scholarly journals Predictive models for personalized asthma attacks based on patient’s biosignals and environmental factors: a systematic review

2021 ◽  
Vol 21 (1) ◽  
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.

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

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.

scholarly journals Polygenic risk prediction models for colorectal cancer: a systematic review

BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Michele Sassano ◽  
Marco Mariani ◽  
Gianluigi Quaranta ◽  
Roberta Pastorino ◽  
Stefania Boccia
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Systematic Review ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Variants ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Discriminatory Accuracy ◽  
Traditional Risk Factors

Abstract Background Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. Methods We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. Results We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. Conclusions Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

scholarly journals Risk Prediction Models for Patients With Acute Heart Failure: A Protocol for Systematic Review, Critical Appraisal, and Meta-Analysis

2021 ◽  
Author(s):  
Xuecheng Zhang ◽  
Kehua Zhou ◽  
Jingjing Zhang ◽  
Ying Chen ◽  
Hengheng Dai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Systematic Review ◽  
Prediction Model ◽  
Acute Heart Failure ◽  
Risk Prediction ◽  
Methodological Quality ◽  
Critical Appraisal ◽  
Prediction Models ◽  
Cochrane Library ◽  
Risk Prediction Models

Abstract Background Nearly a third of patients with acute heart failure (AHF) die or are readmitted within three months after discharge, accounting for the majority of costs associated with heart failure-related care. A considerable number of risk prediction models, which predict outcomes for mortality and readmission rates, have been developed and validated for patients with AHF. These models could help clinicians stratify patients by risk level and improve decision making, and provide specialist care and resources directed to high-risk patients. However, clinicians sometimes reluctant to utilize these models, possibly due to their poor reliability, the variety of models, and/or the complexity of statistical methodologies. Here, we describe a protocol to systematically review extant risk prediction models. We will describe characteristics, compare performance, and critically appraise the reporting transparency and methodological quality of risk prediction models for AHF patients. Method Embase, Pubmed, Web of Science, and the Cochrane Library will be searched from their inception onwards. A back word will be searched on derivation studies to find relevant external validation studies. Multivariable prognostic models used for AHF and mortality and/or readmission rate will be eligible for review. Two reviewers will conduct title and abstract screening, full-text review, and data extraction independently. Included models will be summarized qualitatively and quantitatively. We will also provide an overview of critical appraisal of the methodological quality and reporting transparency of included studies using the Prediction model Risk of Bias Assessment Tool(PROBAST tool) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD statement). Discussion The result of the systematic review could help clinicians better understand and use the prediction models for AHF patients, as well as make standardized decisions about more precise, risk-adjusted management. Systematic review registration : PROSPERO registration number CRD42021256416.

scholarly journals Development and Evaluation of Models to Predict Readmission or Death After Discharge from Intensive Care 

2021 ◽  
Author(s):  
Jamie M Boyd ◽  
Matthew T James ◽  
Danny J Zuege ◽  
Henry Thomas Stelfox
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Intensive Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
Meta Analysis ◽  
Local Data ◽  
Predictive Values ◽  
Risk Prediction Models ◽  
Icu Discharge

Abstract Background Patients being discharged from the intensive care unit (ICU) have variable risks of subsequent readmission or death; however, there is limited understanding of how to predict individual patient risk. We sought to derive risk prediction models for ICU readmission or death after ICU discharge to guide clinician decision-making. Methods Systematic review and meta-analysis to identify risk factors. Development and validation of risk prediction models using two retrospective cohorts of patients discharged alive from medical-surgical ICUs (n = 3 ICUs, n = 11,291 patients; n = 14 ICUs, n = 11,400 patients). Models were developed using literature and data-derived weighted coefficients. Results Sixteen variables identified from the systematic review were used to develop four risk prediction models. In the validation cohort there were 795 (7%) patients who were re-admitted to ICU and 703 (7%) patients who died after ICU discharge. The area under the curve (AUROC) for ICU readmission for the literature (0.615 [95%CI: 0.593, 0.637]) and data (0.652 [95%CI: 0.631, 0.674]) weighted models showed poor discrimination. The AUROC for death after ICU discharge for the literature (0.708 [95%CI: 0.687, 0.728]) and local data weighted (0.752 [95%CI: 0.733, 0.770]) models showed good discrimination. The negative predictive values for ICU readmission and death after ICU discharge ranged from 94%-98%. Conclusions Identifying risk factors and weighting coefficients using systematic review and meta-analysis to develop prediction models is feasible and can identify patients at low risk of ICU readmission or death after ICU discharge.

scholarly journals Glycaemic control at risk? - Impact of temperature, humidity and other physical factors on the analytical quality of point of care testing of blood glucose, a systematic review protocol

2021 ◽  
Author(s):  
Cilia Maria Katharina Sindt ◽  
Lene Noehr-Jensen
Keyword(s):  
Systematic Review ◽  
Blood Glucose ◽  
Environmental Factors ◽  
Search Strategy ◽  
Critical Appraisal ◽  
Current Knowledge ◽  
Meta Analysis ◽  
Review Protocol ◽  
Comprehensive Search

Abstract Background Diabetes is a chronic metabolic disease characterized by increased blood glucose concentration. Self-monitoring of blood glucose (SMBG) is a corner stone in lowering the risk of vascular complications of diabetes and requires reliable measurements by the blood glucose meter and test strips (BG System). Environmental factors as temperature, humidity, altitude, and electromagnetic radiation may affect the measurement of blood glucose to a level that might compromise the glycaemic control. Manufactures often restrict the validation and approved use a limited span of environmental conditions and there is a considerable risk of exposing the equipment to temperature outside the range recommended by manufacturers. This systematic review aims to explore, aggregate, and present the current knowledge of the effect of environmental factors on the quality of results measured by BG systems. Methods Relevant studies will be found by using a comprehensive search strategy in the following databases: EMBASE, PubMed, CINAHL, Academic Search Premier, SweMed+, DANS Easy and Cochrane Library. The search strategy will assess if there is an impact on precision and accuracy of BG systems under varying environmental conditions compared to precision recommended by manufacturers. Both authors will screen titles and in case of disagreement, the study will be included for screening by abstract. After screening of all titles, the procedure will be repeated for screening and reading of abstracts. Full text of papers selected by abstract will be assessed according to in- and exclusions criteria. Homogenous results will be pooled and summarized statistically for meta-analysis. Narrative synthesis will be performed for heterogeneous data that cannot be pooled for meta-analysis. Reporting will follow the Preferred Items for Systematic Reviews and Meta-Analysis 2020 (PRISMA 2020). The risk of bias will be evaluated by choosing an appropriate tool from acknowledged institutions I.e., Joanna Briggs Critical Appraisal for Systematic Reviews Checklist tools or the Critical Appraisal Skills Program (CASP). Discussion This systematic review protocol utilizes rigorous methodology to provide a comprehensive search for outcomes of the impact of environmental factors on BG systems. The outcomes will be examined, and a summary of the current knowledge will be provided.

scholarly journals Interpretation of Ligand-Based Activity Cliff Prediction Models Using the Matched Molecular Pair Kernel

Molecules ◽  
2021 ◽  
Vol 26 (16) ◽  
pp. 4916
Author(s):  
Shunsuke Tamura ◽  
Swarit Jasial ◽  
Tomoyuki Miyao ◽  
Kimito Funatsu
Keyword(s):  
Predictive Models ◽  
Prediction Models ◽  
Support Vector ◽  
Molecular Pair ◽  
Specific Interpretation ◽  
Activity Cliff ◽  
Model Independent ◽  
The Difference ◽  
Intuitive Manner ◽  
Interpretation Method

Activity cliffs (ACs) are formed by two structurally similar compounds with a large difference in potency. Accurate AC prediction is expected to help researchers’ decisions in the early stages of drug discovery. Previously, predictive models based on matched molecular pair (MMP) cliffs have been proposed. However, the proposed methods face a challenge of interpretability due to the black-box character of the predictive models. In this study, we developed interpretable MMP fingerprints and modified a model-specific interpretation approach for models based on a support vector machine (SVM) and MMP kernel. We compared important features highlighted by this SVM-based interpretation approach and the SHapley Additive exPlanations (SHAP) as a major model-independent approach. The model-specific approach could capture the difference between AC and non-AC, while SHAP assigned high weights to the features not present in the test instances. For specific MMPs, the feature weights mapped by the SVM-based interpretation method were in agreement with the previously confirmed binding knowledge from X-ray co-crystal structures, indicating that this method is able to interpret the AC prediction model in a chemically intuitive manner.

scholarly journals Guidelines about physical activity and exercise to reduce cardiometabolic risk factors: protocol for a systematic review and critical appraisal

BMJ Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. e032656 ◽  
Author(s):  
Madelin Siedler ◽  
M Hassan Murad ◽  
Yngve Falck-Ytter ◽  
Philipp Dahm ◽  
Reem A Mustafa ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Systematic Review ◽  
Critical Appraisal ◽  
Current Status ◽  
Cochrane Central Register ◽  
Cardiometabolic Disorders ◽  
Wide Range ◽  
Physical Activity And Exercise

IntroductionPhysical activity can prevent a wide range of diseases, including highly prevalent conditions such as heart disease, diabetes and associated cardiometabolic disorders. Numerous guidelines for the prescription of physical activity and exercise to promote general health and prevent disease are released each year, but the quality of these guidelines is currently unknown. This systematic review and critical appraisal of physical activity and exercise guidelines aims to summarise the current status and quality of these guidelines to provide suggestions to improve the development of future guidelines in this area.Methods and analysisWe will conduct a systematic review of guidelines in Ovid MEDLINE(R) and Epub Ahead of Print, In-Process & Other Non-Indexed Citations, and Daily, Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews and Scopus databases published from database 2000 through 23 October 2019, written in English for the use of physical activity and exercise for the prevention of cardiometabolic disease and related risk factors in otherwise healthy individuals. We will also search the grey literature for additional eligible documents. We will use the Appraisal of Guidelines for Research and Evaluation II tool to assess the quality of eligible recommendations from all included guidelines, as well as perform exploratory analyses on guideline development variables.Ethics and disseminationAs a protocol for the review and critical appraisal of published documents, no potential ethical considerations are discussed. The protocol will guide the development of the review, which will be disseminated to relevant journals for publication.PROSPERO registration numberCRD42019126364

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