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Breathing patterns (respiratory kinematics) contain vital prognostic information. This dimension of physiology is not captured by conventional vital signs. We sought to determine the feasibility and utility of quantifying respiratory kinematics. Using inertial sensors, we analyzed upper rib, lower rib, and abdominal motion of 108 patients with respiratory symptoms during a hospital encounter (582 two-minute recordings). We extracted 34 features based on an explainable correspondence with well-established breathing patterns. K-means clustering revealed that respiratory kinematics had three dimensions apart from the respiratory rate. We represented these dimensions using respiratory rate variability, respiratory alternans (rib-predominant breaths alternating with abdomen-predominant ones), and recruitment of accessory muscles (increased upper rib excursion). Latent profile analysis of the kinematic measures revealed two profiles consistent with the established clinical constructs of labored and unlabored breathing. In logistic regression, the labored breathing profile improved model discrimination for critical illness beyond the Sequential Organ Failure Assessment (SOFA) score (AUROC 0.77 v/s 0.72; p = 0.02). These findings quantitatively confirm the prior understanding that the respiratory rate alone does not adequately represent the complexity of respiratory kinematics; they demonstrate that high-dimensional signatures of labored breathing can be quantified in routine practice settings, and they can improve predictions of clinical deterioration., Competing Interests: Declarations Competing interests SMG, WBA, RDW, SJR, and JRM are co-inventors in United States Patent Application Serial No. 18/018,469 for “METHODS, SYSTEMS, AND COMPUTER READABLE MEDIA FOR ANALYZING RESPIRATORY KINEMATICS”. Other authors declare that they have no competing interests. Ethical approval This study was funded by the Ivy foundation COVID-19 translational research fund. It was approved by University of Virginia Health Sciences Institutional Review Board (study number 20844)., (© 2024. The Author(s).)

Background: A multicenter RCT showed that displaying a heart rate characteristics index (HRCi) predicting late-onset sepsis reduced mortality for VLBW infants. We aimed to assess whether HRCi display had a differential impact for Black versus White infants., Methods: We performed secondary data analysis of Black and White infants enrolled in the HeRO RCT. We evaluated the predictive performance of the HRCi for infants with Black or White maternal race. Using models adjusted for birth weight, we assessed outcomes and interventions for a race × randomization interaction., Results: Among 2607 infants, Black infants had lower birth weight, gestational age, length of stay, and ventilator days, while sepsis and mortality were similar. The HRCi performed equally for sepsis prediction in Black and White infants. We found no differential effect of randomization by race on sepsis, mortality, antibiotic days, length of stay, or ventilator days. However, there was a differential randomization effect by race for blood cultures per patient: White RR 1.11 (95% CrI 1.04-1.18), Black RR 1.00 (0.93-1.07)., Conclusions: The HRCi performed similarly for sepsis prediction in Black and White infants. Randomization to HRCi display increased blood cultures in White but not in Black infants, while the impact on other outcomes or interventions was similar., Impact: Predictive analytics, such as heart rate characteristics (HRC) monitoring for late-onset neonatal sepsis, should have equal impact among patients of different race. Infants with Black or White maternal race randomized to HRC display had similar outcomes, but randomization to the study arm increased a related clinical intervention, blood cultures, in White but not in Black infants. This study provides evidence of a differential effect of predictive models on clinical care by race. The work will promote consideration and analysis of equity in the implementation of predictive analytics., (© 2023. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

Glioblastoma (GBM) is an aggressive central nervous system tumor with a poor prognosis. This study was conducted to determine any comorbid medical conditions that are associated with survival in GBM. Data were collected from medical records of all patients who presented to VCU Medical Center with GBM between January 2005 and February 2015. Patients who underwent surgery/biopsy were considered for inclusion. Cox proportional hazards regression modeling was performed to assess the relationship between survival and sex, race, and comorbid medical conditions. 163 patients met inclusion criteria. Comorbidities associated with survival on individual-characteristic analysis included: history of asthma (Hazard Ratio [HR]: 2.63; 95% Confidence Interval [CI]: 1.24-5.58; p = 0.01), hypercholesterolemia (HR: 1.95; 95% CI: 1.09-3.50; p = 0.02), and incontinence (HR: 2.29; 95% CI: 0.95-5.57; p = 0.07). History of asthma (HR: 2.22; 95% CI: 1.02-4.83; p = 0.04) and hypercholesterolemia (HR: 1.99; 95% CI: 1.11-3.56; p = 0.02) were associated with shorter survival on multivariable analysis. Surgical patients with GBM who had a prior history of asthma or hypercholesterolemia had significantly higher relative risk for mortality on individual-characteristic and multivariable analyses.