Integrated Physiomarker, Biomarker and Clinical Predictive Analytics for Early Warning of Sepsis and Necrotizing Enterocolitis in Very Low Birth Weight Infants: Pilot Randomized Clinical Trial to Test Feasibility of Integrated Predictive Monitoring for Sepsis and NEC
Conditions: Sepsis, Necrotising Enterocolitis Neonatal
Sponsor: University of Virginia

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IRB # 21237: Predictive Modeling for Sepsis and Necrotizing Enterocolitis in Preterm Infants 
NICHD: RO1 HD072071: 
PI: Brynne Sullivan, MD, 
Version dated March 10, 2025 
 
Study Title 
Predictive Modelling for sepsis and necrotizing 
enterocolitis in preterm infants 
Study Intervention 
Develop, test, and validate predictive models for 
sepsis and necrotizing enterocolitis in preterm 
infants 
Indication Studied 
Late onset sepsis and necrotizing enterocolitis 
Sponsor 
NICHD 
Sponsor Protocol 
NICHD: RO1 HD072071 
Number 
UVA IRB #:21237 
Development Phase of Study 
Observational 
Release Date 
Protocol Version 4.0 
Version Date: March 10, 2025 
GCP Statement 
This study is to be performed in full compliance 
with acceptable Good Clinical Practices (GCP) as 
required by U.S. Code of Federal Regulations 
applicable to clinical studies (45CFR46), ICH GCP 
E6 and completion of Human Subjects Protection 
Training. All required study documentation will be 
archived as required by regulatory authorities. 
Principal Investigator 
Brynne Sullivan MD, 
Participating Institutions 
University of Virginia, Charlottesville, VA 
Washington University, St. Louis, MO 
Columbia University, New York, NY 
University of Alabama, Birmingham, AL 
Biostatistician 
Douglas Lake, PhD 

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IRB # 21237: Predictive Modeling for Sepsis and Necrotizing Enterocolitis in Preterm Infants 
NICHD: RO1 HD072071: 
PI: Brynne Sullivan, MD, 
Version dated March 10, 2025 
 
SIGNATURE PAGE 
 
 
 
Signature: 
3/13/2025 
Name: 
Brynne Sullivan, MD 
Date 
Role: PI 
 
INVESTIGATOR’S AGREEMENT 
I confirm that I have read this protocol and I agree to conduct the study as outlined herein. I agree to 
conduct the study in accordance with the ethical principles that have their origin in the Declaration of 
Helsinki and that are consistent with Good Clinical Practices as outlined in ICH E6 and the applicable 
laws and regulations. 
3/13/2025 
 
Site Investigator: 
Signature 
Date 
Brynne Sullivan 
 
Name 
 
 
 
 
 
 
 
 
 
 
Instructions to the Site Investigator: Please sign and date this signature page. File the original 
signature page in the study file at the site and send a copy of the signed and dated page to the UVA 
Study PIs or designee. 

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IRB # 21237: Predictive Modeling for Sepsis and Necrotizing Enterocolitis in Preterm Infants 
NICHD: RO1 HD072071: 
PI: Brynne Sullivan, MD, 
Version dated March 10, 2025 
 
 
STUDY DESIGN 
This is a multicenter observational study to develop predictive models for sepsis and necrotizing 
enterocolitis in preterm infants, using clinical, laboratory, and bedside monitor data. 
• 
No interventions are involved. 
• 
Clinical, laboratory, and imaging data will be collected from the medical record. 
• 
Vital sign data will be collected from the bedside monitors. 
• 
Machine learning will be used to develop, test, and validate predictive algorithms 
SYNOPSIS 
Title: Predictive Modeling for sepsis and necrotizing enterocolitis in preterm infants 
Investigators: Karen Fairchild, M.D.; Brynne Sullivan, M.D. 
Planned Study Initiation: November 2018 
Study Duration: 10 years 
Length of subject participation: Study data are collected from birth until NICU discharge. 
Neurodevelopmental follow-up data will be collected until age 2. 
Objective: To test the hypothesis that validated algorithms combining cardiorespiratory 
metrics from NICU bedside monitors with clinical, demographic and laboratory variables can 
provide early warning for sepsis and NEC in VLBW infants in the NICU. 
Outcomes: 
Primary: 
• 
Late-onset (>3d of age) sepsis 
• 
Other late-onset infections 
• 
Necrotizing enterocolitis 
Secondary: 
• 
Mortality 
• 
Days on mechanical ventilation 
• 
Preterm morbidities including but not limited to brain injury, retinopathy, acute kidney 
injury, and bronchopulmonary dysplasia 
• 
Days on antibiotics 
• 
Length of NICU stay 
• 
Neurodevelopmental outcomes at routine NICU follow-up visits 

Docusign Envelope ID: 453712E0-9B21-403B-BED2-AC4FE73457C9 
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IRB # 21237: Predictive Modeling for Sepsis and Necrotizing Enterocolitis in Preterm Infants 
NICHD: RO1 HD072071: 
PI: Brynne Sullivan, MD, 
Version dated March 10, 2025 
 
Methodology: Observational study with data collection from electronic health record 
Total Number of infants planned: 8500 
Inclusion criteria: 
• 
Birthweight <1500 grams 
Exclusion criteria: 
• 
None 
Investigational intervention: None (data collection only) 
Other study intervention(s): None 
Statistical methods: Descriptive statistics, logistic regression, decision tree, neural network, 
deep learning and others will be utilized. 
Efficacy: Algorithms for sepsis and NEC risk will be developed and externally validated and 
cross-validated. 
Safety: Patient data will be linked and coded within sites (with PHI stored on research drives 
behind fire walls). Only data with limited HIPAA identifiers (dates and study IDs but no key 
to the code) will be shared between sites. 
 
 
LIST OF ABBREVIATIONS 
Abbreviation or specialist term 
Explanation 
BPD 
Bronchopulmonary Dysplasia 
CBC/Diff 
Complete Blood Count and differential 
Corrected Age 
Chronologic age in months, minus #months 
premature (used after NICU discharge) 
CRC 
Clinical Research Coordinator 
CRF 
Case Report Form 
CRP 
C Reactive Protein 
GA 
Gestational Age 
HUS 
Head ultrasound 
IRB 
Institutional Review Board 
IVH 
Intraventricular Hemorrhage 
MOP 
Manual of Procedures 
NEC 
Necrotizing Enterocolitis 
NICU 
Neonatal Intensive Care Unit 
PMA 
Postmenstrual age (gestational age plus 
chronologic age in weeks, used during NICU 
cPVL 
Cystic Periventricular Leukomalacia 

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IRB # 21237: Predictive Modeling for Sepsis and Necrotizing Enterocolitis in Preterm Infants 
NICHD: RO1 HD072071: 
PI: Brynne Sullivan, MD, 
Version dated March 10, 2025 
 
REDCap 
Research Electronic Data Capture 
ROP 
Retinopathy of Prematurity 
UVA 
University of Virginia 
VLBW 
Very Low Birth Weight (<1500 grams) 
 
 
Table of Contents 
 
1 
BACKGROUND AND RATIONALE .......................... Error! Bookmark not defined. 
2 
STUDY OBJECTIVES .......................................................................................... 3 
3 
SUBJECT ELIGIBILITY ....................................................................................... 4 
4 
STUDY PLAN ...................................................................................................... 5 
5 
STATISTICAL CONSIDERATIONS ..................................................................... 6 
6 
STUDY MANAGEMENT ...................................................................................... 7 
7 
REFERENCES ..................................................................................................... 8 

Docusign Envelope ID: 453712E0-9B21-403B-BED2-AC4FE73457C9 
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IRB # 21237: Predictive Modeling for Sepsis and Necrotizing Enterocolitis in Preterm Infants 
NICHD: RO1 HD072071: 
PI: Brynne Sullivan, MD, 
Version dated March 10, 2025 
 
 
1. BACKGROUND AND RATIONALE 
Study Synopsis 
In a multi-NICU study, we are developing, testing, and validating predictive modeling algorithms for early 
detection of sepsis and NEC in VLBW infants. 
Disease Background 
The number of very low birthweight (VLBW, <1500 g) infants born in the US each year remains about 
60,000(1). Their course of post-natal development in the NICU centers on support of ventilation and 
nutrition while systems mature. There are, however, interruptions by the apparently sudden onset of 
inflammatory illnesses such as sepsis and necrotizing enterocolitis (NEC), occurring in approximately 
15% and 8% of VLBW infants, respectively. The mortality is high and there is substantial short- and 
long-term morbidity(2)(3)(4). These illnesses are not really sudden, though – clinical signs of illness 
occur relatively late, when the systemic inflammatory response is well-developed. Effective detection 
systems would allow earlier recognition and treatment. 
 
Many have postulated that new analysis of existing data from standard-of-care bedside monitors should 
give clues that are useful in early diagnosis(5)(6)(7). The University of Virginia group has reduced one 
such analysis to clinical practice. We found abnormal heart rate characteristics (HRC) of reduced 
variability and transient decelerations in the heart rates of septic infants for 12 to 24 hours and more 
prior to clinical suspicion(8). We developed mathematical tools to detect abnormal HRC, validated them 
externally, demonstrated their relationship to clinical data such as lab results and other findings, and 
performed a large randomized trial to assess impact on VLBW outcome(9). The results were important 
– a more than 20% mortality reduction when HRC monitors were displayed to clinicians with no other 
intervention mandated. Since then we have shown that adding other vital sign data, such as oxygen 
saturation, as well as other demographic and clinical data, including laboratory values, might improve 
on heart rate characteristics monitoring for predicting sepsis, NEC, and other outcomes(10)(11). 
Rationale for Study Design 
• 
Routinely monitored cardiorespiratory signals hold much physiologic information that is not synthesized 
in such a way as to alert clinicians to subtle changes that occur very early in the systemic inflammatory 
response. 
• 
The combined efforts of clinicians, mathematicians and engineers can lead to effective new monitoring 
strategies that allow early diagnosis of otherwise invisible clinical deterioration. 
• 
Adding clinical, demographic, and laboratory data to bedside monitor cardiorespiratory data will improve 
prediction and early detection of sepsis and NEC over any of these data alone. 
• 
Improved analytics will favorably impact patient outcomes by assisting clinicians in decisions about 
initiation of antibiotics and other therapies early in the course of illness. 

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IRB # 21237: Predictive Modeling for Sepsis and Necrotizing Enterocolitis in Preterm Infants 
NICHD: RO1 HD072071: 
PI: Brynne Sullivan, MD, 
Version dated March 10, 2025 
 
 
 
2. STUDY OBJECTIVES 
2.1 Hypothesis 
Validated algorithms combining bedside monitor cardiorespiratory-based metrics along with clinical, 
demographic and laboratory variables can provide early warning for sepsis and NEC in VLBW infants 
in the NICU. 
2.2 Specific Aims 
• 
For all VLBW infants, collect baseline demographic and clinical data. 
• 
Characterize, categorize, and catalogue events of sepsis and NEC, and associated clinical and 
laboratory variables, as well as ventilator status at the time of illness 
• 
Identify metrics of heart rate, respiratory rate, and combined cardiorespiratory characteristics 
that change in the preclinical phase of sepsis and NEC 
• 
Develop new or optimally adapted mathematical measures that report on abnormal waveforms 
in the ECG, chest impedance, and/or pulse oximetry signals 
• 
Construct predictive models to predict or detect sepsis or NEC, combining clinical, laboratory, 
and physiologic monitoring data using standard techniques such as (but not limited to) logistic 
regression, and using novel approaches from the field of Big Data analytics such as deep 
neural networks 
• 
Develop and externally validate algorithms on VLBW infants at the other participating NICUs 
over the course of the study. 
 
3. SUBJECT ELIGIBILITY 
Inclusion Criteria 
• 
Birthweight <1500 grams 
Exclusion Criteria 
• 
None 
 
 
4. Study Plan 
Clinical Data Collection 
REDCap will be used by all sites for electronic data entry. All data required for this study should 
be documented in the infant’s medical record as part of routine care. Data entry guidelines are 
specified in the Manual of Procedures (MOP). Both retrospective and prospective data will be 
collected and analyzed. 

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IRB # 21237: Predictive Modeling for Sepsis and Necrotizing Enterocolitis in Preterm Infants 
NICHD: RO1 HD072071: 
PI: Brynne Sullivan, MD, 
Version dated March 10, 2025 
 
Bedside Monitor Data Collection 
At all sites, digital waveforms (3 EKG leads, chest impedance, and the O2 saturation) along with 
q2sec vital signs are stored using BedMasterEx software (Excel Medical), Philips Data 
Warehouse Connect, or other similar data storage systems. These data are labeled by bed 
number and a coded timestamp. Retrospective and prospective data will be used for this study. 
Clinical data and bedside monitor data will also be collected and shared between sites via UVA 
Box. 
 
 
5. STATISTICAL CONSIDERATIONS 
Statistical Methods 
Clinical, demographic, and cardiorespiratory parameters are combined using multivariate 
statistical methods, such as logistic regression, k nearest neighbor analysis, random forests, 
neural nets, and other techniques(12)(13). The period of interest is the 24 hours prior to the 
event. Thus the output of the model is the probability of an event in the next day. We divide by 
the average probability of the event, and present the clinician with the fold-increase in risk of an 
upcoming event. We will have a development/test cohort and validation cohort both within and 
between centers to account for differences in clinical practices that may impact vital sign 
patterns. 
Study Size Considerations 
We anticipate enrolling at least 1667 VLBW infants per site over a 4-year period. With 
incidence of late-onset sepsis and NEC being approximately 15% and 7% respectively, we 
anticipate having about 200 episodes of culture-positive sepsis and 100 episodes of NEC with 
bedside monitor data available for analysis. Generally, 50 events allow for a predictive model 
with 5 predictive variables and (based on our experience with bootstrapping) 95% CI of 0.3 
around the ROC area, so we will have sufficient numbers of infants with events to develop and 
validate models. 
6. STUDY MANAGEMENT 
Institutional Review Board (IRB) Approval and Consent 
This is a purely observational study. Bedside monitor data are currently collected and stored at 
all sites. Routine clinical data are annotated in the electronic health record. For purposes of this 
study, waiver of consent is appropriate since there are no interventions, data are considered de- 
identified under the Privacy Rule since they have only the limited HIPAA identifiers of date and 
Study ID , (Limited Data Set) and securely stored, and requiring consent would impact the study 
results since the consented population would be smaller and may differ from the entire VLBW 
cohort in ways that could impact the validity of the sepsis and NEC algorithms we develop. Data 
will collected either prospectively or retrospectively. 
7. Study Records and Record Retention 

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IRB # 21237: Predictive Modeling for Sepsis and Necrotizing Enterocolitis in Preterm Infants 
NICHD: RO1 HD072071: 
PI: Brynne Sullivan, MD, 
Version dated March 10, 2025 
 
Study documentation includes all electronic case report forms in REDCap, source 
documentation, Sponsor-Investigator correspondence, regulatory documents (e.g., protocol and 
amendments, IRB correspondence and approval) and the like. 
Source documentation will be captured in electronic format and include information from 
patient’s electronic medical records and / or data obtained from various bedside vitals 
monitoring equipment e.g., using BedMasterEx software (Excel Medical), Philips Data 
Warehouse Connect and include all recordings of observations or notations of clinical activities 
and all reports and records necessary for the evaluation and reconstruction of the clinical 
research study. 
Study documentation will be kept on file for the length of time that is required by each 
participating sites IRB. 

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IRB # 21237: Predictive Modeling for Sepsis and Necrotizing Enterocolitis in Preterm Infants 
NICHD: RO1 HD072071: 
PI: Brynne Sullivan, MD, 
Version dated March 10, 2025 
 
 
8. REFERENCES 
1. 
Martin JA, Hamilton BE, Sutton PD, Ventura SJ, Menacker F, Kirmeyer S, et al. Births: 
final data for 2005. Natl Vital Stat Rep. 2007 Dec 5;56(6):1–103. 
2. 
Stoll BJ, Hansen N, Fanaroff AA, Wright LL, Carlo WA, Ehrenkranz RA, et al. Late-onset 
sepsis in very low birth weight neonates: the experience of the NICHD Neonatal 
Research Network. Pediatrics. 2002;110(2 Pt 1):285–91. 
3. 
I. A-C, B.J. S. Neonatal infection and long-term neurodevelopmental outcome in the 
preterm infant. Curr Opin Infect Dis. 2006;19(3):290–7. 
4. 
Greenberg RG, Kandefer S, Do BT, Smith PB, Stoll BJ, Bell EF, et al. Late-onset Sepsis 
in Extremely Premature Infants. Pediatr Infect Dis J. 2017 Aug;36(8):774–9. 
5. 
Sullivan BA, Fairchild KD. Predictive monitoring for sepsis and necrotizing enterocolitis to 
prevent shock. Semin Fetal Neonatal Med. 2015;20:255–61. 
6. 
Lloyd R, O’Toole J, Livingstone V, Hutch W. Predicting 2-y outcome in preterm infants 
using early multimodal physiological monitoring. Pediatr Res. 2016;80(3):382–8. 
7. 
Doheny KK, Palmer C, Browning KN, Jairath P, Liao D, He F, et al. Diminished vagal 
tone is a predictive biomarker of necrotizing enterocolitis-risk in preterm infants. 
Neurogastroenterol Motil. 2014;26(6):832–40. 
8. 
Fairchild KD, O’Shea TM. Heart rate characteristics: physiomarkers for detection of late- 
onset neonatal sepsis. Clin Perinatol. 2010 Sep;37(3):581–98. 
9. 
Moorman JR, Carlo WA, Kattwinkel J, Schelonka RL, Porcelli PJ, Navarrete CT, et al. 
Mortality reduction by heart rate characteristic monitoring in very low birth weight 
neonates: a randomized trial. J Pediatr. 2011;159(6):900–6.e1. 
10. 
Fairchild KD, Lake DE, Kattwinkel J, Moorman JR, Bateman DA, Grieve PG, et al. Vital 
signs and their cross-correlation in sepsis and NEC: A study of 1,065 very-low-birth- 
weight infants in two NICUs. Pediatr Res. 2017;81(2):315–21. 
11. 
Sullivan BA, Wallman-Stokes A, Isler J, Sahni R, Moorman JR, Fairchild KD, et al. Early 
Pulse Oximetry Data Improves Prediction of Death and Adverse Outcomes in a Two- 
Center Cohort of Very Low Birth Weight Infants. Am J Perinatol. 2018 Nov;35(13):1331– 
8. 
12. 
Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: Seven steps for 
development and an ABCD for validation. Vol. 35, European Heart Journal. Oxford 
University Press; 2014. p. 1925–31. 
13. 
Collins GS, Reitsma JB, Altman DG, Moons K. Transparent Reporting of a multivariable 
prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD 
Statement. Ann Intern Med. 2015 Jan 6;162(8):600.