SynthesisBMJ (Clinical research ed.)2019
Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal.
Synthesis in BMJ (Clinical research ed.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 22 of them syntheses that pooled it.
What it found
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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
107 citing papers in PubMed, 22 syntheses or guidelines pooled it, 206 citations in OpenAlex.
- Community-based prediction models of cardiovascular events, acute exacerbations and all-cause mortality in individuals with chronic obstructive pulmonary disease: a systematic review and meta-analysis on behalf of the International Cardiovascular and Respiratory Alliance.BMJ open respiratory research · 2026Pooled it
- Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Prediction models for self-harm and suicide: a systematic review and critical appraisal.BMC medicine · 2025Pooled it
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation.Ewha medical journal · 2025Guideline
- Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Individualized prediction models in ADHD: a systematic review and meta-regression.Molecular psychiatry · 2024Pooled it
- Prognostic risk prediction model for patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD): a systematic review and meta-analysis.Respiratory research · 2024Pooled it
- Predictive models of Alzheimer's disease dementia risk in older adults with mild cognitive impairment: a systematic review and critical appraisal.BMC geriatrics · 2024Pooled it
- The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis.BMC infectious diseases · 2024Pooled it
- Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis.BMJ open · 2023Pooled it
- A pooled analysis of the risk prediction models for mortality in acute exacerbation of chronic obstructive pulmonary disease.The clinical respiratory journal · 2023Pooled it
- Predictors of Readmission, for Patients with Chronic Obstructive Pulmonary Disease (COPD) - A Systematic Review.International journal of chronic obstructive pulmonary disease · 2023Pooled it
- The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Individual participant data meta-analysis with mixed-effects transformation models.Biostatistics (Oxford, England) · 2022Pooled it
- Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Appraising prediction research: a guide and meta-review on bias and applicability assessment using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).Nephrology (Carlton, Vic.) · 2021Pooled it
- Prognostic models for amyotrophic lateral sclerosis: a systematic review.Journal of neurology · 2021Pooled it
- Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.Schizophrenia bulletin · 2021Pooled it
- Prediction Models for Prognosis of Cervical Cancer: Systematic Review and Critical Appraisal.Frontiers in public health · 2021Pooled it
- Palliative Care Initiation in Chronic Obstructive Pulmonary Disease: Prognosis-Based, Symptoms-Based or Needs-Based?International journal of chronic obstructive pulmonary disease · 2020Pooled it
47 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo map and assess prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease (COPD).
designSystematic review. DATA SOURCES: PubMed until November 2018 and hand searched references from eligible articles. ELIGIBILITY CRITERIA FOR STUDY SELECTION: Studies developing, validating, or updating a prediction model in COPD patients and focusing on any potential clinical outcome.
resultsThe systematic search yielded 228 eligible articles, describing the development of 408 prognostic models, the external validation of 38 models, and the validation of 20 prognostic models derived for diseases other than COPD. The 408 prognostic models were developed in three clinical settings: outpatients (n=239; 59%), patients admitted to hospital (n=155; 38%), and patients attending the emergency department (n=14; 3%). Among the 408 prognostic models, the most prevalent endpoints were mortality (n=209; 51%), risk for acute exacerbation of COPD (n=42; 10%), and risk for readmission after the index hospital admission (n=36; 9%). Overall, the most commonly used predictors were age (n=166; 41%), forced expiratory volume in one second (n=85; 21%), sex (n=74; 18%), body mass index (n=66; 16%), and smoking (n=65; 16%). Of the 408 prognostic models, 100 (25%) were internally validated and 91 (23%) examined the calibration of the developed model. For 286 (70%) models a model presentation was not available, and only 56 (14%) models were presented through the full equation. Model discrimination using the C statistic was available for 311 (76%) models. 38 models were externally validated, but in only 12 of these was the validation performed by a fully independent team. Only seven prognostic models with an overall low risk of bias according to PROBAST were identified. These models were ADO, B-AE-D, B-AE-D-C, extended ADO, updated ADO, updated BODE, and a model developed by Bertens et al. A meta-analysis of C statistics was performed for 12 prognostic models, and the summary estimates ranged from 0.611 to 0.769.
conclusionsThis study constitutes a detailed mapping and assessment of the prognostic models for outcome prediction in COPD patients. The findings indicate several methodological pitfalls in their development and a low rate of external validation. Future research should focus on the improvement of existing models through update and external validation, as well as the assessment of the safety, clinical effectiveness, and cost effectiveness of the application of these prognostic models in clinical practice through impact studies. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017069247.
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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.