Telemedicine Control Tower for the Operating Room: Navigating Information, Care and Safety - Randomized Pragmatic Trial
Phase: PHASE3
Conditions: Surgery, Surgery--Complications, Perioperative/Postoperative Complications
Sponsor: Washington University School of Medicine

Protocol for the Effectiveness of an Anesthesiology Control Tower System in 
Improving Perioperative Quality Metrics and Clinical Outcomes: the TECTONICS 
randomized, pragmatic trial  
 
Christopher R. King2, Joanna Abraham1, Thomas G. Kannampallil1, Bradley A. Fritz2, 
Arbi Ben Abdallah2, Yixin Chen3, Bernadette Henrichs2, Mary Politi4, Brian A. Torres2, 
Angela Mickle2, Thaddeus P. Budelier2, Sherry McKinnon2, Stephen Gregory2, Sachin 
Kheterpal5, Troy Wildes2, Michael S. Avidan2*, For the TECTONICS Research Group. 
Michael Avidan2,  Joanna Abraham1, Amrita Aranake-Chrisinger2, Anchal Bansal2, Kara 
Battig2, Arbi Ben Abdallah2, George Benzinger2, Mara Bollini2, Walter Boyle2, Thaddeus 
P. Budelier2, Kathryn Cass2, Laura Cavallone2, Yixin Chen3, Zhicheng Cui3, Daniel 
Emmert2, Mitchell Fingerman2, Bradley Fritz2, Jason Gillihan2,Marie Goez2, Shreya 
Goswami2, Thomas Graetz2, Stephen Gregory2, Ryan Guffey2, Katharine Gurba2, 
Charles Hantler2, Yujie He3, Daniel Helsten2, Bernadette Henrichs2, Erin Herrera2, 
Omokhaye Higo2, Robert Hovis2, Gary Hubbard2, Rocco Hueneke2, Mark Ingram2, Ivan 
Kangrga2, Thomas Kannampallil1, Menelaos Karanikolas2, Christopher R. King2, Sachin 
Kheterpal5, Justin Knittel2, Helga Komen2, Alexander Kronzer2, Anand 
Lakshminarasimhachar2, Alyssa McClellan2, Jacob McDowell2, Sherry McKinnon2, 
Angela Mickle2, Alexander Mohrmann2, David Monks2, Jordan Oberhaus2, Hilary 
Odorizzi2, Daniel Park2, Sarah Perez2, Mary Politi4, Debra Pulley2, Elizabeth Quillen2, 
Rashmi Rathor2, Andrea Reidy2, Cameron Ritter2, Evan Roller2, Elizabeth Schappe2, 
Alexandra Schatz2, Anshuman Sharma2, Tracey Stevens2, Brian Tolly2, Brian Torres2, 
Swarup Varaday2, William Varnum2, Troy Wildes2, William Wise2, Rachel Wolfe2, 
Jinghan Yang3, Muhan Zhang3 
 
1Department of Anesthesiology and Institute for Informatics (I2), Washington University 
School of Medicine, St. Louis, MO 
2Department of Anesthesiology, Washington University School of Medicine, St. Louis, 
MO 
3Department of Computer Science and Engineering, Washington University, St. Louis, 
MO  
4Department of Surgery, Washington University School of Medicine, St. Louis, MO 
5 Department of Anesthesiology, University of Michigan, Ann Arbor, MI   
 
*To whom correspondence should be addressed 
Washington University School of Medicine 
 
Campus Box 8054 
660 South Euclid Avenue 
St. Louis, MO 63110 
 
Protocol version: 14 January, 2020 
 

 
ABSTRACT 
 
Introduction: Perioperative morbidity is a public health priority, and surgical volume is 
increasing rapidly. With advances in technology, there is an opportunity to research the 
utility of a telemedicine-based control center for anesthesia clinicians that assess risk, 
diagnoses negative patient trajectories, and implements evidence-based practices.  
Objectives: The primary objective of this trial is to determine whether an 
anesthesiology control tower (ACT) prevents clinically relevant adverse postoperative 
outcomes including 30-day mortality, delirium, respiratory failure, and acute kidney 
injury. Secondary objectives are to determine whether the ACT improves perioperative 
quality of care metrics including management of temperature, mean arterial pressure, 
mean airway pressure with mechanical ventilation, blood glucose, anesthetic 
concentration, antibiotic redosing, and efficient fresh gas flow.  
Methods and analysis: We are conducting a single center, randomized, controlled, 
phase 3 pragmatic clinical trial. A total of 58 operating rooms are randomized daily to 
receive support from the ACT or not. All adults (eighteen years and older) undergoing 
surgical procedures in these operating rooms are included and followed until 30 days 
after their surgery. Clinicians in operating rooms randomized to ACT support receive 
decision support from clinicians in the ACT. In operating rooms randomized to no 
intervention, the current standard of anesthesia care is delivered. The intention-to-treat 
principle will be followed for all analyses. Differences between groups will be presented 
with 99% confidence intervals; p-values <0.005 will be reported as providing compelling 
evidence, and p-values between 0.05 and 0.005 will be reported as providing 
suggestive evidence. 
Registration: TECTONICS is registered on ClinicalTrials.gov, NCT03923699; 
registered on 23 April 2019. 
 
KEYWORDS 
Telemedicine, Artificial Intelligence, Machine Learning, Forecasting Algorithms, 
Decision Support, Randomized Controlled Trial, Anesthesiology 
 
 
 
 

List of Abbreviations 
ACT 
Anesthesiology Control Tower 
ACTFAST 
Anesthesiology Control Tower—Feedback Alerts to Supplement 
Treatments 
AKI 
Acute Kidney Injury 
BJH 
Barnes-Jewish Hospital 
EHR 
Electronic Health Record 
ML 
Machine Learning 
NINR 
National Institute of Nursing Research 
PHI 
Protected Health Information 
PI 
Principal Investigator 
SMC 
Safety Monitoring Committee 
OR 
Operating Room 
RCT 
Randomized Controlled Trial 
TECTONICS 
Telemedicine Control Tower for the OR: Navigating Information 
Care and Safety Trial 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Introduction 
 
Perioperative complications collectively contribute to numerous deaths around the 
world.1 Following inpatient surgeries, the estimated 30-day mortality is between 1% and 
5%,2-10 and between 5% and 10% of surgical patients will die in the following year.4,5,8,11 
Furthermore, 10% to 20% of surgical patients experience major complications such as 
heart attacks, chronic pain,12 infections and blood clots following their procedures.6,9,10,13 
Although some complications are unavoidable, based on the nature of the particular 

surgical procedure or non-modifiable patient characteristics,9,14,15 others may be 
preventable through early identification of patient risk factors and the use of tailored 
treatments.  
 
Several factors, besides technical aspects of surgeries, contribute towards preventable 
perioperative complications. Clinicians,16-19 including anesthesia care teams (typically 
comprising anesthesiologists and certified registered nurse anesthetists [CRNAs]), 
sometimes fail to implement and adhere to evidence-based standards of care. Many 
clinicians believe that they are in compliance with guidelines, when in reality they are 
not.20 In the United States, differing backgrounds and perspectives of anesthesiologists 
and CRNAs can undermine effective collaboration among team members, potentially 
compromising patient care.21,22 Clinicians also experience cognitive overload in the 
operating room (OR), and limitations in cognitive capacities impair information 
processing 23 and decision making abilities.24-26 In addition, the rapidly evolving OR 
environment and complex patient responses to surgery and anesthesia make it 
challenging for clinicians to accurately assess patients’ shifting risks.  
 
Telemedicine and integrated machine learning (ML) are two promising approaches for 
addressing cognitive and information overload, dynamically focusing resources where 
needed, and reducing accidental variations in care quality. However, there is no 
prospective evidence on telemedicine or ML in the OR context. There is an urgent need 
for rigorous research investigating the utility of a telemedicine-based control center to 
dynamically assess risk, diagnose negative patient trajectories, implement evidence-
based practices, and improve outcomes for surgical patients. A collaborative 
telemedicine solution for the OR, through the early and accurate identification of 
potential risks, could facilitate the development of tailored plans for patient care risk 
mitigation and management. It could also enhance meaningful teamwork between 
CRNAs and anesthesiologists, act as a complementary support for anesthesia care 
teams in the OR, help to decrease cognitive overload and bias, and facilitate evidence-
based care. 
 
To address this deficit, our interdisciplinary team, including academic and clinical 
leaders, has developed a prototype anesthesiology control tower (ACT).30,31 Our 
previous pilot work has demonstrated the usefulness and usability of the ACT.30,32 We 
have also developed ML algorithms for real time decision-support instruments,33-36 
developed the institutional infrastructure to maximize OR integration of the ACT, and 
evaluated the feasibility of conducting a large scale randomized control trial using the 
ACT. 31 In this protocol we outline the Telemedicine Control Tower for the OR: 
Navigating Information, Care and Safety (TECTONICS) trial, which is a large-scale 
randomized controlled trial (RCT) to empirically evaluate the effectiveness of the ACT 

on preventing clinically relevant adverse perioperative outcomes and improving 
perioperative care. 
 
We hypothesize that the integrated ACT system in the TECTONICS trial will improve 
evidence-based quality of perioperative care metrics and prevent clinically relevant 
adverse perioperative outcomes (postoperative delirium, renal failure, respiratory failure, 
and 30-day mortality). 
 
Participants and setting 
 
The study is a single center, randomized, controlled, phase 3, pragmatic superiority trial 
at Barnes Jewish Hospital (BJH) in St. Louis, MO. All adults (18 years and older) 
undergoing surgical procedures in these operating rooms will be included. Children are 
excluded in this study. Labor and operative delivery is conducted in a separate 
administrative area and is also excluded unless it occurs in the main surgical ORs. 
There are no other exclusion criteria related to procedure type, comorbid illnesses, or 
planned disposition other than the requirement that some anesthesia clinician be 
requested (excluding e.g. organ procurement and minor procedures performed without 
anesthesiology services) and the requirement for the procedure to take place in an 
operating room (excluding sedation-based procedure suites such as the cardiac 
diagnostic laboratory). 
 
An estimated 20,000 patients will be enrolled annually, and enrollment will be over four 
years for approximately 80,000 total patients enrolled (Figure 1). Cases started during 
the hours of operation of the ACT will be included regardless of the stop time. 
 
This study has been approved by the Human Research Protection Office at Washington 
University (St. Louis, MO # 201903026) for enrollment with a waiver of consent.  
Participant data is collected from the electronic health record (EHR) of Barnes-Jewish 
and its affiliated hospital and clinic databases until 30-days after their surgery.  
 
 
Interventions  
The participant groups are intraoperative telemedicine support from the ACT 
(“intervention”) and usual care.  
 
Description of the ACT 
The ACT has been established in a location remote from the OR with sophisticated 
hardware and software. It is staffed by at least two clinicians from the research team (an 
anesthesiologist and one or more other clinicians). Intraoperative data streams used by 
the ACT clinicians include real-time access to the hospital’s EHR, web-based 
visualization of vital signs and waveforms, treatment guidelines, protocols for care, as 

well as AlertWatch® (Ann Arbor, MI) (Figure 2). AlertWatch® is a Food and Drug 
Administration-approved patient monitoring and alerting system that displays integrated 
patient information. AlertWatch® was primarily conceptualized for use in individual ORs; 
we have modified the AlertWatch® software based on stakeholder engagement and 
feedback for use in the ACT, creating a customized dashboard and interface specifically 
designed for the telemedicine setting.32 The ACT AlertWatch® version that we 
developed in our preliminary studies is a customized research product with innovative 
information technology and communication components and is distinct from the current 
commercial product in several important respects. Real-time forecasting (from the 
machine-learning algorithms discussed below) of adverse outcomes for individual 
patients is provided to ACT clinicians. BJH is currently installing high definition cameras 
in the ORs; video feeds will be incorporated in the ACT if these are available during the 
trial. Video or audio will not be stored. 
 
Intervention group 
In ORs randomized to intervention, ACT clinicians contact OR clinicians in two phases. 
First, the ACT messages the OR clinician an individualized risk assessment and 
considerations / recommendations based on the preoperative evaluation and real time 
information from the monitors in the OR. The recommendations are geared towards (1) 
preventing major complications (Table 1) based on patients’ specific risk profiles (e.g. 
history of stroke, hypertension, type 1 diabetes, coronary artery disease, valvular heart 
disease, pulmonary disease) and (2) adhering to general quality of care indicators 
(Table 2). The recommendations are based on the best currently available evidence 
(e.g. intensive insulin management in type 1 diabetes). OR clinicians are encouraged to 
reply to the message with specific concerns they would like to discuss, risk 
assessments they believe to be erroneous, and additional monitoring that they would 
like the ACT to perform. The second phase occurs during procedures. The ACT 
monitors physiologic and process alerts generated by AlertWatch® and ML algorithms, 
filters these alerts for those believed relevant and actionable, and contacts the 
anesthesia team where deemed appropriate. OR clinicians receiving notification from 
the ACT may choose to carry out whatever course of action they deem clinically 
appropriate. Anesthesia clinicians in the OR have access to the institution’s “clinical” 
AlertWatch software, but do not have access to the “research” view. 
 
Usual care 
In ORs randomized to usual care, the ACT monitors patients, but the ACT clinicians 
does not contact the OR clinicians unless the ACT clinicians believe it to be clinically 
necessary for patient safety purposes (e.g. neuromuscular blockade without evidence of 
hypnotic agent administered).  Concomitant care is provided in the usual perioperative 
setting with no modifications based on the trial. Anesthesia clinicians in the OR have 

access to the institution’s “clinical” AlertWatch software, but do not have access to the 
“research” view. 
 
Risk forecasting algorithms 
Using data from over 110,000 patients, we developed calibrated ML algorithms to 
predict adverse postoperative outcomes. The details of the dataset and algorithm 
development are published elsewhere.34-36 Briefly, we implemented deep neural 
network models for 30-day mortality, acute kidney injury, and postoperative ventilatory 
failure among other anesthesiology-relevant outcomes based on routinely collected 
clinical data. We tested numerous other prediction methods, and found that deep neural 
networks processing extensive patient information (i.e., demographic characteristics, 
surgical risk, co-morbidities) as well as time series physiological data (e.g., blood 
pressure, temperature, heart rate) predict outcomes such as death with high area under 
receiver operator characteristics curve (0.880), acceptable sensitivity (~50%) and 
excellent specificity (~95%). We adapted models to improve their interpretability and 
incorporated advanced post-processing methods to uncover the data which drives 
individual predictions. The training of these deep neural network models jointly 
estimates data filtering and imputation steps with prediction.33 We implemented 
appropriate data validation and quality filtering steps for the live environment and 
display forecasts of mortality updated every 5 minutes. In contrast to standard 
forecasting models, we have previously demonstrated that ML and data mining 
approaches for patients in ICUs are markedly superior in predicting clinical outcomes 
such as mortality.37 The feasibility of this integration is supported by a previous 
successful trial, where members of our investigative team, using live data from the EHR, 
implemented ML algorithms to guide a rapid response team in medical wards.38-41.  
 
Over the course of the TECTONICS study, with ongoing acquisition of high-resolution 
data and outcomes on thousands of surgical patients, our algorithms will undergo 
regular evaluation and refinement. Periodically updating the model will be necessary, 
which we plan to do at 6-month intervals with newly collected data in the control arm. 
We will also use a human expert to review the face validity of the model’s predictions 
and most important input features. Such a dynamic feedback loop will continuously 
improve and adjust the model. We will test for the overall percentage of correct 
forecasts, the percent of correctly forecasted events, and accuracy when data include 
noise and missing values on several adverse perioperative outcomes. One key metric 
that we will evaluate and compare will be the sensitivity at 95% specificity, since it is 
important to maintain a high specificity (i.e., low false alarm rate) for meaningful 
decision support. Validation techniques will include cross-validation42 and systematically 
different hold-out samples (e.g. distinct time periods or OR locations). 
 
 

Outcome measures and data acquisition 
The primary objective is to determine whether the ACT system is effective in preventing 
clinically relevant adverse perioperative outcomes including thirty-day postoperative 
mortality, postoperative delirium, postoperative respiratory failure and postoperative 
acute kidney injury. Secondary objectives are to determine whether the ACT system is 
effective at improving perioperative quality of care metrics. The study outcomes will be 
assessed according to established criteria (Tables 1 and 2). 
 
Multiple sources are used for standardized data collection. Data on patient outcomes 
and perioperative care metrics is extracted from the EHR. Preoperative patient 
characteristics, comorbidities, surgical and clinical history, as well as perioperative and 
immediate post-operative information are pulled from the EPIC EHR (Verona, WI, USA). 
Additional postoperative patient outcomes data (for sub-studies) will be obtained from 
clinical registries (American College of Surgeons National Surgical Quality Improvement 
Program43, Society of Thoracic Surgeons,44) as well as the EHR. BJH determines vital 
status through multiple mechanisms including follow-up contact and state death 
databases. Non-mortality outcomes are not tracked after discharge from hospital. 
Although the development of incident serious acute kidney injury (AKI) or delirium post-
discharge is possible, we anticipate that these will be uncommon enough to not warrant 
large-scale surveillance. 
 
Data on clinician responses to individual alerts, generated from the AlertWatch® Control 
Tower platform, is logged by ACT staff to a database for qualitative studies and internal 
quality improvement. 
 
Assignment of treatments 
The 58 ORs (all the ORs at BJH excluding “remote” locations, procedure suites, and 
labor and delivery) are 1:1 randomized daily to receive intraoperative support from the 
ACT or usual care without any form of stratification. In other words, participants are 
randomized in clusters whose size randomly depends on the number of cases assigned 
to a room. The randomization script for the TECTONICS trial has been incorporated into 
the AlertWatch® ACT infrastructure, and runs automatically early every morning. 
 
Due to the nature of the intervention it is not possible to blind OR clinicians since 
feedback alerts from the ACT inform them that they are in the intervention group. 
However, patients and those evaluating outcomes are blinded to group assignment. 
 
Sample size calculation and recruitment  
In recent years, more than 20,000 surgeries have been performed annually in the 
Barnes Jewish Hospital main ORs. However, the ACT is only staffed during work-week 
hours and when the assigned attending anesthesiologist is available. During our pilot, 

we made efforts to ensure staffing during all appropriate times; however, under half of 
cases took place during ACT staffed times. We therefore conservatively estimate that 
20,000 patients will participate per year, but we anticipate that more rapid accrual is 
likely based on more complete recent staffing. The TECTONICS trial will be adequately 
powered to answer with precision whether the ACT system has a meaningful impact on 
clinically relevant outcomes (primary outcomes, see Table 3) and quality of care metrics 
(secondary outcomes, see Table 4).  Despite the fact that we are evaluating multiple 
surrogate outcomes, the very large sample size will provide adequate statistical power 
to determine whether or not there is improvement with the ACT system. Individuals with 
multiple surgeries within 30 days will be analyzed with the assignment of their index 
surgery. Individuals with multiple independent encounters (>30 days separation) will be 
treated as distinct observations. Surgeries which take place outside BJH will not be 
accounted for. 
 
Assessing Hawthorne and contamination effects 
We anticipate a possible contamination (or learning) effect over time in the usual care 
group. Clinicians are included in both intervention and control ORs (possibly on the 
same day) and may become sensitized to the standards of practice and the surrogate 
outcome measures being tracked, leading to “overlapping” improvements in these 
measures (as well as clinical outcomes) in both groups over the course of the study. 
This learning effect might manifest most strongly among clinicians who spend time in 
the ACT. Furthermore, with the knowledge that clinical behaviors are being observed, 
there is a high likelihood of a Hawthorne effect.45,46 In the reverse of the usual 
Hawthorne problem, the effect of being observed in the intervention arm represents part 
of the actual effect of the intervention; knowing that they are being observed, clinicians 
may feel more accountable to following perioperative best practices, and this is a 
meaningful effect. However, the non-contact rooms are also aware of the trial and enjoy 
the same improvement, falsely decreasing the estimated effect size. The data we have 
obtained prior to instituting the ACT will be useful in assessing the extent of 
contamination. Specifically, we will assess the intensity/frequency of contamination by 
comparing the outcomes of the control group patients to those of matched patients who 
had similar surgeries, demographics, and health conditions during the immediate period 
prior to the ACT implementation. We will also analyze control arm results in 3-month 
time bins (with baseline data for reference) to evaluate secular tends which may 
represent contamination, as well as the magnitude of clustering design effects. 
 
Analysis of primary and secondary outcomes 
Comparisons between groups during the randomized study will be with parametric and 
non-parametric statistical tests, according to the distributions of the measures of 
interest. Fisher’s exact or χ2 test will be used to assess differences between proportions 
(the majority of assessments). Contingency statistical tests will be used to compare 

occurrence of hypotension, hyperglycemia, hypoglycemia, and hypothermia between 
groups; and unpaired t-test or Mann-Whitney U-test, as appropriate, will be used to 
compare the durations of these occurrences between groups. All comparisons will use 
the intention-to-treat principle.  
 
Results in statistical tests with a p value <0.005 will be viewed as providing compelling 
evidence, whereas results with a p value between 0.05 and 0.005 will be interpreted as 
providing suggestive evidence.47 We will report p-values adjusted for multiple 
hypothesis tests (within the primary and secondary outcome blocks) using permutation 
methods that account for the correlation across outcomes.48 Within the secondary 
outcomes, false-discovery-rate control will be reported.49 
 
 
Handling of missing data 
We anticipate that the prospectively collected data will be high quality with few missing 
outcomes. AKI is informatively missing in patients who are judged as low risk by the 
surgical team and do not have assessments of postoperative creatinine or urine output. 
Delirium is similarly infrequently assessed at our institution in patients who do not 
require intensive care unit admission. A screening bias in both outcomes is possible 
where patients in the treatment arm are more accurately identified as elevated risk and 
checked for complications. Ventilatory failure is unlikely to occur in the 48-hour time 
window among discharged patents. We will report the number of patients without 
assessments for each outcome. The primary analysis will treat patients discharged 
without measures of AKI or delirium as negative. Patients who are informatively 
censored by death will be treated as positive for other outcomes. All outcomes will be 
required to be incident. Individuals without preoperative measures of renal status or 
delirium will be assumed to have normal values.  
 
 
Adverse event and safety monitoring  
This study will have a Safety Monitoring Committee (SMC) according to the National 
Institute of Nursing (NINR) Research Data Safety Monitoring policy. The SMC will be 
comprised of a small group of experts with at least two members independent of the 
study team. They will be responsible for reviewing all adverse events, compliance with 
the IRB requirements, investigator compliance, minimizing risks and protecting the 
confidentiality of participant data. The SMC will review study data every six months 
according to the SMC Plan. 
 
Strengths, limitations and alternative strategies 
The TECTONICS trial has important strengths. It is a pragmatic RCT conducted in a 
high volume, real-world clinical setting that incorporates telemedicine for the OR. The 

adverse outcomes under study are serious and meaningful to patients. The 
TECTONICS trial can be conducted efficiently as many components of the proposed 
study are incorporated into existing infrastructures and processes at Washington 
University: 1) with no known risk associated with the support offered by the ACT, 
participants are included with a waiver of informed consent; 2) the members of the care 
team (anesthesiologists, CRNAs, residents and student registered nurse anesthetists) 
participate in the trial in the course of their routine clinical work; and 3) most of the 
surrogate and clinical outcomes data are obtained from existing IT resources or from 
established and ongoing registries.50 Randomization of ORs is implemented easily, and 
the process for providing feedback alerts from the ACT does not require any lead-in 
time or advanced preparation. The study includes all adult surgical-patients at BJH, 
including both men and women, and those who are recognized to be vulnerable and 
understudied in clinical research. The feasibility of the trial is enhanced by participation 
of a highly committed cadre of CRNAs and attending anesthesiologists, student 
registered nurse anesthetists and residents in the Anesthesiology Department, as well 
as an experienced team of CRNA and anesthesiology investigators that has established 
a track record of scientific collaboration and completion of major trials.4,5,51-56  
 
There are also relevant limitations. The TECTONICS study will be vulnerable both to 
Hawthorne and contamination effects. Although we do not think that these effects can 
be eliminated, we have considered how best to account for them in the analyses. In 
addition, it will not be possible to ensure blinding 57 of clinicians. However, surgical 
patients and those evaluating outcomes will be blinded to group assignment. Another 
major constraint relates to both accuracy and completeness of outcome measures. 
Outcomes routinely tracked in the EHR are often well represented. However, we know 
from previous experience that EHR, registry, and patient reported outcomes data are 
occasionally inaccurate.58 Missing and inaccurate outcomes data will be partially 
mitigated by the large number of patients included in the trial, and are expected to be 
randomly distributed across groups. 
 
Ethics/protection of human subjects 
This study has been approved by the Human Research Protection Office at Washington 
University (St. Louis, MO # 201903026). It satisfies the criteria for a waiver of informed 
consent (there is minimal risk with the intervention, the research could not practically be 
conducted without a waiver, and the rights and welfare of patients are not adversely 
affected by their involvement in the study, and there is no deception requiring additional 
disclosure) and is being conducted accordingly. This protocol was written in compliance 
with the SPIRIT Checklist Guidelines for Interventional Trials. Only the minimum 
necessary private patient information will be collected for the purposes of the study. Any 
protected health data is 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.  
 
 
 
Data management 
The ACT will use clinical applications available to all clinicians to monitor ongoing 
surgical procedures. These applications can only be accessed over the secure hospital 
network or by virtual private network logins. This arrangement meets and/or exceeds 
Health Insurance Portability and Accountability Act standards for Patient Health 
Information (PHI) security. AlertWatch® is an approved clinical application at Barnes-
Jewish Hospital, and therefore maintains these same levels of protection. Access to the 
data collected in this study will be restricted to approved personnel. It is a strict policy 
that PHI content cannot be reviewed outside of this protected environment. Prior to data 
analysis, information will be de-identified. When possible, extracts from this project will 
avoid the use of PHI. De-identified patients can be identified using a special, non-PHI 
primary key, which we have used previously. 
 
The research material obtained in this proposed trial will consist of the already 
established infrastructure and resources of the SATISFY-SOS (NCT02032030), NSQIP 
and STS registry studies, Anesthesiology Control Tower—Feedback Alerts to 
Supplement Treatments (ACTFAST) 1, 2 and 3 studies, the intra-operative electronic 
medical record and the AlertWatch® evidence-based alerting system. Patient 
demographic information and preoperative characteristics are collected and entered into 
the electronic health record as part of routine clinical care at the Center for Preoperative 
Assessment and Planning. Perioperative and intraoperative drug administration and 
vital signs are also charted in the electronic health record.  
 
Data will be maintained for at least 5 years after the end of the grant funding period. 
Further study record retention will be at the discretion of the study investigator. Data 
may be used for future studies not mentioned in the protocol.  
 
Publication/data sharing policy 
The investigative team is comprised of a range of stakeholders, including scientists, 
clinical investigators, and relevant end users. We will each disseminate in our 
respective networks through presentations to relevant stakeholder groups, through 
peer-reviewed publications, and by providing brief summaries for hospital administrators 
and policy makers.  We will also utilize the BJC Collaborative, which aligns multiple 
health networks in the region (including rural settings), as a vehicle for dissemination. 
 

Data from the TECTONICS trial will be made available for analysis in compliance with 
the recommendations of the International Committee of Medical Journal Editors.59 For 
this study, individual participant data that underlie the results of the trial will be made 
available after appropriate de-identification, along with the study protocol and statistical 
analysis plan. We plan to make this information accessible to researchers who provide 
a methodologically appropriate proposal for the purpose of achieving the aims of that 
proposal. Data will be available beginning 9 months and ending 36 months following 
trial publication at a third-party website. Data requestors will need to sign a data access 
agreement to gain access to trial data. Proposals should be directed to 
avidanm@wustl.edu. TECTONICS is registered on clinicaltrials.gov, NCT03923699. 
 
AUTHORSHIP ELIGIBILITY AND CONTRIBUTORSHIP 
Authorship for this study will be given to key personnel involved in study design, data 
collection, and data analysis. There are no publication restrictions and no professional 
writers will be involved in the generation of the manuscript. M. Avidan, A. Ben Abdallah,  
J. Abraham, Y. Chen, B. Fritz, C. King, B. Henrichs, T. Kannampallil, M. Politi, A. 
Sharma, B. Torres, S. Kheterpal, T. Wildes are responsible for conceptualizing study 
design.  
  
All authors, including King, Avidan, Ben Abdallah, Abraham, Chen, Fritz, Henrichs, 
Kannampallil, Politi, Sharma, Torres, Mickle, Budelier, McKinnon, Gregory, Wildes, 
have critically revised the TECTONICS protocol and approved the final version. All 
authors agree to be accountable for the accuracy and integrity of all aspects of the 
TECTONICS trial. No paid writers will be used. 
 
Protocol amendments 
Protocol amendments will be approved by the steering, operations, safety, and data 
management committees, communicated to the IRB and NINR, and posted to 
Clinicaltrials.gov. This protocol corresponds to version 1.1 and was agreed on October 
1 2019. 
 
Conclusion 
While ML and telemedicine have been extensively studied over the past decade, 
telemedicine has often been implemented without a strong research foundation. When it 
has been studied, this has often been done in the context of observational before and 
after cohort trials. ML algorithms have often been studied for risk calculations and 
predictions, but there has been limited investigation of their application to improving 
patient outcomes. In contrast, TECTONICS is designed as a pragmatic, randomized 
clinical trial including telemedicine and ML. The over-arching strength of TECTONICS is 
that it combines and leverages a telemedicine initiative with advanced machine-learning 
algorithms. The net innovation is a fully integrated clinical decision support system, 

comprised of remote surveillance of patient risks in real-time, human expert judgment, 
and computer-generated rules. This realization of the ACT concept provides an 
empowering and unobtrusive socio-technical telemedicine infrastructure and decision 
support solution for OR teams. The ACT also provides a practical and innovative 
solution to the challenge of implementing evidence-based guidelines in the OR. 
Although the ACT system requires an initial modest financial investment, if it proves to 
be effective in promoting and enhancing evidence-based perioperative care, it is 
possible that it could lead to decreased costs via improvements in surgical patient 
outcomes. 
 
The proposed study can have a major impact on healthcare if it demonstrates that the 
ACT system enhances OR care quality and patient safety while simultaneously 
increasing teamwork. Following the TECTONICS study, the ACT system will be further 
refined, and its implementation will be expanded. The logical next step will be to 
conduct a larger multisite trial focusing on expanded clinical and patient-reported 
outcomes. We are well positioned to track such outcomes, based on electronic access 
to ICD codes,58 our experience in building a patient reported outcomes registry,50,58,60 as 
well as our collaborations with NSQIP and the Society for Thoracic Surgeons. 
Importantly, one of the TECTONICS contributors (Kheterpal) is the principal investigator 
of the Multi-Center Perioperative Outcomes Group (MPOG), which includes data from 
>4 million patients, and has an established and sophisticated international IT 
infrastructure. We envisage that future dissemination and implementation of the ACT 
system could occur efficiently using the MPOG infrastructure.  
 
Throughout the study, we will collect data from ACT users on reach (percent of 
clinicians and staff eligible who use and engage with the ACT), adoption (user 
confidence that they will continue to use the ACT after the study ends), implementation 
(user confidence that the ACT can be consistently delivered as intended), and 
maintenance (user confidence that the ACT will produce lasting benefits beyond the 
study).61 When planning for wider-scale implementation, we will use established 
guidelines set-out by the Expert Recommendations in Implementing Change (ERIC) 
team.62  
 
 
Data availability 
Underlying data 
No data are associated with this article. 
 
Reporting guidelines 
Repository: SPIRIT checklist for TECTONICS randomized controlled trial. 
https://doi.org/10.6084/m9.figshare.10255724. 

Data are available under the terms of the CC BY 4.0 license. 
 
Grant information 
Funding for the TECTONICS trial is supplied by NINR (R01 NR017916) and institutional 
support. Funding for the development of TECTONICS and pilot studies was supplied by 
National Science Foundation (1622678); Division of Information and Intelligent Systems; 
Agency for Healthcare Research and Quality (R21 HS24581); Washington University 
Institute of Clinical and Translational Sciences Funding Program (#4265). 
 
 
Competing interests 
None of the authors has conflicts of interest to disclose.  
 
 
 
 
KEY ROLES AND CONTACT INFORMATION 
 
Principal 
Investigator:   
Michael S. Avidan, MBBCh 
Institutions: 
Washington University School of Medicine 
660 South Euclid Ave Box 8054 
St. Louis, MO 63110 
Michael S. Avidan, MBBCh 
P: 314-273-4136 
E: avidanm@wustl.edu 
  
Other Key 
Personnel: 
Arbi Ben Abdallah, PhD 
Associate Professor of Anesthesiology 
Washington University School of Medicine Department of 
Anesthesiology 
660 S. Euclid Avenue 
Campus Box 8054 
St. Louis MO, 63110 
314-286-1769 
aba@wustl.edu 
 
Joanna Abraham, PhD 
Assistant Professor  
Washington University School of Medicine 

Department of Anesthesiology and Institute for Informatics (I2) 
660 S. Euclid Avenue 
Campus Box 8054 
St. Louis MO, 63110 
314-286-1768 
joannaa@wustl.edu 
 
Walter Boyle, MD 
Professor of Anesthesiology 
Washington University School of Medicine  
Department of Anesthesiology 
660 S. Euclid Avenue 
Campus Box 8054 
St. Louis MO, 63110 
 
Yixin Chen, PhD 
Professor of Computer Science and Engineering 
Washington University Department of Computer Science and 
Engineering 
660 S. Euclid Avenue 
St. Louis MO, 63110 
314-935-7528 
Ychen25@wustl.edu 
 
Daniel L. Helsten, MD 
Associate Professor of Anesthesiology 
Washington University School of Medicine 
Department of Anesthesiology 
660 S. Euclid Avenue 
Campus Box 8054 
St. Louis MO, 63110 
314-362-1660 
helstend@wustl.edu 
 
Bernadette Henrichs, PhD, CRNA, CCRN 
Professor and Director, Nurse Anesthesia Program at Goldfarb 
School of Nursing at Barnes-Jewish College  
Director of CRNA Education and Research, Washington 
University School of Medicine 
660 S. Euclid Avenue 
St. Louis MO, 63110 
314-454-8910 
henrichsb@wustl.edu 
 
Thomas G. Kannampallil, PhD 
Assistant Professor  

Washington University School of Medicine 
Department of Anesthesiology and Institute for Informatics (I2) 
660 S. Euclid Avenue 
Campus Box 8054 
St. Louis MO 63110 
314-286-1768 
thomas.k@wustl.edu 
 
Mary Politi, PhD 
Associate Professor Division of Public Health Sciences 
Washington University School of Medicine Department of 
Surgery 
660 Euclid Ave. CB 8100 
St. Louis MO 63110 
314-747-1967 
mpoliti@wustl.edu 
 
 
 
 
Anshuman Sharma, MD 
Department of Anesthesiology 
Professor and Director of Quality Improvement 
Washington University School of Medicine 
660 S. Euclid Avenue 
Campus Box 8054 
St. Louis MO, 63110 
314-457-3147 
Sharma.a@wustl.edu 
 
Brian A. Torres, MSN, BSN, CRNA 
Assistant Director of the Certified Registered Nurse Anesthetist 
(CRNA) 
Washington University School of Medicine 
Department of Anesthesiology 
660 S. Euclid Avenue 
Campus Box 8054 
St. Louis MO, 63110 
 
Troy S. Wildes, MD 
 Associate Professor of Anesthesiology Washington University 
School of Medicine 
Department of Anesthesiology 
660 S. Euclid Avenue 
Campus Box 8054 
St. Louis MO, 63110 

314-362-9208 
wildest@wustl.edu 
  

WORLD HEALTH ORGANIZATION DATA SET 
Primary Registry and 
Trial Identifying Number 
 
ClinicalTrials: NCT03923699 
Date of Registration 
 
23 April 2019 
Secondary Identifying 
Numbers 
 
IRB ID#: 201903026 
Source(s) of Monetary 
Support 
 
 NINR R01NR017916, Institutional funds 
Primary Sponsor 
 
 
Department of Anesthesiology - Washington University School 
of Medicine in St. Louis, MO 
Role of Sponsors 
Sponsors did not contribute to any intellectual study design, 
collection, management, analysis or interpretation of the data 
and have no authority over these activities 
 
Contact for Public 
Queries 
 
Michael Avidan, MBBCh 
Study Principal Investigator 
avidanm@anest.wustl.edu 
314-273-4136 
660 S. Euclid Ave 
Department of Anesthesiology Box 8054 
Washington University School of Medicine 
St. Louis, MO 63110, United States  
 
Contact for Scientific 
Queries 
Michael Avidan, MBBCh 
Study Principal Investigator 
avidanm@anest.wustl.edu 
314-273-4136 
660 S. Euclid Ave 
Department of Anesthesiology Box 8054 
Washington University School of Medicine 
St. Louis, MO 63110, United States  
 
Public Title: 
Protocol for the Telemedicine Control Tower for the OR: 
Navigating Information, Care and Safety (TECTONICS) 

Scientific Title: 
Effectiveness of an anesthesiology control tower system in 
improving perioperative quality metrics and clinical outcomes: 
a randomized, pragmatic trial  
 
Countries of Recruitment 
 
United States 
Health Condition(s) or 
Problem(s) Studied 
 
Determine the Anesthesiology Control Tower (ACT) system’s 
effectiveness at preventing adverse outcomes and improving 
perioperative care quality care metrics. 
 
Description of 
Intervention: 
All 58 main operating rooms will be monitored by the ACT 
team. Operating rooms will be randomized daily to standard of 
care or intervention by the ACT team to assist the anesthesia 
clinicians with clinical decision making.  
 
Key Inclusion and 
Exclusion Criteria 
All patients 18 years or older undergoing surgery at Barnes 
Jewish Hospital in St. Louis, MO 
 
Study Type 
Interventional 
Allocation: Randomized 
Intervention model: parallel assignment 
Blinding: Anesthesia practitioner un-blinded to intervention, 
subject blinded to intervention, data analyst blinded to 
intervention, ACT Clinicians un-blinded to intervention 
Primary purpose: prevention 
Phase III 
 
Date of First Enrollment 
1 July 2019 
 
Target Sample Size 
40,000 
Recruitment Status 
Enrollment 
 
Primary Outcome(s) 
30-day mortality, postoperative delirium, postoperative 
respiratory failure, and postoperative acute kidney injury. 
 
Key Secondary 
Outcomes 
Perioperative quality of care metrics, including temperature 
management, antibiotic redosing, mean arterial pressure 
management, mean airway pressure with mechanical 
ventilation, blood glucose management, measured anesthetic 
concentration, and fresh gas flow rate. 
 

ORGANIZATIONAL STRUCTURE AND RESPONSIBILITIES 
Principal Investigator:  
Michael Avidan, MBBCh 
Responsibilities include: design and conduct of the TECTONICS trial, preparation of protocol and revisions, 
organizing steering committee meetings, and publication of study reports. 
 
Steering Committee: 
Michael Avidan, MBBCh 
Arbi Ben Abdallah, PhD 
Joanna Abraham, PhD 
Walter Boyle, MD 
Yixin Chen, PhD 
Bradley Fritz, MD 
Stephen Gregory, MD 
Daniel L. Helsten, MD 
Bernadette Henrichs, PhD, CRNA, CCRN 
Thomas G. Kannampallil, PhD 
 
Christopher King, MD 
Mary Politi, MD 
Anshuman Sharma, MD 
Brian A. Torres, MSN, BSN, 
CRNA 
Troy Wildes, MD 
 
Responsibilities include: agreement of final protocol, reviewing progress of study and if necessary, changes to the 
protocol, coordinating with principle investigator, and communicating with trial management committee. 
 
Operations Committee:  
Michael Avidan, MBBCh 
Joanna Abraham, PhD 
Walter Boyle, MD 
Yixin Chen, PhD 
Bradley Fritz, MD 
Stephen Gregory, MD 
Daniel L. Helsten, MD 
Bernadette Henrichs, PhD, CRNA, CCRN 
Thomas G. Kannampallil, PhD 
Christopher King, MD 
Mary Politi, MD 
Anshuman Sharma, MD 
Brian A. Torres, MSN, BSN, 
CRNA 
Troy Wildes, MD 
 
Responsibilities include: study planning, organization of steering committee meetings, provides annual risk report 
to the Human Research Protection Office at Washington University, provides reports to Safety Monitoring 
Committee (SMC), reports serious adverse events) to Washington University IRB (Institutional Review Board), 
ethics committee applications, data verification 
 
Data Management Committee:  
Arbi Ben Abdallah, PhD 
Joannah Abraham, PhD 
Bradley Fritz, MD 
Thomas G. Kannampallil, PhD 
Christopher King, MD 
 
Responsibilities include: statistical design of study, data verification, and statistical analysis. 

 
 
 
Data Adjudication Committee: 
Michael Avidan, MBBCh 
Arbi Ben Abdallah, PhD 
Joanna Abraham, PhD 
Walter Boyle, MD 
Yixin Chen, PhD 
Bradley Fritz, MD 
Stephen Gregory, MD 
Daniel L. Helsten, MD 
Bernadette Henrichs,  PhD, 
CRNA, CCRN 
Thomas G. Kannampallil, PhD 
Christopher King, MD 
 
Mary Politi, MD 
Anshuman Sharma, MD 
Brian A. Torres, MSN, BSN, 
CRNA 
Troy Wildes, MD 
Responsibilities include: verify data collection in EMR, contacting trial management committee, 
retraining researchers if necessary. 
  
Safety Monitoring Committee: 
 
Michael Avidan, MBBCh 
Arbi Ben Abdallah, PhD 
Joanna Abraham, PhD 
Walter Boyle, MD 
Yixin Chen, PhD 
Stephen Gregory, MD 
Daniel L. Helsten, MD 
Bernadette Henrichs,  PhD, 
CRNA, CCRN 
Thomas G. Kannampallil, PhD 
 
Mary Politi, MD 
Anshuman Sharma, MD 
Brian A. Torres, MSN, BSN, 
CRNA 
Troy Wildes, MD 
Responsibilities include: reviewing and evaluating the study data to ensure participant safety, study conduct, 
progress, and efficacy, and making recommendations regarding the continuation, modification, and termination of 
the trial.  

 
 
 
 
References 
 
1. 
Nepogodiev D, Martin J, Biccard B, Makupe A, Bhangu A, National Institute for Health 
Research Global Health Research Unit on Global S. Global burden of postoperative death. 
Lancet 2019;393:401. 
2. 
Pearse RM, Moreno RP, Bauer P, et al. Mortality after surgery in Europe: a 7 day cohort 
study. Lancet 2012;380:1059-65. 
3. 
Noordzij PG, Poldermans D, Schouten O, Bax JJ, Schreiner FA, Boersma E. 
Postoperative mortality in The Netherlands: a population-based analysis of surgery-specific risk 
in adults. Anesthesiology 2010;112:1105-15. 
4. 
Kertai MD, Pal N, Palanca BJ, et al. Association of perioperative risk factors and 
cumulative duration of low bispectral index with intermediate-term mortality after cardiac surgery 
in the B-Unaware Trial. Anesthesiology 2010;112:1116-27. 
5. 
Kertai MD, Palanca BJ, Pal N, et al. Bispectral index monitoring, duration of bispectral 
index below 45, patient risk factors, and intermediate-term mortality after noncardiac surgery in 
the B-Unaware Trial. Anesthesiology 2011;114:545-56. 
6. 
Healey MA, Shackford SR, Osler TM, Rogers FB, Burns E. Complications in surgical 
patients. Archives of surgery 2002;137:611-7; discussion 7-8. 
7. 
Finlayson EV, Birkmeyer JD. Operative mortality with elective surgery in older adults. 
Effective clinical practice : ECP 2001;4:172-7. 
8. 
Visser BC, Keegan H, Martin M, Wren SM. Death after colectomy: it's later than we 
think. Archives of surgery 2009;144:1021-7. 
9. 
Turrentine FE, Wang H, Simpson VB, Jones RS. Surgical risk factors, morbidity, and 
mortality in elderly patients. Journal of the American College of Surgeons 2006;203:865-77. 
10. 
Hamel MB, Henderson WG, Khuri SF, Daley J. Surgical outcomes for patients aged 80 
and older: morbidity and mortality from major noncardiac surgery. Journal of the American 
Geriatrics Society 2005;53:424-9. 
11. 
Monk TG, Saini V, Weldon BC, Sigl JC. Anesthetic management and one-year mortality 
after noncardiac surgery. Anesthesia and analgesia 2005;100:4-10. 
12. 
Mashour GA, Shanks A, Tremper KK, et al. Prevention of intraoperative awareness with 
explicit recall in an unselected surgical population: a randomized comparative effectiveness trial. 
Anesthesiology 2012;117:717-25. 

13. 
Story DA, Leslie K, Myles PS, et al. Complications and mortality in older surgical patients 
in Australia and New Zealand (the REASON study): a multicentre, prospective, observational 
study. Anaesthesia 2010;65:1022-30. 
14. 
Lee TH, Marcantonio ER, Mangione CM, et al. Derivation and prospective validation of a 
simple index for prediction of cardiac risk of major noncardiac surgery. Circulation 
1999;100:1043-9. 
15. 
Bilimoria KY, Liu Y, Paruch JL, et al. Development and evaluation of the universal ACS 
NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and 
surgeons. Journal of the American College of Surgeons 2013;217:833-42.e1-3. 
16. 
Cabana MD, Rand CS, Powe NR, et al. Why don't physicians follow clinical practice 
guidelines? A framework for improvement. Jama 1999;282:1458-65. 
17. 
Grol R, Grimshaw J. From best evidence to best practice: effective implementation of 
change in patients' care. Lancet 2003;362:1225-30. 
18. 
Demakis JG, Beauchamp C, Cull WL, et al. Improving residents' compliance with 
standards of ambulatory care: results from the VA Cooperative Study on Computerized 
Reminders. Jama 2000;284:1411-6. 
19. 
Needham DM, Colantuoni E, Mendez-Tellez PA, et al. Lung protective mechanical 
ventilation and two year survival in patients with acute lung injury: prospective cohort study. 
BMJ (Clinical research ed) 2012;344:e2124. 
20. 
Steinman MA, Fischer MA, Shlipak MG, et al. Clinician awareness of adherence to 
hypertension guidelines. The American journal of medicine 2004;117:747-54. 
21. 
Kane M, Smith AF. An American tale - professional conflicts in anaesthesia in the United 
States: implications for the United Kingdom. Anaesthesia 2004;59:793-802. 
22. 
Jameson JK. Transcending intractable conflict in health care: an exploratory study of 
communication and conflict management among anesthesia providers. J Health Commun 
2003;8:563-81. 
23. 
McDonald CJ. Protocol-based computer reminders, the quality of care and the non-
perfectability of man. The New England journal of medicine 1976;295:1351-5. 
24. 
Dawson NV, Arkes HR. Systematic errors in medical decision making: judgment 
limitations. Journal of general internal medicine 1987;2:183-7. 
25. 
Stiegler MP, Ruskin KJ. Decision-making and safety in anesthesiology. Current opinion 
in anaesthesiology 2012;25:724-9. 
26. 
Stiegler MP, Tung A. Cognitive processes in anesthesiology decision making. 
Anesthesiology 2014;120:204-17. 
27. 
Willingham M, Ben Abdallah A, Gradwohl S, et al. Association between intraoperative 
electroencephalographic suppression and postoperative mortality. Br J Anaesth 2014;113:1001-
8. 

28. 
Willingham MD, Karren E, Shanks AM, et al. Concurrence of Intraoperative Hypotension, 
Low Minimum Alveolar Concentration, and Low Bispectral Index Is Associated with 
Postoperative Death. Anesthesiology 2015;123:775-85. 
29. 
Kheterpal S, Tremper KK, Englesbe MJ, et al. Predictors of postoperative acute renal 
failure after noncardiac surgery in patients with previously normal renal function. Anesthesiology 
2007;107:892-902. 
30. 
Murray-Torres TM, Wallace F, Bollini M, Avidan MS, Politi MC. Anesthesiology Control 
Tower: Feasibility Assessment to Support Translation (ACT-FAST)-a feasibility study protocol. 
Pilot Feasibility Stud 2018;4:38. 
31. 
Gregory S, Murray-Torres T, Fritz BA, et al. Study protocol for the Anesthesiology 
Control Tower—Feedback Alerts to Supplement Treatments (ACTFAST-3) trial: a pilot 
randomized controlled trial in intraoperative telemedicine. F1000 Research 2018;7. 
32. 
Murray-Torres T, Casarella A, Bollini M, Wallace F, Avidan MS, Politi MC. 
Anesthesiology Control Tower-Feasibility Assessment to Support Translation (ACTFAST): 
Mixed-Methods Study of a Novel Telemedicine-Based Support System for the Operating Room. 
JMIR Hum Factors 2019;6:e12155. 
33. 
Fritz BA, Chen Y, Murray-Torres TM, et al. Using machine learning techniques to 
develop forecasting algorithms for postoperative complications: protocol for a retrospective 
study. BMJ Open 2018;8:e020124. 
34. 
Cui Z, Fritz BA, King CR, Avidan MS, Chen Y. A Factored Generalized Additive Model 
for Clinical Decision Support in the Operating Room. arXiv:190712596 [cs, stat] 2019. 
35. 
Cui Z, Zhang M, Chen Y. Deep Embedding Logistic Regression. 2019 November 2019. 
p. 176-83. 
36. 
Fritz BA, Cui Z, zhang M, et al. A Deep Learning Model for Predicting 30-Day 
Postoperative Mortality. British Journal of Anaesthesia 2019;Accepted. 
37. 
Wang Y, Chen W, Heard K, et al. Mortality Prediction in ICUs Using A Novel Time-
Slicing Cox Regression Method. Proc American Medical Informatics Annual Fall Symposium 
(AMIA-15) 2015. 
38. 
Kollef MH, Chen Y, Heard K, et al. A randomized trial of real-time automated clinical 
deterioration alerts sent to a rapid response team. J Hosp Med 2014;9:424-9. 
39. 
Bailey TC, Chen Y, Mao Y, et al. A trial of a real-time alert for clinical deterioration in 
patients hospitalized on general medical wards. J Hosp Med 2013;8:236-42. 
40. 
Kollef MH, Heard K, Chen Y, Lu C, Martin N, Bailey T. Mortality and Length of Stay 
Trends Following Implementation of a Rapid Response System and Real-Time Automated 
Clinical Deterioration Alerts. Am J Med Qual 2017;32:12-8. 
41. 
Micek ST, Samant M, Bailey T, et al. Real-time automated clinical deterioration alerts 
predict thirty-day hospital readmission. J Hosp Med 2016;11:768-72. 

42. 
Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and Regression 
Trees: Chapman & Hall; 1984. 
43. 
McNelis J, Castaldi M. "The National Surgery Quality Improvement Project" (NSQIP): a 
new tool to increase patient safety and cost efficiency in a surgical intensive care unit. Patient 
Saf Surg 2014;8:19. 
44. 
Jacobs JP, Shahian DM, Prager RL, et al. Introduction to the STS National Database 
Series: Outcomes Analysis, Quality Improvement, and Patient Safety. Ann Thorac Surg 
2015;100:1992-2000. 
45. 
McCambridge J, Witton J, Elbourne DR. Systematic review of the Hawthorne effect: new 
concepts are needed to study research participation effects. J Clin Epidemiol 2014;67:267-77. 
46. 
McCarney R, Warner J, Iliffe S, van Haselen R, Griffin M, Fisher P. The Hawthorne 
Effect: a randomised, controlled trial. BMC Med Res Methodol 2007;7:30. 
47. 
Johnson VE. Revised standards for statistical evidence. Proc Natl Acad Sci U S A 
2013;110:19313-7. 
48. 
Romano JP, Wolf M. Efficient computation of adjusted p-values for resampling-based 
stepdown multiple testing. Stat Probabil Lett 2016;113:38-40. 
49. 
Storey JD, Taylor JE, Siegmund D. Strong control, conservative point estimation and 
simultaneous conservative consistency of false discovery rates: a unified approach. J Roy Stat 
Soc B 2004;66:187-205. 
50. 
Helsten DL, Ben Abdallah A, Avidan MS, et al. Methodologic Considerations for 
Collecting Patient-reported Outcomes from Unselected Surgical Patients. Anesthesiology 2016. 
51. 
Avidan MS, Zhang L, Burnside BA, et al. Anesthesia awareness and the bispectral 
index. The New England journal of medicine 2008;358:1097-108. 
52. 
Henrichs BM, Avidan MS, Murray DJ, et al. Performance of certified registered nurse 
anesthetists and anesthesiologists in a simulation-based skills assessment. Anesthesia and 
analgesia 2009;108:255-62. 
53. 
Murray DJ, Boulet JR, Avidan M, et al. Performance of residents and anesthesiologists 
in a simulation-based skill assessment. Anesthesiology 2007;107:705-13. 
54. 
Wildes TS, Winter AC, Maybrier HR, et al. Protocol for the Electroencephalography 
Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES) study: a pragmatic, 
randomised clinical trial. BMJ Open 2016;6:e011505. 
55. 
Whitlock EL, Torres BA, Lin N, et al. Postoperative delirium in a substudy of 
cardiothoracic surgical patients in the BAG-RECALL clinical trial. Anesthesia and analgesia 
2014;118:809-17. 
56. 
Avidan MS, Jacobsohn E, Glick D, et al. Prevention of intraoperative awareness in a 
high-risk surgical population. The New England journal of medicine 2011;365:591-600. 

57. 
Gurusamy KS, Gluud C, Nikolova D, Davidson BR. Assessment of risk of bias in 
randomized clinical trials in surgery. Br J Surg 2009;96:342-9. 
58. 
Fritz BA, Escallier KE, Ben Abdallah A, et al. Convergent Validity of Three Methods for 
Measuring Postoperative Complications. Anesthesiology 2016;124:1265-76. 
59. 
Taichman DB, Backus J, Baethge C, et al. Sharing Clinical Trial Data--A Proposal from 
the International Committee of Medical Journal Editors. The New England journal of medicine 
2016;374:384-6. 
60. 
Kronzer VL, Tang RD, Schelble AP, et al. Preoperative Falls and Their Association with 
Functional Dependence and Quality of Life. Anesthesiology 2016. 
61. 
Re-AIM Planning Tool. (Accessed September 29, 2017, at http://re-aim.org/wp-
content/uploads/2016/09/planningtool.pdf ) 
62. 
Powell BJ, Waltz TJ, Chinman MJ, et al. A refined compilation of implementation 
strategies: results from the Expert Recommendations for Implementing Change (ERIC) project. 
Implement Sci 2015;10:21. 
63. 
Johnson ML, Gordon HS, Petersen NJ, et al. Effect of definition of mortality on hospital 
profiles. Med Care 2002;40:7-16. 
64. 
Ely EW, Margolin R, Francis J, et al. Evaluation of delirium in critically ill patients: 
validation of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Crit 
Care Med 2001;29:1370-9. 
65. 
Abbott TEF, Fowler AJ, Pelosi P, et al. A systematic review and consensus definitions 
for standardised end-points in perioperative medicine: pulmonary complications. Br J Anaesth 
2018;120:1066-79. 
66. 
Kellum JA, Lameire N, Group KAGW. Diagnosis, evaluation, and management of acute 
kidney injury: a KDIGO summary (Part 1). Crit Care 2013;17:204. 
67. 
Calvert S, Shaw A. Perioperative acute kidney injury. Perioper Med (Lond) 2012;1:6. 
68. 
Avidan MS, Maybrier HR, Abdallah AB, et al. Intraoperative ketamine for prevention of 
postoperative delirium or pain after major surgery in older adults: an international, multicentre, 
double-blind, randomised clinical trial. Lancet 2017;390:267-75. 
69. 
Fritz BA, Kalarickal PL, Maybrier HR, et al. Intraoperative Electroencephalogram 
Suppression Predicts Postoperative Delirium. Anesthesia and analgesia 2016;122:234-42. 
70. 
Miskovic A, Lumb AB. Postoperative pulmonary complications. Br J Anaesth 
2017;118:317-34. 
71. 
Carmichael P, Carmichael AR. Acute renal failure in the surgical setting. ANZ J Surg 
2003;73:144-53. 
72. 
Masoomi H, Carmichael JC, Dolich M, et al. Predictive factors of acute renal failure in 
colon and rectal surgery. Am Surg 2012;78:1019-23. 

Figures and Tables 
Figure 1. Schematic of study design, patient activity flow.  
 
Figure 2. Summary overview data for a hypothetical patient (AlertWatch® ACT 
Dashboard).    
 
Figure 3. The key workflow and process components of TECTONICS. The team in 
the ACT receives data form the electronic health record, web-interfaced monitors in the 
operating room (OR), video cameras in the OR, multipath convolutional neural network 
machine learning algorithms, and alerting software has been customized to provide 
maximum utility in an ACT. The team weaves together disparate data strands, and 
collaboratively formulates a plan to address the patient’s risk and optimize outcomes. 
The plan is discussed collegially with OR clinicians, who exercise judgement in 
delivering the best individualized perioperative management to each surgical patient. 
Dynamic data from OR patient monitors. (The photo was taken in our prototype ACT). 
CRNA, certified registered nurse anesthetist. 
 
 
Table 1. Primary outcome measures and definitions. 
Measurement 
Definition 
Thirty-day postoperative 
mortality 
Definition postoperative mortality provided by Johnson et al. This 
will include death of any cause occurring in or out of the hospital, 
within 30 days of the index surgery.63 
Postoperative delirium 
Defined as an acute change in consciousness or cognition. It 
has a fluctuating course, and is characterized by inattention, 
disorganized thinking and altered level of consciousness. We 
have trained the nursing staff on our surgical intensive care units 
to assess all patients for postoperative delirium using the 
Confusion Assessment Method for the Intensive Care Unit 
(CAM-ICU) instrument.64 It is administered every 12-24 hours 
depending on clinical context while in the ICU. All delirium 
assessments within 7 days will be included. 
Postoperative 
respiratory failure 
Defined as mechanical ventilation for greater than 24 hours after 
surgery, or unplanned postoperative re-intubation and 
mechanical ventilation within 48 hours of surgery.65 Planned 
staged operations are excluded. 
Postoperative acute 
kidney injury 
Diagnosed when any of the following three criteria are met: (i) an 
increase in serum creatinine by 50% compared with 
preoperative within 7 days, (ii) any increase in serum 
creatinine > 0.3 mg/dL in 48 hours, or (iii) oliguria (urine output 
<0.5 mL/kg/hr for 6-12 hours).33,66,67 
 
 

Table 2. Secondary outcome measures and definitions. 
Measurement 
Definition 
Temperature 
management 
Temperature ≥ 36°C at end of surgery 
Antibiotic redosing 
Antibiotic redosing compliant with guidelines developed by the 
institutional pharmacy and therapeutics committee. 
Mean arterial pressure 
management 
Percentage time during surgery with mean arterial pressure ≥ 60 
mmHg 
Mean airway pressure 
with mechanical 
ventilation 
Percentage time during surgery with mean airway pressure ≤ 30 
cmH2O. 
Blood glucose 
management 
Proportion of patients with blood glucose ≤ 200 mg/dL at end of 
surgery. 
Measured anesthetic 
concentration 
Proportion of patients without ≥ 15 consecutive min of anesthetic 
concentration ≤ 0.3 MAC during anesthetic maintenance period. 
Fresh gas flow rates 
Proportion of patients with efficient fresh gas flow for ≥90% of 
the anesthetic maintenance period 
 
 
Table 3. Primary outcomes to be assessed with estimation of power for each 
metric.  
Primary adverse 
outcomes 
Estimated current 
incidence 
Target with 
ACT 
support 
Power based on 
40,000 patients in 
RCT (p<0.005) 
Thirty-day postoperative 
mortality 
2%2-5 
1.5% 
84% (>80%)* 
Postoperative delirium 
(only patients admitted 
to intensive care units 
[ICUs]) 
25%68,69 
 
21% 
93%% (>80%)* 
(Based on 8,000 
patients admitted to 
ICU) 
Postoperative 
respiratory failure 
2%70 
1.5% 
84% (>80%)* 

Postoperative acute 
kidney injury 
2%71,72 
1.5% 
84% (>80%)* 
*The adjusted power was calculated assuming a cluster-randomized design allowing for 
an intracluster correlation between 0.005 and 0.01 and varying number of patients per 
OR. The current incidence estimates on which these power analyses are based are 
consistent with findings in our previous studies4,5,12,51,55,56,58,68,69 as well as from the 
ACTFAST2 pilot study, where we have approximations of these complications from 
~110,000 (mostly inpatient) surgical patients at our institution over 5 years.  
 
 
Table 4. Secondary outcomes to be assessed with estimation of power for each 
metric.  
Secondary outcome measures 
Estimated 
current 
compliance 
Target with 
ACT 
support 
Power based on 
40,000 patients in 
RCT (p<0.005) 
Temperature ≥ 36°C at end of 
surgery 
60% 
70% 
>99% (>90%)* 
Antibiotic redosing adherence ≥ 90% 
70% 
90% 
>99% (>90%)* 
Percentage time during surgery with 
mean arterial pressure ≥ 60 mmHg 
80% 
85% 
>99% (>90%)* 
Percentage time with peak airway 
pressure ≤ 30 cmH2O 
75% 
85% 
>99% (>90%)* 
Proportion with blood glucose ≤ 200 
mg/dL at end of surgery 
75% 
85% 
>99% (>90%)* 
Proportion without ≥ 15 consecutive 
min of anesthetic concentration ≤ 0.3 
MAC during maintenance period 
95% 
99% 
>99% (>90%)* 
Proportion with efficient fresh gas 
flow for ≥90% of anesthetic period 
75% 
90% 
>99% (>90%)* 
*The adjusted power was calculated assuming a cluster-randomized design allowing for 
an intracluster correlation between 0.01 and 0.03 and varying number of patients per 
OR for a total N=40,000.