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Rajwa B, Naved MMA, Adibuzzaman M, Grama AY, Khan BA, Dundar MM, Rochet JC. Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality. PLOS DIGITAL HEALTH 2024; 3:e0000327. [PMID: 38652722 PMCID: PMC11037536 DOI: 10.1371/journal.pdig.0000327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 03/06/2024] [Indexed: 04/25/2024]
Abstract
As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms. Here, we analyzed the electronic health records of 471 patients with severe COVID-19 to identify clinical characteristics most predictive of mortality. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of death.
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Affiliation(s)
- Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette, Indiana, United States of America
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, United States of America
| | | | - Mohammad Adibuzzaman
- Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon, United States of America
| | - Ananth Y. Grama
- Dept. of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
| | - Babar A. Khan
- Regenstrief Institute, Indianapolis, Indiana, United States of America
| | - M. Murat Dundar
- Dept. of Computer and Information Science, IUPUI, Indianapolis, Indiana, United States of America
| | - Jean-Christophe Rochet
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, United States of America
- Borch Dept. of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, United States of America
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2
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Cheng WJ, Shih HM, Su KP, Hsueh PR. Risk factors for poor COVID-19 outcomes in patients with psychiatric disorders. Brain Behav Immun 2023; 114:255-261. [PMID: 37648008 DOI: 10.1016/j.bbi.2023.08.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/26/2023] [Accepted: 08/26/2023] [Indexed: 09/01/2023] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) has been found to have a greater impact on individuals with pre-existing psychiatric disorders. However, the underlying reasons for this increased risk have yet to be determined. This study aims to investigate the potential factors contributing poor outcomes among COVID-19 patients with psychiatric disorders, including delayed diagnosis of infection, vaccination rates, immune response, and the use of psychotropic medications. METHODS This retrospective cohort study analyzed medical records of 15,783 adult patients who were diagnosed with COVID-19 infection by positive PCR tests between January and September 2022 at a single medical center. We identified psychiatric diagnoses using ICD-9 diagnostic codes from the preceding 3 years before COVID infection. Primary outcome was in-hospital mortality and secondary outcomes were severe illness requiring intensive care or mechanical ventilation, and hospitalization within 45 days after a positive COVID-19 test. We compared the rates of outcomes, viral load, vaccination status at the time of positive test, psychotropic medications prescription within 90 days prior, antiviral medication use, and blood inflammation markers between patients with and without psychiatric disorders. The Cox proportional hazard model was used to examine the association of psychiatric diagnoses, vaccination status, and psychotropic medication prescription with poor outcomes. RESULTS Patients with psychiatric disorders demonstrated higher rates of severe illness (10.4% v.s. 7.1%) and hospitalization (16.4% vs. 11.3%), as well as a shorter duration to in-hospital mortality (6 vs. 12.5 days) compared to non-psychiatric patients. Psychiatric patients had higher vaccination rates and lower levels of inflammatory markers than non-psychiatric patients. Antipsychotic medication use was associated with in-hospital mortality (hazard ratio [HR] = 4.79, 95% confidence interval [CI] = 1.23-18.7), while being unvaccinated was associated with hospitalization (HR = 1.81, 95% CI = 1.29 to 2.54) and severe illness (HR = 3.23, 95% CI = 1.95 to 5.34) among patients with psychiatric disorders. Sedatives prescription was associated with all poor outcomes in general patients. CONCLUSION Considering the narrow time window between a positive COVID-19 test and poor outcomes, healthcare providers should undertake close monitoring of patients with preexisting psychiatric disorders during the initial days after a positive PCR test. Furthermore, caution should be taken when prescribing psychotropic medications, with special attention to antipsychotics.
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Affiliation(s)
- Wan-Ju Cheng
- Department of Public Health, China Medical University, 100 Sec.1, Jingmao Rd., Taichung, Taiwan; National Center for Geriatrics and Welfare Research, National Health Research Institutes, 35 Keyan Road, Miaoli, Taiwan; Department of Psychiatry, China Medical University Hospital, 2 Yude Road, Taichung, Taiwan.
| | - Hong-Mo Shih
- Department of Public Health, China Medical University, 100 Sec.1, Jingmao Rd., Taichung, Taiwan; Department of Emergency Medicine, China Medical University Hospital, Taichung, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan
| | - Kuan-Pin Su
- Mind-Body Interface Research Center (MBI-Lab), China Medical University Hospital, Taichung, Taiwan; An-Nan Hospital, China Medical University, Tainan, Taiwan
| | - Po-Ren Hsueh
- Departments of Laboratory Medicine and Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan; Department of Laboratory Diagnosis, College of Medicine, China Medical University, Taichung, Taiwan; Ph.D Program for Aging, School of Medicine, China Medical University, Taichung, Taiwan
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3
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Molero P, Reina G, Blom JD, Martínez-González MÁ, Reinken A, de Kloet ER, Molendijk ML. COVID-19 risk, course and outcome in people with mental disorders: a systematic review and meta-analyses. Epidemiol Psychiatr Sci 2023; 32:e61. [PMID: 37859501 PMCID: PMC10594644 DOI: 10.1017/s2045796023000719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 08/24/2023] [Accepted: 09/04/2023] [Indexed: 10/21/2023] Open
Abstract
AIMS It has been suggested that people with mental disorders have an elevated risk to acquire severe acute respiratory syndrome coronavirus 2 and to be disproportionally affected by coronavirus disease 19 (COVID-19) once infected. We aimed to analyse the COVID-19 infection rate, course and outcome, including mortality and long COVID, in people with anxiety, depressive, neurodevelopmental, schizophrenia spectrum and substance use disorders relative to control subjects without these disorders. METHODS This study constitutes a preregistered systematic review and random-effects frequentist and Bayesian meta-analyses. Major databases were searched up until 27 June 2023. RESULTS Eighty-one original articles were included reporting 304 cross-sectional and prospective effect size estimates (median n per effect-size = 114837) regarding associations of interest. Infection risk was not significantly increased for any mental disorder that we investigated relative to samples of people without these disorders. The course of COVID-19, however, is relatively severe, and long COVID and COVID-19-related hospitalization are more likely in all patient samples that we investigated. The odds of dying from COVID-19 were high in people with most types of mental disorders, except for those with anxiety and neurodevelopmental disorders relative to non-patient samples (pooled ORs range, 1.26-2.57). Bayesian analyses confirmed the findings from the frequentist approach and complemented them with estimates of the strength of evidence. CONCLUSIONS Once infected, people with pre-existing mental disorders are at an elevated risk for a severe COVID-19 course and outcome, including long COVID and mortality, relative to people without pre-existing mental disorders, despite an infection risk not significantly increased.
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Affiliation(s)
- Patricio Molero
- Department of Psychiatry and Clinical Psychology, Clínica Universidad de Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
| | - Gabriel Reina
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
- Department of Microbiology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Jan Dirk Blom
- Institute of Psychology, Department of Clinical Psychology, Leiden University, Leiden, The Netherlands
- Outpatient Clinic for Uncommon Psychiatric Syndromes, Parnassia Psychiatric Institute, The Hague, The Netherlands
- Department of Psychiatry, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Miguel Ángel Martínez-González
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
- Department of Preventive Medicine and Public Health, School of Medicine, University of Navarra, Pamplona, Spain
- CIBER-OBN, Instituto de Salud Carlos III, Madrid, Spain
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Aischa Reinken
- Institute of Psychology, Department of Clinical Psychology, Leiden University, Leiden, The Netherlands
| | - E. Ronald de Kloet
- Division of Endocrinology, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Marc L. Molendijk
- Institute of Psychology, Department of Clinical Psychology, Leiden University, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden University Medical Center, Leiden, The Netherlands
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Tadayon Najafabadi B, Rayner DG, Shokraee K, Shokraie K, Panahi P, Rastgou P, Seirafianpour F, Momeni Landi F, Alinia P, Parnianfard N, Hemmati N, Banivaheb B, Radmanesh R, Alvand S, Shahbazi P, Dehghanbanadaki H, Shaker E, Same K, Mohammadi E, Malik A, Srivastava A, Nejat P, Tamara A, Chi Y, Yuan Y, Hajizadeh N, Chan C, Zhen J, Tahapary D, Anderson L, Apatu E, Schoonees A, Naude CE, Thabane L, Foroutan F. Obesity as an independent risk factor for COVID-19 severity and mortality. Cochrane Database Syst Rev 2023; 5:CD015201. [PMID: 37222292 PMCID: PMC10207996 DOI: 10.1002/14651858.cd015201] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
BACKGROUND Since December 2019, the world has struggled with the COVID-19 pandemic. Even after the introduction of various vaccines, this disease still takes a considerable toll. In order to improve the optimal allocation of resources and communication of prognosis, healthcare providers and patients need an accurate understanding of factors (such as obesity) that are associated with a higher risk of adverse outcomes from the COVID-19 infection. OBJECTIVES To evaluate obesity as an independent prognostic factor for COVID-19 severity and mortality among adult patients in whom infection with the COVID-19 virus is confirmed. SEARCH METHODS MEDLINE, Embase, two COVID-19 reference collections, and four Chinese biomedical databases were searched up to April 2021. SELECTION CRITERIA We included case-control, case-series, prospective and retrospective cohort studies, and secondary analyses of randomised controlled trials if they evaluated associations between obesity and COVID-19 adverse outcomes including mortality, mechanical ventilation, intensive care unit (ICU) admission, hospitalisation, severe COVID, and COVID pneumonia. Given our interest in ascertaining the independent association between obesity and these outcomes, we selected studies that adjusted for at least one factor other than obesity. Studies were evaluated for inclusion by two independent reviewers working in duplicate. DATA COLLECTION AND ANALYSIS: Using standardised data extraction forms, we extracted relevant information from the included studies. When appropriate, we pooled the estimates of association across studies with the use of random-effects meta-analyses. The Quality in Prognostic Studies (QUIPS) tool provided the platform for assessing the risk of bias across each included study. In our main comparison, we conducted meta-analyses for each obesity class separately. We also meta-analysed unclassified obesity and obesity as a continuous variable (5 kg/m2 increase in BMI (body mass index)). We used the GRADE framework to rate our certainty in the importance of the association observed between obesity and each outcome. As obesity is closely associated with other comorbidities, we decided to prespecify the minimum adjustment set of variables including age, sex, diabetes, hypertension, and cardiovascular disease for subgroup analysis. MAIN RESULTS: We identified 171 studies, 149 of which were included in meta-analyses. As compared to 'normal' BMI (18.5 to 24.9 kg/m2) or patients without obesity, those with obesity classes I (BMI 30 to 35 kg/m2), and II (BMI 35 to 40 kg/m2) were not at increased odds for mortality (Class I: odds ratio [OR] 1.04, 95% confidence interval [CI] 0.94 to 1.16, high certainty (15 studies, 335,209 participants); Class II: OR 1.16, 95% CI 0.99 to 1.36, high certainty (11 studies, 317,925 participants)). However, those with class III obesity (BMI 40 kg/m2 and above) may be at increased odds for mortality (Class III: OR 1.67, 95% CI 1.39 to 2.00, low certainty, (19 studies, 354,967 participants)) compared to normal BMI or patients without obesity. For mechanical ventilation, we observed increasing odds with higher classes of obesity in comparison to normal BMI or patients without obesity (class I: OR 1.38, 95% CI 1.20 to 1.59, 10 studies, 187,895 participants, moderate certainty; class II: OR 1.67, 95% CI 1.42 to 1.96, 6 studies, 171,149 participants, high certainty; class III: OR 2.17, 95% CI 1.59 to 2.97, 12 studies, 174,520 participants, high certainty). However, we did not observe a dose-response relationship across increasing obesity classifications for ICU admission and hospitalisation. AUTHORS' CONCLUSIONS Our findings suggest that obesity is an important independent prognostic factor in the setting of COVID-19. Consideration of obesity may inform the optimal management and allocation of limited resources in the care of COVID-19 patients.
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Affiliation(s)
| | - Daniel G Rayner
- Faculty Health Sciences, McMaster University, Hamilton, Canada
| | - Kamyar Shokraee
- Minimally Invasive Surgery Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Kamran Shokraie
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Parsa Panahi
- Student Research Committee, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Paravaneh Rastgou
- School of Medicine, Tabriz University of Medical Sciences, Tehran, Iran
| | | | - Feryal Momeni Landi
- Functional Neurosurgery Research Center, Shohada Tajrish Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Pariya Alinia
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Neda Parnianfard
- Research Center for Evidence-Based Medicine, Iranian Evidence-Based Medicine (EBM) Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Nima Hemmati
- Minimally Invasive Surgery Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Behrooz Banivaheb
- Minimally Invasive Surgery Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Ramin Radmanesh
- Society of Clinical Research Associates, Toronto, Canada
- Graduate division, Master of Advanced Studies in Clinical Research, University of California, San Diego, California, USA
| | - Saba Alvand
- Liver and Pancreatobiliary Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Parmida Shahbazi
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | | | - Elaheh Shaker
- Non-communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Kaveh Same
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Esmaeil Mohammadi
- Non-communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Abdullah Malik
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | | | - Peyman Nejat
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Alice Tamara
- Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
- Metabolic, Cardiovascular and Aging Cluster, The Indonesian Medical Education and Research Institute, Jakarta, Indonesia
| | - Yuan Chi
- Yealth Network, Beijing Yealth Technology Co., Ltd, Beijing, China
- Cochrane Campbell Global Ageing Partnership, London, UK
| | - Yuhong Yuan
- Department of Medicine, Division of Gastroenterology, McMaster University, Hamilton, Canada
| | - Nima Hajizadeh
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Cynthia Chan
- Michael G. DeGroote School of Medicine, Faculty of Health Sciences, McMaster University, Ontario, Canada
| | - Jamie Zhen
- Michael G. DeGroote School of Medicine, Faculty of Health Sciences, McMaster University, Ontario, Canada
| | - Dicky Tahapary
- Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
- Department of Internal Medicine, Division of Endocrinology and Metabolism, Ontario, Canada
| | - Laura Anderson
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Ontario, Canada
| | - Emma Apatu
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Ontario, Canada
| | - Anel Schoonees
- Centre for Evidence-based Health Care, Division of Epidemiology and Biostatistics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Celeste E Naude
- Centre for Evidence-based Health Care, Division of Epidemiology and Biostatistics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Lehana Thabane
- Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Canada
| | - Farid Foroutan
- Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Canada
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5
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Shi L, Wang Y, Han X, Wang Y, Xu J, Yang H. Comorbid asthma decreased the risk for COVID-19 mortality in the United Kingdom: Evidence based on a meta-analysis. Int Immunopharmacol 2023; 120:110365. [PMID: 37224652 DOI: 10.1016/j.intimp.2023.110365] [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: 03/22/2023] [Revised: 05/15/2023] [Accepted: 05/18/2023] [Indexed: 05/26/2023]
Abstract
The study aimed to investigate the influence of comorbid asthma on the risk for mortality among patients with coronavirus disease 2019 (COVID-19) in the United Kingdom (UK) by utilizing a quantitative meta-analysis. The pooled odds ratio (OR) with 95% confidence interval (CI) was estimated by conducting a random-effects model. Sensitivity analysis, I2 statistic, meta-regression, subgroup analysis, Begg's analysis and Egger's analysis were all implemented. Our results presented that comorbid asthma was significantly related to a decreased risk for COVID-19 mortality in the UK based on 24 eligible studies with 1,209,675 COVID-19 patients (pooled OR = 0.81, 95% CI: 0.71-0.93; I2 = 89.2%, P < 0.01). Coming through further meta-regression to seek the possible cause of heterogeneity, none of elements might be responsible for heterogeneity. A sensitivity analysis proved the stability and reliability of the overall results. Both Begg's analysis (P = 1.000) and Egger's analysis (P = 0.271) manifested that publication bias did not exist. In conclusion, our data demonstrated that COVID-19 patients with comorbid asthma might bear a lower risk for mortality in the UK. Furthermore, routine intervention and treatment of asthma patients with severe acute respiratory syndrome coronavirus 2 infection should be continued in the UK.
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Affiliation(s)
- Liqin Shi
- Department of Epidemiology, School of Public Health, Zhengzhou University, Zhengzhou, 450001 Henan Province, China
| | - Yadong Wang
- Department of Toxicology, Henan Center for Disease Control and Prevention, Zhengzhou, 450016, Henan Province, China
| | - Xueya Han
- Department of Epidemiology, School of Public Health, Zhengzhou University, Zhengzhou, 450001 Henan Province, China
| | - Ying Wang
- Department of Epidemiology, School of Public Health, Zhengzhou University, Zhengzhou, 450001 Henan Province, China
| | - Jie Xu
- Department of Epidemiology, School of Public Health, Zhengzhou University, Zhengzhou, 450001 Henan Province, China
| | - Haiyan Yang
- Department of Epidemiology, School of Public Health, Zhengzhou University, Zhengzhou, 450001 Henan Province, China.
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Cavallaro M, Moran E, Collyer B, McCarthy ND, Green C, Keeling MJ. Informing antimicrobial stewardship with explainable AI. PLOS DIGITAL HEALTH 2023; 2:e0000162. [PMID: 36812617 PMCID: PMC9931350 DOI: 10.1371/journal.pdig.0000162] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 11/14/2022] [Indexed: 01/07/2023]
Abstract
The accuracy and flexibility of artificial intelligence (AI) systems often comes at the cost of a decreased ability to offer an intuitive explanation of their predictions. This hinders trust and discourage adoption of AI in healthcare, exacerbated by concerns over liabilities and risks to patients' health in case of misdiagnosis. Providing an explanation for a model's prediction is possible due to recent advances in the field of interpretable machine learning. We considered a data set of hospital admissions linked to records of antibiotic prescriptions and susceptibilities of bacterial isolates. An appropriately trained gradient boosted decision tree algorithm, supplemented by a Shapley explanation model, predicts the likely antimicrobial drug resistance, with the odds of resistance informed by characteristics of the patient, admission data, and historical drug treatments and culture test results. Applying this AI-based system, we found that it substantially reduces the risk of mismatched treatment compared with the observed prescriptions. The Shapley values provide an intuitive association between observations/data and outcomes; the associations identified are broadly consistent with expectations based on prior knowledge from health specialists. The results, and the ability to attribute confidence and explanations, support the wider adoption of AI in healthcare.
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Affiliation(s)
- Massimo Cavallaro
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Ed Moran
- Department of Infectious Disease, North Bristol NHS Trust, Bristol, United Kingdom
| | - Benjamin Collyer
- Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom
| | - Noel D. McCarthy
- Institute of Population Health, Trinity College Dublin, University of Dublin, Dublin, Ireland
- Warwick Medical School, University of Warwick, Coventry, United Kingdom
| | - Christopher Green
- Institute of Microbiology & Infection, University of Birmingham, Birmingham, United Kingdom
- Department of Infectious Diseases & Tropical Medicine, University Hospitals Birmingham NHS Foundation Trust, United Kingdom
| | - Matt J. Keeling
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
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7
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Lasso G, Khan S, Allen SA, Mariano M, Florez C, Orner EP, Quiroz JA, Quevedo G, Massimi A, Hegde A, Wirchnianski AS, Bortz RH, Malonis RJ, Georgiev GI, Tong K, Herrera NG, Morano NC, Garforth SJ, Malaviya A, Khokhar A, Laudermilch E, Dieterle ME, Fels JM, Haslwanter D, Jangra RK, Barnhill J, Almo SC, Chandran K, Lai JR, Kelly L, Daily JP, Vergnolle O. Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes. PLoS Comput Biol 2022; 18:e1009778. [PMID: 35041647 PMCID: PMC8812869 DOI: 10.1371/journal.pcbi.1009778] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 02/03/2022] [Accepted: 12/20/2021] [Indexed: 02/07/2023] Open
Abstract
The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.
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Affiliation(s)
- Gorka Lasso
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Saad Khan
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Stephanie A. Allen
- Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Margarette Mariano
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Catalina Florez
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Chemistry and Life Science, United States Military Academy at West Point, West Point, New York, United States of America
| | - Erika P. Orner
- Department of Pathology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Jose A. Quiroz
- Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Gregory Quevedo
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Aldo Massimi
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Aditi Hegde
- Eastchester High School, 2 Stewart Place, Eastchester, New York, United States of America
| | - Ariel S. Wirchnianski
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Robert H. Bortz
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Ryan J. Malonis
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - George I. Georgiev
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Karen Tong
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Natalia G. Herrera
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Nicholas C. Morano
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Scott J. Garforth
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Avinash Malaviya
- Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Ahmed Khokhar
- Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Ethan Laudermilch
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - M. Eugenia Dieterle
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - J. Maximilian Fels
- Department of Cell Biology, Harvard Medical School, Boston, Cambridge, Massachusetts, United States of America
- Department of Microbiology, Harvard Medical School, Boston, Cambridge, Massachusetts, United States of America
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, Cambridge, Massachusetts, United States of America
| | - Denise Haslwanter
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Rohit K. Jangra
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Jason Barnhill
- Department of Chemistry and Life Science, United States Military Academy at West Point, West Point, New York, United States of America
- Department of Radiology and Radiological Services, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America
| | - Steven C. Almo
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Kartik Chandran
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Jonathan R. Lai
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Libusha Kelly
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Johanna P. Daily
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Olivia Vergnolle
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
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