scholarly journals Systematic review of prediction models for gestational hypertension and preeclampsia

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0230955 ◽  
Author(s):  
Edward Antwi ◽  
Mary Amoakoh-Coleman ◽  
Dorice L. Vieira ◽  
Shreya Madhavaram ◽  
Kwadwo A. Koram ◽  
...  
Keyword(s):  
Systematic Review ◽  
Prediction Models ◽  
Gestational Hypertension

scholarly journals TRAIL and Cardiovascular Disease—A Risk Factor or Risk Marker: A Systematic Review

2021 ◽  
Vol 10 (6) ◽  
pp. 1252
Author(s):  
Katarzyna Kakareko ◽  
Alicja Rydzewska-Rosołowska ◽  
Edyta Zbroch ◽  
Tomasz Hryszko
Keyword(s):  
Systematic Review ◽  
Risk Factor ◽  
Ischemic Stroke ◽  
Cardiovascular Diseases ◽  
Animal Studies ◽  
Gestational Hypertension ◽  
Cochrane Library ◽  
Major Adverse Cardiovascular Events ◽  
Risk Marker ◽  
Artery Disease

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a pro-apoptotic protein showing broad biological functions. Data from animal studies indicate that TRAIL may possibly contribute to the pathophysiology of cardiomyopathy, atherosclerosis, ischemic stroke and abdominal aortic aneurysm. It has been also suggested that TRAIL might be useful in cardiovascular risk stratification. This systematic review aimed to evaluate whether TRAIL is a risk factor or risk marker in cardiovascular diseases (CVDs) focusing on major adverse cardiovascular events. Two databases (PubMed and Cochrane Library) were searched until December 2020 without a year limit in accordance to the PRISMA guidelines. A total of 63 eligible original studies were identified and included in our systematic review. Studies suggest an important role of TRAIL in disorders such as heart failure, myocardial infarction, atrial fibrillation, ischemic stroke, peripheral artery disease, and pulmonary and gestational hypertension. Most evidence associates reduced TRAIL levels and increased TRAIL-R2 concentration with all-cause mortality in patients with CVDs. It is, however, unclear whether low TRAIL levels should be considered as a risk factor rather than a risk marker of CVDs. Further studies are needed to better define the association of TRAIL with cardiovascular diseases.

scholarly journals Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

2021 ◽  
Vol 18 (9) ◽  
pp. 4499
Author(s):  
Anil Babu Payedimarri ◽  
Diego Concina ◽  
Luigi Portinale ◽  
Massimo Canonico ◽  
Deborah Seys ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Public Transportation ◽  
Prediction Models ◽  
Positive Impact ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Commercial Activities

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

scholarly journals Dietary patterns before and during pregnancy and maternal outcomes: a systematic review

2019 ◽  
Vol 109 (Supplement_1) ◽  
pp. 705S-728S ◽  
Author(s):  
Ramkripa Raghavan ◽  
Carol Dreibelbis ◽  
Brittany L Kingshipp ◽  
Yat Ping Wong ◽  
Barbara Abrams ◽  
...  
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Dietary Patterns ◽  
Access To Health Care ◽  
Gestational Hypertension ◽  
Controlled Trial ◽  
Whole Grains ◽  
Access To Health ◽  
Caucasian Women ◽  
Reduced Risk

ABSTRACT Background Hypertensive disorders of pregnancy (HDP) and gestational diabetes mellitus (GDM) are common maternal complications during pregnancy, with short- and long-term sequelae for both mothers and children. Objective Two systematic review questions were used to examine the relation between 1) dietary patterns before and during pregnancy, 2) HDP, and 3) GDM. Methods A search was conducted from January 1980 to January 2017 in 9 databases including PubMed, Embase, and Cochrane. Two analysts independently screened articles using a priori inclusion and exclusion criteria; data were extracted from included articles, and risk of bias was assessed. After qualitative synthesis, a conclusion statement was drafted for each question and the evidence supporting the conclusion was graded. Results Of the 9103 studies identified, 8 [representing 4 cohorts and 1 randomized controlled trial (RCT)] were included for HDP and 11 (representing 6 cohorts and 1 RCT) for GDM. Limited evidence in healthy Caucasian women with access to health care suggests dietary patterns before and during pregnancy that are higher in vegetables, fruits, whole grains, nuts, legumes, fish, and vegetable oils and lower in meat and refined grains are associated with reduced risk of HDP, including preeclampsia and gestational hypertension. Limited but consistent evidence suggests certain dietary patterns before pregnancy are associated with reduced risk of GDM. These protective dietary patterns are higher in vegetables, fruits, whole grains, nuts, legumes, and fish and lower in red and processed meats. Most of the research was conducted in healthy, Caucasian women with access to health care. Insufficient evidence exists on the associations between dietary patterns before and during pregnancy and risk of HDP in minority women and those of lower socioeconomic status, and dietary patterns during pregnancy and risk of GDM. Conclusions Although some conclusions were drawn from these systematic reviews, more research is needed to address gaps and limitations in the evidence.

scholarly journals RISK PREDICTION MODELS FOR SURVIVAL AFTER HEART TRANSPLANTATION: A SYSTEMATIC REVIEW

2019 ◽  
Vol 35 (10) ◽  
pp. S94-S95
Author(s):  
N. Aleksova ◽  
A. Alba ◽  
V. Molinero ◽  
K. Connolly ◽  
A. Orchanian-Cheff ◽  
...  
Keyword(s):  
Systematic Review ◽  
Heart Transplantation ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models

scholarly journals Systematic Review of Health Economic Impact Evaluations of Risk Prediction Models: Stop Developing, Start Evaluating

2017 ◽  
Vol 20 (4) ◽  
pp. 718-726 ◽  
Author(s):  
Anoukh van Giessen ◽  
Jaime Peters ◽  
Britni Wilcher ◽  
Chris Hyde ◽  
Carl Moons ◽  
...  
Keyword(s):  
Systematic Review ◽  
Health Economic ◽  
Economic Impact ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models

The Use of Prognostic Prediction Models for Mortality or Clinical Deterioration among Hospitalized and Non-hospitalized Adults with COVID-19: A Systematic Review

2021 ◽  
Author(s):  
Patricia Pauline M. Remalante-Rayco ◽  
Evelyn Osio-Salido
Keyword(s):  
Systematic Review ◽  
Emergency Department ◽  
Early Warning ◽  
Prediction Models ◽  
External Validation ◽  
Risk Of Bias ◽  
Clinical Deterioration ◽  
Early Warning Score ◽  
Good Prediction ◽  
Prediction Of Mortality

Objective. To assess the performance of prognostic models in predicting mortality or clinical deterioration among patients with COVID-19, both hospitalized and non-hospitalized Methods. We conducted a systematic review of the literature until March 8, 2021. We included models for the prediction of mortality or clinical deterioration in COVID-19 with external validation. We used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the GRADEpro Guideline Development Tool (GDT) to assess the evidence obtained. Results. We reviewed 33 cohort studies. Two studies had a low risk of bias, four unclear risks, and 27 with a high risk of bias due to participant selection and analysis. For the outcome of mortality, the QCOVID model had excellent prediction with high certainty of evidence but was specific for use in England. The COVID Outcome Prediction in the Emergency Department (COPE) model, the 4C Mortality Score, the Age, BUN, number of comorbidities, CRP, SpO2/FiO2 ratio, platelet count, heart rate (ABC2-SPH) risk score, the Confusion Urea Respiration Blood Pressure (CURB-65) severity score, the Rapid Emergency Medicine Score (REMS), and the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) score had fair to good prediction of death among inpatients, while the quick Sepsis-related Organ Failure Assessment (qSOFA) score had poor to fair prediction. The certainty of evidence for these models was very low to low. For the outcome of clinical deterioration, the 4C Deterioration Score had fair prediction, the National Early Warning Score 2 (NEWS2) score poor to good, and the Modified Early Warning Score (MEWS) had poor prediction. The certainty of evidence for these three models was also very low to low. None of these models had been validated in the Philippine setting. Conclusion. The QCOVID, COPE, ABC2-SPH, 4C, CURB-65, REMS, RISE-UP models for prediction of mortality and the 4C Deterioration and NEWS2 models for prediction of clinical deterioration are potentially useful but need to be validated among patients with COVID-19 of varying severity in the Philippine setting.

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

2021 ◽  
Author(s):  
Nghia H Nguyen ◽  
Dominic Picetti ◽  
Parambir S Dulai ◽  
Vipul Jairath ◽  
William J Sandborn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Risk Prediction ◽  
Statistical Models ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Learning Models ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Machine Learning Models

Abstract Background and Aims There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases (IBD). We synthesized and critically appraised studies comparing machine learning vs. traditional statistical models, using routinely available clinical data for risk prediction in IBD. Methods Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harboring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment (PROBAST) tool. Results We included 13 studies on machine learning-based prediction models in IBD encompassing themes of predicting treatment response to biologics and thiopurines, predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learnings models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated. Conclusions Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

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