Conditions: Hypotension
Keywords: Decision support Decision Support Systems, Clinical Decision Support Techniques Hypotension Intraoperative Hypotension Acute Kidney Injury Postoperative Mortality Blood Pressure
Allocation: NON_RANDOMIZED | Intervention model: SEQUENTIAL
Enrollment: 22435
Healthy volunteers: no

Brief summary:
The purpose of this study is to determine whether a decision support system can improve the adherence to thresholds for low blood pressure by anesthesia providers, which in turn prevents their patients from having organ injury.

Detailed description:
Blood pressure management is an important part of anesthesia. Many factors contribute to a change in blood pressure during a surgical procedure, such as blood loss, manipulation by surgeons, and there are several mechanisms through which anesthesia itself changes blood pressure. Although a high blood pressure also occurs during anesthesia, most of these factors lower a patient's blood pressure. When a patient's blood pressure becomes too low, the internal organs become at risk of receiving not enough blood (low perfusion or hypoperfusion). This low perfusion state can result in organ damage (ischemia) because of an insufficient supply of oxygen and glucose. Hence the important task of anesthesia providers to maintain the blood pressure of patients, using a wide range of drugs and other interventions.

A big challenge in blood pressure management is to know when a low blood pressure indeed results in low perfusion of organs. There is a large variation between patients in how susceptible they are to low blood pressure, as well as a difference between the organs in how easily they are damaged because of low perfusion. Elder patients, or patients with preexisting hypertension, heart problems or other cardiovascular diseases are more prone to a low blood pressure and are more likely to develop organ ischemia when there is a low blood pressure. The kidneys, the heart and the brain are the organs that are most at risk of organ damage. As one cannot measure the perfusion states of individual organs in individual patients, it is very difficult to know 'how low to go' with a patient's blood pressure.

Recent studies have used large datasets of patients to demonstrate that there is statistical association between low blood pressure during surgery and various types of organ injury. As patients are already treated for low blood pressure by anesthesia providers, this suggests that patients have low organ perfusion states despite the current treatment standards. A patient's blood pressure is not simply a dial that can be adjusted to a specific level. Finding the right level of interventions can be difficult in some patients. Consequently, lower blood pressures are common in anesthesia, even with the current standards of blood pressure management.

In this proposed study the investigators will implement two forms of decision support to assist anesthesia providers in blood pressure management. The decision support aims to educate anesthesia providers about the risks of low blood pressures in direct relation to the patients that they treat. One form of decision support will provide automated notifications through pagers and through the anesthesia information management system. These automated notifications pop up when the patient's blood pressure drops below a level that is associated with a risk of organ injury, and thus alerts the anesthesia provider of the blood pressure and its associated risk. The second form of decision support will send a postoperative email the day after the procedure when the patient has had a low blood pressure for particular duration. This email then provides feedback to the anesthesia provider by informing them of the increased risks of organ injury that are associated with that low blood pressure.

The study will look at both a change in patient outcome and a change in blood pressure management and will be performed at the Vanderbilt University Medical Center (VUMC). The change in patient outcome will primarily be studied through the occurrence of acute kidney injury in the first days following the procedure at the VUMC. The change in blood pressure management (provider behavior) will be studied by observing the depth and duration of low pressures during anesthesia, and the number of interventions that have been used to treat the blood pressure. Patient outcome will be studied by comparison of a baseline phase - before the decision support is implemented and uses historic data- and the intervention phase - the period during which the intervention is active. Only routinely collected clinical data will be used for these analyses: no additional data collection is required.

As it is impossible to know which form of decision support will be the most effective, the first three months of the intervention period will be a 'nested cluster-randomized trial'. The anesthesia providers (not the patients) will be randomized to either the automated notifications or the feedback emails. After three months all anesthesia providers will receive both forms of decision support for the remainder of the intervention period. The reason why anesthesia providers are randomized only during the first three months is that cross-over or contamination between the two groups is expected. This contamination could make it impossible to study the effect of the decision support on patient outcome, as there will be no longer any difference between the study groups.

Arms:
arm 1: The intervention period. Several decision support elements are implemented to notify anesthesia providers: attending anesthesiologists and in-room anesthesia providers of intraoperative hypotension (threshold of a mean arterial pressure below 60 mmHg). Two types of decision support will be implemented: near real-time decision support and feedback emails.

Near real-time decision support elements will notify the anesthesia providers of a blood pressure drop below the threshold and display the associated increased risk of acute kidney injury. The notification is presented through the pager system for attending anesthesiologists and through the anesthesia information management system for the in-room anesthesia provider.

All providers will be notified through email within 24 hours after the end of an anesthetic case, when the patient had an episode of intraoperative hypotension that is associated with an increased risk of organ injury due to organ ischemia. arm 2: The 'before' period - or historic control group - during which no decision support for intraoperative hypotension was being used, also known as 'usual care'. This is the three year period prior to the intervention period (the 'Intraoperative hypotension decision support' arm).

Interventions: intervention 1: Attending real-time decision support intervention 2: In-room real-time decision support intervention 3: Attending feedback emails intervention 4: In-room provider feedback emails intervention 5: Anesthesia Information Management System (AIMS) intervention 6: Perioperative Data Warehouse (PDW) intervention 7: General anesthesia intervention 8: Pager system intervention 9: Central neuraxial anesthesia intervention 10: Non-cardiac surgery intervention 11: Propofol intervention 12: Sevoflurane intervention 13: Desflurane intervention 14: Isoflurane intervention 15: Ephedrine intervention 16: Phenylephrine intervention 17: Norepinephrine intervention 18: Epinephrine intervention 19: Dobutamine intervention 20: Dopamine intervention 21: Isoproterenol intervention 22: Milrinone intervention 23: Atropine intervention 24: Glycopyrrolate intervention 25: Vasopressin intervention 26: Terlipressin intervention 27: Sodium Chloride 0.9% intervention 28: Ringer's lactate intervention 29: Hydroxyethyl starch solutions intervention 30: Fresh Frozen Plasma intervention 31: Packed Red Blood Cells intervention 32: Albumin solutions intervention 33: Plasma-Lyte intervention 34: Lidocaine intervention 35: Bupivacaine intervention 36: Levobupivacaine intervention 37: Ropivacaine intervention 38: Mepivacaine intervention 39: Tetracaine intervention 40: Prilocaine intervention 41: Procaine intervention 42: Chloroprocaine intervention 43: Benzocaine intervention 44: Articaine
Intervention descriptions: intervention 1: Near real-time decision support elements will notify the attending anesthesiologists of a blood pressure drop below the threshold for intraoperative hypotension (mean arterial pressure below 60 mmHg). The notification is presented through the pager system. The page will also display the associated increased risk of organ injury due to organ ischemia. intervention 2: Near real-time decision support elements will notify the in-room anesthesia provider of a blood pressure drop below the threshold for intraoperative hypotension (mean arterial pressure below 60 mmHg). The notification is presented through the anesthesia information management system. The decision support system will display the associated increased risk of organ injury due to organ ischemia. intervention 3: Attending anesthesiologists will be notified through email within 24 hours after the end of an anesthetic case, when the patient had an episode of intraoperative hypotension (mean arterial pressure below 60 mmHg or lower for a particular duration) that is associated with an increased risk of organ injury due to organ ischemia. intervention 4: In-room anesthesia providers will be notified through email within 24 hours after the end of an anesthetic case, when the patient had an episode of intraoperative hypotension (mean arterial pressure below 60 mmHg or lower for a particular duration) that is associated with an increased risk of organ injury due to organ ischemia. intervention 5: The anesthesia electronic record keeping system intervention 6: The data warehouse that is used to gather perioperative data and create user reports. In this instance the PDW will be used to send the postoperative feedback emails. intervention 7: Any anesthetic drugs that are used to induce general anesthesia above the level of sedation. intervention 8: The mobile pager system through which alerts can be sent intervention 9: Regional anesthesia effectuated through the placement of local anesthetics around the nerves of the central nervous system, e.g. spinal anesthesia and epidural anesthesia. intervention 10: Any surgical intervention that is not aimed at surgical correction of the heart intervention 11: Anesthetic drug used to maintain general anesthesia intervention 12: Anesthetic drug used to maintain general anesthesia intervention 13: Anesthetic drug used to maintain general anesthesia intervention 14: Anesthetic drug used to maintain general anesthesia intervention 15: Cardiovascular drug used to treat intraoperative hypotension intervention 16: Cardiovascular drug used to treat intraoperative hypotension intervention 17: Cardiovascular drug used to treat intraoperative hypotension intervention 18: Cardiovascular drug used to treat intraoperative hypotension intervention 19: Cardiovascular drug used to treat intraoperative hypotension intervention 20: Cardiovascular drug used to treat intraoperative hypotension intervention 21: Cardiovascular drug used to treat intraoperative hypotension intervention 22: Cardiovascular drug used to treat intraoperative hypotension intervention 23: Cardiovascular drugs used to treat intraoperative hypotension intervention 24: Cardiovascular drug used to treat intraoperative hypotension intervention 25: Cardiovascular drug used to treat intraoperative hypotension intervention 26: Cardiovascular drug used to treat intraoperative hypotension intervention 27: Intravenous fluid used to treat intraoperative hypotension intervention 28: Intravenous fluid used to treat intraoperative hypotension intervention 29: Intravenous fluid used to treat intraoperative hypotension intervention 30: Intravenous fluid used to treat intraoperative hypotension intervention 31: Intravenous fluid used to treat intraoperative hypotension intervention 32: Intravenous fluid used to treat intraoperative hypotension intervention 33: Intravenous fluid used to treat intraoperative hypotension intervention 34: Local anesthetic used for central neuraxial anesthesia. intervention 35: Local anesthetic used for central neuraxial anesthesia. intervention 36: Local anesthetic used for central neuraxial anesthesia. intervention 37: Local anesthetic used for central neuraxial anesthesia. intervention 38: Local anesthetic used for central neuraxial anesthesia. intervention 39: Local anesthetic used for central neuraxial anesthesia. intervention 40: Local anesthetic used for central neuraxial anesthesia. intervention 41: Local anesthetic used for central neuraxial anesthesia. intervention 42: Local anesthetic used for central neuraxial anesthesia. intervention 43: Local anesthetic used for central neuraxial anesthesia. intervention 44: Local anesthetic used for central neuraxial anesthesia.

Protocol document:
Prot_SAP_000.pdf: 
 
 
Decision support for intraoperative low blood pressure – study protocol – version: December 4, 2018 
1 
 
Decision support for intraoperative low 
blood pressure 
The study proposal for an implementation study on decision support for 
intraoperative hypotension 
Version: 
December 4, 2018 
Vanderbilt University Medical Center 
Introduction 
Recently, there is a growing interest in the relation between intraoperative hypotension (IOH) 
and the occurrence of various postoperative adverse events. An increasing number of studies 
show an association between IOH and outcomes such as mortality, acute kidney injury and my-
ocardial infarction.1–5 However, this does not mean that we now understand ‘how low we can 
go’. For example, heterogeneity in IOH definitions, outcome definitions and study populations 
makes it difficult to draw firm conclusions about the exact IOH threshold. Nonetheless, the ob-
servational nature of the data may be the biggest problem, as residual confounding is still likely 
to be of great influence.  
The main cause of the residual confounding in observational IOH studies may be the current 
behavior of anesthesia providers. Blood pressure management is a vital part of anesthesia prac-
tice, and patients undergoing anesthesia are frequently – if not constantly – being treated for 
low blood pressures by anesthesia providers. However, blood pressure management is not a 
one-size-fits-all solution as each patient requires interventions that are tailored his or her needs. 
This complexity of blood pressure management is very difficult to adjust for during the analysis 
of IOH. It is very well possible that IOH is merely related to adverse events because it is a symp-
tom of underlying disease severity and comorbidity, rather than being a proof for organ hy-
poperfusion despite current blood pressure treatment by anesthesia providers. It is thus of 


 
 
Decision support for intraoperative low blood pressure – study protocol – version: December 4, 2018 
2 
 
great importance that we improve our understanding of our current circulatory management. 
In current clinical practice IOH is quite common. Few anesthesia providers would consider a 
mean arterial pressure (MAP) below 60 mmHg* to be clinically acceptable for patients under-
going noncardiac surgery.6 Nonetheless, up to 50% of patients have a MAP below 60 mmHg at 
any time point during their surgical procedure, adding up to ten minutes or more during the 
procedure in 15-25% of all patients.3–5 In view of recent scientific evidence, it thus seems im-
perative that anesthesia providers adhere more strictly to their own clinically acceptable blood 
pressure thresholds. 
 
Clinical decision support may help anesthesia providers to adhere more strictly to their own 
clinically acceptable blood pressure thresholds. An automated notification system may alert the 
anesthesia providers when the blood pressure of their patient drops below 60 mmHg MAP. A 
recent randomized trial didn’t demonstrate any change in blood pressure management as a 
result of automated blood pressure notifications.7 Because of the study design, the authors do 
not provide a satisfactory explanation of why it didn’t change blood pressure management. 
Although randomization at the patient level may seem the appropriate thing to do, there is a 
serious risk of contamination. When on a given day an anesthesia provider received notifica-
tions for the first patient, is it likely that it the provider will be more aware of the blood pressure 
threshold during a next case, even when the subsequent patient was randomized to the control 
group. The contamination could be one explanation for a ‘null-result’ for the study. Another 
explanation would be that the authors did not provide sufficient context to the notifications, 
which has been suggested to be a requirement for the successful implementation of decision 
support.8 The recent randomization trial thus by no means provides sufficient scientific evidence 
that clinical decision support for IOH does not work. 
The current study proposal aims to implement a clinical decision support system for IOH that 
provides both automated notifications and contextual information. The decision support aims to 
educate anesthesia providers about the risks that are associated with IOH. Their response to 
that decision support will allow us to study blood pressure management by anesthesia provid-
                                                            
* a MAP of 60 mmHg corresponds with a blood pressure of systolic 80 over diastolic 50 mmHg 


 
 
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ers, the effect of decision support on that blood pressure management, and the subsequent 
impact on postoperative adverse events. 
Overall research question 
Does a more strict treatment of intraoperative hypotension (IOH) encouraged through a clinical 
decision support system (CDSS) reduce postoperative morbidity and mortality in comparison to 
care-as-usual? 
Specific clinical research questions 
Does a lower incidence of IOH reduce postoperative morbidity and mortality? 
Assuming that the CDSS resulted in a lower incidence of IOH, the postoperative morbidity and 
mortality will be compared between the intervention group and the care-as-usual group 
How does this relation depend on the anesthetic depth? 
Differences in the administration of intraoperative anesthetics and analgesics between the 
study groups will be determined. Postoperative morbidity and mortality will then be studied 
conditional on these differences between study groups. 
How does this relation depend on other treatment choices? 
Differences in the administration of intraoperative vasopressors and inotropes will be compared 
between study groups. Study groups will also be compared for the differences in total amount 
of administered intravenous fluids and blood products.  
How does this relation depend on intraoperative conditions and patient comorbidity? 
The relation between treatment, IOH, and postoperative morbidity and mortality may be stud-
ied conditional on other vital signs, blood loss, and various patient comorbidity. 


 
 
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Specific decision making questions 
Does a CDSS for IOH result in a lower incidence of IOH? 
The incidence of IOH will be compared between study groups, overall and conditional on CDSS 
activity. 
Does a near real-time notification have a greater impact than feedback on performance? 
In this study two types of decision support will be compared. One type consists of near real-time 
notifications that alerts anesthesia providers when the blood pressure drops below a particular 
threshold. The second type is an email system that provides feedback on what has happened 
during the anesthetic case and about the possible consequences. 
What is the role of teamwork in anesthesia management? 
Because the first elements concentrate on the attending anesthesiologist and the latter ele-
ments on the in-room providers, this study will provide insight in the anesthesia teamwork. This 
will be explored by comparing the results according to different roles (e.g. attendings, residents, 
CRNA, SRNA), years of experience and anesthesia service (multispecialty anesthesia, neuroan-
esthesia, etc.). 
How does the ‘directiveness’ of the CDSS influence decision making. 
The staged elements increase in their degree to which they draw the attention of the attending 
anesthesiologists and in-room provider to the problem of IOH. Nevertheless, only the last phase 
provides an actionable recommendation and is thus truly directive. 
How does this relation depend on the anesthetic depth? 
Differences in the administration of intraoperative anesthetics and analgesics between the 
study groups will be determined. The incidence of IOH will then be studied conditional on these 
differences between study groups. 
How does this relation depend on other treatment choices? 
Differences in the administration of intraoperative vasopressors and inotropes will be compared 


 
 
Decision support for intraoperative low blood pressure – study protocol – version: December 4, 2018 
5 
 
between study groups. Study groups will also be compared for the differences in total amount 
of administered intravenous fluids and blood products.  
How does this relation depend on intraoperative conditions and patient comorbidity? 
The relation between treatment and IOH may be studied conditional on other vital signs, blood 
loss, and various patient comorbidity. 
What is the effect of risk education? 
The calibration/simulation will not only help to study decisions, but may also train the providers. 
Methods 
Design and participants 
Domain 
Adult elderly patients scheduled for a non-cardiac surgical procedure with a moderate to high 
surgical risk that have a low blood pressure (IOH) during the procedure. 
Determinant 
A decision support system using two different types of notifications for IOH (for more detailed 
information see paragraph ‘Decision support elements’). 
Outcome 
Postoperative Acute Kidney Injury (AKI, see ‘End points’). 
Study design 
A single center, interrupted time series study with a nested cluster-randomized trial, random-
ized at the level of the anesthesia provider (see Figure 1). is the study will be performed at 
Vanderbilt University Medical Center (VUMC). The study will use a 36-month historic cohort of 
procedures (retrospective data) from the baseline ‘off’ period, further referred to as the prein-


 
 
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tervention period. Subsequently, the decision support system is implemented and its effects are 
recorded during the intervention period using routine clinical data. The decision support system 
consists of four interventional elements (see paragraph ‘Decision support elements’), of which 
two are aimed at the attending anesthesiologist and two are aimed at the in-room anesthesia 
provider (the anesthesiology resident, the certified or student nurse anesthetists (CRNA or 
SRNA)).  
The intervention period starts with the nested cluster-randomized trial. At the start of the 
intervention period, the attending anesthesiologists will be randomized to either of the two 
elements aimed at the attending. One month later, the in-room providers will be randomized to 
either of the elements that are aimed at them. The randomization status of the attending anes-
thesiologists will be maintained. After the third month, all elements will be activated for all us-
ers within that user group. This ends the period of the nested cluster-randomized trial. After the 
fourth month, an add-on for one of the decision support elements will be implemented. The 
exact nature of this add-on modification is discussed in the section ‘Decision support elements’. 
The entire implementation phase will thus be completed in five months. 
The study will then continue to monitor the incidence of the primary end point to reach the 
estimated sample size. The primary outcome is a routinely collected outcome variable, which 
enables us to: 1) use a historic cohort as a baseline ‘off’ period; 2) study the CDSS implementa-
tion in a pragmatic setting; and 3) conduct the study in a more cost-effective way. The total in-
tervention period will be 9 months for the VUMC (Figure 1). 


 
 
Decision support for intraoperative low blood pressure – study protocol – version: December 4, 2018 
7 
 
Figure 1 - Study design 
 
Study population 
All adult inpatients, 60 years and older, scheduled for a non-cardiac surgical procedure under 
general or central neuraxial anesthesia at the Vanderbilt University Medical Center (VUMC).  
Exclusion criteria 
Procedures where only a peripheral nerve blockade is used will be excluded, i.e. patients who 
did not receive either general anesthesia or central neuraxial blockade. Patients with preexisting 
end-stage renal disease will be excluded, operationalized as excluding all patients with a pre-
operative need for dialysis. The following procedures will also be excluded: renal surgery, cardi-
ac surgery, organ transplantation, ophthalmic surgery, endoscopic gastrointestinal procedures, 
and (interventional) radiologic procedures. Additionally, small non-invasive or minimal-
ly-invasive procedures will also be excluded. 


 
 
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Randomization during the nested cluster-randomized trial 
Each attending anesthesiologist and in-room provider will be randomized to either of the two 
interventions that is aimed at their user group. The randomization will occur at the start of the 
nested cluster-randomized trial period or at the time she/he enters the study within that period. 
Randomization at the provider level rather than at the patient level aims to prevent contamina-
tion and therefore dilution of the implementation effect. Randomization of the attending anes-
thesiologists will be stratified for the particular anesthesia expertise groups, e.g. vascular sur-
gery anesthesia and neuroanesthesia. In-room providers will also be randomized per participat-
ing center, but stratified for being a resident, a CRNA or a SRNA. Randomization of CRNAs will 
be stratified on the OR-locations within their center where they mostly work. Stratified ran-
domization will be performed using a block size of four. 
Patients at risk of IOH 
The hypothesis of this study is that prevention of prolonged low blood pressures during anes-
thesia will reduce the incidence of organ injury, i.e. AKI and myocardial injury. The domain of 
this study therefore includes patients at risk of IOH. Identification of patients at risk of IOH is not 
straightforward. In this section we will discuss why identification of patients at risk of IOH is not 
straightforward and how this complicates the design of this study as compared to a regular 
randomized trial.  
In a classical drug-RCT, you know that everyone in the index group will get the intervention, 
although you are not sure for which individuals the intervention will work. A more complex in-
tervention often aims to improve patient outcome by implementing an intervention that is tar-
geted to affect behavior and decision making of either healthcare workers or patients. For ex-
ample, a weight loss program aims to reduce obesity in individuals. The mechanism through 
which the program achieves weight loss may not have the same effect in all individuals, similar 
to our drug trial. On top of that, an additional uncertainty is introduced, because the interven-
tion will also depend on the compliance of the individual to the program.  
When a complex intervention aims to prevent a particular condition – as in this case of IOH 
decision support – there is an additional complicating factor besides the two previously men-


 
 
Decision support for intraoperative low blood pressure – study protocol – version: December 4, 2018 
9 
 
tioned factors of uncertainty. When the condition can be completely prevented, the study pop-
ulation becomes difficult to identify. In our study, the decision support can only prevent AKI in 
patients that would otherwise have had IOH. Thus we should analyze only patients that would 
have had IOH, not patients that would never have IOH. Since we may not know which patients 
would have suffered from IOH in the intervention group, there is no simple comparison. Alter-
natively, we could compare the rates in all patients of both study groups – thus both those that 
would have had and would not have had IOH. However, as anesthesiologists already aim to 
prevent low blood pressures, patients with IOH according to our definitions will be relatively 
rare. Analyzing all patients would thus greatly dilute the effect on AKI and myocardial injury. 
A more efficient way would be to compare patients that would be at risk of IOH rather than 
patients that would definitely have had IOH. A good example is comparing patients with differ-
ent predicted risks when a prediction model is implemented to prevent postoperative nausea 
and vomiting. For IOH there is unfortunately no prediction model available. However, when IOH 
is prevented from occurring because of the implemented decision support, it is likely that they 
will have a low blood pressure that is just above the threshold. Patients that have a blood pres-
sure below a higher threshold will thus be at risk of a blood pressure below a lower threshold, if 
it weren’t for the intervention by the anesthesia providers. The next crucial step would be to 
select that higher threshold. Selecting too high a threshold will dilute the effect, whereas too 
low a threshold and patients may drop out because they are prevented from reaching that 
threshold. Although one cannot be sure what would be the best threshold in advance, it seems 
reasonable that a mean arterial pressure (MAP) below 65 mmHg would be a reasonable indica-
tion of MAP below either 60 mmHg or 55 mmHg.  
Interventions 
The interventions in this study consist of a) the decision support elements; and b) general edu-
cation. 
Decision support elements 
The decision support system consists of four elements that aim to notify anesthesia providers of 


 
 
Decision support for intraoperative low blood pressure – study protocol – version: December 4, 2018 
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the risks of organ injury due to organ ischemia when a patient suffers from IOH. The four ele-
ments will be implemented during different stages of the nested cluster-randomized trial peri-
od. At the end of the trial period all elements will be simultaneously activated (Figure 1).  
 
The decision support system will be custom designed. The VUMC will implement two elements 
that provide near real-time notifications into GasChart, its anesthesia information management 
system (AIMS). The two other interventions consist of feedback emails, which be implemented 
into the email notification system of the Perioperative Data Warehouse (PDW). 
One element of each decision support type will be available for each user group. In other 
words, a near real-time notification system and a feedback email system will be available for the 
attending anesthesiologists as well as for the in-room providers. During the cluster-randomized 
trial period, the near real-time elements will only be activated for a specific case when the an-
esthesia providers were randomized to near real-time decision support within their user group, 
as registered in the anesthesia information management system.  
IOH thresholds associated with organ injury 
The thresholds that will trigger decision support intervention are derived from recent literature. 
The studies were selected based on the results of a systematic review. For AKI, myocardial injury 
and mortality the two highest ranked studies for that end point were selected. For MAPs below 
60, 55 and 50 mmHg and dependent on the duration of hypotension, the results of each study 
were classified into three risk groups (mild, moderate, high) based on the reported odds ratios 
or risk ratios. For each end point, the highest risk category for a particular MAP and duration 
was considered the definitive risk category for that end point. The overall risk of organ injury for 
a particular MAP and total duration was then considered to be the highest of risks of any of the 
end points. Risk thresholds for odds ratios and risk ratios were chosen by the investigators so 
that there was a clinically reasonable incremental increase in risk with lower MAPs and longer 
durations. The exact classification is shown in Tables 1 and 2. 


 
 
Decision support for intraoperative low blood pressure – study protocol – version: December 4, 2018 
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Table 1 - Risk classification based on the odds ratios as reported in literature 
  
  
AKI  
(odds ratios) 
Myocardial injury  
(risk ratios / odds ratios) 
30 day Mortality  
(odds ratios) 
MAP 
min 
Sun et al. 
Walsh et 
al. 
Van Waes et 
al.† 
Pipanmekaporn 
et al.† 
Walsh et al. 
Monk et al. 
Mascha et 
al. 
<60 
≥1 
1.1* 
 
1.1* 
 
 
1.1* 
1.0‡ 
  
>5 
1.1* 
 
1.1* 
 
 
1.1* 
1.1‡ 
  
>10 
1.8 
 
1.5 
 
 
1.1* 
1.1 
  
>20 
1.7* 
 
1.5 
2.6 
 
1.1* 
1.2 
<55 
≥1 
1.4* 
1.2 
1.1* 
 
1.3 
1.1* 
1.1‡ 
  
>5 
1.5* 
1.2 
1.1* 
 
1.5 
1.1* 
1.1‡ 
  
>10 
2.3 
1.3 
1.5 
 
1.8 
1.1* 
1.1 
  
>20 
3.5 
1.5 
1.5 
2.6 
1.8 
1.1* 
1.3 
<50 
≥1 
1.4* 
1.2 
1.3 
 
1.3 
1.2* 
1.1‡ 
  
>5 
1.5* 
1.2 
2.0 
 
1.5 
2.4 
1.1‡ 
  
>10 
2.3 
1.3 
2.0 
 
1.8 
2.4 
1.2 
  
>20 
3.5 
1.5 
2.4 
2.6 
1.8 
2.4 
1.5 
* = not significant; † = the numbers in this column are risk ratios; ‡ = estimated from OR per 10 minutes 
yellow = mild risk; light orange = moderate risk; dark orange = high risk 
risks from higher MAP groups carry over to lower groups if that particular threshold was not reported in the study  
 
 


 
 
Decision support for intraoperative low blood pressure – study protocol – version: December 4, 2018 
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Table 2 - Classification of depth and duration of IOH 
MAP 
total 
minutes 
overall organ 
injury* 
acute kidney 
injury 
myocardial injury  
30 day mortality  
<60 
≥1 
mild risk 
mild risk 
mild risk 
mild risk 
  
>5 
mild risk 
mild risk 
mild risk 
mild risk 
  
>10 
moderate risk 
moderate risk 
moderate risk 
mild risk 
  
>20 
high risk 
moderate risk 
high risk 
mild risk 
<55 
≥1 
mild risk 
mild risk 
mild risk 
mild risk 
  
>5 
moderate risk 
moderate risk 
moderate risk 
mild risk 
  
>10 
high risk 
high risk 
moderate risk 
mild risk 
  
>20 
high risk 
high risk 
high risk 
mild risk 
<50 
≥1 
moderate risk 
mild risk 
moderate risk 
moderate risk 
  
>5 
high risk 
moderate risk 
high risk 
high risk 
  
>10 
high risk 
high risk 
high risk 
high risk 
  
>20 
high risk 
high risk 
high risk 
high risk 
* the risk of overall organ injury (2nd column) is determined by the highest risk classification of the individual risks (3rd to 5th columns). 
The red bar represents the risks of the current patient. 
 
 
Postoperative feedback emails 
The first two elements consist of the same email notification aimed at different anesthesia pro-
viders: the attending anesthesiologists or the in-room providers. The anesthesia providers will 
be notified within 24 hours after the end of a case, when the patient had an episode of IOH that 
is associated with an increased risk of organ injury due to organ ischemia. The feedback email 
will primarily report the risk of AKI at VUMC. In addition, the feedback emails will include a link 
to a more detailed description of all organ risks (see Table 2 and the Example for the Feedback 
emails). Moreover, at the end of each week, the anesthesia providers will receive an email that 
reports which of their patients suffered from postoperative AKI or myocardial injury that week 
(such a report is already a part of clinical routine at VUMC). As the follow-up for AKI is seven 
days, the update may contain patients that had their procedure during the previous 14 days.  


 
 
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Near real-time notifications 
The two near real-time decision support elements will notify the anesthesia provider of a blood 
pressure drop below one of the IOH thresholds. The near real-time notification for the attending 
anesthesiologist will be provided through the paging systems (example text: “MAP<50 for 
10min, high AKI risk”), whereas the in-room providers receive their notification through the 
AIMS of the hospital (see attached Example for the AIMS IOH notification).  
Triggers of near real-time notifications 
The near real-time notifications systems will be triggered by a MAP below 60 mmHg (Figure 2). 
The decision support system will then provide a notification of the AKI risk at VUMC. Depending 
on how much time the MAP of an individual patient was below a particular threshold, the noti-
fication will display the risk of organ ischemia for that patient (mild, moderate or high, see Table 
2). An example in time is shown in Figure 2 – examples 1 and 2.  
The near real-time notifications for the in-room providers will be continuously displayed 
within the AIMS. The near real-time notifications for the anesthesiologists will be sent to their 
pagers. The pager notifications will repeat with a 10-minute interval. Within a single episode of 
IOH there will be a maximum of four notifications the paging system (Figure 2 – example 3). An 
episode of IOH is defined as the time that a patient’s MAP drops below 60 mmHg and the first 
time it reaches a MAP of 60 or higher. When blood pressure has not returned to normal after 
the four notifications – i.e. after 40 minutes – it is assumed that there is a reason for not in-
creasing the blood pressure. In that instance either the MAP is considered to be appropriate for 
that patient or the interventions are not sufficiently effective. This prevents anesthesiologists to 
keep getting notifications throughout the procedure. Even when a new duration threshold is 
reached, the system will not provide a new notification if four notifications within that episode 
have already occurred. However, when the blood pressure normalizes for more than 20 
minutes, a subsequent IOH episode will again provide notifications to the low MAP. Any risk 
that is presented, is based on all the previous low MAP values and durations that occurred dur-
ing the case (Figure 2 – example 4).


 
 
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Figure 2 - Triggers of near real-time notifications 


 
 
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General education 
All attending anesthesiologists and in-room providers will be informed at the start of the nested 
trial period – or when they enter the study at a later time – of the study’s purposes and how the 
decision support will work. The information will be provided by email as well as during sched-
uled educational sessions for each provider group. Further educational sessions will be sched-
uled when required. 
End points 
The primary end points 
The primary end point for the VUMC will be AKI stage I or greater within the first seven postop-
erative days of surgery. The definition of AKI stage I according to KDIGO (Kidney Disease: Im-
proving Global Outcomes) will be used: an absolute increase in serum creatinine of 0.3 mg/dL or 
more (≥26.4 µmol/L) from baseline within 48 hours or a percentage increase in serum creatinine 
to 150% or more from baseline within 7 days. Because of a low availability of information on 
urine output, this criterion will not be used to diagnose AKI. The baseline serum creatinine value 
is the last known preoperative value which was recorded within six months prior to the surgical 
procedure. Preoperative and postoperative creatinine values will be derived from blood sam-
ples that are part of routine clinical care. This may be a primary lab request for creatinine or a 
secondary analysis of blood samples that are otherwise available. No additional blood samples 
will be drawn for the purpose of this study to determine postoperative AKI at the VUMC. 
Secondary end points: patient level 
o 30-day mortality 
 combination of in-hospital mortality and ‘alive-index’ (which checks for 
visits to the hospital in the electronic healthcare record as indication of 
being alive) 
o In-hospital mortality 


 
 
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o length of hospital stay 
o ICU admission 
o length of ICU stay (if any) 
o readmissions 
o AKI stage 2 or greater (to a 200% or more increase in baseline creatinine value) 
o postoperative rise in creatinine levels 
Secondary end points: process level 
 the incidence of IOH 
 the depth and duration of IOH 
 estimated blood loss 
 time to discharge readiness at the PACU 
Secondary end points: behavior level 
 anesthetic/analgesic drug use 
o average concentrations of inhalational anesthesia during MAP < 65 mmHg ep-
isodes 
 average use of the following blood pressure interventions during the entire case 
o vasopressor use 
o inotropic use 
o intravenous fluid administration 
o blood products administration 
 time to first blood pressure intervention after MAP drops below 
o 65 mmHg 
o 60 mmHg 
o 55 mmHg 
o 50 mmHg 


 
 
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Data collection 
Primary and secondary patient end point data are routine clinical data that will be collected 
from the medical records of the hospital electronic patient record keeping system, which are 
already linked to the existing perioperative data warehouses. Behavioral end point data will be 
extracted from the perioperative data warehouse (PDW) of the VUMC. Collection of end point 
data will automatically be performed, guaranteeing an end point assessment that is independ-
ent of the randomization status of providers and procedures. 
Data safety monitoring 
The principal investigator (PI) will be responsible for data safety and monitoring within the 
VUMC. Quality control will include regular data verification and protocol compliance checks. 
Protocol adherence at the VUMC will be monitored by the PI throughout the study. Events de-
termined by the PI to be unanticipated problems involving risks to subjects will be reported by 
the PI to the VUMC IRB within 10 working days of the Investigator's knowledge of the problem. 
All study staff members will be informed about any unanticipated problems involving risks 
to participants or others associated with the research (such as a breach of confidentiality). If any 
protocol changes are needed, the PI will submit an amendment request to the VUMC IRB. Pro-
tocol changes will not be implemented prior to IRB approval unless necessary to eliminate ap-
parent immediate hazards to the research subjects. In such a case, the IRB will be promptly in-
formed of the change following implementation (within 10 working days). 
The PI will permit inspection of all study-related records by the IRB and responsible govern-
mental agencies. Within legal and regulatory restrictions and institutional and ethical considera-
tions, the PI will permit the review of source documents in order to verify data in the study rec-
ords. The PI will provide oversight during the course of the study. All study data will be handled 
confidentially. 
Sample size calculation 
Sample size calculations were performed in R software. As mentioned earlier, we calculated 


 
 
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sample sizes for each of the participating centers, because they will use a different primary end 
point. The required sample sizes were calculated for both primary end points. Sample size cal-
culations assumed a power of 0.8 and a Type 1 error rate (alpha) of 0.05. Based on earlier expe-
riences with decision support intervention studies, we assumed there would be no clustering of 
primary end point data at the provider level.9  
Using historic data from the VUMC, the incidence of any MAP below 65 mmHg were es-
timated at 60% for patients that meet the inclusion criteria. Within this study population – any 
MAP < 65 mmHg – the incidence of AKI stage I was 9.8%. We derived the assumed treatment 
effect from the odds ratio (OR) reported by Sun et al. They reported an odds ratio of 1.8 (origi-
nally 1.84) for postoperative AKI in patients that had a MAP below 60 mmHg for 10 minutes or 
more. Thus the possible treatment effect within patients having a MAP < 60 mmHg for 10 
minutes or more is then an OR of 0.56 (the inverse of 1.8). Within the study population (any 
MAP < 65 mmHg), the incidence of MAP < 60 mmHg for 10 minutes was 43%. Applying the OR 
0.56 to an incidence of 43% resulted in an overall OR of 0.78 within the study population. The 
intervention phase (9 months) is four times shorter than the preintervention phase (36 months), 
resulting in a fraction of 0.20 of the total study period (9 divided by 45). As the intervention 
might not be as efficient during the nested cluster-randomized trial period, we used only half of 
the three months trial period, reducing the fraction of the intervention phase to 0.17. Addition-
ally, we included a 20% margin of error to the sample size of the intervention period. The re-
sulting calculated sample size is 2,005 procedures for the intervention phase and 8,019 proce-
dures for the preintervention phase (Table 3). With an average of 275 procedures per month 
meeting the inclusion criteria, the calculated sample size is feasible within the specified time 
periods. 
The calculations above assume that all episodes of prolonged hypotension will be treat-
ed during the intervention phase, which is unlikely. However, it also assumes that preventing 
shorter amounts of IOH (< 10 minutes) does not have any effect on patient outcome (an OR of 
1.0), which is also unlikely. Consequently, the second assumption may compensate for the first 
assumption. As a sensitivity analysis we calculated the sample sizes for different combination of 
a different treatment percentage and an OR > 1.0 for patients with a MAP < 60 for 1-10 minutes 


 
 
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(Table 3). This sensitivity analysis shows that the calculated sample sizes indeed include an error 
margin for the assumption on the IOH treatment percentages. 
Table 3 - Sample size calculation with sensitivity analyses 
  
 
Sample sizes calculated 
  
Center 
n 
intervention 
phase 
preintervention 
phase 
sample size including 
AKI (VUMC) 
10024 
2005 
8019 
20% error margin 
 
 
 
 
sample size without 
AKI (VUMC) 
9690 
1671 
8019 
20% error margin 
 
 
 
 
80% of hypotension treated 
AKI (VUMC) 
11432 
1971 
9461 
OR 1.1 MAP < 60 for 1-10 min. 
 
 
 
 
70% of hypotension treated 
AKI (VUMC) 
11910 
2054 
9857 
OR 1.2 MAP < 60 for 1-10 min. 
 
 
 
 
Statistical analysis 
Primary end points 
The analysis of the primary end point will be performed under the intention-to-treat principle, 
and only include patients that have had a MAP below 65 mmHg for at least one minute during 
the procedure. As the study is an interrupted time series design, segmented regression models 
will be used for both primary end points. The different study groups within the nested clus-
ter-randomized trial will not be separately analyzed because of a too small sample size.  
A two-sided alpha of 0.05 will be considered to be statistically significant. Continuous varia-
bles will be visually assessed for a normal distribution using histograms and QQ-plots. In de-
scriptive statistics, parametric variables will be expressed as means with standard deviations, 
nonparametric variables will be expressed as medians with interquartile ranges, and discrete 
variables will be expressed as numbers with percentages. Missing data will be multiply imputed 


 
 
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using a regression approach. Before multivariable modelling, all continuous variables will be 
tested for nonlinearity using restricted cubic splines. 
Secondary end points: patient outcome 
Secondary patient outcomes will be primarily be performed similar to the primary end point. 
For continuous end points, either a parametric or a nonparametric approach will be used as is 
appropriate to the distribution of the end points. 
Secondary end points: process level 
In contrast to the primary end point and other secondary patient outcome, clustering at the 
attending level is expected for process level end points. Quantitative analysis on process level 
end points will thus account for clustering using segmented regression models that also includ-
ed random effects. For these mixed effects models we will exclude attending anesthesiologists 
with a low case volume of eligible procedures from the analysis.9 
Secondary end points: behavior 
The impact of the decision support system on the blood pressure management of patients will 
also be studied. This will be done by observing changes in the usage patterns of various treat-
ment options, such as reducing anesthesia, administering vasoactive or inotropic drugs, and 
administering intravenous fluids. The analysis of fluid administration is expected to be restricted 
by the accuracy of the AIMS.  
Because of the intricate relation between the occurrence of IOH and the timing of various 
treatment options, there is currently no straightforward method to analyze the effect of the 
decision support on blood pressure management. Methods for analyzing the behavioral pat-
terns in response to blood pressure thresholds will be explored in a separate observational 
study. This study has already been initiated through the MPOG initiative11 and is expected to be 
completed before the end of the study period. The findings within this study will determine how 
treatment behavior should be defined and studied within the present decision support study. If 
necessary, the study proposal will be amended accordingly. 


 
 
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Additional analyses: specific clinical research questions 
The previous paragraphs describe the analyses of the differences in various end points between 
different study periods. The research questions as described in ‘Specific clinical research ques-
tions’ require additional analyses on the relation between depth and duration of IOH and the 
patient and process level end points. The goal of these additional analyses is to study the rela-
tion between IOH, treatment and outcome. The relation between IOH and various end points 
will be compared between the preintervention and intervention phases. This may seem as a 
very complex way to study the relation between IOH, treatment and outcome. However, as 
treatment is an intermediate variable between IOH and outcome, there is no straightforward 
mathematical way to analyze this relationship using observational data alone. The expected 
differences between the preintervention and intervention phases will be the changes in blood 
pressure measurement. The comparison between these phases will thus allow studying of the 
relation between IOH and various end points may relate to treatment of IOH. As this relation 
will depend on various preoperative and intraoperative factors, several effect modifiers will be 
considered (Table 4). 
Within the scientific literature there is a large variation in how IOH is defined and analyzed. 
Different thresholds are used for either systolic, mean or diastolic blood pressure, or even mul-
tiple thresholds for a given combination. In addition to a threshold definition, the variable for 
IOH can also be modelled in several ways. The thresholds often introduce a cut-off: anything 
above the threshold is considered ‘zero’. All values above the threshold are considered to be the 
same, even when they are close to the threshold. However, everything below the threshold can 
be modelled in several ways: duration of blood pressure below the threshold, the area under 
the threshold (AUT), or simply a ‘one’ – i.e. ‘yes’ the patient’s blood pressure was below the 
threshold. In a separate observational study we aim to evaluate what the best way is to define 
and analyze hypotension as an independent variable. This observational study is expected to be 
completed before the end of the present decision support study. The results of this observa-
tional study will be used to guide the analysis of the relation between IOH and patient and pro-
cess level end points. 
 


 
 
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Table 4 - Variables that will be considered as effect modifiers during the analysis 
Preoperative patient condition 
Age 
ASA physical status or functional status 
Cardiovascular comorbidity 
Hypertension 
Diabetes Mellitus 
History of cardiac disease 
History of cardiovascular disease 
Non-end-stage renal disease 
Intraoperative patient condition 
Other vital signs 
Heart rate 
Oxygen saturation 
Pulse pressure variation 
Blood loss 
Procedural factors 
Surgical phase (induction / surgical preparation / surgical) 
Type of anesthesia (general anesthesia / central neuraxial blockade) 
Type of surgery (vascular, neurosurgery, orthopedic, trauma, ENT) 
Emergency surgery 
Anesthetic depth 
Duration of surgery (stratification for duration < 1 hour, between 1 – 3 hours and > 3 hours) 
Additional analyses: specific decision making questions 
These additional analyses will main consist of analyzing differences in process level and behavior 
level end points between the various study phases, including the nested cluster-randomized 
trial. Effect modification by the variables of Table 4 will also be studied.  
References 
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