Literature DB >> 33090707

Development of a model to predict vestibular schwannoma growth: An opportunity to introduce new wait and scan strategies.

Mayke A Hentschel1, Gerjon Hannink2, Stefan C A Steens3, Jef J S Mulder1, Maroeska M Rovers2,4, Henricus P M Kunst1,5.   

Abstract

OBJECTIVES: To develop a prediction model to predict vestibular schwannoma (VS) growth for patients in a wait and scan (W&S) strategy.
DESIGN: Retrospective cohort study.
SETTING: Tertiary hospital (Radboud university medical center, Nijmegen, the Netherlands). PARTICIPANTS: Patients with unilateral VS, entering a W&S strategy and at least one follow-up MRI available. Data on demographics, symptoms, audiometry and MRI characteristics at time of diagnosis were collected from medical records. MAIN OUTCOME MEASURES: Following multiple imputation, a multivariable Cox regression model was used to select variables, using VS growth (≥2 mm) as outcome. Decision curve analyses (DCA) were performed to compare the model to the current strategy.
RESULTS: Of 1217 analysed VS patients, 653 (53.7%) showed growth during follow-up. Balance complaints (HR 1.57 (95% CI: 1.31-1.88)) and tinnitus complaints in the affected ear (HR 1.36 (95% CI: 1.15-1.61)), Koos grade (Koos 1 is reference, Koos 2 HR 1.03 (95% CI: 0.80-1.31), Koos 3 HR 1.55 (95% CI: 1.16-2.06), Koos 4 HR 2.18 (95% CI: 1.60-2.96)), time since onset of symptoms (IQR HR 0.83 (95% CI: 0.77-0.88) and intrameatal diameter on MRI (IQR HR 1.67 (95% CI: 1.42-1.96)) were selected as significant predictors. The model's discrimination (Harrell's C) was 0.69 (95% CI: 0.67-0.71), and calibration was good. DCA showed that the model has a higher net benefit than the current strategy for probabilities of VS growth of >12%, 15% and 21% for the first consecutive 3 years, respectively.
CONCLUSIONS: Patients with balance and tinnitus complaints, a higher Koos grade, short duration of symptoms and a larger intrameatal diameter at time of diagnosis have a higher probability of future VS growth. After external validation, this model may be used to inform patients about their prognosis, individualise the W&S strategy and improve (cost-)effectiveness.
© 2020 The Authors. Clinical Otolaryngology published by John Wiley & Sons Ltd.

Entities:  

Keywords:  growth; prediction model; vestibular schwannoma; wait and scan

Mesh:

Year:  2020        PMID: 33090707      PMCID: PMC7821120          DOI: 10.1111/coa.13661

Source DB:  PubMed          Journal:  Clin Otolaryngol        ISSN: 1749-4478            Impact factor:   2.597


  39 in total

1.  Prediction of vestibular schwannoma growth: a novel rule based on clinical symptomatology.

Authors:  Ferdinand C A Timmer; Janneke C J M Artz; Andy J Beynon; Rogier T Donders; Jef J S Mulder; Cor W R J Cremers; Kees Graamans
Journal:  Ann Otol Rhinol Laryngol       Date:  2011-12       Impact factor: 1.547

2.  The natural history of vestibular schwannoma.

Authors:  Sven-Eric Stangerup; Per Caye-Thomasen; Mirko Tos; Jens Thomsen
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3.  Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

Authors:  Gary S Collins; Johannes B Reitsma; Douglas G Altman; Karel G M Moons
Journal:  J Clin Epidemiol       Date:  2015-02       Impact factor: 6.437

4.  Identifying at diagnosis the vestibular schwannomas at low risk of growth in a long-term retrospective cohort.

Authors:  J G Wolbers; A H G Dallenga; A van Linge; M Te West; E E Kummer; A Méndez Romero; B K H Pauw; M H Wieringa
Journal:  Clin Otolaryngol       Date:  2016-05-11       Impact factor: 2.597

5.  The Changing Landscape of Vestibular Schwannoma Management in the United States--A Shift Toward Conservatism.

Authors:  Matthew L Carlson; Elizabeth B Habermann; Amy E Wagie; Colin L Driscoll; Jamie J Van Gompel; Jeffrey T Jacob; Michael J Link
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6.  Management of large and giant vestibular schwannomas.

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7.  Vestibular schwannoma: role of conservative management.

Authors:  R Suryanarayanan; R T Ramsden; S R Saeed; R Aggarwal; A T King; S A Rutherford; D G Evans; J E Gillespie
Journal:  J Laryngol Otol       Date:  2009-12-11       Impact factor: 1.469

8.  Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration.

Authors:  Karel G M Moons; Douglas G Altman; Johannes B Reitsma; John P A Ioannidis; Petra Macaskill; Ewout W Steyerberg; Andrew J Vickers; David F Ransohoff; Gary S Collins
Journal:  Ann Intern Med       Date:  2015-01-06       Impact factor: 25.391

9.  Adequate sample size for developing prediction models is not simply related to events per variable.

Authors:  Emmanuel O Ogundimu; Douglas G Altman; Gary S Collins
Journal:  J Clin Epidemiol       Date:  2016-03-08       Impact factor: 6.437

10.  An international comparison of diagnostic and management strategies for vestibular schwannoma.

Authors:  Mayke Hentschel; Maroeska Rovers; Laura Markodimitraki; Stefan Steens; Henricus Kunst
Journal:  Eur Arch Otorhinolaryngol       Date:  2018-11-12       Impact factor: 2.503

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Authors:  Makoto Hosoya; Takeshi Wakabayashi; Koichiro Wasano; Takanori Nishiyama; Nobuyoshi Tsuzuki; Naoki Oishi
Journal:  Diagnostics (Basel)       Date:  2022-04-21

2.  Auditory brainstem response prior to MRI compared to standalone MRI in the detection of vestibular schwannoma: A modelling study.

Authors:  Stan R W Wijn; Mayke A Hentschel; Andy J Beynon; Henricus P M Kunst; Maroeska M Rovers
Journal:  Clin Otolaryngol       Date:  2021-11-24       Impact factor: 2.729

3.  Diagnostic Validity of Auditory Brainstem Response for the Initial Screening of Vestibular Schwannoma.

Authors:  Marn Joon Park; Joong Ho Ahn; Hong Ju Park; Jong Woo Chung; Woo Seok Kang
Journal:  J Audiol Otol       Date:  2021-10-29

4.  Development of a model to predict vestibular schwannoma growth: An opportunity to introduce new wait and scan strategies.

Authors:  Mayke A Hentschel; Gerjon Hannink; Stefan C A Steens; Jef J S Mulder; Maroeska M Rovers; Henricus P M Kunst
Journal:  Clin Otolaryngol       Date:  2020-11-06       Impact factor: 2.597

5.  Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis.

Authors:  Oliver Profant; Zbyněk Bureš; Zuzana Balogová; Jan Betka; Zdeněk Fík; Martin Chovanec; Jan Voráček
Journal:  Sci Rep       Date:  2021-09-15       Impact factor: 4.379

  5 in total

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