The Use of Prognostic Prediction Models for Mortality or Clinical Deterioration among Hospitalized and Non-hospitalized Adults with COVID-19: A Systematic Review

2021 ◽  
Author(s):  
Patricia Pauline M. Remalante-Rayco ◽  
Evelyn Osio-Salido
Keyword(s):  
Systematic Review ◽  
Emergency Department ◽  
Early Warning ◽  
Prediction Models ◽  
External Validation ◽  
Risk Of Bias ◽  
Clinical Deterioration ◽  
Early Warning Score ◽  
Good Prediction ◽  
Prediction Of Mortality

Objective. To assess the performance of prognostic models in predicting mortality or clinical deterioration among patients with COVID-19, both hospitalized and non-hospitalized Methods. We conducted a systematic review of the literature until March 8, 2021. We included models for the prediction of mortality or clinical deterioration in COVID-19 with external validation. We used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the GRADEpro Guideline Development Tool (GDT) to assess the evidence obtained. Results. We reviewed 33 cohort studies. Two studies had a low risk of bias, four unclear risks, and 27 with a high risk of bias due to participant selection and analysis. For the outcome of mortality, the QCOVID model had excellent prediction with high certainty of evidence but was specific for use in England. The COVID Outcome Prediction in the Emergency Department (COPE) model, the 4C Mortality Score, the Age, BUN, number of comorbidities, CRP, SpO2/FiO2 ratio, platelet count, heart rate (ABC2-SPH) risk score, the Confusion Urea Respiration Blood Pressure (CURB-65) severity score, the Rapid Emergency Medicine Score (REMS), and the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) score had fair to good prediction of death among inpatients, while the quick Sepsis-related Organ Failure Assessment (qSOFA) score had poor to fair prediction. The certainty of evidence for these models was very low to low. For the outcome of clinical deterioration, the 4C Deterioration Score had fair prediction, the National Early Warning Score 2 (NEWS2) score poor to good, and the Modified Early Warning Score (MEWS) had poor prediction. The certainty of evidence for these three models was also very low to low. None of these models had been validated in the Philippine setting. Conclusion. The QCOVID, COPE, ABC2-SPH, 4C, CURB-65, REMS, RISE-UP models for prediction of mortality and the 4C Deterioration and NEWS2 models for prediction of clinical deterioration are potentially useful but need to be validated among patients with COVID-19 of varying severity in the Philippine setting.

The use of Early Warning System Scores in Pre-Hospital and Emergency Department Settings to Predict Clinical Deterioration: s Systematic Review and Meta-Analysis

2021 ◽  
Author(s):  
Gigi Guan ◽  
Crystal Man Ying Lee ◽  
Stephen Begg ◽  
Angela Crombie ◽  
George Mnatzaganian
Keyword(s):  
Systematic Review ◽  
Emergency Department ◽  
Publication Bias ◽  
Early Warning ◽  
Early Warning System ◽  
Meta Analysis ◽  
Hospital Setting ◽  
Warning System ◽  
Clinical Deterioration ◽  
Early Warning Score

Abstract Background: It is unclear which Early Warning System (EWS) score best predicts in-hospital deterioration when applied in the emergency department (ED) or pre-hospital setting. Methods: This systematic review and meta-analysis assessed the predictive abilities of five commonly used EWS scores: National Early Warning Score (NEWS) and its updated version NEWS2, Modified Early Warning Score (MEWS), Rapid Acute Physiological Score (RAPS) and Cardiac Arrest Risk Triage (CART). Outcomes of interest included admission to ICU, up-to-≥3-day and 30-day mortality. Pooled estimates were calculated using DerSimonian and Laird random-effects models, constructed by type of EWS score, cut-off points, outcomes, and study setting. Risk of bias was assessed using the Newcastle-Ottawa Scale. Meta-regressions investigated between study heterogeneity. Funnel plots tested for publication bias. Results: A total of 11,565 articles was identified, of which 15 were included. Eight and seven articles conducted in the ED and pre-hospital settings, respectively. In the ED, MEWS and NEWS at cut-off points of 3, 4, or 6 had similar pooled diagnostic odds ratios (DOR) to predict 30-day mortality, ranging from 4.05 (Confidence Interval (CI) 2.35–6.99) to 6.48 (95% CI 1.83–22.89), p = 0.757. The ability of MEWS (cut-off point ≥ 3) to predict ICU admission had a similar pooled DOR of 5.54 (95% CI 2.02–15.21). In the pre-hospital setting, EWS scores failed to predict 30-day mortality. Using high cut-off points of 5, 7, or 9, their predictability improved when assessing up-to-≥3-day mortality with DOR ranging from 11.60 (95%, CI 9.75–13.88) to 20.37 (95% CI 13.16–31.52).Publication bias was not detected. Participants’ age explained 92% of between-study variance. Conclusion: EWS scores’ predictability of clinical deterioration improves when applied on patient populations that are already in the ED or hospital. The high thresholds used and the scores’ failure to predict 30-day mortality make them less suited for use in the pre-hospital setting.

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 Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e044687
Author(s):  
Lauren S. Peetluk ◽  
Felipe M. Ridolfi ◽  
Peter F. Rebeiro ◽  
Dandan Liu ◽  
Valeria C Rolla ◽  
...  
Keyword(s):  
Systematic Review ◽  
Treatment Outcomes ◽  
Prediction Models ◽  
Assessment Tool ◽  
Data Extraction ◽  
External Validation ◽  
Included Study ◽  
Risk Of Bias ◽  
Pulmonary Tb ◽  
Tb Treatment

ObjectiveTo systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB.DesignSystematic review.Data sourcesPubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020.Study selection and data extractionStudies that developed a model to predict pulmonary TB treatment outcomes were included. Study screening, data extraction and quality assessment were conducted independently by two reviewers. Study quality was evaluated using the Prediction model Risk Of Bias Assessment Tool. Data were synthesised with narrative review and in tables and figures.Results14 739 articles were identified, 536 underwent full-text review and 33 studies presenting 37 prediction models were included. Model outcomes included death (n=16, 43%), treatment failure (n=6, 16%), default (n=6, 16%) or a composite outcome (n=9, 25%). Most models (n=30, 81%) measured discrimination (median c-statistic=0.75; IQR: 0.68–0.84), and 17 (46%) reported calibration, often the Hosmer-Lemeshow test (n=13). Nineteen (51%) models were internally validated, and six (16%) were externally validated. Eighteen (54%) studies mentioned missing data, and of those, half (n=9) used complete case analysis. The most common predictors included age, sex, extrapulmonary TB, body mass index, chest X-ray results, previous TB and HIV. Risk of bias varied across studies, but all studies had high risk of bias in their analysis.ConclusionsTB outcome prediction models are heterogeneous with disparate outcome definitions, predictors and methodology. We do not recommend applying any in clinical settings without external validation, and encourage future researchers adhere to guidelines for developing and reporting of prediction models.Trial registrationThe study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782)

scholarly journals Validation of Brighton pediatric early warning score for predicting clinical deterioration in the emergency department

2021 ◽  
Vol 8 (6) ◽  
pp. 211-215
Author(s):  
Rasikapriya Duraisamy ◽  
Jagadeesh Vanaja ◽  
Karuppiah Pandi Ayyappa Samy ◽  
Banupriya Balasubramaniam ◽  
Soundararajan Palanisamy
Keyword(s):  
Emergency Department ◽  
Early Warning ◽  
Clinical Deterioration ◽  
Early Warning Score

scholarly journals Comparison of qSOFA and Hospital Early Warning Scores for prognosis in suspected sepsis in emergency department patients: a systematic review

2021 ◽  
pp. emermed-2020-210416
Author(s):  
Lisa Sabir ◽  
Shammi Ramlakhan ◽  
Steve Goodacre
Keyword(s):  
Emergency Department ◽  
Early Warning ◽  
Literature Search ◽  
Grey Literature ◽  
Cochrane Library ◽  
Good Prediction ◽  
Icu Admission ◽  
Prediction Of Mortality ◽  
Wide Range ◽  
Early Warning Scores

BackgroundSepsis is a major cause of morbidity and mortality and many tools exist to facilitate early recognition. This review compares two tools: the quick Sequential Organ Failure Assessment (qSOFA) and Early Warning Scores (National/Modified Early Warning Scores (NEWS/MEWS)) for predicting intensive care unit (ICU) admission and mortality when applied in the emergency department.MethodsA literature search was conducted using Medline, CINAHL, Embase and Cochrane Library, handsearching of references and a grey literature search with no language or date restrictions. Two authors selected studies and quality assessment completed using QUADAS-2. Area under the receiver operating characteristic curve (AUROC), sensitivities and specificities were compared.Results13 studies were included, totalling 403 865 patients. All reported mortality and six reported ICU admission.The ranges for AUROC estimates varied from little better than chance to good prediction of mortality (NEWS: 0.59–0.88; qSOFA: 0.57–0.79; MEWS 0.56–0.75), however, individual papers generally reported higher AUROC values for NEWS than qSOFA. NEWS values demonstrated a tendency towards better sensitivity for ICU admission (NEWS ≥5, 46%-91%; qSOFA ≥2, 12%–53%) and mortality (NEWS ≥5, 51%–97%; qSOFA ≥2, 14%–71%) but lower specificity (ICU: NEWS ≥5, 25%–91%; qSOFA ≥2, 67%–99%; mortality: NEWS ≥5, 22%–91%; qSOFA ≥2, 58%–99%).ConclusionThe wide range of AUROC estimates and high heterogeneity limit our conclusions. Allowing for this, the NEWS AUROC was consistently higher than qSOFA within individual papers. Both scores allow threshold setting, determined by the preferred compromise between sensitivity and specificity. At established thresholds NEWS tended to higher sensitivity while qSOFA tended to a higher specificity.PROSPERO registration numberCRD42019131414.

scholarly journals Prediction models for acute kidney injury in critically ill patients: a protocol for systematic review and critical appraisal

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e046274
Author(s):  
Danqiong Wang ◽  
Weiwen Zhang ◽  
Jian Luo ◽  
Honglong Fang ◽  
Shanshan Jing ◽  
...  
Keyword(s):  
Systematic Review ◽  
Acute Kidney Injury ◽  
Critical Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Critical Appraisal ◽  
Prediction Models ◽  
External Validation ◽  
Kidney Injury ◽  
Risk Of Bias

IntroductionAcute kidney injury (AKI) has high morbidity and mortality in intensive care units, which can lead to chronic kidney disease, more costs and longer hospital stay. Early identification of AKI is crucial for clinical intervention. Although various risk prediction models have been developed to identify AKI, the overall predictive performance varies widely across studies. Owing to the different disease scenarios and the small number of externally validated cohorts in different prediction models, the stability and applicability of these models for AKI in critically ill patients are controversial. Moreover, there are no current risk-classification tools that are standardised for prediction of AKI in critically ill patients. The purpose of this systematic review is to map and assess prediction models for AKI in critically ill patients based on a comprehensive literature review.Methods and analysisA systematic review with meta-analysis is designed and will be conducted according to the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Three databases including PubMed, Cochrane Library and EMBASE from inception through October 2020 will be searched to identify all studies describing development and/or external validation of original multivariable models for predicting AKI in critically ill patients. Random-effects meta-analyses for external validation studies will be performed to estimate the performance of each model. The restricted maximum likelihood estimation and the Hartung-Knapp-Sidik-Jonkman method under a random-effects model will be applied to estimate the summary C statistic and 95% CI. 95% prediction interval integrating the heterogeneity will also be calculated to pool C-statistics to predict a possible range of C-statistics of future validation studies. Two investigators will extract data independently using the CHARMS checklist. Study quality or risk of bias will be assessed using the Prediction Model Risk of Bias Assessment Tool.Ethics and disseminationEthical approval and patient informed consent are not required because all information will be abstracted from published literatures. We plan to share our results with clinicians and publish them in a general or critical care medicine peer-reviewed journal. We also plan to present our results at critical care international conferences.OSF registration number10.17605/OSF.IO/X25AT.

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

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

scholarly journals Early warning scores for detecting deterioration in adult hospital patients: a systematic review protocol

BMJ Open ◽  
2017 ◽  
Vol 7 (12) ◽  
pp. e019268 ◽  
Author(s):  
Stephen Gerry ◽  
Jacqueline Birks ◽  
Timothy Bonnici ◽  
Peter J Watkinson ◽  
Shona Kirtley ◽  
...  
Keyword(s):  
Systematic Review ◽  
Early Warning ◽  
Assessment Tool ◽  
External Validation ◽  
Risk Of Bias ◽  
Future Research ◽  
Early Warning Scores ◽  
Patients At Risk ◽  
Adult Hospital

IntroductionEarly warning scores (EWSs) are used extensively to identify patients at risk of deterioration in hospital. Previous systematic reviews suggest that studies which develop EWSs suffer methodological shortcomings and consequently may fail to perform well. The reviews have also identified that few validation studies exist to test whether the scores work in other settings. We will aim to systematically review papers describing the development or validation of EWSs, focusing on methodology, generalisability and reporting.MethodsWe will identify studies that describe the development or validation of EWSs for adult hospital inpatients. Each study will be assessed for risk of bias using the Prediction model Risk of Bias ASsessment Tool (PROBAST). Two reviewers will independently extract information. A narrative synthesis and descriptive statistics will be used to answer the main aims of the study which are to assess and critically appraise the methodological quality of the EWS, to describe the predictors included in the EWSs and to describe the reported performance of EWSs in external validation.Ethics and disseminationThis systematic review will only investigate published studies and therefore will not directly involve patient data. The review will help to establish whether EWSs are fit for purpose and make recommendations to improve the quality of future research in this area.PROSPERO registration numberCRD42017053324.

Sign in / Sign up

Export Citation Format

Share Document