|Year : 2013 | Volume
| Issue : 4 | Page : 171-178
A comparative study of the C-reactive protein and the ST-score (ECG) as prognostic indicators in acute myocardial infarction in a rural resource-constrained hospital setting in central India: A cross-sectional study
Jyoti Jain, Udit Ravinder Narang, Vishakha Vinay Jain, Om P Gupta
Department of Medicine, Mahatma Gandhi Institute of Medical Sciences, Sevagram, Wardha, Maharashtra, India
|Date of Web Publication||12-Feb-2014|
Department of Medicine, Mahatma Gandhi Institute of Medical Sciences, Sewagram, Wardha, Maharashtra
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Context: The electrocardiogram remains a crucial tool in identification of acute myocardial infarction (AMI). High sensitivity C-reactive protein (hs-CRP) has been found to be strong predictor of coronary artery disease (CAD) and future cardiovascular events.
Aims: The aim of the present study was to compare hs-CRP and ST-Score (STS) as prognostic indicator in acute ST segment elevated myocardial infarction (STEMI) in central rural India.
Settings and Design: Cross sectional study, rural hospital in central India.
Material and Methods: In all patients of STEMI, STS, ST index and hs-CRP were measured on admission and serially. The Primary outcome was all cause mortality at 30 days. Secondary outcome were heart failure, life threatening arrhythmia, cardiogenic shock, re-infarction, hospital stay and re-admission.
Statistical analysis used: We used Student's t test to compare means, Chi-square test to compare proportions and Mann Whitney test to compare medians. P value <0.05 will be considered significant. Crude odds ratios were computed to assess the strength of association between risk factors and independent variable along with 95% confidence intervals.
Results: STS was significantly higher in patients with poor outcome, when compared with good outcome (20.27mm vs.12.47mm, P = 0.002). On multivariate regression model STS was significant predictor of composite outcome events (OR = 2.74; 95% [CI], 1.46 to 5.17; P = 0.002). The area under the ROC curve was 0.70, with sensitivity of 73.5%, specificity of 58.7%; PPV of 68.3% and NPV of 64.2%. hs-CRP in patients with poor outcome vs. good outcome (6mg/L vs. 3.74mg/L, P = 0.003) and (P = 0.06, 0.85 and 0.12) respectively.
Conclusions: STS on admission is independent predictor while hs- CRP is not in resource constrained settings.
Keywords: Acute ST elevated myocardial infarction, electrocardiogram, hs-CRP, ST-Score, prognostic indicator
|How to cite this article:|
Jain J, Narang UR, Jain VV, Gupta OP. A comparative study of the C-reactive protein and the ST-score (ECG) as prognostic indicators in acute myocardial infarction in a rural resource-constrained hospital setting in central India: A cross-sectional study. Heart Views 2013;14:171-8
|How to cite this URL:|
Jain J, Narang UR, Jain VV, Gupta OP. A comparative study of the C-reactive protein and the ST-score (ECG) as prognostic indicators in acute myocardial infarction in a rural resource-constrained hospital setting in central India: A cross-sectional study. Heart Views [serial online] 2013 [cited 2020 Sep 25];14:171-8. Available from: http://www.heartviews.org/text.asp?2013/14/4/171/126882
| Introduction|| |
Coronary artery disease (CAD) is a leading cause of deaths worldwide. World Health Organization estimates that 60% of the world's cardiac patients will be Indian by 2010.  Investigators continue to propose additional indicators like behavioral, biochemical, environmental and genetic to stratify patients into either low or high risk of developing a cardiovascular event i.e. arrhythmias, heart failure (HF) and death.
Determining infarct-related artery (IRA) patency is important because rescue angioplasty improves outcomes in patients with a persistently occluded IRA. , However, not all hospitals in our country have adequate facilities to perform coronary intervention. In making the clinical decision for the early management in patients with acute myocardial infarction (AMI), an early and specific prognostic indicator is needed ideally which should be simple, quick, reliable, non-invasive, inexpensive and easily applicable especially in resource-constrained setting. The electrocardiogram (ECG) remains a crucial tool in the identification and management of AMI. Electrocardiogram may meet all these requirements if certain calculation criterion were applied. Recent evidences support the role of inflammation as a key pathogenetic mechanism in triggering and progression of athero-thrombotic events.  hs-CRP an acute phase reactant has been linked to the development of an acute plaque and standardized strong predictor of CAD and future cardiovascular events. ,
Thus, the present prospective study was aimed to evaluate efficacy of STS and hs-CRP as a prognostic indicator in patients of more than 18 years of age with acute STEMI, who underwent thrombolysis in a resource-constrained setting in central rural India.
| Materials and Methods|| |
It was a prognostic study carried out in the Department of Medicine from 1 st of May 2007 to 30 th April 2008 at Mahatma Gandhi Institute of Medical Sciences, Sevagram, a rural hospital in central India. The study population consisted of all consecutive patients of more than 18 years of age with acute STEMI who underwent thrombolysis.
The diagnosis of acute STEMI was based on clinical profile, ECG findings and CPK-MB as defined by ESC/ACC/AHA. ,, Inferior wall myocardial infarction (IWMI) was defined as ST elevation of ≥1 mm in 2 of these leads: II, III, aVF and anterior wall myocardial infarction (AWMI) when ST elevation in V5, V6, I and aVL. Criteria for exclusion were presence of acute congestive HF, severe arrhythmias on admission, posterior AMI, right ventricular myocardial infarction, new onset left bundle-branch block. The study was approved by the institutional ethics committee. All participants gave written informed consent.
Baseline characteristics and clinical data were recorded for all patients. All patients received the standard line of management for AMI in accordance with AHA/ACC guidelines. , STS and ST index were calculated in standard 12-lead ECG on admission, at 90 minutes post thrombolysis, six hours, 24 hours, day three and five by two readers separately. The averaged STS measured were used for analysis. In case of a marked difference between the two values i.e. >2 mm, ECG was re-analyzed together in an attempt to reach a consensus. STS was defined as sum of ST-segment elevation in all leads related to infarct location. ST-segment elevation was measured (to the nearest 0.5 mm) 60 ms after J point and relative to PR segment. ,
Sera from all enrolled patients were collected for estimation of hs-CRP on admission, at 6 hours, 24 hours and day 3. Inability to complete the four serial estimations due to early mortality however was not taken as exclusion criteria. The serum was stored at ─20°C till they were tested by serologist who was unaware of the clinical data of the patients. hs-CRP measurement was done using CRP-ULTRA (Latex turbidimetry assay) with a detection limit: 0.05 mg/L to 11.7 mg/L.
Total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, random glucose, CPK-MB, and serum creatinine were obtained on day one in all participants. Patients were monitored for the early complications and mortality during hospital stay and were followed up for 30 days.
The primary endpoint was all cause mortality at 30 days. Secondary endpoints were HF, life-threatening arrhythmias, cardiogenic shock, re-infarction, duration of hospital stay and re-admission. Outcome was defined as composite of primary and secondary outcome as either good or poor outcome.
HF and cardiogenic shock were defined as per ESC/ACC/AHA guidelines. ,, Life-threatening arrhythmias included arrhythmias with hypotension i.e. systolic blood pressure (SBP) <90 mm Hg, bradyarrhythmias and tachyarrhythmias. Re-infarction and re-admission was defined, respectively, as appearance of fresh ECG changes suggestive of infarction or progression of changes from anterior to inferior wall or vise-versa and admission due to any cardiovascular cause like fresh infarction, unstable angina, HF or arrhythmias during the next 30 days.
We analyzed by STATA software (Version 10, Stata Corporation, Texas, USA). We analyzed normally distributed continuous variables by student's t test, proportions by Chi-square test and continuous variables with skewed distribution by Mann-Whitney test. Initial analysis included comparison of the frequencies of demographic variables and risk factors among patients with good and poor outcome. Variables like STS and hs-CRP were not normally distributed, we log-transformed them for analyses. Univariate model with threshold of P < 0.20 to identify statistically significant variables: age, sex, systolic and diastolic blood pressure, Killip class, CPK-MB, hs-CRP, STS and ST indices on admission, and then subsequently assessed. Crude odds ratios (OR) were computed to assess the strength of association between risk factors (covariates) and independent variable (outcome) along with 95% confidence intervals (CIs).
We began building multivariate logistic regression model by a backward stepwise analysis and used a cut-off point of P < 0.05 to eliminate statistically insignificant variables until the best fitting, most parsimonious final model was identified. The initial model included all the variables selected using the criteria described above and variables that did not contribute significantly were dropped. We assessed the impact of elimination of each variable by using the likelihood ratio test of the final model by using the Hosmer-Lemeshow goodness-of-fit test and calibration of the model and its ability to discriminate patients with good or poor outcome by plotting ROC curve. The results of the final model are presented as adjusted OR with 95% CI.
| Results|| |
The baseline characteristics were similar in both patients of AWMI and IWMI with respect to risk factor profile, medication, clinical profile, and biochemical profile except more number of patients of IWMI had diabetes [Table 1].
Primary outcome: Over a period of 30 days a total of 18 patients died: 11 with AWMI and 7 with IWMI. There was no statistical difference between mortality among patients of AWMI and IWMI i.e. 11 vs. 7 (P = 0.95) [Table 2].
The mean hs-CRP level on admission was 4.98 mg/L. This did not differ significantly between patients of AWMI vs. IWMI [Table 3]. The hs-CRP level were more than 3 mg/L in 56 of the 102 patients and was the best cut point to distinguish good outcome and those with poor outcome. The odds of mortality were 3.00 (1.33-6.75) for cut-off value of 3 mg/L [Figure 1].
|Figure 1: The CRP levels were more than 3 mg/L in 56 of the 102 patients. We used ROC curve to create a 3 mg/dl of hs-CRP that maximized sensitivity and specificity. We found that a cut off point was the best cut point to distinguish those with good outcome with those with outcome (area under ROC 0.654; 95% [CI] 0.54|
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hs-CRP on admission was significantly higher in the patients with poor outcome as compared to those with good outcome. However, serial hs-CRP levels did not differ significantly between those with good outcome and poor outcome. On univariate analysis, hs-CRP on admission was significantly associated with adverse outcomes in all AMI patients [Table 4].
Patients with complications and poor outcome, STS were significantly higher as compared with those without complications and good outcome in both AWMI and IWMI [Table 5].
On multivariate regression model, the STS was found to be significant predictor of a composite outcome event (OR = 2.74; 95% [CI], 1.46 to 5.17; P = 0.002). The area under the ROC curve was 0.70 and the model had a sensitivity of 73.5%, specificity of 58.7%; positive predictive value (PPV) of 68.3% and negative predictive value (NPV) of 64.2% [Figure 2].
|Figure 2: On multivariate regression model the STS was found to be significant predictor of a composite outcome event of death, re-infarction, re-admission, arrhythmias and heart failure (OR = 2.74; 95% [CI], 1.46 to 5.17; P = 0.002). The area under the ROC curve was 0.70 and the model had a sensitivity of 73.5%, specificity of 58.7%; positive predictive value (PPV) of 68.3% and negative predictive value (NPV) of 64.2%|
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The mean STS of those died was 19.27 mm (SD 14.9) compared to those who survived 16.16 mm (SD 12.5). We used ROC curve to create a cut-off point of admission STS that maximized sensitivity and specificity and found that at 11 mm (sensitivity: 73% and specificity: 58%) was the best cut-off point to distinguish those with good outcome with those with poor outcome [Figure 3].
|Figure 3: The mean STS of those died was 19.27 mm (SD 14.9) compared to those who survived 16.16 mm (SD 12.5). We found that a cut off point of 11 mm (sensitivity - 73% and specificity 58%) was the best cut off point to distinguish those with good outcome with those with poor outcome (area under ROC 0.66; 95% [CI] 0.59-0.80)|
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Multivariate logistic regression analysis showed that Killip class on admission (OR, 2.39; 95% CI, (1.24-4.58), CPK-MB (OR, 1.008; 95% CI, (1.002-1.014) and STS ≥11 mm (OR, 4.00; 95% CI, (1.60-9.99) were the independent predictors of poor outcome at 30 days while hs-CRP was not. Thus a patient with STS >11 mm was 4 times likely to experience a poor outcome compared to one with STS <11 mm. This model had a sensitivity of 78.5% and specificity of 56.6%, PPV of 68.7% and NPV of 68.4%. It correctly identified 68.63% of all outcomes [Table 6]. [Figure 4] Predictors like age, sex, systolic and diastolic blood pressure were not found to be significantly associated with the outcome on multivariable logistic regression models.
|Table 6: Odds ratio on univariate and multivariate logistic regression analysis|
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|Figure 4: Multivariate logistic regression analysis showed that on admission STS ≥ 11 mm (OR, 4.00; 95% CI, (1.60-9.99) was the independent predictors of poor outcome at 30 days. Thus a patient with STS >11 mm was 4 times likely to experience a poor outcome compared to one with STS <11 mm. This model had a sensitivity of 78.5% and specificity of 56.6%, PPV of 68.7% and NPV of 68.4%|
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| Discussion|| |
We carried out this prospective study with the aim that the serial serum hs-CRP and STS as good indicators for predicting the short-term outcome in acute STEMI patients. The in-hospital and 30 days mortality were 10.78% and 17.6%, respectively, of which 3.92% died within 24 hours. Higher mortality could be because of lower rate of percutaneous intervention (absence of PTCA/CABG facility in our hospital and non-affordability for intervention). There was no statistical difference in the individual outcome events between the patients of AWMI and IWMI except that life-threatening arrhythmia were more common in patients of IWMI (P = 0.007).
In our study hs-CRP has been studied as a prognostic marker in patients with AMI, and mean hs-CRP level on admission was 4.98 mg/L. This did not differ significantly between the patients of AWMI or IWMI, implying that the type of infarct had no relation with the levels of hs-CRP.
Anzai et al. reported the mean peak CRP level as 14.1 ± 11.3 mg/dl (n = 220),  whereas Mach et al. found 1.70 mg/dl (n = 49) as peak levels of CRP.  The difference may be ethnic or geographical. The peak levels of hs-CRP were reached at 24 hours in our study. Kushner et al. reported attainment of peak at 2-4 days and De Beer et al. reported peak levels at 50 hours from the onset of pain. , This difference could be because, in the present study peak was calculated from the time of admission and not from the onset of chest pain, type of patients included, infarct size and geographical differences. The present study showed that hs-CRP levels on admission only were statistically different between the patients who had good outcome or poor outcome (P = 0.003). However, the hs-CRP levels after admission did not differ significantly between the two groups. Hence, further analysis was done using only admission hs-CRP levels. On multivariate analysis, hs-CRP did not emerge as an independent predictor of poor outcomes. This variation in the result could be because of the less number of outcome events in study population.
The present study and other studies which evaluated the role of hs-CRP confirmed that only on admission and higher values best predicted the outcomes in cases of STEMI. We found that 3 mg/L (sensitivity: 64% and specificity: 66%) was the best cut-off point to distinguish those with good outcome from poor outcome, area under ROC 0.66 (95% CI, 0.54-0.78). In 2003, AHA/CDC consensus statement on role of inflammatory markers in CAD had proposed hs-CRP levels of 3 mg/L as high risk, 1 to 3 mg/L as intermediate risk and less than 1 mg/L as low risk categories and high risk tertiles had an approximately two-fold increase in the relative risk compared with low risk tertiles. 
In a study in 215 patients of AMI, the optimal value of hs-CRP was 3.5 mg/L with sensitivity and specificity of 62.5% and 66.0%, respectively.  Cholesterol and Recurrent Events (CARE) trial also showed that patients with raised CRP >3 mg/L had more events as compared to subjects who had levels below 3 mg/L. 
In our study the STS was found to be significantly higher in patients with AWMI than IWMI. The STS was significantly higher in patients with complications and poor outcome with both AWMI and IWMI. This difference persisted throughout, but was maximum at 90-min post thrombolysis. The mean STS for the patients who died was 19.27 mm compared to those who survived 16.16 mm. In a study similar results were found in both AWMI and IWMI with and without complications: 19.4 mm versus 10.3 mm, P < 0.001; 10.4 mm versus 6.9 mm; P < 0.001, respectively, with mean STS of 21.3 mm. 
Schreiber W et al. compared STS and ST-segment deviation scores (SUMSTdev) i.e. the STS and depression in 382 patients with acute MI and found that SUMSTdev was significantly higher in patients with complications, than without complications (AWMI 23.9 mm versus 11.5 mm, respectively, P < 0.001; IWMI 21.6 mm versus 12.0 mm, respectively, P < 0.001). 
In our study, ROC curves showed the performance of admission STS in predicting early complications in acute MI. The mean area under the curve was 0.70. The optimal cut-off point maximizing sensitivity and specificity was found at 11 mm for both AWMI and IWMI with a sensitivity of 73% and a specificity of 58%. The relative risk (RR) was significantly greater in patients above the cut-off point than in those below the cut-off (RR 3.88; 95% [CI], 1.68 to 8.93).
Gwechenberger et al. gave cut-off point of 13 mm for AWMI and 11 mm for IWMI, which maximized sensitivity and specificity. The ROC for AWMI in their study showed the mean SD area under the curve as 0.80 0.04 with sensitivity of 79% and specificity of 73%. The RR was 2.98 (95% [CI], 1.9 to 4.8). ROC curves for IWMI showed mean SD area under the curve as 0.72 0.05 with sensitivity and specificity of 64% and 68%. The RR was 2.1 (95% [CI], 1.4 to 3.2). They showed that an increase of 1.0 mm in STS increases the odds of complications by 1.1 (95% CI, 1.1 to 1.2) for AWMI and by 1.2 (95% CI, 1.1 to 1.4) for IWMI.  We also used the above cut-off point for both AWMI and IWMI and found that STS on admission with cut-off point of 13 had an odds of complication as 5.2 (95% CI, 1.8 to 15.2; P 0.0001) where as cut-off point of 9 mm for IWMI had odds of complication as 4.2, 95% CI, 1.08 to 16.4; P = 0.003.
Schreiber et al. gave the optimal cut-off point for the sum of ST segment deviation as 16 mm for AWMI and 13 mm for IWMI which was much higher than this study and other studies done in past. This difference was because of the authors considered both ST elevation and reciprocal changes of ST depression. 
Thus for the present study we concluded that an increase of 1.0 mm in STS increased the odds of complications by 1.06 (95% CI, 1.01 to 1.2) for AWMI and by 1.09 (95% CI, 0.96 to 1.2) for IWMI, which was comparable to the studies done in the past. Our study showed that on admission STS alone could be used for risk stratification. Neither serial estimation of STS nor estimation for hs-CRP on admission and serially would help in better risk stratification. No other clinical variables available at the time of admission were significantly associated with the occurrence of early complications in patients with AMI.
The difference was observed in patients with both AWMI and IWMI, indicating that the STS can be used independent of the infarct location. In patients with AWMI and early complications increased STS is caused not only by greater ST-segment elevation in each lead but by a higher total number of leads with ST-segment elevations. Therefore, STS may reflect the area of endangered myocardium in patients with AWMI. Our data are in line with the findings of previously published reports demonstrating a relationship between STS and infarct size, serious complications or death. ,
We found no literature on combined effect of hs-CRP and STS. Combined use of STS and hs-CRP did not have any added advantage hence estimation of both are not advocated.
Therefore, inclusion of STsd in patients may lead to an increased STS, resulting in decreased specificity.
The study was a single center study and hence does not reflect the whole population. However, being a single center study, it provides credibility to the study. The other weakness of the study was its small sample, which although was adequate for analysis of mortality and total outcome events but was inadequate for the subgroup analysis. The study had few strengths as well. Firstly, all consecutive patients with acute STEMI were included in the study, eliminating selection bias. Lastly the primary outcome variable was all cause mortality, which is a hard endpoint and thus eliminated bias.
Our study concluded STS on admission proved to be an independent marker of prognostic importance while hs-CRP did not. In an emergency department where electrocardiography is easily available, STS effectively can be used to stratify patients into high-risk and low-risk categories.
It is thus recommended that an aggressive treatment should be initiated with close monitoring in patients who are found to have high values of STS on admission. A large multicenter trial is needed to search for other novel predictor in AMI patients.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]