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We tested the hypothesis that routine monitoring data could describe a detailed and distinct pathophysiologic phenotype of impending hypoglycemia in adult ICU patients. DESIGN:Retrospective analysis leading to model development and validation. SETTING:All ICU admissions wherein patients received insulin therapy during a 4-year period at the University of Virginia Medical Center. Each ICU was equipped with continuous physiologic monitoring systems whose signals were archived in an electronic data warehouse along with the entire medical record. PATIENTS:Eleven thousand eight hundred forty-seven ICU patient admissions. INTERVENTIONS:The primary outcome was hypoglycemia, defined as any episode of blood glucose less than 70 mg/dL where 50% dextrose injection was administered within 1 hour. We used 61 physiologic markers (including vital signs, laboratory values, demographics, and continuous cardiorespiratory monitoring variables) to inform the model. MEASUREMENTS AND MAIN RESULTS:Our dataset consisted of 11,847 ICU patient admissions, 721 (6.1%) of which had one or more hypoglycemic episodes. Multivariable logistic regression analysis revealed a pathophysiologic signature of 41 independent variables that best characterized ICU hypoglycemia. The final model had a cross-validated area under the receiver operating characteristic curve of 0.83 (95% CI, 0.78-0.87) for prediction of impending ICU hypoglycemia. We externally validated the model in the Medical Information Mart for Intensive Care III critical care dataset, where it also demonstrated good performance with an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77-0.81). CONCLUSIONS:We used data from a large number of critically ill inpatients to develop and externally validate a predictive model of impending ICU hypoglycemia. Future steps include incorporating this model into a clinical decision support system and testing its effects in a multicenter randomized controlled clinical trial.

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Objectives To demonstrate how heart rate fragmentation gives novel insights into non-autonomic mechanisms of beat-to-beat variability in cycle length, and predicts survival of cardiology clinic patients, over and above traditional clinical risk factors and measures of heart rate variability. Approach: We studied 2893 patients seen by cardiologists with clinical data including 24-hour Holter monitoring. Novel measures of heart rate fragmentation alongside canonical time and frequency domain measures of heart rate variability, as well as an existing local dynamics score were calculated. A proportional hazards model was utilized to relate the results to survival. Main results: The novel heart rate fragmentation measures were validated and characterized with respect to the effects of age, ectopy and atrial fibrillation. Correlations between parameters were determined. Critically, heart rate fragmentation results could not be accounted for by undersampling respiratory sinus arrhythmia. Increased heart rate fragmentation was associated with poorer survival (p ≪ 0.01 in the univariate model). In multivariable analyses, increased heart rate fragmentation and more abnormal local dynamics (p 0.045), along with increased clinical risk factors (age (p ≪ 0.01), tobacco use (p ≪ 0.01) and history of heart failure (p 0.019)) and lower low- to high-frequency ratio (p 0.022) were all independent predictors of 2-year mortality. Significance: Analysis of continuous ECG data with heart rate fragmentation indices yields information regarding non-autonomic control of beat-to-beat variability in cycle length that is independent of and additive to established parameters for investigating heart rate variability, and predicts mortality in concert with measures of local dynamics, frequency content of heart rate, and clinical risk factors.

BACKGROUND: Sedation is recommended to optimize neuroprotection in neonates with hypoxic ischemic encephalopathy (HIE) undergoing therapeutic hypothermia (TH). Dexmedetomidine is an alternative agent to opioids, which are commonly used but have adverse effects. Both TH and dexmedetomidine can cause bradycardia. In this study, we describe our experience with dexmedetomidine and fentanyl in neonates undergoing TH for HIE, with a focus on heart rate (HR). METHODS: We performed a retrospective chart review from 2011–2019 at a level IV NICU comparing sedation with dexmedetomidine (n = 14), fentanyl (n = 120), or both (n = 32) during TH for HIE. HR trends were compared based on sedation and gestational age. Neonates were included if they underwent TH and received sedation and were excluded if cooling was initiated past 24 hours(h) of life or required ECMO. RESULTS: Of the 166 neonates included, 46 received dexmedetomidine, 14 as monotherapy and 32 in combination with fentanyl. Mean hourly HR from 12–36 h after birth was significantly lower for infants on dexmedetomidine versus fentanyl monotherapy (91±9 vs. 103±11 bpm, p <  0.002). Dexmedetomidine was decreased or discontinued in 22 (47.8%) neonates, most commonly due to inadequate sedation with a low HR. Lower gestational age was associated with higher HR but no significant difference in dexmedetomidine-related HR trends. CONCLUSIONS: Despite an association with lower HR, dexmedetomidine may be successfully used in neonates with HIE undergoing TH. Implementation of a standardized protocol may facilitate dexmedetomidine titration in this population.