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Impact of Mean Arterial Pressure Fluctuation on Mortality in Critically Ill Patients

Gao, Ya, MD1; Wang, Qinfen, MSc2; Li, Jiamei, MD1; Zhang, Jingjing, MD1; Li, Ruohan, MD1; Sun, Lu, MD3; Guo, Qi, MD4; Xia, Yong, PhD2; Fang, Bangjiang, MD, PhD5; Wang, Gang, MD, PhD1

doi: 10.1097/CCM.0000000000003435
Online Clinical Investigations

Objective: The purpose of this study was to investigate the association between mean arterial pressure fluctuations and mortality in critically ill patients admitted to the ICU.

Design: Retrospective cohort.

Setting: All adult ICUs at a tertiary care hospital.

Patients: All adult patients with complete mean arterial pressure records were selected for analysis in the Multiparameter Intelligent Monitoring in Intensive Care II database. Patients in the external cohort were newly recruited adult patients in the Medical Information Mart for Intensive Care III database.

Interventions: None.

Measurements and Main Results: The records of 8,242 patients were extracted. Mean arterial pressure fluctuation was calculated as follows: (mean nighttime mean arterial pressure – mean daytime mean arterial pressure)/mean arterial pressure. Patients were divided into two groups according to the degree of mean arterial pressure fluctuation: group A (between –5% and 5%) and group B (<–5% and >5%). The endpoints of this study were ICU and hospital mortality. Patients in group A (n = 4,793) had higher ICU and hospital mortality than those in group B (n = 3,449; 11.1% vs 8.1%, p < 0.001 and 13.8% vs 10.1%, p < 0.001, respectively). After adjusting for other covariates, the mean arterial pressure fluctuations between –5% and 5% were significantly correlated with ICU mortality (odds ratio, 1.296; 95% CI, 1.103–1.521; p = 0.002) and hospital mortality (odds ratio, 1.323; 95% CI, 1.142–1.531; p < 0.001). This relationship remained remarkable in patients with low or high Sequential Organ Failure Assessment scores in the sensitive analysis. Furthermore, external validation on a total of 4,502 individuals revealed that patients in group A still had significantly higher ICU (p < 0.001) and hospital mortality (p < 0.001) than those in group B.

Conclusions: The reduced mean arterial pressure fluctuation (within –5% and 5%) may be associated with ICU and hospital mortality in critically ill patients.

1Department of Critical Care Medicine, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi'an, China.

2National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China.

3Department of Ultrasound, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi'an, China.

4Department of Cardiology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi'an, China.

5Department of Emergency Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

The sponsors of this research played no role in the research process of this study beyond their important financial contribution.

Dr. Gao and Ms. Q. Wang contributed equally to this work.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals.lww.com/ccmjournal).

The Multiparameter Intelligent Monitoring in Intensive Care II database was supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health (grant number: R01 EB001659).

Dr. G. Wang received the Scientific Fund for the Young Talent of Shaanxi Province (2015KJXX-06). He received support for article research from the government of Shaanxi Province, China. The remaining authors have disclosed that they do not have any potential conflicts of interest.

For information regarding this article, E-mail: gang_wang@mail.xjtu.edu.cn; yxia@nwpu.cn

The hypothalamic suprachiasmatic nucleus (SCN) plays a critical role in orchestrating the circadian rhythms of the human body, such as core body temperature (CBT), hormone levels, blood glucose level, sleep cycles, the immune system, and the autonomic nervous system via a self-regulating molecular mechanism (1). In addition to the desynchronization of critical illness, patients in ICU are exposed to different degrees of artificial light, noise, and various organ supports including ventilation, parenteral nutrition, and medications. Therefore, disrupted circadian rhythms of sleep architecture, CBT, blood glucose, and blood pressure (BP) in ICU patients have been frequently observed, and CBT has been found to be associated with the severity of illness (2–6).

BP is a physiologic parameter presenting various variations as a result of neuroendocrine variables and exogenous factors (7). Several researchers have shown that abnormal BP variation is associated with various organ damage, higher risk for cardiovascular events, and mortality (8–11). In addition, based on our previous research, abnormal circadian BP variation is associated with an elevated risk of various cardiovascular and metabolic conditions (12–16). A previous investigation revealed the prognostic value of the first 5-minute systolic BP (SBP) variability in patients with severe sepsis or septic shock for 28-day ICU mortality (17). However, the relationship between the day-night BP variation and mortality in critically ill patients remains currently unclear. Mean arterial pressure (MAP) serves as an important variable for evaluating organ perfusion in ICU (18). We aimed to explore the associations between MAP fluctuation and ICU and hospital mortality in critically ill patients admitted to the ICU in the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database.

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MATERIALS AND METHODS

Study Design

This was an observational study conducted using patients’ data from the MIMIC-II database, which was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (MIT; Cambridge, MA). This database was compiled by researchers from the MIT and contains all information (including demographic records, medical history, treatments, laboratory tests, physiologic data, and outcome data) for more than 30,000 patients admitted to the ICU at the Beth Israel Deaconess Medical Center (Boston, MA) from 2001 to 2008 (19). All data used for this study were deidentified and available upon request. All authors had completed the Collaborative Institutional Training Initiative program course at the University of Miami and received permission to access the MIMIC database. We used data from the MIMIC-II (version 2.6 MIT; Philips Healthcare, Andover, MA; and Beth Israel Deaconess Medical Center) and extracted the following parameters for each patient: age at ICU admission, sex, ethnicity, first Sequential Organ Failure Assessment (SOFA) score and first Simplified Acute Physiology Score (SAPS-I) score, Elixhauser comorbidity score, International Classification of Diseases-9th Edition comorbidities, vasopressor medications, vasodepressor medications, sedatives, and complete MAP records. In the MIMIC database, vasopressor medications included norepinephrine, neosynephrine, dopamine, isoproterenol, epinephrine, and vasopressin, whereas vasodepressor medications included nitroprusside, nicardipine, labetalol, esmolol, and diltiazem. Furthermore, pentobarbital, atracurium, dexmedetomidine, and diazepam were the sedatives indexed in the database. Data were extracted using Matlab version R2015a (Mathworks, Natick, MA).

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Participants

All adult patients (>15 yr old) with MAP records in the database were selected for analysis. However, individuals were excluded if they 1) were diagnosed as having shock due to any cause; 2) had multiple ICU admissions; 3) stayed in the ICU for less than 1 day; 4) had no SOFA and SAPS-I scores; and 5) had no day MAP records or night MAP records. In addition, patients were not included if greater than 30% of their MAP values less than 50 or greater than 183 mm Hg because extreme records failed to reflect the general characteristics of day-night MAP variation (20).

Invasive MAP values were continuously collected using a bedside monitor (Component Monitoring System IntelliVue MP-70; Philips Healthcare, Andover, MA) during the ICU stay to avoid the inaccuracy of nurse-driven records (21). The mean value of the MAP measurements obtained from 7 AM to 11 PM and 11 PM to 7 PM was defined as the mean daytime and nighttime MAP, respectively. MAP fluctuation was calculated as follows: (mean nighttime MAP – mean daytime MAP)/mean MAP (all MAP values/the number of MAP examinations). Patients were divided into 2 groups based on the degree of MAP fluctuation: group A (fluctuation between –5% and 5%) and group B (fluctuation <–5% and >5%). The endpoints of this study were ICU and hospital mortality.

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Statistical Analysis

Categorical variables were presented as percentages and compared using a chi-square test. Continuous variables were expressed as mean ± SD and compared using a Student t test. Logistic regression models were used to assess the impact of MAP fluctuation on the outcomes (i.e., ICU mortality/hospital mortality). Model 1 was adjusted for age, sex, and ethnicity. Model 2 was adjusted for the covariates included in model 1 plus mean MAP. Model 3 was adjusted for the covariates included in model 2 as well as the SOFA and SAPS-I scores. Model 4 incorporated model 3 along with some of the major comorbidities that could influence mortality (i.e., congestive heart failure, cardiac arrhythmia, hypertension, complicated diabetes, uncomplicated diabetes, metastatic cancer, renal failure, liver disease, lymphoma, coagulopathy, and delirium) based on the univariate analysis results and clinical experience. Besides covariates included in the model 4, model 5 comprised vasopressor medication, vasodepressor medication, and sedatives.

A sensitivity analysis was conducted to determine whether the results persisted even when the severity of the clinical status changed. The SOFA score, a measure of organ dysfunction, has been shown to be associated with patient outcomes (22–24). Possible interactions between MAP fluctuation and SOFA scores were evaluated. Another three sensitivity analyses were conducted in the patients without vasodepressor, vasopressor, or sedative exposure, respectively. All data were analyzed using SPSS version 18.0 (SPSS, Chicago, IL). P values less than 0.05 indicated statistical significance.

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External Validation

The Medical Information Mart for Intensive Care III (MIMIC-III) database is an update from the MIMIC-II (25). We performed the external validation on the newly recruited adult patients with MAP records from MIMIC-III V1.4 only. Based on the exclusion criteria, patients were selected and divided into 2 groups as previously described.

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RESULTS

From the 15,551 adult patients with MAP records that were extracted from the MIMIC-II database, the following patients were excluded: 4,016 with multiple ICU admissions, 1,118 with shock, 1,051 with ICU stays less than 24 hours, 312 without SOFA or SAPS-I score records, 748 without day MAP records or night MAP records, and 64 with more than 30% of extreme MAP records. Finally, a total of 8,242 patients were included in this study (Fig. 1). Patients were divided into group A (n = 4,793) and group B (n = 3,449) based on the MAP fluctuation. The main characteristics of the study participants are shown in Table 1. There were no significant differences in age, sex, or ethnicity between the two groups. Patients in group A had lower MAP fluctuations and higher SOFA (p < 0.001) and SAPS-I (p = 0.002) scores than those in group B. Furthermore, group A had a relatively higher prevalence of congestive heart failure, hypertension, uncomplicated diabetes, renal failure, liver disease, and coagulopathy than group B. Additionally, patients in group A had a higher exposure rate for vasodepressor, vasopressor, and sedatives than those in group B. The mean MAP was not significantly different between these two groups (81.61 vs 81.38 mm Hg; p = 0.333).

TABLE 1

TABLE 1

Figure 1

Figure 1

Surprisingly, we found that patients in group A had higher ICU and hospital mortality than those in group B (11.1% vs 8.1%, p < 0.001 and 13.8% vs 10.1%, p < 0.001, respectively) (Table 1). Logistic regression was performed to verify the association between MAP fluctuation and ICU and hospital mortality. After adjusting for a series of covariates, such as age, sex, ethnicity, mean all-day MAP, SOFA score, SAPS-I score, major comorbidities in regression models, and medications, MAP fluctuations between –5% and 5% were significantly correlated with ICU (odds ratio [OR], 1.296; 95% CI, 1.103–1.521; p = 0.002) and hospital (OR, 1.323; 95% CI, 1.142–1.531; p < 0.001) mortality (Table 2).

TABLE 2

TABLE 2

Subsequently, according to interaction analysis, there was an interaction between MAP fluctuation and different SOFA score groups dichotomized at a cut-off point of 7 (p < 0.001). To diminish the influence of patient severity on mortality, all individuals were divided into two subgroups for further sensitivity analysis: 0 ≤ SOFA score ≤ 7 (n = 4,230) and 8 ≤ SOFA score ≤ 24 (n = 4,012). In the 0 ≤ SOFA score ≤ 7 subgroup, group A had a higher ICU (9.2% vs 6.5%; p = 0.001) and hospital (12.6% vs 8.7%; p < 0.001) mortality than those in group B. This result was also observed in patients who had SOFA scores between 8 and 24 (Supplementary Fig. 1, Supplemental Digital Content 1, http://links.lww.com/CCM/D999; and legend, Supplemental Digital Content 2, http://links.lww.com/CCM/D1000). Additionally, based on the logistic regression analysis conducted for both subgroups, MAP fluctuations between –5% and 5% were a statistically significant factor in both models with or without adjusting for any covariates (Table 3). Furthermore, three sensitivity analyses in the patients without receiving vasodepressor, vasopressor, or sedatives showed that the patients with lower MAP fluctuations were still associated with both ICU and hospital mortality after adjusting a series of covariates (data not shown).

TABLE 3

TABLE 3

In the external cohort, 7,824 adult patients with MAP records were extracted from the MIMIC-III database. Based on the exclusion criteria, a total of 3,322 patients were removed from the analysis, including 2,059 with multiple ICU admissions, 660 with shock, 388 with ICU stays less than 24 hours, 155 without day MAP records or night MAP records, and 60 with more than 30% of extreme MAP records (Fig. 2A). Finally, a total of 4,502 patients were included into the analysis and divided into group A (n = 2,376) and group B (n = 2,126). The results demonstrated that patients in group A had a significantly higher ICU and hospital mortality than those in group B (9.8% vs 6.0%, p < 0.001 and 17.0% vs 11.4%, p < 0.001, respectively) (Fig. 2B).

Figure 2

Figure 2

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DISCUSSION

Circadian rhythms, controlled by a master clock in the SCN of the anterior hypothalamus, are generated to cope with changes in the external environment. To keep synchrony with the environment, circadian rhythms are entrained daily by Zeitgeber signals transmitted to neurons in the SCN by electrical signals (26). There are many different Zeitgeber signals, including light-dark cycle, eating/drinking patterns, environmental temperature, pharmacologic manipulations, and social interactions, with the light being the fundamental factor (27). Additionally, circadian rhythms are also modulated by melatonin, the neurohormone of the pineal gland (28). Patients admitted to the ICU are commonly exposed to completely different circumstances, where rhythmic light-dark cycles are forced to alter, as a result in disturbances of physiologic homeostasis and critical illness.

An observational study in 24 critically ill sedated patients demonstrated that 24-hour profiles of parameters, including cortisol, BP, heart rate, body temperature, and spontaneous motor activity, were all disturbed compared with the well-known rhythmic 24-hour patterns in healthy controls (29). Consistently, many studies also found that the changes in sleep structure, blood glucose, melatonin secretion, and physiologic parameters exist in ICU patients (4 , 6 , 30 , 31). Although the disturbance of circadian rhythms in ICU patients has been verified, relatively little attention has been paid to the outcomes the abnormal circadian rhythm may cause. Therefore, we investigated the relationship between BP variation and mortality in ICU patients.

BP is one of the vital signs and constantly monitored in the ICU. Several studies have been conducted to evaluate the prognostic value of SBP fluctuation and have shown that abnormal SBP fluctuation could predict cardiovascular mortality and all-cause mortality in community-dwelling individuals and hospitalized patients (32–34). Furthermore, we have previously demonstrated that abnormal circadian BP was associated with stable coronary artery disease, early formation of carotid plaques, lacunar infarction, and metabolic conditions (12–16). It was reported that the first 5-minute SBP variability was associated with 28-day mortality in 51 ICU patients with severe sepsis or septic shock. However, the prognostic value of the day-night BP variation reflecting circadian rhythm in critically ill ICU patients in general is unclear (17).

MAP is associated with organ perfusion and mortality (35–37). Although the MAP at admission has been identified as a risk factor for ICU mortality (38), MAP fluctuation, especially day-night variation, has not yet been investigated in any predicting models for ICU mortality. In this present study, we excluded those patients without day or night MAP records, both of which are needed for the calculation of day-night variation. Additionally, we excluded patients with more than 30% extreme MAP values, which might fail to reflect individual characteristics of MAP variation due to sporadic interference. Eventually, we found that lower MAP fluctuations were significantly correlated with ICU mortality (OR, 1.296; 95% CI, 1.103–1.521; p = 0.002) and hospital mortality (OR, 1.323; 95% CI, 1.142–1.531; p < 0.001) after adjusting for a series of covariates. To the best of our knowledge, this was the first study in which the relationship between MAP fluctuation and ICU and hospital mortality has been evaluated using data from the MIMIC-II database.

Predicting mortality in critically ill patients is crucial for critical care management. Traditional risk factors, systematic scoring systems, and novel techniques (i.e., machine learning algorithms) have been investigated to evaluate mortality trends in ICU patients in a large proportion of studies. In these studies, it was revealed that the RBC distribution width, levels of N-terminal pro-brain natriuretic peptide, C-reactive protein, and lactate, and Acute Physiology and Chronic Health Evaluation, SAPS, and SOFA scores could predict ICU mortality (38–46). In this study, we found that patients with lower MAP fluctuations had higher ICU and hospital mortality than other patients. The logistic regression analysis verified the association between lower MAP fluctuations (between –5% and 5%) and both ICU and hospital mortality. Furthermore, we also evaluated the difference in the mean MAP and found that it was not different between the two groups. After adjusting for the mean MAP as a covariate variable in the logistic regression model, the associations of MAP fluctuation with ICU and hospital mortality in critically ill patients remained statistically significant.

The SOFA score is a well-known measure that is robustly correlated with patient outcomes in the ICU setting (23 , 24). Therefore, we conducted a sensitivity analysis in which the patients were divided into two subgroups according to SOFA scores (i.e., ≤7 or >7) according to previous interaction analysis. Surprisingly, the association between MAP fluctuations and mortality persisted in the subgroups. Furthermore, as vasoactive medications and sedatives may affect BP regulation, we performed three sensitivity analyses in patients who were not exposed to vasodepressor, vasopressor, or sedatives, respectively. The logistic regression verified the association of MAP fluctuations with mortality again. Therefore, a lower MAP fluctuation (between –5% and 5%) may serve as an independent risk factor for both ICU and hospital mortality in critically ill patients. Based on this conclusion, further management, such as reducing the number of operations or lowering light intensity level at night, may be taken into account to improve circadian BP rhythm of ICU patient to improve their prognosis.

In normal conditions, the circadian decline rate of SBP is within 10–20%, mostly determined by endogenous neuroendocrine rhythm (7). Critically ill patients suffer from the circumstance alterations in light-dark cycles, auditory disruption, iatrogenic treatment, and psychologic reactions when they are admitted to the ICU. These external changes disturb the endogenous neuroendocrine rhythm and sleep-wake cycle, which may contribute to the abnormal BP variation. Changes in the level of growth hormone, adrenal steroids, thyrotropin, norepinephrine, gonadotropin, and melatonin had been verified existed in ICU patients (47). Among them, abnormal melatonin might associate with a series of pathologic processes such as inflammation, endotoxemia, and CNS diseases (1 , 48). These altered endogenous substances may contribute to higher mortality in ICU patients with lower MAP fluctuation. Furthermore, the external changes impact the ICU patients’ sleep-wake cycle that might exist in the patients with lower MAP fluctuation, has also been verified as a risk factor for mortality (48 , 49). Nevertheless, the exact chronobiologic regulation of MAP variation and outcomes of circadian disruption in ICU patients remains to be investigated in a future study.

Our study has some limitations. First, although we conducted an external validation and successfully verified our finding, there were no SOFA and SAPS-I scores available in the MIMIC-III database for further logistic regression. Second, the MIMIC-II database reflects the real-world clinical setting; therefore, different intervals between BP monitoring measurements may exist (19). We calculated the MAP fluctuation using the average of night MAP, day MAP, and all-day MAP, furthermore, adjusted for a series of covariates in the regression models to diminish the potential influence of mortality. This could be addressed by conducting a well-designed prospective study to evaluate the relationship with predefined frequency of BP measurements in the future. Finally, as limitations of retrospective study, potential biases (i.e., selection bias) are inevitable. Although we have adjusted for as many covariate variables as possible to diminish the possible influences, residual confounding may exit and need to be investigated in the future.

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CONCLUSIONS

In this study, we investigated the relationship between MAP and mortality in critically ill patients and found that patients with lower MAP (between –5% and 5%) fluctuations may be associated with both ICU and hospital mortality. The sensitivity analysis further confirmed this association in patients with either low or high SOFA scores.

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Keywords:

fluctuation; intensive care unit; mean arterial pressure; mortality; Multiparameter Intelligent Monitoring in Intensive Care II; Sequential Organ Failure Assessment scores

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