Signature for Pain Recovery IN Teens (SPRINT): protocol for a multisite prospective signature study in chronic musculoskeletal pain

Laura Simons, Massieh Moayedi, Robert C Coghill, Jennifer Stinson, Martin S Angst, Nima Aghaeepour, Brice Gaudilliere, Christopher D King, Marina López-Solà, Marie-Eve Hoeppli, Emma Biggs, Ed Ganio, Sara E Williams, Kenneth R Goldschneider, Fiona Campbell, Danielle Ruskin, Elliot J Krane, Suellen Walker, Gillian Rush, Marissa Heirich, Laura Simons, Massieh Moayedi, Robert C Coghill, Jennifer Stinson, Martin S Angst, Nima Aghaeepour, Brice Gaudilliere, Christopher D King, Marina López-Solà, Marie-Eve Hoeppli, Emma Biggs, Ed Ganio, Sara E Williams, Kenneth R Goldschneider, Fiona Campbell, Danielle Ruskin, Elliot J Krane, Suellen Walker, Gillian Rush, Marissa Heirich

Abstract

Introduction: Current treatments for chronic musculoskeletal (MSK) pain are suboptimal. Discovery of robust prognostic markers separating patients who recover from patients with persistent pain and disability is critical for developing patient-specific treatment strategies and conceiving novel approaches that benefit all patients. Given that chronic pain is a biopsychosocial process, this study aims to discover and validate a robust prognostic signature that measures across multiple dimensions in the same adolescent patient cohort with a computational analysis pipeline. This will facilitate risk stratification in adolescent patients with chronic MSK pain and more resourceful allocation of patients to costly and potentially burdensome multidisciplinary pain treatment approaches.

Methods and analysis: Here we describe a multi-institutional effort to collect, curate and analyse a high dimensional data set including epidemiological, psychometric, quantitative sensory, brain imaging and biological information collected over the course of 12 months. The aim of this effort is to derive a multivariate model with strong prognostic power regarding the clinical course of adolescent MSK pain and function.

Ethics and dissemination: The study complies with the National Institutes of Health policy on the use of a single internal review board (sIRB) for multisite research, with Cincinnati Children's Hospital Medical Center Review Board as the reviewing IRB. Stanford's IRB is a relying IRB within the sIRB. As foreign institutions, the University of Toronto and The Hospital for Sick Children (SickKids) are overseen by their respective ethics boards. All participants provide signed informed consent. We are committed to open-access publication, so that patients, clinicians and scientists have access to the study data and the signature(s) derived. After findings are published, we will upload a limited data set for sharing with other investigators on applicable repositories.

Trial registration number: NCT04285112.

Keywords: IMMUNOLOGY; Magnetic resonance imaging; PAIN MANAGEMENT; Paediatric anaesthesia; STATISTICS & RESEARCH METHODS.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
Study sequence. After baseline SPRINT assessment of neuroimaging, quantitative sensory testing, immunological markers in blood and self-report questionnaires, healthcare use and clinical endpoints of pain and function are closely tracked every 2 weeks prior to 3-month follow-up, then at 6 months, 9 months and 12 months. SPRINT, Signature for Pain Recovery IN Teens.
Figure 2
Figure 2
Study overview. A cohort of youth with chronic MSK pain enrol in sprint across three participating sites: Stanford, Cincinnati Children’s and Sick Kids in Toronto, Canada. Individuals are thoroughly characterised at baseline. Unbiased machine learning algorithms identify two multivariate models composed of biological and/or psychological markers that predict recovery or persistence of pain and disability in adolescents with MSK pain after multidisciplinary pain treatment. The model will reveal two prognostic signatures to be tested in the R33 validation phase. In an independent cohort of patients, we will capture our metrics at clinic presentation to test the positive and negative prognostic value of the signatures predicting persistence of MSK pain and disability after multidisciplinary pain treatment. MSK, musculoskeletal.
Figure 3
Figure 3
The Elastic Net (EN) analysis pipeline. Neuroimaging (MRI), quantitative sensory testing (QST), immunological (blood) and self-report questionnaire prior knowledge for each feature is extracted by a panel of experts (A) and encoded into a prior knowledge tensor to guide the model optimisation process (B). Individuals within the study cohort (C) provide MRI, questionnaire and QST data, and blood samples, which are subsequently preprocessed (MRI), scores calculated (questionnaire, QST) or stimulated with ligands ex vivo to activate various signalling pathways of the immune system (blood) (D). This produces a a complex set of biopsychosocial features for the prognostic signature (E). This dataset is then fed into the EN algorithm (F) for prognostic modelling of the outcome of interest (G).

References

    1. Huguet A, Miró J. The severity of chronic pediatric pain: an epidemiological study. J Pain 2008;9:226–36. 10.1016/j.jpain.2007.10.015
    1. Coles ML, Weissmann R, Uziel Y. Juvenile primary fibromyalgia syndrome: epidemiology, etiology, pathogenesis, clinical manifestations and diagnosis. Pediatr Rheumatol Online J 2021;19:22. 10.1186/s12969-021-00493-6
    1. Kashikar-Zuck S, Cunningham N, Sil S, et al. . Long-Term outcomes of adolescents with juvenile-onset fibromyalgia in early adulthood. Pediatrics 2014;133:e592–600. 10.1542/peds.2013-2220
    1. Ting TV, Barnett K, Lynch-Jordan A, et al. . 2010 American College of rheumatology adult fibromyalgia criteria for use in an adolescent female population with juvenile fibromyalgia. J Pediatr 2016;169:181–7. 10.1016/j.jpeds.2015.10.011
    1. Yunus MB, Masi AT. Juvenile primary fibromyalgia syndrome. A clinical study of thirty-three patients and matched normal controls. Arthritis Rheum 1985;28:138–45. 10.1002/art.1780280205
    1. Sardini S, Ghirardini M, Betelemme L, et al. . [Epidemiological study of a primary fibromyalgia in pediatric age]. Minerva Pediatr 1996;48:543–50.
    1. Mikkelsson M, Salminen JJ, Kautiainen H. Non-Specific musculoskeletal pain in preadolescents. prevalence and 1-year persistence. Pain 1997;73:29–35. 10.1016/S0304-3959(97)00073-0
    1. Gerloni V, Ghirardini M, Fantini F. Assessment of nonarticular tenderness and prevalence of primary fibromyalgia syndrome in healthy Italian school children. Arthritis & Rheumatism 1998;41.
    1. Lewandowski AS, Palermo TM, Stinson J, et al. . Systematic review of family functioning in families of children and adolescents with chronic pain. J Pain 2010;11:1027–38. 10.1016/j.jpain.2010.04.005
    1. Liossi C, Howard RF. Pediatric chronic pain: biopsychosocial assessment and formulation. Pediatrics 2016;138. 10.1542/peds.2016-0331. [Epub ahead of print: 14 10 2016].
    1. Rajapakse D, Liossi C, Howard RF. Presentation and management of chronic pain. Arch Dis Child 2014;99:474–80. 10.1136/archdischild-2013-304207
    1. IK H, Goldschneider KR, Kashikar-Zuck S. Healthcare utilization and indirect burden among families of pediatric patients with chronic pain. Journal of Musculoskeletal Pain 2008;16:155–64. 10.1080/10582450802161853
    1. Groenewald CB, Essner BS, Wright D, et al. . The economic costs of chronic pain among a cohort of treatment-seeking adolescents in the United States. J Pain 2014;15:925–33. 10.1016/j.jpain.2014.06.002
    1. Groenewald CB, Wright DR, Palermo TM. Health care expenditures associated with pediatric pain-related conditions in the United States. Pain 2015;156:951–7. 10.1097/j.pain.0000000000000137
    1. Borsook D, Kalso E. Transforming pain medicine: adapting to science and society. Eur J Pain 2013;17:1109–25. 10.1002/j.1532-2149.2013.00297.x
    1. Simons LE, Sieberg CB, Conroy C, et al. . Children with chronic pain: response trajectories after intensive pain rehabilitation treatment. J Pain 2018;19:207–18. 10.1016/j.jpain.2017.10.005
    1. Simons LE, Sieberg CB, Pielech M, et al. . What does it take? comparing intensive rehabilitation to outpatient treatment for children with significant pain-related disability. J Pediatr Psychol 2013;38:213–23. 10.1093/jpepsy/jss109
    1. Hechler T, Ruhe A-K, Schmidt P, et al. . Inpatient-based intensive interdisciplinary pain treatment for highly impaired children with severe chronic pain: randomized controlled trial of efficacy and economic effects. Pain 2014;155:118–28. 10.1016/j.pain.2013.09.015
    1. Hirschfeld G, Hechler T, Dobe M, et al. . Maintaining lasting improvements: one-year follow-up of children with severe chronic pain undergoing multimodal inpatient treatment. J Pediatr Psychol 2013;38:224–36. 10.1093/jpepsy/jss115
    1. Palermo TM, Law EF, Zhou C, et al. . Trajectories of change during a randomized controlled trial of internet-delivered psychological treatment for adolescent chronic pain: how does change in pain and function relate? Pain 2015;156:626–34. 10.1097/01.j.pain.0000460355.17246.6c
    1. Holley AL, Wilson AC, Palermo TM. Predictors of the transition from acute to persistent musculoskeletal pain in children and adolescents: a prospective study. Pain 2017;158:794–801. 10.1097/j.pain.0000000000000817
    1. Martin SR, Zeltzer LK. Prioritizing pediatric chronic pain and comprehensive pain treatment in the context of the opioid epidemic. Pain Manag 2018;8:67–70. 10.2217/pmt-2017-0072
    1. Birnie KA, Dib K, Ouellette C, et al. . Partnering for pain: a priority setting partnership to identify patient-oriented research priorities for pediatric chronic pain in Canada. CMAJ Open 2019;7:E654–64. 10.9778/cmajo.20190060
    1. Eccleston C, Fisher E, Howard RF, et al. . Delivering transformative action in paediatric pain: a Lancet Child & Adolescent Health Commission. Lancet Child Adolesc Health 2021;5:47–87. 10.1016/S2352-4642(20)30277-7
    1. Murray CB, Groenewald CB, de la Vega R, et al. . Long-Term impact of adolescent chronic pain on young adult educational, vocational, and social outcomes. Pain 2020;161:439–45. 10.1097/j.pain.0000000000001732
    1. Nemeroff CB. Paradise lost: the neurobiological and clinical consequences of child abuse and neglect. Neuron 2016;89:892–909. 10.1016/j.neuron.2016.01.019
    1. Burke NN, Finn DP, McGuire BE, et al. . Psychological stress in early life as a predisposing factor for the development of chronic pain: clinical and preclinical evidence and neurobiological mechanisms. J Neurosci Res 2017;95:1257–70. 10.1002/jnr.23802
    1. Victoria NC, Murphy AZ. The long-term impact of early life pain on adult responses to anxiety and stress: historical perspectives and empirical evidence. Exp Neurol 2016;275 Pt 2:261–73. 10.1016/j.expneurol.2015.07.017
    1. Walker SM. Translational studies identify long-term impact of prior neonatal pain experience. Pain 2017;158 Suppl 1:S29–42. 10.1097/j.pain.0000000000000784
    1. King CD, Jastrowski Mano KE, Barnett KA, et al. . Pressure pain threshold and anxiety in adolescent females with and without juvenile fibromyalgia: a pilot study. Clin J Pain 2017;33:620–6. 10.1097/AJP.0000000000000444
    1. Kashikar-Zuck S, Cunningham N, Peugh J, et al. . Long-Term outcomes of adolescents with juvenile-onset fibromyalgia into adulthood and impact of depressive symptoms on functioning over time. Pain 2019;160:433–41. 10.1097/j.pain.0000000000001415
    1. Lynch-Jordan AM, Kashikar-Zuck S, Szabova A, et al. . The interplay of parent and adolescent catastrophizing and its impact on adolescents' pain, functioning, and pain behavior. Clin J Pain 2013;29:681–8. 10.1097/AJP.0b013e3182757720
    1. Lasselin J, Kemani MK, Kanstrup M, et al. . Low-Grade inflammation may moderate the effect of behavioral treatment for chronic pain in adults. J Behav Med 2016;39:916–24. 10.1007/s10865-016-9769-z
    1. Ji R-R, Chamessian A, Zhang Y-Q. Pain regulation by non-neuronal cells and inflammation. Science 2016;354:572–7. 10.1126/science.aaf8924
    1. Generaal E, Vogelzangs N, Macfarlane GJ, et al. . Basal inflammation and innate immune response in chronic multisite musculoskeletal pain. Pain 2014;155:1605–12. 10.1016/j.pain.2014.05.007
    1. Vachon-Presseau E. Effects of stress on the corticolimbic system: implications for chronic pain. Prog Neuropsychopharmacol Biol Psychiatry 2018;87:216–23. 10.1016/j.pnpbp.2017.10.014
    1. Vachon-Presseau E, Tétreault P, Petre B, et al. . Corticolimbic anatomical characteristics predetermine risk for chronic pain. Brain 2016;139:1958–70. 10.1093/brain/aww100
    1. Vachon-Presseau E, Centeno MV, Ren W, et al. . The emotional brain as a predictor and amplifier of chronic pain. J Dent Res 2016;95:605–12. 10.1177/0022034516638027
    1. Goubert D, Danneels L, Graven-Nielsen T, et al. . Differences in pain processing between patients with chronic low back pain, recurrent low back pain, and fibromyalgia. Pain Physician 2017;20:307–18.
    1. Marcuzzi A, Wrigley PJ, Dean CM, et al. . From acute to persistent low back pain: a longitudinal investigation of somatosensory changes using quantitative sensory testing-an exploratory study. Pain Rep 2018;3:e641. 10.1097/PR9.0000000000000641
    1. Owens MA, Bulls HW, Trost Z, et al. . An examination of pain Catastrophizing and endogenous pain modulatory processes in adults with chronic low back pain. Pain Med 2016;17:1452–64. 10.1093/pm/pnv074
    1. Chalaye P, Lafrenaye S, Goffaux P, et al. . The role of cardiovascular activity in fibromyalgia and conditioned pain modulation. Pain 2014;155:1064–9. 10.1016/j.pain.2013.12.023
    1. Gerhardt A, Eich W, Treede R-D, et al. . Conditioned pain modulation in patients with nonspecific chronic back pain with chronic local pain, chronic widespread pain, and fibromyalgia. Pain 2017;158:430–9. 10.1097/j.pain.0000000000000777
    1. Potvin S, Marchand S. Pain facilitation and pain inhibition during conditioned pain modulation in fibromyalgia and in healthy controls. Pain 2016;157:1704–10. 10.1097/j.pain.0000000000000573
    1. Georgopoulos V, Akin-Akinyosoye K, Zhang W, et al. . Quantitative sensory testing and predicting outcomes for musculoskeletal pain, disability, and negative affect: a systematic review and meta-analysis. Pain 2019;160:1920–32. 10.1097/j.pain.0000000000001590
    1. Davis KD, Moayedi M. Central mechanisms of pain revealed through functional and structural MRI. J Neuroimmune Pharmacol 2013;8:518–34. 10.1007/s11481-012-9386-8
    1. Apkarian AV, Baliki MN, Farmer MA. Pain: Acute and Chronic. In: Toga AW, ed. Waltham: Brain MappingAcademic Press, 2015: 553–63.
    1. Denk F, McMahon SB, Tracey I. Pain vulnerability: a neurobiological perspective. Nat Neurosci 2014;17:192–200. 10.1038/nn.3628
    1. Gatchel RJ, Peng YB, Peters ML, et al. . The biopsychosocial approach to chronic pain: scientific advances and future directions. Psychol Bull 2007;133:581–624. 10.1037/0033-2909.133.4.581
    1. Harris PA, Taylor R, Thielke R, et al. . Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009;42:377–81. 10.1016/j.jbi.2008.08.010
    1. Harris PA, Taylor R, Minor BL, et al. . The REDCap Consortium: building an international community of software platform partners. J Biomed Inform 2019;95:103208. 10.1016/j.jbi.2019.103208
    1. Price DD, Bush FM, Long S, et al. . A comparison of pain measurement characteristics of mechanical visual analogue and simple numerical rating scales. Pain 1994;56:217–26. 10.1016/0304-3959(94)90097-3
    1. Price DD, McGrath PA, Rafii A, et al. . The validation of visual analogue scales as ratio scale measures for chronic and experimental pain. Pain 1983;17:45–56. 10.1016/0304-3959(83)90126-4
    1. Cleeland CS, Ryan KM. Pain assessment: global use of the brief pain inventory. Ann Acad Med Singap 1994;23:129–38.
    1. McGrath PJ, Walco GA, Turk DC, et al. . Core outcome domains and measures for pediatric acute and chronic/recurrent pain clinical trials: PedIMMPACT recommendations. J Pain 2008;9:771–83. 10.1016/j.jpain.2008.04.007
    1. Claar RL, Walker LS. Functional assessment of pediatric pain patients: psychometric properties of the functional disability inventory. Pain 2006;121:77–84. 10.1016/j.pain.2005.12.002
    1. Nelson SM, Cunningham NR, Kashikar-Zuck S. A conceptual framework for understanding the role of adverse childhood experiences in pediatric chronic pain. Clin J Pain 2017;33:264–70. 10.1097/AJP.0000000000000397
    1. Nelson S, Beveridge JK, Mychasiuk R, et al. . Adverse childhood experiences (ACEs) and internalizing mental health, pain, and quality of life in youth with chronic pain: a longitudinal examination. J Pain 2021;22:1210–20. 10.1016/j.jpain.2021.03.143
    1. Simons LE, Lewandowski Holley A, Phelps E, et al. . Prism: a brief screening tool to identify risk in parents of youth with chronic pain. Pain 2019;160:367–74. 10.1097/j.pain.0000000000001403
    1. Harrison LE, Timmers I, Heathcote LC, et al. . Parent responses to their child's pain: systematic review and meta-analysis of measures. J Pediatr Psychol 2020;45:281–98. 10.1093/jpepsy/jsaa005
    1. Fernandez R, Maecker H. Cytokine-Stimulated Phosphoflow of whole blood using CyTOF mass cytometry. Bio Protoc 2015;5. 10.21769/BioProtoc.1495
    1. López-Solà M, Woo C-W, Pujol J, et al. . Towards a neurophysiological signature for fibromyalgia. Pain 2017;158:34–47. 10.1097/j.pain.0000000000000707
    1. Blankenburg M, Boekens H, Hechler T, et al. . Reference values for quantitative sensory testing in children and adolescents: developmental and gender differences of somatosensory perception. Pain 2010;149:76–88. 10.1016/j.pain.2010.01.011
    1. Walker SM, Franck LS, Fitzgerald M, et al. . Long-Term impact of neonatal intensive care and surgery on somatosensory perception in children born extremely preterm. Pain 2009;141:79–87. 10.1016/j.pain.2008.10.012
    1. Verriotis M, Peters J, Sorger C, et al. . Phenotyping peripheral neuropathic pain in male and female adolescents: pain descriptors, somatosensory profiles, conditioned pain modulation, and child–parent reported disability. Pain 2021;162:1732–48. 10.1097/j.pain.0000000000002172
    1. Walker SM, O'Reilly H, Beckmann J, et al. . Conditioned pain modulation identifies altered sensitivity in extremely preterm young adult males and females. Br J Anaesth 2018;121:636–46. 10.1016/j.bja.2018.05.066
    1. Tsao JCI, Seidman LC, Evans S, et al. . Conditioned pain modulation in children and adolescents: effects of sex and age. The Journal of Pain 2013;14:558–67. 10.1016/j.jpain.2013.01.010
    1. Clinch J, Eccleston C. Chronic musculoskeletal pain in children: assessment and management. Rheumatology 2009;48:466–74. 10.1093/rheumatology/kep001
    1. Robins H, Perron V, Heathcote L, et al. . Pain neuroscience education: state of the art and application in pediatrics. Children 2016;3:43. 10.3390/children3040043
    1. Fisher E, Law E, Dudeney J, et al. . Psychological therapies for the management of chronic and recurrent pain in children and adolescents. Cochrane Database Syst Rev 2018;9:CD003968. 10.1002/14651858.CD003968.pub5
    1. Salaffi F, Stancati A, Silvestri CA, et al. . Minimal clinically important changes in chronic musculoskeletal pain intensity measured on a numerical rating scale. European Journal of Pain 2004;8:283–91. 10.1016/j.ejpain.2003.09.004
    1. Kashikar-Zuck S, Flowers SR, Claar RL, et al. . Clinical utility and validity of the functional disability inventory among a multicenter sample of youth with chronic pain. Pain 2011;152:1600–7. 10.1016/j.pain.2011.02.050
    1. Dworkin RH, Turk DC, Wyrwich KW, et al. . Interpreting the clinical importance of treatment outcomes in chronic pain clinical trials: IMMPACT recommendations. The Journal of Pain 2008;9:105–21. 10.1016/j.jpain.2007.09.005
    1. Palermo TM, Kashikar-Zuck S, Lynch-Jordan A. Topical review: enhancing understanding of the clinical meaningfulness of outcomes to assess treatment benefit from psychological therapies for children with chronic pain. J Pediatr Psychol 2020;45:233–8. 10.1093/jpepsy/jsz077
    1. Lavigne JV. Systematic review: issues in measuring clinically meaningful change in self-reported chronic pediatric pain intensity. J Pediatr Psychol 2016;41:715–34. 10.1093/jpepsy/jsv161
    1. Sil S, Arnold LM, Lynch-Jordan A, et al. . Identifying treatment responders and predictors of improvement after cognitive-behavioral therapy for juvenile fibromyalgia. Pain 2014;155:1206–12. 10.1016/j.pain.2014.03.005
    1. Gaudillière B, Fragiadakis GK, Bruggner RV, et al. . Clinical recovery from surgery correlates with single-cell immune signatures. Sci Transl Med 2014;6:ra131. 10.1126/scitranslmed.3009701
    1. Ganio EA, Stanley N, Lindberg-Larsen V, et al. . Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma. Nat Commun 2020;11:3737. 10.1038/s41467-020-17565-y
    1. Zunder ER, Finck R, Behbehani GK, et al. . Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat Protoc 2015;10:316–33. 10.1038/nprot.2015.020
    1. Behbehani GK, Thom C, Zunder ER, et al. . Transient partial permeabilization with saponin enables cellular barcoding prior to surface marker staining. Cytometry 2014;85:1011–9. 10.1002/cyto.a.22573
    1. Esteban O, Markiewicz CJ, Blair RW, et al. . fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 2019;16:111–6. 10.1038/s41592-018-0235-4
    1. Jenkinson M, Beckmann CF, Behrens TEJ, et al. . Fsl. Neuroimage 2012;62:782–90. 10.1016/j.neuroimage.2011.09.015
    1. Salimi-Khorshidi G, Douaud G, Beckmann CF, et al. . Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage 2014;90:449–68. 10.1016/j.neuroimage.2013.11.046
    1. Griffanti L, Salimi-Khorshidi G, Beckmann CF, et al. . ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. Neuroimage 2014;95:232–47. 10.1016/j.neuroimage.2014.03.034
    1. Glasser MF, Coalson TS, Robinson EC, et al. . A multi-modal parcellation of human cerebral cortex. Nature 2016;536:171–8. 10.1038/nature18933
    1. Destrieux C, Fischl B, Dale A, et al. . Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 2010;53:1–15. 10.1016/j.neuroimage.2010.06.010
    1. Desikan RS, Ségonne F, Fischl B, et al. . An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006;31:968–80. 10.1016/j.neuroimage.2006.01.021
    1. Stanley N, Stelzer IA, Tsai AS, et al. . VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. Nat Commun 2020;11:3738. 10.1038/s41467-020-17569-8
    1. Zou H, Hastie T. Regularization and variable selection via the elastic net. J Royal Statistical Soc B 2005;67:301–20. 10.1111/j.1467-9868.2005.00503.x
    1. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001;98:5116–21. 10.1073/pnas.091062498
    1. Tibshirani R, Hastie T, Narasimhan B, et al. . Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A 2002;99:6567–72. 10.1073/pnas.082099299
    1. Tibshirani R. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society: Series B 1996;58:267–88. 10.1111/j.2517-6161.1996.tb02080.x
    1. Zou H, Hastie T, Tibshirani R. Sparse principal component analysis. Journal of Computational and Graphical Statistics 2006;15:265–86. 10.1198/106186006X113430
    1. Culos A, Tsai AS, Stanley N, et al. . Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions. Nat Mach Intell 2020;2:619–28. 10.1038/s42256-020-00232-8
    1. Aghaeepour N, Kin C, Ganio EA, et al. . Deep immune profiling of an Arginine-Enriched nutritional intervention in patients undergoing surgery. J Immunol 2017;199:2171–80. 10.4049/jimmunol.1700421
    1. Aghaeepour N, Ganio EA, Mcilwain D, et al. . An immune clock of human pregnancy. Sci Immunol 2017;2. 10.1126/sciimmunol.aan2946. [Epub ahead of print: 01 Sep 2017].
    1. Wang L, Zhu J, Zou H. Hybrid huberized support vector machines for microarray classification and gene selection. Bioinformatics 2008;24:412–9. 10.1093/bioinformatics/btm579
    1. Breiman L, Cutler A. RandomForest: Breiman and Cutler’s random forests for classification and regression. In: R package, 2015.
    1. Ripley BD. k-Nearest Neighbour Classification. In: R package, 2015.
    1. Ghaemi MS, DiGiulio DB, Contrepois K, et al. . Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics 2019;35:95–103. 10.1093/bioinformatics/bty537
    1. Jehan F, Sazawal S, Baqui AH, et al. . Multiomics characterization of preterm birth in low- and middle-income countries. JAMA Netw Open 2020;3:e2029655. 10.1001/jamanetworkopen.2020.29655
    1. Wager TD, Atlas LY, Lindquist MA, et al. . An fMRI-based neurologic signature of physical pain. N Engl J Med 2013;368:1388–97. 10.1056/NEJMoa1204471
    1. Tétreault P, Mansour A, Vachon-Presseau E, et al. . Brain connectivity predicts placebo response across chronic pain clinical trials. PLoS Biol 2016;14:e1002570. 10.1371/journal.pbio.1002570
    1. Walker LS, Greene JW. The functional disability inventory: measuring a neglected dimension of child health status. J Pediatr Psychol 1991;16:39–58. 10.1093/jpepsy/16.1.39
    1. von Baeyer CL, Spagrud LJ, McCormick JC, et al. . Three new datasets supporting use of the numerical rating scale (NRS-11) for children's self-reports of pain intensity. Pain 2009;143:223–7. 10.1016/j.pain.2009.03.002
    1. Dworkin RH, Turk DC, Revicki DA, et al. . Development and initial validation of an expanded and revised version of the short-form McGill pain questionnaire (SF-MPQ-2). Pain 2009;144:35–42. 10.1016/j.pain.2009.02.007
    1. Petersen AC, Crockett L, Richards M, et al. . A self-report measure of pubertal status: reliability, validity, and initial norms. J Youth Adolesc 1988;17:117–33. 10.1007/BF01537962
    1. Crombez G, Bijttebier P, Eccleston C, et al. . The child version of the pain catastrophizing scale (PCS-C): a preliminary validation. Pain 2003;104:639–46. 10.1016/S0304-3959(03)00121-0
    1. Kashikar-Zuck S, Carle A, Barnett K, et al. . Longitudinal evaluation of patient-reported outcomes measurement information systems measures in pediatric chronic pain. Pain 2016;157:339–47. 10.1097/j.pain.0000000000000378
    1. Bhandari RP, Feinstein AB, Huestis SE, et al. . Pediatric-Collaborative health outcomes information registry (Peds-CHOIR): a learning health system to guide pediatric pain research and treatment. Pain 2016;157:2033–44. 10.1097/j.pain.0000000000000609
    1. Simons LE, Sieberg CB, Carpino E, et al. . The fear of pain questionnaire (FOPQ): assessment of pain-related fear among children and adolescents with chronic pain. J Pain 2011;12:677–86. 10.1016/j.jpain.2010.12.008
    1. Guite JW, Logan DE, Simons LE, et al. . Readiness to change in pediatric chronic pain: initial validation of adolescent and parent versions of the pain stages of change questionnaire. Pain 2011;152:2301–11. 10.1016/j.pain.2011.06.019
    1. Tisdall MD, Hess AT, Reuter M, et al. . Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI. Magn Reson Med 2012;68:389–99. 10.1002/mrm.23228
    1. López-Solà M, Woo C-W, Pujol J, et al. . Towards a neurophysiological signature for fibromyalgia. Pain 2017;158:34–47. 10.1097/j.pain.0000000000000707
    1. Hirschfeld G, Zernikow B. Cut points for mild, moderate, and severe pain on the vas for children and adolescents: what can be learned from 10 million ANOVAs? Pain 2013;154:2626–32. 10.1016/j.pain.2013.05.048
    1. Goossens ME, Rutten-van Mölken MP, Vlaeyen JW, et al. . The cost diary: a method to measure direct and indirect costs in cost-effectiveness research. J Clin Epidemiol 2000;53:688–95. 10.1016/s0895-4356(99)00177-8

Source: PubMed

Szponzorok és közreműködők

Egészségi állapot

Kábítószer-beavatkozások

3
Iratkozz fel