Sickle cell disease (SCD) is an inherited hemoglobinopathy characterized by polymerization of hemoglobin S upon deoxygenation that results in the formation of rigid sickled-shaped red blood cells that can occlude the microvasculature, which leads to sudden onsets of pain. The severity of vaso-occlusive crises (VOC) is quite variable among patients, which is not fully explained by their genetic and biological profiles. The mechanism that initiates the transition from steady state to VOC remains unknown, as is the role of clinically reported triggers such as stress, cold and pain. The rate of hemoglobin S polymerization after deoxygenation is an important determinant of vaso-occlusion. Similarly, the microvascular blood flow rate plays a critical role as fast-moving red blood cells are better able to escape the microvasculature before polymerization of deoxy-hemoglobin S causes the red cells to become rigid and lodge in small vessels. The role of the autonomic nervous system (ANS) activity in VOC initiation and propagation has been underestimated considering that the ANS is the major regulator of microvascular blood flow and that most triggers of VOC can alter the autonomic balance. Here, we will briefly review the evidence supporting the presence of ANS dysfunction in SCD, its implications in the onset of VOC, and how differences in autonomic vasoreactivity might potentially contribute to variability in VOC severity.

Rationale: The contribution of ventilatory control to the pathogenesis of obstructive sleep apnea (OSA) in preterm-born children is unknown. Objectives: To characterize phenotypes of ventilatory control that are associated with the presence of OSA in preterm-born children during early childhood. Methods: Preterm- and term-born children without comorbid conditions were enrolled. They were categorized into an OSA group and a non-OSA group on the basis of polysomnography. Measurements and Main Results: Loop gain, controller gain, and plant gain, reflecting ventilatory instability, chemoreceptor sensitivity, and blood gas response to a change in ventilation, respectively, were estimated from spontaneous sighs identified during polysomnography. Cardiorespiratory coupling, a measure of brainstem maturation, was estimated by measuring the interval between inspiration and the preceding electrocardiogram R-wave. Cluster analysis was performed to develop phenotypes based on controller gain, plant gain, cardiorespiratory coupling, and gestational age. The study included 92 children, 63 of whom were born preterm (41% OSA) and 29 of whom were born at term (48% OSA). Three phenotypes of ventilatory control were derived with risks for OSA being 8%, 47%, and 77% in clusters 1, 2, and 3, respectively. There was a stepwise decrease in controller gain and an increase in plant gain from clusters 1 to 3. Children in cluster 1 had significantly higher cardiorespiratory coupling and gestational age than clusters 2 and 3. No difference in loop gain was found between clusters. Conclusions: The risk for OSA could be stratified according to controller gain, plant gain, cardiorespiratory coupling, and gestational age. These findings could guide personalized care for children at risk for OSA.

Although respiratory sinus arrhythmia and blood pressure variability have been investigated extensively, there have been far fewer studies of the respiratory modulation of peripheral blood flow in humans. Existing studies have been based primarily on noninvasive measurements using digit photoplethysmography and laser-Doppler flowmetry. The cumulative knowledge derived from these studies suggests that respiration can contribute to fluctuations in peripheral blood flow and volume through a combination of mechanical, hemodynamic, and neural mechanisms. However, the most convincing evidence suggests that the sympathetic nervous system plays the predominant role under normal, resting conditions. This mini-review provides a consolidation and interpretation of the key findings reported in this topical area. Given the need to extract dynamic information from noninvasive measurements under largely “closed-loop” conditions, we propose that the application of analytical tools based on systems theory and mathematical modeling can be of great utility in future studies. In particular, we present an example of how the transfer relation linking respiration to peripheral vascular conductance can be derived using measurements recorded during spontaneous breathing, spontaneous sighs, and ventilator-induced sighs.

In sickle cell disease (SCD), prolonged capillary transit times, resulting from reduced peripheral blood flow, increase the likelihood of rigid red cells entrapment in the microvasculature, predisposing to vaso-occlusive crisis. Since changes in peripheral flow are mediated by the autonomic nervous system (ANS), we tested the hypothesis that the cardiac and peripheral vascular responses to head-up tilt (HUT) are abnormal in SCD. Heart rate, respiration, noninvasive continuous blood pressure and finger photoplethysmogram (PPG) were monitored before, during, and after HUT in SCD, anemic controls and healthy subjects. Percent increase in heart rate from baseline was used to quantify cardiac ANS response, while percent decrease in PPG amplitude represented degree of peripheral vasoconstriction. After employing cluster analysis to determine threshold levels, the HUT responses were classified into 4 phenotypes: (CP) increased heart rate and peripheral vasoconstriction; (C) increased heart rate only; (P) peripheral vasoconstriction only; and (ST) subthreshold cardiac and peripheral vascular responses. Multinomial logistic regression (MLR) was used to relate these phenotypic responses to various parameters representing blood properties and baseline cardiovascular activity. The most common phenotypic response, CP, was found in 82% of non-SCD subjects, including those with chronic anemia. In contrast, 70% of SCD subjects responded abnormally to HUT: C-phenotype =22%, P-phenotype = 37%, or ST-phenotype = 11%. MLR revealed that the HUT phenotypes were significantly associated with baseline cardiac parasympathetic activity, baseline peripheral vascular variability, hemoglobin level and SCD diagnosis. Low parasympathetic activity at baseline dramatically increased the probability of belonging to the P-phenotype in SCD subjects, even after adjusting for hemoglobin level, suggesting a characteristic autonomic dysfunction that is independent of anemia. Further analysis using a mathematical model of heart rate variability revealed that the low parasympathetic activity in P-phenotype SCD subjects was due to impaired respiratory-cardiac coupling rather than reduced cardiac baroreflex sensitivity. By having strong peripheral vasoconstriction without compensatory cardiac responses, P-phenotype subjects may be at increased risk for vaso-occlusive crisis. The classification of autonomic phenotypes based on HUT response may have potential use for guiding therapeutic interventions to alleviate the risk of adverse outcomes in SCD.

Transient increases in peripheral vasoconstriction frequently occur in obstructive sleep apnea and periodic leg movement disorder, both of which are common in sickle cell disease (SCD). These events reduce microvascular blood flow and increase the likelihood of triggering painful vaso-occlusive crises (VOC) that are the hallmark of SCD. We recently reported a significant association between the magnitude of vasoconstriction, inferred from the finger photoplethysmogram (PPG) during sleep, and the frequency of future VOC in 212 children with SCD. In this study, we present an improved predictive model of VOC frequency by employing a two-level stacking machine learning (ML) model that incorporates detailed features extracted from the PPG signals in the same database. The first level contains seven different base ML algorithms predicting each subject's pain category based on the input PPG characteristics and other clinical information, while the second level is a meta model which uses the inputs to the first-level model along with the outputs of the base models to produce the final prediction. Model performance in predicting future VOC was significantly higher than in predicting VOC prior to each sleep study (F1-score of 0.43 vs. 0.35, p-value <0.0001), consistent with our hypothesis of a causal relationship between vasoconstriction and future pain incidence, rather than past pain leading to greater propensity for vasoconstriction. The model also performed much better than our previous conventional statistical model (F1 = 0.33), as well as all other algorithms that used only the base-models for predicting VOC without the second tier meta model. The modest F1 score of the present predictive model was due in part to the relatively small database with substantial imbalance (176:36) between low-pain and high-pain subjects, as well as other factors not captured by the sleep data alone. This report represents the first attempt ever to use non-invasive finger PPG measurements during sleep and a ML-based approach to predict increased propensity for VOC crises in SCD. The promising results suggest the future possibility of embedding an improved version of this model in a low-cost wearable system to assist clinicians in managing long-term therapy for SCD patients.