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Рациональная Фармакотерапия в Кардиологии 2024;20(3):278-284
DOI:10.20996/1819-6446-2024-3003
ISSN 1819-6446 (Print)
ISSN 2225-3653 (Online)
/""""Ч i 4
российское
кардиологическое
общество
ОРИГИНАЛЬНЫЕ ИССЛЕДОВАНИЯ
Predictors and etiology of in-hospital mortality in patients with acute myocardial infarction
Hoang T. H.1,2, Maiskov V. V.3-4, Merai I. A.3-4, Kobalava Z. D.3,4
1Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Vietnam 2Tam Duc Heart Hospital, Ho Chi Minh City, Vietnam 3Peoples' Friendship University of Russia, Moscow, Russia 4Vinogradov Municipal Clinical Hospital, Moscow, Russia
Aim. To identify factors and develop a clinical risk model (nomogram) for in-hospital mortality in patients with acute myocardial infarction (AMI) after timely early invasive diagnosis and revascularization of infarct-related artery (IRA).
Material and methods: We conducted a prospective, single-center observational study that included 712 consecutive patients with AMI [median age 65 (interquartile range 56-74 years), 61% were male, 47.8% with ST-elevation] who underwent coronary angiography <24 hours after symptom onset and successful endovascular revascularization of IRA. The primary endpoint was in-hospital mortality. Logistic regression analysis was used to identify independent prognostic risk factors for inhospital mortality. Based on the multivariate analysis, a nomogram was developed to predict outcome. The discriminative ability of the nomogram was assessed by calculating the area under the receiver operating characteristic (ROC) curve.
Results. The in-hospital mortality rate was 5.06%. The most common cause of in-hospital mortality was acute heart failure (AHF, 75%), followed by myocardial rupture with cardiac tamponade (11.1%). Multivariate analysis revealed that age (odds ratio (OR) 1.07, 95% confidence intervals (CI) 1.01-1.14, p=0.027), Killip class (OR 2.95, 95% CI 1.67-5.23, p<0.001), hemoglobin at admission (OR 0.97, 95% CI 0.95-0.99, p=0.006), and left ventricular ejection fraction (LVEF) <36% (OR 8.87, 95% CI 2.95-26.69, p<0.001), were independent predictors of adverse outcome. The identified predictors were included a nomogram, which demonstrated excellent discrimination in predicting in-hospital mortality (area under the ROC curve = 0.949, 95% CI: 0.925-0.972, p<0.001, sensitivity: 91.3%, specificity: 89.9%) and good calibration (Hosmer-Lemeshow test, p=0.93). Conclusions. Age, hemoglobin at admission, Killip class and left ventricular ejection fraction were independent predictors of inhospital mortality in acute MI. The most common etiology of in-hospital mortality was AHF The nomogram for prediction of in-hospital mortality demonstrated high prognostic potential, allowing for the identification of patients at high-risk of adverse outcome, and targeted therapeutic strategies may be needed to improve the survival of patients with acute MI.
Keywords: acute myocardial infarction, age, hemoglobin, in-hospital mortality, Killip class, left ventricle ejection fraction, nomogram, prognosis.
For citation: Hoang T. H., Maiskov V. V., Merai I. A., Kobalava Z. D. Predictors and etiology of in-hospital mortality in patients with acute myocardial infarction. Rational Pharmacotherapy in Cardiology. 2024;20(3):278-284. DOI: 10.20996/1819-6446-2024-3003. EDN NHUKWW
Предикторы и этиология госпитальной летальности у пациентов с острым инфарктом миокарда
Хоанг Ч. Х.Ч Майсков В. В.3-4, Мерай И.А.3-4, Кобалава Ж. Д.3-4 1 Медицинский университет Фам Нгок Тач, Хошимин, Вьетнам 2Кардиологическая больница Там Дык, Хошимин, Вьетнам 3Российский университет дружбы народов, Москва, Россия 4ГКБ им. В. В. Виноградова ДЗМ, Москва, Россия
Цель. Определить факторы риска и разработать клиническую модель прогностической оценки (номограмму) госпитальной летальности для пациентов острым инфарктом миокарда (ОИМ) после своевременно выполненной ранней инвазивной диагностики и реваскуляризации инфаркт-связанной артерии. Материал и методы. Проведено проспективное одноцентровое наблюдательное исследование, в которое было включено 712 пациентов с ОИМ [медиана возраста 65 лет (межквартильный интервал 56-74 года), 61% мужчины, 47,8% наблюдений с подъёмом сегмента ST] и выполненной коронарографией в сроки не позднее 24 часов от госпитализации и успешной эндоваскулярной реваскуляризацией инфаркт-связанной артерии. Первичной конечной точкой служила госпитальная летальность. Для выявления независимых прогностических факторов риска госпитальной летальности был использован логистический регрессионный анализ. На основе многофакторного анализа была разработана номограмма для прогнозирования исхода. Дискриминационная способность номограммы оценивалась путем расчета площади под характеристической (receiver operating characteristic, ROC) кривой. Результаты. Госпитальная летальность в исследовании составила 5,06%. Самыми распространенными причинами госпитальной летальности были острая сердечная недостаточность (ОСН) (75%) и разрыв миокарда с тампонадой сердца (11,1%). С помощью проведения многофакторного регрессионного анализа было установлено, что возраст (отношение шансов (ОШ) 1,07, 95% доверительный интервал (ДИ) 1,01-1,14, p=0,027), ОСН по Killip (ОШ 2,95, 95% ДИ 1,67-5,23, p<0,001), концентрация гемоглобина при поступлении (ОШ 0,97, 95% ДИ 0,95-0,99, p=0,006) и фракция выброса левого желудочка (ФВЛЖ) <36% (ОШ 8,87, 95% ДИ 2,95-26,69, p<0,001) являлись независимыми предикторами неблагоприятного исхода. Выявленные предикторы были включены в номограмму, которая продемонстрировала отличную дискриминацию при прогнозировании госпитальной летальности (площадь под ROC-кривой = 0,949, 95% ДИ: 0,925-0,972, p<0,001, чувствительность: 91,3%, специфичность: 89,9%) и хорошую калибровку (в соответствии с тестом Хосмера-Лемешоу, p=0,93).
Заключение. Таким образом, возраст, концентрация гемоглобина при поступлении, класс ОСН по Killip и величина ФВЛЖ в нашем исследовании служили независимыми предикторами госпитальной летальности пациентов с ОИМ. Самой распространенной причиной госпитальной летальности была ОСН. Клиническая модель прогностической оценки госпитальной летальности продемонстрировала высокий потенциал. Применение номограммы в рутинной клинической практике позволит дополнительно выявить пациентов высокого риска неблагоприятного исхода на госпитальном этапе и сформировать основные направления терапевтических стратегий, направленных на улучшение выживаемости больных ОИМ.
Ключевые слова: возраст; гемоглобин; госпитальная летальность; Киллип класс; номограмма; острый инфаркт миокарда; прогноз; фракция выброса левого желудочка.
Для цитирования: Хоанг Ч. Х., Майсков В. В., Мерай И. А., Кобалава Ж. Д. Предикторы и этиология госпитальной летальности у пациентов с острым инфарктом миокарда. Рациональная Фармакотерапия в Кардиологии. 2024;20(3):278-284. DOI: 10.20996/1819-6446-2024-3003. EDN NHUKWW
Corresponding Author (Автор, ответственный за переписку): truonghh@pnt.edu.vn
Received/Поступила: 05.01.2024
Review received/Рецензия получена: 04.03.2024
Accepted/Принята в печать: 19.06.2024
Introduction
Cardiovascular disease (CVD) is the most common cause of mortality worldwide and substantially contribute to loss of health and excess health system costs [1, 2]. Acute myocardial infarction (MI) is a common clinical manifestation of CVD. The mortality of patients with acute MI has been decreasing over the last 50 years [3], in part because of improvement in acute MI prevention, diagnosis, and treatment [4]. In-hospital mortality has decreased from 29% in 1969 [5] to <7% today [6, 7]. However, around 9 million people worldwide die after acute MI each year (in 2019, 16% of total death in the world)1, and in-hospital mortality varies substantially across hospitals [7]. Therefore, study of factors predicting the risk of death for a particular medical institution, adapted to its material and technical base and the existing flow of patients with acute MI, is an urgent problem.
Although several risk scores of in-hospital mortality were developed for patients with AMI, including the "Global Registry of Acute Coronary Events" (GRACE), "Thrombolysis in Myocardial Infarction" (TIMI), and "Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA Guidelines" (CRUSADE) scores [8-10], few included a representative sample from routine clinical care. Moreover, continued improvement in acute MI care requires periodic update to the risk models so that hospitals can assess their quality as contemporary care continues to evolve.
This study sought to determine factors associated with for in-hospital mortality in patients presenting with acute MI and develop a prognostic model for in-hospital mortality in a contemporary settings.
Material and methods
Study design and participants
A prospective, single-center observational study at Vinogradov Municipal Clinical Hospital (Moscow, Russia) was conducted. From January 2017 to December 2018, a total of 712 consecutive adults >18 years admitting
1 World Health Organization. The top 10 causes of death. World Health
Organization, 9 December 2020. Available from: https://www.who.int/news-
room/fact-sheets/detail/the-top-10-causes-of-death
with acute MI and undergoing coronary angiography (CAG) <24 hours after symptom onset were recruited. The exclusion criteria were type 3, 4, or type 5 MI. The diagnosis of acute MI was based on the Third universal definition of MI [11]. All patients provided written informed consent. The study was approved by the local Ethics Committee of RUDN University, and carried out in accordance with the Declaration of Helsinki. Data collection and outcomes Variables of interest included clinical characteristics, cardiovascular risk factors, comorbidities, physical examination findings, blood test results, and imaging data (electrocardiography, echocardiography, and CAG). Cardiac troponin I levels were measured using the Access 2 Immunoassay System (Beckman Coulter, USA), with 99th percentile upper reference limit being 0.02 ng/L. The GRACE 2.0 score was used to risk stratification for MI patients [12]. Anemia was defined as a hemoglobin concentration of less than 130 g/L for men or less than 120 g/L for women2. The left ventricular ejection fraction (LVEF) was estimated by the modified biplane Simpson method [13] along with a transthoracic echocardiogram with using the Vivid 7 ultrasound system (General Electric Healthcare, USA).
The primary endpoint was all-cause death during the index hospitalization for acute MI, obtained from medical records.
Statistical analysis
Statistical analysis was performed using IBM SPSS Statistics 25.0 (SPSS Inc., Chicago, IL, USA) and R software (version 3.6.3). Descriptive statistics are presented as frequencies and percentage for categorical variables and as means ± standard deviation for continuous variables if variables adhering to a normal distribution or median (Me) and interquartile range (IQR) for skewed variables. Comparisons of categorical variables were performed by Chi-Square or Fisher's exact tests, while for continuous variables by using the unpaired Student t-test and Mann-Whitney U test. Logistic regression analysis was used to identify factors associating with in-hospital mortality. Odds ratios (OR) and 95% confidence intervals (CI) were presented. The
2 World Health Organization. Nutritional anaemias: report of a WHO scientific group [meeting held in Geneva from 13 to 17 March 1967]. World Heal Organ Tech Rep Ser. 1968;405:1-37. Available from: https://iris.who.int/ handle/10665/40707.
Table 1. The baseline characteristics of the patients
Variables All patients (n=712) Alive (n=676) Dead (n=36) p-value
Age, years, Me (IQR) 65 (56;74) 64 (55;73) 77.5 (67;84) <0.001
Females, n (%) 278 (39) 257 (38) 21 (58.3) 0.021
ST-elevation, n (%) 340 (47.8) 315 (46.6) 25 (69.4) 0.01
Arterial hypertension, n (%) 634(89) 601 (88.9) 33 (91.7) 0.787
Symptoms of CAD, n (%) 328 (46.1 ) 298 (44.1) 30 (83.3) <0.001
Previous MI, n (%) 155 (21.8) 143 (21.2) 12 (33.3) 0.097
Previous revascularization, n (%) 85 (11.9) 82 (12.1) 3 (8.3) 0.790
Previous HF, n (%) 57 (8.0) 53 (7.8) 4 (11.1) 0.521
Diabetes mellitus, n (%) 150 (21.1) 1 37 (20.3) 13 (36.1) 0.034
Previous CVA, n (%) 51 (7.2) 46 (6.8) 5 (13.9) 0.171
Atrial fibrillation, n (%) 73 (10.3) 65 (9.6) 8 (22.2) 0.024
CKD, n (%) 61 (8.6) 50 (7.4) 11 (30.6) <0.001
PAD, n (%) 26 (3.7) 26 (3.8) 0 (0) 0.636
Chronic lung disease, n (%) 115 (16.2) 103 (15.2) 12 (33.3) 0.009
Peptic ulcer disease, n (%) 65 (9.1) 63 (9.3) 2 (5.6) 0.764
Charlson comorbidity index, points, Me (IQR) 4 (3; 6) 4 (3; 5) 7 (6; 8.75) <0.001
Anemia, n (%) 189 (26.5) 167 (24.7) 22(61.1) <0.001
Clinical findings:
Chest pain, n (%) 658 (92.4) 628 (92.9) 30 (83.3) 0.047
Dyspnea, n (%) 124 (17.4) 110 (16.3) 14 (38.9) 0.002
Killip class II-IV, n (%) 160 (22.5) 129 (19.1) 31 (86.1) <0.001
Troponin I, ng/mL, Me (IQR) 0.39 (0.09; 2.85) 0.39 (0.09; 2.74) 1.14 (0.18; 6.22) 0.191
Hemoglobin, g/L, Me (IQR) 136 (123; 147) 137 (124; 147) 118.5 (105.5; 134) <0.001
Creatinine, pmol/L, Me (IQR) 94 (80; 107) 92 (76; 106.7) 106 (92; 160) 0.014
eGFR, ml/min/1.73 m2, Me (IQR) 67 (52; 83) 68 (54,2; 83) 44 (35; 60) <0.001
LVEF, %, Me (IQR) 45 (40; 54) 45 (40; 54) 36 (25; 45) <0.001
Coronary stenosis:
No lesions/Stenosis <50%, n (%) 73 (10.3) 72 (10.7) 1 (2.8) 0.163
Three-vessel CAD, n (%) 390 (54.8) 361 (53.4) 29 (80.6) 0.002
PCI, n (%) 566 (79.5) 538 (79.6) 28 (77.8) 0.832
GRACE score, points, Me (IQR) 117 (98; 141) 115 (97; 137) 167 (148.2; 193) <0.001
CAD — coronary artery disease, CKD — chronic kidney disease, CVA — cerebrovascular accident, eGFR — estimated glomerular filtration rate, GRACE — Global Registry of Acute Coronary Events, HF — heart failure, IQR — interquartile range, LVEF — left ventricular ejection fraction, Me — median, MI — myocardial infarction, PAD — peripheral vascular disease, PCI — percutaneous Coronary Intervention
Table 2. Etiology for in-hospital mortality in patients with acute myocardial infarction
Cause Absolute value, n Frequency, %
Acute heart failure 27 75
Myocardial rupture with cardiac tamponade 4 11.1
Multiple-organ failure 3 8.3
Acute respiratory failure 1 2.8
Acute kidney failure 1 2.8
Pulmonary embolism 1 2.8
Gastrointestinal bleeding 1 2.8
receiver operating characteristic (ROC) curve analysis was performed to determine cut-off level of continuous variables associated with in-hospital mortality. Based on the estimated factors from the multivariable logistic regression model, a nomogram was constructed to assess risk of in-hospital death ("rms" package in R). The discrimination accuracy of a nomogram was assessed the area under the ROC curve (AUC) [14]. The calibration was assessed by Hosmer-Lemeshow chi-square statistics for goodness of fit. A two-tailed p-value <0.05 were considered statistically significant.
Results
Among patients, 434 (61%) were male with median age of 65 (IQR: 56-74) years, 47.8% presented with ST elevation. During the follow-up period, 36 patients (5.06%) died.
The baseline characteristics of the patients are shown in Table 1. Compared with survivors, age, proportion of female gender, ST-elevation, coronary artery disease (CAD), diabetes mellitus, atrial fibrillation, chronic kidney disease, anemia, dyspnea, Killip class II-IV, creatinine
Table 3. Univariate and multivariate analysis for the risk factors in predicting the in-hospital mortality
Variable Univariate Analysis Multivariate Analysis
OR (95% CI) p-value OR (95% CI) p-value
Age, per year 1.09 (1.06-1.13) <0.001 1.07 (1.01-1.14) 0.027
Sex, female 2.28 (1.16-4.51) 0.017 1.46 (0.44-4.91) 0.683
ST elevation 2.60 (1.26-5.38) 0.01 1.89 (0.66-5.40) 0.236
Diabetes mellitus 2.22 (1.10-4.50) 0.026 1.25 (0.43-3.69) 0.681
Atrial fibrillation 2.69 (1.17-6.14) 0.019 2.67 (0.86-8.32) 0.09
Killip class, per class 5.0 (3.41-7.23) <0.001 2.95 (1.67-5.23) <0.001
Hemoglobin, per g/L 0.97 (0.95-0.98) <0.001 0.97 (0.95-0.99) 0.006
eGFR <60 ml/min/1.73 m2 6.21 (2.78-13.89) <0.001 0.82 (0.22-3.03) 0.768
LVEF <36% 13.12 (5.48-31.41) <0.001 8.87 (2.95-26.69) <0.001
Three-vessel CAD 3.61 (1.56-8.37) <0.001 1.41 (0.46-4.30) 0.551
CAD — coronary artery disease, CI — confidence intervals, LVEF — left ventricular ejection fraction, OR — odds ratio
Points
Age, years Killip class
200
LVEF s36%, (0: No; 1: Yes) г Hemoglobin at admission, g/L
Total points
Linear predictor
Risk of in-hospital mortality
10
20
30
40
50
SO
70
80
90
100
25 30 35 40 45 2 50 55 ВО B5 70 75 Э0 E5 SO 4 95
130
160
140
120
100
80
60
40
50
100
150
200
250
300
350
-9 -8 -7 -6 -5
-3 -2 -1
0.01
0.05 0.1 0.2 0.3 0.40.50,60.7 0.8 0.9 0.95 0.975
LVEF — left ventricular ejection fraction
Figure. 1. Nomogram for prediction of in-hospital mortality in acute myocardial infarction. The nomogram included four variables, including age, Killip class, left ventricular ejection fraction and hemoglobin at admission
level, three-vessel CAD, GRACE score were higher in non-survivors. Hemoglobin, LVEF were lower in non-survivors. There were no significant differences in terms of arterial hypertension, previous MI, previous revascularization, previous heart failure (HF), previous cerebrovascular accident, peripheral artery disease, chronic lung disease (asthma and/or chronic obstructive pulmonary disease), peptic ulcer disease, troponin level, non-obstructive CAD, percutaneous coronary intervention (PCI) rate.
In the structure of in-hospital mortality, majority of patients died from acute HF (75%), followed by myocardial rupture with cardiac tamponade (11.1%) and multiple organ failure (8.3%). Other causes of inhospital death are presented in Table 2.
In univariate analysis, age, female gender, ST-elevation, diabetes mellitus, atrial fibrillation, Killip class, hemoglobin at admission, estimated glomerular filtration rate (eGFR), LVEF and three-vessel CAD were associated with in-hospital mortality (all p-value < 0.05) (Table 3). The cut-off for LVEF in ROC curve analysis, was
36% (AUC = 0.692; 0.5548-0.836, p=0.004) for inhospital mortality.
In multivariate analysis, age (OR 1.07, 95% CI 1.01-1.14, p=0.027), Killip class (OR 2.95, 95%CI 1.67-5.23, p<0.001), hemoglobin at admission (OR 0.97, 95% CI 0.95-0.99, p=0.006), and LVEF <36% (OR 8.87, 95% CI 2.95-26.69, p<0.001) were independently associated with the primary endpoint.
Based on the estimated variables in the multivariate model, a nomogram was developed to predict the risk of in-hospital death in patients with MI (Fig. 1). The risk of in-hospital death in MI patients was assessed according to the following equation:
Risk = 1/(1 + e-Z), where Z = -6.683 + 0.083 x Age -0.028 x Hemoglobin at admission + 1.065 x Killip class + 2.377 x LVEF <36%.
A total score was generated by using the number of the points of each factor on the corresponding axis with drawing a vertical line to "Points" axis. Summary the points of all the factors and drawing a vertical line to
ROC Curve
Г J
/
1 / AUC 0 .949 (95% i> CI 0.925 -0.972)
0.0 0.2 0.4 0.6 0.8 1.0 1 - Specificity
AUC — area under the ROC curve, CI — confidence intervals
Figure 2. The receiver-operating characteristic (ROC)
curve analysis of the nomogram for predicting in-hospital mortality in patients with acute myocardial infarction
the "Risk of in-hospital mortality" line to determine the individual's probability of death during hospitalization.
In ROC curves analysis of a developed nomogram for predicting in-hospital mortality, the AUC was 0.949 (95% CI: 0.925-0.972, p<0.001). The sensitivity and specificity were 91.3% and 89.9%, respectively (Fig. 2). A nomogram showed good calibration (chi-square 3.064, p for Hosmer-Lemeshow test 0.93)
Discussion
The present study demonstrated that age, hemoglobin at admission, Killip class and LVEF were independently associated with in-hospital death. Based on results from multivariate analysis, we developed a contemporary risk model to predict in-hospital mortality in patients with acute MI. The model had high predictive potential in predicting in-hospital death.
Age is one of the most important risk factors for a poor outcome in hospitalized patients with acute coronary syndrome (ACS) [15,16]. Median of age among non-survivors in our study was 77.5 years and was an independent factor for in-hospital dearth. Aging causes patients to be physically weaker and frailer. Therefore, old age is an independent predictor of poor prognosis in ACS patients [17]. Additionally, age serves as a prognostic variable in various risk stratification scores
for ACS patients [8-10]. In the TIMI III registry, patients over the age of 75 with unstable angina (UA)/non-ST-segment elevation MI (NSTEMI) had more adverse outcomes both in-hospital and within the first six weeks after discharge than those less than 75 years of age [16]. Age and gender are closely associated with in-hospital mortality in acute MI with women being generally older than men at hospitalization for MI. In our study, the risk of in-hospital mortality for women was 2.3 times higher than for men.
The in-hospital mortality rate in our study was 5.06%, which is lower than the 8.8% reported in a recent metaanalysis involving 615,035 MI patients (comprising both ST-segment elevation MI (STEMI) and NSTEMI/ UA) [18]. During hospitalization, acute HF emerged as the primary cause of death, accounting for three-fourths of the cases. These results are consistent with those of O. G. Sivkov, who examined 1120 patients with acute MI. The author reported an in-hospital mortality rate of 6.0% (n=67), with acute HF contributing to 70.1% of the deaths (32 cases with acute HF and 15 cases with cardiogenic shock), followed by multiple-organ failure at 28.3% [19].
Our multivariate analysis affirmed the continued relevance of Killip classification and LVEF for effective risk stratification in these patients. Killip classification categorizes patients based on physical examination findings that indicate left ventricular dysfunction and HF [20-22]. According to our results, an increase of in Killip degree was associated with an increased odds ratio of 2.95 for in-hospital mortality. These findings are in the line with an analysis of international data from the GRACE registry involving 3917 NSTEMI patients and 4960 UA patients, where Killip class II or III on admission independently predicted in-hospital death (OR, 2.2; p<0.0001) [23]. A broader analysis of data from 26,090 patients across the GUSTO IIb, PURSUIT, PARAGON A, and PARAGON B trials revealed a significant correlation between Killip class and survival [24]. Patients with Killip class II and III/IV had significantly higher mortality rates at 30 days (3% versus 9% and 14%) and six months (5% versus 15% and 23%) compared to patients with Killip class I. Killip class has proven to be a valuable predictor for adverse outcomes in acute MI patients, incorporated into several risk scores [8-10].
The baseline measurement of LVEF has been identified as the most influential determinant of both in-hospital [25, 26] and long-term mortality [27, 28], making its inclusion imperative in risk models for maximal predictive accuracy. In a study by T. Sato et al. [26], involving 1102 acute MI patients undergoing primary PCI, multivariable analysis revealed that LVEF <33% independently predicted in-hospital death, with an AUC of 0.79 (p<0.001). Further, incorporation of LVEF into the TIMI score, as opposed to the TIMI risk score alone, resulted in significant enhancements in predicting in-hospital death (AUC: 0.854 vs. 0.803, p=0.033) in a cohort of 673,673 patients with STEMI
[27]. Regarding long-term risk stratification, LVEF has proven to be a valuable predictor in various scores. For instance, LVEF was included in the CADILLAC score for predicting one-year mortality after primary PCI for acute AMI, with a cutoff value of LVEF <40% [29]. Consistent with these findings, H. K. Kim et al. developed the new KAMIR score from a cohort of 3,997 hospital-discharged patients with acute MI. The KAMIR score, incorporating six independent variables, including LVEF, demonstrated significant differences in predictive accuracy for one-year mortality compared to the GRACE score (AUC 0.83 vs. 0.76, p=0.0089, respectively). LVEF <40% in this study was associated with a hazard ratio (HR) of 2.24 (95% CI 1.47-3.41) for one-year mortality [30]. In our current study, Killip class at presentation, consistently featured in previous scores remained an independent predictor of reduced in-hospital survival, alongside baseline LVEF. This underscores the significance of clinical examination for signs of mild to moderate HF, even when left ventricular function is preserved.
Anemia is increasingly recognized as a condition strongly predictive of adverse outcomes in patients with acute MI, both in the short term [31-33] and long term [33-35]. In a study by J. J. Gonzalez-Ferrer et al., involving 542 high-risk ACS patients, anemia was present in 147 patients (27.1%) at admission [32]. The study found that the admission hemoglobin level (OR=1.4 for each 1 g/dL below normal; 95% CI, 1.1-1.8; p=0.003) was independently associated with in-hospital all-cause mortality or cardiogenic shock, after adjusting for other variables. Another study by M. G. Colombo et al. [36] was performed in 2011 consecutively hospitalized acute MI patients. Mild anemia (defined as hemoglobin concentration of 11 to <12 g/dL in women and 11 to <13 g/dL in men) and moderate to severe anemia (defined as hemoglobin concentration of <11 g/dL) were found in 183 (9.1%) and 100 (5%) patients, respectively. The Cox regression analysis showed significantly increased mortality risks in both patients with mild anemia (HR 1.74, 95% CI 1.23-2.45) and
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The study limitations
Our observational study has such limitations as non-randomization and unmeasured confounding factors. For instance, the exclusion of the variable "previous cerebrovascular accident" despite its known association with increased mortality risk in acute MI patients [37] may compromise the comprehensiveness of our findings. Being single centered with a small sample size is another limitation, though the study's prospective nature adds strength. Thirdly, treatment decisions were left to attending cardiologists, making it difficult to assess the benefits of individualized therapy. At last, the lack of external validation for the nomogram in another acute MI patient cohort limits the generalizability of our findings and reduces the model's clinical implementation. External validation is necessary to confirm the nomogram's effectiveness in real-world scenarios.
Conclusion
Age, Killip class, hemoglobin at admission, and LVEF were identified as factors associated with higher inhospital mortality in patients with acute MI. The most common etiology of in-hospital mortality was acute HF. The nomogram for predicting in-hospital mortality demonstrated high prognostic potential, allowing for the identification of patients at high-risk of adverse outcome, and targeted therapeutic strategies may be needed to improve the survival of patients with acute MI.
Relationships and Activities. None.
Funding. The study was performed with the support of Peoples' Friendship University of Russia.
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Сведения об Авторах/About the Authors
Хоанг Чыонг Хюй [Truong Huy Hoang]
eLibrary SPIN 5684-0275, ORCID 0000-0002-2013-2647
Майсков Виктор Викторович [Victor V. Maiskov]
eLibrary SPIN 7370-7545, ORCID 0009-0002-2135-2606
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Мерай Имад Ахмадович [Imad A. Merai] eLibrary SPIN 4477-7559, ORCID 0000-0001-6818-8845 Кобалава Жанна Давидовна [Zhanna D. Kobalava] eLibrary SPIN 9828-5409, ORCID 0000-0002-5873-1768
