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. 2022 Oct 31;6(6):100116. doi: 10.1016/j.shj.2022.100116

Comparison of Risk Models in the Prediction of 30-Day Mortality in Acute Myocardial Infarction–Associated Cardiogenic Shock

Lauren S Ranard a,1, Kenneth Guber b,1, Justin Fried a, Koji Takeda a, Yuji Kaku a, Dimitrios Karmpaliotis a,c, Gabriel Sayer a, Leroy Rabbani a, Daniel Burkhoff d, Nir Uriel a, Ajay J Kirtane a,d, Amirali Masoumi a,c,∗
PMCID: PMC10242577  PMID: 37288128

Abstract

Background

There are numerous risk-prediction models applied to acute myocardial infarction–related cardiogenic shock (AMI-CS) patients to determine a more accurate prognosis and to assist in patient triage. There is wide heterogeneity among the risk models including the nature of predictors evaluated and their specific outcome measures. The aim of this analysis was to evaluate the performance of 20 risk-prediction models in AMI-CS patients.

Methods

Patients included in our analysis were admitted to a tertiary care cardiac intensive care unit with AMI-CS. Twenty risk-prediction models were computed utilizing vitals assessments, laboratory investigations, hemodynamic markers, and vasopressor, inotropic and mechanical circulatory support available from within the first 24 ​hours of presentation. Receiver operating characteristic curves were used to assess the prediction of 30-day mortality. Calibration was assessed with a Hosmer-Lemeshow test.

Results

Seventy patients (median age 63 years, 67% male) were admitted between 2017 and 2021. The models' area under the curve (AUC) ranged from 0.49 to 0.79, with the Simplified Acute Physiology Score II score having the most optimal discrimination of 30-day mortality (AUC: 0.79, 95% confidence interval [CI]: 0.67-0.90), followed by the Acute Physiology and Chronic Health Evaluation-III score (AUC: 0.72, 95% CI: 0.59-0.84) and the Acute Physiology and Chronic Health Evaluation-II score (AUC: 0.67, 95% CI: 0.55-0.80). All 20 risk scores demonstrated adequate calibration (p > 0.05 for all).

Conclusions

Among the models tested in a data set of patients admitted with AMI-CS, the Simplified Acute Physiology Score II risk score model demonstrated the highest prognostic accuracy. Further investigations are required to improve the discriminative capabilities of these models or to establish new, more streamlined and accurate methods for mortality prognostication in AMI-CS.

Keywords: Acute myocardial infarction cardiogenic shock, Cardiogenic shock, Mortality, Risk prediction, Risk calculator

Introduction

Acute myocardial infarction–related cardiogenic shock (AMI-CS) is characterized by end-organ hypoperfusion as a result of a low cardiac output due to loss of contracting myocardium. This deficiency in end-organ perfusion is often characterized by hypotension, tachycardia, peripheral vasoconstriction, pulmonary and systemic venous congestion, decreased urine output, altered sensorium, acute kidney or liver injury, and lactic acidosis.1 Randomized clinical trials since the 1990s have consistently reported mortality rates between 40% and 60% for patients in AMI-CS.2, 3, 4, 5, 6 Despite the SHOCK trial (Should We Emergently Revascularize Occluded Coronaries for CS), which demonstrated survival benefits from early revascularization in AMI-CS, and the further advancements of primary percutaneous coronary interventions, AMI-CS mortality has remained high.3,7 In recent years, many tertiary care centers have developed treatment teams and algorithms specific for AMI-CS patients with promising results.8,9 Nevertheless, despite advances in reperfusion strategies and hemodynamic support, delayed recognition and regional disparities in care remain fundamental challenges in the treatment of AMI-CS.4,10 As such, tools that lead to effective, early risk stratification of AMI-CS patients may help guide therapy and ultimately reduce mortality.

There is wide heterogeneity in the studies that have evaluated predictors of mortality for AMI-CS; these studies vary on multiple levels including patient population, the nature of the predictors evaluated, and therapies utilized.11 Several scoring systems have been validated to predict the mortality of patients in medical intensive care units.12, 13, 14, 15 Attempts have been made to apply these scoring systems to a wide variety of medical indications including severe trauma, abdominal pathology, chronic obstructive pulmonary disease, acute pancreatitis, sepsis, and postcardiac surgery care.16, 17, 18, 19, 20, 21 A few of these risk scores, namely the Acute Physiology and Chronic Health Evaluation-II (APACHE-II), Acute Physiology and Chronic Health Evaluation-III (APACHE-III), and Simplified Acute Physiology Score II (SAPS-II) models, have been applied to the AMI-CS population and have demonstrated adequate predictive capacities albeit with varying individual results.22, 23, 24 Over the past decade, there has been elaboration of risk scores specific to the AMI-CS population. These risk scores, however, have not been robustly evaluated for their comparative prognostic performance and, at times, include measures that are not readily available at the time of patient presentation. As such, this makes their relative uses in clinical practice challenging.

The aim of this analysis was to compare the predictive performance of a number of risk models in assessing the risk of 30-day, all-cause mortality in patients with AMI-CS treated at our medical center.

Methods

Patient Population

This was a retrospective study including patients who presented with AMI-CS to the Columbia University Irving Medical Center between 2017 and 2021. This study was approved by the Columbia institutional review board. As the study was retrospective, no informed consent was required. Clinical information including vital signs, invasive hemodynamic recordings, the use of inotropic or vasopressor support, the use of mechanical circulatory support, and laboratory investigations were used to identify patients with AMI-CS. Twenty risk-prediction models, listed in Supplemental Table 1 with their respective component variables, were calculated and assessed: APACHE-II, APACHE-III, APACHE-IV, Sequential Organ Failure Assessment (SOFA), SAPS-II, Morrow model, Global Registry of Acute Coronary Events Score, Zwolle model, Primary Angioplasty for the Treatment of Acute ST Elevated Myocardial Infarction Model, Klein model, Thrombolysis in ST-Elevation in Myocardial Infarction Score, Thrombolysis in Non-ST Elevation in Myocardial Infarction Score, Pathophysiology and Prognostic Factors in Cardiogenic Shock Score, Intraaortic Balloon Pump in Cardiogenic Shock Score, Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications, Assessment of Pexelizumab in Acute Myocardial Infarction, Modified Shock Index, SHOCK, Prediction of Cardiogenic Shock Outcome for AMI patients salvaged by Veno-Arterial Extracorporeal Membrane Oxygenation Score, and the Inova Heart and Vascular Institute score.12,13,15,25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 The data entered into the risk score models were from the date of patient presentation.

Outcomes

The primary endpoint of our analysis was 30-day mortality. The secondary endpoints examined were 90-day and 1-year mortality.

Statistical Methods

Categorical variables are reported as frequencies and percentages; statistical differences were analyzed using chi-squared or Fisher exact test as appropriate. Continuous variables are reported as mean and standard deviation; statistical differences were analyzed using Student’s t-test. Receiver operating characteristic curves were constructed for each of the risk scores to assess the discriminative power of the scores for 30-day mortality prediction and to compare them. The calibration of the risk scores was assessed using the Hosmer-Lemeshow goodness-of-fit test. For all statistical tests, a p value of <0.05 was considered statistically significant. All analyses were performed using Stata version 12.1 (Stata Corp, College Station, Texas).

Results

Baseline Characteristics

The study cohort included a total of 70 patients with AMI-CS. Baseline characteristics are demonstrated in Table 1. The median age was 63 years (interquartile range 55-72), and 67.1% were male. The median body mass index was 31 (interquartile range 26-34). Of all the patients in the cohort, 78.6% had been previously diagnosed with hypertension, 67.1% with hyperlipidemia, and 48.6% with diabetes; 22.9% of the cohort had sustained a prior myocardial infarction.

Table 1.

Overall patient characteristics by 30-day survival vs. 30-day mortality

Characteristics All patients, N = 70 30-D survival group, N = 38 30-D mortality group, N = 32 p Value
Age, y 63 (55-72) 62 (53-68) 66 (60-73) 0.47
Male 47 (67.1%) 27 (75%) 20 (62.5%) 0.46
BMI 31.0 (26.5-34.0) 31.0 (26.1-33.8) 30.1 (26.8-34.5) 0.54
Hypertension 55 (78.6%) 27 (75.0%) 28 (87.5%) 0.09
Hyperlipidemia 47 (67.1%) 24 (66.7%) 23 (71.9%) 0.44
Diabetes 34 (48.6%) 15 (41.7%) 19 (59.4%) 0.10
CAD 36 (51.4%) 16 (44.4%) 20 (62.5%) 0.09
CKD 10 (14.3%) 2 (5.6%) 8 (25.0%) <0.05
Prior MI 16 (22.9%) 8 (22.2%) 8 (25.0%) 0.70
Prior CABG 8 (11.4%) 1 (2.8%) 7 (21.9%) <0.05
CCU length of stay, d 17 (7-25) 19 (12-34) 8 (4-19) <0.001
Hospitalization length, d 20 (8-35) 34 (18-45) 8 (4-19) <0.001
Cardiac arrest at admission 27 (41.5%) 15 (41.7%) 13 (40.6%) 0.92
STEMI at admission 42 (60.0%) 26 (76.5%) 16 (50.0%) 0.12
Anterior MI at admission 36 (51.4%) 22 (61.1%) 14 (43.8%) 0.25
Total proximal LAD at admission 20 (28.6%) 13 (36.1%) 7 (21.9%) 0.26
Vasopressor/inotropic support 65 (92.9%) 35 (97.2%) 30 (93.8%) 0.40
Any MCS 67 (95.7%) 36 (94.7%) 31 (96.9%) 0.16
IABP support 27 (38.6%) 20 (55.6%) 7 (21.9%) <0.01
Impella support 42 (60.0%) 19 (52.8%) 23 (71.9%) <0.05
ECMO 51 (72.9%) 24 (66.7%) 27 (84.4%) <0.05
Ventilation 53 (75.7%) 28 (77.8%) 25 (78.1%) 0.50
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Notes. All data are presented as n (%) or median (interquartile range).

BMI, body mass index; CABG, coronary artery bypass graft; CAD, coronary artery disease; CCU, coronary care unit; CKD, chronic kidney disease; ECMO, extracorporeal membrane oxygenation; IABP, intraaortic balloon pump; LAD, left anterior descending artery; MCS, mechanical circulatory support; MI, myocardial infarction; STEMI, ST-elevation myocardial infarction.

Primary Outcome: 30-Day Mortality

The 30-day mortality was 46% (n = 32). In an unadjusted analysis, patients with 30-day mortality had a higher incidence of chronic kidney disease (8 vs. 2 patients, p < 0.05) and were more likely to have had prior coronary artery bypass grafting (7 vs. 1, p < 0.05). Additionally, they were more likely to receive a percutaneous transvalvular left ventricular assist device (Impella) (23 vs. 19, p < 0.05) and/or extracorporeal membrane oxygenation (27 vs. 24, p < 0.05) than those who survived.

There were no significant differences between those who survived versus those who died in terms of the need for a vasopressor or inotropic support, rates of mechanical ventilation, incidence of ST-elevation myocardial infarction, or cardiac arrest at the time of presentation. When patients were stratified by SCAI classification at the time of presentation, a larger percentage of patients who survived to 30 days presented with SCAI shock classification A, B, or C compared with those who died by 30 days. As seen in Figure 1, a majority of patients who died by 30 days presented with SCAI shock classification D or E.43

Figure 1.

Figure 1

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Thirty-day outcomes by SCAI classification. A larger percentage of patients who survived up to 30 days presented with SCAI Shock classification A–C compared to who died. Of those with 30-day mortality, the majority presented with SCAI Shock classification D or E. SCAI stage classification was determined post hoc based on the parameters defined by the most recent expert consensus review statement.42

Abbreviation: SCAI, Society of Cardiovascular Angiography and Interventions.

In terms of risk scores, as shown in Table 2, patients who died by 30 days had significantly higher APACHE II (15 vs. 12, p < 0.05), APACHE III (68 vs. 52, p < 0.01), SAPS-II (49 vs. 41, p < 0.001), SOFA (6 vs. 4, p < 0.05), Morrow (41.3 vs. 34.5, p < 0.05), Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications (13 vs. 11, p < 0.05), Assessment of Pexelizumab in Acute Myocardial Infarction (152 vs. 133, p < 0.01), SHOCK Trial (43 vs. 29, p < 0.05), Intraaortic Balloon Pump in Cardiogenic Shock Score II (5 vs. 3, p < 0.05), and APACHE IV (78 vs. 78, p < 0.05) scores.

Table 2.

Risk scores by 30-day survival vs. 30-day mortality

Risk model Score bounds All patients, N = 70 30-D survival group, N = 38 30-D mortality group, N = 32 p Value
APACHE II 0 to 71 14 (11-17) 12 (10-15) 15 (12-19) <0.05
APACHE III 0 to 299 64 (48-72) 52 (45-63) 68 (51-74) <0.01
SAPS-II 0 to 163 45 (37-50) 41 (36-45) 49 (45-54) <0.001
SOFA 0 to 24 5 (3-6) 4 (2-6) 6 (4-7) <0.05
TIMI-STEMI 0 to 14 7 (6-9) 7 (6-8) 8 (6-9) 0.10
TIMI-NSTEMI 0 to 7 4 (3-5) 4 (3-5) 4 (3-5) 0.50
Morrow model 0 to 100 37.1 (25.4-48.5) 34.5 (24.3-44.1) 41.3 (30.1-51.3) <0.05
GRACE 1 to 372 223 (198.5-246) 216 (198-246) 222 (200-250) 0.68
Zwolle et al. 0 to 16 13 (12-14) 12 (11-13) 13 (12 -14) 0.20
PAMI 0 to 15 6 (4-7) 6 (4-8) 7 (4-9) 0.72
Klein et al. 0 to 262 75 (50-99) 75 (50-100) 75 (68-100) 0.11
CADILLAC 0 to 18 13 (11-14) 11 (9-13) 13 (10-15) <0.05
APEX-AMI 0 to 239 152 (125-163) 133 (105-155) 152 (135-168) <0.01
Modified Shock Index 0 to 3 1.27 (0.9-1.4) 1.1 (0.9-1.4) 1.2 (1.0-1.4) 0.20
CARD-SHOCK 0 to 9 4 (3-5) 4 (3-5) 4 (3-5) <0.05
SHOCK Trial 0 to 102 40 (25-55) 29 (23-48) 43 (28-63) <0.05
ENCOURAGE 0 to 45 19 (17-24) 19 (14-24) 22 (19-24) 0.07
IABP-SHOCK II 0 to 8 5 (3-5) 3 (2-5) 5 (4-6) <0.05
INOVA 0 to 10 5 (3-6) 4 (2-6) 5 (3-6) 0.09
APACHE IV 0 to 286 78 (72-78) 78 (54-78) 78 (78-78) <0.05
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Notes. All data are presented as median (interquartile range).

APACHE-II, Acute Physiology and Chronic Health Evaluation-II; APACHE-III, Acute Physiology and Chronic Health Evaluation-III; APACHE-IV, Acute Physiology and Chronic Health Evaluation-IV; APEX-AMI, Assessment of Pexelizumab in Acute Myocardial Infarction; CADILLAC, Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications; CARD-SHOCK, Pathophysiology and Prognostic Factors in Cardiogenic Shock; ENCOURAGE, Prediction of Cardiogenic shock Outcome for AMI Patients Salvaged by VA-ECMO Score; IABP-SHOCK II, Intraaortic Balloon Pump in Cardiogenic Shock Score; INOVA, Inova Heart and Vascular Institute Score; GRACE, Global Registry of Acute Coronary Events Score; PAMI, Primary Angioplasty for the Treatment of Acute ST Elevated Myocardial Infarction Model; SAPS-II, Simplified Acute Physiology Score; SHOCK, Should We Emergently Revascularize Occluded Coronaries for Cardiogenic Shock Score; SOFA, Sequential Organ Failure Assessment; TIMI-STEMI, Thrombolysis in ST-Elevation in Myocardial Infarction Score; TIMI-NSTEMI, Thrombolysis in Non-ST Elevation in Myocardial Infarction Score.

Secondary Outcomes: 90-Day and 1-Year Mortality

The 90-day mortality was 51% (n = 36), and the 1-year mortality was 56% (n = 39), as seen in Figure 2. Of those who died within 1 year, 82% of the deaths occurred in the first 30 days (32 of 39 patients).

Figure 2.

Figure 2

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Thirty-day, 90-day, and 1-year mortality. The majority of patient deaths occurred within the first 30 days.

Receiver Operating Characteristic Curves

The individual risk score receiver operating characteristic analysis results are shown in Figure 3. The SAPS-II score (area under the curve [AUC]: 0.79, 95% confidence interval [CI]: 0.67-0.90) demonstrated the highest prognostic accuracy for 30-day mortality in this patient cohort, followed by the APACHE-III score (AUC: 0.72, 95% CI: 0.59-0.84) and the APACHE-II score (AUC: 0.67, 95% CI: 0.55-0.80). The poorest performer was the Zwolle model (AUC: 0.49, 95% CI: 0.41-0.56). The Hosmer-Lemeshow goodness-of-fit test demonstrated acceptable calibration for all risk scores (p > 0.05 for all).

Figure 3.

Figure 3

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Receiver operating characteristic curve analysis for 30-day predicted mortality. The SAPS-II demonstrated the highest predictive ability for 30-day mortality (AUC: 0.79, 95% CI: 0.67-0.90), followed by the APACHE-III (AUC: 0.72, 95% CI: 0.59-0.84), the APACHE-II (AUC: 0.67, 95% CI: 0.55-0.80), and SOFA (AUC: 0.67, 95% CI: 0.54-0.79) scores.

Abbreviations: APEX-AMI, Assessment of Pexelizumab in Acute Myocardial Infarction; AUC, area under the curve; CADILLAC, Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications; GRACE, Global Registry of Acute Coronary Events Score; IABP, Intraaortic Balloon Pump; PAMI, Primary Angioplasty for the Treatment of Acute ST Elevated Myocardial Infarction; TIMI, Thrombolysis in ST-Elevation in Myocardial Infarction Score.

Discussion

This study utilized a real-world population of patients with AMI-CS presenting to a tertiary care center. The 30-day mortality of our cohort was 46%, which is similar to what has been seen in other studies examining the AMI-CS population.3,4,44,45 Our study demonstrated that multiple risk scores had similar, adequate predictive power for 30-day mortality. The AUCs for the risk scores ranged from 0.49 to 0.79, with the SAPS-II score having the most optimal discrimination followed by the APACHE-II, APACHE-III, and SOFA models.

The SAPS-II was originally developed for mortality estimation for medical and surgical intensive care unit patients in North America and Europe.26 Paradoxically, in its original conception, it excluded patients with burn injuries, coronary care unit patients, and/or cardiac surgical patients from the analysis. Nevertheless, despite the relative paucity of hemodynamic parameters included in the calculation, the SAPS-II utility in mortality discrimination has been previously observed.46 Kellner et al. evaluated 41 patients with AMI-CS and found that the mean admission APACHE-II, APACHE-III, SAPS-II, and SOFA scores were higher in nonsurvivors vs. survivors and had modest predictive performance (APACHE-II AUC 0.691, APACHE-III AUC 0.786, and SAPS-II AUC 0.790). The maximum score AUCs demonstrated superior performance for prediction of mortality.23 More recently, in a 2019 analysis of a subset of patients with refractory AMI-CS (53.9%), the SAPS-II demonstrated superior mortality-prediction capacity in patients on VA-ECMO when compared to age, SOFA score, and pH alone.47

When considering the parameters employed by the highest-performing indices in our analysis—the SAPS-II, APACHE-II, APACHE-III, and SOFA models—all incorporated the Glasgow Coma Score, the fraction of inspired oxygen, creatinine output, and/or urine output into their respective calculations. Other than the APACHE-IV model, the Glasgow Coma Score was not used in the other 16 indices. The SAPS-II score also included blood urea nitrogen, urine output, and serum bicarbonate concentrations, potentially reflecting a sensitivity toward worsening renal function more than the other scores. The SOFA score was unique among the top performers in that it did not incorporate a measure of chronic health conditions nor the type of hospital admission.

Despite the relative success of the SAPS-II scoring system in our analysis, there was no clear superior model between all 20 analyzed. This is likely best explained by the heterogeneity of the CS population and the inherent challenge of patient generalizability. Furthermore, as mentioned, some scores analyzed were derived and validated in AMI-CS populations, while others in broader cardiogenic shock or septic shock populations. Finally, all risk score models were calculated using single, early, data points within the patients’ clinical courses. Given the multifaceted clinical trajectory of this heterogenous patient population, it may not be possible to utilize a single score to capture a process that inherently has multiple time horizons.

In this vein, there are several limitations to the aforementioned AMI-CS risk models that warrant attention. A significant portion of the scores studied, namely the APACHE II-IV, SHOCK, and Prediction of Cardiogenic shock Outcome for AMI Patients Salvaged by VA-ECMO Score scores, are widely regarded as too complex to calculate at the bedside, hindering their clinical utility. The SHOCK score was also developed from a cohort that preceded the widespread use of primary percutaneous coronary intervention and may not reflect current practice. Finally, none of these scores reflect the importance of time to support in AMI-CS patient outcomes.48,49 As such, when considering a theoretical “ideal” AMI-CS scoring system, such a model should be rigorous enough to incorporate universally available initial metrics at patient presentation such as vitals, physical findings, and cardiometabolic data but also be malleable enough to integrate advanced, hemodynamic parameters such as invasive monitoring, MCS specifications including time to and time on support, and responses to initial therapies into its global assessment.50 Scores should be contemporary, reflecting improvements in standards of care over time, and applicable to specific subgroups of patients, such as those with ST-elevation myocardial infarction or those placed on specific types of MCS.

Limitations

Our study has a few limitations that should be noted. First, this study was conducted at a single academic institution, limiting its generalizability. Second, as this was a retrospective analysis of previously collected data, there may have been variation in available data for risk score calculation. As such, these data need to be prospectively validated in a larger patient population.

Conclusions

In this study, the SAPS-II, APACHE-II, APACHE-III, and SOFA risk score models demonstrated the highest prognostic accuracy for 30-day mortality when applied to a population of AMI-CS patients. Further investigations are required to improve the discriminative capabilities of these models or to establish new, more-streamlined, and accurate methods for mortality prognostication in patients at AMI-CS.

Ethics Statement

This study was approved by the Columbia Institutional Review Board. As the study was retrospective, no informed consent was required.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Disclosure statement

Dr. Amirali Masoumi receives Honoria from Abiomed. The other authors had no conflicts to declare.

Footnotes

Supplemental data for this article can be accessed on the publisher’s website.

Supplementary Material

Supplemental Table 1
mmc1.docx (41.5KB, docx)

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