Predictors of disease severity in children presenting from the community with febrile illnesses: a systematic review of prognostic studies

IntroductionEarly identification of children at risk of severe febrile illness can optimise referral, admission and treatment decisions, particularly in resource-limited settings. We aimed to identify prognostic clinical and laboratory factors that predict progression to severe disease in febrile children presenting from the community.MethodsWe systematically reviewed publications retrieved from MEDLINE, Web of Science and Embase between 31 May 1999 and 30 April 2020, supplemented by hand search of reference lists and consultation with an expert Technical Advisory Panel. Studies evaluating prognostic factors or clinical prediction models in children presenting from the community with febrile illnesses were eligible. The primary outcome was any objective measure of disease severity ascertained within 30 days of enrolment. We calculated unadjusted likelihood ratios (LRs) for comparison of prognostic factors, and compared clinical prediction models using the area under the receiver operating characteristic curves (AUROCs). Risk of bias and applicability of studies were assessed using the Prediction Model Risk of Bias Assessment Tool and the Quality In Prognosis Studies tool.ResultsOf 5949 articles identified, 18 studies evaluating 200 prognostic factors and 25 clinical prediction models in 24 530 children were included. Heterogeneity between studies precluded formal meta-analysis. Malnutrition (positive LR range 1.56–11.13), hypoxia (2.10–8.11), altered consciousness (1.24–14.02), and markers of acidosis (1.36–7.71) and poor peripheral perfusion (1.78–17.38) were the most common predictors of severe disease. Clinical prediction model performance varied widely (AUROC range 0.49–0.97). Concerns regarding applicability were identified and most studies were at high risk of bias.ConclusionsFew studies address this important public health question. We identified prognostic factors from a wide range of geographic contexts that can help clinicians assess febrile children at risk of progressing to severe disease. Multicentre studies that include outpatients are required to explore generalisability and develop data-driven tools to support patient prioritisation and triage at the community level.PROSPERO registration numberCRD42019140542.

Download Full-text

Prognostic Factors for the Occurrence of Post-Operative Shoulder Stiffness After Arthroscopic Rotator Cuff Repair: A Systematic Review

10.21203/rs.3.rs-672350/v1 ◽

2021 ◽

Author(s):

Thomas Stojanov ◽

Linda Modler ◽

Andreas M. Müller ◽

Soheila Aghlmandi ◽

Christian Appenzeller-Herzog ◽

...

Keyword(s):

Prognostic Factors ◽

Prediction Models ◽

Risk Of Bias ◽

Clinical Prediction ◽

Clinical Prediction Models ◽

Shoulder Stiffness ◽

Male Sex ◽

Arthroscopic Rotator Cuff ◽

Cuff Repair

Abstract BackgroundPost-operative shoulder stiffness (POSS) is one of the most frequent complications after arthroscopic rotator cuff repair (ARCR). Factors specifying clinical prediction models for the occurrence of POSS should rely on the literature and expert assessment. Our objective was to map prognostic factors for the occurrence of POSS in patients after an ARCR.MethodsLongitudinal studies of ARCR reporting prognostic factors for the occurrence of POSS with an endpoint of at least 6 months were included. We systematically searched Embase, Medline, and Scopus for articles published between January 1, 2014 and February 12, 2020 and screened cited and citing literature of eligible records and identified reviews. The risk of bias of included studies and the quality of evidence were assessed using the Quality in Prognosis Studies tool and an adapted Grading of Recommendations, Assessment, Development and Evaluations framework. A database was implemented to report the results of individual studies. The review was registered on PROSPERO (CRD42020199257).ResultsSeven cohort studies including 23 257 patients were included after screening 5013 records. POSS prevalence ranged from 0.51% to 8.75% with an endpoint ranging from 6 to 24 months. Due to scarcity of data, no meta-analysis could be performed. Overall risk of bias and quality of evidence was deemed high and low or very low, respectively. Twenty-two potential prognostic factors were identified. Increased age and male sex emerged as protective factors against POSS. Additional factors were reported but do require further analyses to determine their prognostic value.DiscussionAvailable evidence pointed to male sex and increased age as probable protective factors against POSS after ARCR. To establish a reliable pre-specified set of factors for clinical prediction models, our review results require complementation with an expert's opinion.

Download Full-text

Large-scale validation of the Prediction model Risk Of Bias ASsessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination

10.1101/2021.01.20.21250183 ◽

2021 ◽

Author(s):

Esmee Venema ◽

Benjamin S Wessler ◽

Jessica K Paulus ◽

Rehab Salah ◽

Gowri Raman ◽

...

Keyword(s):

High Risk ◽

Prediction Model ◽

Prediction Models ◽

Assessment Tool ◽

Short Form ◽

Risk Of Bias ◽

Clinical Prediction ◽

Model Risk ◽

Clinical Prediction Models ◽

Bias Assessment

AbstractObjectiveTo assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at external validation.Study Design and SettingWe evaluated risk of bias (ROB) on 102 CPMs from the Tufts CPM Registry, comparing PROBAST to a short form consisting of six PROBAST items anticipated to best identify high ROB. We then applied the short form to all CPMs in the Registry with at least 1 validation and assessed the change in discrimination (dAUC) between the derivation and the validation cohorts (n=1,147).ResultsPROBAST classified 98/102 CPMS as high ROB. The short form identified 96 of these 98 as high ROB (98% sensitivity), with perfect specificity. In the full CPM registry, 529/556 CPMs (95%) were classified as high ROB, 20 (4%) low ROB, and 7 (1%) unclear ROB. Median change in discrimination was significantly smaller in low ROB models (dAUC −0.9%, IQR −6.2%–4.2%) compared to high ROB models (dAUC −11.7%, IQR −33.3%–2.6%; p<0.001).ConclusionHigh ROB is pervasive among published CPMs. It is associated with poor performance at validation, supporting the application of PROBAST or a shorter version in CPM reviews.What is newHigh risk of bias is pervasive among published clinical prediction modelsHigh risk of bias identified with PROBAST is associated with poorer model performance at validationA subset of questions can distinguish between models with high and low risk of bias

Download Full-text

Prognostic Prediction Models for Patients with Low Back Pain: Systematic Review Protocol

10.21203/rs.3.rs-88541/v1 ◽

2020 ◽

Author(s):

Fernanda Gonçalves Silva ◽

Leonardo Oliveira Pena Costa ◽

Mark J Hancock ◽

Gabriele Alves Palomo ◽

Luciola da Cunha Menezes Costa ◽

...

Keyword(s):

Systematic Review ◽

Low Back Pain ◽

Back Pain ◽

Prediction Model ◽

Prediction Models ◽

External Validation ◽

Risk Of Bias ◽

Low Back ◽

Clinical Prediction ◽

Clinical Prediction Models

Abstract Background: The prognosis of acute low back pain is generally favourable in terms of pain and disability; however, outcomes vary substantially between individual patients. Clinical prediction models help in estimating the likelihood of an outcome at a certain time point. There are existing clinical prediction models focused on prognosis for patients with low back pain. To date, there is only one previous systematic review summarising the discrimination of validated clinical prediction models to identify the prognosis in patients with low back pain of less than 3 months duration. The aim of this systematic review is to identify existing developed and/or validated clinical prediction models on prognosis of patients with low back pain of less than 3 months duration, and to summarise their performance in terms of discrimination and calibration. Methods: MEDLINE, Embase and CINAHL databases will be searched, from the inception of these databases until January 2020. Eligibility criteria will be: (1) prognostic model development studies with or without external validation, or prognostic external validation studies with or without model updating; (2) with adults aged 18 or over, with ‘recent onset’ low back pain (i.e. less than 3 months duration), with or without leg pain; (3) outcomes of pain, disability, sick leave or days absent from work or return to work status, and self-reported recovery; and (4) study with a follow-up of at least 12 weeks duration. The risk of bias of the included studies will be assessed by the Prediction model Risk Of Bias ASsessment Tool, and the overall quality of evidence will be rated using the Hierarchy of Evidence for Clinical Prediction Rules. Discussion: This systematic review will identify, appraise, and summarize evidence on the performance of existing prediction models for prognosis of low back pain, and may help clinicians to choose the best option of prediction model to better inform patients about their likely prognosis. Systematic review registration: PROSPERO reference number CRD42020160988

Download Full-text

A review of statistical updating methods for clinical prediction models

Statistical Methods in Medical Research ◽

10.1177/0962280215626466 ◽

2016 ◽

Vol 27 (1) ◽

pp. 185-197 ◽

Cited By ~ 27

Author(s):

Ting-Li Su ◽

Thomas Jaki ◽

Graeme L Hickey ◽

Iain Buchan ◽

Matthew Sperrin

Keyword(s):

Prediction Model ◽

Prediction Models ◽

Healthcare Outcomes ◽

Clinical Prediction ◽

Clinical Prediction Models ◽

Clinical Prediction Model ◽

The United Kingdom ◽

Single Strategy ◽

Using Data ◽

Dynamic Updating

A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each population and context; however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing clinical prediction models already developed for use in similar contexts or populations. In addition, clinical prediction models commonly become miscalibrated over time, and need replacing or updating. In this article, we review a range of approaches for re-using and updating clinical prediction models; these fall in into three main categories: simple coefficient updating, combining multiple previous clinical prediction models in a meta-model and dynamic updating of models. We evaluated the performance (discrimination and calibration) of the different strategies using data on mortality following cardiac surgery in the United Kingdom: We found that no single strategy performed sufficiently well to be used to the exclusion of the others. In conclusion, useful tools exist for updating existing clinical prediction models to a new population or context, and these should be implemented rather than developing a new clinical prediction model from scratch, using a breadth of complementary statistical methods.

Download Full-text

Statistical Development and Validation of Clinical Prediction Models

Anesthesiology ◽

10.1097/aln.0000000000003871 ◽

2021 ◽

Author(s):

Steven J. Staffa ◽

David Zurakowski

Keyword(s):

Prediction Model ◽

Prediction Models ◽

External Validation ◽

Model Development ◽

Predictive Ability ◽

Binary Outcome ◽

Clinical Prediction ◽

Clinical Prediction Models ◽

Surgery Research ◽

Development And Validation

Summary Clinical prediction models in anesthesia and surgery research have many clinical applications including preoperative risk stratification with implications for clinical utility in decision-making, resource utilization, and costs. It is imperative that predictive algorithms and multivariable models are validated in a suitable and comprehensive way in order to establish the robustness of the model in terms of accuracy, predictive ability, reliability, and generalizability. The purpose of this article is to educate anesthesia researchers at an introductory level on important statistical concepts involved with development and validation of multivariable prediction models for a binary outcome. Methods covered include assessments of discrimination and calibration through internal and external validation. An anesthesia research publication is examined to illustrate the process and presentation of multivariable prediction model development and validation for a binary outcome. Properly assessing the statistical and clinical validity of a multivariable prediction model is essential for reassuring the generalizability and reproducibility of the published tool.

Download Full-text

Individualised prediction of psychosis in individuals meeting at-risk mental state (ARMS) criteria: protocol for a systematic review of clinical prediction models

Diagnostic and Prognostic Research ◽

10.1186/s41512-019-0066-5 ◽

2019 ◽

Vol 3 (1) ◽

Author(s):

Laura J. Bonnett ◽

Filippo Varese ◽

Catrin Tudur Smith ◽

Allan Flores ◽

Alison R. Yung

Keyword(s):

At Risk ◽

Mental State ◽

Psychotic Disorders ◽

Prediction Models ◽

Assessment Tool ◽

External Validation ◽

Clinical Prediction ◽

Analysis Model ◽

Clinical Prediction Models ◽

At Risk Mental State

Abstract Background Psychotic disorders affect about 3% of the population worldwide and are associated with high personal, social and economic costs. They tend to have their first onset in adolescence. Increasing emphasis has been placed on early intervention to detect illness and minimise disability. In the late 1990s, criteria were developed to identify individuals at high risk for psychotic disorder. These are known as the at-risk mental state (ARMS) criteria. While ARMS individuals have a risk of psychosis much greater than the general population, most individuals meeting the ARMS criteria will not develop psychosis. Despite this, the National Institute for Health and Care Excellence recommends cognitive behavioural therapy (CBT) for all ARMS people. Clinical prediction models that combine multiple patient characteristics to predict individual outcome risk may facilitate identification of patients who would benefit from CBT and conversely those that would benefit from less costly and less intensive regular mental state monitoring. The study will systematically review the evidence on clinical prediction models aimed at making individualised predictions for the transition to psychosis. Methods Database searches will be conducted on PsycINFO, Medline, EMBASE and CINAHL. Reference lists and subject experts will be utilised. No language restrictions will be placed on publications, but searches will be restricted to 1994 onwards, the initial year of the first prospective study using ARMS criteria. Studies of any design will be included if they examined, in ARMS patients, whether more than one factor in combination is associated with the risk of transition to psychosis. Study quality will be assessed using the prediction model risk of bias assessment tool (PROBAST). Clinical prediction models will be summarised qualitatively, and if tested in multiple validation studies, their predictive performance will be summarised using a random-effects meta-analysis model. Discussion The results of the review will identify prediction models for the risk of transition to psychosis. These will be informative for clinicians currently treating ARMS patients and considering potential preventive interventions. The conclusions of the review will also inform the possible update and external validation of prediction models and clinical prediction rules to identify those at high or low risk of transition to psychosis. Trial registration The review has been registered with PROSPERO (CRD42018108488).

Download Full-text

Systematic review of risk prediction studies in bone and joint infection: are modifiable prognostic factors useful in predicting recurrence?

Journal of Bone and Joint Infection ◽

10.5194/jbji-6-257-2021 ◽

2021 ◽

Vol 6 (7) ◽

pp. 257-271

Author(s):

Maria Dudareva ◽

Andrew Hotchen ◽

Martin A. McNally ◽

Jamie Hartmann-Boyce ◽

Matthew Scarborough ◽

...

Keyword(s):

Systematic Review ◽

Prognostic Factors ◽

Primary Outcome ◽

Prediction Models ◽

Joint Infection ◽

Clinical Prediction ◽

Clinical Prediction Models ◽

Orthopaedic Infections ◽

Patient Health ◽

Infection Recurrence

Abstract. Background: Classification systems for orthopaedic infection include patient health status, but there is no consensus about which comorbidities affect prognosis. Modifiable factors including substance use, glycaemic control, malnutrition and obesity may predict post-operative recovery from infection. Aim: This systematic review aimed (1) to critically appraise clinical prediction models for individual prognosis following surgical treatment for orthopaedic infection where an implant is not retained; (2) to understand the usefulness of modifiable prognostic factors for predicting treatment success. Methods: EMBASE and MEDLINE databases were searched for clinical prediction and prognostic studies in adults with orthopaedic infections. Infection recurrence or re-infection after at least 6 months was the primary outcome. The estimated odds ratios for the primary outcome in participants with modifiable prognostic factors were extracted and the direction of the effect reported. Results: Thirty-five retrospective prognostic cohort studies of 92 693 patients were included, of which two reported clinical prediction models. No studies were at low risk of bias, and no externally validated prediction models were identified. Most focused on prosthetic joint infection. A positive association was reported between body mass index and infection recurrence in 19 of 22 studies, similarly in 8 of 14 studies reporting smoking history and 3 of 4 studies reporting alcohol intake. Glycaemic control and malnutrition were rarely considered. Conclusion: Modifiable aspects of patient health appear to predict outcomes after surgery for orthopaedic infection. There is a need to understand which factors may have a causal effect. Development and validation of clinical prediction models that include participant health status will facilitate treatment decisions for orthopaedic infections.

Download Full-text

Facilitating safe discharge through predicting disease progression in moderate COVID-19: a prospective cohort study to develop and validate a clinical prediction model in resource-limited settings

10.1101/2021.12.02.21267170 ◽

2021 ◽

Author(s):

Arjun Chandna ◽

Raman Mahajan ◽

Priyanka Gautam ◽

Lazaro Mwandigha ◽

Karthik Gunasekaran ◽

...

Keyword(s):

Prediction Model ◽

Prediction Models ◽

Validation Cohort ◽

External Validation ◽

Clinical Parameters ◽

Clinical Prediction ◽

Clinical Prediction Models ◽

Resource Limited Settings ◽

Biochemical Biomarkers ◽

Resource Limited

ABSTRACTBackgroundIn locations where few people have received COVID-19 vaccines, health systems remain vulnerable to surges in SARS-CoV-2 infections. Tools to identify patients suitable for community-based management are urgently needed.MethodsWe prospectively recruited adults presenting to two hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 in order to develop and validate a clinical prediction model to rule-out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 < 94%; respiratory rate > 30 bpm; SpO2/FiO2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex and SpO2) and one of seven shortlisted biochemical biomarkers measurable using near-patient tests (CRP, D-dimer, IL-6, NLR, PCT, sTREM-1 or suPAR), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration and clinical utility of the models in a temporal external validation cohort.Findings426 participants were recruited, of whom 89 (21·0%) met the primary outcome. 257 participants comprised the development cohort and 166 comprised the validation cohort. The three models containing NLR, suPAR or IL-6 demonstrated promising discrimination (c-statistics: 0·72 to 0·74) and calibration (calibration slopes: 1·01 to 1·05) in the validation cohort, and provided greater utility than a model containing the clinical parameters alone.InterpretationWe present three clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources.FundingMédecins Sans Frontières, India.RESEARCH IN CONTEXTEvidence before this studyA living systematic review by Wynants et al. identified 137 COVID-19 prediction models, 47 of which were derived to predict whether patients with COVID-19 will have an adverse outcome. Most lacked external validation, relied on retrospective data, did not focus on patients with moderate disease, were at high risk of bias, and were not practical for use in resource-limited settings. To identify promising biochemical biomarkers which may have been evaluated independently of a prediction model and therefore not captured by this review, we searched PubMed on 1 June 2020 using synonyms of “SARS-CoV-2” AND [“biomarker” OR “prognosis”]. We identified 1,214 studies evaluating biochemical biomarkers of potential value in the prognostication of COVID-19 illness. In consultation with FIND (Geneva, Switzerland) we shortlisted seven candidates for evaluation in this study, all of which are measurable using near-patient tests which are either currently available or in late-stage development.Added value of this studyWe followed the TRIPOD guidelines to develop and validate three promising clinical prediction models to help clinicians identify which patients presenting with moderate COVID-19 can be safely managed in the community. Each model contains three easily ascertained clinical parameters (age, sex, and SpO2) and one biochemical biomarker (NLR, suPAR or IL-6), and would be practical for implementation in high-patient-throughput low resource settings. The models showed promising discrimination and calibration in the validation cohort. The inclusion of a biomarker test improved prognostication compared to a model containing the clinical parameters alone, and extended the range of contexts in which such a tool might provide utility to include situations when bed pressures are less critical, for example at earlier points in a COVID-19 surge.Implications of all the available evidencePrognostic models should be developed for clearly-defined clinical use-cases. We report the development and temporal validation of three clinical prediction models to rule-out progression to supplemental oxygen requirement amongst patients presenting with moderate COVID-19. The models are readily implementable and should prove useful in triage and resource allocation. We provide our full models to enable independent validation.

Download Full-text