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
Abstract 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 derivatio...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-50564-9 |
| _version_ | 1797276541331177472 |
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| author | Eric Daniel Tenda Joshua Henrina Andry Setiadharma Dahliana Jessica Aristy Pradana Zaky Romadhon Harik Firman Thahadian Bagus Aulia Mahdi Imam Manggalya Adhikara Erika Marfiani Satriyo Dwi Suryantoro Reyhan Eddy Yunus Prasandhya Astagiri Yusuf |
| author_facet | Eric Daniel Tenda Joshua Henrina Andry Setiadharma Dahliana Jessica Aristy Pradana Zaky Romadhon Harik Firman Thahadian Bagus Aulia Mahdi Imam Manggalya Adhikara Erika Marfiani Satriyo Dwi Suryantoro Reyhan Eddy Yunus Prasandhya Astagiri Yusuf |
| author_sort | Eric Daniel Tenda |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-07T15:29:38Z |
| format | Article |
| id | doaj.art-577fe59643ba4ae6bd8e3beacffb9974 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:29:38Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-577fe59643ba4ae6bd8e3beacffb99742024-03-05T16:29:45ZengNature PortfolioScientific Reports2045-23222024-01-0114111110.1038/s41598-023-50564-9Derivation 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 studyEric Daniel Tenda0Joshua Henrina1Andry Setiadharma2Dahliana Jessica Aristy3Pradana Zaky Romadhon4Harik Firman Thahadian5Bagus Aulia Mahdi6Imam Manggalya Adhikara7Erika Marfiani8Satriyo Dwi Suryantoro9Reyhan Eddy Yunus10Prasandhya Astagiri Yusuf11Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas IndonesiaPulmonology and Critical Care Medicine Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas IndonesiaPulmonology and Critical Care Medicine Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas IndonesiaPulmonology and Critical Care Medicine Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas IndonesiaHematology and Medical Oncology, Department of Internal Medicine, Universitas Airlangga Academic Hospital, Faculty of Medicine Universitas AirlanggaPulmonology and Critical Care Medicine Division, Department of Internal Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Dr. Sardjito General HospitalDepartment of Internal Medicine, Faculty of Medicine Universitas AirlanggaCardiology Division, Department of Internal Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Dr. Sardjito General HospitalPulmonology and Critical Care Medicine Division, Department of Internal Medicine, Faculty of Medicine Universitas Airlangga, Universitas Airlangga Academic HospitalNephrology Division, Department of Internal Medicine, Faculty of Medicine Universitas Airlangga, Universitas Airlangga Academic HospitalMedical Technology Cluster of Indonesian Medical Research Institute (IMERI), Faculty of Medicine Universitas IndonesiaMedical Technology Cluster of Indonesian Medical Research Institute (IMERI), Faculty of Medicine Universitas IndonesiaAbstract 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.https://doi.org/10.1038/s41598-023-50564-9 |
| spellingShingle | Eric Daniel Tenda Joshua Henrina Andry Setiadharma Dahliana Jessica Aristy Pradana Zaky Romadhon Harik Firman Thahadian Bagus Aulia Mahdi Imam Manggalya Adhikara Erika Marfiani Satriyo Dwi Suryantoro Reyhan Eddy Yunus Prasandhya Astagiri Yusuf 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 Scientific Reports |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://doi.org/10.1038/s41598-023-50564-9 |
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