ArticleBMJ open2023
Protocol for the derivation and external validation of a 30-day postoperative pulmonary complications (PPCs) risk prediction model for elderly patients undergoing thoracic surgery: a cohort study in southern China.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Development and External Validation of an Explainable Machine-Learning Model for Predicting Postoperative Pulmonary Complications in Older Adults Undergoing Degenerative Spine Surgery.Journal of clinical medicine · 2026Article
- A machine learning-based predictive model for postoperative pulmonary complications in lung cancer and its SHAP interpretation.Frontiers in oncology · 2026Article
- Development and validation of an explainable machine learning model for predicting postoperative pulmonary complications after lung cancer surgery: a machine learning study.EClinicalMedicine · 2025Article
- Article
- Bidirectional cohort study protocol to construct and validate a prediction model for perioperative pulmonary complications in elderly hip fracture patients.Scientific reports · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPostoperative pulmonary complications (PPCs) occur after up to 60% of non-cardiac thoracic surgery (NCTS), especially for multimorbid elderly patients. Nevertheless, current risk prediction models for PPCs have major limitations regarding derivation and validation, and do not account for the specific risks of NCTS patients. Well-founded and externally validated models specific to elderly NCTS patients are warranted to inform consent and treatment decisions. METHODS AND ANALYSIS: We will develop, internally and externally validate a multivariable risk model to predict 30-day PPCs in elderly NCTS patients. Our cohort will be generated in three study sites in southern China with a target population of approximately 1400 between October 2021 and December 2023. Candidate predictors have been selected based on published data, clinical expertise and epidemiological knowledge. Our model will be derived using the combination of multivariable logistic regression and bootstrapping technique to lessen predictors. The final model will be internally validated using bootstrapping validation technique and externally validated using data from different study sites. A parsimonious risk score will then be developed on the basis of beta estimates derived from the logistic model. Model performance will be evaluated using area under the receiver operating characteristic curve, max-rescaled Brier score and calibration slope. In exploratory analysis, we will also assess the net benefit of Probability of PPCs Associated with THoracic surgery in elderly patients score in the complete cohort using decision curve analysis. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Institutional Review Board of the Affiliated Cancer Hospital and Institute of Guangzhou Medical University, the Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine and the University of Hongkong-Shenzhen Hospital, respectively. The final risk prediction model will be published in an appropriate journal and further disseminated as an online calculator or nomogram for clinical application. Approved and anonymised data will be shared. TRIAL REGISTRATION NUMBER: ChiCTR2100051170.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.