Associations Between Mean Arterial Pressure and Prognosis in Acute Myocardial Infarction Complicated by Cardiogenic Shock Differ Depending on Coma Status

Abstract

Background: In acute myocardial infarction complicated by cardiogenic shock (AMI-CS), low mean arterial pressure (MAP) can reduce cerebral perfusion, potentially resulting in coma. While both MAP and coma on admission are critical prognostic factors, the relationship between them and their prognostic significance based on coma status remains unclear.

Methods and Results: A retrospective analysis of 543 AMI-CS patients was conducted. The overall median MAP was 77 mmHg, with no significant difference between the coma and non-coma groups. The coma group had a higher 30-day mortality compared with the non-coma group (50% vs. 29%; P<0.001). The area under the curve for MAP predicting 30-day mortality was 0.723 (P<0.001) in the coma group, with a cut-off MAP of 76.3 mmHg (sensitivity 0.66, specificity 0.69), but was insignificant in the non-coma group (AUC 0.543; P=0.176). Kaplan-Meier analysis showed higher mortality with low MAP (<77 mmHg) in the coma group, whereas MAP had no significant impact in the non-coma group. Multivariate Cox regression identified low MAP as an independent prognostic factor in the coma group only.

Conclusions: The associations between MAP and prognosis differ depending on the coma status in AMI-CS. Low MAP is a prognostic factor for mortality only in patients with coma. This study highlights the need for treatment strategies tailored to neurological status.

The prognosis for acute myocardial infarction (AMI) patients has improved with the widespread use of early percutaneous coronary intervention, advances in pharmacological and mechanical treatments, and the development of AMI care networks.^1–4 However, the prognosis for AMI complicated by cardiogenic shock (AMI-CS) has remained poor over the past decade, with survival rates of approximately 30–45%.^5–7 In AMI-CS, cardiac output is significantly reduced, leading to insufficient perfusion of peripheral tissues and vital organs.⁸ This decrease in blood flow impairs oxygen delivery, particularly to the brain, which often results in disturbances in consciousness. In the early stages of shock, mild confusion, disorientation, and drowsiness are observed, which can progress to a comatose state as the condition advances.

Mean arterial pressure (MAP) is a key physiological parameter used to assess organ perfusion and is generally regarded as a more accurate indicator of tissue perfusion compared with systolic blood pressure alone.^9,10 Low MAP levels can lead to ischemic injury. Therefore, it is generally recommended that MAP should be maintained to at least 65 mmHg to ensure adequate tissue perfusion in AMI patients with severe systolic dysfunction.⁷ In contrast, an elevated MAP, particularly in patients with impaired cardiac function, may increase afterload, imposing additional stress on the myocardium.

Given the critical roles of both MAP and coma in the clinical trajectory of AMI-CS patients, both parameters are recognized as important prognostic factors.^7,11 Nevertheless, the relationship between MAP and coma in AMI-CS patients remains unclear, particularly with regard to its prognostic significance. Because low MAP can exacerbate ischemic injury and impaired cerebral blood flow, further investigation is warranted to clarify its association of outcomes in AMI-CS patients, both with and without coma. The present study aims to elucidate the prognostic significance of MAP in patients with AMI-CS, with particular focus on coma status as a clinical marker of systemic circulatory failure.

Methods

Study Population

From January 2013 to December 2021, a total of 6,628 consecutive patients diagnosed with acute coronary syndrome (ACS) was enrolled in the Mie ACS Registry, a prospective, multicenter registry based in Mie Prefecture, Japan.^12–14 Cardiogenic shock was defined as Killip 4 characterized by a systolic blood pressure of ≤90 mmHg and signs of peripheral vasoconstriction such as oliguria, cyanosis and diaphoresis.¹⁵ The Killip classification was determined based on the overall clinical course from onset to admission by experienced cardiologists in each institute. From this cohort, patients with unstable angina pectoris (n=651), non-Killip 4 AMI patients (n=5,391), and those without adequate blood pressure data on admission (n=43) were excluded from the analysis. After these exclusions, a total of 543 AMI-CS patients was identified for inclusion in the study. These 543 patients were then classified into 2 groups based on their level of consciousness on hospital admission: a non-coma group (n=398) consisted of patients who did not exhibit coma, and a coma group (n=145) consisted of patients who presented with coma (Figure 1). Coma was defined using the Japan Coma Scale 3 digit code (100–300), indicating a state where patients were unarousable by any forceful stimuli (Supplementary Table), which is equivalent to Glasgow Coma Scale 3–7.¹⁶ The level of consciousness, blood pressure, and pulse rate data were evaluated on arrival to hospital. MAP was calculated using the following formula: MAP = (systolic blood pressure + [2 × diastolic blood pressure]) / 3.

Figure 1.

Flowchart of the study protocol. AMI-CS, acute myocardial infarction complicated by cardiogenic shock.

This registry was approved by the institutional review board or ethics committee of Mie University Graduate School of Medicine and each participating institutional ethics committee (reference no. 2881). The University Hospital Medical Information Network (UMIN) Clinical Trial Registry number is UMIN 000036020. The study was conducted in accordance with the principles of the Declaration of Helsinki.

Clinical Outcome

The primary endpoint of this study was 30-day all-cause mortality. Cumulative survival over a 2-year period was also analyzed in patients who survived beyond 30 days. Clinical outcome data were collected through a combination of outpatient consultations, medical record reviews, and telephone interviews with patients and their families. These assessments were carried out by a well-trained cardiologist to ensure accuracy and consistency in data collection.

Statistical Analysis

Continuous variables were presented as medians with interquartile ranges (IQR) and compared using the non-parametric Mann-Whitney U test, as the data did not follow a normal distribution. Categorical variables were expressed as percentages and compared using the chi-squared test. To evaluate the accuracy of MAP and the Global Registry of Acute Coronary Events (GRACE) score in predicting 30-day all-cause mortality, receiver operating characteristic (ROC) curve analysis was performed. The areas under the ROC curves (AUCs) for these variables were compared using DeLong’s method,¹⁷ which allows for the comparison of ROC curves from different models. The Youden index was applied to identify optimal cut-off values from the ROC curves for predicting mortality. Multivariate Cox proportional hazards regression analysis was conducted to identify independent predictors of 30-day all-cause mortality. Variables that were significant in univariate analysis, along with previously established prognostic factors, were included in the multivariate model. Kaplan-Meier survival analysis was performed to compare time-to-event data between groups stratified by MAP (≥77 vs. <77 mmHg) and coma status (coma vs. non-coma). Subgroup analyses were conducted based on age (≥75 vs. <75 years) and the presence or absence of out-of-hospital cardiac arrest (OHCA). Additionally, a landmark analysis was carried out for patients who survived beyond 30 days, with survival curves plotted for up to 2 years. The log-rank test was applied to assess the statistical significance of differences between the survival curves. All tests were 2-sided, and significance was defined as a P value <0.05. Analyses were performed using SPSS version 24.0 (SPSS, Inc., Chicago, IL, USA) and EZR version 1.52 (Saitama Medical Center, Jichi Medical University, Saitama, Japan), which is a graphical user interface for R version 4.02 (The R Foundation for Statistical Computing, Vienna, Austria).¹⁸

Results

Patient Characteristics

The characteristics of the study population are summarized in Table 1. The median age of the patients was 75 years (IQR 66–83), and 338 (72%) were male. When comparing patients with and without coma, those with coma were significantly younger and had lower rates of hypertension and dyslipidemia. They also exhibited higher levels of hemoglobin, white blood cells, glucose, and C-reactive protein (CRP). The median MAP across all patients was 77 mmHg, with no significant difference between the coma and non-coma groups (P=0.53). Similarly, no significant differences were observed between the groups for systolic and diastolic blood pressures, although heart rate was higher in the coma group. Both the occurrence of OHCA and GRACE scores were significantly higher in the coma group compared with the non-coma group (65% vs. 5%; 259 [242–280] vs. 248 [221–266], respectively). Regarding angiography findings and treatment, patients in the coma group had longer door-to-balloon times and fewer right coronary artery culprit lesions. Additionally, these patients were more frequently treated with extracorporeal membrane oxygenation (ECMO), ECMO+intra-aortic balloon pumping (IABP), continuous hemodiafiltration and ventilation, but were less likely to receive β-blockers or mineralocorticoid receptor antagonists. The use of catecholamines, including dopamine, dobutamine, and norepinephrine, did not differ significantly between the 2 groups. The 30-day mortality rate was significantly higher in the coma group (50%) compared with the non-coma group (29%; P<0.001). Similar results were observed for both cardiovascular (CV) deaths and non-CV deaths (P<0.001).

Table 1.

Patient Characteristics

	Available data	All (n=543)	Non-coma group (n=398)	Coma group (n=145)	P value
Demographic parameters
Age (years)	543	75 [66–83]	77 [68–85]	69 [60–80]	<0.001
Sex, male	543	388 (72)	277 (70)	111 (77)	0.112
Body mass index (kg/m2)	451	23 [20–25]	23 [20–25]	23 [21–26]	0.117
Hypertension	543	295 (54)	232 (58)	63 (43)	0.002
Diabetes	543	195 (36)	145 (36)	50 (34)	0.675
Dyslipidemia	543	177 (33)	147 (37)	30 (21)	<0.001
Smoking	543	118 (22)	80 (20)	38 (26)	0.127
Hemodialysis	543	19 (3.5)	12 (3)	7 (5)	0.309
Prior heart failure admission	543	16 (3)	14 (4)	2 (1)	0.154
Prior MI	543	54 (10)	37 (9)	17 (12)	0.403
SBP (mmHg)	543	103 [83–129]	102 [83–127]	109 [83–140]	0.083
DBP (mmHg)	543	63 [49–80]	62 [50–78]	65 [47–84]	0.808
MAP (mmHg)	543	77 [62–97]	76 [63–94]	79 [61–103]	0.530
HR (beats/min)	542	90 [62–108]	86 [59–105]	96 [74–119]	<0.001
OHCA	543	114 (21)	20 (5)	94 (65)	<0.001
LVEF (%)	425	47 [37–58]	48 [40–58]	50 [35–58]	0.238
GRACE score	520	251 [224–268]	248 [221–266]	259 [242–280]	<0.001
JCS	543
JCS 100		–	–	11 (8)	–
JCS 200		–	–	13 (9)	–
JCS 300		–	–	121 (83)	–
Laboratory data on admission
Creatinine (mg/dL)	543	1.09 [0.89–1.44]	1.09 [0.86–1.46]	1.12 [0.94–1.39]	0.138
Albumin (g/dL)	81	3.7 [3.3–4.1]	3.7 [3.3–4.1]	3.6 [3.3–4.0]	0.531
Hemoglobin (g/dL)	543	13.0 [11–15]	12.9 [11–14]	13.4 [12–15]	0.025
White blood cells (/μL)	543	10,700 [8,550–13,600]	10,500 [8,223–13,300]	11,800 [9,265–15,005]	<0.001
Peak CK (IU/L)	279	1,688 [696–3,353]	1,775 [734–3,935]	1,487 [689–3,072]	0.325
Glucose (mg/dL)	541	222 [159–311]	203 [148–290]	277 [210–362]	<0.001
CRP (mg/dL)	491	0.3 [0.1–1.74]	0.4 [0.1–2.33]	0.2 [0.1–1.0]	0.004
BNP (pg/mL)	312	240 [52–768]	244 [52–831]	204 [56–569]	0.315
Angiography data and treatment
Door-to-balloon time (min)	431	87 [66–117]	81 [65–114]	101 [72–132]	0.009
Culprit lesion
LMT culprit lesion	515	60 (12)	43 (11)	17 (13)	0.664
LAD culprit lesion	515	238 (46)	169 (44)	69 (52)	0.154
RCA culprit lesion	515	172 (33)	138 (36)	34 (25)	0.022
LCX culprit lesion	515	45 (9)	31 (8)	14 (10)	0.415
Multi-vessel disease	543	321 (59)	238 (60)	83 (57)	0.592
Dopamine	543	171 (32)	125 (31)	46 (32)	0.944
Dobutamine	543	193 (36)	147 (37)	46 (32)	0.262
Norepinephrine	543	182 (34)	128 (32)	54 (37)	0.267
CHDF usage	543	63 (12)	38 (10)	25 (17)	0.013
Ventilation	543	247 (46)	123 (31)	124 (86)	<0.001
Final MCS
IABP	540	232 (43)	180 (46)	52 (36)	0.052
Impella	540	15 (3)	12 (3)	3 (2)	0.554
ECMO	540	16 (3)	5 (1)	11 (8)	<0.001
ECMO+IABP	540	56 (10)	28 (7)	28 (19)	<0.001
ECMO+Impella	540	8 (2)	4 (1)	4 (3)	0.133
Medication during hospitalization
β-blocker	543	198 (37)	160 (40)	38 (26)	0.003
ACE-I or ARB	543	321 (59)	245 (62)	76 (52)	0.055
Mineralocorticoid receptor antagonist	543	123 (23)	105 (26)	18 (12)	0.001
Outcome
30-day death	543	187 (34)	114 (29)	73 (50)	<0.001
30-day CV death	543	141 (26)	89 (22)	52 (36)	0.002
30-day non-CV death	543	46 (9)	25 (6)	21 (15)	0.002

Data are presented as median [interquartile range], or n (%). ACE-I, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor antagonists; BNP, brain natriuretic peptide; CABG, coronary artery bypass grafting; CHDF, continuous hemodiafiltration; CK, creatine kinase; CRP, C-reactive protein; CV, cardiovascular; DBP, diastolic blood pressure; ECMO, extracorporeal membrane oxygenation; GRACE, Global Registry of Acute Coronary Event; HR, heart rate; IABP, intra-aortic balloon pumping; JCS, Japan Coma Scale; LAD, left atrial descending artery; LCX, left circumflex artery; LMT, left main trunk artery; LVEF, left ventricular ejection fraction; MAP, mean arterial pressure; MCS, mechanical circulatory support; MI, myocardial infarction; OHCA, out-of-hospital cardiac arrest; PCI, percutaneous coronary intervention; RCA, right coronary artery; SBP, systolic blood pressure.

Relationship Between MAP and Mortality

Patients in each group were divided into quartiles based on MAP levels. In the non-coma group, the Q4 group had the lowest 30-day mortality rate (19%), but no significant differences in mortality were observed between the quartiles (Q1–Q4). However, in the coma group, a significant trend of increasing 30-day mortality was observed with decreasing MAP levels (P<0.001). Specifically, the Q1 and Q2 groups had significantly higher mortality rates compared with the Q4 group, with the Q1 group having approximately 2.6 times the mortality rate of the Q4 group (74% vs. 28%; P<0.05; Figure 2).

Figure 2.

Relationship between mean arterial pressure (MAP) quartiles and 30-day mortality in the non-coma and coma groups.

Prognostic Performance of MAP Compared With GRACE Score

The prognostic performance of MAP and the GRACE score for predicting 30-day mortality was assessed separately for the coma and non-coma groups. In the non-coma group, MAP did not demonstrate significant prognostic performance (ROC-AUC 0.543; P=0.176), whereas the GRACE score showed a relatively strong performance (ROC-AUC 0.660; P<0.001). Conversely, in the coma group, MAP proved to be a better prognostic indicator (ROC-AUC 0.723; P<0.001) compared with the GRACE score, which had a lower prognostic value (Figure 3). The optimal MAP cut-off value for predicting 30-day mortality in the coma group was determined to be 76.3 mmHg (sensitivity 0.66, specificity 0.69).

Figure 3.

Comparison of the prognostic performance of mean arterial pressure (MAP) and the Global Registry of Acute Coronary Event (GRACE) score for 30-day mortality in the non-coma and coma groups.

Associations of Low MAP and Prognosis

Based on the ROC curve analysis and the median MAP value, 77 mmHg was selected as the cut-off point. Kaplan-Meier survival analysis revealed no significant difference in survival between patients with low (<77 mmHg) and high MAP (≥77 mmHg) in the non-coma group (71.2% vs. 71.5%, respectively; P=0.949). In contrast, within the coma group, patients with low MAP had significantly lower cumulative survival compared with those with high MAP (31.4% vs. 66.7%; P<0.001; Figure 4). Similar trends were observed in subgroups stratified by age (≥75 years or <75 years) and by OHCA status, where low MAP was associated with worse survival only in the coma group (Supplementary Figure 1,2). Furthermore, the 30-day outcomes, including both CV and non-CV deaths, as well as the 2-year outcomes among patients who survived beyond the 30-day landmark, demonstrated a similar trend (Figures 5,6).

Figure 4.

Kaplan-Meier curves of cumulative survival for 30-day mortality stratified by mean arterial pressure (MAP) in the non-coma and coma groups.

Figure 5.

Kaplan-Meier curves of cumulative survival for 30-day cardiovascular (CV) death (A) and non-CV death (B) stratified by mean arterial pressure (MAP) in the non-coma and coma groups.

Figure 6.

Kaplan-Meier curves of cumulative survival for 2 years stratified by mean arterial pressure (MAP) in patients who survived >30 days.

Univariate and multivariate Cox regression analyses confirmed that low MAP (<77 mmHg) was not an independent prognostic factor in the non-coma group. Instead, factors such as advanced age, elevated creatinine level, elevated CRP levels, and increased use of ventilator support were identified as significant predictors of mortality. However, in the coma group, low MAP (<77 mmHg) emerged as a significant independent predictor of mortality (hazard ratio [HR] 2.46; 95% confidence interval [CI] 1.45–4.15; P=0.001). Elevated creatinine level, elevated CRP levels, and increased use of ventilator support also remained significant predictors of poor outcomes (Table 2).

Table 2.

Factors Associated With 30-Day Mortality in the Non-Coma Group and the Coma Group

	Non-coma group				Coma group
	Univariate		Multivariate		Univariate		Multivariate
	HR (95% CI)	P value	HR (95% CI)	P value	HR (95% CI)	P value	HR (95% CI)	P value
Age	1.04 (1.02–1.06)	<0.001	1.05 (1.02–1.07)	<0.001	1.00 (0.98–1.02)	0.812
Sex, male	2.36 (1.55–3.58)	<0.001	0.87 (0.56–1.36)	0.544	0.58 (0.35–0.96)	0.035	0.57 (0.32–1.01)	0.053
Hypertension	0.80 (0.55–1.15)	0.230			0.59 (0.36–0.96)	0.033	0.62 (0.35–1.10)	0.103
Diabetes	0.92 (0.63–1.35)	0.678			1.21 (0.75–1.93)	0.438
Dyslipidemia	0.54 (0.35–0.82)	0.004	0.82 (0.53–1.28)	0.381	0.57 (0.30–1.08)	0.085
Prior MI	0.71 (0.35–1.46)	0.354			0.81 (0.40–1.63)	0.552
Smoking	0.55 (0.32–0.94)	0.029	0.74 (0.41–1.33)	0.311	0.64 (0.36–1.14)	0.127
OHCA	0.63 (0.23–1.70)	0.359			0.96 (0.59–1.55)	0.866
MAP <77 mmHg	1.01 (0.70–1.46)	0.95			2.72 (1.67–4.43)	<0.001	2.46 (1.45–4.15)	0.001
Heart rate	1.01 (1.00–1.01)	0.017	1.01 (1.00–1.01)	0.057	1.00 (0.99–1.00)	0.196
Creatinine	1.11 (1.02–1.21)	0.017	1.13 (1.02–1.25)	0.021	1.11 (1.00–1.23)	0.046	1.20 (1.06–1.36)	0.005
Hemoglobin	0.84 (0.78–0.91)	<0.001	0.94 (0.86–1.03)	0.192	0.85 (0.77–0.94)	0.001	0.91 (0.81–1.02)	0.91
Glucose	1.00 (1.00–1.00)	0.046	1.00 (1.00–1.00)	0.064	1.00 (1.00–1.00)	0.612
Peak CK, per 100 pg/mL increase	1.00 (0.99–1.01)	0.995			1.01 (1.00–1.01)	0.114
CRP	1.04 (1.01–1.07)	0.006	1.03 (1.00–1.06)	0.061	1.09 (1.04–1.14)	<0.001	1.07 (1.02–1.13)	0.004
BNP, per 100 pg/mL increase	1.00 (0.997–1.004)	0.767			1.04 (0.99–1.09)	0.169
Door-to-balloon time ≥90 min	1.35 (0.86–2.12)	0.200			1.01 (0.59–1.73)	0.976
MCS support	1.28 (0.87–1.87)	0.213			1.60 (0.94–2.72)	0.086
Ventilator support	2.52 (1.74–3.64)	<0.001	2.44 (1.63–3.67)	<0.001	3.53 (1.29–9.68)	0.014	3.86 (1.32–11.3)	0.014
LMT disease	0.99 (0.54–1.81)	0.979			0.94 (0.45–1.97)	0.869

CI, confidence interval; HR, hazard ratio. Other abbreviations as in Table 1.

Discussion

This study investigated the relationship between coma and MAP in AMI-CS patients, comparing those with and without coma. The key findings are: (1) MAP served as a significant predictor of prognosis in AMI-CS patients with coma, but had no significant prognostic impact in patients without coma; and (2) low MAP (<77 mmHg) was identified as an independent prognostic factor in AMI-CS patients with coma, while this was not the case for those without coma. This is the first study to evaluate AMI-CS by differentiating between patients with and without coma, providing novel insights into risk stratification.

Clinical Significance of Coma in Risk Stratification

Exploring prognostic factors for AMI-CS is essential as it contributes to an understanding of pathophysiology and the establishment of effective treatment strategies. Although previous studies have explored various prognostic factors such as age and blood pressure in AMI-CS patients, the heterogeneity of patient conditions complicates accurate assessments.^11,19,20 Because AMI-CS patients exhibit substantial variability in their clinical conditions, depending on factors such as the severity and duration of the shock, it is preferable to classify patients according to specific clinical indicators. Coma is a critical indicator of insufficient cerebral blood flow and reflects the severity of systemic circulatory failure. Therefore, we focused on utilizing coma status as a risk stratification indicator. In this study, we stratified AMI-CS patients by coma status and demonstrated, for the first time, that risk factors differ based on coma status. Notably, our findings revealed that low MAP is an independent predictor of poor prognosis in patients with coma, whereas this association was not observed in patients without coma. Interestingly, coma appears to be closely related to poor outcomes associated with low MAP, irrespective of OHCA and age. This indicates that, even among the same AMI-CS patients, coma status markedly affects the importance of organ perfusion and its prognostic implications. Thus, this study provides new insights into risk stratification for AMI-CS patients and highlights the need for special attention to patients with coma.

Utility of MAP as an Early Risk Indicator

The rapid identification of high-risk AMI-CS patients in the emergency department and linkage to appropriate care is a challenge for improving survival. The GRACE score is one of the widely used prognostic assessments in ACS patients,²¹ but it was originally derived from a broader ACS cohort and may lack precision in predicting outcomes in severely hemodynamically unstable patients, such as those with cardiogenic shock.²² Furthermore, the GRACE score requires complex calculations and invasive testing, limiting its practical utility in the urgent and unstable setting of AMI-CS. In our study, the GRACE score was a useful prognostic tool for AMI-CS patients without coma, whereas its utility was diminished in those with coma, with MAP emerging as a more reliable indicator. These results indicate that prognostic tools should be adapted to specific clinical conditions, such as coma status, to optimize their effectiveness. Given its simplicity and non-invasive nature, MAP could serve as a critical prognostic tool for risk stratification in AMI-CS patients with coma, particularly in emergency settings where rapid decision-making is essential.

MAP Value for Risk Stratification

While previous studies have proposed a MAP target of 65 mmHg to maintain vital organ perfusion, such as the kidneys, brain, and liver,²³ our study demonstrated that a MAP threshold of 77 mmHg is more predictive of short-term outcomes in AMI-CS patients with coma compared with the previously proposed target of 65 mmHg. In comatose patients, systemic organ perfusion is significantly reduced, and under these conditions a low MAP can easily lead to worsened neurological recovery and the development of multiple organ failure, which is often complicated by systemic inflammatory response syndrome and microvascular alterations.^24–26 In post-cardiac arrest patients, the lower threshold for cerebral autoregulation has been shown to shift to the right, indicating that a higher MAP is required to maintain adequate cerebral perfusion.²⁷ Previous studies have also demonstrated that in patients with cardiogenic shock following cardiac arrest, a higher MAP (around 80 mmHg) in early hospitalization is associated with a favorable clinical outcomes through mechanisms such as reduced myocardial injury and improved blood lactate clearance.^28,29 A MAP of 70 mmHg has been reported as the threshold for favorable neurological outcomes in patients remaining in a coma following resuscitation from cardiac arrest.³⁰ Based on these reports, it is considered that a relatively high MAP at admission in critically ill patients with coma helped to prevent the development of organ failure and neurological damage, serving as a predictor of short-term outcomes. Another possibility is that the poor outcomes in comatose patients with low MAP could be attributed to complications associated with mechanical circulatory support (MCS), such as bleeding or infections. Although the specific causes of death are unknown, this hypothesis is supported by the finding that 19 (90%) of the 21 non-CV deaths, including deaths from bleeding and infections, in the coma group occurred in patients with MCS. In terms of long-term outcomes, secondary factors such as infections and heart failure, arising from severe conditions, such as neurological impairment, multi-organ failure, and SIRS, are thought to significantly impact long-term outcomes.³¹

In contrast, although a higher MAP can serve as a prognostic indicator, its utility as a management indicator remains controversial. In randomized controlled trials involving post-cardiac arrest patients with coma, no significant difference was observed in the percentages of patients dying or experiencing severe disability or coma between the group managed at a low MAP (63 mmHg) and the group managed at a high MAP (77 mmHg).³² Maintaining MAP often requires high doses of catecholamines or afterload-increasing MCS, which may contribute to poor outcomes.^2,29,33 If catecholamine use can be minimized and MCS managed appropriately, a MAP of 77 mmHg may serve as a promising target for blood pressure management. However, further studies are required to determine the optimal MAP target, including effective methods for maintaining blood pressure.

Clinical Implications

Given the heterogeneous nature of AMI-CS, patient stratification based on specific clinical indicators is highly desirable. Prognostic factors differ depending on the coma status, and in patients with coma, MAP is more critical than traditionally reported prognostic factors such as age. Both coma status and MAP can be rapidly assessed, making them valuable for promptly determining treatment strategies. As shown in Figure 2, although an inverse correlation between MAP values and mortality was observed, mortality rates remained persistently high even in patients with MAP >77 mmHg. Therefore, while a MAP of 77 mmHg may serve as a helpful reference for rapid patient stratification, it should not be regarded as a definitive threshold. The optimal MAP target for management has yet to be fully elucidated. With these in mind, clinicians should pay attention to the association of MAP and coma when determining treatment strategies.

Study Limitations

Several limitations of this study must be acknowledged. First, this study was retrospective and observational. Residual confounding variables and missing values may not have been completely adjusted. In terms of analysis of registry data, missing values are unavoidable. Second, hemodynamic parameters, such as MAP, may fluctuate over time in the acute phase of myocardial infarction, which could introduce variability in admission data. A dynamic assessment of MAP, accounting for its changes over the course of treatment, may offer a more comprehensive understanding of its prognostic impact. Third, given the emphasis on coma as a prognostic factor, the study does not include direct measures of cerebral perfusion or detailed neuroimaging, which could have provided more insight into the relationship between MAP, coma, and brain function in AMI-CS patients. Fourth, the absence of information on pre-admission medications, particularly antihypertensive drugs, may have influenced the results. However, given the acute and emergent nature of cardiogenic shock, pre-admission medications are unlikely to have a significant impact on the patient’s hemodynamic status on arrival to hospital, thereby mitigating this limitation to some extent. Fifth, the relatively high blood pressure at admission is a relevant consideration. The primary explanation lies in the fluctuations in blood pressure that occurred from onset to the time of admission and the shock criteria. The Killip classification in this study was determined based on the overall clinical course from onset to admission. Consequently, this study includes patients with preserved blood pressure at admission but who experienced significant hypotension prior to admission, meeting the definition of Killip 4. Our previous study utilizing this registry also reported fluctuations in blood pressure and heart rate parameters from the prehospital phase to the time of hospital arrival.¹⁴ Additionally, we believe that some cases presented with markedly high blood pressure at admission due to prehospital administration of adrenaline. Patient characteristics may also be another contributing factor. Compared with prior studies based on the Japanese shock registry, which analyzed the clinical characteristics and prognostic factors of AMI-CS patients,¹¹ our study demonstrates a slightly higher systolic blood pressure (103 mmHg vs. 75.0 mmHg) and left ventricular ejection fraction (47% vs. 41%). This suggests that relatively milder cases might have been included in our cohort. The lower proportion of patients with prior myocardial infarction (10% vs. 34%) may be one of the reasons for this difference. However, this prior Japanese study also highlighted the prognostic significance of coma and blood pressure, which is consistent with the results of our study. Additionally, a comprehensive review of seminal RCTs in AMI-CS included some studies with patient backgrounds of systolic blood pressure ≥100 mmHg,²⁰ indicating that our study does not include a highly heterogeneous cohort.

Conclusions

The associations between MAP and patient prognosis differs depending on the coma status in AMI-CS. Only in patients with coma did MAP prove to be a significant predictor of prognosis, with low MAP being identified as an independent prognostic factor. Conversely, MAP had no significant prognostic relevance in patients without coma. These findings provide new insights into risk stratification for AMI-CS patients and highlights the importance of tailored treatment approaches based on the patient’s neurological status.

Acknowledgments

This study was supported and funded by the Mie Cardiovascular and Renal Disease Network. The authors thank all participating facilities, the Mie ACS Registry co-investigators, the Mie CCU Network Support Center, and the Mie University Hospital Clinical Research Support Center. The author used OpenAI’s ChatGPT in order to polish the English writing.

Sources of Funding

The Mie ACS Registry was funded by the incorporated non-profit organization Mie Cardiovascular and Renal Disease Network (http://www.medic.mie-u.ac.jp/miecrnet/). This study did not receive any specific funding.

Disclosures

The authors have no relationships to disclose that are relevant to the contents of this manuscript. K.D. received departmental research grant support from Nippon Boehringer Ingelheim Co., Ltd, Abbott Medical Japan LLC, Otsuka Pharmaceutical Co., Ltd, and Daiichi Sankyo Company Limited. The other authors have no financial conflicts of interest to disclose.

IRB Information

This study was approved by the Mie University Hospital Institutional Review Board (reference no. 2881).

Data Availability

The individual deidentified participant data (including data dictionaries) will be shared. Microsoft Excel data used for the analysis will be shared. The data for each table and figure will be shared upon request. The study protocol will also be shared. Data will be available for 1 year after publication of the article. Data will be available to anyone who is interested in this article after publication by contacting the corresponding author. The data will be shared as Microsoft Excel or CSV files via email.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circrep.CR-25-0001

References

• 1.   Goldberg RJ, Spencer FA, Gore JM, Lessard D, Yarzebski J. Thirty-year trends (1975 to 2005) in the magnitude of, management of, and hospital death rates associated with cardiogenic shock in patients with acute myocardial infarction: A population-based perspective. Circulation 2009; 119: 1211–1219.
• 2.   Shibasaki I, Masawa T, Abe S, Ogawa H, Takei Y, Tezuka M, et al. Benefit of veno-arterial extracorporeal membrane oxygenation combined with Impella (ECpella) therapy in acute coronary syndrome with cardiogenic shock. J Cardiol 2022; 80: 116–124.
• 3.   Scott IA, Denaro CP, Bennett CJ, Hickey AC, Mudge AM, Flores JL, et al. Achieving better in-hospital and after-hospital care of patients with acute cardiac disease. Med J Aust 2004; 180: 83–88.
• 4.   Tehrani BN, Sherwood MW, Rosner C, Truesdell AG, Ben Lee S, Damluji AA, et al. A standardized and regionalized network of care for cardiogenic shock. JACC Hear Fail 2022; 10: 768–781.
• 5.   Matoba T, Sakamoto K, Nakai M, Ichimura K, Mohri M, Tsujita Y, et al. Institutional characteristics and prognosis of acute myocardial infarction with cardiogenic shock in Japan?: Analysis from the JROAD/JROAD-DPC Database. Circ J 2021; 85: 1797–1805.
• 6.   Ueki Y, Mohri M, Matoba T, Tsujita Y, Yamasaki M, Tachibana E, et al. Characteristics and predictors of mortality in patients with cardiovascular shock in Japan: Results from the Japanese Circulation Society Cardiovascular Shock registry. Circ J 2016; 80: 852–859.
• 7.   Henry TD, Tomey MI, Tamis-Holland JE, Thiele H, Rao SV, Menon V, et al. Invasive management of acute myocardial infarction complicated by cardiogenic shock: A scientific statement from the American Heart Association. Circulation 2021; 143: E815–E829.
• 8.   Tehrani BN, Truesdell AG, Psotka MA, Rosner C, Singh R, Sinha SS, et al. A standardized and comprehensive approach to the management of cardiogenic shock. JACC Hear Fail 2020; 8: 879–891.
• 9.   Fei Y. Understanding the association between mean arterial pressure and mortality in young adults. Postgrad Med J 2020; 96: 453–454.
• 10.   Fellahi JL, Futier E, Vaisse C, Collange O, Huet O, Loriau J, et al. Perioperative hemodynamic optimization: From guidelines to implementation–An experts’ opinion paper. Ann Intensive Care 2021; 11: 58.
• 11.   Sakamoto K, Matoba T, Mohri M, Ueki Y, Tsujita Y, Yamasaki M, et al. Clinical characteristics and prognostic factors in acute coronary syndrome patients complicated with cardiogenic shock in Japan: Analysis from the Japanese Circulation Society Cardiovascular Shock registry. Heart Vessels 2019; 34: 1241–1249.
• 12.   Masuda J, Kishi M, Kumagai N, Yamazaki T, Sakata K, Higuma T, et al. Rural-urban disparity in emergency care for acute myocardial infarction in Japan. Circ J 2018; 82: 1666–1674.
• 13.   Mizutani H, Fujimoto N, Ito H, Sato T, Moriwaki K, Takasaki A, et al. Prognostic impact of peak aortic jet velocity on patients with acute myocardial infarction. Circ J 2022; 86: 1539–1546.
• 14.   Moriwaki K, Kurita T, Hirota Y, Ito H, Ishise T, Fujimoto N, et al. Prognostic impact of prehospital simple risk index in patients with ST-elevation myocardial infarction. Circ J 2023; 87: 629–639.
• 15.   Killip T, Kimball JT. Treatment of myocardial infarction in a coronary care unit. A two year experience with 250 patients. Am J Cardiol 1967; 20: 457–464.
• 16.   Nakajima M, Okada Y, Sonoo T, Goto T. Development and validation of a novel method for converting the Japan Coma Scale to Glasgow Coma Scale. J Epidemiol 2023; 33: 531–535.
• 17.   Delong ER, Delong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 1988; 44: 837–845.
• 18.   Kanda Y. Investigation of the freely available easy-to-use software “EZR” for medical statistics. Bone Marrow Transplant 2013; 48: 452–458.
• 19.   Pöss J, Köster J, Fuernau G, Eitel I, de Waha S, Ouarrak T, et al. Risk Stratification for patients in cardiogenic shock after acute myocardial infarction. J Am Coll Cardiol 2017; 69: 1913–1920.
• 20.   Tyler JM, Brown C, Jentzer JC, Baran DA, van Diepen S, Kapur NK, et al. Variability in reporting of key outcome predictors in acute myocardial infarction cardiogenic shock trials. Catheter Cardiovasc Interv 2022; 99: 19–26.
• 21.   Granger CB, Goldberg RJ, Dabbous O, Pieper KS, Eagle KA, Cannon CP, et al. Predictors of hospital mortality in the global registry of acute coronary events. Arch Intern Med 2003; 163: 2345–2353.
• 22.   Popovic B, Fay R, Cravoisy-Popovic A, Levy B. Cardiac power index, mean arterial pressure, and simplified acute physiology score II are strong predictors of survival and response to revascularization in cardiogenic shock. Shock 2014; 42: 22–26.
• 23.   Cecconi M, De Backer D, Antonelli M, Beale R, Bakker J, Hofer C, et al. Consensus on circulatory shock and hemodynamic monitoring: Task force of the European Society of Intensive Care Medicine. Intensive Care Med 2014; 40: 1795–1815.
• 24.   Lemiale V, Dumas F, Mongardon N, Giovanetti O, Charpentier J, Chiche JD, et al. Intensive care unit mortality after cardiac arrest: The relative contribution of shock and brain injury in a large cohort. Intensive Care Med 2013; 39: 1972–1980.
• 25.   Jentzer JC, Lawler PR, Van Diepen S, Henry TD, Menon V, Baran DA, et al. Systemic inflammatory response syndrome is associated with increased mortality across the spectrum of shock severity in cardiac intensive care patients. Circ Cardiovasc Qual Outcomes 2020; 13: E006956.
• 26.   De Backer D, Creteur J, Dubois MJ, Sakr Y, Vincent JL. Microvascular alterations in patients with acute severe heart failure and cardiogenic shock. Am Heart J 2004; 147: 91–99.
• 27.   Sundgreen C, Larsen FS, Herzog TM, Knudsen GM, Boesgaard S, Aldershvile J. Autoregulation of cerebral blood flow in patients resuscitated from cardiac arrest. Stroke 2001; 32: 128–132.
• 28.   Ameloot K, Jakkula P, Hästbacka J, Reinikainen M, Pettilä V, Loisa P, et al. Optimum blood pressure in patients with shock after acute myocardial infarction and cardiac arrest. J Am Coll Cardiol 2020; 76: 812–824.
• 29.   Janiczek JA, Winger DG, Coppler P, Sabedra AR, Murray H, Pinsky MR, et al. Hemodynamic resuscitation characteristics associated with improved survival and shock resolution after cardiac arrest. Shock 2016; 45: 613–619.
• 30.   Kilgannon JH, Roberts BW, Jones AE, Mittal N, Cohen E, Mitchell J, et al. Arterial blood pressure and neurologic outcome after resuscitation from cardiac arrest. Crit Care Med 2014; 42: 2083–2091.
• 31.   Kamiński KA, Tycińska AM, Stepek T, Szpakowicz A, Olędzka E, Dobrzycki S, et al. Natural history and risk factors of long-term mortality in acute coronary syndrome patients with cardiogenic shock. Adv Med Sci 2014; 59: 156–160.
• 32.   Kjaergaard J, Møller JE, Schmidt H, Grand J, Mølstrøm S, Borregaard B, et al. Blood-pressure targets in comatose survivors of cardiac arrest. N Engl J Med 2022; 387: 1456–1466.
• 33.   Distelmaier K, Wiedemann D, Lampichler K, Toth D, Galli L, Haberl T, et al. Interdependence of VA-ECMO output, pulmonary congestion and outcome after cardiac surgery. Eur J Intern Med 2020; 81: 67–70.