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 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 (TPP), 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 TPP can predict the risk of mortality, length of hospital stay and peak blood lactate levels. These results indicate that TPP may provide an additional target for blood pressure optimization in patients with circulatory shock.
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Data availability
The raw patient data from the derivation cohort used for this study are part of an institutional data repository and electronic health record with protected health information and cannot be uniformly released for open-source use. The data used for external validation are publicly available through the 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
The computer code used to process raw blood pressure waveform data and to calculate Pcrit and TPP will be made available on publication of the manuscript in the GitHub repository at https://github.com/aguirre-lab/tpp-manuscript-2023.
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Acknowledgements
We thank former members of the clinical outcomes team in the Division of Cardiac Surgery at the Massachusetts General Hospital (MGH) for providing access to outcome data adjudicated for the 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 MGH and from the National Institutes of Health (grant no. HL144515).
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A.D.A. conceived, designed and directed the study. A.C. and R.P.-V. led data collection and analysis and contributed to study design and data interpretation. R.P.-L. and E.P.-T. performed the data analysis and contributed to the interpretation of the results. N.H., H.-S.L., C.G.S. and T.M.S. discussed the study design, results and data interpretation. A.C., R.P.-V. and A.D.A. wrote the manuscript and all authors reviewed, revised and approved it.
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A 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 other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Distributions of Pcrit and TPP for male and female patients and for increments of MAP.
(a, b) Distributions in MAP, Pcrit, and TPP for (A) male (N = 3861) and (B) female (N = 1653) patients. (c, d) Distributions of (C) Pcrit and (D) TPP for quintiles of MAP. Distributions in (A) and (B) were created by averaging the first hour of available data and using r2 > 0.5 as a threshold for valid Pcrit determination. Box plots in (C) and (D) were created by binning the data with an r2 > 0.5 by its MAP value, with each box showing the distribution of Pcrit and TPP of the corresponding quintile (N = 1103 per quintile, except the third quintile which N = 1102). Box plots display the first, second, and third quartiles with whiskers showing the extent of the distribution (median ± 1.5 times the interquartile range) and outliers represented as individual points.
Extended Data Fig. 2 TPP predicts outcomes in patients with both low and high cardiac output.
(a) Analysis comparing the mean TPP and MAP values for patients with low cardiac index (CI, defined as cardiac output divided by body surface area) of < 2.5 L/min/m2 during the first 24 hours in the ICU according to outcomes, with a favorable outcome of short hospital stay (N = 1092 admissions) compared to an unfavorable outcome of long hospital stay or death (N = 332 admissions). Logistic regression identified an optimal TPP threshold of 34 mmHg (N = 834 patients above, 590 below) and an optimal MAP threshold of 74 mmHg (N = 698 above, 726 below) for separating outcomes in this cohort. (b, c) Comparisons of the outcomes of mortality (B) and length of stay (C) for groups stratified by both TPP and MAP thresholds (low MAP and low TPP, N = 351; low MAP and high TPP, N = 375; high MAP and low TPP, N = 239; high MAP and high TPP, N = 459). (d) Analysis of the mean TPP and MAP values for patients with high cardiac index CI ≥ 2.5 L/min/m2 during the first 24 hours in the ICU according to favorable (N = 2953) and unfavorable (N = 522) outcomes as defined in (A), with logistic regression again identifying optimal TPP threshold of 34 mmHg (N = 2085 patients above, 1390 below) and optimal MAP threshold of 74 mmHg (N = 1637 above, 1838 below). (e, f) Comparisons of mortality (E) and length of stay (F) in the high CI cohort for groups stratified again by both TPP and MAP thresholds (low MAP and low TPP, N = 858; low MAP and high TPP, N = 980; high MAP and low TPP, N = 532; high MAP and high TPP, N = 1105). 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 values are shown in Supplementary Tables 2 and 4. Cohort statistics are detailed in Supplementary Tables 2, 3. ****, p < 0.00005. ***, p < 0.0005. **, p < 0.005. *, p < 0.05. n.s., not significant.
Extended Data Fig. 3 External validation of risk stratification by TPP and MAP in the MIMIC-III cohort.
(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 (B) and length of stay (C) 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 4, 5. Cohort statistics are detailed in Supplementary Table 3. ****, p < 0.00005. ***, p < 0.0005. **, p < 0.005. *, p < 0.05. n.s., not significant.
Extended Data Fig. 4 MAP trajectories in response to standard of care therapeutics.
(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 Clustering on TPP value strongly separates patient trajectories in response to standard of care therapeutics.
(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 Clustering on Pcrit in response to standard of care therapeutics.
(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 Second example patient trajectory with hemodynamic instability and long length of stay.
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 Third example patient trajectory with hemodynamic instability and long length of stay.
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 Fourth example patient trajectory with stable hemodynamics and short length of stay.
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 Fifth example patient trajectory with stable hemodynamics and short length of stay.
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.
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Supplementary Figs. 1–17 and Tables 1–5.
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Chandrasekhar, A., Padrós-Valls, R., Pallarès-López, R. et al. Tissue perfusion pressure enables continuous hemodynamic evaluation and risk prediction in the intensive care unit. Nat Med 29, 1998–2006 (2023). https://doi.org/10.1038/s41591-023-02474-6
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DOI: https://doi.org/10.1038/s41591-023-02474-6
