scholarly journals Diagnostic and prognostic prediction models in ventilator-associated pneumonia: Systematic review and meta-analysis of prediction modelling studies

2022 ◽  
Vol 67 ◽  
pp. 44-56
Author(s):  
Tuomas Frondelius ◽  
Irina Atkova ◽  
Jouko Miettunen ◽  
Jordi Rello ◽  
Miia M. Jansson
Keyword(s):  
Systematic Review ◽  
Prediction Models ◽  
Meta Analysis ◽  
Ventilator Associated Pneumonia ◽  
Modelling Studies ◽  
Prediction Modelling ◽  
Prognostic Prediction

scholarly journals Prognostic prediction models for pregnancy complications in women with gestational diabetes: a protocol for systematic review, critical appraisal and meta-analysis

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Shamil D. Cooray ◽  
Jacqueline A. Boyle ◽  
Georgia Soldatos ◽  
Lihini A. Wijeyaratne ◽  
Helena J. Teede
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Gestational Diabetes ◽  
Pregnancy Complications ◽  
Prediction Models ◽  
Absolute Risk ◽  
Meta Analysis ◽  
External Validation ◽  
The Absolute ◽  
Prognostic Prediction

Abstract Background Gestational diabetes (GDM) is increasingly common and has significant implications during pregnancy and for the long-term health of the mother and offspring. However, it is a heterogeneous condition with inter-related factors including ethnicity, body mass index and gestational weight gain significantly modifying the absolute risk of complications at an individual level. Predicting the risk of pregnancy complications for an individual woman with GDM presents a useful adjunct to therapeutic decision-making and patient education. Diagnostic prediction models for GDM are prevalent. In contrast, prediction models for risk of complications in those with GDM are relatively novel. This study will systematically review published prognostic prediction models for pregnancy complications in women with GDM, describe their characteristics, compare performance and assess methodological quality and applicability. Methods Studies will be identified by searching MEDLINE and Embase electronic databases. Title and abstract screening, full-text review and data extraction will be completed independently by two reviewers. The included studies will be systematically assessed for risk of bias and applicability using appropriate tools designed for prediction modelling studies. Extracted data will be tabulated to facilitate qualitative comparison of published prediction models. Quantitative data on predictive performance of these models will be synthesised with meta-analyses if appropriate. Discussion This review will identify and summarise all published prognostic prediction models for pregnancy complications in women with GDM. We will compare model performance across different settings and populations with meta-analysis if appropriate. This work will guide subsequent phases in the prognosis research framework: further model development, external validation and model updating, and impact assessment. The ultimate model will estimate the absolute risk of pregnancy complications for women with GDM and will be implemented into routine care as an evidence-based GDM complication risk prediction model. It is anticipated to offer value to women and their clinicians with individualised risk assessment and may assist decision-making. Ultimately, this systematic review is an important step towards a personalised risk-stratified model-of-care for GDM to allow preventative and therapeutic interventions for the maximal benefit to women and their offspring, whilst sparing expense and harm for those at low risk. Systematic review registration PROSPERO registration number CRD42019115223

scholarly journals Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis

EP Europace ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 684-694 ◽  
Author(s):  
Jelle C L Himmelreich ◽  
Lieke Veelers ◽  
Wim A M Lucassen ◽  
Renate B Schnabel ◽  
Michiel Rienstra ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Systematic Review ◽  
Prediction Models ◽  
Prediction Interval ◽  
Data Extraction ◽  
Meta Analysis ◽  
Aging Research ◽  
Genomic Epidemiology ◽  
C Statistic ◽  
Modelling Studies

Abstract Aims Atrial fibrillation (AF) is a common arrhythmia associated with an increased stroke risk. The use of multivariable prediction models could result in more efficient primary AF screening by selecting at-risk individuals. We aimed to determine which model may be best suitable for increasing efficiency of future primary AF screening efforts. Methods and results We performed a systematic review on multivariable models derived, validated, and/or augmented for AF prediction in community cohorts using Pubmed, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) through 1 August 2019. We performed meta-analysis of model discrimination with the summary C-statistic as the primary expression of associations using a random effects model. In case of high heterogeneity, we calculated a 95% prediction interval. We used the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist for risk of bias assessment. We included 27 studies with a total of 2 978 659 unique participants among 20 cohorts with mean age ranging from 42 to 76 years. We identified 21 risk models used for incident AF risk in community cohorts. Three models showed significant summary discrimination despite high heterogeneity: CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology) [summary C-statistic 0.71; 95% confidence interval (95% CI) 0.66–0.76], FHS-AF (Framingham Heart Study risk score for AF) (summary C-statistic 0.70; 95% CI 0.64–0.76), and CHA2DS2-VASc (summary C-statistic 0.69; 95% CI 0.64–0.74). Of these, CHARGE-AF and FHS-AF had originally been derived for AF incidence prediction. Only CHARGE-AF, which comprises easily obtainable measurements and medical history elements, showed significant summary discrimination among cohorts that had applied a uniform (5-year) risk prediction window. Conclusion CHARGE-AF appeared most suitable for primary screening purposes in terms of performance and applicability in older community cohorts of predominantly European descent.

scholarly journals Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Herdiantri Sufriyana ◽  
Atina Husnayain ◽  
Ya-Lin Chen ◽  
Chao-Yang Kuo ◽  
Onkar Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Pregnancy Outcomes ◽  
Prediction Models ◽  
Meta Analysis ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Pregnancy Care ◽  
Prognostic Prediction ◽  
In Pregnancy

BACKGROUND Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method. OBJECTIVE This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians’ decision making. METHODS Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians’ decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using <i>τ<sup>2</sup></i> and <i>I<sup>2</sup></i>. RESULTS Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; <i>I<sup>2</sup></i>=86%; <i>τ<sup>2</sup></i>=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; <i>I<sup>2</sup></i>=75%; <i>τ<sup>2</sup></i>=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; <i>I<sup>2</sup></i>=75%; <i>τ<sup>2</sup></i>=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; <i>I<sup>2</sup></i>=83%; <i>τ<sup>2</sup></i>=0.07). CONCLUSIONS Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines. CLINICALTRIAL PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106

scholarly journals Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

10.2196/16503 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e16503
Author(s):  
Herdiantri Sufriyana ◽  
Atina Husnayain ◽  
Ya-Lin Chen ◽  
Chao-Yang Kuo ◽  
Onkar Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Pregnancy Outcomes ◽  
Prediction Models ◽  
Meta Analysis ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Pregnancy Care ◽  
Prognostic Prediction ◽  
In Pregnancy

Background Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method. Objective This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians’ decision making. Methods Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians’ decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2. Results Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07). Conclusions Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106

PP23 Accuracy of emergency medical services (EMS) telephone triage in identifying acute coronary syndrome (ACS) for chest pain patients. A systematic review abstract

2021 ◽  
Vol 38 (9) ◽  
pp. A10.2-A10
Author(s):  
Ahmed Alotaibi ◽  
Abdulrhman Alghamdi ◽  
Charles Reynard ◽  
Richard Body
Keyword(s):  
Systematic Review ◽  
Acute Coronary Syndrome ◽  
Chest Pain ◽  
Prediction Models ◽  
Meta Analysis ◽  
Final Diagnosis ◽  
Telephone Triage ◽  
Coronary Syndrome ◽  
Life Threatening ◽  
Pain Patients

IntroductionChest pain is one of the most common reasons for ambulance callouts and presentation to Emergency Departments (EDs). Differentiating patients with serious conditions (e.g. acute coronary syndrome [ACS]) from the majority, who have self-limiting, non-cardiac conditions is extremely challenging. This causes over-triage and over-use of healthcare resources. We aimed to systematically review existing evidence on the accuracy of emergency telephone triage to detect ACS or life-threatening conditions associated with chest pain.MethodsWe conducted a systematic review in accordance with PRISMA guidelines. Two independent investigators searched the Embase, Medline, and Cinahl databases for relevant papers. We included retrospective and prospective cohort studies written in English and investigating EMS telephone triage for chest pain patients linked with final diagnosis of ACS. Studies were summarised in a narrative format as the data were not suitable for meta-analysis.ResultIn total, 553 studies were identified from the literature search and cross-referencing. After excluding 550 studies, three were eligible for inclusion. Among those 3 studies, there are different prediction models developed by authors with variation in variables to detect ACS. The result showed that dispatch triage tools have good sensitivity to detect ACS and life-threatening conditions although they are used to triage sign and symptoms rather than diagnosing the patients. On the other hand, prediction models were built to detect ACS and life-threatening conditions and therefore it showed better sensitivity and NPV.ConclusionEMS dispatch systems accuracy for ACS and life-threatening conditions associated with chest pain is good. Since the dispatch tools were built to triage ambulance response priority based on sign and symptoms, this led to over triage among non-life-threatening chest pain patients. Over triage were slightly reduced by deriving prediction models and showed better sensitivity.

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