Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Narra J
Rumende, Cleopas M.
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Health Professions Immunology & microbiology Medicine & Pharmacology Public Health

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A novel diastolic dysfunction score: A proposed diagnostic predictor for left ventricular dysfunction in obese population Kamelia, Telly; Rumende, Cleopas M.; Makmun, Lukman H.; Timan, Ina S.; Djauzi, Samsuridjal; Prihartono, Joedo; Fardizza, Fauziah; Tabri, Nur A.
Narra J Vol. 5 No. 1 (2025): April 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i1.1564

Abstract

Obesity-related diastolic dysfunction is an emerging contributor to heart failure and cardiovascular mortality. However, effective and accessible diagnostic tools are still limited. Current methods for assessing diastolic dysfunction are often invasive or technologically demanding, making them impractical for routine clinical use and community settings. The aim of this study was to develop a novel, non-invasive scoring system designed to predict diastolic dysfunction in obese adults, addressing this diagnostic gap. This community-based, prospective cross-sectional study was conducted in Jakarta, Indonesia, from March to November 2021, and included 82 participants aged 18 to 60 years, all with a body mass index (BMI) ≥25 kg/m². Patients with acute or critical illnesses, valvular heart diseases, or acute confusional states were excluded. Each participant underwent blood tests, polysomnography, and echocardiography. Of the study population, 80.5% were diagnosed with obstructive sleep apnea (OSA), and 12.2% exhibited diastolic dysfunction, all within the OSA group. The novel scoring system integrates four predictors: oxygen desaturation index (ODI) ≥39 (score 1; prevalence ratio: 4.31 (95% confidence interval (CI): 1.58–11.75)), HbA1C ≥5.95% (score 2; prevalence ratio: 6.32 (95%CI: 2.84–14.06)), pulmonary artery wedge pressure (PAWP) ≥10 mmHg (score 1; prevalence ratio: 5.95 (95%CI: 2.30–15.39)), and global longitudinal strain (GLS) ≥-16.95% (score 1; prevalence ratio: 4.32 (95%CI: 1.87–9.99)). A score of ≥2 predicted diastolic dysfunction with 90% sensitivity, with positive predictive value and negative predictive value of 40.91% and 98.33%, respectively. In conclusion, the diastolic dysfunction score is a simple and practical tool for the early detection of diastolic dysfunction in obese individuals without cardiovascular symptoms.
Designing the CORI score for COVID-19 diagnosis in parallel with deep learning-based imaging models Kamelia, Telly; Zulkarnaien, Benny; Septiyanti, Wita; Afifi, Rahmi; Krisnadhi, Adila; Rumende, Cleopas M.; Wibisono, Ari; Guarddin, Gladhi; Chahyati, Dina; Yunus, Reyhan E.; Pratama, Dhita P.; Rahmawati, Irda N.; Nareswari, Dewi; Falerisya, Maharani; Salsabila, Raissa; Baruna, Bagus DI.; Iriani, Anggraini; Nandipinto, Finny; Wicaksono, Ceva; Sini, Ivan R.
Narra J Vol. 5 No. 2 (2025): August 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i2.1606

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has triggered a global health crisis and placed unprecedented strain on healthcare systems, particularly in resource-limited settings where access to RT-PCR testing is often restricted. Alternative diagnostic strategies are therefore critical. Chest X-rays, when integrated with artificial intelligence (AI), offers a promising approach for COVID-19 detection. The aim of this study was to develop an AI-assisted diagnostic model that combines chest X-ray images and clinical data to generate a COVID-19 Risk Index (CORI) Score and to implement a deep learning model based on ResNet architecture. Between April 2020 and July 2021, a multicenter cohort study was conducted across three hospitals in Jakarta, Indonesia, involving 367 participants categorized into three groups: 100 COVID-19 positive, 100 with non-COVID-19 pneumonia, and 100 healthy individuals. Clinical parameters (e.g., fever, cough, oxygen saturation) and laboratory findings (e.g., D-dimer and C-reactive protein levels) were collected alongside chest X-ray images. Both the CORI Score and the ResNet model were trained using this integrated dataset. During internal validation, the ResNet model achieved 91% accuracy, 94% sensitivity, and 92% specificity. In external validation, it correctly identified 82 of 100 COVID-19 cases. The combined use of imaging, clinical, and laboratory data yielded an area under the ROC curve of 0.98 and a sensitivity exceeding 95%. The CORI Score demonstrated strong diagnostic performance, with 96.6% accuracy, 98% sensitivity, 95.4% specificity, a 99.5% negative predictive value, and a 91.1% positive predictive value. Despite limitations—including retrospective data collection, inter-hospital variability, and limited external validation—the ResNet-based AI model and the CORI Score show substantial promise as diagnostic tools for COVID-19, with performance comparable to that of experienced thoracic radiologists in Indonesia.
Co-Authors Afifi, Rahmi Anggraini Iriani Ari Wibisono Baruna, Bagus DI. Chahyati, Dina Falerisya, Maharani Fardizza, Fauziah Guarddin, Gladhi Ina S. Timan, Ina S. Joedo Prihartono Krisnadhi, Adila Lukman H. Makmun Nandipinto, Finny Nareswari, Dewi Pratama, Dhita P. Rahmawati, Irda N. Salsabila, Raissa Samsuridjal Djauzi Septiyanti, Wita Sini, Ivan R. Tabri, Nur A. Telly Kamelia Wicaksono, Ceva Yunus, Reyhan E. Zulkarnaien, Benny