Low body temperature governs the decline of circulating lymphocytes during hibernation through sphingosine-1-phosphate.

Hibernation is an energy-conserving behavior consisting of periods of inhibited metabolism ('torpor') with lowered body temperature. Torpor bouts are interspersed by arousal periods, in which metabolism increases and body temperature returns to euthermia. In deep torpor, the body temperature typically decreases to 2-10 °C, and major physiological and immunological changes occur. One of these alterations constitutes an almost complete depletion of circulating lymphocytes that is reversed rapidly upon arousal. Here we show that torpor induces the storage of lymphocytes in secondary lymphoid organs in response to a temperature-dependent drop in plasma levels of sphingosine-1-phosphate (S1P). Regulation of lymphocyte numbers was mediated through the type 1 S1P receptor (S1P(1)), because administration of a specific antagonist (W146) during torpor (in a Syrian hamster at ∼8 °C) precluded restoration of lymphocyte numbers upon subsequent arousal. Furthermore, S1P release from erythrocytes via ATP-binding cassette (ABC)-transporters was significantly inhibited at low body temperature (4 °C) but was restored upon rewarming. Reversible lymphopenia also was observed during daily torpor (in a Djungarian hamster at ± 25 °C), during forced hypothermia in anesthetized (summer-active) hamsters (at ± 9 °C), and in a nonhibernator (rat at ∼19 °C). Our results demonstrate that lymphopenia during hibernation in small mammals is driven by body temperature, via altered plasma S1P levels. S1P is recognized as an important bioactive lipid involved in regulating several other physiological processes as well and may be an important factor regulating additional physiological processes in hibernation as well as in mediating the effects of therapeutic hypothermia in patients.

Microarray analysis of gene expression profiles in the rat kidney demonstrates a local inflammatory response induced by cardiopulmonary bypass.

CONTEXT: Cardiopulmonary bypass (CPB) is a commonly used technique in cardiac surgery but is associated with acute, transient, renal dysfunction that has a negative impact on long-term survival. OBJECTIVE: To unravel the molecular pathogenesis of renal injury following CPB. DESIGN: To obtain insight into the pathogenesis of renal dysfunction following CPB, we performed a microarray analysis of renal gene expression in the rat. SETTING: University Medical Centre Groningen. INTERVENTION: Rats underwent CPB or a sham procedure for 60 min and were sacrificed at 60 min, 1 and 5 days after the procedure. MAIN OUTCOME MEASURES: Renal gene expression profile as determined by microarray analysis. RESULTS: Expression of 420 genes was significantly altered in CPB compared to the sham procedure, and in 407 genes, this was evident in the acute phase (60 min) following CPB. Gene ontology analysis revealed 28 of these genes were involved in inflammatory responses, with high expression of genes downstream of mitogen-activated protein-kinase (MAP-kinase) signalling pathways. Potent inducers identified are from the interleukin-6 cytokine family that consists of interleukin-6 and oncostatin M (OSM), which signal through the gp130-cytokine receptor complex. The plasma concentration of interleukin-6 was hugely increased by CPB as measured by ELISA. Expression of genes downstream of these signalling pathways that lead to production of chemokines, adhesion molecules and molecules involved in coagulative pathways, was upregulated. CONCLUSION: CPB induces an acute and local inflammatory response in the kidney, which might contribute to renal injury. The signalling pathways involved identified by gene expression analysis may represent pharmacological targets to limit renal injury following CPB.

Comparison of Machine Learning Models Including Preoperative, Intraoperative, and Postoperative Data and Mortality After Cardiac Surgery.

Importance: A variety of perioperative risk factors are associated with postoperative mortality risk. However, the relative contribution of routinely collected intraoperative clinical parameters to short-term and long-term mortality remains understudied. Objective: To examine the performance of multiple machine learning models with data from different perioperative periods to predict 30-day, 1-year, and 5-year mortality and investigate factors that contribute to these predictions. Design, Setting, and Participants: In this prognostic study using prospectively collected data, risk prediction models were developed for short-term and long-term mortality after cardiac surgery. Included participants were adult patients undergoing a first-time valve operation, coronary artery bypass grafting, or a combination of both between 1997 and 2017 in a single center, the University Medical Centre Groningen in the Netherlands. Mortality data were obtained in November 2017. Data analysis took place between February 2020 and August 2021. Exposure: Cardiac surgery. Main Outcomes and Measures: Postoperative mortality rates at 30 days, 1 year, and 5 years were the primary outcomes. The area under the receiver operating characteristic curve (AUROC) was used to assess discrimination. The contribution of all preoperative, intraoperative hemodynamic and temperature, and postoperative factors to mortality was investigated using Shapley additive explanations (SHAP) values. Results: Data from 9415 patients who underwent cardiac surgery (median [IQR] age, 68 [60-74] years; 2554 [27.1%] women) were included. Overall mortality rates at 30 days, 1 year, and 5 years were 268 patients (2.8%), 420 patients (4.5%), and 612 patients (6.5%), respectively. Models including preoperative, intraoperative, and postoperative data achieved AUROC values of 0.82 (95% CI, 0.78-0.86), 0.81 (95% CI, 0.77-0.85), and 0.80 (95% CI, 0.75-0.84) for 30-day, 1-year, and 5-year mortality, respectively. Models including only postoperative data performed similarly (30 days: 0.78 [95% CI, 0.73-0.82]; 1 year: 0.79 [95% CI, 0.74-0.83]; 5 years: 0.77 [95% CI, 0.73-0.82]). However, models based on all perioperative data provided less clinically usable predictions, with lower detection rates; for example, postoperative models identified a high-risk group with a 2.8-fold increase in risk for 5-year mortality (4.1 [95% CI, 3.3-5.1]) vs an increase of 11.3 (95% CI, 6.8-18.7) for the high-risk group identified by the full perioperative model. Postoperative markers associated with metabolic dysfunction and decreased kidney function were the main factors contributing to mortality risk. Conclusions and Relevance: This study found that the addition of continuous intraoperative hemodynamic and temperature data to postoperative data was not associated with improved machine learning-based identification of patients at increased risk of short-term and long-term mortality after cardiac operations.

Clinical effectiveness of centrifugal pump to produce pulsatile flow during cardiopulmonary bypass in patients undergoing cardiac surgery.

Although the centrifugal pump has been widely used as a nonpulsatile pump for cardiopulmonary bypass (CPB), little is known about its performance as a pulsatile pump for CPB, especially on its efficacy in producing hemodynamic energy and its clinical effectiveness. We performed a study to evaluate whether the Rotaflow centrifugal pump produces effective pulsatile flow during CPB and whether the pulsatile flow in this setting is clinically effective in adult patients undergoing cardiac surgery. Thirty-two patients undergoing CPB for elective coronary artery bypass grafting were randomly allocated to a pulsatile perfusion group (n = 16) or a nonpulsatile perfusion group (n = 16). All patients were perfused with the Rotaflow centrifugal pump. In the pulsatile group, the centrifugal pump was adjusted to the pulsatile mode (60 cycles/min) during aortic cross-clamping, whereas in the nonpulsatile group, the pump was kept in its nonpulsatile mode during the same period of time. Compared with the nonpulsatile group, the pulsatile group had a higher pulse pressure (P < 0.01) and a fraction higher energy equivalent pressure (EEP, P = 0.058). The net gain of pulsatile flow, represented by the surplus hemodynamic energy (SHE), was found much higher in the CPB circuit than in patients (P < 0.01). Clinically, there was no difference between the pulsatile and nonpulsatile groups with regard to postoperative acute kidney injury, endothelial activation, or inflammatory response. Postoperative organ function and the duration of hospital stay were similar in the two patient groups. In conclusion, pulsatile CPB with the Rotaflow centrifugal pump is associated with a small gain of EEP and SHE, which does not seem to be clinically effective in adult cardiac surgical patients.

Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering.

Critically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values. We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25-56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.

Deep Learning for Identification of Acute Illness and Facial Cues of Illness.

Background: The inclusion of facial and bodily cues (clinical gestalt) in machine learning (ML) models improves the assessment of patients' health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves ML-based classification of acutely ill patients. As in previous research in ML analysis of medical images, simulated or augmented data may be used to assess the usability of clinical gestalt. Objective: To assess whether a deep learning algorithm trained on a dataset of simulated and augmented facial photographs reflecting acutely ill patients can distinguish between healthy and LPS-infused, acutely ill individuals. Methods: Photographs from twenty-six volunteers whose facial features were manipulated to resemble a state of acute illness were used to extract features of illness and generate a synthetic dataset of acutely ill photographs, using a neural transfer convolutional neural network (NT-CNN) for data augmentation. Then, four distinct CNNs were trained on different parts of the facial photographs and concatenated into one final, stacked CNN which classified individuals as healthy or acutely ill. Finally, the stacked CNN was validated in an external dataset of volunteers injected with lipopolysaccharide (LPS). Results: In the external validation set, the four individual feature models distinguished acutely ill patients with sensitivities ranging from 10.5% (95% CI, 1.3-33.1% for the skin model) to 89.4% (66.9-98.7%, for the nose model). Specificity ranged from 42.1% (20.3-66.5%) for the nose model and 94.7% (73.9-99.9%) for skin. The stacked model combining all four facial features achieved an area under the receiver characteristic operating curve (AUROC) of 0.67 (0.62-0.71) and distinguished acutely ill patients with a sensitivity of 100% (82.35-100.00%) and specificity of 42.11% (20.25-66.50%). Conclusion: A deep learning algorithm trained on a synthetic, augmented dataset of facial photographs distinguished between healthy and simulated acutely ill individuals, demonstrating that synthetically generated data can be used to develop algorithms for health conditions in which large datasets are difficult to obtain. These results support the potential of facial feature analysis algorithms to support the diagnosis of acute illness.

Ensemble machine learning prediction and variable importance analysis of 5-year mortality after cardiac valve and CABG operations.

Despite having a similar post-operative complication profile, cardiac valve operations are associated with a higher mortality rate compared to coronary artery bypass grafting (CABG) operations. For long-term mortality, few predictors are known. In this study, we applied an ensemble machine learning (ML) algorithm to 88 routinely collected peri-operative variables to predict 5-year mortality after different types of cardiac operations. The Super Learner algorithm was trained using prospectively collected peri-operative data from 8241 patients who underwent cardiac valve, CABG and combined operations. Model performance and calibration were determined for all models, and variable importance analysis was conducted for all peri-operative parameters. Results showed that the predictive accuracy was the highest for solitary mitral (0.846 [95% CI 0.812-0.880]) and solitary aortic (0.838 [0.813-0.864]) valve operations, confirming that ensemble ML using routine data collected perioperatively can predict 5-year mortality after cardiac operations with high accuracy. Additionally, post-operative urea was identified as a novel and strong predictor of mortality for several types of operation, having a seemingly additive effect to better known risk factors such as age and postoperative creatinine.

Adjunct nitrous oxide normalizes vascular reactivity changes after hemorrhagic shock in mice under isoflurane anesthesia.

BACKGROUND: Hemorrhagic shock is associated with changes in vascular responsiveness that may lead to organ dysfunction and, ultimately, multiple organ dysfunction syndrome. Volatile anesthetics interfere with vasoresponsiveness, which may contribute to organ hypoperfusion. In this study, the authors examined the influence of adjunct nitrous oxide on the vascular responsiveness after short-term hemorrhagic shock under isoflurane anesthesia. METHODS: Spontaneously breathing mice (n = 31, 27.6 +/- 0.31 g) were anesthetized with isoflurane (1.4%) or with isoflurane (1.4%) and adjunct nitrous oxide (66%). Both groups were divided into Sham, Shock, and Resuscitated groups. Vascular reactivity to phenylephrine and acetylcholine and expression of cyclooxygenases were studied in the aorta. RESULTS: In the isoflurane-anesthetized groups, the contractile response to phenylephrine was increased in the Shock as compared with the Sham and Resuscitated groups (Emax = 3.2 +/- 0.4, 1.2 +/- 0.4, and 2.5 +/- 0.5 mN, respectively). Adjunct nitrous oxide increased phenylephrine contraction to a similar level in all three groups. In the Sham isoflurane group, acetylcholine caused a biphasic response: An initial relaxation followed by a contractile response sensitive to cyclooxygenases inhibition by indomethacine. The contractile response was abrogated in the isoflurane-anesthetized groups that underwent shock. In all groups, adjunct nitrous oxide preserved the contractile phase. Shock induced a down-regulation of cyclooxygenases-1, which was normalized by adjunct nitrous oxide. CONCLUSION: Adjunct nitrous oxide attenuates shock-induced changes in vascular reactivity and cyclooxygenases expression of mice under isoflurane anesthesia. This implies that vascular reactive properties during anesthesia in hemorrhagic shock conditions may be influenced by the choice of anesthetics.

A Bayesian Network Analysis of the Diagnostic Process and Its Accuracy to Determine How Clinicians Estimate Cardiac Function in Critically Ill Patients: Prospective Observational Cohort Study.

BACKGROUND: Hemodynamic assessment of critically ill patients is a challenging endeavor, and advanced monitoring techniques are often required to guide treatment choices. Given the technical complexity and occasional unavailability of these techniques, estimation of cardiac function based on clinical examination is valuable for critical care physicians to diagnose circulatory shock. Yet, the lack of knowledge on how to best conduct and teach the clinical examination to estimate cardiac function has reduced its accuracy to almost that of "flipping a coin." OBJECTIVE: The aim of this study was to investigate the decision-making process underlying estimates of cardiac function of patients acutely admitted to the intensive care unit (ICU) based on current standardized clinical examination using Bayesian methods. METHODS: Patient data were collected as part of the Simple Intensive Care Studies-I (SICS-I) prospective cohort study. All adult patients consecutively admitted to the ICU with an expected stay longer than 24 hours were included, for whom clinical examination was conducted and cardiac function was estimated. Using these data, first, the probabilistic dependencies between the examiners' estimates and the set of clinically measured variables upon which these rely were analyzed using a Bayesian network. Second, the accuracy of cardiac function estimates was assessed by comparison to the cardiac index values measured by critical care ultrasonography. RESULTS: A total of 1075 patients were included, of which 783 patients had validated cardiac index measurements. A Bayesian network analysis identified two clinical variables upon which cardiac function estimate is conditionally dependent, namely, noradrenaline administration and presence of delayed capillary refill time or mottling. When the patient received noradrenaline, the probability of cardiac function being estimated as reasonable or good P(ER,G) was lower, irrespective of whether the patient was mechanically ventilated (P[ER,G|ventilation, noradrenaline]=0.63, P[ER,G|ventilation, no noradrenaline]=0.91, P[ER,G|no ventilation, noradrenaline]=0.67, P[ER,G|no ventilation, no noradrenaline]=0.93). The same trend was found for capillary refill time or mottling. Sensitivity of estimating a low cardiac index was 26% and 39% and specificity was 83% and 74% for students and physicians, respectively. Positive and negative likelihood ratios were 1.53 (95% CI 1.19-1.97) and 0.87 (95% CI 0.80-0.95), respectively, overall. CONCLUSIONS: The conditional dependencies between clinical variables and the cardiac function estimates resulted in a network consistent with known physiological relations. Conditional probability queries allow for multiple clinical scenarios to be recreated, which provide insight into the possible thought process underlying the examiners' cardiac function estimates. This information can help develop interactive digital training tools for students and physicians and contribute toward the goal of further improving the diagnostic accuracy of clinical examination in ICU patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT02912624; https://clinicaltrials.gov/ct2/show/NCT02912624.