Intelligent Clinical Decision Support for Preoperative Blood Management: A Cluster-Randomized Clinical Trial
Phase: NA
Conditions: Surgery
Sponsor: Washington University School of Medicine

 
1 
Title: 
Intelligent Clinical Decision Support for Preoperative Blood Management 
 
Short Title: 
SPATH-trial 
 
Registration: NCT07223853 
 
Principal Investigator: 
Sunny Lou, MD, PhD 
Assistant Professor 
Department of Anesthesiology 
Washington University School of Medicine 
slou@wustl.edu 
 
Institutions: 
Washington University School of Medicine 
660 S Euclid Ave, Box 8054 
St Louis, MO, 63110 
 
Funding: 
K23HL166880 (National Heart Lung and Blood Institute) 
 
Revision 
Version Date 
Summary of Changes 
Consent 
Change? 
1 
2025.06.14 
Initial Draft 
 
2 
2025.09.16 
Updates to statistical analysis plan and 
clarifications re outcome definitions 
N 
3 
2025.10.15 
Clarifications on study design and audio 
recordings for participants who agree to exit 
interviews 
Y 
4 
2025.11.20 
Add ClinicalTrials.gov registration #, clarify 
adverse event monitoring timeline, add 
clinician subgroup analysis 
Y 
5 
2026.02.03 
Revise randomization strategy, clarify timing of 
survey administration, and revise survey 
questions. 
N 
 
 
 

 
2 
Table of Contents 
Non-Technical Summary .......................................................................................... 4 
1 Background and Rationale ..................................................................................... 5 
Preliminary Data .............................................................................................................. 6 
2 Objectives ............................................................................................................ 9 
2.1 Primary Objective ....................................................................................................... 9 
2.2 Secondary Objectives ................................................................................................. 9 
3 Trial Design ........................................................................................................... 9 
4 Participants, Interventions, Outcomes ................................................................. 10 
4.1 Study Setting ............................................................................................................. 10 
4.2 Participants and Eligibility .......................................................................................... 10 
4.3 Interventions ............................................................................................................. 11 
4.4 Outcomes ................................................................................................................. 15 
4.5 Participant Timeline ................................................................................................... 16 
4.6 Sample Size .............................................................................................................. 16 
4.7 Recruitment .............................................................................................................. 17 
4.8 Allocation ................................................................................................................. 17 
4.9 Blinding ..................................................................................................................... 18 
5 Data Collection, Management, Analysis ............................................................... 18 
5.1 Statistical Methods .................................................................................................... 18 
5.1.1 Analysis for the primary outcome ............................................................................ 19 
6. Monitoring ......................................................................................................... 21 
6.1 Adverse event and safety monitoring .......................................................................... 21 
6.2 Interim Analyses ........................................................................................................ 23 
6.3 Harms ....................................................................................................................... 24 
7. Ethics and Dissemination ................................................................................... 24 
7.1 Protocol .................................................................................................................... 24 
7.2 Consent .................................................................................................................... 24 
7.3 Protections Against Risks .......................................................................................... 26 
7.4 Potential Beneﬁts of the Proposed Research to Participants and Others ...................... 27 
7.5 Importance of Knowledge to be Gained ...................................................................... 27 
7.6 Conﬁdentiality .......................................................................................................... 27 

 
3 
7.7 Declaration of Interests ............................................................................................. 27 
7.8 Dissemination ........................................................................................................... 28 
7.9 Data Sharing Policy .................................................................................................... 28 
 
 
 
 

 
4 
Non-Technical Summary 
 
20 million patients have surgery in the United States every year, with approximately 1 million of 
those patients requiring blood transfusion. Safe and timely transfusion during surgery can be 
life-saving. However, the process of preparing blood for surgical transfusion requires a 
laboratory test known as a “type and screen”, a blood test that measures the patient’s blood 
type and whether they have any red blood cell antibodies, and “crossmatching”, a process 
where blood is screened to ensure its compatibility with the patient. These laboratory tests take 
at least an hour to complete. Therefore, for patients with at least moderate risk of needing blood 
during surgery, we typically run some or all of these tests in the preoperative period so blood is 
readily available when needed during surgery. 
 
We are currently too cautious in how often we are completing these tests for surgical patients, 
largely because we only use information about the planned surgery, and not any information 
about the patient, when we decide which patients need these blood tests. At Barnes Jewish 
Hospital, over 60% of surgical patients have preoperative blood testing done, even though less 
than 7% actually need blood during surgery. To some degree, this is appropriate, because we 
want to be safe. But excessive preparation does come with a cost for the US healthcare system, 
estimated to be on the order of $1 billion dollars a year, and also contributes to blood waste, 
which is a big problem given ongoing blood shortages. 
 
The goal of this study is to evaluate an artificial intelligence system that provides customized 
information on a patient’s risk of needing blood during surgery – using information about both 
the patient and the surgery they are having – to help doctors better prepare blood for patients 
who are likely to need it, while avoiding too much preparation for patients who don’t. This 
artificial intelligence system, called S-PATH, was developed using a large database of over 3 
million surgical patients at over 700 hospitals around the US. It has now been built into the 
electronic health record so that doctors and nurses can see these personalized risk estimates 
as part of their normal workflow. We think that having access to S-PATH’s personalized 
estimates of surgical transfusion risk will help doctors make better decisions about which 
patients need preoperative blood testing before surgery, and we hope that use of this tool will 
improve patient safety and help save money. 
 
This study is a cluster-randomized controlled trial comparing having access to the S-PATH tool 
versus usual care processes. Currently, patients come to our preoperative assessment clinic 
before surgery, and the nurse practitioners who work there decide whether the patient needs 
preoperative blood testing using a chart, called an MSBOS, that lists recommended orders for 
different surgeries. We plan to enroll these nurse practitioners into the study and randomize 
them to either have access to the S-PATH tool in addition to the MSBOS chart versus continuing 
to only use the MSBOS chart. Patients will also be randomized based on the nurse practitioner 
they are assigned to when they come to clinic. 
 
The primary outcome will be the number of type and screen blood testing orders placed before 
surgery. The safety outcome will be use of emergency release blood, which might otherwise be 
given when blood is needed but blood testing is not yet complete. A data safety monitoring 
board will also monitor adverse outcomes related to transfusion such as hemolytic transfusion 
reactions. Secondary outcomes will include the frequency of transfusion during surgery, and 
measures of how frequently the nurse practitioners viewed the S-PATH tool and followed its 
recommendations. We will also examine how clinicians interact with AI and the economic costs 
compare with and without AI access. 

 
5 
 
We plan to consent and train individual nurse practitioners in the appropriate use of the S-PATH 
tool. We are requesting a waiver of informed patient consent because the S-PATH tool is 
minimal risk. Specifically, it provides information on transfusion risk and suggestions on orders 
but the nurse practitioners will be free to decide what they think is most appropriate. In addition, 
the decision for whether the place blood orders will be reviewed again by the surgery and 
anesthesiology teams on the day of surgery. For both control and intervention arms, there is the 
possibility that patients who do not have presurgical blood orders will require transfusion during 
surgery. Emergency release blood is safe and readily available to be used in this situation. For 
both control and intervention arms, there is also the possibility of harm from receiving blood 
transfusion.  
 
1 Background and Rationale 
Preoperative preparation for blood transfusion can be life-saving during surgery. 
Surgery is an indispensable part of health care; it provides treatment across a diverse spectrum 
of diseases, including cardiovascular disease, trauma, cancer, and maternal and child health, 
and helps to save lives, reduce disability, and improve quality of life1. However, surgery also 
entails risk, including the life-threatening risk for major bleeding.2 Timely red blood cell 
administration during surgery can be critical to maintain oxygen delivery to vital organs, thus 
preventing bleeding-associated morbidity and mortality. It is estimated that 20% of the red cells 
transfused in the United States are administered in the surgical setting.3 
Safe red blood cell administration requires several steps in order to ensure appropriate 
matching of the red cell unit to the patient, each of which requires time and labor.4 First, the 
patient’s blood must be drawn to determine their blood type, and to screen for the presence of 
alloantibodies; this is a laboratory test typically referred to as a “type and screen” (T/S), and it 
takes approximately one hour. Next, a donor red cell unit that is compatible with the patient’s 
plasma is identified, tagged as such, and set aside; this step is referred to as a “crossmatch”. 
This step can take minutes to days if the patient has red cell alloantibodies.5 Failure to ensure 
donor unit compatibility with the recipient’s antibodies results in a hemolytic transfusion reaction, 
which can be life-threatening. 
Given the time required to prepare a compatible red cell unit for transfusion, guidelines 
recommend that patients at risk for intraoperative transfusion undergo some portion of the 
pretransfusion testing process in the preoperative period,6,7 so that blood can be available in a 
timely manner when it is needed during surgery.8 With adequate preparation, intraoperative 
transfusion can be safe and effective in reducing complications associated with surgical 
bleeding. 
Excessive preoperative blood preparation can be harmful. 
At the same time, excessive preoperative testing and blood preparation can have substantial 
public health consequences. As advocated by the Choosing Wisely campaign,9 presurgical 
blood orders for patients who do not ultimately require transfusion is wasteful and places an 
unnecessary cost and labor burden on the healthcare system. It is estimated that over 20 million 
surgical procedures are performed in the United States every year.10 Over 50% of these patients 
have presurgical blood orders,8,11 despite intraoperative transfusion occurring in less than 10% 
of surgeries.11,12 Given an estimated cost of $20-100 per patient11,13–15 for presurgical blood 
orders, the US healthcare system likely spends on the order of $1 billion dollars a year on blood 
preparation associated with surgery. Even a 10% reduction in the number of patients with 
presurgical blood orders would achieve meaningful savings. 

 
6 
Excessive orders can also have negative consequences for red cell waste. When crossmatch 
orders are placed, red cell units are set aside where they are unavailable to other patients. This 
forces the blood bank to carry more inventory, which has been associated with an increased 
rate of red cell waste due to expiration.5 Responsible conservation of blood products is critical to 
maintaining the adequacy of the nation’s blood supply, especially given the trend in declining 
blood donations,16 which has only accelerated during the COVID-19 pandemic.17 Concerns 
about the sustainability of the US blood system have been highlighted by two recent reports 
commissioned by the Department of Health and Human Services.18,19 Therefore, avoiding 
presurgical blood orders for patients at low risk of transfusion has significant public health 
implications. 
Preoperative blood orders are currently determined by the surgical procedure alone, 
despite patient factors playing a substantial role in transfusion risk. 
Given the considerations presented above, it is important to accurately estimate a patient’s risk 
of transfusion so that presurgical blood preparation can be performed for patients who need it, 
while avoiding wasteful preparation for patients who do not. The current standard of care is to 
estimate transfusion risk and determine preoperative blood orders based on the planned 
procedure alone, via the creation of a maximum surgical blood ordering schedule (MSBOS).20–23 
For example, guidelines recommend omitting a T/S from patients undergoing procedures with a 
historical procedure-specific transfusion rate less than 5%.21,22 Determination of crossmatch 
orders is less standardized; information on the procedure-specific transfusion rate, mean units 
transfused (transfusion index), and 90th percentile utilization have all been used.20,22,24 A typical 
MSBOS is a table containing a list of procedures with their associated blood order 
recommendations (Table 1).  
Regardless of approach, all prior methods for creating an MSBOS have exclusively used 
procedure characteristics alone to determine presurgical blood orders. However, patient 
characteristics also impact transfusion risk considerably. For example, preoperative anemia, 
renal dysfunction, patient age, gender, and weight have all been associated with an increased 
risk for surgical transfusion.25–29 The central hypothesis motivating this study is that 
implementation of a personalized machine 
learning (ML) model – incorporating both 
patient- and procedure-specific characteristics – 
as a clinical decision support (CDS) system to 
guide presurgical blood orders would result in 
increased ordering precision, i.e., fewer excess 
orders (decreased costs, less waste) and fewer 
missed orders, i.e., patients requiring 
transfusion during surgery without adequate 
preparation (increased patient safety). 
Preliminary Data 
Model development. We developed and validated several ML classifiers to predict the 
probability of intraoperative red cell transfusion using procedure- and patient-specific variables 
commonly available in the preoperative setting. Models were trained using the American 
College of Surgeons (ACS) National Quality Improvement Program (NSQIP) database30 of 3 
million surgical cases across 722 hospitals from the years 2016-2018, and tuned for the specific 
task of guiding presurgical blood orders. Input variables included the procedure, its associated 
procedure-specific transfusion rate, and patient-specific variables including patient 
demographics, comorbidities, and preoperative laboratory values (Figure 2). A complete model 
Procedure 
Routine presurgical 
blood order 
Open heart surgery 
T/S, crossmatch 4 units 
Hysterectomy 
T/S only 
Appendectomy 
None 
Table 1 – Example MSBOS showing typical pre-
surgical blood order recommendations. 

 
7 
pipeline was created to enable reproducible model implementation and prediction for new 
patients, including missing value imputation and data normalization.  
Model performance was measured in comparison to a 
baseline model with only the procedure-specific transfusion 
rate, as is commonly used to create a conventional MSBOS22, 
the current standard of care. The best-performing 
personalized ML model was the gradient boosting machine, 
referred to as the S-PATH model. 
Retrospective and multi-center validation. S-PATH was 
then internally validated across 1,076,441 surgical cases 
captured by NSQIP in 2019. S-PATH generalizability was 
demonstrated by external validation across a cohort of 16,053 
surgical cases at the host institution (BJH), in 2020, and 
across a cohort of 3,455,295 surgical cases performed at 47 
medical centers participating in the Multi-center Perioperative 
Outcomes Group (MPOG) consortium31 from 2020-202132); 
these medical centers represent diverse academic and 
community practice settings in 27 US states. 
Using a novel transfer learning technique based on the 
procedure-specific transfusion rate,33 S-PATH demonstrated 
improved discrimination (i.e., ability to rank-order patients in 
transfusion risk) compared to the baseline MSBOS approach, 
a finding which generalized across internal and external 
validation. For example, area under the receiver operating 
characteristic (AUROC) curve was 0.924 for S-PATH versus 
0.888 for the baseline MSBOS model in internal validation, 
and 0.939 for S-PATH versus 0.908 for the baseline MSBOS 
model in external validation at Washington University; median S-PATH AUROC was 0.926 (IQR 
0.908-0.955) compared with a median MSBOS AUROC of 0.854 (IQR 0.816-0.881) in 47 
MPOG hospitals. 
To determine when to place presurgical T/S orders, guidelines recommend a 5% transfusion 
risk threshold.21,22 Such a threshold would only achieve 83.7% sensitivity for detecting 
transfusion for the baseline MSBOS approach in internal validation, and 91.1% sensitivity in 
external validation at the host institution; in other words, 9-16% of patients requiring transfusion 
would not have had a presurgical T/S (i.e., missed orders) under current MSBOS guidelines, 
which would negatively impact patient safety. The S-PATH model was tuned to achieve 96% 
sensitivity, and consistently required approximately one-third fewer T/S orders to achieve 96% 
sensitivity compared to the baseline MSBOS approach for most hospitals, highlighting the 
generalizability of S-PATH performance and its potential to improve resource utilization while 
improving patient safety. Based on these results, S-PATH is one of the most broadly validated 
machine learning models in healthcare. 
Prospective Validation. S-PATH has been integrated within the electronic health record (EHR) 
at Washington University and has been silently generating predictions since November 2024 for 
patients presenting to our preoperative assessment clinic, which is the intended patient 
population for the proposed cluster-randomized clinical trial. We have found that our current 
care processes for placing preoperative type and screen orders for surgical patients is 
imperfect, as 18% of patients who required transfusion during surgery did not have an active 
type and screen by the start of the procedure; all of these patients were correctly identified as in 
Figure 2 – Model specification 
 
Data sources:  
- 
Training: NSQIP, 2016-2018 
(n=3,049,617) 
- 
Internal validation: NSQIP 
2019 (n=1,076,441) 
- 
External validation: BJH 
2020 (n=16,053); 47 MPOG 
medical centers, 2020-2021 
(n=3,455,295)  
Input Variables 
- 
Patient demographics 
age, weight, height, sex 
- 
Patient comorbidities 
hypertension, heart failure, 
COPD, diabetes, dialysis, 
smoking  
- 
Preoperative laboratory 
values  
hematocrit, platelet count, 
INR, PTT, creatinine, sodium, 
albumin, bilirubin 
- 
Procedure-specific 
transfusion rate 
 
Model outputs 
- 
Predicted risk of red cell 
transfusion 
- 
Recommendation for 
presurgical blood orders 

 
8 
need of a type and screen by S-PATH. In 4 months of prospective validation, S-PATH 
demonstrated 95% sensitivity for identifying patients at risk for surgical transfusion, consistent 
with our original design intention. These results demonstrate the continuing safety and 
consistency of S-PATH predictions when integrated within the EHR in the same manner as it 
would be used in the proposed trial. 
 
Human-centered CDS design. We designed and refined the user interface of the S-PATH 
CDS tool through an iterative human-centered design process.34–36 Specifically, we recruited 29 
stakeholders (i.e., anesthesiologists, nurse anesthetists, preoperative clinic nurse practitioners, 
surgeons, and blood bank physicians) to participate in semi-structured interviews discussing 
design requirements for a CDS tool for presurgical blood ordering and scenario-based 
walkthroughs with a prototype S-PATH CDS design. Usability and workflow issues were 
identified and addressed with revisions to the prototype design.37 
After 3 rounds of iterative refinement, we created an S-PATH CDS interface that is easy to use 
and aligned with clinician workflow at the point-of-care within the Epic EHR (Figure 3). Model 
predicted risk is presented along with a recommendation for presurgical blood orders and an 
explanation for the model’s reasoning.38 Resources are provided to facilitate appropriate 
interpretation of the tool’s results. This design is compliant with the US Food and Drug 
Administration (FDA) draft guidance for machine learning tools in healthcare.39 
Summary. A personalized ML model (S-PATH) to predict risk for intraoperative transfusion was 
developed, validated across local and national settings,12,32,33 and shown to have better 
performance for recommending presurgical blood orders (fewer missed orders, fewer excess 
orders) than the standard of care MSBOS approach. S-PATH was then incorporated within the 
EHR as a CDS tool and made available at the point-of-care with a user interface that is aligned 
with clinician needs and current workflow. The purpose of this study is to evaluate the real-world 
feasibility, acceptability, and preliminary effect of the S-PATH CDS tool in a cluster-randomized 
clinical trial. 
 
 
Figure 3 – S-PATH 
CDS interface. Model 
predictions are 
generated from live 
patient data within the 
EHR without requiring 
clinicians to manually 
input data. A human-
centered design 
process was used to 
refine this design with 
input from stakeholders. 

 
9 
2 Objectives 
The objective of S-PATH cluster-randomized clinical trial are to evaluate the effect of having 
access to ML-based surgical transfusion risk predictions for clinicians working in our 
preoperative assessment clinic. 
2.1 Primary Objective 
The primary objective will be to measure the effect of having access to S-PATH predictions on 
the frequency of preoperative type and screens ordered by clinicians working in the 
preoperative assessment clinic. 
2.2 Secondary Objectives 
2.2.1 Key Secondary Objectives 
Secondary outcomes will include (1) the frequency of patients having an active type and screen 
at the start of surgery (within the first hour), (2) the frequency of red cell transfusion during 
surgery, (3) the frequency of emergency release blood use, (4) the frequency of transfusion 
reaction 
2.2.2 Other Secondary Objectives 
Implementation outcomes will be measured to assess uptake and use of the S-PATH tool by 
participating clinicians. These outcomes will be measured at the clinician level and will include 
(1) frequency of viewing S-PATH predictions, (2) frequency of the clinician’s preoperative type 
and screen order matching S-PATH’s recommendation. These will be assessed monthly 
throughout the study. 
Participants will also be asked to complete a survey regarding their perception on the usability 
of the S-PATH tool, understanding of tool predictions, and trust in the tool prior to the start of 
using the tool in clinical care, and again at the end of the study. 
These results will be used to assess how clinicians interact with AI, and exploratory analyses 
will be conducted to evaluate whether there are clinician-related factors that lead to better 
performance in collaboration with AI. An economic analysis will also be performed to evaluate 
the cost of care with and without access to the AI tool. 
3 Trial Design 
This study will be a prospective open-label cluster-randomized clinical trial, with clusters at the 
level of the clinician working in the preoperative assessment clinic. The study will be conducted 
in three phases resembling a stepped-wedge trial design with two steps (Figure 4). 
In Phase I, all participants will not have access to the tool and will continue routine clinical care 
using the Maximum Surgical Blood Ordering Schedule (MSBOS) to guide type and screen 
ordering decisions.  
In Phase II, half of the nurse practitioner participants will be randomly selected (stratified by 
clinical experience, full-time equivalent work level, and work location) to start having access to 
the S-PATH tool. There will be a wash-in period where participants in the intervention arm will 
view the tool predictions without using them for clinical decision-making. This period is designed 
as a pilot period to allow clinicians to become accustomed to the tool, to allow for the resolution 
of any usability or workflow concerns, and to provide further targeted training in tool use as 
necessary. During Phase II, recruitment for intern physicians will begin, and they will be 
randomized 1:1 to either intervention or control for the duration of their 1-month rotation. 

 
10 
In Phase III, all nurse practitioner participants will have access to the S-PATH tool, including 
those that were randomized to the control arm in Phase II. Just as in Phase II, there will be a 
wash-in period to allow participants who did not previously have access to the tool to become 
accustomed to the tool prior to using it for clinical care. Intern physicians will continue to be 
randomized 1:1 to intervention and control during Phase III each 1 month rotation. 
 
 
4 Participants, Interventions, Outcomes 
4.1 Study Setting 
This will be an open-label, single center, cluster-randomized controlled trial at Barnes Jewish 
Hospital in St. Louis, MO, a large tertiary care academic medical center affiliated with 
Washington University School of Medicine (WashU) in St Louis. This study will be conducted 
within the Center for Preoperative Assessment and Planning (CPAP), an outpatient clinical 
service that evaluates approximately 90% of all elective surgical patients having surgery at 
Barnes Jewish Hospital. This clinic is staffed primarily by nurse practitioners (NP), who practice 
independently with oversight by an attending anesthesiologist. There are also two intern 
physicians who rotate in the clinic each month. The majority of preoperative blood orders at BJH 
are placed during the CPAP clinic visit. Each NP typically evaluates 30 patients per month in-
person. Each intern physician typically evaluates 120 patients in person during their month-long 
rotation. Current clinic workflow is to assign patients to CPAP clinicians based on availability; 
clinicians do not specialize in specific surgical areas. 
4.2 Participants and Eligibility 
4.2.1 Clinician participants. Clinician participants for this study will be recruited from clinicians 
working in the preoperative assessment clinic, including nurse practitioners and intern 
physicians. Clinician subjects will provide informed consent for their participation in this study. 
We estimate there will be 46 NPs and 16 intern physicians eligible for recruitment throughout 
the duration of this study. NP participants will be randomized to either have early (i.e, Phase II) 
or late (i.e., Phase III) access to the S-PATH tool as shown in Figure 4. Intern participants will be 
randomized to either have access to the S-PATH tool or to control, i.e., with use of the existing 
Maximum Blood Ordering Schedule; early and late availability of the tool is not feasible for this 
population as they only spend 1 month on the CPAP clinical rotation. 
Figure 4 – S-PATH trial design. In Phase I, all 
participants will use the MSBOS per usual care 
(control). In Phase II, half of the nurse practitioner 
(NP) participants will be randomized to have 
access to the S-PATH tool (intervention). In 
addition, half of the intern physicians each month 
will be randomized to the intervention group. In 
Phase III, all NP participants will have access to the 
tool, and intern physicians will continue to be 
randomized 1:1. Between each phase, there will be 
a wash-in period to allow participants who will use 
the tool in the next phase to become accustomed to 
how the tool works. 

 
11 
4.2.2. Patient participants. Patients who are evaluated by enrolled clinicians in-person at the 
preoperative assessment clinic will also be included in the study, with their randomization group 
determined by the clinician assigned to evaluate them.  
We are requesting a waiver of informed patient consent as the S-PATH clinical decision support 
tool is minimal risk as described in 7.3 Consent below. Based on our preliminary data, we 
estimate that approximately 1,100 patients per month meeting the below inclusion and exclusion 
criteria will be evaluated in person at CPAP clinic, for an estimated total of 13,200 patients 
throughout the duration of this 12 month study. 
4.2.3. Patient inclusion criteria. Patients will be included in the study if they are evaluated in-
person at CPAP clinic and have an S-PATH model prediction. We expect approximately 70% of 
patients being seen in-person at CPAP clinic to have model predictions. Reasons for not having 
a prediction include unscheduled walk-in appointments and patients having rare procedures for 
which no reliable historical transfusion information is available to make a prediction. 
4.2.4. Patient exclusion criteria. Patients will be excluded if they have a history of red cell 
alloantibodies or if they are not scheduled to have their surgery in one of the main operating 
rooms at Barnes Jewish Hospital (e.g., scheduled for surgery at one of the other BJC hospitals 
or in a remote procedural area like gastroenterology or interventional radiology).  
4.3 Interventions 
4.3.1 Usual care. In the control periods, participants will make usual care decisions on whether 
patients should have preoperative type and screen orders, guided by the existing Maximum 
Surgical Blood Ordering Schedule (MSBOS), which is currently in common use in CPAP clinic. 
4.3.2 S-PATH intervention. In the intervention periods, participants will have access to the S-
PATH clinical decision support tool in addition to the MSBOS. The S-PATH tool will be integrated 
within the electronic health record (EHR), such that it will show the patient’s predicted 
transfusion risk, an explanation for how it arrived at that risk estimate, and a suggestion for 
whether to order a preoperative type and screen (Figure 5). Patients with >3% risk of surgical 
transfusion are recommended to have a preoperative type and screen. The interface also links 
out to a quick-start guide, which provides additional information on how the model was trained, 
how to appropriately interpret its predictions and explanations, important limitations in its use, 
and measures of model performance (Figure 6, also available at https://sites.wustl.edu/spath-
tool/). 
The S-PATH interface was designed following a human-centered design process, with input 
from 29 stakeholders representing surgery, anesthesiology, preoperative assessment clinic, and 
transfusion medicine (see Preliminary Studies for more information). This design is compliant 
with the US Food and Drug Administration draft guidance for machine learning tools in 
healthcare.39 

 
12 
 
Figure 5 – Example S-
PATH interface integrated 
within existing EHR 
navigator pages that 
clinicians working in the 
preoperative assessment 
clinic routinely visit. 

 
13 
Figure 6 – Snippet of model information website, available at https://sites.wustl.edu/spath-tool/ . This website also 
provides information on the demographics of the cohort the model was trained on, and model performance. 
 

 
14 
Prior to use of the tool in clinical care, participants will be trained in appropriate tool use, 
including discussion of the method by which it was trained, expected performance and ways in 
which it was validated, and key limitations to its use. The training will also emphasize that 
clinicians are to continue to use their clinical judgement when deciding whether to place a 
preoperative type and screen order, and that they are free to disregard the model prediction and 
recommendation for any reason. A sample reference card to help clinicians appropriately 
interpret S-PATH results is shown in Figure 7. This reference card and the quick start guide 
website shown in Figure 6 may be modified based on participant feedback. A link to a feedback 
form will be available within the S-PATH interface to allow clinicians to report problems, 
concerns, or near-miss events concerning the S-PATH tool to the study team. 
Figure 7 – Decision guide to help clinicians appropriately interpret S-PATH predictions. 
 
4.3.1. Case screening. Patients will be excluded from the study if they have a history of red cell 
alloantibodies, or if their surgery is not scheduled in one of the main operating rooms at Barnes 
Jewish Hospital (e.g., scheduled for surgery at one of the other BJC hospitals or in a remote 
procedural area like gastroenterology or interventional radiology). These exclusion criteria will 
be electronically applied. The location of the surgery and the patient’s potential history of red cell 
alloantibodies will be automatically retrieved from the EHR the first time that patient’s chart is 
opened using the preoperative assessment clinic context. If the patient meets any of the 
exclusion criteria, model predictions will not be shown; instead, a message will print explaining 
the reason for the patient’s exclusion from the study. 
4.3.2. Follow up for patients with new results from laboratory tests drawn during their 
CPAP visit. Patients frequently have laboratory tests drawn at their preoperative assessment 
clinic visit. The results of some of these tests may result in the model recommendation changing 
from “Consider NOT ordering a preoperative type and screen for patients at low risk of 
transfusion” to “Consider ordering a preoperative type and screen for patients at medium to high 
risk of transfusion”. To detect these patients, model predictions will be generated the day after 
the CPAP visit and the day prior to surgery for patients in the intervention arm, and a report will 
identify patients with predictions greater than the 3% risk threshold the day after their CPAP visit 
and who did not receive a type and screen test during their CPAP visit. CPAP currently 
dedicates one NP to following up on the previous day’s laboratory results. This NP will be 
assigned to review this report and order a type and screen as necessary to be drawn on the day 
of surgery. 
4.3.3. Modifications to the S-PATH model. We do not plan to modify the S-PATH model after 
the study starts. However, we may modify the model pipeline and display code if there changes 

 
15 
in the EHR that prevent the model from displaying correctly, retrieving appropriate data, or 
producing appropriate predictions. 
4.3.4. Adherence. Clinicians will not be required to adhere to model recommendations. Rather, 
in both control and intervention groups, they will be encouraged to use their clinical judgement 
to determine appropriate preoperative blood orders, with the MSBOS (control) or MSBOS and 
S-PATH (intervention) for reference.  
Because access to the S-PATH tool is controlled electronically, crossover between control and 
intervention groups will be minimized.  
4.3.5. Concomitant Care. Clinicians involved in the patient’s surgical care downstream of the 
preoperative assessment clinic visit will continue to provide care without any restrictions from 
the trial. These clinicians include the day-of-surgery anesthesia, surgical, and nursing teams, 
who will review the patient’s case and are free to order additional type and screen tests based 
on their clinical judgement. These clinicians will not have access to S-PATH predictions. They 
will not be blinded to treatment allocation, as enrolled clinicians may write into their preoperative 
assessment notes that their decision was guided either by the MSBOS or the S-PATH tool. 
4.4 Outcomes 
All outcomes will be binary, assessed at the patient / surgical case level, and aggregated to 
provide overall frequencies. 
 
4.4.1 Primary Outcome Measure 
The primary outcome will be placement of a type and screen order during the preoperative clinic 
assessment visit, evaluated at the patient / surgical case level. This includes orders placed and 
collected during the preoperative clinic assessment visit, as well as orders signed during the 
preoperative clinic assessment visit or subsequent follow up care and held to be drawn on the 
day of surgery. 
4.4.2 Secondary Outcome Measures 
The following secondary outcomes will be measured at the patient / surgical case level: 
• 
Valid type and screen order at the start of surgery (defined as resulted prior to 1h 
after anesthesia start). The 1 hour buffer is used to account for orders that were drawn 
prior to surgery but may not have resulted by the time the patient enters the operating 
room. This will include orders placed by the clinicians working in preoperative 
assessment clinic and any orders that may be placed by the day of surgery anesthesia 
or surgical teams. This will not include type and screen orders that have expired by the 
start of surgery. This is a secondary efficacy outcome that reflects type and screen 
ordering decisions by all members of the patient’s care team. 
• 
Need for red cell transfusion during surgery. Administration of allogeneic packed red 
blood cells during surgery will be retrieved from the electronic health record. 
Documentation of transfusion is mandatory and the electronic health record scan is used 
to confirm the correct patient. This is a secondary efficacy outcome, as lack of a type 
and screen may prevent discretionary intraoperative transfusion. 
• 
Emergency release blood use during surgery. Administration of emergency release 
allogeneic packed red blood cells will be retrieved from the electronic health record. 
These are either documented as volumes under the MTP tab or individually scanned as 
uncrossmatched red cells. This is the primary safety outcome of the study as emergency 
release blood may be administered if red cell transfusion is urgently indicated but a type 
and screen is not available. The frequency of this outcome will be reported overall 

 
16 
across both groups, and stratified by whether the patient had an active type and screen 
at the start of surgery. 
• 
Red cell transfusion during surgery without an active type and screen at the start 
of surgery. This is the secondary safety outcome intended to capture false negative 
results, i.e., patients who required transfusion during surgery but did not have a 
preoperative type and screen. This outcome will be collected electronically from the EHR 
and will capture patients for whom the type and screen was collected after the start of 
surgery so that crossmatched red cells could be issued. 
• 
Transfusion reaction. Transfusion reactions are reported by clinical teams to the blood 
bank, and the transfusion medicine service investigates and classifies each transfusion 
reaction. These investigation reports will be transmitted to the study team for adverse 
event reporting and outcome collection. This is also a secondary safety outcome for the 
study. Transfusion reactions will be reported stratified by category, as hemolytic 
transfusion reactions are the most relevant to the trial. 
Implementation outcomes will be measured at the clinician / participant-level monthly to assess 
uptake and use of the S-PATH tool. 
• 
Frequency of viewing S-PATH predictions – retrieved from EHR audit log data, which 
shows the types of patient data that the clinician accesses. 
• 
Frequency of the clinician accepting S-PATH’s type and screen recommendation, i.e., 
frequency of the clinician’s decision on whether to order a type and screen matching the 
order recommended by the model. 
Clinician participants will also be asked to complete a survey regarding their perception on the 
usability of the S-PATH tool, understanding of tool predictions, and trust in the tool prior to the 
start of using the tool in clinical care, and again at the end of the study. 
4.5 Participant Timeline 
Clinician participants will be recruited to participate. Following recruitment, nurse practitioner 
participants will be randomized to either early (i.e., month 4 of the study) or late (i.e., month 8 of 
the study) access to the S-PATH tool. Intern physician participants will be randomized to either 
have access to the S-PATH tool or to usual care; it is not practical to randomize intern 
physicians to early or late access to S-PATH as they will only work in the preoperative 
assessment clinic for 1 month. 
Following randomization, clinicians in the intervention arm will receive 1:1 training on 
appropriate interpretation and use of the S-PATH tool, and questions will be addressed. 
Clinicians will be asked to complete a survey on S-PATH usability and trust in S-PATH 
predictions at the beginning and end of their time in the intervention group. Clinician participants 
will also be debriefed at the end of the study to collect their feedback and impressions on use of 
the S-PATH tool. 
All scheduled patient encounters at the preoperative assessment clinic will be scored daily from 
the day prior to the encounter to four days following the encounter. Scores will only be shown to 
clinicians in the intervention arm. Scoring on the days following the encounter is intended to 
identify patients in the intervention arm whose predictions may have changed as the result of 
laboratory tests performed during the preoperative assessment clinic visit; this report will be 
reviewed daily and a type and screen will be ordered to be collected on the day of surgery as 
deemed appropriate. 
4.6 Sample Size 

 
17 
We powered this study to ensure adequate sample size to evaluate whether the primary 
outcome (frequency of type and screen orders) differs between the control and intervention 
arms. Using preliminary data based on silent collection of S-PATH predictions from November 
12, 2024 through March 31, 2025, we anticipate that each clinician will evaluate approximately 
20 patients per month, and approximately 70% of patients will have S-PATH predictions; thus 
we estimate 14 patients per month per clinician will be eligible for inclusion in the study. Our 
preliminary data indicates that S-PATH may recommend type and screen orders for 46% of 
patients, while usual care may order type and screens for 71% of patients, an absolute 
difference of 25 percentage points. 
There are 42 nurse practitioners (NPs) and 16 intern physicians eligible to be recruited for this 
study. For power analysis purposes, we make the conservative assumption that we will be able 
to recruit and retain 75% of eligible NPs (N = 32), who will each evaluate 14 patients eligible for 
S-PATH predictions per month over the course of the study. Using a stepped wedge cluster-
randomized design with 2 steps (16 clusters switching at each step) and assuming an intra-
class correlation coefficient of 0.1,40–42 a two-sided α of 0.05, and 4032 patient encounters over 
the 32 NPs, we anticipate that we will have >99% power to detect a difference of 25% between 
arms for the primary outcome (46% in the intervention arm and 71% in the control arm). Even 
under more conservative assumptions (62% in intervention, 71% in control; ICC of 0.50) we 
anticipate > 84% power. Therefore, we anticipate that we will have ample power to detect the 
effect of interest in this study over a wide range of realistic scenarios. 
4.7 Recruitment 
Individual clinicians will be approached in-person to solicit consent for participation in this study. 
Informed consent will be solicited, including discussion of risks and benefits of participation in 
this study. Clinicians will be shown the consent document and key information form, either as a 
paper document or as a RedCap electronic version; either way, a member of the study team will 
be present in person to address any questions that arise. Clinicians will have as much time as 
they need to consider participating in the study. The study team member that performs these 
recruitment tasks will not be in the supervision hierarchy for any of the clinicians eligible for 
recruitment to minimize the possibility of coercion or undue influence in the consent process. 
We are requesting a waiver of informed patient consent because the intervention is minimal risk 
as described in 7.3 Consent. Patient accrual will occur electronically based on the 
randomization group of the clinician to which they are assigned when they arrive at their 
preoperative assessment clinic visit. 
4.8 Allocation 
Nurse practitioner participants will be randomized 1:1 to either early (Phase II) access to S-
PATH or late (Phase III) access to S-PATH. A computerized covariate-constrained 
randomization method with random component will be used to provide randomized treatment 
assignments balanced by age, years of preoperative clinic experience, and expected number of 
inpatient preoperative clinic encounters during the study.43–46 To approximate the total 
randomization space, 100,000 unique allocations of clinicians to arms will be randomly 
generated. For each scheme, an l1 balance metric (defined as the sum of absolute differences 
between arm means weighted by inverse standard deviation) will be calculated. The constrained 
randomization space will be restricted to the top 1% of schemes (the 1,000 allocations achieving 
the best balance), from which the final assignment will be randomly selected to preserve the 
random component of the design. All balance covariates will be included as fixed effects in the 
primary analysis to ensure statistical power and validity. 

 
18 
Intern physician participants will be directly randomized (without constraint) 1:1 to either access 
to S-PATH or to usual care at the beginning of their 1-month preoperative assessment clinic 
rotation. 
4.9 Blinding 
This is an open-label study. Clinician participants will be aware of their randomization group as 
clinicians in the intervention arm need to be trained in S-PATH use. Similarly, the study team will 
be aware of the clinician’s randomization group to provide training and technical support.  
Patients and other downstream clinicians (day of surgery anesthesia and surgical teams) will 
not be directly aware of the patient’s randomization group. However, the preoperative 
assessment clinic note may disclose whether S-PATH was used in clinical decision-making, and 
both patients and other clinicians are able to read these notes. 
5 Data Collection, Management, Analysis 
All outcomes of the study will be electronically collected from the electronic health record (EHR) 
data warehouse, with the exception of suspected transfusion reactions, which will be transmitted 
by the Blood Bank to the study team as they occur. Data collected for this study will include 
patient demographic information and comorbidities as documented in the preoperative 
assessment clinic visit, preoperative laboratory test results, information on the date and time of 
the preoperative assessment clinic visit, the planned surgery, the surgery location, the clinician 
evaluating the patient in preoperative assessment clinic visits and their demographic 
characteristics, history of red cell alloantibodies, any type and screen orders placed prior to 
surgery and subsequent results, and transfusion of any blood products during surgery. To 
measure use of the S-PATH tool, electronic health record access logs for participating clinicians 
will also be retrieved. 
Only the minimum necessary private patient information will be collected for the purposes of the 
study. Any protected health data will be kept in a secure digital environment that is digitally 
encrypted, password protected and limited to research team only. De-identified data may be 
kept and used in future studies not pre-specified in the above protocol. The investigators are 
responsible for ensuring the accuracy, completeness, legibility, and timeliness of the data 
collected. All data will be managed by Washington University IT Department and housed on 
secure password protected, encrypted servers. 
In compliance with NIH guidelines, following completion of the study, all data will be deidentified 
prior to depositing the study data in the WashU Libraries Open Scholarship Digital Research 
Materials Repository. For deidentification purposes, clinician participants will be given a 
anonymized participant ID key and patient participants will be given an anonymized patient ID 
key distinct from their medical record number. Curation will be guided by the Data Curation 
Network (DCN) CURATED model, from which the dissemination information package (DIP) is 
created, given a registered DOI, retained in the repository and archived for a minimum of 10 
years. The archival information package (AIP) will contain the original submission information 
package (SIP), the DIP, PREMIS compliant metadata, the readme file, and documentation of 
curation processes. Data may be used for future studies not mentioned in the protocol.   
5.1 Statistical Methods 
The primary statistical analysis will include both NPs and intern physicians in the same model, 
with the primary unit of analysis being the patient / surgical case level. Each patient / surgical 
case will have their randomization status determined by the randomization status of the clinician 
who evaluated the patient in the preoperative assessment clinic. NP participants will have been 
randomized either to the early (Phase II) or late (Phase III) access to S-PATH group, meaning 

 
19 
that each NP will spend time in both the control and intervention groups depending on time 
point. Intern physicians will have been randomized 1:1 to either control or intervention for the 
duration of their 1-month rotation. A sensitivity analysis will be performed to analyze patients 
evaluated by NPs and intern physicians separately to ensure that biases were not introduced by 
including both groups in the same analysis, as they had different exposures to the intervention. 
An intention-to-treat approach will be used; all patients and clinicians will be analyzed with the 
intervention group to which they were randomly assigned regardless of whether the clinician 
used the S-PATH tool or placed an order consistent with S-PATH’s recommendation. 
5.1.1. Descriptive statistics 
Basic descriptive statistics will be computed overall and stratified by randomization group for all 
demographic characteristics of participating clinicians and patients. Imbalance between 
intervention groups will be assessed using univariable statistical tests, such as the Mann-
Whitney U test for continuous variables and Chi-square test for categorical variables. 
5.1.2 Analysis for the primary outcome 
Placement of type and screen orders: The primary outcome for this cluster-randomized trial is 
the placement of type and screen orders by enrolled clinicians at the preoperative assessment 
clinic visit, assessed at the patient level as a binary outcome. The frequency with which these 
type and screen orders were placed will be calculated separately for patients / surgical cases in 
the intervention condition and control condition. To test the statistical null hypothesis that there 
is no difference in the proportion of patients with type and screen orders in the control vs the 
intervention arm, we will employ a generalized estimating equations (GEE) approach. The 
primary analysis will be a modified logistic regression47 with robust standard errors clustered on 
provider to estimate relative risks and risk differences. We will model our data similarly to the 
Hussey and Hughes48 approach for analyzing stepped wedge studies, which includes a fixed 
effect for the treatment group (SPATH vs MSBOS) to test for differences between groups, and a 
fixed temporal variable to adjust for variation over calendar time. We will also adjust for a set of 
baseline covariates at the patient-level (age, sex, weight, race, preoperative hematocrit, 
procedure-specific transfusion rate, preoperative INR, platelet count, and partial thromboplastin 
time) and clinician-level (balancing variables: age, years of experience, expected number of 
encounters) to enhance precision of the treatment-effect estimate and account for chance 
imbalances.49 A significant difference between the treatment groups (P < 0.05), with higher 
proportions of type and screen orders in the control compared to the intervention group, will 
support the hypothesis that access to the S-PATH CDS system results in fewer type and screen 
orders. 
 
5.1.3 Analyses for secondary outcomes 
 
Emergency release blood utilization: The safety outcome is binary, assessed at the patient 
level, and will be analyzed using a noninferiority analysis. This noninferiority analysis will assess 
whether access to the S-PATH CDS system is noninferior to usual care with regard to the use of 
emergency release blood. Based on our preliminary data, emergency release blood use is rare, 
and currently occurs for 1 in 200 (0.5%) surgical cases at the host institution. In discussion with 
key stakeholders and by comparison with results from other institutions that revised their 
Maximum Surgical Blood Ordering Schedule (MSBOS), we determined that a 1.2-fold increase 
in emergency release blood use would be clinically meaningful. To test this hypothesis, we will 
estimate the relative risk for emergency release blood use using a GEE and a noninferiority limit 
of 1.2 for the relative risk. If the upper confidence limit for the relative risk estimate is below the 
noninferiority margin of 1.2, we will reject the null hypothesis that the intervention is worse than 

 
20 
the control and conclude that the intervention is noninferior with regards to emergency release 
blood use. 
 
Other secondary outcomes: The following secondary outcomes are all binary outcomes 
assessed at the patient level, and they will be analyzed using a GEE approach clustered by 
clinician as described for the primary outcome: 
- 
Active type and screen orders at the start of surgery 
- 
Red cell transfusion during surgery 
- 
Red cell transfusion during surgery without an active type and screen at the start of 
surgery 
- 
Transfusion reaction 
 
5.1.4. Sensitivity and Subgroup Analyses 
Sensitivity Analyses. As a sensitivity analysis, we plan to prospectively evaluate the S-PATH’s 
predictions as if they had been followed, regardless of the observed type and screen ordering 
decision made by the enrolled clinician. Specifically, we will compare the frequency of the 
primary outcome between the intervention and control groups as if the S-PATH recommendation 
had been followed in the intervention group. We will also compare the frequency of red cell 
transfusion during surgery without a type and screen using this approach, as this will represent 
the false negative rate of S-PATH versus usual care. 
As additional sensitivity analysis, we will analyze data collected during Phase II of the trial 
(when the early group of NPs had started using S-PATH but the late group of NPs were still 
performing usual care) as a parallel cluster-randomized trial using a generalized estimating 
equation approach assuming a binomial distribution and log link function. We anticipate that this 
approach may have lower power but will be less subject to temporal trends given the shorter 
duration of Phase II (3 months). 
As mentioned in section 5.1, we also plan to analyze data from patients seen by NP participants 
separately from patients seen by intern physicians, using the same analytic techniques as 
described in the primary analysis. The study protocol for training and exposure to the S-PATH 
intervention did differ between these two groups, so this sensitivity analysis is designed to 
address the possibility that biases might have been introduced by aggregating data from both 
provider groups in the main analysis. 
Subgroup Analyses. Subgroups will be examined to evaluate the effectiveness of the 
intervention across the following predefined subgroups for the primary and safety endpoints: 
patient gender, patient race, and surgical service.  
5.1.5. Implementation outcomes 
This study is designed as a hybrid Type I implementation-effectiveness trial.50 Therefore, we 
plan to evaluate implementation outcomes to assess the implementation strategy for the S-
PATH intervention and to identify barriers or facilitators S-PATH use. 
Clinician-focused implementation outcomes: These outcomes will be assessed at the clinician 
level monthly and in aggregate and described with simple descriptive statistics. The frequency 
of viewing S-PATH predictions will be reported as the proportion of that clinicians’ eligible 
patient encounters where they viewed the S-PATH tool. The frequency of the clinician accepting 
S-PATH’s recommendation will be reported as the proportion of that clinician’s eligible patient 
encounters where their type and screen decision matched S-PATH’s recommendation. 
Associations between clinician characteristics (such as clinician type, experience, responses to 
pre- and post-study surveys) and clinician-focused implementation outcomes will be explored 

 
21 
using simple descriptive statistics, the chi-square test statistic, or logistic regression models 
appropriate. 
Clinician-AI interaction: We will also explore the performance of the clinician assisted by AI 
compared with the performance of the S-PATH model alone. Specifically, we will evaluate the 
primary outcome (frequency of type and screen orders placed during the CPAP visit) and the 
secondary safety outcome (frequency of receiving red cell transfusion during surgery without a 
preoperative type and screen), aggregated at the clinician level, as compared with S-PATH 
model predictions. We anticipate some clinicians may perform better than the S-PATH alone 
while others may perform worse. We will explore associations between clinician characteristics 
(similar to as described above) and overall performance relative to S-PATH alone using simple 
descriptive statistics or logistic regression models as appropriate. 
Patient- and procedure-level predictors of non-acceptance. Rationale: Non-acceptance of S-
PATH recommendations may suggest misalignment of the model with clinician judgement; in 
other words, clinicians may disagree with S-PATH because they have safety concerns about the 
recommended order. The goal of this exploratory analysis is to identify patient subgroups for 
whom clinicians were more likely to not accept S-PATH recommendations; these represent 
opportunities for improving the model or the implementation strategy. 
We will first compute simple descriptive statistics illustrating S-PATH non-acceptance rates 
stratified by procedure code, surgical service, procedure-specific transfusion rate, patient-level 
demographic characteristics (age, sex, weight), and preoperative laboratory values. These 
variables are chosen as those that most influence the model’s prediction or clinician behaviors 
based on preliminary work. Continuous variables will be categorized into quartiles for this 
univariable analysis. The Chi-square test with Bonferroni correction will be used to assess for 
statistically significant differences in non-acceptance between levels of each variable. Variables 
with significant univariable differences in non-acceptance by level will be included in a 
multivariable logistic regression model to identify independent predictors of S-PATH non-
acceptance. 
5.1.6 Economic analysis 
To estimate the potential cost savings of S-PATH use, we will use the Medicare reimbursement 
rate for a blood type and antibody screen based on the 2025 Medicare Clinical Laboratory Fee 
Schedule ($15.75),51 and the median acquisition price for packed red blood cells based on the 
2023 National Blood Collection and Utilization Survey.52 Per-patient estimated costs will be the 
sum of blood type and antibody screens performed and red cell units transfused, and will be 
compared between trial arms. 
5.1.7. Exit interviews 
At the end of their study participation, clinician participants will be sent a survey via email, which 
will solicit feedback on their impressions of the S-PATH tool. Clinicians will also have the option 
to opt into participating in an exit interview. Semistructured exit interviews will be conducted 
virtually with a member of the study team using the Zoom videoconferencing platform. A 
semistructured interview guide will be used to elicit feedback on barriers and facilitators to S-
PATH use. Interviews will be recorded and transcribed using the Zoom platform, and analyzed 
using an inductive open coding approach. 
6. Monitoring 
6.1 Adverse event and safety monitoring 
6.1.1 Data Safety Monitoring Board 

 
22 
A Data Safety Monitoring Board will be convened to monitor participant safety during the trial. 
Members will include: Michael Mathis (expertise in machine learning, electronic health record 
data), Jessica Spence (expertise in cluster-randomized clinical trials and statistics), Steven 
Frank (expertise in transfusion medicine), Justin Starren (expertise in medical ethics and 
informatics). The DSMB will meet prior to Phase II of the trial to review procedures for the 
protection of human subjects and baseline safety data. The DSMB will meet again prior to the 
initiation of Phase III of the trial to reviewing interim safety outcomes. 
A DSMB in the context of this pragmatic clinical trial exists for the purpose of providing the 
investigators, the IRB, and the sponsor with objective, scientific monitoring of the conduct of the 
study from the standpoint of ensuring (1) the protection and safety of human subjects and (2) 
the validity and integrity of the trial. The DSMB serves as an independent group advisory to the 
funding agency. To fulfill its functions, the DSMB will review the original protocol and any 
subsequent amendments, perform expedited monitoring of all serious adverse events (SAEs), 
perform ongoing monitoring of non-SAEs and safety outcomes, determine whether study 
procedures should be changed or the study should be halted because of serious safety 
concerns and/or major problems with the study conduct, and perform periodic review of the 
completeness and validity of data to be used for analysis of safety and efficacy. The DSMB will 
also monitor implementation of procedures to ensure privacy and data confidentiality. 
No formal stopping rules for safety, efficacy, or futility are planned. However, blinded interim 
analysis of the safety outcomes of the trial will be reviewed at the mid-trial DSMB meeting (see 
6.2 Interim Analyses below)  
If at any time during the course of the study, the DSMB judges that risk to subjects may 
significantly outweighs the potential benefit, the DSMB shall have the discretion and 
responsibility to request all necessary information for detailed analyses, and if warranted, 
recommend that the study be terminated. Factors that might lead to consideration of stopping 
would include a significant number of AEs that can reasonably be attributed to participation in 
the study, clearly more harm in one group than the other, inability to recruit and measure the 
required number of participants to conduct the primary outcome analyses, poor intervention 
adoption, serious protocol violations, or other circumstances that would render the study unlikely 
to produce scientifically valid findings. The DSMB will carefully weigh the risk of completing the 
trial as planned against the risk of prematurely stopping the trial for safety or futility. 
6.1.2 Safety Monitoring 
The primary patient-related risk relevant to presurgical blood orders is the possibility of patients 
requiring intraoperative transfusion in the absence of presurgical blood orders (i.e., false 
negative recommendations). This can occur with usual care and with use of the S-PATH tool. 
When this happens, transfusion can be delayed to allow for blood preparation orders to be 
placed and processed. Alternatively, if the need for transfusion is urgent, emergency release 
blood (universal donor, type O) can be administered. Although emergency release blood is very 
safe, in rare circumstances (0.01-0.1%),53–56 a patient might have a hemolytic transfusion 
reaction (for example, if they have unanticipated red cell alloantibodies), which are usually mild 
and clinically insignificant. The other risks associated with transfusion (infection, volume 
overload, acute lung injury etc.) are otherwise identical between emergency release and cross-
matched blood. 
Therefore, adverse event and safety monitoring for this trial will focus on monitoring for the 
following: (1) patients receiving intraoperative transfusion without a type and screen prior to the 
start of surgery, (2) patients receiving emergency release blood who did not have a type and 
screen prior to the start of surgery, and (3) hemolytic transfusion reactions in patients receiving 
emergency release blood who did not have a type and screen prior to the start of surgery. All 3 

 
23 
of these occurrences will be considered adverse events (AE). Serious adverse events (SAE) will 
be defined as AE that result in death, organ failure, or results in persistent or significant 
disability. All suspected AEs and SAEs will be logged by the study coordinator in the study 
REDCap project and forwarded to the PI for review, adjudication, and investigation to determine 
whether they are expected and whether they are plausibly related to the study. AE summary 
reports can be generated at any time for the DSMB and the IRB, for both regularly scheduled 
reviews as well as in the event of any SAEs requiring expedited reporting. 
Monitoring for intraoperative transfusion without a preoperative type and screen and for 
emergency release blood use will occur electronically by querying the EHR data warehouse as 
described in 4.4.2. 
Monitoring for transfusion reaction will occur by leveraging the Blood Bank’s existing monitoring 
framework for detecting transfusion-related adverse events. Specifically, the Blood Bank is an 
AABB-accredited and FDA-regulated facility that is mandated to track, investigate, and report on 
all transfusion service adverse events. The study coordinator will communicate with Blood Bank 
staff to retrieve the names of all surgical patients who were reported to have a transfusion-
associated AE.  
Manual chart review will be performed for all patients with adverse events to monitor for 
subsequent SAE. Specifically, patients will be followed by chart review until hospital discharge 
or for 30 days after the index transfusion event (whichever comes sooner) to ascertain whether 
a SAE occurred. Mortality resulting from transfusion will be reported to the Blood Bank and the 
FDA as required. 
At each DSMB meeting, safety information for this study will be reported to the DSMB by group 
but with the true identity of the study groups masked. This will maintain proper blinding of the 
DSMB and study personnel responsible for the scientific oversight and direction (PI and 
statistical advisors) and outcome data acquisition and analysis (coordinator and analysts). 
However, if there are extraordinary concerns regarding participant safety, the DSMB may 
request unblinded data, e.g., on unexpected SAEs, in order to determine the nature and extent 
of harm of the interventions under study. When this occurs, a protocol to be established with 
input from the DSMB during the trial initiation phase will be followed to ensure continued 
masking of the study personnel as noted. 
6.2 Interim Analyses 
Blinded interim reporting on safety outcomes will be presented at the mid-trial DSMB meeting 
for review. These safety outcomes include, in decreasing order of expected prevalence, (1) the 
frequency of transfusion without an active type and screen within the first hour of surgery, (2) 
the frequency of patients receiving emergency release blood, (3) the frequency of patients 
experiencing hemolytic and other types of transfusion reaction. 
Statistical analyses of these outcomes will be presented with blinding of the intervention groups. 
Unadjusted binomial confidence intervals will be presented along with the prevalence of each 
safety outcome in each blinded study group. The DSMB will consider stopping the trial if there 
are non-overlapping confidence intervals for the prevalence of any safety outcome between the 
trial arms. Based on preliminary data on safety outcome 1 (the current frequency of patients 
receiving transfusion without a T&S prior to the start of surgery = 0.4%) and assuming 
recruitment targets described in 4.6 Sample Size, we anticipate being powered to detect a 3-fold 
difference in the prevalence of this outcome by the mid-trial DSMB meeting. 
The DSMB will evaluate the data to determine whether the trial should continue as planned, be 
modified, or be stopped. The DSMB may also request additional statistical analyses, including 
full statistical adjustment for the safety outcomes, if needed to clarify emerging safety signals. If 

 
24 
extraordinary concerns arise regarding patient safety, the DSMB may request unblinded data 
prior to making a recommendation. 
 
6.3 Harms 
Expedited reporting. All unexpected SAEs must be reported to the DSMB, IRB, and Blood Bank 
within 5 business days of discovery, regardless of any judgment of their relatedness to the study 
treatment. All SAEs that result in patient death must be reported to the IRB, DSMB, and Blood 
Bank within 1 business day. This reporting timeframe is consistent with the Washington 
University IRB’s requirements as described below. All relevant information will be reported to the 
DSMB for each SAE including information about the event and its outcome, dosing history of a 
suspect medication/treatment (if applicable), concomitant medications, the participant’s medical 
history and current conditions, and all relevant laboratory data. An initial notification by email 
with all related study forms shall be made to the DSMB within 1 or 5 business days of the 
detection of an unexpected SAE depending on severity. Any additional information about the 
case that may be obtained after the initial notification shall be communicated to the DSMB in a 
timely manner. The DSMB may require a conference call to review all the relevant information 
and the site study physician’s determination of whether there was any possible relevance to the 
study, and discuss and approve the investigators’ Corrective and Preventive Action Plan, if 
warranted.   
IRB requirements for AE reporting.  The Washington University IRB requires that within 5 
business days of the PI learning of an unanticipated problem (UP) or major protocol deviation, 
the PI informs the Director of IRB panel and all relevant oversight committees at the university, 
and that mortality be reported within 1 business day. Within 15 business days of the PI 
becoming aware of non-serious AE, changes in risk/benefit, or events requiring report to the 
sponsor, these will be reported. An annual report will be submitted to the IRB and consortium 
institutions and to the sponsor summarizing all AEs, serious or not. 
7. Ethics and Dissemination 
7.1 Protocol  
Any modifications to the protocol which may impact on the conduct of the study, potential benefit 
of the patient or may affect patient safety, including changes of study objectives, study design, 
patient population, sample sizes, study procedures, or significant administrative aspects will 
require a formal amendment to the protocol. Such amendment will be agreed upon by the 
WUSTL IRB. As discussed above, small changes to the S-PATH interface or reference materials 
are anticipated and will not be regarded as protocol modifications. 
7.2 Consent 
Clinician participants will provide written informed consent. This study is seeking approval for a 
waiver of informed patient consent. 
This waiver is justified because:  
1. The intervention is not an FDA-regulated device. Figure 8 shows the Food and Drug 
Administration’s guidance on the criteria for clinical decision support software that are 
not considered medical devices.57 The S-PATH tool meets all of the criteria:  
a. S-PATH does not acquire, process, or analyze medical images, signals, or 
patterns. It explicitly does not use any time-series features, and only considers 
the patient’s most recent laboratory values and other demographic and surgical 
information.  

 
25 
b. S-PATH displays surgical transfusion risk and the values for the patient’s risk 
factors that contribute to its risk estimate, all of which are medical information 
that normally is communicated between healthcare professionals. The relevance 
of the surgical transfusion risk estimate to the clinical decision to order a type and 
screen is well understood. Existing guidelines for the creation of a Maximum 
Surgical Blood Ordering Schedule (MSBOS), the current standard of care 
approach, advocate for the use of the procedure-specific historical transfusion 
risk as the risk estimate and specifically describe use of a 5% risk threshold for 
ordering a type and screen. S-PATH displays the same procedure-specific 
historical transfusion used for MSBOS creation, and also provides a more 
personalized risk estimate based on the patient’s characteristics. 
c. S-PATH provides information and recommendations to a healthcare professional 
and does not substitute, replace, or direct judgement. S-PATH provides a 
recommendation for whether to consider ordering a type and screen, but clinician 
participants are free to make the clinical decision they feel is appropriate. 
d. S-PATH provides the basis for its recommendations so that the clinician can 
independently review the basis for the recommendations and do not primarily rely 
on its recommendations to make a decision. As shown in Figure 5, S-PATH 
provides information on which of the patient’s characteristics that most 
contributed to its risk prediction so the clinician can evaluate the accuracy of its 
recommendation. Training will be provided specifically to help clinicians identify 
situations where S-PATH might be misled. Clinicians will also have access to the 
existing MSBOS when making their decisions. 
2. The intervention is minimal risk – The intervention does not expose clinician or patient 
participant to risks beyond what is routinely encountered in everyday life. The reasons 
for this are threefold: (1) The intervention (S-PATH tool) provides information and 
recommendations to the clinician in CPAP clinic, but it does not override their clinical 
Figure 8 – FDA guidance illustrating criteria for a clinical decision support tool to not be regulated as a medical device. 

 
26 
judgement. In addition, there are many clinician stakeholders who review the type and 
screen status of the patient prior to surgery, including the surgeon and day of surgery 
anesthesia team. (2) For both the control (routine care) and intervention (S-PATH) arms, 
there is the possibility that patients who do not have a preoperative type and screen will 
require intraoperative transfusion. Our preliminary data indicates that this may occur less 
frequently with S-PATH compared to usual care (see Preliminary Data / Prospective 
Validation). Emergency release blood is readily available (within 5 minutes) and is 
routinely used in this situation. (3) For both control and intervention arms, there is the 
possibility that patients with unnecessary presurgical blood orders will receive 
unnecessary discretionary transfusions, resulting in increased exposure to all 
transfusion-associated risks. Therefore, there are risks to both inadequate and 
excessive presurgical blood orders for both intervention and control. 
3. The research could not be conducted without the waiver. Given the sample size required 
to adequately power this study (3,780 patients, see 4.5 Sample Size), it is not feasible to 
consent individual patients. 
4. The rights and welfare of patients are not adversely affected. The use of a waiver of 
consent in this situation would not violate any legal statutes given that the intervention is 
minimal risk. 
7.3 Protections Against Risks 
IRB approval of the study protocols and procedures will be obtained prior to implementation, 
and the Data Safety Monitoring Board will review the procedures for the protection of human 
subjects at the outset of the project. 
 
Use of the S-PATH system is a variation of routine clinical care; instead of using the 
conventional Maximum Surgical Blood Ordering Schedule (MSBOS) to determine presurgical 
blood orders based on the patient’s planned procedure (routine care), the S-PATH system 
incorporates both patient- and procedure-specific information to produce its estimated surgical 
transfusion risk and recommendation for blood orders. For both routine care and the S-PATH 
intervention, the clinician remains the final arbiter of the blood ordering decision. 
 
As part of the consent and training process, clinician participants will be informed that the 
recommendations of the S-PATH system in no way overrides their clinical judgement. In 
addition, it will be emphasized that S-PATH use is optional, and that clinicians are free to use 
the conventional MSBOS alongside the S-PATH system. For clinicians in the intervention arm, 
training will be provided on the limitations of the S-PATH system, including clinical scenarios 
where S-PATH accuracy might be reduced. Limitations are also explicitly incorporated into the 
user interface in a way that is easily interpretable. Patients with a prior history of red cell 
alloantibodies, for whom type and screen and crossmatching blood would take longer time, are 
explicitly excluded from this study. 
 
We will implement the following measures to minimize risks to clinician and patient participants: 
- 
Risk for patients requiring intraoperative transfusion in the absence of presurgical blood 
orders. If the need for transfusion is urgent to protect patient safety, emergency release 
blood can be administered. Emergency release blood is currently available within all of 
the operating room areas at Barnes Jewish Hospital, and can be retrieved within 5 
minutes. It is already routinely utilized for this purpose, i.e., for patients encountering 
unexpected surgical bleeding where the time to obtain crossmatched blood exceeds 
clinical need. Anesthesiology clinicians who provide intraoperative care will be notified of 
this study, and additional educational materials will be provided about the availability of 

 
27 
emergency release blood prior to study initiation. The frequency of emergency release 
blood utilization and adverse events associated with transfusion will be monitored and 
reviewed by the Data Safety Monitoring Board (DSMB) as described in the Data Safety 
Monitoring Plan. An unblinded statistician will conduct statistical analyses on these 
safety outcomes for each meeting for DSMB members to review. 
- 
Risk of disruption to clinical workflow. To minimize this risk, we conducted a human-
centered design process in partnership with stakeholders and users to align the S-PATH 
system with clinician workflow and to ensure its ease of use. Training will be provided to 
clinicians in the intervention arm prior to trial initiation to reduce the burden of learning a 
new workflow. A wash-in period will be provided to allow clinicians to become 
accustomed to the S-PATH interface before potentially using it in clinical care. In 
addition, S-PATH use will be optional. Contact information for the study team will be 
provided to participants so that technical issues and concerns about workflow impact 
can be promptly addressed.  
- 
Risk for breach of confidentiality. Clinician participants will be assigned an anonymous 
identifier. Identifying information will be kept separate from study data in a password-
protected file. All study data, including log files, electronic health record data, and audio 
files, will be stored on a password-protected HIPAA-compliant server managed by 
Washington University Research Information Services (RIS). All paper study data will be 
stored in a locked filing cabinet in the PI’s office. 
7.3.1 Vulnerable Subjects 
No protected patient classes are specifically involved in this study. 
7.4 Potential Benefits of the Proposed Research to Participants and Others 
There is a potential for participants to benefit from the S-PATH intervention; however, there is no 
guarantee. For clinician participants, S-PATH use for presurgical blood orders may simplify 
clinical workflow because S-PATH is integrated within the electronic health record, unlike the 
conventional MSBOS. Many of the clinically relevant variables are automatically transferred and 
displayed by the CDS system, which facilitates the ease of data review.  
For patient participants, personalized identification of surgical transfusion risk as performed by 
the S-PATH system might improve patient safety by recommending blood orders for patients at 
higher risk of transfusion due to patient-specific factors, who otherwise might have been missed 
by the conventional MSBOS. There may also be patients who are spared the cost and 
discomfort of unnecessary presurgical blood type testing because the S-PATH system identifies 
that their personalized transfusion risk is lower than otherwise might be assumed based on the 
conventional MSBOS. A reduced prevalence of type and screen orders among low risk patients 
may also decrease the risk of discretionary transfusion and its antecedent risks. 
7.5 Importance of Knowledge to be Gained 
Evaluation of the S-PATH system is important because of its potential to change presurgical 
blood ordering practice, with considerable public health impact for patient safety, blood 
conservation, and reduced healthcare costs. In addition, insights derived from this study will 
contribute to the advancement of the delivery science for artificial intelligence in healthcare. 
7.6 Confidentiality 
Identifying data will be protected as described in 5 Data Collection and Management. 
7.7 Declaration of Interests 
The investigators of no financial conflicts of interest. Computer code for the S-PATH model is 

 
28 
available on a public repository58 and there are no plans for commercialization. 
7.8 Dissemination 
This study will be registered on ClinicalTrials.gov and results information will be submitted to 
ClinicalTrials.gov as outlined in the policy and according to the specific timelines stated in NIH 
policy. Washington University has an internal policy in place to ensure that clinical trials 
registration and results reporting occur in compliance with policy requirements. 
We will present the research findings for the trial at national scientific meetings and publish the 
results in peer reviewed journals. The trial protocol will be also be published in a peer reviewed 
journal. 
7.9 Data Sharing Policy 
Per NIH guidelines, all data are planned to be preserved and shared after anonymization. 
Recruitment progress and final results will be documented at clinicaltrials.gov. To anonymize the 
data, all clinicians and patients will be assigned random identifiers to replace clinician names 
and patient medical record numbers. No dates for surgical encounters will be retained. All other 
incidental identifiers (such as mentions of care team member names, encounter numbers, or 
hospital units) will be stripped. 
Data will be deposited in the WashU Libraries Open Scholarship Digital Research Materials 
Repository. Curation is guided by the Data Curation Network (DCN) CURATED model, from 
which the dissemination information package (DIP) is created, given a registered DOI, retained 
in the repository and archived for a minimum of 10 years. The archival information package 
(AIP) will contain the original submission information package (SIP), the DIP, PREMIS compliant 
metadata, the readme file, and documentation of curation processes. 
Data will be findable for the research community through the Becker Library commons. For all 
publications, a separate study ID will be created. Each study will be assigned a separate digital 
object identifier (DOI). This data DOI will be referenced in the publication to allow the research 
community easy access to the exact data used in the publication. 
Data will be made available within 12 months of the grant period ending or on publication of a 
manuscript using the data, whichever comes first. Data will be retained in the WashU Libraries 
Open Scholarship Digital Research Materials Repository for at least 10 years. 
 
 
 

 
29 
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