FRI0129 DEVELOPMENT OF A PREDICTION MODEL FOR MAXIMUM METHOTREXATE (MTX) DOSE WITHOUT HEPATOTOXICITY USING AN INDEX OF ERYTHROCYTE MTX-POLYGLUTAMATE (MTXPG) LEVELS SPECULATED BY CLINICAL AND GENETIC MARKERS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 646-647
Author(s):  
S. Kumagai ◽  
S. Takahashi ◽  
M. Takahashi ◽  
T. Saito ◽  
K. Yoshida ◽  
...  
Keyword(s):  
Prediction Model ◽  
Genetic Markers ◽  
Prediction Models ◽  
Univariate Analysis ◽  
Clinical Pathology ◽  
Clinical Prediction Models ◽  
Clinical Model ◽  
Upper Limits ◽  
Calculated Dose ◽  
Rbc Count

Background:MTX is transported into cells and retained long after polyglutamation. MTXPG level can predict response and possibly adverse effects of MTX. We reported erythrocyte MTXPG concentrations efficiently discriminated patients with and without hepatotoxicity1. We also developed genetic and clinical prediction models for efficacy and hepatotoxicity of MTX2. In the present study, we firstly investigated the effects of clinical and secondly genetic variables on the concentration of total MTXPG and determined oral maximum MTX dose without hepatotoxicity using these variables.Objectives:To develop a prediction model for maximum MTX dose without hepatotoxicity.Methods:Concentrations of erythrocyte MTX-PG (PG1 to PG4) were detected by LC-MS/MS and calculated total MTXPG as sum of them. MTX-PGn levels were measured in 265 RA patients including 40 patients with elevated AST or ALT (≥ 60 U/L; 1.5 times of upper limits) and the 6 SNPs of 6 gens related to MTXPG metabolism were identified by RT-PCR.Results:Total concentrations of MTXPG were 141.3 ± 86.5 and 87.6 ± 47.8 nmol/L (mean±SD) in 40 RA patients with hepatotoxicity and 225 patients without, respectively (p<0.0001). By ROC analysis, the two groups were most efficiently discriminated with cutoff concentration of 100.0 nmol/L (AUC 0.731). Next, genetic and clinical model to speculate the MTXPG concentration was established by multivariate analysis using 4 clinical and 3 genetic variables which were selected from 20 clinical and 6 genetic variables by univariate analysis (p<0.1). Finally, a speculation model for MTXPG concentration by 4 clinical variables (MTX dose, BMI, RBC count, and creatinine) and one genetic variable (GGH c.452C>T) was developed (Figure). When MTXPG concentration of 100 nmol/L was applied to the model, maximum MTX dose without hepatotoxicity was calculated for each patient asMTX dose (mg) = {100 (MTXPG) – 96 + 1.7*BMI + 28*RBC - 120*creatinine - 19.3*GGH(C/T)} / 7.7. Real dose of oral MTX exceeded the calculated dose in 23 of 40 patients (57.5%) with hepatotoxicity, whereas it exceeded in 95 of 223 patients (42.6%) without hepatotoxicity (OR 1.82, p=0.081).Conclusion:Maximum MTX dose without hepatotoxicity was speculated by several clinical and genetic markers without measurement of erythrocyte MTX-PG concentrations.References:[1]Takahashi M, et al: Clinical Pathology (Rinsho Byori), 67:433-442, 2019.[2]Onishi A, et al: The Pharmacogenomics J, doi.org/10.1038/s41397-019-0134-9, 2019Disclosure of Interests:Shunichi Kumagai Grant/research support from: Astellas, Chugai, Mitsubishi Tanabe Co.Ltds, Consultant of: Sysmex Co.Ltd, Speakers bureau: many companies, Soshi Takahashi: None declared, Miho Takahashi: None declared, Toshiharu Saito: None declared, Katsuyuki Yoshida: None declared, Motoko Katayama: None declared, Saki Mukohara: None declared, Norihiko Amano: None declared, Akira Onishi Speakers bureau: AO received a speaker fee from Chugai, Ono Pharmaceutical, Eli Lilly, Mitsubishi-Tanabe, Asahi-Kasei, and Takeda, Masakazu Shinohara: None declared, Saori Hatachi: None declared

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

2021 ◽  
Vol 6 (1) ◽  
pp. e003451
Author(s):  
Arjun Chandna ◽  
Rainer Tan ◽  
Michael Carter ◽  
Ann Van Den Bruel ◽  
Jan Verbakel ◽  
...  
Keyword(s):  
At Risk ◽  
Prognostic Factors ◽  
Prediction Model ◽  
Disease Severity ◽  
Prediction Models ◽  
Severe Disease ◽  
Risk Of Bias ◽  
Clinical Prediction ◽  
Children At Risk ◽  
Clinical Prediction Models

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.

scholarly journals Tumor marker based survival analysis for patients with pseudomyxoma peritonei of appendiceal origin: A retrospective cohort study

2020 ◽  
Author(s):  
Mingjian Bai ◽  
Shilong Wang ◽  
Ruiqing Ma ◽  
Ying Cai ◽  
Yiyan Lu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Pseudomyxoma Peritonei ◽  
Roc Analysis ◽  
Prediction Models ◽  
Radical Surgery ◽  
Univariate Analysis ◽  
Barthel Index Score ◽  
Discrimination Ability ◽  
First Time ◽  
D Dimer

Abstract Background Pseudomyxoma peritonei (PMP) is a rare disease, the prognosis of overall survival (OS) is affected by many factors, present study aim to screen independent prediction indicators for PMP and establish prediction model for OS rates in PMP.Methods 119 PMP patients received cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) in our center for the first time were included between 01/06/2013 and 22/11/2019 . The log-rank test was used to compare the OS rate among groups, subsequently, variables with P<0.10 were subjected to multivariate Cox model for screening independent prediction indicators. Finally, the prediction models for OS in PMP will be established.Results Univariate analysis showed that Barthel Index Score, albumin, D-dimer, CEA, CA125, CA19-9, CA724, CA242, PCI, degree of radical surgery, histopathological grade were significant predictors for OS in PMP. At multivariate analysis, sex, D-dimer, CA125, CA19-9, and degree of radical surgery were independently associated with OS rate in PMP. ROC analysis was performed to calculate discrimination ability of prediction model and the area under curves (AUC) was 0.902 (95%CI: 0.823-0.954). Finally, nomogram was plotted by the independent predictive factors for PMP.Conclusions Several factors (sex, degree of radical surgery, D-dimer, preoperative CA125 and CA19-9) have independent prognostic value for survival in PMP, the tumor based prediction model has better prediction value, more researches are need to verify and improve the prediction model.

scholarly journals A review of statistical updating methods for clinical prediction models

2016 ◽  
Vol 27 (1) ◽  
pp. 185-197 ◽  
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.

scholarly journals Tumor marker based survival analysis for patients with pseudomyxoma peritonei of appendiceal origin: A retrospective cohort study

2020 ◽  
Author(s):  
Mingjian Bai ◽  
Shilong Wang ◽  
Ruiqing Ma ◽  
Ying Cai ◽  
Yiyan Lu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Pseudomyxoma Peritonei ◽  
Prediction Models ◽  
Radical Surgery ◽  
Cox Model ◽  
Univariate Analysis ◽  
Rank Test ◽  
Internal Validation ◽  
Discrimination Ability ◽  
D Dimer

Abstract Background Pseudomyxoma peritonei (PMP) is a rare disease, the prognosis of overall survival (OS) is affected by many factors, present study aim to screen independent prediction indicators and establish a nomogram for PMP. Methods 119 PMP patients received cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) in our center for the first time were included between 01/06/2013 and 22/11/2019 . The log-rank test was used to compare the OS rate among groups, subsequently, variables with P<0.10 were subjected to multivariate Cox model for screening independent prediction indicators. Finally, the nomogram prediction models will be established. Results Univariate analysis showed that Barthel Index Score, Albumin, D-Dimer, CEA, CA125, CA19-9, CA724, CA242, PCI, degree of radical surgery, histopathological grade were significant predictors for OS in PMP. At multivariate analysis, Sex, D-Dimer, CA125, CA19-9, PCI, and degree of radical surgery were independently associated with OS rate in PMP. A nomogram was plotted based on the independent predictive factors for PMP and undergone internal validation, ROC analysis was performed to calculate discrimination ability of prediction model, the area under curves (AUC) was 0.880 (95% CI : 0.806- 0.933). Conclusions Several factors (Sex, D-Dimer, CA125, CA19-9, PCI, and degree of radical surgery) have independent prognostic value for survival in PMP, the tumor based prediction model has a better prediction value, more researches are need to verify and improve the prediction model.

Statistical Development and Validation of Clinical Prediction Models

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.

Individual prediction of non-adherence to oral mercaptopurine (6MP) in children with acute lymphoblastic leukemia (ALL): Results from COG AALL03N1 study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 10530-10530 ◽  
Author(s):  
Anna Lynn Hoppmann ◽  
Yanjun Chen ◽  
Wendy Landier ◽  
Lindsey Hageman ◽  
Mary V. Relling ◽  
...  
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Time Of Day ◽  
Red Cell ◽  
Training Set ◽  
Test Set ◽  
Predictive Values ◽  
Final Model ◽  
Clinical Model ◽  
Race Ethnicity

10530 Background: Poor adherence to 6MP (measured electronically [MEMS]) increases relapse risk in children with ALL (Bhatia et al. JAMA Oncol 2015). Adherence is difficult to assess clinically and non-adherers are more likely to over-report 6MP intake (Landier et al. Blood 2017). Key sociodemographic/clinical factors (Bhatia et al. JCO 2012) and red cell methyl-mercaptopurine (MMP, a 6MP metabolite) levels (Hoppmann et al. ASCO 2017) are associated with non-adherence and could potentially identify non-adherers. Methods: We developed a prediction model for 6MP non-adherence (MEMS adherence rate < 90%), using receiver operating characteristic (ROC) analyses in 407 children with ALL receiving 6MP (mean age 7.7±4.4y; 68% males; 35% Caucasians, 34% Hispanics, 16% African Americans, 15% Asians). The cohort was divided into a training set (n = 250) and test set (n = 157) using stratified random sampling (stratified by race/ethnicity, gender, age and 6MP non-adherence). We used logistic regression with backward variable elimination, guided by change in area under ROC (AUC), to create a prediction model in the training set, using only clinical and sociodemographic variables (Clinical Model). We then generated a model that added 6MP dose-intensity (6MPDI)-adjusted red cell MMP levels to the Clinical Model (Final Model). All models were validated in the test set. Results: Predictors retained in the Training Clinical Model included: age, race/ethnicity, absolute neutrophil count, 6MPDI, family structure, and taking 6MP at the same vs varied time of day (AUC = 0.79; 95%CI 0.72-0.85). The Training Final Model (adding 6MPDI-adjusted MMP to the Clinical Model) yielded an AUC = 0.79 (95%CI 0.72-0.86). The Test Final Model (AUC = 0.79, 95%CI 0.69-0.88) showed significantly superior discrimination compared to the Test Clinical Model (AUC = 0.74, 95%CI 0.63-0.85; P = 0.002). Using a binary classifier with predicted probability of non-adherence ≥0.5, the Test Final Model had an accuracy of 79%, and positive and negative predictive values of 71% and 80%, respectively. Conclusions: We created, validated, and compared 2 risk-prediction models for 6MP non-adherence in children undergoing maintenance chemotherapy. While inclusion of red cell MMP levels provided superior discrimination in identifying non-adherent patients, the Clinical Model (without MMP levels) performed adequately well, and could be used in the clinical setting.

scholarly journals Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy

Cancers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1051
Author(s):  
Elsa Parr ◽  
Qian Du ◽  
Chi Zhang ◽  
Chi Lin ◽  
Ahsan Kamal ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Stereotactic Body Radiotherapy ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Pancreatic Tumors ◽  
Survival Prediction ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Clinical Model

(1) Background: Radiomics use high-throughput mining of medical imaging data to extract unique information and predict tumor behavior. Currently available clinical prediction models poorly predict treatment outcomes in pancreatic adenocarcinoma. Therefore, we used radiomic features of primary pancreatic tumors to develop outcome prediction models and compared them to traditional clinical models. (2) Methods: We extracted and analyzed radiomic data from pre-radiation contrast-enhanced CTs of 74 pancreatic cancer patients undergoing stereotactic body radiotherapy. A panel of over 800 radiomic features was screened to create overall survival and local-regional recurrence prediction models, which were compared to clinical prediction models and models combining radiomic and clinical information. (3) Results: A 6-feature radiomic signature was identified that achieved better overall survival prediction performance than the clinical model (mean concordance index: 0.66 vs. 0.54 on resampled cross-validation test sets), and the combined model improved the performance slightly further to 0.68. Similarly, a 7-feature radiomic signature better predicted recurrence than the clinical model (mean AUC of 0.78 vs. 0.66). (4) Conclusion: Overall survival and recurrence can be better predicted with models based on radiomic features than with those based on clinical features for pancreatic cancer.

scholarly journals 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

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

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 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

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.

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