Objectives: This study aims to investigate the risk factors associated with severity and death from COVID-19 through a systematic review and meta-analysis of the published documents in Iran. Methods: A systematic search was performed based on all articles indexed in Scopus, Embase, Web of Science (WOS), PubMed, and Google Scholar in English and Scientific Information Database (SID) and Iranian Research Institute for Information Science and Technology (IRA)NDOC indexes in Persian. To assess quality, we used the Newcastle Ottawa Scale. Publication bias was assessed using Egger's tests. Forest plots were used for a graphical description of the results. We used HRs, and ORs reported for the association between risk factors and COVID-19 severity and death. Results: Sixty-nine studies were included in the meta-analysis, of which 62 and 13 had assessed risk factors for death and severity, respectively. The results showed a significant association between death from COVID-19 and age, male gender, diabetes, hypertension, cardiovascular disease (CVD), cerebrovascular disease, chronic kidney disease (CKD), Headache, and Dyspnea. We observed a significant association between increased white blood cell (WBC), decreased Lymphocyte, increased blood urea nitrogen (BUN), increased creatinine, vitamin D deficiency, and death from COVID-19. There was only a significant relationship between CVD and disease severity. Conclusion: It is recommended that the predictive risk factors of COVID-19 severity and death mentioned in this study to be used for therapeutic and health interventions, to update clinical guidelines and determine patients’ prognoses.

Risk Factors Associated with Severity and Death from COVID-19 in Iran: A Systematic Review and Meta-Analysis Study

Coronavirus increases mortality rate in people with underlying disease. The purpose of the present research was to compare the clinical outcomes in Covid-19 patients with and without underlying diabetes disease using propensity score matching. A matched case–control study was conducted on 459 diabetic patients with Covid-19 (case group) and 459 non-diabetic patients with Covid-19 (control group). Matching in two groups was performed using propensity score matching method. The effect of covariates on the clinical outcome of the patients (recovery-death) was assessed using logistic regression and the associations of factors with the patients' survival were determined using Cox proportional hazards regression model. Data were analyzed using R software. The mean (standard deviation) age of patients in the case and control groups were 65.77 (12.2) and 65.8 (12.24), respectively. 196 patients (43%) in the case group, and 249 patients (54%) in the control group were male (with P-value < 0.05). The logistic regression model showed that the variables of age, level of blood oxygen (SpO2), ICU admission, length of hospitalization, cancer and diabetes affected patients' death. Furthermore, the resuts of the Cox regression showed that the variables of age, level of blood oxygen (SpO2), ICU admission,cancer and diabetes were related to survival of the patients. It was found that diabetes was significantly associated with mortality from COVID-19 with odds ratio of 2.88 (95% CI: 1.80–4.69; P < 0.01) and hazard ratio of 1.45 (95% CI: 1.01–2.03; P = 0.05). The underlying diabetes significantly increases the mortality among patients with Covid-19, so special care should be taken for this high risk group if they develop Covid-19.

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The effect of underlying diabetes disease on clinical outcome and survival in patients with Covid-19: a propensity score matching study

: Background and Aim: Coronavirus is still a life-threatening disease around the world. In patients with this disease, having an underlying disease reduces the effectiveness of treatment and increases mortality. This study aimed to examine the effect of cancer history on mortality rates in patients with COVID-19. Materials and Methods: This study was a case-control study involving 60 cancer patients with COVID-19 as the case group and 180 non-cancer patients with COVID-19 as the control group. A matching method based on propensity score was used to select patients in the control group. The effect of treatment on the outcome (recovery death) was studied with logistic regression, and the factors affecting patient survival were analyzed with Cox models. R software was used to analyze the data. Results: The mean (SD) age of patients in the case and control groups was 61.37 (13.47) and 63.19 (13.95) years, respectively. In the case group, 37 patients (61.7%), and in the control group, 114 patients (63.3%) were male. 23 cancer patients (38.3%) and 26 non-cancer patients (14.4%) died. The results of logistic regression as well as the Cox model showed the variables of age, blood oxygen level (SpO2), admission to the intensive care unit, and cancer history as significant for patient death (P <0.05). Conclusion: To study the effect of demographic, clinical, and laboratory results on the risk of death among COVID-19 patients with and without a cancer history, control group in this study was selected by PSM method. The results of this study have indicated cancer history to be one of the factors affecting the mortality of patients with COVID-19 in addition to age variables, blood oxygen levels (SpO2), and admission to the intensive care unit (ICU).

Fatemeh Ghadamgahi

Papers

The effect of underlying diabetes disease on clinical outcome and survival in patients with Covid-19: a propensity score matching study

Mortality Rate and Effects of COVID-19 in Cancer Patients versus Non-cancer Patients: A Propensity Score Matching Study