Trial reportClinical interventions in aging2023
Lasso-Based Machine Learning Algorithm for Predicting Postoperative Lung Complications in Elderly: A Single-Center Retrospective Study from China.
Trial report in Clinical interventions in aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 22 citations in OpenAlex.
- Construction and validation of a predictive nomogram for 28-day mortality in critically ill patients with toxic encephalopathy.BMC medical informatics and decision making · 2026Article
- Surveillance of postoperative respiratory complications in Japanese patients with hip fractures using ARISCAT score.Musculoskeletal surgery · 2026Article
- LASSO-based machine learning algorithm for prediction of dysphagia in patients suffering anterior cervical discectomy and fusion.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Integrated transcriptomic analysis and experimental validation identify ACADL as a mitochondrial tumor suppressor via the FOXO3a/PUMA axis in lung adenocarcinoma.European journal of medical research · 2026Article
- Inflammatory biomarkers and physiological reserve: an explainable machine learning model for predicting postoperative pulmonary complications in elderly laparoscopic surgery.Frontiers in cellular and infection microbiology · 2026Article
- A novel tumor microenvironment-related gene signature for prognostic prediction of intrahepatic cholangiocarcinoma.Scientific reports · 2025Article
- Article
- Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future-A Systematic Review.Journal of clinical medicine · 2025Review
- Prediction model for postoperative pulmonary complications after thoracoscopic surgery with machine learning algorithms and SHapley Additive exPlanations.Journal of thoracic disease · 2025Article
- Interpretable machine learning model to predict 90-day radiographically confirmed pneumonia after chemotherapy initiation in non-Hodgkin lymphoma: development and internal validation of a single-center cohort.Frontiers in medicine · 2025Article
- Multiparametric-MRI habitat radiomics analysis for discriminating pathological types of brain metastases.Frontiers in oncology · 2025Article
- Draw on advantages and avoid disadvantages: CT-derived individualized radiomic signature for predicting chemo-radiotherapy sensitivity in unresectable advanced non-small cell lung cancer.Journal of cancer research and clinical oncology · 2024Article
- POLE -related gene signature predicts prognosis, immune feature, and drug therapy in human endometrioid carcinoma.Heliyon · 2024Article
- A prediction model of elderly hip fracture mortality including preoperative red cell distribution width constructed based on the random survival forest (RSF) and Cox risk ratio regression.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2024Article
- Ultrasound-based deep learning radiomics nomogram for risk stratification of testicular masses: a two-center study.Journal of cancer research and clinical oncology · 2024Article
- Concordance between ARISCAT risk score and cardiopulmonary exercise test values in risk prediction of postoperative pulmonary complications of major abdominal surgeries in a tertiary cancer hospital: A cross-sectional study.Health science reports · 2023Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The predictive effect of systemic inflammatory factors on postoperative pulmonary complications in elderly patients remains unclear. In addition, machine learning models are rarely used in prediction models for elderly patients. Patients and Methods: We retrospectively evaluated elderly patients who underwent general anesthesia during a 6-year period. Eligible patients were randomly assigned in a 7:3 ratio to the development group and validation group. The Least logistic absolute shrinkage and selection operator (LASSO) regression model and multiple logistic regression analysis were used to select the optimal feature. The discrimination, calibration and net reclassification improvement (NRI) of the final model were compared with "the Assess Respiratory Risk in Surgical Patients in Catalonia" (ARISCAT) model. Results: Of the 9775 patients analyzed, 8.31% developed PPCs. The final model included age, preoperative SpO2, ANS (the Albumin/NLR Score), operation time, and red blood cells (RBC) transfusion. The concordance index (C-index) values of the model for the development cohort and the validation cohort were 0.740 and 0.748, respectively. The Conclusion: Based on LASSO machine learning algorithm, we constructed a prediction model superior to ARISCAT model in predicting the risk of PPCs. Clinicians could utilize these predictors to optimize prospective and preventive interventions in this patient population.
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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.