Hypotension in ICU Patients Receiving Vasopressor Therapy

Hypotension During Vasopressor Infusion Occurs in Predictable Clusters: A Multicenter Analysis

Background: Published evidence indicates that mean arterial pressure (MAP) below a goal range (hypotension) is associated with worse outcomes, though MAP management failures are common. We sought to characterize hypotension occurrences in ICUs and consider the implications for MAP management. Methods: Retrospective analysis of 3 hospitals' cohorts of adult ICU patients during continuous vasopressor infusion. Two cohorts were general, mixed ICU patients and one was exclusively acute spinal cord injury patients. "Hypotension-clusters" were defined where there were ≥10 min of cumulative hypotension over a 60-min period and "constant hypotension" was ≥10 continuous minutes. Trend analysis was performed (predicting future MAP using 14 min of preceding MAP data) to understand which hypotension-clusters could likely have been predicted by clinician awareness of MAP trends. Results: In cohorts of 155, 66, and 16 ICU stays, respectively, the majority of hypotension occurred within the hypotension-clusters. Failures to keep MAP above the hypotension threshold were notable in the bottom quartiles of each cohort, with hypotension durations of 436, 167, and 468 min, respectively, occurring within hypotension-clusters per day. Mean arterial pressure trend analysis identified most hypotension-clusters before any constant hypotension occurred (81.2%-93.6% sensitivity, range). The positive predictive value of hypotension predictions ranged from 51.4% to 72.9%. Conclusions: Across 3 cohorts, most hypotension occurred in temporal clusters of hypotension that were usually predictable from extrapolation of MAP trends.

Development and Validation of a Prediction Model for Acute Hypotensive Events in Intensive Care Unit Patients

Background: Persistent hypotension in the intensive care unit (ICU) is associated with increased mortality. Predicting acute hypotensive events can lead to timely intervention. We aimed to develop a prediction model of acute hypotensive events in patients admitted to the ICU. Methods: We included adult patients admitted to the Nagoya City University (NCU) Hospital ICU between January 2018 and December 2021 for model training and internal validation. The MIMIC-III database was used for external validation. A hypotensive event was defined as a mean arterial pressure < 60 mmHg for at least 5 min in 10 min. The input features were age, sex, and time-series data for vital signs. We compared the area under the receiver-operating characteristic curve (AUROC) of three machine-learning algorithms: logistic regression, the light gradient boosting machine (LightGBM), and long short-term memory (LSTM). Results: Acute hypotensive events were found in 1325/1777 (74.6%) and 2691/5266 (51.1%) of admissions in the NCU and MIMIC-III cohorts, respectively. In the internal validation, the LightGBM model had the highest AUROC (0.835), followed by the LSTM (AUROC 0.834) and logistic regression (AUROC 0.821) models. Applying only blood pressure-related features, the LSTM model achieved the highest AUROC (0.843) and consistently showed similar results in external and internal validation. Conclusions: The LSTM model using only blood pressure-related features had the highest AUROC with comparable performance in external validation.

Nurse management of noradrenaline infusions in intensive care units: An observational study

BACKGROUND

Intensive care nurse management of noradrenaline (norepinephrine) infusions is a common and essential clinical competency for patient haemodynamic support. Nurses titrate and wean noradrenaline infusions to a target blood pressure in a dynamic, high-risk, and unpredictable environment. Titration and weaning are complex interventions, and blood pressure goals are often variable.

OBJECTIVES

The aim was to examine how nurses used blood pressure targets when escalating, weaning, and titrating noradrenaline in intensive care patients admitted for haemodynamic management and explore patient blood pressure responses to changes in noradrenaline doses.

METHODS

In this naturalistic observational study, noradrenaline dose changes were classified as escalation, weaning, and titration changes and analysed to explore nursing practice. The study was undertaken in two adult medical/surgical intensive care units in Melbourne, Australia. Participants included intensive care nurses and patients who received noradrenaline infusions for haemodynamic support.

RESULTS

Observations of 14 nurse-patient dyads provided 25 h of blood pressure and noradrenaline dose data. Patient participants received weight-adjusted maximum noradrenaline doses of between 0.06 mcg/kg/min and 0.87 mcg/kg/minute, with those in the escalation group receiving dose increases of up to 5 mcg to achieve blood pressure goals. During weaning, patients maintained or increased their blood pressure as noradrenaline doses were decreased. Nurses consistently maintained blood pressures at higher than target goals, and despite constant fluctuations, they only documented blood pressure readings hourly.

CONCLUSIONS

Intensive care nurses managed noradrenaline to achieve mean arterial pressure targets that were variable and not evidence based. The disconnection between observed blood pressure fluctuations and nurse documentation of patient blood pressures was reflected in titration practices. Discrepancies between documented and actual blood pressures raised issues about data used by nurses and doctors to inform clinical practice on noradrenaline management.

Precision Automated Critical Care Management: Closed-loop critical care for the treatment of distributive shock in a swine model of ischemia-reperfusion

BACKGROUND

Goal-directed blood pressure management in the intensive care unit can improve trauma outcomes but is labor-intensive. Automated critical care systems can deliver scaled interventions to avoid excessive fluid or vasopressor administration. We compared a first-generation automated drug and fluid delivery platform, Precision Automated Critical Care Management (PACC-MAN), to a more refined algorithm, incorporating additional physiologic inputs and therapeutics. We hypothesized that the enhanced algorithm would achieve equivalent resuscitation endpoints with less crystalloid utilization in the setting of distributive shock.

METHODS

Twelve swine underwent 30% hemorrhage and 30 minutes of aortic occlusion to induce an ischemia-reperfusion injury and distributive shock state. Next, animals were transfused to euvolemia and randomized into a standardized critical care (SCC) of PACC-MAN or an enhanced version (SCC+) for 4.25 hours. SCC+ incorporated lactate and urine output to assess global response to resuscitation and added vasopressin as an adjunct to norepinephrine at certain thresholds. Primary and secondary outcomes were decreased crystalloid administration and time at goal blood pressure, respectively.

RESULTS

Weight-based fluid bolus volume was lower in SCC+ compared with SCC (26.9 mL/kg vs. 67.5 mL/kg, p = 0.02). Cumulative norepinephrine dose required was not significantly different (SCC+: 26.9 μg/kg vs. SCC: 13.76 μg/kg, p = 0.24). Three of 6 animals (50%) in SCC+ triggered vasopressin as an adjunct. Percent time spent between 60 mm Hg and 70 mm Hg, terminal creatinine and lactate, and weight-adjusted cumulative urine output were equivalent.

CONCLUSION

Refinement of the PACC-MAN algorithm decreased crystalloid administration without sacrificing time in normotension, reducing urine output, increasing vasopressor support, or elevating biomarkers of organ damage. Iterative improvements in automated critical care systems to achieve target hemodynamics in a distributive-shock model are feasible.

Relative sparsity for medical decision problems

Existing statistical methods can estimate a policy, or a mapping from covariates to decisions, which can then instruct decision makers (eg, whether to administer hypotension treatment based on covariates blood pressure and heart rate). There is great interest in using such data-driven policies in healthcare. However, it is often important to explain to the healthcare provider, and to the patient, how a new policy differs from the current standard of care. This end is facilitated if one can pinpoint the aspects of the policy (ie, the parameters for blood pressure and heart rate) that change when moving from the standard of care to the new, suggested policy. To this end, we adapt ideas from Trust Region Policy Optimization (TRPO). In our work, however, unlike in TRPO, the difference between the suggested policy and standard of care is required to be sparse, aiding with interpretability. This yields "relative sparsity," where, as a function of a tuning parameter,λ $$ \lambda $$ , we can approximately control the number of parameters in our suggested policy that differ from their counterparts in the standard of care (eg, heart rate only). We propose a criterion for selectingλ $$ \lambda $$ , perform simulations, and illustrate our method with a real, observational healthcare dataset, deriving a policy that is easy to explain in the context of the current standard of care. Our work promotes the adoption of data-driven decision aids, which have great potential to improve health outcomes.

Incidence, Severity and Clinical Factors Associated with Hypotension in Patients Admitted to an Intensive Care Unit: A Prospective Observational Study

Background: The majority of patients admitted to the intensive care unit (ICU) experience severe hypotension which is associated with increased morbidity and mortality. At present, prospective studies examining the incidence and severity of hypotension using continuous waveforms are missing. Methods: This study is a prospective observational cohort study in a mixed surgical and non-surgical ICU population. All patients over 18 years were included and continuous arterial pressure waveforms data were collected. Mean arterial pressure (MAP) below 65 mmHg for at least 10 s was defined as hypotension and a MAP below 45 mmHg as severe hypotension. The primary outcome was the incidence of hypotension. Secondary outcomes were the severity of hypotension expressed in time-weighted average (TWA), factors associated with hypotension, the number and duration of hypotensive events. Results: 499 patients were included. The incidence of hypotension (MAP < 65 mmHg) was 75% (376 out of 499) and 9% (46 out of 499) experienced severe hypotension. Median TWA was 0.3 mmHg [0−1.0]. Associated clinical factors were age, male sex, BMI and cardiogenic shock. There were 5 (1−12) events per patients with a median of 52 min (5−170). Conclusions: In a mixed surgical and non-surgical ICU population the incidence of hypotension is remarkably high.

End-user evaluation of an interface for clinical decision support using predictive algorithms

There have been decades of interest in advanced computational algorithms with potential for clinical decision support systems (CDSS), yet these have not been widely implemented in clinical practice. One major barrier to dissemination may be a user-friendly interface that integrates into clinical workflows. Complicated or non-intuitive displays may confuse users and may even increase patient management errors. We recently developed a graphical user interface (GUI) intended to integrate a predictive hemodynamic model into the workflow of nurses caring for patients on vasopressors in the intensive care unit (ICU). Here, we evaluated user perceptions of the usability of this system. The software was installed in the room of an ICU patient, running for at least 4 hours with the display hidden. Afterward, we showed nurses a video recording of the session and surveyed their perceptions about the software's potential safety and usefulness. We collected data for nine patients. Overall, nurses expressed reasonable enthusiasm that the software would be useful and without serious safety concerns. However, there was a wide diversity of opinions about what specific aspects of the software would be useful and what aspects were confusing. In several instances, the same elements of the GUI were cited as most useful by some nurses and most confusing by others. Our findings validate that it is possible to develop GUIs for CDSS that are perceived as potentially useful and without substantial risk but also reinforce the diversity of user perceptions about novel CDSS technology. Clinical Relevance- This end-user evaluation of a novel CDSS highlights the importance of end-user experience in the workflow integration of advanced computational algorithms for bedside decision support during critical care.

Predicting Hypertensive Events with Time-Series Analysis of Mean Arterial Pressure

We investigated whether a statistical model used previously to predict hypotension from mean arterial pressure (MAP) time series analysis could predict hypertension. We performed a retrospective analysis of minute-by-minute MAP records from two cohorts of intensive care unit (ICU) patients. The first cohort was comprised of surgical and medical ICUs while the second cohort was comprised of acute spinal cord injury (ASCI) patients in a neurological ICU. At each time point with physiological MAP, time series analysis was used to predict the median MAP for the subsequent 20 min. This method was used to predict hypertensive episodes, i.e., intervals of 20 or more minutes where at least half of the MAP measurements were > 105 mmHg. Advance prediction of hypertensive episodes was similar in the two cohorts (69.15% vs. 82.61%, respectively), as was positive predictive value of the hypertension predictions (67.42% vs. 71.57%). The results suggest that the methodology may be useable for predicting hypertension from time-series analysis of MAP. Patients requiring continuous vasopressor infusion are at risk of hypertension and excessive vasoconstriction. We found evidence that time-series analysis previously validated for predicting hypotension may also be usable for predicting hypertension.

Recommendations for the Development of Telemedicine in Poland Based on the Analysis of Barriers and Selected Telemedicine Solutions

Technological development around the world has led to the digitalisation of the health system. Along with the digitalisation of the health sector, financial, legal, awareness-related, technological and IT barriers appeared. The aim of the article is to present recommendations for the development of telemedicine services in Poland on the basis of a list of implementation barriers and ways of resolving them in the USA and selected European countries. A literature review was conducted in accordance with the PRISMA-ScR, using the PubMed and Google Scholar databases, Scopus and the OECD iLibrary. A total of 59 literature positions were used, which constituted the references. The article presented the implemented and effective solutions in selected countries. Based on these solutions, recommendations for the development of telemedicine in Poland were presented, as well as successes in the form of telemedicine startups, which can inspire other countries. The analysis of the publications discussed in the article shows that the implementation of telemedicine services should begin with the elimination of barriers limiting the development of telemedicine systems. An important issue in their elimination is to analyse their interconnections and implement such solutions which would have a multi-area coverage.

Dose equivalence between metaraminol and norepinephrine in critical care

AIMS

The aim of this study was to determine the conversion dose ratio between continuous infusion metaraminol and norepinephrine in critically ill patients with shock.

METHODS

A retrospective cohort study was conducted in adult patients with shock admitted to an intensive care unit from 29 October 2018 to 30 October 2019 and who transitioned from metaraminol monotherapy to norepinephrine monotherapy. Mean arterial pressure (MAP) and infusion doses for both drugs were collected at hourly intervals; 2 hours before to 5 hours after switching from metaraminol monotherapy to norepinephrine monotherapy. The conversion dose ratio was defined as the ratio of metaraminol (μg.kg^-1 .min^-1) : norepinephrine (μg.kg^-1 .min^-1 ) required to achieve a similar MAP.

RESULTS

A total of 43 out of 144 eligible patients were included. The median age was 68 years (IQR 56-76) and 22 (51%) were male. There was no significant difference between the baseline MAP during metaraminol monotherapy (median 71 mm Hg, IQR 66-76) and the post-transition MAP during norepinephrine monotherapy (median 70 mm Hg, IQR 66-73) (P = .09). The median conversion dose ratio between metaraminol and norepinephrine was 13 (IQR 7-24). In the sensitivity analyses, the median conversion dose ratio using the maximum and the mean norepinephrine infusion dose was 8 (IQR 5-16) and 12 (IQR 8-23), respectively.

CONCLUSION

A conversion dose ratio of 10:1 (metaraminol μg.kg^-1 .min^-1 :norepinephrine μg.kg^-1 .min^-1 ) may be used in critically ill patients with shock to account for ease of calculations and variability of the conversion ratio in the primary and sensitivity analyses.

Sepsis in two hospitals in Rwanda: A retrospective cohort study of presentation, management, outcomes, and predictors of mortality

Purpose

Few studies have assessed the presentation, management, and outcomes of sepsis in low-income countries (LICs). We sought to characterize these aspects of sepsis and to assess mortality predictors in sepsis in two referral hospitals in Rwanda.

Materials and methods

This was a retrospective cohort study in two public academic referral hospitals in Rwanda. Data was abstracted from paper medical records of adult patients who met our criteria for sepsis.

Results

Of the 181 subjects who met eligibility criteria, 111 (61.3%) met our criteria for sepsis without shock and 70 (38.7%) met our criteria for septic shock. Thirty-five subjects (19.3%) were known to be HIV positive. The vast majority of septic patients (92.7%) received intravenous fluid therapy (median = 1.0 L within 8 hours), and 94.0% received antimicrobials. Vasopressors were administered to 32.0% of the cohort and 46.4% received mechanical ventilation. In-hospital mortality for all patients with sepsis was 51.4%, and it was 82.9% for those with septic shock. Baseline characteristic mortality predictors were respiratory rate, Glasgow Coma Scale score, and known HIV seropositivity.

Conclusions

Septic patients in two public tertiary referral hospitals in Rwanda are young (median age = 40, IQR = 29, 59) and experience high rates of mortality. Predictors of mortality included baseline clinical characteristics and HIV seropositivity status. The majority of subjects were treated with intravenous fluids and antimicrobials. Further work is needed to understand clinical and management factors that may help improve mortality in septic patients in LICs.

Clinical Characteristics, Management, and Outcome of the First 19 Patients With Pneumonia Due to the 2019 Novel Coronavirus Disease Treated in an Intensive Care Unit in the Republic of Cyprus

Background

The widespread reach of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its consequences have severely affected the consistency of healthcare systems around the world and caused millions of deaths to date. Understanding the coronavirus disease 2019 (COVID-19) manifestation, progression, and management is crucial for the healthcare personnel caring for COVID-19 patients within the intensive care unit (ICU), as well as for the patients' health progression.

Methods

A prospective observational study was used to investigate the progression of critically ill COVID-19 positive patients who were admitted to the ICU of Nicosia General Hospital from March 10 to May 1, 2020. All patients over the age of 18 were included in the study; their data were anonymously collected using the institution’s electronic medical record system and analyzed in Microsoft Excel (Microsoft Corporation, Redmond, WA). Pregnant women, children, and prisoners were excluded.

Results

During the study period, a total of 19 patients with a positive result on a reverse-transcriptase polymerase chain reaction (RT-PCR) were included in the study; 74% were men and their mean age was 64 years. Sixty-three percent of the patients were obese, 53% had a history of confirmed hypertension, 68% were admitted with severe respiratory failure, and all of them required invasive mechanical ventilation. Patients were categorized into four groups of ventilation based on the H or L ventilation phenotype in association with co-morbidities. Prone position in the first mechanical ventilation days was found to be more advantageous in L than H phenotype patients, 68% required vasopressor support, and 42% developed acute kidney injury (AKI) during their ICU stay. Diarrhea was with a median day of onset of eight days. Lactate levels above 2 mmol/L in the first four days of admission were correlated with a negative outcome. Nine patients (47%) were successfully discharged from the ICU while 10 (53%) died during their stay.

Conclusion

In critically ill patients, male gender and obesity are significant risk factors for ICU admission due to COVID-19, and early prone position, mechanical ventilation, and low positive end-expiratory pressures (PEEP) values may be beneficial, especially in the L phenotype category patients. Patients' ventilation phenotype during ICU admission and hospitalization seemed to determine the outcome.

Clinical improvement might have been higher and possibly ICU mortality lower if remdesivir was available. Hydroxychloroquine did not seem to improve patient outcomes, a consistent find, as suggested by other studies; on the contrary, it may have contributed to increased mortality rates.

Target value of mean arterial pressure in patients undergoing continuous renal replacement therapy due to acute kidney injury

Background

Although patients undergoing continuous renal replacement therapy (CRRT) due to acute kidney injury (AKI) frequently have instability in mean arterial pressure (MAP), no consensus exists on the target value of MAP related to high mortality after CRRT.

Methods

A total of 2,292 patients who underwent CRRT due to AKI in three referral hospitals were retrospectively reviewed. The MAPs were divided into tertiles, and the 3^rd tertile group served as a reference in the analyses. The major outcome was all-cause mortality during the intensive care unit period. The odds ratio (OR) of mortality was calculated using logistic regression after adjustment for multiple covariates. The nonlinear relationship regression model was applied to determine the threshold value of MAP related to increasing mortality.

Results

The mean value of MAP was 80.7 ± 17.3 mmHg at the time of CRRT initiation. The median intensive care unit stay was 5 days (interquartile range, 2–12 days), and during this time, 1,227 (55.5%) patients died. The 1^st tertile group of MAP showed an elevated risk of mortality compared with the 3^rd tertile group (adjusted OR, 1.28 [1.03–1.60]; P = 0.029). In the nonlinear regression analysis, the threshold value of MAP was calculated as 82.7 mmHg. Patients with MAP < 82.7 mmHg had a higher mortality rate than those with ≥ 82.7 mmHg (adjusted OR, 1.21 [1.01–1.45]; P = 0.037).

Conclusions

Low MAP at CRRT initiation is associated with a high risk of mortality, particularly when it is < 82.7 mmHg. This value may be used for risk classification and as a potential therapeutic target.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-020-02227-4.

Development and Validation of a Predictive Model for Hemodynamic Responses to Resuscitation during Uncontrolled Hemorrhage

We investigated whether a statistical model could predict mean arterial pressure (MAP) during uncontrolled hemorrhage; such a model could be used for automated decision support, to help clinicians decide when to provide intravascular volume to achieve MAP goals. This was a secondary analysis of adult swine subjects during uncontrolled splenic bleeding. By protocol, after developing severe hypotension (MAP < 60 mmHg), subjects were resuscitated with either saline (NS) or fresh frozen plasma (FFP), determined randomly. Vital signs were documented at quasi-regular time-step intervals, until either subject death or 300 min. Subjects were randomly separated 50%/50% into training/validation sets, and regression models were developed to predict MAP for each subsequent (i.e., future) time-step. Median time-steps for serially recorded vital signs were +15 min. 5 subjects survived the protocol; 17 died after a median time of 87 min (IQR 78 - 134). The final model consisted of: current MAP; heart rate (HR); prior NS; imminent NS; and imminent FFP. The 95% limits-of-agreement between true subsequent MAP vs. predicted subsequent MAP were +10/-11 mmHg for the 79 time-steps in the training set; and +14/-13 for the 64 time-steps in the validation set. A total of 10 sudden death events (i.e., rapid, fatal MAP decrease within one single time-step) were excluded from analysis. In conclusion, for uncontrolled hemorrhage in a swine model, it was possible to estimate the next documented MAP value on the basis of the subject's current documented MAP; HR; prior NS; and the volume of resuscitation about to be administered. However, the model was unable to predict "sudden death" events. The applicability to populations with wider heterogeneity of hemorrhage patterns and with comorbidities requires further investigation.

Predictors of pressure injury development in critically ill adults: A retrospective cohort study

OBJECTIVE

The purpose of this research was to identify predictors of pressure injury, using data from the electronic health records of critically ill adults.

METHODOLOGY

A retrospective cohort study was conducted using logistic regression models to examine risk factors adjusted for age, gender, race/ethnicity and length of stay.

SETTING

The study cohort included 1587 adults in intensive care units within an urban academic medical centre.

MAIN OUTCOME MEASURES

The presence or absence of a hospital-acquired pressure injury was determined during monthly skin integrity prevalence surveys. All pressure injuries were independently confirmed by two Certified Wound Care Nurses.

RESULTS

Eighty-one (5.1%) of the 1587 cohort patients developed pressure injuries. After adjusting for confounders, the clinical variables associated with pressure injury development included mean arterial pressure <60 mmHg and lowest Total Braden score up to two weeks prior to the date of HAPI development or date of prevalence survey for the comparison group.

CONCLUSIONS

This study provides a more comprehensive understanding about pressure injury risk in critically ill adults, identifying extrinsic and intrinsic factors associated with pressure injury development. Prospective multisite studies are needed to further examine these potential contributors to pressure injury development within the context of adherence to prevention interventions.

Automated Blood Pressure Control

Arterial pressure management is a crucial task in the operating room and intensive care unit. In high-risk surgical and in critically ill patients, sustained hypotension is managed with continuous infusion of vasopressor agents, which most commonly have direct α agonist activity like phenylephrine or norepinephrine. The current standard of care to guide vasopressor infusion is manual titration to an arterial pressure target range. This approach may be improved by using automated systems that titrate vasopressor infusions to maintain a target pressure. In this article, we review the evidence behind blood pressure management in the operating room and intensive care unit and discuss current and potential future applications of automated blood pressure control.

Artificial intelligence in health care: accountability and safety

The prospect of patient harm caused by the decisions made by an artificial intelligence-based clinical tool is something to which current practices of accountability and safety worldwide have not yet adjusted. We focus on two aspects of clinical artificial intelligence used for decision-making: moral accountability for harm to patients; and safety assurance to protect patients against such harm. Artificial intelligence-based tools are challenging the standard clinical practices of assigning blame and assuring safety. Human clinicians and safety engineers have weaker control over the decisions reached by artificial intelligence systems and less knowledge and understanding of precisely how the artificial intelligence systems reach their decisions. We illustrate this analysis by applying it to an example of an artificial intelligence-based system developed for use in the treatment of sepsis. The paper ends with practical suggestions for ways forward to mitigate these concerns. We argue for a need to include artificial intelligence developers and systems safety engineers in our assessments of moral accountability for patient harm. Meanwhile, none of the actors in the model robustly fulfil the traditional conditions of moral accountability for the decisions of an artificial intelligence system. We should therefore update our conceptions of moral accountability in this context. We also need to move from a static to a dynamic model of assurance, accepting that considerations of safety are not fully resolvable during the design of the artificial intelligence system before the system has been deployed.

Forecasting Hypotension during Vasopressor Infusion via Time Series Analysis

For optimal management of hypotension during continuous vasopressor infusion, this study investigated two forecasting models, logistic regression (LR) and auto-regressive (AR) models, to predict sustained hypotension episodes (SHEs) in the ICU, before the SHE occurred. Two investigational models were compared to a simple threshold detector, which alerts whenever the BP is less than the specific hypotension threshold. Datasets were collected from 207 patients treated for a variety of clinical indications in two different hospitals (Hospital 1 & 2). For the 60 mmHg hypotension threshold, LR model predicted SHEs an average of 7.0 min before (Hospital 1) and 2.5 min before (Hospital 2), and the AR model predicted SHEs 10.5 min and 2.0 min before (Hospital 1 and 2 respectively). Both were significantly better than the threshold method and without higher false alarm rates. The AR model offered the flexibility to predict for different hypotension thresholds.

Towards precision critical care management of blood pressure in hemorrhagic stroke patients using dynamic linear models

Finding optimal blood pressure (BP) target and BP treatment after acute ischemic or hemorrhagic strokes is an area of controversy and a significant unmet need in the critical care of stroke victims. Numerous large prospective clinical trials have been done to address this question but have generated neutral or conflicting results. One major limitation that may have contributed to so many neutral or conflicting clinical trial results is the "one-size fit all" approach to BP targets, while the optimal BP target likely varies between individuals. We address this problem with the Acute Intervention Model of Blood Pressure (AIM-BP) framework: an individualized, human interpretable model of BP and its control in the acute care setting. The framework consists of two components: one, a model of BP homeostasis and the various effects that perturb it; and two, a parameter estimator that can learn clinically important model parameters on a patient by patient basis. By estimating the parameters of the AIM-BP model for a given patient, the effectiveness of antihypertensive medication can be quantified separately from the patient's spontaneous BP trends. We hypothesize that the AIM-BP is a sufficient framework for estimating parameters of a homeostasis perturbation model of a stroke patient's BP time course and the AIM-BP parameter estimator can do so as accurately and consistently as a state-of-the-art maximum likelihood estimation method. We demonstrate that this is the case in a proof of concept of the AIM-BP framework, using simulated clinical scenarios modeled on stroke patients from real world intensive care datasets.

Reduced Rank Least Squares for Real-Time Short Term Estimation of Mean Arterial Blood Pressure in Septic Patients Receiving Norepinephrine

Norepinephrine (NE), an endogenous catecholamine, is a mainstay treatment for septic shock, which is a life-threatening manifestation of severe infection. NE counteracts the loss in blood pressure associated with septic shock. However, an NE infusion that is too low fails to counteract the blood pressure drop, and an NE infusion that is too high can cause a hypertensive crisis and heart attack. Ideally, the NE infusion rate should maintain a patient’s mean arterial blood pressure (MAP) above 65 mmHg. There are a few data-driven, quantitative models to predict the MAP, and incorporate NE effects. This paper presents a model, driven by intensive care unit (ICU) measurable data and known NE inputs, to predict the future MAP of an ICU patient. We derive a least square estimation model for MAP based on available ICU data, including heart period, NE infusion rate, and respiration wave. We learn the parameters of our model from initial patient data and then use this information to predict future MAP data. We assess our model with data from 12 septic patients. Our model successfully predicts and tracks MAP when the NE infusion rate changes. Specifically, we predict MAP 3 to 20 min in the future with the mean error of less than 4 to 7 mmHg over 12 patients. Conclusion: this new approach creates the potential to advance methods for predicting NE infusion rate in septic patients. Significance: successfully predicted patients’ MAP could reduce catastrophic human error and lessen clinicians’ workload.

Closed-loop hemodynamic management

As the operating room and intensive care settings become increasingly complex, the required vigilance practitioners must dedicate to a wide array of clinical systems has increased concordantly. The resulting shortage of available attention to these various clinical tasks creates a vacuum for the introduction of systems that can administer well-established goal-directed therapies without significant provider feedback. Recently, there has been an explosion of academic exploration into creating such automated systems, with a strong specific focus on hemodynamic control. Within this field, the largest focus has been on goal-directed fluid therapy as systems automating vasopressor administration have only recently become viable options. Our goal in this review article is to summarize the validity of the relevant goal-directed hemodynamic systems and explore the expanding role of automation within these systems.