Postoperative Pulmonary ComplicationsPEPPERMINTNCT05789953
PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach
An observational study of Postoperative Pulmonary Complications, run by Britta Trautwein at 1 site in Germany.
Trial summary
Postoperative pulmonary complications (POPC) are common after general anaesthesia and are a major cause of increased morbidity and mortality in surgical patients. However, prevention and treatment methods for POPC that are considered effective, tie up human and technical resources. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Postoperative Pulmonary Complications
- How many people
- 512 participants
- Where
- 1 siteGermany
- Study dates
- 25 Apr 2023to Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: adult patients in a university hospital
Who may be able to join
- adult patients
- elective, surgical procedure
- general anaesthesia
Who cannot join
- patients younger than 18 years of age
- outpatient surgery
- postoperative admission to intensive care unit
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Germany1 site, 1 recruiting
- Recruiting
University Hospital Ulm
Ulm, 89081
Britta Trautwein, 004973150060227, britta.trautwein@uniklinik-ulm.de
Simone Kagerbauer, 004973150060254, simone.kagerbauer@uniklinik-ulm.de
About this study
Postoperative pulmonary complications (POPC) are common after general anaesthesia and are a major cause of increased morbidity and mortality in surgical patients. However, prevention and treatment methods for POPC that are considered effective, tie up human and technical resources. The aim of the planned research project is therefore to enable reliable identification of high-risk patients on the basis of a tailored machine learning algorithm using perioperative clinical routine data and sonographic imaging data collected in the recovery room. The randomized clinical trial will include 512 patients undergoing elective surgery in general anaesthesia. The primary outcome will be the development of POPC. The goal of the study is to detect postoperative pulmonary complications before they become clinically manifest.
Read the detailed description
The incidence of postoperative pulmonary complications (POPC) is reported to be 9-40%, depending on the surgical procedure. Various preoperative risk factors are known, but usually cannot be modified. A major problem of older publications was that for a long time there existed no clear definition of the outcome parameter "pulmonary complication". It was not until 2018 that a standardised definition was developed by the Standardised Endpoints for Perioperative Medicine (StEP) collaboration. Due to the high clinical relevance - POPC are the main cause of postoperative morbidity and mortality - clinical scoring systems for the preoperative prediction of POPC have been developed, but their predictive quality still needs to be improved. The currently best evaluated score for predicting postoperative pulmonary complications (ARISCAT: Assess Respiratory Risk in Surgical Patients in Catalonia) has sufficient sensitivity but lacks specificity. Therefore, machine learning methods for determining risk from preoperative routine data are also being tested.
Sonography is becoming increasingly important as a non-invasive examination method that can be performed at the bedside. Various sonographic scores and models have already been developed to predict pulmonary complications. Image processing methods and machine learning, in particular deep learning are also increasingly being used in ultrasound diagnostics. A combination of routine clinical data and imaging data to develop a machine learning algorithm has not yet been tested. However, augmented algorithms using pre- and intraoperative clinical information in addition to ultrasound imaging promise better predictive accuracy than the respective individual methods. In addition, prospective clinical evaluation of machine learning algorithm-based prediction models is lacking to date, although they show good values for "area under the receiver operating characteristic" (AUROC), accuracy and precision in the respective test and validation datasets, which are considered common measures of the predictive quality of such models.
Measures for the prevention of POPC are known, but are probably not consistently applied in clinical routine due to the increased demand, especially for human resources. Therefore, the aim of the study is to identify patients at risk of POPC on the basis of a machine learning algorithm.
All patients are undergoing the same study protocol to develop the machine learning model. Perioperative clinical routine data are going to be assessed as per standard. Postoperatively, a standardized lung sonography is going to be performed in the recovery room. Patients will then be visited on the ward on postoperative day 1, 3 and 7 for clinical examination to detect POPC according to the criteria elaborated by the StEP- collaboration.
According to the case number calculation, 512 adult patients undergoing elective, surgical procedures under general anaesthesia are going to be included. Perioperative routine data will be assessed and stored in a hospital-internal database, as well as data from postoperative clinical examination. Image data from lung sonography will be archived in the PACS for further processing. Based on the collected data, a machine learning algorithm based on neural networks will be trained to predict POPC. The model is created with the anonymized data using the statistics-oriented programming language R and the framework TensorFlow, a deep learning software library based on the programming language Python. The prediction quality of the created prediction model is assessed using the area under the receiver operator characteristics (AUROC) as well as the area under the precision recall curve (AUPRC) and compared with the values of the ARISCAT score, a common score to estimate the risk of POPC.
Precise risk assessment by means of an augmented machine-learning algorithm that uses clinical routine as well as imaging data has great potential to improve patient outcomes and could also help to reduce health care costs.
- Conditions
- Postoperative Pulmonary Complications
- Keywords
- machine learning, lung ultrasound
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Development of the machine learning model | Perioperative clinical routine data are going to be assessed as per standard. Postoperatively, a standardized lung sonography is going to be performed in the recovery room. Patients will then be visited on the ward on postoperative day 1, 3 and 7 for clinical examination to detect postoperative pulmonary complications according to the criteria elaborated by the StEP- collaboration. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of patients with postoperative pulmonary complications (POPC)
POPC according to criteria by the StEP-collaboration. This includes a clinical examination and interview of the patients on postoperative day 1,3 and 7.
Measured: postoperative day 7 or day of discharge
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Mar 2023
- Study start25 Apr 2023
- Primary completionSep 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for Postoperative Pulmonary ComplicationsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 512 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Britta Trautwein
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Britta Trautwein, University Hospital Ulm
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05789953
- Sponsor's protocol ID
- UHUlm
- Acronym
- PEPPERMINT
- Status last verified
- May 2026
- First submitted
- 16 Mar 2023
- Last updated
- 8 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Due to German regulatory regulations, the investigators are not allowed to publish individual patient data. They can provide the data to researchers upon reasonable request after appraisal by the data protection officer.
Publications
Fernandez-Bustamante A, Frendl G, Sprung J, Kor DJ, Subramaniam B, Martinez Ruiz R, Lee JW, Henderson WG, Moss A, Mehdiratta N, Colwell MM, Bartels K, Kolodzie K, Giquel J, Vidal Melo MF. Postoperative Pulmonary Complications, Early Mortality, and Hospital Stay Following Noncardiothoracic Surgery: A Multicenter Study by the Perioperative Research Network Investigators. JAMA Surg. 2017 Feb 1;152(2):157-166. doi: 10.1001/jamasurg.2016.4065.
PubMed 27829093Ferreyra GP, Baussano I, Squadrone V, Richiardi L, Marchiaro G, Del Sorbo L, Mascia L, Merletti F, Ranieri VM. Continuous positive airway pressure for treatment of respiratory complications after abdominal surgery: a systematic review and meta-analysis. Ann Surg. 2008 Apr;247(4):617-26. doi: 10.1097/SLA.0b013e3181675829.
PubMed 18362624Miskovic A, Lumb AB. Postoperative pulmonary complications. Br J Anaesth. 2017 Mar 1;118(3):317-334. doi: 10.1093/bja/aex002.
PubMed 28186222Abbott TEF, Fowler AJ, Pelosi P, Gama de Abreu M, Moller AM, Canet J, Creagh-Brown B, Mythen M, Gin T, Lalu MM, Futier E, Grocott MP, Schultz MJ, Pearse RM; StEP-COMPAC Group. A systematic review and consensus definitions for standardised end-points in perioperative medicine: pulmonary complications. Br J Anaesth. 2018 May;120(5):1066-1079. doi: 10.1016/j.bja.2018.02.007. Epub 2018 Mar 27.
PubMed 29661384Ball L, Pelosi P. Predictive scores for postoperative pulmonary complications: time to move towards clinical practice. Minerva Anestesiol. 2016 Mar;82(3):265-7. Epub 2015 Sep 4. No abstract available.
PubMed 26344668Nithiuthai J, Siriussawakul A, Junkai R, Horugsa N, Jarungjitaree S, Triyasunant N. Do ARISCAT scores help to predict the incidence of postoperative pulmonary complications in elderly patients after upper abdominal surgery? An observational study at a single university hospital. Perioper Med (Lond). 2021 Dec 8;10(1):43. doi: 10.1186/s13741-021-00214-3.
PubMed 34876228Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications. JAMA Netw Open. 2021 Mar 1;4(3):e212240. doi: 10.1001/jamanetworkopen.2021.2240.
PubMed 33783520Szabo M, Bozo A, Darvas K, Soos S, Ozse M, Ivanyi ZD. The role of ultrasonographic lung aeration score in the prediction of postoperative pulmonary complications: an observational study. BMC Anesthesiol. 2021 Jan 14;21(1):19. doi: 10.1186/s12871-021-01236-6.
PubMed 33446103van Sloun RJG, Demi L. Localizing B-Lines in Lung Ultrasonography by Weakly Supervised Deep Learning, In-Vivo Results. IEEE J Biomed Health Inform. 2020 Apr;24(4):957-964. doi: 10.1109/JBHI.2019.2936151. Epub 2019 Aug 19.
PubMed 31425126Brusasco C, Santori G, Tavazzi G, Via G, Robba C, Gargani L, Mojoli F, Mongodi S, Bruzzo E, Tro R, Boccacci P, Isirdi A, Forfori F, Corradi F; UCARE (Ultrasound in Critical care and Anesthesia Research Group). Second-order grey-scale texture analysis of pleural ultrasound images to differentiate acute respiratory distress syndrome and cardiogenic pulmonary edema. J Clin Monit Comput. 2022 Feb;36(1):131-140. doi: 10.1007/s10877-020-00629-1. Epub 2020 Dec 12.
PubMed 33313979Trautwein B, Beer M, Blobner M, Jungwirth B, Kagerbauer SM, Gotz M. Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. PLoS One. 2025 Aug 19;20(8):e0329076. doi: 10.1371/journal.pone.0329076. eCollection 2025.
PubMed 40828817
Study at a glance
From the registry record, last updated by the sponsor on 8 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach
- ClinicalTrials.gov ID
- NCT05789953
- Acronym / sponsor ID
- PEPPERMINT / UHUlm
- Conditions
- Postoperative Pulmonary Complications
- Keywords
- machine learning, lung ultrasound
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 512 participants (estimated)
- Lead sponsor
- Britta TrautweinUniversity, hospital or other
- Study start
- 25 Apr 2023 (actual)
- Primary completion
- Sep 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 29 Mar 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Postoperative pulmonary complications (POPC) are common after general anaesthesia and are a major cause of increased morbidity and mortality in surgical patients. However, prevention and treatment methods for POPC that are considered effective, tie up human and technical resources. The aim of the planned research project is therefore to enable reliable identification of high-risk patients on the basis of a tailored machine learning algorithm using perioperative clinical routine data and sonographic imaging data collected in the recovery room. The randomized clinical trial will include 512 patients undergoing elective surgery in general anaesthesia. The primary outcome will be the development of POPC. The goal of the study is to detect postoperative pulmonary complications before they become clinically manifest.
Detailed description
The incidence of postoperative pulmonary complications (POPC) is reported to be 9-40%, depending on the surgical procedure. Various preoperative risk factors are known, but usually cannot be modified. A major problem of older publications was that for a long time there existed no clear definition of the outcome parameter "pulmonary complication". It was not until 2018 that a standardised definition was developed by the Standardised Endpoints for Perioperative Medicine (StEP) collaboration. Due to the high clinical relevance - POPC are the main cause of postoperative morbidity and mortality - clinical scoring systems for the preoperative prediction of POPC have been developed, but their predictive quality still needs to be improved. The currently best evaluated score for predicting postoperative pulmonary complications (ARISCAT: Assess Respiratory Risk in Surgical Patients in Catalonia) has sufficient sensitivity but lacks specificity. Therefore, machine learning methods for determining risk from preoperative routine data are also being tested.
Sonography is becoming increasingly important as a non-invasive examination method that can be performed at the bedside. Various sonographic scores and models have already been developed to predict pulmonary complications. Image processing methods and machine learning, in particular deep learning are also increasingly being used in ultrasound diagnostics. A combination of routine clinical data and imaging data to develop a machine learning algorithm has not yet been tested. However, augmented algorithms using pre- and intraoperative clinical information in addition to ultrasound imaging promise better predictive accuracy than the respective individual methods. In addition, prospective clinical evaluation of machine learning algorithm-based prediction models is lacking to date, although they show good values for "area under the receiver operating characteristic" (AUROC), accuracy and precision in the respective test and validation datasets, which are considered common measures of the predictive quality of such models.
Measures for the prevention of POPC are known, but are probably not consistently applied in clinical routine due to the increased demand, especially for human resources. Therefore, the aim of the study is to identify patients at risk of POPC on the basis of a machine learning algorithm.
All patients are undergoing the same study protocol to develop the machine learning model. Perioperative clinical routine data are going to be assessed as per standard. Postoperatively, a standardized lung sonography is going to be performed in the recovery room. Patients will then be visited on the ward on postoperative day 1, 3 and 7 for clinical examination to detect POPC according to the criteria elaborated by the StEP- collaboration.
According to the case number calculation, 512 adult patients undergoing elective, surgical procedures under general anaesthesia are going to be included. Perioperative routine data will be assessed and stored in a hospital-internal database, as well as data from postoperative clinical examination. Image data from lung sonography will be archived in the PACS for further processing. Based on the collected data, a machine learning algorithm based on neural networks will be trained to predict POPC. The model is created with the anonymized data using the statistics-oriented programming language R and the framework TensorFlow, a deep learning software library based on the programming language Python. The prediction quality of the created prediction model is assessed using the area under the receiver operator characteristics (AUROC) as well as the area under the precision recall curve (AUPRC) and compared with the values of the ARISCAT score, a common score to estimate the risk of POPC.
Precise risk assessment by means of an augmented machine-learning algorithm that uses clinical routine as well as imaging data has great potential to improve patient outcomes and could also help to reduce health care costs.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Development of the machine learning model | – | –Perioperative clinical routine data are going to be assessed as per standard. Postoperatively, a standardized lung sonography is going to be performed in the recovery room. Patients will then be visited on the ward on postoperative day 1, 3 and 7 for clinical examination to detect postoperative pulmonary complications according to the criteria elaborated by the StEP- collaboration. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- adult patients in a university hospital
Key inclusion criteria (3 listed)
- adult patients
- elective, surgical procedure
- general anaesthesia
Key exclusion criteria (3 listed)
- patients younger than 18 years of age
- outpatient surgery
- postoperative admission to intensive care unit
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Number of patients with postoperative pulmonary complications (POPC)
Time frame: postoperative day 7 or day of discharge
POPC according to criteria by the StEP-collaboration. This includes a clinical examination and interview of the patients on postoperative day 1,3 and 7.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 May 2026.
- Background: PubMed 27829093Fernandez-Bustamante A, Frendl G, Sprung J, Kor DJ, Subramaniam B, Martinez Ruiz R, Lee JW, Henderson WG, Moss A, Mehdiratta N, Colwell MM, Bartels K, Kolodzie K, Giquel J, Vidal Melo MF. Postoperative Pulmonary Complications, Early Mortality, and Hospital Stay Following Noncardiothoracic Surgery: A Multicenter Study by the Perioperative Research Network Investigators. JAMA Surg. 2017 Feb 1;152(2):157-166. doi: 10.1001/jamasurg.2016.4065.
- Background: PubMed 18362624Ferreyra GP, Baussano I, Squadrone V, Richiardi L, Marchiaro G, Del Sorbo L, Mascia L, Merletti F, Ranieri VM. Continuous positive airway pressure for treatment of respiratory complications after abdominal surgery: a systematic review and meta-analysis. Ann Surg. 2008 Apr;247(4):617-26. doi: 10.1097/SLA.0b013e3181675829.
- Background: PubMed 28186222Miskovic A, Lumb AB. Postoperative pulmonary complications. Br J Anaesth. 2017 Mar 1;118(3):317-334. doi: 10.1093/bja/aex002.
- Background: PubMed 29661384Abbott TEF, Fowler AJ, Pelosi P, Gama de Abreu M, Moller AM, Canet J, Creagh-Brown B, Mythen M, Gin T, Lalu MM, Futier E, Grocott MP, Schultz MJ, Pearse RM; StEP-COMPAC Group. A systematic review and consensus definitions for standardised end-points in perioperative medicine: pulmonary complications. Br J Anaesth. 2018 May;120(5):1066-1079. doi: 10.1016/j.bja.2018.02.007. Epub 2018 Mar 27.
- Background: PubMed 26344668Ball L, Pelosi P. Predictive scores for postoperative pulmonary complications: time to move towards clinical practice. Minerva Anestesiol. 2016 Mar;82(3):265-7. Epub 2015 Sep 4. No abstract available.
- Background: PubMed 34876228Nithiuthai J, Siriussawakul A, Junkai R, Horugsa N, Jarungjitaree S, Triyasunant N. Do ARISCAT scores help to predict the incidence of postoperative pulmonary complications in elderly patients after upper abdominal surgery? An observational study at a single university hospital. Perioper Med (Lond). 2021 Dec 8;10(1):43. doi: 10.1186/s13741-021-00214-3.
- Background: PubMed 33783520Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications. JAMA Netw Open. 2021 Mar 1;4(3):e212240. doi: 10.1001/jamanetworkopen.2021.2240.
- Background: PubMed 33446103Szabo M, Bozo A, Darvas K, Soos S, Ozse M, Ivanyi ZD. The role of ultrasonographic lung aeration score in the prediction of postoperative pulmonary complications: an observational study. BMC Anesthesiol. 2021 Jan 14;21(1):19. doi: 10.1186/s12871-021-01236-6.
- Background: PubMed 31425126van Sloun RJG, Demi L. Localizing B-Lines in Lung Ultrasonography by Weakly Supervised Deep Learning, In-Vivo Results. IEEE J Biomed Health Inform. 2020 Apr;24(4):957-964. doi: 10.1109/JBHI.2019.2936151. Epub 2019 Aug 19.
- Background: PubMed 33313979Brusasco C, Santori G, Tavazzi G, Via G, Robba C, Gargani L, Mojoli F, Mongodi S, Bruzzo E, Tro R, Boccacci P, Isirdi A, Forfori F, Corradi F; UCARE (Ultrasound in Critical care and Anesthesia Research Group). Second-order grey-scale texture analysis of pleural ultrasound images to differentiate acute respiratory distress syndrome and cardiogenic pulmonary edema. J Clin Monit Comput. 2022 Feb;36(1):131-140. doi: 10.1007/s10877-020-00629-1. Epub 2020 Dec 12.
- Derived from this study: PubMed 40828817Trautwein B, Beer M, Blobner M, Jungwirth B, Kagerbauer SM, Gotz M. Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. PLoS One. 2025 Aug 19;20(8):e0329076. doi: 10.1371/journal.pone.0329076. eCollection 2025.
- Individual participant dataSharing planned: No. Due to German regulatory regulations, the investigators are not allowed to publish individual patient data. They can provide the data to researchers upon reasonable request after appraisal by the data protection officer.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Postoperative Pulmonary Complications, run by Britta Trautwein at 1 site in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 8 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Britta Trautwein, MD (00731 500 60227, britta.trautwein@uniklinik-ulm.de), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of patients with postoperative pulmonary complications (POPC) (time frame: postoperative day 7 or day of discharge). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05789953, which also lists 11 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 May 2026.