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Farshadmand, J.;  Hai, O.;  Zeltser, R.;  Makaryus, A.N. Cardiac Power Hemodynamic Measurements in Cardiovascular Conditions. Encyclopedia. Available online: (accessed on 19 June 2024).
Farshadmand J,  Hai O,  Zeltser R,  Makaryus AN. Cardiac Power Hemodynamic Measurements in Cardiovascular Conditions. Encyclopedia. Available at: Accessed June 19, 2024.
Farshadmand, Jonathan, Ofek Hai, Roman Zeltser, Amgad N. Makaryus. "Cardiac Power Hemodynamic Measurements in Cardiovascular Conditions" Encyclopedia, (accessed June 19, 2024).
Farshadmand, J.,  Hai, O.,  Zeltser, R., & Makaryus, A.N. (2022, December 06). Cardiac Power Hemodynamic Measurements in Cardiovascular Conditions. In Encyclopedia.
Farshadmand, Jonathan, et al. "Cardiac Power Hemodynamic Measurements in Cardiovascular Conditions." Encyclopedia. Web. 06 December, 2022.
Cardiac Power Hemodynamic Measurements in Cardiovascular Conditions

Despite numerous advancements in prevention, diagnosis and treatment, cardiovascular disease has remained the leading cause of mortality globally for the past. Part of the explanation for this trend is persistent difficulty in determining the severity of cardiac conditions in order to allow for the deployment of prompt therapies.  In heart failure (HF) patients showing cardiac power index (CPI) and cardiac power output (CPO) as valuable tools to assess cardiac function in the acute setting; and, additionally, CPO was found to be an essential tool in patients with critical cardiac illness (CCI), as the literature showed that CPO was statistically correlated with mortality. Cardiac power and the derived measures obtained from this relatively easily obtained variable can allow for essential estimations of prognostic outcomes in cardiac patients.

cardiac power prognosis of cardiac power cardiac power index Cardiac Power Output

1. Introduction

Despite numerous advancements in prevention, diagnosis, evaluation and treatment, cardiovascular disease is the leading cause of morbidity and mortality in the US and globally, and has been for the past 20 years [1]. Due to the high mortality surrounding cardiovascular diseases, there has been extensive research into determining the function and prognostic utility of hemodynamic parameters and biomarkers. Some of the more commonly used prognostic variables include left ventricular ejection fraction (EF), cardiac output (CO), cardiac index (CI), pulmonary capillary wedge pressure (PCWP), blood pressure (diastolic, systolic and/or mean) and brain-natriuretic peptide (BNP). There are several issues, however, with using each of these variables, and their prognostic power varies across cardiac conditions. Both EF [2] and CO/CI [3] are dependent on preload and afterload and may therefore inaccurately represent cardiac function, especially in critical illness such as cardiogenic shock [4]. PCWP captures ventricular compliance and volume status more than actual cardiac pumping ability and it may therefore fail to predict symptomatic improvement [5] or outcome [6] in various cardiac conditions. Similarly, BNP is a measure of ventricular volume overload as opposed to cardiac performance and may therefore be normal in conditions such as heart failure with preserved ejection fraction (HFpEF), CS, and restrictive cardiomyopathies [7][8], despite severe disease. By combining both pressure and blood flow, cardiac power (CP) gives a better picture of the overall pumping ability of the heart. It can also be measured noninvasively using echocardiography, a common procedure in the care of cardiac patients and bioimpedance [9][10]. Specifically, resting CP is easily measured without having to subject patients to additional invasive procedures, which could lead to further decompensation in their condition. CP has been shown to be a powerful predictor of mortality in cardiogenic shock [6][11][12][13][14], chronic heart failure [15][16] and sepsis [17][18], among other conditions. Despite showing prognostic utility across a wide range of conditions, however, it is not routinely used to guide treatment of cardiac or critically ill patients.

2. Aortic Stenosis

With regards to transcatheter aortic valve replacement (TAVR) due to symptomatic severe aortic stenosis (AS), it was found that all four studies in this category showed a strong and significant correlation between CPI and mortality, where the lowest study population was over 750 subjects. All of these articles calculated and compared CPI post-TAVR placement between living and deceased groups, where one article found a p < 0.03 [19] whereas the other three articles showed a significance of p < 0.001 [20][21][22]. Two of the articles were also able to determine very similar CPI cutoff values (0.49 [22] and 0.48 [20]) that were found to be statistically significant in determining one-year mortality. Lastly, one article showed that CPI had the highest relative importance in predicting 1-year mortality post TAVR placement, being approximately three times more impactful than the second most influential variable [21].

3. Septic Shock

This group contained three articles, where all three articles compared CPI between survivors and non-survivors and had slightly different definitions of septic shock. One study showed a very strong significance (p < 0.0001) with a population size of 39 [23]; another showed just under statistical significance between groups with p = 0.058 with a population size of 70 [17]; and the third article, which had a population size of 141, showed no statistical significance with p > 0.05 [18]. One article also tested the significance of CPO in determining mortality in septic shock and compared ICU survivors and non-survivors with a significant finding of p < 0.0001 [23]. However, of the studies that performed univariate analysis, they all compared CPI to one-month mortality approximately and both showed a significance of p < 0.05 [17][18]. One of the studies also performed a multivariate study which showed that 28-day mortality was independently associated with a decrease in CPI with OR: 1.84 [1.18–2.87] per 0.1 W/m2 decrease in CPI; p = 0.008 [17].

4. Post-Myocardial Infarction

The post-MI group only had two articles in this category, as patients who had an MI, which was determined based on ST-segment elevation that rapidly resulted in heart failure or cardiogenic shock, were placed in the HF or cardiogenic shock category, respectively. Both of these studies measured in-hospital mortality using thoracic electrical bioimpedance to measure CPI and CPO [24][25], but one study continued to follow their patients, also measuring 1- and 5-year mortality [24]. The study that only measured in-hospital mortality had a sample size of 232 [25]. This initially showed CPO and CPI to be significantly correlated with diabetes (p < 0.001 for CPI, p = 0.005 for CPO), which then went on to show that, for all patients, diabetic and non-diabetic, CPO and CPI were significant in univariate analysis with in-hospital mortality within all three groups, but not on multivariate analysis [25]. The second article, which had a patient population of 208, showed that CPO and CPI was determined to be significant at in-hospital mortality, 1-year mortality and 5-year mortality in univariate analysis. (CPO at 5-year mortality had a HR of 4.33 [1.73–10.84] with p = 0.002). However, on multivariate analysis CPI was not significant with in-hospital mortality, 1-year mortality, or 5-year mortality. When determining the significance on multivariate analysis for CPO, it was found to only be significant for 5-year mortality [24].

5. Heart Failure

Within the heart failure group, the definition or context around heart failure varied by a significant amount based on each article. For example, some articles were in the context of an acute exacerbation while others were in a stable chronic heart failure setting. The smaller study compared CPI to mortality at 90 days and found no statistical difference in resting CPI between survivors and non-survivors, but did not perform univariate or multivariate analysis [26]. The larger study’s endpoints were all cause mortality, heart transplant, or ventricular assistant device placement and had an average follow up of 3.3 years [16].
With regards to the HF group when looking into CPO, there were a total of 10 articles with a mean, median and range of the population sample of 20,478; 160; and 50–18,733, respectively. The study duration and endpoints varied between articles, but the majority of the articles looked at mortality for at least one year. Of the ten articles, 7 articles compared resting CPO to their correlated endpoint, where 5/7 articles showed strong significance between CPO and prognosis. Two articles also produced CPO cut-off values to determine resting CPO’s correlation with prognosis. One of the articles created a CPO cutoff of 0.54 which showed that patients below a CPO of 0.54 have a significantly lower probability of survival (p < 0.0001) and should be classified as high-risk patients [27]. The other study created a CPO cutoff value of 0.6, which showed that falling below this value increased the odds of worsening heart failure by 4.0 and 2.6 at 7 days and 30 days, respectively. However, at the 6-month mark, baseline CPO < 0.6 was no longer found to be significant with an OR of 0.4. The article also found that a decrease in CPO at the 6 hr mark showed OR of 3.4, 3.0 and 2.0 at the 7-day, 30 day and 6 months follow up marks. Lastly, patients who met an initial baseline CPO < 0.6 and decrease in CPO at the 6 h mark, were found to have OR = 8.3, 95% CI = 1.2–56.0, p = 0.004 at 7 days, OR = 14.3, 95% CI = 2.7–76.5, p = 0.002 at 30 days and OR = 3.1, 95% CI = 0.6–15.5, p = 0.174 at 6 months [28]. Upon univariate analysis, a total of 8 articles examined the relationship between CPO and prognosis, where only 3 [15][27][28] showed statistical significance. Furthermore, upon multivariate analysis, only 1 [27] out of 7 articles found any significant relationship between CPO and prognosis.

6. Cardiogenic Shock

The cardiogenic shock category was the largest category with 13 articles included, so, similarly to the HF group, this group was also divided into articles that discussed CPI or CPO. Within these articles, the definition of cardiogenic shock had some slight variation, but for the majority was defined by SBP < 90 mmHg for >30 min or use of inotropes/vasopressors to maintain a SBP > 90 mmHg, some evidence of end-organ hypoperfusion and caused by a cardiogenic etiology. However, in this group, there were two articles that discussed CPI and CPO together, so their respective findings were included into each category. There was a total of 10 articles with regards to CPI in cardiogenic shock, with most using mortality as an endpoint but at various follow-up intervals. The articles had a mean, median and range of population sample of 163, 91 and 68–541, respectively. Of the ten articles, four did not compare CPI to an endpoint or another group and only measured the median CPI of the patients. The other 6 articles which compared CPI between survivors and non-survivors included 5/6 showing statistical significance [13][29][30][31][32]. Of the ten articles, 7 performed univariate and multivariate analysis which all found CPI to be statistically significant in both analyses [6][12][13][14][30][32][33]. Only four articles performed any analysis with regards to prognosis with the use of a CPI cut-off value. One article showed an AUC = 0.81 with sensitivity = 39.3 and specificity = 88.9, when hourly time integral of CPI drops below 0.8. However, when using a cut-off of 0.4, the analysis showed an AUC = 0.79, Sensitivity = 39.3 and Specificity = 90.5 [32]. Another study found that CPI < 0.2 had 80% specificity for 28-day mortality [30]. The last study combined having CPI < 0.28 with a history of ischemic cardiomyopathy and this showed that chance for survival was less than 20% in 4 weeks, p = 0.005 [13]. These three articles all showed varying degrees of using cut-off values for CPI in determining prognosis, but strong specificity in helping rule in patients with poor prognosis in cardiogenic shock.
There was a total of five articles that discussed CPO in cardiogenic shock, including the two articles that discussed both CPO and CPI simultaneously. Of the five articles, 4/5 measured mortality as their endpoint at various time points and the mean, median and range of the population samples was 204, 171 and 28–541, respectively. Of the five articles, three discussed mean CPO with regards to prognosis, whereas the other two only reported their group’s average CPO, but did not determine significance or compare it to any alternate group. Of the three articles, one article found CPO not to be statistically significant p = 0.0625; another article also found it not to be significant, but found p > 0.01 and did not include the actual p-value [34], and the last article found CPO to be significant at two time points after their procedure, but found that pre-operative CPO was not significant between survivors and non-survivors [35]. Within this group, four of the articles created CPO cut offs, where one article found that all patients with CPO < 0.35 died in 1 year follow up [34]. The three other articles created similar cut-off values [6]. One of the articles with a cut-off of 0.6 showed that it was significantly correlated to prognosis [11], whereas the last one calculated a sensitivity of 38% and specificity of 88% in predicting mortality [35]. Only three of the articles performed univariate and multivariate analysis, but all three showed the significance of CPO with mortality [6][11][35].

7. Critical Cardiac Disease

Another category was critical cardiac disease in the CCU or CICU which included two studies. The definition of critical cardiac disease varied between the two studies. As for the CCU group, it was defined as having a primary cardiac diagnosis undergoing PAC, where for the CICU group it was defined as patients admitted to the CICU and meeting any stage of the Society for Cardiovascular Angiography and Intervention for shock. Both of these studies’ end points were in-hospital mortality and compared this to CPO. The CICU study had a total of 5453 patients [36] while the CCU study had a total of 349 patients [3]. The smaller study found that, when using a CPO cut-off of 0.53, 49% of patients with CPO ≤ 0.53 died in the hospital, while only 20% with CPO > 0.53 died in the hospital and this was shown to be significant with a p < 0.001, PPV of 49% and NPV of 80% [3]. Furthermore, upon univariate analysis, CPO was found to have OR of 0.66, 95% CI of 0.55–0.79 and p < 0.001. On multivariate analysis of CPO, it showed an OR of 0.65, 95% CI of 0.54–0.78, p < 0.001 [3]. In the CICU study, the patients were stratified into stages based on the Society for Cardiovascular Angiography and Interventions. Within each stage, the median CPO and CPI were calculated as well as the percentage of patients that had a CPO < 0.6. The CPI, CPO and CPO < 0.6 groups per stage all showed great significance with p < 0.001. Within multivariate analysis of CPO, the adjusted OR for having a CPO < 0.6 was 1.859 with 95% CI 1.291–2.678 and p < 0.001, without including other variables in the analysis. The adjusted OR for CPO < 0.6 when combined with other variables, dropped to 1.504 with 95% CI of 0.939–2.408, p = 0.09 and was no longer statistically significant [36].

8. Other Diseases

A few articles included were not able to be fit into a categorical section with other similar papers, which is why an “Other” category was created.

8.1. Non-Cardiac Related Illness

One article in this group determined the use of CPO and CPI in non-cardiac related illness based on their correlation to in-hospital mortality for 32 patients. Initial hospital admission CPO for survivors had a median CPO of 1.14 (IQR: 0.9–1.52) and non-survivors had a median CPO of 0.89 (0.67–0.99) which had a p < 0.05. Survivors and non-survivors were also compared at optimal (t1) and max (tmax) fluid resuscitation for CPO and this showed a significant difference between the two groups at these additional time points; t1 p < 0.0001 and tmax p < 0.0001. CPI for the survivors was found to be 0.48 and for non-survivors was 0.51, which was found not to be significant with p > 0.05; however, at t1, CPI for survivors was 0.88, while for non-survivors this was 0.57 with p < 0.05 [37].

8.2. Heart Transplant

An article on heart transplant compared CPI for 140 patients using primary graft dysfunction (PGD) in 30 days as their primary end point. Patients with no severe PGD had an initial median CPI of 0.44 (IQR: 0.37–0.53) while patients with severe PGD had median CPI of 0.29 (IQR: 0.23–0.33) with p < 0.001. Patients were also seen to have significant CPI differences at the 6-h mark, p < 0.001. Furthermore, the study was able to show that using an initial CPI cut off of 0.34 and a CPI cutoff of 0.33 at the six-hour mark showed that 79% of patients who had CPI values above cut-off values at their respective time points, survived, while patients who were below both cut-off points only had a survival rate of 2%, showing a high NPV [38].

8.3. Chronic Kidney Disease

Another article dealt with CPI in chronic kidney disease with 349 patients and found that patients with a low CPI defined as MAP <  88 mmHg and CI  <  3.25  L/min/m2 had the highest one year mortality rate of 23.4%, whereas patients with a high CPI, defined as MAP ≥ 88 mmHg and CI  ≥  3.25  L/min/m2, had a mortality rate of 5.6% with p = 0.06 and with no univariate or multivariate analysis carried out in the study on CPI [39].

8.4. Ischemic Cardiomyopathy

The next article was in regard to ischemic cardiomyopathy which mainly focused on peak CPO and included 111 patients, but did find significance in univariate analysis of resting CPO in these patients. Resting CPO had a HR of 1.10 (95% CI: 0.33; 3.64) with regards to mortality, with median follow up of 29 months [40].

8.5. Extracorporeal Circulation

Lastly, Clark et al.’s article with regards to post extracorporeal circulation for cardiac procedures analyzed CPO and CPI in relation to survival 7 days post-procedure in 181 patients. This showed that CPO and CPI of the deceased patients in the study was approximately half the value of surviving patients with p < 0.03, but they were unable to perform univariate or multivariate analysis due to the small number of deaths in the patient pool [41].


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