Improving postpartum hemorrhage risk prediction using longitudinal electronic medical records.

OBJECTIVE: Postpartum hemorrhage (PPH) remains a leading cause of preventable maternal mortality in the United States. We sought to develop a novel risk assessment tool and compare its accuracy to tools used in current practice. MATERIALS AND METHODS: We used a PPH digital phenotype that we developed and validated previously to identify 6639 PPH deliveries from our delivery cohort (N = 70 948). Using a vast array of known and potential risk factors extracted from electronic medical records available prior to delivery, we trained a gradient boosting model in a subset of our cohort. In a held-out test sample, we compared performance of our model with 3 clinical risk-assessment tools and 1 previously published model. RESULTS: Our 24-feature model achieved an area under the receiver-operating characteristic curve (AUROC) of 0.71 (95% confidence interval [CI], 0.69-0.72), higher than all other tools (research-based AUROC, 0.67 [95% CI, 0.66-0.69]; clinical AUROCs, 0.55 [95% CI, 0.54-0.56] to 0.61 [95% CI, 0.59-0.62]). Five features were novel, including red blood cell indices and infection markers measured upon admission. Additionally, we identified inflection points for vital signs and labs where risk rose substantially. Most notably, patients with median intrapartum systolic blood pressure above 132 mm Hg had an 11% (95% CI, 8%-13%) median increase in relative risk for PPH. CONCLUSIONS: We developed a novel approach for predicting PPH and identified clinical feature thresholds that can guide intrapartum monitoring for PPH risk. These results suggest that our model is an excellent candidate for prospective evaluation and could ultimately reduce PPH morbidity and mortality through early detection and prevention.

A comprehensive digital phenotype for postpartum hemorrhage.

OBJECTIVE: We aimed to establish a comprehensive digital phenotype for postpartum hemorrhage (PPH). Current guidelines rely primarily on estimates of blood loss, which can be inaccurate and biased and ignore complementary information readily available in electronic medical records (EMR). Inaccurate and incomplete phenotyping contributes to ongoing challenges in tracking PPH outcomes, developing more accurate risk assessments, and identifying novel interventions. MATERIALS AND METHODS: We constructed a cohort of 71 944 deliveries from the Mount Sinai Health System. Estimates of postpartum blood loss, shifts in hematocrit, administration of uterotonics, surgical interventions, and diagnostic codes were combined to identify PPH, retrospectively. Clinical features were extracted from EMRs and mapped to common data models for maximum interoperability across hospitals. Blinded chart review was done by a physician on a subset of PPH and non-PPH patients and performance was compared to alternate PPH phenotypes. PPH was defined as clinical diagnosis of postpartum hemorrhage documented in the patient's chart upon chart review. RESULTS: We identified 6639 PPH deliveries (9% prevalence) using our phenotype-more than 3 times as many as using blood loss alone (N = 1,747), supporting the need to incorporate other diagnostic and intervention data. Chart review revealed our phenotype had 89% accuracy and an F1-score of 0.92. Alternate phenotypes were less accurate, including a common blood loss-based definition (67%) and a previously published digital phenotype (74%). CONCLUSION: We have developed a scalable, accurate, and valid digital phenotype that may be of significant use for tracking outcomes and ongoing clinical research to deliver better preventative interventions for PPH.

Excitatory/Inhibitory Responses Shape Coherent Neuronal Dynamics Driven by Optogenetic Stimulation in the Primate Brain.

Coherent neuronal dynamics play an important role in complex cognitive functions. Optogenetic stimulation promises to provide new ways to test the functional significance of coherent neural activity. However, the mechanisms by which optogenetic stimulation drives coherent dynamics remain unclear, especially in the nonhuman primate brain. Here, we perform computational modeling and experiments to study the mechanisms of optogenetic-stimulation-driven coherent neuronal dynamics in three male nonhuman primates. Neural responses arise from stimulation-evoked, temporally dynamic excitatory (E) and inhibitory (I) activity. Spiking activity is more likely to occur during E/I imbalances. Thus the relative difference in the driven E and I responses precisely controls spike timing by forming a brief time interval of increased spiking likelihood. Experimental results agree with parameter-dependent predictions from the computational models. These results demonstrate that optogenetic stimulation driven coherent neuronal dynamics are governed by the temporal properties of E/I activity. Transient imbalances in excitatory and inhibitory activity may provide a general mechanism for generating coherent neuronal dynamics without the need for an oscillatory generator.SIGNIFICANCE STATEMENT We examine how coherent neuronal dynamics arise from optogenetic stimulation in the primate brain. Using computational models and experiments, we demonstrate that coherent spiking and local field potential activity is generated by stimulation-evoked responses of excitatory and inhibitory activity in networks, extending the growing literature on neuronal dynamics. These responses create brief time intervals of increased spiking tendency and are consistent with previous observations in the literature that balanced excitation and inhibition controls spike timing, suggesting that optogenetic-stimulation-driven coherence may arise from intrinsic E/I balance. Most importantly, our results are obtained in nonhuman primates and thus will play a leading role in driving the use of causal manipulations with optogenetic tools to study higher cognitive functions in the primate brain.

Competition for visual selection in the oculomotor system.

During behavior, the oculomotor system is tasked with selecting objects from an ever-changing visual field and guiding eye movements to these locations. The attentional priority given to visual targets during selection can be strongly influenced by external stimulus properties or internal goals based on previous experience. Although these exogenous and endogenous drivers of selection are known to operate across partially overlapping timescales, the form of their interaction over time remains poorly understood. Using a novel choice task that simultaneously manipulates stimulus- and goal-driven attention, we demonstrate that exogenous and endogenous attentional biases change linearly as a function of time after stimulus onset and have an additive influence on the visual selection process in rhesus macaques (Macaca mulatta). We present a family of computational models that quantify this interaction over time and detail the history dependence of both processes. The computational models reveal the existence of a critical 140-180 ms attentional "switching" time, when stimulus- and goal-driven processes simultaneously favor competing visual targets. These results suggest that the brain uses a linear sum of attentional biases to guide visual selection.