Defining Pre-Asthma: A Systematic Review and Meta-Analysis Protocol for Multidimensional Early Prediction Models for Adult Asthma in Undiagnosed Populations

Defining Pre-Asthma: A Systematic Review and Meta-Analysis Protocol for Multidimensional Early Prediction Models for Adult Asthma in Undiagnosed Populations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Defining Pre-Asthma: A Systematic Review and Meta-Analysis Protocol for Multidimensional Early Prediction Models for Adult Asthma in Undiagnosed Populations Yilai Li, Lishan Yuan, Lei Wang, Li Zhang, Ying Liu, Lei Liu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6750481/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background : Asthma poses substantial global health challenges due to its variable clinical manifestations and unmet needs in early risk stratification. Current prediction models lack generalizability for those with susceptibility, particularly when early symptoms are nonspecific. A rigorous synthesis of existing models for adult asthma is needed to evaluate their validity, prioritize predictors, and guide targeted prevention strategies. Objective : This study aims to synthesize early asthma prediction models in undiagnosed populations, evaluating their performance metrics and clinical utility to try defining "pre-asthma" by integrating multidimensional predictors, specifically demographic, genetic, environmental, phenotypic/endotypic biomarkers, and symptom trajectories. Methods: Following the CHARMS framework, we will systematically search PubMed, Web of Science, Embase, Cochrane Library, Scopus and IEEE Xplore (inception–April 2025) for studies developing or externally validating adult asthma prediction models. Data extraction and risk of bias assessment (by PROBAST and TRIPOD criteria) will be performed independently by two reviewers. Meta-analysis using random-effects models will synthesize the performance measures, with heterogeneity explored by meta-regression. Methodological rigor and clinical relevance of predictors will be evaluated to establish evidence-based recommendations and used to define "pre-asthma". Study registration number : CRD420251047047 pre-asthma asthma prediction models machine learning systematic review critical appraisal meta-analysis Patient and public involvement This study is a systematic evaluation of what has been reported in the literature. It does not involve patient and public participation in the design, conduct, or reporting of this study. Strengths and limitations of this study: This study will to synthesize early asthma prediction models in undiagnosed populations, evaluating their performance metrics and clinical utility to try defining "pre-asthma" by integrating multidimensional predictors. A highly sensitive search strategy and robust quality assessment criteria (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) will be used to appraise existing early asthma prediction models in undiagnosed adult populations. Exclusion of journal articles published in languages other than English is a limitation of this study. INTRODUCTION Asthma is a heterogeneous disorder exhibiting considerable variability in both clinical phenotypes and underlying endotypes.( 1 , 2 ) It represents a significant global health concern, affecting approximately 1–29% of the population across different regions—totaling nearly 300 million individuals worldwide—and accounting for an estimated 1,000 deaths each day.( 1 , 3 , 4 ) Clinical manifestations typically arise only when airway inflammation exceeds a critical threshold or when substantial structural damage has occurred.( 5 , 6 ) This progression underscores the importance of early detection and intervention, as by the time symptoms manifest, the disease may have progressed to an irreversible stage with no possibility of cure. Understanding the underlying mechanisms of asthma and the triggers that lead to asthma is crucial for developing effective treatment strategies and reducing the risk of developing asthma. It is even more necessary for us to accurately understand the characteristics of the “pre-asthma stage”, identify treatable features, intervene early, and ultimately reduce the burden of asthma. Recently, the concept of early preemptive diagnosis has evolved, such as pre-COPD.( 7 , 8 ) Pre-asthma has been attempted to be defined as a subclinical condition characterized by mild, intermittent lower airway inflammation or hypersensitivity, with the potential to progress to asthma.( 9 ) However, this definition may be limited due to the absence of specific biomarkers, unclear temporal progression, limited consideration of non-inflammatory mechanisms, and genetic and environmental factors.( 10 , 11 ) It also faces challenges in early identification and has limited utility in understanding etiology and risk stratification. Besides, relying on asthma assessment criteria to evaluate individuals with suspected “pre-asthma” is unsuitable, as these populations, by definition, have not yet developed asthma. These issues highlight the need for a more precise and comprehensive identification and definition of “pre-asthma”. Early identification of individuals at risk for asthma is crucial for implementing prevention strategies and personalized interventions. Existing prediction models for asthma are limited to exacerbation risk in established asthmatic patients, relying on biomarkers like blood eosinophils and FeNO for risk assessment.( 12 , 13 ) These models, however, are not suitable for undiagnosed individuals with asthma risk factors, regardless of whether they exhibit early symptoms. Given the multifactorial nature of asthma, a more effective approach for early prediction may involve categorizing cases based on specific and multidimensional characteristics and biomarkers. The predictive value of single-dimensiona lcommon asthma assessments, such as bronchial provocation, lung function, allergy tests, chest imaging, FeNO measurement, and blood eosinophil counts, is limited by their low sensitivity and specificity.( 14 – 16 ) Recent advancements in machine learning have spurred the development of innovative predictive models, which serve as effective tools for enhancing the accuracy of predicting asthma onset.( 17 ) However, the heterogeneity in model design, including the selection of predictors and the definition of outcomes, coupled with methodological flaws, has hindered their clinical application. Therefore, there is an urgent need for a systematic review of existing evidence to evaluate model performance, identify reliable predictors for early assessment of an individual’s risk of developing asthma, define the concept of “pre-asthma,” explore interventions targeting early risk factors to prevent asthma onset, and guide future research directions.. This study aims to conduct a systematic review, critical appraisal, and meta-analysis of early prediction models for asthma development in undiagnosed populations. We will rigorously assess the validity (calibration and discrimination) and clinical utility of these models to identify key predictors and clinical features characterizing a novel “pre-asthma” stage. By analogy with pre-COPD, we will establish an evidence-based definition and diagnostic criteria for “pre-asthma.” This review will focus on the multidimensional integration of demographic and clinical characteristics, genetic predisposition, environmental exposures, phenotypic/endotypic biomarkers, and temporal dynamics to advance early risk identification. METHODS AND ANALYSIS Protocol Registration and Methodology Framework This protocol has been developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement. ( 18 ) We will design and conduct this systematic review according to the CHARMS. ( 19 , 20 ) This protocol was registered on the PROSPERO international registry of systematic reviews (CRD420251047047). Eligibility criteria for study selection We will include studies that outlines the development and external validation of novel multi-variable models for forecasting adult asthma cases. We provide a comprehensive outline of the PICOTS (population, intervention, comparator, outcomes, timing, setting) for this systematic review (Table 1 ) according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guideline (TRIPOD).( 21 , 22 ) We will identify and include the prediction model studies based on the following criteria in Table 1 . Table 1 The PICOTS description about study PICOTS Elements Population (P) 1. Studies reporting on prediction models proposed for asthma diagnosed after 18 years (adult asthma) 2. Asthma may have been diagnosed by any criteria, including but not limited to those outlined in established guidelines such as the GINA or local asthma diagnostic guidelines 3. The standard diagnostic criteria of asthma have been reported 4. Participants included in the primary studies should not have been diagnosed with asthma at the time of inclusion Intervention (I) 1. Any prediction model to predict the risk of the onset of asthma in participants undiagnosed asthma 2. Prediction model development studies with and without external validation and external model validation studies with or without model updating Comparator (C) Not applicable Outcomes (O) Risk of the onset of asthma after 18 years reported by prediction models (adult asthma) Timing (T) 1. Predictive variables measured at any timepoint during the clinical course of developing into asthma 2. No specific limitation applied in prediction horizon Setting (S) No limitation. Prediction models designed for use by healthcare professionals in any clinical settings, and which include participants enrolled at any time prior to an asthma diagnosis Types of studies and limits Studies ( 1 ) reported the development or validation multivariable model(s) of asthma with or without external validation with at least two predictors; ( 2 ) that evaluated or updated the quantitative measure of model performance of an existing model in an independent population in terms of overall performance, discriminative ability and calibration of a certain prediction model. Any identified and relevant review articles will be used to identify eligible primary studies. Studies will be limited to those conducted in humans. There will be no limits on the year of publication. This search will be limited to reports in English, and for which full-text access is available. No restrictions will be placed on sex or gender, race, comorbidities, or other characteristics. Animal studies, conference abstracts, editorials, case reports, letters, commentaries, book chapters, protocols, guidelines, unpublished articles and surveys will be excluded. A study will be excluded from consideration if the crucial missing data (such as study information, sample size, and model performance indicators) cannot be adequately supplemented. Databases We will search the following databases from their inception to April 2025: PubMed, EMBASE, Web of Science, Scopus, Cochrane Library, and IEEE Xplore. Search strategy This study will adopt a three-step search strategy to ensure comprehensive literature coverage. Initially, two experienced reviewers will develop the search strategy, incorporating terms related to asthma and predictive modeling, and conduct a preliminary search on PubMed. Subsequently, two independent reviewers will perform the searches. In the final phase, a thorough review of the selected literature and related references will be conducted to identify additional studies that meet the inclusion criteria. The search strategy was built using keywords including asthma-related terms and prediction modelling-related terms. The following search strategy with related key words was developed with an example of PubMed: (("Asthma"[MeSH] OR "Asthma/diagnosis"[MeSH] OR "Asthma/epidemiology"[MeSH] OR asthm*[tiab]) AND ("Prognosis"[MeSH] OR "risk assess*"[MeSH] OR "machine learning"[MeSH] OR ("predict* model"[tiab] OR "prognostic model"[tiab] OR "risk model"[tiab] OR "predict* rule"[tiab] OR "risk stratification"[tiab] OR nomogram*[tiab] OR "predictive analy*"[tiab] OR "artificial intelligence"[tiab] OR "random forest"[tiab] OR "neural network"[tiab] OR "supervised learning"[tiab] OR "regression analy*"[tiab] OR "logistic regression"[tiab] OR "cox regression"[tiab] OR "lasso regression"[tiab] OR "ridge regression"[tiab] OR "elastic net"[tiab] OR "survival analysis"[tiab] OR "bayesian model"[tiab] OR "support vector machine"[tiab] OR "gradient boosting"[tiab] OR "xgboost"[tiab] OR "ensemble learning"[tiab] OR "internal validation"[tiab] OR "cross validation"[tiab] OR "overfitting"[tiab] OR "variable selection"[tiab] OR "feature engineering"[tiab] OR "sensitivity and specificity"[tiab] OR "positive predictive value"[tiab] OR "calibration curve"[tiab] OR "brier score"[tiab])) AND ("validation stud*"[pt] OR "discrimination"[tiab] OR "calibration"[tiab] OR "AUC"[tiab] OR "C-statistic"[tiab] OR "external validation"[tiab] OR "model performance"[tiab] OR "clinical utility"[tiab])) NOT ("Animals"[MeSH] NOT "Humans"[MeSH]). Additionally, manual review of references in the selected literature will be conducted to identify potentially relevant studies. Study selection Reviewers will undergo formal training at the Cochrane China Center prior to the formal selection of studies. The eligibility criteria will be explained and discussed in detail to ensure that all reviewers have a uniform understanding. Search results will be combined using Endnote, and duplicates will be removed. Two reviewers (YL and LY) will independently screen the titles and abstracts of every article according to the selection criteria. Subsequently, both reviewers will independently read the screened full texts of the studies and rigorously evaluate them against the predefined eligibility criteria. For each excluded article, the reasons for exclusion will be specified. Disagreements or doubts will be resolved by consensus, and if consensus cannot be reached, the full texts of the studies will be independently assessed for further evaluation. Any conflicts will be resolved through discussion with the senior advisors (XZ and LW), if necessary. Data extraction Two independent reviewers (YL and LY) will perform the data extraction, utilizing a standardized data extraction form for all included studies. The form was developed based on the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (the CHARMS checklist). [ ( 19 , 20 ) ] For each eligible study, we plan to extract information on study aims, data source, participants, stakeholders; algorithms, potential predictors, sample size, missing data, diagnostic criteria for asthma, predicted outcomes, model development, model performance (i.e., discrimination, calibration, clinical utility, and classification), final multivariable models, and interpretation of presented models. Disagreements on data elements will be resolved through discussion or consultation with a third reviewer when consensus cannot be reached. Information not reported in the publication will be obtained from the authors whenever possible. Additionally, if insufficient information is obtained, the study will be excluded from the review. Finally, the reviewers will cross-check the extracted data and review the full text to identify and correct discrepancies, ensuring accuracy. Critical appraisal To achieve an inter-rater reliability kappa coefficient of over 0.8 for risk of bias assessment, the assessors will pre-evaluate a sample of qualified studies. The results of the pilot risk of bias assessment will be discussed among the review authors and assessors. Each selected study will be independently evaluated for the risk of bias and reporting transparency by two reviewers (YL and LY), and the results will be cross-checked. Any disagreements will be resolved through discussion to reach a consensus, and consultation with the senior authors (XZ and LW) will be sought if necessary. Risk of bias and applicability assessment We will critically assess each included prediction model using the PROBAST technique, a tool designed to assess the risk of bias and applicability of prediction model studies.( 19 , 23 ) The PROBAST tool comprises 20 signalling questions divided into four domains: participants, predictors, outcomes, and statistical analysis. ( 19 , 23 ) An overall rating for each domain will be assigned as low, high, or unclear risk of bias. The applicability assessment examines whether the model development/validation study aligns with our systematic review question regarding the target population, predictors, or outcome of interest. Furthermore, a high number of variables (over ten) and the inclusion of many continuous variables (> 4) will be seen as barriers to routine use. Additionally, the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) assessment tool will be employed to appraise the methodological quality of the studies.( 24 – 26 ) Reporting transparency assessment The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD statement), which provides a checklist of 22 items deemed essential for reporting transparency of a prediction model study, will be used to evaluate the transparency of the reporting of the included studies.( 21 , 22 ) We will report the overall TRIPOD adherence score for each study, which was developed to assess the consistency in measuring adherence to the TRIPOD statement. The TRIPOD adherence score is calculated by dividing the sum of the adhered TRIPOD items by the total number of applicable TRIPOD items.( 21 , 22 ) Data synthesis and analysis We will provide a qualitative overview of the model used for each individual study. The characteristics of the models will be tabulated to show classification measures such as sensitivity, specificity, and the AUROC. Clinical utility will be assessed based on ease of use, defined by whether they exist in a clinically useful form, such as a risk score or online calculator. The clinical impact will be measured by convenience of use, risk of bias, applicability, and accuracy of the model. Data will be synthesised by performing meta-analysis by type of prediction modelling study if feasible and identified prediction models are sufficiently homogenous. Derivation and validation of models will be considered separately. Meta-analysis will be conducted in accordance with the Meta-analysis of Observational Studies in Epidemiology group guidelines. Data will be synthesized by meta-analysis if at least five studies are included in a subset with an analogous clinical question assessed repeatedly. A meta-analysis of performance in validation for a common prediction model at the same time point will be performed if at least three studies are included. Performance measures such as discrimination (e.g., AUROC, C-statistics) and calibration (e.g., calibration slope) will be pooled and analyzed using a random effects model. The restricted maximum likelihood and Hartung-Knapp-Sidik-Jonkman methods will be used to estimate between-study heterogeneity and 95% confidence intervals for the average performance.( 27 , 28 ) The clinical and methodological heterogeneity across studies will be evaluated by examining the variability in participants' characteristics (e.g., age, sex distribution, and setting), the definitions and measurement methodologies employed in asthma diagnosis, as well as the risk of bias. Cochran's Q and the I 2 statistics will be calculated for statistical assessment of heterogeneity. If there is clinical and methodological heterogeneity among the included studies, the random effects model approach will be used instead of the fixed effect approach. Potential sources of heterogeneity will be investigated by undertaking meta-regression analyses. If more than ten studies are incorporated into the review, graphical exploration of reporting bias will be conducted using a funnel plot, and statistical assessment will be performed via Egger's test. As recommended, a p-value less than 0.05 will be deemed indicative of publication bias. The meta-analysis will be carried out using R Software V.4.4.3 (R Core Team, Vienna, Austria, available at: https://www.R-project.org ). Subgroup analysis Where there are sufficient eligible studies (≥ 10 studies) included in the review, we planned to conduct subgroup analyses: ( 1 ) age; ( 2 ) sex; ( 3 ) diagnostic criteria for asthma; ( 4 ) asthma phenotype; ( 5 ) type of prediction model (i.e., development or validation); ( 6 ) method of predictive model building; ( 7 ) settings and location; ( 8 ) source of data; ( 9 ) time points; ( 10 ) follow-up duration; and ( 11 ) study quality (risk of bias). Further subgroup analysis will be dependent on the final data extraction. Sensitivity analysis Sensitivity analyses will be conducted by excluding studies with a high risk of bias and those with low reporting transparency to explore their influence on effect size.( 27 ) Summary of findings The findings of this systematic review will be reported in adherence to the TRIPOD guideline and the PRISMA statement. Abbreviations AUROC Area Under the Receiver Operating Characteristic Curve COPD chronic obstructive pulmonary disease C-statistics concordance statistics DTA diagnostic test accuracy FeNO fractional exhaled nitric oxide FEV1 forced expiratory volume in one second GINA Global Initiative for Asthma GRADE the Grading of Recommendations, Assessment, Development, and Evaluation PEF peak expiratory flow PICOTS population, intervention, comparator, outcomes, timing, setting PM2.5 2.5-micrometer Particulate Matter PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses PRS polygenic risk scores TRIPOD Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis Declarations Amendments The protocol for this systematic review will be amended as needed during the peer review process. The author would publish when the study is complete. Ethics approval and consent to participate Ethical approval is not required for this systematic review and meta-analyses as it involves secondary analysis of published literature and does not involve patient or public information. The results will be submitted for publication in a peer-reviewed journal and presented at a relevant conference. Data generated during the research will be available from the corresponding author upon reasonable request. Consent for publication All authors approved and contributed to the final manuscript. Availability of data and materials Not applicable. Competing interests The authors declare that they have no competing interests Funding This work was supported by 1.3.5 project for disciplines of excellence-Clinical Research Fund, West China Hospital, Sichuan University (2023HXFH045), 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYGD23002), National Natural Science Foundation of China (82100032, 82200037,82300036). Authors' contributions XZ conceived the research idea and planned the entire method of undertaking the study. XZ, LW, YL and LY wrote the draft protocol. XZ, LW, LZ, YL, LL, LL, MF, GW, SZ, YY, CZ and DK provided all the guidance. All authors critically revised the manuscript. All authors revised and approved the final version of the manuscript. Acknowledgements The authors would like to acknowledge the contribution of the Cochrane China Center at West China Hospital of Sichuan University for the support and guidance in designing the search methodology. Competing interests None declared. Patient consent for publication Not applicable. References Global Initiative for Asthma. Global Strategy for Asthma Management and Prevention. https://ginasthma.org/2025-gina-strategy-report/ . Accessed 26 May 2025. Kotlia P, Pant J, Lohani MC. Identifying asthma risk factors and developing predictive models for early intervention using machine learning. Biomed Pharmacol J. 2025;18(December Spl Edition):295–314. Mortimer K, Reddel HK, Pitrez PM, Bateman ED. Asthma management in low and middle income countries: case for change. Eur Respir J. 2022;60(3):2103179. Asher MI, Rutter CE, Bissell K, Chiang CY, El Sony A, Ellwood E, et al. Worldwide trends in the burden of asthma symptoms in school-aged children: global asthma network phase I cross-sectional study. Lancet. 2021;398(10311):1569–80. Busse WW. The relationship of airway hyperresponsiveness and airway inflammation. Chest. 2010;138(2):S4–10. Xu Z, Li R, Wang L, Wu Y, Tian Y, Su Y, et al. Pathogenic role of different phenotypes of immune cells in airway allergic diseases: a study based on Mendelian randomization. Front Immunol. 2024;15:1349470. Global Initiative for Chronic Obstructive Lung Disease. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease. https://goldcopd.org/2025-gold-report/ . Accessed 26 May 2025. Han MK, Agusti A, Celli BR, Criner GJ, Halpin DMG, Roche N, et al. From GOLD 0 to pre-COPD. Am J Respir Crit Care Med. 2021;203(4):414–23. Scadding GK, McDonald M, Backer V, Scadding G, Bernal-Sprekelsen M, Conti DM, et al. Pre-asthma: a useful concept for prevention and disease-modification? A EUFOREA paper. Part 1-allergic asthma. Front Allergy. 2023;4:1291185. Yue M, Gaietto K, Han YY, Rosser FJ, Xu Z, Qoyawayma C, et al. Transcriptomic profiles in nasal epithelium and asthma endotypes in youth. JAMA. 2025;333(4):307. Azim A, Barber C, Dennison P, Riley J, Howarth P. Exhaled volatile organic compounds in adult asthma: a systematic review. Eur Respir J. 2019;54(3):1900056. Liu A, Zhang Y, Yadav C, Chen W. An updated systematic review on asthma exacerbation risk prediction models between 2017 and 2023: risk of bias and applicability. J Asthma Allergy. 2025;18:579–89. Simmalee K, Kawamatawong T, Vitte J, Demoly P, Lumjiaktase P. Exploring the pathogenesis and clinical implications of asthma, chronic obstructive pulmonary disease (COPD), and asthma-COPD overlap (ACO): a narrative review. Front Med. 2025;12:1514846. Hartl S, Breyer MK, Burghuber OC, Ofenheimer A, Schrott A, Urban MH, et al. Blood eosinophil count in the general population: typical values and potential confounders. Eur Respir J. 2020;55(5):1901874. Winkel P, Statland BE, Saunders AM, Osborn H, Kupperman H. Within-day physiologic variation of leukocyte types in healthy subjects as assayed by two automated leukocyte differential analyzers. Am J Clin Pathol. 1981;75(5):693–700. Högman M. ERS technical standard: global lung function initiative reference values for exhaled nitric oxide (FENO). Zhong X, Song J, Lei C, Wang X, Wang Y, Yu J, et al. Machine learning-based screening of asthma biomarkers and related immune infiltration. Front Allergy. 2025;6:1506608. Preferred reporting items. for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2016;i4086. Fernandez-Felix BM, López-Alcalde J, Roqué M, Muriel A, Zamora J. CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models. BMC Med Res Methodol. 2023;23(1):44. Moons KGM, De Groot JAH, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 2014;11(10):e1001744. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Br J Surg. 2015;102(3):148–58. Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1–73. Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med. 2019;170(1):W1–33. Schünemann HJ, Jaeschke R, Cook DJ, Bria WF, El-Solh AA, Ernst A, et al. An official ATS statement: grading the quality of evidence and strength of recommendations in ATS guidelines and recommendations. Am J Respir Crit Care Med. 2006;174(5):605–14. Iorio A, Spencer FA, Falavigna M, Alba C, Lang E, Burnand B, et al. Use of GRADE for assessment of evidence about prognosis: Rating confidence in estimates of event rates in broad categories of patients. BMJ. 2015;350(mar16 7):h870–870. Foroutan F, Guyatt G, Trivella M, Kreuzberger N, Skoetz N, Riley RD, et al. GRADE concept paper 2: concepts for judging certainty on the calibration of prognostic models in a body of validation studies. J Clin Epidemiol. 2022;143:202–11. Debray TPA, Damen JAAG, Snell KIE, Ensor J, Hooft L, Reitsma JB et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ. 2017;i6460. IntHout J, Ioannidis JP, Borm GF. The hartung-knapp-sidik-jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-laird method. BMC Med Res Methodol. 2014;14(1):25. Supplementary Files PRISMAPSystRevchecklist.docx funding.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 02 May, 2026 Reviewers agreed at journal 26 Sep, 2025 Reviewers invited by journal 10 Aug, 2025 Editor assigned by journal 25 Jul, 2025 First submitted to journal 29 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6750481","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498235552,"identity":"f92ee05f-3b91-45d6-9cf6-268a4cf2d9e4","order_by":0,"name":"Yilai Li","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yilai","middleName":"","lastName":"Li","suffix":""},{"id":498235553,"identity":"52d01a02-7394-4c5c-b7c8-044443adbb29","order_by":1,"name":"Lishan Yuan","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Lishan","middleName":"","lastName":"Yuan","suffix":""},{"id":498235554,"identity":"b21af7ec-e57a-4279-9efc-1744ac0d12ee","order_by":2,"name":"Lei Wang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""},{"id":498235555,"identity":"5c2d1f7c-802a-428a-a1f3-0e2991adc4e1","order_by":3,"name":"Li Zhang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhang","suffix":""},{"id":498235556,"identity":"57c78153-a149-406b-88b6-e54d82239a63","order_by":4,"name":"Ying Liu","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Liu","suffix":""},{"id":498235557,"identity":"3c7a5719-23fe-4ad8-a447-5291e4f892d6","order_by":5,"name":"Lei Liu","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Liu","suffix":""},{"id":498235558,"identity":"fba22598-f3b3-41be-86aa-370f71c9b38a","order_by":6,"name":"Lingyun Lu","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Lingyun","middleName":"","lastName":"Lu","suffix":""},{"id":498235559,"identity":"92408263-27eb-4ae6-ac2b-50833bb3e079","order_by":7,"name":"Min Feng","email":"","orcid":"","institution":"University of Technology Sydney","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Feng","suffix":""},{"id":498235560,"identity":"da541344-ddcf-46bc-95cf-8599952fa172","order_by":8,"name":"Gang Wang","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Wang","suffix":""},{"id":498235561,"identity":"3aa9042f-d52e-420b-ac7a-c7434d6635bc","order_by":9,"name":"Shuwen Zhang","email":"","orcid":"","institution":"China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuwen","middleName":"","lastName":"Zhang","suffix":""},{"id":498235562,"identity":"6a56c923-4c46-44ee-8894-fd8432efc3f8","order_by":10,"name":"Yulai Yuan","email":"","orcid":"","institution":"Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yulai","middleName":"","lastName":"Yuan","suffix":""},{"id":498235563,"identity":"87f39a49-afab-4c30-a304-d535d3e24e6d","order_by":11,"name":"Chongyang Zhao","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Chongyang","middleName":"","lastName":"Zhao","suffix":""},{"id":498235564,"identity":"131d7d0f-4063-4fbc-81bc-1b12716e5261","order_by":12,"name":"Deying Kang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Deying","middleName":"","lastName":"Kang","suffix":""},{"id":498235565,"identity":"64926e8d-a3b6-4d8d-b924-dd46c7aede90","order_by":13,"name":"Xin Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDACCTjJw/ggocKGNC3MBg/OpBGtBQR42CQfth0irEN+dvOzh1/bLPIMjp89VpHAdoCBv707Aa8WxjnHzI1lzkgUG5zJS7uRwHOHQeLM2Q14tTBLJJhJS1RIJG67wWN2I0HiGYOBRC5+LWwS6d+kJQwgWgoSDA4T1sIjkWMm+QFqC0NCAhFaJCRyyqQZzkgk7j+TYyyRcCCNh6Bf5Gekb5P82VaXOLP9jOHHn/9s5Pjbe/FrAQFmHmSXElQOAow/iFI2CkbBKBgFIxYAADoJRr3cVK9+AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7357-3788","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-05-26 11:50:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6750481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6750481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89092455,"identity":"33e7e640-967a-4ad7-aee5-d70690801752","added_by":"auto","created_at":"2025-08-14 14:57:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":705339,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6750481/v1/6161d54e-8776-4b04-b359-aa3c812ff7b3.pdf"},{"id":89091111,"identity":"8614d2a9-8207-42d4-9288-9a5d56b4b35a","added_by":"auto","created_at":"2025-08-14 14:49:07","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":34416,"visible":true,"origin":"","legend":"","description":"","filename":"PRISMAPSystRevchecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-6750481/v1/a6f405474c71d70c681fcb2f.docx"},{"id":89092453,"identity":"c51471e4-0b02-40a5-a7ae-8a45741a29b2","added_by":"auto","created_at":"2025-08-14 14:57:07","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":16196,"visible":true,"origin":"","legend":"","description":"","filename":"funding.docx","url":"https://assets-eu.researchsquare.com/files/rs-6750481/v1/5d680377387e4cd0671f2c62.docx"}],"financialInterests":"","formattedTitle":"Defining Pre-Asthma: A Systematic Review and Meta-Analysis Protocol for Multidimensional Early Prediction Models for Adult Asthma in Undiagnosed Populations","fulltext":[{"header":"Patient and public involvement","content":"\u003cp\u003eThis study is a systematic evaluation of what has been reported in the literature. It does not involve patient and public participation in the design, conduct, or reporting of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations of this study:\u0026nbsp;\u003c/strong\u003eThis study will to synthesize early asthma prediction models in undiagnosed populations, evaluating their performance metrics and clinical utility to try defining \"pre-asthma\" by integrating multidimensional predictors. A highly sensitive search strategy and robust quality assessment criteria (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) will be used to appraise existing early asthma prediction models in undiagnosed adult populations.\u003c/p\u003e\n\u003cp\u003eExclusion of journal articles published in languages other than English is a limitation of this study.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eAsthma is a heterogeneous disorder exhibiting considerable variability in both clinical phenotypes and underlying endotypes.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) It represents a significant global health concern, affecting approximately 1\u0026ndash;29% of the population across different regions\u0026mdash;totaling nearly 300\u0026nbsp;million individuals worldwide\u0026mdash;and accounting for an estimated 1,000 deaths each day.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Clinical manifestations typically arise only when airway inflammation exceeds a critical threshold or when substantial structural damage has occurred.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) This progression underscores the importance of early detection and intervention, as by the time symptoms manifest, the disease may have progressed to an irreversible stage with no possibility of cure. Understanding the underlying mechanisms of asthma and the triggers that lead to asthma is crucial for developing effective treatment strategies and reducing the risk of developing asthma. It is even more necessary for us to accurately understand the characteristics of the \u0026ldquo;pre-asthma stage\u0026rdquo;, identify treatable features, intervene early, and ultimately reduce the burden of asthma.\u003c/p\u003e\u003cp\u003eRecently, the concept of early preemptive diagnosis has evolved, such as pre-COPD.(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) Pre-asthma has been attempted to be defined as a subclinical condition characterized by mild, intermittent lower airway inflammation or hypersensitivity, with the potential to progress to asthma.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) However, this definition may be limited due to the absence of specific biomarkers, unclear temporal progression, limited consideration of non-inflammatory mechanisms, and genetic and environmental factors.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) It also faces challenges in early identification and has limited utility in understanding etiology and risk stratification. Besides, relying on asthma assessment criteria to evaluate individuals with suspected \u0026ldquo;pre-asthma\u0026rdquo; is unsuitable, as these populations, by definition, have not yet developed asthma. These issues highlight the need for a more precise and comprehensive identification and definition of \u0026ldquo;pre-asthma\u0026rdquo;. Early identification of individuals at risk for asthma is crucial for implementing prevention strategies and personalized interventions.\u003c/p\u003e\u003cp\u003eExisting prediction models for asthma are limited to exacerbation risk in established asthmatic patients, relying on biomarkers like blood eosinophils and FeNO for risk assessment.(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) These models, however, are not suitable for undiagnosed individuals with asthma risk factors, regardless of whether they exhibit early symptoms. Given the multifactorial nature of asthma, a more effective approach for early prediction may involve categorizing cases based on specific and multidimensional characteristics and biomarkers. The predictive value of single-dimensiona lcommon asthma assessments, such as bronchial provocation, lung function, allergy tests, chest imaging, FeNO measurement, and blood eosinophil counts, is limited by their low sensitivity and specificity.(\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eRecent advancements in machine learning have spurred the development of innovative predictive models, which serve as effective tools for enhancing the accuracy of predicting asthma onset.(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) However, the heterogeneity in model design, including the selection of predictors and the definition of outcomes, coupled with methodological flaws, has hindered their clinical application. Therefore, there is an urgent need for a systematic review of existing evidence to evaluate model performance, identify reliable predictors for early assessment of an individual\u0026rsquo;s risk of developing asthma, define the concept of \u0026ldquo;pre-asthma,\u0026rdquo; explore interventions targeting early risk factors to prevent asthma onset, and guide future research directions..\u003c/p\u003e\u003cp\u003eThis study aims to conduct a systematic review, critical appraisal, and meta-analysis of early prediction models for asthma development in undiagnosed populations. We will rigorously assess the validity (calibration and discrimination) and clinical utility of these models to identify key predictors and clinical features characterizing a novel \u0026ldquo;pre-asthma\u0026rdquo; stage. By analogy with pre-COPD, we will establish an evidence-based definition and diagnostic criteria for \u0026ldquo;pre-asthma.\u0026rdquo; This review will focus on the multidimensional integration of demographic and clinical characteristics, genetic predisposition, environmental exposures, phenotypic/endotypic biomarkers, and temporal dynamics to advance early risk identification.\u003c/p\u003e"},{"header":"METHODS AND ANALYSIS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eProtocol Registration and Methodology Framework\u003c/h2\u003e\u003cp\u003eThis protocol has been developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) We will design and conduct this systematic review according to the CHARMS. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) This protocol was registered on the PROSPERO international registry of systematic reviews (CRD420251047047).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEligibility criteria for study selection\u003c/h3\u003e\n\u003cp\u003eWe will include studies that outlines the development and external validation of novel multi-variable models for forecasting adult asthma cases. We provide a comprehensive outline of the PICOTS (population, intervention, comparator, outcomes, timing, setting) for this systematic review (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guideline (TRIPOD).(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) We will identify and include the prediction model studies based on the following criteria in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe PICOTS description about study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePICOTS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElements\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation (P)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1. Studies reporting on prediction models proposed for asthma diagnosed after 18 years (adult asthma)\u003c/p\u003e\u003cp\u003e2. Asthma may have been diagnosed by any criteria, including but not limited to those outlined in established guidelines such as the GINA or local asthma diagnostic guidelines\u003c/p\u003e\u003cp\u003e3. The standard diagnostic criteria of asthma have been reported\u003c/p\u003e\u003cp\u003e4. Participants included in the primary studies should not have been diagnosed with asthma at the time of inclusion\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntervention (I)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1. Any prediction model to predict the risk of the onset of asthma in participants undiagnosed asthma\u003c/p\u003e\u003cp\u003e2. Prediction model development studies with and without external validation and external model validation studies with or without model updating\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComparator (C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcomes (O)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRisk of the onset of asthma after 18 years reported by prediction models (adult asthma)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTiming (T)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1. Predictive variables measured at any timepoint during the clinical course of developing into asthma\u003c/p\u003e\u003cp\u003e2. No specific limitation applied in prediction horizon\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSetting (S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo limitation. Prediction models designed for use by healthcare professionals in any clinical settings, and which include participants enrolled at any time prior to an asthma diagnosis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eTypes of studies and limits\u003c/h3\u003e\n\u003cp\u003eStudies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) reported the development or validation multivariable model(s) of asthma with or without external validation with at least two predictors; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) that evaluated or updated the quantitative measure of model performance of an existing model in an independent population in terms of overall performance, discriminative ability and calibration of a certain prediction model. Any identified and relevant review articles will be used to identify eligible primary studies. Studies will be limited to those conducted in humans. There will be no limits on the year of publication. This search will be limited to reports in English, and for which full-text access is available. No restrictions will be placed on sex or gender, race, comorbidities, or other characteristics. Animal studies, conference abstracts, editorials, case reports, letters, commentaries, book chapters, protocols, guidelines, unpublished articles and surveys will be excluded. A study will be excluded from consideration if the crucial missing data (such as study information, sample size, and model performance indicators) cannot be adequately supplemented.\u003c/p\u003e\n\u003ch3\u003eDatabases\u003c/h3\u003e\n\u003cp\u003eWe will search the following databases from their inception to April 2025: PubMed, EMBASE, Web of Science, Scopus, Cochrane Library, and IEEE Xplore.\u003c/p\u003e\n\u003ch3\u003eSearch strategy\u003c/h3\u003e\n\u003cp\u003eThis study will adopt a three-step search strategy to ensure comprehensive literature coverage. Initially, two experienced reviewers will develop the search strategy, incorporating terms related to asthma and predictive modeling, and conduct a preliminary search on PubMed. Subsequently, two independent reviewers will perform the searches. In the final phase, a thorough review of the selected literature and related references will be conducted to identify additional studies that meet the inclusion criteria.\u003c/p\u003e\u003cp\u003eThe search strategy was built using keywords including asthma-related terms and prediction modelling-related terms. The following search strategy with related key words was developed with an example of PubMed: ((\"Asthma\"[MeSH] OR \"Asthma/diagnosis\"[MeSH] OR \"Asthma/epidemiology\"[MeSH] OR asthm*[tiab]) AND (\"Prognosis\"[MeSH] OR \"risk assess*\"[MeSH] OR \"machine learning\"[MeSH] OR (\"predict* model\"[tiab] OR \"prognostic model\"[tiab] OR \"risk model\"[tiab] OR \"predict* rule\"[tiab] OR \"risk stratification\"[tiab] OR nomogram*[tiab] OR \"predictive analy*\"[tiab] OR \"artificial intelligence\"[tiab] OR \"random forest\"[tiab] OR \"neural network\"[tiab] OR \"supervised learning\"[tiab] OR \"regression analy*\"[tiab] OR \"logistic regression\"[tiab] OR \"cox regression\"[tiab] OR \"lasso regression\"[tiab] OR \"ridge regression\"[tiab] OR \"elastic net\"[tiab] OR \"survival analysis\"[tiab] OR \"bayesian model\"[tiab] OR \"support vector machine\"[tiab] OR \"gradient boosting\"[tiab] OR \"xgboost\"[tiab] OR \"ensemble learning\"[tiab] OR \"internal validation\"[tiab] OR \"cross validation\"[tiab] OR \"overfitting\"[tiab] OR \"variable selection\"[tiab] OR \"feature engineering\"[tiab] OR \"sensitivity and specificity\"[tiab] OR \"positive predictive value\"[tiab] OR \"calibration curve\"[tiab] OR \"brier score\"[tiab])) AND (\"validation stud*\"[pt] OR \"discrimination\"[tiab] OR \"calibration\"[tiab] OR \"AUC\"[tiab] OR \"C-statistic\"[tiab] OR \"external validation\"[tiab] OR \"model performance\"[tiab] OR \"clinical utility\"[tiab])) NOT (\"Animals\"[MeSH] NOT \"Humans\"[MeSH]). Additionally, manual review of references in the selected literature will be conducted to identify potentially relevant studies.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStudy selection\u003c/h2\u003e\u003cp\u003eReviewers will undergo formal training at the Cochrane China Center prior to the formal selection of studies. The eligibility criteria will be explained and discussed in detail to ensure that all reviewers have a uniform understanding. Search results will be combined using Endnote, and duplicates will be removed. Two reviewers (YL and LY) will independently screen the titles and abstracts of every article according to the selection criteria. Subsequently, both reviewers will independently read the screened full texts of the studies and rigorously evaluate them against the predefined eligibility criteria. For each excluded article, the reasons for exclusion will be specified. Disagreements or doubts will be resolved by consensus, and if consensus cannot be reached, the full texts of the studies will be independently assessed for further evaluation. Any conflicts will be resolved through discussion with the senior advisors (XZ and LW), if necessary.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData extraction\u003c/h3\u003e\n\u003cp\u003eTwo independent reviewers (YL and LY) will perform the data extraction, utilizing a standardized data extraction form for all included studies. The form was developed based on the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (the CHARMS checklist).\u003csup\u003e[\u003c/sup\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003csup\u003e]\u003c/sup\u003e For each eligible study, we plan to extract information on study aims, data source, participants, stakeholders; algorithms, potential predictors, sample size, missing data, diagnostic criteria for asthma, predicted outcomes, model development, model performance (i.e., discrimination, calibration, clinical utility, and classification), final multivariable models, and interpretation of presented models. Disagreements on data elements will be resolved through discussion or consultation with a third reviewer when consensus cannot be reached. Information not reported in the publication will be obtained from the authors whenever possible. Additionally, if insufficient information is obtained, the study will be excluded from the review. Finally, the reviewers will cross-check the extracted data and review the full text to identify and correct discrepancies, ensuring accuracy.\u003c/p\u003e\n\u003ch3\u003eCritical appraisal\u003c/h3\u003e\n\u003cp\u003eTo achieve an inter-rater reliability kappa coefficient of over 0.8 for risk of bias assessment, the assessors will pre-evaluate a sample of qualified studies. The results of the pilot risk of bias assessment will be discussed among the review authors and assessors. Each selected study will be independently evaluated for the risk of bias and reporting transparency by two reviewers (YL and LY), and the results will be cross-checked. Any disagreements will be resolved through discussion to reach a consensus, and consultation with the senior authors (XZ and LW) will be sought if necessary.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eRisk of bias and applicability assessment\u003c/h2\u003e\u003cp\u003eWe will critically assess each included prediction model using the PROBAST technique, a tool designed to assess the risk of bias and applicability of prediction model studies.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) The PROBAST tool comprises 20 signalling questions divided into four domains: participants, predictors, outcomes, and statistical analysis. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) An overall rating for each domain will be assigned as low, high, or unclear risk of bias. The applicability assessment examines whether the model development/validation study aligns with our systematic review question regarding the target population, predictors, or outcome of interest. Furthermore, a high number of variables (over ten) and the inclusion of many continuous variables (\u0026gt;\u0026thinsp;4) will be seen as barriers to routine use. Additionally, the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) assessment tool will be employed to appraise the methodological quality of the studies.(\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eReporting transparency assessment\u003c/h2\u003e\u003cp\u003eThe Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD statement), which provides a checklist of 22 items deemed essential for reporting transparency of a prediction model study, will be used to evaluate the transparency of the reporting of the included studies.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) We will report the overall TRIPOD adherence score for each study, which was developed to assess the consistency in measuring adherence to the TRIPOD statement. The TRIPOD adherence score is calculated by dividing the sum of the adhered TRIPOD items by the total number of applicable TRIPOD items.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eData synthesis and analysis\u003c/h2\u003e\u003cp\u003eWe will provide a qualitative overview of the model used for each individual study. The characteristics of the models will be tabulated to show classification measures such as sensitivity, specificity, and the AUROC. Clinical utility will be assessed based on ease of use, defined by whether they exist in a clinically useful form, such as a risk score or online calculator. The clinical impact will be measured by convenience of use, risk of bias, applicability, and accuracy of the model.\u003c/p\u003e\u003cp\u003eData will be synthesised by performing meta-analysis by type of prediction modelling study if feasible and identified prediction models are sufficiently homogenous. Derivation and validation of models will be considered separately. Meta-analysis will be conducted in accordance with the Meta-analysis of Observational Studies in Epidemiology group guidelines. Data will be synthesized by meta-analysis if at least five studies are included in a subset with an analogous clinical question assessed repeatedly. A meta-analysis of performance in validation for a common prediction model at the same time point will be performed if at least three studies are included. Performance measures such as discrimination (e.g., AUROC, C-statistics) and calibration (e.g., calibration slope) will be pooled and analyzed using a random effects model. The restricted maximum likelihood and Hartung-Knapp-Sidik-Jonkman methods will be used to estimate between-study heterogeneity and 95% confidence intervals for the average performance.(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe clinical and methodological heterogeneity across studies will be evaluated by examining the variability in participants' characteristics (e.g., age, sex distribution, and setting), the definitions and measurement methodologies employed in asthma diagnosis, as well as the risk of bias. Cochran's Q and the I\u003csup\u003e2\u003c/sup\u003e statistics will be calculated for statistical assessment of heterogeneity. If there is clinical and methodological heterogeneity among the included studies, the random effects model approach will be used instead of the fixed effect approach. Potential sources of heterogeneity will be investigated by undertaking meta-regression analyses. If more than ten studies are incorporated into the review, graphical exploration of reporting bias will be conducted using a funnel plot, and statistical assessment will be performed via Egger's test. As recommended, a p-value less than 0.05 will be deemed indicative of publication bias. The meta-analysis will be carried out using R Software V.4.4.3 (R Core Team, Vienna, Austria, available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org\u003c/span\u003e\u003cspan address=\"https://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup analysis\u003c/h2\u003e\u003cp\u003eWhere there are sufficient eligible studies (\u0026ge;\u0026thinsp;10 studies) included in the review, we planned to conduct subgroup analyses: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) sex; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) diagnostic criteria for asthma; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) asthma phenotype; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) type of prediction model (i.e., development or validation); (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) method of predictive model building; (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) settings and location; (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) source of data; (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) time points; (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) follow-up duration; and (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) study quality (risk of bias). Further subgroup analysis will be dependent on the final data extraction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity analysis\u003c/h2\u003e\u003cp\u003eSensitivity analyses will be conducted by excluding studies with a high risk of bias and those with low reporting transparency to explore their influence on effect size.(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eSummary of findings\u003c/h2\u003e\u003cp\u003eThe findings of this systematic review will be reported in adherence to the TRIPOD guideline and the PRISMA statement.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea Under the Receiver Operating Characteristic Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eC-statistics\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econcordance statistics\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDTA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ediagnostic test accuracy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFeNO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efractional exhaled nitric oxide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFEV1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eforced expiratory volume in one second\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGINA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlobal Initiative for Asthma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGRADE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe Grading of Recommendations, Assessment, Development, and Evaluation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePEF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epeak expiratory flow\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePICOTS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epopulation, intervention, comparator, outcomes, timing, setting\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePM2.5\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e2.5-micrometer Particulate Matter\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePRISMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePreferred Reporting Items for Systematic Reviews and Meta-Analyses\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePRS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epolygenic risk scores\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTRIPOD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTransparent reporting of a multivariable prediction model for individual prognosis or diagnosis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAmendments \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol for this systematic review will be amended as needed during the peer review process. The author would publish when the study is complete.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval is not required for this systematic review and meta-analyses as it involves secondary analysis of published literature and does not involve patient or public information. The results will be submitted for publication in a peer-reviewed journal and presented at a relevant conference. Data generated during the research will be available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved and contributed to the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by 1.3.5 project for disciplines of excellence-Clinical Research Fund, West China Hospital, Sichuan University (2023HXFH045), 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYGD23002), National Natural Science Foundation of China (82100032, 82200037,82300036).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXZ conceived the research idea and planned the entire method of undertaking the study. XZ, LW, YL and LY wrote the draft protocol. XZ, LW, LZ, YL, LL, LL, MF, GW, SZ, YY, CZ and DK provided all the guidance. All authors critically revised the manuscript. All authors revised and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the contribution of the Cochrane China Center at West China Hospital of Sichuan University for the support and guidance in designing the search methodology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal Initiative for Asthma. Global Strategy for Asthma Management and Prevention. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ginasthma.org/2025-gina-strategy-report/\u003c/span\u003e\u003cspan address=\"https://ginasthma.org/2025-gina-strategy-report/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 26 May 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKotlia P, Pant J, Lohani MC. Identifying asthma risk factors and developing predictive models for early intervention using machine learning. Biomed Pharmacol J. 2025;18(December Spl Edition):295\u0026ndash;314.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMortimer K, Reddel HK, Pitrez PM, Bateman ED. Asthma management in low and middle income countries: case for change. Eur Respir J. 2022;60(3):2103179.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsher MI, Rutter CE, Bissell K, Chiang CY, El Sony A, Ellwood E, et al. Worldwide trends in the burden of asthma symptoms in school-aged children: global asthma network phase I cross-sectional study. Lancet. 2021;398(10311):1569\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBusse WW. The relationship of airway hyperresponsiveness and airway inflammation. Chest. 2010;138(2):S4\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu Z, Li R, Wang L, Wu Y, Tian Y, Su Y, et al. Pathogenic role of different phenotypes of immune cells in airway allergic diseases: a study based on Mendelian randomization. Front Immunol. 2024;15:1349470.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlobal Initiative for Chronic Obstructive Lung Disease. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://goldcopd.org/2025-gold-report/\u003c/span\u003e\u003cspan address=\"https://goldcopd.org/2025-gold-report/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 26 May 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan MK, Agusti A, Celli BR, Criner GJ, Halpin DMG, Roche N, et al. From GOLD 0 to pre-COPD. Am J Respir Crit Care Med. 2021;203(4):414\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScadding GK, McDonald M, Backer V, Scadding G, Bernal-Sprekelsen M, Conti DM, et al. Pre-asthma: a useful concept for prevention and disease-modification? A EUFOREA paper. Part 1-allergic asthma. Front Allergy. 2023;4:1291185.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYue M, Gaietto K, Han YY, Rosser FJ, Xu Z, Qoyawayma C, et al. Transcriptomic profiles in nasal epithelium and asthma endotypes in youth. JAMA. 2025;333(4):307.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzim A, Barber C, Dennison P, Riley J, Howarth P. Exhaled volatile organic compounds in adult asthma: a systematic review. Eur Respir J. 2019;54(3):1900056.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu A, Zhang Y, Yadav C, Chen W. An updated systematic review on asthma exacerbation risk prediction models between 2017 and 2023: risk of bias and applicability. J Asthma Allergy. 2025;18:579\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimmalee K, Kawamatawong T, Vitte J, Demoly P, Lumjiaktase P. Exploring the pathogenesis and clinical implications of asthma, chronic obstructive pulmonary disease (COPD), and asthma-COPD overlap (ACO): a narrative review. Front Med. 2025;12:1514846.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHartl S, Breyer MK, Burghuber OC, Ofenheimer A, Schrott A, Urban MH, et al. Blood eosinophil count in the general population: typical values and potential confounders. Eur Respir J. 2020;55(5):1901874.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWinkel P, Statland BE, Saunders AM, Osborn H, Kupperman H. Within-day physiologic variation of leukocyte types in healthy subjects as assayed by two automated leukocyte differential analyzers. Am J Clin Pathol. 1981;75(5):693\u0026ndash;700.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eH\u0026ouml;gman M. ERS technical standard: global lung function initiative reference values for exhaled nitric oxide (FENO).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhong X, Song J, Lei C, Wang X, Wang Y, Yu J, et al. Machine learning-based screening of asthma biomarkers and related immune infiltration. Front Allergy. 2025;6:1506608.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePreferred reporting items. for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2016;i4086.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFernandez-Felix BM, L\u0026oacute;pez-Alcalde J, Roqu\u0026eacute; M, Muriel A, Zamora J. CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models. BMC Med Res Methodol. 2023;23(1):44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoons KGM, De Groot JAH, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 2014;11(10):e1001744.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCollins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Br J Surg. 2015;102(3):148\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med. 2019;170(1):W1\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSch\u0026uuml;nemann HJ, Jaeschke R, Cook DJ, Bria WF, El-Solh AA, Ernst A, et al. An official ATS statement: grading the quality of evidence and strength of recommendations in ATS guidelines and recommendations. Am J Respir Crit Care Med. 2006;174(5):605\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIorio A, Spencer FA, Falavigna M, Alba C, Lang E, Burnand B, et al. Use of GRADE for assessment of evidence about prognosis: Rating confidence in estimates of event rates in broad categories of patients. BMJ. 2015;350(mar16 7):h870\u0026ndash;870.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eForoutan F, Guyatt G, Trivella M, Kreuzberger N, Skoetz N, Riley RD, et al. GRADE concept paper 2: concepts for judging certainty on the calibration of prognostic models in a body of validation studies. J Clin Epidemiol. 2022;143:202\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDebray TPA, Damen JAAG, Snell KIE, Ensor J, Hooft L, Reitsma JB et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ. 2017;i6460.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIntHout J, Ioannidis JP, Borm GF. The hartung-knapp-sidik-jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-laird method. BMC Med Res Methodol. 2014;14(1):25.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"systematic-reviews","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sysr","sideBox":"Learn more about [Systematic Reviews](http://systematicreviewsjournal.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/sysr/default.aspx","title":"Systematic Reviews","twitterHandle":"@MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"pre-asthma, asthma, prediction models, machine learning, systematic review, critical appraisal, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-6750481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6750481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Asthma poses substantial global health challenges due to its variable clinical manifestations and unmet needs in early risk stratification. Current prediction models lack generalizability for those with susceptibility, particularly when early symptoms are nonspecific. A rigorous synthesis of existing models for adult asthma is needed to evaluate their validity, prioritize predictors, and guide targeted prevention strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study aims to synthesize early asthma prediction models in undiagnosed populations, evaluating their performance metrics and clinical utility to try defining \"pre-asthma\" by integrating multidimensional predictors, specifically demographic, genetic, environmental, phenotypic/endotypic biomarkers, and symptom trajectories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eFollowing the CHARMS framework, we will systematically search PubMed, Web of Science, Embase, Cochrane Library, Scopus and IEEE Xplore (inception–April 2025) for studies developing or externally validating adult asthma prediction models. Data extraction and risk of bias assessment (by PROBAST and TRIPOD criteria) will be performed independently by two reviewers. Meta-analysis using random-effects models will synthesize the performance measures, with heterogeneity explored by meta-regression. Methodological rigor and clinical relevance of predictors will be evaluated to establish evidence-based recommendations and used to define \"pre-asthma\".\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy registration number\u003c/strong\u003e: CRD420251047047\u003c/p\u003e","manuscriptTitle":"Defining Pre-Asthma: A Systematic Review and Meta-Analysis Protocol for Multidimensional Early Prediction Models for Adult Asthma in Undiagnosed Populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-14 14:49:02","doi":"10.21203/rs.3.rs-6750481/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2026-05-02T11:56:05+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-26T12:41:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-10T07:52:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-25T08:48:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Systematic Reviews","date":"2025-05-29T10:46:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"systematic-reviews","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sysr","sideBox":"Learn more about [Systematic Reviews](http://systematicreviewsjournal.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/sysr/default.aspx","title":"Systematic Reviews","twitterHandle":"@MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8ee180e9-0f62-46ef-9fcd-2f2d1f882a13","owner":[],"postedDate":"August 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-22T15:01:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-14 14:49:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6750481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6750481","identity":"rs-6750481","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}