Prediction models in gynaecology: Transparent reporting needed for clinical application

Recent efforts to improve the reliability and efficiency of scientific research have caught the attention of researchers conducting prediction modeling studies (PMSs). Use of prediction models in oral health has become more common over the past decades for predicting the risk of diseases and treatment outcomes. Risk of bias and insufficient reporting present challenges to the reproducibility and implementation of these models. A recent tool for bias assessment and a reporting guideline—PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis)—have been proposed to guide researchers in the development and reporting of PMSs, but their application has been limited. Following the standards proposed in these tools and a systematic review approach, a literature search was carried out in PubMed to identify oral health PMSs published in dental, epidemiologic, and biostatistical journals. Risk of bias and transparency of reporting were assessed with PROBAST and TRIPOD. Among 2,881 papers identified, 34 studies containing 58 models were included. The most investigated outcomes were periodontal diseases (42%) and oral cancers (30%). Seventy-five percent of the studies were susceptible to at least 4 of 20 sources of bias, including measurement error in predictors ( n = 12) and/or outcome ( n = 7), omitting samples with missing data ( n = 10), selecting variables based on univariate analyses ( n = 9), overfitting ( n = 13), and lack of model performance assessment ( n = 24). Based on TRIPOD, at least 5 of 31 items were inadequately reported in 95% of the studies. These items included sampling approaches ( n = 15), participant eligibility criteria ( n = 6), and model-building procedures ( n = 16). There was a general lack of transparent reporting and identification of bias across the studies. Application of the recommendations proposed in PROBAST and TRIPOD can benefit future research and improve the reproducibility and applicability of prediction models in oral health.

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Breast Cancer Risk Prediction Models: Challenges in Clinical Application

10.1188/19.cjon.256-259 ◽

2019 ◽

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Risk Prediction ◽

Clinical Application ◽

Prediction Models ◽

Risk Prediction Models

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Multi-perspective predictive modeling for acute kidney injury in general hospital populations using electronic medical records

JAMIA Open ◽

10.1093/jamiaopen/ooy043 ◽

2018 ◽

Vol 2 (1) ◽

pp. 115-122 ◽

Cited By ~ 7

Author(s):

Jianqin He ◽

Yong Hu ◽

Xiangzhou Zhang ◽

Lijuan Wu ◽

Lemuel R Waitman ◽

...

Keyword(s):

Acute Kidney Injury ◽

Clinical Application ◽

Lead Time ◽

Model Building ◽

Prediction Models ◽

Tertiary Care ◽

Area Under The Curve ◽

Kidney Injury ◽

Multiple Perspectives ◽

Future Health

Abstract Objectives Acute kidney injury (AKI) in hospitalized patients puts them at much higher risk for developing future health problems such as chronic kidney disease, stroke, and heart disease. Accurate AKI prediction would allow timely prevention and intervention. However, current AKI prediction researches pay less attention to model building strategies that meet complex clinical application scenario. This study aims to build and evaluate AKI prediction models from multiple perspectives that reflect different clinical applications. Materials and Methods A retrospective cohort of 76 957 encounters and relevant clinical variables were extracted from a tertiary care, academic hospital electronic medical record (EMR) system between November 2007 and December 2016. Five machine learning methods were used to build prediction models. Prediction tasks from 4 clinical perspectives with different modeling and evaluation strategies were designed to build and evaluate the models. Results Experimental analysis of the AKI prediction models built from 4 different clinical perspectives suggest a realistic prediction performance in cross-validated area under the curve ranging from 0.720 to 0.764. Discussion Results show that models built at admission is effective for predicting AKI events in the next day; models built using data with a fixed lead time to AKI onset is still effective in the dynamic clinical application scenario in which each patient’s lead time to AKI onset is different. Conclusion To our best knowledge, this is the first systematic study to explore multiple clinical perspectives in building predictive models for AKI in the general inpatient population to reflect real performance in clinical application.

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

Neuroscience & Biobehavioral Reviews ◽

10.1016/j.neubiorev.2021.02.032 ◽

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

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Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T 2 *‐weighted images of cervical spondylotic myelopathy

JOR Spine ◽

10.1002/jsp2.1178 ◽

2021 ◽

Author(s):

Meng‐Ze Zhang ◽

Han‐Qiang Ou‐Yang ◽

Liang Jiang ◽

Chun‐Jie Wang ◽

Jian‐Fang Liu ◽

...

Keyword(s):

Machine Learning ◽

Clinical Application ◽

Cervical Spondylotic Myelopathy ◽

Prediction Models ◽

Learning Methods ◽

Machine Learning Methods ◽

Optimal Machine

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Machine learning prediction models in orthopedic surgery: A systematic review in transparent reporting

Journal of Orthopaedic Research® ◽

10.1002/jor.25036 ◽

2021 ◽

Author(s):

Olivier Q. Groot ◽

Paul T. Ogink ◽

Amanda Lans ◽

Peter K. Twining ◽

Neal D. Kapoor ◽

...

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Orthopedic Surgery ◽

Prediction Models ◽

Transparent Reporting

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Quantitative structure activity/pharmacokinetics relationship studies of HIV-1 protease inhibitors using three modelling methods

Medicinal Chemistry ◽

10.2174/1573406415666190826154505 ◽

2019 ◽

Vol 15 ◽

Author(s):

Dan Han ◽

Jianjun Tan ◽

Jingrui Men ◽

Chunhua Li ◽

Xiaoyi Zhang

Keyword(s):

Clinical Application ◽

Computational Models ◽

Prediction Models ◽

Good Choice ◽

Support Vector ◽

Quantitative Structure ◽

Least Squares Regression ◽

Structure Activity ◽

Modelling Methods ◽

Hiv 1

Background: HIV-1 protease inhibitor (PIs) is a good choice of ADIS patients. Nevertheless, for PIs, there are several bugs in clinical application, like drug resistance, the large dose, the high costs and so on, among which, the poor pharmacokinetics property is one of the important reasons that leads to the failure of its clinical application. Objective: We aimed to build computational models for studying the relationship between PIs structure and its pharmacological activities. Method: We collected experimental values of koff/Ki and structures of 50 PIs through a careful literature and database search. Quantitative structure activity/pharmacokinetics relationship (QSAR/QSPR) models were constructed by support vector machine (SVM), partial-least squares regression (PLSR) and back-propagation neural network (BPNN). Results: For QSAR models, SVM, PLSR and BPNN all generated reliable prediction models with the r2 of 0.688, 0.768 and 0.787, respectively, and "r" _"pred" ^"2" of 0.748, 0.696 and 0.640, respectively. For QSPR models, the optimum models of SVM, PLSR and BPNN got the r2 of 0.952, 0.869 and 0.960, respectively, and the "r" _"pred" ^"2" of 0.852, 0.628 and 0.814, respectively. Conclusion: Among these three modelling methods, SVM showed superior ability to PLSR and BPNN both in QSAR/QSPR modelling of PIs, thus, we suspected that SVM was more suitable for predicting activities of PIs. In addition, 3D-MoRSE descriptors may have a tight relationship with the Ki values of PIs, and the GETAWAY descriptors have significant influence for both koff and Ki in PLSR equations.

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