scholarly journals Clinical prediction models for bronchopulmonary dysplasia: a systematic review and external validation study

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Wes Onland ◽  
Thomas P Debray ◽  
Matthew M Laughon ◽  
Martijn Miedema ◽  
Filip Cools ◽  
...  
Keyword(s):  
Systematic Review ◽  
Bronchopulmonary Dysplasia ◽  
Validation Study ◽  
Prediction Models ◽  
External Validation ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
External Validation Study

Using interpretability approaches to update “black-box” clinical prediction models: an external validation study in nephrology

2021 ◽  
Vol 111 ◽  
pp. 101982
Author(s):  
Harry Freitas da Cruz ◽  
Boris Pfahringer ◽  
Tom Martensen ◽  
Frederic Schneider ◽  
Alexander Meyer ◽  
...  
Keyword(s):  
Validation Study ◽  
Prediction Models ◽  
External Validation ◽  
Black Box ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
External Validation Study

scholarly journals A systematic review of the quality of clinical prediction models in in vitro fertilisation

2020 ◽  
Vol 35 (1) ◽  
pp. 100-116 ◽  
Author(s):  
M B Ratna ◽  
S Bhattacharya ◽  
B Abdulrahim ◽  
D J McLernon
Keyword(s):  
Systematic Review ◽  
Live Birth ◽  
Prediction Models ◽  
Conflicts Of Interest ◽  
External Validation ◽  
Post Treatment ◽  
Treatment Model ◽  
Clinical Prediction ◽  
Clinical Prediction Models

Abstract STUDY QUESTION What are the best-quality clinical prediction models in IVF (including ICSI) treatment to inform clinicians and their patients of their chance of success? SUMMARY ANSWER The review recommends the McLernon post-treatment model for predicting the cumulative chance of live birth over and up to six complete cycles of IVF. WHAT IS KNOWN ALREADY Prediction models in IVF have not found widespread use in routine clinical practice. This could be due to their limited predictive accuracy and clinical utility. A previous systematic review of IVF prediction models, published a decade ago and which has never been updated, did not assess the methodological quality of existing models nor provided recommendations for the best-quality models for use in clinical practice. STUDY DESIGN, SIZE, DURATION The electronic databases OVID MEDLINE, OVID EMBASE and Cochrane library were searched systematically for primary articles published from 1978 to January 2019 using search terms on the development and/or validation (internal and external) of models in predicting pregnancy or live birth. No language or any other restrictions were applied. PARTICIPANTS/MATERIALS, SETTING, METHODS The PRISMA flowchart was used for the inclusion of studies after screening. All studies reporting on the development and/or validation of IVF prediction models were included. Articles reporting on women who had any treatment elements involving donor eggs or sperm and surrogacy were excluded. The CHARMS checklist was used to extract and critically appraise the methodological quality of the included articles. We evaluated models’ performance by assessing their c-statistics and plots of calibration in studies and assessed correct reporting by calculating the percentage of the TRIPOD 22 checklist items met in each study. MAIN RESULTS AND THE ROLE OF CHANCE We identified 33 publications reporting on 35 prediction models. Seventeen articles had been published since the last systematic review. The quality of models has improved over time with regard to clinical relevance, methodological rigour and utility. The percentage of TRIPOD score for all included studies ranged from 29 to 95%, and the c-statistics of all externally validated studies ranged between 0.55 and 0.77. Most of the models predicted the chance of pregnancy/live birth for a single fresh cycle. Six models aimed to predict the chance of pregnancy/live birth per individual treatment cycle, and three predicted more clinically relevant outcomes such as cumulative pregnancy/live birth. The McLernon (pre- and post-treatment) models predict the cumulative chance of live birth over multiple complete cycles of IVF per woman where a complete cycle includes all fresh and frozen embryo transfers from the same episode of ovarian stimulation. McLernon models were developed using national UK data and had the highest TRIPOD score, and the post-treatment model performed best on external validation. LIMITATIONS, REASONS FOR CAUTION To assess the reporting quality of all included studies, we used the TRIPOD checklist, but many of the earlier IVF prediction models were developed and validated before the formal TRIPOD reporting was published in 2015. It should also be noted that two of the authors of this systematic review are authors of the McLernon model article. However, we feel we have conducted our review and made our recommendations using a fair and transparent systematic approach. WIDER IMPLICATIONS OF THE FINDINGS This study provides a comprehensive picture of the evolving quality of IVF prediction models. Clinicians should use the most appropriate model to suit their patients’ needs. We recommend the McLernon post-treatment model as a counselling tool to inform couples of their predicted chance of success over and up to six complete cycles. However, it requires further external validation to assess applicability in countries with different IVF practices and policies. STUDY FUNDING/COMPETING INTEREST(S) The study was funded by the Elphinstone Scholarship Scheme and the Assisted Reproduction Unit, University of Aberdeen. Both D.J.M. and S.B. are authors of the McLernon model article and S.B. is Editor in Chief of Human Reproduction Open. They have completed and submitted the ICMJE forms for Disclosure of potential Conflicts of Interest. The other co-authors have no conflicts of interest to declare. REGISTRATION NUMBER N/A

Clinical Prediction Models for Patients With Nontraumatic Knee Pain in Primary Care: A Systematic Review and Internal Validation Study

2017 ◽  
Vol 47 (8) ◽  
pp. 518-529 ◽  
Author(s):  
Guus Panken ◽  
Arianne P. Verhagen ◽  
Caroline B. Terwee ◽  
Martijn W. Heymans
Keyword(s):  
Systematic Review ◽  
Primary Care ◽  
Knee Pain ◽  
Validation Study ◽  
Prediction Models ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Internal Validation

scholarly journals Prediction models for the mortality risk in chronic dialysis patients: a systematic review and independent external validation study

2017 ◽  
Vol Volume 9 ◽  
pp. 451-464 ◽  
Author(s):  
Chava Ramspek ◽  
Pauline Voskamp ◽  
Frans van Ittersum ◽  
Raymond Krediet ◽  
Friedo Dekker ◽  
...  
Keyword(s):  
Systematic Review ◽  
Mortality Risk ◽  
Validation Study ◽  
Prediction Models ◽  
External Validation ◽  
Chronic Dialysis ◽  
Dialysis Patients ◽  
External Validation Study

Towards clinical application of prediction models for transition to psychosis: A systematic review and external validation study in the PRONIA sample

2021 ◽  
Vol 125 ◽  
pp. 478-492
Author(s):  
Marlene Rosen ◽  
Linda T. Betz ◽  
Frauke Schultze-Lutter ◽  
Katharine Chisholm ◽  
Theresa K. Haidl ◽  
...  
Keyword(s):  
Systematic Review ◽  
Clinical Application ◽  
Validation Study ◽  
Prediction Models ◽  
External Validation ◽  
External Validation Study

scholarly journals Prognostic Prediction Models for Patients with Low Back Pain: Systematic Review Protocol

2020 ◽  
Author(s):  
Fernanda Gonçalves Silva ◽  
Leonardo Oliveira Pena Costa ◽  
Mark J Hancock ◽  
Gabriele Alves Palomo ◽  
Luciola da Cunha Menezes Costa ◽  
...  
Keyword(s):  
Systematic Review ◽  
Low Back Pain ◽  
Back Pain ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Risk Of Bias ◽  
Low Back ◽  
Clinical Prediction ◽  
Clinical Prediction Models

Abstract Background: The prognosis of acute low back pain is generally favourable in terms of pain and disability; however, outcomes vary substantially between individual patients. Clinical prediction models help in estimating the likelihood of an outcome at a certain time point. There are existing clinical prediction models focused on prognosis for patients with low back pain. To date, there is only one previous systematic review summarising the discrimination of validated clinical prediction models to identify the prognosis in patients with low back pain of less than 3 months duration. The aim of this systematic review is to identify existing developed and/or validated clinical prediction models on prognosis of patients with low back pain of less than 3 months duration, and to summarise their performance in terms of discrimination and calibration. Methods: MEDLINE, Embase and CINAHL databases will be searched, from the inception of these databases until January 2020. Eligibility criteria will be: (1) prognostic model development studies with or without external validation, or prognostic external validation studies with or without model updating; (2) with adults aged 18 or over, with ‘recent onset’ low back pain (i.e. less than 3 months duration), with or without leg pain; (3) outcomes of pain, disability, sick leave or days absent from work or return to work status, and self-reported recovery; and (4) study with a follow-up of at least 12 weeks duration. The risk of bias of the included studies will be assessed by the Prediction model Risk Of Bias ASsessment Tool, and the overall quality of evidence will be rated using the Hierarchy of Evidence for Clinical Prediction Rules. Discussion: This systematic review will identify, appraise, and summarize evidence on the performance of existing prediction models for prognosis of low back pain, and may help clinicians to choose the best option of prediction model to better inform patients about their likely prognosis. Systematic review registration: PROSPERO reference number CRD42020160988

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