Prevalence and Temporal Distribution of Extrasystoles in Septic ICU Patients: The Feasibility of Predicting Fluid Responsiveness Using Extrasystoles

Background. Extrasystoles may be useful for predicting the response to fluid therapy in hemodynamically unstable patients but their prevalence is unknown. The aim of this study was to estimate the availability of extrasystoles in intensive care unit patients diagnosed with sepsis. The study aim was not to validate the fluid responsiveness prediction ability of extrasystoles. Methods. Twenty-four-hour ECG recordings from a convenience sample of 50 patients diagnosed with sepsis were extracted from the MIMIC-II waveform database, and ECGs were visually examined for correct QRS complex detection. Custom-made algorithms identified potential extrasystoles based on RR intervals. Two raters visually confirmed or rejected the potential extrasystoles and then classified them as ventricular, supraventricular, or unknown origin. Extrasystole availability was calculated as extrasystolic coverage for each 24 h ECG recording, that is, the percentage of the 24 h recording where an extrasystole had occurred in the preceding 30 minutes. Results. Mean extrasystolic coverage was 53.3% (confidence interval: [42.8; 63.6]%) and ventricular extrasystolic coverage was 21.4 [13.5; 29.8]%. Interrater reliability was strong for confirming/rejecting extrasystoles. Conclusions. Extrasystoles are available for fluid responsiveness prediction in septic patients in about half of the time. With this extrasystolic availability, we believe the method to be considered for clinical use, provided that future studies validate the method’s fluid responsiveness prediction ability

Temporal discounting of reward and the cost of time in motor control

Why do movements take a characteristic amount of time, and why do diseases that affect the reward system alter control of movements? Suppose that the purpose of any movement is to position our body in a more rewarding state. People and other animals discount future reward as a hyperbolic function of time. Here, we show that across populations of people and monkeys there is a correlation between discounting of reward and control of movements. We consider saccadic eye movements and hypothesize that duration of a movement is equivalent to a delay of reward. The hyperbolic cost of this delay not only accounts for kinematics of saccades in adults, it also accounts for the faster saccades of children, who temporally discount reward more steeply. Our theory explains why saccade velocities increase when reward is elevated, and why disorders in the encoding of reward, for example in Parkinson's disease and schizophrenia, produce changes in saccade. We show that delay of reward elevates the cost of saccades, reducing velocities. Finally, we consider coordinated movements that include motion of eyes and head and find that their kinematics is also consistent with a hyperbolic, reward-dependent cost of time. Therefore, each voluntary movement carries a cost because its duration delays acquisition of reward. The cost depends on the value that the brain assigns to stimuli, and the rate at which it discounts this value in time. The motor commands that move our eyes reflect this cost of time.status: publishe

Temporal discounting of reward and the cost of time in motor control.

Why do movements take a characteristic amount of time, and why do diseases that affect the reward system alter control of movements? Suppose that the purpose of any movement is to position our body in a more rewarding state. People and other animals discount future reward as a hyperbolic function of time. Here, we show that across populations of people and monkeys there is a correlation between discounting of reward and control of movements. We consider saccadic eye movements and hypothesize that duration of a movement is equivalent to a delay of reward. The hyperbolic cost of this delay not only accounts for kinematics of saccades in adults, it also accounts for the faster saccades of children, who temporally discount reward more steeply. Our theory explains why saccade velocities increase when reward is elevated, and why disorders in the encoding of reward, for example in Parkinson's disease and schizophrenia, produce changes in saccade. We show that delay of reward elevates the cost of saccades, reducing velocities. Finally, we consider coordinated movements that include motion of eyes and head and find that their kinematics is also consistent with a hyperbolic, reward-dependent cost of time. Therefore, each voluntary movement carries a cost because its duration delays acquisition of reward. The cost depends on the value that the brain assigns to stimuli, and the rate at which it discounts this value in time. The motor commands that move our eyes reflect this cost of time

Early prediction of hemodynamic interventions in the intensive care unit using machine learning

Abstract Background Timely recognition of hemodynamic instability in critically ill patients enables increased vigilance and early treatment opportunities. We develop the Hemodynamic Stability Index (HSI), which highlights situational awareness of possible hemodynamic instability occurring at the bedside and to prompt assessment for potential hemodynamic interventions. Methods We used an ensemble of decision trees to obtain a real-time risk score that predicts the initiation of hemodynamic interventions an hour into the future. We developed the model using the eICU Research Institute (eRI) database, based on adult ICU admissions from 2012 to 2016. A total of 208,375 ICU stays met the inclusion criteria, with 32,896 patients (prevalence = 18%) experiencing at least one instability event where they received one of the interventions during their stay. Predictors included vital signs, laboratory measurements, and ventilation settings. Results HSI showed significantly better performance compared to single parameters like systolic blood pressure and shock index (heart rate/systolic blood pressure) and showed good generalization across patient subgroups. HSI AUC was 0.82 and predicted 52% of all hemodynamic interventions with a lead time of 1-h with a specificity of 92%. In addition to predicting future hemodynamic interventions, our model provides confidence intervals and a ranked list of clinical features that contribute to each prediction. Importantly, HSI can use a sparse set of physiologic variables and abstains from making a prediction when the confidence is below an acceptable threshold. Conclusions The HSI algorithm provides a single score that summarizes hemodynamic status in real time using multiple physiologic parameters in patient monitors and electronic medical records (EMR). Importantly, HSI is designed for real-world deployment, demonstrating generalizability, strong performance under different data availability conditions, and providing model explanation in the form of feature importance and prediction confidence