Evidence map›Paper›PMID 32164641›Full record

ArticleBMC medical informatics and decision making2020

Development and internal validation of prediction models for colorectal cancer survivors to estimate the 1-year risk of low health-related quality of life in multiple domains.

Dóra Révész, Sander M J van Kuijk, Floortje Mols, Fränzel J B van Duijnhoven, Renate M Winkels, Huub Hoofs, I Jmert Kant, Luc J Smits, Stéphanie O Breukink, Lonneke V van de Poll-Franse and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 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. Predicting health-related quality of life for patients with gastroesophageal cancer.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026
    Observational
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
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

14 authors.

Dóra RévészDepartment of Epidemiology, GROW - School for Oncology and Developmental Biology, Maastricht University, P. Debyeplein 1, 6200, MD, Maastricht, the Netherlands. Dora.Revesz@maastrichtuniversity.nl.ORCID 0000-0003-0435-6808
Sander M J van KuijkClinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, P. Debyelaan 25, PO Box 5800, Maastricht, 6202, AZ, the Netherlands.
Floortje MolsDepartment of Medical and Clinical Psychology, CoRPS - Center of Research on Psychology in Somatic diseases, Tilburg University, Warandelaan 2, 5037, AB, Tilburg, the Netherlands.
Fränzel J B van DuijnhovenDivision of Human Nutrition, Wageningen University & Research, Stippeneng 4, 6708, WE, Wageningen, the Netherlands.
Renate M WinkelsDepartment of Public Health Sciences, Penn State Cancer Institute, 500 University, Hershey, PA, 17033, USA.
Huub HoofsDepartment of Epidemiology, CAPHRI School for Public Health and Primary Care, Faculty of Health, Medicine and Life Sciences, Maastricht University, P. Debyeplein 1, 6200, MD, Maastricht, the Netherlands.
I Jmert KantDepartment of Epidemiology, CAPHRI School for Public Health and Primary Care, Faculty of Health, Medicine and Life Sciences, Maastricht University, P. Debyeplein 1, 6200, MD, Maastricht, the Netherlands.
Luc J SmitsDepartment of Epidemiology, CAPHRI School for Public Health and Primary Care, Faculty of Health, Medicine and Life Sciences, Maastricht University, P. Debyeplein 1, 6200, MD, Maastricht, the Netherlands.
Stéphanie O BreukinkDepartment of Surgery, Maastricht University Medical Centre, P. Debyelaan 25, 6229, HX, Maastricht, the Netherlands.
Lonneke V van de Poll-FranseClinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, P. Debyelaan 25, PO Box 5800, Maastricht, 6202, AZ, the Netherlands.
Ellen KampmanDivision of Human Nutrition, Wageningen University & Research, Stippeneng 4, 6708, WE, Wageningen, the Netherlands.
Sandra BeijerNetherlands Comprehensive Cancer Organisation (IKNL), Godebaldkwartier 419, 3511, DT, Utrecht, the Netherlands.
Matty P WeijenbergDepartment of Epidemiology, GROW - School for Oncology and Developmental Biology, Maastricht University, P. Debyeplein 1, 6200, MD, Maastricht, the Netherlands.
Martijn J L BoursDepartment of Epidemiology, GROW - School for Oncology and Developmental Biology, Maastricht University, P. Debyeplein 1, 6200, MD, Maastricht, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany colorectal cancer (CRC) survivors experience persisting health problems post-treatment that compromise their health-related quality of life (HRQoL). Prediction models are useful tools for identifying survivors at risk of low HRQoL in the future and for taking preventive action. Therefore, we developed prediction models for CRC survivors to estimate the 1-year risk of low HRQoL in multiple domains.

methodsIn 1458 CRC survivors, seven HRQoL domains (EORTC QLQ-C30: global QoL; cognitive, emotional, physical, role, social functioning; fatigue) were measured prospectively at study baseline and 1 year later. For each HRQoL domain, scores at 1-year follow-up were dichotomized into low versus normal/high. Separate multivariable logistic prediction models including biopsychosocial predictors measured at baseline were developed for the seven HRQoL domains, and internally validated using bootstrapping.

resultsAverage time since diagnosis was 5 years at study baseline. Prediction models included both non-modifiable predictors (age, sex, socio-economic status, time since diagnosis, tumor stage, chemotherapy, radiotherapy, stoma, micturition, chemotherapy-related, stoma-related and gastrointestinal complaints, comorbidities, social inhibition/negative affectivity, and working status) and modifiable predictors (body mass index, physical activity, smoking, meat consumption, anxiety/depression, pain, and baseline fatigue and HRQoL scores). Internally validated models showed good calibration and discrimination (AUCs: 0.83-0.93).

conclusionsThe prediction models performed well for estimating 1-year risk of low HRQoL in seven domains. External validation is needed before models can be applied in practice.

Indexed as

Models, StatisticalQuality of LifeAgedCancer SurvivorsColorectal NeoplasmsComorbidityFemaleHumansMaleMiddle AgedRiskCancer survivorsColorectal cancerInternal validationModel developmentPrediction modelsQuality of life

Identifiers

PMID32164641
PMCPMC7068880

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.