Tissue perfusion pressure enables continuous hemodynamic evaluation and risk prediction in the intensive care unit

Anand Chandrasekhar; Raimon Padrós-Valls; Roger Pallares-Lopez; Eric Palanques-Tost; Nicholas Houstis; Thoralf M Sundt; Hae-Seung Lee; Charles G Sodini; Aaron D Aguirre

doi:10.1038/s41591-023-02474-6

. Author manuscript; available in PMC: 2026 Feb 26.

Published in final edited form as: Nat Med. 2023 Aug 7;29(8):1998–2006. doi: 10.1038/s41591-023-02474-6

Tissue perfusion pressure enables continuous hemodynamic evaluation and risk prediction in the intensive care unit

Anand Chandrasekhar ^1,^2,^†, Raimon Padrós-Valls ^2,^3,^†, Roger Pallares-Lopez ^2,³, Eric Palanques-Tost ^2,³, Nicholas Houstis ^2,⁴, Thoralf M Sundt ⁵, Hae-Seung Lee ¹, Charles G Sodini ^1,⁶, Aaron D Aguirre ^2,^3,^4,^7,^*

PMCID: PMC12935171 NIHMSID: NIHMS2068997 PMID: 37550417

Abstract

Treatment of circulatory shock in critically-ill patients requires management of blood pressure using invasive monitoring, but uncertainty remains as to optimal individual blood pressure targets. Critical closing pressure, which refers to the arterial pressure when blood flow stops, can provide a fundamental measure of vascular tone in response to disease and therapy, but it has not previously been possible to measure this parameter routinely in clinical care. Here we first describe a method to continuously measure critical closing pressure in the systemic circulation using readily available blood pressure monitors and then show that tissue perfusion pressure, defined as the difference between mean arterial pressure and critical closing pressure, provides unique information compared to other hemodynamic parameters. Using analyses of 5,988 admissions to a modern cardiac intensive care unit, and externally validated with 864 admissions to another institution, we show that tissue perfusion pressure can predict risk of mortality, length of hospital stay, and peak blood lactate levels. These results indicate that tissue perfusion pressure may provide an additional target for blood pressure optimization in patients with circulatory shock.

Circulatory shock is one of the most common reasons for admission to an intensive care unit (ICU) and results from inadequate blood pressure and blood flow to support organ function. Causes of circulatory shock include heart failure, overwhelming infection or sepsis, and hemorrhage. Prompt treatment is required to reverse the cause and to restore adequate blood pressure to prevent severe organ injury and death. Consensus guidelines for treatment of shock provide general targets for mean arterial pressure (MAP) that can be used to adjust medications¹, but optimal individual pressure goals for patients with various diseases and comorbidities remain uncertain^2-4. This has been the subject of much study, with prospective and randomized clinical trials looking at different patient populations failing to show a mortality benefit for higher versus lower MAP goals^5-8. Results of these studies highlight that MAP alone is an inadequate single measure of tissue perfusion and new approaches are needed to guide clinical care.

The pressure drop across the circulation depends upon both the inflow arterial pressure (MAP) and the outflow pressure, which is conventionally taken as central venous pressure (CVP). It is known, however, that the systemic circulation has a critical closing pressure (Pcrit), which is the arterial pressure when blood flow stops and the circulation collapses^9,10. The actual perfusion pressure driving flow then should be measured as the difference between MAP and Pcrit. Critical closing pressure has been measured in careful animal experiments and in controlled clinical situations such as cardiac surgery where the circulation has been stopped and flow goes to zero. It has not been possible, however, to reliably measure Pcrit in patients with an intact circulation¹¹.

We demonstrate a method to continuously estimate the critical closing pressure of the systemic circulation from standard blood pressure monitors and subsequently define the concept of tissue perfusion pressure (TPP). We analyze the parameter in nearly 6000 patients in a cardiac surgery ICU and characterize TPP relative to other commonly used measures of clinical hemodynamics including MAP, systemic vascular resistance (SVR), and cardiac output (CO). We then demonstrate the relationship of TPP to clinical outcomes in critically-ill patients and validate the risk prediction power in an external cohort of over 800 admissions. Finally, we provide individual examples of how TPP monitoring can provide unique information in the ICU.

Results

Physiological Model: TPP = MAP–Pcrit is a measure of tissue perfusion

The systemic circulation behaves as a Starling resistor, with Pcrit defined as the arterial pressure below which blood flow stops. Pcrit is generally higher than CVP, resulting in a “waterfall edge” to the arterial pressure whereby changes in CVP do not impact TPP, measured as MAP – Pcrit, as shown in Fig. 1a. To explain the role of Pcrit and the concept of TPP, we define a physiological model of the systemic vasculature, with terms defined for large and small vessels, shown in Fig. 1b, and a lumped parameter circuit model shown in Fig. 1c. At steady state, CO is the flow through the systemic circulation, which generates a MAP in proportion to the magnitude of the Starling resistor (Rs). The pressure drop across Rs is then TPP, which equals MAP minus Pcrit. Under most scenarios, external tissue pressure (ETP) is a function of the intrathoracic or intra-abdominal pressure and would impact the compliance of large vessels, whereas Pcrit is largely impacted by vascular tone at the level of smaller arterioles. Notably, the hemodynamic measurement SVR, which is defined as (MAP–CVP)/CO, does not feature in this model because the Starling resistor effect redefines the pressure drop across the circulation.

Estimation of Pcrit from arterial blood pressure waveforms

Pcrit can be estimated in a patient with intact circulation by knowing at least two points on a plot of CO versus MAP (Fig. 1d) and extrapolating the best-fit line to zero CO¹¹. An assumption here is that the resistance (Rs) at the time of measurement of CO and MAP remains fixed. Acquisition of these data in standard clinical care would typically require specialized equipment and maneuvers to modulate CO. To simplify, we use a common approximation for CO as pulse pressure (PP) multiplied by heart rate (HR)¹². We tested the approximation by comparing changes in PP*HR with changes in measured CO and found concordance consistent with that seen between other CO estimation methods (Supplementary Fig. 1)^13-15. In the limit that pulsatile flow goes to zero, PP*HR also goes to zero, and the Pcrit pressure intercept on a PP*HR versus MAP plot should be the same as when using paired CO and MAP measurements. Pcrit can now be estimated continuously from available arterial blood pressure (ABP) monitoring devices such as an indwelling arterial catheter.

Our approach still requires at least two points measured at similar resistance Rs. To achieve this, we leverage natural variability in ABP over short periods of time. As shown using data from an ICU patient in Fig. 2a, there is significant variation over seconds to minutes in the pressures. On the shortest scale, the variability results from respiration-induced changes in ventricular filling (preload) or heart rate that leads to stroke volume and CO variation. Over longer time scales, autonomic contributions to HR and vascular tone can drive pressure changes. Fig. 2b demonstrates the frequency spectrum of the PP waveform with respiration as the dominant frequency. Using beat-to-beat detection of PP and cardiac cycle length (1/HR), every beat of the heart can contribute data to the PP*HR versus MAP plot. Over a predefined interval where resistance is assumed constant, these data create a scatterplot of the variability, as shown in Fig. 2c. In this work, we select a time interval of 1 minute for analysis, which allows a compromise between high time resolution and sufficient data points for reasonable fitting. A linear fit to the scatterplot is then made, and the zero-flow intercept recorded as Pcrit. Using this approach, Pcrit and TPP could be calculated every minute for each patient, as shown in Fig. 2d. This analysis was applied to ABP waveforms from the first 24 hours of data for nearly 6000 patients in a cardiac surgical ICU (Supplementary Table S1), resulting in almost 144,000 hours of analyzed data. Comparison of Pcrit estimates over different time windows, showed relative stability between 30 sec and 2 min (Supplementary Figs. 2-3) and variation in the linear fit over time intervals of many minutes (Supplementary Fig. 4) consistent with changing Rs and Pcrit over longer periods. Over our selected 1-minute interval, high quality linear fit was achieved over a range of MAP and PP*HR variation (Supplementary Fig. 5), and variability in both PP and HR contributed to the estimates of Pcrit (Supplementary Figs. 6-7). Complete details of the algorithms and the analyses are provided in the Methods.

Fig. 2. — (A) Example blood pressure data showing that natural variation in beat-to-beat blood pressure occurs over short time scales, resulting in modulation of systolic (SBP), mean (MAP), diastolic (DBP), and pulse pressures (PP) over seconds to minutes. PP is defined as SBP-DBP and ΔT_w as the cardiac cycle length measured between two consecutive DBP values. (B) Frequency spectrum from Fourier transformation of PP data in (A), with fundamental frequency equal to the respiration rate. The mean of the signal was subtracted before computing the Fourier transform to facilitate display. (C) Plot of MAP versus PP*HR for the defined 1-minute time interval of data in (A) produces a scatter cloud of data that can be fit with a line to determine the pressure-axis intercept representing Pcrit. The coefficient of correlation (r²) was used to quantify the accuracy of the fit, with r² > 0.3 taken as a threshold to determine Pcrit. (D) Plot of continuous MAP and PP data over time along with the serial calculations of Pcrit and tissue perfusion pressure (TPP), defined as MAP minus Pcrit.

Relationship of TPP and Pcrit to Conventional Hemodynamic Measures

Standard management of patients with circulatory shock relies upon invasive hemodynamic measurements including the MAP, CVP, CO, and SVR. Fig. 3 presents data from 1911 patients in the cardiac surgical ICU with time-aligned sets of measurements (5438 sets). As shown in Fig. 3a, Pcrit provides unique information compared to MAP and CVP, with no obvious dependencies between the variables. Whereas MAP in this ICU population follows a tight and approximately normal distribution, Pcrit and TPP have broader and more complex distributions, reflecting underlying heterogeneity in perfusion pressure characteristics of the patients (Fig. 3b). Distributions in Pcrit and TPP were similar for male and female patients, and there was considerable heterogeneity in Pcrit and TPP metrics at all MAP levels (Extended Data Fig. 1). The relationship between TPP and SVR shows a modest linear relationship at low SVR for incremental ranges of Pcrit and flattens out at higher SVR (Fig. 3c). Notably, TPP has a complex relationship with CO, without consistent increase or decrease across a range of CO values and Pcrit intervals (Fig. 3d). With both SVR and CO relationships, there is a stepwise increase in TPP with incrementally lower Pcrit levels. To gain insight into factors that control TPP, we calculated the vasoactive inotrope score (VIS) for patients with available data (N=5544) and compared it to TPP. The VIS represents a measure of the total vasoactive support required to maintain hemodynamic goals, with higher VIS generally representing more severe circulatory shock^16,17. Fig. 3e demonstrates a highly significant relationship between the maximum VIS (VISmax) over 24 hours after ICU admission and the mean TPP, with higher VISmax corresponding to lower mean TPP. Furthermore, VISmax predicts mortality with high significance, reflecting the severity of circulatory shock (Fig. 3f). Risk for additional outcomes, including reoperation, prolonged ventilation, and length of hospital stay, are also stratified with high statistical significance by both maximum and mean VIS over 24 hours (Supplementary Figs. 8-9). Analyses of subsets of patients with cardiac pacing, acute heart failure, right heart failure, different cardiac surgery procedures, and mechanical ventilation demonstrate the ability for Pcrit and TPP to identify differences in the hemodynamic profiles (Supplementary Figs. 6, 10-13). Pcrit values estimated from simultaneous upper and lower extremity catheters also highlight similar overall distributions (Supplementary Fig. 14).

TPP adds value to MAP for risk prediction

For patients with hypotension in the ICU, the standard MAP target would be 65 mmHg by most consensus guidelines¹, although limitations of this goal in various patient populations have been widely discussed^3,4. In order to gain intuition on how TPP can impact clinical practice, we analyzed optimal TPP and MAP thresholds that stratify outcomes in our patient cohort. Fig. 4a presents analyses from 4899 patients separated by adjudicated outcomes according to our institutional Society for Thoracic Surgeons (STS) database (see Methods). Patients with short stay (< 14 days) were compared against those with a composite of long stay (≥ 14 days) or death during hospitalization in terms of both average MAP and TPP values over the first 24 hours of post-operative ICU stay. Both MAP and TPP were significantly different between the short stay and long stay / death groupings (p < 0.0005). Using logistic regression, an optimal TPP threshold of 34 mmHg and an optimal MAP threshold of 74 mmHg best separated these outcome groups. When stratified by these thresholds for MAP and TPP, differences in mortality, mean length of stay, and maximum lactate for patient groups were highly significant (Figs. 4b-d). For both high and low MAP groupings, the mortality and mean length of stay remained highly significantly different between cohorts above and below the TPP threshold (Fig. 4e-f). High versus low MAP groupings provided separation with high significance within TPP categories for mortality but not for length of stay. Further stratifying the groups by high and low cardiac index (CO divided by body surface area), TPP continued to show additive value over MAP for separating mortality and length of stay, particularly for the low cardiac index grouping (Extended Data Fig. 2). These analyses highlight that TPP provides additional discriminatory information beyond MAP in critically-ill patients managed with standard of care hemodynamic targets.

Fig. 4. — (A) Analysis of the mean TPP and MAP values for patients during the first 24 hours in the ICU according to outcomes, with a favorable outcome of short hospital stay (N = 4045 admissions) compared to an unfavorable outcome of long hospital stay or death (N = 854 admissions). Logistic regression identified an optimal TPP threshold of 34 mmHg and an optimal MAP threshold of 74 mmHg to separate outcome groups. (B-D) Comparisons of mortality (B), length of stay (C), and maximum blood lactate value (D) for patients grouped above and below the optimal TPP and MAP thresholds. (N = 2919 above TPP threshold, N = 1980 patients below TPP threshold; N = 2335 above MAP threshold, N = 2564 below MAP threshold). (E-F) Comparisons of the outcomes of mortality (E) and length of stay (F) for groups stratified by both TPP and MAP thresholds (low MAP and low TPP, N = 1209; low MAP and high TPP, N = 1355; high MAP and low TPP, N = 771; high MAP and high TPP, N = 1564). Data are displayed as the mean and 95% confidence intervals. P-values in (A), (C), (D), and (F) are calculated using a one-sided ANOVA test; p-values in (B) and (E) are calculated using a two-sided chi-square test. The exact p-values are shown in Supplementary Tables S2 and S4-S5. Cohort statistics are further detailed in Supplementary Tables S2 and S3. ****, p < 0.00005. ***, p < 0.0005. **, p < 0.005. *, p < 0.05. n.s., not significant.

External validation was performed using data from the Medical Information Mart for Intensive Care (MIMIC) III database^18-20. We identified 864 admissions with ABP waveforms for 24 hours after cardiac surgery and associated outcomes for mortality and length of stay. Supplementary Fig. 15 compares outcomes between the MGH and MIMIC databases. We applied algorithms developed on MGH data to MIMIC data to determine MAP, Pcrit, and TPP. Distributions are shown for the MIMIC population in Extended Data Fig. 3a and in Supplementary Fig. 16a-c with overall similarities to the MGH population. Optimal thresholds to separate outcomes of short stay versus long stay or death were 73 mmHg for MAP and 36 mmHg for TPP (Supplementary Fig. 16d), which are also similar to those derived on MGH data. For true external validation, we used thresholds from the MGH cohort for testing the MIMIC cohort, with results for outcomes presented in Extended Data Fig. 3b-f. In the MIMIC cohort, the MAP threshold does not reach statistical significance (taken as p < 0.05) in separating groups according to mortality, length of stay, or maximum lactate. TPP however can separate all outcomes with statistical significance. Stratifying further by high and low MAP, TPP still provides statistically significant separation for length of stay in the high MAP group. TPP also has strong trends toward significance in separating mortality for both MAP groups and length of stay for the low MAP group (p ≤ 0.07). Lower significance of separation in the external cohort is likely due to the small size of the cohort (864 vs 4899 in MGH) and to relatively lower length of stay in the MIMIC cohort. External validation analyses reenforce that TPP adds value to MAP. Details on patient groupings for MGH and MIMIC cohorts as well as statistical summaries are provided in Supplementary Tables S2-S5.

Evolution of TPP Trajectories and Relationship to Outcomes

We next evaluated how TPP varies with therapy guided by standard hemodynamic targets. The population consists of patients after cardiac surgery, most of whom underwent cardiopulmonary bypass. Treatment for these patients in the first 24 hours in the ICU consists of optimizing cardiovascular function, including MAP and CO, using a combination of volume resuscitation and vasoactive medications, and optimizing respiratory status. Hemodynamic measurements can be labile as patients frequently have a combination of bleeding, vasodilation, and impaired cardiac function. We organized time series data for each patient with sampling every 4 hours over the first 24 hours of ICU stay, including data for MAP, TPP, CO, and blood lactate. We then performed K-means clustering on TPP trajectories of all patients with full available data (N = 3592) to identify four most common paths in the recovery period. To equally weight values and shape, we included both absolute values of TPP and normalized trajectories (see Methods). Figure 5 presents results of the clustering analysis, with each curve representing the mean trajectory for a cluster and the error bars showing 95% confidence intervals. As shown in Fig. 5a, there can be markedly different paths, with some patients having uniformly low TPP, while others have consistently high TPP, and still others have either increasing or decreasing trajectories over the course of recovery. Notably, corresponding MAP trajectories for these clusters have small relative ranges in values (Fig. 5b) but show correlation with the TPP clusters in that the highest TPP cluster has highest MAP and the lowest TPP cluster has lowest MAP (inset). Lower TPP overall correlates with higher lactate values (Fig. 5c), consistent with worse tissue perfusion. Cardiac output trajectories display an expected increase in CO in initial hours as patients are resuscitated (Fig. 5d). However, CO clusters do not separate well based upon TPP, consistent with the analysis of Fig. 3. We then looked at adjudicated outcomes for the various clusters using the STS database²¹. Mortality and reoperation rates are significantly different in the lowest TPP cluster (Fig. 5e,f). Similarly, clustering by TPP identifies groups with higher incidence of prolonged mechanical ventilation and increased length of stay (Fig. 5g,h). For comparison, we performed similar clustering analyses according to MAP value and shape (Extended Data Fig. 4), TPP value alone (Extended Data Fig. 5), Pcrit value and shape (Extended Data Fig. 6), and SVR value and shape (Supplementary Fig. 17). MAP clusters can also separate adverse outcomes but with less power than TPP clusters. Clustering on TPP value alone provided the most potent discrimination of outcomes.

Fig. 5. — (A) K-means clustering performed on TPP value and shape trajectories identifies four distinct mean trajectories in TPP over the first 24 hours after cardiac surgery. (B-D) Associated trajectories in MAP (B), blood lactate (C), and cardiac output (D) for each TPP cluster. (E-H) Patient outcomes for mortality (E), reoperation rate (F), prolonged mechanical ventilation (G), and length of hospital stay (H) compared according to TPP cluster. Trajectories display the mean and 95% confidence intervals of the mean at each 4-hour increment of time. Bar charts display mean and 95% confidence intervals also. The p-values were determined via a two-sided chi-square test with Bonferroni correction for multiple-comparisons (E), (F), (G) and one-sided ANOVA followed up by Tukey’s HSD test for multiple-comparisons (H). ****, p < 0.00005. ***, p < 0.0005. **, p < 0.005. *, p < 0.05. The number of ICU admissions in each cluster is illustrated in the inset legend in (D).

Continuous TPP monitoring in clinical practice

The method described here offers real-time monitoring of Pcrit and TPP along with MAP and other clinical variables. Fig. 6 demonstrates an individual patient’s hemodynamic time course over 24 hours in the ICU, with Pcrit and TPP calculated with 1-min resolution. This was a woman in her seventies with severe aortic stenosis, calcific mitral valve disease, and coronary artery disease who underwent an aortic valve replacement, mitral valve replacement, and coronary artery bypass grafting. Her post-operative course was complicated by right ventricular dysfunction, atrial fibrillation, and profound circulatory shock and acidosis. She was hemodynamically unstable, as evidenced by the high VIS (Fig. 6g), resulting in high Pcrit (Fig. 6c), and low TPP (Fig. 6d). With volume resuscitation (Fig. 6e), vasoactive support, and resolution of atrial fibrillation she had progressive improvement in hemodynamics after 6 hours, with concomitant decrease in Pcrit and increase in TPP, and a delayed peak and decline in lactate levels (Fig. 6f). Notably, variability in MAP does not fully track that of Pcrit and TPP, reflecting that these metrics provide additional information to distinguish the trajectory and response to therapy. This patient had prolonged length of stay but eventually recovered and was discharged from the hospital. Additional patient vignettes are provided in Extended Data Figs. 7-10 to further highlight the utility of TPP as an additive hemodynamic measure.

Discussion

We demonstrated an approach for continuous measurement of critical closing pressure in patients using readily available blood pressure signals and without the need to perturb the circulation or cardiovascular function. Prior attempts at measuring Pcrit in small human studies were made using circulatory arrest and modeling²² or by modulating filling pressures and CO through maneuvers with a ventilator²³. These methods are not suitable for routine clinical use and cannot provide continuous updates. Using our method, we made continuous serial measurements of Pcrit in thousands of patients. We extend the concept of Pcrit by defining the metric of TPP, which describes the pressure drop driving flow across the intact circulation. Using human data matched with clinical variables and outcomes, we further show that TPP provides unique information compared to conventional hemodynamic measures of MAP, CO, and SVR and that TPP provides risk prediction in critically-ill patients.

The described method for measuring Pcrit takes advantage of intrinsic variability in cardiac output, which occurs as the result of multiple sources including respiration, HR variation, or drift in blood pressure with volume status or autonomic tone. The shortest time scale variation, typically respiration, defines the limit of temporal resolution. Importantly, the method assumes that vascular resistance (Rs) is constant within short time windows (e.g. seconds to minutes) over which one would make therapeutic adjustments. A second key assumption is that PP*HR is a valid approximation to CO. There are limits to this assumption since the compliance of the arterial system changes with age and comorbid conditions such as hypertension, atherosclerosis, and diabetes. However, our approach utilizes small-scale variation in PP*HR and MAP, making it more reasonable to assume linearity of the arterial pressure-volume relationship around any given timepoint. Furthermore, when extrapolating to zero flow, PP and stroke volume both go to zero at Pcrit. Analyses provided in Supplementary Figs. support these assumptions. It should also be noted that the slope of our fit line represents a scaled version of Rs. Without true cardiac output measurements, we cannot directly derive Rs.

The method utilizes beat-to-beat data to create a scatterplot of PP*HR vs MAP over the period of interest. Such high frequency sampling makes it robust against individual faulty measurements or artifacts, which occur frequently in real-world data due to equipment failures, patient movement, or other sources. We utilize basic filtering on the cloud of data to remove artifacts and determine accuracy of any given linear fit using the r² value. There are multiple approaches that can be taken to optimize fitting, but we found that simple filtering and fitting procedures were sufficient for robust serial measurement of Pcrit. It should also be noted that any source of variation in PP*HR can generate useful contributions to the fit. For example, a patient in atrial fibrillation will have beat-to-beat variability in HR and PP.

Our method estimates Pcrit in an intact circulation and reflects the state of the vasculature at specific pressure and flow conditions. It is likely higher than the Pcrit that would be measured if the heart stopped and flow went to zero as a result of autoregulatory mechanisms at low pressure and flow states²². Very limited prior data exists on Pcrit in the intact circulation. Maas et al. used ventilator maneuvers to measure Pcrit in 10 cardiac surgery patients and reported mean 45mmHg with standard deviation of 11 mmHg at a mean MAP of 85 mmHg²³, which is largely consistent with our measurements (Extended Data Fig. 1c).

Pcrit provides unique hemodynamic information to MAP, CO, and CVP. SVR is calculated from these measurements and is therefore not a direct measure of vascular tone. Pcrit effectively provides a unique measure of systemic vascular tone and may therefore enable new insights on how tone varies with disease or therapeutic interventions. It is postulated that Pcrit varies from one vascular bed to another^11,24-29. For example, the Pcrit in the brain will be different from the Pcrit in the skeletal muscles, liver, or kidney. Analyses provided here were performed on ABP waveforms from standard catheters used in the ICU, which are predominantly in radial or brachial arteries. They therefore represent specific Pcrit and TPP measurements, and further work will be necessary to understand how Pcrit and TPP vary between vascular beds at baseline and during circulatory shock and how these organ-specific measurements differentially respond to therapy. Nonetheless, the clinical utility of our approach derives from the ready availability of this data in clinical care. TPP has analogy to the concept of cerebral perfusion pressure, CPP, which is equal to MAP minus intracranial pressure (ICP) and is widely understood in neurological critical care^30-32. While Pcrit is generally determined by vascular tone, there may be scenarios where intrathoracic, intra-abdominal, or muscular compartment pressures are pathologically high and can dominate the vascular Pcrit.

TPP can provide an additional target for blood pressure management in patients with heart failure or circulatory shock. Randomized clinical trial data has not consistently shown benefits of higher versus lower MAP targets in critically-ill patients, although some analyses of cohorts from trials have suggested benefit^33,34. As a result, there remains uncertainty as to whether different MAP targets can benefit select patient populations^2-4. One reason for lack of benefit seen in clinical trials may be underlying heterogeneity in the patient hemodynamic characteristics that is not captured by MAP alone. From our analyses, TPP identifies patients with similar MAP values but different tissue perfusion characteristics. TPP may therefore serve as an additional target to MAP to allow individualization of hemodynamic management, including titration of vasoactive medications and optimization of fluid balance.

This work impacts broader discussions of optimal monitoring strategies for patients with circulatory shock. Other perfusion measures such as lactate levels or urine output generally lag in time the current perfusion status and are not available as continuous measures. The gold-standard pulmonary artery catheter (PAC) can directly measure CO as well as pressures in the heart, but the routine use of PAC’s has been questioned due to additional risks associated with these catheters³⁵. There have also been several less invasive CO monitors developed for commercialization, including some that work from an arterial line³⁶. Our analyses suggest that Pcrit and TPP may provide a readily available alternative for advanced hemodynamic monitoring of critically-ill patients. Moreover, the general technique can be applied to any type of noninvasive blood pressure monitor that provides high frequency of data. This opens the possibility for monitoring patients on standard medical floors or in the ambulatory setting.

The data used for this work includes primary high frequency waveforms from bedside monitors as well as vital signs, lab data, and medication records from the electronic health record. The population studied here consists of patients admitted to a cardiac surgery ICU due to availability of gold standard hemodynamic measurements, including the continuous ABP waveforms, as well as carefully adjudicated outcomes from the STS database. This population also experiences a range of conditions leading to shock, including hemorrhage, vasodilation, infection, and heart failure.

Limitations to this study include the retrospective nature of the analyses, and further prospective trials are needed to define the use of Pcrit and TPP in clinical management. In addition, the results must be interpreted carefully when extrapolating to other patient populations, including non-surgical critically-ill patients with sepsis and heart failure. While the physiology should generally hold, we have not yet tested the metrics at scale in other populations. Importantly, different optimal TPP thresholds may exist for risk prediction in different populations depending upon the disease state, standard of care management strategies, and outcomes of interest.

Online Methods

Study Design and Primary Dataset

The study was performed under a protocol approved by the Institutional Review Board at the Massachusetts General Hospital (Protocol 2020P003053). A retrospective analysis was performed on data collected from a cohort of patients in the cardiac surgical intensive care unit (ICU) at the Massachusetts General Hospital (MGH).

This population was chosen because of the availability of standard invasive hemodynamic measurements for comparison, including pulmonary artery catheter data, and because of the availability of well-adjudicated outcomes data as part of the MGH institutional Society for Thoracic Surgery (STS) database²¹. Patients admitted to the ICU with indwelling radial, brachial, or femoral artery blood pressure (BP) catheters, during the period of 11-07-2015 to 06-14-2021, were included in this investigation. High frequency sampled waveforms (120 Hz) of the blood pressure were available for all included patients. The BP waveforms were part of a research archive consisting of waveform data collected and saved from bedside telemetry monitors using the BedMaster platform from Excel Medical. The study analyses also utilized laboratory data, vital signs, and discrete hemodynamic measurements taken from the hospital’s electronic health record and electronic data warehouse (Epic EDW).

Data during the first 24 hours of admission to the ICU and the adjudicated outcomes data (from the STS database) were used for analyses described in this manuscript. A cohort of 6804 admissions to the MGH cardiac surgical ICU (6591 patients) was identified with appropriate waveform data, including data from multiple admissions of the same individuals (N = 200). A total of 5729 admissions (5521 patients) had well-adjudicated outcomes from the STS database.

Code for data processing and analysis was written in Python using open-source libraries including scikit-learn, NumPy, and SciPy.

External Validation Dataset

A second independent dataset was used for external validation of analyses performed on the primary dataset. This dataset was obtained from the publicly available Medical Information Mart for Intensive Care (MIMIC) III database^18-20, which contains high-frequency arterial line waveform data (125 Hz) for patients in the cardiac ICU matched to select electronic health record information such as demographics, vital signs, and laboratory reports, as well as available outcomes data for length of stay in the hospital and in-hospital mortality. Data in MIMIC-III was collected between 2001 to 2012. Institutional Review Boards of the Beth Israel Deaconess Medical Center (2001-P-001699/14) and the Massachusetts Institute of Technology (no. 0403000206) approved the use of MIMIC-III data collection protocol.

We identified a total of 878 ICU admissions data (860 patients, age = 71.3±31.5 years, female= 33.6%, mortality = 3.75%) admitted to the cardiac surgery ICU with continuous arterial blood pressure waveforms available during the first 24 hours of their stay in the ICU and with available outcomes data for length of stay and mortality. From these ICU admissions, records with poor quality blood pressure waveforms were excluded, leaving a total of 864 ICU admissions (846 patients) for final analysis. We did not have access to formally adjudicated STS outcomes for the MIMIC cohort. Length of stay in the hospital, in-hospital mortality, and the lactate values were available, however, from the matched clinical records of the patient in the MIMIC-III database. Length of stay was calculated as the time spent in the hospital since the admission into the cardiac ICU, and the lactate value was determined as the maximum lactate value during the patients’ first 24 hours in the ICU.

Detection of beat-to-beat features (PP, MAP, HR) of a BP waveform

The BP waveforms recorded at the radial, brachial, or femoral arteries were used to measure systolic blood pressure (SBP), diastolic blood pressure (DBP), pule pressure (PP), mean arterial pressure (MAP), and heart rate (HR) as illustrated in Fig. 2a. From the primary dataset, radial or brachial ABP waveforms were available for 6008 ICU admissions, and femoral BP was used for those individuals (N=79) without an upper extremity BP waveform. A combination of femoral and radial BP waveforms was used for analysis on 796 ICU admissions due to the absence of upper extremity BP waveform for short intervals of time. In the external dataset, ABP waveforms from 864 admissions were used.

Maximum and minimum locations, detected from every cardiac cycle of the BP waveform were used to calculate the PP, MAP, and HR beat by beat during the first 24 hours in the ICU (see Fig. 2a). The maximum and minimum locations of the BP waveform were detected as follows. First, the waveforms were low-pass filtered with a cutoff frequency of 2 Hz to remove high-frequency signals. Then, minima and maxima from these filtered waveforms were used as an initial estimate to locate the maximum and minimum of every cardiac beat. Specifically, a window of 100 ms, centered on the initial estimate, was assessed on the unfiltered waveform to locate the true maximum and minimum locations. A single cardiac cycle is the BP waveform between two adjacent minima. The height and time average of the BP waveform within a single cardiac cycle was used as PP and MAP, respectively, and the time-width of the cardiac cycle (ΔT_w) was used to obtain HR (= 60/ΔT_w). MAP, PP, and HR were calculated using the above algorithm on BP data from all ICU admissions. The same detection algorithms were applied to the MGH and MIMIC cohorts.

Estimation of Pcrit and TPP from MAP, PP, and HR

Pcrit was estimated from the pressure-axis intercept of the graph between MAP and the product of PP and HR (PP*HR). The intercept was obtained by fitting a line to the plot of MAP and PP*HR (see Fig. 2c). MAP, PP, and HR obtained from all cardiac cycles within a time window of 1 minute were used to estimate a single value of Pcrit. The Pcrit estimation algorithm was iteratively repeated (stride = 1 minute) on all the data recorded for a duration of 24 hours. The scikit learn library from Python was used to implement the linear fit, and NumPy was used for computations.

A set of rules were followed to improve the robustness of Pcrit estimation. First, PP*HR data points outside the 5 to 95 percentiles were removed to filter extreme outliers that may be due to sources of measurement artifact. Second, estimated Pcrit values were discarded if the slope or pressure-axis intercept of the best-fit line were negative, or the coefficient of determination (r²) of the fit was less than 0.3. By default, 0.3 was used as the threshold for r² based filtering unless specified otherwise in a specific analysis. Once Pcrit was calculated for every 1-minute time window, TPP was estimated as the difference between the average MAP over the 1-minute interval and the estimated Pcrit value.

From the analyzed 6804 ICU admissions in the primary dataset, 6693 admissions had a valid TPP value estimated based on the above rules. 111 ICU admissions did not have a single value of Pcrit or TPP estimated from the first 24 hours of admission to the cardiac ICU. These individuals had a short duration of BP waveforms with substantial artifact or noise. Another exclusion criterion was imposed on the minimum number of TPP values detected from an individual; ICU admissions with TPP values estimated for a duration of less than 1 hour were excluded from further analysis. Based on these inclusion/exclusion rules, Pcrit and TPP estimated from a cohort of 5988 ICU admissions (5532 patients) from the primary dataset were used in all analyses described in this manuscript. Basic details for these individuals are available in Supplementary Table S1.

Pcrit values estimated from the above algorithm were compared with the Central Venous Pressure (CVP) and MAP of the patient population over the first 24 hours in the cardiac ICU. In addition, the relationship between TPP, systemic vascular resistance (SVR), and cardiac output (CO) was investigated. CVP and CO were measured using a central catheter and the thermodilution technique, respectively. Values of SVR were obtained from those recorded in the electronic health record (Epic EDW) according to clinical standard calculations. Patients without a CVP measurement, or those CVP datapoints of a patient with a magnitude of more than 30 mmHg (assumed to be inaccurate) were excluded from the analysis. Also, Pcrit was estimated from the linear fit of MAP and PP*HR with an r² > 0.5 taken as a minimum threshold in order to provide higher accuracy of comparisons with other hemodynamic parameters in Fig. 3a, 3c. and 3d. Based on the previously described exclusion criteria, a cohort of 1911 ICU admissions was included in the final analysis presented in Fig. 3a, 3c, and 3d. Fig. 3a shows all the paired values (5438) of CVP, MAP, and Pcrit for these ICU admissions. SVR, CO, and TPP from these patients were grouped based on Pcrit into four cohorts – Group 1: 0≤Pcrit<30 mmHg; Group 2: 30≤Pcrit<45 mmHg; Group 3: 45≤Pcrit<60 mmHg; Group 4: 60≤Pcrit<75 mmHg, as illustrated in Fig. 3c and d. The mean TPP, SVR and CO as well as +/− one standard deviation of the data for each Pcrit group are shown.

The average Pcrit, MAP and TPP values estimated from the first hour of the data available from the cohort of 5988 ICU admissions were studied to investigate the relationship between the distribution of these variables. Pcrit measurements with r² > 0.5 were included for analysis. A set of 5514 ICU admissions were identified based on these criteria, and the resulting histogram of Pcrit, MAP, and TPP is illustrated in Fig. 3b. The above analysis was repeated after stratifying the patient data based on gender and the results are illustrated in the Extended Data Fig. 1.

Vasoactive-Inotropic Score (VIS) analysis

The impact of vasoactive drugs on the TPP was evaluated, as illustrated in Fig. 3e-f and Supplementary Fig. 8-9, using the vasoactive-inotropic score (VIS). Out of the 5988 ICU admissions investigated, 5544 were included in the analysis to compare VIS and TPP. Patients without medication records corresponding to the first 24 hours of their postoperative period in the ICU were excluded from the analysis. VIS was calculated using Eq. (1)¹⁶.

VIS = 10000 \times Vasopressin dose (in U ∕ kg ∕ min) + 100 \times Epinephrine dose (in μ g ∕ kg ∕ min) + 100 \times Norepinephrine dose (in μ g ∕ kg ∕ min) + 50 \times Levosimendan dose (in μ g ∕ kg ∕ min) + 25 \times Olprinone dose (in μ g ∕ kg ∕ min) + 20 \times Methylene blue dose (in mg ∕ kg ∕ hr) + 10 \times Milrinone dose (in μ g ∕ kg ∕ min) + 10 \times Phenylephrine dose (in μ g ∕ kg ∕ min) + 10 \times Terlipressin dose (in μ g ∕ min) + 0.25 \times Angiotensin II dose (in ng ∕ kg ∕ min) + 1 \times Dobutamine dose (in μ g ∕ kg ∕ min) + 1 \times Dopamine dose (in μ g ∕ kg ∕ min) + 1 \times Enoximone dose (in μ g ∕ kg ∕ min)

(1)

This formula accounts for a range of possible medications, with most patients receiving only a small fraction of these and norepinephrine being most common. The maximum value of VIS evaluated for the first 24 hours was compared with the respective mean TPP of the patient during the same period. Five categories of VIS were defined¹⁷ – Group 1: 0<VIS≤5; Group 2: 5<VIS≤15; Group 3: 15<VIS≤30; Group 4: 30<VIS≤45, and Group 5: 45<VIS. Note that the above study was also performed by comparing the mean value of VIS to the average TPP over the first 24 hours in the ICU as illustrated in Supplementary Fig. 9. The mean value gives a better estimate of the total exposure that the patient has had to vasoactive medications.

In all comparative studies discussed here, a standard list of additional outcomes—patient mortality, reoperation rate, prolonged ventilation, and length of stay (LoS)—were investigated for every VIS group. Patient mortality, reoperation rate, and prolonged ventilation data were obtained from the MGH STS database (binary adjudicated outcomes), and LoS was calculated as the time spent in the hospital since admission to the cardiac ICU after the surgery. All patients declared dead, regardless of cause, during hospitalization or before the end of the 30^th postoperative day after discharge from the hospital were used for calculating patient mortality. Reoperation due to any cardiac reason, tamponade, or bleeding were utilized while computing reoperation rate. An individual with a ventilation or reintubation time of more than 24 hours was included in the list of patients with prolonged ventilation. The percentage patient mortality of a VIS group was calculated as the ratio of individuals dead over the total number of ICU admissions in the group having information in the STS database. A similar approach was used to calculate the percentage of individuals that needed reoperation and prolonged ventilation in each VIS group. The average LoS of individuals in each group was also calculated and presented in Supplementary Figs. 8-9.

Standard box plots were used to show the distribution of TPP values in each VIS group. A one-side ANOVA test followed up by Tukey’s HSD test for multiple-comparisons was performed to compare the TPP distribution for each VIS group. The mean of outcomes for individuals in these groups were represented via bar charts, and 95% confidence interval was used to indicate mean variation. The 95% confidence interval and p-values for the binary outcomes (patient mortality, reoperation rate, and prolonged ventilation data) were calculated using the Bernoulli distribution and chi-square test, respectively. The T-distribution and one-side ANOVA test followed up by Tukey’s HSD test for multiple-comparisons were used to calculate the 95% confidence interval and p-value for the mean value of the LoS for these groups, respectively.

Clinical outcomes analyses: mortality, length of stay, blood lactate

The additive value of TPP as a target for therapy was investigated using a primary cohort of 5988 ICU admissions from the MGH database (see Supplementary Table S1). The mean value of patients’ TPP over the first 24 hours in the cardiac ICU and the mortality, LoS, and maximum blood lactate levels were compared, as illustrated in Fig. 4, Supplementary Table S1, and Supplementary Table S2. In addition to other variables as discussed earlier, cardiac index (CI) was used for the analysis discussed here. CI, determined as a ratio of cardiac output over patient’s body surface area, is used routinely in clinical care and was available from measurements recorded in the hospital electronic health record for the primary cohort. Individuals without CI, MAP, or TPP measurements over the stipulated time were excluded from the analysis, leaving a total of 4899 ICU admissions for the current analysis.

The average CI, TPP, and MAP over the first 24 hours in the cardiac-ICU were used for the following analyses. First, patients were stratified based on their CI into three groups - Group 1 (low CI): 1≤CI<2.5 Lmin⁻¹m⁻², Group 2 (normal-high CI): 2.5≤CI<6 Lmin⁻¹m⁻², and Group 3 (all patients): 1<CI<2.5 Lmin⁻¹m⁻². Patients in each of the above groups were further categorized into two groups based on LoS - Group A: LoS≤14 days and Group B: a composite of LoS>14 days or death. Details of these subgroups are illustrated in Supplementary Table S2. For Groups 1 to 3, the optimal TPP and MAP values that separate Group A from B were obtained via logistic regression.

Logistic regression on Groups A and B was implemented as follows. First, a balanced dataset was created from the groups. For instance, among the cohort with a low CI (< 2.5 Lmin⁻¹m⁻²), 1092 and 332 subjects were identified in Groups A and B, respectively. Then, 332 random ICU admissions were located from Group A to create a balanced dataset with Group B. Next, a logistic regression model was trained on MAP and TPP values of this balanced dataset to obtain a threshold for optimal separation. The scikit learn library from Python was used to implement the logistic regression model. Multiple optimal TPP and MAP thresholds were obtained via logistic regression by selecting 5 different and random sets of 332 ICU admissions from Group A. Finally, an average of the optimal MAP and TPP, rounded to the closest integer, was determined. The above method to calculate the optimal threshold was implemented separately on the cohorts in Groups 1-3. Average optimal thresholds of 34 and 74 mmHg were determined independently for TPP and MAP, respectively, for the Groups 1-3. A one-side ANOVA test was used to compute the p-values for optimal threshold analyses, shown in Supplementary Table S2.

Patients in Group 3 representing all patients were stratified into subgroups above and below the optimal MAP and TPP thresholds and outcomes were compared as shown in Fig. 4b-d. Patients in Groups 1-3 were further stratified into additional subgroups based on both optimal MAP and TPP—Group P: TPP<34 mmHg, MAP<74 mmHg; Group Q: TPP<34 mmHg, MAP≥74 mmHg; Group R: TPP≥34 mmHg, MAP < 74 mmHg; and Group S: TPP≥34 mmHg, MAP≥ 74 mmHg. Details of the individuals in each subgroup are illustrated in Supplementary Table S3. The average and 95% confidence intervals for outcomes of mortality, LoS, and maximum lactate for the abovementioned subgroups were calculated and statistical comparisons made (see Fig. 4b-f, Extended Data Fig. 2b-f and Supplementary Tables S4-S5). A chi-square test for independence was used to compute the p-values comparing mortality of different groups and a one-side ANOVA test was used to compute the p-value for the lactate and length of stay analyses. The confidence intervals for continuous variables were estimated using the T-distribution and for binary variables using the Bernoulli distribution.

The outcomes analyses were repeated for the MIMIC external validation cohort using identical methods. Stratification by the MGH optimal thresholds was used for outcomes comparison for true external validation. Outcomes of mortality, LoS, and maximum lactate levels were used. Additional stratification by CI for the external cohort was not possible due to the lack of sufficient available CI data for the cohort. Results for external validation are presented in Extended Data Fig. 3 and Supplementary Tables 3-5.

Clustering analyses

The relationship between TPP, MAP, arterial blood lactate concentration (ABLC), and CO was investigated on 5988 ICU admissions recovering from cardiac surgery over the first 24 hours of their post-operative period. Individuals without any of the above measurements over the period of interest were not included. Also, markedly out-of-range measurements for an individual were excluded from the analysis. Specifically, MAP values of more than 120 mmHg or less than 30 mmHg were excluded. ABLC values were restricted to a range of 0 to 20 mmolL⁻¹. CO values of less than 1 Lmin⁻¹ and more than 15 Lmin⁻¹ were ignored. In addition, a TPP measurement of more than 100 mmHg was ignored for the current clustering-based analysis. Based on the above inclusion-exclusion criteria, clustering was performed on a total of 3592 ICU admissions in the current analysis.

For clustering analysis, 24-hour trajectories of all the variables of interest—TPP, MAP, ABLC, Pcrit, SVR and CO— were created for every patient by averaging values within a time window of 4 hours (stride = 4 hours) for a duration of 1 day. Thereby, a 24-hour trajectory of all the variables, defined via a maximum number of 6 datapoints, was obtained for each patient. Special care was needed for variables with missing datapoints in the 24-hour trajectory. Missing datapoints were left undefined and replaced with “Nan”—a mathematical construct that programming languages use for undefined variables—for temporal alignment of the 24-hour trajectories.

For Fig. 5, clustering was performed using the absolute value and the shape of the TPP’s 24-hour trajectory. TPP trajectories were organized in the form of a 3592x6 matrix, T, with the trajectory of each patient as a row vector. Then, the T matrix was standardized with the global mean (G_u) and standard deviation (G_s) to create a V matrix; V = (T – G_u)/G_s. The relative value of the trajectories is encoded in the V matrix. Similarly, a matrix representing the shape, hereby referred to as the S matrix, of the trajectories was created by standardizing every row vector in T with the row’s corresponding mean and standard deviation. Finally, a new matrix, SV, was created by concatenating the rows of the S and V matrixes; SV = [S V] such that the value and shape of the trajectory were encoded in a single matrix. A K-means algorithm was applied to the SV matrix to cluster groups of trajectories with unique shapes and values. Only TPP trajectories without any missing data were input into the K-means algorithm. A total of 3592 ICU admissions were available with fully defined TPP trajectories, and the final clustering was performed on these trajectories.

Four main clusters—C1, C2, C3, and C4—were defined based on the above algorithm. The mean trajectory and 95% confidence interval of the mean are indicated for each of the 4 TPP clusters in Fig. 5a. The T-distribution was used to obtain the 95% confidence interval of the mean. The corresponding MAP, ABLC, and CO trajectories for these clusters were identified and plotted as shown in Fig. 5b, c, and d, respectively. STS outcomes were also obtained for all the ICU admissions within each cluster. Three main STS outcomes—mortality, reoperation rate and prolonged ventilation—were studied for patients in these clusters. In addition, LoS was also included in the outcome analysis. The mean and 95% confidence interval of the mean were plotted for each of the clusters as shown in Fig. 5e-h. The confidence interval was calculated using the Bernoulli distribution and T-distribution for all the STS outcomes and LoS computations, respectively. The p-values were determined via a chi-square test with Bonferroni correction for multiple-comparisons and one-side ANOVA followed up by Tukey’s HSD test for multiple-comparisons for the STS outcomes and LoS computations, respectively.

The above clustering analyses to identify C1-C4 were also repeated for the MAP, Pcrit, and SVR trajectories. The results are presented in Extended Data Figs. 4 and 6 and Supplementary Fig. 17. Also, clustering analysis was performed using the value of TPP alone (V matrix) and results are presented in Extended Data Fig. 5.

Statistical methods

Standard statistical methods were used for all the analyses discussed in the manuscript. Box plots were utilized to illustrate the distribution of a parameter in a group. The first and third quartiles of the parameter were represented with an upper and lower line of the box plot. The median value of the parameter is shown as the middle line of the box plot. After removing the outliers, the lowest and highest parameter within the group is indicated with solid horizontal lines. Outliers are represented as individual points.

Bar charts were utilized to show a parameter’s mean value in a specific group or cluster. The mean value of the variable is indicated via the height of the bar. The error bars on these plots reveal the 95% confidence interval of the mean value. Undefined terms were excluded from the computation of the mean value.

The significance of a parameter compared to the same estimated from different groups was determined using p-values. A p-value less than 0.05 is indicated with a single (*), and the degree of significance increases with the number of (*). For instance, a p-value less than 0.005 is marked with two (*). A maximum of four (*) are displayed for all p-values less than 0.00005.

Extended Data

Extended Data Fig. 3. — (A) Distributions of MAP, Pcrit, and TPP for the external validation cohort. (B-D) Comparisons of the outcomes of mortality (B), length of stay (C), and maximum blood lactate value (D) for patient groups in the MIMIC cohort defined according to optimal thresholds derived from the MGH cohort (TPP threshold 34 mmHg: N = 589 patients above threshold, 275 patients below threshold; MAP threshold 74 mmHg: N = 400 patients above threshold, 463 patients below threshold). (E-F) Comparisons of the outcomes of mortality (E) and length of stay (F) for groups from the external cohort when stratified by both TPP and MAP thresholds (low MAP and low TPP, N = 163; low MAP and high TPP, N = 301; high MAP and low TPP, N = 112; high MAP and high TPP, N = 288). Data are displayed as the mean and 95% confidence intervals. P-values in (C), (D), and (F) are calculated using a one-sided ANOVA test, p-values in (B), and (E) are calculated using a two-sided chi-square test. The exact values are shown in Supplementary Tables S4-S5. Cohort statistics are detailed in Supplementary Table S3. ****, p < 0.00005. ***, p < 0.0005. **, p < 0.005. *, p < 0.05. n.s., not significant.

Extended Data Fig. 4. — (A-D) K-means clustering performed on MAP value and shape also identifies four distinct mean trajectories in MAP (B) over the first 24 hours after cardiac surgery with associated trajectories in TPP (A), blood lactate (C), and cardiac output (D). (E-H) Patient outcomes of mortality (E), reoperation rate (F), prolonged mechanical ventilation (G), and length of hospital stay (H) compared according to MAP clusters. Trajectories display the mean and 95% confidence intervals of the mean at each 4-hour increment of time. Bar charts display mean and 95% confidence intervals also. The p-values were determined via a two-sided chi-square test with Bonferroni correction for multiple-comparisons (E), (F), (G) and one-side ANOVA followed up by Tukey’s HSD test for multiple-comparisons (H). ****, p < 0.00005. ***, p < 0.0005. **, p < 0.005. *, p < 0.05. The number of ICU admissions for each cluster is defined in the inset legend in (D).

Extended Data Fig. 5. — (A) K-means clustering performed on TPP value alone distinguishes four distinct mean trajectories in TPP over the first 24 hours after cardiac surgery. (B-D) Associated trajectories for MAP (B), blood lactate (C), and cardiac output (D) for each TPP cluster. (E-H) Patient outcomes of mortality (E), reoperation rate (F), prolonged mechanical ventilation (G), and length of hospital stay (H) compared according to TPP clusters. Trajectories display the mean and 95% confidence intervals of the mean at each 4-hour increment of time. Bar charts display mean and 95% confidence intervals also. The p-values were determined via a two sided chi-square test with Bonferroni correction for multiple-comparisons (E), (F), (G) and one-side ANOVA followed up by Tukey’s HSD test for multiple-comparisons (H). ****, p < 0.00005. ***, p < 0.0005. **, p < 0.005. *, p < 0.05. The number of ICU admissions for each cluster is defined in the inset legend in (D).

Extended Data Fig. 6. — (A) K-means clustering performed on Pcrit value and trajectory shape distinguishes four distinct mean trajectories over the first 24 hours after cardiac surgery. (B-D) Associated trajectories for MAP (B), SVR (C), and cardiac output (D) for each Pcrit cluster. (E-H) Patient outcomes of mortality (E), reoperation rate (F), prolonged mechanical ventilation (G), and length of hospital stay (H) compared according to Pcrit clusters. Trajectories display the mean and 95% confidence intervals of the mean at each 4-hour increment of time. Bar charts display mean and 95% confidence intervals also. The p-values were determined via a two-sided chi-square test with Bonferroni correction for multiple-comparisons (E), (F), (G) and one-side ANOVA followed up by Tukey’s HSD test for multiple-comparisons (H). ****, p < 0.00005. ***, p < 0.0005. **, p < 0.005. *, p < 0.05. The number of ICU admissions for each cluster is defined in the inset legend in (D).

Extended Data Fig. 7. — Individual patient data is shown for the first 24 hours of ICU admission from the case of a woman in her seventies with coronary artery disease, carotid stenosis, atrial fibrillation, and heart failure who underwent multivessel coronary artery bypass grafting, an atrial ablation procedure (MAZE), and a carotid endarterectomy. Data includes (A) MAP, (B) Pulse pressure multiplied by heart rate (PP*HR), (C) Pcrit, (D) TPP, (E) fluid input / output (i/O), (F) lactate levels, and (G) vasoactive inotrope score (VIS). The time averaged trends for MAP and PP*HR are shown in red with an averaging window size of 20 seconds. The patient developed rapid atrial fibrillation and hypotension several hours after arrival to the ICU which corresponded to a rise Pcrit, a drop in TPP, and marked increase in vasopressor requirement. With fluid resuscitation and cardioversion, the patient subsequently improved and weaned from vasopressors. The patient had a long length of stay but was successfully discharged from the hospital.

Extended Data Fig. 8. — Individual patient data is shown for the first 24 hours of ICU admission from the case of a man in his seventies presenting with an acute myocardial infarction and mitral regurgitation who underwent multivessel coronary artery bypass grafting, an atrial ablation procedure (MAZE), and mitral valve repair. Data includes (A) MAP, (B) Pulse pressure multiplied by heart rate (PP*HR), (C) Pcrit, (D) TPP, (E) fluid input / output (I/O), (F) lactate levels, and (G) vasoactive inotrope score (VIS). The time averaged trends for MAP and PP*HR are shown in red with an averaging window size of 20 seconds. His post-operative course was notable for a rising inotrope and vasopressor requirement, metabolic acidosis and marked lactate elevation in the setting of ischemic ECG changes. He required significant volume resuscitation and blood transfusion with improvement but had a persistent requirement for vasoactive support over 24 hours. Improvement in clinical status is reflected in dynamic changes in Pcrit (decrease) and TPP (increase). The patient had a long length of stay but was successfully discharged from the hospital.

Extended Data Fig. 9. — Individual patient data is shown for the first 24 hours of ICU admission from the case of a man in his sixties presenting with severe aortic stenosis who underwent an aortic valve replacement. Data includes (A) MAP, (B) Pulse pressure multiplied by heart rate (PP*HR), (C) Pcrit, (D) TPP, (E) fluid input / output (I/O), (F) lactate levels, and (G) vasoactive inotrope score (VIS). The time averaged trends for MAP and PP*HR are shown in red with an averaging window size of 20 seconds. His post-operative course was notable for a modest vasopressor requirement but overall high TPP and low Pcrit throughout the first 24 hours. He was extubated at hour 3, left the ICU at hour 28, and was discharged home after a routine hospital course on day 7.

Extended Data Fig. 10. — Individual patient data is shown for the first 24 hours of ICU admission from the case of a man in his seventies with a history of hypertension, diabetes, and chronic kidney disease who presented with anginal chest pain and underwent two-vessel coronary artery bypass grafting. Data includes (A) MAP, (B) Pulse pressure multiplied by heart rate (PP*HR), (C) Pcrit, (D) TPP, (E) fluid input / output (I/O), (F) lactate levels, and (G) vasoactive inotrope score (VIS). The time averaged trends for MAP and PP*HR are shown in red with an averaging window size of 20 seconds. Dynamic responses in Pcrit and TPP are seen with fluid balance in this patient. He received 3 liters of intravenous fluid in the first 5 hours, with a decrease in Pcrit and an increase in TPP resulting. Diuretic medication was started at hour 14 and with high urine output between hours 15 and 18 resulting in negative fluid balance, a rise in Pcrit and a drop in TPP are observed. The patient did not require vasopressors in the post-operative setting, was extubated at hour 6, and discharged from the hospital on day 6.

Supplementary Material

SUPPLEMENTAL INFO

NIHMS2068997-supplement-SUPPLEMENTAL_INFO.pdf^{(8.5MB, pdf)}

Acknowledgments

The authors thank former members of the clinical outcomes team in the Division of Cardiac Surgery at the Massachusetts General Hospital (MGH) for providing access to outcomes data adjudicated for the Society for Thoracic Surgeons (STS) institutional database. C.G.S. H.S.L. and A.C acknowledge funding from the MIT Center for Integrated Circuits and Systems. A.D.A. acknowledges funding from the Wellman Center at Massachusetts General Hospital and from the NIH (HL144515).

Footnotes

Competing Interests Statement

A provisional patent application entitled “System and methods for measuring critical closing pressure and tissue perfusion pressure in patients” was filed by The General Hospital Corporation with the U.S. Patent and Trademark Office. A.C., R.P.V., and A.D.A. are listed as inventors. The remaining authors declare no competing interests.

Data Availability

The raw patient data from the derivation cohort used for this study is part of an institutional data repository and electronic health record with protected health information and cannot be uniformly released for open-source use. Data used for external validation is publicly available through the Medical Information Mart for Intensive Care (MIMIC) III database, with links provided in the references. More detailed data access to the derivation cohort will require institutional review board approval and relevant data use agreement from Mass General Brigham. Inquiries regarding data availability can be directed to the corresponding author.

Code Availability

Computer code used to process raw blood pressure waveform data and to calculate Pcrit and TPP will be made available upon publication of the manuscript in the GitHub repository: https://github.com/aguirre-lab/tpp-manuscript-2023

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

SUPPLEMENTAL INFO

NIHMS2068997-supplement-SUPPLEMENTAL_INFO.pdf^{(8.5MB, pdf)}

Data Availability Statement

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PERMALINK

Tissue perfusion pressure enables continuous hemodynamic evaluation and risk prediction in the intensive care unit

Anand Chandrasekhar

Raimon Padrós-Valls

Roger Pallares-Lopez

Eric Palanques-Tost

Nicholas Houstis

Thoralf M Sundt

Hae-Seung Lee

Charles G Sodini

Aaron D Aguirre

Abstract

Results

Physiological Model: TPP = MAP–Pcrit is a measure of tissue perfusion

Fig. 1. Critical closing pressure (Pcrit) and tissue perfusion pressure (TPP) describe the state of the systemic arterial circulation.

Estimation of Pcrit from arterial blood pressure waveforms

Fig. 2. Pcrit and TPP can be measured continuously with high temporal resolution from arterial blood pressure data.

Relationship of TPP and Pcrit to Conventional Hemodynamic Measures

Fig. 3. Pcrit and TPP provide unique information compared to conventional hemodynamic metrics.

TPP adds value to MAP for risk prediction

Fig. 4. TPP predicts outcomes in patients in the cardiac surgical intensive care unit.

Evolution of TPP Trajectories and Relationship to Outcomes

Fig. 5. TPP trajectories in response to standard of care therapeutics identify patient groups with worse outcomes.

Continuous TPP monitoring in clinical practice

Fig. 6. Continuous monitoring of Pcrit and TPP provide dynamic hemodynamic information on patients.

Discussion

Online Methods

Study Design and Primary Dataset

External Validation Dataset

Detection of beat-to-beat features (PP, MAP, HR) of a BP waveform

Estimation of Pcrit and TPP from MAP, PP, and HR

Vasoactive-Inotropic Score (VIS) analysis

Clinical outcomes analyses: mortality, length of stay, blood lactate

Clustering analyses

Statistical methods

Extended Data

Extended Data Fig. 1. Distributions of Pcrit and TPP for male and female patients and for increments of MAP.

Extended Data Fig. 2. TPP predicts outcomes in patients with both low and high cardiac output.

Extended Data Fig. 3. External validation of risk stratification by TPP and MAP in the MIMIC-III cohort.

Extended Data Fig. 4. MAP trajectories in response to standard of care therapeutics.

Extended Data Fig. 5. Clustering on TPP value strongly separates patient trajectories in response to standard of care therapeutics.

Extended Data Fig. 6. Clustering on Pcrit in response to standard of care therapeutics.

Extended Data Fig. 7. Second example patient trajectory with hemodynamic instability and long length of stay.

Extended Data Fig. 8. Third example patient trajectory with hemodynamic instability and long length of stay.

Extended Data Fig. 9. Fourth example patient trajectory with stable hemodynamics and short length of stay.

Extended Data Fig. 10. Fifth example patient trajectory with stable hemodynamics and short length of stay.

Supplementary Material

Acknowledgments

Footnotes

Data Availability

Code Availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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