Dataset Information

Multisuture and Syndromic Craniosynostoses: Simplifying the Complex.

ABSTRACT: Most complex craniosynostoses are managed the same way as syndromic craniosynostoses (SCs), as these patients often experience similar problems regarding cognition and increased intracranial pressure (ICP). The evaluation and treatment plan for craniosynostoses is complex, and this, additionally, is complicated by the age at presentation. In this article, the authors review the complexity of SCs in the presentation and management. An algorithm is necessary for such multifaceted and multidimensional pathology as craniosynostoses. In most algorithms, posterior calvarial distraction is a consistent early option for complex craniosynostoses presenting early with raised ICP. Addressing the airway early is critical when significant airway issues are there. All other surgical interventions are tailored on the basis of presentation and age.

SUBMITTER: Udayakumaran S

PROVIDER: S-EPMC9648657 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Multisuture and Syndromic Craniosynostoses: Simplifying the Complex.

Udayakumaran Suhas S Krishnadas Arjun A Subash Pramod P

Journal of pediatric neurosciences 20220919 Suppl 1

Most complex craniosynostoses are managed the same way as syndromic craniosynostoses (SCs), as these patients often experience similar problems regarding cognition and increased intracranial pressure (ICP). The evaluation and treatment plan for craniosynostoses is complex, and this, additionally, is complicated by the age at presentation. In this article, the authors review the complexity of SCs in the presentation and management. An algorithm is necessary for such multifaceted and multidimensio ...[more]

PMID: 36388010

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Project description:BackgroundPersonalizing medical treatments based on patient-specific risks and preferences can improve patient health. However, models to support personalized treatment decisions are often complex and difficult to interpret, limiting their clinical application.MethodsWe present a new method, using machine learning to create meta-models, for simplifying complex models for personalizing medical treatment decisions. We consider simple interpretable models, interpretable ensemble models, and noninterpretable ensemble models. We use variable selection with a penalty for patient-specific risks and/or preferences that are difficult, risky, or costly to obtain. We interpret the meta-models to the extent permitted by their model architectures. We illustrate our method by applying it to simplify a previously developed model for personalized selection of antipsychotic drugs for patients with schizophrenia.ResultsThe best simplified interpretable, interpretable ensemble, and noninterpretable ensemble models contained at most half the number of patient-specific risks and preferences compared with the original model. The simplified models achieved 60.5% (95% credible interval [crI]: 55.2-65.4), 60.8% (95% crI: 55.5-65.7), and 83.8% (95% crI: 80.8-86.6), respectively, of the net health benefit of the original model (quality-adjusted life-years gained). Important variables in all models were similar and made intuitive sense. Computation time for the meta-models was orders of magnitude less than for the original model.LimitationsThe simplified models share the limitations of the original model (e.g., potential biases).ConclusionsOur meta-modeling method is disease- and model- agnostic and can be used to simplify complex models for personalization, allowing for variable selection in addition to improved model interpretability and computational performance. Simplified models may be more likely to be adopted in clinical settings and can help improve equity in patient outcomes.

Dataset Information

Multisuture and Syndromic Craniosynostoses: Simplifying the Complex.

Publications

Multisuture and Syndromic Craniosynostoses: Simplifying the Complex.

Similar Datasets

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