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Prediction Equations for Calculating Maximal Inspiratory Pressure from Spirometry and Thoracic Ultrasound After COVID-19 with Gastroesophageal Reflux Disease in Indonesian Adults: A Cross-sectional Study

ACTA MEDICA INDONESIANA · Vol. 56 No. 3 (2024)

ISSN
2338-2732, 0125-9326
Published
2024-10-25
Publisher
The Indonesian Society of Internal Medicine
Language
eng

Abstract

Background: This study aimed to determine the prediction equations for calculating maximal inspiratory pressure using spirometry and thoracic ultrasonography (USG) after COVID-19 with gastroesophageal reflux disease (GERD). Methods: This cross-sectional study was conducted from January to December 2022 and included Indonesian adults recruited by consecutive sampling after they developed COVID-19 with GERD symptoms. The following tests were used: spirometry (forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1); thoracic USG (left diaphragm excursion (LDE) and right diaphragm excursion (RDE); and respirometry (maximal inspiratory pressure (MIP). The data were analyzed using Pearson correlational analysis and multiple linear regression. Results: Sixty-two participants were recruited: mean age 37.23 ± 9.76 years and average MIP 49.85 ± 18.13 cmH2O. MIP correlated significantly with FVC (r = 0.307; p = 0.015), LDE (r = 0.249; p = 0.051), FEV1(r = 0.186; p = 0.147), and RDE (r = 0.156; p = 0.221). We developed two models based on their applicability. Model 1 provides an MIP prediction equation for health facilities that have only spirometry: 23.841 – (20.455 × FEV1) + (26.190 × FVC). Model 2 provides an MIP prediction equation for health facilities that have both spirometry and thoracic USG: 3.530 – (20.025 × FEV1) + (25.354 × FVC) + (4.819 × LDE). Conclusion: In this study, measures of respiratory function correlated significantly with diaphragm excursion. MIP can be predicted from spirometry and thoracic USG data. Healthcare facilities can choose the prediction equation model that best meets their situation.

Keywords

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Co-authorOrganisationJoint articles
Siti SetiatiAffiliation unknown6
Hamzah ShatriAffiliation unknown5
Dadang MakmunAffiliation unknown4
Murdani AbdullahAffiliation unknown4
Adityo SusiloAffiliation unknown4
Ikhwan RinaldiAffiliation unknown3
Andhika RachmanAffiliation unknown3
Idrus AlwiAffiliation unknown3
Cleopas Martin RumendeAffiliation unknown2
Saskia Aziza NursyirwanAffiliation unknown2
Amanda Pitarini UtariAffiliation unknown2
Anna ArianeAffiliation unknown2
Achmad FauziAffiliation unknown2
Astrid SulistomoAffiliation unknown2
Budi WiwekoAffiliation unknown2
Cosphiadi IrawanAffiliation unknown2
Evy YunihastutiAffiliation unknown2
Rahmi IstantiAffiliation unknown2
Lies Dina LiastutiAffiliation unknown2
Harry IsbagioAffiliation unknown1
Bambang SetyohadiAffiliation unknown1
Faisal YunusAffiliation unknown1
Widjajalaksmi WidjajalaksmiAffiliation unknown1
Dwi R HelianthiAffiliation unknown1
Christina SimadibrataAffiliation unknown1
Adiningsih SrilestariAffiliation unknown1
Edy R WahyudiAffiliation unknown1
Radhiyatam MardhiyahAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://www.actamedindones.org/index.php/ijim/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Prediction Equations for Calculating Maximal Inspiratory Pressure from Spirometry and Thoracic Ultrasound  After COVID-19 with Gastroesophageal Reflux Disease  in Indonesian Adults: A Cross-sectional Study</dc:title>
	<dc:creator>Widjanantie, Siti Chandra</dc:creator>
	<dc:creator>Syam, Ari Fahrial</dc:creator>
	<dc:creator>Nusdwinuringtyas, Nury</dc:creator>
	<dc:creator>Susanto, Agus Dwi</dc:creator>
	<dc:creator>Hidayat, Rudy</dc:creator>
	<dc:creator>Kekalih, Aria</dc:creator>
	<dc:creator>Rachmawati, Maria Regina</dc:creator>
	<dc:creator>Maryastuti, Maryastuti</dc:creator>
	<dc:creator>Damayanti, Triya</dc:creator>
	<dc:creator>Prathiwi, Putri</dc:creator>
	<dc:creator>Choi, Won Ah</dc:creator>
	<dc:creator>Kang, Seong Woong</dc:creator>
	<dc:subject xml:lang="en-US">COVID-19</dc:subject>
	<dc:subject xml:lang="en-US">prediction equation</dc:subject>
	<dc:subject xml:lang="en-US">maximum inspiratory pressure</dc:subject>
	<dc:subject xml:lang="en-US">spirometry</dc:subject>
	<dc:subject xml:lang="en-US">thoracic ultrasound</dc:subject>
	<dc:subject xml:lang="en-US">Gastroenterology</dc:subject>
	<dc:description xml:lang="en-US">Background: This study aimed to determine the prediction equations for calculating maximal inspiratory pressure using spirometry and thoracic ultrasonography (USG) after COVID-19 with gastroesophageal reflux disease (GERD).  Methods: This cross-sectional study was conducted from January to December 2022 and included Indonesian adults recruited by consecutive sampling after they developed COVID-19 with GERD symptoms. The following tests were used: spirometry (forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1); thoracic USG (left diaphragm excursion (LDE) and right diaphragm excursion (RDE); and respirometry (maximal inspiratory pressure (MIP). The data were analyzed using Pearson correlational analysis and multiple linear regression. Results: Sixty-two participants were recruited: mean age 37.23 ± 9.76 years and average MIP 49.85 ± 18.13 cmH2O. MIP correlated significantly with FVC (r = 0.307; p = 0.015), LDE (r = 0.249; p = 0.051), FEV1(r = 0.186; p = 0.147), and RDE (r = 0.156; p = 0.221). We developed two models based on their applicability. Model 1 provides an MIP prediction equation for health facilities that have only spirometry: 23.841 – (20.455 × FEV1) + (26.190 × FVC). Model 2 provides an MIP prediction equation for health facilities that have both spirometry and thoracic USG: 3.530 – (20.025 × FEV1) + (25.354 × FVC) + (4.819 × LDE). Conclusion: In this study, measures of respiratory function correlated significantly with diaphragm excursion. MIP can be predicted from spirometry and thoracic USG data. Healthcare facilities can choose the prediction equation model that best meets their situation.</dc:description>
	<dc:publisher xml:lang="en-US">The Indonesian Society of Internal Medicine</dc:publisher>
	<dc:date>2024-10-25</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">cross-sectional study</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>http://www.actamedindones.org/index.php/ijim/article/view/2675</dc:identifier>
	<dc:source xml:lang="en-US">Acta Medica Indonesiana; Vol. 56 No. 3 (2024): Acta Medica Indonesiana; 314</dc:source>
	<dc:source>2338-2732</dc:source>
	<dc:source>0125-9326</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>http://www.actamedindones.org/index.php/ijim/article/view/2675/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Siti Chandra Widjanantie, Ari Fahrial Syam, Nury Nusdwinuringtyas, Agus Dwi Susanto, Rudy Hidayat, Aria Kekalih, Maria Regina Rachmawati, Maryastuti -, Triya Damayanti, Putri Prathiwi, Won Ah Choi, Seong Woong Kang</dc:rights>
</oai_dc:dc>