shi S, Festa N, Afilalo J, Popma JJ, Khabbaz KR, Laham RJ, et al. Comparative utility of frailty to a general prognostic score in identifying patients at risk for poor outcomes after aortic valve replacement. BMC Geriatr 2020; 20(1): 38. https://doi.org/10.1186/s12877-020-1440-4.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatr 1961; 4: 561571. https://doi.org/10.1001/archpsyc.1961.01710120031004.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Beck AT, Steer RA, Ball R, Ranieri W. Comparison of Beck depression inventories -IA and -II in psychiatric outpatients. J Pers Assess 1996; 67(3): 588597. https://doi.org/10.1207/s15327752jpa6703_13.

    • Search Google Scholar
    • Export Citation
  • 15.

    Kopp MS. Psychophysiological characteristics of anxiety patients and controls. Psychother Psychosom 1989; 52(1–3): 7479. https://doi.org/10.1159/000288302.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Szekely A, Balog P, Benko E, Breuer T, Szekely J, Kertai MD, et al. Anxiety predicts mortality and morbidity after coronary artery and valve surgery–a 4-year follow-up study. Psychosom Med 2007; 69(7): 625631. https://doi.org/10.1097/PSY.0b013e31814b8c0f.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Caldwell RA, Pearson JL, Chin RJ. Stress-moderating effects: social support in the context of gender and locus of control. Pers Soc Psychol Bull 1987; 13(1): 517. https://doi.org/10.1177/0146167287131001.

    • Search Google Scholar
    • Export Citation
  • 18.

    Wuorela M, Lavonius S, Salminen M, Vahlberg T, Viitanen M, Viikari L. Self-rated health and objective health status as predictors of all-cause mortality among older people: a prospective study with a 5-, 10-, and 27-year follow-up. BMC Geriatr 2020; 20(1): 120. https://doi.org/10.1186/s12877-020-01516-9.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Siahpush M, Spittal M, Singh GK. Happiness and life satisfaction prospectively predict self-rated health, physical health, and the presence of limiting, long-term health conditions. Am J Health Promot 2008; 23(1): 1826. https://doi.org/10.4278/ajhp.061023137.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    OʼSúilleabháin PS, Gallagher S, Steptoe A. Loneliness, living alone, and all-cause mortality: the role of emotional and social loneliness in the elderly during 19 Years of follow-up. Psychosom Med 2019; 81(6): 521526. https://doi.org/10.1097/PSY.0000000000000710.

    • Search Google Scholar
    • Export Citation
  • 21.

    Gopinath B, Rochtchina E, Anstey KJ, Mitchell P. Living alone and risk of mortality in older, community-dwelling adults. JAMA Intern Med 2013; 173(4): 320321. https://doi.org/10.1001/jamainternmed.2013.1597.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Sohail I, Jonker L, Stanton A, Walker M, Joseph T. Physiological POSSUM as an indicator for long-term survival in vascular surgery. Eur J Vasc Endovasc Surg 2013; 46(2): 223226. https://doi.org/10.1016/j.ejvs.2013.05.018.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    The jamovi project [Computer Software]. jamovi; 2021 [cited 2023 February 6]. Version 1.6:[open statistical software for the desktop and cloud]. Available from: https://www.jamovi.org.

  • 24.

    Lee H, Lee E, Jang IY. Frailty and comprehensive geriatric assessment. J Korean Med Sci 2020; 35(3): e16. https://doi.org/10.3346/jkms.2020.35.e16.

  • 25.

    van Mourik MS, Velu JF, Lanting VR, Limpens J, Bouma BJ, Piek JJ, et al. Preoperative frailty parameters as predictors for outcomes after transcatheter aortic valve implantation: a systematic review and meta-analysis. Neth Heart J 2020; 28(5): 280292. https://doi.org/10.1007/s12471-020-01379-0.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Afilalo J, Lauck S, Kim DH, Lefèvre T, Piazza N, Lachapelle K, et al. Frailty in older adults undergoing aortic valve replacement: the FRAILTY-AVR study. J Am Coll Cardiol 2017; 70(6): 689700. https://doi.org/10.1016/j.jacc.2017.06.024.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Kishimoto Y, Yoshikawa Y, Morimoto K, Onohara T, Horie H, Kumagai K, et al. Impact of frailty on early and mid-term outcomes of hybrid aortic arch repair. Surg Today 2022; 52(8): 11941201. https://doi.org/10.1007/s00595-021-02443-x.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28.

    Green P, Woglom AE, Genereux P, Daneault B, Paradis JM, Schnell S, et al. The impact of frailty status on survival after transcatheter aortic valve replacement in older adults with severe aortic stenosis: a single-center experience. JACC Cardiovasc Interv 2012; 5(9): 974981. https://doi.org/10.1016/j.jcin.2012.06.011.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29.

    Stortecky S, Schoenenberger AW, Moser A, Kalesan B, Jüni P, Carrel T, et al. Evaluation of multidimensional geriatric assessment as a predictor of mortality and cardiovascular events after transcatheter aortic valve implantation. JACC Cardiovasc Interv 2012; 5(5): 489496. https://doi.org/10.1016/j.jcin.2012.02.012.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.

    Turcotte LA, Zalucky AA, Stall NM, Downar J, Rockwood K, Theou O, et al. Baseline frailty as a predictor of survival after critical care: a retrospective cohort study of older adults receiving home care in Ontario, Canada. Chest 2021; 160(6): 21012111. https://doi.org/10.1016/j.chest.2021.06.009.

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    McIsaac DI, MacDonald DB, Aucoin SD. Frailty for perioperative clinicians: a narrative review. Anesth Analg 2020; 130(6): 14501460. https://doi.org/10.1213/ane.0000000000004602.

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Editor-in-Chief

László ROSIVALL (Semmelweis University, Budapest, Hungary)

Managing Editor

Anna BERHIDI (Semmelweis University, Budapest, Hungary)

Co-Editors

  • Gábor SZÉNÁSI (Semmelweis University, Budapest, Hungary)
  • Ákos KOLLER (Semmelweis University, Budapest, Hungary)
  • Zsolt RADÁK (University of Physical Education, Budapest, Hungary)
  • László LÉNÁRD (University of Pécs, Hungary)
  • Zoltán UNGVÁRI (Semmelweis University, Budapest, Hungary)

Assistant Editors

  • Gabriella DÖRNYEI (Semmelweis University, Budapest, Hungary)
  • Zsuzsanna MIKLÓS (Semmelweis University, Budapest, Hungary)
  • György NÁDASY (Semmelweis University, Budapest, Hungary)

Hungarian Editorial Board

  • György BENEDEK (University of Szeged, Hungary)
  • Zoltán BENYÓ (Semmelweis University, Budapest, Hungary)
  • Mihály BOROS (University of Szeged, Hungary)
  • László CSERNOCH (University of Debrecen, Hungary)
  • Magdolna DANK (Semmelweis University, Budapest, Hungary)
  • László DÉTÁRI (Eötvös Loránd University, Budapest, Hungary)
  • Zoltán GIRICZ (Semmelweis University, Budapest, Hungary and Pharmahungary Group, Szeged, Hungary)
  • Zoltán HANTOS (Semmelweis University, Budapest and University of Szeged, Hungary)
  • Zoltán HEROLD (Semmelweis University, Budapest, Hungary) 
  • László HUNYADI (Semmelweis University, Budapest, Hungary)
  • Gábor JANCSÓ (University of Pécs, Hungary)
  • Zoltán KARÁDI (University of Pecs, Hungary)
  • Miklós PALKOVITS (Semmelweis University, Budapest, Hungary)
  • Gyula PAPP (University of Szeged, Hungary)
  • Gábor PAVLIK (University of Physical Education, Budapest, Hungary)
  • András SPÄT (Semmelweis University, Budapest, Hungary)
  • Gyula SZABÓ (University of Szeged, Hungary)
  • Zoltán SZELÉNYI (University of Pécs, Hungary)
  • Lajos SZOLLÁR (Semmelweis University, Budapest, Hungary)
  • József TOLDI (MTA-SZTE Neuroscience Research Group and University of Szeged, Hungary)
  • Árpád TÓSAKI (University of Debrecen, Hungary)

International Editorial Board

  • Dragan DJURIC (University of Belgrade, Serbia)
  • Christopher H.  FRY (University of Bristol, UK)
  • Stephen E. GREENWALD (Blizard Institute, Barts and Queen Mary University of London, UK)
  • Tibor HORTOBÁGYI (University of Groningen, Netherlands)
  • George KUNOS (National Institutes of Health, Bethesda, USA)
  • Massoud MAHMOUDIAN (Iran University of Medical Sciences, Tehran, Iran)
  • Tadaaki MANO (Gifu University of Medical Science, Japan)
  • Luis Gabriel NAVAR (Tulane University School of Medicine, New Orleans, USA)
  • Hitoo NISHINO (Nagoya City University, Japan)
  • Ole H. PETERSEN (Cardiff University, UK)
  • Ulrich POHL (German Centre for Cardiovascular Research and Ludwig-Maximilians-University, Planegg, Germany)
  • Andrej A. ROMANOVSKY (University of Arizona, USA)
  • Anwar Ali SIDDIQUI (Aga Khan University, Karachi, Pakistan)
  • Csaba SZABÓ (University of Fribourg, Switzerland)
  • Eric VICAUT (Université de Paris, UMRS 942 INSERM, France)

 

Editorial Correspondence:
Physiology International
Semmelweis University
Faculty of Medicine, Institute of Translational Medicine
Nagyvárad tér 4, H-1089 Budapest, Hungary
Phone/Fax: +36-1-2100-100
E-mail: pi@semmelweis-univ.hu

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2023  
Web of Science  
Journal Impact Factor 2.2
Rank by Impact Factor Q3 (Physiology)
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Scopus  
CiteScore 3.4
CiteScore rank Q2 (Physical Therapy, Sports Therapy and Rehabilitation)
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SJR Q rank Q2

Physiology International
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Physiology International
Language English
Size B5
Year of
Foundation
2006 (1950)
Volumes
per Year
1
Issues
per Year
4
Founder Magyar Tudományos Akadémia
Founder's
Address
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2498-602X (Print)
ISSN 2677-0164 (Online)

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