Evidence map›Paper›PMID 39175031›Full record

ArticleParasites & vectors2024

Relapses in canine leishmaniosis: risk factors identified through mixed-effects logistic regression.

Juliana Sarquis, Letícia Martins Raposo, Carolina R Sanz, Ana Montoya, Juan Pedro Barrera, Rocío Checa, Blanca Perez-Montero, María Luisa Fermín Rodríguez, Guadalupe Miró

Abstract read
In one paragraph

Article in Parasites & vectors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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  4. Copy numbers of theCurrent research in parasitology & vector-borne diseases · 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

9 authors.

Juliana SarquisAnimal Health Department, Faculty of Veterinary Medicine, Complutense University of Madrid, Madrid, Spain.
Letícia Martins RaposoDepartment of Quantitative Methods, Universidade Federal Do Estado Do Rio de Janeiro, Rio de Janeiro, Brazil.
Carolina R SanzAnimal Health Department, Faculty of Veterinary Medicine, Complutense University of Madrid, Madrid, Spain.
Ana MontoyaAnimal Health Department, Faculty of Veterinary Medicine, Complutense University of Madrid, Madrid, Spain.
Juan Pedro BarreraAnimal Health Department, Faculty of Veterinary Medicine, Complutense University of Madrid, Madrid, Spain.
Rocío ChecaAnimal Health Department, Faculty of Veterinary Medicine, Complutense University of Madrid, Madrid, Spain.
Blanca Perez-MonteroDepartament of Medicine and Animal Surgery, Faculty of Veterinary Medicine, Complutense University of Madrid, Madrid, Spain.
María Luisa Fermín RodríguezDepartament of Medicine and Animal Surgery, Faculty of Veterinary Medicine, Complutense University of Madrid, Madrid, Spain.
Guadalupe MiróAnimal Health Department, Faculty of Veterinary Medicine, Complutense University of Madrid, Madrid, Spain. gmiro@ucm.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCanine leishmaniosis (CanL), caused by Leishmania infantum, is an important vector-borne parasitic disease in dogs with implications for human health. Despite advancements, managing CanL remains challenging due to its complexity, especially in chronic, relapsing cases. Mathematical modeling has emerged as a powerful tool in various medical fields, but its application in understanding CanL relapses remains unexplored.

methodsThis retrospective study aimed to investigate risk factors associated with disease relapse in a cohort of dogs naturally infected with L. infantum. Data from 291 repeated measures of 54 dogs meeting the inclusion criteria were included. Two logistic mixed-effects models were created to identify clinicopathological variables associated with an increased risk of clinical relapses requiring a leishmanicidal treatment in CanL. A backward elimination approach was employed, starting with a full model comprising all potential predictors. Variables were iteratively eliminated on the basis of their impact on the model, considering both statistical significance and model complexity. All analyses were conducted using R software, primarily employing the lme4 package, and applying a significance level of 5% (P < 0.05).

resultsThis study identified clinicopathological variables associated with an increased risk of relapses requiring a leishmanicidal treatment. Model 1 revealed that for each 0.1 increase in the albumin/globulin ratio (A/G) ratio, the odds of requiring treatment decreased by 45%. Conversely, for each unit increase in the total clinical score (CS), the odds of requiring treatment increase by 22-30%. Indirect immunofluorescence antibody test (IFAT) was not a significant risk factor in model 1. Model 2, incorporating individual albumin and globulins values, showed that dogs with high IFAT titers, hyper beta-globulinemia, hypoalbuminemia, anemia, and high CS were at increased risk of relapse. Both models demonstrated a good fit and explained a substantial amount of variability in treatment decisions.

conclusionsDogs exhibiting higher CS, dysproteinemia, anemia, and high IFAT titers are at increased risk of requiring leishmanicidal treatment upon clinical relapse in CanL. Regular monitoring and assessment of risk factors prove essential for early detection of relapses and effective intervention in CanL cases. The contrasting findings between the two models highlight the complexity of aspects influencing treatment decisions in this disease and the importance of tailored management strategies to improve outcomes for affected dogs.

Indexed as

Dog DiseasesLeishmania infantumLeishmaniasis, VisceralRecurrenceAnimalsDogsFemaleLeishmaniasisLogistic ModelsMaleRetrospective StudiesRisk FactorsDysproteinemiaLeishmaniaLeishmaniosisLogistic regressionProtein electrophoresisRelapsesRetrospective studyTreatment

Identifiers

PMID39175031
PMCPMC11342489

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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.