Genetic Aspects and Immune Responses in Covid-19: Important Organ Involvement

Ghale-Noie, Zari Naderi; Salmaninejad, Arash; Bergquist, Robert; Mollazadeh, Samaneh; Hoseini, Benyamin; Sahebkar, Amirhossein

doi:10.1007/978-3-030-71697-4_1

Cited by 5 publications

(8 citation statements)

References 158 publications

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“…The predictors found in the current study are mostly coherent with previously reported factors associated with the severity of disease. 1 , 2 , 4 , 27 However, we aimed to use machine learning for risk stratification for prediction goals because it has been shown that machine learning has excellent reliability and optimal performance for the assessment of the relationship between data from different domains and outputs. 11 , 13 , 19 The model developed in this study was a better-performed classifier to classify ICU- and ICU+ compared to previous studies.…”

Section: Discussionmentioning

confidence: 99%

“…Coronavirus disease-2019 (COVID-19), a widespread and ongoing global acute viral respiratory infection, has been the third coronavirus in the human population in the past two decades. 1 At present, the diagnosis of COVID-19 infection is confirmed by nucleic acid test of reverse transcription-polymerase chain reaction (RT-PCR). 1 , 2 Chest CT had higher sensitivity for COVID-19 diagnosis (about 98%) than initial RT-PCR with a reported positive rate of 32–60% for throat swab samples.…”

Section: Introductionmentioning

confidence: 99%

“… 1 At present, the diagnosis of COVID-19 infection is confirmed by nucleic acid test of reverse transcription-polymerase chain reaction (RT-PCR). 1 , 2 Chest CT had higher sensitivity for COVID-19 diagnosis (about 98%) than initial RT-PCR with a reported positive rate of 32–60% for throat swab samples. 3 , 4 …”

Section: Introductionmentioning

confidence: 99%

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Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran

Sabetian¹,

Azimi²,

Kazemi³

et al. 2022

Indian Journal of Critical Care Medicine

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A bstract Background Prioritizing the patients requiring intensive care may decrease the fatality of coronavirus disease-2019 (COVID-19). Aims and objectives To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care. Materials and methods In 2021, 506 suspected COVID-19 patients, with clinical presentations along with radiographic findings, were laboratory confirmed and included in the study. The primary end-point was patients with COVID-19 requiring intensive care, defined as actual admission to the intensive care unit (ICU). The data were randomly partitioned into training and testing sets (70% and 30%, respectively) without overlapping. A decision-tree algorithm and multivariate logistic regression were performed to develop the models for predicting the cases based on their first 24 hours data. The predictive performance of the models was compared based on the area under the receiver operating characteristic curve (AUC), sensitivity, and accuracy of the models. Results A 10-fold cross-validation decision-tree model predicted cases requiring intensive care with the AUC, accuracy, and sensitivity of 97%, 98%, and 94.74%, respectively. The same values in the machine-learning logistic regression model were 75%, 85.62%, and 55.26%, respectively. Creatinine, smoking, neutrophil/lymphocyte ratio, temperature, respiratory rate, partial thromboplastin time, white blood cell, Glasgow Coma Scale (GCS), dizziness, international normalized ratio, O 2 saturation, C-reactive protein, diastolic blood pressure (DBP), and dry cough were the most important predictors. Conclusion In an Iranian population, our decision-based machine-learning method offered an advantage over logistic regression for predicting patients requiring intensive care. This method can support clinicians in decision-making, using patients’ early data, particularly in low- and middle-income countries where their resources are as limited as Iran. How to cite this article Sabetian G, Azimi A, Kazemi A, Hoseini B, Asmarian N, Khaloo V, et al. Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study based on Machine-learning Approach from Iran. Indian J Crit Care Med 2022;26(6):688–695. Ethics approval This study was approved by the Ethical Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.018).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran

Sabetian¹,

Azimi²,

Kazemi³

et al. 2022

Indian Journal of Critical Care Medicine

Self Cite

View full text Add to dashboard Cite

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“…Understanding the susceptibility or resistance to a particular disease associated with the presence of specific alleles can be useful in drug manufacturing and development and in identifying at-risk populations [ 26 ]. In this regard, COVID-19 can be prevented by lifelong immunological memory using a vaccine based on natural protective immunity to SARS-CoV-2 infection [ 27 , 28 ]. Therefore, it is important to find the reasons for the different clinical responses of infected individuals.…”

Section: Introductionmentioning

confidence: 99%

The role of HLA genotypes in understanding the pathogenesis of severe COVID-19

Arab

Mollazadeh

Ghayourbabaei

et al. 2023

Egypt J Med Hum Genet

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The coronavirus disease 2019 (COVID-19) pandemic has caused human tragedy through the global spread of the viral pathogen SARS-CoV-2. Although the underlying factors for the severity of COVID-19 in different people are still unknown, several gene variants can be used as predictors of disease severity, particularly variations in viral receptor genes such as angiotensin-converting enzyme 2 (ACE2) or major histocompatibility complex (MHC) genes. The reaction of the immune system, as the most important defense strategy in the case of viruses, plays a decisive role. The innate immune system is important both as a primary line of defense and as a trigger of the acquired immune response. The HLA-mediated acquired immune response is linked to the acquired immune system. In various diseases, it has been shown that genetic alterations in components of the immune system can play a crucial role in how the body responds to pathogens, especially viruses. One of the most important host genetic factors is the human leukocyte antigen (HLA) profile, which includes HLA classes I and II and may be symbolic of the diversity of immune response and genetic predisposition in disease progression. COVID-19 will have direct contact with the acquired immune system as an intracellular pathogen after exposure to the proteasome and its components through class I HLA. Therefore, it is assumed that in different genotypes of the HLA-I class, an undesirable supply causes an insufficient activation of the immune system. Insufficient binding of antigen delivered by class I HLA to host lymphocytes results in uncertain identification and insufficient activation of the acquired immune system. The absence of secretion of immune cytokines such as interferons, which play an important role in controlling viral infection in the early stages, is a complication of this event. Understanding the allelic diversity of HLA in people infected with coronavirus compared with uninfected people of one race not only allows identification of people with HLA susceptible to COVID-19 but also provides better insight into the behavior of the virus, which helps to take effective preventive and curative measures earlier.

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“…Since March 11, 2020, the coronavirus disease 2019 (COVID-19) outbreak has been declared a global pandemic by the World Health Organization (WHO) due to its rapid spread across the globe. 1 – 3 This pandemic is caused by a novel virus, SARS-CoV-2, which shares similarities with the virus responsible for the severe acute respiratory syndrome (SARS) outbreak in 2003. 1 SARS-CoV-2 belongs to the Coronaviridae family and possesses a single-stranded positive-sense RNA genome.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department

Hoseini¹,

Rahmatinejad²,

Reihani³

et al. 2023

Indian Journal of Critical Care Medicine

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A bstract Background The study aimed to compare the prognostic accuracy of six different severity-of-illness scoring systems for predicting in-hospital mortality among patients with confirmed SARS-COV2 who presented to the emergency department (ED). The scoring systems assessed were worthing physiological score (WPS), early warning score (EWS), rapid acute physiology score (RAPS), rapid emergency medicine score (REMS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA). Materials and methods A cohort study was conducted using data obtained from electronic medical records of 6,429 confirmed SARS-COV2 patients presenting to the ED. Logistic regression models were fitted on the original severity-of-illness scores to assess the models’ performance using the Area Under the Curve for ROC (AUC-ROC) and Precision-Recall curves (AUC-PR), Brier Score (BS), and calibration plots were used to assess the models’ performance. Bootstrap samples with multiple imputations were used for internal validation. Results The mean age of the patients was 64 years (IQR:50–76) and 57.5% were male. The WPS, REMS, and NEWS models had AUROC of 0.714, 0.705, and 0.701, respectively. The poorest performance was observed in the RAPS model, with an AUROC of 0.601. The BS for the NEWS, qSOFA, EWS, WPS, RAPS, and REMS was 0.18, 0.09, 0.03, 0.14, 0.15, and 0.11 respectively. Excellent calibration was obtained for the NEWS, while the other models had proper calibration. Conclusion The WPS, REMS, and NEWS have a fair discriminatory performance and may assist in risk stratification for SARS-COV2 patients presenting to the ED. Generally, underlying diseases and most vital signs are positively associated with mortality and were different between the survivors and non-survivors. How to cite this article Rahmatinejad Z, Hoseini B, Reihani H, Hanna AA, Pourmand A, Tabatabaei SM, et al . Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department. Indian J Crit Care Med 2023;27(6):416–425.

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Genetic Aspects and Immune Responses in Covid-19: Important Organ Involvement

Cited by 5 publications

References 158 publications

Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran

Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran

The role of HLA genotypes in understanding the pathogenesis of severe COVID-19

Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department

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