A survey of clinical prediction tools in colorectal and lung cancers and melanoma.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 1592-1592 ◽  
Author(s):  
Alyson L. Mahar ◽  
Susan Halabi ◽  
Lisa M. McShane ◽  
Patricia A. Groome ◽  
Carolyn C. Compton ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Model Building ◽  
Performance Metrics ◽  
External Validation ◽  
Model Development ◽  
Risk Scores ◽  
Prediction Tool ◽  
Clinical Prediction ◽  
Prediction Tools ◽  
Methodological Guidelines

1592 Background: Clinical prediction in cancer depends on a myriad of prognostic factors, and relies on sound methodology for model building and validation. Increased understanding of complex tumour biology allows for simultaneous consideration of biological markers and standard clinical and pathological factors for prediction. We evaluated published studies supporting existing prediction tools in three cancers. Methods: Scientific literature and online resources were searched for clinical prediction tools for survival in three cancers: colorectal, lung, and melanoma. A priori criteria determined by the Molecular Modellers Working Group of the AJCC were evaluated and included: defined patient population, consideration of standard prognostic variables, model development approaches, validation strategies, performance metrics, presentation form of prediction tool, and intended clinical use. Results: Seventy-eight tools intended for prediction of survival were identified for the three cancers: 41 in colorectal, 23 in lung, and 14 in melanoma. Clinical presentations varied within each: 23 of the colorectal cancer tools focused on advanced disease with liver metastases and the remaining varied by stage; 16 lung cancer tools focused on NSCLC and 7 on SCLC. Even in narrowly defined situations, there was no consensus on key variables; for example, no variables were common to all 8 prediction tools for metastatic lung cancer. Variable definitions were missing or vague and the form of the model was often not provided, hampering independent validation and usability. Only 32/78 tools were supported by appropriate internal validity statistics and 21/78 with external validation. Often the development of risk scores did not create groups for whom treatment decisions would be similar. Conclusions: The quality of the literature supporting clinical prediction tools is variable, and the accuracy and utility of many existing tools is undetermined. Methodological guidelines for prediction tool development and validation should be adopted and adhered to. Studies developing and validating clinical prediction tools in cancer must be reported in complete and transparent fashion to facilitate proper interpretation and judgment of utility.

scholarly journals Towards the best kidney failure prediction tool: a systematic review and selection aid

2019 ◽  
Vol 35 (9) ◽  
pp. 1527-1538 ◽  
Author(s):  
Chava L Ramspek ◽  
Ype de Jong ◽  
Friedo W Dekker ◽  
Merel van Diepen
Keyword(s):  
Systematic Review ◽  
Kidney Failure ◽  
External Validation ◽  
Model Development ◽  
Risk Of Bias ◽  
Future Research ◽  
Prediction Tool ◽  
Clinical Value ◽  
Wide Range ◽  
And Performance

Abstract Background Prediction tools that identify chronic kidney disease (CKD) patients at a high risk of developing kidney failure have the potential for great clinical value, but limited uptake. The aim of the current study is to systematically review all available models predicting kidney failure in CKD patients, organize empirical evidence on their validity and ultimately provide guidance in the interpretation and uptake of these tools. Methods PubMed and EMBASE were searched for relevant articles. Titles, abstracts and full-text articles were sequentially screened for inclusion by two independent researchers. Data on study design, model development and performance were extracted. The risk of bias and clinical usefulness were assessed and combined in order to provide recommendations on which models to use. Results Of 2183 screened studies, a total of 42 studies were included in the current review. Most studies showed high discriminatory capacity and the included predictors had large overlap. Overall, the risk of bias was high. Slightly less than half the studies (48%) presented enough detail for the use of their prediction tool in practice and few models were externally validated. Conclusions The current systematic review may be used as a tool to select the most appropriate and robust prognostic model for various settings. Although some models showed great potential, many lacked clinical relevance due to being developed in a prevalent patient population with a wide range of disease severity. Future research efforts should focus on external validation and impact assessment in clinically relevant patient populations.

scholarly journals Development and external validation of a prognostic multivariable model on admission for hospitalized patients with COVID-19

2020 ◽  
Author(s):  
Jianfeng Xie ◽  
Daniel Hungerford ◽  
Hui Chen ◽  
Simon T Abrams ◽  
Shusheng Li ◽  
...  
Keyword(s):  
Lactate Dehydrogenase ◽  
Prediction Model ◽  
Lymphocyte Count ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Added Value ◽  
Clinical Prediction ◽  
Healthcare Resources ◽  
D Dimer

SummaryBackgroundCOVID-19 pandemic has developed rapidly and the ability to stratify the most vulnerable patients is vital. However, routinely used severity scoring systems are often low on diagnosis, even in non-survivors. Therefore, clinical prediction models for mortality are urgently required.MethodsWe developed and internally validated a multivariable logistic regression model to predict inpatient mortality in COVID-19 positive patients using data collected retrospectively from Tongji Hospital, Wuhan (299 patients). External validation was conducted using a retrospective cohort from Jinyintan Hospital, Wuhan (145 patients). Nine variables commonly measured in these acute settings were considered for model development, including age, biomarkers and comorbidities. Backwards stepwise selection and bootstrap resampling were used for model development and internal validation. We assessed discrimination via the C statistic, and calibration using calibration-in-the-large, calibration slopes and plots.FindingsThe final model included age, lymphocyte count, lactate dehydrogenase and SpO2 as independent predictors of mortality. Discrimination of the model was excellent in both internal (c=0·89) and external (c=0·98) validation. Internal calibration was excellent (calibration slope=1). External validation showed some over-prediction of risk in low-risk individuals and under-prediction of risk in high-risk individuals prior to recalibration. Recalibration of the intercept and slope led to excellent performance of the model in independent data.InterpretationCOVID-19 is a new disease and behaves differently from common critical illnesses. This study provides a new prediction model to identify patients with lethal COVID-19. Its practical reliance on commonly available parameters should improve usage of limited healthcare resources and patient survival rate.FundingThis study was supported by following funding: Key Research and Development Plan of Jiangsu Province (BE2018743 and BE2019749), National Institute for Health Research (NIHR) (PDF-2018-11-ST2-006), British Heart Foundation (BHF) (PG/16/65/32313) and Liverpool University Hospitals NHS Foundation Trust in UK.Research in contextEvidence before this studySince the outbreak of COVID-19, there has been a pressing need for development of a prognostic tool that is easy for clinicians to use. Recently, a Lancet publication showed that in a cohort of 191 patients with COVID-19, age, SOFA score and D-dimer measurements were associated with mortality. No other publication involving prognostic factors or models has been identified to date.Added value of this studyIn our cohorts of 444 patients from two hospitals, SOFA scores were low in the majority of patients on admission. The relevance of D-dimer could not be verified, as it is not included in routine laboratory tests. In this study, we have established a multivariable clinical prediction model using a development cohort of 299 patients from one hospital. After backwards selection, four variables, including age, lymphocyte count, lactate dehydrogenase and SpO2 remained in the model to predict mortality. This has been validated internally and externally with a cohort of 145 patients from a different hospital. Discrimination of the model was excellent in both internal (c=0·89) and external (c=0·98) validation. Calibration plots showed excellent agreement between predicted and observed probabilities of mortality after recalibration of the model to account for underlying differences in the risk profile of the datasets. This demonstrated that the model is able to make reliable predictions in patients from different hospitals. In addition, these variables agree with pathological mechanisms and the model is easy to use in all types of clinical settings.Implication of all the available evidenceAfter further external validation in different countries the model will enable better risk stratification and more targeted management of patients with COVID-19. With the nomogram, this model that is based on readily available parameters can help clinicians to stratify COVID-19 patients on diagnosis to use limited healthcare resources effectively and improve patient outcome.

scholarly journals PREDICTing Mortality in the Emergency Department: External Validation and Derivation of a Clinical Prediction Tool

2017 ◽  
Vol 24 (7) ◽  
pp. 822-831 ◽  
Author(s):  
Rajat N. Moman ◽  
Caitlin E. Loprinzi Brauer ◽  
Katherine M. Kelsey ◽  
Rachel D. Havyer ◽  
Christine M. Lohse ◽  
...  
Keyword(s):  
Emergency Department ◽  
External Validation ◽  
Prediction Tool ◽  
Clinical Prediction ◽  
Clinical Prediction Tool

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.

scholarly journals Lung cancer in symptomatic patients presenting in primary care: a systematic review of risk prediction tools

2017 ◽  
Vol 67 (659) ◽  
pp. e396-e404 ◽  
Author(s):  
Mia Schmidt-Hansen ◽  
Sabine Berendse ◽  
Willie Hamilton ◽  
David R Baldwin
Keyword(s):  
Lung Cancer ◽  
Systematic Review ◽  
Primary Care ◽  
Risk Prediction ◽  
External Validation ◽  
Clinical Decision ◽  
Cochrane Library ◽  
Specialist Care ◽  
Prediction Tools ◽  
Primary Care Data

BackgroundLung cancer is the leading cause of cancer deaths. Around 70% of patients first presenting to specialist care have advanced disease, at which point current treatments have little effect on survival. The issue for primary care is how to recognise patients earlier and investigate appropriately. This requires an assessment of the risk of lung cancer.AimThe aim of this study was to systematically review the existing risk prediction tools for patients presenting in primary care with symptoms that may indicate lung cancerDesign and settingSystematic review of primary care data.MethodMedline, PreMedline, Embase, the Cochrane Library, Web of Science, and ISI Proceedings (1980 to March 2016) were searched. The final list of included studies was agreed between two of the authors, who also appraised and summarised them.ResultsSeven studies with between 1482 and 2 406 127 patients were included. The tools were all based on UK primary care data, but differed in complexity of development, number/type of variables examined/included, and outcome time frame. There were four multivariable tools with internal validation area under the curves between 0.88 and 0.92. The tools all had a number of limitations, and none have been externally validated, or had their clinical and cost impact examined.ConclusionThere is insufficient evidence for the recommendation of any one of the available risk prediction tools. However, some multivariable tools showed promising discrimination. What is needed to guide clinical practice is both external validation of the existing tools and a comparative study, so that the best tools can be incorporated into clinical decision tools used in primary care.

scholarly journals External validation of two prediction tools for patients at risk for recurrent Clostridioides difficile infection

2021 ◽  
Vol 14 ◽  
pp. 175628482097738
Author(s):  
Tessel M. van Rossen ◽  
Laura J. van Dijk ◽  
Martijn W. Heymans ◽  
Olaf M. Dekkers ◽  
Christina M. J. E. Vandenbroucke-Grauls ◽  
...  
Keyword(s):  
At Risk ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
External Validation ◽  
Risk Scores ◽  
Prediction Tools ◽  
Clostridioides Difficile ◽  
Clostridioides Difficile Infection ◽  
Patients At Risk ◽  
Definition Of

Background: One in four patients with primary Clostridioides difficile infection (CDI) develops recurrent CDI (rCDI). With every recurrence, the chance of a subsequent CDI episode increases. Early identification of patients at risk for rCDI might help doctors to guide treatment. The aim of this study was to externally validate published clinical prediction tools for rCDI. Methods: The validation cohort consisted of 129 patients, diagnosed with CDI between 2018 and 2020. rCDI risk scores were calculated for each individual patient in the validation cohort using the scoring tools described in the derivation studies. Per score value, we compared the average predicted risk of rCDI with the observed number of rCDI cases. Discrimination was assessed by calculating the area under the receiver operating characteristic curve (AUC). Results: Two prediction tools were selected for validation (Cobo 2018 and Larrainzar-Coghen 2016). The two derivation studies used different definitions for rCDI. Using Cobo’s definition, rCDI occurred in 34 patients (26%) of the validation cohort: using the definition of Larrainzar-Coghen, we observed 19 recurrences (15%). The performance of both prediction tools was poor when applied to our validation cohort. The estimated AUC was 0.43 [95% confidence interval (CI); 0.32–0.54] for Cobo’s tool and 0.42 (95% CI; 0.28–0.56) for Larrainzar-Coghen’s tool. Conclusion: Performance of both prediction tools was disappointing in the external validation cohort. Currently identified clinical risk factors may not be sufficient for accurate prediction of rCDI.

scholarly journals Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review

2021 ◽  
Author(s):  
Cynthia Yang ◽  
Jan A. Kors ◽  
Solomon Ioannou ◽  
Luis H. John ◽  
Aniek F. Markus ◽  
...  
Keyword(s):  
Systematic Review ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Final Model ◽  
Development And Validation ◽  
Over Time

Objectives This systematic review aims to provide further insights into the conduct and reporting of clinical prediction model development and validation over time. We focus on assessing the reporting of information necessary to enable external validation by other investigators. Materials and Methods We searched Embase, Medline, Web-of-Science, Cochrane Library and Google Scholar to identify studies that developed one or more multivariable prognostic prediction models using electronic health record (EHR) data published in the period 2009-2019. Results We identified 422 studies that developed a total of 579 clinical prediction models using EHR data. We observed a steep increase over the years in the number of developed models. The percentage of models externally validated in the same paper remained at around 10%. Throughout 2009-2019, for both the target population and the outcome definitions, code lists were provided for less than 20% of the models. For about half of the models that were developed using regression analysis, the final model was not completely presented. Discussion Overall, we observed limited improvement over time in the conduct and reporting of clinical prediction model development and validation. In particular, the prediction problem definition was often not clearly reported, and the final model was often not completely presented. Conclusion Improvement in the reporting of information necessary to enable external validation by other investigators is still urgently needed to increase clinical adoption of developed models.

scholarly journals Assessing prognosis and prediction of treatment response in early rheumatoid arthritis: systematic reviews

2018 ◽  
Vol 22 (66) ◽  
pp. 1-294 ◽  
Author(s):  
Rachel Archer ◽  
Emma Hock ◽  
Jean Hamilton ◽  
John Stevens ◽  
Munira Essat ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Prediction Models ◽  
Meta Analysis ◽  
External Validation ◽  
Model Development ◽  
Predictive Performance ◽  
Assessment Tools ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Early Ra

Background Rheumatoid arthritis (RA) is a chronic, debilitating disease associated with reduced quality of life and substantial costs. It is unclear which tests and assessment tools allow the best assessment of prognosis in people with early RA and whether or not variables predict the response of patients to different drug treatments. Objective To systematically review evidence on the use of selected tests and assessment tools in patients with early RA (1) in the evaluation of a prognosis (review 1) and (2) as predictive markers of treatment response (review 2). Data sources Electronic databases (e.g. MEDLINE, EMBASE, The Cochrane Library, Web of Science Conference Proceedings; searched to September 2016), registers, key websites, hand-searching of reference lists of included studies and key systematic reviews and contact with experts. Study selection Review 1 – primary studies on the development, external validation and impact of clinical prediction models for selected outcomes in adult early RA patients. Review 2 – primary studies on the interaction between selected baseline covariates and treatment (conventional and biological disease-modifying antirheumatic drugs) on salient outcomes in adult early RA patients. Results Review 1 – 22 model development studies and one combined model development/external validation study reporting 39 clinical prediction models were included. Five external validation studies evaluating eight clinical prediction models for radiographic joint damage were also included. c-statistics from internal validation ranged from 0.63 to 0.87 for radiographic progression (different definitions, six studies) and 0.78 to 0.82 for the Health Assessment Questionnaire (HAQ). Predictive performance in external validations varied considerably. Three models [(1) Active controlled Study of Patients receiving Infliximab for the treatment of Rheumatoid arthritis of Early onset (ASPIRE) C-reactive protein (ASPIRE CRP), (2) ASPIRE erythrocyte sedimentation rate (ASPIRE ESR) and (3) Behandelings Strategie (BeSt)] were externally validated using the same outcome definition in more than one population. Results of the random-effects meta-analysis suggested substantial uncertainty in the expected predictive performance of models in a new sample of patients. Review 2 – 12 studies were identified. Covariates examined included anti-citrullinated protein/peptide anti-body (ACPA) status, smoking status, erosions, rheumatoid factor status, C-reactive protein level, erythrocyte sedimentation rate, swollen joint count (SJC), body mass index and vascularity of synovium on power Doppler ultrasound (PDUS). Outcomes examined included erosions/radiographic progression, disease activity, physical function and Disease Activity Score-28 remission. There was statistical evidence to suggest that ACPA status, SJC and PDUS status at baseline may be treatment effect modifiers, but not necessarily that they are prognostic of response for all treatments. Most of the results were subject to considerable uncertainty and were not statistically significant. Limitations The meta-analysis in review 1 was limited by the availability of only a small number of external validation studies. Studies rarely investigated the interaction between predictors and treatment. Suggested research priorities Collaborative research (including the use of individual participant data) is needed to further develop and externally validate the clinical prediction models. The clinical prediction models should be validated with respect to individual treatments. Future assessments of treatment by covariate interactions should follow good statistical practice. Conclusions Review 1 – uncertainty remains over the optimal prediction model(s) for use in clinical practice. Review 2 – in general, there was insufficient evidence that the effect of treatment depended on baseline characteristics. Study registration This study is registered as PROSPERO CRD42016042402. Funding The National Institute for Health Research Health Technology Assessment programme.

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