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Hamar Á, Mohammed D, Váradi A, Herczeg R, Balázsfalvi N, Fülesdi B, László I, Gömöri L, Gergely PA, Kovacs GL, Jáksó K, Gombos K. COVID-19 mortality prediction in Hungarian ICU settings implementing random forest algorithm. Sci Rep 2024; 14:11941. [PMID: 38789490 PMCID: PMC11126653 DOI: 10.1038/s41598-024-62791-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 05/19/2024] [Indexed: 05/26/2024] Open
Abstract
The emergence of newer SARS-CoV-2 variants of concern (VOCs) profoundly changed the ICU demography; this shift in the virus's genotype and its correlation to lethality in the ICUs is still not fully investigated. We aimed to survey ICU patients' clinical and laboratory parameters in correlation with SARS-CoV-2 variant genotypes to lethality. 503 COVID-19 ICU patients were included in our study beginning in January 2021 through November 2022 in Hungary. Furthermore, we implemented random forest (RF) as a potential predictor regarding SARS-CoV-2 lethality among 649 ICU patients in two ICU centers. Survival analysis and comparison of hypertension (HT), diabetes mellitus (DM), and vaccination effects were conducted. Logistic regression identified DM as a significant mortality risk factor (OR: 1.55, 95% CI 1.06-2.29, p = 0.025), while HT showed marginal significance. Additionally, vaccination demonstrated protection against mortality (p = 0.028). RF detected lethality with 81.42% accuracy (95% CI 73.01-88.11%, [AUC]: 91.6%), key predictors being PaO2/FiO2 ratio, lymphocyte count, and chest Computed Tomography Severity Score (CTSS). Although a smaller number of patients require ICU treatment among Omicron cases, the likelihood of survival has not proportionately increased for those who are admitted to the ICU. In conclusion, our RF model supports more effective clinical decision-making among ICU COVID-19 patients.
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Affiliation(s)
- Ágoston Hamar
- Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary
- Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
| | - Daryan Mohammed
- Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
| | - Alex Váradi
- Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
- Institute of Metagenomics, University of Debrecen, Debrecen, Hungary
| | - Róbert Herczeg
- Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
| | - Norbert Balázsfalvi
- Department of Anaesthesiology and Intensive Care, University of Debrecen, Debrecen, Hungary
| | - Béla Fülesdi
- Department of Anaesthesiology and Intensive Care, University of Debrecen, Debrecen, Hungary
| | - István László
- Department of Anaesthesiology and Intensive Care, University of Debrecen, Debrecen, Hungary
| | - Lídia Gömöri
- Doctoral School of Neuroscience, University of Debrecen, Debrecen, Hungary
| | | | - Gabor Laszlo Kovacs
- Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary
- Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
| | - Krisztián Jáksó
- Department of Anaesthesiology and Intensive Care, Clinical Centre, University of Pécs, Pécs, Hungary
| | - Katalin Gombos
- Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary.
- Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary.
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Andrea Gallego Aristizabal P, Paola Lujan Chavarría T, Isabel Vergara Hernández S, Rincón Acosta F, Paula Sánchez Carmona M, Andrea Salazar Ospina P, Jose Atencia Florez C, Mario Barros Liñán C, Jaimes F. External validation of two clinical prediction models for mortality in COVID-19 patients (4C and NEWS2), in three centers in Medellín, Colombia: Assessing the impact of vaccination over time. Infect Dis Now 2024; 54:104921. [PMID: 38703825 DOI: 10.1016/j.idnow.2024.104921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 12/19/2023] [Accepted: 04/29/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVES External validation of the 4C and NEWS2 scores for the prediction of in-hospital mortality in COVID-19 patients, and evaluation of its operational performance in two time periods: before and after the start of the vaccination program in Colombia. METHODS Retrospective cohort in three high complexity hospitals in the city of Medellín, Colombia, between June 2020 and April 2022. RESULTS The areas under the ROC curve (AUC) for the 4C mortality risk score and the NEWS2 were 0.75 (95% CI 0.73-0.78) and 0.68 (95% CI 0.66-0.71), respectively. For the 4C score, the AUC for the first and second periods was 0.77 (95% CI 0.74-0.80) and 0.75 (95% CI 0.71-0.78); whilst for the NEWS2 score, it was 0.68 (95% CI 0.65-0.71) and 0.69 (95% CI 0.64-0.73). The calibration for both scores was adequate, albeit with reduced performance during the second period. CONCLUSIONS The 4C mortality risk score proved to be the more adequate predictor of in-hospital mortality in COVID-19 patients in this Latin American population. The operational performance during both time periods remained similar, which shows its utility notwithstanding major changes, including vaccination, as the pandemic evolved.
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Affiliation(s)
- Paola Andrea Gallego Aristizabal
- Department of Internal Medicine, Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia; Hospital Universitario San Vicente Fundación, Medellín, Colombia
| | - Tania Paola Lujan Chavarría
- Department of Internal Medicine, Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia; Hospital Universitario San Vicente Fundación, Medellín, Colombia.
| | | | | | | | | | - Carlos Jose Atencia Florez
- Department of Internal Medicine, Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia; Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia; Hospital Universitario San Vicente Fundación, Medellín, Colombia
| | - Carlos Mario Barros Liñán
- Department of Internal Medicine, Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia; Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia; Hospital Alma Mater, Medellín, Colombia
| | - Fabián Jaimes
- Department of Internal Medicine, Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia; Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia; Hospital Universitario San Vicente Fundación, Medellín, Colombia
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Haimovich AD, Burke RC, Nathanson LA, Rubins D, Taylor RA, Kross EK, Ouchi K, Shapiro NI, Schonberg MA. Geriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients. JAMA Netw Open 2024; 7:e2414213. [PMID: 38819823 PMCID: PMC11143461 DOI: 10.1001/jamanetworkopen.2024.14213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/31/2024] [Indexed: 06/01/2024] Open
Abstract
Importance Emergency department (ED) visits by older adults with life-limiting illnesses are a critical opportunity to establish patient care end-of-life preferences, but little is known about the optimal screening criteria for resource-constrained EDs. Objectives To externally validate the Geriatric End-of-Life Screening Tool (GEST) in an independent population and compare it with commonly used serious illness diagnostic criteria. Design, Setting, and Participants This prognostic study assessed a cohort of patients aged 65 years and older who were treated in a tertiary care ED in Boston, Massachusetts, from 2017 to 2021. Patients arriving in cardiac arrest or who died within 1 day of ED arrival were excluded. Data analysis was performed from August 1, 2023, to March 27, 2024. Exposure GEST, a logistic regression algorithm that uses commonly available electronic health record (EHR) datapoints and was developed and validated across 9 EDs, was compared with serious illness diagnoses as documented in the EHR. Serious illnesses included stroke/transient ischemic attack, liver disease, cancer, lung disease, and age greater than 80 years, among others. Main Outcomes and Measures The primary outcome was 6-month mortality following an ED encounter. Statistical analyses included area under the receiver operating characteristic curve, calibration analyses, Kaplan-Meier survival curves, and decision curves. Results This external validation included 82 371 ED encounters by 40 505 unique individuals (mean [SD] age, 76.8 [8.4] years; 54.3% women, 13.8% 6-month mortality rate). GEST had an external validation area under the receiver operating characteristic curve of 0.79 (95% CI, 0.78-0.79) that was stable across years and demographic subgroups. Of included encounters, 53.4% had a serious illness, with a sensitivity of 77.4% (95% CI, 76.6%-78.2%) and specificity of 50.5% (95% CI, 50.1%-50.8%). Varying GEST cutoffs from 5% to 30% increased specificity (5%: 49.1% [95% CI, 48.7%-49.5%]; 30%: 92.2% [95% CI, 92.0%-92.4%]) at the cost of sensitivity (5%: 89.3% [95% CI, 88.8-89.9]; 30%: 36.2% [95% CI, 35.3-37.1]). In a decision curve analysis, GEST outperformed serious illness criteria across all tested thresholds. When comparing patients referred to intervention by GEST with serious illness criteria, GEST reclassified 45.1% of patients with serious illness as having low risk of mortality with an observed mortality rate 8.1% and 2.6% of patients without serious illness as having high mortality risk with an observed mortality rate of 34.3% for a total reclassification rate of 25.3%. Conclusions and Relevance The findings of this study suggest that both serious illness criteria and GEST identified older ED patients at risk for 6-month mortality, but GEST offered more useful screening characteristics. Future trials of serious illness interventions for high mortality risk in older adults may consider transitioning from diagnosis code criteria to GEST, an automatable EHR-based algorithm.
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Affiliation(s)
- Adrian D. Haimovich
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Ryan C. Burke
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Larry A. Nathanson
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - David Rubins
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - R. Andrew Taylor
- Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Erin K. Kross
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington, Seattle
- Cambia Palliative Care Center of Excellence at UW Medicine, Seattle, Washington
| | - Kei Ouchi
- Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Nathan I. Shapiro
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Mara A. Schonberg
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
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Horvath VJ, Békeffy M, Németh Z, Szelke E, Fazekas-Pongor V, Hajdu N, Svébis MM, Pintér J, Domján BA, Mészáros S, Körei AE, Kézdi Á, Kocsis I, Kristóf K, Kempler P, Rozgonyi F, Takács I, Tabák AG. The effect of COVID-19 vaccination status on all-cause mortality in patients hospitalised with COVID-19 in Hungary during the delta wave of the pandemic. GeroScience 2024; 46:1881-1894. [PMID: 37755581 PMCID: PMC10828407 DOI: 10.1007/s11357-023-00931-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 09/04/2023] [Indexed: 09/28/2023] Open
Abstract
The high mortality of patients with coronavirus disease 2019 (COVID-19) is effectively reduced by vaccination. However, the effect of vaccination on mortality among hospitalised patients is under-researched. Thus, we investigated the effect of a full primary or an additional booster vaccination on in-hospital mortality among patients hospitalised with COVID-19 during the delta wave of the pandemic. This retrospective cohort included all patients (n = 430) admitted with COVID-19 at Semmelweis University Department of Medicine and Oncology in 01/OCT/2021-15/DEC/2021. Logistic regression models were built with COVID-19-associated in-hospital/30 day-mortality as outcome with hierarchical entry of predictors of vaccination, vaccination status, measures of disease severity, and chronic comorbidities. Deceased COVID-19 patients were older and presented more frequently with cardiac complications, chronic kidney disease, and active malignancy, as well as higher levels of inflammatory markers, serum creatinine, and lower albumin compared to surviving patients (all p < 0.05). However, the rates of vaccination were similar (52-55%) in both groups. Based on the fully adjusted model, there was a linear decrease of mortality from no/incomplete vaccination (ref) through full primary (OR 0.69, 95% CI: 0.39-1.23) to booster vaccination (OR 0.31, 95% CI 0.13-0.72, p = 0.006). Although unadjusted mortality was similar among vaccinated and unvaccinated patients, this was explained by differences in comorbidities and disease severity. In adjusted models, a full primary and especially a booster vaccination improved survival of patients hospitalised with COVID-19 during the delta wave of the pandemic. Our findings may improve the quality of patient provider discussions at the time of admission.
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Affiliation(s)
- Viktor J Horvath
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary.
| | - Magdolna Békeffy
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary
| | - Zsuzsanna Németh
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary
| | - Emese Szelke
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary
| | - Vince Fazekas-Pongor
- Department of Public Health, Semmelweis University Faculty of Medicine, Budapest, Hungary
| | - Noémi Hajdu
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary
| | - Márk M Svébis
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary
| | - József Pintér
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary
| | - Beatrix A Domján
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary
| | - Szilvia Mészáros
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary
| | - Anna E Körei
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary
| | - Árpád Kézdi
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary
| | - Ibolya Kocsis
- Department of Laboratory Medicine, Semmelweis University Faculty of Medicine, Budapest, Hungary
| | - Katalin Kristóf
- Department of Laboratory Medicine, Semmelweis University Faculty of Medicine, Budapest, Hungary
| | - Péter Kempler
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary
| | - Ferenc Rozgonyi
- Department of Laboratory Medicine, Semmelweis University Faculty of Medicine, Budapest, Hungary
| | - István Takács
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary
| | - Adam G Tabák
- Department of Internal Medicine and Oncology, Semmelweis University Faculty of Medicine, 2/a Korányi S. Str, 1083, Budapest, Hungary
- Department of Public Health, Semmelweis University Faculty of Medicine, Budapest, Hungary
- UCL Brain Sciences, University College London, London, UK
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la Roi-Teeuw HM, Luijken K, Blom MT, Gussekloo J, Mooijaart SP, Polinder-Bos HA, van Smeden M, Geersing GJ, van den Dries CJ. Limited incremental predictive value of the frailty index and other vulnerability measures from routine care data for mortality risk prediction in older patients with COVID-19 in primary care. BMC PRIMARY CARE 2024; 25:70. [PMID: 38395766 PMCID: PMC10885372 DOI: 10.1186/s12875-024-02308-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 02/13/2024] [Indexed: 02/25/2024]
Abstract
BACKGROUND During the COVID-19 pandemic, older patients in primary care were triaged based on their frailty or assumed vulnerability for poor outcomes, while evidence on the prognostic value of vulnerability measures in COVID-19 patients in primary care was lacking. Still, knowledge on the role of vulnerability is pivotal in understanding the resilience of older people during acute illness, and hence important for future pandemic preparedness. Therefore, we assessed the predictive value of different routine care-based vulnerability measures in addition to age and sex for 28-day mortality in an older primary care population of patients with COVID-19. METHODS From primary care medical records using three routinely collected Dutch primary care databases, we included all patients aged 70 years or older with a COVID-19 diagnosis registration in 2020 and 2021. All-cause mortality was predicted using logistic regression based on age and sex only (basic model), and separately adding six vulnerability measures: renal function, cognitive impairment, number of chronic drugs, Charlson Comorbidity Index, Chronic Comorbidity Score, and a Frailty Index. Predictive performance of the basic model and the six vulnerability models was compared in terms of area under the receiver operator characteristic curve (AUC), index of prediction accuracy and the distribution of predicted risks. RESULTS Of the 4,065 included patients, 9% died within 28 days after COVID-19 diagnosis. Predicted mortality risk ranged between 7-26% for the basic model including age and sex, changing to 4-41% by addition of comorbidity-based vulnerability measures (Charlson Comorbidity Index, Chronic Comorbidity Score), more reflecting impaired organ functioning. Similarly, the AUC of the basic model slightly increased from 0.69 (95%CI 0.66 - 0.72) to 0.74 (95%CI 0.71 - 0.76) by addition of either of these comorbidity scores. Addition of a Frailty Index, renal function, the number of chronic drugs or cognitive impairment yielded no substantial change in predictions. CONCLUSION In our dataset of older COVID-19 patients in primary care, the 28-day mortality fraction was substantial at 9%. Six different vulnerability measures had little incremental predictive value in addition to age and sex in predicting short-term mortality.
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Affiliation(s)
- Hannah M la Roi-Teeuw
- Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Stratenum 6.131, PO Box 85500, 3508 GA, Utrecht, The Netherlands.
| | - Kim Luijken
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Marieke T Blom
- Department of General Practice, Amsterdam UMC Location Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jacobijn Gussekloo
- LUMC Center for Medicine for Older People, Department of Public Health and Primary Care, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Simon P Mooijaart
- LUMC Center for Medicine for Older People, Department of Public Health and Primary Care, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Harmke A Polinder-Bos
- Section of Geriatric Medicine, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Maarten van Smeden
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Geert-Jan Geersing
- Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Stratenum 6.131, PO Box 85500, 3508 GA, Utrecht, The Netherlands
| | - Carline J van den Dries
- Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Stratenum 6.131, PO Box 85500, 3508 GA, Utrecht, The Netherlands
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Nieto-Gutierrez W, Campos-Chambergo J, Gonzalez-Ayala E, Oyola-Garcia O, Alejandro-Mora A, Luis-Aguirre E, Pasquel-Santillan R, Leiva-Aguirre J, Ugarte-Gil C, Loyola S. Prediction models of COVID-19 fatality in nine Peruvian provinces: A secondary analysis of the national epidemiological surveillance system. PLOS GLOBAL PUBLIC HEALTH 2024; 4:e0002854. [PMID: 38285714 PMCID: PMC10824411 DOI: 10.1371/journal.pgph.0002854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/05/2024] [Indexed: 01/31/2024]
Abstract
There are initiatives to promote the creation of predictive COVID-19 fatality models to assist decision-makers. The study aimed to develop prediction models for COVID-19 fatality using population data recorded in the national epidemiological surveillance system of Peru. A retrospective cohort study was conducted (March to September of 2020). The study population consisted of confirmed COVID-19 cases reported in the surveillance system of nine provinces of Lima, Peru. A random sample of 80% of the study population was selected, and four prediction models were constructed using four different strategies to select variables: 1) previously analyzed variables in machine learning models; 2) based on the LASSO method; 3) based on significance; and 4) based on a post-hoc approach with variables consistently included in the three previous strategies. The internal validation was performed with the remaining 20% of the population. Four prediction models were successfully created and validate using data from 22,098 cases. All models performed adequately and similarly; however, we selected models derived from strategy 1 (AUC 0.89, CI95% 0.87-0.91) and strategy 4 (AUC 0.88, CI95% 0.86-0.90). The performance of both models was robust in validation and sensitivity analyses. This study offers insights into estimating COVID-19 fatality within the Peruvian population. Our findings contribute to the advancement of prediction models for COVID-19 fatality and may aid in identifying individuals at increased risk, enabling targeted interventions to mitigate the disease. Future studies should confirm the performance and validate the usefulness of the models described here under real-world conditions and settings.
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Affiliation(s)
- Wendy Nieto-Gutierrez
- Facultad de Salud Pública, Universidad Peruana Cayetano Heredia, Lima, Perú
- Universidad Científica del Sur, Lima, Perú
| | - Jaid Campos-Chambergo
- Dirección de Epidemiología e Investigación, Dirección Regional de Salud Lima Provincias, Lima, Perú
| | - Enrique Gonzalez-Ayala
- Dirección de Epidemiología e Investigación, Dirección Regional de Salud Lima Provincias, Lima, Perú
| | - Oswaldo Oyola-Garcia
- Dirección de Epidemiología e Investigación, Dirección Regional de Salud Lima Provincias, Lima, Perú
| | - Alberti Alejandro-Mora
- Dirección de Epidemiología e Investigación, Dirección Regional de Salud Lima Provincias, Lima, Perú
| | - Eliana Luis-Aguirre
- Dirección de Epidemiología e Investigación, Dirección Regional de Salud Lima Provincias, Lima, Perú
| | - Roly Pasquel-Santillan
- Dirección de Epidemiología e Investigación, Dirección Regional de Salud Lima Provincias, Lima, Perú
| | - Juan Leiva-Aguirre
- Dirección de Epidemiología e Investigación, Dirección Regional de Salud Lima Provincias, Lima, Perú
| | - Cesar Ugarte-Gil
- Facultad de Medicina, Universidad Peruana Cayetano Heredia, Lima, Perú
- Instituto de Medicina Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Perú
- Department of Epidemiology, School of Public and Population Health, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Steev Loyola
- Dirección de Epidemiología e Investigación, Dirección Regional de Salud Lima Provincias, Lima, Perú
- Facultad de Medicina, Universidad Peruana Cayetano Heredia, Lima, Perú
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Xue P, Xi H, Chen H, He S, Liu X, Du B. Predictive value of clinical features and CT radiomics in the efficacy of hip preservation surgery with fibula allograft. J Orthop Surg Res 2023; 18:940. [PMID: 38062463 PMCID: PMC10704794 DOI: 10.1186/s13018-023-04431-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Despite being an effective treatment for osteonecrosis of the femoral head (ONFH), hip preservation surgery with fibula allograft (HPS&FA) still experiences numerous failures. Developing a prediction model based on clinical and radiomics predictors holds promise for addressing this issue. METHODS This study included 112 ONFH patients who underwent HPS&FA and were randomly divided into training and validation cohorts. Clinical data were collected, and clinically significant predictors were identified using univariate and multivariate analyses to develop a clinical prediction model (CPM). Simultaneously, the least absolute shrinkage and selection operator method was employed to select optimal radiomics features from preoperative hip computed tomography images, forming a radiomics prediction model (RPM). Furthermore, to enhance prediction accuracy, a clinical-radiomics prediction model (CRPM) was constructed by integrating all predictors. The predictive performance of the models was evaluated using receiver operating characteristic curve (ROC), area under the curve (AUC), DeLong test, calibration curve, and decision curve analysis. RESULTS Age, Japanese Investigation Committee classification, postoperative use of glucocorticoids or alcohol, and non-weightbearing time were identified as clinical predictors. The AUC of the ROC curve for the CPM was 0.847 in the training cohort and 0.762 in the validation cohort. After incorporating radiomics features, the CRPM showed improved AUC values of 0.875 in the training cohort and 0.918 in the validation cohort. Decision curves demonstrated that the CRPM yielded greater medical benefit across most risk thresholds. CONCLUSION The CRPM serves as an efficient prediction model for assessing HPS&FA efficacy and holds potential as a personalized perioperative intervention tool to enhance HPS&FA success rates.
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Affiliation(s)
- Peng Xue
- The First School of Clinical Medicine of Nanjing University of Chinese Medicine, Nanjing, 210029, China
- Department of Orthopedics, The Affiliated Hospital of Nanjing University of Chinese Medicine, Hanzhong Road 155, Nanjing, 210029, China
| | - Hongzhong Xi
- The First School of Clinical Medicine of Nanjing University of Chinese Medicine, Nanjing, 210029, China
- Department of Orthopedics, The Affiliated Hospital of Nanjing University of Chinese Medicine, Hanzhong Road 155, Nanjing, 210029, China
| | - Hao Chen
- The First School of Clinical Medicine of Nanjing University of Chinese Medicine, Nanjing, 210029, China
- Department of Orthopedics, The Affiliated Hospital of Nanjing University of Chinese Medicine, Hanzhong Road 155, Nanjing, 210029, China
| | - Shuai He
- The First School of Clinical Medicine of Nanjing University of Chinese Medicine, Nanjing, 210029, China
- Department of Orthopedics, The Affiliated Hospital of Nanjing University of Chinese Medicine, Hanzhong Road 155, Nanjing, 210029, China
| | - Xin Liu
- The First School of Clinical Medicine of Nanjing University of Chinese Medicine, Nanjing, 210029, China.
- Department of Orthopedics, The Affiliated Hospital of Nanjing University of Chinese Medicine, Hanzhong Road 155, Nanjing, 210029, China.
| | - Bin Du
- The First School of Clinical Medicine of Nanjing University of Chinese Medicine, Nanjing, 210029, China.
- Department of Orthopedics, The Affiliated Hospital of Nanjing University of Chinese Medicine, Hanzhong Road 155, Nanjing, 210029, China.
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Tasca KI, Alves CG, Grotto RMT, de Moraes LN, Assato PA, Fortaleza CMCB. Dichotomous outcomes vs. survival regression models for identification of predictors of mortality among patients with severe acute respiratory illness during COVID-19 pandemics. Front Public Health 2023; 11:1271177. [PMID: 38125848 PMCID: PMC10732580 DOI: 10.3389/fpubh.2023.1271177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 11/13/2023] [Indexed: 12/23/2023] Open
Abstract
Introduction As the studies predicting mortality in severe acute respiratory illness (SARI) have inferred associations either from dichotomous outcomes or from time-event models, we identified some clinical-epidemiological characteristics and predictors of mortality by comparing and discussing two multivariate models. Methods To identify factors associated with death among all SARI hospitalizations occurred in Botucatu (Brazil)/regardless of the infectious agent, and among the COVID-19 subgroup, from March 2020 to 2022, we used a multivariate Poisson regression model with binomial outcomes and Cox proportional hazards (time-event). The performance metrics of both models were also analyzed. Results A total of 3,995 hospitalized subjects were included, of whom 1338 (33%) tested positive for SARS-CoV-2. We identified 866 deaths, of which 371 (43%) were due to the COVID-19. In the total number of SARI cases, using both Poisson and Cox models, the predictors of mortality were the presence of neurological diseases, immunosuppression, obesity, older age, and need for invasive ventilation support. However, the Poisson test also revealed that admission to an intensive care unit and the COVID-19 diagnosis were predictors of mortality, with the female gender having a protective effect against death. Likewise, Poisson proved to be more sensitive and specific, and indeed the most suitable model for analyzing risk factors for death in patients with SARI/COVID-19. Conclusion Given these results and the acute course of SARI and COVID-19, to compare the associations and their different meanings is essential and, therefore, models with dichotomous outcomes are more appropriate than time-to-event/survival approaches.
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Affiliation(s)
- Karen Ingrid Tasca
- Department of Infectious Diseases, Botucatu Medical School (FMB), São Paulo State University (Unesp), Botucatu, São Paulo, Brazil
| | - Camila Gonçalves Alves
- Department of Infectious Diseases, Botucatu Medical School (FMB), São Paulo State University (Unesp), Botucatu, São Paulo, Brazil
| | - Rejane Maria Tommasini Grotto
- Department of Biotechnology and Bioprocess, School of Agriculture (FCA), São Paulo State University (Unesp), Botucatu, São Paulo, Brazil
- Clinical Hospital of Botucatu Medical School (HCFMB), Botucatu, Brazil
| | - Leonardo Nazario de Moraes
- Department of Biotechnology and Bioprocess, School of Agriculture (FCA), São Paulo State University (Unesp), Botucatu, São Paulo, Brazil
- Clinical Hospital of Botucatu Medical School (HCFMB), Botucatu, Brazil
| | - Patrícia Akemi Assato
- Department of Biotechnology and Bioprocess, School of Agriculture (FCA), São Paulo State University (Unesp), Botucatu, São Paulo, Brazil
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Yang Y, Huan X, Guo D, Wang X, Niu S, Li K. Performance of deep learning-based autodetection of arterial stenosis on head and neck CT angiography: an independent external validation study. LA RADIOLOGIA MEDICA 2023; 128:1103-1115. [PMID: 37464200 DOI: 10.1007/s11547-023-01683-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 07/10/2023] [Indexed: 07/20/2023]
Abstract
PURPOSE To externally validate the performance of automated stenosis detection on head and neck CT angiography (CTA) and investigate the impact factors using an independent bi-center dataset with digital subtraction angiography (DSA) as the ground truth. MATERIAL AND METHODS Patients who underwent head and neck CTA and DSA between January 2019 and December 2021 were retrospectively included. The degree of stenosis was automatically evaluated using CerebralDoc based on CTA. The performance of CerebralDoc across levels (per-patient, per-region, per-vessel, and per-segment) and thresholds (≥ 50%, ≥ 70%, and = 100%) was evaluated. Logistic regression was performed to identify independent factors associated with false negative results. RESULTS 296 patients were analyzed. Specificity across levels and thresholds was high, exceeding 92%. The area under the curve ranged from poor (0.615, 95% CI: 0.544, 0.686; at the region-based analysis for stenosis ≥ 70%) to excellent (0.945, 95% CI: 0.905, 0.985; at the patient-based analysis for stenosis ≥ 50%). Sensitivity ranged from 0.714 (95% CI: 0.675, 0.750) at the segment-based analysis for stenosis ≥ 70% to 0.895 (95% CI: 0.849, 0.919) at the patient-based analysis for stenosis ≥ 50%. The multiple logistic regression analysis revealed that false negative results were primarily more likely to specific stenosis locations (particularly the M2 segment and skull base segment of the internal carotid artery) and occlusion. CONCLUSIONS CerebralDoc has the potential to automated stenosis detection on head and neck CTA, but further efforts are needed to optimize its performance.
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Affiliation(s)
- Yongwei Yang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
- Department of Radiology, the Fifth People's Hospital of Chongqing, Chongqing, China
| | - Xinyue Huan
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
| | - Dajing Guo
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
| | - Xiaolin Wang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
| | - Shengwen Niu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
| | - Kunhua Li
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China.
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10
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Miele L, Dajko M, Savino MC, Capocchiano ND, Calvez V, Liguori A, Masciocchi C, Vetrone L, Mignini I, Schepis T, Marrone G, Biolato M, Cesario A, Patarnello S, Damiani A, Grieco A, Valentini V, Gasbarrini A. Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves. Intern Emerg Med 2023; 18:1415-1427. [PMID: 37491564 PMCID: PMC10412472 DOI: 10.1007/s11739-023-03310-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 05/10/2023] [Indexed: 07/27/2023]
Abstract
Increased values of the FIB-4 index appear to be associated with poor clinical outcomes in COVID-19 patients. This study aimed to develop and validate predictive mortality models, using data upon admission of hospitalized patients in four COVID-19 waves between March 2020 and January 2022. A single-center cohort study was performed on consecutive adult patients with Covid-19 admitted at the Fondazione Policlinico Gemelli IRCCS (Rome, Italy). Artificial intelligence and big data processing were used to retrieve data. Patients and clinical characteristics of patients with available FIB-4 data derived from the Gemelli Generator Real World Data (G2 RWD) were used to develop predictive mortality models during the four waves of the COVID-19 pandemic. A logistic regression model was applied to the training and test set (75%:25%). The model's performance was assessed by receiver operating characteristic (ROC) curves. A total of 4936 patients were included. Hypertension (38.4%), cancer (12.15%) and diabetes (16.3%) were the most common comorbidities. 23.9% of patients were admitted to ICU, and 12.6% had mechanical ventilation. During the study period, 762 patients (15.4%) died. We developed a multivariable logistic regression model on patient data from all waves, which showed that the FIB-4 score > 2.53 was associated with increased mortality risk (OR = 4.53, 95% CI 2.83-7.25; p ≤ 0.001). These data may be useful in the risk stratification at the admission of hospitalized patients with COVID-19.
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Affiliation(s)
- Luca Miele
- Dipartimento di Scienze Mediche e Chirurgiche (DiSMeC), Fondazione Policlinico Gemelli IRCCS, Università Cattolica del S. Cuore, 8, Largo Gemelli, 00168 Rome, Italy
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Marianxhela Dajko
- Dipartimento di Scienze Mediche e Chirurgiche (DiSMeC), Fondazione Policlinico Gemelli IRCCS, Università Cattolica del S. Cuore, 8, Largo Gemelli, 00168 Rome, Italy
| | - Maria Chiara Savino
- Department Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
| | - Nicola D. Capocchiano
- Gemelli Generator Real World Data Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Valentino Calvez
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Antonio Liguori
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Carlotta Masciocchi
- Gemelli Generator Real World Data Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Lorenzo Vetrone
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Irene Mignini
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Tommaso Schepis
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Giuseppe Marrone
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Marco Biolato
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Alfredo Cesario
- Gemelli Digital Medicine and Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Stefano Patarnello
- Gemelli Generator Real World Data Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Andrea Damiani
- Gemelli Generator Real World Data Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Antonio Grieco
- Dipartimento di Scienze Mediche e Chirurgiche (DiSMeC), Fondazione Policlinico Gemelli IRCCS, Università Cattolica del S. Cuore, 8, Largo Gemelli, 00168 Rome, Italy
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Vincenzo Valentini
- Department Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
| | - Antonio Gasbarrini
- Dipartimento di Scienze Mediche e Chirurgiche (DiSMeC), Fondazione Policlinico Gemelli IRCCS, Università Cattolica del S. Cuore, 8, Largo Gemelli, 00168 Rome, Italy
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Gemelli against COVID Group
- Dipartimento di Scienze Mediche e Chirurgiche (DiSMeC), Fondazione Policlinico Gemelli IRCCS, Università Cattolica del S. Cuore, 8, Largo Gemelli, 00168 Rome, Italy
- Department of Medicina e Chirurgia Traslazionale, Università Cattolica Del Sacro Cuore, Rome, Italy
- Department Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
- Gemelli Generator Real World Data Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Gemelli Digital Medicine and Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
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11
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Noitz M, Meier J. [Risk Factors for COVID-19 Mortality]. Anasthesiol Intensivmed Notfallmed Schmerzther 2023; 58:362-372. [PMID: 37385242 DOI: 10.1055/a-1971-5095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
The COVID-19 pandemic has changed the world significantly within the last two years and has put a major burden on health care systems worldwide. Due to the imbalance between the number of patients requiring treatment and the shortage of necessary healthcare resources, a new mode of triage had to be established. The allocation of resources and definition of treatment priorities could be supported by taking the actual short-term mortality risk of patients with COVID-19 into account. We therefore analyzed the current literature for criteria to predict mortality in COVID-19.
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12
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Stracci F, Gili A, Caruso E, Polosa R, Ambrosio G. Value of hospital datasets of COVID-19 patients across different pandemic periods: challenges and opportunities. Intern Emerg Med 2023; 18:969-971. [PMID: 36592272 PMCID: PMC9807090 DOI: 10.1007/s11739-022-03162-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 11/20/2022] [Indexed: 01/03/2023]
Affiliation(s)
- Fabrizio Stracci
- Department of Medicine and Surgery, Public Health Section, University of Perugia, Perugia, Italy
| | - Alessio Gili
- Department of Medicine and Surgery, Public Health Section, University of Perugia, Perugia, Italy
| | - Enza Caruso
- Department of Political Sciences, University of Perugia, Perugia, Italy
| | - Riccardo Polosa
- Center of Excellence for the Acceleration of HArm Reduction (CoEHAR), University of Catania, Catania, Italy
- Department of Clinical & Experimental Medicine, University of Catania, Catania, Italy
- ECLAT Srl, Spin-off of the University of Catania, Catania, Italy
| | - Giuseppe Ambrosio
- Department of Medicine and Surgery, Cardiology and Cardiovascular Pathophysiology Section, University of Perugia, Perugia, Italy.
- CERICLET-Centro Ricerca Clinica E Traslazionale, University of Perugia, Perugia, Italy.
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13
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Zahra A, Luijken K, Abbink EJ, van den Berg JM, Blom MT, Elders P, Festen J, Gussekloo J, Joling KJ, Melis R, Mooijaart S, Peters JB, Polinder-Bos HA, van Raaij BFM, Smorenberg A, la Roi-Teeuw HM, Moons KGM, van Smeden M. A study protocol of external validation of eight COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting. Diagn Progn Res 2023; 7:8. [PMID: 37013651 PMCID: PMC10069944 DOI: 10.1186/s41512-023-00144-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/27/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has a large impact worldwide and is known to particularly affect the older population. This paper outlines the protocol for external validation of prognostic models predicting mortality risk after presentation with COVID-19 in the older population. These prognostic models were originally developed in an adult population and will be validated in an older population (≥ 70 years of age) in three healthcare settings: the hospital setting, the primary care setting, and the nursing home setting. METHODS Based on a living systematic review of COVID-19 prediction models, we identified eight prognostic models predicting the risk of mortality in adults with a COVID-19 infection (five COVID-19 specific models: GAL-COVID-19 mortality, 4C Mortality Score, NEWS2 + model, Xie model, and Wang clinical model and three pre-existing prognostic scores: APACHE-II, CURB65, SOFA). These eight models will be validated in six different cohorts of the Dutch older population (three hospital cohorts, two primary care cohorts, and a nursing home cohort). All prognostic models will be validated in a hospital setting while the GAL-COVID-19 mortality model will be validated in hospital, primary care, and nursing home settings. The study will include individuals ≥ 70 years of age with a highly suspected or PCR-confirmed COVID-19 infection from March 2020 to December 2020 (and up to December 2021 in a sensitivity analysis). The predictive performance will be evaluated in terms of discrimination, calibration, and decision curves for each of the prognostic models in each cohort individually. For prognostic models with indications of miscalibration, an intercept update will be performed after which predictive performance will be re-evaluated. DISCUSSION Insight into the performance of existing prognostic models in one of the most vulnerable populations clarifies the extent to which tailoring of COVID-19 prognostic models is needed when models are applied to the older population. Such insight will be important for possible future waves of the COVID-19 pandemic or future pandemics.
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Affiliation(s)
- Anum Zahra
- Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht University, Utrecht, the Netherlands.
| | - Kim Luijken
- Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht University, Utrecht, the Netherlands
| | - Evertine J Abbink
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jesse M van den Berg
- Department of General Practice, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, the Netherlands
- PHARMO Institute for Drug Outcomes Research, Utrecht, the Netherlands
| | - Marieke T Blom
- Department of General Practice, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, the Netherlands
| | - Petra Elders
- Department of General Practice, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | | | - Jacobijn Gussekloo
- Department of Public Health and Primary Care & Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Karlijn J Joling
- Department of Medicine for Older People, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health, Aging & Later Life, Amsterdam, the Netherlands
| | - René Melis
- Department of Geriatric Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Simon Mooijaart
- Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Jeannette B Peters
- Department of Pulmonary Diseases, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Harmke A Polinder-Bos
- Department of Internal Medicine, Section of Geriatric Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Bas F M van Raaij
- Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Annemieke Smorenberg
- Department of Internal Medicine, Section of Geriatric Medicine, Amsterdam UMC, Amsterdam, the Netherlands
| | - Hannah M la Roi-Teeuw
- Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht University, Utrecht, the Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht University, Utrecht, the Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht University, Utrecht, the Netherlands
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Kwok SWH, Wang G, Sohel F, Kashani KB, Zhu Y, Wang Z, Antpack E, Khandelwal K, Pagali SR, Nanda S, Abdalrhim AD, Sharma UM, Bhagra S, Dugani S, Takahashi PY, Murad MH, Yousufuddin M. An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems. Respir Res 2023; 24:79. [PMID: 36915107 PMCID: PMC10010216 DOI: 10.1186/s12931-023-02386-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/07/2023] [Indexed: 03/14/2023] Open
Abstract
BACKGROUND We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores. METHODS This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis. RESULTS Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores. CONCLUSION The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.
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Affiliation(s)
| | - Guanjin Wang
- Department of Information Technology, Murdoch University, Murdoch, Australia
| | - Ferdous Sohel
- Department of Information Technology, Murdoch University, Murdoch, Australia
| | - Kianoush B Kashani
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Ye Zhu
- Robert D. and Patricia E. Kern Centre for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Zhen Wang
- Robert D. and Patricia E. Kern Centre for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Eduardo Antpack
- Division of Hospital Internal Medicine, Mayo Clinic Health System, Austin, MN, USA
| | | | - Sandeep R Pagali
- Division of Hospital Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Sanjeev Nanda
- Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Ahmed D Abdalrhim
- Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Umesh M Sharma
- Division of Hospital Internal Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Sumit Bhagra
- Department of Endocrine and Metabolism, Mayo Clinic Health System, Austin, MN, USA
| | - Sagar Dugani
- Division of Hospital Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Mohammad H Murad
- Robert D. and Patricia E. Kern Centre for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA.,Division of Preventive Medicine, Mayo Clinic, Rochester, MN, USA
| | - Mohammed Yousufuddin
- Division of Surgery, Mayo Clinic, Rochester, MN, USA. .,Hospital Internal Medicine, Mayo Clinic Health System, Mayo Clinic, 1000 1st Drive NW, Austin, MN, USA.
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15
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Predictive tools in psychosis: what is 'good enough'? Nat Rev Neurol 2023; 19:191-192. [PMID: 36879034 PMCID: PMC9987363 DOI: 10.1038/s41582-023-00787-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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Abstract
BACKGROUND Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external validation sufficient to claim that a prediction model works well in the intended clinical context? MAIN BODY We argue to the contrary because (1) patient populations vary, (2) measurement procedures vary, and (3) populations and measurements change over time. Hence, we have to expect heterogeneity in model performance between locations and settings, and across time. It follows that prediction models are never truly validated. This does not imply that validation is not important. Rather, the current focus on developing new models should shift to a focus on more extensive, well-conducted, and well-reported validation studies of promising models. CONCLUSION Principled validation strategies are needed to understand and quantify heterogeneity, monitor performance over time, and update prediction models when appropriate. Such strategies will help to ensure that prediction models stay up-to-date and safe to support clinical decision-making.
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Abu Elhassan UE, Alqahtani SM, Al Saglan NS, Hawan A, Alqahtani FS, Almtheeb RS, Abdelwahab MS, AlFlan MA, Alfaifi AS, Alqahtani MA, Alshafa FA, Alsalem AA, Al-Imamah YA, Abdelwahab OS, Attia MF, Mahmoud IM. Utility of the 4C ISARIC mortality score in hospitalized COVID-19 patients at a large tertiary Saudi Arabian center. Multidiscip Respir Med 2023; 18:917. [PMID: 37692055 PMCID: PMC10483479 DOI: 10.4081/mrm.2023.917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 06/16/2023] [Indexed: 09/12/2023] Open
Abstract
Background The International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) 4C mortality score has been used before as a valuable tool for predicting mortality in COVID-19 patients. We aimed to address the utility of the 4C score in a well-defined Saudi population with COVID-19 admitted to a large tertiary referral hospital in Saudi Arabia. Methods A retrospective study was conducted that included all adults COVID‑19 patients admitted to the Armed Forces Hospital Southern Region (AFHSR), between January 2021 and September 2022. The receiver operating characteristic (ROC) curve depicted the diagnostic performance of the 4C Score for mortality prediction. Results A total of 1,853 patients were enrolled. The ROC curve of the 4C score had an area under the curve of 0.73 (95% CI: 0.702-0.758), p<0.001. The sensitivity and specificity with scores >8 were 80% and 58%, respectively, the positive and negative predictive values were 28% and 93%, respectively. Three hundred and sixteen (17.1%), 638 (34.4%), 814 (43.9%), and 85 (4.6%) patients had low, intermediate, high, and very high values, respectively. There were significant differences between survivors and non-survivors with regard to all variables used in the calculation of the 4C score. Multivariable logistic regression analysis revealed that all components of the 4C score, except gender and O2 saturation, were independent significant predictors of mortality. Conclusions Our data support previous international and Saudi studies that the 4C mortality score is a reliable tool with good sensitivity and specificity in the mortality prediction of COVID-19 patients. All components of the 4C score, except gender and O2 saturation, were independent significant predictors of mortality. Within the 4C score, odds ratios increased proportionately with an increase in the score value. Future multi-center prospective studies are warranted.
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Affiliation(s)
- Usama E. Abu Elhassan
- Department of Internal Medicine, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
- Department of Pulmonary Medicine, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Saad M.A. Alqahtani
- Department of Internal Medicine, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
| | - Naif S. Al Saglan
- Department of Internal Medicine, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
| | - Ali Hawan
- Department of Pathology and Laboratory Medicine, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
| | - Faisal S. Alqahtani
- Infectious Diseases and Notification Unit, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
| | - Roaa S. Almtheeb
- Department of Pathology and Laboratory Medicine, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
| | - Magda S.R. Abdelwahab
- Department of Anesthesia and Intensive Care, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Mohammed A. AlFlan
- Department of Internal Medicine, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
| | - Abdulaziz S.Y. Alfaifi
- Department of Internal Medicine, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
| | - Mohammed A. Alqahtani
- Department of Internal Medicine, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
| | - Fawwaz A. Alshafa
- Department of Internal Medicine, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
| | - Ali A. Alsalem
- Department of Internal Medicine, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
| | - Yahya A. Al-Imamah
- Department of Internal Medicine, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
| | | | - Mohammed F. Attia
- Department of Critical Care, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
| | - Ibrahim M.A. Mahmoud
- Department of Critical Care, Armed Forces Hospital Southern Region (AFHSR), Khamis Mushayt, Saudi Arabia
- Department of Critical Care, Faculty of Medicine, Cairo University, Cairo, Egypt
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18
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A two-gene marker for the two-tiered innate immune response in COVID-19 patients. PLoS One 2023; 18:e0280392. [PMID: 36649304 PMCID: PMC9844909 DOI: 10.1371/journal.pone.0280392] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 12/28/2022] [Indexed: 01/18/2023] Open
Abstract
For coronavirus disease 2019 (COVID-19), a pandemic disease characterized by strong immune dysregulation in severe patients, convenient and efficient monitoring of the host immune response is critical. Human hosts respond to viral and bacterial infections in different ways, the former is characterized by the activation of interferon stimulated genes (ISGs) such as IFI27, while the latter is characterized by the activation of anti-bacterial associated genes (ABGs) such as S100A12. This two-tiered innate immune response has not been examined in COVID-19. In this study, the activation patterns of this two-tiered innate immune response represented by IFI27 and S100A12 were explored based on 1421 samples from 17 transcriptome datasets derived from the blood of COVID-19 patients and relevant controls. It was found that IFI27 activation occurred in most of the symptomatic patients and displayed no correlation with disease severity, while S100A12 activation was more restricted to patients under severe and critical conditions with a stepwise activation pattern. In addition, most of the S100A12 activation was accompanied by IFI27 activation. Furthermore, the activation of IFI27 was most pronounced within the first week of symptom onset, but generally waned after 2-3 weeks. On the other hand, the activation of S100A12 displayed no apparent correlation with disease duration and could last for several months in certain patients. These features of the two-tiered innate immune response can further our understanding on the disease mechanism of COVID-19 and may have implications to the clinical triage. Development of a convenient two-gene protocol for the routine serial monitoring of this two-tiered immune response will be a valuable addition to the existing laboratory tests.
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19
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Qiu W, Shi Q, Chen F, Wu Q, Yu X, Xiong L. The derived neutrophil to lymphocyte ratio can be the predictor of prognosis for COVID-19 Omicron BA.2 infected patients. Front Immunol 2022; 13:1065345. [PMID: 36405724 PMCID: PMC9666892 DOI: 10.3389/fimmu.2022.1065345] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 10/20/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Several systemic inflammatory biomarkers have been associated with poor overall survival (OS) and disease severity in patients with coronavirus disease 2019 (COVID-19). However, it remains unclear which markers are better for predicting prognosis, especially for COVID-19 Omicron BA.2 infected patients. The present study aimed to identify reliable predictors of prognosis of COVID-19 Omicron BA.2 from inflammatory indicators. METHODS A cohort of 2645 COVID-19 Omicron BA.2 infected patients were retrospectively analyzed during the Omicron BA.2 surge in Shanghai between April 12, 2022, and June 17, 2022. The patients were admitted to the Shanghai Fourth People's Hospital, School of Medicine, Tongji University. Six systemic inflammatory indicators were included, and their cut-off points were calculated using maximally selected rank statistics. The analysis involved Kaplan-Meier curves, univariate and multivariate Cox proportional hazard models, and time-dependent receiver operating characteristic curves (time-ROC) for OS-associated inflammatory indicators. RESULTS A total of 2347 COVID-19 Omicron BA.2 infected patients were included. All selected indicators proved to be independent predictors of OS in the multivariate analysis (all P < 0.01). A high derived neutrophil to lymphocyte ratio (dNLR) was associated with a higher mortality risk of COVID-19 [hazard ratio, 4.272; 95% confidence interval (CI), 2.417-7.552]. The analyses of time-AUC and C-index showed that the dNLR (C-index: 0.844, 0.824, and 0.718 for the 5th, 10th, and 15th day, respectively) had the best predictive power for OS in COVID-19 Omicron BA.2 infected patients. Among different sub-groups, the dNLR was the best predictor for OS regardless of age (0.811 for patients aged ≥70 years), gender (C-index, 0.880 for men and 0.793 for women) and disease severity (C-index, 0.932 for non-severe patients and 0.658 for severe patients). However, the platelet to lymphocyte ratio was superior to the other indicators in patients aged <70 years. CONCLUSIONS The prognostic ability of the dNLR was higher than the other evaluated inflammatory indicators for all COVID-19 Omicron BA.2 infected patients.
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Affiliation(s)
- Weiji Qiu
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China,Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China,Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China,Clinical Research Centre for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China
| | - Qiqing Shi
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China,Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China,Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China,Clinical Research Centre for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China
| | - Fang Chen
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China,Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China,Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China,Clinical Research Centre for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China
| | - Qian Wu
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China,Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China,Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China,Clinical Research Centre for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China
| | - Xiya Yu
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China,Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China,Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China,Clinical Research Centre for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China,*Correspondence: Xiya Yu, ; Lize Xiong,
| | - Lize Xiong
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China,Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China,Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China,Clinical Research Centre for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China,*Correspondence: Xiya Yu, ; Lize Xiong,
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20
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Louisa M, Cahyadi D, Nilasari D, Soetikno V. Lack of Correlation Between Soluble Angiotensin-Converting Enzyme 2 and Inflammatory Markers in Hospitalized COVID-19 Patients with Hypertension. Infect Drug Resist 2022; 15:4799-4807. [PMID: 36045873 PMCID: PMC9420737 DOI: 10.2147/idr.s369771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 08/10/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose This study aimed to investigate the correlation of plasma soluble angiotensin-converting enzyme 2, sACE2, and several inflammatory markers in COVID-19 patients requiring hospitalization with hypertension. Additionally, we analyzed the effects of renin-angiotensin-aldosterone-system, RAAS, inhibitors on the levels of sACE2 and inflammatory marker levels in patients with COVID-19. Patients and Methods This cross-sectional study involved patients with COVID-19 who required hospitalization on a stable dose of antihypertensive drugs. The study included three hospitals in Jakarta and Tangerang, Indonesia, between December 2020 and June 2021. We classified eligible subjects into two groups: patients with COVID-19 treated with antihypertensive RAAS inhibitors or non-RAAS inhibitors. Results We found no correlation between sACE2 and all the inflammatory and coagulation markers studied (high-sensitivity C-reactive protein, IL-6, IL-10, IL6/IL10, tumor necrosis factor-α, neutrophil-to-lymphocyte ratio, and D-dimer) in COVID-19 patients with hypertension. Further analysis showed lower sACE2 concentrations and IL-6/IL-10 ratio in patients treated with RAAS inhibitors vs those treated with non-RAAS inhibitors. Conclusion We found no correlation between ACE2 and inflammatory markers. Using RAAS inhibitors resulted in a lower sACE2 and IL-6/IL-10 ratio. The type of antihypertensive treatments has a neutral effect on disease severity and outcome in COVID-19 patients with hypertension. However, to firmly-established these effects, our findings should be confirmed in a much larger population.
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Affiliation(s)
- Melva Louisa
- Department of Pharmacology and Therapeutics, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
| | - Daniel Cahyadi
- Master Program in Biomedical Sciences, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
| | - Dina Nilasari
- Department of Clinical Research, Siloam Hospitals, Jakarta, Indonesia.,Faculty of Medicine, University of Hasanuddin, Makassar, South Sulawesi, Indonesia
| | - Vivian Soetikno
- Department of Pharmacology and Therapeutics, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
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21
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Abbasi K. Long covid and apheresis: a miracle cure sold on a hypothesis of hope. BMJ : BRITISH MEDICAL JOURNAL 2022. [DOI: 10.1136/bmj.o1733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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22
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Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369:m1328. [PMID: 32265220 PMCID: PMC7222643 DOI: 10.1136/bmj.m1328] [Citation(s) in RCA: 1622] [Impact Index Per Article: 405.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
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Affiliation(s)
- Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Marc M J Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Darren L Dahly
- HRB Clinical Research Facility, Cork, Ireland
- School of Public Health, University College Cork, Cork, Ireland
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten De Vos
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT Stadius, KU Leuven, Leuven, Belgium
| | - Paul Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Maria C Haller
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Palliative and Advanced Illness Research Center and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Liesbet Henckaerts
- Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
- Department of General Internal Medicine, KU Leuven-University Hospitals Leuven, Leuven, Belgium
| | - Pauline Heus
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Michael Kammer
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Nina Kreuzberger
- Evidence-Based Oncology, Department I of Internal Medicine and Centre for Integrated Oncology Aachen Bonn Cologne Dusseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna Lohmann
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Kim Luijken
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Jie Ma
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - David J McLernon
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jamie C Sergeant
- Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Chunhu Shi
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
| | - Nicole Skoetz
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Luc J M Smits
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
| | - Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Matthew Sperrin
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - René Spijker
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Bas van Bussel
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Florien S van Royen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jan Y Verbakel
- EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Wallisch
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jack Wilkinson
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | | | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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