Clinical prediction models

Mijderwijk, Hendrik-Jan; Beez, Thomas; Hänggi, Daniel; Nieboer, Daan

doi:10.1007/s00381-020-04577-8

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Building a model involves the selection of possible predictors and combining them into a multivariate model, which is usually analyzed by logistic regression or Cox regression. Logistic regression analysis is most commonly used to predict binary events, while Cox regression is often used for long-term prognostic results ( 15 ). This paper is a retrospective study, and the content of the study is the risk prediction of renal damage in IgAV.…”

Section: Discussionmentioning

confidence: 99%

Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis

Yang

et al. 2022

Front. Pediatr.

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ObjectivesTo explore the risk factors for renal damage in childhood immunoglobulin A vasculitis (IgAV) within 6 months and construct a clinical model for individual risk prediction.MethodsWe retrospectively analyzed the clinical data of 1,007 children in our hospital and 287 children in other hospitals who were diagnosed with IgAV. Approximately 70% of the cases in our hospital were randomly selected using statistical product service soltions (SPSS) software for modeling. The remaining 30% of the cases were selected for internal verification, and the other hospital's cases were reviewed for external verification. A clinical prediction model for renal damage in children with IgAV was constructed by analyzing the modeling data through single-factor and multiple-factor logistic regression analyses. Then, we assessed and verified the degree of discrimination, calibration and clinical usefulness of the model. Finally, the prediction model was rendered in the form of a nomogram.ResultsAge, persistent cutaneous purpura, erythrocyte distribution width, complement C3, immunoglobulin G and triglycerides were independent influencing factors of renal damage in IgAV. Based on these factors, the area under the curve (AUC) for the prediction model was 0.772; the calibration curve did not significantly deviate from the ideal curve; and the clinical decision curve was higher than two extreme lines when the prediction probability was ~15–82%. When the internal and external verification datasets were applied to the prediction model, the AUC was 0.729 and 0.750, respectively, and the Z test was compared with the modeling AUC, P > 0.05. The calibration curves fluctuated around the ideal curve, and the clinical decision curve was higher than two extreme lines when the prediction probability was 25~84% and 14~73%, respectively.ConclusionThe prediction model has a good degree of discrimination, calibration and clinical usefulness. Either the internal or external verification has better clinical efficacy, indicating that the model has repeatability and portability.Clinical trial registration:www.chictr.org.cn, identifier ChiCTR2000033435.

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Section: Discussionmentioning

confidence: 99%

Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis

Yang

et al. 2022

Front. Pediatr.

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“…In healthcare applications, machine learning models are trained using patient data to provide an estimate of a patient's current clinical state (diagnosis) or future clinical state (prediction). Though diagnostic and prognostic classification models estimate the same statistical quantity (i.e., the conditional probability of a clinical state or event), diagnosis and prognosis differ greatly in their interpretation and use cases 12 . To complicate matters further, it is common in the machine learning literature to refer to any point estimate (i.e., the model or algorithm's "best guess") of this type as a "prediction" 13 .…”

Section: Introductionmentioning

confidence: 99%

Second opinion needed: communicating uncertainty in medical machine learning

2021

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There is great excitement that medical artificial intelligence (AI) based on machine learning (ML) can be used to improve decision making at the patient level in a variety of healthcare settings. However, the quantification and communication of uncertainty for individual predictions is often neglected even though uncertainty estimates could lead to more principled decision-making and enable machine learning models to automatically or semi-automatically abstain on samples for which there is high uncertainty. In this article, we provide an overview of different approaches to uncertainty quantification and abstention for machine learning and highlight how these techniques could improve the safety and reliability of current ML systems being used in healthcare settings. Effective quantification and communication of uncertainty could help to engender trust with healthcare workers, while providing safeguards against known failure modes of current machine learning approaches. As machine learning becomes further integrated into healthcare environments, the ability to say “I’m not sure” or “I don’t know” when uncertain is a necessary capability to enable safe clinical deployment.

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“…There has been an increase in the number of articles reporting on prediction models in pediatric neurosurgery [4]. A well-known and widely used prediction model in pediatric neurosurgery is the endoscopic third ventriculostomy success score (EVTSS) [1].…”

Section: Introductionmentioning

confidence: 99%

Application of clinical prediction modeling in pediatric neurosurgery: a case study

et al. 2021

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There has been an increasing interest in articles reporting on clinical prediction models in pediatric neurosurgery. Clinical prediction models are mathematical equations that combine patient-related risk factors for the estimation of an individual’s risk of an outcome. If used sensibly, these evidence-based tools may help pediatric neurosurgeons in medical decision-making processes. Furthermore, they may help to communicate anticipated future events of diseases to children and their parents and facilitate shared decision-making accordingly. A basic understanding of this methodology is incumbent when developing or applying a prediction model. This paper addresses this methodology tailored to pediatric neurosurgery. For illustration, we use original pediatric data from our institution to illustrate this methodology with a case study. The developed model is however not externally validated, and clinical impact has not been assessed; therefore, the model cannot be recommended for clinical use in its current form.

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Clinical prediction models

Cited by 5 publications

References 4 publications

Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis

Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis

Second opinion needed: communicating uncertainty in medical machine learning

Application of clinical prediction modeling in pediatric neurosurgery: a case study

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