ESC HEART FAILURE ORIGINAL ARTICLE ESC Heart Failure 2022; 9: 606–613 Published online 22 November 2021 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ehf2.13707 Risk stratification in heart failure decompensation in the community: HEFESTOS score José-María Verdu-Rotellar1,2,3* , Rosa Abellana4, Helene Vaillant-Roussel5, Lea Gril Jevsek6, Radost Assenova7, Djurdjica Kasuba Lazic8, Peter Torsza9, Liam George Glynn10, Heidrun Lingner11, Jacopo Demurtas12,13, Hans Thulesius14,15, Miguel Angel Muñoz1,2,3 and HEFESTOS group† 1 Gerencia Territorial de Barcelona, Institut Català de la Salut, Barcelona, Spain; 2Unitat de Suport a la Recerca de Barcelona, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 3School of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain; 4Departament de Fonaments Clinics, Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain; 5Faculty of Medicine, UPU ACCePPT, Department of General Practice, CHU, Direction de La Recherche Clinique et de l’Innovation, Clermont Auvergne University, Clermont-Ferrand, France; 6Medical Faculty, University of Maribor, Maribor, Slovenia; 7Department of Urology and General Medicine, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria; 8Department of Family Medicine “Andrija Stampar” School of Public Health, School of Medicine University of Zagreb, Zagreb, Croatia; 9Semmelweis University, Budapest, Hungary; 10Health Research Institute and Graduate Entry Medical School, University of Limerick, Limerick, Ireland; 11Hannover Medical School-Center for Public Health and Healthcare, Hannover, Germany; 12Primary Care Department, Azienda Usl Toscana Sud Est, Grosseto, Italy; 13Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy; 14Department of Clinical Sciences, Lund University, Lund, Sweden; and 15Department of Medicine and Optometry, Linnaeus University, Växjö, Sweden Abstract Aims Because evidence regarding risk stratification predicting prognosis of patients with heart failure (HF) decompensation attended in primary care is lacking, we developed and externally validated a model to forecast death/hospitalization during the first 30 days after an episode of decompensation. The predictive model is based on variables easily obtained in primary care settings. Methods and results HEFESTOS is a multinational study consisting of a derivation cohort of HF patients recruited in 14 primary healthcare centres in Barcelona and a validation cohort from primary healthcare in 9 other European countries. The derivation and validation cohorts included 561 and 250 patients, respectively. Percentages of women in the derivation and validation cohorts were 56.3% and 47.6% (P = 0.026), respectively. Mean age was 82.2 years (SD 8.03) in the derivation cohort, and 79.3 years (SD 10.3) in the validation one (P = 0.001). HF with preserved ejection fraction represented 72.1% in the derivation cohort and 58.8% in the validation one (P = 0.004). Mortality/hospitalization during the first 30 days after a decom- pensation episode was 30.5% and 26% (P = 0.225) for the derivation and validation cohorts, respectively. Multivariable logistic regression models were performed to develop a score of risk. The identified predictors were worsening of dyspnoea [odds ratio (OR): 2.5; P = 0.001], orthopnoea (OR: 2.16; P = 0.01), paroxysmal nocturnal dyspnoea (OR: 2.25; P = 0.01), crackles (OR: 2.35; P = 0.01), New York Heart Association functional class III/IV (OR: 2.11; P = 0.001), oxygen saturation ≤ 90% (OR: 4.98; P < 0.001), heart rate > 100 b.p.m. (OR: 2.72; P = 0.002), and previous hospitalization due to HF (OR: 2.45; P < 0.001). The model showed an area under the curve (AUC) of 0.807, 95% confidence interval (CI): [0.770; 0.845] in the derivation cohort and AUC 0.73, 95% CI: [0.660; 0.808] in the validation one. No significant differences between both cohorts were observed (P = 0.08). Regarding probability of hospitalization/death, three risk groups were defined: low <5%, medium 5–20%, and high >20%. Outcome incidence was 2.7% for the low-risk group, 12.8% for medium risk, and 46.2% for high risk in the derivation cohort, and 9.1%, 12.9%, and 39.6% in the validation one. Conclusions The HEFESTOS score, based on variables easily accessible in a community setting and validated in an external European cohort, properly predicted the risk of death/hospitalization during the first 30 days after an HF decompensation episode. Keywords Primary care; Heart failure; Decompensation; Risk stratification Received: 18 June 2021; Revised: 1 October 2021; Accepted: 29 October 2021 *Correspondence to: José-María Verdu-Rotellar, Unitat de Suport a la Recerca, Gerència Territorial de Barcelona, Institut Català de la Salut-IDIAP Jordi Gol Departament de Salut, Generalitat de Catalunya, Carrer Sardenya 375, Entlo, 08025 Barcelona, Spain. Tel: 0034 932 073 760; Fax: 0034 933070812. Email: verdujm@gmail.com † The list of the HEFESTOS investigators is given in the Supporting Information, Appendix S1. © 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. Risk stratification in heart failure decompensation in the community 607 Introduction The derivation cohort consisted of 561 heart failure pa- tients recruited in 14 primary care centres in Barcelona Heart failure decompensation is a major cause of hospitaliza- (Spain) between March 2015 and June 2019. The validation tion and is associated with high in-hospital mortality follow- cohort included 250 primary care patients from 9 European ing discharge and increased readmission rates. Moreover, it countries (France, Ireland, Germany, Italy, Slovenia, Croatia, represents one of the greatest economic and health burdens Bulgaria, Hungary, and Sweden) recruited between March for the public healthcare system.1–4 2017 and June 2019. Heart failure decompensation is defined as the worsening Inclusion criteria were patients aged ≥45 years with a diag- of the symptoms and characteristic signs of this disease that nosis of heart failure in their medical records and attended in require unplanned visits to outpatient clinics and emergency primary care for heart failure decompensation. Recruitment departments, or hospitalization.5,6 was carried out after the participants provided informed, A number of studies in different countries have developed signed consent. models that predict the risk of hospitalization or death in pa- Heart failure decompensation was considered as such tients with this condition. These heterogeneous models, how- when the patient had any of the following signs or symptoms: ever, present high variability and have not been widely increased dyspnoea, unexplained weight gain, or appear- implemented in routine clinical practice. Almost all of them ance/increase of peripheral oedema. included hospitalized patients, and the few that assessed pa- Patients were excluded if they presented severe psychiat- tients attended in primary care or outpatient clinics were ric illness or cognitive impairment, were unable to complete based on variables not accessible to the majority of primary clinical examinations, or had been hospitalized in the previ- healthcare professionals.7,8 In addition, they showed a re- ous 30 days due to heart failure decompensation. duced predictive capacity in patients with low risk of death.9 The Helsinki ethics declaration was followed at all times. Most patients presenting heart failure decompensation Participants provided informed consent, and the study proto- are attended in primary care and contact their family doctor col was approved by the research ethics committees of all the during the month prior to being hospitalized.10 participant countries. In the primary care setting, some laboratory tests such as Data were collected using specifically designed webpage natriuretic peptides and echocardiographic parameters, forms, and quality checks were carried out every month. which could help to better stratify risk of heart failure, are Potential explanatory variables were sociodemographic data, not usually available. comorbidity, clinical examination, pulse oximetry, electrocar- The objective of our study was, therefore, to develop and diography, and medication. Ejection fraction assessments validate a risk score based on clinical variables easily accessi- were collected when available. Variables were recorded at ble in primary care to predict hospitalization/death at the time of study inclusion. short term (30 days) of patients presenting heart failure The outcome variables were hospitalization for causes re- decompensation. lated to heart failure decompensation and/or mortality from any cause in the 30 days after the index consultation date. This information was obtained by accessing medical records and/or by telephone contacts with patients or their relatives. Methods HEFESTOS is an international, multicentre, prospective cohort study. It is focused on creating a predictive prognostic model Statistical analysis for heart failure patients who suffer a decompensation episode initially treated in primary care. Categorical data are expressed using frequencies and per- The project was originally designed in Spain where a pilot centages, and continuous ones with means and standard de- study was carried out to test its feasibility (March–July 2015). viations (SD). The χ 2 test was used to study the association The protocol was discussed within the European General between outcome and categorical variables, and Student’s Research Network where family doctors from nine countries t-test to assess differences between continuous variables. agreed to collaborate in the project. The European research The associations were also evaluated in terms of odds ratio. team worked on standardizing methodological procedures Clinically meaningful variables showing a significant level in and extended the study to their respective countries. The univariate analysis (P < 0.05) were thereafter included in study protocol was written in English and translated into the multivariable logistic model. A backward stepwise the languages of the participant countries (French, German, method was employed to identify independent risk predic- Italian, Slovenian, Croatian, Bulgarian, Hungarian, and tors with P < 0.05 for inclusion or deletion. The overall per- Swedish). Ethical approval was sought and granted from each formance of the model was calculated with the Brier score country taking part. and Nagelkerke R2. ESC Heart Failure 2022; 9: 606–613 DOI: 10.1002/ehf2.13707 608 J.-M. Verdu-Rotellar et al. The discriminative ability of the model was assessed by patients attended in primary care and not initially referred Harrell’s C-index (area under the receiver-operating charac- to the hospital, 12.5% were eventually admitted, and 2.2% teristic curve). The calibration of the model was checked died in the following 30 days. using the Hosmer–Lemeshow goodness-of-fit test (by deciles Of the patients who needed to be hospitalized or died in of the predicted probability) and plotting the observed and the first 30 days after the episode, those attended in primary predicted probabilities of the model grouped into tenths care settings presented worse clinical parameters [crackles, using deciles. The external validation of the predictive higher respiratory and heart rate, lower ejection fraction, par- model was also evaluated in terms of calibration and oxysmal dyspnoea, orthopnoea, and worse functional New discrimination. York Heart Association (NYHA) functional class class]. We In creating the prognostic risk score, each final predictor did not find any association with the outcomes regarding co- had its beta-coefficient divided by the smallest figure and morbidity with the exception of renal failure and chronic ob- then rounded to the nearest integer number.10 The predic- structive pulmonary disease. Such conditions were more tors of a particular patient thus ranged from 0 to 23. Analysis commonly observed in patients who were hospitalized or de- was performed using R software for Windows Version 4.0.3 ceased. Previous hospitalization due to heart failure decom- (R project for statistical computing; Vienna, Austria). pensation (between 31 and 365 days prior to inclusion) was also related to outcome occurrence (Table 2). All these vari- ables were included in the analysis to create the multivariate predictive model. Results The model confirmed that previous hospitalization due to heart failure decompensation, presence of crackles, paroxys- A total of 561 consecutively recruited patients were included mal nocturnal dyspnoea, orthopnoea, NYHA III/IV status, in the derivation cohort (women = 56.3%), and 250 patients worsening in NYHA functional status, having a heart (women = 47.6%) in the validation one. Mean age was 82.2 rate > 100 b.p.m., and oxygen saturation ≤ 90% were inde- (SD 8.03) and 79.3 (SD 10.3) years in the derivation and vali- pendent predictors for hospitalization/death in the first dation cohorts, respectively. 30 days following the decompensation episode. In the validation cohort, there was a greater percentage of This predictive model demonstrated a good discrimination men and participants were younger and presented lower co- ability with an area under the curve (AUC) at 30 days of morbidity, with the exception of coronary heart disease, 0.807, 95% confidence interval (CI): [0.770; 0.845]. Figure 1 which was higher (Table 1). shows the receiver-operating characteristic curve (ROC) of Mortality or hospitalization during the first 30 days after an the predicted probabilities (black line). In addition, overall episode of decompensation was 30.5% for the derivation performance using the Bier score had a rating of 0.015, which cohort and 26.0% for the validation one (P = 0.225). Of the was below 0.25, and a Nagelkerke’s R2 of 0.341. In terms of 561 derivation cohort patients, 450 were attended in primary agreement between the predicted and observed probabilities care settings (56% oral treatment adjustment, 43% intrave- of the risk of hospitalization/death, the χ 2 Hosmer and nous diuretic medication), and 111 were referred to the hos- Lemeshow test was 9 (P = 0.3), indicating no evidence of poor pital emergency room. fit. Additionally, the calibration plots of the model showed a Among those referred to the emergency room, 85% were good calibration, because the triangles lay around a 45 line hospitalized and 8.1% died in the following 30 days. Of the of the plot with a slope of 1 [Figure 2A]. Table 1 Characteristics of participant heart failure patients according to the study cohorts [All] Spain cohort Europe cohort N = 811 N = 561 N = 250 OR P value N Sex 0.026 811 Male 376 (46.4%) 245 (43.7%) 131 (52.4%) Ref. Female 435 (53.6%) 316 (56.3%) 119 (47.6%) 0.70 [0.52;0.95] Age (mean, SD) 81.3 (8.89) 82.2 (8.03) 79.3 (10.3) 0.96 [0.95;0.98] 0.001 808 Diabetes mellitus 358 (44.3%) 267 (47.6%) 91 (36.7%) 0.64 [0.47;0.87] 0.005 809 Cardiac ischaemia 321 (39.8%) 191 (34.0%) 130 (52.8%) 2.17 [1.60;2.95] 0.001 807 Chronic or paroxysmal atrial fibrillation 454 (56.1%) 334 (59.5%) 120 (48.4%) 0.64 [0.47;0.86] 0.004 809 Stroke 120 (14.9%) 82 (14.6%) 38 (15.4%) 1.06 [0.69;1.61] 0.861 808 Chronic renal disease 342 (42.3%) 256 (45.6%) 86 (34.7%) 0.63 [0.46;0.86] 0.005 809 Smoking 77 (9.58%) 46 (8.2%) 31 (12.8%) 1.64 [1.00;2.65] 0.059 804 Chronic obstructive pulmonary disease 223 (27.6%) 169 (30.1%) 54 (21.8%) 0.65 [0.45;0.92] 0.018 809 Hypertension 684 (88.0%) 489 (87.2%) 195 (90.3%) 1.36 [0.83;2.33] 0.283 777 OR, odds ratio; SD, standard deviation. ESC Heart Failure 2022; 9: 606–613 DOI: 10.1002/ehf2.13707 Risk stratification in heart failure decompensation in the community 609 Table 2 Summary description according to hospitalization or/and death during the first 30 days after an episode of decompensation in heart failure patients [All] No outcome Hospitalization or death N = 561 N = 390 N = 171 Odds ratio P value N Demographics Sex 561 Male 245 (43.7%) 167 (42.8%) 78 (45.6%) Ref. Ref. Female 316 (56.3%) 223 (57.2%) 93 (54.4%) 0.89 [0.62;1.28] 0.540 Age (mean, SD) 82.2 (8.03) 82.0 (8.02) 82.7 (8.05) 1.01 [0.99;1.03] 0.369 561 Personal history Diabetes mellitus 267 (47.6%) 179 (45.9%) 88 (51.5%) 1.25 [0.87;1.79] 0.226 561 Cardiac ischaemia 191 (34.0%) 130 (33.3%) 61 (35.7%) 1.11 [0.76;1.62] 0.590 561 Chronic or paroxysmal atrial fibrillation 334 (59.5%) 229 (58.7%) 105 (61.4%) 1.12 [0.77;1.62] 0.554 561 Stroke 82 (14.6%) 57 (14.6%) 25 (14.6%) 1.00 [0.59;1.65] 0.990 561 Chronic renal disease 256 (45.6%) 167 (42.8%) 89 (52.0%) 1.45 [1.01;2.08] 0.044 561 Smoking 46 (8.20%) 34 (8.72%) 12 (7.02%) 0.80 [0.38;1.54] 0.512 561 Chronic obstructive pulmonary disease 169 (30.1%) 106 (27.2%) 63 (36.8%) 1.56 [1.06;2.29] 0.023 561 Hypertension 489 (87.2%) 337 (86.4%) 152 (88.9%) 1.25 [0.73;2.24] 0.426 561 Previous hospitalization in the last year 132 (23.5%) 74 (19.0%) 58 (33.9%) 2.19 [1.46;3.29] <0.001 561 Anamnesis and exploration Crackles 347 (61.9%) 209 (53.6%) 138 (80.7%) 3.60 [2.37;5.61] <0.001 561 Heart rate > 100 b.p.m. 66 (11.8%) 34 (8.72%) 32 (18.7%) 2.41 [1.42;4.07] 0.001 561 Respiratory frequency > 24 per minute 86 (15.4%) 46 (11.9%) 40 (23.4%) 2.27 [1.41;3.63] 0.001 559 Body mass index 30.9 (6.08) 31.1 (6.19) 30.3 (5.80) 0.98 [0.95;1.01] 0.159 546 Temperature 36.0 (0.58) 36.0 (0.56) 36.1 (0.62) 1.38 [1.01;1.89] 0.046 557 Oedema 452 (80.6%) 312 (80.0%) 140 (81.9%) 1.13 [0.71;1.81] 0.613 561 Systolic blood pressure (mmHg) 136 (22.1) 135 (23.2) 137 (19.6) 1.00 [1.00;1.01] 0.374 809 Diastolic blood pressure (mmHg) 71.9 (12.3) 72.1 (11.8) 71.4 (13.4) 0.99 [0.98;1.01] 0.493 561 Ejection fraction rate 561 ≥50% 381 (67.9%) 281 (72.1%) 100 (58.5%) Ref. Ref. 40–49% 50 (8.91%) 30 (7.69%) 20 (11.7%) 1.87 [1.00;3.44] 0.049 ≤39% 71 (12.7%) 40 (10.3%) 31 (18.1%) 2.18 [1.28;3.67] 0.004 Unknown 59 (10.5%) 39 (10.0%) 20 (11.7%) 1.44 [0.79;2.57] 0.228 Signs and symptoms Paroxysmal nocturnal dyspnoea 160 (28.5%) 75 (19.2%) 85 (49.7%) 4.14 [2.80;6.14] <0.001 561 Orthopnoea 272 (48.5%) 150 (38.5%) 122 (71.3%) 3.97 [2.70;5.90] <0.001 561 NYHA 561 I or II 153 (27.3%) 135 (34.6%) 18 (10.5%) Ref. Ref. III or IV 408 (72.7%) 255 (65.4%) 153 (89.5%) 4.46 [2.68;7.83] <0.001 Worsening in NYHA status 416 (74.2%) 267 (68.5%) 149 (87.1%) 3.10 [1.92;5.21] <0.001 561 Weight gain 192 (34.2%) 116 (29.7%) 76 (44.4%) 1.89 [1.30;2.74] 0.001 561 Worsening or occurrence of ankle oedemas 364 (65.1%) 242 (62.4%) 122 (71.3%) 1.50 [1.02;2.23] 0.040 559 Time elapsed since the first symptoms 9.31 (9.74) 9.80 (10.5) 8.18 (7.68) 0.98 [0.96;1.00] 0.073 553 Complementary explorations ECG 557 Normal 72 (12.9%) 54 (14.0%) 18 (10.6%) Ref. Ref. Abnormal 485 (87.1%) 333 (86.0%) 152 (89.4%) 1.36 [0.78;2.47] 0.279 Oxygen saturation ≤ 90% 37 (6.61%) 11 (2.83%) 26 (15.2%) 6.09 [2.99;13.2] <0.001 560 Treatments ACEi/ARB 421 (75.0%) 278 (71.3%) 143 (83.6%) 2.05 [1.31;3.30] 0.003 561 Beta-blockers 409 (72.9%) 284 (72.8%) 125 (73.1%) 1.01 [0.68;1.53] 1.000 561 Aldosterone 121 (21.6%) 74 (19.0%) 47 (27.5%) 1.62 [1.06;2.46] 0.032 561 Loop diuretics 472 (84.1%) 321 (82.3%) 151 (88.3%) 1.61 [0.96;2.82] 0.071 561 ACEi, angiotensin-converting enzyme inhibitors; ARB, angiotensin receptor blockers; ECG, electrocardiogram; NYHA, New York Heart Association; SD, standard deviation. External validation Scoring system The AUC of the model in the validation cohort was 0.73, 95% In order to build a score able to predict the risk of CI: [0.660; 0.808] (Figure 1, grey line), which was a little lower hospitalization/death at 30 days, we divided the model re- than in derivate one although no significantly statistical differ- gression coefficients by the beta coefficient of males (0.35) ences between them were observed (P = 0.08). The calibra- and rounded them to the nearest integer (Table 3). The score tion plot of the model also demonstrated good calibration ranked from 0 to 23: 0 points corresponding to female gen- between the observed and predicted probabilities with the der, no previous hospitalization, not presenting crackles or exception of strongly predicted probabilities [Figure 2B]. paroxysmal nocturnal dyspnoea, NYHA class I/II, without ESC Heart Failure 2022; 9: 606–613 DOI: 10.1002/ehf2.13707 610 J.-M. Verdu-Rotellar et al. NYHA worsening, heart rate < 100 b.p.m., and oxygen Thus, low risk for hospitalization/death corresponded to saturation > 90%. The estimated risk for 0 points was scores ≤3, medium risk 4–7, and high risk ≥8. In Figure 3, 0.017, and for 23 points 0.980. The estimated risk for each the predicted probabilities in days for hospitalization/death score point is shown in Figure 3. The AUC for the score in 30 days after the decompensation episode are plotted. the derivation cohort was 0.89, 95% CI: [0.78; 0.84] and In the derivation cohort, cumulative incidence for low-risk 0.73, 95% CI: [0.66; 0.81] in the validation one. patients was 2.7%, medium risk 12.8%, and high risk 46.2%. In accordance with published studies,7,8,11,12 we created In the validation cohort, accumulated incidences were similar three groups of risk. A <5% probability of hospitalization/ with the exception of the low-risk group (Table 4). death 30 days after the decompensation episode indicated A URL has been created to facilitate clinicians and re- patients at low risk, 5–20% medium risk, and >20% high risk. searchers access to the calculator: https://rabellana. shinyapps.io/HEFESTOS_Score/. Figure 1 Receiver-operating characteristic curve of the predicted proba- bilities of short-term hospitalization or death in decompensated heart failure patients. Discussion Summary of main findings This study presents a multinational, externally validated score, the HEFESTOS, which predicts hospitalization/death at short term (30 days) due to a heart failure decompensation episode in patients attended in primary care. It is based on clinical variables easily available for primary care professionals. The identified independent predictors were hospitalization in the previous year, presence of crackles, paroxysmal noctur- nal dyspnoea, or orthopnoea, NYHA III/IV, worsening in NYHA status, heart rate > 100 b.p.m., and oxygen saturation ≤ 90%. This score facilitates risk stratification in community-living heart failure patients. C-statistics of 0.81, 95% CI: [0.77; 0.84] in the derivation and 0.74, 95% CI: [0.66; 0.81] in the validation cohorts, respectively, were reported. To date, these are the highest ranges of predictive scores published in studies on heart failure decompensation.7,8 Figure 2 Calibration plot of the predictive model for short-term heart failure hospitalization or death. (A) Derivation cohort. (B) Validation cohort. The triangles denote the mean predicted and observed event probabilities for patients grouped into tenths using deciles. The grey dashed line denotes perfect calibration. The distribution of calculated predicted probabilities is overlaid along the horizontal axis. 1 indicates patients with hospitalization or death 30 days after an episode, and 0 patients who did not undergo an event. ESC Heart Failure 2022; 9: 606–613 DOI: 10.1002/ehf2.13707 Risk stratification in heart failure decompensation in the community 611 Table 3 Multivariable predictors to predict hospitalization or death 30 days after an episode of decompensation in heart failure patients Odds ratio (95% CI) Beta coefficient P value Points (additive score) Risk groups (Intercept) 4.050 <0.001 Low risk Male 1.43 (0.93; 2.17) 0.350 0.107 1 Score ≤ 3 points a Previous hospitalization 2.45 (1.51; 3.99) 0.895 <0.001 3 Crackles 2.35 (1.45; 3.89) 0.856 0.001 2 Paroxysmal nocturnal dyspnoea 2.25 (1.41; 3.59) 0.809 0.001 2 Medium risk Orthopnoea 2.16 (1.36; 3.46) 0.770 0.001 2 Scores = 4–7 points NYHA class III–IV 2.11 (1.17; 3.94) 0.745 0.016 2 Worsening in NYHA status 2.50 (1.46; 4.45) 0.917 0.001 3 High risk Heart rate > 100 b.p.m. 2.72 (1.46; 5.11) 1.002 0.002 3 Scores ≥ 8 Oxygen saturation ≤ 90% 4.98 (2.16; 12.18) 1.606 <0.001 5 CI, confidence interval; NYHA, New York Heart Association. a Due to heart failure decompensation (between 31 and 365 days prior to inclusion). Figure 3 Predicted probabilities of hospitalization or death 30 days after Table 4 Number of hospitalizations or deaths 30 days after an episode of decompensation in heart failure patients. episode of decompensation in heart failure patients, among the total of patients in each stratum, and cumulative incidence, according to derivation and validation cohorts and risk score groups Derivation cohort Validation cohort Number of patients Number of patients Risk score groups (cumulative (cumulative (score points) incidence) incidence) Low risk (≤3 points) 2/73 (2.7%) 2/27 (9.09%) Medium risk 21/163 (12.8%) 7/54 (12.9%) (between 4 and 7 points) High risk (≥8 points) 148/322 (46.22%) 50/126 (39.68%) scores13–15 were accurate and had been properly validated. The predicted outcome most frequently used was 30 day mortality. Among the scores that predict short-term hospitalization, the RENDISCORE study12 elaborated a model to predict read- mission at 1 month and was based on previously hospitalized patients, some of whom were recruited in primary care. It in- cluded natriuretic peptides, symptoms and signs of left heart failure, and glomerular filtration rate < 60 mg/dL. The hospi- tal readmission percentage was 3.1%, which was lower than our findings. This may be explained by patients being incor- porated at hospital discharge and, as a consequence, Comparison with existing literature clinically stable at the inclusion date.12 The MEESSI model15 was developed and validated to strat- In comparison with other authors, the participants in this ify risk of death of heart failure patients 1 month after being real-world, observational study were older, there was a attended in a hospital emergency room. For patients with higher proportion of women, and a greater number of heart characteristics similar to those of our participants, it had, failure patients with preserved ejection fraction.7,8 All of however, low predictive capacity. which concurs with the characteristics of patients treated in The general triage scales in emergency departments such the community.1 as the Canadian Triage Acuity Scale (CTAS), Manchester Most scores predicting heart failure decompensation Triage System (MTS), and Triage Andorran Model - Triage events have been performed in hospitalized patients and in- Spanish System (MAT-SET) have not demonstrated usefulness cluded variables not available in routine primary care prac- for the prediction of hospitalization or death at 30 days in pa- tice. The discrimination capacity of such models varies tients with heart failure decompensation.16 between 0.54 and 0.86.8 Systematic reviews7,8 have consid- Thibodeau and Drazner17 showed that clinical examination ered that, among the studies analysed, only three of the using variables such as those included in our model is crucial ESC Heart Failure 2022; 9: 606–613 DOI: 10.1002/ehf2.13707 612 J.-M. Verdu-Rotellar et al. in the management of heart failure patients. Such evalua- Many countries participated; there may therefore tions provide crucial prognostic information and may help be differences in protocols and healthcare system charac- to guide decision-making. teristics with respect to criteria in managing heart failure Regarding the variables included in our model, hospital decompensation. Nevertheless, such variability could also admission in the previous year has been shown in multiple be seen as a strength, because we aimed to validate the studies to be a risk factor for hospitalization.1,18,19 model in differing countries and medical systems. It is NYHA functional class III or IV15 and reduced oxygen possible that we included patients presenting more symp- saturation12–15,19–21 as well as elevated heart rates13,14,22 tomatic decompensations and neglected those with less have also been included in other models. In addition, symp- severe decompensations that might affect the prediction toms of left ventricle overload have been reported to be pre- score. dictive for both heart failure diagnosis and Given the characteristics of an observational study carried hospitalization.12,15 out in real clinical practice in primary care and the consider- The elevated mean age of patients included in our popula- able number of researchers involved, it has been difficult to tion may explain the differences in prognosis as this variable ensure strictly consecutive recruitment. Whilst this could im- was not statistically significant. Because of the narrow inter- ply a selection bias, it does not, however, affect the validity of val of age, and most patients being older than 75 years, these the model. differences were not expected unlike other studies with a wider age range. An international consensus document on heart failure Conclusions management has provided guidelines from an emergency medical perspective. It has been suggested that not more The HEFESTOS score is a usable tool based on variables easily than 2% of patients discharged for a heart failure decompen- collected in primary care. It stratifies the risk of death or sation should die in the first 30 days and that the rate of hospitalization in heart failure patients presenting readmissions should be lower than 10%. The lack of risk decompensation. stratification of these patients before decision-making has, however, been identified as one of the possible reasons for poorer outcomes.11 Most patients who were hospitalized or died had been ini- Conflict of interest tially referred by primary care professionals to the emergency room. Nevertheless, a considerable percentage of those not None declared. referred at the moment of initial evaluation finally needed to be admitted to hospital (12.5%) and 2% died. These figures clearly exceeded the benchmarks in the consensus Funding recommendations.11 We consider that the proposed score may improve prioritization with respect to referring patients This work was supported by the Carlos III Institute of Health, to hospital and intensify clinical follow-up of those at moder- Ministry of Economy and Competitiveness (Spain) (Instituto ate or high risk (more than 3 points in the score). de Salud Carlos III), awarded on the 2014 call under the Health Strategy Action 2013–2016, within the National Re- search Program oriented to Societal Challenges, within the Strengths and limitations Technical, Scientific and Innovation Research National Plan 2013–2016, co-funded with European Union ERDF funds Given the characteristics of the population attended in pri- (PI14/00583) and The European General Practice Research mary care, such as advanced age, high proportion of women, Network (EGPRN) (HP/2015.036). and predominance of preserved ejection fraction, the exter- nal validity of this score is applicable in settings and geo- graphical areas with similar characteristics to those of the Supporting information present study. Validation studies will be required to apply the score in other populations with different characteristics Additional supporting information may be found online in the to the patients included in the present study. Nevertheless, Supporting Information section at the end of the article. the high proportion of women, unlike other previously pub- lished scoring systems, is a strength of our study. Data S1. Supporting information. ESC Heart Failure 2022; 9: 606–613 DOI: 10.1002/ehf2.13707 Risk stratification in heart failure decompensation in the community 613 References 1. Farré N, Vela E, Clèries M, Bustins M, 8. Miró Ò, Rossello X, Platz E, Masip J, adverse events. Acad Emerg Med 2013; Cainzos-Achirica M, Enjuanes C, Moliner Gualandro DM, Peacock WF, Price S, 20: 17–26. P, Ruiz S, Verdú-Rotellar JM, Comín- Cullen L, DiSomma S, de Oliveira MT 15. Miró Ò, Rossello X, Gil V, Martín- Colet J. 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Cardiol 2017; 34: 168–179. heart failure at high risk for serious ESC Heart Failure 2022; 9: 606–613 DOI: 10.1002/ehf2.13707