scholarly journals Solitary Pulmonary Nodule Malignancy Predictive Models Applicable to Routine Clinical Practice: A Systematic Review 

2021 ◽  
Author(s):  
Marina Senent Valero ◽  
María Pastor-Valero ◽  
Julián Librero
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Clinical Practice ◽  
Prediction Model ◽  
Pulmonary Nodule ◽  
Predictive Models ◽  
Solitary Pulmonary Nodule ◽  
Routine Clinical Practice ◽  
Risk Of Bias ◽  
Diagnostic Management

Abstract Background: Solitary Pulmonary Nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. Facing clinical experience as a guide for decision-making in the management of SPN, the application of predictive models of malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. For this, it is necessary to know and evaluate the current state of knowledge in relation to predictive models of malignancy of SPN in the general or low-risk population. The present systematic review was carried out with the purpose of critically assess studies aimed at developing predictive models of Solitary Pulmonary Nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice. Methods: we performed a systematic review through the following bibliographic databases: MEDLINE, SCOPUS and Cochrane Library. We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to describe the type of predictive model included in each study. We used The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles. The Fleischner guidelines for the management of incidental SPN were used as reference to describe the variables included in the models. Results: 186 references were retrieved and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of malignancy included in the models were, in order of frequency: age, diameter, spiculated edge of the SPN, calcification, and smoking history. Variables such as race, growth rate of the SPN, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon among others were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient follow-up of the population, and lack of external validation, compromising their applicability for clinical practice.Conclusions: The studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management. Systematic review registration: PROSPERO International prospective register of systematic reviews: CRD42020161559.

scholarly journals Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Marina Senent-Valero ◽  
Julián Librero ◽  
María Pastor-Valero
Keyword(s):  
Systematic Review ◽  
Clinical Practice ◽  
High Risk ◽  
Pulmonary Nodule ◽  
Predictive Models ◽  
Solitary Pulmonary Nodule ◽  
Routine Clinical Practice ◽  
Exclusion Criteria ◽  
Diagnostic Management ◽  
High Risk Populations

Abstract Background Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice. Methods We performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles. Results A total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice. Conclusions The studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management. Systematic review registration PROSPERO CRD42020161559

scholarly journals Protocol for systematic review: patient decision aids for total hip and knee arthroplasty decision-making

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Lissa Pacheco-Brousseau ◽  
Marylène Charette ◽  
Dawn Stacey ◽  
Stéphane Poitras
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Knee Arthroplasty ◽  
Patient Involvement ◽  
Decision Aids ◽  
Risk Of Bias ◽  
Decision Making Process ◽  
Patient Decision Aids ◽  
Patient Decision ◽  
Hip And Knee Arthroplasty

Abstract Background Total hip and knee arthroplasty are a highly performed surgery; however, patient satisfaction with surgery results and patient involvement in the decision-making process remains low. Patient decision aids (PtDAs) are tools used in clinical practices to facilitate active patient involvement in healthcare decision-making. Nonetheless, PtDA effects have not been systematically evaluated for hip and knee total joint arthroplasty (TJA) decision-making. The aim of this systematic review is to determine the effect of patient decision aids compared to alternative of care on quality and process of decision-making when provided to adults with hip and knee osteoarthritis considering primary elective TJA. Methods This systematic review will follow the Cochrane Handbook for Systematic Reviews. This protocol was reported based on the PRISMA-P checklist guidelines. Studies will be searched in CINAHL, MEDLINE, Embase, PsycINFO, and Web of Science. Eligible studies will be randomized control trial (RCT) evaluating the effect of PtDA on TJA decision-making. Descriptive and meta-analysis of outcomes will include decision quality (knowledge and values-based choice), decisional conflict, patient involvement, decision-making process satisfaction, actual decision made, health outcomes, and harm(s). Risk of bias will be evaluated with Cochrane’s risk of bias tool for RCTs. Quality and strength of recommendations will be appraised with Grades of Recommendation, Assessment, Development and Evaluation (GRADE). Discussion This review will provide a summary of RCT findings on PtDA effect on decision-making quality and process of adults with knee and hip osteoarthritis considering primary elective TJA. Further, it will provide evidence comparing different types of PtDA used for TJA decision-making. This review is expected to inform further research on joint replacement decision-making quality and processes and on ways PtDAs facilitate shared decision-making for orthopedic surgery. Systematic review registration PROSPERO CRD42020171334

scholarly journals Systematic review of clinician-directed nudges in healthcare contexts

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e048801
Author(s):  
Briana S Last ◽  
Alison M Buttenheim ◽  
Carter E Timon ◽  
Nandita Mitra ◽  
Rinad S Beidas
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Assessment Tool ◽  
Risk Of Bias ◽  
Minimal Risk ◽  
Clinical Decisions ◽  
Snowball Sampling ◽  
Healthcare Settings ◽  
Default Options

ObjectiveNudges are interventions that alter the way options are presented, enabling individuals to more easily select the best option. Health systems and researchers have tested nudges to shape clinician decision-making with the aim of improving healthcare service delivery. We aimed to systematically study the use and effectiveness of nudges designed to improve clinicians’ decisions in healthcare settings.DesignA systematic review was conducted to collect and consolidate results from studies testing nudges and to determine whether nudges directed at improving clinical decisions in healthcare settings across clinician types were effective. We systematically searched seven databases (EBSCO MegaFILE, EconLit, Embase, PsycINFO, PubMed, Scopus and Web of Science) and used a snowball sampling technique to identify peer-reviewed published studies available between 1 January 1984 and 22 April 2020. Eligible studies were critically appraised and narratively synthesised. We categorised nudges according to a taxonomy derived from the Nuffield Council on Bioethics. Included studies were appraised using the Cochrane Risk of Bias Assessment Tool.ResultsWe screened 3608 studies and 39 studies met our criteria. The majority of the studies (90%) were conducted in the USA and 36% were randomised controlled trials. The most commonly studied nudge intervention (46%) framed information for clinicians, often through peer comparison feedback. Nudges that guided clinical decisions through default options or by enabling choice were also frequently studied (31%). Information framing, default and enabling choice nudges showed promise, whereas the effectiveness of other nudge types was mixed. Given the inclusion of non-experimental designs, only a small portion of studies were at minimal risk of bias (33%) across all Cochrane criteria.ConclusionsNudges that frame information, change default options or enable choice are frequently studied and show promise in improving clinical decision-making. Future work should examine how nudges compare to non-nudge interventions (eg, policy interventions) in improving healthcare.

scholarly journals Virtual Reality for Health Professions Education: Systematic Review and Meta-Analysis by the Digital Health Education Collaboration (Preprint)

2018 ◽  
Author(s):  
Bhone Myint Kyaw ◽  
Nakul Saxena ◽  
Pawel Posadzki ◽  
Jitka Vseteckova ◽  
Charoula Konstantia Nikolaou ◽  
...  
Keyword(s):  
Systematic Review ◽  
Virtual Reality ◽  
Clinical Practice ◽  
Health Professionals ◽  
Cognitive Skills ◽  
Meta Analysis ◽  
Health Professions ◽  
Risk Of Bias ◽  
Traditional Learning ◽  
Digital Education

BACKGROUND Virtual reality (VR) is a technology that allows the user to explore and manipulate computer-generated real or artificial three-dimensional multimedia sensory environments in real time to gain practical knowledge that can be used in clinical practice. OBJECTIVE The aim of this systematic review was to evaluate the effectiveness of VR for educating health professionals and improving their knowledge, cognitive skills, attitudes, and satisfaction. METHODS We performed a systematic review of the effectiveness of VR in pre- and postregistration health professions education following the gold standard Cochrane methodology. We searched 7 databases from the year 1990 to August 2017. No language restrictions were applied. We included randomized controlled trials and cluster-randomized trials. We independently selected studies, extracted data, and assessed risk of bias, and then, we compared the information in pairs. We contacted authors of the studies for additional information if necessary. All pooled analyses were based on random-effects models. We used the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach to rate the quality of the body of evidence. RESULTS A total of 31 studies (2407 participants) were included. Meta-analysis of 8 studies found that VR slightly improves postintervention knowledge scores when compared with traditional learning (standardized mean difference [SMD]=0.44; 95% CI 0.18-0.69; I2=49%; 603 participants; moderate certainty evidence) or other types of digital education such as online or offline digital education (SMD=0.43; 95% CI 0.07-0.79; I2=78%; 608 participants [8 studies]; low certainty evidence). Another meta-analysis of 4 studies found that VR improves health professionals’ cognitive skills when compared with traditional learning (SMD=1.12; 95% CI 0.81-1.43; I2=0%; 235 participants; large effect size; moderate certainty evidence). Two studies compared the effect of VR with other forms of digital education on skills, favoring the VR group (SMD=0.5; 95% CI 0.32-0.69; I2=0%; 467 participants; moderate effect size; low certainty evidence). The findings for attitudes and satisfaction were mixed and inconclusive. None of the studies reported any patient-related outcomes, behavior change, as well as unintended or adverse effects of VR. Overall, the certainty of evidence according to the GRADE criteria ranged from low to moderate. We downgraded our certainty of evidence primarily because of the risk of bias and/or inconsistency. CONCLUSIONS We found evidence suggesting that VR improves postintervention knowledge and skills outcomes of health professionals when compared with traditional education or other types of digital education such as online or offline digital education. The findings on other outcomes are limited. Future research should evaluate the effectiveness of immersive and interactive forms of VR and evaluate other outcomes such as attitude, satisfaction, cost-effectiveness, and clinical practice or behavior change.

scholarly journals Systematic review of predictive models of microbial water quality at freshwater recreational beaches

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256785
Author(s):  
Cole Heasley ◽  
J. Johanna Sanchez ◽  
Jordan Tustin ◽  
Ian Young
Keyword(s):  
Systematic Review ◽  
Water Quality ◽  
Predictive Models ◽  
Fecal Indicator Bacteria ◽  
Grey Literature ◽  
Predictive Modelling ◽  
The United States ◽  
Risk Of Bias ◽  
Indicator Bacteria ◽  
Fecal Indicator

Monitoring of fecal indicator bacteria at recreational waters is an important public health measure to minimize water-borne disease, however traditional culture methods for quantifying bacteria can take 18–24 hours to obtain a result. To support real-time notifications of water quality, models using environmental variables have been created to predict indicator bacteria levels on the day of sampling. We conducted a systematic review of predictive models of fecal indicator bacteria at freshwater recreational sites in temperate climates to identify and describe the existing approaches, trends, and their performance to inform beach water management policies. We conducted a comprehensive search strategy, including five databases and grey literature, screened abstracts for relevance, and extracted data using structured forms. Data were descriptively summarized. A total of 53 relevant studies were identified. Most studies (n = 44, 83%) were conducted in the United States and evaluated water quality using E. coli as fecal indicator bacteria (n = 46, 87%). Studies were primarily conducted in lakes (n = 40, 75%) compared to rivers (n = 13, 25%). The most commonly reported predictive model-building method was multiple linear regression (n = 37, 70%). Frequently used predictors in best-fitting models included rainfall (n = 39, 74%), turbidity (n = 31, 58%), wave height (n = 24, 45%), and wind speed and direction (n = 25, 47%, and n = 23, 43%, respectively). Of the 19 (36%) studies that measured accuracy, predictive models averaged an 81.0% accuracy, and all but one were more accurate than traditional methods. Limitations identifed by risk-of-bias assessment included not validating models (n = 21, 40%), limited reporting of whether modelling assumptions were met (n = 40, 75%), and lack of reporting on handling of missing data (n = 37, 70%). Additional research is warranted on the utility and accuracy of more advanced predictive modelling methods, such as Bayesian networks and artificial neural networks, which were investigated in comparatively fewer studies and creating risk of bias tools for non-medical predictive modelling.

scholarly journals Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study

10.2196/21547 ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. e21547
Author(s):  
Jenna M Reps ◽  
Chungsoo Kim ◽  
Ross D Williams ◽  
Aniek F Markus ◽  
Cynthia Yang ◽  
...  
Keyword(s):  
United States ◽  
Health Care ◽  
Decision Making ◽  
Emergency Department ◽  
Prediction Model ◽  
Emergency Department Visit ◽  
External Validation ◽  
The United States ◽  
Risk Of Bias ◽  
Data Sets

Background SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the “prediction model risk of bias assessment” criteria, and it has not been externally validated. Objective The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. Methods We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia. Results The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68. Conclusions Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.

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.

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