Predictors of Outcome in Critically Ill Cardiac Patients with a very long ICU stay–a retrospective bicentric study

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Schultz, Valentin Ritschl, Tanja Stamm, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8748230/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Outcome predictors in critically ill cardiac patients with a very long ICU stay remain poorly defined. This study aimed to identify such predictors in this population. Methods This is a retrospective bi–centric study including critically ill surgical and medical cardiac patients with an ICU length of stay > 30 days. ICU–mortality was the primary endpoint; 1–year mortality was the secondary endpoint. A priori defined outcome predictors were analyzed by means of univariable and multivariable logistic regressions. Additionally, classification and regression tree (CART) analysis was employed to explore non–linear relationships and interactions. Results A total of 210 patients, among them 107 (51.0 %) surgical and 103 (49.0%) medical, were included. ICU mortality was 24.3 %. In the entire cohort, 1–year mortality was 46.7 %, whereas among patients who survived the ICU it was 29.6 %. Factors independently associated with ICU–mortality included baseline frailty (odds ratio (OR), 3.16 [95%–confidence interval (CI) 2.26–4.70] per point), prolonged continuous renal support (OR, 1.07 [1.03–1.13] per day), prolonged invasive ventilation (OR, 1.07 [1.01–1.14] per day) and prolonged vasopressor use (OR, 1.09 [1.02–1.17] per day), whereas prolonged inotrope use (OR, 0.93 [0.87–0.99] per day) was associated with lower ICU–mortality. CART analysis identified various patient groups with distinctly diverging ICU mortality. Conclusion In critically ill cardiac patients with a very long stay in ICU, irrespective of surgical or medical status, baseline frailty, prolonged renal support, invasive ventilation, and vasopressor use were independent predictors of ICU–mortality, whereas prolonged inotrope use was inversely associated with mortality. External validation is needed before these findings can be used for decision support. Trial registration The study was registered and approved by the Medical University of Vienna IRB on 31 st of August 2021 (EC number 1669/2021). Critical Care & Emergency Medicine Intensive care critical care cardiac disease cardiac surgery heart failure prolonged stay outcome prediction prediction model Figures Figure 1 Figure 2 Background Patients with a very long intensive care unit (ICU) stay often face a poor prognosis, regardless of pathology [1–5]. High mortality is particularly evident in patients with heart failure and cardiogenic shock or post–cardiac surgery [6–8]. Furthermore, extended stay in the ICU is often associated with significant psychological stress and discomfort, for example due to invasive procedures and transport, or dyspnea during respiratory weaning; in addition, it often results in long–term sequelae [9–12]. Overall, outcome prediction in such patients is highly challenging, and has been poorly studied, mostly due to the small size of patient cohorts and the heterogeneity of the diseases and their processes. In fact, patient demographics and baseline characteristics [8,13,14], and disease severity scores [15,16], using data collected in the first 24 hours in ICU, lack significant predictive capacity. In non–cardiac ICU populations, the need for invasive mechanical ventilation (MV) and renal replacement therapy (RRT) has been associated with increased mortality [17,18]. However, it is uncertain if this also applies to ICU patients with a primarily cardiac pathology and a very long stay in ICU. Thus, ICU physicians may face the dilemma, whether to perform further invasive and curative treatment or initiate palliative end-of-life care planning in this patient population without reliable, objective data to support their decision-making process. Furthermore, advancements in care, like extracorporeal membrane oxygenation (ECMO) and ventricular assist devices [13,14], can prolong postoperative ICU treatment almost infinitely, thus raising additional ethical questions despite being supportive in distinct scenarios. In light of the lack of evidence, and in order to provide a prognostic framework to caregivers, patients and their next–of–kin, this study aimed to identify predictors of clinical outcome in critically ill cardiac patients with a very long ICU. We defined very long ICU stay as greater than 30 days based on prior literature identifying this group as an extreme prolonged stay population [19–21]. We hypothesized that specific clinical and treatment related variables would serve as independent predictors of outcome in this population. Methods Design and ethical considerations This analysis is based on a retrospective study in patients admitted to a postoperative cardiovascular ICU and a medical cardiovascular ICU in a tertiary care hospital between 10/2016 and 8/2022. Due to the organizational structure of the hospital, the ICUs operate fully independently from each other, under different organizational, management and staffing structures, and do not possess any relevant consensus regarding standard operating procedures. The study protocol was approved by the Medical University of Vienna ethics committee (EC number 1669/2021). Because of the retrospective nature of the study, the requirement for individual informed consent was waived. Patients Patients were eligible for analysis if: (1) aged over 14 years; (2) admitted before or after cardiac surgery or due to a primary cardiac pathology, (3) cared for > 30 days in the ICU within the predefined period. Patients who were readmitted to the ICU or with missing frailty scale were excluded from analysis. Data collected For all patients, the following demographics and baseline characteristics were extracted at ICU admission: age, sex, BMI, Simplified Acute Physiology Score (SAPS) 3, preoperative or pre–ICU admission frailty (Clinical frailty scale 0 to 9, CFS[22]), and the main reasons for hospital and ICU admission. Duration of organ support was captured up to day 30 after ICU admission was calculated from the day of initiation (actual number of support days) for continuous renal replacement therapy (CRRT), invasive MV, mechanical circulatory support (MCS), including ECMO, micro–axial continuous flow, and temporary right ventricular assist devices (RVAD), vasopressors (including norepinephrine ≥ 0.1mcg/kg/min or vasopressin ≥ 2IU/h), inotropes (including dobutamine ≥ 2 mcg/kg/min, milrinone ≥ 0,15mcg/kg/min, or levosimendan at any dose), administration of blood products (including packed red blood cells, fresh frozen plasma, and platelets), and daily fluid balances exceeding +1 L. We also captured, whether patients received immunosuppressive therapy or antibiotic therapy due to an infection with multi–drug resistant (MDR) pathogens. All data were extracted from the electronic patient medical records (IntelliSpace Critical Care and Anesthesia (ICCA), Philips Health Systems, Amsterdam, Netherlands). Hospital discharge status, and mortality 1 year after ICU admission were extracted from the national death register (Zentrales Personenstandsregister ZPR). Definitions ICU mortality was defined as all–cause death in the ICU; 1–year mortality was defined as all–cause death within 1 year after ICU admission and was analyzed both for the entire cohort and for ICU survivors. In–hospital mortality was defined as all-cause mortality within the index hospital stay. Outcomes The primary outcome was ICU–mortality: 1–year mortality after ICU admission served as the secondary outcome. Statistical analysis The sample size was the number of patients who stayed in the ICU > 30 days during the 6–year study period. Categorical variables are reported as counts and percentages, continuous variables as mean ± SD. χ²–tests, t–tests, and ANOVA were used where appropriate. We fitted two prespecified multivariable logistic regression models, one for ICU– mortality in the full cohort, and one for 1–year mortality in ICU survivors. Predictors were defined a priori based on clinical relevance and included age, sex, CFS before ICU admission, use and days of mechanical ventilation, use of and days on CRRT, use and days of hemodynamic support, and immunosuppression or infection with a multi–resistant pathogen at or within the first 30 ICU days. Collinearity was screened using variance inflation factors. No variable selection or cross-validation was applied to these regression models, as they were explanatory rather than predictive. To assess potential departures from linearity in the duration variables (invasive ventilation, CRRT, and hemodynamic support), we compared models with log–transformed terms [log(x+1)] and with restricted cubic splines (natural splines; 3 degrees of freedom per variable), evaluating changes in model fit with likelihood–ratio tests and information criteria (AIC/BIC). Results are presented as adjusted odds ratios (ORs) with 95% confidence intervals. For interpretable rule–based patterns and to capture potential non–linearities and interactions, we trained classification and regression trees (CART), first for ICU–survival, and then separately for 1–year survival among ICU survivors. Trees were tuned via the complexity parameter using 5–fold cross–validation and pruned to minimize overfitting. As an internal robustness check, we repeated the CART analysis separately within the two separate ICUs. Results are as split rules, node risks, and variable importance descriptively. All analyses were performed in R (version 4.5.1, R Foundation for Statistical Computing, http://www.r-project.org). All tests were two–sided with α = 0.05. Results Patients Of 242 patients with an ICU > 30 days, 210 patients were included ( Figure 1 ). Of these, 51 (24.3%) died in the ICU. Among the 159 ICU survivors, 12 (7.5 %) died before hospital discharge, and 35 (22.0 %) died after hospital discharge within the first year following ICU admission. This resulted in an overall 1–year mortality of 46.7%. ICU non–survivors had higher baseline CFS scores, while 1–year non–survivors were older, had higher baseline CFS scores, and more frequently had an infection with MDR pathogens ( Table 1 ). Table 1. Patient Demographics and Baseline Characteristics in ICU Survivors versus Non–survivors and in 1–year Survivors versus Non–survivors all (N = 210) ICU survivors (N = 159) ICU non–survivors (N = 51) P 1–year survivors (N = 112) 1–year non–survivors (N = 98) P age, years 62.4 (15.5) 61.5 (16.1) 65.3 (13.2) 0.12 58.5 (17.4) 65.8 (12.1) <0.01 sex, female 63 (30.0) 48 (28.3) 15 (29.4) 1.00 39 (34.8) 24 (24.5) 0.1 BMI 27.2 (5.7) 27.3 (6.0) 26.7 (4.9) 0.52 27.4 (6.0) 26.8 (5.4) 0.53 clinical frailty scale score 4.6 (1.7) 4.1 (1.3) 6.3 (1.8) <0.01 4.0 (1.3) 5.3 (1.9) <0.01 immunosuppression 29.0 (14.0) 23 (14.7) 6 (11.8) 0.76 19 (17.0) 10 (10.2) 0.16 infection with MDR pathogen 37.0 (17.9) 26 (16.7) 11 (21.6) 0.56 14 (12.5) 23 (23.5) 0.04 SAPS 3 score 64.8 (14.3) 64.9 (14.6) 64.4 (13.6) 0.84 63.1 (13.6) 66.7 (15.0) 0.08 admission type 0.42 0.28 surgical (%) 107 (51.0) 78 (49.1) 29 (56.9) 61 (54.5) 46 (46.9) medical 103 (49.0) 81 (50.9) 22 (43.1) 51 (45.5) 52 (53.1) medical history 0.62 0.49 cardiomyopathy 57 (27.1) 44 (27.7) 13 (25.5) 34 (30.4) 23 (23.5) myocardial infarction 25 (11.9) 22 (13.8) 3 (5.9) 15 (13.4) 10 (10.2) respiratory insufficiency 24 (11.5) 19 (12.0) 5 (9.8) 11 (9.8) 13 (13.3) aortic disease 18 (8.6) 14 (8.8) 4 (7.9) 13 (11.6) 5 (5.1) aortic valve disease 16 (7.6) 10 (6.3) 6 (11.8) 8 (7.1) 8 (8.2) mitral valve disease 16 (7.6) 12 (7.6) 4 (7.9) 5 (4.5) 11 (11.2) following CPR 12 (5.7) 8 (5.0) 4 (7.8) 2 (1.8) 10 (10.2) endocarditis 12 (5.7) 7 (4.4) 5 (9.8) 5 (4.5) 7 (7.1) coronary artery disease 8 (3.8) 5 (3.1) 3 (5.9) 3 (2.7) 5 (5.1) sepsis 6 (2.9) 3 (1.9) 3 (5.9) 2 (1.8) 4 (4.1) neurologic dysfunction 3 (1.4) 3 (1.9) 0 (0.0) 3 (2.7) 0 (0.0) hemothorax 2 (1.0) 1 (0.6) 1 (2.0) 1 (0.9) 1 (1.0) electrical storm 2 (1.0) 2 (1.3) 0 (0.0) 2 (1.8) 0 (0.0) tricuspid valve disease 1 (0.5) 1 (0.6) 0 (0.0) 1 (0.9) 0 (0.0) pulmonary valve disease 1 (0.5) 1 (0.6) 0 (0.0) 1 (0.9) 0 (0.0) other 7 (3.3) 7 (4.5) 0 (0.0) 6 (5.4) 1 (1.0) Data are means (SD) or numbers (%). Abbreviations: BMI, body mass index; MDR, Multiple Drug Resistant; SAPS, Simplified Acute Physiology Score; ICU, intensive care unit; CPR, cardiopulmonary resuscitation. ICU non–survivors, compared to ICU survivors, received CRRT, MCS, and vasopressors for longer periods within the first 30 days after ICU admission, while 1–year non–survivors, compared to 1–year survivors, received CRRT and vasopressors for longer periods within the first 30 days after ICU admission ( Table 2 ). ICU length of stay did not differ between survivors and non–survivors. Predominant causes of death in the ICU were multiorgan failure, permanent brain injury, and sepsis. The predominant causes of in hospital mortality after ICU discharge were permanent brain injury, ischemic stroke, and respiratory failure. The most frequently reported causes of death after hospital discharge were coronary artery disease, acute coronary syndrome, and complications of diabetes ( Supplemental Table 1 ). Table 2. Organ Support in the first 30 days after ICU Admission, and Total Length of Stay in ICU, in ICU Survivors versus Non–survivors, and in 1–year Survivors versus Non–survivors all (N = 210) ICU survivors (N = 159) ICU non–survivors (N = 51) P 1–year survivors (N = 112) 1–year non–survivors (N = 98) P Organ Support days receiving CRRT 10.3 (11.6) 8.0 (10.9) 17.5 (11.3) <0.01 7.3 (10.7) 13.7 (11.9) <0.01 days receiving invasive MV 23.6 (9.6) 23.0 (9.9) 25.5 (8.3) 0.11 22.6 (10.1) 24.8 (8.8) 0.10 days receiving MCS 5.2 (7.8) 4.5 (7.0) 7.2 (9.7) 0.03 4.6 (7.3) 5.8 (8.4) 0.26 days receiving vasopressors 10.7 (8.6) 9.0 (7.2) 16.2 (10.3) <0.01 9.0 (7.1) 12.7 (9.8) 0.02 days receiving inotropes 8.6 (9.1) 8.2 (8.8) 10.1 (10.1) 0.18 8.5 (9.2) 8.8 (9.2) 0.83 days receiving transfusions 0.5 (1.9) 0.4 (1.0) 0.7 (3.4) 0.41 0.4 (8.9) 0.6 (2.6) 0.60 days with positive fluid balance 3.5 (4.5) 3.7 (4.6) 2.7 (4.3) 0.17 3.4 (4.3) 3.6 (4.8) 0.82 LOS in the ICU days in ICU 54.5 (25.3) 52.8 (24.0) 58.0 (29.0) 0.20 52.0 (25.0) 56.3 (25.5) 0.22 Data are means (SD). Abbreviations: CRRT, continuous renal replacement therapy; MV, mechanical ventilation; MCS, Mechanical Circulatory Support (ECMO, percutaneous RVAD, RVAD, Impella); LOS, length of stay Factors associated with ICU mortality and with 1–year mortality Factors independently associated with ICU–mortality included frailty, prolonged CRRT, prolonged invasive MV and prolonged vasopressor use. On the contrary, prolonged inotrope use was associated with lower ICU–mortality ( Table 3 ). In the overall cohort, factors associated with 1–year mortality were age, frailty, prolonged CRRT and prolonged invasive MV. In the group of ICU survivors, only age was independently associated with 1–year mortality ( Supplemental Table 2 ). Use of log–transforms and non–linear spline transformations did not improve model fit. Table 3. Risk factors associated with ICU mortality, 1-year mortality Multivariate logistic regression for ICU mortality OR (95% CI) for ICU mortality† P OR (95% CI) for 1-year mortality† P age (per year) 1.00 (0.96-1.04) 0.88 1.03 (1.01-1.06) 0.01 frailty (per point) 3.16 (2.26-4.70) <0.01 1.62 (1.31-2.05) <0.01 female sex (yes) 1.59 (0.56-4.54) 0.38 1.59 (0.87-2.92) 0.13 immunosuppression (yes) 0.85 (0.19-3.26) 0.82 1.70 (0.75-3.89) 0.21 infection with MDR pathogen (yes) 0.61 (0.18-1.89) 0.41 1.53 (0.62-3.78) 0.35 invasive ventilation (per day) 1.07 (1.01-1.14) 0.03 1.04 (1.00-1.09) 0.05 CRRT (per day) 1.07 (1.03-1.13) <0.01 1.05 (1.02-1.09) <0.01 MCS (per day) 1.06 (1.00-1.14) 0.06 1.04 (0.99-1.09) 0.15 vasopressor support (per day) 1.09 (1.02-1.17) 0.01 1.01 (0.96-1.06) 0.82 inotrope support (per day) 0.93 (0.87-0.99) 0.04 0.98 (0.94-1.02) 0.34 transfusion (per day) 1.10 (0.81-1.23) 0.98 1.04 (0.88-1.23) 0.65 positive fluid balance (per day) 0.95 (0.88-1.02) 0.17 1.02 (0.94-1.11) 0.68 † for all 210 patients; Data are presented as means with (SD) or as absolute values (%) or Odds Ratio (95% CI); Abbreviations: ICU, intensive care unit; MDR, Multiple Drug Resistant; CRRT, continuous renal replacement therapy; MCS, mechanical circulatory support (ECMO, Impella, percutaneous RVAD); OR, Odds Ratio; CFS, Clinical Frailty Scale. Outcome prediction Using CART analysis, we identified five distinct subgroups of patients with sharply diverging ICU mortality using baseline CFS, CRRT days, and vasopressor days ( Figure 2 ). Patients with CFS ≥ 7 who needed significant vasopressors ≥ 5 days had a high predicted ICU–mortality (92.3%, Group I ). In contrast, patients with CFS of ≥ 7 who needed significant vasopressors < 5 days had a lower predicted ICU mortality (46.2%, Group II ). Patients with CFS < 7 who underwent CRRT on ≥ 21 days and needed significant vasopressors ≥ 22 days had a high predicted ICU mortality (90%, Group III ). Contrariwise, in patients with CFS < 7, predicted ICU mortality was low if they underwent CRRT on ≥ 21 days but needed significant vasopressors on < 22 days (20% Group IV ). Predicted ICU mortality was very low if they underwent CRRT on < 21 days (4.6%, Group V ). In order to ensure robustness, CART was performed for each individual ICU cohort independently, which confirmed consistent identifiability of the subgroups across cohorts. Discussion This study sought to identify factors associated with ICU and 1–year outcome in critically ill cardiac patients with a very long ICU stay, and to investigate whether duration of organ support (medications, organ function support) was associated with outcome beyond demographic and admission characteristics. The main findings are summarized as follows: (1) baseline frailty, prolonged CRRT, prolonged invasive ventilation, and prolonged vasopressor use were independently associated with higher ICU mortality; whereas (2) prolonged inotrope use was independently associated with lower ICU mortality; (3) in the overall cohort, age, frailty, prolonged CRRT and prolonged invasive MV were associated with higher 1-year mortality while; (4) among ICU survivors; and (5) there were five distinct patient subgroups in whom combinations of frailty, CRRT, and vasopressor days predicted ICU mortality ranging from below 5% to higher than 90%. This study has several strengths. The analysis focused on a clearly defined and homogeneous population of patients with a very long ICU stay, with strict inclusion and exclusion criteria that minimized heterogeneity and ensured completeness of baseline and organ support data. All data were systematically extracted from electronic health records. This data capture enabled a nuanced assessment of both preexisting patient vulnerability and duration of organ support. Outcomes were rigorously defined, with 1–year mortality assessed through linkage with a national death registry, providing reliable long–term follow–up. The analytical approach was robust, combining conventional regression models to identify independent predictors and classification and regression tree analysis to reveal clinically interpretable subgroups. The structured design, standardized definitions, and detailed longitudinal data enhance the reproducibility and clinical applicability of the findings, supporting their relevance for prognosis, risk stratification, and informed decision–making in this specific ICU population. Traditional outcome prediction models in critically ill patients, such as the acute physiology and chronic health evaluation (APACHE) [23] and SAPS [24], rely primarily on demographic and physiologic data captured within the first 24 hours of ICU admission [25]. However, in patients with a very long ICU stay, these early baseline characteristics may lose prognostic value, because the clinical trajectory over the subsequent ICU stay becomes an important determinant of outcome, as shown in patients with COVID–19 ARDS and in those with severe trauma [5,26,27]. Scores incorporating sequential organ support, such as the Sequential Organ Failure Assessment (SOFA) [28], and studies of delta SOFA [29,30] highlight that evolving physiologic trajectories provide superior prognostic information compared to static early measures. Such scores are widely used in general ICU populations to track evolving organ dysfunction and estimate short–term mortality. Our findings extend this concept to patients with very long stay in a cardiac ICU setting, demonstrating that ICU mortality is associated with prolonged CRRT, prolonged invasive ventilation, and prolonged vasopressor use, whereas prolonged inotrope use is independently associated with lower ICU mortality. Among baseline characteristics, only frailty was associated with ICU mortality, whereas age was the only factor associated with 1–year mortality among ICU survivors, underscoring that dynamic patterns of organ support capture risk beyond initial demographic and physiologic measures. Our findings are in line with prior work showing that baseline frailty is an important determinant of short–term outcomes in critical illness [31,32]. We refine this evidence by demonstrating that frailty was associated with ICU and 1–year mortality in cardiac ICU patients with a very long ICU stay, in contrast to age, which was associated only with 1–year mortality among ICU survivors. These suggest that chronological age may exert its effect later in the recovery trajectory, whereas frailty reflects a more immediate physiologic vulnerability during the ICU stay. Our findings are in agreement with frailty being an important predictor for outcome in surgical and medical patients alike [32,33]. It was somewhat surprising that prolonged inotrope use was associated with lower ICU mortality, in contrast to vasopressor use. The reasons for this finding remain speculative. One possibility is that inotropes were administered selectively in patients in whom frequent hemodynamic measurements showed that they were effectively improving cardiac performance; in other words, inotropes may provide benefit only when they successfully augment cardiac output parameters, whereas vasopressors are given more broadly to maintain perfusion pressure under severe circulatory distress. Another consideration is that short–term improvements in cardiac function, as observed with agents such dobutamine [34], milrinone [35], or levosimendan [36], may translate into immediate survival benefit in patients with a prolonged ICU stay, even if such effects do not extend to long–term outcomes [37–40]. Factors, such as tolerance development [41], do not appear to account for this association. Overall, these findings suggest that the relationship between inotrope therapy and outcome is complex and context-dependent, and highlight the need for further study to identify which patients derive true benefit. The CART analysis provides a detailed perspective by showing that specific combinations and duration of CRRT and vasopressors, together with baseline frailty, define clinically meaningful ICU mortality risk subgroups among very long stay cardiac ICU patients. This approach offers a more individualized and actionable way to predict outcomes than overall mortality tools, particularly in patients with the worst prognosis who consume the most resources [42–44] . Our findings, which are hypothesis–generating rather than allowing outcome prediction at this point, highlight subgroups that merit further study and external validation in other centers. Uncertainty regarding outcome remains for all groups of patients, likely reflecting the challenge of incorporating neurological function into risk prediction [45]. This uncertainty is particularly evident in group 2, possibly due to the small size of this particular subgroup. For all subgroups, any outcome prediction should at best provide supportive guidance rather than certainty. Our study has several limitations, including the retrospective design and loss of some patients due to lack of frailty scores. It was a bi–centric study, which may limit generalizability. Nevertheless, the cohort of 210 very long stay ICU patients represents a substantial and clinically relevant sample, as such patients are rare and among the sickest patients in cardiac ICUs. External validation of the developed outcome prediction model in other centers and patient cohorts will be necessary to assess broader applicability. We used CFS for estimation of frailty. For practical reasons, frailty assessment differed by patient group: in surgical patients, CFS was determined preoperatively. In medical patients, it was assessed at ICU admission, using information provided by the patients or their relatives and based on the patient’s frailty status before the acute illness resulted in admission to the ICU. We modelled duration of therapies beyond a certain threshold, but not their absolute intensity or the timepoint of their initiation. While the risk factors for ICU mortality were predefined to enhance the validity of the investigation, it is possible that other unseen factors with association with outcome were missed. Functional outcomes, such as neurological outcomes and quality of life were not captured. Ischemic or hemorrhagic stroke is not uncommon in critically ill patients after cardiac surgery and strongly influences outcome [46]. This may have contributed to some of the observed inaccuracies in outcome prediction, particularly in CART group II. Finally, as with any observational study, residual confounding and unmeasured variables may have influenced the results despite detailed data collection and predefined analysis plans. Conclusion In critically ill cardiac patients with a very long ICU stay, both baseline characteristics and the duration of organ support were importantly associated to outcome. Frailty and prolonged use of CRRT, invasive ventilation, and vasopressors may identify patients at high risk of ICU mortality, whereas prolonged inotrope use was associated with lower risk. Importantly, CART analysis delineated clinically meaningful subgroups with distinctly different mortality risk, highlighting the potential for individualized, trajectory–based risk assessment and some degree of outcome prediction in this highly–specific ICU population. Abbreviations ANOVA, Analysis of Variance APACHE, acute physiology and chronic health evaluation ARDS, acute respiratory distress syndrome BMI, body mass index CART, classification and regression tree CFS, Clinical frailty scale COVID-19, Coronavirus disease 2019 CRRT, continuous renal replacement therapy EC, ethics committee ECMO, extracorporeal membrane oxygenation ICCA, IntelliSpace Critical Care and Anesthesia ICU, intensive care unit IRB, institutional review board MCS, mechanical circulatory support MDR, multi-drug resistant MV, mechanical ventilation RRT, renal replacement therapy RVAD, right ventricular assist device SAPS, Simplified Acute Physiology Score SOFA, sequential organ Failure Assessment ZPR, Zentrales Personenstandsregister Declarations Ethics approval The study was registered and approved by the Medical University of Vienna IRB on 31 st of August 2021 (EC umber 1669/2021). Consent for publication The need for individual patient consent was waived by the IRB due to the retrospective nature of the investigation. Availability of data and materials The datasets used and analyzed during the study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding Institutional funding from the Medical University of Vienna, Vienna, Austria. Authors’ contributions BZ, ET developed the concept of the study; BZ, GH, LW collected the data and VR and TS performed the statistical analysis; BZ, ET, PWG, MJS interpreted the findings and drafted a first version of the manuscript; GH, RZ, SCS, NJS gave critical input to the manuscript. All authors read and approved the manuscript, and gave consent for submission. Acknowledgements Not applicable. References Combes A, Costa M-A, Trouillet J-L, Baudot J, Mokhtari M, Gibert C, et al. 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Schmidt M, Banzett RB, Raux M, Morélot-Panzini C, Dangers L, Similowski T, et al. Unrecognized suffering in the ICU: addressing dyspnea in mechanically ventilated patients. Intens Care Med 2014;40:1–10. https://doi.org/10.1007/s00134-013-3117-3. Wahl GW, Swinburne AJ, Fedullo AJ, Lee KPD, Bixby K. Long-term Outcome When Major Complications Follow Coronary Artery Bypass Graft Surgery Recovery After Complicated Coronary Artery Bypass Graft Surgery. Chest 1996;110:1394–8. https://doi.org/10.1378/chest.110.6.1394. Ryan TA, Rady MY, Bashour CA, Leventhal M, Lytle B, Starr NJ. Predictors of Outcome in Cardiac Surgical Patients With Prolonged Intensive Care Stay. Chest 1997;112:1035–42. https://doi.org/10.1378/chest.112.4.1035. Quintairos A, Pilcher D, Salluh JIF. ICU scoring systems. Intensiv Care Med 2023;49:223–5. https://doi.org/10.1007/s00134-022-06914-8. Suistomaa M, Niskanen M, Kari A, Hynynen M, Takala J. Customized prediction models based on APACHE II and SAPS II scores in patients with prolonged length of stay in the ICU. Intens Care Med 2002;28:479–85. Gaudry S, Grolleau F, Barbar S, Martin-Lefevre L, Pons B, Boulet É, et al. Continuous renal replacement therapy versus intermittent hemodialysis as first modality for renal replacement therapy in severe acute kidney injury: a secondary analysis of AKIKI and IDEAL-ICU studies. Crit Care 2022;26:93. https://doi.org/10.1186/s13054-022-03955-9. Goligher EC, Dres M, Fan E, Rubenfeld GD, Scales DC, Herridge MS, et al. Mechanical Ventilation–induced Diaphragm Atrophy Strongly Impacts Clinical Outcomes. Am J Respir Crit Care Med 2018;197:204–13. https://doi.org/10.1164/rccm.201703-0536oc. Ong AW, Omert LA, Vido D, Goodman BM, Protetch J, Rodriguez A, et al. Characteristics and outcomes of trauma patients with ICU lengths of stay 30 days and greater: a seven-year retrospective study. Crit Care 2009;13:R154–R154. https://doi.org/10.1186/cc8054. Friedrich JO, Wilson G, Chant C. Long-term outcomes and clinical predictors of hospital mortality in very long stay intensive care unit patients: a cohort study. Crit Care 2006;10:R59. https://doi.org/10.1186/cc4888. Karth GD, Meyer B, Bauer S, Nikfardjam M, Heinz G. Outcome and functional capacity after prolonged intensive care unit stay. Wien Klin Wochenschr 2006;118:390–6. https://doi.org/10.1007/s00508-006-0616-z. Rockwood K, Theou O. Using the Clinical Frailty Scale in Allocating Scarce Health Care Resources. Can Geriatr J 2020;23:254–9. https://doi.org/10.5770/cgj.23.463. Zimmerman JE, Kramer AA, McNair DS, Malila FM. Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients. Crit Care Med 2006;34:1297–310. https://doi.org/10.1097/01.ccm.0000215112.84523.f0. Moreno RP, Metnitz PGH, Almeida E, Jordan B, Bauer P, Campos RA, et al. SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensiv Care Med 2005;31:1345–55. https://doi.org/10.1007/s00134-005-2763-5. Vincent J-L, Moreno R. Clinical review: Scoring systems in the critically ill. Crit Care 2010;14:207. https://doi.org/10.1186/cc8204. Zanella A, Florio G, Antonelli M, Bellani G, Berselli A, Bove T, et al. Time course of risk factors associated with mortality of 1260 critically ill patients with COVID-19 admitted to 24 Italian intensive care units. Intensiv Care Med 2021;47:995–1008. https://doi.org/10.1007/s00134-021-06495-y. Eriksson J, Nelson D, Holst A, Hellgren E, Friman O, Oldner A. Temporal patterns of organ dysfunction after severe trauma. Crit Care 2021;25:165. https://doi.org/10.1186/s13054-021-03586-6. Vincent J-L, Moreno R, Takala J, Willatts S, Mendonça AD, Bruining H, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. Intensiv Care Med 1996;22:707–10. https://doi.org/10.1007/bf01709751. Ferreira FL, Bota DP, Bross A, Mélot C, Vincent J-L. Serial Evaluation of the SOFA Score to Predict Outcome in Critically Ill Patients. JAMA 2001;286:1754–8. https://doi.org/10.1001/jama.286.14.1754. Karakike E, Kyriazopoulou E, Tsangaris I, Routsi C, Vincent J-L, Giamarellos-Bourboulis EJ. The early change of SOFA score as a prognostic marker of 28-day sepsis mortality: analysis through a derivation and a validation cohort. Crit Care 2019;23:387. https://doi.org/10.1186/s13054-019-2665-5. Andertun SG, Wissendorff-Ekdahl A, Ullén S, Cronberg T, Friberg H, Jakobsen JC, et al. Impact of frailty on mortality, functional outcome, and health status after out-of-hospital cardiac arrest: insights from the TTM2-trial. Intensiv Care Med 2025:1–11. https://doi.org/10.1007/s00134-025-08185-5. Muscedere J, Waters B, Varambally A, Bagshaw SM, Boyd JG, Maslove D, et al. The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis. Intensiv Care Med 2017;43:1105–22. https://doi.org/10.1007/s00134-017-4867-0. Muscedere J, Bagshaw SM, Kho M, Mehta S, Cook DJ, Boyd JG, et al. Frailty, Outcomes, Recovery and Care Steps of Critically Ill Patients (FORECAST): a prospective, multi-centre, cohort study. Intensiv Care Med 2024;50:1064–74. https://doi.org/10.1007/s00134-024-07404-9. Mathew R, Santo PD, Jung RG, Marbach JA, Hutson J, Simard T, et al. Milrinone as Compared with Dobutamine in the Treatment of Cardiogenic Shock. N Engl J Med 2021;385:516–25. https://doi.org/10.1056/nejmoa2026845. Rodenas‐Alesina E, Scolari FL, Wang VN, Brahmbhatt DH, Mihajlovic V, Fung NL, et al. Improved mortality and haemodynamics with milrinone in cardiogenic shock due to acute decompensated heart failure. ESC Hear Fail 2023;10:2577–87. https://doi.org/10.1002/ehf2.14379. Slawsky MT, Colucci WS, Gottlieb SS, Greenberg BH, Haeusslein E, Hare J, et al. Acute Hemodynamic and Clinical Effects of Levosimendan in Patients With Severe Heart Failure. Circulation 2000;102:2222–7. https://doi.org/10.1161/01.cir.102.18.2222. Gustafsson F, Damman K, Nalbantgil S, Laake LWV, Tops LF, Thum T, et al. Inotropic therapy in patients with advanced heart failure. A clinical consensus statement from the Heart Failure Association of the European Society of Cardiology. Eur J Hear Fail 2023;25:457–68. https://doi.org/10.1002/ejhf.2814. Petursson P, Gudmundsson T, Råmunddal T, Angerås O, Rawshani A, Mohammad MA, et al. Inotropes and mortality in patients with cardiogenic shock: an instrumental variable analysis from the SWEDEHEART registry. Eur Hear J Cardiovasc Pharmacother 2025;11:57–65. https://doi.org/10.1093/ehjcvp/pvae078. Sato R, Ariyoshi N, Hasegawa D, Crossey E, Hamahata N, Ishihara T, et al. Effects of Inotropes on the Mortality in Patients With Septic Shock. J Intensiv Care Med 2019;36:211–9. https://doi.org/10.1177/0885066619892218. Chen Q-H, Zheng R-Q, Lin H, Shao J, Yu J, Wang H-L. Effect of levosimendan on prognosis in adult patients undergoing cardiac surgery: a meta-analysis of randomized controlled trials. Crit Care 2017;21:253. https://doi.org/10.1186/s13054-017-1848-1. Unverferth DV, Blanford M, Kates RE, Leier CV. Tolerance to dobutamine after a 72 hour continuous infusion. Am J Med 1980;69:262–6. https://doi.org/10.1016/0002-9343(80)90387-3. Chin-Yee N, D’Egidio G, Thavorn K, Heyland D, Kyeremanteng K. Cost analysis of the very elderly admitted to intensive care units. Critical Care Lond Engl 2017;21:109. https://doi.org/10.1186/s13054-017-1689-y. Chen C-L, Wang S-T, Cheng W-C, Chen C-Y, Chen W-C, Lin Y-C, et al. Outcomes and Prognostic Factors in Patients with Hematologic Malignancies in the Intensive Care unit: A Single-Center Cohort Study of 233 Cases in Taiwan 2021. https://doi.org/10.21203/rs.3.rs-152665/v1. Sadick V, Bowcock E, Lane S, Seppelt I. Survival and predictors of outcome among patients with decompensated liver disease in a non‐liver transplant intensive care unit. Pessimism is historical and unjustified. Intern Med J 2019;49:745–52. https://doi.org/10.1111/imj.14151. Moreno R, Rhodes A, Piquilloud L, Hernandez G, Takala J, Gershengorn HB, et al. The Sequential Organ Failure Assessment (SOFA) Score: has the time come for an update? Crit Care 2023;27:15. https://doi.org/10.1186/s13054-022-04290-9. Yu P-J, Cassiere HA, Fishbein J, Esposito RA, Hartman AR. Outcomes of Patients With Prolonged Intensive Care Unit Length of Stay After Cardiac Surgery. J Cardiothor Vasc An 2016;30:1550–4. https://doi.org/10.1053/j.jvca.2016.03.145. Additional Declarations The authors declare no competing interests. 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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-8748230","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583449151,"identity":"40878813-0116-445c-bf44-3619b18e46c6","order_by":0,"name":"Bernhard Zapletal","email":"","orcid":"","institution":"Medical University of Vienna, Vienna, Austria, Department of Anaesthesiology, General Intensive Care and Pain Medicine, Division of Cardiac Thoracic Vascular Anesthesia and Intensive Care Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bernhard","middleName":"","lastName":"Zapletal","suffix":""},{"id":583449152,"identity":"b2309b5d-7603-4606-9b8e-91e00487e316","order_by":1,"name":"Marcus J. Schultz","email":"","orcid":"","institution":"Medical University of Vienna, Vienna, Austria, Department of Anaesthesiology, General Intensive Care and Pain Medicine, Division of Cardiac Thoracic Vascular Anesthesia and Intensive Care Medicine","correspondingAuthor":false,"prefix":"","firstName":"Marcus","middleName":"J.","lastName":"Schultz","suffix":""},{"id":583449153,"identity":"3ad46faf-ef3c-4bcc-bae3-a0a978b33528","order_by":2,"name":"Valentin Ritschl","email":"","orcid":"","institution":"Medical University of Vienna, Vienna, Austria, Center for Medical Data Science, Institute of Outcomes Research","correspondingAuthor":false,"prefix":"","firstName":"Valentin","middleName":"","lastName":"Ritschl","suffix":""},{"id":583449154,"identity":"9ace579c-7c67-4517-a74c-c5f6e107c2fb","order_by":3,"name":"Tanja Stamm","email":"","orcid":"","institution":"Medical University of Vienna, Vienna, Austria, Center for Medical Data Science, Institute of Outcomes Research","correspondingAuthor":false,"prefix":"","firstName":"Tanja","middleName":"","lastName":"Stamm","suffix":""},{"id":583449155,"identity":"b4f1d368-04bf-4af7-b7ea-acac64f62e32","order_by":4,"name":"Pedro D. Wendel–Garcia","email":"","orcid":"","institution":"Medical University of Vienna, Vienna, Austria, Department of Anaesthesiology, General Intensive Care and Pain Medicine, Division of Cardiac Thoracic Vascular Anesthesia and Intensive Care Medicine","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"D.","lastName":"Wendel–Garcia","suffix":""},{"id":583449156,"identity":"82ba286d-4a21-4b36-bda8-04699bc38744","order_by":5,"name":"Gottfried Heinz","email":"","orcid":"","institution":"Medical University of Vienna, Vienna, Austria, University Department of Medicine II, Division of Cardiology","correspondingAuthor":false,"prefix":"","firstName":"Gottfried","middleName":"","lastName":"Heinz","suffix":""},{"id":583449157,"identity":"c30e56f4-cf4b-4db9-95d8-8d8706e5f7cb","order_by":6,"name":"Robert Zilberszac","email":"","orcid":"","institution":"Medical University of Vienna, Vienna, Austria, University Department of Medicine II, Division of Cardiology","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Zilberszac","suffix":""},{"id":583449158,"identity":"0125aa62-0795-4bd5-b3df-7b8c27117a8e","order_by":7,"name":"Laurenz Wolner","email":"","orcid":"","institution":"Medical University of Vienna, Vienna, Austria, Department of Cardiac and Thoracic Aortic Surgery","correspondingAuthor":false,"prefix":"","firstName":"Laurenz","middleName":"","lastName":"Wolner","suffix":""},{"id":583449159,"identity":"3f4b306f-c292-4134-967b-f140b6fcee61","order_by":8,"name":"Simon Corrado Serafini","email":"","orcid":"","institution":"University of Genoa, Genova, Italy, Department of Surgical Sciences and Integrated Diagnostics","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"Corrado","lastName":"Serafini","suffix":""},{"id":583449160,"identity":"805054c3-61dd-4243-ba80-74213c66f079","order_by":9,"name":"Nikolaos J Skubas","email":"","orcid":"","institution":"Cleveland Clinic, Cleveland, Ohio, United States of America, Division of Cardiothoracic Anesthesiology, Department of Anesthesiology","correspondingAuthor":false,"prefix":"","firstName":"Nikolaos","middleName":"J","lastName":"Skubas","suffix":""},{"id":583449161,"identity":"dcdd82a1-6724-4035-a353-f1b809ef847b","order_by":10,"name":"Edda M. Tschernko","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBAC+4YEAwbGfwwMBkAOYwODDWEtBgxALQwGcC1pcBkJYrUcJkILe/LGDwwGdfLm7L3PHs6oOB9tcCOB8cMPBrs6nH7heVYswWBw2HBnz3Fzww1nbuduuJHALNnDkIzbFokcA6CWAwkGN9LYJB+2AbWcOcAgzcDAjE+L8Q+gwxIM7j8DaTkH0sL8m4GhHp8WM6AtzEBb2NgkN7YdyN1wvIENaMth3Fp4npVZJAD9suEM0GEzziTnzjze2GbZY3BcsgGX99uTN9/4AAwxg+PH2CR7Kuxy+w4zH77xo6KaH5ctYJCAygVGDziaRsEoGAWjYBSQDQAMfFWdGtv12wAAAABJRU5ErkJggg==","orcid":"","institution":"Medical University of Vienna, Vienna, Austria, Department of Anaesthesiology, General Intensive Care and Pain Medicine, Division of Cardiac Thoracic Vascular Anesthesia and Intensive Care Medicine","correspondingAuthor":true,"prefix":"","firstName":"Edda","middleName":"M.","lastName":"Tschernko","suffix":""}],"badges":[],"createdAt":"2026-01-31 08:57:09","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8748230/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8748230/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101790693,"identity":"ab14b78c-7864-42a1-9831-cf27afa99387","added_by":"auto","created_at":"2026-02-03 16:06:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":240739,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patients.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CV-ICU, cardiovascular intensive care unit; LOS, length of stay; ICU, intensive care unit.\u003c/p\u003e","description":"","filename":"finalFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8748230/v1/b9b34c4afdf905094825e64c.jpg"},{"id":101790689,"identity":"e5b3fc93-04a2-4f60-86d1-2394224e3b91","added_by":"auto","created_at":"2026-02-03 16:06:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":483116,"visible":true,"origin":"","legend":"\u003cp\u003eDecision tree based on the CART analysis.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CV-ICU, cardiovascular intensive care unit; LOS, length of stay; ICU, intensive care unit.\u003c/p\u003e","description":"","filename":"finalFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8748230/v1/af7738a202e983581be3b0c9.jpg"},{"id":101881264,"identity":"c01826ed-1205-420d-a08c-dc8ba05b689a","added_by":"auto","created_at":"2026-02-04 15:11:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1421040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8748230/v1/36371300-d4a1-4939-9530-fd60654b07c6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003ePredictors of Outcome in Critically Ill Cardiac Patients with a very long ICU stay–a retrospective bicentric study\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003ePatients with a very long intensive care unit (ICU) stay often face a poor prognosis, regardless of pathology [1\u0026ndash;5]. High mortality is particularly evident in patients with heart failure and cardiogenic shock or post\u0026ndash;cardiac surgery [6\u0026ndash;8]. Furthermore, extended stay in the ICU is often associated with significant psychological stress and discomfort, for example due to invasive procedures and transport, or dyspnea during respiratory weaning; in addition, it often results in long\u0026ndash;term sequelae [9\u0026ndash;12].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, outcome prediction in such patients is highly challenging, and has been poorly studied, mostly due to the small size of patient cohorts and the heterogeneity of the diseases and their processes. In fact, patient demographics and baseline characteristics [8,13,14], and disease severity scores [15,16], using data collected in the first 24 hours in ICU, lack significant predictive capacity. In non\u0026ndash;cardiac ICU populations, the need for invasive mechanical ventilation (MV) and renal replacement therapy (RRT) has been associated with increased mortality [17,18]. However, it is uncertain if this also applies to ICU patients with a primarily cardiac pathology and a very long stay in ICU. Thus, ICU physicians may face the dilemma, whether to perform further invasive and curative treatment or initiate palliative end-of-life care planning in this patient population without reliable, objective data to support their decision-making process. Furthermore, advancements in care, like extracorporeal membrane oxygenation (ECMO) and ventricular assist devices [13,14], can prolong postoperative ICU treatment almost infinitely, thus raising additional ethical questions despite being supportive in distinct scenarios.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn light of the lack of evidence, and in order to provide a prognostic framework to caregivers, patients and their next\u0026ndash;of\u0026ndash;kin, this study aimed to identify predictors of clinical outcome in critically ill cardiac patients with a very long ICU. We defined very long ICU stay as greater than 30 days based on prior literature identifying this group as an extreme prolonged stay population [19\u0026ndash;21]. We hypothesized that specific clinical and treatment related variables would serve as independent predictors of outcome in this population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eDesign and ethical considerations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis analysis is based on a retrospective study in patients admitted to a postoperative cardiovascular ICU and a medical cardiovascular ICU in a tertiary care hospital between 10/2016 and 8/2022. Due to the organizational structure of the hospital, the ICUs operate fully independently from each other, under different organizational, management and staffing structures, and do not possess any relevant consensus regarding standard operating procedures. The study protocol was approved by the Medical University of Vienna ethics committee (EC number 1669/2021). Because of the retrospective nature of the study, the requirement for individual informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatients\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatients were eligible for analysis if: (1) aged over 14 years; (2) admitted before or after cardiac surgery or due to a primary cardiac pathology, (3) cared for \u0026gt; 30 days in the ICU within the predefined period.\u0026nbsp;Patients who were readmitted to the ICU or with missing frailty scale were excluded from analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData collected\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor all patients, the following demographics and baseline characteristics were extracted at ICU admission: age, sex, BMI, Simplified Acute Physiology Score (SAPS) 3, preoperative or pre\u0026ndash;ICU admission frailty (Clinical frailty scale 0 to 9, CFS[22]), and the main reasons for hospital and ICU admission.\u003c/p\u003e\n\u003cp\u003eDuration of organ support was captured up to day 30 after ICU admission was calculated from the day of initiation (actual number of support days) for continuous renal replacement therapy (CRRT), invasive MV, mechanical circulatory support (MCS), including ECMO, micro\u0026ndash;axial continuous flow, and temporary right ventricular assist devices (RVAD), vasopressors (including norepinephrine \u0026ge; 0.1mcg/kg/min or vasopressin \u0026ge; 2IU/h), inotropes (including dobutamine \u0026ge; 2 mcg/kg/min, milrinone \u0026ge; 0,15mcg/kg/min, or levosimendan at any dose), administration of blood products (including packed red blood cells, fresh frozen plasma, and platelets), and daily fluid balances exceeding +1 L. We also captured, whether patients received immunosuppressive therapy or antibiotic therapy due to an infection with multi\u0026ndash;drug resistant (MDR) pathogens. All data were extracted from the electronic patient medical records (IntelliSpace Critical Care and Anesthesia (ICCA), Philips Health Systems, Amsterdam, Netherlands). Hospital discharge status, and mortality 1 year after ICU admission were extracted from the national death register (Zentrales Personenstandsregister ZPR).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDefinitions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eICU mortality was defined as all\u0026ndash;cause death in the ICU; 1\u0026ndash;year mortality was defined as all\u0026ndash;cause death within 1 year after ICU admission and was analyzed\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eboth for the entire cohort and for ICU survivors.\u003c/p\u003e\n\u003cp\u003eIn\u0026ndash;hospital mortality was defined as all-cause mortality within the index hospital stay.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOutcomes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was ICU\u0026ndash;mortality: 1\u0026ndash;year mortality after ICU admission served as the secondary outcome.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe sample size was the number of patients who stayed in the ICU \u0026gt; 30 days during the 6\u0026ndash;year study period.\u003c/p\u003e\n\u003cp\u003eCategorical variables are reported as counts and percentages, continuous variables as mean \u0026plusmn; SD. \u0026chi;\u0026sup2;\u0026ndash;tests, t\u0026ndash;tests, and ANOVA were used where appropriate.\u003c/p\u003e\n\u003cp\u003eWe fitted two prespecified multivariable logistic regression models, one for ICU\u0026ndash; mortality in the full cohort, and one for 1\u0026ndash;year mortality in ICU survivors. Predictors were defined a priori based on clinical relevance and included age, sex, CFS before ICU admission, use and days of mechanical ventilation, use of and days on CRRT, use and days of hemodynamic support, and immunosuppression or infection with a multi\u0026ndash;resistant pathogen at or within the first 30 ICU days.\u0026nbsp;Collinearity was screened using variance inflation factors. No variable selection or cross-validation was applied to these regression models, as they were explanatory rather than predictive. To assess potential departures from linearity in the duration variables (invasive ventilation, CRRT, and\u0026nbsp;hemodynamic support), we compared models with log\u0026ndash;transformed terms [log(x+1)] and with restricted cubic splines (natural splines; 3 degrees of freedom per variable), evaluating changes in model fit with likelihood\u0026ndash;ratio tests and information criteria (AIC/BIC). Results are presented as adjusted odds ratios (ORs) with 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003eFor interpretable rule\u0026ndash;based patterns and to capture potential non\u0026ndash;linearities and interactions, we trained classification and regression trees (CART), first for ICU\u0026ndash;survival, and then separately for 1\u0026ndash;year survival among ICU survivors. Trees were tuned via the complexity parameter using 5\u0026ndash;fold cross\u0026ndash;validation and pruned to minimize overfitting. As an internal robustness check, we repeated the CART analysis separately within the two separate ICUs. Results are as split rules, node risks, and variable importance descriptively.\u003c/p\u003e\n\u003cp\u003eAll analyses were performed in R (version 4.5.1, R Foundation for Statistical Computing, http://www.r-project.org). All tests were two\u0026ndash;sided with \u0026alpha; = 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003ePatients\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOf 242 patients with an ICU \u0026gt; 30 days, 210 patients were included (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Of these, 51 (24.3%) died in the ICU. Among the 159 ICU survivors, 12 (7.5 %) died before hospital discharge, and 35 (22.0 %) died after hospital discharge within the first year following ICU admission. This resulted in an overall 1\u0026ndash;year mortality of 46.7%. ICU non\u0026ndash;survivors had higher baseline CFS scores, while 1\u0026ndash;year non\u0026ndash;survivors were older, had higher baseline CFS scores, and more frequently had an infection with MDR pathogens (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 44.074%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003ePatient Demographics and Baseline Characteristics in ICU Survivors versus Non\u0026ndash;survivors and in 1\u0026ndash;year Survivors versus Non\u0026ndash;survivors\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.8467%;\"\u003e\n \u003cp\u003eall\u003c/p\u003e\n \u003cp\u003e(N = 210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.5054%;\"\u003e\n \u003cp\u003eICU survivors\u003c/p\u003e\n \u003cp\u003e(N = 159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003eICU non\u0026ndash;survivors\u003c/p\u003e\n \u003cp\u003e(N = 51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e1\u0026ndash;year survivors\u003c/p\u003e\n \u003cp\u003e(N = 112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e1\u0026ndash;year non\u0026ndash;survivors\u003c/p\u003e\n \u003cp\u003e(N = 98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61027%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003eage, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e62.4 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e61.5 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e65.3 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e58.5 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e65.8 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61027%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003esex, female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e63 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e48 (28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e15 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e39 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e24 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61027%;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e27.2 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e27.3 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e26.7 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e27.4 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e26.8 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61027%;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003eclinical frailty scale score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e4.6 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e4.1 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e6.3 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e4.0 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e5.3 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61027%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003eimmunosuppression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e29.0 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e23 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e6 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e19 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e10 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61027%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003einfection with MDR pathogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e37.0 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e26 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e11 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e14 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e23 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61027%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003eSAPS 3 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e64.8 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e64.9 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e64.4 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e63.1 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e66.7 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61027%;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003eadmission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61027%;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;surgical (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e107 (51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e78 (49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e29 (56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e61 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e46 (46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61027%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;medical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e103 (49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e81 (50.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e22 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e51 (45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e52 (53.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61027%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003emedical history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61027%;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;cardiomyopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e57 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e44 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e13 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e34 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e23 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;myocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e25 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e22 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e3 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e15 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e10 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;respiratory insufficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e24 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e19 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e5 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e11 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e13 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;aortic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e18 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e14 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e4 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e13 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e5 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;aortic valve disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e16 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e10 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e6 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e8 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e8 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;mitral valve disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e16 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e12 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e4 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e5 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e11 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;following CPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e12 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e8 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e4 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e2 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e10 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;endocarditis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e12 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e7 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e5 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e5 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e7 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;coronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e8 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e5 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e3 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e3 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e5 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;sepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e6 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e3 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e3 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e2 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e4 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;neurologic dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e3 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e3 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e3 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;hemothorax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e2 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e1 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e1 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e1 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;electrical storm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e2 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e2 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e2 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;tricuspid valve disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e1 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e1 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;pulmonary valve disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e1 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e1 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.9172%;\"\u003e\n \u003cp\u003e7 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4349%;\"\u003e\n \u003cp\u003e7 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2584%;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9995%;\"\u003e\n \u003cp\u003e6 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.7407%;\"\u003e\n \u003cp\u003e1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 54.4317%;\"\u003e\n \u003cp\u003eData are means (SD) or numbers (%).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAbbreviations: BMI, body mass index; MDR, Multiple Drug Resistant; SAPS, Simplified Acute Physiology Score; ICU, intensive care unit; CPR, cardiopulmonary resuscitation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eICU non\u0026ndash;survivors, compared to ICU survivors, received CRRT, MCS, and vasopressors for longer periods within the first 30 days after ICU admission, while 1\u0026ndash;year non\u0026ndash;survivors, compared to 1\u0026ndash;year survivors, received CRRT and vasopressors for longer periods within the first 30 days after ICU admission (\u003cstrong\u003eTable 2\u003c/strong\u003e). ICU length of stay did not differ between survivors and non\u0026ndash;survivors. Predominant causes of death in the ICU were multiorgan failure, permanent brain injury, and sepsis. The predominant causes of in hospital mortality after ICU discharge were permanent brain injury, ischemic stroke, and respiratory failure. The most frequently reported causes of death after hospital discharge were coronary artery disease, acute coronary syndrome, and complications of diabetes (\u003cstrong\u003eSupplemental Table 1\u003c/strong\u003e).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eOrgan Support in the first 30 days after ICU Admission, and Total Length of Stay in ICU, in ICU Survivors versus Non\u0026ndash;survivors, and in 1\u0026ndash;year Survivors versus Non\u0026ndash;survivors\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003eall\u003c/p\u003e\n \u003cp\u003e(N = 210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67262%;\"\u003e\n \u003cp\u003eICU survivors\u003c/p\u003e\n \u003cp\u003e(N = 159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.97024%;\"\u003e\n \u003cp\u003eICU non\u0026ndash;survivors\u003c/p\u003e\n \u003cp\u003e(N = 51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.99405%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e1\u0026ndash;year survivors\u003c/p\u003e\n \u003cp\u003e(N = 112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1369%;\"\u003e\n \u003cp\u003e1\u0026ndash;year non\u0026ndash;survivors\u003c/p\u003e\n \u003cp\u003e(N = 98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 100%;\"\u003e\n \u003cp\u003eOrgan Support\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;days receiving CRRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e10.3 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67262%;\"\u003e\n \u003cp\u003e8.0 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.97024%;\"\u003e\n \u003cp\u003e17.5 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.99405%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e7.3 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1369%;\"\u003e\n \u003cp\u003e13.7 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;days receiving invasive MV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e23.6 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67262%;\"\u003e\n \u003cp\u003e23.0 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.97024%;\"\u003e\n \u003cp\u003e25.5 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.99405%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e22.6 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1369%;\"\u003e\n \u003cp\u003e24.8 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;days receiving MCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e5.2 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67262%;\"\u003e\n \u003cp\u003e4.5 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.97024%;\"\u003e\n \u003cp\u003e7.2 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.99405%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e4.6 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1369%;\"\u003e\n \u003cp\u003e5.8 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;days receiving vasopressors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e10.7 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67262%;\"\u003e\n \u003cp\u003e9.0 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.97024%;\"\u003e\n \u003cp\u003e16.2 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.99405%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e9.0 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1369%;\"\u003e\n \u003cp\u003e12.7 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;days receiving inotropes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e8.6 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67262%;\"\u003e\n \u003cp\u003e8.2 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.97024%;\"\u003e\n \u003cp\u003e10.1 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.99405%;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e8.5 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1369%;\"\u003e\n \u003cp\u003e8.8 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;days receiving transfusions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e0.5 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67262%;\"\u003e\n \u003cp\u003e0.4 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.97024%;\"\u003e\n \u003cp\u003e0.7 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.99405%;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e0.4 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1369%;\"\u003e\n \u003cp\u003e0.6 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;days with positive fluid\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;balance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e3.5 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67262%;\"\u003e\n \u003cp\u003e3.7 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.97024%;\"\u003e\n \u003cp\u003e2.7 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.99405%;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e3.4 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1369%;\"\u003e\n \u003cp\u003e3.6 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eLOS in the ICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.97024%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.99405%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1369%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;days in ICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e54.5 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67262%;\"\u003e\n \u003cp\u003e52.8 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.97024%;\"\u003e\n \u003cp\u003e58.0 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.99405%;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e52.0 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1369%;\"\u003e\n \u003cp\u003e56.3 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 100%;\"\u003e\n \u003cp\u003eData are means (SD).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAbbreviations: CRRT, continuous renal replacement therapy; MV, mechanical ventilation; MCS, Mechanical Circulatory Support (ECMO, percutaneous RVAD, RVAD, Impella); LOS, length of stay\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eFactors associated with ICU mortality and with 1\u0026ndash;year mortality\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFactors independently associated with ICU\u0026ndash;mortality included frailty, prolonged CRRT, prolonged invasive MV and prolonged vasopressor use. On the contrary, prolonged inotrope use was associated with lower ICU\u0026ndash;mortality (\u003cstrong\u003eTable 3\u003c/strong\u003e). In the overall cohort, factors associated with 1\u0026ndash;year mortality were age, frailty, prolonged CRRT and prolonged invasive MV. In the group of ICU survivors, only age was independently associated with 1\u0026ndash;year mortality (\u003cstrong\u003eSupplemental Table 2\u003c/strong\u003e). Use of log\u0026ndash;transforms and non\u0026ndash;linear spline transformations did not improve model fit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eRisk factors associated with ICU mortality, 1-year mortality\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"668\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 37.4726%;\"\u003e\n \u003cp\u003eMultivariate logistic regression for ICU mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.8323%;\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.9698%;\"\u003e\u003cspan style='color: rgb(0, 0, 0); font-family: \"Times New Roman\"; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;'\u003eOR (95% CI) for ICU mortality\u0026dagger;\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 18.8623%;\" colspan=\"3\"\u003e\u003cspan style='color: rgb(0, 0, 0); font-family: \"Times New Roman\"; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;'\u003eP\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 17.0659%;\" colspan=\"2\"\u003eOR (95% CI) for 1-year mortality\u0026dagger;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13.024%;\" colspan=\"3\"\u003eP\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 14.812%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;age (per year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9698%;\"\u003e\n \u003cp\u003e1.00 (0.96-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 5.3111%;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 6.9634%;\"\u003e\n \u003cp\u003e1.03 (1.01-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 3.3637%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 14.812%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;frailty (per point)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9698%;\"\u003e\n \u003cp\u003e3.16 (2.26-4.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 5.3111%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 6.9634%;\"\u003e\n \u003cp\u003e1.62 (1.31-2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 3.3637%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 14.812%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;female sex (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.9698%;\"\u003e\n \u003cp\u003e1.59 (0.56-4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 5.3111%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 6.9634%;\"\u003e\n \u003cp\u003e1.59 (0.87-2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 3.3637%;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 14.812%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; immunosuppression (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.9698%;\"\u003e\n \u003cp\u003e0.85 (0.19-3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 5.3111%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 6.9634%;\"\u003e\n \u003cp\u003e1.70 (0.75-3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 3.3637%;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 14.812%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;infection with MDR pathogen (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.9698%;\"\u003e\n \u003cp\u003e0.61 (0.18-1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 5.3111%;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 6.9634%;\"\u003e\n \u003cp\u003e1.53 (0.62-3.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 3.3637%;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 14.812%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;invasive ventilation (per day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9698%;\"\u003e\n \u003cp\u003e1.07 (1.01-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 5.3111%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 6.9634%;\"\u003e\n \u003cp\u003e1.04 (1.00-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 3.3637%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 14.812%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;CRRT (per day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9698%;\"\u003e\n \u003cp\u003e1.07 (1.03-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 5.3111%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 6.9634%;\"\u003e\n \u003cp\u003e1.05 (1.02-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 3.3637%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 14.812%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;MCS (per day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9698%;\"\u003e\n \u003cp\u003e1.06 (1.00-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 5.3111%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 6.9634%;\"\u003e\n \u003cp\u003e1.04 (0.99-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 3.3637%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 14.812%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;vasopressor support (per day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9698%;\"\u003e\n \u003cp\u003e1.09 (1.02-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 18.7612%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4544%;\"\u003e\n \u003cp\u003e1.01 (0.96-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 3.3637%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 14.812%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;inotrope support (per day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9698%;\"\u003e\n \u003cp\u003e0.93 (0.87-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 18.7612%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4544%;\"\u003e\n \u003cp\u003e0.98 (0.94-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 3.3637%;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 14.812%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;transfusion (per day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9698%;\"\u003e\n \u003cp\u003e1.10 (0.81-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 18.7612%;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4544%;\"\u003e\n \u003cp\u003e1.04 (0.88-1.23)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 3.3637%;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 14.812%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;positive fluid balance (per day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9698%;\"\u003e\n \u003cp\u003e0.95 (0.88-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 18.7612%;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4544%;\"\u003e\n \u003cp\u003e1.02 (0.94-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 3.3637%;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026dagger; for all 210 patients;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData are presented as means with (SD) or as absolute values (%) or Odds Ratio (95% CI); Abbreviations: ICU, intensive care unit; MDR, Multiple Drug Resistant; CRRT, continuous renal replacement therapy; MCS, mechanical circulatory support (ECMO, Impella, percutaneous RVAD); OR, Odds Ratio; CFS, Clinical Frailty Scale.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOutcome prediction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCART analysis,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewe identified five distinct subgroups of patients with sharply diverging ICU mortality using baseline CFS, CRRT days, and vasopressor days (\u003cstrong\u003eFigure 2\u003c/strong\u003e). Patients with CFS \u0026ge; 7 who needed significant vasopressors \u0026ge; 5 days had a high predicted ICU\u0026ndash;mortality (92.3%, \u003cstrong\u003eGroup I\u003c/strong\u003e). In contrast, patients with CFS of \u0026ge; 7 who needed significant vasopressors \u0026lt; 5 days had a lower predicted ICU mortality (46.2%, \u003cstrong\u003eGroup II\u003c/strong\u003e). Patients with CFS \u0026lt; 7 who underwent CRRT on \u0026ge; 21 days \u003cem\u003eand\u003c/em\u003e needed significant vasopressors \u0026ge; 22 days had a high predicted ICU mortality (90%, \u003cstrong\u003eGroup III\u003c/strong\u003e). Contrariwise, in patients with CFS \u0026lt; 7, predicted ICU mortality was low if they underwent CRRT on \u0026ge; 21 days \u003cem\u003ebut\u0026nbsp;\u003c/em\u003eneeded significant vasopressors on \u0026lt; 22 days (20% \u003cstrong\u003eGroup IV\u003c/strong\u003e). Predicted ICU mortality was very low if they underwent CRRT on \u0026lt; 21 days (4.6%, \u003cstrong\u003eGroup V\u003c/strong\u003e). In order to ensure robustness, CART was performed for each individual ICU cohort independently, which confirmed consistent identifiability of the subgroups across cohorts.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study sought to identify factors associated with ICU and 1\u0026ndash;year outcome in critically ill cardiac patients with a very long ICU stay, and to investigate whether duration of organ support (medications, organ function support) was associated with outcome beyond demographic and admission characteristics. The main findings are summarized as follows: (1) baseline frailty, prolonged CRRT, prolonged invasive ventilation, and prolonged vasopressor use were independently associated with higher ICU mortality; whereas (2) prolonged inotrope use was independently associated with lower ICU mortality; (3) in the overall cohort, age, frailty, prolonged CRRT and prolonged invasive MV were associated with higher 1-year mortality while; (4) among ICU survivors; and (5) there were five distinct patient subgroups in whom combinations of frailty, CRRT, and vasopressor days predicted ICU mortality ranging from below 5% to higher than 90%.\u003c/p\u003e\n\u003cp\u003eThis study has several strengths. The analysis focused on a clearly defined and homogeneous population of patients with a very long ICU stay, with strict inclusion and exclusion criteria that minimized heterogeneity and ensured completeness of baseline and organ support data. All data were systematically extracted from electronic health records. This data capture enabled a nuanced assessment of both preexisting patient vulnerability and duration of organ support. Outcomes were rigorously defined, with 1\u0026ndash;year mortality assessed through linkage with a national death registry, providing reliable long\u0026ndash;term follow\u0026ndash;up. The analytical approach was robust, combining conventional regression models to identify independent predictors and classification and regression tree analysis to reveal clinically interpretable subgroups. The structured design, standardized definitions, and detailed longitudinal data enhance the reproducibility and clinical applicability of the findings, supporting their relevance for prognosis, risk stratification, and informed decision\u0026ndash;making in this specific ICU population.\u003c/p\u003e\n\u003cp\u003eTraditional outcome prediction models in critically ill patients, such as the acute physiology and chronic health evaluation (APACHE) [23] and SAPS [24], rely primarily on demographic and physiologic data captured within the first 24 hours of ICU admission [25]. However, in patients with a very long ICU stay, these early baseline characteristics may lose prognostic value, because the clinical trajectory over the subsequent ICU stay becomes an important determinant of outcome, as shown in patients with COVID\u0026ndash;19 ARDS and in those with severe trauma [5,26,27]. Scores incorporating sequential organ support, such as the Sequential Organ Failure Assessment (SOFA) [28],\u0026nbsp;and studies of delta SOFA\u0026nbsp;[29,30]\u0026nbsp;highlight that evolving physiologic trajectories provide superior prognostic information compared to static early measures.\u0026nbsp;Such scores\u0026nbsp;are widely used in general ICU populations to track evolving organ dysfunction and estimate short\u0026ndash;term mortality. Our findings extend this concept to patients with very long stay in a cardiac ICU setting, demonstrating that\u0026nbsp;ICU mortality is associated with prolonged CRRT, prolonged invasive ventilation, and prolonged vasopressor use, whereas prolonged inotrope use is independently associated with lower ICU mortality. Among baseline characteristics, only frailty was associated with ICU mortality, whereas age was the only factor associated with 1\u0026ndash;year mortality among ICU survivors, underscoring that dynamic patterns of organ support capture risk beyond initial demographic and physiologic measures.\u003c/p\u003e\n\u003cp\u003eOur findings are in line with prior work showing that baseline frailty is an important determinant of short\u0026ndash;term outcomes in critical illness [31,32]. We refine this evidence by demonstrating that frailty was associated with ICU and 1\u0026ndash;year mortality in cardiac ICU patients with a very long ICU stay, in contrast to age, which was associated only with 1\u0026ndash;year mortality among ICU survivors. These suggest that chronological age may exert its effect later in the recovery trajectory, whereas frailty reflects a more immediate physiologic vulnerability during the ICU stay. Our findings are in agreement with frailty being an important predictor for outcome in surgical and medical patients alike [32,33].\u003c/p\u003e\n\u003cp\u003eIt was somewhat surprising that prolonged inotrope use was associated with lower ICU mortality, in contrast to vasopressor use. The reasons for this finding remain speculative. One possibility is that inotropes were administered selectively in patients in whom frequent hemodynamic measurements showed that they were effectively improving cardiac performance; in other words, inotropes may provide benefit only when they successfully augment cardiac output parameters, whereas vasopressors are given more broadly to maintain perfusion pressure under severe circulatory distress. Another consideration is that short\u0026ndash;term improvements in cardiac function, as observed with agents such dobutamine [34], milrinone [35], or levosimendan [36], may translate into immediate survival benefit in patients with a prolonged ICU stay, even if such effects do not extend to long\u0026ndash;term outcomes [37\u0026ndash;40]. Factors, such as tolerance development [41], do not appear to account for this association.\u0026nbsp;Overall, these findings suggest that the relationship between inotrope therapy and outcome is complex and context-dependent, and highlight the need for further study to identify which patients derive true benefit.\u003c/p\u003e\n\u003cp\u003eThe CART analysis provides a detailed perspective by showing that specific combinations and duration of\u0026nbsp;CRRT and vasopressors, together with baseline frailty, define clinically meaningful ICU mortality risk subgroups among\u0026nbsp;very long stay cardiac ICU patients.\u0026nbsp;This approach offers a more individualized and actionable way to predict outcomes than overall mortality tools, particularly in\u0026nbsp;patients with the worst prognosis who consume the most resources \u003cspan lang=\"NL\"\u003e[42\u0026ndash;44]\u003c/span\u003e.\u0026nbsp;Our findings, which are hypothesis\u0026ndash;generating rather than allowing outcome prediction at this point, highlight subgroups that merit further study and external validation in other centers. Uncertainty regarding outcome remains for all groups of patients, likely reflecting the challenge of incorporating neurological function into risk prediction [45]. This uncertainty is particularly evident in group 2, possibly due to the small size of this particular subgroup. For all subgroups, any outcome prediction should at best provide supportive guidance rather than certainty.\u003c/p\u003e\n\u003cp\u003eOur study has several limitations, including the retrospective design and loss of some patients due to lack of frailty scores. It was a bi\u0026ndash;centric study, which may limit generalizability. Nevertheless, the cohort of 210 very long stay ICU patients represents a substantial and clinically relevant sample, as such patients are rare and among the sickest patients in cardiac ICUs. External validation of the developed outcome prediction model in other centers and patient cohorts will be necessary to assess broader applicability. We used CFS for estimation of frailty. For practical reasons, frailty assessment differed by patient group: in surgical patients, CFS was determined preoperatively. In medical patients, it was assessed at ICU admission, using information provided by the patients or their relatives and based on the patient\u0026rsquo;s frailty status before the acute illness resulted in admission to the ICU. We modelled duration of therapies beyond a certain threshold, but not their absolute intensity or the timepoint of their initiation. While the risk factors for ICU mortality were predefined to enhance the validity of the investigation, it is possible that other unseen factors with association with outcome were missed. Functional outcomes, such as neurological outcomes and quality of life were not captured. Ischemic or hemorrhagic stroke is not uncommon in critically ill patients after cardiac surgery and strongly influences outcome [46]. This may have contributed to some of the observed inaccuracies in outcome prediction, particularly in CART group II. Finally, as with any observational study, residual confounding and unmeasured variables may have influenced the results despite detailed data collection and predefined analysis plans.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn critically ill cardiac patients with a very long ICU stay, both baseline characteristics and the duration of organ support were importantly associated to outcome. Frailty and prolonged use of CRRT, invasive ventilation, and vasopressors may identify patients at high risk of ICU mortality, whereas prolonged inotrope use was associated with lower risk. Importantly, CART analysis delineated clinically meaningful subgroups with distinctly different mortality risk, highlighting the potential for individualized, trajectory\u0026ndash;based risk assessment and some degree of outcome prediction in this highly\u0026ndash;specific ICU population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eANOVA, Analysis of Variance\u003c/p\u003e\n\u003cp\u003eAPACHE, acute physiology and chronic health evaluation\u003c/p\u003e\n\u003cp\u003eARDS, acute respiratory distress syndrome\u003c/p\u003e\n\u003cp\u003eBMI, body mass index\u003c/p\u003e\n\u003cp\u003eCART, classification and regression tree\u003c/p\u003e\n\u003cp\u003eCFS, Clinical frailty scale\u003c/p\u003e\n\u003cp\u003eCOVID-19, Coronavirus disease 2019\u003c/p\u003e\n\u003cp\u003eCRRT, continuous renal replacement therapy\u003c/p\u003e\n\u003cp\u003eEC, ethics committee\u003c/p\u003e\n\u003cp\u003eECMO, extracorporeal membrane oxygenation\u003c/p\u003e\n\u003cp\u003eICCA, IntelliSpace Critical Care and Anesthesia\u003c/p\u003e\n\u003cp\u003eICU, intensive care unit\u003c/p\u003e\n\u003cp\u003eIRB, institutional review board\u003c/p\u003e\n\u003cp\u003eMCS, mechanical circulatory support\u003c/p\u003e\n\u003cp\u003eMDR, multi-drug resistant\u003c/p\u003e\n\u003cp\u003eMV, mechanical ventilation\u003c/p\u003e\n\u003cp\u003eRRT, renal replacement therapy\u003c/p\u003e\n\u003cp\u003eRVAD, right ventricular assist device\u003c/p\u003e\n\u003cp\u003eSAPS, Simplified Acute Physiology Score\u003c/p\u003e\n\u003cp\u003eSOFA, sequential organ Failure Assessment\u003c/p\u003e\n\u003cp\u003eZPR, Zentrales Personenstandsregister\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was registered and approved by the Medical University of Vienna IRB on 31\u003csup\u003est\u003c/sup\u003e of August 2021 (EC umber 1669/2021).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe need for individual patient consent was waived by the IRB due to the retrospective nature of the investigation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInstitutional funding from the Medical University of Vienna, Vienna, Austria.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBZ, ET developed the concept of the study; BZ, GH, LW collected the data and VR and TS performed the statistical analysis; BZ, ET, PWG, MJS interpreted the findings and drafted a first version of the manuscript; GH, RZ, SCS, NJS gave critical input to the manuscript. All authors read and approved the manuscript, and gave consent for submission.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCombes A, Costa M-A, Trouillet J-L, Baudot J, Mokhtari M, Gibert C, et al. Morbidity, mortality, and quality-of-life outcomes of patients requiring \u0026gt;or=14 days of mechanical ventilation. Crit Care Med 2003;31:1373\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eCarson SS, Garrett J, Hanson LC, Lanier J, Govert J, Brake MC, et al. A prognostic model for one-year mortality in patients requiring prolonged mechanical ventilation* Crit Care Med 2008;36:2061\u0026ndash;9. https://doi.org/10.1097/ccm.0b013e31817b8925.\u003c/li\u003e\n\u003cli\u003eDibiasi C, Kimberger O, Bologheanu R, Staudinger T, Heinz G, Zauner C, et al. 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Wien Klin Wochenschr 2006;118:390\u0026ndash;6. https://doi.org/10.1007/s00508-006-0616-z.\u003c/li\u003e\n\u003cli\u003eRockwood K, Theou O. Using the Clinical Frailty Scale in Allocating Scarce Health Care Resources. Can Geriatr J 2020;23:254\u0026ndash;9. https://doi.org/10.5770/cgj.23.463.\u003c/li\u003e\n\u003cli\u003eZimmerman JE, Kramer AA, McNair DS, Malila FM. Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today\u0026rsquo;s critically ill patients. Crit Care Med 2006;34:1297\u0026ndash;310. https://doi.org/10.1097/01.ccm.0000215112.84523.f0.\u003c/li\u003e\n\u003cli\u003eMoreno RP, Metnitz PGH, Almeida E, Jordan B, Bauer P, Campos RA, et al. SAPS 3\u0026mdash;From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensiv Care Med 2005;31:1345\u0026ndash;55. https://doi.org/10.1007/s00134-005-2763-5.\u003c/li\u003e\n\u003cli\u003eVincent J-L, Moreno R. Clinical review: Scoring systems in the critically ill. Crit Care 2010;14:207. https://doi.org/10.1186/cc8204.\u003c/li\u003e\n\u003cli\u003eZanella A, Florio G, Antonelli M, Bellani G, Berselli A, Bove T, et al. Time course of risk factors associated with mortality of 1260 critically ill patients with COVID-19 admitted to 24 Italian intensive care units. Intensiv Care Med 2021;47:995\u0026ndash;1008. https://doi.org/10.1007/s00134-021-06495-y.\u003c/li\u003e\n\u003cli\u003eEriksson J, Nelson D, Holst A, Hellgren E, Friman O, Oldner A. Temporal patterns of organ dysfunction after severe trauma. Crit Care 2021;25:165. https://doi.org/10.1186/s13054-021-03586-6.\u003c/li\u003e\n\u003cli\u003eVincent J-L, Moreno R, Takala J, Willatts S, Mendon\u0026ccedil;a AD, Bruining H, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. Intensiv Care Med 1996;22:707\u0026ndash;10. https://doi.org/10.1007/bf01709751.\u003c/li\u003e\n\u003cli\u003eFerreira FL, Bota DP, Bross A, M\u0026eacute;lot C, Vincent J-L. Serial Evaluation of the SOFA Score to Predict Outcome in Critically Ill Patients. JAMA 2001;286:1754\u0026ndash;8. https://doi.org/10.1001/jama.286.14.1754.\u003c/li\u003e\n\u003cli\u003eKarakike E, Kyriazopoulou E, Tsangaris I, Routsi C, Vincent J-L, Giamarellos-Bourboulis EJ. The early change of SOFA score as a prognostic marker of 28-day sepsis mortality: analysis through a derivation and a validation cohort. Crit Care 2019;23:387. https://doi.org/10.1186/s13054-019-2665-5.\u003c/li\u003e\n\u003cli\u003eAndertun SG, Wissendorff-Ekdahl A, Ull\u0026eacute;n S, Cronberg T, Friberg H, Jakobsen JC, et al. Impact of frailty on mortality, functional outcome, and health status after out-of-hospital cardiac arrest: insights from the TTM2-trial. Intensiv Care Med 2025:1\u0026ndash;11. https://doi.org/10.1007/s00134-025-08185-5.\u003c/li\u003e\n\u003cli\u003eMuscedere J, Waters B, Varambally A, Bagshaw SM, Boyd JG, Maslove D, et al. The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis. Intensiv Care Med 2017;43:1105\u0026ndash;22. https://doi.org/10.1007/s00134-017-4867-0.\u003c/li\u003e\n\u003cli\u003eMuscedere J, Bagshaw SM, Kho M, Mehta S, Cook DJ, Boyd JG, et al. Frailty, Outcomes, Recovery and Care Steps of Critically Ill Patients (FORECAST): a prospective, multi-centre, cohort study. Intensiv Care Med 2024;50:1064\u0026ndash;74. https://doi.org/10.1007/s00134-024-07404-9.\u003c/li\u003e\n\u003cli\u003eMathew R, Santo PD, Jung RG, Marbach JA, Hutson J, Simard T, et al. Milrinone as Compared with Dobutamine in the Treatment of Cardiogenic Shock. N Engl J Med 2021;385:516\u0026ndash;25. https://doi.org/10.1056/nejmoa2026845.\u003c/li\u003e\n\u003cli\u003eRodenas‐Alesina E, Scolari FL, Wang VN, Brahmbhatt DH, Mihajlovic V, Fung NL, et al. Improved mortality and haemodynamics with milrinone in cardiogenic shock due to acute decompensated heart failure. ESC Hear Fail 2023;10:2577\u0026ndash;87. https://doi.org/10.1002/ehf2.14379.\u003c/li\u003e\n\u003cli\u003eSlawsky MT, Colucci WS, Gottlieb SS, Greenberg BH, Haeusslein E, Hare J, et al. Acute Hemodynamic and Clinical Effects of Levosimendan in Patients With Severe Heart Failure. Circulation 2000;102:2222\u0026ndash;7. https://doi.org/10.1161/01.cir.102.18.2222.\u003c/li\u003e\n\u003cli\u003eGustafsson F, Damman K, Nalbantgil S, Laake LWV, Tops LF, Thum T, et al. Inotropic therapy in patients with advanced heart failure. A clinical consensus statement from the Heart Failure Association of the European Society of Cardiology. Eur J Hear Fail 2023;25:457\u0026ndash;68. https://doi.org/10.1002/ejhf.2814.\u003c/li\u003e\n\u003cli\u003ePetursson P, Gudmundsson T, R\u0026aring;munddal T, Anger\u0026aring;s O, Rawshani A, Mohammad MA, et al. Inotropes and mortality in patients with cardiogenic shock: an instrumental variable analysis from the SWEDEHEART registry. Eur Hear J Cardiovasc Pharmacother 2025;11:57\u0026ndash;65. https://doi.org/10.1093/ehjcvp/pvae078.\u003c/li\u003e\n\u003cli\u003eSato R, Ariyoshi N, Hasegawa D, Crossey E, Hamahata N, Ishihara T, et al. Effects of Inotropes on the Mortality in Patients With Septic Shock. J Intensiv Care Med 2019;36:211\u0026ndash;9. https://doi.org/10.1177/0885066619892218.\u003c/li\u003e\n\u003cli\u003eChen Q-H, Zheng R-Q, Lin H, Shao J, Yu J, Wang H-L. Effect of levosimendan on prognosis in adult patients undergoing cardiac surgery: a meta-analysis of randomized controlled trials. Crit Care 2017;21:253. https://doi.org/10.1186/s13054-017-1848-1.\u003c/li\u003e\n\u003cli\u003eUnverferth DV, Blanford M, Kates RE, Leier CV. Tolerance to dobutamine after a 72 hour continuous infusion. Am J Med 1980;69:262\u0026ndash;6. https://doi.org/10.1016/0002-9343(80)90387-3.\u003c/li\u003e\n\u003cli\u003eChin-Yee N, D\u0026rsquo;Egidio G, Thavorn K, Heyland D, Kyeremanteng K. Cost analysis of the very elderly admitted to intensive care units. Critical Care Lond Engl 2017;21:109. https://doi.org/10.1186/s13054-017-1689-y.\u003c/li\u003e\n\u003cli\u003eChen C-L, Wang S-T, Cheng W-C, Chen C-Y, Chen W-C, Lin Y-C, et al. Outcomes and Prognostic Factors in Patients with Hematologic Malignancies in the Intensive Care unit: A Single-Center Cohort Study of 233 Cases in Taiwan 2021. https://doi.org/10.21203/rs.3.rs-152665/v1.\u003c/li\u003e\n\u003cli\u003eSadick V, Bowcock E, Lane S, Seppelt I. Survival and predictors of outcome among patients with decompensated liver disease in a non‐liver transplant intensive care unit. Pessimism is historical and unjustified. Intern Med J 2019;49:745\u0026ndash;52. https://doi.org/10.1111/imj.14151.\u003c/li\u003e\n\u003cli\u003eMoreno R, Rhodes A, Piquilloud L, Hernandez G, Takala J, Gershengorn HB, et al. The Sequential Organ Failure Assessment (SOFA) Score: has the time come for an update? Crit Care 2023;27:15. https://doi.org/10.1186/s13054-022-04290-9.\u003c/li\u003e\n\u003cli\u003eYu P-J, Cassiere HA, Fishbein J, Esposito RA, Hartman AR. Outcomes of Patients With Prolonged Intensive Care Unit Length of Stay After Cardiac Surgery. J Cardiothor Vasc An 2016;30:1550\u0026ndash;4. https://doi.org/10.1053/j.jvca.2016.03.145.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Medical University of Vienna","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Intensive care, critical care, cardiac disease, cardiac surgery, heart failure, prolonged stay, outcome prediction, prediction model","lastPublishedDoi":"10.21203/rs.3.rs-8748230/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8748230/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOutcome predictors in critically ill cardiac patients with a very long ICU stay remain poorly defined. This study aimed to identify such predictors in this population.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis is a retrospective bi–centric study including critically ill surgical and medical cardiac patients with an ICU length of stay \u0026gt; 30 days. ICU–mortality was the primary endpoint; 1–year mortality was the secondary endpoint. A priori defined outcome predictors were analyzed by means of univariable and multivariable logistic regressions. Additionally, classification and regression tree (CART) analysis was employed to explore non–linear relationships and interactions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 210 patients, among them 107 (51.0 %) surgical and 103 (49.0%) medical, were included. ICU mortality was 24.3 %. In the entire cohort, 1–year mortality was 46.7 %, whereas among patients who survived the ICU it was 29.6 %. Factors independently associated with ICU–mortality included baseline frailty (odds ratio (OR), 3.16 [95%–confidence interval (CI) 2.26–4.70] per point), prolonged continuous renal support (OR, 1.07 [1.03–1.13] per day), prolonged invasive ventilation (OR, 1.07 [1.01–1.14] per day) and prolonged vasopressor use (OR, 1.09 [1.02–1.17] per day), whereas prolonged inotrope use (OR, 0.93 [0.87–0.99] per day) was associated with lower ICU–mortality. CART analysis identified various patient groups with distinctly diverging ICU mortality.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusion\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn critically ill cardiac patients with a very long stay in ICU, irrespective of surgical or medical status, baseline frailty, prolonged renal support, invasive ventilation, and vasopressor use were independent predictors of ICU–mortality, whereas prolonged inotrope use was inversely associated with mortality. External validation is needed before these findings can be used for decision support.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTrial registration\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was registered and approved by the Medical University of Vienna IRB on 31\u003csup\u003est\u003c/sup\u003e of August 2021 (EC number 1669/2021).\u003c/p\u003e","manuscriptTitle":"Predictors of Outcome in Critically Ill Cardiac Patients with a very long ICU stay–a retrospective bicentric study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 16:06:46","doi":"10.21203/rs.3.rs-8748230/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e72ac56-0d28-47e1-9a14-71b31027ea4e","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62076753,"name":"Critical Care \u0026 Emergency Medicine"}],"tags":[],"updatedAt":"2026-02-03T16:06:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 16:06:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8748230","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8748230","identity":"rs-8748230","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}