scholarly journals A systematic review of prediction models to diagnose COVID-19 in adults admitted to healthcare centers

2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Médéa Locquet ◽  
Anh Nguyet Diep ◽  
Charlotte Beaudart ◽  
Nadia Dardenne ◽  
Christian Brabant ◽  
...  
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
High Risk ◽  
Prediction Models ◽  
Assessment Tool ◽  
Risk Of Bias ◽  
Medical Setting ◽  
Eligibility Criteria ◽  
Hospital System ◽  
Model Study

Abstract Background The COVID-19 pandemic is putting significant pressure on the hospital system. To help clinicians in the rapid triage of patients at high risk of COVID-19 while waiting for RT-PCR results, different diagnostic prediction models have been developed. Our objective is to identify, compare, and evaluate performances of prediction models for the diagnosis of COVID-19 in adult patients in a health care setting. Methods A search for relevant references has been conducted on the MEDLINE and Scopus databases. Rigorous eligibility criteria have been established (e.g., adult participants, suspicion of COVID-19, medical setting) and applied by two independent investigators to identify suitable studies at 2 different stages: (1) titles and abstracts screening and (2) full-texts screening. Risk of bias (RoB) has been assessed using the Prediction model study Risk of Bias Assessment Tool (PROBAST). Data synthesis has been presented according to a narrative report of findings. Results Out of the 2334 references identified by the literature search, 13 articles have been included in our systematic review. The studies, carried out all over the world, were performed in 2020. The included articles proposed a model developed using different methods, namely, logistic regression, score, machine learning, XGBoost. All the included models performed well to discriminate adults at high risks of presenting COVID-19 (all area under the ROC curve (AUROC) > 0.500). The best AUROC was observed for the model of Kurstjens et al (AUROC = 0.940 (0.910–0.960), which was also the model that achieved the highest sensitivity (98%). RoB was evaluated as low in general. Conclusion Thirteen models have been developed since the start of the pandemic in order to diagnose COVID-19 in suspected patients from health care centers. All these models are effective, to varying degrees, in identifying whether patients were at high risk of having COVID-19.

scholarly journals Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e035045
Author(s):  
Morris Ogero ◽  
Rachel Jelagat Sarguta ◽  
Lucas Malla ◽  
Jalemba Aluvaala ◽  
Ambrose Agweyu ◽  
...  
Keyword(s):  
Systematic Review ◽  
Low Income ◽  
Assessment Tool ◽  
Data Extraction ◽  
Model Development ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Risk Scores ◽  
Eligibility Criteria ◽  
Bias Assessment

ObjectivesTo identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs).DesignSystematic review of peer-reviewed journals.Data sourcesMEDLINE, CINAHL, Google Scholar and Web of Science electronic databases since inception to August 2019.Eligibility criteriaWe included model development studies predicting in-hospital paediatric mortality in LMIC.Data extraction and synthesisThis systematic review followed the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies framework. The risk of bias assessment was conducted using Prediction model Risk of Bias Assessment Tool (PROBAST). No quantitative summary was conducted due to substantial heterogeneity that was observed after assessing the studies included.ResultsOur search strategy identified a total of 4054 unique articles. Among these, 3545 articles were excluded after review of titles and abstracts as they covered non-relevant topics. Full texts of 509 articles were screened for eligibility, of which 15 studies reporting 21 models met the eligibility criteria. Based on the PROBAST tool, risk of bias was assessed in four domains; participant, predictors, outcome and analyses. The domain of statistical analyses was the main area of concern where none of the included models was judged to be of low risk of bias.ConclusionThis review identified 21 models predicting in-hospital paediatric mortality in LMIC. However, most reports characterising these models are of poor quality when judged against recent reporting standards due to a high risk of bias. Future studies should adhere to standardised methodological criteria and progress from identifying new risk scores to validating or adapting existing scores.PROSPERO registration numberCRD42018088599.

scholarly journals Safety and efficiency of gasless laparoscopy: a systematic review protocol

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Haitham Shoman ◽  
Simone Sandler ◽  
Alexander Peters ◽  
Ameer Farooq ◽  
Magdalen Gruendl ◽  
...  
Keyword(s):  
Systematic Review ◽  
Assessment Tool ◽  
Data Extraction ◽  
Meta Analysis ◽  
A Priori ◽  
Risk Of Bias ◽  
Conventional Laparoscopy ◽  
Eligibility Criteria ◽  
Gasless Laparoscopy ◽  
Resource Setting

Abstract Background Gasless laparoscopy, developed in the early 1990s, was a means to minimize the clinical and financial challenges of pneumoperitoneum and general anaesthesia. It has been used in a variety of procedures such as in general surgery and gynecology procedures including diagnostic laparoscopy. There has been increasing evidence of the utility of gasless laparoscopy in resource limited settings where diagnostic imaging is not available. In addition, it may help save costs for hospitals. The aim of this study is to conduct a systematic review of the available evidence surrounding the safety and efficiency of gasless laparoscopy compared to conventional laparoscopy and open techniques and to analyze the benefits that gasless laparoscopy has for low resource setting hospitals. Methods This protocol is developed by following the Preferred Reporting Items for Systematic review and Meta-Analysis–Protocols (PRISMA-P). The PRISMA statement guidelines and flowchart will be used to conduct the study itself. MEDLINE (Ovid), Embase, Web of Science, Cochrane Central, and Global Index Medicus (WHO) will be searched and the National Institutes of Health Clinical Trials database. The articles that will be found will be pooled into Covidence article manager software where all the records will be screened for eligibility and duplicates removed. A data extraction spreadsheet will be developed based on variables of interest set a priori. Reviewers will then screen all included studies based on the eligibility criteria. The GRADE tool will be used to assess the quality of the studies and the risk of bias in all the studies will be assessed using the Cochrane Risk assessment tool. The RoB II tool will assed the risk of bias in randomized control studies and the ROBINS I will be used for the non-randomized studies. Discussion This study will be a comprehensive review on all published articles found using this search strategy on the safety and efficiency of the use of gasless laparoscopy. The systematic review outcomes will include safety and efficiency of gasless laparoscopy compared to the use of conventional laparoscopy or laparotomy. Trial registration The study has been registered in PROSPERO under registration number: CRD42017078338

scholarly journals Risk Prediction Models for Liver Cancer in Adults: A Systematic Review and Meta-Analysis

2021 ◽  
Author(s):  
Maomao Cao ◽  
He Li ◽  
Dianqin Sun ◽  
Siyi He ◽  
Yadi Zheng ◽  
...  
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
Liver Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Risk Models ◽  
Risk Prediction Models ◽  
Prediction Risk

Abstract Background Prediction of liver cancer risk is beneficial to define high-risk population of liver cancer and guide clinical decisions. We aimed to review and critically appraise the quality of existing risk-prediction models for liver cancer. Methods This systematic review followed the guidelines of CHARMS (Checklist for Critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) and Preferred Reporting Items for Systematic Reviews and Meta (PRISMA). We searched for PubMed, Embase, Web of Science, and the Cochrane Library from inception to July 2020. Prediction model Risk Of Bias Assessment Tool was used to assess the risk of bias of all potential articles. A narrative description and meta-analysis were conducted. Results After removal irrespective and duplicated citations, 20 risk prediction publications were finally included. Within the 20 studies, 15 studies performed model derivation and validation process, three publications only conducted developed procedure without validation and two articles were used to validate existing models. Discrimination was expressed as area under curve or C statistic, which was acceptable for most models, ranging from 0.64 to 0.96. Calibration of the predictions model were rarely assessed. All models were graded at high risk of bias. The risk bias of applicability in 13 studies was considered low. Conclusions This systematic review gives an overall review of the prediction risk models for liver cancer, pointing out several methodological issues in their development. No prediction risk models were recommended due to the high risk of bias.Systematic review registration: This systematic has been registered in PROSPERO (International Prospective Register of Systemic Review: CRD42020203244).

scholarly journals Clinical prognostic models for severe dengue: a systematic review protocol

2019 ◽  
Vol 4 ◽  
pp. 12 ◽  
Author(s):  
Thang Dao Phuoc ◽  
Long Khuong Quynh ◽  
Linh Vien Dang Khanh ◽  
Thinh Ong Phuc ◽  
Hieu Le Sy ◽  
...  
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Prediction Models ◽  
Assessment Tool ◽  
Prognostic Models ◽  
Current Status ◽  
Severe Dengue ◽  
High Morbidity ◽  
Reference List ◽  
Eligibility Criteria

Background: Dengue is a common mosquito-borne, with high morbidity rates recorded in the annual. Dengue contributes to a major disease burden in many tropical countries. This demonstrates the urgent need in developing effective approaches to identify severe cases early. For this purpose, many multivariable prognostic models using multiple prognostic variables were developed to predict the risk of progression to severe outcomes. The aim of the planned systematic review is to identify and describe the existing clinical multivariable prognostic models for severe dengue as well as examine the possibility of combining them. These findings will suggest directions for further research of this field. Methods: This protocol has followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta – Analyses Protocol (PRISMA-P). We will conduct a comprehensive search of Pubmed, Embase, and Web of Science. Eligibility criteria include being published in peer-review journals, focusing on human subjects and developing the multivariable prognostic model for severe dengue, without any restriction on language, location and period of publication, and study design. The reference list will be captured and removed from duplications. We will use the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to extract data and Prediction study risk of bias assessment tool (PROBAST) to assess the study quality. Discussion: This systematic review will describe the existing prediction models, summarize the current status of prognostic research on dengue, and report the possibility to combine the models to optimize the power of each paradigm. PROSPERO registration: CRD42018102907

scholarly journals The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal

2020 ◽  
Vol 17 (9) ◽  
pp. 3048 ◽  
Author(s):  
Shamil D. Cooray ◽  
Lihini A. Wijeyaratne ◽  
Georgia Soldatos ◽  
John Allotey ◽  
Jacqueline A. Boyle ◽  
...  
Keyword(s):  
Systematic Review ◽  
Body Mass Index ◽  
High Risk ◽  
Gestational Diabetes ◽  
Body Mass ◽  
Pregnancy Complications ◽  
Critical Appraisal ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Prognostic Prediction

Gestational diabetes (GDM) increases the risk of pregnancy complications. However, these risks are not the same for all affected women and may be mediated by inter-related factors including ethnicity, body mass index and gestational weight gain. This study was conducted to identify, compare, and critically appraise prognostic prediction models for pregnancy complications in women with gestational diabetes (GDM). A systematic review of prognostic prediction models for pregnancy complications in women with GDM was conducted. Critical appraisal was conducted using the prediction model risk of bias assessment tool (PROBAST). Five prediction modelling studies were identified, from which ten prognostic models primarily intended to predict pregnancy complications related to GDM were developed. While the composition of the pregnancy complications predicted varied, the delivery of a large-for-gestational age neonate was the subject of prediction in four studies, either alone or as a component of a composite outcome. Glycaemic measures and body mass index were selected as predictors in four studies. Model evaluation was limited to internal validation in four studies and not reported in the fifth. Performance was inadequately reported with no useful measures of calibration nor formal evaluation of clinical usefulness. Critical appraisal using PROBAST revealed that all studies were subject to a high risk of bias overall driven by methodologic limitations in statistical analysis. This review demonstrates the potential for prediction models to provide an individualised absolute risk of pregnancy complications for women affected by GDM. However, at present, a lack of external validation and high risk of bias limit clinical application. Future model development and validation should utilise the latest methodological advances in prediction modelling to achieve the evolution required to create a useful clinical tool. Such a tool may enhance clinical decision-making and support a risk-stratified approach to the management of GDM. Systematic review registration: PROSPERO CRD42019115223.

scholarly journals Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e044687
Author(s):  
Lauren S. Peetluk ◽  
Felipe M. Ridolfi ◽  
Peter F. Rebeiro ◽  
Dandan Liu ◽  
Valeria C Rolla ◽  
...  
Keyword(s):  
Systematic Review ◽  
Treatment Outcomes ◽  
Prediction Models ◽  
Assessment Tool ◽  
Data Extraction ◽  
External Validation ◽  
Included Study ◽  
Risk Of Bias ◽  
Pulmonary Tb ◽  
Tb Treatment

ObjectiveTo systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB.DesignSystematic review.Data sourcesPubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020.Study selection and data extractionStudies that developed a model to predict pulmonary TB treatment outcomes were included. Study screening, data extraction and quality assessment were conducted independently by two reviewers. Study quality was evaluated using the Prediction model Risk Of Bias Assessment Tool. Data were synthesised with narrative review and in tables and figures.Results14 739 articles were identified, 536 underwent full-text review and 33 studies presenting 37 prediction models were included. Model outcomes included death (n=16, 43%), treatment failure (n=6, 16%), default (n=6, 16%) or a composite outcome (n=9, 25%). Most models (n=30, 81%) measured discrimination (median c-statistic=0.75; IQR: 0.68–0.84), and 17 (46%) reported calibration, often the Hosmer-Lemeshow test (n=13). Nineteen (51%) models were internally validated, and six (16%) were externally validated. Eighteen (54%) studies mentioned missing data, and of those, half (n=9) used complete case analysis. The most common predictors included age, sex, extrapulmonary TB, body mass index, chest X-ray results, previous TB and HIV. Risk of bias varied across studies, but all studies had high risk of bias in their analysis.ConclusionsTB outcome prediction models are heterogeneous with disparate outcome definitions, predictors and methodology. We do not recommend applying any in clinical settings without external validation, and encourage future researchers adhere to guidelines for developing and reporting of prediction models.Trial registrationThe study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782)

scholarly journals Effects of cosmetic and other camouflage interventions on appearance-related and psychological outcomes among adults with visible differences in appearance: a systematic review

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e046634
Author(s):  
Shadi Gholizadeh ◽  
Danielle B Rice ◽  
Andrea Carboni-Jiménez ◽  
Linda Kwakkenbos ◽  
Jill Boruff ◽  
...  
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
Adverse Effects ◽  
Data Extraction ◽  
Routine Care ◽  
Psychological Outcomes ◽  
Risk Of Bias ◽  
Eligibility Criteria ◽  
Randomised Controlled ◽  
Poor Reporting

ObjectiveVisible differences in appearance are associated with poor social and psychological outcomes. Effectiveness of non-surgical cosmetic and other camouflage interventions is poorly understood. The objective was to evaluate effects of cosmetic and other camouflage interventions on appearance-related outcomes, general psychological outcomes and adverse effects for adults with visible appearance differences.DesignSystematic review.Data sourcesMEDLINE (Ovid), EMBASE (Ovid), PsycINFO (Ovid) CINAHL and Cochrane Central databases searched from inception to 24 October 2020. Two reviewers independently reviewed titles and abstracts and full texts.Eligibility criteriaRandomised controlled trials in any language on non-surgical cosmetic or other camouflage interventions that reported appearance-related outcomes, general psychological outcomes or adverse effects for adults with visible appearance differences.Data extraction and synthesisTwo reviewers independently extracted data, assessed intervention reporting using the Template for Intervention Description and Replication checklist, and assessed risk of bias using the Cochrane risk of bias tool. Outcomes included appearance-related outcomes, general psychological outcomes (eg, depression, anxiety) and adverse effects.ResultsOne head-to-head trial and five trials with waiting list or routine care comparators were included. All had unclear or high risk of bias in at least five of seven domains. Effect sizes could not be determined for most outcomes due to poor reporting. Between-group statistically significant differences were not reported for any appearance-related outcomes and for only 5 of 25 (20%) other psychological outcomes. Given heterogeneity of populations and interventions, poor reporting and high risk of bias, quantitative synthesis was not possible.ConclusionsConclusions about effectiveness of non-surgical cosmetic or other camouflage interventions could not be drawn. Well-designed and conducted trials are needed. Without such evidence, clinicians or other qualified individuals should engage with patients interested in cosmetic interventions in shared decision making, outlining potential benefits and harms, and the lack of evidence to inform decisions.PROSPERO registration numberCRD42018103421.

scholarly journals Prediction Models for Future High-Need High-Cost Healthcare Use: a Systematic Review

2022 ◽  
Author(s):  
Ursula W. de Ruijter ◽  
Z. L. Rana Kaplan ◽  
Wichor M. Bramer ◽  
Frank Eijkenaar ◽  
Daan Nieboer ◽  
...  
Keyword(s):  
Systematic Review ◽  
Care Management ◽  
Prediction Models ◽  
Assessment Tool ◽  
Meta Analysis ◽  
Model Performance ◽  
Case Finding ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Healthcare Use

Abstract Background In an effort to improve both quality of care and cost-effectiveness, various care-management programmes have been developed for high-need high-cost (HNHC) patients. Early identification of patients at risk of becoming HNHC (i.e. case finding) is crucial to a programme’s success. We aim to systematically identify prediction models predicting future HNHC healthcare use in adults, to describe their predictive performance and to assess their applicability. Methods Ovid MEDLINE® All, EMBASE, CINAHL, Web of Science and Google Scholar were systematically searched from inception through January 31, 2021. Risk of bias and methodological quality assessment was performed through the Prediction model Risk Of Bias Assessment Tool (PROBAST). Results Of 5890 studies, 60 studies met inclusion criteria. Within these studies, 313 unique models were presented using a median development cohort size of 20,248 patients (IQR 5601–174,242). Predictors were derived from a combination of data sources, most often claims data (n = 37; 62%) and patient survey data (n = 29; 48%). Most studies (n = 36; 60%) estimated patients’ risk to become part of some top percentage of the cost distribution (top-1–20%) within a mean time horizon of 16 months (range 12–60). Five studies (8%) predicted HNHC persistence over multiple years. Model validation was performed in 45 studies (76%). Model performance in terms of both calibration and discrimination was reported in 14 studies (23%). Overall risk of bias was rated as ‘high’ in 40 studies (67%), mostly due to a ‘high’ risk of bias in the subdomain ‘Analysis’ (n = 37; 62%). Discussion This is the first systematic review (PROSPERO CRD42020164734) of non-proprietary prognostic models predicting HNHC healthcare use. Meta-analysis was not possible due to heterogeneity. Most identified models estimated a patient’s risk to incur high healthcare expenditure during the subsequent year. However, case-finding strategies for HNHC care-management programmes are best informed by a model predicting HNHC persistence. Therefore, future studies should not only focus on validating and extending existing models, but also concentrate on clinical usefulness.

Voice, Articulation, and Prosody Contribute to Listener Perceptions of Speaker Gender: A Systematic Review and Meta-Analysis

2018 ◽  
Vol 61 (2) ◽  
pp. 266-297 ◽  
Author(s):  
Yeptain Leung ◽  
Jennifer Oates ◽  
Siew Pang Chan
Keyword(s):  
Systematic Review ◽  
Assessment Tool ◽  
Meta Analysis ◽  
Voice Quality ◽  
Significant Risk ◽  
Verbal Communication ◽  
Risk Of Bias ◽  
Eligibility Criteria ◽  
Gender Perception ◽  
Meta Analyses

PurposeThe aim of this study was to provide a systematic review of the aspects of verbal communication contributing to listener perceptions of speaker gender with a view to providing clinicians with guidance for the selection of the training goals when working with transsexual individuals.MethodPreferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) guidelines were adopted in this systematic review. Studies evaluating the contribution of aspects of verbal communication to listener perceptions of speaker gender were rated against a new risk of bias assessment tool. Relevant data were extracted, and narrative synthesis was then conducted. Meta-analyses were conducted when appropriate data were available.ResultsThirty-eight articles met the eligibility criteria. Meta-analysis showed speaking fundamental frequency contributing to 41.6% of the variance in gender perception. Auditory-perceptual and acoustic measures of pitch, resonance, loudness, articulation, and intonation were found to be associated with listeners' perceptions of speaker gender. Tempo and stress were not significantly associated. Mixed findings were found as to the contribution of a breathy voice quality to gender perception. Nonetheless, there exists significant risk of bias in this body of research.ConclusionsSpeech and language clinicians working with transsexual individuals may use the results of this review for goal setting. Further research is required to redress the significant risk of bias.

scholarly journals Determinants of COVID-19 outcomes: A systematic review.

2021 ◽  
Author(s):  
Shirley Crankson ◽  
Subhash Pokhrel ◽  
Nana Kwame Anokye
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
English Language ◽  
Assessment Tool ◽  
Meta Analysis ◽  
Population Level ◽  
The Elderly ◽  
Healthcare Research ◽  
Risk Of Bias ◽  
Eligibility Criteria

Background: The current pandemic, COVID-19, caused by a novel coronavirus SARS-CoV-2 has claimed over a million lives worldwide in a year, warranting the need for more research into the wider determinants of COVID-19 outcomes to support evidence-based policies. Objective: This study aimed to investigate what factors determined the mortality and length of hospitalisation in individuals with COVID-19. Data Source: This is a systematic review with data from four electronic databases: Scopus, Google Scholar, CINAHL and Web of Science. Eligibility Criteria: Studies were included in this review if they explored determinants of COVID-19 mortality or length of hospitalisation, were written in the English Language, and had available full-text. Study appraisal and data synthesis: The authors assessed the quality of the included studies with the Newcastle Ottawa Scale and the Agency for Healthcare Research and Quality checklist, depending on their study design. Risk of bias in the included studies was assessed with risk of bias assessment tool for non-randomised studies. A narrative synthesis of the evidence was carried out. Results: The review included 22 studies from nine countries, with participants totalling 239,830. The included studies quality was moderate to high. The identified determinants were categorised into demographic, biological, socioeconomic and lifestyle risk factors, based on the Dahlgren and Whitehead determinant of health model. Increasing age (ORs 1.04-20.6, 95%CIs 1.01-22.68) was the common demographic determinant of COVID-19 mortality while living with diabetes (ORs 0.50-3.2, 95%CIs -0.2-0.74) was one of the most common biological determinants of COVID-19 length of hospitalisation. Review limitation: Meta-analysis was not conducted because of included studies heterogeneity. Conclusion: COVID-19 outcomes are predicted by multiple determinants, with increasing age and living with diabetes being the most common risk factors. Population-level policies that prioritise interventions for the elderly population and the people living with diabetes may help mitigate the outbreak's impact.

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