Evidence map›Paper›PMID 33723204›Full record

ArticleAnnals of transplantation2021

Use of Imputation and Decision Modeling to Improve Diagnosis and Management of Patients at Risk for New-Onset Diabetes After Transplantation.

Vidit N Munshi, Soroush Saghafian, Curtiss B Cook, Sumhith Veda Aradhyula, Harini A Chakkera

Open access · hybridAbstract read
In one paragraph

Article in Annals of transplantation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.5field-weighted citation impact, top 40% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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.

Vidit N MunshiDepartment of Health Policy, Harvard University, Cambridge, MA, USA.
Soroush SaghafianHarvard Kennedy School, Harvard University, Cambridge, MA, USA.
Curtiss B CookDepartment of Endocrinology, Mayo Clinic, Scottsdale, AZ, USA.
Sumhith Veda AradhyulaHarvard Kennedy School, Harvard University, Cambridge, MA, USA.
Harini A ChakkeraDepartment of Surgery/Nephrology, Mayo Clinic, Scottsdale, AZ, USA.
Harvard University Press · USMayo Clinic in Arizona · USHarvard University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND New-onset diabetes after transplantation (NODAT) is a complication of solid organ transplantation. We sought to determine the extent to which NODAT goes undiagnosed over the course of 1 year following transplantation, analyze missed or later-diagnosed cases of NODAT due to poor hemoglobin A1c (HbA1c) and fasting blood glucose (FBG) collection, and to estimate the impact that improved NODAT screening metrics may have on long-term outcomes. MATERIAL AND METHODS This was a retrospective study utilizing 3 datasets from a single center on kidney, liver, and heart transplantation patients. Retrospective analysis was supplemented with an imputation procedure to account for missing data and project outcomes under perfect information. In addition, the data were used to inform a simulation model used to estimate life expectancy and cost-effectiveness of a hypothetical intervention. RESULTS Estimates of NODAT incidence increased from 27% to 31% in kidney transplantation patients, from 31% to 40% in liver transplantation patients, and from 45% to 67% in heart transplantation patients, when HbA1c and FBG were assumed to be collected perfectly at all points. Perfect screening for kidney transplantation patients was cost-saving, while perfect screening for liver and heart transplantation patients was cost-effective at a willingness-to-pay threshold of $100 000 per life-year. CONCLUSIONS Improved collection of HbA1c and FBG is a cost-effective method for detecting many additional cases of NODAT within the first year alone. Additional research into both improved glucometric monitoring as well as effective strategies for mitigating NODAT risk will become increasingly important to improve health in this population.

Indexed as

Decision Support TechniquesDiabetes MellitusKidney TransplantationCost-Benefit AnalysisFemaleGlycated HemoglobinHumansImmunosuppressive AgentsMaleRetrospective StudiesRisk FactorsGlycated Hemoglobinhemoglobin A1c protein, humanImmunosuppressive Agents

Identifiers

PMID33723204
PMCPMC7980500
OpenAlexW3131264515

What OpenQuestion holds

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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.