External validation and update of prediction models for unfavorable outcomes in hospitalized patients with COVID-19 in Japan.
Ontology highlight
ABSTRACT: Introduction
Most of the currently used prognostic models for COVID-19 are based on Western cohorts, but it is unknown whether any are applicable to patients with COVID-19 in Japan.Methods
This retrospective cohort study included 160 patients with COVID-19 who were admitted to the National Center for Global Health and Medicine between January 26, 2020 and July 25, 2020. We searched PubMed for prognostic models for COVID-19. The predicted outcome was initiation of respiratory support or death. Performance of the candidate models was evaluated according to discrimination and calibration. We recalibrated the intercept of each model with our data. We also updated each model by adding β2-microglobulin (β2MG) to the model and recalculating the intercept and the coefficient of β2MG.Results
Mean patient age was 49.8 years, 68% were male, 88.7% were Japanese. The study outcomes occurred in 15 patients, including two deaths. Two-hundred sixty-nine papers were screened, and four candidate prognostic models were assessed. The model of Bartoletti et al. had the highest area under receiver operating characteristic curve (AUC) (0.88; 95% confidence interval 0.81-0.96). All four models overestimated the probability of occurrence of the outcome. None of the four models showed statistically significant improvement in AUCs by adding β2MG.Conclusions
Our results suggest that the existing prediction models for COVID-19 overestimate the probability of occurrence of unfavorable outcomes in a Japanese cohort. When applying a prediction model to a different cohort, it is desirable to evaluate its performance according to the prevalent health situation in that region.
SUBMITTER: Yamada G
PROVIDER: S-EPMC8041181 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:IntroductionExternal validation studies are essential to study the generalizability of prediction models. Recently a permutation test, focusing on discrimination as quantified by the c-statistic, was proposed to judge whether a prediction model is transportable to a new setting. We aimed to evaluate this test and compare it to previously proposed procedures to judge any changes in c-statistic from development to external validation setting.MethodsWe compared the use of the permutation test to the use of benchmark values of the c-statistic following from a previously proposed framework to judge transportability of a prediction model. In a simulation study we developed a prediction model with logistic regression on a development set and validated them in the validation set. We concentrated on two scenarios: 1) the case-mix was more heterogeneous and predictor effects were weaker in the validation set compared to the development set, and 2) the case-mix was less heterogeneous in the validation set and predictor effects were identical in the validation and development set. Furthermore we illustrated the methods in a case study using 15 datasets of patients suffering from traumatic brain injury.ResultsThe permutation test indicated that the validation and development set were homogenous in scenario 1 (in almost all simulated samples) and heterogeneous in scenario 2 (in 17%-39% of simulated samples). Previously proposed benchmark values of the c-statistic and the standard deviation of the linear predictors correctly pointed at the more heterogeneous case-mix in scenario 1 and the less heterogeneous case-mix in scenario 2.ConclusionThe recently proposed permutation test may provide misleading results when externally validating prediction models in the presence of case-mix differences between the development and validation population. To correctly interpret the c-statistic found at external validation it is crucial to disentangle case-mix differences from incorrect regression coefficients.
| S-EPMC4755533 | biostudies-literature
Project description:Most cases of COVID-19 are non-severe, but some patients require urgent hospital care. In the past, it has been established that adrenal hyperactivity predicts poorer prognosis in severely ill patients. We wanted to verify if cortisol levels can be tied to clinical outcomes and the degree of inflammation in hospitalized COVID-19 patients. We recruited 180 adult patients with PCR-confirmed COVID-19. The group was divided into smaller subgroups based on the glucocorticoid treatment status; the subgroups were evaluated in three separate time points. The assessment involved hormonal function (cortisol, ACTH), inflammatory markers, and occurrence of the pre-selected endpoints (death, hospitalization ≥10 days, non-invasive ventilation or high-flow oxygenation, mechanical ventilation, vasopressors). In the evaluated group, 121 patients showed signs of abnormal adrenal function. There was a clear correlation between cortisol and IL-6 concentrations in all three time points regardless of glucocorticoid treatment. A total of 71.1% of patients displaying abnormal cortisol production met the preselected endpoints. Our analysis showed that a cutoff cortisol concentration prognosing endpoint occurrence could be set at 15.45 μg/dL for patients not treated with glucocorticoids. Cortisol concentration can be seen as an independent prognostic factor for unfavorable outcomes in selected adults hospitalized with COVID-19.
| S-EPMC9313159 | biostudies-literature
Project description:BackgroundMany dementia prediction models have been developed, but only few have been externally validated, which hinders clinical uptake and may pose a risk if models are applied to actual patients regardless. Externally validating an existing prediction model is a difficult task, where we mostly rely on the completeness of model reporting in a published article. In this study, we aim to externally validate existing dementia prediction models. To that end, we define model reporting criteria, review published studies, and externally validate three well reported models using routinely collected health data from administrative claims and electronic health records.MethodsWe identified dementia prediction models that were developed between 2011 and 2020 and assessed if they could be externally validated given a set of model criteria. In addition, we externally validated three of these models (Walters' Dementia Risk Score, Mehta's RxDx-Dementia Risk Index, and Nori's ADRD dementia prediction model) on a network of six observational health databases from the United States, United Kingdom, Germany and the Netherlands, including the original development databases of the models.ResultsWe reviewed 59 dementia prediction models. All models reported the prediction method, development database, and target and outcome definitions. Less frequently reported by these 59 prediction models were predictor definitions (52 models) including the time window in which a predictor is assessed (21 models), predictor coefficients (20 models), and the time-at-risk (42 models). The validation of the model by Walters (development c-statistic: 0.84) showed moderate transportability (0.67-0.76 c-statistic). The Mehta model (development c-statistic: 0.81) transported well to some of the external databases (0.69-0.79 c-statistic). The Nori model (development AUROC: 0.69) transported well (0.62-0.68 AUROC) but performed modestly overall. Recalibration showed improvements for the Walters and Nori models, while recalibration could not be assessed for the Mehta model due to unreported baseline hazard.ConclusionWe observed that reporting is mostly insufficient to fully externally validate published dementia prediction models, and therefore, it is uncertain how well these models would work in other clinical settings. We emphasize the importance of following established guidelines for reporting clinical prediction models. We recommend that reporting should be more explicit and have external validation in mind if the model is meant to be applied in different settings.
| S-EPMC9720950 | biostudies-literature
Project description:Concordance measures are frequently used for assessing the discriminative ability of risk prediction models. The interpretation of estimated concordance at external validation is difficult if the case-mix differs from the model development setting. We aimed to develop a concordance measure that provides insight into the influence of case-mix heterogeneity and is robust to censoring of time-to-event data. We first derived a model-based concordance (mbc) measure that allows for quantification of the influence of case-mix heterogeneity on discriminative ability of proportional hazards and logistic regression models. This mbc can also be calculated including a regression slope that calibrates the predictions at external validation (c-mbc), hence assessing the influence of overall regression coefficient validity on discriminative ability. We derived variance formulas for both mbc and c-mbc. We compared the mbc and the c-mbc with commonly used concordance measures in a simulation study and in two external validation settings. The mbc was asymptotically equivalent to a previously proposed resampling-based case-mix corrected c-index. The c-mbc remained stable at the true value with increasing proportions of censoring, while Harrell's c-index and to a lesser extent Uno's concordance measure increased unfavorably. Variance estimates of mbc and c-mbc were well in agreement with the simulated empirical variances. We conclude that the mbc is an attractive closed-form measure that allows for a straightforward quantification of the expected change in a model's discriminative ability due to case-mix heterogeneity. The c-mbc also reflects regression coefficient validity and is a censoring-robust alternative for the c-index when the proportional hazards assumption holds. Copyright © 2016 John Wiley & Sons, Ltd.
| S-EPMC5550798 | biostudies-literature
Project description:Risk calculators can facilitate shared medical decision-making 1 . Demographics, comorbidities, medication use, geographic region, and other factors may increase the risk for COVID-19-related complications among patients with IBD 2,3 . Develop an individualized prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with IBD. This study developed and validated prognostic penalized logistic regression models 4 using reports to Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease (SECURE-IBD) from March-October 2020. Model development was done using a training data set (85% of cases reported March 13 - September 15, 2020), and model validation was conducted using a test data set (the remaining 15% of cases plus all cases reported September 16-October 20, 2020). COVID-19 related:Hospitalization+: composite outcome of hospitalization, ICU admission, mechanical ventilation, or deathICU+: composite outcome of ICU admission, mechanical ventilation, or deathDeathWe assessed the resulting models' discrimination using the area under the curve (AUC) of the receiver-operator characteristic (ROC) curves and reported the corresponding 95% confidence intervals (CIs). We included 2709 cases from 59 countries (mean age 41.2 years [s.d. 18], 50.2% male). A total of 633 (24%) were hospitalized, 137 (5%) were admitted to the ICU or intubated, and 69 (3%) died. 2009 patients comprised the training set and 700 the test set.The models demonstrated excellent discrimination, with a test set AUC (95% CI) of 0.79 (0.75, 0.83) for Hospitalization+, 0.88 (0.82, 0.95) for ICU+, and 0.94 (0.89, 0.99) for Death. Age, comorbidities, corticosteroid use, and male gender were associated with higher risk of death, while use of biologic therapies was associated with a lower risk. Prognostic models can effectively predict who is at higher risk for COVID-19-related adverse outcomes in a population of IBD patients. A free online risk calculator ( https://covidibd.org/covid-19-risk-calculator/ ) is available for healthcare providers to facilitate discussion of risks due to COVID-19 with IBD patients. The tool numerically and visually summarizes the patient's probabilities of adverse outcomes and associated CIs. Helping physicians identify their highest-risk patients will be important in the coming months as cases rise in the US and worldwide. This tool can also serve as a model for risk stratification in other chronic diseases. Question: How well can a multivariable risk model predict the risk of hospitalization, intensive care unit (ICU) stay, or death due to COVID-19 in patients with inflammatory bowel disease (IBD)?Findings: Multivariable prediction models developed using data from an international voluntary registry of IBD patients and available for use online ( https://covidibd.org/ ) have very good discrimination for predicting hospitalization (Test set AUC 0.79) and excellent discrimination for ICU admission (Test set AUC 0.88) and death (Test set AUC 0.94). The models were developed with a training sample of 2009 cases and validated in an independent test sample of 700 cases comprised of a random sub-sample of cases and all cases entered in the registry during a one-month period after model development. Meaning: This risk prediction model may serve as an effective tool for healthcare providers to facilitate conversations about COVID-19-related risks with IBD patients.
| S-EPMC7836127 | biostudies-literature
Project description:The value of a developed prediction model depends on its performance outside the development sample. The key is therefore to externally validate the model on a different but related independent data. In this study, we propose a permutation method to assess heterogeneity in external validation for risk prediction models. The permutation p value measures the extent of homology between development and validation datasets. If p < 0.05, the model may not be directly transported to the external validation population without further revision or updating. Monte-Carlo simulations are conducted to evaluate the statistical properties of the proposed method, and two microarray breast cancer datasets are analyzed for demonstration. The permutation method is easy to implement and is recommended for routine use in external validation for risk prediction models.
| S-EPMC4301917 | biostudies-literature
Project description:BackgroundThis study aimed to compare and externally validate risk scores developed to predict incident colorectal cancer (CRC) that include variables routinely available or easily obtainable via self-completed questionnaire.MethodsExternal validation of fourteen risk models from a previous systematic review in 373 112 men and women within the UK Biobank cohort with 5-year follow-up, no prior history of CRC and data for incidence of CRC through linkage to national cancer registries.ResultsThere were 1719 (0.46%) cases of incident CRC. The performance of the risk models varied substantially. In men, the QCancer10 model and models by Tao, Driver and Ma all had an area under the receiver operating characteristic curve (AUC) between 0.67 and 0.70. Discrimination was lower in women: the QCancer10, Wells, Tao, Guesmi and Ma models were the best performing with AUCs between 0.63 and 0.66. Assessment of calibration was possible for six models in men and women. All would require country-specific recalibration if estimates of absolute risks were to be given to individuals.ConclusionsSeveral risk models based on easily obtainable data have relatively good discrimination in a UK population. Modelling studies are now required to estimate the potential health benefits and cost-effectiveness of implementing stratified risk-based CRC screening.
| S-EPMC5846069 | biostudies-literature
Project description:BackgroundEarly distinction between mild and serious infections (SI) is challenging in children in ambulatory care. Clinical prediction models (CPMs), developed to aid physicians in clinical decision-making, require broad external validation before clinical use. We aimed to externally validate four CPMs, developed in emergency departments, in ambulatory care.MethodsWe applied the CPMs in a prospective cohort of acutely ill children presenting to general practices, outpatient paediatric practices or emergency departments in Flanders, Belgium. For two multinomial regression models, Feverkidstool and Craig model, discriminative ability and calibration were assessed, and a model update was performed by re-estimation of coefficients with correction for overfitting. For two risk scores, the SBI score and PAWS, the diagnostic test accuracy was assessed.ResultsA total of 8211 children were included, comprising 498 SI and 276 serious bacterial infections (SBI). Feverkidstool had a C-statistic of 0.80 (95% confidence interval 0.77-0.84) with good calibration for pneumonia and 0.74 (0.70-0.79) with poor calibration for other SBI. The Craig model had a C-statistic of 0.80 (0.77-0.83) for pneumonia, 0.75 (0.70-0.80) for complicated urinary tract infections and 0.63 (0.39-0.88) for bacteraemia, with poor calibration. The model update resulted in improved C-statistics for all outcomes and good overall calibration for Feverkidstool and the Craig model. SBI score and PAWS performed extremely weak with sensitivities of 0.12 (0.09-0.15) and 0.32 (0.28-0.37).ConclusionsFeverkidstool and the Craig model show good discriminative ability for predicting SBI and a potential for early recognition of SBI, confirming good external validity in a low prevalence setting of SBI. The SBI score and PAWS showed poor diagnostic performance.Trial registrationClinicalTrials.gov, NCT02024282. Registered on 31 December 2013.
| S-EPMC10114467 | biostudies-literature
Project description:BackgroundThe coronavirus disease 2019 pandemic has placed unprecedented stress on health systems and has been associated with elevated risk for delirium. The convergence of pandemic resource limitation and clinical demand associated with delirium requires careful risk stratification for targeted prevention efforts.ObjectivesTo develop an incident delirium predictive model among coronavirus disease 2019 patients.MethodsWe applied supervised machine learning to electronic health record data for inpatients with coronavirus disease 2019 at three hospitals to build an incident delirium diagnosis prediction model. We validated this model in three different hospitals. Both hospital cohorts included academic and community settings.ResultsAmong 2907 patients across 6 hospitals, 488 (16.8%) developed delirium. Applying the predictive model in the external validation cohort of 755 patients, the c-index was 0.75 (0.71-0.79) and the lift in the top quintile was 2.1. At a sensitivity of 80%, the specificity was 56%, negative predictive value 92%, and positive predictive value 30%. Equivalent model performance was observed in subsamples stratified by age, sex, race, need for critical care and care at community vs. academic hospitals.ConclusionMachine learning applied to electronic health records available at the time of inpatient admission can be used to risk-stratify patients with coronavirus disease 2019 for incident delirium. Delirium is common among patients with coronavirus disease 2019, and resource constraints during a pandemic demand careful attention to the optimal application of predictive models.
| S-EPMC7933786 | biostudies-literature
Project description:ObjectivesDevelop an individualised prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with inflammatory bowel disease (IBD).Design and settingThis study developed and validated prognostic penalised logistic regression models using reports to the international Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease voluntary registry from March to October 2020. Model development was done using a training data set (85% of cases reported 13 March-15 September 2020), and model validation was conducted using a test data set (the remaining 15% of cases plus all cases reported 16 September-20 October 2020).ParticipantsWe included 2709 cases from 59 countries (mean age 41.2 years (SD 18), 50.2% male). All submitted cases after removing duplicates were included.Primary and secondary outcome measuresCOVID-19 related: (1) Hospitalisation+: composite outcome of hospitalisation, ICU admission, mechanical ventilation or death; (2) Intensive Care Unit+ (ICU+): composite outcome of ICU admission, mechanical ventilation or death; (3) Death. We assessed the resulting models' discrimination using the area under the curve of the receiver operator characteristic curves and reported the corresponding 95% CIs.ResultsOf the submitted cases, a total of 633 (24%) were hospitalised, 137 (5%) were admitted to the ICU or intubated and 69 (3%) died. 2009 patients comprised the training set and 700 the test set. The models demonstrated excellent discrimination, with a test set area under the curve (95% CI) of 0.79 (0.75 to 0.83) for Hospitalisation+, 0.88 (0.82 to 0.95) for ICU+ and 0.94 (0.89 to 0.99) for Death. Age, comorbidities, corticosteroid use and male gender were associated with a higher risk of death, while the use of biological therapies was associated with a lower risk.ConclusionsPrognostic models can effectively predict who is at higher risk for COVID-19-related adverse outcomes in a population of patients with IBD. A free online risk calculator (https://covidibd.org/covid-19-risk-calculator/) is available for healthcare providers to facilitate discussion of risks due to COVID-19 with patients with IBD.
| S-EPMC8593277 | biostudies-literature