Risk factors for mortality of 557 adult patients with COVID 19 in Babol, Northern Iran: a retrospective cohort study

Javanian, Mostafa; Bayani, Masoumeh; Shokri, Mehran; Sadeghi–Haddad–Zavareh, Mahmoud; Babazadeh, Arefeh; Ghadimi, R; Sepidarkish, Mahdi; Bijani, Ali; Yahyapour, Yousef; Barary, Mohammad; Hasanpour, Amir Hossein; Ebrahimpour, Soheil

doi:10.4149/bll_2021_003

Cited by 14 publications

(13 citation statements)

References 21 publications

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“…The emergence of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has resulted in many individuals becoming infected, more than four million deaths, and has placed an unprecedented burden on public health services worldwide. 1 , 2 , 3 Vaccinations against severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a crucial step in ending the current worldwide pandemic. However, several particularly vulnerable groups in the population were not included in sufficient numbers in coronavirus disease 2019 (Covid‐19) vaccine trials.…”

Section: Introductionmentioning

confidence: 99%

Coronavirus disease 2019 (Covid‐19) vaccination recommendations in special populations and patients with existing comorbidities

Afshar

Babazadeh

Janbakhsh

et al. 2021

Reviews in Medical Virology

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Summary Vaccination against severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a crucial step in ending the current worldwide pandemic. However, several particularly vulnerable groups in the population were not included in sufficient numbers in coronavirus disease 2019 (Covid‐19) vaccine trials. Therefore, as science advances, the advice for vaccinating these special populations against Covid‐19 will continue to evolve. This focused review provides the latest recommendations and considerations for these special populations (i.e., patients with rheumatologic and autoimmune disorders, cancer, transplant recipients, chronic liver diseases, end‐stage renal disease, neurologic disorders, psychiatric disorders, diabetes mellitus, obesity, cardiovascular diseases, chronic obstructive pulmonary disease, human immunodeficiency virus, current smokers, pregnant and breastfeeding women, the elderly, children, and patients with allergic reactions) using the currently available research evidence.

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Section: Introductionmentioning

confidence: 99%

Coronavirus disease 2019 (Covid‐19) vaccination recommendations in special populations and patients with existing comorbidities

Afshar

Babazadeh

Janbakhsh

et al. 2021

Reviews in Medical Virology

View full text Add to dashboard Cite

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“…The coronavirus disease 2019 (COVID‐19) has various presentations, from a flu‐like illness to acute respiratory distress syndrome (ARDS), multi‐organ failure (MOF), and death. 1 , 2 , 3 Some of these life‐threatening manifestations are the symptoms of immune dysregulation or the so‐called COVID‐19‐related cytokine storm syndrome (COVID‐CSS), 4 which results in long‐hauler syndrome and multisystem inflammatory syndrome in children (MIS‐C). 5 Even though cytokines are vital regulators of our immunoinflammatory response, their excessive release can be harmful.…”

Section: Introductionmentioning

confidence: 99%

The role of cytokines and their antagonists in the treatment of COVID‐19 patients

Afshar

Barary

Babazadeh

et al. 2022

Reviews in Medical Virology

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The coronavirus disease 2019 (COVID‐19) has various presentations, of which immune dysregulation or the so‐called cytokine storm syndrome (COVID‐CSS) is prominent. Even though cytokines are vital regulators of body immunoinflammatory responses, their exaggerated release can be harmful. This hyperinflammatory response is more commonly observed during severe COVID‐19 infections, caused by the excessive release of pro‐inflammatory cytokines, such as interleukin‐1 (IL‐1), IL‐6, IL‐8, tumour necrosis factor, granulocyte‐macrophage colony‐stimulating factor, and interferon‐gamma, making their blockers and antagonists of great interest as therapeutic options in this condition. Thus, the pathophysiology of excessive cytokine secretion is outlined, and their most important blockers and antagonists are discussed, mainly focussing on tocilizumab, an interleukin‐6 receptor blocker approved to treat severe COVID‐19 infections.

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“…Many studies have been done since the beginning of the disease, and each has used different methods to investigate the mortality rate and its causes. In the research conducted in Babol, Northern Iran, from February to April 2020, out of 557 patients admitted, 121 died [ 2 ]. In another study conducted in Tehran, Iran, from March 2020 up to December 2020, out of 205,654 patients admitted, 20,472 died [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran

Moslehi

Rabiei

Soltanian

et al. 2022

BMC Med Inform Decis Mak

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Background Due to the high mortality of COVID-19 patients, the use of a high-precision classification model of patient’s mortality that is also interpretable, could help reduce mortality and take appropriate action urgently. In this study, the random forest method was used to select the effective features in COVID-19 mortality and the classification was performed using logistic model tree (LMT), classification and regression tree (CART), C4.5, and C5.0 tree based on important features. Methods In this retrospective study, the data of 2470 COVID-19 patients admitted to hospitals in Hamadan, west Iran, were used, of which 75.02% recovered and 24.98% died. To classify, at first among the 25 demographic, clinical, and laboratory findings, features with a relative importance more than 6% were selected by random forest. Then LMT, C4.5, C5.0, and CART trees were developed and the accuracy of classification performance was evaluated with recall, accuracy, and F1-score criteria for training, test, and total datasets. At last, the best tree was developed and the receiver operating characteristic curve and area under the curve (AUC) value were reported. Results The results of this study showed that among demographic and clinical features gender and age, and among laboratory findings blood urea nitrogen, partial thromboplastin time, serum glutamic-oxaloacetic transaminase, and erythrocyte sedimentation rate had more than 6% relative importance. Developing the trees using the above features revealed that the CART with the values of F1-score, Accuracy, and Recall, 0.8681, 0.7824, and 0.955, respectively, for the test dataset and 0.8667, 0.7834, and 0.9385, respectively, for the total dataset had the best performance. The AUC value obtained for the CART was 79.5%. Conclusions Finding a highly accurate and qualified model for interpreting the classification of a response that is considered clinically consequential is critical at all stages, including treatment and immediate decision making. In this study, the CART with its high accuracy for diagnosing and classifying mortality of COVID-19 patients as well as prioritizing important demographic, clinical, and laboratory findings in an interpretable format, risk factors for prognosis of COVID-19 patients mortality identify and enable immediate and appropriate decisions for health professionals and physicians.

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Risk factors for mortality of 557 adult patients with COVID 19 in Babol, Northern Iran: a retrospective cohort study

Cited by 14 publications

References 21 publications

Coronavirus disease 2019 (Covid‐19) vaccination recommendations in special populations and patients with existing comorbidities

Coronavirus disease 2019 (Covid‐19) vaccination recommendations in special populations and patients with existing comorbidities

The role of cytokines and their antagonists in the treatment of COVID‐19 patients

Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran

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