Evidence map›Paper›PMID 35172984›Full record

ArticleBMJ open respiratory research2022

Reference equations for pulmonary diffusing capacity using segmented regression show similar predictive accuracy as GAMLSS models.

Gerald Stanley Zavorsky, Jiguo Cao

Abstract read
In one paragraph

Article in BMJ open respiratory research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
  3. Pulmonary diffusing capacity for nitric oxide in disease: a scoping review.European respiratory review : an official journal of the European Respiratory Society · 2026
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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

2 authors.

Gerald Stanley ZavorskyPulmonary Services, Department of Medicine, UC Davis, Sacramento, California, USA gerryzavorsky@gmail.com.ORCID 0000-0002-4473-1601
Jiguo CaoDepartment of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo determine whether generalised additive models of location, scale and shape (GAMLSS) developed for pulmonary diffusing capacity are superior to segmented (piecewise) regression models, and to update reference equations for pulmonary diffusing capacity for carbon monoxide (DLCO) and nitric oxide (DLNO), which may be affected by the equipment used for its measurement.

methodsData were pooled from five studies that developed reference equations for DLCO and DLNO (n=530 F/546 M; 5-95 years old, body mass index 12.4-39.0 kg/m

resultsIn males, GAMLSS models were 7% worse to 3% better compared to segmented regression for DLCO and DLNO. In females, GAMLSS models were 2% worse to 5% better compared to segmented linear regression for DLCO and DLNO. The Hyp'Air Compact measured DLNO and alveolar volume (VA) that was approximately 16-20 mL/min/mm Hg and 0.2-0.4 L higher, respectively, compared to the Jaeger MasterScreen Pro. The measured DLCO was similar between devices after controlling for altitude.

conclusionsFor the development of pulmonary function reference equations, we propose that segmented linear regression can be used instead of GAMLSS due to its simplicity, especially when the predictive accuracy is similar between the two models, overall.

Indexed as

Carbon MonoxidePulmonary Diffusing CapacityAdolescentAdultAgedAged, 80 and overChildChild, PreschoolFemaleHumansLungMaleMiddle AgedNitric OxideYoung AdultCarbon MonoxideNitric Oxideequipment evaluationslung physiologyrespiratory measurement

Identifiers

PMID35172984
PMCPMC8852756

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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.