Objective Trajectories of physiomarkers over time can be useful to define phenotypes of disease progression and as predictors of clinical outcomes. The aim of this study was to identify phenotypes of the time course of late-onset sepsis in premature infants in Neonatal Intensive Care Units. Methods We examined the trajectories of a validated continuous physiomarker, abnormal heart rate characteristics, using functional data analysis and clustering techniques. Participants We analyzed continuous heart rate characteristics data from 2989 very low birth weight infants (<1500 grams) from nine NICUs from 2004–2010. Result Despite the relative homogeneity of the patients, we found extreme variability in the physiomarker trajectories. We identified phenotypes that were indicative of seven and 30 day mortality beyond that predicted by individual heart rate characteristics values or baseline demographic information. Conclusion Time courses of a heart rate characteristics physiomarker reveal snapshots of illness patterns, some of which were more deadly than others.

1It is thought that the brain does not simply react to sensory feedback, but rather uses an 2 internal model of the body to predict the consequences of motor commands before sensory 3 feedback arrives. Time-delayed sensory feedback can then be used to correct for the 4 unexpected-perturbations, motor noise, or a moving target. The cerebellum has been implicated 5 in this predictive control process. Here we show that the feedback gain in patients with cerebellar 6 ataxia matches that of healthy subjects, but that patients exhibit substantially more phase lag. 7 This difference is captured by a computational model incorporating a Smith predictor in healthy 8 subjects that is missing in patients, supporting the predictive role of the cerebellum in feedback 9 control. Lastly, we improve cerebellar patients' movement control by altering (phase advancing) 10 the visual feedback they receive from their own self movement in a simplified virtual reality 11 setup.12 16 slow movements accurately. However, feedback is time delayed, and thus it never represents the 17 current state of the body during movement. Because of this, it is thought that we depend on 18 internal models of the body that are built based on prior experience. These models can be rapidly 19 accessed and thus provide a fast internal prediction system to estimate how a movement will 20 unfold, enabling us to better understand where our limbs are at any given moment. This allows 21 us to make fast and accurate movements despite long-latency feedback. 22People with cerebellar damage show a characteristic pattern of incoordination during 24 movement that is referred to as ataxia. When reaching, they make curved movements that miss 25 intended targets and require multiple corrections. This pattern of over-and undershooting a 26 target (dysmetria) and oscillatory corrections (intention tremor) are hallmarks of cerebellar 27 ataxia. One hypothesis that might explain ataxia is that the predictive estimation and control 28 provided by cerebellar circuits is dysfunctional or lost (Wolpert, Miall, and Kawato 1998; R. 29 Chris Miall et al. 2007).30 31 Normally, the estimation of limb state (i.e., position and velocity) benefits from 32 integrating proprioceptive measurements with an internal predictive control model during a 33 movement (Paillard and Brouchon 1974; Adamovich et al. 1998; Fuentes and Bastian 2010). 34However, patients with cerebellar damage do not seem to receive this benefit (N. H. Bhanpuri, 35 Okamura, and Bastian 2013; Weeks, Therrien, and Bastian 2017). Worse, it is possible that their 36 predictive model actually conveys incorrect state information during active movements, which 37 could corrupt rather than enhance proprioceptive estimation of limb state. This difficulty of 38 predicting the future state of limbs during active movement leads to movements that are poorly 39 directed and scaled, requiring ongoing corrections to reach a goal location. 41Patients with cerebellar ataxia may rely more heavily on visual feedback to correct 42 dysmet...

Background: The increasing incidence of bronchopulmonary dysplasia in premature babies may be due in part to immature ventilatory control, contributing to hypoxemia. The latter responds to ventilation and/or oxygen therapy, treatments associated with adverse sequelae. This is an overview of the Prematurity-Related Ventilatory Control Study which aims to analyze the under-utilized cardiorespiratory continuous waveform monitoring data to delineate mechanisms of immature ventilatory control in preterm infants and identify predictive markers. Methods: Continuous ECG, heart rate, respiratory and oxygen saturation data will be collected throughout the NICU stay in 500 infants <29wks gestation across 5 centers. Mild permissive hypercapnia, and hyperoxia and/or hypoxia assessments will be conducted in a subcohort of infants (28 days and 52wks) along with inpatient questionnaires, urine, serum, and DNA samples. Results: Primary outcomes will be respiratory status at 40wks and quantitative measures of immature breathing plotted on a standard curve for infants matched at 36–37wks. Physiologic and/or biologic determinants will be collected to enhance the predictive model linking ventilatory control to outcomes. Conclusion: By incorporating bedside monitoring variables along with biomarkers that predict respiratory outcomes we aim to elucidate individualized cardiopulmonary phenotypes and mechanisms of ventilatory control contributing to adverse respiratory outcomes in premature infants.