Evidence map›Paper›PMID 32170136›Full record

ArticleScientific reports2020

Development and internal validation of a depression severity prediction model for tinnitus patients based on questionnaire responses and socio-demographics.

Uli Niemann, Petra Brueggemann, Benjamin Boecking, Birgit Mazurek, Myra Spiliopoulou

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Tinnitus and Influencing Comorbidities.Laryngo- rhino- otologie · 2023
    Review
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Uli NiemannFaculty of Computer Science, Otto von Guericke University Magdeburg, Universitätsplatz 2, Magdeburg, 39106, Germany. uli.niemann@ovgu.de.
Petra BrueggemannTinnitus Center, Charité Universitaetsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany.
Benjamin BoeckingTinnitus Center, Charité Universitaetsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany.
Birgit MazurekTinnitus Center, Charité Universitaetsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany.
Myra SpiliopoulouFaculty of Computer Science, Otto von Guericke University Magdeburg, Universitätsplatz 2, Magdeburg, 39106, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tinnitus is a complex condition that is associated with major psychological and economic impairments - partly through various comorbidities such as depression. Understanding the interaction between tinnitus and depression may thus improve either symptom cluster's prevention, diagnosis and treatment. In this study, we developed and validated a machine learning model to predict depression severity after outpatient therapy (T1) based on variables obtained before therapy (T0). 1,490 patients with chronic tinnitus (comorbid major depressive disorder: 52.2%) who completed a 7-day multimodal treatment encompassing tinnitus-specific components, cognitive behavioural therapy, physiotherapy and informational counselling were included. 185 variables were extracted from self-report questionnaires and socio-demographic data acquired at T0. We used 11 classification methods to train models that reliably separate between subclinical and clinical depression at T1 as measured by the general depression questionnaire. To ensure highly predictive and robust classifiers, we tuned algorithm hyperparameters in a 10-fold cross-validation scheme. To reduce model complexity and improve interpretability, we wrapped model training around an incremental feature selection mechanism that retained features that contributed to model prediction. We identified a LASSO model that included all 185 features to yield highest predictive performance (AUC = 0.87 ± 0.04). Through our feature selection wrapper, we identified a LASSO model with good trade-off between predictive performance and interpretability that used only 6 features (AUC = 0.85 ± 0.05). Thus, predictive machine learning models can lead to a better understanding of depression in tinnitus patients, and contribute to the selection of suitable therapeutic strategies and concise and valid questionnaire design for patients with chronic tinnitus with or without comorbid major depressive disorder.

Indexed as

Disease SusceptibilityAdultDepressionFemaleHumansMachine LearningMaleMiddle AgedPrognosisSeverity of Illness IndexSocioeconomic FactorsSurveys and QuestionnairesTinnitusWorkflow

Identifiers

PMID32170136
PMCPMC7069984

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.