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OBJECTIVE: The evaluation of an infant for suspected sepsis often includes obtaining blood for laboratory tests. The shortcomings of the current practice are that the infant has to appear clinically ill for the diagnosis to be entertained, and the conventional laboratory tests are invasive. We have found that the clinical diagnosis of neonatal sepsis is preceded by abnormal heart rate characteristics (HRC) of reduced variability and transient decelerations, and we have devised a predictive HRC monitoring strategy based on multivariable logistic regression analysis that was developed at one tertiary care NICU and validated at another. We hypothesized that HRC monitoring, which is continuous and noninvasive, might be an adjunct to conventional laboratory tests in the diagnosis of neonatal sepsis. The objective of this study was to test the hypothesis that HRC monitoring adds information to conventional laboratory tests in diagnosing neonatal sepsis. METHODS: We prospectively collected heart rate data in 678 consecutive infants who stayed >7 days in the University of Virginia NICU from July 1999 to July 2003. We prospectively measured HRC and noted 149 episodes of sepsis with positive blood cultures for which data were available in 137. We obtained all laboratory test results for ratio of immature to total neutrophil count, white blood cell count, glucose, platelet count, HCO3, arterial partial pressure of carbon dioxide, and pH. We tested hypotheses using multivariable logistic regression modeling adjusted for repeated measures. RESULTS: We found that the HRC index, which was available 92% of the time, was highly significantly associated with sepsis (receiver-operating characteristic [ROC] area: 0.73). The ratio of immature to total neutrophil count, white blood cell count (available 4%-8% of the time, usually around the time of suspected sepsis), and blood glucose and pH (available 28% and 38% of the time) were also significantly associated with sepsis (ROC area: 0.75). HRC and laboratory values added independent information to each other, and a predictive model using all significant variables had ROC area of 0.82. CONCLUSIONS: HRC monitoring adds independent information to laboratory tests in the diagnosis of culture-positive neonatal sepsis. [ABSTRACT FROM AUTHOR]

Abstract Background The purpose of this study was to examine the career decision-making process of International Medical Graduates (IMGs). There are two main types of IMGs who apply for licensure in Canada. Canadian International Medical Graduates (CIMGs) were Canadian citizens before leaving to study medicine in a foreign country, in comparison to those non-CIMGs who had studied medicine in a foreign country before immigrating to Canada. Given that their motivations for becoming a doctor in Canada may differ, it is important to examine how they decided to become a doctor for each group separately. Methods A total of 46 IMGs participated in a semi-structured interview - 20 were CIMGs and 26 were non-CIMGs. Results An iterative process of content analysis was conducted to categorize responses from five open-ended questions according to the Ego Identity Statuses theory of career decision-making. Event contingency analysis identified a significant difference between CIMGs and non-CIMGs, Fisher’s exact test (1) = 18.79, p Conclusion About half of the Canadian citizens who had studied medicine in a foreign country had explored different careers before making a commitment to medicine, and half had not. No IMGs, however, who studied medicine in another country before immigrating to Canada, had explored various career opportunities before selecting medicine.

Background: The Congestive heart failure, Hypertension, Age ≥ 75 years, Diabetes mellitus, Stroke (CHADS(2)) score is used to predict the need for oral anticoagulation for stroke prophylaxis in patients with atrial fibrillation. The Congestive heart failure, Hypertension, Age ≥ 75 years, Diabetes mellitus, Stroke, Vascular disease, Age 65-74 years, Sex category (CHA(2)DS(2)-VASc) schema has been proposed as an improvement. Our objective is to determine how adoption of the CHA(2)DS(2)-VASc score alters anticoagulation recommendations.Methods: Between 2004 and 2008, 1664 patients were seen at the University of Virginia Atrial Fibrillation Center. We calculated the CHADS(2) and CHA(2)DS(2)-VASc scores for each patient. The 2006 American College of Cardiology/American Heart Association/Heart Rhythm Society guidelines for atrial fibrillation management were used to determine anticoagulation recommendations based on the CHADS(2) score, and the 2010 European Society of Cardiology guidelines were used to determine anticoagulation recommendations based on the CHA(2)DS(2)-VASc score.Results: The average age was 62±13 years, and 34% were women. Average CHADS(2) and CHA(2)DS(2)-VASc scores were 1.1±1.1 and 1.8±1.5, respectively (P<.0001). The CHADS(2) score classified 33% as requiring oral anticoagulation. The CHA(2)DS(2)-VASc score classified 53% as requiring oral anticoagulation. For women, 31% had a CHADS(2) score ≥ 2, but 81% had a CHA(2)DS(2)-VASc score ≥ 2 (P = .0001). Also, 32% of women with a CHADS(2) score of zero had a CHA(2)DS(2)-VASc score ≥ 2. For men, 25% had a CHADS(2) score ≥ 2, but 39% had a CHA(2)DS(2)-VASc score ≥ 2 (P<.0001).Conclusion: Compared with the CHADS(2) score, the CHA(2)DS(2)-VASc score more clearly defines anticoagulation recommendations. Many patients, particularly older women, are redistributed from the low- to high-risk categories. [ABSTRACT FROM AUTHOR]

Background: A pulse oximetry warning system (POWS) to analyze heart rate and oxygen saturation data and predict risk of sepsis was developed for very low birth weight (VLBW) infants., Methods: We determined the clinical correlates and positive predictive value (PPV) of a high POWS score in VLBW infants. In a two-NICU retrospective study, we identified times when POWS increased above 6 (POWS spike). We selected an equal number of control times, matched for gestational and chronologic age. We reviewed records for infection and non-infection events around POWS spikes and control times. We calculated the frequencies and PPV of a POWS spike for infection or another significant event., Results: We reviewed 111 POWS spike times and 111 control times. Days near POWS spikes were more likely to have clinical events than control days (77% vs 50%). A POWS spike had 52% PPV for suspected or confirmed infection and 77% for any clinically significant event. Respiratory deterioration occurred near more POWS spike times than control times (34% vs 18%)., Conclusions: In a retrospective cohort, infection and respiratory deterioration were common clinical correlations of a POWS spike. POWS had a high PPV for significant clinical events with or without infection., Impact: There are significant gaps in understanding the best approach to implementing continuous sepsis prediction models so that clinicians can best respond to early signals of deterioration. Infection and respiratory deterioration were common clinical events identified at the time of a high predictive model score. Understanding the clinical correlates of a high-risk early warning score will inform future implementation efforts., (© 2024. The Author(s).)

Purpose: Late alopecia, defined as incomplete hair regrowth > 6 months following cytotoxic chemotherapy or > 6 months from initiation of endocrine therapy, negatively impacts quality of life and may affect dose intensity of adjuvant therapy. This study investigates the effect of oral minoxidil in women with chemotherapy and/or endocrine therapy-induced late alopecia., Methods: The rate of clinical response was assessed by standardized photography and quantitated with trichoscopy., Results: Two hundred and sixteen patients (mean age 57.8 ± 13.7) were included. The most common cancer diagnosis was breast, in 170 patients (79.1%). Alopecia developed after chemotherapy in 31 (14.4%) patients, endocrine monotherapy in 65 (30.1%) patients, and chemotherapy followed by endocrine therapy in 120 (55.6%) patients. In 119 patients, standardized photography assessments were used to determine clinical change in alopecia after a median of 105 (IQR = 70) days on oral minoxidil and revealed improvement in 88 (74%) patients. Forty-two patients received quantitative trichoscopic assessments at baseline and at follow-up after a median of 91 (IQR = 126) days on oral minoxidil. Patients had clinically and statistically significant increases in frontal hair shaft density (from 124.2 hairs/cm 2 at initial to 153.2 hairs/cm 2 at follow-up assessment, p = 0.008) and occipital shaft density (from 100.3 hairs/cm 2 at initial to 123.5 hairs/cm 2 at follow-up assessment. p = 0.004). No patients discontinued oral minoxidil due to adverse events., Conclusions: Overall, oral minoxidil was well tolerated by patients and may benefit both frontal and occipital late alopecia in cancer survivors treated with cytotoxic and/or endocrine therapy by increasing hair shaft and follicle density., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: The objective of this study was to examine the association of cardiorespiratory events, including apnea, periodic breathing, intermittent hypoxemia (IH), and bradycardia, with late-onset sepsis for extremely preterm infants (<29 weeks of gestational age) on vs off invasive mechanical ventilation., Study Design: This is a retrospective analysis of data from infants enrolled in Pre-Vent (ClinicalTrials.gov identifier NCT03174301), an observational study in 5 level IV neonatal intensive care units. Clinical data were analyzed for 737 infants (mean gestational age: 26.4 weeks, SD 1.71). Monitoring data were available and analyzed for 719 infants (47 512 patient-days); of whom, 109 had 123 sepsis events. Using continuous monitoring data, we quantified apnea, periodic breathing, bradycardia, and IH. We analyzed the relationships between these daily measures and late-onset sepsis (positive blood culture >72 hours after birth and ≥5-day antibiotics)., Results: For infants not on a ventilator, apnea, periodic breathing, and bradycardia increased before sepsis diagnosis. During times on a ventilator, increased sepsis risk was associated with longer events with oxygen saturation <80% (IH80) and more bradycardia events before sepsis. IH events were associated with higher sepsis risk but did not dynamically increase before sepsis, regardless of ventilator status. A multivariable model including postmenstrual age, cardiorespiratory variables (apnea, periodic breathing, IH80, and bradycardia), and ventilator status predicted sepsis with an area under the receiver operator characteristic curve of 0.783., Conclusion: We identified cardiorespiratory signatures of late-onset sepsis. Longer IH events were associated with increased sepsis risk but did not change temporally near diagnosis. Increases in bradycardia, apnea, and periodic breathing preceded the clinical diagnosis of sepsis., Competing Interests: Declaration of Competing Interest Some authors have financial conflicts of interest. JRM and DEL own stock in Medical Prediction Sciences Corporation. JRM is a consultant for Nihon Kohden Digital Health Solutions, proceeds donated to the University of Virginia. ZAV is a consultant for Medtronic. All other authors have no financial conflicts to disclose. No authors have any nonfinancial conflicts of interest to disclose. Funding Support: We acknowledge the following NIH grants for funding the work presented in this manuscript. University of Virginia (NCT03174301): U01 HL133708, K23 HD097254, HL133708-05S1. Case Western Reserve University: U01 HL133643. Northwestern University: U01 HL133704. University of Alabama at Birmingham: U01 HL133536, K23 HL157618. University of Miami: U01 HL133689. Washington University: U01 HL133700, F., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective. Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from>700extremely preterm infants to identify physiologic features that predict respiratory outcomes. Approach . We calculated a subset of 33 HCTSA features on>7 M 10 min windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on>3500HCTSA algorithms. We hypothesized that the best HCTSA algorithms would compare favorably to optimal PreVent physiologic predictor IH90&#95;DPE (duration per event of intermittent hypoxemia events below 90%). Main Results. The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90&#95;DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90&#95;DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850). Significance . These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90&#95;DPE as an optimal predictor of respiratory outcomes., (Creative Commons Attribution license.)

Background: In extremely preterm infants, persistence of cardioventilatory events is associated with long-term morbidity. Therefore, the objective was to characterize physiologic growth curves of apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants during the first few months of life., Methods: The Prematurity-Related Ventilatory Control study included 717 preterm infants <29 weeks gestation. Waveforms were downloaded from bedside monitors with a novel sharing analytics strategy utilized to run software locally, with summary data sent to the Data Coordinating Center for compilation., Results: Apnea, periodic breathing, and intermittent hypoxemia events rose from day 3 of life then fell to near-resolution by 8-12 weeks of age. Apnea/intermittent hypoxemia were inversely correlated with gestational age, peaking at 3-4 weeks of age. Periodic breathing was positively correlated with gestational age peaking at 31-33 weeks postmenstrual age. Females had more periodic breathing but less intermittent hypoxemia/bradycardia. White infants had more apnea/periodic breathing/intermittent hypoxemia. Infants never receiving mechanical ventilation followed similar postnatal trajectories but with less apnea and intermittent hypoxemia, and more periodic breathing., Conclusions: Cardioventilatory events peak during the first month of life but the actual postnatal trajectory is dependent on the type of event, race, sex and use of mechanical ventilation., Impact: Physiologic curves of cardiorespiratory events in extremely preterm-born infants offer (1) objective measures to assess individual patient courses and (2) guides for research into control of ventilation, biomarkers and outcomes. Presented are updated maturational trajectories of apnea, periodic breathing, intermittent hypoxemia, and bradycardia in 717 infants born <29 weeks gestation from the multi-site NHLBI-funded Pre-Vent study. Cardioventilatory events peak during the first month of life but the actual postnatal trajectory is dependent on the type of event, race, sex and use of mechanical ventilation. Different time courses for apnea and periodic breathing suggest different maturational mechanisms., (© 2023. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

Rationale: Immature control of breathing is associated with apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants. However, it is not clear if such events independently predict worse respiratory outcome. Objectives: To determine if analysis of cardiorespiratory monitoring data can predict unfavorable respiratory outcomes at 40 weeks postmenstrual age (PMA) and other outcomes, such as bronchopulmonary dysplasia at 36 weeks PMA. Methods: The Prematurity-related Ventilatory Control (Pre-Vent) study was an observational multicenter prospective cohort study including infants born at <29 weeks of gestation with continuous cardiorespiratory monitoring. The primary outcome was either "favorable" (alive and previously discharged or inpatient and off respiratory medications/O 2 /support at 40 wk PMA) or "unfavorable" (either deceased or inpatient/previously discharged on respiratory medications/O 2 /support at 40 wk PMA). Measurements and Main Results: A total of 717 infants were evaluated (median birth weight, 850 g; gestation, 26.4 wk), 53.7% of whom had a favorable outcome and 46.3% of whom had an unfavorable outcome. Physiologic data predicted unfavorable outcome, with accuracy improving with advancing age (area under the curve, 0.79 at Day 7, 0.85 at Day 28 and 32 wk PMA). The physiologic variable that contributed most to prediction was intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <90%. Models with clinical data alone or combining physiologic and clinical data also had good accuracy, with areas under the curve of 0.84-0.85 at Days 7 and 14 and 0.86-0.88 at Day 28 and 32 weeks PMA. Intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <80% was the major physiologic predictor of severe bronchopulmonary dysplasia and death or mechanical ventilation at 40 weeks PMA. Conclusions: Physiologic data are independently associated with unfavorable respiratory outcome in extremely preterm infants.

Background: Heart rate characteristics aid early detection of late-onset sepsis (LOS), but respiratory data contain additional signatures of illness due to infection. Predictive models using cardiorespiratory data may improve early sepsis detection. We hypothesized that heart rate (HR) and oxygenation (SpO 2 ) data contain signatures that improve sepsis risk prediction over HR or demographics alone., Methods: We analyzed cardiorespiratory data from very low birth weight (VLBW, <1500 g) infants admitted to three NICUs. We developed and externally validated four machine learning models to predict LOS using features calculated every 10 m: mean, standard deviation, skewness, kurtosis of HR and SpO 2 , and cross-correlation. We compared feature importance, discrimination, calibration, and dynamic prediction across models and cohorts. We built models of demographics and HR or SpO 2 features alone for comparison with HR-SpO 2 models., Results: Performance, feature importance, and calibration were similar among modeling methods. All models had favorable external validation performance. The HR-SpO 2 model performed better than models using either HR or SpO 2 alone. Demographics improved the discrimination of all physiologic data models but dampened dynamic performance., Conclusions: Cardiorespiratory signatures detect LOS in VLBW infants at 3 NICUs. Demographics risk-stratify, but predictive modeling with both HR and SpO 2 features provides the best dynamic risk prediction., Impact: Heart rate characteristics aid early detection of late-onset sepsis, but respiratory data contain signatures of illness due to infection. Predictive models using both heart rate and respiratory data may improve early sepsis detection. A cardiorespiratory early warning score, analyzing heart rate from electrocardiogram or pulse oximetry with SpO 2 , predicts late-onset sepsis within 24 h across multiple NICUs and detects sepsis better than heart rate characteristics or demographics alone. Demographics risk-stratify, but predictive modeling with both HR and SpO 2 features provides the best dynamic risk prediction. The results increase understanding of physiologic signatures of neonatal sepsis., (© 2022. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

Objective: Predictive analytics tools variably take into account data from the electronic medical record, lab tests, nursing charted vital signs and continuous cardiorespiratory monitoring to deliver an instantaneous prediction of patient risk or instability. Few, if any, of these tools reflect the risk to a patient accumulated over the course of an entire hospital stay., Approach: We have expanded on our instantaneous CoMET predictive analytics score to generate the cumulative CoMET score (cCoMET), which sums all of the instantaneous CoMET scores throughout a hospital admission relative to a baseline expected risk unique to that patient., Main Results: We have shown that higher cCoMET scores predict mortality, but not length of stay, and that higher baseline CoMET scores predict higher cCoMET scores at discharge/death. cCoMET scores were higher in males in our cohort, and added information to the final CoMET when it came to the prediction of death., Significance: We have shown that the inclusion of all repeated measures of risk estimation performed throughout a patients hospital stay adds information to instantaneous predictive analytics, and could improve the ability of clinicians to predict deterioration, and improve patient outcomes in so doing., (Creative Commons Attribution license.)

The idea that we can detect subacute potentially catastrophic illness earlier by using statistical models trained on clinical data is now well-established. We review evidence that supports the role of continuous cardiorespiratory monitoring in these predictive analytics monitoring tools. In particular, we review how continuous ECG monitoring reflects the patient and not the clinician, is less likely to be biased, is unaffected by changes in practice patterns, captures signatures of illnesses that are interpretable by clinicians, and is an underappreciated and underutilized source of detailed information for new mathematical methods to reveal., (Copyright © 2022. Published by Elsevier Inc.)

Acute respiratory failure requiring the initiation of invasive mechanical ventilation remains commonplace in the pediatric intensive care unit (PICU). Early recognition of patients at risk for respiratory failure may provide clinicians with the opportunity to intervene and potentially improve outcomes. Through the development of a random forest model to identify patients at risk for requiring unplanned intubation, we tested the hypothesis that subtle signatures of illness are present in physiological and biochemical time series of PICU patients in the early stages of respiratory decompensation. We included 116 unplanned intubation events as recorded in the National Emergency Airway Registry for Children in 92 PICU admissions over a 29-month period at our institution. We observed that children have a physiologic signature of illness preceding unplanned intubation in the PICU. Generally, it comprises younger age, and abnormalities in electrolyte, hematologic and vital sign parameters. Additionally, given the heterogeneity of the PICU patient population, we found differences in the presentation among the major patient groups - medical, cardiac surgical, and non-cardiac surgical. At four hours prior to the event, our random forest model demonstrated an area under the receiver operating characteristic curve of 0.766 (0.738 for medical, 0.755 for cardiac surgical, and 0.797 for non-cardiac surgical patients). The multivariable statistical models that captured the physiological and biochemical dynamics leading up to the event of urgent unplanned intubation in a PICU can be repurposed for bedside risk prediction., Competing Interests: Authors LPM and MTC were employed by company Nihon Kohden Digital Health Solutions. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2022 Spaeder, Moorman, Moorman, Adu-Darko, Keim-Malpass, Lake and Clark.)

This paper proposes a novel deep learning architecture involving combinations of Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers that can be used to perform segmentation and classification of 5 cardiac rhythms based on ECG recordings. The algorithm is developed in a sequence to sequence setting where the input is a sequence of five second ECG signal sliding windows and the output is a sequence of cardiac rhythm labels. The novel architecture processes as input both the spectrograms of the ECG signal as well as the heartbeats' signal waveform. Additionally, we are able to train the model in the presence of label noise. The model's performance and generalizability is verified on an external database different from the one we used to train. Experimental result shows this approach can achieve an average F1 scores of 0.89 (averaged across 5 classes). The proposed model also achieves comparable classification performance to existing state-of-the-art approach with considerably less number of training parameters.

To seek new signatures of illness in heart rate and oxygen saturation vital signs from Neonatal Intensive Care Unit (NICU) patients, we implemented highly comparative time-series analysis to discover features of all-cause mortality in the next 7 days. We collected 0.5 Hz heart rate and oxygen saturation vital signs of infants in the University of Virginia NICU from 2009 to 2019. We applied 4998 algorithmic operations from 11 mathematical families to random daily 10 min segments from 5957 NICU infants, 205 of whom died. We clustered the results and selected a representative from each, and examined multivariable logistic regression models. 3555 operations were usable; 20 cluster medoids held more than 81% of the information, and a multivariable model had AUC 0.83. New algorithms outperformed others: moving threshold, successive increases, surprise, and random walk. We computed provenance of the computations and constructed a software library with links to the data. We conclude that highly comparative time-series analysis revealed new vital sign measures to identify NICU patients at the highest risk of death in the next week., (© 2022. The Author(s).)

Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result's metadata. An ontology for Evidence Graphs, EVI ( https://w3id.org/EVI ), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software., (© 2021. The Author(s).)

Background: Neonatal intensive care unit (NICU) patients are at increased risk for autism spectrum disorder (ASD). Autonomic nervous system aberrancy has been described in children with ASD, and we aimed to identify heart rate (HR) patterns in NICU patients associated with eventual ASD diagnosis., Methods: This retrospective cohort study included NICU patients from 2009 to 2016 with archived HR data and follow-up beyond age 3 years. Medical records provided clinical variables and ASD diagnosis. HR data were compared in infants with and without ASD., Results: Of the 2371 patients, 88 had ASD, and 689,016 h of data were analyzed. HR skewness (HRskw) was significantly different between ASD and control infants. Preterm infants at early postmenstrual ages (PMAs) had negative HRskw reflecting decelerations, which increased with maturation. From 34 to 42 weeks PMA, positive HRskw toward accelerations was higher in males with ASD. In 931 males with at least 4 days of HR data, overall ASD prevalence was 5%, whereas 11% in the top 5th HRskw percentile had ASD., Conclusion: High HRskw in NICU males, perhaps representing autonomic imbalance, was associated with increased ASD risk. Further study is needed to determine whether HR analysis identifies highest-risk infants who might benefit from earlier screening and therapies., Impact: In a large retrospective single-center cohort of NICU patients, we found that high positive skewness of heart rate toward more accelerations was significantly associated with increased risk of eventual autism spectrum disorder diagnosis in male infants but not in females. Existing literature describes differences in heart rate characteristics in children, adolescents, and adults with autism spectrum disorders, but the finding from our study in NICU infants is novel. Heart rate analysis during the NICU stay might identify, among an inherently high-risk population, those infants with especially high risk of ASD who might benefit from earlier screening and therapies., (© 2021. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

Objective: Apnea is common among infants in the neonatal intensive care unit (NICU). Our group previously developed an automated algorithm to quantitate central apneas with associated bradycardia and desaturation (ABDs). Sex differences in lung disease are well described in preterm infants, but the influence of sex on apnea has not been established., Study Design: This study includes infants < 34 weeks' gestation admitted to the University of Virginia NICU from 2009 to 2014 with at least 1 day of bedside monitor data available when not on mechanical ventilation. Waveform and vital sign data were analyzed using a validated algorithm to detect ABD events of low variance in chest impedance signal lasting at least 10 seconds with associated drop in heart rate to < 100 beats/minute and drop in oxygen saturation to < 80%. Male and female infants were compared for prevalence of at least one ABD event during the NICU stay, treatment with caffeine, occurrence of ABDs at each week of postmenstrual age, and number of events per day., Results: Of 926 infants studied (median gestational age 30 weeks, 53% male), median days of data analyzed were 19 and 22 for males and females, respectively. There was no sex difference in prevalence of at least one ABD event during the NICU stay (males 62%, females 64%, p  = 0.47) or in the percentage of infants treated with caffeine (males 64%, females 67%, p  = 0.40). Cumulative prevalence of ABDs from postmenstrual ages 24 to 36 weeks was comparable between sexes. Males had 18% more ABDs per day of data, but this difference was not statistically significant ( p  = 0.16)., Conclusion: In this large cohort of infants < 34 weeks' gestation, we did not detect a sex difference in prevalence of central ABD events. There was a nonsignificant trend toward a greater number of ABDs per day in male infants., Key Points: · Central apnea is pervasive among preterm infants in the NICU, but potential disparities between males and females have not been thoroughly studied.. · Identification of risk factors for central apnea can lead to improved treatment protocols.. · The rate and prevalence of central apnea events accompanied by bradycardia and desaturation does not significantly differ between male and female preterm infants.., Competing Interests: None declared., (Thieme. All rights reserved.)

Objective: The goal of predictive analytics monitoring is the early detection of patients at high risk of subacute potentially catastrophic illnesses. An excellent example of a targeted illness is respiratory failure leading to urgent unplanned intubation, where early detection might lead to interventions that improve patient outcomes. Previously, we identified signatures of this illness in the continuous cardiorespiratory monitoring data of intensive care unit (ICU) patients and devised algorithms to identify patients at rising risk. Here, we externally validated three logistic regression models to estimate the risk of emergency intubation developed in Medical and Surgical ICUs at the University of Virginia., Approach: We calculated the model outputs for more than 8000 patients in the University of California-San Francisco ICUs, 240 of whom underwent emergency intubation as determined by individual chart review., Main Results: We found that the AUC of the models exceeded 0.75 in this external population, and that the risk rose appreciably over the 12 h before the event., Significance: We conclude that there are generalizable physiological signatures of impending respiratory failure in the continuous cardiorespiratory monitoring data., (Creative Commons Attribution license.)

Background: Diagnosing sepsis remains challenging. Data compiled from continuous monitoring and electronic health records allow for new opportunities to compute predictions based on machine learning techniques. There has been a lack of consensus identifying best practices for model development and validation towards early identification of sepsis., Objective: To evaluate the modeling approach and statistical methodology of machine learning prediction models for sepsis in the adult hospital population., Methods: PubMed, CINAHL, and Cochrane databases were searched with the Preferred Reporting Items for Systematic Reviews guided protocol development. We evaluated studies that developed or validated physiologic sepsis prediction models or implemented a model in the hospital environment., Results: Fourteen studies met the inclusion criteria, and the AUROC of the prediction models ranged from 0.61 to 0.96. We found a variety of sepsis definitions, methods used for event adjudication, model parameters used, and modeling methods. Two studies tested models in clinical settings; the results suggested that patient outcomes were improved with implementation of machine learning models., Conclusion: Nurses have a unique perspective to offer in the development and implementation of machine learning models detecting patients at risk for sepsis. More work is needed in developing model harmonization standards and testing in clinical settings., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: Continuous heart rate (HR) and oxygenation (SpO 2 ) metrics can be useful for predicting adverse events in very low birth weight (VLBW) infants. To optimize the utility of these tools, inter-site variability must be taken into account., Methods: For VLBW infants at three neonatal intensive care units (NICUs), we analyzed the mean, standard deviation, skewness, kurtosis, and cross-correlation of electrocardiogram HR, pulse oximeter pulse rate, and SpO 2 . The number and durations of bradycardia and desaturation events were also measured. Twenty-two metrics were calculated hourly, and mean daily values were compared between sites., Results: We analyzed data from 1168 VLBW infants from birth through day 42 (35,238 infant-days). HR and SpO 2 metrics were similar at the three NICUs, with mean HR rising by ~10 beats/min over the first 2 weeks and mean SpO 2 remaining stable ~94% over time. The number of bradycardia events was higher at one site, and the duration of desaturations was longer at another site., Conclusions: Mean HR and SpO 2 were generally similar among VLBW infants at three NICUs from birth through 6 weeks of age, but bradycardia and desaturation events differed in the first 2 weeks after birth. This highlights the importance of developing predictive analytics tools at multiple sites., Impact: HR and SpO 2 analytics can be useful for predicting adverse events in VLBW infants in the NICU, but inter-site differences must be taken into account in developing predictive algorithms. Although mean HR and SpO 2 patterns were similar in VLBW infants at three NICUs, inter-site differences in the number of bradycardia events and duration of desaturation events were found. Inter-site differences in bradycardia and desaturation events among VLBW infants should be considered in the development of predictive algorithms., (© 2021. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

Objective. To develop a standardized format for exchanging clinical and physiologic data generated in the intensive care unit. Our goal was to develop a format that would accommodate the data collection pipelines of various sites but would not require dataset-specific schemas or ad-hoc tools for decoding and analysis. Approach. A number of centers had independently developed solutions for storing clinical and physiologic data using Hierarchical Data Format-Version 5 (HDF5), a well-supported standard already in use in multiple other fields. These individual solutions involved design choices that made the data difficult to share despite the underlying common framework. A collaborative process was used to form the basis of a proposed standard that would allow for interoperability and data sharing with common analysis tools. Main Results. We developed the HDF5-based critical care data exchange format which stores multiparametric data in an efficient, self-describing, hierarchical structure and supports real-time streaming and compression. In addition to cardiorespiratory and laboratory data, the format can, in future, accommodate other large datasets such as imaging and genomics. We demonstated the feasibility of a standardized format by converting data from three sites as well as the MIMIC III dataset. Significance. Individual approaches to the storage of multiparametric clinical data are proliferating, representing both a duplication of effort and a missed opportunity for collaboration. Adoption of a standardized format for clinical data exchange will enable the development of a digital biobank, facilitate the external validation of machine learning models and be a powerful tool for sharing multiparametric, high frequency patient level data in multisite clinical trials. Our proposed solution focuses on supporting standardized ontologies such as LOINC allowing easy reading of data regardless of the source and in so doing provides a useful method to integrate large amounts of existing data., (© 2021 Institute of Physics and Engineering in Medicine.)

Background: In premature infants, clinical changes frequently occur due to sepsis or non-infectious conditions, and distinguishing between these is challenging. Baseline risk factors, vital signs, and clinical signs guide decisions to culture and start antibiotics. We sought to compare heart rate (HR) and oxygenation (SpO2) patterns as well as baseline variables and clinical signs prompting sepsis work-ups ultimately determined to be late-onset sepsis (LOS) and sepsis ruled out (SRO)., Methods: At three NICUs, we reviewed records of very low birth weight (VLBW) infants around their first sepsis work-up diagnosed as LOS or SRO. Clinical signs prompting the evaluation were determined from clinician documentation. HR-SpO2 data, when available, were analyzed for mean, standard deviation, skewness, kurtosis, and cross-correlation. We used LASSO and logistic regression to assess variable importance and associations with LOS compared to SRO., Results: We analyzed sepsis work-ups in 408 infants (173 LOS, 235 SRO). Compared to infants with SRO, those with LOS were of lower GA and BW, and more likely to have a central catheter and mechanical ventilation. Clinical signs cited more often in LOS included hypotension, acidosis, abdominal distension, lethargy, oliguria, and abnormal CBC or CRP(p < 0.05). HR-SpO2 data were available in 266 events. Cross-correlation HR-SpO2 before the event was associated with LOS after adjusting for GA, BW, and postnatal age. A model combining baseline, clinical and HR-SpO2 variables had AUC 0.821., Conclusion: In VLBW infants at 3-NICUs, we describe the baseline, clinical, and HR-SpO2 variables associated with LOS versus SRO.

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., (© The Author(s) 2020.)

Objective: Trajectories of physiomarkers over time can be useful to define phenotypes of disease progression and as predictors of clinical outcomes. The aim of this study was to identify phenotypes of the time course of late-onset sepsis in premature infants in Neonatal Intensive Care Units., Methods: We examined the trajectories of a validated continuous physiomarker, abnormal heart rate characteristics, using functional data analysis and clustering techniques., Participants: We analyzed continuous heart rate characteristics data from 2989 very low birth weight infants (<1500 grams) from nine NICUs from 2004-2010., Result: Despite the relative homogeneity of the patients, we found extreme variability in the physiomarker trajectories. We identified phenotypes that were indicative of seven and 30 day mortality beyond that predicted by individual heart rate characteristics values or baseline demographic information., Conclusion: Time courses of a heart rate characteristics physiomarker reveal snapshots of illness patterns, some of which were more deadly than others., (© The Author(s) 2020.)

Patients who deteriorate while on the acute care ward and are emergently transferred to the Intensive Care Unit (ICU) experience high rates of mortality. To date, risk scores for clinical deterioration applied to the acute care wards rely on static or intermittent inputs of vital sign and assessment parameters. We propose the use of continuous predictive analytics monitoring, or data that relies on real-time physiologic monitoring data captured from ECG, documented vital signs, laboratory results, and other clinical assessments to predict clinical deterioration. A necessary step in translation to practice is understanding how an alert threshold would perform if applied to a continuous predictive analytic that was trained to detect clinical deterioration. The purpose of this study was to evaluate the positive predictive value of 'risk spikes', or large abrupt increases in the output of a statistical model of risk predicting clinical deterioration. We studied 8111 consecutive patient admissions to a cardiovascular medicine and surgery ward with continuous ECG data. We first trained a multivariable logistic regression model for emergent ICU transfer in a test set and tested the characteristics of the model in a validation set of 4059 patient admissions. Then, in a nested analysis we identified large, abrupt spikes in risk (increase by three units over the prior 6 h; a unit is the fold-increase in risk of ICU transfer in the next 24 h) and reviewed hospital records of 91 patients for clinical events such as emergent ICU transfer. We compared results to 59 control patients at times when they were matched for baseline risk including the National Warning Score (NEWS). There was a 3.4-fold higher event rate for patients with risk spikes (positive predictive value 24% compared to 7%, p = 0.006). If we were to use risk spikes as an alert, they would fire about once per day on a 73-bed acute care ward. Risk spikes that were primarily driven by respiratory changes (ECG-derived respiration (EDR) or charted respiratory rate) had highest PPV (30-35%) while risk spikes driven by heart rate had the lowest (7%). Alert thresholds derived from continuous predictive analytics monitoring are able to be operationalized as a degree of change from the person's own baseline rather than arbitrary threshold cut-points, which can likely better account for the individual's own inherent acuity levels. Point of care clinicians in the acute care ward settings need tailored alert strategies that promote a balance in recognition of clinical deterioration and assessment of the utility of the alert approach.

Purpose: Rash develops in approximately 50% of patients receiving alpelisib for breast cancer, often requiring dose modifications. Here, we describe the clinicopathologic, laboratory, and management characteristics of alpelisib-related dermatologic adverse events (dAEs)., Methods: A single center-retrospective analysis was conducted. Data were abstracted from electronic medical records., Results: A total of 102 patients (mean age 56 years, range 27-83) receiving alpelisib most frequently in combination with endocrine therapy (79, 77.5%) were included. We identified 41 (40.2%) patients with all-grade rash distributed primarily along the trunk (78%) and extremities (70%) that developed approximately within two weeks of treatment initiation (mean 12.8 ± 1.5 days) and lasted one-week (mean duration 7.1 ± 0.8 days). Of 29 patients with documented morphology of alpelisib-related dAEs, 26 (89.7%) had maculopapular rash. Histology showed perivascular and interface lymphocytic dermatitis. All-grade rash correlated with an increase in serum eosinophils from 2.7 to 4.4%, p < 0.05, and prophylaxis with non-sedating antihistamines (n = 43) was correlated with a reduction of grade 1/2 rash (OR 0.39, p = 0.09). Sixteen (84.2%) of 19 patients with grade 3 dAEs resulted in interruption of alpelisib, which were managed with antihistamines, topical and systemic corticosteroids. We did not observe rash recurrence in 12 (75%) patients who were re-challenged., Conclusions: A maculopapular rash associated with increased blood eosinophils occurs frequently with alpelisib. While grade 3 rash leads to alpelisib therapy interruption, dermatologic improvement is evident with systemic corticosteroids; and most patients can continue oncologic treatment at a maintained or reduced dose upon re-challenge with alpelisib.

Background: Displaying heart rate characteristic (HRC) scores was associated with lower sepsis-associated mortality in very low birth weight (VLBW) infants in a multicenter randomized controlled trial (HeRO trial). The aim of this study was to test whether HRC indices rise before diagnosis of urinary tract infection (UTI) or meningitis, with and without concomitant BSI., Methods: Blood, urine, and cerebrospinal fluid (CSF) culture data after 3 days of age and within 120 days of study enrollment were analyzed from 2989 VLBW infants. The HRC index was analyzed 12 h prior to positive cultures compared to 36 h prior, using paired signed-rank tests., Results: UTI, meningitis, and BSI were diagnosed in 10%, 2%, and 24% of infants, respectively. The mean hourly HRC index was significantly higher 12 h prior to diagnosis of UTI and BSI compared to 36 h prior (UTI 2.07 versus 1.81; BSI 2.62 versus 2.25, both p < 0.0001). The baseline HRC index was higher for meningitis, compared to UTI or BSI, but without a statistically significant rise in the day prior to meningitis diagnosis., Conclusions: In a large cohort of VLBW infants enrolled in the HeRO trial, the HRC index increased in the 24-h period prior to diagnosis of UTI and BSI but not meningitis.

Objectives: Early detection of subacute potentially catastrophic illnesses using available data is a clinical imperative, and scores that report risk of imminent events in real time abound. Patients deteriorate for a variety of reasons, and it is unlikely that a single predictor such as an abnormal National Early Warning Score will detect all of them equally well. The objective of this study was to test the idea that the diversity of reasons for clinical deterioration leading to ICU transfer mandates multiple targeted predictive models., Design: Individual chart review to determine the clinical reason for ICU transfer; determination of relative risks of individual vital signs, laboratory tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer; and logistic regression modeling for the outcome of ICU transfer for a specific clinical reason., Setting: Cardiac medical-surgical ward; tertiary care academic hospital., Patients: Eight-thousand one-hundred eleven adult patients, 457 of whom were transferred to an ICU for clinical deterioration., Interventions: None., Measurements and Main Results: We calculated the contributing relative risks of individual vital signs, laboratory tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer, and used logistic regression modeling to calculate receiver operating characteristic areas and relative risks for the outcome of ICU transfer for a specific clinical reason. The reasons for clinical deterioration leading to ICU transfer were varied, as were their predictors. For example, the three most common reasons-respiratory instability, infection and suspected sepsis, and heart failure requiring escalated therapy-had distinct signatures of illness. Statistical models trained to target-specific reasons for ICU transfer performed better than one model targeting combined events., Conclusions: A single predictive model for clinical deterioration does not perform as well as having multiple models trained for the individual specific clinical events leading to ICU transfer., Competing Interests: Dr. Moorman is Chief Medical Officer and shareholder and Dr. Clark is Chief Scientific Officer and shareholder in Advanced Medical Predictive Devices, Diagnostics, and Displays, Charlottesville, VA. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2020 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Objective: To test whether the composite outcome of death or neurodevelopmental impairment (NDI) at 18-22 months corrected age for infants ≤1000 g at birth is decreased by continuous monitoring of heart rate characteristics during neonatal intensive care., Study Design: We studied a subset of participants enrolled in a multicenter randomized trial of heart rate characteristics monitoring. Survivors were evaluated at 18-22 months corrected age with a standardized neurologic examination and the Bayley Scales of Infant Development-III (BSID-III). NDI was defined as Gross Motor Function Classification System of >2 (moderate or severe cerebral palsy), BSID-III language or cognitive scores of <70, severe bilateral hearing impairment, and/or bilateral blindness., Results: The composite outcome, death or NDI, was obtained for 628 of 884 study infants (72%). The prevalence of this outcome was 44.4% (136/306) among controls (infants randomized to heart rate characteristics monitored but not displayed) and 38.9% (125/322) among infants randomized to heart rate characteristics monitoring displayed (relative risk, 0.87; 95% CI, 0.73-1.05; P = .17). Mortality was reduced from 32.0% (99/307) among controls to 24.8% (81/326) among monitoring displayed infants (relative risk, 0.75; 95% CI, 0.59 to 0.97; P = .028). The composite outcomes of death or severe CP and death or mildly low Bayley cognitive score occurred less frequently in the displayed group (P < .05)., Conclusions: We found no difference in the composite outcome of death or NDI for extremely preterm infants whose heart rate characteristics were and were not displayed during neonatal intensive care. Two outcomes that included mortality or a specific NDI were less frequent in the displayed group., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: There are limited evidence-based published blood pressure ranges for premature neonates. The aim of the study was to determine blood pressure ranges in a large cohort of premature neonates based on gestational and post-menstrual age., Methods: Retrospective observational study of premature neonates admitted to the neonatal intensive care unit at our institution between January 2009 and October 2015. We stratified data by gestational and post-menstrual age groups as well as by method of blood pressure measurement (non-invasive vs. invasive)., Results: Over two billion blood pressure values in 1708 neonates were analyzed to generate heat maps and establish percentile-based reference ranges. The median gestational age of the cohort was 31 weeks (IQR 28-33 weeks). We found moderate correlation (r = 0.57) between simultaneously obtained non-invasive and invasive blood pressure measurements., Conclusions: Our results can serve as a reference during the bedside assessment of the critically-ill neonate.

Objective: The ratio of the partial pressure of arterial oxygen to fraction of inspired oxygen is a key component of the sequential organ failure assessment score that operationally defines sepsis. But, it is calculated infrequently due to the need for the acquisition of an arterial blood gas. So, we sought to find an optimal imputation strategy for the estimation of sepsis-defining hypoxemic respiratory failure using oximetry instead of an arterial blood gas., Approach: We retrospectively studied a sample of non-intubated acute-care patients with oxygen saturation recorded  ⩽10 min before arterial blood sampling (N  =  492 from 2013-2017). We imputed ratios of the partial pressure of arterial oxygen to the fraction of inspired oxygen and sepsis criteria from existing imputation equations (Hill, Severinghaus-Ellis, Rice, and Pandharipande) and compared them with the ratios and sepsis criteria measured from arterial blood gases. We devised a modified model-based equation to eliminate the bias of the results., Main Results: Hypoxemia severity estimates from the Severinghaus-Ellis equation were more accurate than those from other existing equations, but showed significant proportional bias towards under-estimation of hypoxemia severity, especially at oxygen saturations  >96%. Our modified equation eliminated bias and surpassed others on all imputation quality metrics., Significance: Our modified imputation equation, [Formula: see text] is the first one that is free of bias at all oxygen saturations. It resulted in ratios of partial pressure of arterial oxygen to fraction of inspired oxygen and sepsis respiratory criteria closest to those obtained by arterial blood gas testing and is the optimal imputation strategy for non-intubated acute-care patients.

Background: Early recognition of patients at risk for sepsis is paramount to improve clinical outcomes. We hypothesized that subtle signatures of illness are present in physiological and biochemical time series of pediatric-intensive care unit (PICU) patients in the early stages of sepsis., Methods: We developed multivariate models in a retrospective observational cohort to predict the clinical diagnosis of sepsis in children. We focused on age as a predictor and asked whether random forest models, with their potential for multiple cut points, had better performance than logistic regression., Results: One thousand seven hundred and eleven admissions for 1425 patients admitted to a mixed cardiac and medical/surgical PICU were included. We identified, through individual chart review, 187 sepsis diagnoses that were not within 14 days of a prior sepsis diagnosis. Multivariate models predicted sepsis in the next 24 h: cross-validated C-statistic for logistic regression and random forest were 0.74 (95% confidence interval (CI): 0.71-0.77) and 0.76 (95% CI: 0.73-0.79), respectively., Conclusions: Statistical models based on physiological and biochemical data already available in the PICU identify high-risk patients up to 24 h prior to the clinical diagnosis of sepsis. The random forest model was superior to logistic regression in capturing the context of age.

Background: Preterm very low birth weight (VLBW) infants experience physiologic maturation and transitions off therapies from 32 to 35 weeks postmenstrual age (PMA), which may impact episodic bradycardia and oxygen desaturation. We sought to characterize bradycardias and desaturations from 32 to 35 weeks PMA and test whether events at 32 weeks PMA are associated with NICU length of stay., Methods: For 265 VLBW infants from 32 to 35 weeks PMA, we quantified the number and duration of bradycardias (HR <100 for ≥4 s) and desaturations (SpO 2 <80% for ≥10 s) and compared events around discontinuation of CPAP, caffeine, and supplemental oxygen. We modeled associations between clinical variables, bradycardias and desaturations at 32 weeks PMA, and discharge PMA., Results: Desaturations decreased from 60 to 41 per day at 32 and 35 weeks, respectively (p < 0.01). Duration of desaturations and number and duration of bradycardias decreased to a smaller extent (p < 0.05), and there was a non-significant trend toward increased desaturations after stopping CPAP and caffeine. Controlling for clinical variables, longer duration of bradycardias and desaturations at 32 weeks PMA was associated with later discharge PMA., Conclusion: Delayed recovery from bradycardias and desaturations at 32 weeks PMA, perhaps reflecting less physiologic resilience, is associated with prolonged NICU stay for VLBW infants.

Predictive analytics monitoring, the use of patient data to provide continuous risk estimation of deterioration, is a promising new application of big data analytical techniques to the care of individual patients. We tested the hypothesis that continuous display of novel electronic risk visualization of respiratory and cardiovascular events would impact intensive care unit (ICU) patient outcomes. In an adult tertiary care surgical trauma ICU, we displayed risk estimation visualizations on a large monitor, but in the medical ICU in the same institution we did not. The risk estimates were based solely on analysis of continuous cardiorespiratory monitoring. We examined 4275 individual patient records within a 7 month time period preceding and following data display. We determined cases of septic shock, emergency intubation, hemorrhage, and death to compare rates per patient care pre-and post-implementation. Following implementation, the incidence of septic shock fell by half (p < 0.01 in a multivariate model that included age and APACHE) in the surgical trauma ICU, where the data were continuously on display, but by only 10% (p = NS) in the control Medical ICU. There were no significant changes in the other outcomes. Display of a predictive analytics monitor based on continuous cardiorespiratory monitoring was followed by a reduction in the rate of septic shock, even when controlling for age and APACHE score.

Background: Bradycardia and oxygen desaturation episodes are common among preterm very low birth weight (VLBW) infants in the Neonatal Intensive Care Unit (NICU), and their association with adverse outcomes such as bronchopulmonary dysplasia (BPD) is unclear., Methods: For 502 VLBW infants we quantified bradycardias (HR < 100 for ≥ 4 s) and desaturations (SpO 2  < 80% for ≥ 10 s), combined bradycardia and desaturation (BD) events, and percent time in events in the first 4 weeks after birth (32 infant-years of data). We tested logistic regression models of clinical risks (including a respiratory acuity score incorporating FiO 2 and level of respiratory support) to estimate the risks of BPD or death and secondary outcomes. We then tested the additive value of the bradycardia and desaturation metrics for outcomes prediction., Results: BPD occurred in 187 infants (37%). The clinical risk model had ROC area for BPD of 0.874. Measures of desaturation, but not bradycardia, significantly added to the predictive model. Desaturation metrics also added to clinical risks for prediction of severe intraventricular hemorrhage, retinopathy of prematurity and prolonged length of stay in the NICU., Conclusions: Oxygen desaturations in the first month of the NICU course are associated with risk of BPD and other morbidities in VLBW infants.

Background: We previously showed, in a single-center study, that early heart rate (HR) characteristics predicted later adverse outcomes in very low birth weight (VLBW) infants. We sought to improve predictive models by adding oxygenation data and testing in a second neonatal intensive care unit (NICU)., Methods: HR and oxygen saturation (SpO 2 ) from the first 12 hours and first 7 days after birth were analyzed for 778 VLBW infants at two NICUs. Using multivariate logistic regression, clinical predictive scores were developed for death, severe intraventricular hemorrhage (sIVH), bronchopulmonary dysplasia (BPD), treated retinopathy of prematurity (tROP), late-onset septicemia (LOS), and necrotizing enterocolitis (NEC). Ten HR-SpO 2 measures were analyzed, with first 12 hours data used for predicting death or sIVH and first 7 days for the other outcomes. HR-SpO 2 models were combined with clinical models to develop a pulse oximetry predictive score (POPS). Net reclassification improvement (NRI) compared performance of POPS with the clinical predictive score., Results: Models using clinical or pulse oximetry variables alone performed well for each outcome. POPS performed better than clinical variables for predicting death, sIVH, and BPD (NRI > 0.5, p  < 0.01), but not tROP, LOS, or NEC., Conclusion: Analysis of early HR-SpO 2 characteristics adds to clinical risk factors to predict later adverse outcomes in VLBW infants., Competing Interests: None., (Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.)

Objective: There are limited evidence-based published heart rate ranges for premature neonates. We determined heart rate ranges in premature neonates based on gestational and post-menstrual age., Study Design: Retrospective observational study of premature neonates admitted to the neonatal intensive care unit at the University of Virginia between January 2009 and October 2015. We included gestational ages between 23 0/7 weeks and 34 6/7 weeks. We stratified data by gestational and post-menstrual age groups., Results: Over two billion heart rate values in 1703 neonates were included in our study. We established percentile-based reference ranges based on gestational and post-menstrual age. Our results demonstrate a slight increase in the initial weeks after birth, followed by a gradual decline with age. The baseline heart rate is lower with advancing gestational age., Conclusions: Knowing heart rate reference ranges in the premature neonatal population can be helpful in the bedside assessment of the neonate.

Objective: Predictive analytics monitoring that informs clinicians of the risk for failed extubation would help minimize both the duration of mechanical ventilation and the risk of emergency re-intubation in ICU patients. We hypothesized that dynamic monitoring of cardiorespiratory data, vital signs, and lab test results would add information to standard clinical risk factors., Methods: We report model development in a retrospective observational cohort admitted to either the medical or surgical/trauma ICU that were intubated during their ICU stay and had available physiologic monitoring data (n  =  1202). The primary outcome was removal of endotracheal intubation (i.e. extubation) followed within 48 h by reintubation or death (i.e. failed extubation). We developed a standard risk marker model based on demographic and clinical data. We also developed a novel risk marker model using dynamic data elements-continuous cardiorespiratory monitoring, vital signs, and lab values., Results: Risk estimates from multivariate predictive models in the 24 h preceding extubation were significantly higher for patients that failed. Combined standard and novel risk markers demonstrated good predictive performance in leave-one-out validation: AUC of 0.64 (95% CI: 0.57-0.69) and 1.6 alerts per week to identify 32% of extubations that will fail. Novel risk factors added significantly to the standard model., Conclusion: Predictive analytics monitoring models can detect changes in vital signs, continuous cardiorespiratory monitoring, and laboratory measurements in both the hours preceding and following extubation for those patients destined for extubation failure.

Objective: To examine the effect of heart rate characteristics (HRC) monitoring on length of stay among very low birth weight (VLBW; <1500 g birth weight) neonates in the HeRO randomized controlled trial (RCT)., Study Design: We performed a retrospective analysis of length of stay metrics among 3 subpopulations (all patients, all survivors, and survivors with positive blood or urine cultures) enrolled in a multicenter, RCT of HRC monitoring., Results: Among all patients in the RCT, infants randomized to receive HRC monitoring were more likely than controls to be discharged alive and prior to day 120 (83.6% vs 80.1%, P = .014). The postmenstrual age at discharge for survivors with positive blood or urine cultures was 3.2 days lower among infants randomized to receive HRC monitoring when compared with controls (P = .026). Although there were trends in other metrics toward reduced length of stay in HRC-monitored patients, none reached statistical significance., Conclusions: HRC monitoring is associated with reduced mortality in VLBW patients and a reduction in length of stay among infected surviving VLBW infants., Trial Registration: ClinicalTrials.gov: NCT00307333., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background:  Analysis of subtle vital sign changes could facilitate earlier treatment of acute inflammatory illnesses. We previously showed that high cross-correlation of heart rate and oxygen saturation (XCorr-HR-SpO 2 ) occurs in some very low birthweight (VLBW) infants with sepsis, and hypothesized that this corresponds to apnea., Methods:  In 629 VLBW infants, we analyzed XCorr-HR-SpO 2 in relation to central apnea with bradycardia and desaturation (ABD), BD with or without central apnea (BD), and percent time in periodic breathing (PB) throughout the neonatal intensive care unit (NICU) stay (75 infant-years). We reviewed 100 days with extremely high XCorr-HR-SpO 2 (>0.7) and control days for clinical associations. Next, we identified all cases of late-onset septicemia (LOS) and necrotizing enterocolitis (NEC) and analyzed change in XCorr-HR-SpO 2 before diagnosis., Results:  Mean XCorr-HR-SpO 2 was ∼0.10, and increasing XCorr-HR-SpO 2 was associated with increasing ABD, BD, and PB (correlation coefficients >0.93). Days with maximum XCorr-HR-SpO 2 >0.7 were more likely to have an adverse event than control days (49% versus 13%). In 93 cases of LOS or NEC, there was a 67% increase in XCorr-HR-SpO 2 in the 24-hour period prior to diagnosis compared with the previous day ( p  < 0.01)., Conclusion:  High XCorr-HR-SpO 2 is associated with apnea and adverse events including LOS and NEC., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.)

Background: Continuous predictive monitoring has been employed successfully to predict subclinical adverse events. Should low values on these models, however, reassure us that a patient will not have an adverse outcome? Negative predictive values of such models could help predict safe patient discharge. The goal of this study was to validate the negative predictive value of an ensemble model for critical illness (using previously developed models for respiratory instability, hemorrhage, and sepsis) based on bedside monitoring data in the intensive care units and intermediate care unit., Methods: We calculated the relative risk of 3 critical illnesses for all patients every 15 minutes (n= 124,588) for 2,924 patients downgraded from the surgical intensive care units and intermediate care unit between May 2014 to May 2016. We constructed an ensemble model to estimate at the time of intensive care units or intermediate care unit discharge the probability of favorable outcome after downgrade., Results: Outputs form the ensemble model stratified patients by risk of favorable and bad outcomes in both intensive care units/intermediate care unit; area under the receiver operating characteristic curve = .639/.629 respectively for favorable outcomes and .645/.641 for adverse events. These performance characteristics are commensurate with published models for predicting readmission. The ensemble model remained a statistically significant predictor after adjusting for hospital duration of stay and admitting service. The rate of favorable outcome in the highest and lowest deciles in the intensive care units were 76.2% and 27.3% (2.8-fold decrease) and 88.3% and 33.2% in the intermediate care unit (2.7-fold decrease), respectively., Conclusion: An ensemble model for critical illness predicts favorable outcome after downgrade and safe patient discharge (hospital stay <7 days, no readmission, upgrade, or death)., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)