P9‐45: Use of smartphone applications in identification of risk factors of asthma attack: A systematic review

Abstract Background Asthma is a chronic disease that exacerbates due to various risk factors, including the patient’s biosignals and environmental conditions. It is affecting on average 7% of the world population. Preventing an asthma attack is the main challenge for asthma patients, which requires keeping track of any risk factor that can cause a seizure. Many researchers developed asthma attacks prediction models that used various asthma biosignals and environmental factors. These predictive models can help asthmatic patients predict asthma attacks in advance, and thus preventive measures can be taken. This paper introduces a review of these models to evaluate the used methods, model’s performance, and determine the need to improve research in this field. Method A systematic review was conducted for the research articles introducing asthma attack prediction models for children and adults. We searched the PubMed, ScienceDirect, Springer, and IEEE databases from January 2000 to December 2020. The search includes the prediction models that used biosignal, environmental, and both risk factors. The research article’s quality was assessed and scored based on two checklists, the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and the Critical Appraisal Skills Programme clinical prediction rule checklist (CASP). The highest scored articles were selected to review. Result From 1068 research articles we reviewed, we found that most of the studies used asthma biosignal factors only for prediction, few of the studies used environmental factors, and limited studies used both of these factors. Fifteen different asthma attack predictive models were selected for this review. we found that most of the studies used traditional prediction methods, like Support Vector Machine and regression. We have identified the pros and cons of the reviewed asthma attack prediction models and propose solutions to advance the studies in this field. Conclusion Asthma attack predictive models become more significant when using both patient’s biosignal and environmental factors. There is a lack of utilizing advanced machine learning methods, like deep learning techniques. Besides, there is a need to build smart healthcare systems that provide patients with decision-making systems to identify risk and visualize high-risk regions.

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Predictive Models for Personalized Asthma Attacks Based on Patient’s Biosignals and Environmental Factors: A Systematic Review

10.21203/rs.3.rs-770597/v1 ◽

2021 ◽

Author(s):

Eman T. Alharbi ◽

Farrukh Nadeem ◽

Asma Cherif

Keyword(s):

Risk Factors ◽

Systematic Review ◽

Environmental Factors ◽

Predictive Models ◽

Critical Appraisal ◽

Prediction Models ◽

Research Articles ◽

Support Vector ◽

World Population ◽

Asthma Attack

Abstract Background: Asthma is a chronic disease that exacerbates due to various risk factors, including the patient's biosignals and environmental conditions. It is affecting on average 7% of the world population. Preventing an asthma attack is the main challenge for asthma patients, which requires keeping track of any risk factor that can cause a seizure. Many researchers developed asthma attacks prediction models that used various asthma biosignals and environmental factors. These predictive models can help asthmatic patients predict asthma attacks in advance, and thus preventive measures can be taken. This paper introduces a review of these models to evaluate the used methods, model's performance, and determine the need to improve research in this field.Method: A systematic review was conducted for the research articles introducing asthma attack prediction models for children and adults. We searched the PubMed, ScienceDirect, Springer, and IEEE databases from January 2000 to December 2020. The search includes the prediction models that used biosignal, environmental, and both risk factors. The research article's quality was assessed and scored based on two checklists, the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and the Critical Appraisal Skills Programme clinical prediction rule checklist (CASP). The highest scored articles were selected to review.Result: From 1068 research articles we reviewed, we found that most of the studies used asthma biosignal factors only for prediction, few of the studies used environmental factors, and limited studies used both of these factors. Fifteen different asthma attack predictive models were selected for this review. we found that most of the studies used traditional prediction methods, like Support Vector Machine and regression. We have identified the pros and cons of the reviewed asthma attack prediction models and propose solutions to advance the studies in this field.Conclusion: Asthma attack predictive models become more significant when using both patient's biosignal and environmental factors. There is a lack of utilizing advanced machine learning methods, like deep learning techniques. Besides, there is a need to build smart healthcare systems that provide patients with decision-making systems to identify risk and visualize high-risk regions.

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RISK FACTORS FOR FALSE-POSITIVE AND FALSE-NEGATIVE FECAL IMMUNOCHEMICAL TEST RESULTS IN COLORECTAL CANCER SCREENING: SYSTEMATIC REVIEW AND META-ANALYSIS

10.1055/s-0038-1637308 ◽

2018 ◽

Author(s):

CM de Klerk ◽

LM Vendrig ◽

PM Bossuyt ◽

E Dekker

Keyword(s):

Colorectal Cancer ◽

Risk Factors ◽

Systematic Review ◽

Cancer Screening ◽

Colorectal Cancer Screening ◽

False Positive ◽

Meta Analysis ◽

False Negative ◽

Test Results ◽

Immunochemical Test

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Risk factors related to surgical site infections among surgical patients in developing world: a systematic review

International Journal of Medicine in Developing Countries ◽

10.24911/ijmdc.51-1598614458 ◽

2020 ◽

pp. 1984-1992

Author(s):

Faihan Alotaibi ◽

Faisal Alnemari ◽

Alwaleed Alsufyani ◽

Aisha Al-sanea ◽

Abeer Al-Nashri ◽

...

Keyword(s):

Risk Factors ◽

Systematic Review ◽

Developing World ◽

Surgical Site Infections ◽

Surgical Patients

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Stress, anxiety, depression and burnout in frontline health care workers during COVID-19 pandemic: a brief systematic review and new data from Russia

10.31234/osf.io/je3ch ◽

2021 ◽

Author(s):

Ekaterina Mosolova ◽

Dmitry Sosin ◽

Sergey Mosolov

Keyword(s):

Risk Factors ◽

Systematic Review ◽

Female Gender ◽

Assessment Tools ◽

Care Workers ◽

Younger Age ◽

Wide Range ◽

Associated Risk Factors ◽

Anxiety Depression ◽

The Impact

During the COVID-19 pandemic, healthcare workers (HCWs) have been subject to increased workload while also exposed to many psychosocial stressors. In a systematic review we analyze the impact that the pandemic has had on HCWs mental state and associated risk factors. Most studies reported high levels of depression and anxiety among HCWs worldwide, however, due to a wide range of assessment tools, cut-off scores, and number of frontline participants in the studies, results were difficult to compare. Our study is based on two online surveys of 2195 HCWs from different regions of Russia during spring and autumn epidemic outbreaks revealed the rates of anxiety, stress, depression, emotional exhaustion and depersonalization and perceived stress as 32.3%, 31.1%, 45.5%, 74.2%, 37.7% ,67.8%, respectively. Moreover, 2.4% of HCWs reported suicidal thoughts. The most common risk factors include: female gender, nurse as an occupation, younger age, working for over 6 months, chronic diseases, smoking, high working demands, lack of personal protective equipment, low salary, lack of social support, isolation from families, the fear of relatives getting infected. These results demonstrate the need for urgent supportive programs for HCWs fighting COVID-19 that fall into higher risk factors groups.

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