Evidence map›Paper›PMID 31061037›Full record

ArticleBMJ open2019

Counterintuitive results from observational data: a case study and discussion.

Erik Doty, David J Stone, Ned McCague, Leo Anthony Celi

Abstract read
In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Observational
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

4 authors.

Erik DotyDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
David J StoneUniversity of Virginia School of Medicine, Charlottesville, Virginia, USA.
Ned McCagueLaboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Leo Anthony CeliMassachusetts Institute of Technology, Cambridge, Massachusetts, USA.ORCID 0000-0001-6712-6626

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo explore the issue of counterintuitive data via analysis of a representative case in which the data obtained was unexpected and inconsistent with current knowledge. We then discuss the issue of counterintuitive data while developing a framework for approaching such findings.

designThe case study is a retrospective analysis of a cohort of coronary artery bypass graft (CABG) patients. Regression was used to examine the association between perceived pain in the intensive care unit (ICU) and selected outcomes.

settingMedical Information Mart for Intensive Care-III, a publicly available, de-identified critical care patient database.

participants844 adult patients from the database who underwent CABG surgery and were extubated within 24 hours after ICU admission. OUTCOMES: 30 day mortality, 1 year mortality and hospital length of stay (LOS).

resultsIncreased pain levels were found to be significantly associated with reduced mortality at 30 days and 1 year, and shorter hospital LOS. A one-point increase in mean pain level was found to be associated with a reduction in the odds of 30 day and 1 year mortality by a factor of 0.457 (95% CI 0.304 to 0.687, p<0.01) and 0.710 (95% CI 0.571 to 0.881, p<0.01) respectively, and a 0.916 (95% CI -1.159 to -0.673, p<0.01) day decrease in hospital LOS.

conclusionThe finding of an association between increased pain and improved outcomes was unexpected and clinically counterintuitive. In an increasingly digitised age of medical big data, such results are likely to become more common. The reliability of such counterintuitive results must be carefully examined. We suggest several issues to consider in this analytic process. If the data is determined to be valid, consideration must then be made towards alternative explanations for the counterintuitive results observed. Such results may in fact indicate that current clinical knowledge is incomplete or not have been firmly based on empirical evidence and function to inspire further research into the factors involved.

Indexed as

AgedAged, 80 and overCoronary Artery BypassDatabases, FactualFemaleHospital MortalityHumansIntensive Care UnitsLength of StayLogistic ModelsMaleMiddle AgedMultivariate AnalysisPain MeasurementReproducibility of ResultsRetrospective Studieslength of staymortalitypain

Identifiers

PMID31061037
PMCPMC6502001

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LicenceCC BY-NC
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Registered trials

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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.