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European Journal of Heart Failure 2001 3(2):233-241; doi:10.1016/S1388-9842(00)00154-9
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© 2001 European Society of Cardiology

Worsening of heart failure during hospital course of an unselected cohort of 2507 patients with myocardial infarction is a factor of poor prognosis: the PRIMA study

Guy de Gevigneya,*, René Ecochardb, Muriel Rabilloudb, Sandrine Gaillarda, Edouard Cheneaua, Corinne Ducreuxa, Danièle Caoa, Hugues Milonc, François Delahayea and PRIMA group1

a Hôpital Cardiovasculaire et Pneumologique Louis Pradel, BP Lyon Montchat 69394-Lyon Cedex 03, France
b Département d'Information et de Biostatistiques Médicales des Hospices Civils de Lyon 162, avenue Lacassagne, 69424-Lyon Cedex 03, France
c Hôpital de la Croix-Rousse 103, Grande-Rue de la Croix-Rousse, 69317-Lyon Cedex 04, France

* Corresponding author. Tel.: +33-4-72-11-80-07; fax: +33-4-72-35-73-10. E-mail address: francois.delahaye{at}chu-lyon.fr (F. Delahaye).


   Abstract
Top
 Notes
 Abstract
1. Introduction
 2. Patients and methods
 3. Results
 4. Discussion
 5. Conclusion
 Appendix A. List of...
 References
 
Worsening of heart failure in patients with myocardial infarction is seldom studied, elderly patients often are not included, and multivariate analysis is uncommon. The prospective PRIMA study (Prise en charge de l'Infarctus du Myocarde Aigu; management of acute myocardial infarction) sought to determine the incidence of worsening heart failure, its risk factors, and its prognostic importance in patients with myocardial infarction, regardless of age and hospital facilities, in the ‘real world’ in a region in France, using multivariate analysis. Data were prospectively collected in all patients with myocardial infarction admitted in all hospitals in three departments in the Rhône–Alpes region in France between 1 September 1993 and 31 January 1995. Among the 2507 patients included, 33% were in Killip classes II–IV at admission. After exclusion of patients with admission Killip class IV, 416 patients (17% of the cohort, 24% of women and 14% of men) had worsening of Killip class during the first 5 days. In-hospital mortality (overall, 14%) increased dramatically with Killip class at admission (9% in class I, 62% in class IV) and with worsening of Killip class during the first 5 days (36.5 vs. 8.5% if no worsening). In multivariate analysis, older age, diabetes mellitus and anterior Q-wave myocardial infarction were significant predictors of Killip class at admission and of its worsening; Killip class > I at admission was a significant predictor of Killip-class worsening. The significant predictors of in-hospital mortality were older age, Killip class III at admission and worsening of Killip class during the first 5 days. This large, unselected cohort revealed that, among patients with myocardial infarction, heart failure and its worsening are frequent, especially in the elderly, and dramatically worsen the in-hospital mortality.

Key Words: Myocardial infarction • Heart failure • Worsening • In-hospital mortality • Prognosis

Received May 22, 2000; Revised September 15, 2000; Accepted November 30, 2000


   1. Introduction
Top
 Notes
 Abstract
 1. Introduction
2. Patients and methods
 3. Results
 4. Discussion
 5. Conclusion
 Appendix A. List of...
 References
 
Whereas the presence of heart failure (HF) during the acute phase of myocardial infarction (MI) is a well known factor of poorer prognosis, the prognostic influence of its worsening during hospital course is not known. Indeed, in patients with MI, the left ventricular function, assessed either by clinical or radiological features (presence or absence of HF evaluated by the Killip classification [1]), or by invasive or non-invasive methods (isotopic, echographic or angiographic left ventricular ejection fraction, LVEF) [2–6], appears to be one of the most potent prognostic factors of in-hospital and 1-year mortality. The 1-year mortality is three–fourfold higher in patients with clinical HF [2–5], three-fold higher if LVEF is ≤40% [3], sixfold higher if LVEF is ≤25% as compared with LVEF >50% [4]. Moreover, clinical HF and left ventricular function are not highly correlated, and they are independent prognostic factors [5,7].

The frequency, predictive factors and prognostic influence of the worsening of HF during the hospital course of MI are almost unknown. Many studies were conducted in university hospitals only, elderly patients were often not included, and multivariate analyses were seldom performed. The PRIMA study (Prise en charge de l'Infarctus du Myocarde Aigu; management of acute MI) prospectively included all patients with acute MI, regardless of age and admission hospital-type in a geographic area. The present study sought to describe the frequency, risk factors and influence on in-hospital mortality, of HF at admission and its subsequent worsening during the first 5 days after MI.


   2. Patients and methods
Top
 Notes
 Abstract
 1. Introduction
 2. Patients and methods
3. Results
 4. Discussion
 5. Conclusion
 Appendix A. List of...
 References
 
2.1. Participating centers
All of the 48 medical units admitting patients with MI in three neighboring administrative departments in the Rhône–Alpes region in France were contacted. All of them accepted to participate in the study.

2.2. Patient selection
All consecutive patients with MI admitted to the participating centers between 1 September 1993 and 31 January 1995 were prospectively enrolled in the study if they met the following criteria: diagnosis of MI; resident and hospitalized in one of the three departments; and French nationality (in order to have the vital status from the birthplace councils). Diagnosis of acute MI was based on the presence of at least two of the three following criteria: chest pain lasting more than 30 min; ST segment elevation ≥1 mm; or serum total creatine-phospho-kinase (CPK) elevation to >twofold the upper normal limit. Patients with post-operative MI, and children less than 15 years old, were not included in the study. All patients gave their written informed consent. The study was approved by the Commission Nationale Informatique et Liberté (National Computing and Freedom Committee).

2.3. Data collection
In-hospital data were collected by three physicians who were specially trained for the study. They interviewed the patients and the physicians in charge of the patients, and examined the medical charts. HF was evaluated by the Killip classification: I, no clinical or radiological signs of HF; II, HF (rales, S3 gallop, venous hypertension, radiological interstitial edema); III, severe HF (frank pulmonary edema); IV, cardiogenic shock [1]. Two variables were studied: Killip class at admission in the hospital, and worsening of Killip class during the first 5 days (after exclusion of patients with Killip class IV at admission, in whom worsening was not possible).

Two different and independent operators were responsible for data entry. To check whether the cohort was exhaustive, all MI entered in the French diagnosis-related groups (DRG) system were examined. Concordance between the PRIMA database and the DRG system was 99%, and all missing records were retrieved and included in the database.

2.4. Statistical methods
Statistical analyses were performed using the SAS software package [8]. Results are expressed as mean±S.D., or %. Univariate analysis using the chi-square or Fisher exact test (when the number of patients was too small) was performed to examine the influence of the following variables on the two HF variables: gender; age (terciles); cardiovascular risk factors (smoking, hypertension, and diabetes mellitus); presence of comorbidities (peripheral arterial disease, pulmonary disease, liver disease, renal failure, cerebrovascular accident or transient ischemic attack, cancer, and alcoholism); history of MI or of angina pectoris; location of MI and presence/absence of Q waves, in three classes (anterior Q-wave; non anterior Q-wave; non Q-wave); type of hospital (university, community, private); time between symptom onset and initial medical intervention; use of thrombolysis; and for worsening of Killip class, ratio of maximal CPK/normal upper limit (terciles). Due to multiple testing, a P value <0.001 was considered significant.

A logistic regression model was used for each of the two HF variables to examine the independent influence of the variables listed above [9]. Age was introduced as a continuous variable. The variables introduced in the model were selected by a stepwise procedure. Only significant variables (P<0.05) were re-entered into the model in order to obtain more precise estimates of the odds ratios (OR).

The multivariate analysis of in-hospital mortality was performed using the Cox proportional-hazards model. All the previously cited variables and the two HF variables were tested. In order to look at the influence of worsening of Killip class during the first 5 days, we excluded the patients who died during the first 5 days. Assumptions for using statistical multivariate models were tested.


   3. Results
Top
 Notes
 Abstract
 1. Introduction
 2. Patients and methods
 3. Results
4. Discussion
 5. Conclusion
 Appendix A. List of...
 References
 
A total of 2507 patients with Killip data available, hospitalized for MI in the 48 participating hospitals, were included in the PRIMA study. The mean age of the population was 68±14 years, significantly higher in women (76±12 years) than in men (64±14 years, P<0.001).

3.1. Killip class at admission
Distribution of Killip class at admission according to baseline characteristics is presented in Table 1. One third of the patients were in class II–IV. In univariate analysis, Killip class at admission was more severe in women, in older patients, in smokers, in patients with diabetes mellitus, in those with hypertension, in patients with comorbidities, in patients with a history of MI or angina pectoris, in patients with anterior Q-wave MI, and in patients who did not receive thrombolysis. Multivariate analysis showed that age, smoking, diabetes mellitus, several comorbidities, history of MI and Q-wave anterior location of MI were significant predictors of Killip class at admission (Table 2).


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  		Table 1 Killip class at admission according to baseline characteristics

 


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  		Table 2 Multivariate analysis of significant predictors of Killip class at admission (>I versus =I)

 
3.2. Worsening of Killip class during the first 5 days
After exclusion of patients with admission Killip class IV, 416 patients (17% of the cohort, 24% of women and 14% of men) had worsening of Killip class during the first 5 days:
  • Among patients with initial Killip class I, 284 (17%) got worse: 201 achieved class II, 32 class III and 51 class IV.
  • Among patients with initial Killip class II, 93 (17%) got worse: 57 achieved class III and 36 class IV.
  • Among patients with initial Killip class III, 39 (15%) got worse and achieved class IV.

In univariate analysis, worsening of initial Killip class was more frequent in women, in older patients, in non-smokers, in patients with anterior Q-wave MI, in those with higher CPK levels and in patients hospitalized in community hospitals (Table 3). Whereas higher Killip class at admission was not a significant predictor of worsening of Killip class during the first 5 days in univariate analysis, it was so in multivariate analysis (Table 4). The other predictors were older age, diabetes mellitus, anterior Q-wave location of MI, and higher CPK levels.


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  		Table 3 Worsening of Killip class during the first 5 days (after exclusion of patients with Killip class IV at admission)

 


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  		Table 4 Multivariate analysis of significant predictors of worsening of Killip class during the first 5 days

 
3.3. In-hospital mortality
During a mean in-hospital stay of 16±15 days, overall in-hospital mortality was 14%. It increased dramatically with Killip class at admission and with worsening of Killip class:
  • for Killip class at admission, it increased from 9% in class I to 19% in class II, 32% in class III and 62% in class IV (P<0.001) (class>I: 26%); and
  • for worsening of Killip class during the first 5 days, it was 8.5% in patients with no worsening vs. 36.5% in patients with worsening (P<0.001).

In multivariate analysis, the significant predictors of in-hospital mortality were only older age, Killip class III at admission and worsening of Killip class during the first 5 days (Table 5).


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  		Table 5 Multivariate analysis of significant predictors of in-hospital mortality

 

   4. Discussion
Top
 Notes
 Abstract
 1. Introduction
 2. Patients and methods
 3. Results
 4. Discussion
5. Conclusion
 Appendix A. List of...
 References
 
The negative influence of Killip class at admission on in-hospital mortality has been established for a long time [3,5,13,18,19,25], but our study shows that worsening of Killip class over the first days is also a very potent predictor of a poorer prognosis. Older age, diabetes mellitus and anterior location of MI were predictors, both of a higher Killip class at admission, and of worsening of HF. The latter was more frequent in patients whose Killip class at admission was >I.

4.1. Incidence and risk factors of heart failure
Although the definition of HF used in patients with MI may differ between studies, the most common one is that given by Killip and Kimball in 1967 [1]. In their study, 33% of the patients were class I, 38% class II, 10% class III, and 19% class IV. In another study 10 years later, the % of patients in each class was 37, 18, 11 and 33%, respectively [10]. The percent of patients in class I observed in our study (67%) is similar to that found in other recent series [11–13]. In the TRACE study, clinical HF occurred in 51% of the patients at some time [6]. The incidence of HF is very dependent on the age of the population studied: in the Emanuelsonn et al. study [14], HF was very frequent because of the age distribution (median age of the patients with HF, 75 years).

In the TRACE study, the frequency of HF increased dramatically with age: 24% in patients ≤55 years old, 72% in patients >75 years old [15]. However, this may be due to a more frequent diastolic dysfunction or poorer peripheral organ function in the elderly, rather than to a higher prevalence of HF due to MI in these patients.

The incidence of HF has not changed over the last decades, despite the thrombolytic era [13,16]. In univariate analysis, it was more frequent in patients with a history of angina pectoris in our study. This is similar to some series [14], but not to all [17]. Likewise, HF was more frequent in patients with previous MI [14,18], but this was not observed in another study [17]. HF is much more frequent in patients who have a history of HF [14,19]. A history of HF was more frequent in women in several series [20–22], but this was not observed by Bueno et al. [19], perhaps because patients with a history of MI were excluded from this study. However, the TRACE study has shown that mortality was higher in patients with a history of HF than in those whose HF occurred during hospitalization [15].

In our study, smoking was a predictive factor in multivariate analysis, along with diabetes mellitus, which is a known risk factor for HF at admission or during hospitalization in patients with MI [14,19].

In our study, as in others, thrombolysis appears to be performed more often in patients without HF, even though the contrary would be expected due to the higher benefit of thrombolysis in patients at higher risk: 47 vs. 37% in the TRACE study [6], 49 vs. 36% in Rott et al. study [13], but there was no influence of the time interval between symptom onset and initial medical intervention in our study, as in the Emanuelsson et al. study [14].

The maximal CPK level correlates highly with HF, either already present at admission [14], or occurring during hospitalization (Killip class III–IV: 3% when CPK level ≤200 IU/l vs. 27% when CPK level >800 IU/l) [23], or persisting HF [24]. In our study, CPK was predictive of worsening of Killip class.

HF was more frequent in anterior Q-wave MI than in non-anterior or non-Q-wave MI. In the TRACE study, MI was non-Q-wave in 18% of patients with HF vs. 23% of the patients without HF (P<0.001); it was anterior in 30% of patients with HF vs. 22% of the patients without HF (P<0.001) [6]. In the study by Maisel et al., HF was present in 59% of inferior MI and in 71% of anterior MI [18]. However, this was not observed in other series [14,19].

The higher mortality in women has been observed in several studies [13,20,22]. In the Bueno et al. study, Killip class at admission was >I in 22% of the men and 39% of the women; % for Killip class >I during hospitalization were 32 and 53%, respectively [19]. However, in our study, although female gender was associated with a higher Killip class in univariate analysis, regardless of the Killip variable tested, it did not remain so in the multivariate analysis, most probably because of age. Age is a very potent factor for HF [13,14,19]. In a study of 4259 Danish patients with MI, two thirds of the patients >80 years had HF, and cardiogenic shock was two-fold more frequent in patients >80 years of age than in patients 60–69 years old [25].

4.2. Worsening of heart failure during hospitalization
Worsening of HF during hospitalization has rarely been described in the literature. In our study, worsening occurred in 17% of the patients with Killip class I–III at admission. In the TRACE study, 14% of patients developed HF >2 days after MI [6]. In the Leor et al. study, among patients with Killip class I at admission, cardiogenic shock occurred in 3% of the patients during the first 24 h after admission. Predictors were age, female gender, history of angina pectoris, maximal lacticodeshydrogenase level, and hyperglycemia [12]. In our study, age, diabetes mellitus, CPK level, and location of MI were predictive. Worsening of HF occurred less often in patients with Killip class I at admission compared to those with Killip class >I (although this was not so in univariate analysis; univariate analyses should always be completed with multivariate analyses). In a study in 202 patients, HF at admission was present in 16% of the surviving patients and in 91% of the deceased patients, whereas HF after the third day was present in 6% of the surviving patients and in 36% of the deceased patients [26]. Thrombolysis was not predictive of HF at admission, or of its worsening.

4.3. Prognostic influence of heart failure and its worsening
The prognostic importance of HF has been well established [3,5,13,18,19,25,27–34]. In patients >70 years, Killip class was the only predictor of mortality in multivariate analysis [35]. In-hospital mortality is 3–10% in Killip class I, 10–30% in class II, approximately 35% in class III, and approximately 60% in class IV [13,14]. In the GUSTO-I study, cardiogenic shock, which occurred in 7% of the patients, brought about 58% of the in-hospital mortality [28]. The increase in mortality with the increase in Killip class has not changed since the initial description [1], even though mortality has decreased over time in each class [13]. This decrease cannot be attributed solely to the introduction of thrombolysis in the late 1980s, since mortality in patients who did not receive thrombolysis in the 1990s was lower than that in patients in the early 80s [13].

Prognosis was very poor in patients with worsening of HF in our study: in-hospital mortality was 8% in patients without worsening vs. 36% in patients with worsening of HF. In the Cleempoel et al. study [26], early HF was an independent predictive factor of mortality in multivariate analysis. In the AIRE study, transient HF, which occurred in 12% of the patients, was a predictor of mortality, but less so than persistent HF. In patients without HF, 1-year mortality was 6%, 11% in patients with transient HF, and 25% in patients with persistent HF [36]. Similarly, in the TRACE study, the mortality was lower in patients with transient HF than in patients with persistent HF, but higher than in patients without HF (P<0.001) [6]. In the GUSTO-I study, in-hospital mortality was higher in patients in whom cardiogenic shock occurred during hospitalization than in patients with cardiogenic shock at admission; moreover, in patients developing cardiogenic shock during hospitalization, in-hospital mortality was higher in patients in Killip class II or III at admission than in those in Killip class I [28].

HF affects in-hospital mortality as well as 1-year mortality: in the TRACE study, patients with HF had a higher 1-year mortality than patients without HF (28 vs. 7%, P<0.001) [6].

Clinical HF and LVEF are two independent predictors of mortality which are additive [5,6,14,37,38]: in the TRACE study, HF and reduction of echocardiographic wall-motion index predicted mortality regardless of each other [6].

4.4. Limitations of the study
The cohort may not be exhaustive, because firstly, we relied on the reports of the physicians in charge of the patients rather than actively searching the medical charts of all patients admitted to cardiology or intensive care units; and secondly, some patients who were resident in the three departments may have sought care in hospitals outside the departments. However, our findings were consistent with those of the French diagnosis-related groups’ database, which suggests that only a few cases, at worst, were missed.


   5. Conclusion
Top
 Notes
 Abstract
 1. Introduction
 2. Patients and methods
 3. Results
 4. Discussion
 5. Conclusion
Appendix A. List of...
 References
 
The simple description of clinical and radiological HF during hospital course of MI using the Killip classification allows risk stratification as soon as the patient is admitted to hospital, and thus rapid and appropriate management (medical treatment alone or invasive procedure) [13]. Combining Killip classification with echographic measurement of the wall-motion index easily allows identification of low-risk patients [39]. For prognosis appraisal, initial Killip classification appears more judicious than simple grading of HF/no HF [14].

Worsening of HF, which occurred in 17% of our patients, was associated with a higher in-hospital mortality (no worsening, 8%; worsening, 36%). Killip class >I is associated, not only with a higher in-hospital mortality, but also with a higher 1-year mortality in patients surviving the hospital phase [3,13,27,32–34]. This reinforces the need for very aggressive management of patients with MI and HF in order to limit the occurrence of HF and its consequences.


   Appendix A. List of PRIMA centers
Top
 Notes
 Abstract
 1. Introduction
 2. Patients and methods
 3. Results
 4. Discussion
 5. Conclusion
 Appendix A. List of...
References
 

Isère: Bourgoin-Jallieu; la Côte Saint-André; Grenoble (centre hospitalier universitaire, clinique des Eaux Claires); la Mure; le Pont de Beauvoisin; Saint-Laurent du Pont; Saint-Marcellin; Saint-Martin d'Hères; Vienne; and Voiron (centre hospitalier général, clinique de Chartreuse).
Loire: Feurs; Firminy; Montbrison; Rive de Gier; Roanne; Saint-Chamond; Saint-Etienne (clinique la Croix, hôpital Bellevue, hôpital de la Charité, hôpital Nord, hôpital de Saint-Jean-Bonnefond, policlinique Beaulieu); Saint-Galmier; and Saint-Just et Saint-Rambert.
Rhône: Condrieu; Givors; Lyon and surroundings (centre hospitalier Lyon Sud, clinique Charcot, clinique du Grand Large, clinique des Minguettes, clinique Mutualiste E André, clinique de la Roseraie, clinique de la Sauvegarde, clinique du Tonkin, hôpital Cardiovasculaire et Pneumologique, hôpital A Charrial, hôpital de la Croix-Rousse, hôpital Desgenettes, hôpital E Herriot, hôpital de l'Hôtel Dieu, hôpital de Sainte-Foy les Lyon, hôpital Saint-Joseph, infirmerie protestante, policlinique de Rillieux); Tarare; and Villefranche sur Saône.
Co-ordinating center: F. Delahaye, C. Colin, R. Ecochard, and G. de Gevigney.


   Acknowledgements
 
We are indebted to all physicians in the participating centers who made this study possible, and to A.F. Myard, B. Riche, and S. Excoffier, for their technical assistance. This study was supported by grants from: Programme Hospitalier de Recherche Clinique, Ministry of Health, Paris; Réseau National de Santé Publique, Paris; Fédération Française de Cardiologie, Paris; and Laboratoire Lipha-Santé, Lyon.


   Notes
Top
 Notes
Abstract
 1. Introduction
 2. Patients and methods
 3. Results
 4. Discussion
 5. Conclusion
 Appendix A. List of...
 References
 
1 List in Appendix A. Back


   References
Top
 Notes
 Abstract
 1. Introduction
 2. Patients and methods
 3. Results
 4. Discussion
 5. Conclusion
 Appendix A. List of...
 References
 

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