Optimizing Vancomycin Therapy in Children
Conditions: Sepsis, Critical Illness, Infections, Drug Toxicity
Sponsor: Children's Hospital of Philadelphia

Title: Optimizing Vancomycin Therapy in Children (Opt Vanc) 
 
ClinicalTrials.gov ID: NCT05691309 
 
Sponsor: Children's Hospital of Philadelphia 
 
Document Date: May 12, 2023 
 

 
  
Title: 
Optimizing Vancomycin Therapy in Children II 
Short Title: 
Optimizing Vancomycin II 
Sponsor: 
National Institutes of Health 
eIRB Number: 
22-020240 
Protocol Date: 
 January 23, 2023 
Amendment 1 Date: March 9, 2023 
Amendment 4 Date:  
Amendment 2 Date: May 12, 2023 
Amendment 5 Date: 
Amendment 3 Date: 
Amendment 6 Date: 
 
Kevin J. Downes, MD (Principal Investigator) 
Division of Infectious Diseases, Children’s Hospital of Philadelphia 
Roberts Center for Pediatric Research  
2716 South Street, Room 10360 
Philadelphia, PA, 19146 
Phone: 215-590-4024 
Email: downeskj@chop.edu 
 
 

 
  
 
 
 
 
 
ii
TABLE OF CONTENTS 
Table of Contents ................................................................................................................... ii 
Abbreviations and Definitions of Terms ............................................................................. iv 
Abstract ................................................................................................................................. vi 
Table 1: Schedule of Study Procedures ............................................................................. vii 
1 
BACKGROUND INFORMATION AND RATIONALE ....................................................................... 1 
1.1 
INTRODUCTION ..................................................................................................................................... 1 
1.2 
RELEVANT LITERATURE AND DATA ..................................................................................................... 1 
1.3 
COMPLIANCE STATEMENT .................................................................................................................... 5 
2 
STUDY OBJECTIVES .............................................................................................................................. 5 
3 
INVESTIGATIONAL PLAN .................................................................................................................... 5 
3.1 
GENERAL SCHEMA OF STUDY DESIGN ................................................................................................. 5 
3.2 
STUDY DURATION, ENROLLMENT AND NUMBER OF SITES ................................................................... 6 
3.2.1 
Duration of Study Participation ...................................................................................................... 6 
3.2.2 
Total Number of Study Sites/Total Number of Subjects Projected .................................................. 6 
3.3 
STUDY POPULATION ............................................................................................................................. 6 
3.3.1 
Inclusion Criteria ............................................................................................................................ 6 
3.3.2 
Exclusion Criteria ........................................................................................................................... 6 
4 
STUDY PROCEDURES ............................................................................................................................ 8 
4.1 
SCREENING VISIT ................................................................................................................................. 8 
4.2 
OBSERVATIONAL PERIOD ..................................................................................................................... 8 
4.2.1 
Visit 1 .............................................................................................................................................. 8 
4.2.2 
Visit 2 .............................................................................................................................................. 9 
4.2.3 
Daily blood and urine samples ........................................................................................................ 9 
4.2.4 
Weight measurement ..................................................................................................................... 10 
4.2.5 
Medical record review .................................................................................................................. 10 
4.3 
SUBJECT COMPLETION/WITHDRAWAL ............................................................................................... 10 
5 
STUDY EVALUATIONS AND MEASUREMENTS ............................................................................ 11 
5.1 
SCREENING AND MONITORING EVALUATIONS AND MEASUREMENTS ................................................ 11 
5.1.1 
Screening for eligible subjects ...................................................................................................... 11 
5.1.2 
Informed consent ........................................................................................................................... 11 
5.1.3 
Medical record review .................................................................................................................. 11 
5.1.4 
Cystatin C measurement ................................................................................................................ 11 
5.1.5 
NGAL measurement ...................................................................................................................... 12 
5.1.6 
Physical examination .................................................................................................................... 12 
5.1.7 
PK sample collection ..................................................................................................................... 12 
5.1.8 
Future use of stored samples ......................................................................................................... 12 
5.2 
BAYESIAN AUC24 ESTIMATION .......................................................................................................... 13 
6 
STATISTICAL CONSIDERATIONS .................................................................................................... 14 
6.1 
PRIMARY ENDPOINTS ......................................................................................................................... 14 
6.2 
SECONDARY ENDPOINTS .................................................................................................................... 14 
6.3 
CONTROL OF BIAS AND CONFOUNDING .............................................................................................. 14 
6.4 
STATISTICAL METHODS ...................................................................................................................... 14 
6.4.1 
Baseline Data ................................................................................................................................ 14 
6.4.2 
Analysis of Primary Outcome of Interest ...................................................................................... 14 
6.5 
SAMPLE SIZE AND POWER .................................................................................................................. 15 
7 
SAFETY MANAGEMENT ..................................................................................................................... 16 

 
 
 
 
 
 
 
iii 
7.1 
CLINICAL ADVERSE EVENTS .............................................................................................................. 16 
7.2 
ADVERSE EVENT REPORTING ............................................................................................................. 16 
8 
STUDY ADMINISTRATION ................................................................................................................. 17 
8.1 
DATA COLLECTION AND MANAGEMENT ............................................................................................ 17 
8.2 
CONFIDENTIALITY .............................................................................................................................. 18 
8.3 
REGULATORY AND ETHICAL CONSIDERATIONS .................................................................................. 18 
8.3.1 
Data and Safety Monitoring Plan ................................................................................................. 18 
8.3.2 
Risk Assessment ............................................................................................................................. 18 
8.3.3 
Potential Benefits of Study Participation ...................................................................................... 19 
8.3.4 
Risk-Benefit Assessment ................................................................................................................ 19 
8.4 
RECRUITMENT STRATEGY .................................................................................................................. 19 
8.5 
INFORMED CONSENT/ASSENT AND HIPAA AUTHORIZATION ............................................................ 19 
8.6 
PAYMENT TO SUBJECTS/FAMILIES ...................................................................................................... 20 
9 
PUBLICATION ........................................................................................................................................ 20 
10 
REFERENCES ......................................................................................................................................... 21 
Appendix ............................................................................................................................... 26 
 
 

 
  
 
 
 
 
 
iv
ABBREVIATIONS AND DEFINITIONS OF TERMS 
°C 
Degrees centigrade 
AE 
Adverse event 
AKI 
Acute kidney injury 
Anchoring dose 
Dose of vancomycin preceding performance of TDM/PK sampling 
AUC 
Area under the curve 
CHOP 
Children’s Hospital of Philadelphia 
CHPS 
Center for Human Phenomic Science 
CITI 
Collaborative Institutional Training Initiative 
Cmin  
Minimum blood plasma concentration 
CysC 
Cystatin C 
DOB 
Date of Birth  
ECMO 
Extracorporeal Membrane Oxygenation 
ELISA 
Enzyme-Linked Immunosorbent Assay 
GFR 
Glomerular Filtration Rate  
ICU 
Intensive Care Unit 
IRB 
Institutional Review Board  
IV 
Intravenous 
Kg 
Kilogram 
KIM-1 
Kidney Injury Molecule-1 
mg 
Milligram 
mL 
Milliliter 
min 
Minute 
MIC 
Minimum Inhibitory Concentration  
MRN 
Medical Record Number 

 
  
 
 
 
 
 
v
NGAL 
Neutrophil Gelatinase-Associated Lipocalin 
NIH 
National Institutes of Health 
PD 
Pharmacodynamics  
PI 
Principle Investigator 
PICU 
Pediatric Intensive Care Unit 
PIM-3 
Pediatric Index of Mortality-3 
PK 
Pharmacokinetics 
PK sampling 
Vancomycin concentrations drawn for research 
PopPK 
Population PK 
REDCap 
Secure web application for building and managing online surveys and 
databases 
ROC 
Receiver Operating Characteristic 
SAN 
Storage Area Network 
SCr 
Serum Creatinine 
SIRS 
Systemic Inflammatory Response Syndrome 
t½  
Elimination half-life 
TDM 
Therapeutic Drug Monitoring 
TDM samples 
Vancomycin concentrations drawn for clinical care 
UCr 
Urine Creatinine 
 

 
  
 
 
 
 
 
vi
ABSTRACT 
Context:   
Vancomycin is the drug of choice for treatment of serious gram-positive infections, 
exhibiting area under the curve (AUC24)-dependent efficacy, as well as toxicity. Currently, 
vancomycin dosing is based on a child’s weight and renal function, determined by 
measurement of serum creatinine (SCr). But, the ability of SCr to guide vancomycin dosing 
is poor in critically ill children because of the dynamic nature of critical illness. Cystatin C 
(CysC) is a recently available plasma biomarker that better estimates kidney function and 
drug clearance than SCr. By using population pharmacokinetic (popPK) models as prior 
information, Bayesian dose adaptation can incorporate a patient’s characteristics (i.e. age, 
renal function, etc.), dosage history, and optimally timed drug concentrations to estimate 
patient-specific PK parameters and facilitate personalized vancomycin dosing that improves 
therapeutic target attainment. Through prior work, we have developed popPK models that 
can be used to improve vancomycin dosing in critically ill children through Bayesian 
estimation approaches. 
Objectives:  
The objective of the study is to evaluate the ability of Bayesian dose adaptation based on our 
previously developed popPK models for intravenous (IV) vancomycin to accurately predict 
vancomycin AUC24 in critically ill children.   
Study Design:  
Observational study. 
Setting/Participants: 
Participants will be recruited from the Children’s Hospital of Philadelphia. A total of 20 
evaluable participants aged 1-17 years receiving standard of care IV vancomycin for a 
suspected or confirmed infection will be enrolled.  
Study Interventions and Measures:  
Study intervention(s): Dosing of vancomycin will be determined by clinical teams. We will 
use Bayesian dose adaptation software that incorporates our previously developed 
vancomycin popPK model(s) to predict each subject’s vancomycin AUC24 based on a single, 
optimally-timed vancomycin concentration. 
Study measures include: urine samples, blood samples, demographics, anthropomorphic and 
clinical data.   
 

 
 
 
 
 
 
 
 
vii
TABLE 1: SCHEDULE OF STUDY PROCEDURES  
Study Phase 
Screening 
Visit 1a 
Visit 2a 
Study Days  
1 
1-2 
2-5 
Review 
inclusion/exclusion 
criteria 
X 
 
 
Informed consent 
X 
 
 
Medical records review 
X 
X 
X 
Weightb 
X 
 
 
Urine samples 
 
X 
X 
Blood samples 
 
X 
X 
 
a Study visits will be based on the timing of therapeutic drug monitoring (TDM) performed 
for clinical care. The first instance of TDM after enrollment will serve as Visit 1 for the 
study; Visit 2 will be the second instance of TDM after enrollment. If TDM will not be 
repeated by the clinical team, Visit 2 will be specified by the study team and occur within 72 
hours of Visit 1.   
b Weight will be collected if one is not available from within 48 hours of vancomycin 
initiation. Weight can be collected at any time during study participation. 
 

 
  
 
  
 
 
 
1
1 
BACKGROUND INFORMATION AND RATIONALE 
1.1 
Introduction 
Sepsis is a leading cause of death in children with an in-hospital mortality of 25%.1,2 Broad-
spectrum antibiotics are necessary and the timely initiation of effective therapy improves 
survival.3 Vancomycin is a commonly used antibiotic in children with suspected or proven 
sepsis in the pediatric intensive care unit (PICU),4 exhibiting area under the curve (AUC24)-
dependent efficacy,5-7 as well as toxicity.8 Increased vancomycin concentrations can lead to 
acute kidney injury (AKI),8-12 which is an independent risk factor for PICU mortality,12-16 
and critically ill children sustain AKI in approximately 25% of vancomycin treatment 
courses.10,11,13  
At CHOP, vancomycin therapeutic drug monitoring (TDM) involves measurement of serum 
creatinine (SCr), a traditional marker of kidney function, and collection of two vancomycin 
serum concentrations, which are used to calculate AUC24. These two vancomycin 
concentrations must be collected at specific times, which can be difficult for clinical teams 
to coordinate, and collection of two blood samples is often uncomfortable for patients. 
While SCr is traditionally used as a marker of kidney function, its ability to guide 
vancomycin dosing in critically ill children, who often have dynamic physiology, is poor.17-
19 Newly discovered biomarkers, such as plasma cystatin C (CysC), may better estimate 
kidney function and drug clearance than SCr.20-24 
By using population pharmacokinetic (popPK) models as prior information, Bayesian dose 
adaptation can incorporate a patient’s characteristics (i.e. age, renal function, etc.), dosage 
history, and drug concentrations to estimate patient-specific PK parameters. This 
information can then be used to facilitate personalized vancomycin dosing that improves 
therapeutic target attainment and reduces AKI.25 Most available popPK models are not 
specific to critically ill children and are therefore insufficient to guide dosing in this 
population via Bayesian dose adaption.  
Through a previous study (IRB 18-014851), we have developed robust population PK 
models for vancomycin in critically ill children, incorporating newer biomarkers (plasma 
CysC and urinary NGAL). We will test the ability of our model(s) to predict vancomycin 
AUC24 using Bayesian dose adaptation in this pilot study. 
1.2 
Relevant Literature and Data 
Vancomycin is the drug of choice for treatment of serious gram-positive infections in 
children: Vancomycin is a glycopeptide antibiotic with excellent activity against gram-
positive bacteria. Due to the rising prevalence of methicillin-resistant Staphylococcus aureus 
(MRSA) infections, current clinical practice guidelines from the Infectious Diseases Society 
of America (IDSA) recommend its use empirically in children with suspected serious 
infections such as bacteremia and meningitis.26 At our institution, gram-positive organisms 
account for nearly 75% of all isolates causing bacteremia, with S. aureus the most common 
pathogen,27 and vancomycin is administered empirically in all patients with suspected 
sepsis. 
Current dosing and monitoring strategies for vancomycin are inadequate in critically 
ill children: Therapeutic drug monitoring (TDM) is used primarily to minimize drug 

 
  
 
  
 
 
 
2
toxicity and, to a lesser extent, maximize therapeutic efficacy. In adults with MRSA 
infections, a ratio of the vancomycin area under the curve over 24 hours (AUC24) to the 
minimum inhibitory concentration (MIC) of the bacteria >400 is the therapeutic target 
associated with improved clinical outcomes.5-7,28 Direct measurement of AUC24 is 
technically difficult, however, requiring multiple blood draws, and is not routinely 
performed in clinical practice. Thus, vancomycin trough concentrations (Cmin; levels 
obtained immediately prior to a dose) had been traditionally used as surrogate therapeutic 
targets for AUC24. Although simple to interpret, a single trough measurement does not 
capture the inter-individual variability in PK present in most populations and the correlation 
between troughs and AUC24 in children is poor.17,18,29,30 As a result, newer guidelines call 
for estimation of vancomycin AUC24 in all children and adults.31 
Critically ill patients frequently undergo physiologic changes that alter the distribution and 
clearance of medications.32 Altered fluid balance leads to an increased volume of 
distribution and lower plasma drug concentrations, while organ dysfunction, including acute 
kidney injury (AKI), may impair drug clearance.32 Higher doses of vancomycin are 
generally needed in critically ill children to achieve therapeutic concentrations.33-35 As a 
result of these physiologic alterations, a one-size-fits-all weight-based dosing strategy for 
antibiotics is suboptimal. 
Vancomycin has a narrow therapeutic index and exhibits AUC-dependent nephrotoxicity.8 
Due to several factors (decreased renal perfusion, receipt of concurrent nephrotoxic 
medications), critically ill children sustain AKI during 15-25% of vancomycin courses,10-13 
which is associated with increased mortality and development of chronic kidney disease.13-
15,36-38 Meanwhile, delayed attainment of vancomycin therapeutic targets increases the risk 
of treatment failure,5,6,39,40 and sub-inhibitory concentrations can contribute to selection of 
resistant organisms.41-43 Considering the tenuous balance between vancomycin efficacy and 
toxicity in critically ill children, and their complex physiology, clinicians need reliable 
approaches to attain target AUC24 to maximize efficacy, minimize toxicity, and prevent the 
development of resistance.  
The current approach to TDM of vancomycin at CHOP involves measurement of two serum 
concentrations obtained 1-1.5 hours after infusion of a dose and immediately prior to the 
next dose. These two concentrations are then used to calculate AUC24 using the Sawchuk-
Zaske method. Limitations to this approach are: a) collection of two samples can be 
uncomfortable for patients, b) obtaining two samples at these specific times can be difficult 
for clinical teams, c) mistimed samples may be uninformative with this method, and d) 
samples must be collected at steady-state, which is not always possible in critically ill 
patients, who often have dynamic physiology.  
Novel acute kidney injury biomarkers can improve estimation of vancomycin AUC24 
and clearance: Vancomycin is renally eliminated and total body clearance (CL) is related to 
glomerular filtration rate (GFR).44 Direct GFR measurement in the critical care setting is 
impractical and clinicians utilize surrogate markers such as SCr to estimate GFR (eGFR). 
Creatinine is a suboptimal marker of kidney function in children as values can be affected by 
age, gender, medications, and muscle mass.45 Changes in SCr resulting from AKI are also 
not evident until significant renal mass has been affected.46  

 
  
 
  
 
 
 
3
Cystatin C (CysC) is an endogenous cysteine protease inhibitor widely expressed by 
nucleated cells and produced at a constant rate in the body.47 It is freely filtered across the 
glomerular membrane, not secreted by renal tubules, and, therefore, is felt to be a good 
marker of GFR.47 Studies have described its superiority over SCr for GFR estimation and 
earlier AKI detection in critically ill children including those with sepsis.48-54 In previous 
popPK work, we have found that CysC is a more informative covariate on vancomycin CL 
than SCr in critically ill children.55 
Neutrophil gelatinase-associated lipocalin (NGAL) is another promising biomarker for the 
early detection of AKI in critically ill children.54,56,57 Plasma and urinary NGAL increase 
prior to changes in SCr in critically ill children with sepsis.54,58 In a study of 21 adult 
patients with severe burns receiving vancomycin, NGAL served as a better predictor of 
vancomycin concentrations than creatinine clearance: R2 = 0.58 vs. 0.30, respectively.23  
Given the known limitations of SCr in children, the clinical use of novel AKI biomarkers 
can improve estimation of kidney function and vancomycin CL, detect children at risk for 
subsequent AKI, and promote individualized vancomycin dosing. 
Bayesian dose adaptation facilitates rapid attainment of evidence-based therapeutic 
targets in individual patients: The use of Bayesian dose adaptation can provide robust data 
about drug exposure and ensure that pharmacokinetic/pharmacodynamic (PK/PD) targets are 
attained in individual patients.32 Briefly, Bayesian dose adaptation is a tool to more 
accurately estimate the probability distribution of PK parameter values (i.e. clearance, 
volume of distribution) in individual patients based on how the drug has behaved in prior 
patients.30 By using population PK models as prior information (Bayesian prior), Bayesian 
approaches incorporate a patient’s characteristics (i.e. age, gender, renal function, etc.), 
dosage history, and drug concentrations to estimate patient-specific PK parameters 
(Bayesian posterior) that more accurately reflect drug behavior in that specific patient.25 The 
individual Bayesian posterior model can then be used to predict a drug concentration 
following a dose or calculate the dose needed to attain a target concentration (Bayesian 
adaptive control).25 Importantly, Bayesian methods do not require waiting for steady-state 
concentrations and drug sampling can occur at any time. Therefore, more rapid attainment of 
therapeutic goals can be achieved.25 And, an important potential benefit of a Bayesian 
approach to patients, compared to the Sawchuk-Zaske method, is that vancomycin AUC24 
can be estimated from a single drug concentration (i.e limited sampling). 
We have developed population PK models for vancomycin in critically ill children to 
inform Bayesian approaches: Through a previous study (IRB 18-014851), we have 
developed robust population PK models for vancomycin in critically ill children. Briefly, we 
enrolled 50 critically ill children treated with IV vancomycin and collected up to 5 PK 
samples and urinary and plasma biomarkers on each. We performed nonparametric popPK 
modeling for vancomycin using data from 30 subjects (model development group). During 
popPK model development, cystatin C (CysC) was a superior renal function estimator (i.e 
covariate on clearance) compared to serum creatinine (Akaike Information Criteria [AIC] 
793.3 vs. 807.2). And, inclusion of urinary NGAL (uNGAL) as a covariate on vancomycin 
clearance further improved the model compared to the model with only CysC (AIC 785.0 vs 
793.3).  

 
  
 
  
 
 
 
4
We then sought to evaluate the ability of popPK models to estimate AUC24 using Bayesian 
adaptation in the other 20 subjects (validation group). We utilized 3 separate popPK models: 
1) the model that incorporated urinary NGAL and plasma cystatin C (uNGAL model), 2) a 
model that incorporated only plasma cystatin C (CysC model), and 3) a model that 
incorporated only serum creatinine (SCr model). We evaluated the accuracy of these 3 
models to estimate AUC24 using limited sampling (1 or 2 optimal sampling times) compared 
to non-compartmental AUC24 from 4 or 5 vancomycin measurements. Our a priori target for 
model validation was bias <10% and correlation >0.9, compared to non-compartmental 
AUC24. 
We found that the uNGAL model and the CysC model performed similarly in terms of 
AUC24 estimation. When using a single, optimally timed sample, the uNGAL model 
estimated AUC with a median bias of 3.5% and had a correlation with non-compartmental 
analysis AUC24 of 0.868. The bias and correlation of the CysC model were 2.5% and 0.891, 
respectively. Meanwhile, the bias and correlation of the SCr model were -0.9% and 0.825, 
respectively. When using the 2 optimally timed samples, the bias and correlation of the 
uNGAL model, CysC model and SCr model were: 0.9% and 0.924, 1.8% and 0.928, and -
1.2% and 0.860, respectively. Thus, we concluded that the CysC and uNGAL models could 
estimate AUC24 using 2 optimally timed samples with adequate accuracy and that these two 
models performed similarly.  
Upon further evaluation, the 20 subjects included in the validation analyses differed slightly 
from the 30 subjects used during model development. The validation population was slightly 
heavier (37.6 vs 25.9 kg) and had better kidney function (estimated GFR based on Cystatin 
C: 156 vs 130 ml/min/1.73 m2) at vancomycin initiation. Urinary NGAL was also slightly 
lower in the validation cohort (35.5 vs 59.8 ng/mg urine creatinine), suggesting less kidney 
injury. These differences may have contributed to the suboptimal correlation between AUCs 
when using a single optimal sample, especially given the small sample size of our validation 
cohort (n=20).  
We next combined data from all 50 subjects enrolled in the project (IRB 18-014851) and 
developed new popPK models. As before, CysC was the most informative covariate on 
vancomycin CL. And, as before, uNGAL was also an informative covariate on vancomycin 
CL. Interestingly, in this population of 50 subjects, plasma NGAL (pNGAL) led to a similar 
reduction in AIC as uNGAL, with similar bias and imprecision. Through this process, we 
have developed a new CysC model, as well as a new uNGAL model and a pNGAL model. 
Inclusion of all 50 subjects in our model(s) improved the bias and precision of parameter 
estimation and should improve the generalizability of our model(s) across a spectrum of 
illness severity. These models will serve as the focus of the current observational study.  
Because the uNGAL and CysC models performed similarly in terms of AUC24 estimation 
during our initial model development and validation analyses, the CysC model will be the 
primary model of interest for the current study.  This is because: a) CysC is a clinically 
validated and available test, b) CysC results can be available in real-time to inform dosing 
via Bayesian estimation at the bedside, and c) the CysC model is simpler (i.e. has fewer 
covariates) and will be easier to implement clinically in the future. The pNGAL and uNGAL 
models will serve as the basis for exploratory analyses (see Section 5.2). 

 
  
 
  
 
 
 
5
1.3 
Compliance Statement 
This study will be conducted in full accordance with all applicable Children’s Hospital of 
Philadelphia Research Policies and Procedures and all applicable Federal and state laws and 
regulations including 45 CFR 46. All episodes of noncompliance will be documented. 
The investigators will perform the study in accordance with this protocol, will obtain 
consent and assent (unless a waiver is granted), and will report unanticipated problems 
involving risks to subjects or others in accordance with Children’s Hospital of Philadelphia 
IRB Policies and Procedures and all federal requirements. Collection, recording, and 
reporting of data will be accurate and will ensure the privacy, health, and welfare of research 
subjects during and after the study. 
2 
STUDY OBJECTIVES 
The purpose of the study is to evaluate the feasibility of Bayesian dose adaptation based on 
our CysC model and a single optimally timed PK sample to predict vancomycin AUC24 in 
critically ill children.  
Secondary objectives are to: 
• Determine the accuracy of Bayesian AUC estimation using an optimally timed PK 
sample compared to Bayesian estimation using ≥2 samples.  
• Compare AUC24 derived from Bayesian estimation using a single, optimally timed 
sample to AUC24 calculated using standard clinical methods (Zaske-Sawchuk 
method).  
• Assess the ability of our model to predict Visit 2 AUC24 using Bayesian estimation.  
• Compare AUC24 derived from Bayesian estimation using the uNGAL and pNGAL 
models to the CysC model (exploratory set of analyses).  
3 
INVESTIGATIONAL PLAN 
3.1 
General Schema of Study Design 
This is an observational pilot study. We will identify 20 evaluable subjects receiving 
vancomycin for standard of care in the CHOP PICU. For each subject, we will measure 
CysC upon enrollment and at the time of any clinical SCr measurement following 
enrollment. We will use the MMOpt function of Pmetrics™ to determine the optimal 
sampling time to estimate AUC24 for each subject. At the time of clinical TDM (Visit 1), we 
will collect a single PK sample at the optimal sampling time. We will then utilize Bayesian 
dosing software (InsightRx, Inc.), informed by our CysC popPK model, to estimate each 
subject’s PK parameters (clearance, volume of distribution, etc.) and vancomycin AUC24. 
We will compare each subject’s Visit 1 AUC24 using the optimally timed PK sample to: a) 
AUC24 estimated using the Bayesian software and all available vancomycin measurements 
(TDM + PK samples; primary outcome), and b) AUC24 calculated for clinical care via TDM 
(secondary outcome). This will provide information about the accuracy and feasibility of 
real-time Bayesian estimation based on optimal sampling. If the subject has only a single 
vancomycin concentration obtained for clinical TDM, we will draw the optimally timed 

 
  
 
  
 
 
 
6
sample as well as a “peak” concentration, which will allow us to calculate AUC24 as is done 
clinically (Zaske-Sawchuk method). 
We will then repeat the process of optimal sampling time determination and TDM/PK 
sampling 24-72 hours later (Visit 2). We will use the Bayesian dosing software and data 
available prior to Visit 2 to predict AUC24 at Visit 2. We will compare the predicted AUC24 
to the measured AUC24; the measured AUC24 will incorporate vancomycin concentrations 
collected at Visit 2. 
As an exploratory analysis, we will also collect urine samples twice daily from enrollment 
through Visit 2 for measurement of urinary NGAL. Further, we will collect any residual 
plasma samples available in the CHOP lab from clinical chemistry tests for measurement of 
plasma NGAL. We will then repeat the process of Bayesian AUC24 estimation using the 
Bayesian software and the uNGAL and pNGAL models. Measurement of NGAL will be 
performed after study enrollment is completed. 
3.2 
Study Duration, Enrollment and Number of Sites 
3.2.1 Duration of Study Participation 
Enrolled subjects will be followed for the duration of their hospitalization or 30 days, 
whichever is shorter.  
3.2.2 Total Number of Study Sites/Total Number of Subjects Projected 
All subjects will be enrolled at the Children’s Hospital of Philadelphia.  
We will plan to enroll 20 evaluable subjects. Subjects will be considered evaluable if they 
provide CysC and PK samples at both study visits (Visits 1 and 2). Subjects that are not 
evaluable may be replaced.  
Because administration of vancomycin may be transient or stopped for clinical care reasons 
prior to PK sampling, we anticipate that 40 subjects will provide consent/assent in order to 
enroll 20 evaluable subjects. 
3.3 
Study Population 
3.3.1 Inclusion Criteria 
1) Males and females 1-17 years of age, 
2) Administered intravenous vancomycin via intermittent infusion, 
3) Eligible for vancomycin therapeutic drug monitoring, per the subject’s clinical team, 
and  
4) Parental/guardian permission (informed consent). 
3.3.2 Exclusion Criteria 
1) Receipt of renal replacement therapy, plasmapheresis, or extracorporeal membrane 
oxygenation (ECMO), or 

 
  
 
  
 
 
 
7
2) Unable to provide urine and blood samples. 
Subjects that do not meet all of the enrollment criteria may not be enrolled. Any violations 
of these criteria must be reported in accordance with IRB Policies and Procedures.  

 
  
 
  
 
 
 
8
4 
STUDY PROCEDURES 
4.1 
Screening Visit 
• Informed Consent. 
• Review of inclusion/exclusion criteria – following consent and prior to performance 
of any study procedures. 
4.2 
Observational Period  
A schematic for study procedures is shown in Appendix A.  
Following informed consent, study subjects will complete two study visits within 4 days to 
provide blood and urine samples. No more than a teaspoon (3mL) of blood will be collected 
on any given day of study participation, and no more than two teaspoons (9mL) of blood 
will be collected over the course of the study from a given participant. After their active 
participation is complete, their data will continue to be collected from the EMR through the 
end of their follow-up period. 
4.2.1 Visit 1 
At Visit 1 subjects will provide 1 blood sample (0.5 – 1.0 mL each) for the measurement of 
vancomycin in serum in the CHOP clinical lab. 
Subjects will undergo TDM for clinical care. This involves collection of 1 or 2 blood 
samples for measurement of vancomycin concentrations in serum in the CHOP clinical lab. 
Vancomycin concentrations ordered by the clinical team for TDM will be referred to as 
“TDM samples.” The timing and ordering of all TDM sampling in enrolled subjects will be 
directed by the clinical team. Because TDM samples are drawn for clinical care, they will 
not count towards blood volume limits for research. 
The optimal sample collection time to estimate AUC using Bayesian estimation will be 
determined by using our CysC model and each subject’s dosing information and informative 
covariates (i.e. weight, age, CysC). We will use the MMOpt function of Pmetrics™, a 
library package within R used for population modeling and simulation,59 to determine the 
optimal sampling time. The timing of this sample collection may or may not align with one 
of the TDM samples. If timing does not align, a research-only blood sample will be 
collected, called the “PK sample”. If the optimal sample collection time does align with one 
of the TDM samples, a PK sample will not be collected. The results of the PK sample will 
not be reported in the patient’s electronic medical record to avoid confusion to learners and 
providers who may not be fully acquainted with AUC24 calculations and TDM practices. If 
the subject has only a single vancomycin concentration obtained for clinical TDM, we will 
draw the optimally timed sample as well as a “peak” concentration, which will allow us to 
calculate AUC24 as is done clinically (Zaske-Sawchuk method). 
When possible, blood samples will be drawn in conjunction with clinical care to minimize 
blood draws. Blood will be collected via an existing central venous, arterial or peripheral 
venous catheter, separate and distinct from the infusing catheter, when possible. If a separate 
catheter for blood collection is not present, a peripheral venous catheter for blood collection 

 
  
 
  
 
 
 
9
may be placed, if permission given by the subject’s legal guardian and treating PICU 
physician. If arterial or venous access is not possible, blood may be collected via capillary 
stick. 
Samples will be collected by qualified study staff or CHOP nurses. 
4.2.2 Visit 2 
At Visit 2 subjects will provide 2 blood samples (0.5 – 1.0 mL) for the measurement of 
vancomycin in serum in the CHOP clinical lab 
If dosing adjustments are made by the clinical team after Visit 1, TDM may be repeated to 
confirm the adequacy of the dosing change. If 2 TDM samples are obtained for clinical care, 
no additional PK samples will be drawn. Otherwise, we will collect 2 PK samples. The 
timing of these PK samples will be determined using the MMOpt function of PmetricsTM. 
The results of the PK samples will not be reported in the patient’s electronic medical record. 
When possible, blood samples will be drawn in conjunction with clinical care to minimize 
blood draws. Blood will be collected via an existing central venous, arterial or peripheral 
venous catheter, separate and distinct from the infusing catheter, when possible. If a separate 
catheter for blood collection is not present, a peripheral venous catheter for blood collection 
may be placed, if permission given by the subject’s legal guardian and treating PICU 
physician. If arterial or venous access is not possible, blood may be collected via capillary 
stick. 
Samples will be collected by qualified study staff or CHOP nurses. 
4.2.3 Daily blood and urine samples 
• From the time of enrollment through completion of Visit 2, we will measure CysC at 
least once daily. This test will be performed using the same sample used for SCr 
measurement or another clinical blood test, thus no additional blood will be 
collected. On the day of enrollment, a residual sample from a clinical test performed 
earlier in the day can be used.  In the event that a subject does not have labs 
performed clinically on a given day, we will collect 1 mL for measurement of CysC; 
we will also keep residual plasma from this sample. No more than 5 mL/kg may be 
drawn for research purposes in a single day and no more than 9.5 mL/kg may be 
drawn over any eight-week period for children.  
 
• If available, we will collect or utilize residual blood samples stored in the CHOP 
Clinical Labs from clinical tests performed from the start of the subject’s 
vancomycin course through the end of active study participation. These samples will 
be used for CysC or NGAL measurement, and for future research if the subject 
consents to future use. 
 
• From the time of enrollment through completion of Visit 2, we will collect urine 
samples twice daily for measurement of urinary NGAL in a research lab. The goal 
time for these urine samples will be 6-10am and 3-7pm, although we will allow for 
collection outside of these windows if necessary. Urine samples will be collected via 

 
  
 
  
 
 
 
10 
indwelling urinary catheter (if present), clean intermittent catheterization (if 
performed for clinical care), urine cup or urine bag. 
4.2.4 Weight measurement 
Weight will be measured if is one not available from clinical care within 48 hours of 
vancomycin initiation. Weight can be measured at any time during study participation. 
4.2.5 Medical record review 
Data/information will be collected. We will collect demographic, anthropomorphic and 
clinical data, including diagnoses, laboratory and imaging results, and concomitant 
medications. Outcomes will be tracked including infection outcomes, PICU length of stay 
and mortality. 
4.3 
Subject Completion/Withdrawal 
Subjects may withdraw from the study at any time without prejudice to their care.  They 
may also be discontinued from the study at the discretion of the investigator due to change 
in diagnosis, treatment, or lack of adherence to study schedule.  
Subjects will be considered evaluable if they provide CysC and PK samples at both study 
visits (Visits 1 and 2). Subjects that are not evaluable may be replaced. 

 
  
 
  
 
 
 
11 
5 
STUDY EVALUATIONS AND MEASUREMENTS 
5.1 
Screening and Monitoring Evaluations and Measurements 
5.1.1 Screening for eligible subjects 
Utilizing a daily roster of PICU patients, eligible subjects will be identified each day by a 
study team member through screening of the PICU census, discussions with the PICU 
research team, review of electronic medical records and discussion with clinical team. 
5.1.2 Informed consent  
Subjects’ parents or legal guardians will be consented and subjects will be enrolled 
following initiation of IV vancomycin. 
5.1.3 Medical record review 
Medical records will be reviewed for collection of data from 48 hours prior to the start of 
vancomycin therapy through hospital discharge or 30 days from initiation of vancomycin, 
whichever is shorter: 
• Demographics 
• Anthropomorphic measures (weight, body surface area) 
• Vital signs 
• Diagnoses 
• Vancomycin dosing information 
• Vancomycin TDM information (including calculated AUC24 according to clinical 
team) 
• Microbiology results* 
• Laboratory results** 
• Severity of illness scores  
• Respiratory support 
• Medications 
• Infection outcomes at 14 and 30 days after vancomycin initiation  
• PICU and hospital length of stay  
• Discharge status (alive/dead) 
* Data will be included from vancomycin initiation through 30 days post-treatment to 
identify infection outcomes. 
** Data will be included from 90 days prior to vancomycin initiation through initiation of 
vancomycin treatment to generate baseline data such as renal function. 
5.1.4 Cystatin C measurement 
CysC will be measured via particle-enhanced turbidimetric immunoassay (PETIA) in the 
CHOP Chemistry Laboratory. Results of the CysC measurement will be reported in a 
subject’s electronic medical record.  
Up to 5 mL blood will be collected for CysC measurement for this study. 

 
  
 
  
 
 
 
12 
5.1.5 NGAL measurement 
Urine samples will be collected via indwelling urinary catheter, clean intermittent 
catheterization (if performed for clinical care), or urine cup/bag. Urine volumes will be 
recorded and samples will be kept on ice or refrigerated at 4°C until centrifugation (3000 
RPM at 4°C for 15 minutes). The supernatants will be divided and stored at -80°C.  
Each urine sample, along with any residual plasma collected for the study, will be used to 
quantify NGAL via ELISA in the Translational Core Laboratory. Urinary creatinine will 
also be measured on each urine sample, to correct for urinary volume.  
5.1.6 Physical examination 
Weight may be measured for research purposes if these measurements were not completed 
as standard of care. Weight will be collected if one not available from within 48 hours of 
vancomycin initiation. 
5.1.7 PK sample collection 
Following the Visit 1 anchoring dose for TDM, we will collect a single 0.5-1.0 mL blood 
sample for quantification of vancomycin in serum. This PK sample will be collected at the 
optimal time to estimate vancomycin AUC24 via Bayesian estimation. If the timing of the 
PK sample coincides with a TDM sample, the PK sample will not be drawn. If the subject 
has only a single vancomycin concentration obtained for clinical TDM, we will draw the 
optimally timed sample as well as a “peak” concentration, which will allow us to calculate 
AUC24 as is done clinically (Zaske-Sawchuk method). 
Within 72 hours of Visit 1, we will collect 2 additional PK samples. The timing of these 
specimens will be informed by MMOpt and used to estimate vancomycin AUC24 via 
Bayesian estimation. If a subject undergoes clinical TDM within 72 hours of Visit 1, no 
additional samples will be collected. 
Vancomycin concentrations will be measured by chemiluminescent microparticle 
immunoassay (Abbott Diagnostics) in the CHOP Chemistry Lab. Results of vancomycin 
concentrations drawn specifically for our study (i.e. PK samples) will not be reported in the 
patient’s medical chart. 
Up to 3 mL will be collected for vancomycin concentration measurement for this study. 
5.1.8 Future use of stored samples 
The consent form will include a section to ask for permission to retain any specimens 
collected for possible use in future research studies, such as analyses of other biomarkers. 
We will plan to retain samples indefinitely following the conclusion of the study.  
 
There are no benefits to subjects in the collection, storage and subsequent research use of 
specimens. Reports about future research done with subjects’ samples will not be kept in 
their health records, but subjects’ samples may be kept with the study records or in other 
secure areas. Subjects can decide if they want their samples to be used for future research or 
have their samples destroyed at the end of the study. A subject’s decision can be changed at 
any time prior to the end of the study by notifying the study personnel in writing. However, 
if a subject consents to future use and samples have already been used for research purposes, 
the information from that research may still be used. 

 
  
 
  
 
 
 
13 
 
5.2 
Bayesian AUC24 estimation 
We have developed nonparametric population PK models for vancomycin using Pmetrics™, 
a library package within R used for population modeling and simulation.59 As described in 
section 1.2, we have developed three popPK models in our previous work that have 
performed similarly in terms of their accuracy and precision for AUC24 estimation. The first 
is based on plasma CysC as a renal function biomarker (CysC model) and the others include 
urinary NGAL (uNGAL model) and plasma NGAL (pNGAL model) on vancomycin CL. 
The CysC model is our primary model of interest while the NGAL models are exploratory 
models. 
For each subject enrolled, we will utilize the Multiple Model optimal (MMopt) sampling 
algorithm in Pmetrics™ to identify the single optimal sampling time to estimate AUC24.60 
MMopt finds the optimal times based on when all the PK curves generated by the support 
points in the nonparametric model, in this case the CysC model, are most separated (e.g. the 
time points that are most informative). Thus, MMopt will inform the optimal sampling time 
for each subject, based on the subject’s dosing information, covariate data (i.e. CysC result), 
and anticipated sampling window. 
Through a Collaborative Research Agreement with InsightRx, Inc. (2021-1898), the CysC 
and NGAL models can be incorporated into the InsightRx clinical dosing software program 
for Bayesian estimation in our study. Data will be de-identified prior to entry into their web-
based interface. If integration of our models into InsightRx software cannot be 
accomplished, we will use BestDoseTM or Pmetrics™, which are freely available, R-based 
PK software programs.  
For each subject, we will enter the relevant vancomycin dosing information and Visit 1 
covariate data (e.g. CysC concentration, weight, age, etc.) into the program. We will 
additionally enter their single, optimally timed, Visit 1 vancomycin concentration. From 
this, we will generate estimates of each subject’s PK (CL, volume of distribution, 
elimination rate, etc.) and their Visit 1 vancomycin AUC24, which will be recorded in 
REDCap. We will then add any additional Visit 1 vancomycin concentrations obtained for 
TDM to the software, re-estimate the subject’s PK and AUC24, and record these values in 
REDCap. We will also calculate each subject’s PK and AUC24 using TDM samples via the 
Zaske-Sawchuk method, as will be performed clinically.  
We will then use Bayesian estimation to predict each subject’s Visit 2 AUC24 based on the 
relevant dosing information, covariate data, and optimally timed Visit 1 PK. We will also 
predict each subject’s Visit 2 vancomycin concentrations, based on the time that TDM or PK 
samples are collected. 
Finally, we will re-estimate the Visit 2 PK and AUC24 incorporating all relevant dosing 
information, covariate data, and vancomycin concentrations.  
The uNGAL and pNGAL models will be utilized retrospectively, since measurement of 
urinary and plasma NGAL will only take place after completion of enrollment. 
 
 

 
  
 
  
 
 
 
14 
6 
STATISTICAL CONSIDERATIONS 
6.1 
Primary Endpoints 
Vancomycin AUC24 at Visit 1 estimated using Bayesian estimation and the single, optimally 
timed vancomycin PK sample (AUCoptimal). 
6.2 
Secondary Endpoints 
Vancomycin AUC24 at Visit 1 estimated using Bayesian estimation and all available 
vancomycin concentrations (AUCfull_1) 
Vancomycin AUC24 at Visit 1 calculated using Zaske-Sawchuk method and TDM 
vancomycin concentrations (AUCclinical_1) 
Predicted vancomycin AUC24 at Visit 2 using Bayesian estimation and the single, optimally 
timed vancomycin PK sample from visit 1 (AUCpredicted). 
Vancomycin AUC24 at Visit 2 estimated using Bayesian estimation and all available 
vancomycin concentrations (AUCfull_2) 
Vancomycin AUC24 at Visit 2 calculated using Zaske-Sawchuk method and TDM or PK 
vancomycin concentrations (AUCclinical_2) 
Vancomycin concentrations at Visits 1 and 2 
 
6.3 
Control of Bias and Confounding  
Subjects will be identified based on eligibility criteria defined above. Among those eligible 
for participation, selection of subjects will not be influenced by subjects’ cultural 
background, age, or socioeconomic status. We will minimize information bias by collecting 
data from the medical record in a systematic manner using a common data abstraction tool. 
All efforts will be made to record data accurately.  
6.4 
Statistical Methods 
6.4.1 Baseline Data  
Baseline and demographic characteristics will be summarized by standard descriptive 
summaries (e.g. means and standard deviations for continuous variables such as age and 
percentages for categorical variables such as sex). 
6.4.2 Analysis of Primary Outcome of Interest 
The primary analysis will compare the Visit 1 AUC24 estimated using Bayesian estimation 
and the single, optimally timed vancomycin PK sample (AUCoptimal) to the Visit 1 AUC24 
estimated using Bayesian estimation and all available vancomycin concentrations (AUCfull). 
Since we have shown through our previous work that vancomycin AUC24 can be accurately 
estimated with ≥2 concentrations, Bayesian estimation from all available concentrations (PK 
sample + TDM samples) will serve as the primary comparator of interest.  

 
  
 
  
 
 
 
15 
We will determine the bias [(AUCoptimal – AUCfull / AUCfull) * 100] and imprecision 
[absolute value of (AUCoptimal – AUCfull / AUCfull) * 100] for each subject, and, we will 
summarize these measures across subjects. Bias <10% will be considered excellent and 
<20% will be considered good.  
A series of secondary comparisons will also be performed: 
• AUCoptimal to AUCclinical_1 
• AUCpredicted to AUCfull_2 
• AUCpredicted to AUCclinical_2 
• Estimated Visit 1 vancomycin concentrations after Bayesian estimation with the 
optimally timed PK sample to measured vancomycin concentrations 
• Predicted Visit 2 vancomycin concentrations after Bayesian estimation with the 
optimally timed PK sample to measured vancomycin concentrations 
• Comparison of PK parameters derived from Bayesian estimation vs clinical 
calculations 
All comparisons will be made using the CysC model as the model embedded in the Bayesian 
estimation software.  
As an exploratory analysis, we will embed the Bayesian software with the uNGAL and 
pNGAL models and repeat all AUC estimations. Each AUC estimation (AUCoptimal, 
AUCfull_1, and AUCfull_2) will be compared to the estimate generated using the CysC model, 
as well as to AUCclinical_1 and AUCclinical_2, as appropriate. 
6.5 
Sample Size and Power 
This is a pilot study with the primary objective to demonstrate the feasibility of real-time 
Bayesian estimation for vancomycin at the bedside using CysC. Sample size was not 
determined by power calculations since no statistical comparisons are planned for this study. 
Results of this pilot study will serve as preliminary data to inform future grant applications 
and large-scale studies that will investigate the impact of AUC-driven dosing on clinical 
outcomes.  

 
  
 
  
 
 
 
16 
7 
SAFETY MANAGEMENT 
7.1 
Clinical Adverse Events  
Vancomycin will be administered by the CHOP clinical team. All routine administration and 
monitoring practices will be followed by the CHOP clinical team. Decisions regarding 
initiation, dosing, monitoring, and discontinuation of vancomycin will be solely at the 
discretion of the clinical team. Monitoring of adverse events (AEs) will not be conducted as 
part of this observational study. Results of vancomycin concentrations drawn for research 
purposes will not be made available to the clinical team. Results of Bayesian estimation will 
also not be shared with the clinical team.  
7.2 
Adverse Event Reporting 
Since the study procedures are not greater than minimal risk, SAEs are not expected. If any 
unanticipated problems related to the research involving risks to subjects or others happen 
during the course of this study (including SAEs) these will be reported to the IRB in 
accordance with CHOP IRB SOP 408: Unanticipated Problems Involving Risks to Subjects. 
AEs that are not serious but that are notable and could involve risks to subjects will be 
summarized in narrative or other format and submitted to the IRB at the time of continuing 
review.  

 
  
 
  
 
 
 
17 
8 
STUDY ADMINISTRATION 
8.1 
Data Collection and Management 
Data entry and storage will be facilitated using REDCap, which enables secure capture of 
research data as coded, data-dictionary-compliant observations through web-based 
electronic case report forms and surveys. Real-time transactional data such as variables 
associated with an updated clinical encounter in the EHR can be captured programmatically 
through the REDCap application-programming interface. Whenever possible, study data will 
be entered directly into REDCap to minimize the use of paper forms.  
Research urine samples, and any residual plasma samples, will be labeled with a unique 
Subject ID and sample number. Paper forms may be used to collect research information 
related to sample collection such as collection date/times, collection site, and other pertinent 
collection, storage, and sample processing information. All paper forms will be coded with a 
Subject ID and direct patient identifiers (name, DOB, MRN) will be omitted. It is possible 
that research samples will need to be temporarily labeled with a clinical patient label, but 
this label will be removed and destroyed prior to any longer-term storage in the Clinical 
Pharmacology Lab in Abramson Research Center.  
Vancomycin and CysC samples will be sent to the CHOP Chemistry Laboratory and labeled 
with patient stickers, as is routine clinical care. Results of CysC will be made available in 
the patient’s EMR. Results of vancomycin concentrations drawn specifically for the study 
will not be made available, since these PK samples are not part of routine care. 
Pharmacokinetic data and research test results will be stored on a secure Research SAN. 
Subject ID numbers will be used in lieu of direct patient identifiers (name, DOB, MRN), 
although these files may contain dates and times.  
All data entered into InsightRx will be de-identified. Dates will be shifted prior to entry into 
the InsightRx interface to ensure confidentiality of the data. 
1. Confidentiality 
• All research materials are inaccessible to anyone other than the investigators 
and other project personnel approved for the conduct of this research by the 
IRB of the Children’s Hospital of Philadelphia. 
• The REDCap project will link PHI and Subject ID numbers. Paper data forms 
will be coded with a Subject ID number; these files will also contain dates. 
Electronic data will be stored in REDCap or on the CHOP Research SAN. 
Electronic files will be coded and will be stored on the CHOP Research SAN. 
No identifying information about the subjects will be extracted or reproduced 
on collection forms or labels. 
• Electronic files will be stored on the CHOP Research SAN accessible only to 
CHOP research staff who have completed their CITI training.   
2. Security 

 
  
 
  
 
 
 
18 
• Electronic files will be stored on the CHOP secure network that is backed up 
at least daily. 
3. Anonymization, de-identification or destruction 
• A coded data set will be used to replace the name, MRN and other readily 
identifiable fields with a unique Subject ID number. No identifying 
information about the subjects will be extracted or reproduced on collection 
forms or labels. Each subject in the study will be assigned a unique Subject 
ID to be used on all data forms and labels. The link between direct identifiers 
and Subject ID numbers will be maintained in REDCap. Only the 
investigators and the IRB approved project staff at CHOP will have access to 
this information. All personally identifiable information will be kept strictly 
confidential and on secure servers at CHOP.  
8.2 
Confidentiality 
All data and records generated during this study will be kept confidential in accordance with 
Institutional policies and HIPAA on subject privacy, and the Investigator and other site 
personnel will not use such data and records for any purpose other than conducting the 
study. Safeguards to maintain confidentiality have been described above. 
No identifiable data will be used for future study without first obtaining IRB approval. The 
investigator will obtain a data use agreement between provider (the PI) and any recipient 
researchers (including others at CHOP) before sharing a limited dataset (dates and zip 
codes).  
8.3 
Regulatory and Ethical Considerations 
8.3.1 Data and Safety Monitoring Plan 
The Principal Investigator will be responsible for data and safety monitoring. The only risks 
anticipated to patients are those from blood draws and potential loss of confidentiality.  
8.3.2 Risk Assessment 
The study is minimal risk for all subjects and potential for minor discomfort is possible.  
Risks associated with collection of blood: 
Blood samples will be collected primarily via pre-existing vascular catheters. For subjects 
that do not have adequate access, investigators may place a research IV catheter to collect 
blood samples. Placing an IV may cause some pain, and bleeding or bruising at the spot 
where the needle enters your body. Rarely, it may cause fainting. The longer an IV catheter 
is left in place, the more common it is for redness or infection to develop. If placement of a 
research IV is not possible and clinically-placed central lines or arterial lines are not 
available, blood samples may be collected via venipuncture, which may cause some pain, 
bleeding or bruising at the spot where the needle enters your body. If arterial or venous 
access is not possible, blood may be collected via capillary stick. Rarely, taking blood may 
cause fainting or infection. To reduce risk, bedside nurses will be used to obtain samples and 

 
  
 
  
 
 
 
19 
follow all CHOP protocols. Phlebotomists and/or accredited/approved research staff may 
draw blood samples via venipuncture. 
Risks associated with collection of urine: 
Urine samples introduce no additional risk due to collection from pre-existing urinary 
catheter, clean intermittent catheterization, urine cup, or urine bag.  
To minimize loss of confidentiality, study files containing PHI will be password protected 
and only accessed by the CHOP study team. 
8.3.3 Potential Benefits of Study Participation 
There is no direct benefit to study participation, but patients involved in the study may incur 
an indirect benefit from participation. CysC results will be made available to a subject’s 
treating physicians. Measurement of CysC concentrations could facilitate identification of 
individuals with impaired renal function and/or promote needed dose adjustments or renally 
eliminated drugs, such as vancomycin.  
Participation in this study may also help future children treated with IV vancomycin. This 
study will demonstrate the feasibility of Bayesian estimation based on a robust population 
PK model of IV vancomycin in critically ill children. Results from this study will inform 
future studies and may inform clinical care of future children.  
8.3.4 Risk-Benefit Assessment 
The proposed research is justified, considering that the risk associated with participation is 
minimal compared to the potential and anticipated benefits. With experienced healthcare 
providers and trained staff conducting blood draws, and appropriate confidentiality measures 
in place, the risk to the subjects should be negligible. 
8.4 
Recruitment Strategy 
Eligible subjects will be identified through daily screening of the PICU census by study 
staff. Further, study staff will be notified by Antimicrobial Stewardship team, clinical 
pharmacists, or via an EPIC alert, when a patient in the PICU is receiving IV vancomycin 
and eligible for clinical AUC24 monitoring. Principal Investigator or qualified research staff 
will approach the eligible subjects and their parent(s)/legal guardian(s) in the PICU for 
consent. The screening for this study qualifies for a waiver of HIPAA authorization since it 
is no more than minimal risk and it will not be feasible to obtain consent and HIPAA 
authorization prior to identification of eligible subjects (initiation of vancomycin).  
8.5 
Informed Consent/Assent and HIPAA Authorization 
In obtaining consent, research staff will explain the risks and benefits of participation to the 
child and their parent/legal guardian. The family will be able to ask any study-related 
questions and will be given time to consider their decision. If requested, the research staff 
will leave the family to consider study participation and come back later. After all study-
related questions are answered and families have had time to consider their decision, written 
informed consent and HIPAA authorization (using a combined form) will be obtained by the 

 
  
 
  
 
 
 
20 
research staff. Assent will be obtained from children, as possible. We expect that the 
majority of eligible patients will have altered mental status due to the severity of their 
infections that necessitate a level of care provided in the PICU, in which case assent will be 
deferred. 
The informed consent will cover the child’s participation including collection of biological 
samples and electronic health data. The in-person consent process will take place at 
Children’s Hospital of Philadelphia. 
Because procedures involved in the study are time-sensitive, and parents/guardians may not 
be present in a patient’s room at all times, we may verbally consent the subject’s 
parent/guardian over the phone. Subjects’ parents/guardians will be provided comprehensive 
information using the verbal consent and qualified staff will go over the study procedures 
with them and answer any questions that they might have. Documentation of consent will be 
made by the person consenting by phone. A copy of the completed verbal consent form will 
be provided to the parent/guardian of the study subject. 
8.6 
Payment to Subjects/Families 
The subject’s family will be provided with a $20 gift card upon study completion for their 
time and effort associated with completion of all study procedures for the study. 
9 
PUBLICATION 
We plan to publish the results in a peer-reviewed journal. No individually identifiable PHI 
will be published. 
 
 

 
  
 
  
 
 
 
21 
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APPENDIX 
Figure 1. Schematic of study procedures. Subjects will be enrolled following initiation of 
vancomycin. A blood sample (for CysC) will be collected following enrollment and then 
with each SCr clinical measurement (at least daily). At the time of therapeutic drug 
monitoring (TDM), an additional, optimally timed PK sample will be collected following 
the same dose used for TDM. Within 72 hours of initial TDM, additional blood (for CysC) 
will be obtained, prior to repeat performance of vancomycin TDM. If TDM sampling will 
not occur a second time, 2 samples will be drawn for measurement of vancomycin 
concentrations for purposes of the study. We will also collect urine samples twice daily, as 
well as residual plasma from clinical samples, from enrollment through Visit 2, which will 
be used for measurement of NGAL after completion of enrollment.