Evidence map›Paper›PMID 42136999›Full record

ArticlePediatric quality & safety

Improving Timeliness of Sweat Test for Cystic Fibrosis in All Infants with Out-of-range Screen.

Jaclyn Davis, Rachel Gordon, Kristen Richard, Cecelia DeBlasi, Lena Joseph, Christine Thayer, Emily Barsky, Gregory S Sawicki

Abstract read
In one paragraph

Article in Pediatric quality & safety. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

8 authors.

Jaclyn DavisFrom the Division of Pulmonary Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Mass.
Rachel GordonFrom the Division of Pulmonary Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Mass.
Kristen RichardFrom the Division of Pulmonary Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Mass.
Cecelia DeBlasiFrom the Division of Pulmonary Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Mass.
Lena JosephFrom the Division of Pulmonary Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Mass.
Christine ThayerFrom the Division of Pulmonary Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Mass.
Emily BarskyFrom the Division of Pulmonary Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Mass.
Gregory S SawickiFrom the Division of Pulmonary Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Mass.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Early diagnosis of cystic fibrosis (CF) leads to better outcomes. CF newborn screening occurs in the United States, and the ultimate diagnosis requires referral for sweat chloride testing (SCT), ideally within 28 days of life. At our center, 60% of CF newborn SCTs occurred outside the recommended window. Recognizing known differences in CF detection by newborn screen across race and ethnicity groups, we also identified a gap in understanding of potential inequities in SCT timeliness in our center. Methods: We used the Model for Improvement to understand key drivers for late SCT, focusing on optimizing internal processes and partnership with primary care providers and patient families. We implemented iterative Plan-Do-Study-Act cycles to effect change. Additionally, we developed a tracking tool to evaluate timeliness by self-reported race, ethnicity, and primary language. Results: We improved the rate of timely SCT from 40% to greater than 80% within the first project year, which was sustained over the subsequent year. We identified disparities in SCT timeliness by demographic characteristics, with those identified as Hispanic, Black, and multiracial having lower rates of timely SCT compared with those identified as White. There was a trend toward a lower rate of timely SCT in those speaking a non-English language as their primary language. Conclusions: This work provided a framework for CF center-based improvement of the SCT process after an out-of-range CF screen, recognizing the importance of partnership with referring providers and understanding the patient voice. After uncovering disparities within our CF center, further work is needed to understand the drivers of these inequities and to develop and study solutions.

Identifiers

PMID42136999
PMCPMC13169250

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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.