Bench-to-bedside review: Mechanisms of critical illness – classifying microcirculatory flow abnormalities in distributive shock

Over 30 years ago Weil and Shubin proposed a re-classification of shock states and identified hypovolemic, cardiogenic, obstructive and distributive shock. The first three categories have in common that they are associated with a fall in cardiac output. Distributive shock, such as occurs during sepsis and septic shock, however, is associated with an abnormal distribution of microvascular blood flow and metabolic distress in the presence of normal or even supranormal levels of cardiac output. This Bench-to-bedside review looks at the recent insights that have been gained into the nature of distributive shock. Its pathophysiology can best be described as a microcirculatory and mitochondrial distress syndrome, where time and therapy form an integral part of the definition. The clinical introduction of new microcirculatory imaging techniques, such as orthogonal polarization spectral and side-stream dark-field imaging, have allowed direct observation of the microcirculation at the bedside. Images of the sublingual microcirculation during septic shock and resuscitation have revealed that the distributive defect of blood flow occurs at the capillary level. In this paper, we classify the different types of heterogeneous flow patterns of microcirculatory abnormalities found during different types of distributive shock. Analysis of these patterns gave a five class classification system to define the types of microcirculatory abnormalities found in different types of distributive shock and indicated that distributive shock occurs in many other clinical conditions than just sepsis and septic shock. It is likely that different mechanisms defined by pathology and treatment underlie these abnormalities observed in the different classes. Functionally, however, they all cause a distributive defect resulting in microcirculatory shunting and regional dysoxia. It is hoped that this classification system will help in the identification of mechanisms underlying these abnormalities and indicate optimal therapies for resuscitating septic and other types of distributive shock

Withdrawing intra-aortic balloon pump support paradoxically improves microvascular flow

Introduction: The Intra-Aortic Balloon Pump (IABP) is frequently used to mechanically support the heart. There is evidence that IABP improves microvascular flow during cardiogenic shock but its influence on the human microcirculation in patients deemed ready for discontinuing IABP support has not yet been studied. Therefore we used sidestream dark field imaging (SDF) to test our hypothesis that human microcirculation remains unaltered with or without IABP support in patients clinically ready for discontinuation of mechanical support. Methods: We studied 15 ICU patients on IABP therapy. Measurements were performed after the clinical decision was made to remove the balloon catheter. We recorded global hemodynamic parameters and performed venous oximetry during maximal IABP support (1:1) and 10 minutes after temporarily stopping the IABP therapy. At both time points, we also recorded video clips of the sublingual microcirculation. From these we determined indices of microvascular perfusion including perfused vessel density (PVD) and microvascular flow index (MFI). Results: Ceasing IABP support lowered mean arterial pressure (74 +/- 8 to 71 +/- 10 mmHg; P = 0.048) and increased diastolic pressure (43 +/- 10 to 53 +/- 9 mmHg; P = 0.0002). However, at the level of the microcirculation we found an increase of PVD of small vessels <20 mu m (5.47 +/- 1.76 to 6.63 +/- 1.90; P = 0.0039). PVD for vessels >20 mu m and MFI for both small and large vessels were unaltered. During the procedure global oxygenation parameters (ScvO(2)/SvO(2)) remained unchanged. Conclusions: In patients deemed ready for discontinuing IABP support according to current practice, SDF imaging showed an increase of microcirculatory flow of small vessels after ceasing IABP therapy. This observation may indicate that IABP impairs microvascular perfusion in recovered patients, although this warrants confirmatio

Machine learning in intensive care medicine: ready for take-off?

In 1986 the world was shaken by the Challenger space shuttle disaster. In the years that followed, the American National Aeronautics and Space Administration (NASA) called for a strategy change in space technology development [1]. Allowing technology to be developed without a specific space program in mind was central to the new strategy [2]. In order to evaluate resulting projects with no direct contribution to a space mission, NASA introduced the general concept of technology readiness levels (TRLs) [3]. These nine levels, adopted by many EU institutions, assess the maturity level of technology and estimate its readiness to fly

Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example.

OBJECTIVES: Critical care medicine is a natural environment for machine learning approaches to improve outcomes for critically ill patients as admissions to ICUs generate vast amounts of data. However, technical, legal, ethical, and privacy concerns have so far limited the critical care medicine community from making these data readily available. The Society of Critical Care Medicine and the European Society of Intensive Care Medicine have identified ICU patient data sharing as one of the priorities under their Joint Data Science Collaboration. To encourage ICUs worldwide to share their patient data responsibly, we now describe the development and release of Amsterdam University Medical Centers Database (AmsterdamUMCdb), the first freely available critical care database in full compliance with privacy laws from both the United States and Europe, as an example of the feasibility of sharing complex critical care data. SETTING: University hospital ICU. SUBJECTS: Data from ICU patients admitted between 2003 and 2016. INTERVENTIONS: We used a risk-based deidentification strategy to maintain data utility while preserving privacy. In addition, we implemented contractual and governance processes, and a communication strategy. Patient organizations, supporting hospitals, and experts on ethics and privacy audited these processes and the database. MEASUREMENTS AND MAIN RESULTS: AmsterdamUMCdb contains approximately 1 billion clinical data points from 23,106 admissions of 20,109 patients. The privacy audit concluded that reidentification is not reasonably likely, and AmsterdamUMCdb can therefore be considered as anonymous information, both in the context of the U.S. Health Insurance Portability and Accountability Act and the European General Data Protection Regulation. The ethics audit concluded that responsible data sharing imposes minimal burden, whereas the potential benefit is tremendous. CONCLUSIONS: Technical, legal, ethical, and privacy challenges related to responsible data sharing can be addressed using a multidisciplinary approach. A risk-based deidentification strategy, that complies with both U.S. and European privacy regulations, should be the preferred approach to releasing ICU patient data. This supports the shared Society of Critical Care Medicine and European Society of Intensive Care Medicine vision to improve critical care outcomes through scientific inquiry of vast and combined ICU datasets

PW: Fast-track microcirculation analysis

The recently published report ‘How to evaluate the microcirculation’ [1] should be praised for standardizing analysis of human microcirculation data. This standardization will enable better comparison between studies. Having worked extensively with both orthogonal polarization spectral (OPS) and sidestream dark field (SDF) imaging and most analysis software, I feel the proposed analysis is extremely useful but is also equally time consuming. Despite advances in computer analysis, current practice is still predominantly manual. I therefore wish to make a comment that may greatly simplify the procedure. The report suggests determining the microvascular flow index (MFI), the perfused vessel density (PVD) and the percentage of perfused vessels (PPV). For the MFI a grid is used dividing the screen into four quadrants, and the vessels are scored according to observed flow: 0 = none, 1 = intermittent, 2 = sluggish, 3 = continuous. For the PVD and the PPV, three equidistant horizontal and vertical lines are drawn and a different score is used: absent, intermittent, present (for details see [1]). I propose using the same grid for the MFI, the PPV and the PVD. Dividing the MFI quadrants into four sections more effectively creates the PPV and PVD lines (see Figure 1). Each vessel is then scored according to the MFI criteria. The PPV and the PVD are calculated as usual. Vessels with MFI scores of 2 or 3 are classified as having flow present. Finally, the MFI is calculated as ordinary. I used this method for a recent study [2]. Trzeciak and colleagues used a similar approach but with different scoring definitions [3]. Combining scores and the grid saves time. In addition, the approach potentially allows for distinction between sluggish and continuous flow for PVD and PPV determinations

Case Report: Bilateral reexpansion pulmonary edema following treatment of a unilateral hemothorax [v1; ref status: indexed, http://f1000r.es/4yb]

Bilateral re-expansion pulmonary edema (RPE) is an extremely rare entity. We report the unique case of bilateral RPE following a traumatic, unilateral hemopneumothorax in a young healthy male. Bilateral RPE occurred only one hour after drainage of a unilateral hemopneumothorax. The patient was treated with diuretics and supplemental oxygen. Diagnosis was confirmed by excluding other causes, using laboratory findings, chest radiography, pulmonary and cardiac ultrasound and high resolution computed tomography. His recovery was uneventful. The pathophysiology of bilateral RPE is not well known. Treatment is mainly supportive and consists of diuretics, mechanical ventilation, inotropes and steroids. In case of a pulmonary deterioration after the drainage of a traumatic pneumothorax, bilateral RPE should be considered after exclusion of more common causes of dyspnea

Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis

Introduction: In recent years, reinforcement learning (RL) has gained traction in the healthcare domain. In particular, RL methods have been explored for haemodynamic optimization of septic patients in the Intensive Care Unit. Most hospitals however, lack the data and expertise for model development, necessitating transfer of models developed using external datasets. This approach assumes model generalizability across different patient populations, the validity of which has not previously been tested. In addition, there is limited knowledge on safety and reliability. These challenges need to be addressed to further facilitate implementation of RL models in clinical practice. Method: We developed and validated a new reinforcement learning model for hemodynamic optimization in sepsis on the MIMIC intensive care database from the USA using a dueling double deep Q network. We then transferred this model to the European AmsterdamUMCdb intensive care database. T-Distributed Stochastic Neighbor Embedding and Sequential Organ Failure Assessment scores were used to explore the differences between the patient populations. We apply off-policy policy evaluation methods to quantify model performance. In addition, we introduce and apply a novel deep policy inspection to analyse how the optimal policy relates to the different phases of sepsis and sepsis treatment to provide interpretable insight in order to assess model safety and reliability. Results: The off-policy evaluation revealed that the optimal policy outperformed the physician policy on both datasets despite marked differences between the two patient populations and physician's policies. Our novel deep policy inspection method showed insightful results and unveiled that the model could initiate therapy adequately and adjust therapy intensity to illness severity and disease progression which indicated safe and reliable model behaviour. Compared to current physician behavior, the developed policy prefers a more liberal use of vasopressors with a more restrained use of fluid therapy in line with previous work. Conclusion: We created a reinforcement learning model for optimal bedside hemodynamic management and demonstrated model transferability between populations from the USA and Europe for the first time. We proposed new methods for deep policy inspection integrating expert domain knowledge. This is expected to facilitate progression to bedside clinical decision support for the treatment of critically ill patients