Evidence map›Paper›PMID 29754203›Full record

ArticlePediatric cardiology2018

Prospective Analysis of Decision Making During Joint Cardiology Cardiothoracic Conference in Treatment of 107 Consecutive Children with Congenital Heart Disease.

Sophie Duignan, Aedin Ryan, Dara O'Keeffe, Damien Kenny, Colin J McMahon

Abstract read
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Article in Pediatric cardiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.0field-weighted citation impact, top 9% of its field
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

11 citing papers in PubMed, 20 citations in OpenAlex.

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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 at 3 institutions in 1 country.

Sophie DuignanDepartment of Paediatric Cardiology, Our Lady's Children's Hospital, Crumlin, Dublin 12, Ireland.
Aedin RyanDepartment of Paediatric Cardiology, Our Lady's Children's Hospital, Crumlin, Dublin 12, Ireland.
Dara O'KeeffeSimulation Centre, Department Medical Education, Royal College of Surgeons Ireland, Dublin, Ireland.
Damien KennyDepartment of Paediatric Cardiology, Our Lady's Children's Hospital, Crumlin, Dublin 12, Ireland.
Colin J McMahonDepartment of Paediatric Cardiology, Our Lady's Children's Hospital, Crumlin, Dublin 12, Ireland. cmcmahon992004@yahoo.com.
Children's Health Ireland at Crumlin · IEOur Lady's Hospital · IERoyal College of Surgeons in Ireland · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The complexity and potential biases involved in decision making have long been recognised and examined in both the aviation and business industries. More recently, the medical community have started to explore this concept and its particular importance in our field. Paediatric cardiology is a rapidly expanding field and for many of the conditions we treat, there is limited evidence available to support our decision-making. Variability exists within decision-making in paediatric cardiology and this may influence outcomes. There are no validated tools available to support and examine consistent decision-making for various treatment strategies in children with congenital heart disease in a multidisciplinary cardiology and cardiothoracic institution. Our primary objective was to analyse the complexity of decision-making for children with cardiac conditions in the context of our joint cardiology and cardiothoracic conference (JCC). Two paediatric cardiologists acted as investigators by observing the weekly joint cardiology-cardiothoracic surgery conference and prospectively evaluating the degree of complexity of decision-making in the management of 107 sequential children with congenital heart disease discussed. Additionally, the group consensus on the same patients was prospectively assessed to compare this to the independent observers. Of 107 consecutive children discussed at our JCC conference 32 (27%) went on to receive surgical intervention, 20 (17%) underwent catheterisation and 65 (56%) received medical treatment. There were 53 (50%) cases rated as simple by one senior observer, while 54 (50%) were rated as complex to some degree. There was high inter-observer agreement with a Krippendorff's alpha of ≥ 0.8 between 2 observers and between 2 observers and the group consensus as a whole for grading of the complexity of decision-making. Different decisions were occasionally made on patients with the same data set. Discussions revisiting the same patient, in complex cases, resulted in different management decisions being reached in this series. Anchoring of decision-making was witnessed in certain cases. Potential application of decision making algorithms is discussed in making decisions in paediatric cardiology patients. Decision-making in our institution's joint cardiology-cardiothoracic conference proved to be complex in approximately half of our patients. Inconsistency in decision-making for patients with the same diagnosis, and different decisions made for the same complex patient at different time points confounds the reliability of the decision-making process. These novel data highlight the absence of evidence-based medicine for many decisions, occasional lack of consistency and the impact of anchoring, heuristics and other biases in complex cases. Validated decision-making algorithms may assist in providing consistency to decision-making in this setting.

Indexed as

AlgorithmsCardiac Surgical ProceduresCardiologyChildClinical Decision-MakingCongresses as TopicConsensusHeart Defects, CongenitalHumansInfantInfant, NewbornMaleProspective StudiesReproducibility of ResultsThoracic SurgeryBiasConferenceCongenital heart diseaseDecision makingHeuristicsMultidisciplinary

Identifiers

PMID29754203
OpenAlexW2800252108

What OpenQuestion holds

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Read underepoch 390

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.