scholarly journals Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

BMJ ◽  
2020 ◽  
pp. m1328 ◽  
Author(s):  
Laure Wynants ◽  
Ben Van Calster ◽  
Gary S Collins ◽  
Richard D Riley ◽  
Georg Heinze ◽  
...  
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
General Population ◽  
Prediction Model ◽  
Critical Appraisal ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Link Type ◽  
Diagnostic Models

Abstract Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of becoming infected with covid-19 or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 14 217 titles were screened, and 107 studies describing 145 prediction models were included. The review identified four models for identifying people at risk in the general population; 91 diagnostic models for detecting covid-19 (60 were based on medical imaging, nine to diagnose disease severity); and 50 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequently reported predictors of diagnosis and prognosis of covid-19 are age, body temperature, lymphocyte count, and lung imaging features. Flu-like symptoms and neutrophil count are frequently predictive in diagnostic models, while comorbidities, sex, C reactive protein, and creatinine are frequent prognostic factors. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.68 to 0.99 in prognostic models. All models were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and vague reporting. Most reports did not include any description of the study population or intended use of the models, and calibration of the model predictions was rarely assessed. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Hence, we do not recommend any of these reported prediction models for use in current practice. Immediate sharing of well documented individual participant data from covid-19 studies and collaboration are urgently needed to develop more rigorous prediction models, and validate promising ones. The predictors identified in included models should be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/ , registration https://osf.io/wy245 . Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 2 of the original article published on 7 April 2020 ( BMJ 2020;369:m1328), and previous updates can be found as data supplements ( https://www.bmj.com/content/369/bmj.m1328/related#datasupp ).

scholarly journals Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

2020 ◽  
Author(s):  
Laure Wynants ◽  
Ben Van Calster ◽  
Marc MJ Bonten ◽  
Gary S Collins ◽  
Thomas PA Debray ◽  
...  
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
General Population ◽  
Critical Appraisal ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Medical Decision ◽  
List Type ◽  
Diagnostic Models

AbstractObjectiveTo review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) models to identify individuals at high risk for COVID-19 in the general population.DesignRapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection.Data sourcesPubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 24th March 2020.Study selectionStudies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles, abstracts and full text.Data extractionData from included studies were extracted independently by at least two authors based on the CHARMS checklist, and risk of bias was assessed using PROBAST. Data were extracted on various domains including the participants, predictors, outcomes, data analysis, and prediction model performance.Results2696 titles were screened. Of these, 27 studies describing 31 prediction models were included for data extraction and critical appraisal. We identified three models to predict hospital admission from pneumonia and other events (as a proxy for covid-19 pneumonia) in the general population; 18 diagnostic models to detect COVID-19 infection in symptomatic individuals (13 of which were machine learning utilising computed tomography (CT) results); and ten prognostic models for predicting mortality risk, progression to a severe state, or length of hospital stay. Only one of these studies used data on COVID-19 cases outside of China. Most reported predictors of presence of COVID-19 in suspected patients included age, body temperature, and signs and symptoms. Most reported predictors of severe prognosis in infected patients included age, sex, features derived from CT, C-reactive protein, lactic dehydrogenase, and lymphocyte count.Estimated C-index estimates for the prediction models ranged from 0.73 to 0.81 in those for the general population (reported for all 3 general population models), from 0.81 to > 0.99 in those for diagnosis (reported for 13 of the 18 diagnostic models), and from 0.85 to 0.98 in those for prognosis (reported for 6 of the 10 prognostic models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and poor statistical analysis, including high risk of model overfitting. Reporting quality varied substantially between studies. A description of the study population and intended use of the models was absent in almost all reports, and calibration of predictions was rarely assessed.ConclusionCOVID-19 related prediction models are quickly entering the academic literature, to support medical decision making at a time where this is urgently needed. Our review indicates proposed models are poorly reported and at high risk of bias. Thus, their reported performance is likely optimistic and using them to support medical decision making is not advised. We call for immediate sharing of the individual participant data from COVID-19 studies to support collaborative efforts in building more rigorously developed prediction models and validating (evaluating) existing models. The aforementioned predictors identified in multiple included studies could be considered as candidate predictors for new models. We also stress the need to follow methodological guidance when developing and validating prediction models, as unreliable predictions may cause more harm than benefit when used to guide clinical decisions. Finally, studies should adhere to the TRIPOD statement to facilitate validating, appraising, advocating and clinically using the reported models.Systematic review registration protocolosf.io/ehc47/, registration: osf.io/wy245Summary boxesWhat is already known on this topic-The sharp recent increase in COVID-19 infections has put a strain on healthcare systems worldwide, necessitating efficient early detection, diagnosis of patients suspected of the infection and prognostication of COVID-19 confirmed cases.-Viral nucleic acid testing and chest CT are standard methods for diagnosing COVID-19, but are time-consuming.-Earlier reports suggest that the elderly, patients with comorbidity (COPD, cardiovascular disease, hypertension), and patients presenting with dyspnoea are vulnerable to more severe morbidity and mortality after COVID-19 infection.What this study adds-We identified three models to predict hospital admission from pneumonia and other events (as a proxy for COVID-19 pneumonia) in the general population.-We identified 18 diagnostic models for COVID-19 detection in symptomatic patients.-13 of these were machine learning models based on CT images.-We identified ten prognostic models for COVID-19 infected patients, of which six aimed to predict mortality risk in confirmed or suspected COVID-19 patients, two aimed to predict progression to a severe or critical state, and two aimed to predict a hospital stay of more than 10 days from admission.-Included studies were poorly reported compromising their subsequent appraisal, and recommendation for use in daily practice. All studies were appraised at high risk of bias, raising concern that the models may be flawed and perform poorly when applied in practice, such that their predictions may be unreliable.

scholarly journals Prognostic Models for Knee Osteoarthritis: A Protocol for Systematic Review, Critical Appraisal and Meta-Analysis

2020 ◽  
Author(s):  
Jingyu Zhong ◽  
Liping Si ◽  
Guangcheng Zhang ◽  
Jiayu Huo ◽  
Yue Xing ◽  
...  
Keyword(s):  
Systematic Review ◽  
Knee Osteoarthritis ◽  
Prediction Model ◽  
Critical Appraisal ◽  
Prediction Models ◽  
Data Extraction ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Rapid Progression ◽  
Prognostic Prediction

Abstract Background: Osteoarthritis is the most common degenerative joint disease diagnosed in clinical practice. It is associated with significant socioeconomic burden and poor quality of life, a large proportion of which is due to knee osteoarthritis (KOA), mainly driven by total knee arthroplasty (TKA). As the difficulty of being detected early and deficiency of disease-modifying drug, the focus of KOA is shifting to disease prevention and the treatment to delay its rapid progression. Thus, the prognostic prediction models are called for, to stratify individuals to guide clinical decision making. The aim of our review is to identify and characterize reported multivariable prognostic models for KOA which concern about three clinical concerns: (1) the risk of developing KOA in general population; (2) the risk of receiving TKA in KOA patients; and (3) the outcome of TKA in KOA patients who plan to receive TKA.Methods: Studies will be identified by searching seven electronic databases. Title and abstract screening and full-text review will be accomplished by two independent reviewers. Data extraction instrument and critical appraisal instrument will be developed before formal assessment, and will be modified during a training phase in advance. Study reporting transparency, methodological quality, and risk of bias will be assessed according to Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and Prediction model Risk Of Bias ASsessment Tool (PROBAST). Prognostic prediction models will be summarized qualitatively. Quantitative metrics on predictive performance of these models will be synthesized with meta-analyses if appropriate.Discussion: Our systematic review will collate evidence from prognostic prediction models that can be used through the whole process of KOA. The review may identify models which are capable of allowing personalized preventative and therapeutic interventions to be precisely targeted at those individuals who are at the highest risk. To accomplish the prediction models to cross the translational gaps between an exploratory research method and a valued addition to precision medicine workflows, research recommendations relating to model development, validation or impact assessment will be made.Systematic review registration: PROSPERO (registered, waiting for assessment, ID 203543)

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 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 Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jingyu Zhong ◽  
Liping Si ◽  
Guangcheng Zhang ◽  
Jiayu Huo ◽  
Yue Xing ◽  
...  
Keyword(s):  
Systematic Review ◽  
General Population ◽  
Knee Osteoarthritis ◽  
Critical Appraisal ◽  
Prediction Models ◽  
Prognostic Models ◽  
Cochrane Library ◽  
British Library ◽  
Prognostic Prediction ◽  
To Receive

Abstract Background Osteoarthritis is the most common degenerative joint disease. It is associated with significant socioeconomic burden and poor quality of life, mainly due to knee osteoarthritis (KOA), and related total knee arthroplasty (TKA). Since early detection method and disease-modifying drug is lacking, the key of KOA treatment is shifting to disease prevention and progression slowing. The prognostic prediction models are called for to guide clinical decision-making. The aim of our review is to identify and characterize reported multivariable prognostic models for KOA about three clinical concerns: (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA. Methods The electronic datasets (PubMed, Embase, the Cochrane Library, Web of Science, Scopus, SportDiscus, and CINAHL) and gray literature sources (OpenGrey, British Library Inside, ProQuest Dissertations & Theses Global, and BIOSIS preview) will be searched from their inception onwards. Title and abstract screening and full-text review will be accomplished by two independent reviewers. The multivariable prognostic models that concern on (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA will be included. Data extraction instrument and critical appraisal instrument will be developed before formal assessment and will be modified during a training phase in advance. Study reporting transparency, methodological quality, and risk of bias will be assessed according to the TRIPOD statement, CHARMS checklist, and PROBAST tool, respectively. Prognostic prediction models will be summarized qualitatively. Quantitative metrics on the predictive performance of these models will be synthesized with meta-analyses if appropriate. Discussion Our systematic review will collate evidence from prognostic prediction models that can be used through the whole process of KOA. The review may identify models which are capable of allowing personalized preventative and therapeutic interventions to be precisely targeted at those individuals who are at the highest risk. To accomplish the prediction models to cross the translational gaps between an exploratory research method and a valued addition to precision medicine workflows, research recommendations relating to model development, validation, or impact assessment will be made. Systematic review registration PROSPERO CRD42020203543

scholarly journals Applications of Artificial Intelligence and Machine Learning in Diagnosis and Prognosis of COVID-19 infection: A systematic review

2021 ◽  
Vol 10 (1) ◽  
pp. 93
Author(s):  
Mahdieh Montazeri ◽  
Ali Afraz ◽  
Mitra Montazeri ◽  
Sadegh Nejatzadeh ◽  
Fatemeh Rahimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Prediction Models ◽  
External Validation ◽  
Area Under The Curve ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Discriminative Performance ◽  
Internal Validation ◽  
Diagnostic Models

Introduction: Our aim in this study was to summarize information on the use of intelligent models for predicting and diagnosing the Coronavirus disease 2019 (COVID-19) to help early and timely diagnosis of the disease.Material and Methods: A systematic literature search included articles published until 20 April 2020 in PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv databases. The search strategy consisted of two groups of keywords: A) Novel coronavirus, B) Machine learning. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. Studies were critically reviewed for risk of bias using prediction model risk of bias assessment tool.Results: We gathered 1650 articles through database searches. After the full-text assessment 31 articles were included. Neural networks and deep neural network variants were the most popular machine learning type. Of the five models that authors claimed were externally validated, we considered external validation only for four of them. Area under the curve (AUC) in internal validation of prognostic models varied from .94 to .97. AUC in diagnostic models varied from 0.84 to 0.99, and AUC in external validation of diagnostic models varied from 0.73 to 0.94. Our analysis finds all but two studies have a high risk of bias due to various reasons like a low number of participants and lack of external validation.Conclusion: Diagnostic and prognostic models for COVID-19 show good to excellent discriminative performance. However, these models are at high risk of bias because of various reasons like a low number of participants and lack of external validation. Future studies should address these concerns. Sharing data and experiences for the development, validation, and updating of COVID-19 related prediction models 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 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 Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients with Primary or Secondary Brain Tumors: A Systematic Review

2021 ◽  
Author(s):  
Fariba Tohidinezhad ◽  
Dario Di Perri ◽  
Catharina M.L. Zegers ◽  
Jeanette Dijkstra ◽  
Monique Anten ◽  
...  
Keyword(s):  
High Risk ◽  
Radiation Dose ◽  
Brain Tumors ◽  
Prediction Model ◽  
Prediction Models ◽  
Verbal Learning ◽  
Risk Of Bias ◽  
Neurocognitive Impairments ◽  
Radiation Induced ◽  
Neurocognitive Decline

Abstract Purpose: Although an increasing body of literature suggests a relationship between brain irradiation and deterioration of neurocognitive function, it remains as the standard therapeutic and prophylactic modality in patients with brain tumors. This review was aimed to abstract and evaluate the prediction models for radiation-induced neurocognitive decline in patients with primary or secondary brain tumors.Methods: MEDLINE was searched on October 31, 2021 for publications containing relevant truncation and MeSH terms related to “radiotherapy”, “brain”, “prediction model”, and “neurocognitive impairments”. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool.Results: Of 3,580 studies reviewed, 23 prediction models were identified. Age, tumor location, education level, baseline neurocognitive score, and radiation dose to the hippocampus were the most common predictors in the models. The Hopkins verbal learning (n=7) and the trail making tests (n=4) were the most frequent outcome assessment tools. All studies used regression (n=14 linear, n=8 logistic, and n=4 Cox) as machine learning method. All models were judged to have a high risk of bias mainly due to issues in the analysis.Conclusion: Existing models have limited quality and are at high risk of bias. Following recommendations are outlined in this review to improve future models: develop a standardized instrument for neurocognitive assessment in patients with brain tumors; adherence to model development and validation guidelines; careful choice of candidate predictors according to the literature and domain expert consensus; and considering radiation dose to brain substructures as they can provide important information on specific neurocognitive impairments.

scholarly journals Dementia risk in the general population: large-scale external validation of prediction models in the AGES-Reykjavik study

2021 ◽  
Author(s):  
Jet M. J. Vonk ◽  
Jacoba P. Greving ◽  
Vilmundur Gudnason ◽  
Lenore J. Launer ◽  
Mirjam I. Geerlings
Keyword(s):  
High Risk ◽  
General Population ◽  
Large Scale ◽  
Prediction Models ◽  
External Validation ◽  
Predictive Performance ◽  
Population Based ◽  
Cognitive Testing ◽  
Prognostic Models ◽  
Dementia Risk

AbstractWe aimed to evaluate the external performance of prediction models for all-cause dementia or AD in the general population, which can aid selection of high-risk individuals for clinical trials and prevention. We identified 17 out of 36 eligible published prognostic models for external validation in the population-based AGES-Reykjavik Study. Predictive performance was assessed with c statistics and calibration plots. All five models with a c statistic > .75 (.76–.81) contained cognitive testing as a predictor, while all models with lower c statistics (.67–.75) did not. Calibration ranged from good to poor across all models, including systematic risk overestimation or overestimation for particularly the highest risk group. Models that overestimate risk may be acceptable for exclusion purposes, but lack the ability to accurately identify individuals at higher dementia risk. Both updating existing models or developing new models aimed at identifying high-risk individuals, as well as more external validation studies of dementia prediction models are warranted.

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