Abstract 12398: Cardiopulmonary Resuscitation Duration Determines Phenotype of Post-Arrest Brain Injury

Introduction: Patients resuscitated from out-of-hospital cardiac arrest (OHCA) have variable severity of brain injury. Signatures of severe injury on brain imaging and EEG including diffuse cerebral edema and burst suppression with identical bursts (BSIB). Current therapies for these patterns of injury are inadequate and patient outcomes are poor. Hypothesis: We hypothesize distinct phenotypes of brain injury are associated with increasing CPR duration. Methods: We identified from our prospective registry OHCA patients treated between January 2010 to July 2019. We abstracted CPR duration, best neurological examination < 6 hours from OHCA, initial brain CT grey-to-white ratio (GWR), and initial EEG pattern. We defined cerebral edema as GWR <1.20. We defined BSIB according to American Clinical Neurophysiology Society guidelines. We considered four phenotypes on presentation: awake; comatose with neither BSIB nor cerebral edema; BSIB; and cerebral edema. BSIB and cerebral edema were considered as non-mutually exclusive outcomes. We compared duration of CPR across groups using Kruskal-Wallis tests with Bonferroni correction for multiple hypothesis testing. We report the probability of presenting phenotype at the median CPR duration for each group using local regression. Results: We included 2,721 patients, of whom 582 (21%) were awake, 1,428 (52%) had coma without BSIB or edema, 372 (14%) had BSIB and 356 (13%) had cerebral edema. Only 47 (2%) had both BSIB and edema. Median CPR duration was 16 [IQR 8-28] minutes overall. Median CPR duration increased in a stepwise manner across groups: awake 6 [3-12] minutes; comatose without BSIB or edema 16 [9-27] minutes; BSIB 21 [14-30] minutes; cerebral edema 32 [22-46] minutes (all P <0.001). The probability of observing each phenotype at the median CPR duration for each was: awake (0.42); comatose without BSIB or edema (0.72); BSIB (0.34); cerebral edema (0.29). Conclusions: The brain injury phenotype is related to CPR duration, which is a surrogate for severity of ischemic injury. The sequence of most likely brain injury phenotype with progressively longer CPR duration is awake, coma without BSIB or edema, BSIB, and finally cerebral edema.

Target Tracking Using Kalman Filter Based Algorithms

Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.

Visualizing the Costs and Benefits of Correcting P-Values for Multiple Hypothesis Testing in Omics Data

The multiple hypothesis testing problem is inherent in high-throughput quantitative genomic, transcriptomic, proteomic, and other “omic” screens. The correction of p-values for multiple testing is a critical element of quantitative omic data analysis, yet many researchers are unfamiliar with the sensitivity costs and false discovery rate (FDR) benefits of p-value correction. We developed models of quantitative omic experiments, modeled the costs and benefits of p-value correction, and visualized the results with color-coded volcano plots. We developed an R Shiny web application for further exploration of these models which we call the Simulator of P-value Multiple Hypothesis Correction (SIMPLYCORRECT). We modeled experiments in which no analytes were truly differential between the control and test group (all null hypotheses true), all analytes were differential, or a mixture of differential and non-differential analytes were present. We corrected p-values using the Benjamini-Hochberg (BH), Bonferroni, and permutation FDR methods and compared the costs and benefits of each. By manipulating variables in the models, we demonstrated that increasing sample size or decreasing variability can reduce or eliminate the sensitivity cost of p-value correction and that permutation FDR correction can yield more hits than BH-adjusted and even unadjusted p-values in strongly differential data. SIMPLYCORRECT can serve as a tool in education and research to show how p-value adjustment and various parameters affect the results of quantitative omics experiments.