How to conduct a systematic review and meta-analysis of prognostic model studies

Damen, Johanna Aag; Moons, Karel G.M.; Smeden, Maarten van; Hooft, Lotty

doi:10.1016/j.cmi.2022.07.019

Cited by 44 publications

(24 citation statements)

References 58 publications

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“…In this retrospective analysis of data from CPET conducted at the tertiary care sports diagnostic center, we examine the relationship between directly measured and predicted HRmax by the 13 commonly used regression equations. We demonstrate that: ( By definition, external validation is 'assessing the predictive agreement of a prediction model in a research population other than the one from which the model was developed' [23]. We underline the lack of external validation studies on EA performed comprehensively for numerous HRmax equations.…”

Section: Discussionmentioning

confidence: 89%

Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists

Kasiak¹,

Wiecha²,

Cieśliński³

et al. 2023

Preprint

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Maximal heart rate (HRmax) is a widely used measure of cardiorespiratory fitness. Prediction of HRmax is an alternative to cardiopulmonary exercise testing (CPET), but its accuracy among endurance athletes (EA) requires evaluation. This study aimed to externally validate HRmax prediction models in the EA independently for running and cycling CPET. 4043 runners (age=33.58 (8.12) years; 83.53% males; BMI=23.66 (2.54) kg·m−2) and 1026 cyclists (age=36.88 (9.03) years; 89.67% males; BMI=24.04 (2.65) kg·m−2) underwent maximum CPET. Student t-test, mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE) were applied to externally validate 8 running and 5 cycling HRmax equations. HRmax was 184.60 (9.79) beats·min−1 and 182.66 (10.28) beats·min−1 respectively for running and cycling, p=0.001. Measured and predicted HRmax differed significantly (p=0.001) for 9 of 13 (69.23%) models. HRmax was overestimated by 8 (61.54%) and underestimated by 5 (38.46%) formulae. Overestimated HRmax ranged 0.08-4.94 beats·min−1 and underestimated HRmax ranged 0.03-4.90 beats·min−1. MAE and RMSE were 0.18-4.94 beats·min−1 and 9.13-10.47, respectively. MAPE ranged 3.95-4.69%. Prediction models do not allow for accurate estimation of HRmax. HRmax was more often underestimated than overestimated. Predicted HRmax can be implemented for EA as a supplemental method but CPET is the preferable approach.

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Section: Discussionmentioning

confidence: 89%

Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists

Kasiak¹,

Wiecha²,

Cieśliński³

et al. 2023

Preprint

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“…Random-effects models were to be used for the meta-analyses. However, after data extraction, we found that the data on prognostic factors and models were too heterogeneous and poorly reported to perform a meta-analysis, and following Damen et al (2022), we decided not to adopt a meta-analytic approach (42).…”

Section: Discussionmentioning

confidence: 99%

“…This work was conducted according to the guidelines for systematic reviews of prognostic studies ( 42 , 43 , 44 , 45 ). The methodological approach was registered in the PROSPERO database (CRD42022327028).…”

Section: Methodsmentioning

confidence: 99%

Accuracy of papillary thyroid cancer prognostic nomograms: a systematic review

Garo¹,

Deandreis

Campennì

et al. 2023

Endocrine Connections

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Objective: To perform a systematic review to evaluate the predictive performance of available nomograms for thyroid cancer patients. Design and methods: PROSPERO registration: CRD42022327028. A systematic search was conducted without time and language restrictions. PICOT question: (Population): patients with papillary thyroid cancer; (Comparator Prognostic Factor): single-arm studies; (Outcomes): Overall survival, disease-free survival, cancer-specific survival, recurrence, central lymph node metastases, or lateral lymph node metastases; (Timing): all periods; (Setting): Hospital setting. Risk of Bias was assessed through PROBAST tool. Results: Eighteen studies with a total of 20 prognostic models were included in the systematic review (90,969 PTC patients). Fourteen models were at high risk of bias and four were at unclear risk of bias. The greatest concerns arose in the analysis domain. The accuracy of nomograms for overall survival was assessed in only one study and appeared limited (0.77, 95%CI: 0.75-0.79). The accuracy of nomograms for disease-free survival ranged from 0.65 (95% CI: 0.55-0.75) to 0.92 (95%CI: 0.91-0.95). The C-index for predicting lateral lymph node metastasis ranged from 0.72 to 0.92 (95%CI: 0.86-0.97). For central lymph node metastasis, the C-index of externally validated studies ranged from 0.706 (95%CI: 0.685-0.727) to 0.923 (95%CI: 0.893-0.946). Conclusions: Our work highlights the extremely high heterogeneity among nomograms and the critical lack of external validation studies that limit the applicability of nomograms in clinical practice. Further studies, ideally using commonly adopted risk factors as backbone to develop nomograms are required.

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“…To evaluate the features of the included CPM trials and clinical variables, descriptive analyses were conducted 18 . Discrimination refers to a predictive model's ability to differentiate between a subject's developing and non-developing outcomes and is typically evaluated by the consistency (C) statistic 19 , which corresponds to the area under the curve (AUC) 17 . Due to the possibility of heterogeneity between studies in meta-analysis of prediction models, random effects models were utilized in all analyses.…”

Section: Discussionmentioning

confidence: 99%

“…Reviewers collected information from the relevant published works using the CHARMS 15 checklist as a guideline, and they retrieved items such as (1) study demographic characteristics, (2) the de nition of clinical outcome events, and (3) the number of clinical outcome events: PICC-RVT identi ed through an ultrasound examination, and (4) information regarding the CPM, including a description of the prediction model/algorithm/protocol, statistical methods for CPM construction, the number of patients enrolled in the CPM, the number and de nition of candidate predictors, and a performance index of the model (such as the area under the receiver operating characteristic curve [AUC], sensitivity, speci city, positive predictive value [PPV], negative predictive value [EPV], odds ratio [OR] 13,17 .…”

Section: Data Extractionmentioning

confidence: 99%

Clinical Prediction Models for Peripherally Inserted Central Catheter-Related Venous Thrombosis: A Systematic Review and Meta-analysis

Wang

et al. 2023

Preprint

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Background PICC-related venous thrombosis (PICC-RVT) is one of the most serious complications of PICC. Clinical prediction models (CPMs) are statistical models that incorporate a number of variables and forecast the likelihood of outcome events using a few risk factors.Objective To analyze the reported PICC-RVT risk prediction models' bias risks and to conduct out a meta-analysis.Methods PICC-RVT research reports were available in PubMed, Web of Science, CINAHL, Scopus, ProQuest, and Cochrane Library. Research data from articles fulfilling eligibility conditions were collected and analyzed using the Systematic Reviews of Prediction Modeling Studies checklist. The included studies were assessed for bias using PROBAST. A meta-analysis of the C statistics of the included studies was performed using R software.Results A total of 714 articles were acquired from the electronic database; 5 of these were included in our study. All models were evaluated to have "low risk" in the clinical application domain using PROBAST evaluation. Regarding bias risk, three models were labeled as "high risk," one as "uncertain," and one as "low risk." The C-statistics for each model, which were in the range of 0.636 to 0.802.Conclusions The model's discrimination and prediction are acceptable, and most of the models in our review lack external validation. Our findings suggest reporting predictive model creation or validation utilizing TRIPOD criteria.

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How to conduct a systematic review and meta-analysis of prognostic model studies

Cited by 44 publications

References 58 publications

Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists

Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists

Accuracy of papillary thyroid cancer prognostic nomograms: a systematic review

Clinical Prediction Models for Peripherally Inserted Central Catheter-Related Venous Thrombosis: A Systematic Review and Meta-analysis

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