Footprint analysis in flatfoot assessment

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Abstract

BACKGROUND: A flatfoot is the most common condition in the practice of a pediatric orthopedist. A flatfoot is primarily diagnosed based on the assessment of the degree of the foot flattening. Along with clinical examination, footprint analysis is often used in practice due to the safety of this study and convenience and ease of implementation.

AIM: This study aimed to determine how much footprints can correlate with the clinical assessment of flatfoot in children and which footprint indices are the most valuable in flatfoot assessment.

MATERIALS AND METHODS: The study included the survey results of 76 children aged 7–15 years of the St. Petersburg 49th school “School of Health” for 2021–2022. In this study, anthropometric data, clinical parameters (value of the heel valgus, arch angle, and Friedland index) and footprint indices and angles (Schwartz and Clarke angle, Chippaux–Smirak index, Staheli index, Cavanagh and Rodgers index, and Irwin index) were analyzed. In the study, the average values were calculated, and correlation and regression analyses were performed.

RESULTS: The footprint parameters did not have moderate and strong correlations with clinical parameters. Footprint parameters that assessed the area of the barefoot zone on the footprints (Irwin index and Cavanagh and Rodgers index) showed statistically significant moderate and strong correlations among plantographic parameters. Among linear and angular footprint parameters, the Chippaux–Smirak index showed statistically significant moderate and strong correlations.

CONCLUSIONS: The footprint criteria weakly correlated with the foot shape criteria in a clinical assessment, which does not allow us to interpolate the footprint’s data to the clinical evaluation data of the foot. The Cavanagh and Rodgers index, Irwin index, and Chippaux–Smirak index had statistically significant moderate and strong correlations with other indices, which makes them more valuable in the assessment of feet according to the footprint analysis.

About the authors

Andrey V. Sapogovskiy

H. Turner National Medical Research Center for Children’s Orthopedics and Trauma Surgery

Email: sapogovskiy@gmail.com
ORCID iD: 0000-0002-5762-4477
SPIN-code: 2068-2102
Scopus Author ID: 57193257532

MD, PhD, Cand. Sci. (Med.)

Russian Federation, Saint Petersburg

Alla V. Ovechkina

H. Turner National Medical Research Center for Children’s Orthopedics and Trauma Surgery

Email: ovechkina.spb@mail.ru
ORCID iD: 0000-0002-3172-0065
SPIN-code: 7049-6674
Scopus Author ID: 6507566283

MD, PhD, Cand. Sci. (Med.), Assistant Professor, Honored Doctor of the Russian Federation

Russian Federation, Saint Petersburg

Ilya A. Abramov

Murmansk Regional Clinical Multidisciplinary Center

Email: ia.murman@yandex.ru
ORCID iD: 0000-0003-4653-4203

MD, Paediatric Orthopaedic Surgeon

Russian Federation, Murmansk

Olga E. Agranovich

H. Turner National Medical Research Center for Children’s Orthopedics and Trauma Surgery

Email: olga_agranovich@yahoo.com
ORCID iD: 0000-0002-6655-4108
SPIN-code: 4393-3694
Scopus Author ID: 56913386600
ResearcherId: B-3334-2019

MD, PhD, Dr. Sci. (Med.)

Russian Federation, Saint Petersburg

Anastasia I. Shubina

H. Turner National Medical Research Center for Children’s Orthopedics and Trauma Surgery

Email: shubinaasia@gmail.com
ORCID iD: 0000-0001-7843-9564

MD, PhD student

Russian Federation, Saint Petersburg

Tatyana G. Budkevich

Boarding school No. 49 of St. Petersburg’s Petrodvortsovy district “School of Health”

Author for correspondence.
Email: Bt-tata@mail.ru
ORCID iD: 0000-0002-4278-5454

MD, PhD, Cand. Sci. (Med.)

Russian Federation, Saint Petersburg

References

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Supplementary files

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2. Fig. 3. Graphs of regression models between plantographic indices with strong correlations (RUS)

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3. Table 2. Correlation matrix of the studied parameters (Spearman coefficient)

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4. Fig. 1. Method for assessing the foot shape, including measuring the rearfoot valgus (a), angle of the longitudinal arch of the foot (Dahle angle) (b), and Friedland index (c)

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5. Fig. 2. Plantogram analysis techniques: a, Schwartz, and Clarke angle; b, Chippaux–Smirak index; c, Staheli index; d, Cavanagh, and Rodgers index; e, Irwin index

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6. Fig. 3. Graphs of regression models between plantographic indices with strong correlations

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Copyright (c) 2023 Sapogovskiy A.V., Ovechkina A.V., Abramov I.A., Agranovich O. ., Shubina A.I., Budkevich T.G.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
 


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