ASA Prediction Using Health Data and Medication Use
Conditions: ASA-PS Classification
Sponsor: Erasmus Medical Center

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TEMPLATE RESEARCH PROTOCOL 
for non-WMO-applicable research 
 
 
23-04-2024, version 4.0 
 
 
 
 
 
 
 
 
 
 
Full title of protocol  
ASA prediction using health data and medication use 
Short title or Acronym 
ASA prediction 
Protocol ID / Panama number 
12006 
Version 
3.0 
 

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Date 
23-04-2024 
Coordinating investigator1/ 
Project leader 
S.F. van den Heuvel – s.vandenheuvel@erasmusmc.nl 
Principal investigator2 
(in Dutch: hoofdonderzoeker/ 
uitvoerder) 
 
<Multicenter research3: 
principal investigator per site> 
J.-W. Korstanje – j.korstanje@erasmusmc.nl 
Sponsor4 (in Dutch: 
verrichter/opdrachtgever) 
Erasmus MC 
Subsidizing party5 
Health Holland – Ministry of Economic Affairs 
 
 
Name 
Signature 
Date 
Sponsor or legal representative: <please 
include name and function> 
<For non-commercial research,> 
Head of Department:  
C. van der Marel 
 
Anesthesiology 
24-04-2024 
Coordinating Investigator/Project 
leader/Principal Investigator: <please include 
name and function> 
J.-W. Korstanje 
prinicipal investigator 
24-04-2024 
 
1. Coordinating investigator: Investigator who bears the responsibility for the coordination of 
investigators operating in the various centers participating in multicenter research. Not all 
multicenter research will have a coordinating investigator. There is no requirement to appoint one. 
A project leader has the responsibility to develop a research protocol and to complete the study 
within the predefined goals. 
2. Principal investigator: Investigator who has the overall responsibility to comply and to complete 
the study within the predefined goals. 
3. Multicenter research: as an alternative you can also state that these are specified in the list with 
participating centers including principal investigator. This separate document with version date 
must be uploaded under category I1.    
4. Sponsor: The party that commissions the organization or performance of the research, for 
example a pharmaceutical company, academic hospital, scientific organization or the 
investigator’s employee. A party that provides funding for a study but does not commission it is 
not regarded as the sponsor, but referred to as a subsidizing party. 
5. Subsidizing party: A party that provides funding for a study but does not commission it 
 
 

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Table of Contents 
 
List of abbreviations and relevant definitions ................................................................................................. 4 
Summary ........................................................................................................................................................ 5 
 
Introduction and rationale .................................................................................................................. 6 
 
Objective(s) ........................................................................................................................................ 6 
 
Study type .......................................................................................................................................... 6 
 
Study population ................................................................................................................................ 7 
 
Methods ............................................................................................................................................. 7 
 
Incidental findings ............................................................................................................................ 11 
 
Statistical analysis  ........................................................................................................................... 11 
 
Ethical considerations ...................................................................................................................... 12 
 
Handling and storage of data / images / sound recordings / photos / film recordings .................... 13 
 
Handling and storage of human material ..................................................................................... 14 
 
Exchange, sharing or transfer of data and/or human material and/or images ............................ 14 
 
Amendments ................................................................................................................................ 14 
 
End of study report  ...................................................................................................................... 15 
 
Publication.................................................................................................................................... 15 
 
References ................................................................................................................................... 15 
 
Attachments ................................................................................................................................. 15 
 
 
Please note that it is not allowed to remove paragraphs from this template protocol. If a paragraph is not 
applicable, please mention this in the specific paragraph, preferably with a short motivation.  

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List of abbreviations and relevant definitions* 
 
CTA 
Clinical Trial Agreement 
De novo biobank 
A new data, human material or imaging collection 
DMP 
Data Management Plan 
DPIA 
Data Protection Impact Assessment 
DTA 
Data Transfer Agreement 
Exception consent 
Form Care for data Template, in Dutch: Formulier uitzondering toestemming 
GCP 
Good Clinical Practice 
GDPR 
General Data Protection Regulation in Dutch: Algemene Verordening 
Gegevensbescherming 
IC 
Informed Consent 
IFU 
Instruction For Use 
MTA 
Material Transfer Agreement 
NWTC 
Non-WMO Review Committee; in Dutch: Niet WMO Toetsingscommissie 
UAVG 
Dutch Act on Implementation of the General Data Protection Regulation; in 
Dutch: Uitvoeringswet Algemene Verordening Gegevensbescherming 
WMO 
Medical Research Involving Human Subjects Act, in Dutch: Wet Medisch-
wetenschappelijk Onderzoek met Mensen 
 
*Please add any new definitions that are used in the research protocol 
 
 
 
 
 
 
 

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Summary 
 
The summary should give a brief description of the central question that the research is intended to 
answer and its justification. It should specify the hypothesis (if applicable) and the research objectives. In 
addition, the synopsis should briefly describe the design, population, methods and procedures of the 
study. Finally, if applicable, the nature and extent of the burden and risks should be indicated.  
Rationale: The development of a machine learning algorithm that predicts American Society of 
Anesthesiologist-Physical Status (ASA-PS) based on preoperative variables would not only improve 
clinical decision-making in patient risk stratification but also offer a more reliable tool for administrative 
and regulatory uses. Therefore, the development of such a machine learning tool presents a significant 
opportunity to advance both the science and practice of perioperative care. Incorporating medication use 
into the algorithm could further enhance its predictive power, as it is closely linked to systemic disease. 
This addition could help refine the ASA-PS classification, making it an even more valuable tool in the 
clinical setting. 
Objective(s): To develop a machine learning algorithm that can predict the American Society of 
Anesthesiologists Physical Score (ASA-PS) I-IV based on gender, age, BMI, standard health 
questionnaire and medication use (ATC) and determine its performance. 
Study type: A retrospective cohort study of routinely collected health data of patients who had surgery in 
the Erasmus MC. 
Study population: All patients, 18 years or older, who underwent elective surgery in the Erasmus MC 
since the introduction of HiX and who have had a preoperative anesthesiological consultation. 
Methods: Multiple machine learning algorithms predicting ASA-PS using age, sex, BMI, health 
questionnaires and ATC codes of used medication will be developed, validated and evaluated. 
Burden and risks: none 
Recruitment and consent: Based on art. 24 of the “Uitvoeringswet AVG”, this research will be performed 
under the exception consent: 
- 
Obtaining informed consent of all patients in this database (>90.000 patients) demands an 
unreasonable effort. 
- 
The study serves a general purpose (triage of patients for personalized preoperative screening) 
- 
Data that is processed is stored in pseudonymized format and cannot lead to individual patients. 
- 
Patients that have registered an opt-out for scientific research are excluded 
 
 
 
 

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 Introduction and rationale 
 
The American Society of Anesthesiologists Physical Status (ASA-PS) classification system is a widely 
used tool for assessing surgical fitness and other clinical contexts. However, its inherent subjectivity and 
heavy reliance on clinician judgment can lead to inconsistencies in patient risk stratification, a critical 
component of perioperative care. Furthermore, the ASA-PS system has been adopted for various 
administrative and regulatory purposes beyond its original intent, such as quality assessment by the 
Dutch Health and Youth Care Inspectorate (IGJ), compensation decisions by private payers in the USA, 
patient triage, and determining suitability for certain types of surgery. 
Given the broad and critical applications of the ASA-PS system, enhancing its precision and objectivity is 
of paramount importance. One way to achieve this is through the development of a machine learning 
algorithm that predicts ASA-PS based on preoperative variables. Anesthesiologists base the ASA-PS 
score on the presence of systemic diseases, which can be inferred from medication use. By leveraging 
data such as ATC codes, BMI, sex, age, routinely collected preoperative health data, and medication use, 
this algorithm could provide a more consistent and objective measure of ASA-PS. 
This would not only improve clinical decision-making in patient risk stratification but also offer a more 
reliable tool for administrative and regulatory uses. Therefore, the development of such a machine 
learning tool presents a significant opportunity to advance both the science and practice of perioperative 
care. Incorporating medication use into the algorithm could further enhance its predictive power, as it is 
closely linked to systemic disease. This addition could help refine the ASA-PS classification, making it an 
even more valuable tool in the clinical setting. 
 
 Objective(s) 
 
To develop a machine learning algorithm that can predict the American Society of Anesthesiologists 
Physical Score (ASA-PS) based on sex, BMI, age and medication use (ATC) and routinely collected 
preoperative health data and determine its performance. 
 
 Study type 
 
3.1. Study type  
 
☒ Retrospective 
☐ Prospective 
☐ Combination Retrospective/Prospective 
 
3.2.  Single center / Multicenter 
 
☒ Single center 
☐ Multicenter 
 
3.3  Check all the applicable boxes 
 
☒ Medical records (re-use of data from healthcare, including AI) 
☐ Case report 

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☐ Re-use data from research 
☐ Evaluations of quality of healthcare (retrospective 
☐ Research with additional use of residual material from regular healthcare 
☐ Research with re-use of human material from research or existing biobank 
☐ De novo biobank 
☐ Phase IV research 
☐ Healthcare evaluation research (prospective) 
☐ Research with medical devices 
☐ Research with In Vitro Diagnostic Tests 
☐ Other research, describe 
 
 
 Study population 
 
4.1. Study population 
 
☒ Adults (16 years and older) 
☐ Minors (younger than 16 years) 
☐ Incapacitated adults (16 years and older) 
☐ Incapacitated minors (younger than 16 years) 
 
4.2. Population (base)  
All patients who underwent a surgical, diagnostic or therapeutic procedure within the surgical suite 
of the Erasmus MC since 2018 (introduction new digital Hospital Information System, HiX) and 
who had a ASA-PS class recorded. 
 
4.3. Inclusion criteria 
In order to be eligible to participate in this study, a subject must meet all of the following criteria: 
- 
Underwent a surgical, diagnostic or therapeutic procedure within the surgical suite of the 
Erasmus MC 
- 
ASA score recorded in electronic medical record  
- 
A verified medication list in HiX, or a filled out preoperative anesthesiological health 
questionnaire registered in HiX 
 
4.4. Exclusion criteria 
A potential subject who meets any of the following criteria will be excluded from participation in 
this study:  
- 
Age <18 at moment of surgery 
- 
ASA V-VI 
- 
Opt-out registered in HiX 
 
4.5. Sample size calculation 

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It is widely acknowledged that robust development and accurate validation of AI-based prediction 
models necessitate large sample sizes. (van Royen et al. EHJ 2023) However, no sample size 
calculators are currently available for development of AI models to perform a formal a priori 
sample size calculation.  
Nevertheless, we used sample size guidelines for regression-based prediction models as a 
starting point (Riley et al.,2020). For an upper limit of the minimum required sample size (for a 
relatively unbalanced, multiclassification model) we used a c-statistic of 0.77 (Mudumbai et al., 
2019) for a prediction of ASA I-IV, 302 candidate parameters, and a prevalence of 0.04 (for ASA 
IV, Model #10 in Table 1) and targeted a shrinkage of 0.9. This led to a sample size of 67,116.  
For a lower limit of the minimum required sample size (for a more balanced, binary classification 
model) we used a c-statistic of 0.88 (Zhang et al., 2018) for a prediction of ASA I&II vs. III&IV, 302 
candidate parameters, and a prevalence of 0.31 (for ASA III&IV, model #15 in Table 1), targeting a 
shrinkage of 0.9. This led to a sample size of 6,663. 
Based on ratios observed in the dataset and an approximate size of the dataset of approximately 
150,000 cases, we estimate approximately 30,000 to remain after applying inclusion and 
exclusion criteria for these specific models, satisfying at least the minimal criteria. 
A larger sample size may be needed due to the higher complexity of AI-based prediction models, 
low prevalence (lower than 0.5) and expected high noise (van Royen et al. EHJ 2023). Therefore, 
a posteriori approach of plotting model errors against increasing sample percentages will also be 
used to justify the sample size (Rajput et al., 2023). 
 
5.1. Research methods 
Data collection: Health data obtained during the preoperative screening process for procedures 
registered in HiX from 2018-2023 will be extracted from the HiX database using a PowerBI query 
developed by ICON Healthcare in collaboration with the department of Anesthesiology under a 
data processing agreement. The patient ID’s will be cross-referenced with the hospital’s “opt-out’ 
registry (“Baseline”) and patients with an opt-out will be removed from the database. The resulting 
database will be stored on a secure, encrypted drive within DRE (Research environment) only 
available to the Principal Investigator. An extra Pseudonymized patient ID column will be added. 
An extraction will then be performed of the following data: Pseudonymized patient ID, 
demographic information (sex, age), Body Mass Index (BMI), answers to questions in the 
standard anesthetic preoperative health questionnaire, medication use (represented by ATC 
codes), and ASA-PS scores. This resulting database will be used to construct the necessary 
datasets and be further preprocessed. (See DMP for further details).  
Construction of data sets: all data will be processed according to the flow diagram in Figure 1. 
The split into a training and test set will be stratified on ASA-PS scores. 
 

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Data Preprocessing: The constructed (training and test) datasets data will be cleaned to handle 
missing values, outliers, and inconsistencies. Of patients that are present in the dataset with 
multiple procedures, procedures are first prioritized on the availability of both a verified medication  
list and a filled out preoperative health questionnaire, then the oldest record will selected and 
retained. Categorical variables, such as sex, will be encoded appropriately for the machine 
learning model. A variable on the total number of medication will be constructed. Other new 
features (variables) can be created based on existing data to improve the performance of our 
model (feature engineering). The exact nature of these new features is not predetermined, as they 
will be constructed based on the insights we gain during the exploratory data analysis and model 
training phases. Medication use will be recoded from the number of medicines a participant uses 
per ATC code, to the use of a medicine within a particular ATC code (yes/no). Variables will be 
normalized as required by the specific machine learning method.  
Statistical Analysis and Model Validation: The development of a machine learning algorithm 
will be structured around the creation of predictive models using the H2O AutoML package, 
applicable in both Python and R environments. These models will undergo training on a 
meticulously preprocessed dataset, with a k-fold cross-validation strategy where 80% of the data 
is allocated for training purposes and the remaining 20% for validation. The primary outcome 
variable in focus is the ASA-PS score, with predictor variables encompassing age, sex, body 
mass index (BMI), responses to a health-related questionnaire, and ATC codes. 
A suite of machine learning algorithms, bundled in the AutoML package, will be employed, 
including the Generalized Linear Model, Random Forest, Gradient Boosting Machine, and Deep 
Learning techniques. To enhance predictive performance, a Stacked Ensemble method, also 
known as Stacked Generalization, will be utilized to amalgamate the strengths of individual 
algorithms. The ensemble will be guided by a 'superlearner' or 'metalearner', with the Generalized 
Linear Model serving as the default choice.  
Predictions for the response variable will be approached in two distinct manners: 
1. A multiclass classification challenge, distinguishing between ASA scores I through IV. 
2. A binary classification task, differentiating between the clinically significant groupings of ASA 
scores I&II and III&IV. 

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To tackle the multiclass classification, two primary strategies will be adopted: 
1. The construction of a Multiclass Classification Ensemble, designed to model the ordinal ASA 
scores as a single entity. 
2. The implementation of a Two-Stage One-vs-One Majority Voting scheme, where six binary 
ensemble models will be developed to discern between each pair of ASA scores. The collective 
output of these models will be synthesized through a majority voting mechanism, as facilitated by 
the diceR package in R. 
The following combination of dependent variables (features) will be explored: 
1. 
Demographics and BMI only 
To evaluate the predictive power of base characteristics of patients. 
2. 
Medication combined with demographic data (age, gender) and BMI 
To evaluate the predictive power of a model based on medication and demographic data 
that should always be readily available. 
3. 
Health Questionnaire combined with demographic data (age, gender) and BMI. 
As 2, but answers to a general anesthetic health questionnaire instead of medication. 
4. 
Health Questionnaire and medication combined with demographic data (age, 
gender) and BMI. 
A combination of all above features. 
This results in the following combinations of dependent (response) and dependent (predictor) 
 
variables: 
Model 
Dependent (response) 
Independent (predictor) 
Strategy 
1 
ASA 1 vs. 2 vs. 3 vs. 4 
Demographics + BMI 
Ensemble (multiclass) 
2 
ASA 1 vs. 2 vs. 3 vs. 4 
Demographics + BMI 
Two-stage OVO-Majority voting 
3 
ASA 1 vs. 2 vs. 3 vs. 4 
Model 1 + Medication 
Ensemble (multiclass) 
4 
ASA 1 vs. 2 vs. 3 vs. 4 
Model 2 + Medication 
Two-stage OVO-Majority voting 
5 
ASA 1 vs. 2 vs. 3 vs. 4 
Model 1 + Questionnaire 
Ensemble (multiclass) 
6 
ASA 1 vs. 2 vs. 3 vs. 4 
Model 2 + Questionnaire 
Two-stage OVO-Majority voting 
7 
ASA 1 vs. 2 vs. 3 vs. 4 
Model 1 + Medication + Questionnaire 
Ensemble (multiclass) 
8 
ASA 1 vs. 2 vs. 3 vs. 4 
Model 2 + Medication + Questionnaire 
Two-stage OVO-Majority voting 
9 
ASA 1 & 2 vs. 3 & 4 
Demographics + BMI 
Ensemble (binary) 
10 
ASA 1 & 2 vs. 3 & 4 
Model 9 + Medication 
Ensemble (binary) 
11 
ASA 1 & 2 vs. 3 & 4 
Model 9 + Questionnaire 
Ensemble (binary) 
12 
ASA 1 & 2 vs. 3 & 4 
Model 9 + Medication + Questionnaire 
Ensemble (binary) 
 
Model development process is inherently iterative, commencing with the aforementioned  
methodologies. As the project progresses, the pursuit of the most efficacious model will lead to 
the exploration of additional refinements following the initial model constructions. Examples of 
possible refinements are: dimensionality reduction, dataset balancing, the modeling of data 
subsets, the incorporation of domain knowledge leading to the creation of more sophisticated 
features, and the identification of independent variables that may suffer from the perfect 
separation problem. This comprehensive approach ensures a robust and systematic development 
of a machine learning algorithm tailored to the specificities of the ASA-PS score prediction. 
Validation: The models will be validated using a separate holdout test set (10% of original 
dataset). This will ensure that our models generalize well to unseen data. The validation process 

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will involve applying the models to the test set and comparing the predicted outcomes with the 
actual outcomes.  
Model Evaluation: The performance of the models will be evaluated using metrics such as 
accuracy, precision, recall, AUC and F1-score. Confusion matrices and ROC’s will be used to 
visualize the performance of our models. Misclassifications will be manually reviewed to gain 
qualitative insights into the model’s performance, with special emphasis on “catastrophic error 
examples”, e.g. ASA I instead of IV, or ASA IV instead of I. 
SHAP (Shapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic 
Explanations) will be explored as techniques for interpreting the models. The calibration of the 
models will be evaluated using calibration plots. The optimal method for a priori sample size 
calculation in machine learning algorithms lacks consensus. Consequently, we will utilize the 
entire available dataset and conduct a post hoc assessment by plotting model errors against 
increasing sample percentages (Rajput et al., 2023). To detect possible bias in the final model, the 
‘fairness’ of the model will be assessed from different perspectives for different subpopulations. 
5.2. Standard clinical care versus extra for research  
Not applicable 
 
5.3. Burden and risks 
No burden or risk because of retrospective study design and pseudonymized data. 
 
5.4. Medical device(s) / In vitro diagnostic tests 
Not applicable. 
Incidental findings 
 
6.1. 
Chance of incidental findings 
Is there a chance of incidental findings? 
☐ Yes 
☒ No 
 
6.2. 
Procedures 
Not applicable. 
Statistical analysis 
Describe how data will be analyzed 
 
7.1 
Main study parameters/endpoints 
Please describe the main study parameters/endpoints. 
The dependent response variable will be the ASA-PS class, both as a four-level variable (ASA-
PS I, II, III and IV) and a two-level variable (ASA-PS I and II versus ASA-PS III and IV). The 
ASA-PS class was assigned to the patient and recorded in the patients file in the EMR by an 
anesthesiologist of resident anesthesiology as a part of the routinely performed preoperative 
anesthesiological screening in preparation for a procedure. 
 
7.2 
Secondary study parameters/endpoints 
Please describe the secondary parameters/endpoints, how the analysis will be done for the 
outcome parameters. 
- 
Performance metrics: The correct classification of the ASA-PS score will be evaluated using 
performance metrics of the machine learning algorithms. Common performance metrics 
include: Accuracy (the proportion of correctly predicted instances), precision (the ratio of true 

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positive predictions to the total positive predictions), recall (sensitivity or the ratio of true 
positive predictions to the actual positive instances), F1-score (the harmonic mean of 
precision and recall) and the Area Under the Receiver Operating Characteristic Curve (AUC-
ROC, Measures the model’s ability to discriminate between positive and negative instances). 
- 
Calibration: calibration plots will be used to assess the agreement between predictions and 
the event rate (i.e. correct classification). 
- 
Misclassifications. A manual review of a selection of misclassifications will be performed by 
two anesthesiologists to qualitatively assess the cause of the misclassification. 
- 
Explainability: SHAP and/or LIME can offer insights into the contribution of each feature to the 
prediction of individual instances. 
- 
Optimal sample size: Analysis of the learning curves to determine if additional data would 
likely improve the model’s performance or if the current dataset is sufficient.  
- 
Fairness: To detect possible bias in the final model, the ‘fairness’ of the model will be 
assessed from different perspectives for different subpopulations. 
 
 
7.3 
Other study parameters 
Please describe the other parameters/endpoints. 
As independent predictor variables information will be used that could be collected 
preoperatively with minimal effort for both patient and healthcare professional without the need 
for hospital visits or invasive procedures (e.g. blood sampling, ECG recording). We will extract 
basic demographic information (age, sex), general health information collected using a 
preoperative anesthesiological health questionnaire, medication use registered by the hospital 
pharmacy using ATC codes, and the Body Mass Index (BMI), all valid at the time of the 
preoperative anesthesiological screening and subsequent ASA-PS classification 
 
7.4 
Analysis 
Please describe how the analysis will be done for the outcome parameters. 
The performance of the models will be evaluated using metrics such as accuracy, precision, 
recall, AUC-ROC, AUC-PR and F1-score. Confusion matrices and ROC’s will be used to 
visualize the performance of our models. Misclassifications will be manually reviewed to gain 
qualitative insights into the model’s performance, with special emphasis on “catastrophic error 
examples”, e.g. ASA I instead of IV, or ASA IV instead of I. Explainability will be explored using 
SHAP and LIME. The optimal sample size will be assessed using the learning curves of the 
models with increasing sample size. Fairness will be assessed by evaluating the performance 
on subpopulations. 
Ethical considerations 
 
8.1 
Regulation statement 
The study will be conducted according to the principles of the Declaration of Helsinki (version, 
date, see for the most recent version:), Gedragscode Gezondheidsonderzoek 2022 and in 
accordance with other guidelines, regulations and Acts (if applicable, please specify). 
 
8.2 
Informed consent 
Will the subjects be asked for informed consent? 
☐ Yes (Upload Participant Information Letter and Informed Consent) 
☐ No, consent already given in previous study (Upload Participant Information Letter and 
Informed Consent previous study) 
☒ No, this research will be performed under the exception consent (Upload form Care for data 
Template, in Dutch: Formulier uitzondering toestemming) 
☐ Other (e.g. partly, indirectly) Please describe the situation. 

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8.3 
Recruitment and informed consent procedures 
If yes, please give a description of the recruitment and informed consent procedures. How and 
by whom (investigator, supervising doctor, other person) participants will be informed about the 
study and asked for their consent, how much time will they be given to consider the decision. 
The patient information letter with informed consent form should be attached as a separate 
document. 
Not applicable 
 
8.4 
Exception consent 
If no, exception consent: the opt-out registry will be consulted for all patients included in the 
database. See the attached Form Care for data Template. 
 
Handling and storage of data / images / sound recordings / photos / film recordings 
 
9.1 
Data / images / sound recordings / photos / film recordings 
Please describe which data / images / sound recordings / photos / film recordings is/are used, 
are they obtained for regular healthcare purposes or in the context a research project, or a 
combination. 
Health data obtained during the preoperative screening process for procedures registered in Hix 
from 2018-2023 will be extracted from the HiX database using a PowerBI query developed by 
ICON Healthcare in collaboration with the department of Anesthesiology. This database will be 
stored on a secure, encrypted drive within DRE (Research environment). There an extraction 
will be performed of the following data: Pseudonymized patient ID, demographic information 
(sex, age), Body Mass Index (BMI), answers to questions in the standard anesthetic 
preoperative health questionnaire, medication use (represented by ATC codes), and ASA-PS 
scores. This resulting database will be used to construct the necessary datasets and be further 
preprocessed. (See DMP for further details) 
 
9.2 
Privacy protection 
Describe how subject’s privacy is protected. Describe how, when and by whom data is coded 
(unique code without initials or date of birth) and how the key table is safeguarded, mention the 
General Data Protection Regulation. 
 
Please note: The handling of personal data has to comply with the EU General Data Protection 
Regulation and the Dutch Act on Implementation of the General Data Protection Regulation (in 
Dutch: Algemene Verordening Gegevensbescherming en Uitvoeringswet Algemene Verordening 
Gegevensbescherming). 
 
Before further preprocessing the data is stored on an encrypted, secure location in a DRE 
Workspace. There, patient hospital IDs (PID) are deterministically encrypted using encryption 
packages in R. The key is stored on a separate password protected location within Microsoft 
Azure only available to the principal investigator. Age at the time of the preoperative screening is 
calculated using the birth date and date of the preoperative screening. Only ‘age’ is used for 
further analyses. All other data is not traceable to individual patients. 
 
9.3 
Handling and storage of data 
Describe how data is handled and stored (i.e., which data management system/data capture 
system), anonymous / pseudonymized / coded / identifiable, who has access to the coded 
source data, how long data will be kept, which steps are taken to ensure data security, what 
happens with the data after the research has been completed. 
 

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In line with Erasmus MC guidelines, data will be kept 10 years after it is collected. The coded 
database will be stored on DRE. The key will be stored on a separate location (Sharepoint) only 
available to the principal investigator. 
 
9.4 
Handling and storage of images / sound recordings / photos / film recordings 
Describe how images / sound recordings / photos / film recordings are handled and stored, how 
the subject’s privacy is protected, anonymous / pseudonymized / coded / identifiable, what 
happens with images after the research has been completed. 
 
Not applicable. 
 
9.5 
Approval of access to data / images / sound recordings / photos / film recordings 
Describe how the access is approved. Is access granted by the Data Board, Department, 
Principal investigator of the collection or other?  
Not applicable 
Handling and storage of human material 
 
10.1 Human material 
Please describe which human material is used. 
Not applicable 
 
 
10.2 Check all the boxes which are applicable to the human material origin: 
☐ Residual material from regular healthcare 
☐ Research (material acquired from a previous study).  
Add the reference of the study i.e., MEC-number Erasmus MC. 
☐ Re-use of human material from existing biobank 
      Describe whether the human material originates from research into the same disease.  
☐ Other, please specify 
 
10.3 Handling and storage of human material  
Not applicable 
 
10.4 Biobank 
Not applicable 
 
 
10.5 Approval of access to human material  
Not applicable 
 
11. Exchange, sharing or transfer of data and/or human material and/or images 
Data will be shared on DataVerse according to Erasmus MC policies. Please see the DMP. 
 
 
12. Amendments 

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Amendments are changes made to the research after a favorable opinion by the NWTC has been 
given.  
All amendments must be submitted to the NWTC that gave the favorable opinion.  
Substantial amendments must be approved by the Niet WMO Toetsingscommissie before they can 
be implemented. 
 
13. End of study report 
Within one year after the end of the study a final study report will be submitted with the results of the 
study, including any publications/abstracts of the study. 
 
In case the final study report will not be available within one year, another term should be defined 
including the reasons. 
 
 
14. Publication 
Do you have the intention to submit the study results in a manuscript for publication in a journal: 
☒ Yes  
☐ No, please motivate 
 
Describe when the final study report with the results of the study will be submitted. 
 
15. References 
Include a list of all key references published in peer review journals that are relevant for the study 
and are discussed in the protocol 
 
16. Attachments 
☐ Participant information letter and Informed consent document  
☒ Care for data Template – Formulier uitzondering toestemming  
☐ Questionnaires 
☒ Data Management Plan 
☐ Data Transfer Agreement 
☐ Material Transfer Agreement 
☐ Clinical Trial (Site) Agreement 
☐ Other, please describe