ESC HEART FAILURE ORIGINAL RESEARCH ARTICLE ESC Heart Failure 2021; 8: 1398–1407 Published online 31 January 2021 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ehf2.13224 Mortality risk prediction in elderly patients with cardiogenic shock: results from the CardShock study Mari Hongisto1*, Johan Lassus2, Tuukka Tarvasmäki2, Alessandro Sionis3, Jordi Sans-Rosello3, Heli Tolppanen2, Anu Kataja1, Toni Jäntti2, Tuija Sabell2, Matias Greve Lindholm4, Marek Banaszewski5, Jose Silva Cardoso6, John Parissis7, Salvatore Di Somma8, Valentina Carubelli9, Raija Jurkko2, Josep Masip10, Veli-Pekka Harjola1 and for the CardShock Study Investigators and the GREAT Network 1 Division of Emergency Medicine, Department of Emergency Medicine and Services, Helsinki University Hospital, PO Box 900, Helsinki, 00029 HUS, Finland; 2Cardiology, University of Helsinki and Heart and Lung Centre, Helsinki University Hospital, Helsinki, Finland; 3Cardiology Department, Hospital de la Santa Creu I Sant Pau, Universitat Autònoma de Barcelona, Biomedical Research Institute IIB-Sant Pau, CIBER-CV, Barcelona, Spain; 4Department of Cardiology, Zealand University Hospital, Roskilde, Denmark; 5Intensive Cardiac Therapy Clinic, National Institute of Cardiology, Warsaw, Poland; 6CINTESIS—Center for Health Technology and Services Research, Department of Cardiology, Faculty of Medicine, University of Porto, São João University Medical Centre, Porto, Portugal; 7ER and Heart Failure Unit, Attikon University Hospital, Athens, Greece; 8Department of Medical Surgery, Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy; 9Cardiology Division, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University and Civil Hospital of Brescia, Brescia, Italy; 10Critical Care Department, Hospital Sant Joan Despi Moisès Broggi, Consorci Sanitari Integral, University of Barcelona, Barcelona, Spain Abstract Aims This study aimed to assess the utility of contemporary clinical risk scores and explore the ability of two biomarkers [growth differentiation factor-15 (GDF-15) and soluble ST2 (sST2)] to improve risk prediction in elderly patients with cardio- genic shock. Methods and results Patients (n = 219) from the multicentre CardShock study were grouped according to age (elderly ≥75 years and younger). Characteristics, management, and outcome between the groups were compared. The ability of the CardShock risk score and the IABP-SHOCK II score to predict in-hospital mortality and the additional value of GDF-15 and sST2 to improve risk prediction in the elderly was evaluated. The elderly constituted 26% of the patients (n = 56), with a higher proportion of women (41% vs. 21%, P < 0.05) and more co-morbidities compared with the younger. The primary aetiology of shock in the elderly was acute coronary syndrome (84%), with high rates of percutaneous coronary intervention (87%). Compared with the younger, the elderly had higher in-hospital mortality (46% vs. 33%; P = 0.08), but 1 year post-discharge survival was excellent in both age groups (90% in the elderly vs. 88% in the younger). In the elderly, the risk prediction models demonstrated an area under the curve of 0.75 for the CardShock risk score and 0.71 for the IABP-SHOCK II score. Incorporating GDF-15 and sST2 improved discrimination for both risk scores with areas under the curve ranging from 0.78 to 0.84. Conclusions Elderly patients with cardiogenic shock have higher in-hospital mortality compared with the younger, but post-discharge outcomes are similar. Contemporary risk scores proved useful for early mortality risk prediction also in the elderly, and risk stratification could be further improved with biomarkers such as GDF-15 or sST2. Keywords Cardiogenic shock; Elderly; Risk prediction; Biomarker; GDF-15; sST2 Received: 12 May 2020; Revised: 14 December 2020; Accepted: 7 January 2021 *Correspondence to: Mari Hongisto, Division of Emergency Medicine, Department of Emergency Medicine and Services, Helsinki University Hospital, PO Box 900, 00029 HUS Helsinki, Finland. Tel: +358 40 586 3263; Fax: +358 9 471 66650. Email: mari.hongisto@hus.fi [Correction added on 11 February 2021, after first online publication: The name of the author Jordi Sans Roselló has been corrected in this version.] Introduction treatment, but, on the other hand, they are vulnerable to treatment-related complications. Furthermore, there The management of cardiogenic shock (CS) in the is remarkable individual variation in functional and elderly poses a clinical challenge. On one hand, the elderly cognitive reserves among this age group, presenting a are at the highest risk for adverse outcomes and hurdle to the objective evaluation of the prognosis in acute therefore have the greatest potential to benefit from settings. © 2021 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. Mortality risk prediction in elderly patients with cardiogenic shock 1399 Early assessment of shock severity is crucial in order to to be over 18 years old, and they had to be included within single out patients at high risk of death. Accurate risk stratifi- 6 h of the identification of the shock. cation could guide the treatment and help in the allocation Baseline characteristics and previous medical history were of clinical resources, through identification of patients recorded. Biochemical and clinical findings, as well as haemo- most likely to benefit from the highly intense and costly treat- dynamic parameters, were documented at detection of shock ment options. Age itself elevates the risk of myocardial and at pre-specified time points until 96 h after inclusion. infarction-related CS, and advanced age is an additional Patients were treated according to local practice in each hos- known risk factor for CS mortality.1,2 Risk prediction models pital, and treatment procedures were registered. The primary have been introduced to facilitate risk assessment and the outcome was all-cause in-hospital mortality. In addition, 1 year prediction of mortality in the acute phase of CS. Two risk pre- mortality was assessed. After hospital discharge, three pa- diction scores, the CardShock risk score and the IABP-SHOCK tients were lost to follow-up. In the mortality analyses, their II score, have been developed specifically for CS and have cases were censored at the time of hospital discharge. Written shown good performance in both early risk stratification informed consent was obtained from the patient or, according and prediction of short-term mortality.1,3,4 However, the util- to local regulations, from a close person or a relative. Vital sta- ity of these risk score models in the elderly remains unclear. tus during follow-up was determined through direct contact Biomarkers have become significant prognostic tools in many with the patient or next of kin or through population and hos- cardiovascular diseases.5,6 Most recently, growth differentia- pital registers. The study was approved by the following local tion factor-15 (GDF-15) and soluble ST2 (sST2) have been ethics committees: Athens: Ethics Committee of Attikon found to be valuable in risk stratification in heart failure University Hospital; Barcelona: Health Research Ethics and CS,7–10 but data on elderly patients with CS in the con- Committee of the Hospital de Sant Pau; Brescia: Ethics temporary era remain scarce. Committee of the Province of Brescia; Brno: Ethics Committee We examined the clinical picture, management, and out- of University Hospital Brno; Helsinki: The Ethics Committee, comes of patients aged ≥75 years in a prospective, Department of Medicine, The Hospital District of Helsinki multicentre study on CS. Our aim was to compare the key and Uusimaa; Porto: Ethics Committee of São João Hospital features between survivors and non-survivors and to assess Center/Porto Medical School; Rome: Ethical Committee the performance of the contemporary risk prediction scores Sant’Andrea Hospital; Warsaw: Local Bioethics Committee of in the elderly. Finally, we investigated the ability of GDF-15 the Institute of Cardiology; and Copenhagen: the study was and sST2 to improve early risk stratification in this age group. approved by the Danish Protection Agency with reference number GEH-2014-013 and I-Suite number 02731. The study was conducted in accordance with the Declaration of Helsinki. Serial blood sampling was performed at baseline and Methods thereafter at 12 h intervals up to 48 h, and plasma samples were stored in aliquots frozen at 80 ( 70)°C until assayed. The CardShock study (NCT01374867 at https://www. Creatinine, C-reactive protein, high-sensitivity troponin ClinicalTrials.gov is a prospective, observational, multicentre T (hsTnT), N-terminal pro-B-type natriuretic peptide study on CS, including both acute coronary syndrome (NT-proBNP), and GDF-15 (Roche Diagnostics, Basel, (ACS)-related and non-ACS-related aetiologies. Nine tertiary Switzerland) were analysed centrally at ISLAB (Kuopio, hospitals in eight European countries participated between Finland). sST2 was measured at INSERM UMR-S 942 October 2010 and December 2012, enrolling 219 patients. (Paris, France) using a quantitative sandwich monoclonal The detailed design and the primary results of the study have enzyme-linked immunosorbent assay (Presage sST2 Assay; been published elsewhere.1 Critical Diagnostics, San Diego, CA, USA). Arterial blood lac- tate and pH were analysed locally. Estimated glomerular fil- tration rate (eGFR) was calculated from creatinine values Inclusion criteria and data collection using the Chronic Kidney Disease Epidemiology Collaboration equation.11 Acute kidney injury was defined and staged ac- Besides an acute cardiac cause, the inclusion criteria cording to the Kidney Disease: Improving Global Outcomes consisted of systolic blood pressure <90 mmHg (after criteria based on creatinine value.12 adequate fluid challenge) for 30 min, or need for vasopressor therapy to maintain systolic blood pressure >90 mmHg, and signs of hypoperfusion (altered mental status/confusion, cold Risk prediction models periphery, oliguria <0.5 mL/kg/h for the previous 6 h, or blood lactate >2 mmol/L). The exclusion criterion was shock We assessed the ability of two published risk prediction caused either by ongoing hemodynamically significant ar- models, the CardShock risk score and the IABP-SHOCK II rhythmias or by cardiac or non-cardiac surgery. Patients had score, to predict in-hospital mortality in the elderly. ESC Heart Failure 2021; 8: 1398–1407 DOI: 10.1002/ehf2.13224 1400 M. Hongisto et al. The CardShock risk score consists of seven variables mea- A two-sided P-value <0.05 was regarded as statistically signif- sured at admission in patients with CS of various aetiologies icant. All statistical analyses were performed with SPSS 22.0 (age >75 years; eGFR; blood lactate; confusion on admission; statistical software (IBM Corp., Armonk, NY, USA). left ventricular ejection fraction <40%; previous myocardial infarction or coronary artery bypass grafting; and ACS aetiology) with a maximum of 9 points. The score categorizes Results the patients into low-risk (0–3 points), intermediate-risk (4–5 points), and high-risk (6–9 points) groups.1 Clinical characteristics, presentation, and The IABP-SHOCK II score is derived from the Intra-aortic treatment of cardiogenic shock in the elderly Balloon Pump in Cardiogenic Shock II (IABP-SHOCK II) study in patients with CS due to ACS who are undergoing percutane- Table 1 outlines the baseline characteristics of the elderly and ous coronary intervention (PCI). It consists of six variables (age the younger patients. The elderly constituted 26% (n = 56) of >73 years; prior stroke; glucose at admission >10.6 mmol/L; the patients, with a mean age of 81 ± 4 years. They were creatinine at admission >132.6 mmol/L; thrombolysis in myo- more frequently female (41% vs. 21%, P = 0.003) and had cardial infarction flow grade <3 after PCI; and arterial blood more co-morbidities overall, with generalized arteriosclerosis lactate at admission >5 mmol/L) giving a maximum of 9 being particularly high compared with younger patients. points. The patients can be classified according to the points Overall, ACS (81%) was the principal aetiology of shock, and into low-risk (0–2 points), intermediate-risk (3–4 points), and its frequency did not differ between the age groups. high-risk (5–9 points) categories.3 The two age groups were largely similar with respect to clinical presentation and biochemical findings, as summarized in Table 2, except for a few differences. Compared with the Statistical analysis younger group, the elderly were less likely to have sinus rhythm at baseline, and a higher proportion of them had left We categorized the patients by age into (i) ≥75 years old ventricular ejection fraction >40%. The elderly group had (elderly group) and (ii) <75 years old (younger group). Elderly worse renal function (creatinine and eGFR) at admission but patients were further compared with respect to their with a similar incidence of acute kidney injury as the younger in-hospital survival status (survivors vs. non-survivors). patients. Biomarker levels of congestion/cardiac stress Results are presented here as number (n) and percentage (NT-proBNP) were higher in the elderly, whereas the extent (%); mean with standard deviation (SD); or median with of myocardial injury as measured by hsTnT did not differ inter-quartile range, as appropriate. Group comparisons were between the groups [peak hsTnT 3680 (1270–14 281) ng/L performed using the χ 2 test and Fisher’s exact test for cate- in the elderly vs. 3849 (927–12 585) ng/L in the younger; gorical variables and Student’s t-test and Mann–Whitney U P = 0.8]. The extent of coronary artery disease assessed by test for continuous variables. The Kaplan–Meier method coronary angiogram in the elderly (one-vessel disease 20%; was used to elucidate the timing of events during 30 day multivessel disease 59%; and left main disease 11%) was and 1 year follow-up in relation to age group, and statistical comparable with that of the younger group. assessment was performed using the log-rank test. The management of CS, including invasive assessment by To assess the ability of the CardShock risk score and coronary angiography and coronary interventions, mechani- IABP-SHOCK II score to predict in-hospital mortality in the cal ventilation, and use of vasoactive pharmacotherapy, was elderly, and to evaluate the additional value of GDF-15 and similar between the age groups, as shown in Table 3. The sST2 on the risk prediction models, receiver operating charac- use of intra-aortic balloon pump was more common in the teristic curve analysis was performed. We used previously younger group (61% vs. 39%, P = 0.004). No differences in defined cut-off values of the biomarkers (GDF- the prescription of antithrombotic medication were observed 15 > 7000 ng/L and sST2 > 500 ng/mL) for this analysis.7,8 between the age groups (data not shown). The distribution of the elderly patients and observed mortal- ity within risk categories of both risk prediction models were calculated. The additional value of the biomarkers in mortal- ity prediction was assessed via the likelihood ratio test for Outcomes and comparing elderly survivors and nested models. For comparison, the CardShock risk score non-survivors was further validated in an external cohort of patients with CS with unselected aetiology from a single-centre prospective The elderly had numerically higher in-hospital (46% vs. 33%, study. The validation was performed in all patients as well as P = 0.08) and 1 year (52% vs. 41%; P = 0.17) mortality. separately in the elderly (≥75 years) and the younger Kaplan–Meier survival curves for in-hospital mortality for all (<75 years) patients. Logistic regression was used to investi- patients and 1 year mortality for those surviving hospitaliza- gate the interaction between age and risk prediction models. tion stratified by the age group are shown in Figure 1. Of ESC Heart Failure 2021; 8: 1398–1407 DOI: 10.1002/ehf2.13224 Mortality risk prediction in elderly patients with cardiogenic shock 1401 Table 1 Clinical characteristics, medical history, and shock aetiology ≥75 years <75 years Elderly survivors Elderly non-survivors n = 56 (26%) n = 163 (74%) P-value n = 30 (54%) n = 26 (46%) P-value Age (years) 81 (4) 62 (9) <0.001 81 (5) 81 (4) 0.7 80 (78–83) 63 (57–69) 0.003 Female, n (%) 23 (41) 34 (21) 0.003 13 (43) 10 (39) 0.6 2 BMI (kg/m ) 26 (5) 27 (4) 0.2 26 (5) 26 (4) 0.9 Resuscitated, n (%) 16 (29) 46 (28) 1.0 7 (23) 9 (35) 0.4 Medical history, n (%) Hypertension 39 (70) 93 (57) 0.10 21 (70) 18 (69) 1.0 Diabetes 17 (30) 44 (27) 0.6 8 (27) 9 (35) 0.5 History of MI/CABG 19 (34) 38 (23) 0.12 7 (23) 12 (46) 0.07 Prior stroke/TIA 8 (14) 12 (7) 0.12 4 (13) 4 (15) 1.0 PAD 11 (20) 10 (6) 0.003 3 (10) 8 (31) 0.05 Chronic heart failure 7 (13) 29 (18) 0.4 2 (7) 5 (19) 0.2 History of AF 11 (20) 21 (13) 0.2 4 (13) 7 (27) 0.2 Renal insufficiency 11 (20) 14 (9) 0.03 4 (13) 7 (27) 0.2 Smoking history 24 (43) 111 (68) <0.001 14 (47) 10 (39) 0.6 Shock aetiology, n (%) ACS 47 (84) 130 (80) 0.5 25 (83) 22 (85) 1.0 STEMI 37 (66) 112 (69) 0.7 21 (70) 16 (62) 0.5 ACS, acute coronary syndrome; AF, atrial fibrillation; BMI, body mass index; CABG, coronary artery bypass grafting; MI, myocardial infarc- tion; PAD, peripheral artery disease; STEMI, ST-elevation myocardial infarction; TIA, transient ischaemic attack. Data are presented as numbers and percentages (%), means (SD), and median (inter-quartile range). note, among patients discharged alive, 1 year survival was risk score [5.6 (SD 1.5) vs. 4.0 (1.8); P < 0.001] and comparable between the age groups (Figure 1 and Table 3). IABP-SHOCK II score [4.8 (SD 1.4) vs. 3.2 (1.4); P < 0.001] Causes of death did not differ between the groups compared with the younger. The difference remained signifi- (Supporting Information, Table S1). In addition, an explor- cant even after excluding the age variable from the model atory analysis of the patients with ACS aetiology undergoing [CardShock risk score 4.6 (1.4) vs. 4.0 (1.8); P = 0.01 and PCI (n = 142) showed no difference in in-hospital (41% vs. IABP-SHOCK II score 3.8 (1.4) vs. 3.2 (1.4); P = 0.02]. 38%, P = 0.7) and 1 year (44% vs. 46%, P = 0.8) mortality rates The elderly survivors had a lower risk profile than between the elderly and the younger groups. non-survivors according to both risk models (CardShock risk Elderly survivors and non-survivors were largely similar score 4.9 vs. 6.4; P < 0.001 and IABP-SHOCK II 4.4 vs. 5.6; with respect to age, sex, medical history, and shock aetiology P = 0.009). While both risk models categorized the majority (Table 1). In contrast, the non-survivors suffered from more of the non-survivors (65% in CardShock risk score and 85% severe shock already at baseline, requiring more intense in IABP-SHOCK II) into the high-risk group, a significant pro- respiratory and haemodynamic support, as depicted in Tables portion of the elderly survivors were classified as 2 and 3. Interestingly, non-survivors had significantly higher intermediate-risk patients (Figure 3). Age did not have an GDF-15 levels compared with survivors (Table 2). Survivors effect on the ability of the CardShock risk score or the were more likely to undergo coronary angiogram (93% vs. IABP-SHOCK II score to predict outcome (Pinteraction 0.82 and 65%, P = 0.009). The revascularization rate was, however, still 0.47, respectively). very high (94% had PCI) among non-survivors undergoing an- Among the elderly, the CardShock risk score had an area giogram (Table 3). under the curve (AUC) of 0.75 (vs. 0.82 in the younger group) and the IABP-SHOCK II score an AUC of 0.71 (vs. 0.73 in the younger group) for prediction of in-hospital mortality. Each Performance of risk scores for prediction of of the biomarkers increased the discrimination of both the in-hospital mortality in the elderly CardShock and IABP-SHOCK II risk scores (Table 4). Adding the combination of GDF-15 and sST2 to the risk prediction Compared with the younger patients, the CardShock and models did not improve discrimination compared with adding IABP-SHOCK II risk scores more frequently categorized the only one biomarker at a time (Table 4). elderly patients into the intermediate-risk and high-risk For comparison, the CardShock risk score had an AUC of groups and less frequently into the low-risk group (Figure 0.75 [95% confidence interval (CI) 0.69–0.80] for all patients 2). Both scores were useful for mortality risk prediction in (n = 262), 0.77 (95% CI 0.67–0.87) for patients ≥75 years the elderly; in the low-risk category, the outcome was old (n = 83), and 0.75 (95% CI 0.68–0.82) for patients favourable, and the mortality increased with higher risk cate- <75 years old (n = 179) for predicting in-hospital mortality gory (Figure 2). The elderly demonstrated a higher CardShock in the validation cohort. ESC Heart Failure 2021; 8: 1398–1407 DOI: 10.1002/ehf2.13224 1402 Table 2 Clinical presentation and biochemical findings at baseline ≥75 years <75 years Elderly survivors Elderly non-survivors n = 56 (26%) n = 163 (74%) P-value n = 30 (54%) n = 26 (46%) P-value Systolic blood pressure (mmHg) 78 (11) 77 (15) 0.7 81 (10) 75 (11) 0.08 Heart rate (b.p.m.) 86 (31) 92 (27) 0.2 78 (29) 93 (33) 0.10 Sinus rhythm, n (%) 30 (54) 128 (79) 0.001 20 (67) 11 (42) 0.09 Left ventricular ejection 36 (15) 32 (14) 0.12 41 (15) 31 (13) 0.03 fraction (LVEF) (%) LVEF < 40%, n (%) 28 (50) 107 (66) 0.03 13 (43) 15 (58) 0.3 Confusion, n (%) 44 (79) 104 (64) 0.06 21 (70) 23 (89) 0.09 Oliguria, n (%) 35 (63) 86 (53) 0.19 11 (37) 24 (92) <0.001 Biochemical findings Haemoglobin (g/L) 124 (21) 130 (23) 0.12 124 (19) 123 (21) 0.6 CRP (mg/L) 19 (3–65) 14 (5–49) 0.9 19 (5–74) 8 (2–65) 0.3 Creatinine (μmol/L) 121 (96–159) 96 (73–138) 0.004 116 (92–137) 127 (106–173) 0.3 2 eGFR (mL/min/1.73 m ) 44 (32–62) 70 (45–95) <0.001 44 (32–66) 35 (30–56) 0.3 ALT (U/L) 37 (20–86) 46 (20–101) 0.4 29 (19–71) 50 (22–90) 0.4 Glucose (mmol/L) 12.7 (9.0–15.6) 10.3 (7.6–16.1) 0.3 11.2 (7.9–14.4) 12.7 (9.4–19.0) 0.9 hsTnT (ng/L) 1568 (358–4174) 2307 (403–5418) 0.7 1479 (212–6249) 1568 (441–4140) 0.9 Peak hsTnT (ng/L) 3680 (1270–14 281) 3849 (927–12 585) 0.8 3521 (1194–8036) 4291 (1345–16 665) 0.4 NT-proBNP (ng/L) 4322 (1599–16 786) 2367 (400–8082) 0.02 4965 (838–18 786) 3706 (1609–16 547) 0.9 Arterial pH 7.29 (7.18–7.37) 7.32 (7.21–7.39) 0.2 7.31 (7.25–7.41) 7.23 (7.10–7.31) 0.02 Lactate (mmol/L) 2.8 (2.0–7.9) 2.9 (1.6–5.3) 0.16 2.3 (1.6–3.3) 6.4 (2.8–8.6) <0.001 Lactate >5 mmol/L, n (%) 20 (36) 44 (27) 0.2 5 (17) 15 (58) 0.001 GDF-15 (ng/L at 12 h) (n = 154) 10 871 (6562–30 074) 8440 (3846–16 033) 0.05 6912 (3901–13 309) 29 647 (9775–42 406) <0.001 sST2 (ng/mL at 12 h) (n = 154) 602 (372–1244) 633 (343–1028) NS 482 (311–923) 907 (543–1574) 0.2 ALT, alanine aminotransferase; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; GDF-15, growth differentiation factor-15; hsTnT, high-sensitivity troponin T; NS, not significant; NT-proBNP, N-terminal pro-B-type natriuretic peptide; sST2, soluble ST2. Data are presented as numbers and percentages (%), means (SD), and median (inter-quartile range). ESC Heart Failure 2021; 8: 1398–1407 DOI: 10.1002/ehf2.13224 M. Hongisto et al. Mortality risk prediction in elderly patients with cardiogenic shock 1403 Table 3 Treatment of the shock, length of hospital stay, and outcomes ≥75 years <75 years Elderly survivors Elderly non-survivors n = 56 (26%) n = 163 (74%) P-value n = 30 (54%) n = 26 (46%) P-value Treatment, n (%) Angiogram 45 (80) 137 (84) 0.2 28 (93) 17 (65) 0.009 a PCI 39 (87) 110 (80) 0.3 23 (82) 16 (94) 0.2 TIMI flow <3 prior PCI (n = 167) 38 (95) 114 (90) 0.5 22 (92) 15 (100) 0.5 a CABG 1 (2) 8 (6) 0.5 1 (4) 0 — IABP 22 (39) 100 (61) 0.004 10 (33) 12 (46) 0.3 Invasive mechanical ventilation 31 (55) 106 (65) 0.2 11 (37) 20 (77) 0.003 Use of vasoactive medication, n (%) Noradrenaline 40 (71) 124 (76) 0.5 19 (63) 21 (81) 0.15 Dobutamine 25 (45) 84 (52) 0.4 10 (33) 15 (58) 0.07 Adrenaline 13 (23) 33 (20) 0.6 3 (10) 10 (39) 0.01 Levosimendan 12 (21) 41 (25) 0.6 7 (23) 5 (19) 0.7 Outcomes Incidence of AKI, n (%) (n = 154) 15 (36) 32 (29) 0.4 1 (4) 14 (74) <0.001 b Length of hospital stay (days) 10 (5–18) 14 (8–27) 0.07 In-hospital mortality, n (%) 26 (46) 54 (33) 0.08 One year mortality (n = 216), n (%) 29 (52) 67 (41) 0.17 One year mortality (among hospital 3 (10) 13 (12) 1.0 survivors, n = 139), n (%) AKI, acute kidney injury; CABG, coronary artery bypass grafting; IABP, intra-aortic balloon pump; PCI, percutaneous coronary angiogram; TIMI, thrombolysis in myocardial infarction flow. Data are presented as numbers and percentages (%), means (SD), and median (inter-quartile range). a Proportion of those who underwent angiogram. b Reported only for those who survived to hospital discharge. Discussion influence on patient profiles. The most frail elderly patients judged to have poor prognosis may not have been trans- Within this prospective, multicentre study on CS with unse- ferred to tertiary centres. Consequently, mortality in all lected aetiology, the elderly constituted one-fourth of the elderly patients may be higher than we found in our study. population. They had a higher in-hospital mortality rate com- The decision whether to transfer the patient or not is made pared with the younger, despite active revascularization. by the physician in charge, often based on the patient’s clin- However, those surviving to hospital discharge had a ical condition. favourable long-term prognosis. Contemporary CS risk predic- Accurate risk prediction is essential in the critically ill and tion scores showed good ability for mortality risk stratifica- in the elderly in particular. Considering the complex patho- tion also in the elderly. The discriminative performance of physiology and clinical picture of CS especially in the elderly, the scores could be further improved by biomarkers such as tools for objective risk stratification are needed to help treat- GDF-15 and sST2. ment decisions at all levels of care. The favourable prognosis The elderly constituted 26% of the patients with CS in this in discharged elderly patients with CS highlights the impor- study. In prior studies, the proportion of the elderly has var- tance of well-balanced treatment decisions early during the ied between 29% and 37%.2,13,14 In our study, both the clini- in-hospital phase. We evaluated two contemporary risk cal presentation and management strategies were similar in scores developed specifically in CS and found satisfactory the elderly and the younger patients with CS. Considering performance in the elderly. Both scores have recently been the predominance of ACS aetiology also in the elderly, the externally validated in patients with CS.4,16,17 Furthermore, PCI rate was very reasonable; indeed, it was higher than in according to our additional validation, CardShock risk score previous studies. The PCI rate among the elderly patients performed well in the elderly in the study population cohort with CS in previous studies varied between 26% and 51% de- as well. The risk scores are easy to use, and they can be ap- pending on the study period and the study design including plied early in the management of patients with CS. These risk hospital facilities.2,14 The elderly had 40% higher in-hospital scores could serve as useful tools for clinicians taking care of mortality compared with the younger. However, the outcome elderly patients with CS in their daily practice. In addition, at 1 year in the elderly hospital survivors was surprisingly they provide objective risk stratification and may therefore good and comparable with the younger. Other studies have be an aid in allocating resources adequately. reported similar rates of short-term and 1 year mortality in In the current study, the discriminative ability of the risk the elderly.2,13,15 scores was, however, somewhat lower in the elderly com- All the centres in our study were tertiary hospitals with an pared with the younger. Age is included as a variable in both on-site catheterization laboratory, which might have had an scores, and certain other variables, such as renal function and ESC Heart Failure 2021; 8: 1398–1407 DOI: 10.1002/ehf2.13224 1404 M. Hongisto et al. Figure 1 Survival in patients with cardiogenic shock by the age group. Figure 2 In-hospital mortality by the risk categories in the elderly and Kaplan–Meier survival curves for all-cause mortality in the elderly the younger with cardiogenic shock. Distribution of the patients (%; bars) (≥75 years old) (dashed line) and the younger (<75 years old) (solid line) and in-hospital mortality (%, dashed lines) according to the risk category patients with cardiogenic shock. (A) In-hospital mortality (46% in the el- (low, intermediate, and high) in the elderly (≥75 years) and in the youn- derly and 33% in the younger) for all patients. (B) One year mortality ger (<75 years) patients with cardiogenic shock in (A) CardShock risk (10% in the elderly and 12% in the younger) for those surviving score and (B) IABP-SHOCK II score. hospitalization. prior manifestation of atherosclerosis, reflect the greater un- captured by traditional risk score variables, which could be derlying burden of diseases in the elderly. Consequently, particularly useful in the elderly. In view of the ageing popu- most elderly patients are classified into intermediate-risk or lation, more studies on accurate risk stratification and the high-risk groups. Although the majority of the non-survivors optimal management of elderly patients with CS seem were appropriately categorized as high risk, many elderly sur- warranted. vivors were in the intermediate-risk group, leaving room for improved risk classification. We found that incorporating biomarkers into the risk Limitations prediction improved mortality risk discrimination in the elderly. Considering the complex pathophysiology of CS bio- There are some limitations to be acknowledged. First, markers such as GDF-15, a marker of oxidative stress and as- although the number of the patients in the prospective sociating with biomarkers of hypoperfusion in CS,8 and sST2, CardShock study was reasonable, the proportion of elderly a marker of cardiac stress and inflammation,18 likely provide was limited, creating some statistical uncertainty in additional prognostic information related to CS that is not between-group comparisons. This is a common problem for ESC Heart Failure 2021; 8: 1398–1407 DOI: 10.1002/ehf2.13224 Mortality risk prediction in elderly patients with cardiogenic shock 1405 Figure 3 Elderly survivors and non-survivors and the risk model catego- available differed between the CardShock and the ries. Distribution (%) of the elderly (≥75 years) in-hospital survivors and IABP-SHOCK II risk scores. This is mostly due to the variable non-survivors in different risk categories in (A) CardShock risk score (n = 53) and (B) IABP-SHOCK II score (n = 33). Numbers above the bars thrombolysis in myocardial infarction flow in the indicate the number of the patients in each category. IABP-SHOCK II score, which was missing in patients who did not undergo coronary angiogram (e.g. non-ACS aetiology). Only a few patients were excluded for other missing vari- ables. Furthermore, GDF-15 and sST2 concentrations were not available from all patients limiting the number of the patients included in the analyses assessing the additional prognostic value of the biomarkers. Secondly, age is one of the variables in both scores giving one point to the elderly au- tomatically. This may contribute to higher score levels and lesser dispersion of the scores among the elderly potentially diminishing the predictive capability of the risk models. Thirdly, having been developed in this study population (patients with CS with different aetiologies), CardShock risk score will perform better in this patient population compared with IABP-SHOCK II score, which was developed in a different patient population (patients with CS due to ACS). Neverthe- less, IABP-SHOCK II score performed well in this study popu- lation as well. Finally, all treatment decisions were at the discretion of the physician in charge. Nevertheless, this study reflects real-life practice in European tertiary care hospitals, and the choice of treatment strategy was made after careful evaluation based on each individual patient’s global health and clinical presentation. Conclusions A quarter of patients with CS are elderly. Despite being simi- lar to younger patients in terms of clinical presentation and active revascularization, elderly patients have a higher in-hospital mortality rate. Those surviving to hospital dis- charge, however, were comparable with younger discharges most studies in CS.19 Nevertheless, only a small number of in having a good long-term prognosis. Contemporary mortal- patients were included in the analyses of the risk scores’ per- ity risk prediction scores are useful for risk stratification also formance and biomarkers. The number of the patients in the elderly. The added value of biomarker-based risk Table 4 AUC for the CardShock risk score and for the IABP-SHOCK II score in combination with GDF-15 or sST2 or both biomarkers to discriminate between in-hospital survivors and non-survivors in the elderly (≥75 years old) and in the younger (<75 years old) ≥75 years <75 years 2a a 2a a Model AUC (95% CI) χ P AUC (95% CI) χ P CardShock risk score 0.75 (0.60–0.91) (n = 40) 0.82 (0.74–0.90) (n = 106) +GDF-15 0.82 (0.69–0.95) 4.92 0.03 0.85 (0.78–0.92) 4.67 0.03 +sST2 0.80 (0.66–0.93) 2.56 0.1 0.83 (0.76–0.91) 1.08 0.3 +GDF-15 + sST2 0.81 (0.68–0.94) 5.76 0.06 0.85 (0.78–0.93) 4.76 0.09 IABP-SHOCK II score 0.71 (0.47–0.94) (n = 28) 0.73 (0.61–0.84) (n = 81) +GDF-15 0.84 (0.69–0.99) 8.78 0.003 0.81 (0.71–0.90) 8.64 0.003 +sST2 0.78 (0.59–0.96) 5.14 0.02 0.79 (0.69–0.89) 4.75 0.03 +GDF-15 + sST2 0.83 (0.68–0.98) 9.56 0.008 0.81 (0.72–0.90) 9.42 0.009 AUC, area under the curve; CI, confidence interval; GDF-15, growth differentiation factor-15; sST2, soluble ST2. 2 χ and P-values are shown for comparison of nested models. a ESC Heart Failure 2021; 8: 1398–1407 DOI: 10.1002/ehf2.13224 1406 M. Hongisto et al. stratification in elderly patients with CS needs to be con- Funding firmed in larger, prospective cohorts. This work was supported by Aarne Koskelo Foundation (Aarne Koskelon Säätiö), the Finnish Cardiac Foundation Acknowledgements (Suomen Kardiologinen Seura), and the state funding for university-level health research. M.H. received a personal The authors thankfully acknowledge the contribution of the scholarship from the Department of Emergency Medicine CardShock Study investigators in all participating hospitals and Services of Helsinki University Hospital (HUS Akuutti) and the GREAT network. Laboratory kits for analysis of NT- and a personal grant from Aarne Koskelo Foundation. The proBNP, hsTnT, and GDF-15 were kindly provided by Roche funders had no role in study design, data collection and Diagnostics, Basel, Switzerland. analysis, decision to publish, or preparation of the manuscript. Conflict of interest Supporting information J.L. has received fees for lectures and advisory board meet- Additional supporting information may be found online in the ings from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Supporting Information section at the end of the article. Novartis, Pfizer, Roche Diagnostics, and Vifor Pharma. J.P. re- ceived honoraria for advisory meetings and lectures from Table S1. Causes of death as reported by local investiga- Orion Pharma and Roche Diagnostics. V.C. received consult- tors. Note: more than 1 cause of death per patient was ing honoraria from CVie Therapeutics Limited, Servier, and accepted. Windtree Therapeutics. All other authors have no conflicts Figure S1. Distribution of the age in the elderly. Histogram of to declare. the distribution of the age in the elderly patients. References 1. Harjola VP, Lassus J, Sionis A, Køber L, 5. 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Bonnefoy-Cudraz E, Angoulvant D, Harjola VP, Lassus J, Thiele H, Sionis A. ESC Heart Failure 2021; 8: 1398–1407 DOI: 10.1002/ehf2.13224