Implementation Trial of a Validated Clinical Decision Rule for Pediatric Abusive Head Trauma (NIH Grant Number P50HD089922)
Phase: NA
Conditions: Pediatric Abusive Head Trauma
Sponsor: Milton S. Hershey Medical Center

Study Protocol and Statistical Analysis Plan 
 
 
Official Title: Implementation Trial of a Validated Clinical Decision Rule for Pediatric Abusive Head Trauma 
 
Identifiers: NCT03162354   Unique Protocol ID: STUDY00005613   Secondary IDs: P50HD089922 
 
Document Date: January 29, 2016 
 

SPECIFIC AIMS: Project 2 
Abusive head trauma (AHT) is the leading cause of traumatic death and disability in early childhood, 
affecting at least 690,000 children annually in the United States.1-7 Physicians who care for young victims of 
trauma must make important decisions to launch or forgo child-abuse evaluations. These decisions can be 
difficult, and the stakes are high. Research has shown that AHT was missed or unrecognized in 30% of head- 
injured children, more than 25% of children with unrecognized AHT suffered additional inflicted injuries when 
returned to abusive caregivers, and 10% of children with unrecognized AHT subsequently died or were killed— 
80% of whom could have been saved by earlier recognition of abuse.8 Moreover, a flawed decision to launch 
an abuse evaluation can increase parental stress, expose the child to additional risks, prolong hospital stays, 
and increase health care costs.9, 10 Unfortunately, physicians routinely struggle to define a “reasonable 
suspicion” of abuse11-18 and have demonstrated biases: Younger children of minority race, from single-parent 
households, and of low socioeconomic status get evaluated for abuse more often.8, 11-13, 17, 19-21 
The principal investigator (PI; Hymel) conceptualized, designed, and directed sequential multicenter 
studies to derive and validate an effective AHT screening tool.22, 23 This screening tool comes in the form of a 
clinical decision rule (CDR)—an evidence-based tool developed to guide a discrete decision in the continuum 
of AHT care.24 Applied at the time of pediatric intensive care unit (PICU) admission, the CDR recommends 
abuse evaluations for all “high risk” patients defined as presenting with closed head trauma and any one or 
more of the following: (1) acute respiratory compromise; (2) bruising of the torso, ear(s), or neck; (3) bilateral or 
interhemispheric subdural hemorrhage(s) or fluid collection(s); and (4) any skull fracture(s) other than an 
isolated unilateral non-diastatic linear parietal skull fracture.23 Our preliminary studies of acutely head-injured 
children (N = 500) admitted to 18 PICUs demonstrated that application of this CDR significantly increased AHT 
detection (sensitivity 0.96) and decreased unnecessary abuse evaluations (specificity 0.43).23, 25 Though 
promising, the CDR has not yet been fully tested in a formal randomized trial. 
The proposed CDR clinical trial will demonstrate the CDR’s impact on AHT screening accuracy. We 
hypothesize that CDR application will increase AHT detection and reduce unnecessary abuse evaluations. We 
will conduct a stratified cluster randomized trial (SCRT) at eight US PICUs randomly assigned to intervention 
(n = 4) or control (n = 4) conditions. The SCRT will compare AHT screening accuracy after the deployment of 
active multifaceted implementation strategies designed to promote CDR acceptance, utilization, and accuracy 
at the intervention sites. These strategies include provider training with onsite visits, site-specific feedback, 
tailored booster emails to Providers, CDR badge cards, and a mobile-device “AHT Probability Calculator”. A 
clear demonstration of the CDR’s positive clinical impacts and an evaluation of avenues to promote 
sustainability will inform and accelerate subsequent efforts to disseminate the CDR more broadly. The 
following specific aims are proposed: 
Aim 1. Conduct a SCRT across eight PICUs to assess the CDR’s impact on AHT screening accuracy. In 
comparison to non-intervention control sites, we hypothesize that CDR application as an AHT screening tool at 
the intervention sites will be associated with higher percentages of high-risk patients evaluated for child abuse 
and lower percentages of low-risk patients evaluated for child abuse. 
Aim 2. Identify site-, provider- and patient-specific factors that predict CDR application in PICU settings. 
We hypothesize that sites with higher patient volumes will demonstrate higher percentages of high-risk patients 
evaluated for child abuse and lower percentages of low-risk patients evaluated for child abuse. We also 
hypothesize that Providers with child-abuse expertise, high exposure to the CDR implementation strategies, 
and high CDR acceptability will demonstrate higher percentages of high-risk patients evaluated for child abuse 
and lower percentages of low-risk patients evaluated for child abuse. Although we suspect that CDR 
application will reduce patient disparities at intervention sites, overall, we predict that patients younger than 6 
months or those of minority race will be evaluated for abuse more often than patients older than 6 months or 
nonminority. 
Exploratory Aim 3. Once the SCRT has concluded, conduct a 12-month sustainability trial at intervention 
sites whereby implementation strategies are systematically omitted one-by-one in order to discern their relative 
importance to sustainability. 
Impact. This simple, safe, inexpensive, reliable, readily accessible, evidence-based, and validated CDR will be 
the first child-abuse screening tool to undergo a formal clinical trial. Applied accurately and consistently, the 
CDR will decrease the negative impacts of physicians’ inherent biases and practice disparities, significantly 
increase AHT detection, decrease unnecessary abuse evaluations (and their associated risks), and reduce 
AHT-associated health care costs. Most importantly, the CDR will save lives—the lives of children who will 
otherwise suffer additional fatal inflicted injuries if/when their AHT is missed or unrecognized.8, 25, 

A. SIGNIFICANCE 
Among all forms of violence directed against infants and young children, AHT is the leading cause of 
traumatic death and disability, impacting at least 690,000 U.S. children annually. 1-7 To help confirm or exclude 
AHT masquerading as accidental head trauma, physicians must decide whether or not to launch a workup for 
child abuse in their young, acutely head-injured patients. The signs and symptoms of accidental head injuries 
are non-specific and can be the same as those from abuse, associated external injuries are not universal, and 
decisions are further complicated by the fact that patients are often unable to describe their traumas because 
they are so young and have head injuries. In addition, caregiver accounts of their children’s head injury events 
can be absent, minimized, changing, or fabricated, and for physicians, discussing abuse with parents is often 
uncomfortable, and familial psychosocial risk factors can impair physicians’ objectivity. Not surprisingly then, 
physicians often struggle to define a “reasonable suspicion” of abuse11-18 and have been shown to exhibit 
biases regarding child maltreatment based on family racial, socioeconomic, and marital status.8, 11-13, 19-21 In 
addition, physicians differ in their medical decisions to evaluate young victims of trauma for abuse, with abuse 
being diagnosed more frequently in large centers than in small centers for patients with equivalent injuries. 8, 12, 
17-21 Finally, the validity of AHT as a medical diagnosis is now openly and aggressively disputed in medical 
journals, in the lay press, and in courtrooms.26-33 As a result, many physicians are fearful of making 
unwarranted AHT diagnoses and being challenged in court for doing so, leading them to avoid working up 
children for abuse in the first place. 
A1. The stakes are high. A doctor’s flawed decision to forgo an abuse evaluation in an abused head- 
injured child puts that child at substantial risk for further abuse and/or death when returned to his or her 
abusive caregiver(s).8 In their sentinel 1997 study of “missed” AHT, Jenny, Hymel (the PI of this study), and 
their co-authors reported that (1) AHT was missed or unrecognized in 3 of 10 abused head-injured children 
when those children first presented for medical care, (2) more than 1 in 4 children with missed or unrecognized 
AHT suffered additional inflicted injuries when they were returned to their abusive caregiver(s), (3) almost 1 in 
10 children with missed AHT subsequently died or were killed, and (4) 4/5 of these deaths might have been 
prevented by earlier recognition of their abuse.8 
Launching an unnecessary abuse evaluation in a child who has suffered an accidental head injury can also 
have adverse consequences, including increased parental stress, strain on the doctor-parent relationship, 
exposure of the child to additional risks (e.g., radiation, sedation), false positive results that could trigger a 
misguided child-protection and/or police investigation, prolonged hospital stays, and increased health care 
costs.9, 10 Years after Jenny, Hymel, and colleagues’ missed AHT study,8 physicians continue to miss AHT-- 
and to order unnecessary child-abuse workups. As described in preliminary studies, we estimate that more 
than 1 in 10 AHT victims admitted for intensive care are still missed or misdiagnosed and that 2 of every 3 
patients with accidental head trauma undergo an unnecessary workup for abuse.25 
A2. To improve the accuracy of current AHT screening and evaluation practices, physicians need 
an effective screening tool for AHT. An effective AHT screening tool (1) is simple, safe, readily available, 
highly reliable, inexpensive, and evidence-based; (2) will perform with levels of sensitivity and specificity higher 
than achieved in current AHT screening practices; (3) has been broadly validated in different geographic 
regions, in large and small PICUs, and in the same institutions over time; (4) can be applied at or near the time 
of PICU admission; (5) will facilitate simple, rapid bedside calculation of an evidence-based patient-specific 
estimate of the probability of abuse; and (6) will provide a clear recommendation to complete an abuse 
evaluation if/when appropriate to do so. Finally, in the absence of a gold standard, an effective AHT screening 
tool must perform well across a broad spectrum of criteria physicians use to diagnose abuse. 
A3. Unfortunately, an AHT screening tool with all of these qualities did not exist—until now. The PI’s 
team conceptualized, designed, and directed sequential multicenter studies of 500 acutely head-injured 
children across 18 participating sites to derive and validate an effective AHT screening tool.22, 23 This new AHT 
screening tool is simple, safe, readily available, highly reliable, inexpensive, and evidence-based. Its AHT 
screening performance has been broadly validated in different geographic regions, disparate clinical settings, 
and against a broad spectrum of definitional criteria for AHT.23 Findings revealed that, applied accurately and 
consistently, it would have correctly identified (categorized as high risk) 96% of patients who met our original 
criteria for AHT, 98% of patients ultimately diagnosed with AHT, and 99% of patients whose completed skeletal 
surveys and/or retinal exams revealed corroborating findings of abuse.22, 23 Based on these strong preliminary 
studies, the AHT screening tool presented here is fully ready for a clinical trial in active PICU settings. 
A4. The new AHT screening tool comes in the form of a CDR. A clinical prediction rule is an evidence- 
based tool that measures and then combines the specific predictive contributions of multiple clinical findings or 
test results to estimate or predict the probability of a diagnosis, prognosis, or response to therapy in an 

individual patient.34 A clinical prediction rule rises to the level of a clinical decision rule (CDR) if and when 
physicians use the tool to guide a specific clinical decision. Our screening tool was developed to serve first and 
foremost as an effective decision rule. Our rule was 
derived and validated in strict accordance with 
established guidelines35-39 and was developed to 
function effectively as an AHT screening tool in PICU 
settings.22, 23. It includes four highly reliable predictors 
(see Table A1) and makes a recommendation to 
complete an abuse evaluation on all patients that it 
categorizes as high risk (see Table 1). Because the 
CDR’s predictor variables are all readily available, 
physicians can apply this AHT screening tool when 
they need it—at or near the time of PICU 
admission—to inform or guide their early decisions to either launch or forgo child-abuse evaluations in their 
young, acutely head-injured patients.22, 23 Physicians will be able to apply this validated AHT screening tool as 
a clinical decision rule.22-25 If applied as recommended, it will categorize all patients who present for PICU 
admission with any one or more of its four variables as high risk and make a recommendation to evaluate all 
high risk patients for abuse.23, 25 
A5. Applied accurately and consistently, the CDR could have widespread beneficial impacts. As 
summarized in Preliminary Studies (section C), accurate and consistent application of this new, broadly 
validated CDR could: (1) reduce disparities in current AHT screening and evaluation practices; (2) minimize the 
negative impacts of clinicians’ inexperience, uncertainty, and inherent biases; (3) significantly increase AHT 
detection (87% to 96%); (4) increase the overall diagnostic yield of patients’ completed abuse evaluations (i.e., 
the percentage of patients whose completed skeletal surveys and/or retinal exams reveal corroborating 
findings of abuse; 49% to 56%); (5) lower evidence-based estimates of missed AHT among patients not 
evaluated for abuse (0.19 to 0.07); (6) decrease unnecessary abuse evaluations of patients with accidental 
head trauma (67% to 60%); (7) lower the cost per correctly identified child with AHT (by 15.1%); and (8) 
dramatically lower overall AHT-associated acute health care costs in PICU settings (by 74.2%).25 Most 
importantly, the CDR will save lives—the lives of children who will otherwise suffer fatal inflicted injuries 
because their AHT is missed or unrecognized.8, 25 
B. INNOVATION 
B1. The CDR addresses a high-stakes clinical decision that no other AHT screening tool addresses. 
In the realm of pediatric AHT, only two other CDRs are currently under development. Maguire and colleagues 
have derived and validated an assistive prediction rule that measures the association between AHT and 
various combinations of six clinical findings.40, 41 Because two of these six findings can only be confirmed (or 
excluded) after an abuse evaluation has been launched, their prediction rule cannot be applied as an AHT 
screening tool at or near the time of PICU admission, when physicians need evidence to inform or guide their 
early decisions to launch or forgo an abuse evaluation. Berger and colleagues are working to validate a more 
directive decision rule designed to identify infants at increased risk of brain injury or AHT who might benefit 
from neuroimaging in the emergency department.42 These two rules and our CDR target three different 
decision points along the continuum of AHT clinical care. 
B2. The CDR casts a broad screening net to miss as few cases of AHT as possible. The CDR 
demonstrated sensitivity of 0.96 and specificity of 0.43 in our completed validation study.23 That is, the CDR 
would have categorized 96% of AHT patients as high risk and 43% of non-AHT patients as low risk. Positive 
and negative predictive values were 0.55 and 0.93, respectively. In other words, the proportion of high-risk 
patients with AHT was 55%, and the proportion of low-risk patients with non-AHT was 93%. The CDR was 
designed to be highly sensitive to high-risk patients in need of evaluation. This includes opting for lower 
specificity in favor of higher sensitivity to be liberal in our inclusion of patients at high risk. 
B3. The CDR’s patient-specific estimates of abuse probability are good predictors of the results of 
patients’ completed abuse evaluations. There are 16 combinations of the CDR’s four prediction variables, 
leading to 16 subpopulations. Across these subpopulations, the CDR abuse probability correlated positively 
and very strongly (R = 0.71) with the overall diagnostic yields,25 that is, those corroborated by completed 
skeletal surveys and/or retinal exams. 
B4. Our proposed CDR clinical trial is itself highly innovative. Our proposed SCRT will be the very first 
formal clinical trial of a validated child-abuse screening tool. Our matched pair cluster randomized trial design 
will facilitate the: (1) assessment of the CDR’s impact on relevant clinical outcomes; (2) identification of site- 

specific, provider-specific, and patient-specific factors that impact the CDR’s acceptability, utilization, and 
accuracy; and (3) assessment of the effectiveness of the planned multifaceted CDR implementation strategies. 
Finally CDR implementation will result in: (1) higher diagnostic yields (i.e., abuse corroborated by clinical tests), 
(2) lower evidence-based estimates of “missed AHT” among patients not evaluated for abuse, and (3) lower 
percentages of patients with accidental head trauma evaluated for abuse. 
C. PRELIMINARY STUDIES 
Our proposed CDR clinical trial represents the vital third step in the study PI’s long-term research plan to 
develop an effective PICU-based AHT screening tool. The PI’s commitment to the development and execution 
of this long-term research plan began when he and Dr. Jenny met the infant with missed AHT who would 
became the inspiration for their sentinel study of missed AHT.8 This program of work required the formation of 
a dedicated research network, the application of established methodologies for CDR development, and the 
successful execution of sequential multicenter studies to derive and validate an effective AHT screening 
tool.22,23 Our preliminary studies include the results of our prospective multicenter CDR derivation and 
validation studies and secondary analyses designed to: (1) further validate the CDR’s screening performance; 
(2) assess site-by-site disparities in current AHT screening and evaluation practices; (3) demonstrate the 
CDR’s application as an assistive prediction tool; (4) estimate the magnitude of the CDR’s potential clinical 
impacts and cost benefit; (5) identify factors that could impact the CDR’s acceptability, utilization, and accuracy 
when applied in PICU settings; and (6) estimate baseline CDR acceptability among potential physician users. 
C1. CDR derivation and validation studies. These two prospective cross-sectional observational 
studies22,23 were conducted in accordance with established standards for CDR development35-39 at 18 sites in 
the United States and Canada. Ten sites participated in both studies. In both studies, clinicians were kept 
blinded to the CDR’s final form and content. The two studies used the same inclusion and exclusion criteria, 
data forms, methods, and a priori definitional criteria for AHT. To minimize circular reasoning, these criteria 
contained no references to any of the clinical variables considered for inclusion in the CDR. Eligible patients 
were children less than three years old who were hospitalized for intensive care of symptomatic, acute, closed, 
traumatic, cranial, or intracranial injuries confirmed on initial head CT or MRI. Patients were excluded if their 
head injuries resulted from collisions involving motor vehicles or if initial neuroimaging revealed clear evidence 
of pre-existing brain malformation, disease, infection, or hypoxia-ischemia. 
The CDR derivation study.22 As shown in Table C1, we conducted the derivation study between February 
2010 and August 2011 at 14 PICU sites in order to identify a cluster of “predictor variables” that would perform 
effectively as an AHT screening tool. Our derivation study (1) captured historical, clinical, and radiological data 
for 209 eligible patients; (2) identified 13 highly discriminating (p ≤ 0.001) and highly reliable (κ = 0.68-0.95) 
clinical variables readily available at or near the time of PICU admission; (3) calculated each variable’s isolated 
predictive qualities (i.e., sensitivity, specificity, predictive values, likelihood ratios, post-test probabilities); (4) 
applied a recursive partitioning algorithm designed to penalize missed cases of AHT; and (5) identified four 
clusters of these 13 variables that, alone or in combination, detected AHT with a sensitivity ≥ 0.92. These four 
clusters of variables became our four candidate CDRs. 
The CDR validation study.23 To verify the candidate CDRs’ AHT screening performance in a new, 
equivalent patient population, we conducted a CDR validation study between March 2012 and July 2013 at 14 
participating sites. Our validation study (1) captured equivalent historical, clinical, and radiological data for 291 
additional eligible patients, (2) confirmed AHT 
screening performance of all four candidate CDRs 
in a new equivalent patient population, and (3) 
determined that a four-variable CDR (see Table A1) 
demonstrated the optimal combination of simplicity 
and AHT screening performance. All measures of 
this four-variable CDR’s screening performance 
matched or exceeded equivalent results from the 
completed derivation study. Most importantly, the 
CDR demonstrated sensitivity of 0.96 in both 
studies (see Table C1).22, 23 As Table C1 shows, 
379 (76%) of these 500 eligible patients would have 
been categorized as high risk. 
C2. Secondary analyses. We conducted multiple secondary analyses of the captured data regarding the 
500 acutely head-injured patients in our derivation and validation study datasets. 
CDR’s abuse probability can be used to aid in investigative and legal responses to suspected AHT. For 

example, we showed that the mean probability of children reported to child protective services was 0.64 
whereas the mean probability of children not reported was 0.20. 
The CDR has been broadly validated. Analysis of the CDR’s AHT screening performance in novel patient 
cohorts revealed that the CDR performed with high sensitivity (0.95 to 0.98) in different geographical regions, 
in large and small PICUs (0.94 to 0.99), and in the same PICUs over time (0.97 to 0.99).23 
The CDR is highly sensitive no matter how we define AHT. Gold standard definitional criteria for AHT do 
not exist. As defined by our original a priori criteria, the prevalence of AHT in our combined patient population 
was 0.44. The CDR would have correctly identified (i.e., categorized as high risk) 96% of patients who met 
these criteria. Applying several modified (more inclusive and less inclusive) AHT criteria to iteratively re-sort 
the patient population, our resulting calculations of AHT prevalence ranged from 0.28 to 0.52. Subsequent 
analyses revealed that the CDR would have correctly identified more than 96% of AHT patients no matter how 
we defined AHT. The CDR would also have correctly identified 98% of patients ultimately diagnosed with 
probable or definitive AHT by their treating and/or consulting physicians, and 99% of patients whose completed 
skeletal surveys and/or retinal exams revealed corroborating findings of abuse.23 
Current AHT screening and evaluation practices demonstrate site-by-site disparities. The percentage of 
eligible patients (N = 500) who underwent both skeletal survey and retinal examination varied from 33% to 
100% across the 18 participating sites. The percentage of eligible patients who underwent at least one of these 
abuse evaluations varied from 43% to 100%, and the percentage who underwent neither abuse evaluation 
varied from 0% to 57%. 
Applied as an assistive prediction tool (i.e., the AHT Probability Calculator), the CDR facilitates the simple 
calculation of an evidence-based, patient-specific estimate of abuse probability. To calculate patient-specific 
evidence-based estimates of abuse probability, we (1) divided the patient population (N = 500) into its 16 
subpopulations defined by patients’ unique combinations of the CDR’s four predictor variables, (2) calculated 
the percentage of patients 
within each subpopulation who 
met our a priori definitional 
criteria for AHT, and (3) 
assigned that same value— 
expressed as an estimate of 
abuse probability—to every patient within that same subpopulation. The resulting patient-specific estimates of 
abuse probability varied widely (from 0.06 to 1.00) across the 16 patient subpopulations. For the 121 low-risk 
patients (who presented with none of the CDR’s four predictor variables), their estimated probability of abuse 
was 0.07 (95% CI: 0.04-0.14).25 As discussed above, the CDR’s estimates of abuse probability correlated 
positively with the overall diagnostic yields of patients’ completed skeletal surveys and/or retinal exams.25 
The CDR will improve AHT screening accuracy. In our completed CDR derivation and validation studies, 
PICU Providers and child abuse consultants completed abuse evaluations on only 277 (73%) of 379 patients 
the CDR would have categorized as high risk, and completed abuse evaluation on 70 (58%) of 121 patients 
the CDR would have categorized as low risk. It is on these preliminary findings that we base the power 
calculations of the proposed SCRT. These observations led us to complete analyses designed to estimate the 
CDR’s potential impact on AHT screening accuracy.25 To do so, we applied mean patient-specific estimates of 
abuse probability to predict additional positive abuse evaluations (i.e., cases of missed AHT) among subgroups 
of patients lacking skeletal surveys and/or retinal exams and then used these predictions to extrapolate and 
compare AHT detection in “current PICU screening for AHT” versus “AHT screening guided accurately and 
consistently by the CPR.” Our results (see Table C2) suggest that the CDR could (1) increase AHT detection 
(from 87% to 96%), (2) increase the overall diagnostic yield of patients’ completed abuse evaluations (from 
49% to 56%), (3) reduce (mean) evidence-based estimates of abuse probability (i.e., estimates of missed AHT) 
among patients who are not evaluated for abuse (from 0.19 to 0.07), and (4) reduce unnecessary abuse 
evaluations of patients with non-AHT (from 67% to 60%).25 
The CDR could also decrease AHT-associated acute health care costs in PICU settings. Applying average 
skeletal survey, retinal exam, and visit costs obtained from the MarketScan Commercial Claims and 
Encounters database, we estimate that accurate and consistent application of the CDR in PICU settings could 
reduce the average cost per correctly identified child with AHT from $458 to $389—a reduction of 15.1%. 
Applying published data on re-injury rates in children with missed AHT8 and the costs of emergency 
department/clinic visits and hospital re-admissions resulting from re-injury9, we estimate a 72.4% reduction in 
total AHT-associated acute health care costs stemming from the anticipated reduction in missed AHT cases. 
Non-clinical factors appear to impact physicians’ decisions to launch or forgo child-abuse evaluations in 
Table C2. Extrapolated Measures 
of AHT screening accuracy in… 
Current PICU 
Screening for AHT 
AHT Screening 
 
% 
Guided by the CPR 
change 
…AHT detection 
191 (87%) of 220 
212 (96%) of 220 
↑ 9% 
…Overall diagnostic yields 
191 (49%) of 391 
212 (56%) of 379 
↑ 7% 
…Abuse probability among patients 
not evaluated thoroughly for abuse 
0.19 
0.07 
↓ 12% 

their young acutely head-injured patients. Exploratory analyses identified four non-clinical variables associated 
with significant differences (p < .05) in the percentages of high- or low-risk patients evaluated for child abuse. 
These include primary caregiver denial of any head trauma; patient of minority race/ethnicity; patient cruising 
or walking prior to PICU admission; and patient age less than 6 months at time of PICU admission. 
A preliminary assessment of CDR acceptability yielded favorable results. The Ottawa Acceptability of 
Decision Rules Instrument (OADRI) is a validated 12-item instrument that evaluates decision rule acceptability 
among clinicians.43 The OADRI’s final score is calculated as the mean of all 12 items, resulting in a score that 
ranges from 0 to 6 with 6 being greatest acceptability. To estimate the CDR’s baseline acceptability among 
potential users, we distributed a brief, voluntary, anonymous survey to pediatric intensivists and child-abuse 
pediatricians who participate in the dedicated listservs of the PALISI research network and the Ray E. Helfer 
Society. Survey participants were provided a one-page document that summarized the CDR’s derivation, 
validation, and potential clinical impact.22, 23, 25 Participants were asked to answer the OADRI’s 12 questions “as 
if they were considering using the rule.” A total of 30 pediatric intensivists, 51 child-abuse pediatricians, and 2 
other clinicians completed the survey. The mean OADRI score for the total sample (N= 83) was 4.86 (SD 
=1.32). These participants endorsed items indicating that the CDR was “easy to use”, “easy to understand”, 
“useful”, “clear”, “supported by colleagues”, and “will benefit patients”. Though not generalizable, these results 
suggest that clinicians will consider the CDR’s adoption as an AHT screening tool in PICU settings. 
Summary. The PI’s (Hymel) preliminary studies clearly demonstrate that CDR is simple, safe, inexpensive, 
reliable, evidence-based, and highly sensitive. It was derived and validated in strict accordance with 
established standards for CDR development. Secondary analyses demonstrate its clear potential to improve 
patient safety (i.e., to reduce the likelihood that patients with AHT go undetected) and clinical efficiency (i.e., to 
reduce unnecessary abuse evaluations and their associated costs). These preliminary studies also 
demonstrate that roughly 76% of head emergency room trauma cases are considered high risk, but only 73% 
of these high-risk cases receive an evaluation for abuse. To enhance clinical practice, the CDR needs (and is 
now ready for) a formal clinical trial. To conduct this trial we have assembled an extraordinary team. Dr. 
Chinchilli (Co-I)—an experienced biostatistician with a broad background in clinical trials, longitudinal data 
analysis, multivariate analysis, crossover trials, and measures of agreement. Dr. Wang (Co-I)—a biostatistician 
with expertise in analyses of large-scale longitudinal studies—has collaborated with the Study PI (Hymel) as 
well as other clinical investigators to develop and implement advanced statistical methodologies. Finally, Dr. 
Dias (Co-I)—a pediatric neurosurgeon recognized internationally for his AHT clinical expertise and his research 
on AHT prevention will apply his vast clinical experience in monitoring the long-term care and outcomes of a 
cohort of head-injured patients whose clinical data will contribute to the TCCMS Admin Core’s data repository. 
D. APPROACH 
D1. Overview. To assess the CDR’s impact on AHT screening accuracy, we propose a stratified cluster 
randomized trial (SCRT) at eight university-based US PICUs to test “AHT screening guided by a CDR” 
compared to “AHT screening as usual.” Participating sites will include Connecticut Children's Medical Center, 
Hartford; Virginia Commonwealth University Medical Center, Richmond, VA; University of Nebraska Medical 
Center, Omaha, NE; The Children's Mercy Hospital, Kansas, MO; Baylor College of Medicine, Houston, TX; 
University of Texas Health Science Center at San Antonio, San Antonio, TX; University of Utah, Salt Lake City, 
UT; and University of Kansas Medical Center, Wichita, KS. The eight PICU sites, located in diverse regions of 
the U.S. and representative of large and small PICU settings, will be matched into four pairs based on each 
site’s projected volume of high-risk patients. One PICU from each pair (4 total) will then be randomized to the 
intervention arm of the SCRT. The remaining 4 PICUs will serve as controls. Randomization sequences will be 
kept by the study biostatistician (Dr. Chinchilli) and will be concealed from Dr. Wong, who will be blinded to 
group membership and responsible for formal data analyses of Aims 1 and 2.44 In this SCRT sites will be 
stratified by patient volume (number of patients seen for AHT via PICU census): there will be four high-volume 
sites, two of which will be assigned to the intervention and two to control, and four low-volume sites, also 
equally distributed into the intervention and control groups. Further, in this trial, patients are clustered within 
Providers which are clustered within sites and thus our power analysis and analysis plan are based on this 
clustering. Dr. Chinchilli will use a pseudorandom number generator in the statistical software R to obtain the 
randomization schedule within each stratification. Based on patient volume reports at each of the PICUs, we 
anticipate 416 eligible patients over a 24-month period (see Figure D1). If eligible patient enrollments are low, 
we will extend this period by 6 months. Our preliminary studies (Table C2) indicate that ~76% of eligible cases 
will be deemed high risk based on “standard of care” (N=316). Based on our preliminary studies, we anticipate 
that only 73% of these high risk cases will receive an evaluation for abuse (N=115). Our conservative estimate 
(see section D3) is that we will increase this evaluation rate to 90% by implementing the CDR (N=142). 

Study personnel and coordination. Project 2 involves the coordination of personnel at the Penn State 
Hershey College of Medicine (COM) and at the 8 participating PICU sites. At the COM, the PI (Dr. Hymel) will 
oversee all activities. Dr. Hymel will supervise the Overall Project Coordinator (OPC) at the COM and will work 
closely with the biostatisticians. The OPC will be responsible for communicating with all PICU site research 
personnel, compiling all data in REDCap, working with Dr. Wong on data management, generating site-specific 
feedback and the monthly booster emails (see section D3), and working with site research personnel at the 
end of trial to ensure all data are completed. 
Each PICU site includes a Site PI, Site Research Coordinator (SRC), and Providers who are defined as 
PICU physicians and child abuse consultants. At each participating PICU, the Site PI will be responsible for 
reviewing and communicating site-specific feedback and conducting information-sharing sessions (see section 
D3). The SRCs will be responsible for ensuring data is compiled. The data includes the information from the 
data forms (Appendix B2) and information gleaned from the EMR (see Table D1). Data form and EMR 
information is transferred to REDCap by the SRCs. Providers are the participants of the clinical trial. It is these 
Providers who will apply the CDR, consider its recommendations, and make decisions to either launch or forgo 
child-abuse evaluations. It is their reasoning and decision-making behavior that will be captured on the data 
forms by the SRC and transferred to REDCap for analysis. 
Clinical trial. As outlined in Figure D1, the first year will consist of project set-up and training of the 
implementation strategies. Prior to full implementation (during Months 10–12), Providers at the intervention 
sites will undergo online training to learn about the screening tool and its application, and Providers at both the 
intervention and control sites will gain familiarity with the study design, methods, data forms, and procedures. 
The SCRT will commence by the end of Month 12. Over the next two years (Months 12–48), active strategies 
(D2.2) will be deployed at the intervention sites to promote PICU Providers’ adoption of the CDR. These 
Providers will complete four measures of CDR acceptability (OADRI, see Appendix B6) at six-month intervals 
during the trial. In addition, Site Research Coordinators will work with Providers to ensure that all patient- 
specific (both the intervention and control sites) and provider-specific (only the intervention sites) data are 
continuously captured throughout the trial. 
Figure D1. Study flow and timeline 
 
 
 
 
 
 
 
 
 
 
Using these data, we will compare the number of high-risk and low-risk patients who are actually evaluated 
for child abuse (i.e., patients who undergo both skeletal survey and retinal exam) at the intervention and 
control sites to assess the CDR’s impact on AHT screening accuracy (Aim 1). Beginning in Month 36, we will 
conduct analyses using data collected during Months 12-36 (see Appendix B2) to determine whether site- 
specific, provider-specific, and/or patient-specific factors impact AHT screening accuracy (Aim 2). Finally, to 
address our Exploratory Aim regarding the sustained impact of our implementation efforts, we will measure 
CDR utilization at the intervention sites over the 12-month period following the discontinuation of active CDR 
implementation strategies (Exploratory Aim 3). 
D2. Participants. To test our hypotheses with sufficient power, we need to capture the historical, clinical, 
and radiological data for 416 eligible acutely head-injured infants and young children. Replicating the criteria 
used in our completed CDR derivation and validation studies,22, 23 eligible patients will be children under three 
years of age admitted to the PICU for the management of symptomatic, acute, closed, traumatic, cranial, or 
intracranial injuries confirmed by computed tomography (CT) or magnetic resonance imaging (MRI). Patients 
will be excluded if their acute head injuries resulted from a collision involving a motor vehicle or if initial 
neuroimaging reveals clear evidence of pre-existing brain malformation, disease, infection, or hypoxia- 
ischemia. Eligible patients will be classified as high or low risk. A high-risk patient is defined as presenting to 
the PICU with closed head trauma and at least one of the four following conditions: (1) acute respiratory 
compromise; (2) bruising of the torso, ear(s), or neck; (3) bilateral or interhemispheric subdural hemorrhage(s) 
CDR 
Months 
0 
12 
24 
36 
48 
60 
CDR 
Low Risk 
N=100 
Eligible 
N=416 
High Risk 
N=316 
Standard of Care 
Intervention 
N=158 
Evaluated 
N=142 (90%) 
Setup 
Register SCRT 
Training 
SCRT 8 PICUs (Aim 1) 
4 = Intervention Sites (N=208) 
Sustainability 
Trial (Aim 3) 
N=104 
Return on 
Investment 
Analysis (DOC); 
Bridge to Policy 
(Admin Core) 
4 = Control Sites (N=208) 
Analyses of 
Factors that 
Impact CDR 
Success 
(Aim 2) 
Sustain-­­ 
ability Trial 
Analyses 
(Aim 3) 
(see Fig. D2)
Control 
N=158 
Evaluated 
N=115 (73%) 
Intervention 
N=50 
Evaluated 
N=24 (48%) 
Control 
N=50 
Evaluated 
N=29 (58%) 

or fluid collection(s); and (4) any skull fracture(s) other than an isolated unilateral non-diastatic linear parietal 
skull fracture. A low-risk patient will have none of these presenting conditions. In our preliminary studies, 
Providers completed abuse evaluations on only 73% of their high-risk patients (see Section D1). Based on 
census estimates from participating PICUs, we expect 304 high-risk patients and 112 low-risk patients (see 
Figure D1). Based on the census from participating Pediatric Intensive Care Units, we anticipate the patients 
being 74% White, 17% African American, 9% other with 26% of Hispanic ethnicity. We anticipate that the 
gender distribution of the patients will be 48% females. 
Although we need complete data for 416 patients to test our hypotheses, the actual subjects of this trial will 
be the Providers (as defined in section D1). We anticipate that participating sites will obtain waivers of parental 
consent related to capturing patient-related data (see Human Subjects). Only Providers will be required to 
provide informed consent for study participation. Based on current and projected staffing compositions of, we 
anticipate an average of 5 Providers at each low patient-volume PICU and an average of 15 for each high 
patient-volume PICU. Hence we expect to consent 80 Providers across the 8 participating sites. 
D3. Intervention. Getting physicians to adopt novel best practices is difficult45 and usually requires a 
flexible, multifaceted strategy that identifies and targets local barriers to practice change.45-58 Adopting an 
analogous plan to facilitate practice change, we have prepared a cadre of active and adaptive CDR 
implementation strategies designed to promote CDR adoption as an AHT screening tool at the intervention 
sites. Directed specifically to Providers at the intervention sites, these strategies will include: (1) initial online 
training (Appendix B1) regarding the CDR’s derivation, validation, application as an AHT screening tool, and 
potential beneficial impacts which will be augmented with onsite visits by PI Hymel; (2) monthly booster emails 
designed to reinforce and sustain the effectiveness of the initial online training (Appendix B5); (3) Provider 
reports of their application of the CDR and active consideration of its recommendations for each eligible new 
patient admission to the PICU—e.g., “Which of the following explain your current reluctance to evaluate your 
‘higher risk’ patient for abuse?” (see Appendix B2); and (4) ongoing Provider access to a fully developed online 
or smartphone “AHT Probability Calculator” app (Appendix B7) that will facilitate the simple, rapid bedside 
estimate of abuse probability and provide real-time data capture regarding use and probability of each case 
considered. More specifically, the calculator predicts the likelihood that a child’s head injury was caused by 
abuse based on the 16 combinations of the CDR’s four prediction variables (see Section B3). Each of the 16 
combinations has a specific, associated probability of abuse, which the calculator displays to help guide 
Providers’ decisions. Finally, (5) Providers will carry a CDR badge card listing the four CDR prediction 
variables (see Table A1). 
Online training. The initial online computer-based training will include background information such as 
statistics on AHT, the four variables included in the CDR, how/when to apply the CDR, and beneficial impacts 
of CDR application. A series of scenarios of high-risk and low-risk cases is presented; Providers use the AHT 
probability calculator to derive an estimate of abuse probability and then chose whether or not to further 
evaluate the child for abuse. Of the first 6 scenarios, if 5 out of 6 (83%) cases are correctly identified as 
needing/not needing evaluation, the training evaluation is completed. If the 83% criterion is not achieved, 
additional scenarios are presented until a score of 83% is achieved (Appendix B1). The online training will be 
available on a secure website created for the study. Providers will also receive a CDR badge for quick access 
to the CDR as another aid for screening of potential abuse. The PI (Hymel) will conduct on-site visits with 
Providers to train research coordinators, augment the online training, and facilitate conversation on barriers to 
implementation. This training has been piloted with four experienced child abuse pediatricians who have 
reviewed the content, layout, and structure. 
Site-specific feedback. To identify and address local barriers to practice change, site PIs will receive 
iterative site-specific feedback via email regarding their Providers’ AHT screening performance over the prior 
six months (see example, Appendix B4). This feedback will be provided for four (six-month) cycles during the 
trial’s 24 months of active data capture and will include the following information: (1) a list (in descending order 
of frequency) of local Providers’ self-reported barriers to CDR utilization, accuracy, and/or acceptability (e.g., 
“Which of the following explain your current reluctance to evaluate your “higher risk” patient for abuse?”—see 
Appendix B2); (2) Providers’ most mean score on the OADRI (see Section D1 above); and (3) graphs that 
compare outcome measures of CDR utilization across the four intervention sites over the previous six months. 
Upon receipt of site-specific feedback, site PIs will schedule and facilitate a local information-sharing session 
so their Providers can review the feedback and discuss strategies for overcoming local barriers to CDR 
utilization, accuracy, and acceptability. 
Tailored booster emails to Providers. Monthly emails will be sent to Providers that will include feedback 
indicating their CDR utilization compared to the other intervention site Providers; a recommendation, statistic, 

or reminder from the online training; a recent or relevant research article; and an invitation to ask questions or 
provide comments. 
CDR Badge Card. Physicians will receive a CDR badge card listing the four CDR prediction variables 
(Table A1). The badge will allow another avenue for a quick screening of AHT and can be used in conjunction 
with the AHT Probability Calculator when a physician is unsure whether a patient is at high versus low risk and 
need for evaluation. This CDR card will affix to their ID badge for quick and easy access. 
AHT Probability Calculator. Providers will download a smartphone or mobile-device “AHT Probability 
Calculator” (Appendix B7) that will facilitate simple, rapid bedside estimates of abuse probability. Based on our 
preliminary studies, the calculator predicts the likelihood that a child’s head injury was caused by abuse based 
on the 16 possible combinations of the CDR’s four prediction variables (Section B3). Each of the 16 
combinations has a specific, associated probability of abuse, which the calculator displays to help guide 
Providers’ decisions. When calculators are used, data will be captured via the apps statistics for later analyses. 
Adherence. Strict adherence to the CDR’s recommendations will be encouraged—but not required—for 
several reasons: First, the CDR is imperfect—it will categorize some patients with non-abusive head trauma as 
high risk and will categorize a few patients with AHT as low risk. Second, Providers’ clinical judgment will 
sometimes conflict with the CDR’s categorization. Third, many Providers will not agreed to completely bypass 
their clinical judgement in favor of strict adherence to the CDR’s categorizations (high versus low risk) or 
recommendations. Finally, individual and/or institutional barriers will likely limit the CDR’s accurate and 
consistent application. To be conservative, we predict the CDR implementation will motivate intervention site 
Providers to complete abuse evaluations on 90% of their high-risk patients during this SCRT, and we have 
thus powered Aim 1 to detect a 17% increase in high-risk patients being screened (i.e., 73% to 90%). 
D4. Data capture to evaluate CDR utilization. Under current “standard of care” practice, patients 
presenting with 
head trauma are 
identified through 
daily screening of 
PICU admission 
logs for initial risk 
level (see Figure 
D1). The CDR will 
be applied where 
high-risk patients 
should receive a 
child abuse 
evaluation. A 
completed skeletal 
survey and a 
completed retinal 
exam constitute a child abuse evaluation. At both intervention and control sites, Research Coordinators will 
record whether or not patients receive a subsequent child abuse evaluation (see Figure D1 and Appendices 4- 
6); all data will be entered via REDCap on a secure server. Table D1 summarizes the variables used in all 
aims, how they are defined, and the sources of each. As outlined above, Site Research Coordinators will be 
responsible for extracting EMR data and completing all data forms in REDCap. 
D5. Measures for Aim 1. The two predicted results relevant to Aim 1 include: (1) a higher percentage of 
high risk patients evaluated for abuse via a skeletal survey and/or retinal exam; and (2) a lower percentage of 
low risk patients evaluated for abuse. To assess these outcomes, we will calculate the number of patients 
categorized as either high or low risk based on admitting criteria and will capture data throughout the active 
trial (Months 12–48) regarding completed skeletal surveys and completed retinal exams. Although we are not 
specifically powered to detect differences in the following clinical outcome measures, we will nonetheless 
calculate these as indicators of the clinical impact of the CDR: (1) the overall diagnostic yield of patients’ 
completed abuse evaluations, and (2) the mean estimate of missed AHT among patients lacking evaluations 
for abuse. We will obtain information for each of these indicators from data collected from Providers during the 
active trial (Months 12–48) using the forms found in Appendix B2. 
D6. Measures for Aim 2. For Aim 2, we will determine what—if any—site-, provider-, and patient-specific 
factors impact the percentage of high-risk patients who are evaluated for AHT and the percentage of low-risk 
patients who are evaluated for AHT. Site-specific variables. To determine high and low patient volumes, we will 

calculate the number of eligible patients (described in D2) who pass through each intervention site to 
determine which sites had the highest number of eligible patients and which had the lowest number of eligible 
patients (based on information from the data-collection forms found in Appendix B2). We will then combine 
screening-accuracy data from the two sites with the highest patient volumes and the two sites with the lowest 
patient volumes and will use those data to make comparisons between the two groups. Provider-specific 
variables. For each case of AHT that arrives in the PICU, the provider will report (using the forms found in 
Appendix B2) who consulted on case: a standard Provider (without child-abuse expertise), a Provider with 
child-abuse expertise, or both. As a measure of training “dosage,” we will also capture the Providers’ exposure 
to the CDR implementation strategies (see Table D2). Providers will receive points when they complete their 
initial online training, read the monthly booster emails, apply the CDR and actively consider its 
recommendations with each new patient, use the AHT Probability Calculator, refer to their CDR badge card, 
and attend the information-sharing sessions. In some cases, the data will be calculated automatically (e.g., 
when a Provider opens an email or completes the online training, both of which can be tracked), whereas the 
rest of the data will be collected using forms found in Appendix B2 (e.g., “Which of the following explain your 
current reluctance to evaluate your ‘higher risk’ patient for abuse?”). Finally, acceptability will be assessed 
every six months and quantified via scores on the OADRI (see C2.8), Using these data, we will compare 
screening accuracy among Providers to determine the effect of each variable on our two outcomes. 
Patient-specific variables. The age and race/ethnicity of patients will recorded as will information on 
whether caregivers specifically deny head trauma and whether patients have begun to walk/cruise given 
findings from our preliminary studies (Appendix B3). We will conduct group comparisons to determine what—if 
any—patient-specific factors are linked to the two 
outcomes of interest. 
D7. Measures for Exploratory Aim 3. We will 
conduct a sustainability trial at the implementation 
sites once the SCRT is completed (at 36 months; 
see Figure D2). To test the impact of gradually 
repealing the implementation strategies on 
sustainability, the local information sessions will be 
discontinued beginning immediately after the SCRT 
has ended and the tailored booster emails will no 
longer be sent to physicians six months after the 
SCRT has ended. Finally, when these CDR 
implementation strategies have been discontinued, intervention site Providers will continue to capture patient- 
specific data and complete a final measure of CDR acceptability. Physicians will complete a final phone call 
interview focused on barriers to sustainability. The data collection phases of Aim 3 will provide important 
Figure D2. Flow and timeline of sustainability trial (Aim 3) 
 
 
 
 
 
quantitative and qualitative information about the utility of the most resource-consumptive implementation 
supports for CDR implementation. Phasing out the most consumptive and least utilized supports, without loss 
in CDR use, makes it more likely that hospitals and physicians are able to sustain implementation. If CDR 
acceptability and utilization falls, then the importance of these supports will be better understood. 
This goal will be to evaluate whether Providers continue to use the CRD when implementation support is 
reduced. We will systematically reduce high-cost implementation supports one by one and evaluate the impact 
of these reductions on: (1) CDR use (the percent of high-risk patients evaluated for abuse), and (2) Provider 
acceptability as measured by the OARDI—both of which will be assessed at 36, 42 and 48 months. As Figure 
D2 depicts, site-specific feedback will be discontinued at 36 months, and email boosters to Providers will be 
Months 
12 
24 
36 
42 
48 
CDR Badge Cards 
Mobile-­­device AHT Probability Calculator 
Email Boosters to providers 
Site-­­specific feedback/Information-­­sharing 
Low-­­Cost 
Implem 
rategies 
entation St 
High-­­Cost 
Implementation Strategies 
SCRT 
Phase A 
Phase B 
Sustainability Trial (Aim 3) 

discontinued at 48 months. We anticipate that another 104 patients will be eligible during this 12-month period 
(Figure D1). This nested approach will allow us to compare the impact of site-specific feedback (Phase A) and 
email boosters (Phase B) to full implementation support (during the SCRT) and to one another. This 
sustainability trial will employed us to test whether CDR utilization and acceptability can be maintained when 
only low-cost strategies are used. Further, brief interviews will be conducted with Providers about utilization 
and acceptability one year after active implementation supports are removed. This strategy will provide key 
information about the process of sustaining the CDR and cost reduction possibilities in nation-wide 
dissemination. 
D8. Power Analysis. To estimate the statistical power for the target sample size of 304 high-risk patients 
based on the first primary measure of CDR utilization (see section D5), we simulated 1,000 datasets via the 
generalized linear mixed-effects model described below by excluding the patient-level covariates under the 
following set of assumptions: [1] Sites 1, 3, 5, and 7 are randomized to the control group, whereas sites 2, 4, 6, 
and 8 are randomized to the intervention group; [2] The mean percentages of high-risk patients evaluated for 
abuse (i.e., skeletal survey and retinal exam) are 73% in the control group and 90% in the intervention group 
based in part on observations from our completed preliminary derivation and validation studies (section D2). 
We analyzed each of the 1,000 simulated datasets, each of sample size 304, via SAS PROC GLIMMIX, 
Version 9.4, with respect to the primary null hypothesis (Aim 1). The estimated statistical power (proportion of 
simulated datasets that yielded a statistically significant result for Aim 1) was 95.7%. Therefore, our target 
sample size of 304 high-risk patients has excellent statistical power for addressing Aim 1. For Aim 2 we 
consider the patient-level factor of minority race and/or ethnicity for power estimation to investigate its 
significant impact on the percentage of low-risk patients at least partially evaluated for abuse using the 
intervention group only. Our preliminary studies (section C) indicate that minority patients will have a 1.5 higher 
log odds of being evaluated for abuse than non-minorities. The estimated power (proportion of simulated data 
sets that yielded a statistically significant result for Aim 2) was 90% for testing the null hypothesis 𝐻!: 𝛽 = 0 
against the alternative hypothesis 𝐻!: 𝛽 = 1.5. 
D9. Data Analyses. Data Management. Supervised by Dr. Hymel, the Overall Project Coordinator (OPC) 
will be responsible for monitoring the data as they are input via REDCap by Site Research Coordinators. The 
OPC will work closely with Dr. Wong to develop data quality assessment reports, including accrual and attrition 
reports, which will be shared with the PI and the DSMB. Dr. Wong will be blinded to group membership. 
Steps Taken Prior to Conducting Formal Analyses. The primary statistical package used will be SAS, 
version 9.2. Prior to formal analyses, means, medians, ranges, standard deviations and descriptive measures 
for nonnormality will be computed for each continuous variable, as well as frequencies for categorical 
variables, within each group. Any data values identified as outliers will be further examined to determine if they 
are data entry errors, in which case they will be modified when possible. Outliers will generally be kept in the 
final analysis, unless substantial evidence is available for their deletion. If the distribution of a continuous 
outcome variable varies substantially from a normal distribution, a transformation to approximate normality will 
be performed, and all formal analyses will be carried out with the transformed variable. If groups exhibit 
heterogeneous variances, an appropriate statistical method will be used to account for this fact. Outcome 
variables will also be examined over time at the group and the individual level graphically as well as via 
descriptive statistics stratified on time and group. Furthermore, bivariate relations between outcome variables 
and potential covariates will be examined to determine if substantial deviations from linearity exist. 
Intent-to-Treat Analysis Plan and Plan for Addressing Missing Data. The primary approach for the analyses 
is intent-to-treat, meaning that Providers will be kept in the group to which they were randomized, regardless of 
protocol violations or drop-out. Drop out will be defined as a Provider who leaves the site. Every effort will be 
made to minimize missing data, analyses will be performed to explore plausible missing data mechanisms59, 
and predictors of missingness will be investigated. If missing data are substantial, maximum likelihood 
estimation will be employed where predictor variables associated with missingness will be added to the 
statistical model. This approach generally provides valid inferences when data are missing at random 60, 61 and 
is more appropriate than many commonly employed approaches to data imputation62. 
D10. Aim 1 Analyses. We will use a linear-mixed model approach for longitudinal data. We let 𝑌!"#$ denote 
the binary response of evaluation for abuse with both skeletal survey and retinal exam for the 𝑙!!  high risk patient 
from the 𝑘!!  Provider within the 𝑗!!  site of the 𝑖!!  group, where 𝑖   =  0, 1 (corresponding to the control and 
intervention group), 𝑗 = 1, 2, 3, 4 (corresponding to site), 𝑘 = 1, 2, … , 𝐾!" (corresponding to Provider), and 
𝑙 = 1, 2, … , 𝐿!"(corresponding to high risk patient). We construct the following bivariate logit function for 
generalized linear mixed-effects models: 𝑙𝑜𝑔! !"  !!"#$!!  
!"  !!"#$!! = 𝜇! + 𝑢!" + 𝜈!"# , where 𝜇! and 𝜇! represent means 

! 
on the logit scale for the control and intervention sites from health Providers, 𝑢!"  is a random effect for the 𝑗!! 
site of the 𝑖!!  group, and 𝜈!"#  is a random effect for the 𝑘!!  Provider within the 𝑗!!  site of the 𝑖!!  group. We 
assume that the 𝑢!"s are independent and identically distributed according to a normal distribution with a mean 
of zero and a variance of 𝜎!; the 𝜈 
s are independent and identically distributed according to a normal 
! 
!"# 
distribution with a mean of zero and a variance of 𝜎!, and the 𝑢!" s, the 𝜈!"# s are mutually independent. We will 
apply an iterative maximum likelihood (ML) algorithm to derive the estimates of the parameters via SAS PROC 
GLIMMIX, Version 9.4. The adjusted relative risk (RR) for comparing the intervention and control groups 
among health Provider is 𝑅𝑅 = 1 + exp −𝜇! 
 / 1 + exp −𝜇! , and the primary null hypothesis is 𝐻!: 𝑅𝑅 = 1, 
which is equivalent to 𝐻!: 𝜇! = 𝜇!, where we will construct a one-df F-test based on the ML estimates. We will 
apply a variety of sensitivity analyses to determine the robustness of the results based on this primary 
statistical model. In particular, we will investigate heterogeneity of variance by requiring 𝜎! to vary according to 
the matched pair of sites and 𝜔! to vary according to the type of health care Provider. 
Of note, the same approach and statistical models described above can be applied to the second 
measure in Aim 1 (see D5), where 𝑌!"#$ is denoted as the binary response of at least partial evaluation for 
abuse (i.e., skeletal survey and/or retinal exam) for the 𝑙!!  low-risk patient from the 𝑘!!  Provider within the 𝑗!! 
site of the 𝑖!!  group. In addition, with regard to additional indicators of the clinical impact of the CDR (see D5), 
preliminary investigation by using aggregated data over a 24-month period (Month 12-36) can be conducted 
via two-sample proportional tests with the significance level of .05. 
D11. Aim 2 Analyses. We use the same general model introduced in Aim 1, but specify predictors at 
various levels, and when data are only applicable to the treatment group, we analyze data within the treatment 
group only. For example, we expect that sites with higher patient volumes will be associated with higher 
percentages of CDR application, 𝑙𝑜𝑔! !"  !!"#$!!  
!"  !!"#$!! = 𝜇! + 𝑢!" + 𝜈!"# + 𝛽𝑥!" , where xij is the yield for the jth site 
within the ith intervention group. With respect to Provider-level risk factors, we will use the following model to 
examine the association between proportion of CDR application and the level of child-abuse expertise of the 
Provider: 𝑙𝑜𝑔! !"  !!"#$!!  
!"  !!"#$!! = 𝜇! + 𝑢!" + 𝜈!"# + 𝛽𝑥!"# , where x  
ijk is the level of child-abuse expertise for the kth 
Provider within the jth site within the ith intervention group. Because data for the Provider-level predictors of 
high exposure to the CDR implementation strategies and high CDR acceptability will only be available within 
the treatment group, we will examine each of these factors separately as predictors of the proportion of CDR 
application using the following model within the treatment group only: 𝑙𝑜𝑔! !"  !!!"#!!  
!"  !!!"#!! = 𝜇! + 𝑢!! + 𝜈!!" + 
𝛽𝑥!!", where x1jk is either the level of exposure to the CDR implementation strategy or level of CDR 
acceptability for the kth Provider within the jth site within the intervention group (i.e., group 1). Finally, we will 
examine the patient-level risk factors of minority status and child age/developmental level (cruise/walk) within 
the following model: 𝑙𝑜𝑔! !"  !!"#$!!  
!"  !!"#$!! = 𝜇! + 𝑢!" + 𝜈!"# + 𝛽𝑥!"#$ , where x  
ijkl is child age or minority status for the 
ith patient within the kth Provider within the jth site within the ith intervention group. It is important to note that 
when examining models that include both intervention groups, we will also explore the possibility of any 
potential intervention group by factor interactions before moving forward with the models with main effects 
only. For all Aim 2 models, β is the parameter of interest with the corresponding null hypothesis of H!: β = 0. 
Also within Aim 2, we will assess CDR acceptability using both averages and longitudinal data based on 
data at Month 12, 18, 24, 30, and 36 (after the intervention strategies are discontinued for the Providers using 
the score based on the OADRI, a validated 12-item instrument (see D6). Specifically, we will use a linear- 
mixed model approach for longitudinal data to explore the possibility of linear change over time using a 
random-effects model in addition to fitting a linear mixed-effects model that calculates Provider-specific means 
to evaluate the acceptability of the treatment. 
D12. Aim 3 Analyses. Similar to the longitudinal model used in Aim 2, we will use a linear-mixed model 
approach for longitudinal data to explore the possibility of linear change over time (i.e., from 36 to 42 months 
for Phase A, and from 42 to 48 months for Phase B; see Figure D2). We will use a random-effects model in 
addition to fitting a linear mixed-effects model that calculates both CDR acceptability (OARDI scores) and CDR 
application over time and evaluate whether reducing email boosters and/or site-specific feedback have an 
impact on sustainability. If there is no linear effect, then we can conclude that reducing these high-cost 
implantation strategies does not significantly reduce the effects of the intervention over time. If linear effects 
are significant (and negative), then we can conclude that one or more of the high-cost strategies should be 

maintained in order to sustain the effectiveness of the intervention. 
E. RIGOR, TRANSPARENCY AND REPRODUCIBILITY 
In compliance with NOT-OD-16-011 and TCCMS standards (see Admin Core Aim 1), we have designed 
Project 2 to enhance scientific rigor by articulating our scientific premise in terms of the need for an evidence- 
based tool to improve care for AHT children. We have outlined the strengths and weaknesses of published 
research (including our own) to clearly represent why the proposed SCRT is a sound next step in the CDR 
process. We also have provided details of the design of the SCRT, its Aims, and the intervention and data 
collection plans. Our methods include clear exclusionary criteria, a randomized trial, objective tools, and 
multiple data sources including Providers as well as electronic medical records. One of our statisticians will be 
responsible for the randomization; the other will be blinded to group membership and will conduct subsequent 
analyses. Our analysis plan was developed in consultation with clinical trials experts and is state-of-the-art, 
robust, and unbiased. We have a priori hypotheses and have been clear when analyses are exploratory. We 
will register this SCRT with clinicaltrials.gov prior to enrollment and have a committed DSMB and solid plan for 
monitoring the trial. To enhance transparency and reproducibility, de-identified clinical data from Project 2 will 
be included in the TCCMS’s data repository (within the project’s Administrative Core) for future hypothesis- 
generating research that will further advance the field. Permission will also be sought to re-contact the 520 
families involved in both the SCRT and the Sustainability Trial for future TCCMS research studies that will grow 
new science examining: (1) the exacerbated impact of child abuse on head trauma, and (2) how the 
fundamentally different types of brain injuries manifest in disparate developmental trajectories. Mark Dias 
(Project 2 Co-I) will begin crafting such an R01 in Year 4. 
F. IMPACT 
The consequences of AHT can be grave, including the strong possibility of long-lasting, often permanent 
neuropsychological, behavioral, emotional, and scholastic difficulties as well as the potential for continued 
abuse, and possibly death, if AHT is missed or unrecognized. However, as we have described, the decision to 
evaluate a child for AHT is complicated by myriad Provider-, site-, and patient-specific factors. The goal of this 
CDR, which is the first child-abuse screening tool to undergo a formal clinical trial, is to help Providers 
effectively overcome barriers and to quickly, reliably, and easily determine when a head-injured child should be 
evaluated for abuse, thereby eliminating any hesitations that may be inherent in this challenging context. As a 
result, the CDR will decrease the negative impacts of physicians’ inherent biases and practice disparities, 
significantly increase overall AHT detection, decrease unnecessary abuse evaluations (and their associated 
risks), and reduce AHT-associated health care costs—all while lessening physicians’ fear of making the 
“wrong” decision. Most importantly, however, the CDR will save the lives of children who would otherwise fall 
through the cracks--the head-injured children who are abused but who would be missed using current AHT 
evaluation criteria. Approximately 35,000 children under the age of 3 present with head trauma injuries 
admitted to PICUs each year in the U.S.63, 64 Preliminary studies suggest that although 76% of these 
presenting cases are high risk (~26,000), only 73% of these cases are currently evaluated for abuse (~19,000). 
We expect that adopting the CDR will lead to increased evaluation rates of high-risk children by 17%--from 
73% to 90%, or an additional 5,000 abuse evaluations. Published estimates indicate that AHT is missed or 
unrecognized in 30% of head-injured children, more than 25% of children with unrecognized AHT suffer 
additional inflicted injuries, and 10% of children with unrecognized AHT subsequently die.8 Broad uptake of our 
CDR would translate into roughly 1400 cases of AHT being recognized that would have been missed, 
preventing re-abuse of about 350 children and saving the lives of nearly 100 children every year. Note that 
these are conservative estimates based on a 90% CDR utilization. These numbers would be even more 
staggering if the CDR were to become the standard of care and if its utilization rates were to rise to 100% 
nationally. Moreover, having a clear decision rule that is recommended as standard of care would provide 
physicians and emergency room Providers increased confidence in their decision to evaluate high risk children 
for abuse, thus reducing bias and alleviating potential fear of litigation for having done so. If the proposed 
SCRT is successful, the TCCMS will leverage these findings to change practice. For example, a demonstration 
project for the DOC’s Aim 1 is to calculate the hospital ‘return-on-investment’ (ROI) from CDR adoption. To 
ensure that this is accomplished, the budget for Project 1 includes paying sites for an additional month after the 
SCRT is complete to collet the data from for the ROI from the EMR (e.g., length of stay; see Table D1). These 
potential cost-savings, along with the SCRT findings, will be packaged by the DOC into translational messages 
designed to resonate with hospital administrators, policy-makers, and practitioners. Finally, the Admin Core 
Aim 3 includes plans to work with the American Academy of Pediatrics (AAP) toward a formal endorsement of 
the CDR as standard of care (see letter of support from Dr. Robert Block; former AAP President).