Cited by 657Meta-analysis and systematic reviewsStatistical Methods in EpidemiologyClinical practice guidelines implementation

The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.

A guide to systematic review and meta-analysis of prediction model performance

Open access

Debray, Thomas P A, Damen, Johanna A A G, Snell, Kym I E, Ensor, Joie, Hooft, Lotty, Reitsma, Johannes B, Riley, Richard D, Moons, Karel G M·2017·BMJ

DOI 10.1136/bmj.i6460

Open this paper in CoChat — free account Open PDF

Abstract

Validation of prediction models is highly recommended and increasingly common in the literature. A systematic review of validation studies is therefore helpful, with meta-analysis needed to summarise the predictive performance of the model being validated across different settings and populations. This article provides guidance for researchers systematically reviewing and meta-analysing the existing evidence on a specific prediction model, discusses good practice when quantitatively summarising the predictive performance of the model across studies, and provides recommendations for interpreting meta-analysis estimates of model performance. We present key steps of the meta-analysis and illustrate each step in an example review, by summarising the discrimination and calibration performance of the EuroSCORE for predicting operative mortality in patients undergoing coronary artery bypass grafting.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1136/bmj.i6460

Cites

49 of 49 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Two new Later Stone Age sites from the Final Pleistocene in the Falémé Valley, eastern Senegal
Ndiaye, M., Lespez, L., Tribolo, C., Rassé, M., Hadjas, I., Davidoux, S., Huysecom, É., & Douze, K. (2024) · Monash University Research Portal (Monash University) · doi:10.3929/ethz-b-000667478 · Cited by 24,214
The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gotzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · BMJ · doi:10.1136/bmj.b2700 · Cited by 17,648
Estimating the mean and variance from the median, range, and the size of a sample
Hozo, S. P., Djulbegovic, B., & Hozo, I. (2005) · BMC Medical Research Methodology · doi:10.1186/1471-2288-5-13 · Cited by 9,062
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Self-citation
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0697 · Cited by 3,267
Self-citation
Interpretation of random effects meta-analyses
Riley, R. D., Higgins, J. P. T., & Deeks, J. J. (2011) · BMJ · doi:10.1136/bmj.d549 · Cited by 2,884
Self-citation
Chapter 9: Analysing Data and Undertaking Meta-Analyses
Deeks, J. J., Higgins, J. P. T., & Altman, D. G. (2008) · Cited by 2,782
The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method
IntHout, J., Ioannidis, J. P., & Borm, G. F. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-25 · Cited by 2,419
Preferred Reporting Items for a Systematic Review and Meta-analysis of Individual Participant Data: The PRISMA-IPD Statement
Stewart, L. A., Clarke, M., Rovers, M., Riley, R. D., Simmonds, M., Stewart, G., & Tierney, J. F. (2015) · JAMA · doi:10.1001/jama.2015.3656 · Cited by 2,134
Self-citation
Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist
Moons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018
Self-citation

Show all 48 references

Sorted by citations

Items 401–500 of 648

Arm Based on LEg blood pressures (ABLE-BP): can systolic leg blood pressure measurements predict systolic brachial blood pressure? Protocol for an individual participant data meta-analysis from the INTERPRESS-IPD Collaboration
McDonagh, S. T. J., Sheppard, J. P., Warren, F. C., Boddy, K., Farmer, L., Shore, H., Williams, P., Lewis, P., Baumber, R., Fordham, J., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-040481 · Cited by 3
Prediction Models for Perioperative Blood Transfusion in Patients Undergoing Gynecologic Surgery: A Systematic Review
Pan, Z.-M., & Charoenkwan, K. (2024) · Diagnostics · doi:10.3390/diagnostics14182018 · Cited by 3
Postoperative recurrence prediction model for atrial fibrillation: a meta-analysis
Chen, C.-F. (2024) · American Journal of Translational Research · doi:10.62347/ijep7120 · Cited by 3
Prediction of atrial fibrillation after a stroke event: A systematic review with meta-analysis
Helbitz, A., Haris, M., Younsi, T., Romer, E., Ginks, W. R., Raveendra, K., Hayward, C., Shuweihdi, F., Larvin, H., Cameron, A., et al. (2025) · Heart Rhythm · doi:10.1016/j.hrthm.2025.01.026 · Cited by 3
Development and Spatial External Validation of a Predictive Model of Survival Based on Random Survival Forest Analysis for People Living With HIV and AIDS After Highly Active Antiretroviral Therapy in China: Retrospective Cohort Study
Li, X., Li, Y., Zhu, Z., Tan, B., Zhou, X., Shi, H., Li, X., Zhu, P., & Xu, Y. (2025) · Journal of Medical Internet Research · doi:10.2196/71257 · Cited by 3
Hierarchical imputation of categorical variables in the presence of systematically and sporadically missing data
Jolani, S. (2025) · Research Synthesis Methods · doi:10.1017/rsm.2025.10017 · Cited by 3
Clinical prediction models for postoperative blood transfusion after total knee arthroplasty: a systematic review and meta-analysis
Chen, J., Zhong, X., Zhai, Y., Zhao, C., Lan, J., Chen, L., & Xia, Z. (2025) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-025-09164-z · Cited by 3
Imputing missing values with external data: Applications for multisite settings and federated analyses
Thiesmeier, R., Bottai, M., & Orsini, N. (2025) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x251398605 · Cited by 3
One Heartbeat Away from a Prediction Model for Cardiovascular Diseases in Patients with Chronic Kidney Disease: A Systematic Review
Smit, L. C. M., Bots, M. L., van der Leeuw, J., Damen, J. A., Blankestijn, P. J., Verhaar, M. C., & Vernooij, R. W. M. (2023) · Cardiorenal Medicine · doi:10.1159/000529791 · Cited by 3
Self-citation
Tutorial: dos and don’ts in clinical prediction research for venous thromboembolism
Nemeth, B., Smeets, M. J. R., Cannegieter, S. C., & van Smeden, M. (2024) · Research and Practice in Thrombosis and Haemostasis · doi:10.1016/j.rpth.2024.102480 · Cited by 3
Systematic review of methods used in prediction models with recurrent event data
Watson, V., Smith, C. T., & Bonnett, L. J. (2024) · Diagnostic and Prognostic Research · doi:10.1186/s41512-024-00173-5 · Cited by 3
Prognostic models in multiple sclerosis: progress and challenges in clinical integration
Havla, J. B., Reeve, K. A., Ön, B. I., Mansmann, U., & Held, U. (2024) · Neurological Research and Practice · doi:10.1186/s42466-024-00338-z · Cited by 3
Prediction models for intradialytic hypotension in hemodialysis patients: A protocol for systematic review and critical appraisal
Li, Z., Yang, L., Xi, Z., Wen, Y., Zeng, X., Ma, D., & Lei, Y. (2024) · PLoS ONE · doi:10.1371/journal.pone.0310191 · Cited by 3
Systematic review and critical appraisal of predictive models for diabetic peripheral neuropathy: Existing challenges and proposed enhancements
Sun, C., Lin, Y.-H., Ling, G.-X., Gao, H., Feng, X., & Sun, C.-Q. (2025) · World Journal of Diabetes · doi:10.4239/wjd.v16.i4.101310 · Cited by 3
Oxygen therapy in early warning scores: a systematic review and meta-analysis
Harrison, C. H., Tupper, P., Gerry, S., Michael, V., Bedford, J., Smith, C. E., Subbe, C. P., Redfern, O., & Watkinson, P. (2025) · Thorax · doi:10.1136/thorax-2024-222663 · Cited by 4
Minimum sample size calculation for radiomics-based binary outcome prediction models: Theoretical framework and practical example
Cao, Q., Jiang, Z., Wang, Z., Wee, L., Dekker, A. L. A. J., Zhang, Z., & Zhu, J. (2025) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2025.111134 · Cited by 3
Hidden risks of predictive models in healthcare
Alderman, J. E., Riley, R. D., Parekh, D., Summers, C., Liu, X., & Denniston, A. K. (2025) · BMJ Evidence-Based Medicine · doi:10.1136/bmjebm-2025-113730 · Cited by 3
Self-citation
Development and external validation of prediction models for critical outcomes of unvaccinated COVID-19 patients based on demographics, medical conditions and dental status
Su, N., Donders, M.-C., Ho, J. T. F., Vespasiano, V., de Lange, J., & Loos, B. G. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e15283 · Cited by 3
Accounting for time dependency in meta‐analyses of concordance probability estimates
Schmid, M. C., Friede, T., Klein, N., & Weinhold, L. (2023) · Research Synthesis Methods · doi:10.1002/jrsm.1655 · Cited by 3
Risk prediction models for mortality and readmission in patients with acute heart failure: A protocol for systematic review, critical appraisal, and meta-analysis
Zhang, X., Zhou, K., You, L., Zhang, J., Chen, Y., Dai, H., Wan, S., Guan, Z., Hu, M., Kang, J., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0283307 · Cited by 3
Prognostic factors predicting an unprovoked seizure recurrence in children and adults following a first unprovoked seizure
Adan, G., Neligan, A., Nevitt, S. J., Pullen, A., Sander, J. W. A. S., & Marson, A. G. (2021) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013848 · Cited by 3
Validating and updating GRASP: An evidence-based framework for grading and assessment of clinical predictive tools
Khalifa, M., Magrabi, F., & Gallego, B. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100161 · Cited by 2
Evaluating the predictive value of clinical models for HBV-related hepatocellular carcinoma: A meta-analysis
Huang, L., Feng, L.-H., Yang, L., Hu, B., Liang, H., Ren, A., Wang, H., He, W., Deng, C., Su, M., et al. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1529201 · Cited by 2
Real-time motion sickness measurement: Feasibility and application of the real-time motion sickness scale (RMS)
Song, Y., Bae, J., Shin, J., Yang, J., Kim, S., Bahn, S., & Yun, M. H. (2025) · Applied Ergonomics · doi:10.1016/j.apergo.2025.104507 · Cited by 2
Precision of predictive nomograms for lymph node metastasis of thyroid cancer from Chinese real-world study: a systematic review and meta-analysis
Wu, Y., Su, Y., Zhao, Y., Mourdi, N., & Wang, Z. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1617563 · Cited by 2
Risk prediction models for discharge disposition in patients with stroke: a systematic review and meta-analysis
Xu, C., Xiang, L., Luo, Y., Li, H., Tai, L., Liu, Y., He, K., Du, M., & Zhang, X. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1637606 · Cited by 2
Towards fair AI in Estonia’s public service: discussing and disseminating bias prevention in automated decision-making
Joamets, K. (2025) · TalTech Journal of European Studies · doi:10.2478/bjes-2025-0037 · Cited by 3
Performance of clinical prediction models for identifying postmenopausal osteoporosis: a systematic review and meta-analysis
Huang, X., Gan, Y., Guo, X., Li, Y., Wang, L., Qin, J., Sun, C., Yin, Y., Chen, M., Xie, Y., et al. (2026) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-025-09389-y · Cited by 2
Risk prediction models for detecting a new diagnosis of heart failure within 5 years in the community: a systematic review
Thaitirarot, C., Sze, S., Jones, N. R., Barker, J., Chan, A., Hobbs, F. D. R., Taylor, K., & Taylor, C. J. (2026) · BMJ Open · doi:10.1136/bmjopen-2024-093419 · Cited by 2
Prediction models for CAR-T Cell therapy-related adverse events in hematologic malignancies: a systematic review and meta-analysis
Ye, L., Liao, L., Wang, L., Zhao, X., Zeng, J., & Xu, X. (2026) · BMC Cancer · doi:10.1186/s12885-026-15877-8 · Cited by 2
Artificial Intelligence-Based Prognostic Models for Postoperative Outcomes in Vitreoretinal Surgery
Al-Ani, A., Connors, L., Mikhail, D., Alkhawaja, M., Yang, L., Ambikkumar, A., Punja, K. G., Costello, F., Gooi, P., & Munro, M. (2026) · Ophthalmology Retina · doi:10.1016/j.oret.2026.04.022 · Cited by 2
Performance of prediction models for incident heart failure and heart failure hospitalization in individuals with type 2 diabetes: a systematic review and meta-analysis
Mircescu, A., Allan, W., Haris, M., Hurdus, B., Helbitz, A., Ginks, W., Wu, J., Sattar, N. A., Petrie, M. C., Ajjan, R., et al. (2026) · European Journal of Heart Failure · doi:10.1093/ejhf/xuag090 · Cited by 2
Comparative predictive accuracy of machine learning versus traditional statistical methods for diabetes‐related complications: A systematic review and analysis
Ghafoor, B., Zaccardi, F., Khunti, K. K., & Shabnam, S. (2026) · Diabetic Medicine · doi:10.1111/dme.70398 · Cited by 2
Machine learning vs. traditional methods for predicting postoperative cardiac complications after non‐cardiac surgery: a systematic review and Bayesian network meta‐analysis
Dhaliwal, S., Chen, S., Papas, C., Hughes, I. P., Cavallucci, D., & O’Rourke, N. (2026) · Anaesthesia · doi:10.1111/anae.70237 · Cited by 2
Prognostic models for predicting recurrence and survival in women with endometrial cancer
Wan, Y.-L. L., Sayed, I. E., Walker, T. D. J., Russell, B., Badrick, E., McAleenan, A., Edmondson, R. J., & Crosbie, E. J. (2021) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd014625 · Cited by 2
Prognostic models of quality of life after total knee replacement: A systematic review
Ohyama, Y., Iwamura, T., Hoshino, T., & Miyata, K. (2023) · Physiotherapy Theory and Practice · doi:10.1080/09593985.2023.2211716 · Cited by 2
Knowledge, attitudes and practice on sudden infant death: study protocol of a scoping review
Rodrigues, L., de Souza, A. V. P., Sánchez, O. D. R., & Carmona, E. V. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-080107 · Cited by 2
SERUM TRANSACTIVE RESPONSE DNA BINDING PROTEIN 43 ASSOCIATES WITH POOR SHORT-TERM NEUROLOGIC OUTCOME AFTER RETURN OF SPONTANEOUS CIRCULATION FOLLOWING CARDIAC ARREST
Sun, Z., Yu, D., Li, P., Wang, L., Chen, Y., Wei, X., & Gong, P. (2024) · Shock · doi:10.1097/shk.0000000000002378 · Cited by 2
Drug Exposure As a Predictor in Diabetic Retinopathy Risk Prediction Models—A Systematic Review and Meta-Analysis
Bantounou, M. A., Nahar, T. A. K., Plascevic, J., Kumar, N. S., Nath, M., Myint, P. K., & Philip, S. (2024) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2024.07.012 · Cited by 3
The Patient, the Multidisciplinary Team and the Assessment
Martin, F. C. (2017) · Practical issues in geriatrics · doi:10.1007/978-3-319-62503-4_3 · Cited by 2
Effectiveness of Self-Hydration Management Interventions in Middle-Aged and Older Adults: A Systematic Review and Meta-Analysis
Atasoy, E., Akyol, M. A., Söylemez, B. A., & Küçükgüçlü, Ö. (2025) · Journal of Gerontological Nursing · doi:10.3928/00989134-20250326-01 · Cited by 2
Arm Based on LEg blood pressures (ABLE-BP): can systolic ankle blood pressure measurements predict systolic arm blood pressure? An individual participant data meta-analysis from the INTERPRESS-IPD Collaboration
McDonagh, S. T. J., Warren, F. C., Sheppard, J. P., Boddy, K., Farmer, L., Shore, H., Williams, P., Lewis, P., Fordham, A. J., Martin, U., et al. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-094389 · Cited by 2
Prediction models for venous thromboembolism in ambulatory adults with pancreatic and gastro-oesophageal cancer: protocol for systematic review and meta-analysis
Zaheer, A., Naumovski, N., Toohey, K. L., Niyonsenga, T., Yip, D., Brown, N. A. T., & Mortazavi, R. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-056431 · Cited by 2
Prediction models for acute kidney injury in critically ill patients: a protocol for systematic review and critical appraisal
Wang, D., Zhang, W., Luo, J. F., Fang, H., Jing, S., & Mei, Z. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-046274 · Cited by 2
An evidence synthesis approach for combining different data sources illustrated using entomological efficacy of insecticides for indoor residual spraying
Green, N., Agossa, F. R., Yovogan, B., Oxborough, R. M., Kitau, J., Müller, P., Constant, E., Rowland, M., Tchacaya, E. F. S., Benjamin, K. G., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0263446 · Cited by 2
Improving prediction model systematic review methodology: Letter to the Editor
Bullock, G. S., Hughes, T., Sergeant, J. C., Callaghan, M. J., Collins, P. G. S., & Riley, R. D. (2021) · Translational Sports Medicine · doi:10.1002/tsm2.240 · Cited by 2
Self-citation
Molecular biomarkers for predicting complete response to preoperative chemoradiation in people with locally advanced rectal cancer
Lacavalerie, P. D., Lord, S. J., Morgan, M. J., Caldon, C. E., & Kohonen‐Corish, M. R. J. (2024) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd014718 · Cited by 1
The challenges for developing prognostic prediction models for acute kidney injury in hospitalized children: A systematic review
Wang, C., Liu, X., Zhang, C., Yan, R., Li, Y., & Peng, X. (2024) · Pediatric Investigation · doi:10.1002/ped4.12458 · Cited by 1
Systematic review and meta-analysis of cardiovascular event risk prediction models in maintenance hemodialysis patients
Zhou, Y., He, Y., Zhu, P., Yan, R., Tang, R., Wang, L., & He, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-07586-2 · Cited by 1
Aspiration risk prediction models in patients with nasogastric enteral nutrition: a systematic review and meta-analysis
Chen, J., Su, Y., Li, T., Mao, X., Jiang, Q., Yang, Q., Wen, Q., Pu, Z., & Liu, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-12252-8 · Cited by 1
Predictive factors and models for major adverse cardiovascular and limb events in patients with peripheral arterial disease
Song, P., Xiong, H., Quan, X., Chen, Q., Wang, D., Liu, X., & Shi, M. (2025) · VASA · doi:10.1024/0301-1526/a001228 · Cited by 2
Exploratory analysis of predictive models in the field of myelitis: a systematic review and meta-analysis
Huang, J., Zhao, Y., Zhang, Z., Cheng, Y., & Wu, B. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1669338 · Cited by 1
Decision Support Tools for Adjusting Perioperative Opioid Dosing as Individualised Postoperative Pain Management: A Systematic Review
Sunde, P. B., Rasmussen, I., Folkersen, C., Saito, A., Olsen, M. H., Laigaard, J., Papadomanolakis‐Pakis, N., Lunn, T. H., Meyhoff, C. S., Mathiesen, O., et al. (2025) · Acta Anaesthesiologica Scandinavica · doi:10.1111/aas.70146 · Cited by 1
Interventions to increase adherence to antiretroviral therapy in people living with HIV – what works? A systematic review and meta-analysis
Moon, Z., Kılıç, A., Amirova, A., Campbell, L. J., Cooper, V., Bondaronek, P., Chalder, T., Chisari, C., Glendinning, E., King, K., et al. (2025) · AIDS Care · doi:10.1080/09540121.2025.2598805 · Cited by 1
Predictive models for adverse pregnancy outcomes in fetal growth restriction: a systematic review and meta-analysis
Peng, J., Fang, Y., Shen, W., Hu, J., Deng, X., & Yin, L. (2026) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-026-08904-w · Cited by 1
Diagnostic Accuracy of MUM1 (IRF4) Immunohistochemistry in Chronic Endometritis: A Systematic Review and Bayesian Meta-Analysis
Mihoci, A. M., Socolov, D. G., Mihoci, E. C., Toma, L. M., Pruteanu, A. I., & Socolov, R. V. (2026) · Diagnostics · doi:10.3390/diagnostics16081167 · Cited by 1
Predictive performance of CT-based artificial intelligence for predicting variceal bleeding in portal hypertension: a systematic review and meta-analysis
Zhu, C., Liu, Q., Tao, W., Fu, B., Yang, F., Yang, K., Bao, Y., Cao, B., Liu, L., Ma, J., et al. (2026) · Abdominal Radiology · doi:10.1007/s00261-026-05564-4 · Cited by 1
Characteristics of Cardiovascular Disease Prediction Models Considering Mental Disorders: A Systematic Review
Siddiqi, S., Haggerty, T., Akther, A., Rusu, S., Blair, D.-L., Eccles, H., Yu, J., Shorr, R., Gupta, A., Bouchard, K., et al. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.046964 · Cited by 1
Computational and AI-Enabled Imaging Biomarkers for Predicting and Assessing Immunotherapy Response in Oral Squamous Cell Carcinoma: A Systematic Review with Functional Meta-Synthesis
Ardila, C. M., Vivares‐Builes, A. M., & Pineda‐Vélez, E. (2026) · Medical Sciences · doi:10.3390/medsci14050554 · Cited by 1
Improvements of Statistical Practices Were Strongly Needed Among Prognostic Prediction Models For Obstetric Care
Tan, J., Liu, C., Yang, M. A., Xiong, Y., Huang, S., Qi, Y., Chen, M., Thabane, L., Liu, X., He, L., et al. (2022) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.14757 · Cited by 1
Spin and Hacking in Machine Learning Prediction Model Studies in Dentistry: A Systematic Review
Cheng, L., Pan, Y., Wo, P., Zheng, Y., Yi, Y., Park, J. W., & Xiong, X. (2025) · Oral Diseases · doi:10.1111/odi.70023 · Cited by 1
Prediction of sentinel lymph node status in patients with early breast cancer using breast imaging as an alternative to surgical staging—a systematic review and meta-analysis
Rejmer, C., Hjärtström, M., Bendahl, P., Dihge, L., Skarping, I. D., Zhang, D., Dustler, M., & Rydén, L. (2025) · Systematic Reviews · doi:10.1186/s13643-025-03005-9 · Cited by 1
Risk prediction models for sarcopenia in maintenance hemodialysis patients: a systematic review and meta-analysis
Chen, L., Shen, H., Dong, Y., Xu, X., Zhong, Q., Zhuang, D., & Zhao, M. (2025) · BMC Nephrology · doi:10.1186/s12882-025-04662-7 · Cited by 1
Evaluating methodological quality of prognostic prediction models on patient-reported outcome measurements after total hip and total knee arthroplasty
Lin, J. Y., Natarajan, P., Liu, V. K., Jenkin, D., Chang, W., Naylor, J., & Adie, S. (2026) · Bone & Joint Open · doi:10.1302/2633-1462.71.bjo-2025-0014.r1 · Cited by 1
The risk prediction models for sarcopenia in older adults: a systematic review and critical appraisal
Lin, T., Liao, H., Su, L., Xu, P., Tu, X., Dai, L., Luo, J., Xiang, Q., Ge, N., & Yue, J. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1751954 · Cited by 1
Improving 10-year cardiovascular risk prediction in patients with established cardiovascular disease: flexible addition of risk predictors on top of the SMART2 risk score
Holtrop, J., Gynnild, M. N., Richards, M., Lim, C.-E., Lääperi, M., Deo, S. V., Dalen, H., Ueda, P., Rajagopalan, S., Chlabicz, M., et al. (2026) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwag086 · Cited by 1
Are aging clocks based on routine clinical indicators trustworthy and applicable? A systematic review and critical appraisal
Zhang, J., Hu, J., Gao, Y., Pang, Z., Mo, L., & Wu, I. X. Y. (2026) · The Journals of Gerontology Series A · doi:10.1093/gerona/glag032 · Cited by 1
Development and validation of a prognostic model for neurological deterioration in acute posterior circulation cerebral infarction
Guo, Z., Zhang, Q. J., Wu, J., & Yi, L. (2026) · BMC Neurology · doi:10.1186/s12883-026-04823-7 · Cited by 1
Integrating Copernicus Earth Observation and Artificial Intelligence for Habitat Suitability Modeling of Pinctada radiata in Semi-Enclosed Coastal Watersheds of Central Greece
Pafras, D., Conides, A. J., Vafidis, D., Georgos, K., & Klaoudatos, D. (2026) · Journal of Watershed Ecology · doi:10.70322/jwe.2026.10003 · Cited by 1
Prognostic Models for Mortality in Patients with Diabetic Foot Ulcers: A Systematic Review
Zhuang, H., Gu, Y., Guo, J., Zhu, J., Li, X., & Zhang, W. (2026) · Advances in Wound Care · doi:10.1177/21621918261429115 · Cited by 1
Clinical utility of biomarkers for outcomes prediction in adults with suspected sepsis presenting to the emergency department: a synthesis of current evidence
Imamura, M., Duggan, S. N., Vadiveloo, T., Cooper, J. G., Kaye, C. T., Manson, P. D., Virgili, G., Aucott, L. S., Clarke, M. J., & Brazzelli, M. G. (2025) · Health Technology Assessment · doi:10.3310/gjmb1730 · Cited by 1
Predictive model for severe intraventricular hemorrhage risk in preterm infants: a systematic review and meta-analysis
Zhan, Z., Zhang, J., Rong, H., Shen, F., & Chong, Q. (2026) · Frontiers in Pediatrics · doi:10.3389/fped.2026.1785442 · Cited by 1
Prediction models for progression from diabetic kidney disease to end-stage renal disease: a systematic review and meta-analysis
Shi, Y., Dong, Z., Shan, X., Wang, X., Huang, S., Liu, H., & Zhang, L. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1812362 · Cited by 1
Knowledge, attitudes, and practices related to the use of artificial intelligence in nursing education and practice: a systematic review and meta-analysis
Almarwani, A. M., & Hebeshy, M. I. (2026) · BMC Nursing · doi:10.1186/s12912-026-04524-z · Cited by 1
Machine Learning Models for Predicting Pain, Fatigue, Depression, Anxiety, and Malnutrition in Cancer Patients: A Systematic Review and Meta‐Analysis
Reheman, M., Li, Y., Chen, Y., Yan, Q., & Hu, X. (2026) · Journal of Nursing Scholarship · doi:10.1111/jnu.70123 · Cited by 1
Artificial Intelligence and Machine Learning-based prediction of tuberculosis treatment failure: A systematic review and meta-analysis
Kamulegeya, R., Nabatanzi, R., Semugenze, D., Mugala, F., Takuwa, M., Nasinghe, E., Musinguzi, D., Namiiro, S. M., Katumba, A., Ssengooba, W., et al. (2026) · PLoS ONE · doi:10.1371/journal.pone.0355124 · Cited by 2
Model for end stage liver disease for prediction of mortality in people with cirrhosis
D’Amico, G., Perricone, G., Morabito, A., Latteri, F., Filì, D., Affronti, A., Pietrosi, G., Maida, M. F., Rizzo, G. E. M., Bronte, F., et al. (2021) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013849 · Cited by 1
Multivariable prediction models for long-term outcomes after hip fracture: A protocol for a systematic review
Walsh, M. E., Kristensen, P. K., Hjelholt, T. J., Hurson, C. J., Walsh, C., & Blake, C. M. (2022) · HRB Open Research · doi:10.12688/hrbopenres.13575.1 · Cited by 1
Clinical prediction models assessing response to radiotherapy for rectal cancer: protocol for a systematic review
Karageorgou, M., Hughes, D. M., Myint, A. S., Pritchard, D. M., & Bonnett, L. J. (2022) · Diagnostic and Prognostic Research · doi:10.1186/s41512-022-00132-y · Cited by 1
Protocol for a systematic review and meta-analysis on preoperative risk factors for failure after fixed sling implantation for post-prostatectomy stress urinary incontinence
Sacco, E., Marino, F., Gavi, F., Moretto, S., Gandi, C., Bientinesi, R., Pierconti, F., & Bassi, P. (2023) · F1000Research · doi:10.12688/f1000research.129182.1 · Cited by 1
Development of a nomogram for the early prediction of PACU VAS in patients undergoing laparoscopic radical resection of colorectal cancer with fentanyl
Zhou, Y., Huang, J., Cao, L., Gao, Y., Li, Y., Wang, B., Pan, B., Guo, W., & Cang, J. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e18560 · Cited by 1
Development and validation of a prognostic nomogram for predicting cancer-specific survival in advanced endometrial carcinoma after surgery: a retrospective analysis of the SEER Database
Zheng, C., Chen, W., Zheng, Z., Liang, X., Xu, X., Fang, D., Ma, R.-J., Fan, F., Ni, Y., Zhang, P., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-070893 · Cited by 1
Indicazioni per lo sviluppo di una revisione sistematica di accuratezza diagnostica per un esame di laboratorio clinico
PECORARO, V., Pezzati, P., Fasano, T., & Trenti, T. (2025) · Biochimica Clinica · doi:10.23736/s0393-0564.25.00010-x · Cited by 1
Opportunities and challenges in a world of data abundance
Holly, C. M. (2017) · The JBI Database of Systematic Reviews and Implementation Reports · doi:10.11124/jbisrir-2017-003629 · Cited by 1
The added value of different biomarkers to the Revised Cardiac Risk Index to predict major adverse cardiac events and all-cause mortality after noncardiac surgery
Vernooij, L. M., Damen, J. A., van Klei, W. A., Moons, K. G. M., & Peelen, L. M. (2018) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013139 · Cited by 1
Self-citation
Systematic review and meta-analysis protocol for development and validation of a prediction model for gestational hypertension in Africa
Feleke, S. F., Dessie, A. M., Anley, D. T., Yimer, A., Geremew, H., Anteneh, R. M., & Fantaye, A. K. (2023) · SAGE Open Medicine · doi:10.1177/20503121231153508 · Cited by 1
Prognostic research in treated populations
Pajouheshnia, R. (2018) · Utrecht University Repository (Utrecht University) · Cited by 1
Methods for individual participant data meta-analysis in prediction research
de Jong, V. M. T. (2020) · doi:10.33540/469 · Cited by 1
Re: “Diagnostic Accuracies of the Ultrasound and Magnetic Resonance Imaging ADNEX Scoring Systems for Ovarian Adnexal Mass: Systematic Review and Meta-Analysis”
CALSTER, B. V., Bourne, T., Froyman, W., Timmerman, D., & Valentin, L. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.07.031 · Cited by 1
Validating and Updating Grasp: An Evidence-Based Framework for Grading and Assessment of Clinical Predictive ToolsPreprint
Khalifa, M., Magrabi, F., & Gallego, B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4788792 · Cited by 1
Clinical prediction models for acute kidney injury
Feng, Y., Blot, S. I., & Gallagher, M. (2024) · Intensive and Critical Care Nursing · doi:10.1016/j.iccn.2024.103852 · Cited by 1
External validation of ultrasound-based models for discrimination between benign and malignant adnexal masses in Italy: the prospective multicenter IOTA phase 6 studyPreprint
Moro, F., Momi, M., Bertoldo, V., Ledger, A., Barreñada, L., Ceusters, J., Sturla, D., Ghezzi, F., Mor, E., Fornari, L., et al. (2024) · medRxiv · doi:10.1101/2024.12.23.24319517 · Cited by 1
Personalizing obstetric care
Meertens (2018) · doi:10.26481/dis.20180831lm
SLT versus ALT in Glaucoma Patients Treated Previously with 360-degree SLT—A Randomised, Single-Blind, Equivalence Clinical Trial
Guo, H. (2018) · Scholarship@Western (Western University)
Contributions to Semiparametric Methods for Incomplete Data
Evans, K. (2017) · Digital Access to Scholarship at Harvard (DASH) (Harvard University)
Classifying colorectal cancer or colorectal polyps in endoscopic setting using convolutional neural network: protocol for a systematic review and meta-analysis
Keshtkar, K., Keshtkar, A., & Safarpour, A. R. (2020) · F1000Research · doi:10.12688/f1000research.25548.1
Prognostic Prediction Models for Patients with Low Back Pain: Systematic Review ProtocolPreprint
Silva, F. G., Costa, L. O. P., Hancock, M. J., Palomo, G. A., Costa, L. D. C. M., & da Silva, T. M. (2020) · doi:10.21203/rs.3.rs-88541/v1
Prediction Models for Tuberculosis Treatment Outcomes
Peetluk, L. S. (2021)
Pre-hospital Trauma Systems
van der Sluijs, R. (2021) · doi:10.33540/511
Real-world prognosis of eyes with diabetic macular oedema receiving treatment with vascular endothelial growth factor (VEGF) inhibitors
Bhandari, S., Nguyen, V., Fraser‐Bell, S., Tanna, G. L. D., & Gillies, M. C. (2021) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd015030

Authors: Debray, T. P. A., Damen, J. A. A. G., Snell, K. I. E., Ensor, J., Hooft, L., Reitsma, J. B., Riley, R. D., & Moons, K. G. M.
Affiliations: University Medical Center Utrecht (NL)
Keele University (GB)
Corresponding author: Debray, Thomas P A
ORCID: Debray, Thomas P A
Damen, Johanna A A G
Snell, Kym I E
Ensor, Joie
Hooft, Lotty
Reitsma, Johannes B
Riley, Richard D
Moons, Karel G M
Venue: BMJ
Publisher: BMJ
Published: 2017-01-05
Volume: 356
Pages: i6460
ISSN: 1756-1833 (ISSN-L)
DOI: 10.1136/bmj.i6460
OpenAlex: W2570878489
Cited by: 657
References: 48
Citations by year: 2026: 109 · 2025: 98 · 2024: 94 · 2023: 81 · 2022: 64 · 2021: 82 · 2020: 56 · 2019: 33 · 2018: 27 · 2017: 11
FWCI: 8.62
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.bmj.org/licenses/tdm/1.0/terms-and-conditions.html (tdm)
Locations: BMJ (publishedVersion)
PubMed (publishedVersion)
Pure Amsterdam UMC (publishedVersion)
Keele Research Repository (Keele University) (submittedVersion, cc-by)
Data Archiving and Networked Services (DANS) (submittedVersion)
Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
HighWire Press Open Archive (submittedVersion)
University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
Funders: Medical Research Council (MR/L004933/1, G0902393)
Topics: Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
Keywords: prediction model validation, systematic reviews, meta-analysis, predictive performance, discrimination (statistics), calibration, EuroSCORE, postoperative mortality, coronary artery bypass grafting, risk prediction model
MeSH terms: Systematic Reviews as Topic, Coronary Artery Bypass, Decision Support Techniques, Humans, Meta-Analysis as Topic, Risk Assessment, Validation Studies as Topic

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A guide to systematic review and meta-analysis of prediction model performance

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks