Automated Blood Pressure Control

Rinehart, Joseph; Lee, Sean; Saugel, Bernd; Joosten, Alexandre

doi:10.1055/s-0040-1713083

Cited by 5 publications

(2 citation statements)

References 108 publications

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“…Based on this, considering the large proportion of low-risk patients who undergo conventional common surgery, we should weigh the risk-benefit ratio when using expensive noninvasive hemodynamic devices, as it is a valuable tool or a superfluous toy for the right patients [ 105 ]. Fully automated individualized hemodynamic management has broad prospects with advances in computer technology, especially in high-risk patients, as supported by recent studies [ 75 , 106 , 107 ].…”

Section: Individualized Hemodynamic Management In Perioperative Care ...mentioning

confidence: 99%

Provisional Decision-Making for Perioperative Blood Pressure Management: A Narrative Review

Song

Jiang

2022

Oxidative Medicine and Cellular Longevity

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Blood pressure (BP) is a basic determinant for organ blood flow supply. Insufficient blood supply will cause tissue hypoxia, provoke cellular oxidative stress, and to some extent lead to organ injury. Perioperative BP is labile and dynamic, and intraoperative hypotension is common. It is unclear whether there is a causal relationship between intraoperative hypotension and organ injury. However, hypotension surely compromises perfusion and causes harm to some extent. Because the harm threshold remains unknown, various guidelines for intraoperative BP management have been proposed. With the pending definitions from robust randomized trials, it is reasonable to consider observational analyses suggesting that mean arterial pressures below 65 mmHg sustained for more than 15 minutes are associated with myocardial and renal injury. Advances in machine learning and artificial intelligence may facilitate the management of hemodynamics globally, including fluid administration, rather than BP alone. The previous mounting studies concentrated on associations between BP targets and adverse complications, whereas few studies were concerned about how to treat and multiple factors for decision-making. Hence, in this narrative review, we discussed the way of BP measurement and current knowledge about baseline BP extracting for surgical patients, highlighted the decision-making process for BP management with a view to providing pragmatic guidance for BP treatment in the clinical settings, and evaluated the merits of an automated blood control system in predicting hypotension.

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Section: Individualized Hemodynamic Management In Perioperative Care ...mentioning

confidence: 99%

Provisional Decision-Making for Perioperative Blood Pressure Management: A Narrative Review

Song

Jiang

2022

Oxidative Medicine and Cellular Longevity

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“…Detailed physiological data including cardiac stroke volume and precise times for initiation and changes in the SNP infusion were recorded, providing a robust data source for calibration of the model and identification of appropriate parameter ranges and/or simulation of individuals. With a focus on potential use in the development of future closed-loop pharmacologic interventions [17][18][19][20], we hypothesized that the present model could be extended to include the effects of SNP with a root-mean-squared error of less than 10% for MAP itself, as was achieved with the original model.…”

Section: Introductionmentioning

confidence: 99%

Physiological Modeling of Hemodynamic Responses to Sodium Nitroprusside

Rinehart

Coeckelenbergh

Srivastava

et al. 2023

JPM

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Background: Computational modeling of physiology has become a routine element in the development, evaluation, and safety testing of many types of medical devices. Members of the Food and Drug Administration have recently published a manuscript detailing the development, validation, and sensitivity testing of a computational model for blood volume, cardiac stroke volume, and blood pressure, noting that such a model might be useful in the development of closed-loop fluid administration systems. In the present study, we have expanded on this model to include the pharmacologic effect of sodium nitroprusside and calibrated the model against our previous experimental animal model data. Methods: Beginning with the model elements in the original publication, we added six new parameters to control the effect of sodium nitroprusside: two for the onset time and clearance rates, two for the stroke volume effect (which includes venodilation as a “hidden” element), and two for the direct effect on arterial blood pressure. Using this new model, we then calibrated the predictive performance against previously collected animal study data using nitroprusside infusions to simulate shock with the primary emphasis on MAP. Root-mean-squared error (RMSE) was calculated, and the performance was compared to the performance of the model in the original study. Results: RMSE of model-predicted MAP to actual MAP was lower than that reported in the original model, but higher for SV and CO. The individually fit models showed lower RMSE than using the population average values for parameters, suggesting the fitting process was effective in identifying improved parameters. Use of partially fit models after removal of the lowest variance population parameters showed a very minor decrement in improvement over the fully fit models. Conclusion: The new model added the clinical effects of SNP and was successfully calibrated against experimental data with an RMSE of <10% for mean arterial pressure. Model-predicted MAP showed an error similar to that seen in the original base model when using fluid shifts, heart rate, and drug dose as model inputs.

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