Predictive value of the preliminary findings in the severity of COVID-19 disease and the effect on therapeutic approaches

In this retrospective multicenter case series study, the predictive value of initial findings of confirm COVID‐19 cases in determining outcome of the disease was assessed. Patients were divided into two groups based on the outcome: low risk (hospitalization in the infectious disease ward and discharge) and high risk (hospitalization in ICU or death). A total of 164 patients with positive PCR‐RT were enrolled in this study. About 36 patients (22%) were in the high‐risk group and 128 (78%) were in the low‐risk group. Results of statistical analysis revealed a significant relationship between age, fatigue, history of cerebrovascular disease, organ failure, white blood cells (WBC), neutrophil‐to‐lymphocyte ratio (NLR), and derived neutrophil‐to‐lymphocyte ratio (dNLR) with increased risk of disease. The artificial neural network (ANN) could predict the high‐risk group with an accuracy of 87.2%. Preliminary findings of COVID‐19 patients can be used in predicting their outcome and ANN can determine the outcome of patients with appropriate accuracy (87.2%). Most treatment in Covid‐19 are supportive and depend on the severity of the disease and its complications. The first step in treatment is to determine the severity of the disease. This study can improve the treatment of patients by predicting the severity of the disease using the initial finding of patients and improve the management of disease with differentiating high‐risk from low‐risk groups.

Age distribution of accidents in hospitals affiliated to Iranian Ministry of Health

Background:

Accidents, as the third important cause of mortality, are among main challenges of communities due their costs and irreversible damages. This study aimed to investigate the age distribution of people referred to the hospitals affiliated by Iran’s ministry of health because of accidents.

Methods:

In a cross-sectional study, using a CDC checklist, data related to the injured people who referred to the hospitals affiliated by Iran’s ministry of health in a one year period was gathered from hospital records, as well as interview and observation. The collected data was analyzed by descriptive and analytical statistics in SPSS11 software.

Results:

The commonest age group involved in accidents during the year 1395 (2016) was middle-aged (37%) followed by youth (25%). The commonest accidents in children were: fall (23%), traffic accidents (19%), and burn (13%). In adolescents, the commonest accidents were: traffic accidents (32%), fall (16%), and violence and poisoning (5%). The commonest accidents in youth were: traffic accidents (35%), violence (9%), and poisoning (6%). In middle-aged group, the commonest accidents were: traffic accidents (30%), fall (12%), and violence and poisoning (7%). The commonest accidents in elderly were: fall and traffic accidents (25%), burn (4%), and violence and poisoning (3%).

Conclusions:

Making plan and policies to prevent accidents and injuries is a health priority and a key tool for improving the safety of the community. Also, informing the all age groups of the community about preventive actions is necessary.

Keywords:

Road Traffic Injury, Trauma, Hospital