Predictors of Health-Related Quality-of-Life after Cardiac Surgery: Findings from the ANesthesiology-QUality-Registry (ANQUR) and Frailty-Management

Predictors of Health-Related Quality-of-Life after Cardiac Surgery: Findings from the ANesthesiology-QUality-Registry (ANQUR) and Frailty-Management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictors of Health-Related Quality-of-Life after Cardiac Surgery: Findings from the ANesthesiology-QUality-Registry (ANQUR) and Frailty-Management Max Briefs, Janis Fliegenschmidt, Jan Wiesemann, Catharina Middeke, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9117218/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Assessment of long-term patient-reported outcome allows identification of vulnerable populations undergoing cardiac surgery. Incorporation of findings into clinical practice may enhance risk-stratification, prevent perioperative complications, and improve outcome. Frailty is prevalent in up to a third of cardiac patients and structured peri-procedural programs addressing frailty and delirium may be relevant to long-term outcome. This study aims to identify predictors of health-related quality-of-life one year after cardiac surgery and assess outcome in patients enrolled in an extensive frailty- and delirium-pathway. Methods Patients undergoing cardiac surgery at a high-volume German heart center were enrolled in an anesthesiology quality registry and health-related quality of life (HR-QoL) was assessed using the Short Form-12 (SF-12) questionnaire at 1-year-follow-up. Corresponding factors were analyzed for their association with individual outcome. The cohort comprised 812 patients. A subgroup of 190 patients participated in a frailty and delirium management program, providing extended preoperative screening and postoperative supervision. Results Female sex [B -2.56, 95% CI (-4.28 – -0.85)], increase in age [B -4.00, 95% CI (-6.17 – -1.83)] and weight [B -4.57, 95% CI (-6.53 – -2.60)], preoperative anemia [B -3.43, 95% CI (-5.77 – -1.10)], vascular comorbidities [B -3.37, 95% CI (-6.57 – -0.16)], smoking [B -2.73, 95% CI (-5.00 – -0.46)], increase in symptom burden [B -6.07, 95% CI (-11.55 – -0.59)] and physical frailty [B -9.90, 95% CI (-17.72 – -2.09)] were independently associated with lower physical outcome scores. Cognitive scores were higher in older patients [B 4.33, 95% CI (6.17–1.83)] and lower in smokers [B -2.18, 95% CI (-3.61 – -0.27)]. Conclusion Independent predictors of impaired HR-QoL at 1-year follow-up could be identified, suggesting a phenotype at risk. Physical frailty independently predicted poorer physical outcome, emphasizing potential for prehabilitation and frailty-management. Findings should be interpreted considering selection- and response-bias and absence of baseline HR-QoL-assessment. Trial registration This observational study complied with the Declaration of Helsinki and was approved by the Ethics Committee of the Medical Faculty of the Ruhr-University Bochum on 17th November 2022 (Registration-Number: 2022 − 947). Minor additions to the questionnaire were approved on 19th August 2024 (Registration-Number: 2022 − 947_1). Graphical abstract (Publication license available) Cardiac Surgical Procedures Quality of Life Follow-Up Studies Patient Reported Outcome Measures Frailty Cognitive Dysfunction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Background In light of an ongoing demographic shift, hospitalized populations become older and increasingly comorbid, resulting in an incline in economic burden. [ 1 – 4 ] To minimize perioperative complications and financial downsides thereof, extensive risk-stratification is warranted. [ 5 ] In addition to short-term physician reported follow-up, patient-reported outcome (PRO) and health-related quality of life (HR-QoL) are increasingly recognized for evaluation of subjective long-term outcome. [ 6 , 7 ] HR-QoL represents the most frequently assessed PRO and can be evaluated using the generic Short-Form-12-questionnaire (SF-12), which operationalizes QoL and yields both a physical and a mental component summary score. [ 8 – 11 ] The SF-12 user manual was first published in 1994, and the instrument has been validated in German, and standardized to the general population. [ 10 – 12 ] In addition to QoL-assessment, incorporation of findings into clinical practice is crucial and may enable identification of vulnerable populations, who might benefit from extended supervision and periprocedural care. Potentially modifiable predictors of impaired HR-QoL in patients undergoing cardiac surgery – such as weight, a history of smoking or length of stay – have been previously identified, though a lack of consistency across studies was apparent. [ 13 ] Prehabilitation represents a structured effort for preoperative modification of predictors of impaired outcome and aims to improve patients’ resilience and postoperative outcome by optimizing individual risk profile and thereby reducing overall complications, their severity and limiting further decline. [ 14 , 15 ] One exemplary pillar of preoperative optimization is patient-blood-management (PBM); PBM can be defined as a patient-centered approach to improve outcomes and reduce risks while promoting patient safety and empowerment. [ 16 , 17 ] Furthermore, incorporating multimodal concepts of nutrition and exercise can benefit both surgical and subjective outcomes. These interventions – applied not only in prehabilitation prior to surgery but also in rehabilitation or recovery after surgery (ERAS) – include specific strategies such as physical activation or guidance, nutritional counseling, and optimization, representing only a subset of potential targets for improving patient centered care. [ 18 , 19 ] Frailty is described as a concept of age-related decline in physical function accompanied by increased vulnerability to external stressors and is prevalent in over 30% of patients undergoing cardiac surgery. [ 20 , 21 ] The association between frailty, coexisting comorbidities, and increased odds of impaired outcomes in cardiac patients suggests that preoperative modification of frailty may represent a potential strategy to improve outcomes and prevent complications. [ 21 , 22 ] This study aims to identify determinants of clinical outcomes in patients undergoing cardiac surgery, while also focusing on prognostic significance of physical and cognitive frailty. 2 Methods 2.1 Patient recruitment In total, 1.136 patients who underwent cardiac surgery at a large-volume German heart center gave informed consent to participation in an Anesthesiology-Quality-Registry (ANQUR) in 2023. Patients were preoperatively informed and consent was recorded during the premedication consultation. Postoperatively, HR-QoL-data was assessed at 1-year-follow up, using the Short-Form-12-questionnaire (SF-12). The final cohort comprised 812 patients. A subgroup comprised 190 patients, who were additionally included in a specialized frailty- and delirium-pathway and underwent extensive preoperative screening for physical or cognitive frailty as well as assessment of risk for delirium (Fig. 1 ). 2.2 Data collection and definition of variables All available SF-12 data were included in the registry, and standardized scoring was performed for fully completed questionnaires. In accordance with the user manual, the Physical Component Summary (PCS) and Mental Component Summary (MCS) scores were calculated for each patient, representing individual HR-QoL, and were therefore defined as the primary endpoints. The investigated variables comprised demographic and periprocedural factors, as well as parameters assessed within the frailty management program. All data were extracted from internal databases and pseudonymized prior to analysis. For analytical purposes, non-continuous variables were categorized as appropriate. Demographic variables included sex and age. Sex was recorded as male or female, with no additional categories available. Age was analyzed as a continuous variable and additionally categorized into three predefined groups: 18–64, 65–74, and ≥ 75 years. Preoperative variables included hemoglobin levels, analyzed both as a continuous variable and categorized according to the World Health Organization (WHO) classification of anemia; LDL cholesterol and lipoprotein(a) [Lp(a)] levels as indicators of lipid profile; body mass index (BMI), assessed continuously and categorically; preexisting conditions including hypertension, diabetes mellitus, and peripheral arterial disease (PAD); smoking status; and left ventricular ejection fraction (LVEF). [ 23 ] Intraoperative factors included duration of anesthesia and surgical intervention, as well as the type of procedure, categorized as isolated valve surgery, isolated on- (OPCAB) and off-pump coronary artery bypass grafting (CABG), combined CABG and other procedures, aortic surgery, ventricular assist device (VAD) implantation, and heart transplantation. Postoperative variables comprised length of stay in the intensive care unit (ICU) and total hospital length of stay (LOS). Subgroup patients provided detailed data on frailty and postoperative delirium. Preoperative assessment included physical and cognitive frailty scoring, using an adaptation of the Fried Frailty Score and the Mini-Cog©, which allowed categorization of patients as robust, pre-frail, or frail, and identification of lower or higher likelihood of clinically significant cognitive impairment. Postoperative delirium is also recorded in subgroup patients. The frailty score includes gait-speed, assessed via timed-up-and-go-test, handgrip strength measured with a dynamometer, evaluation of unintentional weight loss, subjective exhaustion, and physical activity. The Mini-Cog©-Score consists of a three-word-recall task and a clock-drawing-test. [ 24 – 26 ] 3 Results 3.1 Baseline characteristics Mean age of the cohort was 67.38 years, 28.4% were female. 1-year-mortality in all patients receiving cardiac surgery was 5.55%. For all demographic and periprocedural data of the cohort and subgroup, see Table 1 . Table 1 Demographic and periprocedural baseline data of the analyzed cohort and additional data on physical and cognitive frailty in the subgroup (BMI: body mass index; Dur.: duration; ICU: intensive care unit; LDL: low density lipoprotein; Lp(a): lipoprotein(a); LVEF: left ventricular ejection fraction; NYHA: New York Heart Association; PAD: peripheral arterial disease; Postop.: postoperative; Preop.: preoperative; SD: standard deviation) Overall Overall n 812 n 812 Sex: Female (%) 231 (28.4) Smoking status (%) Age (mean (SD)) 67.38 (9.62) No smoking 447 (55.3) Age-categories (%) Active smoking 115 (14.2) 18–64 years 298 (36.5) Former smoking 246 (30.4) 65–74 years 326 (40.2) LVEF (mean (SD)) 54.48 (9.50) ≥ 75 years 189 (23.3) LVEF-categories (%) ≥ 50% 674 (83.0) 41–49% 61 (7.5) ≤ 40% 77 (9.5) Preop. hemoglobin (mg/dl, mean (SD)) 14.09 (1.73) Dur. anesthesia (minutes, mean (SD)) 332.85 (70.27) Preop. anemia (%) Dur. intervention (minutes, mean (SD)) 211.51 (62.23) None 681 (84.4) Type of surgery (%) Mild 94 (11.6) Isolated valve 377 (46.4) Moderate or severe 32 (4.0) Isolated Off-Pump CABG 197 (24.3) Preop. LDL (mean (SD)) Isolated OPCAB 9 (1.1) < 55 mg/dl 517 (64.9) Aortic procedures 114 (14.0) 55–69 mg/dl 97 (12.2) CABG combinations 101 (12.4) ≥ 70 mg/dl 182 (22.9) VAD 4 (0.5) Preop. Lp(a) ≥ 50 mg/dl (%) 271 (38.9) Others 10 (1.2) BMI (mean (SD)) 27.39 (5.20) Dur. postop. ICU-stay (days, mean (SD)) 2.21 (4.54) BMI-categories (%) Dur. in-hospital stay (days, mean (SD)) 13.75 (9.36) Underweight 11 (1.4) Postop. infection (%) 30 (3.7) Normal weight 236 (29.5) Frailty-program (%) 190 (23.4) Overweight 320 (40.0) Obese 233 (29.1) n 190 Hypertension (%) 663 (81.7) Frailty-Score (%) Diabetes mellitus (%) 173 (21.3) Robust 67 (35.3) PAD (%) 46 (5.7) Pre-Frail 92 (48.4) NYHA-classification (%) Frail 31 (16.3) I 84 (10.4) Mini-Cog©-Score (%) II 264 (32.6) Lower likelihood 151 (79.5) III 440 (54.3) Higher likelihood 14 (7.4) IV 22 (2.7) Postop. delirium (%) 12 (6.3) 3.2 Outcome Mean physical outcome (PCS) was 45.91 (SD 10.31) and mean mental outcome (MCS) was 50.12 (SD 10.71). Female sex [B = -1.8 (95% CI -3.3 – -0.2)] and increase in age [B = -5.0 (95% CI -6.8 – -3.1)] were associated with significantly lower PCS, while older age was associated with significantly higher MCS [B = 3.7 (95% CI 1.7–5.6)] (Fig. 2 ). Patients preoperatively presenting with anemia [B = -6.8 (95% CI -10.5 – -3.2)], increased BMI [B = -5.1 (95% CI -6.9 – -3.2)], positive history of hypertension [B = -3.3 (95% CI -5.1 – -1.5)], diabetes mellitus [B = -2.9 (95% CI -4.6 – -1.2)], vascular comorbidities [B = -4.6 (95% CI -7.7 – -1.5)] and smoking [B = -2.2 (95% CI -3.8 – -0.6)] presented significantly lower physical outcome scores – smoking having an additional negative association with MCS [B = -2.1 (95% CI -3.7 – -0.4)]. Increase in NYHA-classification [B = -10.9 (95% CI -15.6 – -6.2)] and decrease in LVEF [B = -3.8 (95% CI -6.3 – -1.4)] were also associated with significantly lower PCS (Fig. 2 ). Prolonged duration of anesthesia [B = -0.02 (95% CI -0.03 – -0.01)] and surgical intervention [B = -0.01 (95% CI -0.02 – -0.001)], as well as prolonged ICU-stay [B = -0.4 (95% CI -0.6 – -0.3)] and total length of stay [B = -0.2 (95% CI -0.3 – -0.1)], were all associated with significant lower physical outcome scores (PCS). Compared with isolated valve procedures, patients undergoing isolated Off-Pump CABG also exhibited lower physical outcome scores [B = -2.0 (95% CI -3.75 – -0.26)] (Fig. 2 ), with no relevant differences in mean age or preoperative risk evaluation (Euroscore II) but a higher prevalence of PAD in patients undergoing off-pump CABG (11.2% vs. 3.7%; p < 0.001). Patients undergoing isolated OPCAB, heart transplantation or VAD-implantation were excluded from this analysis due to insufficient sample size. Patients undergoing any postoperative infection (POI) presented significantly lower PCS [B = -7.8 (95% CI -11.5 – -4.1)] and MCS [B = -4.2 (95% CI -8.1 – -0.3)]. Participation in the frailty- and delirium-pathway was associated with significantly lower PCS [B = -2.2 (95% CI -3.9 – -0.6)], as was preoperative classification as physically pre-frail [B = -5.6 (95% CI -8.8 – -2.4)] or frail [B = -9.6 (95% CI -14.0 – -5.2)] (Fig. 3 , Supplementary Table 1). A multivariate analysis identified female sex, age ≥ 75 years, anemia, overweight and obesity, vascular comorbidities (PAD), a history of smoking and increased symptom burden were identified as independent predictors of lower physical outcome scores, as assessed by the SF-12-questionnaire. Smoking and isolated CABG independently predicted lower cognitive outcome scores, while increased age was independently associated with significantly higher MCS (Fig. 4 , Supplementary Table 2). Participation in the frailty management program was not associated with significant changes in either physical or cognitive outcome. 3.3 Subgroup analysis: Frail patients The distribution of frailty status showed no significant difference between off-pump bypasses and isolated valve procedures. in A multivariate subgroup-analysis identified preoperative physical pre-frailty frailty as independent predictors of lower physical outcome scores (Fig. 5 , Supplementary Table 3). Cognitive frailty was not associated with significant changes in PCS or MCS. 4 Discussion The results of this prospective observational study demonstrate several important findings regarding prediction of health-related quality-of-life in patients undergoing cardiac surgery. Multivariate analyses revealed an independent association of female sex, an increase in age, weight and symptom burden, preoperative anemia, preexisting vascular conditions and a history of smoking with impaired physical outcome. Regarding mental outcome, older age was associated with improved outcome, while a history of smoking was linked to an impairment of mental status. Subgroup analysis identified physical frailty and pre-frailty as independent predictors of impaired physical outcome. According to user manual, the SF-12-scores are norm based and standardized to the general population with a mean of 50 and a standard deviation of 10, lower scores reflecting poorer outcome. [ 10 , 12 ] In this analysis, mean physical outcome was reduced by approximately half a standard deviation (45.91), whereas mean mental outcome did not differ from that of the general population (50.12). A comparable pattern emerged in the relationship between age and outcomes: older age was associated with impaired physical health, whereas mental health tended to improve with age. These findings may indicate that physical health is generally more susceptible to influencing factors, such as surgery, than mental health. Preoperative anemia and frailty, both independently associated with worse outcomes, represent potentially modifiable risk factors whose optimization could reduce complications and improve postoperative results. Patient Blood Management (PBM), which aims to optimize red blood cell mass, minimize blood loss, and enhance a patient’s tolerance to anemia, constitutes an important strategy for the structural management of perioperative anemia and has been shown to have clinically relevant effects on outcomes. [ 16 , 17 ] In our analysis, preoperative anemia was confirmed as a significant predictor of postoperative outcome, underscoring the ongoing need to implement and refine PBM strategies. Preoperative frailty deems a similar approach of perioperative management: an enhanced preoperative assessment of cognition, function and frailty, perioperative support including family, and postoperative care focusing on delirium-prevention and functional recovery are recommended to minimize complications and improve outcome. [ 27 , 28 ] The observed association between physical frailty and impaired physical outcomes supports evidence linking frailty to adverse events, including increased mortality, prolonged hospital stays, and higher risk of delirium. [ 29 – 31 ] By focusing on preoperative optimization and extended risk stratification to reduce perioperative complications and overall length of stay, physicians may help mitigate the economic burden for patients undergoing complex procedures, particularly those with extensive comorbidities or exhibiting physical or mental vulnerability. [ 32 , 33 ] Overall, the results of this analysis are in line with previously identified predictors of HR-QoL. A systematic review, published by Sanders et al. in 2022, identified 103 independent predictors of HR-QoL after cardiac surgery, including BMI, smoking and length of stay in the hospital and on the ICU, while emphasizing a lack of consistency across studies. [ 13 ] A follow-up study on 272 patients, published by Perrotti et al. in 2019, demonstrated the association between dyspnea, older age and impaired physical outcome. [ 34 ] Regarding this analysis’ subgroup, an observational study on 133 patients, published by Nakano et al. in 2020, revealed an association of frailty with functional decline after cardiac surgery. [ 22 ] All findings suggest a distinct phenotype at risk for impaired outcome after surgery, which could benefit from extensive patient-centered care and perioperative management. This study presents strengths and weaknesses. By analyzing similar sample sizes as comparable studies, it offers further insights into prediction of HR-QoL after cardiac surgery and adds to consistency in literature, while emphasizing the vulnerability of frail patients undergoing high-risk cardiac surgery. Similar findings suggest broader implications for cardiac patients and highlight the importance of partially modifiable factors in the perioperative period. The single-center design and the absence of a preoperative HR-QoL assessment represent weaknesses of this observational study. Selection bias, due to inclusion of only patients capable and compliant enough to participate, and response bias, because immediate outcomes, mortality, or disability may have influenced questionnaire return, may also have affected the results. Future research should incorporate preoperative assessment of HR-QoL to evaluate absolute effects on QoL and should be designed as multicenter studies to include a broader patient spectrum, increase sample size, and enhance generalizability of the findings. Furthermore, identification of high-risk patients is possible, but clinical translation remains crucial for improving care and optimizing outcome; subjective, objective and economically. 5 Conclusion In patients undergoing cardiac surgery at a large-volume German heart center, independent predictors of HR-QoL at follow-up were identified. Female sex, increase in age, weight and symptom burden, a preoperative anemia, vascular comorbidities and a history of smoking were associated with impaired physical outcome one year after cardiac surgery. Mental outcome improved with age and was impaired in patients with a history of smoking. A subgroup analysis identified physical frailty as an independent predictor of impaired physical outcome. These findings suggest a distinct phenotype at risk for impaired outcome after surgery, highlighting the need for preemptive risk-stratification to identify and extensively care for vulnerable patients to minimize perioperative complications and improve patient-reported outcome. Abbreviations ANQUR Anesthesiology-QUality-Registry BMI Body Mass Index CABG Coronary Artery Bypass Graft CI Confidence Interval ERAS Enhanced Recovery after Surgery HR-QoL Health-Related Quality-of-Life ICU Intensive Care Unit LDL Low Density Lipoprotein LOS Length of Stay Lp(a) Lipoprotein (a) LVEF Left Ventricular Ejection Fraction MCS Mental Component Summary NYHA New York Heart Association OPCAB Off-Pump Coronary Artery Bypass PAD Peripheral Arterial Disease PBM Patient Blood Management PCS Physical Component Summary POI Postoperative Infection PRO Patient-Reported Outcome SD Standard Deviation SF-12 Short-Form 12 VAD Ventricular Assist Device WHO World Health Organization Declarations Ethics approval and consent to participate : This prospective observational study complied with the Declaration of Helsinki and was approved by the Ethics Committee of the Medical Faculty of the Ruhr-University Bochum on 17 th November 2022 (Registration-Number: 2022-947). Minor additions to the questionnaire were approved on 19 th August 2024 (Registration-Number: 2022-947_1). Written informed consent was recorded of all participants. Consent for publication : Not applicable. Availability of data and materials : The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests : The authors declare that they have no competing interests. Funding : This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author’s contributions : MB: Data Curation, Formal analysis, Writing - Original Draft and Editing; JF: Conceptualization, Methodology, Writing - Review & Editing; JW: Resources; CM: Resources, Project administration; AB: Writing - Review & Editing; RS: Writing - Review & Editing; NH: Writing - Review & Editing; JG: Writing - Review & Editing; VVD: Supervision, Writing - Review & Editing Acknowledgements : Not applicable. References Milan V, Fetzer S, Hagist C. Healing, surviving, or dying? - projecting the German future disease burden using a Markov illness-death model. BMC Public Health. 2021;21:123. https://doi.org/10.1186/s12889-020-09941-6 . Peters E, Pritzkuleit R, Beske F, Katalinic A. Demografischer Wandel und Krankheitshäufigkeiten. Bundesgesundheitsbl. 2010;53:417–26. https://doi.org/10.1007/s00103-010-1050-y . Puth M-T, Weckbecker K, Schmid M, Münster E. Prevalence of multimorbidity in Germany: impact of age and educational level in a cross-sectional study on 19,294 adults. 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Eine narrative Zusammenfassung einer Co-Management-Strategie in der Prävention des postoperativen Delirs in der Herzchirurgie als Umsetzung eines Qualitätsvertrages., Fliegenschmidt J, Middeke C, Ruggeri S, Bunge C, Schütte E, Hulde N et al. Delirprävention im Co-Management mit Qualitätsvertrag: Die OP-Paten Eine narrative Zusammenfassung einer Co-Management-Strategie in der Prävention des postoperativen Delirs in der : 2024;782024:389–94. https://doi.org/10.19224/ai2024.389 Sieber F, McIsaac DI, Deiner S, Azefor T, Berger M, Hughes C, et al. 2025 American Society of Anesthesiologists Practice Advisory for Perioperative Care of Older Adults Scheduled for Inpatient Surgery. Anesthesiology. 2025;142:22. https://doi.org/10.1097/ALN.0000000000005172 . Zietlow KE, Wong S, Heflin MT, McDonald SR, Sickeler R, Devinney M, et al. Geriatric Preoperative Optimization: A Review. Am J Med. 2022;135:39–48. https://doi.org/10.1016/j.amjmed.2021.07.028 . Kojima G, Iliffe S, Walters K. Frailty index as a predictor of mortality: a systematic review and meta-analysis. Age Ageing. 2018;47:193–200. https://doi.org/10.1093/ageing/afx162 . Gracie TJ, Caufield-Noll C, Wang N-Y, Sieber FE. The Association of Preoperative Frailty and Postoperative Delirium: A Meta-analysis. Anesth Analgesia. 2021;133:314. https://doi.org/10.1213/ANE.0000000000005609 . Prokopidis K, Nortcliffe A, Okoye C, Venturelli M, Lip GYH, Isanejad M. Length of stay and prior heart failure admission in frailty and heart failure: A systematic review and meta-analysis. ESC Heart Fail. 2025;12:2417–26. https://doi.org/10.1002/ehf2.15300 . Thanakiattiwibun C, Siriussawakul A, Virotjarumart T, Maneeon S, Tantai N, Srinonprasert V, et al. Multimorbidity, healthcare utilization, and quality of life for older patients undergoing surgery: A prospective study. Med (Baltim). 2023;102:e33389. https://doi.org/10.1097/MD.0000000000033389 . Snowden CP, Anderson H. Preoperative optimization: rationale and process: is it economic sense? Curr Opin Anaesthesiol. 2012;25:210–6. https://doi.org/10.1097/ACO.0b013e32834ef903 . Perrotti A, Ecarnot F, Monaco F, Dorigo E, Monteleone P, Besch G, et al. Quality of life 10 years after cardiac surgery in adults: a long-term follow-up study. Health Qual Life Outcomes. 2019;17:88. https://doi.org/10.1186/s12955-019-1160-7 . Additional Declarations No competing interests reported. Supplementary Files ANQURHCHsupplements.docx floatimage1.jpeg Graphical abstract (Publication license available) Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Mar, 2026 Reviews received at journal 28 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 18 Mar, 2026 Editor assigned by journal 18 Mar, 2026 Editor invited by journal 18 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 17 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9117218","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609077982,"identity":"e4b605c1-5ab2-42ee-becb-676aa8a2d109","order_by":0,"name":"Max Briefs","email":"data:image/png;base64,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","orcid":"","institution":"Heart and Diabetes Center North Rhine-Westphalia","correspondingAuthor":true,"prefix":"","firstName":"Max","middleName":"","lastName":"Briefs","suffix":""},{"id":609077987,"identity":"0ea0f424-2a9a-49d8-b800-908b7e1a93d2","order_by":1,"name":"Janis Fliegenschmidt","email":"","orcid":"","institution":"Heart and Diabetes Center North Rhine-Westphalia","correspondingAuthor":false,"prefix":"","firstName":"Janis","middleName":"","lastName":"Fliegenschmidt","suffix":""},{"id":609078020,"identity":"6870b4cd-3677-4b12-b513-d4ba797bbe4a","order_by":2,"name":"Jan Wiesemann","email":"","orcid":"","institution":"Heart and Diabetes Center North Rhine-Westphalia","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Wiesemann","suffix":""},{"id":609078021,"identity":"24b4dfd1-6785-4437-8a12-b24ed46e8816","order_by":3,"name":"Catharina Middeke","email":"","orcid":"","institution":"Heart and Diabetes Center North Rhine-Westphalia","correspondingAuthor":false,"prefix":"","firstName":"Catharina","middleName":"","lastName":"Middeke","suffix":""},{"id":609078023,"identity":"a6d63b01-c9af-410a-92be-869d5116f9ab","order_by":4,"name":"Astrid Bergmann","email":"","orcid":"","institution":"Heart and Diabetes Center North Rhine-Westphalia","correspondingAuthor":false,"prefix":"","firstName":"Astrid","middleName":"","lastName":"Bergmann","suffix":""},{"id":609078025,"identity":"e16bbd22-bfff-44d6-aaf7-30dd5dde803d","order_by":5,"name":"René Schramm","email":"","orcid":"","institution":"Heart and Diabetes Center North Rhine-Westphalia","correspondingAuthor":false,"prefix":"","firstName":"René","middleName":"","lastName":"Schramm","suffix":""},{"id":609078036,"identity":"589a21e1-a373-4359-bdf5-88730d215cee","order_by":6,"name":"Nikolai Hulde","email":"","orcid":"","institution":"Heart and Diabetes Center North Rhine-Westphalia","correspondingAuthor":false,"prefix":"","firstName":"Nikolai","middleName":"","lastName":"Hulde","suffix":""},{"id":609078037,"identity":"a9d5dfeb-3ed6-44c3-bf3b-c483195e8055","order_by":7,"name":"Jan Gummert","email":"","orcid":"","institution":"Heart and Diabetes Center North Rhine-Westphalia","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Gummert","suffix":""},{"id":609078039,"identity":"70fcc11e-13d8-447b-919e-004ae5522f67","order_by":8,"name":"Vera von Dossow","email":"","orcid":"","institution":"Heart and Diabetes Center North Rhine-Westphalia","correspondingAuthor":false,"prefix":"","firstName":"Vera","middleName":"","lastName":"von Dossow","suffix":""}],"badges":[],"createdAt":"2026-03-13 17:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9117218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9117218/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105563764,"identity":"01f60154-5ee8-4541-8a21-e13a09fc9d1a","added_by":"auto","created_at":"2026-03-27 12:47:44","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141112,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFlowchart of patient-selection (ANQUR: ANesthesiology-QUality-Registry, SF-12: Short-Form-12)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9117218/v1/8c6e98c98681ca5695baeda1.jpeg"},{"id":105150256,"identity":"84e00ecb-526d-43c2-9428-13b7e6a43f58","added_by":"auto","created_at":"2026-03-22 15:01:04","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":220867,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSF-12-scores by sex and age-category, grade of preoperative anemia, weight categorized by body-mass-index (BMI), symptom burden according to NYHA-classification and type of performed procedure (SF-12: Short-Form-12; MCS: mental component summary; PCS: physical component summary; BMI: body mass index; NYHA: New York Heart Association, CABG: coronary artery bypass graft)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9117218/v1/10510400c1b8941be18fcf8b.jpeg"},{"id":105150262,"identity":"015856f9-afc4-4c17-88f0-b49a14ca9ee7","added_by":"auto","created_at":"2026-03-22 15:01:05","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":151491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSF-12-scores by classification of physical frailty (SF-12: Short-Form-12; MCS: mental component summary; PCS: physical component summary)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9117218/v1/04a34baa010b49fbadc47b81.jpeg"},{"id":105150258,"identity":"b5b4d787-3392-4511-9f48-ab1ddc8fdb83","added_by":"auto","created_at":"2026-03-22 15:01:05","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":166888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIndependent predictors of HR-QoL as identified in multivariate analyses (CI: confidence interval; MCS: mental component summary; PCS: physical component summary; PAD: peripheral arterial disease; NYHA: New York Heart Association; OPCAB: On-Pump coronary artery bypass; CABG: coronary artery bypass graft)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9117218/v1/683907bd46b337c41a586429.jpeg"},{"id":105563806,"identity":"e9095d9c-28c4-4ea2-8af2-066e540978d1","added_by":"auto","created_at":"2026-03-27 12:47:53","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":43489,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIndependent predictors of HR-QoL as identified in multivariate analyses in the subgroup (HR-QoL: health-related quality-of-life; SF-12: Short-Form-12; MCS: mental component summary; PCS: physical component summary)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9117218/v1/60a9d5e57bc23a8c04b01606.jpeg"},{"id":105569082,"identity":"941d501e-4954-4f8a-a14e-2effabcd38f5","added_by":"auto","created_at":"2026-03-27 13:11:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1666330,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9117218/v1/7caf1b97-d6b5-4516-850c-e004733669b6.pdf"},{"id":105563303,"identity":"baa1a6d8-8b22-4b9d-bbe1-60c73391035f","added_by":"auto","created_at":"2026-03-27 12:46:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29282,"visible":true,"origin":"","legend":"","description":"","filename":"ANQURHCHsupplements.docx","url":"https://assets-eu.researchsquare.com/files/rs-9117218/v1/e2b8c074a7eb8b34aa159d70.docx"},{"id":105563275,"identity":"cce29407-a7f6-41e7-8e48-5d74a35d04a9","added_by":"auto","created_at":"2026-03-27 12:46:36","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":273260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract (Publication license available)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9117218/v1/3c55a9a5a74f5c61052b0459.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictors of Health-Related Quality-of-Life after Cardiac Surgery: Findings from the ANesthesiology-QUality-Registry (ANQUR) and Frailty-Management","fulltext":[{"header":"1 Background","content":"\u003cp\u003eIn light of an ongoing demographic shift, hospitalized populations become older and increasingly comorbid, resulting in an incline in economic burden. [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] To minimize perioperative complications and financial downsides thereof, extensive risk-stratification is warranted. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] In addition to short-term physician reported follow-up, patient-reported outcome (PRO) and health-related quality of life (HR-QoL) are increasingly recognized for evaluation of subjective long-term outcome. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] HR-QoL represents the most frequently assessed PRO and can be evaluated using the generic Short-Form-12-questionnaire (SF-12), which operationalizes QoL and yields both a physical and a mental component summary score. [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] The SF-12 user manual was first published in 1994, and the instrument has been validated in German, and standardized to the general population. [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] In addition to QoL-assessment, incorporation of findings into clinical practice is crucial and may enable identification of vulnerable populations, who might benefit from extended supervision and periprocedural care. Potentially modifiable predictors of impaired HR-QoL in patients undergoing cardiac surgery \u0026ndash; such as weight, a history of smoking or length of stay \u0026ndash; have been previously identified, though a lack of consistency across studies was apparent. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003cp\u003ePrehabilitation represents a structured effort for preoperative modification of predictors of impaired outcome and aims to improve patients\u0026rsquo; resilience and postoperative outcome by optimizing individual risk profile and thereby reducing overall complications, their severity and limiting further decline. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] One exemplary pillar of preoperative optimization is patient-blood-management (PBM); PBM can be defined as a patient-centered approach to improve outcomes and reduce risks while promoting patient safety and empowerment. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Furthermore, incorporating multimodal concepts of nutrition and exercise can benefit both surgical and subjective outcomes. These interventions \u0026ndash; applied not only in prehabilitation prior to surgery but also in rehabilitation or recovery after surgery (ERAS) \u0026ndash; include specific strategies such as physical activation or guidance, nutritional counseling, and optimization, representing only a subset of potential targets for improving patient centered care. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFrailty is described as a concept of age-related decline in physical function accompanied by increased vulnerability to external stressors and is prevalent in over 30% of patients undergoing cardiac surgery. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] The association between frailty, coexisting comorbidities, and increased odds of impaired outcomes in cardiac patients suggests that preoperative modification of frailty may represent a potential strategy to improve outcomes and prevent complications. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] This study aims to identify determinants of clinical outcomes in patients undergoing cardiac surgery, while also focusing on prognostic significance of physical and cognitive frailty.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patient recruitment\u003c/h2\u003e \u003cp\u003e In total, 1.136 patients who underwent cardiac surgery at a large-volume German heart center gave informed consent to participation in an Anesthesiology-Quality-Registry (ANQUR) in 2023. Patients were preoperatively informed and consent was recorded during the premedication consultation. Postoperatively, HR-QoL-data was assessed at 1-year-follow up, using the Short-Form-12-questionnaire (SF-12). The final cohort comprised 812 patients. A subgroup comprised 190 patients, who were additionally included in a specialized frailty- and delirium-pathway and underwent extensive preoperative screening for physical or cognitive frailty as well as assessment of risk for delirium (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection and definition of variables\u003c/h2\u003e \u003cp\u003eAll available SF-12 data were included in the registry, and standardized scoring was performed for fully completed questionnaires. In accordance with the user manual, the Physical Component Summary (PCS) and Mental Component Summary (MCS) scores were calculated for each patient, representing individual HR-QoL, and were therefore defined as the primary endpoints. The investigated variables comprised demographic and periprocedural factors, as well as parameters assessed within the frailty management program. All data were extracted from internal databases and pseudonymized prior to analysis. For analytical purposes, non-continuous variables were categorized as appropriate.\u003c/p\u003e \u003cp\u003eDemographic variables included sex and age. Sex was recorded as male or female, with no additional categories available. Age was analyzed as a continuous variable and additionally categorized into three predefined groups: 18\u0026ndash;64, 65\u0026ndash;74, and \u0026ge;\u0026thinsp;75 years. Preoperative variables included hemoglobin levels, analyzed both as a continuous variable and categorized according to the World Health Organization (WHO) classification of anemia; LDL cholesterol and lipoprotein(a) [Lp(a)] levels as indicators of lipid profile; body mass index (BMI), assessed continuously and categorically; preexisting conditions including hypertension, diabetes mellitus, and peripheral arterial disease (PAD); smoking status; and left ventricular ejection fraction (LVEF). [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] Intraoperative factors included duration of anesthesia and surgical intervention, as well as the type of procedure, categorized as isolated valve surgery, isolated on- (OPCAB) and off-pump coronary artery bypass grafting (CABG), combined CABG and other procedures, aortic surgery, ventricular assist device (VAD) implantation, and heart transplantation. Postoperative variables comprised length of stay in the intensive care unit (ICU) and total hospital length of stay (LOS).\u003c/p\u003e \u003cp\u003eSubgroup patients provided detailed data on frailty and postoperative delirium. Preoperative assessment included physical and cognitive frailty scoring, using an adaptation of the Fried Frailty Score and the Mini-Cog\u0026copy;, which allowed categorization of patients as robust, pre-frail, or frail, and identification of lower or higher likelihood of clinically significant cognitive impairment. Postoperative delirium is also recorded in subgroup patients. The frailty score includes gait-speed, assessed via timed-up-and-go-test, handgrip strength measured with a dynamometer, evaluation of unintentional weight loss, subjective exhaustion, and physical activity. The Mini-Cog\u0026copy;-Score consists of a three-word-recall task and a clock-drawing-test. [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e \u003cp\u003eMean age of the cohort was 67.38 years, 28.4% were female. 1-year-mortality in all patients receiving cardiac surgery was 5.55%. For all demographic and periprocedural data of the cohort and subgroup, see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and periprocedural baseline data of the analyzed cohort and additional data on physical and cognitive frailty in the subgroup (BMI: body mass index; Dur.: duration; ICU: intensive care unit; LDL: low density lipoprotein; Lp(a): lipoprotein(a); LVEF: left ventricular ejection fraction; NYHA: New York Heart Association; PAD: peripheral arterial disease; Postop.: postoperative; Preop.: preoperative; SD: standard deviation)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e812\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e812\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex: Female (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSmoking status (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(9.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(55.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge-categories (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eActive smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(14.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;64 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFormer smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(30.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;74 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLVEF (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(9.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLVEF-categories (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(83.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41\u0026ndash;49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreop. hemoglobin (mg/dl, mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eDur. anesthesia (minutes, mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e332.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(70.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreop. anemia (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eDur. intervention (minutes, mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e211.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(62.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eType of surgery (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsolated valve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(46.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate or severe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsolated Off-Pump CABG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(24.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreop. LDL (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsolated OPCAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;55 mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAortic procedures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;69 mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCABG combinations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(12.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70 mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreop. Lp(a)\u0026thinsp;\u0026ge;\u0026thinsp;50 mg/dl (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(5.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eDur. postop. ICU-stay (days, mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI-categories (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eDur. in-hospital stay (days, mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(9.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePostop. infection (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eFrailty-program (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(23.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e190\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(81.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eFrailty-Score (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes mellitus (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(35.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePAD (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePre-Frail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(48.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNYHA-classification (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(16.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMini-Cog\u0026copy;-Score (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(79.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigher likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(7.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePostop. delirium (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Outcome\u003c/h2\u003e \u003cp\u003eMean physical outcome (PCS) was 45.91 (SD 10.31) and mean mental outcome (MCS) was 50.12 (SD 10.71). Female sex [B = -1.8 (95% CI -3.3 \u0026ndash; -0.2)] and increase in age [B = -5.0 (95% CI -6.8 \u0026ndash; -3.1)] were associated with significantly lower PCS, while older age was associated with significantly higher MCS [B\u0026thinsp;=\u0026thinsp;3.7 (95% CI 1.7\u0026ndash;5.6)] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatients preoperatively presenting with anemia [B = -6.8 (95% CI -10.5 \u0026ndash; -3.2)], increased BMI [B = -5.1 (95% CI -6.9 \u0026ndash; -3.2)], positive history of hypertension [B = -3.3 (95% CI -5.1 \u0026ndash; -1.5)], diabetes mellitus [B = -2.9 (95% CI -4.6 \u0026ndash; -1.2)], vascular comorbidities [B = -4.6 (95% CI -7.7 \u0026ndash; -1.5)] and smoking [B = -2.2 (95% CI -3.8 \u0026ndash; -0.6)] presented significantly lower physical outcome scores \u0026ndash; smoking having an additional negative association with MCS [B = -2.1 (95% CI -3.7 \u0026ndash; -0.4)]. Increase in NYHA-classification [B = -10.9 (95% CI -15.6 \u0026ndash; -6.2)] and decrease in LVEF [B = -3.8 (95% CI -6.3 \u0026ndash; -1.4)] were also associated with significantly lower PCS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProlonged duration of anesthesia [B = -0.02 (95% CI -0.03 \u0026ndash; -0.01)] and surgical intervention [B = -0.01 (95% CI -0.02 \u0026ndash; -0.001)], as well as prolonged ICU-stay [B = -0.4 (95% CI -0.6 \u0026ndash; -0.3)] and total length of stay [B = -0.2 (95% CI -0.3 \u0026ndash; -0.1)], were all associated with significant lower physical outcome scores (PCS). Compared with isolated valve procedures, patients undergoing isolated Off-Pump CABG also exhibited lower physical outcome scores [B = -2.0 (95% CI -3.75 \u0026ndash; -0.26)] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with no relevant differences in mean age or preoperative risk evaluation (Euroscore II) but a higher prevalence of PAD in patients undergoing off-pump CABG (11.2% vs. 3.7%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients undergoing isolated OPCAB, heart transplantation or VAD-implantation were excluded from this analysis due to insufficient sample size.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePatients undergoing any postoperative infection (POI) presented significantly lower PCS [B = -7.8 (95% CI -11.5 \u0026ndash; -4.1)] and MCS [B = -4.2 (95% CI -8.1 \u0026ndash; -0.3)]. Participation in the frailty- and delirium-pathway was associated with significantly lower PCS [B = -2.2 (95% CI -3.9 \u0026ndash; -0.6)], as was preoperative classification as physically pre-frail [B = -5.6 (95% CI -8.8 \u0026ndash; -2.4)] or frail [B = -9.6 (95% CI -14.0 \u0026ndash; -5.2)] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA multivariate analysis identified female sex, age\u0026thinsp;\u0026ge;\u0026thinsp;75 years, anemia, overweight and obesity, vascular comorbidities (PAD), a history of smoking and increased symptom burden were identified as independent predictors of lower physical outcome scores, as assessed by the SF-12-questionnaire. Smoking and isolated CABG independently predicted lower cognitive outcome scores, while increased age was independently associated with significantly higher MCS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary Table\u0026nbsp;2). Participation in the frailty management program was not associated with significant changes in either physical or cognitive outcome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Subgroup analysis: Frail patients\u003c/h2\u003e \u003cp\u003eThe distribution of frailty status showed no significant difference between off-pump bypasses and isolated valve procedures. in A multivariate subgroup-analysis identified preoperative physical pre-frailty frailty as independent predictors of lower physical outcome scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Supplementary Table\u0026nbsp;3). Cognitive frailty was not associated with significant changes in PCS or MCS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe results of this prospective observational study demonstrate several important findings regarding prediction of health-related quality-of-life in patients undergoing cardiac surgery.\u003c/p\u003e \u003cp\u003eMultivariate analyses revealed an independent association of female sex, an increase in age, weight and symptom burden, preoperative anemia, preexisting vascular conditions and a history of smoking with impaired physical outcome. Regarding mental outcome, older age was associated with improved outcome, while a history of smoking was linked to an impairment of mental status. Subgroup analysis identified physical frailty and pre-frailty as independent predictors of impaired physical outcome.\u003c/p\u003e \u003cp\u003eAccording to user manual, the SF-12-scores are norm based and standardized to the general population with a mean of 50 and a standard deviation of 10, lower scores reflecting poorer outcome. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] In this analysis, mean physical outcome was reduced by approximately half a standard deviation (45.91), whereas mean mental outcome did not differ from that of the general population (50.12). A comparable pattern emerged in the relationship between age and outcomes: older age was associated with impaired physical health, whereas mental health tended to improve with age. These findings may indicate that physical health is generally more susceptible to influencing factors, such as surgery, than mental health.\u003c/p\u003e \u003cp\u003ePreoperative anemia and frailty, both independently associated with worse outcomes, represent potentially modifiable risk factors whose optimization could reduce complications and improve postoperative results. Patient Blood Management (PBM), which aims to optimize red blood cell mass, minimize blood loss, and enhance a patient\u0026rsquo;s tolerance to anemia, constitutes an important strategy for the structural management of perioperative anemia and has been shown to have clinically relevant effects on outcomes. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] In our analysis, preoperative anemia was confirmed as a significant predictor of postoperative outcome, underscoring the ongoing need to implement and refine PBM strategies. Preoperative frailty deems a similar approach of perioperative management: an enhanced preoperative assessment of cognition, function and frailty, perioperative support including family, and postoperative care focusing on delirium-prevention and functional recovery are recommended to minimize complications and improve outcome. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] The observed association between physical frailty and impaired physical outcomes supports evidence linking frailty to adverse events, including increased mortality, prolonged hospital stays, and higher risk of delirium. [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] By focusing on preoperative optimization and extended risk stratification to reduce perioperative complications and overall length of stay, physicians may help mitigate the economic burden for patients undergoing complex procedures, particularly those with extensive comorbidities or exhibiting physical or mental vulnerability. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOverall, the results of this analysis are in line with previously identified predictors of HR-QoL. A systematic review, published by Sanders et al. in 2022, identified 103 independent predictors of HR-QoL after cardiac surgery, including BMI, smoking and length of stay in the hospital and on the ICU, while emphasizing a lack of consistency across studies. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] A follow-up study on 272 patients, published by Perrotti et al. in 2019, demonstrated the association between dyspnea, older age and impaired physical outcome. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] Regarding this analysis\u0026rsquo; subgroup, an observational study on 133 patients, published by Nakano et al. in 2020, revealed an association of frailty with functional decline after cardiac surgery. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] All findings suggest a distinct phenotype at risk for impaired outcome after surgery, which could benefit from extensive patient-centered care and perioperative management.\u003c/p\u003e \u003cp\u003eThis study presents strengths and weaknesses. By analyzing similar sample sizes as comparable studies, it offers further insights into prediction of HR-QoL after cardiac surgery and adds to consistency in literature, while emphasizing the vulnerability of frail patients undergoing high-risk cardiac surgery. Similar findings suggest broader implications for cardiac patients and highlight the importance of partially modifiable factors in the perioperative period. The single-center design and the absence of a preoperative HR-QoL assessment represent weaknesses of this observational study. Selection bias, due to inclusion of only patients capable and compliant enough to participate, and response bias, because immediate outcomes, mortality, or disability may have influenced questionnaire return, may also have affected the results.\u003c/p\u003e \u003cp\u003eFuture research should incorporate preoperative assessment of HR-QoL to evaluate absolute effects on QoL and should be designed as multicenter studies to include a broader patient spectrum, increase sample size, and enhance generalizability of the findings. Furthermore, identification of high-risk patients is possible, but clinical translation remains crucial for improving care and optimizing outcome; subjective, objective and economically.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn patients undergoing cardiac surgery at a large-volume German heart center, independent predictors of HR-QoL at follow-up were identified. Female sex, increase in age, weight and symptom burden, a preoperative anemia, vascular comorbidities and a history of smoking were associated with impaired physical outcome one year after cardiac surgery. Mental outcome improved with age and was impaired in patients with a history of smoking. A subgroup analysis identified physical frailty as an independent predictor of impaired physical outcome.\u003c/p\u003e \u003cp\u003eThese findings suggest a distinct phenotype at risk for impaired outcome after surgery, highlighting the need for preemptive risk-stratification to identify and extensively care for vulnerable patients to minimize perioperative complications and improve patient-reported outcome.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANQUR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnesthesiology-QUality-Registry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCABG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronary Artery Bypass Graft\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eERAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnhanced Recovery after Surgery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR-QoL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth-Related Quality-of-Life\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow Density Lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLength of Stay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLp(a)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLipoprotein (a)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeft Ventricular Ejection Fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMental Component Summary\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNYHA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNew York Heart Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOPCAB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOff-Pump Coronary Artery Bypass\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeripheral Arterial Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePatient Blood Management\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhysical Component Summary\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePOI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePostoperative Infection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePatient-Reported Outcome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSF-12\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShort-Form 12\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVentricular Assist Device\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e: This prospective observational study complied with the Declaration of Helsinki and was approved by the Ethics Committee of the Medical Faculty of the Ruhr-University Bochum on 17\u003csup\u003eth\u003c/sup\u003e November 2022 (Registration-Number: 2022-947). Minor additions to the questionnaire were approved on 19\u003csup\u003eth\u003c/sup\u003e August 2024 (Registration-Number: 2022-947_1). Written informed consent was recorded of all participants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor\u0026rsquo;s contributions\u003c/em\u003e: MB: Data Curation, Formal analysis, Writing - Original Draft and Editing; JF: Conceptualization, Methodology, Writing - Review \u0026amp; Editing; JW: Resources; CM: Resources, Project administration; AB: Writing - Review \u0026amp; Editing; RS: Writing - Review \u0026amp; Editing; NH: Writing - Review \u0026amp; Editing; JG: Writing - Review \u0026amp; Editing; VVD: Supervision, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMilan V, Fetzer S, Hagist C. 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Age Ageing. 2018;47:193\u0026ndash;200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ageing/afx162\u003c/span\u003e\u003cspan address=\"10.1093/ageing/afx162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGracie TJ, Caufield-Noll C, Wang N-Y, Sieber FE. The Association of Preoperative Frailty and Postoperative Delirium: A Meta-analysis. Anesth Analgesia. 2021;133:314. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1213/ANE.0000000000005609\u003c/span\u003e\u003cspan address=\"10.1213/ANE.0000000000005609\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProkopidis K, Nortcliffe A, Okoye C, Venturelli M, Lip GYH, Isanejad M. Length of stay and prior heart failure admission in frailty and heart failure: A systematic review and meta-analysis. ESC Heart Fail. 2025;12:2417\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ehf2.15300\u003c/span\u003e\u003cspan address=\"10.1002/ehf2.15300\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThanakiattiwibun C, Siriussawakul A, Virotjarumart T, Maneeon S, Tantai N, Srinonprasert V, et al. Multimorbidity, healthcare utilization, and quality of life for older patients undergoing surgery: A prospective study. Med (Baltim). 2023;102:e33389. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/MD.0000000000033389\u003c/span\u003e\u003cspan address=\"10.1097/MD.0000000000033389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnowden CP, Anderson H. Preoperative optimization: rationale and process: is it economic sense? Curr Opin Anaesthesiol. 2012;25:210\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/ACO.0b013e32834ef903\u003c/span\u003e\u003cspan address=\"10.1097/ACO.0b013e32834ef903\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerrotti A, Ecarnot F, Monaco F, Dorigo E, Monteleone P, Besch G, et al. Quality of life 10 years after cardiac surgery in adults: a long-term follow-up study. Health Qual Life Outcomes. 2019;17:88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12955-019-1160-7\u003c/span\u003e\u003cspan address=\"10.1186/s12955-019-1160-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiac Surgical Procedures, Quality of Life, Follow-Up Studies, Patient Reported Outcome Measures, Frailty, Cognitive Dysfunction","lastPublishedDoi":"10.21203/rs.3.rs-9117218/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9117218/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssessment of long-term patient-reported outcome allows identification of vulnerable populations undergoing cardiac surgery. Incorporation of findings into clinical practice may enhance risk-stratification, prevent perioperative complications, and improve outcome. Frailty is prevalent in up to a third of cardiac patients and structured peri-procedural programs addressing frailty and delirium may be relevant to long-term outcome. This study aims to identify predictors of health-related quality-of-life one year after cardiac surgery and assess outcome in patients enrolled in an extensive frailty- and delirium-pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients undergoing cardiac surgery at a high-volume German heart center were enrolled in an anesthesiology quality registry and health-related quality of life (HR-QoL) was assessed using the Short Form-12 (SF-12) questionnaire at 1-year-follow-up. Corresponding factors were analyzed for their association with individual outcome. The cohort comprised 812 patients. A subgroup of 190 patients participated in a frailty and delirium management program, providing extended preoperative screening and postoperative supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFemale sex [B -2.56, 95% CI (-4.28 – -0.85)], increase in age [B -4.00, 95% CI (-6.17 – -1.83)] and weight [B -4.57, 95% CI (-6.53 – -2.60)], preoperative anemia [B -3.43, 95% CI (-5.77 – -1.10)], vascular comorbidities [B -3.37, 95% CI (-6.57 – -0.16)], smoking [B -2.73, 95% CI (-5.00 – -0.46)], increase in symptom burden [B -6.07, 95% CI (-11.55 – -0.59)] and physical frailty [B -9.90, 95% CI (-17.72 – -2.09)] were independently associated with lower physical outcome scores. Cognitive scores were higher in older patients [B 4.33, 95% CI (6.17–1.83)] and lower in smokers [B -2.18, 95% CI (-3.61 – -0.27)].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndependent predictors of impaired HR-QoL at 1-year follow-up could be identified, suggesting a phenotype at risk. Physical frailty independently predicted poorer physical outcome, emphasizing potential for prehabilitation and frailty-management. Findings should be interpreted considering selection- and response-bias and absence of baseline HR-QoL-assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis observational study complied with the Declaration of Helsinki and was approved by the Ethics Committee of the Medical Faculty of the Ruhr-University Bochum on 17th November 2022 (Registration-Number: 2022 − 947). Minor additions to the questionnaire were approved on 19th August 2024 (Registration-Number: 2022 − 947_1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGraphical abstract (Publication license available)\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Predictors of Health-Related Quality-of-Life after Cardiac Surgery: Findings from the ANesthesiology-QUality-Registry (ANQUR) and Frailty-Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-22 15:01:00","doi":"10.21203/rs.3.rs-9117218/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-30T10:22:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-28T08:47:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201645439096140485236153290733443463387","date":"2026-03-19T18:37:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-19T09:45:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15002363155542166998082731341707913327","date":"2026-03-18T10:14:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-18T09:55:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-18T09:53:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-18T05:24:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T23:09:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Anesthesiology","date":"2026-03-17T16:17:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b048a2c1-e3cd-4872-b837-888bee7c2eee","owner":[],"postedDate":"March 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T06:55:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-22 15:01:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9117218","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9117218","identity":"rs-9117218","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}