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

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.

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Predicting major adverse cardiovascular events for secondary prevention: protocol for a systematic review and meta-analysis of risk prediction models

BMJ Open ◽

10.1136/bmjopen-2019-034564 ◽

2020 ◽

Vol 10 (7) ◽

pp. e034564

Author(s):

Ralph K Akyea ◽

Jo Leonardi-Bee ◽

Folkert W Asselbergs ◽

Riyaz S Patel ◽

Paul Durrington ◽

...

Keyword(s):

Secondary Prevention ◽

Cardiovascular Events ◽

Prediction Models ◽

Assessment Tool ◽

Meta Analysis ◽

Risk Of Bias ◽

Prognostic Models ◽

Major Adverse Cardiovascular Events ◽

Bibliographic Databases ◽

Analysis Model

IntroductionCardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. With advances in early diagnosis and treatment of CVD and increasing life expectancy, more people are surviving initial CVD events. However, models for stratifying disease severity risk in patients with established CVD for effective secondary prevention strategies are inadequate. Multivariable prognostic models to stratify CVD risk may allow personalised treatment interventions. This review aims to systematically review the existing multivariable prognostic models for the recurrence of CVD or major adverse cardiovascular events in adults with established CVD diagnosis.Methods and analysisBibliographic databases (Ovid MEDLINE, EMBASE, PsycINFO and Web of Science) will be searched, from database inception to April 2020, using terms relating to the clinical area and prognosis. A hand search of the reference lists of included studies will also be done to identify additional published studies. No restrictions on language of publications will be applied. Eligible studies present multivariable models (derived or validated) of adults (aged 16 years and over) with an established diagnosis of CVD, reporting at least one of the components of the primary outcome of major adverse cardiovascular events (defined as either coronary heart disease, stroke, peripheral artery disease, heart failure or CVD-related mortality). Reviewing will be done by two reviewers independently using the pre-defined criteria. Data will be extracted for included full-text articles. Risk of bias will be assessed using the Prediction model study Risk Of Bias ASsessment Tool (PROBAST). Prognostic models will be summarised narratively. If a model is tested in multiple validation studies, the predictive performance will be summarised using a random-effects meta-analysis model to account for any between-study heterogeneity.Ethics and disseminationEthics approval is not required. The results of this study will be submitted to relevant conferences for presentation and a peer-reviewed journal for publication.PROSPERO registration numberCRD42019149111.

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Predicting recurrent atrial fibrillation after catheter ablation: a systematic review of prognostic models

EP Europace ◽

10.1093/europace/euaa041 ◽

2020 ◽

Vol 22 (5) ◽

pp. 748-760 ◽

Cited By ~ 4

Author(s):

Janine Dretzke ◽

Naomi Chuchu ◽

Ridhi Agarwal ◽

Clare Herd ◽

Winnie Chua ◽

...

Keyword(s):

Atrial Fibrillation ◽

Systematic Review ◽

Catheter Ablation ◽

Assessment Tool ◽

Model Performance ◽

Risk Of Bias ◽

Prognostic Models ◽

Risk Scores ◽

Bibliographic Databases ◽

Recurrent Atrial Fibrillation

Abstract Aims We assessed the performance of modelsf (risk scores) for predicting recurrence of atrial fibrillation (AF) in patients who have undergone catheter ablation. Methods and results Systematic searches of bibliographic databases were conducted (November 2018). Studies were eligible for inclusion if they reported the development, validation, or impact assessment of a model for predicting AF recurrence after ablation. Model performance (discrimination and calibration) measures were extracted. The Prediction Study Risk of Bias Assessment Tool (PROBAST) was used to assess risk of bias. Meta-analysis was not feasible due to clinical and methodological differences between studies, but c-statistics were presented in forest plots. Thirty-three studies developing or validating 13 models were included; eight studies compared two or more models. Common model variables were left atrial parameters, type of AF, and age. Model discriminatory ability was highly variable and no model had consistently poor or good performance. Most studies did not assess model calibration. The main risk of bias concern was the lack of internal validation which may have resulted in overly optimistic and/or biased model performance estimates. No model impact studies were identified. Conclusion Our systematic review suggests that clinical risk prediction of AF after ablation has potential, but there remains a need for robust evaluation of risk factors and development of risk scores.

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

BMJ Open ◽

10.1136/bmjopen-2019-035045 ◽

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.

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Safety and efficiency of gasless laparoscopy: a systematic review protocol

Systematic Reviews ◽

10.1186/s13643-020-01365-y ◽

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

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

10.21203/rs.3.rs-138018/v1 ◽

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).

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Clinical prognostic models for severe dengue: a systematic review protocol

Wellcome Open Research ◽

10.12688/wellcomeopenres.15033.2 ◽

2019 ◽

Vol 4 ◽

pp. 12 ◽

Cited By ~ 1

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

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

10.1101/2021.01.28.21250718 ◽

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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Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol

BMJ Open ◽

10.1136/bmjopen-2019-036388 ◽

2020 ◽

Vol 10 (4) ◽

pp. e036388

Author(s):

Mohammad Ziaul Islam Chowdhury ◽

Iffat Naeem ◽

Hude Quan ◽

Alexander A Leung ◽

Khokan C Sikdar ◽

...

Keyword(s):

Risk Factors ◽

Systematic Review ◽

Risk Prediction ◽

Prediction Models ◽

Assessment Tool ◽

Meta Analysis ◽

Grey Literature ◽

Random Effect ◽

Regression Coefficients ◽

Risk Prediction Models

IntroductionHypertension is one of the most common medical conditions and represents a major risk factor for heart attack, stroke, kidney disease and mortality. The risk of progression to hypertension depends on several factors, and combining these risk factors into a multivariable model for risk stratification would help to identify high-risk individuals who should be targeted for healthy behavioural changes and/or medical treatment to prevent the development of hypertension. The risk prediction models can be further improved in terms of accuracy by using a metamodel updating technique where existing hypertension prediction models can be updated by combining information available in existing models with new data. A systematic review and meta-analysis will be performed of hypertension prediction models in order to identify known risk factors for high blood pressure and to summarise the magnitude of their association with hypertension.Methods and analysisMEDLINE, Embase, Web of Science, Scopus and grey literature will be systematically searched for studies predicting the risk of hypertension among the general population. The search will be based on two key concepts: hypertension and risk prediction. The summary statistics from the individual studies will be the regression coefficients of the hypertension risk prediction models, and random-effect meta-analysis will be used to obtain pooled estimates. Heterogeneity and publication bias will be assessed, along with study quality, which will be assessed using the Prediction Model Risk of Bias Assessment Tool checklist.Ethics and disseminationEthics approval is not required for this systematic review and meta-analysis. We plan to disseminate the results of our review through journal publications and presentations at applicable platforms.

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Parent–Infant Interventions to Promote Parental Sensitivity During NICU Hospitalization: Systematic Review and Meta-Analysis

Journal of Early Intervention ◽

10.1177/1053815121991928 ◽

2021 ◽

pp. 105381512199192

Author(s):

Andréane Lavallée ◽

Gwenaëlle De Clifford-Faugère ◽

Ariane Ballard ◽

Marilyn Aita

Keyword(s):

Systematic Review ◽

Systematic Reviews ◽

Standard Care ◽

Assessment Tool ◽

Meta Analysis ◽

Risk Of Bias ◽

Parental Sensitivity ◽

Assessment Development ◽

Implementation Failure ◽

Meta Analyses

This systematic review and meta-analysis examined the effectiveness of parent–infant interventions for parents of preterm infants on parental sensitivity compared to standard care or active comparators. This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO; registration ID: CRD42016047083). Database searches were performed from inception to 2020 to identify eligible randomized controlled trials. Two review authors independently selected studies, extracted data, and assessed the risk of bias using the Cochrane risk of bias assessment tool and quality of evidence using the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) guidelines. A total of 19 studies ( n = 2,111 participants) were included and 14 were suitable to be pooled in our primary outcome meta-analysis. Results show no significant effect of parent–infant interventions over standard care or basic educational programs, on parental sensitivity. Results may not necessarily be due to the ineffectiveness of the interventions but rather due to implementation failure or high risk of bias of included studies.

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Acupuncture combined with medication for opioid use disorder in adults: a protocol for systematic review and meta-analysis

BMJ Open ◽

10.1136/bmjopen-2019-034554 ◽

2020 ◽

Vol 10 (6) ◽

pp. e034554

Author(s):

Zhihan Chen ◽

Rui Wang ◽

Min Zhang ◽

Yitong Wang ◽

Yulan Ren

Keyword(s):

Systematic Review ◽

Assessment Tool ◽

Meta Analysis ◽

Opioid Use Disorder ◽

Risk Of Bias ◽

Control Group ◽

Opioid Use ◽

Bias Assessment ◽

Assess Quality ◽

Randomised Controlled

IntroductionOpioid use disorder (OUD) is a worldwide health problem. Clinical trials indicated that acupuncture combined with medication is effective in OUD, however, there are different conclusions presented by previous trials. This study is designed to evaluate the efficacy and safety of acupuncture combined with medication in OUD.Methods and analysisPubMed, CENTRAL, Embase, Web of Science, CINAHL, PsycINFO, ProQuest Dissertation and Theses, AMED, OpenGrey, Clinicaltrials.gov and who.int/trialsearch will be searched in September 2019 without a language restriction. Randomised controlled trials (RCTs) and quasi-RCTs which included participants with OUD receiving acupuncture therapy combined with medication versus control group will be included in this study. Two reviewers will independently screen studies, extract data, assess risk of bias by the Cochrane risk of bias assessment tool and assess quality of evidence by Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Any disagreements will be arbitrated by the third reviewer. Data synthesis and analysis will be conducted by using RevMan V.5.3. Subgroup analyses, sensitivity analysis, meta-regression and reporting bias assessment will be conducted if necessary and appropriate.Ethics and disseminationOn account of the nature of this systematic review and meta-analysis, ethical approval is not required. The results will be published in a peer-reviewed journal.PROSPERO registration numberCRD42019123436.

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