Abstract
Background:
Infective endocarditis (IE) is associated with high morbidity, therefore the identification of high risk patients is crucial. Age-creatinine-ejection fraction (ACEF) score is a risk stratification tool already used in different clinical settings. The aims of our study is to evaluate its ability to identify IE patients with worst prognosis.
Methods:
Consecutive patients hospitalized in our infectious diseases hospital for IE were retrospectively included (January 2023 - February 2025). Clinical, laboratory, echocardiographic data and ACEF score were recorded at admission.
Results:
One hundred forty-three patients were included. Mean age was 65±14 years (69% males). Native valve was involved in 48% and Staphylococcus aureus was responsible in 16% patients. Thirty patients died during follow up. At adjusted Cox regression model, higher ACEF score (AdjHR= 2.944; CI 1.153-7.513; p= 0.024), diabetes mellitus (AdjHR= 4.842; CI 1.017-4.322; p 0.022), cardiac surgery indicated but not performed (AdjHR 5.695; CI 1.503-21.574; p= 0.010) and Staphylococcus aureus infection (AdjHR 1.058; CI 1.012-1.280; p <0.001) were associated with worst outcome. In Kaplan-Meier analysis, these populations had significantly higher in-hospital and early mortality (P-log-rank= 0.024; 0.022; 0.010 and <0.001).
Conclusions:
ACEF score is a simple risk score, integrating clinical and echocardiographic informations, that can be easily calculated in IE. High ACEF score ≥1.30 was associated to worst prognosis in terms of higher mortality during follow up, in both surgically or medically treated subjects. Our results suggest the possible use of ACEF score to stratify risk in newly diagnosed IE, representing a quick tool for clinicians in everyday practice.
Keywords: Age-Creatinine-Ejection Fraction score, ejection fraction, infective endocarditis, risk stratification
INTRODUCTION
Infective endocarditis (IE) is an uncommon disease, which still represents a challenge for the clinicians and public healthcare system.[1] The management of these patients is laborious, starting from the multiparametric diagnosis through therapeutical approach and timing, with a still high burden in terms of morbidity and mortality. Due to patients’ complexity, the necessity to address higher risk patients to referral centers having all diagnostic and therapeutic resources is fundamental. Patients with clear complicated clinical evolution should be promptly transferred.[1] However, many patients without these characteristics can still carry a bad prognosis and the necessity to identify those at higher risk of mortality is crucial.[2,3] Some characteristics as older age, multiple comorbidities or Staphylococcus aureus etiology are more clearly linked to higher mortality. However, other aspects are not yet elucidated, as the impact of sex, echocardiographic parameters or the better risk score before surgery.[4]
Specific risk stratification scores for IE are lacking, and those used are multiparametric and time-consuming.[4,5] In this complex clinical scenario, an easy score with few variables can represent a useful tool to help clinicians in everyday routine. Age-Creatinine-Ejection Fraction (ACEF) score has been proposed in 2009 for mortality risk evaluation in elective cardiac surgery.[6] Its simplicity led to its spread in various other contests as acute coronary syndromes and even valvular surgery in IE.[7,8]
Despite its potentiality, very few studies have evaluated ACEF score in IE.[8,9] The aim of our study was therefore to investigate the validity of ACEF score for the evaluation of in-hospital and short-term mortality in a population of patients hospitalized for IE in a single-center and real-world contest.
MATERIALS AND METHODS
Patient population and study design
All consecutive patients admitted from January 2023 to February 2025 in our infectious diseases hospital (Cotugno Hospital, AO dei Colli, Napoli) and with a confirmed diagnosis of IE during hospitalization were retrospectively evaluated. IE diagnostic workup was performed following updated guidelines and diagnosis was made according to the latest 2023 European Society of Cardiology modified diagnostic criteria.[1] Patients with local pocket device infection without systemic widespread of infection were excluded, as patients with unstable hemodynamic condition at admission. Clinical, laboratory, and echocardiographic characteristics were recorded for each patient and multiple blood samples were collected for culture. A comprehensive transthoracic echocardiogram was performed in all patients at admission and repeated during hospitalization as clinically needed, following the most recent guidelines.[1,10,11] Transesophageal echocardiography was also performed in all patients for the diagnosis and during follow-up, according to the major recommendations and using 3D method when needed[1,12,13] [Figures 1 and 2]. All echocardiographic examinations were reviewed by two expert operators blinded to outcome results. Clinical or telephonic follow-up was performed at 6 months after the hospital discharge. Our study was conducted in accordance with the Declaration of Helsinki and approval by the institutional Ethics Committee.
Figure 1.
Transesophageal echocardiographic examination showing voluminous >1 cm vegetation, indented and very mobile during cardiac cycle, aderent to posterior mitral leaflet (arrow)
Figure 2.
3D transesophageal echocardiographic examination showing vegetation on biological valve cusp (thick arrow). Mitroaortic junction also appears thickened (thin arrow)
Statistical analysis
The Kolmogorov–Smirnov and the Shapiro–Wilk tests were used to evaluate the distribution of continuous data. Mean ± standard deviation and median with interquartile range were used to express, respectively, the normally and nonnormally distributed variables. Categorical variables were expressed as numbers with percentages. Student t-test and Mann–Whitney U-test were used to compare, respectively, the continuous normally and nonnormally distributed variables. Chi-squared test or Fisher exact test when appropriate was used to compare the categorical variables. The unadjusted (univariate) and adjusted (multivariate) risk ratios (RR) both for the outcomes of interest were calculated using Cox regression model and presented as RR with their 95% confidence intervals (CI). All variables showing a P < 0.05 for the association with the primary outcome events at univariable analysis were tested in the multivariable model, with special care to avoid the collinearity. Finally, primary outcome events recurrence free-rates during follow-up were evaluated with Kaplan–Meier method and compared with the Log-rank test. For all tests, a P < 0.05 was considered statistically significant. All the analyses were performed with SPSS Statistics, Version 20.0 (Statistical Product and Service Solution [SPSS], IBM SPSS Statistics, Chicago, Illinois, United States of America).
RESULTS
One hundred and forty-three patients were included in our study (mean age 65 ± 14 years, 69% men). Demographic, clinical, and echocardiographic characteristics are listed in Table 1. Endocarditis relapse was present in 19 patients (13%), native valve was the most frequently involved (48%) and in particular aortic valve (51%). Mitral valve IE was present in 27%, while tricuspid valve infection in 16% and device infection in 24% of the population. Other sites IE include ascending aorta involvement (native or prosthetic material). Both left and right heart involvement was recorded in only 6 patients (4% of the population) and multiple valve infection was present in 21% patients. The pathogen could be identified in most cases (69% of cases) and S. aureus was the most represented (16%).
Table 1.
Demographic, clinical and echocardiographic characteristics of study population, based on outcome
| Overall (n=143) | Death (n=30) | Survival (n=113) | P | |||||
|---|---|---|---|---|---|---|---|---|
| Demographic data | ||||||||
| Age (years) | 65±14 | 71±11 | 64±14 | 0.005 | ||||
| Men | 98 (69) | 21 (70) | 77 (68) | 0.846 | ||||
| Weight (kg) | 73±20 | 76±19 | 71±21 | 0.437 | ||||
| General clinical data | ||||||||
| Hypertension | 90 (63) | 20 (67) | 70 (62) | 0.634 | ||||
| Diabetes mellitus | 43 (30) | 13 (43) | 30 (27) | 0.075 | ||||
| Coronary artery disease | 35 (24) | 11 (37) | 24 (21) | 0.085 | ||||
| Atrial fibrillation/flutter | 34 (24) | 7 (23) | 27 (24) | 0.949 | ||||
| CKD | 57 (40) | 22 (73) | 35 (31) | <0.001 | ||||
| COPD | 26 (18) | 6 (20) | 20 (18) | 0.631 | ||||
| PWID | 9 (6) | 1 (3) | 8 (7) | 0.453 | ||||
| Previous CS | 52 (36) | 10 (33) | 42 (37) | 0.698 | ||||
| Systolic arterial pressure (mmHg) | 121±20 | 121±20 | 122±20 | 0.758 | ||||
| Admission heart rate (bpm) | 80±17 | 83±16 | 79±17 | 0.101 | ||||
| ACEF score, value | 1.44±0.65 | 1.90±0.98 | 1.32±0.47 | <0.001 | ||||
| IE-type | ||||||||
| Endocarditis relapse | 19 (13) | 4 (13) | 15 (13) | 0.993 | ||||
| Native valve IE | 69 (48) | 14 (47) | 55 (49) | 0.845 | ||||
| Prosthetic valve IE | 53 (37) | 10 (33) | 43 (38) | 0.634 | ||||
| Mitral valve IE | 39 (27) | 11 (37) | 28 (25) | 0.194 | ||||
| Aortic valve IE | 73 (51) | 13 (43) | 60 (53) | 0.342 | ||||
| Tricuspid valve IE | 23 (16) | 3 (10) | 20 (18) | 0.308 | ||||
| Device IE | 34 (24) | 6 (20) | 28 (25) | 0.585 | ||||
| Other site IE | 7 (5) | 2 (7) | 5 (4) | 0.613 | ||||
| Multiple valve IE | 30 (21) | 5 (17) | 25 (22) | 0.514 | ||||
| Left side endocarditis | 31 (2) | 7 (23) | 24 (21) | 0.562 | ||||
| Left and right side IE | 6 (4) | 0 | 6 (5) | 0.197 | ||||
| IE-related clinical data | ||||||||
| Admission antibiotic therapy | 83 (58) | 18 (60) | 65 (58) | 0.807 | ||||
| Admission anticoagulant | 85 (59) | 18 (60) | 67 (59) | 0.944 | ||||
| Pathogen identified | 98 (69) | 20 (67) | 78 (69) | 0.805 | ||||
| Admission for fever | 85 (59) | 16 (53) | 69 (61) | 0.443 | ||||
| Admission for IE | 58 (41) | 14 (47) | 44 (39) | 0.443 | ||||
| Spondylodiscitis | 11 (7) | 6 (20) | 5 (4) | 0.004 | ||||
| Peripheral embolization | 69 (48) | 16 (53) | 53 (47) | 0.531 | ||||
| CS performed | 59 (41) | 6 (20) | 53 (47) | 0.002 | ||||
| CS indication but not performed | 24 (17) | 14 (47) | 10 (9) | <0.001 | ||||
| Hospital-stay length (days) | 39±44 | 24±27 | 43±46 | 0.029 | ||||
| Microbiological data | ||||||||
| Staphylococcus aureus | 23 (16) | 10 (33) | 13 (12) | 0.004 | ||||
| Staphylococcus CoagNeg | 20 (14) | 4 (13) | 16 (14) | 0.880 | ||||
| Streptococcus | 20 (14) | 0 | 20 (18) | 0.012 | ||||
| Enterococcus | 18 (13) | 3 (10) | 15 (13) | 0.609 | ||||
| Gram-negative | 17 (12) | 2 (7) | 15 (13) | 0.307 | ||||
| Fungal IE | 2 (1) | 2 (7) | 0 | 0.006 | ||||
| Laboratory data | ||||||||
| Creatinine (mg/dL) | 1.4±1.1 | 2.1±1.5 | 1.2±0.9 | <0.001 | ||||
| Hemoglobin (g/dL) | 11±2 | 10.4±2.0 | 11.2±2.0 | 0.087 | ||||
| Procalcitonin (ng/mL) | 2.4±7.0 | 4.7±9.2 | 1.8±6.3 | 0.059 | ||||
| CRP (mg/dL) | 10.5±11.4 | 12.0±8.8 | 10.0±12.0 | 0.405 | ||||
| Admission glucose (mg/dL) | 123±47 | 135±55 | 120±45 | 0.130 | ||||
| NTproBNP (ng/mL) | 5813±8856 | 12,775±8966 | 3863±7944 | 0.016 | ||||
| Echocardiographic parameters | ||||||||
| Vegetation | 119 (83) | 27 (90) | 93 (82) | 0.308 | ||||
| Multiple vegetations | 45 (31) | 9 (30) | 36 (32) | 0.353 | ||||
| Vegetation (mm) | 15±7 | 16±7 | 14±7 | 0.145 | ||||
| Ejection fraction (%) | 53±10 | 48±14 | 55±9 | 0.001 | ||||
| TAPSE (mm) | 21±4 | 20±4 | 21±3 | 0.512 | ||||
| PAPs (mmHg) | 36±12 | 42±14 | 34±11 | 0.003 | ||||
| Significant valvulopathy | 54 (38) | 11 (37) | 43 (38) | 0.889 | ||||
| Local complications | 65 (45) | 13 (43) | 52 (46) | 0.793 |
Data are expressed as mean±SD or n (%). SD=Standard deviation, ACEF=Age-Creatinin-Ejection Fraction, CKD=Chronic kidney disease, COPD=Chronic obstructive pulmonary disease, CRP=C-reactive protein, CS=Cardiac surgery, IE=Infective endocarditis, NTproBNP=N-terminal pro brain natriuretic peptide, TAPSE=Tricuspid, annular plane systolic excursion, PAPs=Pulmonary artery systolic pressure, PWID=Patient who inject drug
Thirty patients died during the follow-up period. Most patients died during hospitalization (27 patients) and for IE-related complications (13 patients for ongoing sepsis; 6 patients for complications due to septic embolizations). No statistical difference was recorded in terms of IE-type [relapse, number of valves, prosthetic or native valve, respectively P = 0.993, P = 0.634, P = 0.845; Table 1]. Patients with chronic kidney disease, referral for spondilodiscitis, and elevated admission N-terminal probrain natriuretic peptide (NTproBNP) levels (respectively, P < 0.001; P = 0.004; P = 0.004) experimented worst prognosis in terms of mortality. Patients submitted to cardiac surgery, with reduced left ventricular ejection fraction or elevated systolic pulmonary arterial pressure also carried worst outcomes (respectively, P = 0.002, P = 0.001, and P = 0.003) [Table 1]. Finally, ACEF score was significantly higher in patients who died (P < 0.001).
No significant differences were recorded between patients with lower or higher ACEF score values (ACEF score < or ≥1.30) in terms of IE type, IE-related clinical data, or microbiological agent [Table 2]. Higher ACEF score patients were more frequently affected by coronary artery diseas (P = 0.005), chronic kidney disease (P = 0.004) and had higher levels of procalcitonin and NTproBNP at admission (respectively, P = 0.018 and P < 0.001). ACEF score was not significantly higher in patients treated with surgery compared to medical therapy (P = 0.390).
Table 2.
Demographic, clinical, and echocardiographic characteristics of the study population, based on age-creatinin-ejection fraction score
| Overall (n=143) | ACEF score<1.30 (n=74) | ACEF score≥1.30 (n=69) | P | |||||
|---|---|---|---|---|---|---|---|---|
| Demographic data | ||||||||
| Age (years) | 65±14 | 64±14 | 67±14 | 0.204 | ||||
| Men | 98 (69) | 48 (65) | 50 (72) | 0.328 | ||||
| Weight (kg) | 73±20 | 72±18 | 74±23 | 0.802 | ||||
| General clinical data | ||||||||
| Hypertension | 90 (63) | 44 (59) | 46 (67) | 0.373 | ||||
| Diabetes mellitus | 43 (30) | 17 (23) | 26 (38) | 0.055 | ||||
| Coronary artery disease | 35 (24) | 11 (15) | 24 (35) | 0.005 | ||||
| Atrial fibrillation/flutter | 34 (24) | 17 (23) | 17 (25) | 0.815 | ||||
| CKD | 57 (40) | 21 (28) | 36 (52) | 0.004 | ||||
| COPD | 26 (18) | 10 (14) | 16 (23) | 0.177 | ||||
| PWID | 9 (6) | 8 (11) | 1 (1) | 0.021 | ||||
| Previous CS | 52 (36) | 26 (35) | 26 (38) | 0.752 | ||||
| Systolic arterial pressure (mmHg) | 121±20 | 121±19 | 122±21 | 0.711 | ||||
| Admission heart rate (bpm) | 80±17 | 81±16 | 80±18 | 0.705 | ||||
| ACEF score, value | 1.44±0.65 | 1.04±0.19 | 1.86±0.70 | - | ||||
| IE-type | ||||||||
| Endocarditis relapse | 19 (13) | 7 (9) | 12 (17) | 0.163 | ||||
| Native valve IE | 69 (48) | 41 (55) | 28 (41) | 0.076 | ||||
| Prosthetic valve IE | 53 (37) | 25 (34) | 28 (41) | 0.400 | ||||
| Mitral valve IE | 39 (27) | 25 (34) | 14 (20) | 0.070 | ||||
| Aortic valve IE | 73 (51) | 38 (51) | 35 (51) | 0.940 | ||||
| Tricuspid valve IE | 23 (16) | 14 (19) | 9 (13) | 0.339 | ||||
| Device IE | 34 (24) | 15 (20) | 19 (28) | 0.308 | ||||
| Other site IE | 7 (5) | 4 (5) | 3 (4) | 0.770 | ||||
| Multiple valve IE | 30 (21) | 16 (22) | 14 (20) | 0.845 | ||||
| Left side endocarditis | 96 (67) | 50 (68) | 46 (67) | 0.909 | ||||
| Left and right side IE | 6 (4) | 2 (3) | 4 (6) | 0.356 | ||||
| IE-related clinical data | ||||||||
| Admission antibiotic therapy | 83 (58) | 46 (62) | 37 (54) | 0.301 | ||||
| Admission anticoagulant | 85 (59) | 39 (53) | 46 (67) | 0.089 | ||||
| Pathogen identified | 98 (69) | 51 (69) | 47 (68) | 0.918 | ||||
| Admission for fever | 85 (59) | 43 (58) | 42 (61) | 0.737 | ||||
| Admission for IE | 58 (41) | 31 (427) | 27 (39) | 0.737 | ||||
| Spondylodiscitis | 11 (8) | 5 (7) | 6 (9) | 0.664 | ||||
| Peripheral embolization | 69 (48) | 37 (50) | 32 (46) | 0.665 | ||||
| CS performed | 59 (41) | 34 (46) | 25 (36) | 0.390 | ||||
| CS indication but not performed | 24 (17) | 12 (16) | 12 (17) | 0.851 | ||||
| Hospital-stay length (days) | 39±44 | 39±24 | 40±58 | 0.878 | ||||
| Microbiological data | ||||||||
| Staphylococcus aureus | 23 (16) | 12 (16) | 11 (16) | 0.966 | ||||
| Staphylococcus CoagNeg | 20 (14) | 9 (12) | 11 (16) | 0.513 | ||||
| Streptococcus | 20 (14) | 13 (18) | 7 (10) | 0.201 | ||||
| Enterococcus | 18 (13) | 8 (11) | 10 (14) | 0.505 | ||||
| Gram-negative | 17 (12) | 7 (9) | 10 (14) | 0.351 | ||||
| Fungal IE | 2 (1) | 1 (1) | 1 (1) | 0.960 | ||||
| Laboratory data | ||||||||
| Creatinine (mg/dL) | 1.4±1.1 | 0.9±0.4 | 1.8±1.5 | <0.001 | ||||
| Hemoglobin (g/dL) | 11±2 | 11.0±1.9 | 11.0±2.2 | 0.894 | ||||
| Procalcitonin (ng/mL) | 2.4±7.0 | 1.0±2.5 | 3.9±9.7 | 0.018 | ||||
| CRP (mg/dL) | 10.5±11.4 | 10.6±10.8 | 10.3±12.0 | 0.865 | ||||
| Admission glucose (mg/dL) | 123±47 | 118±36 | 128±57 | 0.213 | ||||
| NTproBNP (ng/mL) | 5813±8856 | 1223±1288 | 11,714±10,870 | <0.001 | ||||
| Echocardiographic parameters | ||||||||
| Vegetation | 120 (84) | 55 (74) | 65 (94) | 0.186 | ||||
| Multiple vegetations | 45 (31) | 25 (34) | 20 (29) | 0.840 | ||||
| Vegetation (mm) | 15±7 | 15±7 | 14±7 | 0.916 | ||||
| Ejection fraction (%) | 53±10 | 58±5 | 48±12 | <0.001 | ||||
| TAPSE (mm) | 21±4 | 21±3 | 21±3 | 0.643 | ||||
| PAPs (mmHg) | 36±12 | 36±13 | 36±11 | 0.785 | ||||
| Significant valvulopathy | 54 (38) | 27 (36) | 27 (39) | 0.745 | ||||
| Local complications | 65 (45) | 33 (45) | 32 (46) | 0.831 |
Data are expressed as mean±SD or n (%). ACEF=Age-Creatinin-Ejection Fraction, CKD=Chronic kidney disease, COPD=Chronic obstructive pulmonary disease, CRP=C-reactive protein, CS=Cardiac surgery, IE=Infective endocarditis, NTproBNP=N-terminal pro brain natriuretic peptide, TAPSE=Tricuspid, annular plane systolic excursion, PAPs=Pulmonary artery systolic pressure, PWID=Patient who inject drug, SD=Standard deviation
At adjusted Cox regression model, only higher ACEF score (adjusted hazard ratio [AdjHR] =2.944; CI 1.153–7.513; P = 0.024), diabetes mellitus (AdjHR = 4.842; CI 1.017–4.322; P 0.022), cardiac surgery indicated but not performed (AdjHR 5.695; CI 1.503–21.574; P = 0.010) and S. aureus infection (AdjHR 1.058; CI 1.012–1.280; P < 0.001) were associated with worst outcome during the follow-up [Table 3]. Moreover, in Kaplan–Meier analysis, these populations showed a significantly higher in-hospital and early mortality (P-log-rank = 0.024; P-log-rank = 0.022; P-log-rank = 0.010 and P-log-rank < 0.001, respectively) [Figure 3].
Table 3.
Unadjusted and adjusted odds ratio for mortality
| HR | CI (95%) | P | Adjusted HR | CI (95%) | P | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age (years) | 1.041 | 1.008–1.076 | 0.016 | |||||||||
| Diabetes mellitus, n (%) | 2.096 | 1.017–4.322 | 0.045 | 4.842 | 1.262–18.578 | 0.022 | ||||||
| CKD, n (%) | 4.874 | 2.168–10.956 | <0.001 | |||||||||
| Admission heart rate (bpm) | 1.023 | 1.001–1.046 | 0.038 | |||||||||
| ACEF score | 2.925 | 1.975–4.334 | <0.001 | 2.944 | 1.153–7.513 | 0.024 | ||||||
| Spondylodiscitis | 0.365 | 0.149–0.896 | 0.028 | |||||||||
| CS indication but not performed | 6.594 | 2.644–16.448 | <0.001 | 5.695 | 1.503–21.574 | 0.010 | ||||||
| Hospital-stay length | 0.937 | 0.912–0.963 | <0.001 | 0.946 | 0.914–0.978 | 0.001 | ||||||
| Staphylococcus aureus | 1.291 | 1.136–1.262 | 0.002 | 1.058 | 1.012–1.280 | <0.001 | ||||||
| Creatinine (mg/dL) | 1.408 | 1.187–1.671 | <0.001 | |||||||||
| Ejection fraction (%) | 0.947 | 0.921–0.973 | <0.001 | |||||||||
| PAPs (mmHg) | 1.037 | 1.014–1.061 | 0.002 |
HR=Hazard ratio, CI=Confidence interval, ACEF=Age-Creatinin-Ejection Fraction, CKD=Chronic kidney disease, CS=Cardiac surgery, PAPs=Pulmonary artery systolic pressure
Figure 3.
Kaplan–Meier analysis: Significant increased mortality risk in patients with higher Age-Creatinine-Ejection Fraction score (a), diabetes mellitus (b), cardiac surgery indicated but not performed (c) and Staphylococcus aureus infection (d). ACEF = Age-Creatinine-Ejection Fraction
DISCUSSION
In our population, the presence of ACEF score ≥1.30 was associated with higher in hospital and short-term death. Patients’ age, renal function, and baseline ejection fraction were associated with worst outcome, more than specific IE-related characteristics as prosthetic or native valve IE, first episode or relapse, vegetation length, or embolization. Moreover, as previously underlined, avoiding surgery when indicated represents a clear turning point which carries a negative impact on prognosis.[14,15]
IE patients are very delicate and heterogeneous in terms of prognosis and management. The identification of patients with higher mortality is a crucial point and is based upon patients’ characteristics, entity of cardiac and systemic involvement, and causative micro-organism. ESC guidelines recommend the transfer of patients with complicated clinical evolution to referral hospitals, capable of a multispecialty management of disease.[1] The correct patient overview is not always easy, due to the fact that many factors have to be checked and may themselves require a high expertise. For example, transesophageal echocardiography, especially 3D, represents a fundamental tool for the study of cardiac involvement and complications,[13,16] however its availability can be limited in peripherical centers. On the other hand, the absence of a defined local “endocarditis network” can determine a difficulty in inter-hospitals communication and patients transfer. In the real-world practice, patients with unstable hemodynamic or clear surgical indication are more easily transferred, but those who need monitoring and longer hospitalization can be more difficult to manage. In this contest, the possibility to screen patients with simple accurate risk scores represents a good tool to help clinicians in making decisions and address patients to the needed intensity of care.
Many risk stratification scores have been proposed in IE patients over the years. Cardiac surgery tools as EURO score and STS score have been modified to better fit endocarditis patients, but their accuracy was not clearly improved in this contest and score calculation could result complex and time-consuming. Specific endocarditis scores have also been suggested, focusing in most cases in surgical risk estimation more than global IE risk evaluation, with variable performance and without a clear superiority of one score compared to another.[17,18] In this complex scenario, a simple and quick score can be useful and may help not only for surgical indication but also in the management of patients when diagnosis of IE is made. ACEF score combines 3 simple data as age, renal and cardiac function, which are usually available for every patient. Previous studies have highlighted the importance of ACEF score in patients treated surgically. Wei et al. retrospectively analyzed 1019 patients with IE, screened in 6 years, and found that ACEF score >0.8 was associated with higher in-hospital and long-term mortality. In patients who underwent surgical treatment compared to conservative approach, its predictive value was higher especially to assess in-hospital death.[9] In another study, 130 patients with IE suitable for valve surgery over a 9-year period were retrospectively included and ACEF score with postoperative lactate levels were reported.[8] The presence of higher ACEF score associated with higher lactate levels predicted worst mortality during the follow-up. In our population of 143 patients with IE diagnosis during 2 years’ period, 41% of patients underwent surgery and ACEF score was not significantly different in these patients compared to medically treated ones. The score was calculated at admission and when the value was ≥1.30, it was able to identify patients with higher in-hospital and early mortality, in both surgically and medically treated subjects. ACEF score does not want to replace the need of more accurate risk evaluation scores but can be used as first screening tool in patients with the diagnosis of IE. Patients with higher score need a close monitoring and when the score is integrated with other more specific endocarditis parameters (i.e. echocardiographic information, embolic dissemination, and microbiology response), it could guide in decision-making as transfer to referral centers or cardiac surgery indication.
Study limitations
Our study has some limitations. It is a single-center experience in an infectious diseases hospital and this aspect may have influenced the management of patients and may not be generalized in other contexts. This is a retrospective study and data were collected from existing medical records that may have missed relevant clinical information. Moreover, multivariable analysis was performed to adjust for confounding factors, but residual confounding variables may have been missed even due to the relatively small population.
CONCLUSION
ACEF score is a simple risk score that can be easily calculated in patients with IE diagnosis. In our population, high ACEF score ≥1.30 identifies patients with higher mortality during follow-up, in both surgically or medically treated subjects. Our results suggest its possible use to stratify risk in newly diagnosed IE, guiding decision-making in these patients. Further studies are required to better identify ACEF score usefulness and applicability in different clinical and local contests.
Ethical statement
The study was approved by the territorial Ethic Committee n.2337-2025.
Conflicts of interest
There are no conflicts of interest.
Funding Statement
Nil.
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