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Gourgoura K, Rivadeneyra P, Stanghellini E, Caroni C, Bartolucci F, Curcio R, Bartoli S, Ferranti R, Folletti I, Cavallo M, Sanesi L, Dominioni I, Santoni E, Morgana G, Pasticci MB, Pucci G, Vaudo G. Modelling the long-term health impact of COVID-19 using Graphical Chain Models brief heading: long COVID prediction by graphical chain models. BMC Infect Dis 2024; 24:885. [PMID: 39210315 PMCID: PMC11360819 DOI: 10.1186/s12879-024-09777-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 08/20/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Long-term sequelae of SARS-CoV-2 infection, namely long COVID syndrome, affect about 10% of severe COVID-19 survivors. This condition includes several physical symptoms and objective measures of organ dysfunction resulting from a complex interaction between individual predisposing factors and the acute manifestation of disease. We aimed at describing the complexity of the relationship between long COVID symptoms and their predictors in a population of survivors of hospitalization for severe COVID-19-related pneumonia using a Graphical Chain Model (GCM). METHODS 96 patients with severe COVID-19 hospitalized in a non-intensive ward at the "Santa Maria" University Hospital, Terni, Italy, were followed up at 3-6 months. Data regarding present and previous clinical status, drug treatment, findings recorded during the in-hospital phase, presence of symptoms and signs of organ damage at follow-up were collected. Static and dynamic cardiac and respiratory parameters were evaluated by resting pulmonary function test, echocardiography, high-resolution chest tomography (HRCT) and cardiopulmonary exercise testing (CPET). RESULTS Twelve clinically most relevant factors were identified and partitioned into four ordered blocks in the GCM: block 1 - gender, smoking, age and body mass index (BMI); block 2 - admission to the intensive care unit (ICU) and length of follow-up in days; block 3 - peak oxygen consumption (VO2), forced expiratory volume at first second (FEV1), D-dimer levels, depression score and presence of fatigue; block 4 - HRCT pathological findings. Higher BMI and smoking had a significant impact on the probability of a patient's admission to ICU. VO2 showed dependency on length of follow-up. FEV1 was related to the self-assessed indicator of fatigue, and, in turn, fatigue was significantly associated with the depression score. Notably, neither fatigue nor depression depended on variables in block 2, including length of follow-up. CONCLUSIONS The biological plausibility of the relationships between variables demonstrated by the GCM validates the efficacy of this approach as a valuable statistical tool for elucidating structural features, such as conditional dependencies and associations. This promising method holds potential for exploring the long-term health repercussions of COVID-19 by identifying predictive factors and establishing suitable therapeutic strategies.
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Affiliation(s)
- K Gourgoura
- Department of Economics, University of Perugia, Perugia, Italy
| | - P Rivadeneyra
- University of Padova, Padua, Italy
- University of Camerino, Camerino, Italy
| | - E Stanghellini
- Department of Economics, University of Perugia, Perugia, Italy
| | - C Caroni
- National Technical University of Athens, Athens, Greece
| | - F Bartolucci
- Department of Economics, University of Perugia, Perugia, Italy
| | - R Curcio
- Unit of Internal Medicine, Santa Maria Terni Hospital, Terni, Italy
| | - S Bartoli
- Unit of Clinical Psychology, Santa Maria Terni Hospital, Terni, Italy
| | - R Ferranti
- Unit of Radiology, Santa Maria Terni Hospital, Terni, Italy
| | - I Folletti
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
- Section of Occupational Medicine, Santa Maria Terni Hospital, Terni, Italy
| | - M Cavallo
- Unit of Internal Medicine, Santa Maria Terni Hospital, Terni, Italy
| | - L Sanesi
- Unit of Internal Medicine, Santa Maria Terni Hospital, Terni, Italy
| | - I Dominioni
- Unit of Internal Medicine, Santa Maria Terni Hospital, Terni, Italy
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - E Santoni
- Unit of Internal Medicine, Santa Maria Terni Hospital, Terni, Italy
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - G Morgana
- Unit of Internal Medicine, Santa Maria Terni Hospital, Terni, Italy
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - M B Pasticci
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
- Infectious Diseases Unit, Santa Maria Terni Hospital, Terni, Italy
| | - G Pucci
- Unit of Internal Medicine, Santa Maria Terni Hospital, Terni, Italy.
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy.
| | - G Vaudo
- Unit of Internal Medicine, Santa Maria Terni Hospital, Terni, Italy
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
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Casal-Guisande M, Comesaña-Campos A, Núñez-Fernández M, Torres-Durán M, Fernández-Villar A. Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Prediction of Dyspnea after 12 Months of an Acute Episode of COVID-19. Biomedicines 2024; 12:854. [PMID: 38672208 PMCID: PMC11047904 DOI: 10.3390/biomedicines12040854] [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: 12/28/2023] [Revised: 03/01/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice.
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Affiliation(s)
- Manuel Casal-Guisande
- Fundación Pública Galega de Investigación Biomédica Galicia Sur, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
- NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36312 Vigo, Spain; (M.N.-F.); (A.F.-V.)
- Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain;
| | - Alberto Comesaña-Campos
- Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain;
- Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36312 Vigo, Spain
| | - Marta Núñez-Fernández
- NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36312 Vigo, Spain; (M.N.-F.); (A.F.-V.)
- Pulmonary Department, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
| | - María Torres-Durán
- NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36312 Vigo, Spain; (M.N.-F.); (A.F.-V.)
- Pulmonary Department, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
- Centro de Investigación Biomédica en Red, CIBERES ISCIII, 28029 Madrid, Spain
| | - Alberto Fernández-Villar
- NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36312 Vigo, Spain; (M.N.-F.); (A.F.-V.)
- Pulmonary Department, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
- Centro de Investigación Biomédica en Red, CIBERES ISCIII, 28029 Madrid, Spain
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Malheiro DT, Bernardez-Pereira S, Parreira KCJ, Pagliuso JGD, de Paula Gomes E, de Mesquita Escobosa D, de Araújo CI, Pimenta BS, Lin V, de Almeida SM, Tuma P, Laselva CR, Neto MC, Klajner S, Teich VD, Kobayashi T, Edmond MB, Marra AR. Prevalence, predictors, and patient-reported outcomes of long COVID in hospitalized and non-hospitalized patients from the city of São Paulo, Brazil. Front Public Health 2024; 11:1302669. [PMID: 38317683 PMCID: PMC10839020 DOI: 10.3389/fpubh.2023.1302669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 12/20/2023] [Indexed: 02/07/2024] Open
Abstract
Background Robust data comparing long COVID in hospitalized and non-hospitalized patients in middle-income countries are limited. Methods A retrospective cohort study was conducted in Brazil, including hospitalized and non-hospitalized patients. Long COVID was diagnosed at 90-day follow-up using WHO criteria. Demographic and clinical information, including the depression screening scale (PHQ-2) at day 30, was compared between the groups. If the PHQ-2 score is 3 or greater, major depressive disorder is likely. Logistic regression analysis identified predictors and protective factors for long COVID. Results A total of 291 hospitalized and 1,118 non-hospitalized patients with COVID-19 were included. The prevalence of long COVID was 47.1% and 49.5%, respectively. Multivariable logistic regression showed female sex (odds ratio [OR] = 4.50, 95% confidence interval (CI) 2.51-8.37), hypertension (OR = 2.90, 95% CI 1.52-5.69), PHQ-2 > 3 (OR = 6.50, 95% CI 1.68-33.4) and corticosteroid use during hospital stay (OR = 2.43, 95% CI 1.20-5.04) as predictors of long COVID in hospitalized patients, while female sex (OR = 2.52, 95% CI 1.95-3.27) and PHQ-2 > 3 (OR = 3.88, 95% CI 2.52-6.16) were predictors in non-hospitalized patients. Conclusion Long COVID was prevalent in both groups. Positive depression screening at day 30 post-infection can predict long COVID. Early screening of depression helps health staff to identify patients at a higher risk of long COVID, allowing an early diagnosis of the condition.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Vivian Lin
- Hospital Israelita Albert Einstein, São Paulo, Brazil
| | | | - Paula Tuma
- Hospital Israelita Albert Einstein, São Paulo, Brazil
| | | | | | | | | | - Takaaki Kobayashi
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, United States
| | - Michael B. Edmond
- West Virginia University School of Medicine, Morgantown, WV, United States
| | - Alexandre R. Marra
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, United States
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