مجله پیاورد سلامت
سال شانزدهم شماره 2 (خرداد و تیر 1401)

Atracurium, as one of the neuromuscular relaxants, is one of the most important irritants of the allergic reaction during anesthesia, which causes the release of histamine. This study was conducted with the aim of determining the effect of hydrocortisone on hemodynamic changes resulting from atracurium drug in patients with upper limb fractures under general anesthesia.

In this double-blind randomized clinical trial study, 50 patients with upper limb fractures under general anesthesia, 18 to 60 years old with ASA class 1 and 2, were randomly assigned to two groups of hydrocortisone and placebo (distilled water). In intervention group, 30 minutes before entering the operating room, vial oxycort (hydrocortisone) was administered intravenously and bolus with 300 ml of normal saline. Hemodynamic changes in patients before administration of hydrocortisone, 30 minute after administration, 5 minutes after the peak effect of atracurium (before intubation) and after extubation were recorded in the relevant checklist and comparisons were made between the two groups.

There was no significant difference between the two groups in terms of age, gender and duration of surgery. Systolic blood pressure 5 minutes after the peak effect of atracurium (before intubation) in the control group was lower than the intervention group and this difference was statistically significant (P=0.02). Thirteen minute after hydrocortisone administration, mean blood pressure systolic and diastolic patients decreased and this decrease continued after administration of histamine release atracurium (before intubation); but after extubation, the patients’ mean blood pressure has increased. Also, after administration of atracurium, the mean heart rate of patients decreased by 14.44 units as compared to before administration of hydrocortisone and this decrease was significant (P=0.001).

The results of this study show that hydrocortisone can be used as an effective factor in maintaining hemodynamic stability in patients under general anesthesia. However, its use as a factor in maintaining hemodynamics has not yet been widespread and needs further investigation.

Clinical learning is an important part of the health field, where the student interacts with the environment and applies the learned concepts in practice. Clinical environments such as operating rooms are challenging for students due to their special complexity and can have a negative impact on their learning process. In order to identify students ‘learning challenges in the operating room environment, the present study was conducted to explain students’ experiences in the field of clinical learning challenges.

The present qualitative study was performed by contract content analysis method in 2022 in Shahrekord University of Medical Sciences. Fourteen surgical technology students were purposefully selected and data were collected using in-depth semi-structured individual and group interviews and analyzed using the Granheim and Landman approaches.

The participants were interviewed over a period of 5 months. 9 face-to-face interviews were conducted with 14 participants. There were 6 individual interviews and 3 group interviews. The average duration of the interview was 30 minutes. The interviews continued until data saturation and when no new themes or categories were obtained from the interviews. The findings included a theme of “unfavorable learning environment” and three categories of “confusion in learning educational content, improper professional behavior of staff and insufficient self-confidence”. The main challenge that students faced in the field of clinical learning was the unfavorable learning environment. Conditions such as confusion in learning educational content, improper professional behavior of staff and insufficient self-confidence experienced by the students in the operating room, cause the students to find the learning atmosphere in the operating room unfavorable.

Improving the behavior and performance of staff and physicians in accordance with the standards of professional and ethical behavior and its regular evaluation from the perspective of students and other colleagues can play an effective role in maintaining professional conditions. Also, using experienced instructors who have the role of facilitating communication and learning of students in the operating room environment will play an effective role in reducing fear and controlling inappropriate behaviors of staff towards students. Educational officials are advised to solve the existing problems in order to improve the educational atmosphere of the operating room.

Cancer is the second leading cause of death in the world, which leads to the death of more than 10 million people in the world every year. Its early diagnosis, management and proper treatment play an important role in reducing complications and mortality. One of the support tools in early diagnosis, treatment and management of this disease are Clinical Decision Support System (CDSS), which are divided into two groups, rule-based and non-rule-based. Rule-based decision support systems are created based on clinical guidelines, while non-rule-based decision support systems use machine learning. In this research, the effects of decision support systems, rule-based and non-rule-based, on cancer diagnosis, treatment and management were measured.

The present study was conducted using a systematic review method, which was conducted by searching the Web of Science, Scopus, IEEE and PubMED databases until 12/31/2021. After removing duplicates and evaluating the characteristics of the inclusion and exclusion criteria, studies related to the goal were selected. The selection of articles was based on the title, abstract and full text The data collection tool was the data extraction form, which included year of study, type of study, system of body, organ of body, the service provided by the decision support system, type of decision support system, effect, effect index and the score of effect index. Narrative synthesis were used for data analysis.

Out of 768 articles, 16 articles related to the objectives of the study were identified. Studies were presented in two categories of clinical decision-support systems: Rule-based and non-Rule based. The effects evaluated in the clinical decision support systems were Rule-based, dose adjustment, symptoms, adherence to treatment guidelines, care time, smoking, need for chemotherapy and pain management, all of which except pain management were significant and positive. The effects evaluated were in the category of non-Rule based clinical decision support systems, diagnostic and therapeutic decisions, controlling neutropenia, all of which were significant and positive except controlling neutropenia.

The results obtained for the effectiveness of both Rule-based and non-Rule-based decision support systems indicated different benefits of these two categories. Therefore, using their combination in the field of cancer can bring very useful results.

Health information seeking behavior can be influenced by several factors and variables such as personality traits, beliefs, values, tendencies, contextual factors and personal emotions. Health information has a direct relationship with the quality of life of people in society, it can be influential in decisions related to personal and social health and improve people’s performance in this field. This study aimed to determine the relationship between personality types of graduate students of Isfahan University of medical Sciences (IUMS) and their health information seeking behavior based on the Miller model.

This was an applied survey study. The study population was all graduate students of IUMS. The sample size was determined using the Morgan table of 297 people. Data collection tools are Neo Five Personality Factor Questionnaire and Miller Information Behavior Questionnaire. Data were analyzed by SPSS software.

The results showed that there is an inverse relationship between personality type “Psychotic” and the component of “active search for health information”. Also there is a direct relationship between persons who are “Eager for new experiences” and “conscientiousness” with the component of “active search for health information”. But there is no significant relationship between personality types “extroversion” and “agreeability” with the components of health informing behavior. The studied students are not responsible and conscientious in terms of personality type, they often prefer solitude and are introverted and conservative. But more than half of them are balanced in the dimension of “agreeability”.

This study showed that people who are eager for new experiences and conscientious, search health information actively, but Psychotic persons are not willing to be active in seeking health information This means that the more responsible and conscientious people are, or the more eager they are for new experiences, the more actively they search for health information. Therefore, it is better for health policy makers to plan in such a way that the necessary health information is provided to them interactively based on the personality of the people.

Currently, utilizing different nutritional indicators to investigate the association between diet and various diseases is considered in previous studies, which is related with some chronic diseases. However, no studies have studied the connection between the indicators with the rate of metabolism at rest (RMR). Therefore, the present study aimed to determine the relationship between dietary index and insulin load with resting metabolic rate (RMR) in overweight and obese women

A total of 280 healthy overweight and obese women (aged 18-50 years) who were referred to community health centers of Tehran University of medical sciences were included. In this study anthropometrics measurements such as weight, height, waist circumference, waist to hip ratio, body mass index, fat percentage, and fat-free mass were evaluated for every participant. Data on dietary intakes were collected using 147 semi-quantitative food frequency questionnaire (FFQ). DII and DIL were calculated using food insulin index values published earlier. To assess the RMR, indirect calorimetry was used. 

Mean age of study participants was 36.4±8.38 years. Although a significant association was seen between DII and RMR in a crude model (P=0.04); adjusting for different confounders made this significant relationship between DII and RMR insignificant. In addition, the dietary insulin index had no significant relationship with the amount of RMR/kg (p=0.63) and RMR/FFM (p=0.73).

Based on the results of this cross-sectional study, it seems that the insulin index and insulin load of the diet are not associated to the rate of resting metabolism. However, due to the limitations of this study, findings can only confirm or reject the hypothesis under further studies. It is also necessary to determine the role of dietary insulin indicators on human health, especially with a Study Prospective Design

Breast cancer is one of the most common and aggressive malignancies in women. Timely diagnosis of breast cancer plays an important role in preventing the progression of this disease, timely treatment measures, and aftermath reducing the mortality rate of these patients. Machine learning has the potential ability to diagnose diseases quickly and cost-effectively. This study aims to design a CDSS based on the rules extracted from the decision tree algorithm with the best performance to diagnose breast cancer in a timely and effective manner.

The data of 597 suspected people with breast cancer (255 patients and 342 healthy people) were retrospectively extracted from the electronic database of Ayatollah Taleghani Hospital in Abadan city with 24 characteristics, mainly pertained to lifestyle and medical histories. After selecting the most important variables by using the Chi-square Pearson and one-way analysis of variance (P<0.05), the performance of selected data mining algorithms including RF, J-48, DS, RT and XG -Boost was evaluated for breast cancer diagnosis in Weka 3.4 software. Finally, the breast cancer diagnostic system was designed based on the best model and through C# programming language and Dot Net Framework V3.5.4.

Fourteen variables including personal history of breast cancer, breast sampling, and chest X-ray, high blood pressure, increased LDL blood cholesterol, presence of mass in upper inner quadrant of the breast, hormone therapy with estrogen, hormone therapy with Estrogen-progesterone, family history of breast cancer, age, history of other cancers, waist-to-hip ratio and fruit and vegetable consumption showed a significant relationship with the output class at the P<0.05. Based on the results of the performance evaluation of selected algorithms, the RF model with sensitivity, specificity, accuracy, and F- measure equal to 0.97, 0.99, 0.98, 0.974, respectively, AUC=0.936 had higher performance than other selected algorithms and was suggested as the best model for breast cancer diagnosis.

It seems that using modifiable variables such as lifestyle and reproductive-hormonal characteristics as input to the RF algorithm to design the CDSS, can detect breast cancer cases with optimal accuracy. In addition, the proposed system can be effectively adapted in real clinical environments for quick and effective disease diagnosis.

The outbreak of the Covid-19 pandemic has led to the emergence of new information needs for people with diverse information literacy. Both infected and healthy people feel the need to have essential and practical information about this pandemic. One of the concerns of Covid-19 patients is their need for reliable and sufficient information about various aspects of the disease. Understanding the information needs of patients and the experiences of people who have been infected and recovered from the disease can be a suitable and reliable source of information. So the aim of this study was to identify the information needs of patients with Covid-19.

This research was employed a qualitative approach using conventional content analysis. Key informants were Covid-19 recovered patients in Kashan city who had a history of admission in medical centers. Sampling consisted 17 participants (11 males and 6 females) who were selected from almost different social classes through purposeful method. The data were collected using a semi-structured interview and the saturation point was reached at 17 interviews. Data were analyzed using the Diekelmann’s seven-stage method.

The results of this study included 430 primary codes that after removing and merging duplicate codes, seven main themes and 30 categories were identified in the field of information needs were extracted. Main themes and categories included understanding the nature of the disease (the origin of the disease, knowledge about the symptoms, transmission and types of mutations), prevention (health protocols, prevention equipments, and vaccination), treatment (diagnostic tests, type of disease treatment, disease process, costs, psychological support), nutrition (the type of nutrition for prevention, during illness and after recovery), communication with others (type, length and conditions of quarantine, how to communicate with others), statistics, and information sources (up-to-date, valid, and types).

The most critical information needs of Covid-19 patients include information about the nature, treatment, and preventive measures of the disease. Social media and oral information such as doctors, friends, and acquaintances were also reported as the most important sources of information. Therefore, this study suggests that health managers provide the most up-to-date and reliable information and news related to Covid-19 through the most appropriate and accessible media.

Identifying risk factors is recommended as the first step for depression management in children and adolescents. This study aims to determine the data elements required for developing a clinical decision support system for screening major depression in young people.

This research was a descriptive-analytical study. The research population included a variety of mental health specialists that were both psychologists and students in psychiatry and guidance & counseling majors as well as electronic databases including Scopus, Pubmed, Embase, PsychInfo, WOS and Clinical key. The data collection tool was a questionnaire designed in three main sections which was answered by a convenient sample of 8 people who were specialists in the field. To analyze the extracted data Content Validity Ratio (CVR) and Mean measures were calculated for each item in questionnaire. Content Validity Index (CVI) and Cronbach’s Alpha (using SPSS software) were calculated which were equal to 0.74 and 0.824 respectively which confirmed validity and reliability of the research tool. 

 According to Lawshe’s table, data elements with CVR between 0 and 0.75 and Mean less than 1.5, like “Ethnicity and race” (CVR=-0.25, Mean=1.125), were rejected. Items such as “Gender” (CVR=0.5) with a CVR equal to or less than 0.75, as well as items with a CVR between 0 and 0.75 and a Mean equal to or more than 1.5, like “Marital status” (CVR=0.5, Mean=1.625) were retained and considered to be included as the minimum data set for screening major depression in ages 10 to 25 years. Data elements were categorized in three categories: Demographic, Clinical and Psychosocial

Clinical decision support systems can facilitate providing healthcare at different levels such as screening major depression. These systems can be used for screening major depression risk factors to improve accessibility to mental health practitioners, assure the implementation of guidelines and provide a common language between different levels of healthcare. Determining the minimum data set for screening major depression in ages 10 to 25 years, is the first step toward developing a clinical decision support system for screening individuals for major depression.