Prediction Models and Scores in Pulmonary Hypertension: A Systematic Review

2020 ◽  
Vol 26 ◽  
Author(s):  
Sophia Anastasia Mouratoglou ◽  
Ahmed A. Bayoumy ◽  
Anton Vonk Noordegraaf
Keyword(s):  
Systematic Review ◽  
Risk Stratification ◽  
Prediction Models ◽  
Prognostic Significance ◽  
Morbidity And Mortality ◽  
Prognostic Models ◽  
Risk Scores ◽  
Patient Treatment ◽  
Discrimination Ability ◽  
Mesh Terms

Background:: pulmonary arterial hypertension (PAH) is a serious disease with increased morbidity and mortality. The need of an individualized patient treatment approach necessitates the use of risk assessment in PAH patients. That may include a range of hemodynamic, clinical, imaging and biochemical parameters, derived from clinical studies and registry data. Objective:: in current systematic review, we summarize the available data on risk prognostic models and scores in PAH and we explore the possible concordance amongst different risk stratification tools in PAH. Methods:: PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines aided the performance of this systematic review. Eligible studies were identified through literature search in the electronic databases PubMed, Science Direct, Google Scholar and Cochrane with the use of various combinations of MeSH and non-MeSH terms, with focus on PAH Results:: overall, 25 studies were included in the systematic review, out of them, 9 were studies deriving prognostic equations and risk scores and 16 were validating studies of an existing score. The majority of risk stratification scores use hemodynamic data for the assessment of prognosis, while other also include clinical and demographic variables in their equations. The risk discrimination in the overall PAH population, was adequate, especially in differentiating the low versus high risk patients, but their discrimination ability in the intermediate groups remained lower. Current ESC/ERS proposed risk stratification score utilizes a limited number of parameters with prognostic significance, whose prognostic ability is validated in European patient populations. Conclusion:: despite improvement in risk estimation of prognostic tools of the disease, PAH morbidity and mortality remain high, necessitating the need for the risk scores to undergo periodic re-evaluation and refinements to incorporate new data on predictors of disease progression and mortality and, thereby, maintain their clinical utility

scholarly journals Biomarkers for Clinical Decision-Making in the Management of Pulmonary Embolism

2017 ◽  
Vol 63 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Evangelos Giannitsis ◽  
Hugo A Katus
Keyword(s):  
Pulmonary Embolism ◽  
Risk Stratification ◽  
Natriuretic Peptides ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Severity Index ◽  
Prognostic Models ◽  
Risk Scores ◽  
Fatty Acid Binding ◽  
Cardiac Troponins

Abstract BACKGROUND Pulmonary embolism (PE) is associated with high all-cause and PE-related mortality and requires individualized management. After confirmation of PE, a refined risk stratification is particularly warranted among normotensive patients. Previous prognostic models favored combinations of echocardiography or computed tomography suggestive of right ventricular (RV) dysfunction together with biomarkers of RV dysfunction (natriuretic peptides) or myocardial injury (cardiac troponins) to identify candidates for thrombolysis or embolectomy. In contrast, current predictive models using clinical scores such as the Pulmonary Embolism Severity Index (PESI) or its simplified version (sPESI) rather seek to identify patients, not only those at higher risk requiring observation for early detection of hemodynamic decompensation, and the need for initiation of rescue reperfusion therapy, but also those at low risk qualifying for early discharge and outpatient treatment. Almost all prediction models advocate the additional measurement of biomarkers along with imaging of RV dysfunction as part of a comprehensive algorithm. CONTENT The following mini-review will provide an updated overview on the individual components of different algorithms with a particular focus on guideline-recommended and new, less-established biomarkers for risk stratification, and how biomarkers should be implemented and interpreted. SUMMARY Ideally, biomarkers should be part of a comprehensive risk stratification algorithm used together with clinical risk scores as a basis, and/or imaging. For this purpose, cardiac troponins, including high-sensitivity troponin generations, natriuretic peptides, and h-FABP (heart-type fatty acid–binding protein) are currently recommended in guidelines. There is emerging evidence for several novel biomarkers that require further validation before being applied in clinical practice.

Risk Stratification in Acute Pulmonary Embolism: The Latest Algorithms

2021 ◽  
Vol 42 (02) ◽  
pp. 183-198
Author(s):  
Georgios A. Triantafyllou ◽  
Oisin O'Corragain ◽  
Belinda Rivera-Lebron ◽  
Parth Rali
Keyword(s):  
Pulmonary Embolism ◽  
Risk Stratification ◽  
Prediction Models ◽  
Bleeding Risk ◽  
Hemodynamic Instability ◽  
Reperfusion Therapy ◽  
Mortality Prediction ◽  
Risk Scores ◽  
Short Term Mortality ◽  
Urgent Patient

AbstractPulmonary embolism (PE) is a common clinical entity, which most clinicians will encounter. Appropriate risk stratification of patients is key to identify those who may benefit from reperfusion therapy. The first step in risk assessment should be the identification of hemodynamic instability and, if present, urgent patient consideration for systemic thrombolytics. In the absence of shock, there is a plethora of imaging studies, biochemical markers, and clinical scores that can be used to further assess the patients' short-term mortality risk. Integrated prediction models incorporate more information toward an individualized and precise mortality prediction. Additionally, bleeding risk scores should be utilized prior to initiation of anticoagulation and/or reperfusion therapy administration. Here, we review the latest algorithms for a comprehensive risk stratification of the patient with acute PE.

scholarly journals Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies

Gut ◽  
2018 ◽  
Vol 68 (4) ◽  
pp. 672-683 ◽  
Author(s):  
Todd Smith ◽  
David C Muller ◽  
Karel G M Moons ◽  
Amanda J Cross ◽  
Mattias Johansson ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Systematic Review ◽  
Prediction Models ◽  
Large Population ◽  
External Validation ◽  
Population Based ◽  
Prognostic Models ◽  
Uk Biobank ◽  
Asymptomatic Individuals ◽  
The Uk

ObjectiveTo systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts.DesignModels were identified through an update of a published systematic review and validated in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted probability).ResultsThe systematic review and its update identified 16 models from 8 publications (8 colorectal, 5 colon and 3 rectal). The number of participants included in each model validation ranged from 41 587 to 396 515, and the number of cases ranged from 115 to 1781. Eligible and ineligible participants across the models were largely comparable. Calibration of the models, where assessable, was very good and further improved by recalibration. The C-statistics of the models were largely similar between validation cohorts with the highest values achieved being 0.70 (95% CI 0.68 to 0.72) in the UK Biobank and 0.71 (95% CI 0.67 to 0.74) in EPIC.ConclusionSeveral of these non-invasive models exhibited good calibration and discrimination within both external validation populations and are therefore potentially suitable candidates for the facilitation of risk stratification in population-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained.

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 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 Prediction Models for Severe Manifestations and Mortality due to COVID-19: A Rapid Systematic Review

2021 ◽  
Author(s):  
Jamie L. Miller ◽  
Masafumi Tada ◽  
Michihiko Goto ◽  
Nicholas Mohr ◽  
Sangil Lee
Keyword(s):  
Systematic Review ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Clinical Signs ◽  
Risk Of Bias ◽  
Low Risk ◽  
Prognostic Models ◽  
Organ Support ◽  
Public Data

ABSTRACTBackgroundThroughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available.ObjectiveThis systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19.MethodsSearches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and July 2020 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized.ResultsA primary review found 292 articles relevant based on title and abstract. After further review, 246 were excluded based on the defined inclusion and exclusion criteria. Forty-six articles were included in the qualitative analysis. Inter observer agreement on inclusion was 0.86 (95% confidence interval: 0.79 - 0.93). When the PROBAST tool was applied, 44 of the 46 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Two studied reported prediction models, 4C Mortality Score from hospital data and QCOVID from general public data from UK, and were rated as low risk of bias and low concerns for applicability.ConclusionSeveral prognostic models are reported in the literature, but many of them had concerning risks of biases and applicability. For most of the studies, caution is needed before use, as many of them will require external validation before dissemination. However, two articles were found to have low risk of bias and low applicability can be useful tools.

scholarly journals Prognostic models for mortality after cardiac surgery in patients with infective endocarditis: a systematic review and aggregation of prediction models

2021 ◽  
Author(s):  
Borja M. Fernandez-Felix ◽  
Laura Varela Barca ◽  
Esther Garcia-Esquinas ◽  
Andrea Correa-Pérez ◽  
Nuria Fernández-Hidalgo ◽  
...  
Keyword(s):  
Systematic Review ◽  
Cardiac Surgery ◽  
Infective Endocarditis ◽  
Prediction Models ◽  
Prognostic Models

scholarly journals Systematic review of risk prediction scores for surgical site infection or periprosthetic joint infection following joint arthroplasty

2017 ◽  
Vol 145 (9) ◽  
pp. 1738-1749 ◽  
Author(s):  
S. K. KUNUTSOR ◽  
M. R. WHITEHOUSE ◽  
A. W. BLOM ◽  
A. D. BESWICK
Keyword(s):  
Systematic Review ◽  
Risk Prediction ◽  
Prediction Models ◽  
Joint Infection ◽  
External Validation ◽  
Surgical Site Infections ◽  
Predictive Performance ◽  
Risk Scores ◽  
Accurate Identification ◽  
Discriminative Ability

SUMMARYAccurate identification of individuals at high risk of surgical site infections (SSIs) or periprosthetic joint infections (PJIs) influences clinical decisions and development of preventive strategies. We aimed to determine progress in the development and validation of risk prediction models for SSI or PJI using a systematic review. We searched for studies that have developed or validated a risk prediction tool for SSI or PJI following joint replacement in MEDLINE, EMBASE, Web of Science and Cochrane databases; trial registers and reference lists of studies up to September 2016. Nine studies describing 16 risk scores for SSI or PJI were identified. The number of component variables in a risk score ranged from 4 to 45. The C-index ranged from 0·56 to 0·74, with only three risk scores reporting a discriminative ability of >0·70. Five risk scores were validated internally. The National Healthcare Safety Network SSIs risk models for hip and knee arthroplasties (HPRO and KPRO) were the only scores to be externally validated. Except for HPRO which shows some promise for use in a clinical setting (based on predictive performance and external validation), none of the identified risk scores can be considered ready for use. Further research is urgently warranted within the field.

Predictors of Maternal Mortality and Prognostic Models in Obstetric Patients

2017 ◽  
Vol 38 (02) ◽  
pp. 191-200 ◽  
Author(s):  
Jezid Miranda ◽  
Jose Rojas-Suarez ◽  
Andrew Levinson
Keyword(s):  
High Risk ◽  
Maternal Morbidity ◽  
Prediction Models ◽  
Severity Of Illness ◽  
Scoring Systems ◽  
Morbidity And Mortality ◽  
Prognostic Models ◽  
High Risk Patients ◽  
Maternal Morbidity And Mortality ◽  
Risk Patients

AbstractThe use of predictive models has been proposed as a potential tool to reduce maternal morbidity and mortality, by aiding in the timely identification of potential high-risk patients. Prognostic models in critical care have been used to characterize the severity of illness of specific diseases. Physiological changes in pregnancy may result in general critical illness prediction models overestimating mortality in obstetric patients. Models that specifically reflect the unique characteristics of obstetric patients may have better prognostic value. Recently developed tools have focused on identifying at-risk patients before they require intensive care unit (ICU) admission to target early interventions and prevent acute clinical decompensation. The aim of the newest scoring systems, specifically designed for groups of obstetric patients receiving non-ICU care, is to reduce maternal morbidity and mortality by identifying early high-risk patients and initiating prompt effective medical responses.

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