Alireza AtashiI,II; Shahram AminiIII; Mohammad Abbasi TashniziIV; Ali Asghar MoeinipourIV; Mathias Hossain AazamiV; Fariba TohidnezhadI; Erfan GhasemiVI; Saeid EslamiI,VII,VIII
ABSTRACTIntroduction: The European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) is a prediction model which maps 18 predictors to a 30-day post-operative risk of death concentrating on accurate stratification of candidate patients for cardiac surgery.
AUC = Area under the receiver operating characteristic curve
CABG = Coronary artery bypass grafting
CCS = Canadian Cardiovascular Society
EF = Ejection factor
EuroSCORE = European System for Cardiac Operative Risk Evaluation
NYHA = New York Heart Association
RAMR = Risk-adjusted mortality ratio
STS = Society of Thoracic Surgeons
A growing literature shows the pervasiveness and importance of the need for reliable information on the cost-effectiveness of adult cardiac surgeries. Moreover, potential post-operative adverse events highlight the significance of perioperative clinical decision making. Various prediction models have been developed aiming to estimate risk-adjusted mortality, morbidity and length of intensive care unit stay following cardiac surgeries. European System for Cardiac Operative Risk Evaluation (EuroSCORE) is a risk stratification tool which incorporates 18 variables describing patient, heart and proposed surgery to predict 30-day post operative risk of death. Predictive power of EuroSCORE II has been evaluated on different samples of target population in European countries. Vast majority of these studies have reported acceptable calibration (How many patients with a risk prediction of x% have experienced the outcome?) and discrimination (Who are the patients who have experienced the outcome associated with higher risk predictions and who are those that do not?) measures in comparison to Society of Thoracic Surgeons (STS) [esp. for patients undergoing coronary artery bypass grafting (CABG) procedure].
An international evaluation study was performed by Roques et al., in 2000, to assess the predictive ability of EuroSCORE II on 18676 patients from six European countries (Germany, Spain, England, France, Italy, and Finland). Despite clinical and epidemiological differences, EuroSCORE II provided acceptable predictions for all datasets (esp. for Spanish patients). Geissler et al. compared six prediction models using a single-center 2-year dataset, which resulted in the best performance measures for EuroSCORE II. While previous studies published admissible application of EuroSCORE II for patients undergoing CABG[6,7], conflict reports exist for Australian samples.
Similar studies in Iran reflect poor applicability of EuroSCORE II within patients undergoing different types of cardiac surgeries[9,10]. Diverse surgical techniques and potential risk factors already have been stabilized in different communities may mislead prediction models and result in erroneous interpretations. Thus, mathematical localization studies are required in different geographical borders to assure its proper predictive function before routine clinical use. This study is conducted to investigate the accuracy of quantitative prioritization scores estimated by EuroSCORE II in an Iranian population.
Participants and Setting
A retrospective single-center cohort study was conducted to include all consecutive patients undergoing cardiac surgeries at Emam Reza hospital, Northeast Iran from January 1, 2014 to December 31, 2015. Once the patient was hospitalized a cardiologist or a general physician evaluated pre- peri- and postoperative state to fill out the pre-designed structural paper form.
A total of 2907 patients were included and 30-day outcome was discovered using hospital information system or direct contact with patients' family. About 11.2% (N=326) of records were excluded due to major variables' missing values and all data items were rechecked to verify their consistency, reliability and integrity. In some cases (less than 3% of records) by the physicians' recommendation, the missing data were imputed with normal values.
First, univariate and multivariate analysis of relevant EuroSCORE II prognostic factors were performed aiming to identify significant covariates which contributed to higher risk. EuroSCORE II was calculated and inserted in dataset using online calculator (Available at: http://riskcalc.sts.org/stswebriskcalc/#). The data were aggregated in a unique electronic dataset, summarized considering the demographic and clinical characteristics and were used for statistical analysis. The relation of each variable was addressed and the number of patients due to different values were compared to the original EuroSCORE II population. Then, the overall model performance was reported using Brier Score (A score function which measures the closeness of predictions to actual outcomes and result in a value from 0 for a perfect model to 0.25 for a non-informative model). The area under the receiver operating characteristic curve (AUC) statistic was used to indicate the discriminative ability of model (while 1 refers to perfect discrimination, a value of 0.5 shows random classification). The Hosmer-Lemeshow goodness-of-fit test was employed to test the fitness of model to data by comparing observed to predicted mortality by decile of predicted probability). Analysis were performed using Medcalc-188.8.131.52 and R-3.3.1 (Resource Selection package).
Patients' Baseline Characteristics
The mean age among the total of 2581 patients was 56.3±13.88 years (minimum=17 and maximum=93). The mortality rate was 3.3% (N=84). The mean height and weight of patients were 1.64±0.1 meters and 68.4±13.4 kilograms, respectively. About 7.8% (N=201) of patients aged 75 years and older and 22.2% (N=572) were diabetic. While 6.1% (N=158) were involved with a type of chronic kidney disease, 15.1% (N=24) underwent dialysis regularly; 10.6% (N=274) were current or past smokers and 2.2% (N=56) of patients were diagnosed with COPD. Table 1 summarizes some comparable information of our patients with the original EuroSCORE II population.
|Variable||Frequencies (%) or mean (SD) [range] of original EuroSCORE II Population (N=22381)||Frequencies (%) or mean (SD) [range] of our Population (N=2581)|
|Age||64.6 (12.5) [18-95]||56.3 (13.88) [17-94]|
|Male||15462 (69.1%)||1537 (59.6%)|
|Height (cm)||168.5 (9.6) [100-213]||164.1 (10.0) [104-199]|
|Weight (kg)||77.9 (15.9) [30-182]||68.4 (13.4) [28-132]|
|BMI (kg/m2)||27.4 (4.8) [9.6-82.6]||25.4 (4.8) [10.1-62.6]|
|Diabetes on insulin||5643 (25.2%)||572 (22.2%)|
|Class IV||NA||96 (3.6%)|
|Chronic pulmonary disease||2384 (10.7%)||56 (2.2%)|
|Serum creatinine (mg/dl)||1.13 (0.92)||1.09 (0.98)|
|LV function (ejection fraction)|
|Recent MI||NA||161 (6.2%)|
|Pulmonary hypertension||NA||190 (7.3%)|
|Previous cardiac surgery||NA||8 (0.3%)|
As all procedures were elective operations, there were no urgent surgeries. Also, 23 patients undergoing valve surgery were suffering from active endocarditis, extra cardiac arteriopathy. Poor mobility was observed in 48 patients. No patient with Canadian Cardiovascular Society (CCS) class 4 or with critical preoperative state was observed. Also, none of surgeries were on thoracic aorta. Some other details are presented in Tables 1 and 2. As these patients had no mortality, these factors were excluded for regression analysis. The univariate and multivariate analysis are presented in Table 3.
|Variable||Number of Patients N (%)||Mortality N (%)||Mortality Predicted truly (N) by EuroSCORE II|
|Gender||Male||1537 (59.6%)||40 (47.6%)||27|
|Female||1044 (40.4%)||44 (52.4%)||29|
|21-40||301 (11.7%)||6 (7.1%)||3|
|41-60||1215 (47.1%)||30 (35.7%)||21|
|61-80||979 (37.9%)||45 (53.6%)||31|
|>80||46 (1.8%)||3 (3.6%)||1|
|BMI (kg/m2)||≤18.5||120 (4.6%)||10 (11.9%)||8|
|[18.5-23]||608 (23.6%)||21 (25%)||14|
|[23-25]||437 (16.9%)||10 (11.9%)||8|
|[25-30]||832 (32.2%)||28 (33.4%)||21|
|>30||364 (14.1%)||7 (8.4%)||5|
|Valve Surgery (weight of the intervention)||Isolated CABG||2071 (80.2%)||54 (64.3%)||37|
|AVR||76 (2.9%)||2 (2.4%)||2|
|MVR||195 (7.6%)||14 (16.7%)||11|
|TVR||16 (0.6%)||2 (2.4%)||2|
|MVR+TVR||54 (2.1%)||4 (4.7%)||1|
|AVR+MVR||47 (1.8%)||3 (3.6%)||2|
|AVR+MVR+TVR||18 (0.7%)||1 (1.2%)||__|
|2 procedures||122 (4.7%)||7 (8.3%)||6|
|3 procedures||19 (0.7%)||1 (1.2%)||__|
|Other||77 (3%)||4 (4.7%)||3|
|Ejection fraction||≤50||1150 (44.6%)||64 (76.1%)||31|
|51-70||782 (30.3%)||19 (22.6%)||10|
|>70||20 (0.8%)||1 (1.2%)||1|
|Diabetes mellitus||Yes||572 (22.2%)||32 (38.1%)||19|
|No||2009 (77.8%)||52 (61.9%)||37|
|COPD||Yes||56 (2.2%)||3 (3.6%)||3|
|No||2525 (97.8%)||81 (96.4%)||51|
|Previous cardiac surgery||Yes||8 (0.3%)||3 (3.6%)||1|
|No||2573 (99.7%)||81 (96.4%)||55|
|Recent MI||Yes||161 (6.2%)||7 (8.3%)||4|
|No||2420 (93.8%)||77 (91.7%)||52|
|NYHA||Class II||1008 (37.0%)||7 (8.3%)||2|
|Class III||957 (37.1%)||24 (28.5%)||20|
|Class IV||96(3.6%)||4 (4.7%)||4|
|Renal failure||Yes||158 (6.1%)||14 (16.6%)||10|
|No||2423 (93.9%)||70 (83.3%)||46|
|Dialysis||Yes||23 (0.9%)||2 (2.4%)||2|
|No||2558 (99.1%)||82 (97.6%)||54|
|Pulmonary hypertension||Yes||190 (7.3%)||15 (17.9%)||10|
|No||2391 (92.6%)||64 (82.1%)||46|
BMI=body mass index; CABG=coronary artery bypass grafting; AVR=aortic valve replacement; MVR=mitral valve replacement; TVR=tricuspid valve replacement; ASD=atrial septal defect; PVR=pulmonary valve replacement; COPD=chronic obstructive pulmonary disease; N/A=not applicable; MI=myocardial infarction
|Characteristic||Univariate Analysis||Multivariate Analysisa|
|β (95% CI)||P value||β (95% CI)||P value|
|Age (year)a||56.3±13.88||0.063 (0.058 to 0.068)||<0.001||0.058 (0.051 to 0.065)||<0.001|
|Gender||Female||3.5±1.95||1 [References]||<0.001||1 [Reference]|
|Male||2.6±2.16||-0.88 (-1.05 to -0.72)||-0.82 (-0.98 to -0.66)||<0.001|
|Creatinine clearance||<50||4.4±2.16||1 [References]||<0.001||1 [Reference]|
|50-85||2.9±1.99||-1.49 (-1.70 to -1.28)||-0.92 (-1.13 to -0.71)||<0.001|
|>85||1.8±1.53||-2.57 (-2.8 to -2.34)||-1.06 (-1.32 to -0.79)||<0.001|
|Dialysis||3.0±2.31||-1.33 (-2.12 to -0.55)||-0.78 (-1.73 to -0.02)||0.045|
|Chronic lung disease||No||2.9±2.11||1 [References]||0.061||1 [Reference]|
|Yes||3.5±2.43||0.54 (-0.03 to 1.10)||0.15 (-0.31 to 0.61)||0.529|
|Diabetes on insulin||No||3.0±2.12||1 [References]||0.859||1 [Reference]|
|Yes||2.9±2.14||-0.02 (-0.22 to 0.18)||-0.19 (-0.37 to -0.01)||0.043|
|NYHA||Class I||2.4±1.97||1 [References]||<0.001||1 [Reference]|
|Class II||2.9±2.18||0.47 (0.18 to 0.77)||0.2 (-0.002 to 0.40)||0.052|
|Class III||3.3±2.09||0.86 (0.56 to 1.17)||0.14 (-0.07 to 0.35)||0.199|
|Class IV||3.8±1.98||1.44 (0.81 to 2.07)||0.52 (0.08 to 0.95)||0.019|
|Left ventricular function||≤50||3.5±2.19||1 [References]||<0.001||1 [Reference]|
|51-70||2.3±1.84||-1.22 (-1.40 to -1.03)||-0.87 (-1.03 to -0.70)||<0.001|
|>70||2.0 ± 1.76||-1.52 (-2.43 to -0.61)||-1.37 (-2.06 to -0.69)||<.001|
|Recent myocardial infarction||No||2.8±2.07||1 [References]||<0.001||1 [Reference]|
|Yes||4.7±2.19||1.81 (1.48 to 2.14)||1.86 (1.56 to 2.15)||<0.001|
|Pulmonary hypertension||No||2.8±2.03||1 [References]||<0.001||1 [Reference]|
|Yes||5.2±1.99||2.41 (2.11 to 2.71)||2.04 (1.77 to 2.31)||<0.001|
|Weight of the intervention||Isolated CABG||2.7±2.06||1 [References]||<0.001||1 [Reference]|
|Single non-CABG||4.1±2.02||1.38 (1.14 to 1.62)||1.48 (1.25 to 1.72)||<0.001|
|2 Procedures||4.2±1.93||1.55 (1.21 to 1.90)||1.89 (1.56 to 2.22)||<0.001|
|3 Procedures||4.1±1.6||1.45 (0.74 to 2.15)||1.58 (0.91 to 2.25)||<0.001|
a Using linear regression, all variables were found to be associated with EuroSCORE II, except for chronic lung disease, diabetes on insulin, and NYHA.
Patients' Heart Status
Using New York Heart Association (NYHA), 37.1% (N=957) were classified as stage III cardiac failure patients, 6.2% (N=161) patients had a previous congestive heart failure during three months before surgery, 1% (N=26) of patients had atrial fibrillation. While 61.4% of surgeries were on-pump, the rest of procedures were performed off-pump. Table 2 shows more information about biological and clinical characteristics of patients.
As mentioned before, the overall mortality was 3.3%. When applied to the current data set, the EuroSCORE II predicted a mortality of 4.7%. This means that the current risk-adjusted mortality ratio (RAMR=observed/predicted) for the previous additive model is about 0.67 and not adequately enough for outcome prediction or resource allocation programs.
The Brier Score lower than 0.05 indicates acceptable overall performance. However, poor discrimination may be revealed by AUC=0.667 (cut off=3.0, sensitivity=61.90, and specificity=66.24). Also, the Hosmer-Lemeshow test showed unacceptable matching of predicted probabilities to observed events (P-value<0.01) (Table 4). Performance measures of EuroSCORE II are presented in Figure 1 and Table 4.
|Scoring System||Overall Performance||Discrimination||Calibration|
|AUC||SE||95% CI||Sensitivity||Specificity||H-L Test|
|EuroSCORE II||0.047 (0.0-1.0) [0.12]||0.667||0.0307||0.648-0.685||0.619||0.662||Chi2(8)=936.66, P<0.01|
Our single-center study, based on consecutive patients who underwent cardiac surgery revealed that EuroSCORE II demonstrated a moderate statistical overall performance with poor discrimination and calibration measures remain as concerning issues regarding 30-day post-operative mortality prediction after adult cardiac surgery. The analysis of ROC curve showed that the EuroSCORE II discrimination power is less than optimal (AUC=0.667) for outcome prediction and less accurate for resource allocation programs, because, references consider an AUC value more than 0.7 as an acceptable value for least useful prediction models. Although, the Brier score less than 0.05 indicates good overall performance for the model, the Hosmer-Lemeshow test showed unacceptable matching of predicted probabilities to observed events. In general, EuroSCORE II did not predict the outcome for our population as well as it did for the European populations. Thus, recalibration process seems to be essential for Iranian population prior to daily clinical use.
It is well known that risk assessment is central in the evaluation of the perioperative risk. The application of risk stratification tools gives an objective appraisal of risk for both physicians and patients and presents a good estimation for allocation of resources. However, some features may not be fully covered by the models including center-to-center variability in outcomes, type of surgery required and inherent complexity of some diseases. On the other hand, the patient populations defer significantly between institutions and countries. Thus, the comparison of absolute numbers such as the mortality rates is not feasible. A large variety of risk scores have been developed due to differences in patient populations and comparisons of observed mortality versus expected mortality have been reported[15-17].
Comparison to Similar Studies
EuroSCORE was first developed in 1999 to estimate postsurgical mortality in a European population who underwent cardiac surgery (70% of procedures were CABG). A meta-analysis by Parolari et al. revealed poor performance of EuroSCORE II in valve surgery, in 2010. Moreover, as the quality of medical techniques continues to improve over time, model expiration was considered as an inevitable topic in this area [2,18-20]. To address the surgical type bias in the modified version of model, reasonable number of patients who experienced CABG and valve surgeries were included in the development dataset. The new version of EuroSCORE reflected acceptable discrimination power among both European and non-European samples[17,21-23]. The new model also provided more acceptable predictions for surgeries other than CABG[21-23]. However, evaluation studies in Iran reported poor performance measures for both EuroSCORE I and II[9,10]. Further studies concentrating on recalibrated version of the model published unacceptable results[2,15,21,23].
The observed 30-day mortality rate in our sample (3.3%) was similar to those published by Roques et al. (3.4%), Nashef et al. (3.9%), Geissler et al. (4%), and Pitkänen et al. (2%). While Mir Mohammad Sadeghi et al. reported similar mortality rate in Isfahan (central Iran), four years later Jamaati et al. evaluated EuroSCORE II on a sample containing 12.2 mortality rate. An AUC of 66.7% in our study is lower than all similar studies including 78% by Geissler et al., 77% by Pitkänen et al., and 75.4% by Antunes et al.. This is while similar studies in Iran confirmed the poor discriminative ability of EuroSCORE II[9,10].
Currently, a great interest for prediction models as powerful tools for outcome prediction, cost-effectiveness strategies, reasonable resource allocation, and consequently quality control process have been growing[1,9,25].
Due to the results of our study, despite the little differences between two populations (Tables 1 to 3) the EuroSCORE II may not be completely reliable for risk periodization or resource allocation programs in Iran. Poor performance measures for EuroSCORE II highlights the need for reformulating this risk stratification tool aiming to improve post cardiac surgery outcome predictions in Iran. It may be done by calibrating mortality risk scoring model (e.g. EuroSCORE model) for the region or creating new models with accurate localized parameter sets[11,20].
Although sampling was done in one of the largest hospitals performing various cardiac procedures and the study has adequate sample size, including just one center may affect the generalizability of results to the entire country.
Our study showed that the EuroSCORE II discrimination power is less than optimal for outcome prediction and less accurate for resource allocation programs. It highlights the need for recalibration this risk stratification tool aiming to improve post cardiac surgery outcome predictions in Iran.
The authors would like to express their appreciation to the staff of cardiac surgery ICU of Emam Reza hospital, Mashhad, for their tremendous support in data collection. We thank Proff. Ameen Abu-Hanna for remarks on an earlier version of this paper. This study was a part of the first author's PhD thesis which was supported by a grant from Mashhad University of Medical Sciences Research Councils.
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Authors' roles & responsibilities
AA Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; drafting the work; final approval of the version to be published
SA Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; data gathering management; final approval of the version to be published
MAT Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; data gathering management; final approval of the version to be published
AAM Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; data gathering management; final approval of the version to be published
MHA Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; data gathering management; final approval of the version to be published
FT Statistical analysis and interpretation of data for the work; final approval of the version to be published
EG Statistical analysis and interpretation of data for the work; final approval of the version to be published
SE Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; data gathering management; final approval of the version to be published
Article receive on Monday, February 13, 2017