Riset dan Pengabdian Masyarakat
Do Research Sosial Service and Innovation
Publikasi Scopus 2024 per tanggal 30 Juni 2024 (499 artikel)
Publikasi Scopus 2024 per tanggal 30 Juni 2024 (499 artikel)
Tenda, Eric Daniel (57189692101); Henrina, Joshua (57218482646); Setiadharma, Andry (58837878100); Aristy, Dahliana Jessica (58837378800); Romadhon, Pradana Zaky (57211318799); Thahadian, Harik Firman (58082324500); Mahdi, Bagus Aulia (57208279588); Adhikara, Imam Manggalya (58082150300); Marfiani, Erika (57218766909); Suryantoro, Satriyo Dwi (57222345502); Yunus, Reyhan Eddy (57215658457); Yusuf, Prasandhya Astagiri (57192156597)
Derivation and validation of novel integrated inpatient mortality prediction score for COVID-19 (IMPACT) using clinical, laboratory, and AI—processed radiological parameter upon admission: a multicentre study
2024
Scientific Reports
14
1
2149
2
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183040813&doi=10.1038%2fs41598-023-50564-9&partnerID=40&md5=993a2db75f9c61381c865251785f80e6
Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas Indonesia, Jl. Pangeran Diponegoro No. 71, RW. 5, Kenari, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta, 10430, Indonesia; Medical Technology Cluster of Indonesian Medical Research Institute (IMERI), Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia; Hematology and Medical Oncology, Department of Internal Medicine, Universitas Airlangga Academic Hospital, Faculty of Medicine Universitas Airlangga, Surabaya, Indonesia; Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Dr. Sardjito General Hospital, Yogyakarta, Indonesia; Department of Internal Medicine, Faculty of Medicine Universitas Airlangga, Surabaya, Indonesia; Cardiology Division, Department of Internal Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Dr. Sardjito General Hospital, Yogyakarta, Indonesia; Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Faculty of Medicine Universitas Airlangga, Universitas Airlangga Academic Hospital, Surabaya, Indonesia; Nephrology Division, Department of Internal Medicine, Faculty of Medicine Universitas Airlangga, Universitas Airlangga Academic Hospital, Surabaya, Indonesia; Department of Radiology, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia; Department of Medical Physiology and Biophysics, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
Limited studies explore the use of AI for COVID-19 prognostication. This study investigates the relationship between AI-aided radiographic parameters, clinical and laboratory data, and mortality in hospitalized COVID-19 patients. We conducted a multicentre retrospective study. The derivation and validation cohort comprised of 512 and 137 confirmed COVID-19 patients, respectively. Variable selection for constructing an in-hospital mortality scoring model was performed using the least absolute shrinkage and selection operator, followed by logistic regression. The accuracy of the scoring model was assessed using the area under the receiver operating characteristic curve. The final model included eight variables: anosmia (OR: 0.280; 95%CI 0.095–0.826), dyspnoea (OR: 1.684; 95%CI 1.049–2.705), loss of consciousness (OR: 4.593; 95%CI 1.702–12.396), mean arterial pressure (OR: 0.928; 95%CI 0.900–0.957), peripheral oxygen saturation (OR: 0.981; 95%CI 0.967–0.996), neutrophil % (OR: 1.034; 95%CI 1.013–1.055), serum urea (OR: 1.018; 95%CI 1.010–1.026), affected lung area score (OR: 1.026; 95%CI 1.014–1.038). The Integrated Inpatient Mortality Prediction Score for COVID-19 (IMPACT) demonstrated a predictive value of 0.815 (95% CI 0.774–0.856) in the derivation cohort. Internal validation resulted in an AUROC of 0.770 (95% CI 0.661–0.879). Our study provides valuable evidence of the real-world application of AI in clinical settings. However, it is imperative to conduct prospective validation of our findings, preferably utilizing a control group and extending the application to broader populations. © 2024, The Author(s).
Arterial Pressure; COVID-19; Humans; Inpatients; Logistic Models; Retrospective Studies; ROC Curve; arterial pressure; clinical trial; coronavirus disease 2019; diagnostic imaging; hospital patient; human; multicenter study; receiver operating characteristic; retrospective study; statistical model
Delft Imaging System; Indonesian Collaboration Research Program, (NKB-1061UN2, RST/HKP.05.00/2022); Universitas Indonesia, UI
Funding text 1: The research team received support from the Indonesian Collaboration Research Program Grant NKB-1061UN2.RST/HKP.05.00/2022. Additionally, a joint research arrangement has been signed between Universitas Indonesia, RSPUN dr. Cipto Mangunkusumo Hospital, and Delft Imaging System, outlining the rights and obligations of each party regarding data confidentiality during and after the re
Nature Research
20452322
38272920
Article
Q1
973
4401