Evidence map›Paper›PMID 31398358›Full record

ArticleThe Annals of thoracic surgery2019

The Quest for Precision Medicine: Unmeasured Patient Factors and Mortality After Congenital Heart Surgery.

Sara K Pasquali, Michael Gaies, Mousumi Banerjee, Wenying Zhang, Janet Donohue, Mark Russell, J William Gaynor

Abstract readMulticenter Study
In one paragraph

Article in The Annals of thoracic surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  10. A machine learning-based pulmonary venous obstruction prediction model using clinical data and CT image.International journal of computer assisted radiology and surgery · 2021
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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

7 authors.

Sara K PasqualiDivision of Cardiology, Department of Pediatrics and Communicable Diseases, University of Michigan C.S. Mott Children's Hospital, Ann Arbor, Michigan. Electronic address: pasquali@med.umich.edu.
Michael GaiesDivision of Cardiology, Department of Pediatrics and Communicable Diseases, University of Michigan C.S. Mott Children's Hospital, Ann Arbor, Michigan.
Mousumi BanerjeeDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan.
Wenying ZhangDivision of Cardiology, Department of Pediatrics and Communicable Diseases, University of Michigan C.S. Mott Children's Hospital, Ann Arbor, Michigan.
Janet DonohueDivision of Cardiology, Department of Pediatrics and Communicable Diseases, University of Michigan C.S. Mott Children's Hospital, Ann Arbor, Michigan.
Mark RussellDivision of Cardiology, Department of Pediatrics and Communicable Diseases, University of Michigan C.S. Mott Children's Hospital, Ann Arbor, Michigan.
J William GaynorDepartment of Surgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.

Funding

Michigan Institute for Clinical and Health Research (MICHR)UL1TR002240 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LUMENG, JULIE C, MASHOUR, GEORGE ALEXANDER · 2017 to 2022
$54.9M
Understanding Quality and Costs in Congenital Heart SurgeryR01HL122261 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI PASQUALI, SARA · 2014 to 2019
$2.8M
Explaining Variation in Mortality after Pediatric Cardiac SurgeryK08HL116639 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GAIES, MICHAEL · 2014 to 2018
$767k
NCATS NIH HHS UL1 TR002240NHLBI NIH HHS K08 HL116639NHLBI NIH HHS R01 HL122261
6 · The paper itself

Abstract

backgroundEmerging data across many fields suggest that unique patient characteristics can impact disease manifestation and response to therapy, supporting "precision medicine" approaches and more individualized and targeted therapeutic strategies. In children undergoing congenital heart surgery, current risk models primarily focus on the population level, and their utility in understanding precise characteristics that place individual patients at risk for poor outcome remains unclear.

methodsWe analyzed index surgeries in the Pediatric Cardiac Critical Care Consortium (PC

resultsA total of 8406 operations (22 hospitals) were included. We found that only 30% of the total between-patient variation in mortality in our cohort was explained by the patient factors included in our model. Age, The Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery score, and preoperative mechanical ventilation explained the greatest proportion of variation. Of the variation that remained unexplained, 95% was attributable to unmeasured patient factors. In stratified analyses, these results were consistent across patient subgroups.

conclusionsPatient factors typically included in congenital heart surgery risk models explain only a small portion of total variation in mortality. A better understanding of other underrecognized factors is critical in further defining risk profiles and in developing more individualized and tailored therapeutic strategies.

Indexed as

Outcome Assessment, Health CareRegistriesAdolescentAdultCardiac Surgical ProceduresChildChild, PreschoolFemaleHeart Defects, CongenitalHospital MortalityHumansIncidenceInfantInfant, NewbornMalePostoperative Complications

Identifiers

PMID31398358
PMCPMC7075483

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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.