Postoperative PneumoniaML-PNEUMO-BTNCT07321262
Machine Learning to Predict Postoperative Pneumonia in Brain Tumor Patients
An observational study of Postoperative Pneumonia and Brain Tumors, run by Ming Yang at 1 site in China.
Trial summary
Postoperative pneumonia (POP) is a common and serious complication after elective craniotomy for brain tumor resection. POP often develops within the first week after surgery and may lead to prolonged hospitalization, higher medical costs, and increased risk of severe illness. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Postoperative Pneumonia, Brain Tumors
- How many people
- 1,856 participants
- Where
- 1 siteChina
- Study dates
- 1 Aug 2024to 1 Sep 2025
- How long will I take part?
- 7 Days
- Phase
- Observational
- Are there results?
- None posted in the registry
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 undergoing elective craniotomy for intracranial brain tumor resection at participating centers. The study includes a retrospective model-development cohort and multiple prospective validation cohorts. Routinely collected perioperative electronic medical record data are used to develop and validate an interpretable prediction model for early postoperative pneumonia (within 7 postoperative days).
Who may be able to join
- Age ≥ 18 years.
- Undergoing elective craniotomy for intracranial brain tumor resection.
- Perioperative clinical data available in the electronic medical record to derive required predictors.
- Expected postoperative survival ≥ 7 days.
Who cannot join
- Evidence of active infection (including pneumonia) prior to surgery.
- Thoracic surgery or severe chest trauma within 30 days prior to craniotomy.
- Spinal tumors or extracranial peripheral nerve tumors.
- Pregnancy or lactation.
- Hospice care, expected survival < 7 days, or insufficient data completeness for model calculation.
Trial locations and contacts
1 site in 1 country.
China1 site
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
Beijing, Beijing Municipality, 100021
About this study
Official title: An Interpretable and Clinically Deployable Machine Learning Model for Predicting Early Postoperative Pneumonia of Brain Tumor: a Multicenter Diagnostic Study
Postoperative pneumonia (POP) is a common and serious complication after elective craniotomy for brain tumor resection. POP often develops within the first week after surgery and may lead to prolonged hospitalization, higher medical costs, and increased risk of severe illness. Because symptoms can be subtle in neurosurgical patients, POP may be detected late, limiting timely prevention and treatment.
This study will evaluate whether a machine-learning-based clinical decision support tool can help clinicians identify patients at high risk for POP early and improve perioperative preventive care. The tool uses routinely collected clinical information to estimate an individual patient's POP risk and provides an easy-to-understand explanation of key risk drivers. Based on the predicted risk level (low, moderate, high, or very high), the system suggests standardized preventive care pathways (e.g., perioperative airway management, targeted antibiotic strategies per local practice, and nutritional support), while allowing clinicians to override recommendations at any time.
Read more
Participants will be adults undergoing their first elective craniotomy for brain tumor resection at participating neurosurgical centers. The primary outcome is the occurrence of POP within 7 days after surgery, defined using CDC/NHSN criteria. Secondary outcomes include antibiotic use intensity, length of hospital stay, direct medical cost, and clinician decision confidence. Participants will be followed at postoperative days 1, 3, and 7 using electronic medical record review and phone confirmation when needed.
The goal of this study is to determine whether integrating an explainable AI risk prediction tool into routine care can reduce POP and improve the quality and efficiency of perioperative management after brain tumor surgery.
Read the detailed description
Rationale Postoperative pneumonia (POP) remains a frequent and clinically important complication after elective craniotomy for brain tumor resection, contributing to prolonged hospitalization, increased cost, and worse clinical outcomes. Conventional POP risk assessment is often experience-based or relies on simplified scoring approaches, which may not adequately capture nonlinear interactions among perioperative factors. This study implements an explainable machine-learning (ML) prediction model within routine perioperative workflows and evaluates whether model-assisted care can improve POP prevention and related resource utilization compared with usual care.
Decision support system
An explainable gradient boosting machine (GBM) model is used to estimate individual POP risk from routinely available perioperative variables. Interpretability is provided using SHAP-based explanations at two complementary levels:
Population level: summarizes the most influential predictors and selected interaction patterns to support clinical understanding and model governance.
Patient level: generates an individualized contribution visualization (e.g., waterfall-style), highlighting the main drivers of a specific patient's risk estimate.
The system automatically assigns a risk tier (low, moderate, high, or very high) and links each tier to standardized prevention pathway templates (e.g., airway management optimization, antimicrobial stewardship-consistent strategies per local policy, and nutritional support). The tool does not mandate treatment; clinicians may accept, modify, or override any suggestion.
Evaluation framework The overall project includes retrospective model development/optimization, prospective external validation/calibration, and a pragmatic implementation evaluation. The registered interventional evaluation uses a multicenter, cluster randomized crossover design with monthly alternating periods of model-assisted care versus usual care. Allocation procedures, eligibility criteria, planned enrollment, and endpoint definitions/time windows are specified in the corresponding record modules (Study Design, Arms/Interventions, Outcome Measures, and Eligibility) to avoid duplication in this section.
Implementation and integration The model is deployed as a lightweight web service with unified APIs and data-exchange formats to enable non-disruptive integration with hospital information systems (HIS) and electronic medical records (EMR). A web-based front end and a Python-based back end support RESTful calls and are designed for low-latency inference (target single-prediction latency <200 ms), suitable for perioperative and inpatient workflows.
Data governance and model updating To support long-term generalizability across hospitals and mitigate dataset shift, the project establishes a closed-loop maintenance process ("local de-identification → cloud retraining → model version management → edge deployment"). Model updates are version-controlled and deployed under governance procedures consistent with local regulations, institutional policies, and applicable ethics approvals.
- Conditions
- Postoperative Pneumonia, Brain Tumors
- Keywords
- postoperative pneumonia, brain tumor surgery, perioperative management, machine learning, explainable artificial intelligence, SHAP, clinical decision support system
- Medical subject headings
- Brain Neoplasms
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 |
|---|---|---|
| Retrospective model development cohort (CAMS/PUMC, 2022-2024) | Retrospective cohort collected at the Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS/PUMC) between Jan 1, 2022 and Oct 31, 2024. Adult patients undergoing elective craniotomy for intracranial brain tumor resection were included. This cohort was used for model development, comparison of candidate algorithms, and internal validation; it was randomly split 8:2 into a training set (n=609) and an internal validation set (n=152). Early postoperative pneumonia (POP) was defined by CDC criteria within 7 days postoperatively. No study-mandated intervention was applied; routine perioperative care was provided. | |
| Prospective internal validation cohort (Test P, CAMS/PUMC) | Prospective cohort enrolled at CAMS/PUMC between Nov 1, 2024 and Apr 30, 2025 (Test P; n=224). Adult patients undergoing elective craniotomy for brain tumor resection were included. This cohort prospectively validated the finalized interpretable prediction model for early POP using routinely available perioperative EMR variables. Early POP was defined per CDC criteria within 7 postoperative days. Clinical management followed standard-of-care without any study-assigned intervention. | |
| Prospective external validation cohort (Test A, Anhui Medical Univ) | Prospective external validation cohort recruited at the First Affiliated Hospital of Anhui Medical University from Aug 1, 2024 to Apr 30, 2025 (Test A; n=329). Adult patients undergoing elective craniotomy for brain tumor resection were included. The cohort independently validated the finalized POP prediction model using routine perioperative data. Early POP was defined by CDC criteria within 7 days after surgery. No investigational intervention was administered; all care followed local standard practice. | |
| Prospective external validation cohort (Test S, Shandong Cancer Hospital) | Prospective external validation cohort recruited at Shandong Cancer Hospital from Aug 1, 2024 to Apr 30, 2025 (Test S; n=440). Adult patients undergoing elective craniotomy for intracranial brain tumor resection were included. This cohort was used to externally validate the finalized interpretable model for early POP prediction using routinely collected perioperative variables. Early POP was defined according to CDC criteria within 7 postoperative days. Participants received routine perioperative management per institutional standards without study-mandated interventions. | |
| Prospective external validation cohort (Test J, Jinan Fourth People's Hospital) | Prospective external validation cohort recruited at Jinan Fourth People's Hospital from Aug 1, 2024 to Apr 30, 2025 (Test J; n=102). Adult patients undergoing elective craniotomy for brain tumor resection were included. The cohort externally validated the finalized prediction model for early POP based on routine perioperative EMR data. Early POP was defined per CDC criteria within 7 postoperative days. There was no study-assigned intervention; all patients received standard perioperative care per local protocols. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Incidence of early postoperative pneumonia (POP)
Early POP was diagnosed according to CDC criteria and recorded in the electronic medical record, assessed as a binary outcome (POP vs no POP) within 7 postoperative days.
Measured: Within 7 days after craniotomy (postoperative day 0-7)
Secondary outcomes (4)
Discrimination of the finalized prediction model (AUC)
Area under the receiver operating characteristic curve (AUC) for the finalized interpretable logistic regression model in predicting early POP (binary outcome within 7 days).
Measured: Within 7 days after craniotomy (postoperative day 0-7)
Calibration performance of the finalized prediction model
Calibration assessed using calibration curve and summary statistics (e.g., calibration intercept and slope; Brier score), comparing predicted probabilities with observed early POP outcomes.
Measured: Within 7 days after craniotomy (postoperative day 0-7)
Clinical utility of the finalized prediction model (Decision Curve Analysis)
Net benefit of the finalized model evaluated by decision curve analysis across clinically relevant threshold probabilities for early POP.
Measured: Within 7 days after craniotomy (postoperative day 0-7)
Classification performance of the finalized prediction model at a pre-specified cutoff
Sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score calculated at a pre-specified probability threshold (e.g., determined in the training cohort using Youden index) and applied to validation cohorts.
Measured: Within 7 days after craniotomy (postoperative day 0-7)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Jan 2026
- Study start1 Aug 2024
- Primary completion1 Apr 2025
- Study completion1 Sep 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (16)Papers the sponsor has linked to this study.
- Other trials for Postoperative PneumoniaStudies 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
- Other
- Enrolment
- 1,856 participants (actual)
- Target duration
- 7 Days
Sponsor and investigators
- Lead sponsor
- Ming Yang
- Sponsor type
- University, hospital or other
- Collaborators
- The First Affiliated Hospital of Anhui Medical University, Shandong Cancer Hospital and Institute
- Responsible party
- Sponsor-investigator, Ming Yang, Cancer Institute and Hospital, Chinese Academy of Medical Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07321262
- Sponsor's protocol ID
- NCC2025C-1426
- Other IDs
- 2024-I2M-3-014
- Acronym
- ML-PNEUMO-BT
- Status last verified
- Jan 2026
- First submitted
- 22 Dec 2025
- Last updated
- 8 Jan 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: IPD sharing is currently undecided. The study uses detailed multi-center electronic medical record data, and additional assessment is needed to determine whether an adequately de-identified dataset can be shared without meaningful risk of re-identification and in compliance with IRB/ethics approvals, institutional policies, and applicable privacy regulations. We will revisit the IPD sharing plan after study completion and publication of primary results.
Publications
Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems. 2017;30
Azodi CB, Tang J, Shiu SH. Opening the Black Box: Interpretable Machine Learning for Geneticists. Trends Genet. 2020 Jun;36(6):442-455. doi: 10.1016/j.tig.2020.03.005. Epub 2020 Apr 17.
PubMed 32396837Han J, Yao T, Gao L, Gao H, Chen Y, Wang Y, Cao Y, Liu C, Qiu F, Jia K, Huang H. Development and validation of a risk prediction model related to inflammatory and nutritional indexes for postoperative pulmonary infection after radical colorectal cancer surgery. BMJ Open. 2025 Jan 8;15(1):e087426. doi: 10.1136/bmjopen-2024-087426.
PubMed 39779267Zhao C, Xiang B, Zhang J, Yang P, Liu Q, Wang S. Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning. Front Physiol. 2024 Dec 4;15:1501854. doi: 10.3389/fphys.2024.1501854. eCollection 2024.
PubMed 39697615Chen Y, Ma M, Qu D, Xu C. Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures. Sci Rep. 2025 Jul 1;15(1):22409. doi: 10.1038/s41598-025-04623-y.
PubMed 40595785Churpek MM, Carey KA, Edelson DP, Singh T, Astor BC, Gilbert ER, Winslow C, Shah N, Afshar M, Koyner JL. Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury. JAMA Netw Open. 2020 Aug 3;3(8):e2012892. doi: 10.1001/jamanetworkopen.2020.12892.
PubMed 32780123Koyner JL, Adhikari R, Edelson DP, Churpek MM. Development of a Multicenter Ward-Based AKI Prediction Model. Clin J Am Soc Nephrol. 2016 Nov 7;11(11):1935-1943. doi: 10.2215/CJN.00280116. Epub 2016 Sep 15.
PubMed 27633727Canet J, Gallart L, Gomar C, Paluzie G, Valles J, Castillo J, Sabate S, Mazo V, Briones Z, Sanchis J; ARISCAT Group. Prediction of postoperative pulmonary complications in a population-based surgical cohort. Anesthesiology. 2010 Dec;113(6):1338-50. doi: 10.1097/ALN.0b013e3181fc6e0a.
PubMed 21045639Lan J, Wei Y, Zhu Y, Zhang Y, Zhang S, Mo L, Wei D, Lei Y. Risk Factors for Post-Operative Pulmonary Infection in Patients With Brain Tumors: A Systematic Review and Meta-Analysis. Surg Infect (Larchmt). 2023 Sep;24(7):588-597. doi: 10.1089/sur.2023.130. Epub 2023 Aug 10.
PubMed 37582199Jing X, Wang X, Zhuang H, Fang X, Xu H. Multiple Machine Learning Approaches Based on Postoperative Prediction of Pulmonary Complications in Patients With Emergency Cerebral Hemorrhage Surgery. Front Surg. 2022 Jan 18;8:797872. doi: 10.3389/fsurg.2021.797872. eCollection 2021.
PubMed 35127804Sughrue ME, Rutkowski MJ, Shangari G, Chang HQ, Parsa AT, Berger MS, McDermott MW. Risk factors for the development of serious medical complications after resection of meningiomas. Clinical article. J Neurosurg. 2011 Mar;114(3):697-704. doi: 10.3171/2010.6.JNS091974. Epub 2010 Jul 23.
PubMed 20653395Longo M, Agarwal V. Postoperative Pulmonary Complications Following Brain Tumor Resection: A National Database Analysis. World Neurosurg. 2019 Jun;126:e1147-e1154. doi: 10.1016/j.wneu.2019.03.058. Epub 2019 Mar 15.
PubMed 30880210Gonzalez-Bonet LG, Tarazona-Santabalbina FJ, Lizan Tudela L. [Neurosurgery in the elderly patient: Geriatric neurosurgery]. Neurocirugia (Astur). 2016 Jul-Aug;27(4):155-66. doi: 10.1016/j.neucir.2015.11.003. Epub 2016 Jan 4. Spanish.
PubMed 26762658Karhade AV, Cote DJ, Larsen AM, Smith TR. Neurosurgical Infection Rates and Risk Factors: A National Surgical Quality Improvement Program Analysis of 132,000 Patients, 2006-2014. World Neurosurg. 2017 Jan;97:205-212. doi: 10.1016/j.wneu.2016.09.056. Epub 2016 Sep 23.
PubMed 27671880Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control. 2008 Jun;36(5):309-32. doi: 10.1016/j.ajic.2008.03.002. No abstract available.
PubMed 18538699
Study at a glance
From the registry record, last updated by the sponsor on 8 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- An Interpretable and Clinically Deployable Machine Learning Model for Predicting Early Postoperative Pneumonia of Brain Tumor: a Multicenter Diagnostic Study
- ClinicalTrials.gov ID
- NCT07321262
- Acronym / sponsor ID
- ML-PNEUMO-BT / NCC2025C-1426
- Conditions
- Postoperative Pneumonia, Brain Tumors
- Keywords
- postoperative pneumonia, brain tumor surgery, perioperative management, machine learning, explainable artificial intelligence, SHAP, clinical decision support system
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,856 participants (actual)
- Lead sponsor
- Ming YangUniversity, hospital or other
- Collaborators
- The First Affiliated Hospital of Anhui Medical University, Shandong Cancer Hospital and Institute
- Study start
- 1 Aug 2024 (actual)
- Primary completion
- 1 Apr 2025 (actual)
- Study completion
- 1 Sep 2025 (actual)
- First posted
- 7 Jan 2026 (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 pneumonia (POP) is a common and serious complication after elective craniotomy for brain tumor resection. POP often develops within the first week after surgery and may lead to prolonged hospitalization, higher medical costs, and increased risk of severe illness. Because symptoms can be subtle in neurosurgical patients, POP may be detected late, limiting timely prevention and treatment.
This study will evaluate whether a machine-learning-based clinical decision support tool can help clinicians identify patients at high risk for POP early and improve perioperative preventive care. The tool uses routinely collected clinical information to estimate an individual patient's POP risk and provides an easy-to-understand explanation of key risk drivers. Based on the predicted risk level (low, moderate, high, or very high), the system suggests standardized preventive care pathways (e.g., perioperative airway management, targeted antibiotic strategies per local practice, and nutritional support), while allowing clinicians to override recommendations at any time.
Participants will be adults undergoing their first elective craniotomy for brain tumor resection at participating neurosurgical centers. The primary outcome is the occurrence of POP within 7 days after surgery, defined using CDC/NHSN criteria. Secondary outcomes include antibiotic use intensity, length of hospital stay, direct medical cost, and clinician decision confidence. Participants will be followed at postoperative days 1, 3, and 7 using electronic medical record review and phone confirmation when needed.
The goal of this study is to determine whether integrating an explainable AI risk prediction tool into routine care can reduce POP and improve the quality and efficiency of perioperative management after brain tumor surgery.
Detailed description
Rationale Postoperative pneumonia (POP) remains a frequent and clinically important complication after elective craniotomy for brain tumor resection, contributing to prolonged hospitalization, increased cost, and worse clinical outcomes. Conventional POP risk assessment is often experience-based or relies on simplified scoring approaches, which may not adequately capture nonlinear interactions among perioperative factors. This study implements an explainable machine-learning (ML) prediction model within routine perioperative workflows and evaluates whether model-assisted care can improve POP prevention and related resource utilization compared with usual care.
Decision support system
An explainable gradient boosting machine (GBM) model is used to estimate individual POP risk from routinely available perioperative variables. Interpretability is provided using SHAP-based explanations at two complementary levels:
Population level: summarizes the most influential predictors and selected interaction patterns to support clinical understanding and model governance.
Patient level: generates an individualized contribution visualization (e.g., waterfall-style), highlighting the main drivers of a specific patient's risk estimate.
The system automatically assigns a risk tier (low, moderate, high, or very high) and links each tier to standardized prevention pathway templates (e.g., airway management optimization, antimicrobial stewardship-consistent strategies per local policy, and nutritional support). The tool does not mandate treatment; clinicians may accept, modify, or override any suggestion.
Evaluation framework The overall project includes retrospective model development/optimization, prospective external validation/calibration, and a pragmatic implementation evaluation. The registered interventional evaluation uses a multicenter, cluster randomized crossover design with monthly alternating periods of model-assisted care versus usual care. Allocation procedures, eligibility criteria, planned enrollment, and endpoint definitions/time windows are specified in the corresponding record modules (Study Design, Arms/Interventions, Outcome Measures, and Eligibility) to avoid duplication in this section.
Implementation and integration The model is deployed as a lightweight web service with unified APIs and data-exchange formats to enable non-disruptive integration with hospital information systems (HIS) and electronic medical records (EMR). A web-based front end and a Python-based back end support RESTful calls and are designed for low-latency inference (target single-prediction latency <200 ms), suitable for perioperative and inpatient workflows.
Data governance and model updating To support long-term generalizability across hospitals and mitigate dataset shift, the project establishes a closed-loop maintenance process ("local de-identification → cloud retraining → model version management → edge deployment"). Model updates are version-controlled and deployed under governance procedures consistent with local regulations, institutional policies, and applicable ethics approvals.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 7 Days
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| Retrospective model development cohort (CAMS/PUMC, 2022-2024) | – | –Retrospective cohort collected at the Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS/PUMC) between Jan 1, 2022 and Oct 31, 2024. Adult patients undergoing elective craniotomy for intracranial brain tumor resection were included. This cohort was used for model development, comparison of candidate algorithms, and internal validation; it was randomly split 8:2 into a training set (n=609) and an internal validation set (n=152). Early postoperative pneumonia (POP) was defined by CDC criteria within 7 days postoperatively. No study-mandated intervention was applied; routine perioperative care was provided. |
| Prospective internal validation cohort (Test P, CAMS/PUMC) | – | –Prospective cohort enrolled at CAMS/PUMC between Nov 1, 2024 and Apr 30, 2025 (Test P; n=224). Adult patients undergoing elective craniotomy for brain tumor resection were included. This cohort prospectively validated the finalized interpretable prediction model for early POP using routinely available perioperative EMR variables. Early POP was defined per CDC criteria within 7 postoperative days. Clinical management followed standard-of-care without any study-assigned intervention. |
| Prospective external validation cohort (Test A, Anhui Medical Univ) | – | –Prospective external validation cohort recruited at the First Affiliated Hospital of Anhui Medical University from Aug 1, 2024 to Apr 30, 2025 (Test A; n=329). Adult patients undergoing elective craniotomy for brain tumor resection were included. The cohort independently validated the finalized POP prediction model using routine perioperative data. Early POP was defined by CDC criteria within 7 days after surgery. No investigational intervention was administered; all care followed local standard practice. |
| Prospective external validation cohort (Test S, Shandong Cancer Hospital) | – | –Prospective external validation cohort recruited at Shandong Cancer Hospital from Aug 1, 2024 to Apr 30, 2025 (Test S; n=440). Adult patients undergoing elective craniotomy for intracranial brain tumor resection were included. This cohort was used to externally validate the finalized interpretable model for early POP prediction using routinely collected perioperative variables. Early POP was defined according to CDC criteria within 7 postoperative days. Participants received routine perioperative management per institutional standards without study-mandated interventions. |
| Prospective external validation cohort (Test J, Jinan Fourth People's Hospital) | – | –Prospective external validation cohort recruited at Jinan Fourth People's Hospital from Aug 1, 2024 to Apr 30, 2025 (Test J; n=102). Adult patients undergoing elective craniotomy for brain tumor resection were included. The cohort externally validated the finalized prediction model for early POP based on routine perioperative EMR data. Early POP was defined per CDC criteria within 7 postoperative days. There was no study-assigned intervention; all patients received standard perioperative care per local protocols. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients undergoing elective craniotomy for intracranial brain tumor resection at participating centers. The study includes a retrospective model-development cohort and multiple prospective validation cohorts. Routinely collected perioperative electronic medical record data are used to develop and validate an interpretable prediction model for early postoperative pneumonia (within 7 postoperative days).
Key inclusion criteria (4 listed)
- Age ≥ 18 years.
- Undergoing elective craniotomy for intracranial brain tumor resection.
- Perioperative clinical data available in the electronic medical record to derive required predictors.
- Expected postoperative survival ≥ 7 days.
Key exclusion criteria (5 listed)
- Evidence of active infection (including pneumonia) prior to surgery.
- Thoracic surgery or severe chest trauma within 30 days prior to craniotomy.
- Spinal tumors or extracranial peripheral nerve tumors.
- Pregnancy or lactation.
- Hospice care, expected survival < 7 days, or insufficient data completeness for model calculation.
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
Incidence of early postoperative pneumonia (POP)
Time frame: Within 7 days after craniotomy (postoperative day 0-7)
Early POP was diagnosed according to CDC criteria and recorded in the electronic medical record, assessed as a binary outcome (POP vs no POP) within 7 postoperative days.
4 secondary outcomes
Discrimination of the finalized prediction model (AUC)
Time frame: Within 7 days after craniotomy (postoperative day 0-7)
Calibration performance of the finalized prediction model
Time frame: Within 7 days after craniotomy (postoperative day 0-7)
Clinical utility of the finalized prediction model (Decision Curve Analysis)
Time frame: Within 7 days after craniotomy (postoperative day 0-7)
Classification performance of the finalized prediction model at a pre-specified cutoff
Time frame: Within 7 days after craniotomy (postoperative day 0-7)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Jan 2026.
- BackgroundLundberg SM, Lee S-I. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems. 2017;30
- Background: PubMed 32396837Azodi CB, Tang J, Shiu SH. Opening the Black Box: Interpretable Machine Learning for Geneticists. Trends Genet. 2020 Jun;36(6):442-455. doi: 10.1016/j.tig.2020.03.005. Epub 2020 Apr 17.
- Background: PubMed 39779267Han J, Yao T, Gao L, Gao H, Chen Y, Wang Y, Cao Y, Liu C, Qiu F, Jia K, Huang H. Development and validation of a risk prediction model related to inflammatory and nutritional indexes for postoperative pulmonary infection after radical colorectal cancer surgery. BMJ Open. 2025 Jan 8;15(1):e087426. doi: 10.1136/bmjopen-2024-087426.
- Background: PubMed 39697615Zhao C, Xiang B, Zhang J, Yang P, Liu Q, Wang S. Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning. Front Physiol. 2024 Dec 4;15:1501854. doi: 10.3389/fphys.2024.1501854. eCollection 2024.
- Background: PubMed 40595785Chen Y, Ma M, Qu D, Xu C. Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures. Sci Rep. 2025 Jul 1;15(1):22409. doi: 10.1038/s41598-025-04623-y.
- Background: PubMed 32780123Churpek MM, Carey KA, Edelson DP, Singh T, Astor BC, Gilbert ER, Winslow C, Shah N, Afshar M, Koyner JL. Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury. JAMA Netw Open. 2020 Aug 3;3(8):e2012892. doi: 10.1001/jamanetworkopen.2020.12892.
- Background: PubMed 27633727Koyner JL, Adhikari R, Edelson DP, Churpek MM. Development of a Multicenter Ward-Based AKI Prediction Model. Clin J Am Soc Nephrol. 2016 Nov 7;11(11):1935-1943. doi: 10.2215/CJN.00280116. Epub 2016 Sep 15.
- Background: PubMed 21045639Canet J, Gallart L, Gomar C, Paluzie G, Valles J, Castillo J, Sabate S, Mazo V, Briones Z, Sanchis J; ARISCAT Group. Prediction of postoperative pulmonary complications in a population-based surgical cohort. Anesthesiology. 2010 Dec;113(6):1338-50. doi: 10.1097/ALN.0b013e3181fc6e0a.
- Background: PubMed 37582199Lan J, Wei Y, Zhu Y, Zhang Y, Zhang S, Mo L, Wei D, Lei Y. Risk Factors for Post-Operative Pulmonary Infection in Patients With Brain Tumors: A Systematic Review and Meta-Analysis. Surg Infect (Larchmt). 2023 Sep;24(7):588-597. doi: 10.1089/sur.2023.130. Epub 2023 Aug 10.
- Background: PubMed 35127804Jing X, Wang X, Zhuang H, Fang X, Xu H. Multiple Machine Learning Approaches Based on Postoperative Prediction of Pulmonary Complications in Patients With Emergency Cerebral Hemorrhage Surgery. Front Surg. 2022 Jan 18;8:797872. doi: 10.3389/fsurg.2021.797872. eCollection 2021.
- Background: PubMed 20653395Sughrue ME, Rutkowski MJ, Shangari G, Chang HQ, Parsa AT, Berger MS, McDermott MW. Risk factors for the development of serious medical complications after resection of meningiomas. Clinical article. J Neurosurg. 2011 Mar;114(3):697-704. doi: 10.3171/2010.6.JNS091974. Epub 2010 Jul 23.
- Background: PubMed 30880210Longo M, Agarwal V. Postoperative Pulmonary Complications Following Brain Tumor Resection: A National Database Analysis. World Neurosurg. 2019 Jun;126:e1147-e1154. doi: 10.1016/j.wneu.2019.03.058. Epub 2019 Mar 15.
- Individual participant dataSharing planned: Undecided. IPD sharing is currently undecided. The study uses detailed multi-center electronic medical record data, and additional assessment is needed to determine whether an adequately de-identified dataset can be shared without meaningful risk of re-identification and in compliance with IRB/ethics approvals, institutional policies, and applicable privacy regulations. We will revisit the IPD sharing plan after study completion and publication of primary results.
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Medical Device Clinical Trial Without CE Marking to Evidence Safety and Performance of the INBENTUS VERSATILE Ventilator in Patients Requiring Assisted/Controlled Mechanical Ventilation and Weaning.
Acute Respiratory Distress Syndrome (ARDS), Pneumonia
Not yet recruiting
Effect of Positioning and ACBT on Pulmonary Complications After Cardiac Surgery
Postoperative Pulmonary Complications (PPCs), Cardiac Surgery With Sternotomy
Not yet recruiting
High-Flow Nasal Cannula After Major Abdominal Surgery to Prevent Postoperative Pulmonary Complications
Postoperative Complications, Atelectasis
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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
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 Pneumonia and Brain Tumors, run by Ming Yang at 1 site in China.
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 4 inclusion and 5 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: finished, as of the sponsor's last update on 8 Jan 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 China. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Incidence of early postoperative pneumonia (POP) (time frame: Within 7 days after craniotomy (postoperative day 0-7)). 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 NCT07321262, which also lists 16 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 Jan 2026.