The Utility of rRT-PCR in Diagnosis and Assessment of Case-fatality rates of COVID-19 In the Iranian Population. Positive Test Results are a Marker for Illness Severity

The utility of PCR-based testing in characterizing patients with COVID-19 and the severity of their disease remains unknown. We performed an observational study among patients presenting to hospitals in Iran who were tested for 2019-nCoV viral RNA by rRT-PCR between the fourth week of February 2020 to the fourth week of March 2020. Frequency of symptoms, comorbidities, intubation, and mortality rates were compared between COVID-19 positive vs. negative patients. 96103 patients were tested from 879 hospitals. 18754 (19.5%) tested positive for COVID-19. Positive testing was more frequent in those 50 years or older. The prevalence of cough (54.5% vs. 49.7%), fever (49.5% vs. 44.7%), and respiratory distress (43.0% vs. 39.0%) but not hypoxia (46.9% vs. 56.7%) was higher in COVID-19 positive vs. negative patients (p<0.001 for all). More patients had cardiovascular diseases (10.6% vs. 9.5%, p<0.001) and type 2 diabetes mellitus (10.8% vs. 8.7%, p<0.001) among COVID-19 positive vs. negative patients. There were fewer patients with cancer (1.1%, vs. 1.4%, p<0.001), asthma (1.9% vs. 2.5%, p<0.001), or pregnant (0.4% vs. 0.6%, =0.001) in COVID-19 positive vs. negative groups. COVID-19 positive vs. negative patients required more intubation (7.7% vs. 5.2%, p<0.001) and had higher mortality (14.6% vs. 6.3%, p<0.001). Odds ratios for death of positive vs negative patients range from 2.01 to 3.10 across all age groups. In conclusion, COVID-19 test-positive vs. test-negative patients had more severe symptoms and comorbidities, required higher intubation, and had higher mortality. rRT-PCR positive result provided diagnosis and a marker of disease severity in Iranians.

Smoking and Diabetes Control in Adults With Type 1 and Type 2 Diabetes: A Nationwide Study From the 2018 National Program for Prevention and Control of Diabetes of Iran.

OBJECTIVES: Smoking is among the top leading causes of morbidity and mortality worldwide. To date, studies on the association between smoking and diabetes complications and metabolic control have shown conflicting results. In this study, we aimed to assess the association of smoking with micro- and macrovascular complications of diabetes and lipid and glycemic indices. METHODS: We used the National Program for Prevention and Control of Diabetes of Iran database of 99,651 adult patients with diabetes across Iran. Multiple logistic regression models were used to examine the association between smoking and diabetes complications including cardiovascular disease, neuropathy, nephropathy and retinopathy. This association was adjusted for age, sex, duration of diabetes, glycated hemoglobin (A1C), hypertension, hyperlipidemia, medication, obesity and type of diabetes. RESULTS: Smoking was associated with cardiovascular disease, nephropathy, retinopathy and neuropathy (odds ratios [ORs] for patients with type 1 diabetes were 1.51, 2.29, 2.70 and 2.40, respectively; for patients with type 2 diabetes, ORs were 1.27, 1.21, 1.51 and 1.70, respectively; all with p values <0.001). Among patients with type 1 diabetes, smoking was significantly (p<0.05) associated with A1C (OR, 2.12), 2-h postglucose level (OR, 1.30), triglycerides (OR, 1.48) and high-density lipoprotein (HDL) control (OR, 1.34). Among patients with type 2 diabetes, smoking was significantly associated with A1C (OR, 1.09) and HDL control (OR, 1.21). CONCLUSIONS: Smoking was associated with multiple diabetes complications including cardiovascular disease, neuropathy, nephropathy and retinopathy and worse A1C and HDL control in both patients with type 1 and type 2 diabetes. It was also associated with worse 2-h postglucose level and triglyceride control among patients with type 1 diabetes. Our findings signify that national programs for smoking prevention and cessation may be beneficial to diabetes control in Iran.

Estimating the Survival of Patients With Lung Cancer: What Is the Best Statistical Model? / 예방의학회지

OBJECTIVES@#: Investigating the survival of patients with cancer is vitally necessary for controlling the disease and for assessing treatment methods. This study aimed to compare various statistical models of survival and to determine the survival rate and its related factors among patients suffering from lung cancer.@*METHODS@#: In this retrospective cohort, the cumulative survival rate, median survival time, and factors associated with the survival of lung cancer patients were estimated using Cox, Weibull, exponential, and Gompertz regression models. Kaplan-Meier tables and the log-rank test were also used to analyze the survival of patients in different subgroups.@*RESULTS@#: Of 102 patients with lung cancer, 74.5% were male. During the follow-up period, 80.4% died. The incidence rate of death among patients was estimated as 3.9 (95% confidence [CI], 3.1 to 4.8) per 100 person-months. The 5-year survival rate for all patients, males, females, patients with non-small cell lung carcinoma (NSCLC), and patients with small cell lung carcinoma (SCLC) was 17%, 13%, 29%, 21%, and 0%, respectively. The median survival time for all patients, males, females, those with NSCLC, and those with SCLC was 12.7 months, 12.0 months, 16.0 months, 16.0 months, and 6.0 months, respectively. Multivariate analyses indicated that the hazard ratios (95% CIs) for male sex, age, and SCLC were 0.56 (0.33 to 0.93), 1.03 (1.01 to 1.05), and 2.91 (1.71 to 4.95), respectively.@*CONCLUSIONS@#: Our results showed that the exponential model was the most precise. This model identified age, sex, and type of cancer as factors that predicted survival in patients with lung cancer.

Estimating the Survival of Patients With Lung Cancer: What Is the Best Statistical Model? / 예방의학회지

OBJECTIVES:: Investigating the survival of patients with cancer is vitally necessary for controlling the disease and for assessing treatment methods. This study aimed to compare various statistical models of survival and to determine the survival rate and its related factors among patients suffering from lung cancer. METHODS:: In this retrospective cohort, the cumulative survival rate, median survival time, and factors associated with the survival of lung cancer patients were estimated using Cox, Weibull, exponential, and Gompertz regression models. Kaplan-Meier tables and the log-rank test were also used to analyze the survival of patients in different subgroups. RESULTS:: Of 102 patients with lung cancer, 74.5% were male. During the follow-up period, 80.4% died. The incidence rate of death among patients was estimated as 3.9 (95% confidence [CI], 3.1 to 4.8) per 100 person-months. The 5-year survival rate for all patients, males, females, patients with non-small cell lung carcinoma (NSCLC), and patients with small cell lung carcinoma (SCLC) was 17%, 13%, 29%, 21%, and 0%, respectively. The median survival time for all patients, males, females, those with NSCLC, and those with SCLC was 12.7 months, 12.0 months, 16.0 months, 16.0 months, and 6.0 months, respectively. Multivariate analyses indicated that the hazard ratios (95% CIs) for male sex, age, and SCLC were 0.56 (0.33 to 0.93), 1.03 (1.01 to 1.05), and 2.91 (1.71 to 4.95), respectively. CONCLUSIONS:: Our results showed that the exponential model was the most precise. This model identified age, sex, and type of cancer as factors that predicted survival in patients with lung cancer.