Methods for Stratification and Validation Cohorts: A Scoping Review

Moral, Teresa Torres; Sánchez-Niubò, Albert; Monistrol-Mula, Anna; Gerardi, Chiara; Banzi, Rita; Garcia, Paula A. Agudelo; Demotes-Mainard, Jacques; Haro, Josep María

doi:10.3390/jpm12050688

Cited by 5 publications

(5 citation statements)

References 77 publications

(240 reference statements)

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“…Optimal patient stratification is required to sustain the precision medicine revolution occurring in the clinical setting (43). While significant progress has been made in classification problems, particularly in domains like single-cell transcriptomic analysis (44,45), unsupervised clustering of patients based on clinical information is still in the developmental stage (7,46). Notably, the existing challenges in clinical stratification are often addressed using ad-hoc solutions that consider mixed data types, missing values, or highly correlated variables.…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, the World Health Organization has acknowledged patient stratification as a valuable approach for enhancing population health management and providing better-tailored services (6). In conceptual terms, patient stratification can be described as the process of grouping or clustering patients based on specific characteristics or patterns without relying on labelled data or information about future outcomes (7). Therefore, contrary to scores developed using classical statistical approaches based on the clinical course, stratification can capture features explaining patients' heterogeneity independently of their association with patient outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous attempts have been made to identify subgroups within clinical datasets (7)(8)(9). However, the lack of a universally applicable approach poses a significant challenge in the field of clustering analysis.…”

Section: Introductionmentioning

confidence: 99%

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ClustALL: A robust clustering strategy for stratification of patients with acutely decompensated cirrhosis

Palomino-Echeverria,

Huergou,

Ortega-Legarre

et al. 2023

Preprint

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Patient heterogeneity represents a significant challenge for both individual patient management and clinical trial design, especially in the context of complex diseases. Most existing clinical classifications are based on scores built to predict patients’ outcomes. These classical methods may thus miss features that contribute to heterogeneity without necessarily translating into prognostic implications.To address patient heterogeneity at hospital admission, we developed ClustALL, a computational pipeline designed to handle common clinical data challenges such as mixed data types, missing values, and collinearity. ClustALL also facilitates the unsupervised identification of multiple and robust stratifications. We applied ClustALL to a prospective European multicentre cohort of patients with acutely decompensated cirrhosis (AD) (n=766), a highly heterogeneous disease. ClustALL identified five robust stratifications for patients with AD, using only data at hospital admission. All stratifications included markers of impaired liver function and number of organ dysfunction or failure, and most included precipitating events. When focusing on one of these stratifications, patients were categorized into three clusters characterized by typical clinical features but also having a prognostic value. Re-assessment of patient stratification during follow-up delineated patients’ outcomes, with further improvement of the prognostic value of the stratification. We validated these findings in an independent prospective multicentre cohort of patients from Latin America (n=580).In conclusion, this study developed ClustALL, a novel and robust stratification method capable of addressing challenges tied to intricate clinical data and applicable to complex diseases. By applying ClustALL to patients with AD, we identified three patient clusters, offering insights that could guide future clinical trial design.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ClustALL: A robust clustering strategy for stratification of patients with acutely decompensated cirrhosis

Palomino-Echeverria,

Huergou,

Ortega-Legarre

et al. 2023

Preprint

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“…Compared to systematic reviews that aim to answer specific questions, scoping reviews are used to determine the scope o available evidence in a given field and examine how research is conducted in that field This scoping review is part of the PERMIT project (PERsonalized MedIcine Trials), which aims to map the methods for personalized medicine research and build recommendations for robustness and reproducibility of different stages of the development programs. Although several categorization may be proposed, the PERMIT project considers four main building blocks of the personalized medicine research pipeline: (1) design, building and management of stratification and validation cohorts; (2) application of machine learning methods for patient stratification; (3) use of preclinical methods for translational development, including the use of preclinical models used to assign treatments to patient clusters; and (4) evaluation of treatments in randomized clinical trials [22][23][24]. This scoping review covers the third building block in this framework.…”

Section: Methodsmentioning

confidence: 99%

Evaluating Translational Methods for Personalized Medicine—A Scoping Review

Fosse

Oldoni²,

Gerardi³

et al. 2022

JPM

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The introduction of personalized medicine, through the increasing multi-omics characterization of disease, brings new challenges to disease modeling. The scope of this review was a broad evaluation of the relevance, validity, and predictive value of the current preclinical methodologies applied in stratified medicine approaches. Two case models were chosen: oncology and brain disorders. We conducted a scoping review, following the Joanna Briggs Institute guidelines, and searched PubMed, EMBASE, and relevant databases for reports describing preclinical models applied in personalized medicine approaches. A total of 1292 and 1516 records were identified from the oncology and brain disorders search, respectively. Quantitative and qualitative synthesis was performed on a final total of 63 oncology and 94 brain disorder studies. The complexity of personalized approaches highlights the need for more sophisticated biological systems to assess the integrated mechanisms of response. Despite the progress in developing innovative and complex preclinical model systems, the currently available methods need to be further developed and validated before their potential in personalized medicine endeavors can be realized. More importantly, we identified underlying gaps in preclinical research relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. To achieve a broad implementation of predictive translational models in personalized medicine, these fundamental deficits must be addressed.

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“…Ideally, robust and reproducible methods should cover all the steps between the generation of the hypothesis (e.g. a given stratum of patients could better respond to a treatment), its validation, and preclinical development, up to the definition of its value in a clinical setting [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Recommendations for robust and reproducible preclinical research in personalised medicine

Fosse

Oldoni²,

Bietrix³

et al. 2023

BMC Med

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Background Personalised medicine is a medical model that aims to provide tailor-made prevention and treatment strategies for defined groups of individuals. The concept brings new challenges to the translational step, both in clinical relevance and validity of models. We have developed a set of recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. Methods These recommendations have been developed following four main steps: (1) a scoping review of the literature with a gap analysis, (2) working sessions with a wide range of experts in the field, (3) a consensus workshop, and (4) preparation of the final set of recommendations. Results Despite the progress in developing innovative and complex preclinical model systems, to date there are fundamental deficits in translational methods that prevent the further development of personalised medicine. The literature review highlighted five main gaps, relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. We identified five points of focus for the recommendations, based on the consensus reached during the consultation meetings: (1) clinically relevant translational research, (2) robust model development, (3) transparency and education, (4) revised regulation, and (5) interaction with clinical research and patient engagement. Here, we present a set of 15 recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. Conclusions Appropriate preclinical models should be an integral contributor to interventional clinical trial success rates, and predictive translational models are a fundamental requirement to realise the dream of personalised medicine. The implementation of these guidelines is ambitious, and it is only through the active involvement of all relevant stakeholders in this field that we will be able to make an impact and effectuate a change which will facilitate improved translation of personalised medicine in the future.

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Methods for Stratification and Validation Cohorts: A Scoping Review

Cited by 5 publications

References 77 publications

ClustALL: A robust clustering strategy for stratification of patients with acutely decompensated cirrhosis

ClustALL: A robust clustering strategy for stratification of patients with acutely decompensated cirrhosis

Evaluating Translational Methods for Personalized Medicine—A Scoping Review

Recommendations for robust and reproducible preclinical research in personalised medicine

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