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Development and Validation of a Risk Stratification Model Using Disease Severity Hierarchy for Mortality or Major Cardiovascular Event.

ABSTRACT: Importance:Clinical domain knowledge about diseases and their comorbidities, severity, treatment pathways, and outcomes can facilitate diagnosis, enhance preventive strategies, and help create smart evidence-based practice guidelines. Objective:To introduce a new representation of patient data called disease severity hierarchy that leverages domain knowledge in a nested fashion to create subpopulations that share increasing amounts of clinical details suitable for risk prediction. Design, Setting, and Participants:This retrospective cohort study included 51?969 patients aged 45 to 85 years, with 10?674 patients who received primary care at the Mayo Clinic between January 2004 and December 2015 in the training cohort and 41?295 patients who received primary care at Fairview Health Services from January 2010 to December 2017 in the validation cohort. Data were analyzed from May 2018 to December 2019. Main Outcomes and Measures:Several binary classification measures, including the area under the receiver operating characteristic curve (AUC), Gini score, sensitivity, and positive predictive value, were used to evaluate models predicting all-cause mortality and major cardiovascular events at ages 60, 65, 75, and 80 years. Results:The mean (SD) age and proportions of women and white individuals were 59.4 (10.8) years, 6324 (59.3%) and 9804 (91.9%), respectively, in the training cohort and 57.4 (7.9) years, 21?975 (53.1%), and 37?653 (91.2%), respectively, in the validation cohort. During follow-up, 945 patients (8.9%) in the training cohort died, while 787 (7.4%) had major cardiovascular events. Models using the new representation achieved AUCs for predicting death in the training cohort at ages 60, 65, 75, and 80 years of 0.96 (95% CI, 0.94-0.97), 0.96 (95% CI, 0.95-0.98), 0.97 (95% CI, 0.96-0.98), and 0.98 (95% CI, 0.98-0.99), respectively, while standard methods achieved modest AUCs of 0.67 (95% CI, 0.55-0.80), 0.66 (95% CI, 0.56-0.79), 0.64 (95% CI, 0.57-0.71), and 0.63 (95% CI, 0.54-0.70), respectively. Conclusions and Relevance:In this study, the proposed patient data representation accurately predicted the age at which a patient was at risk of dying or developing major cardiovascular events substantially better than standard methods. The representation uses known relationships contained in electronic health records to capture disease severity in a natural and clinically meaningful way. Furthermore, it is expressive and interpretable. This novel patient representation can help to support critical decision-making, develop smart guidelines, and enhance health care and disease management by helping to identify patients with high risk.

SUBMITTER: Ngufor C

PROVIDER: S-EPMC7368174 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Development and Validation of a Risk Stratification Model Using Disease Severity Hierarchy for Mortality or Major Cardiovascular Event.

Ngufor Che C Caraballo Pedro J PJ O'Byrne Thomas J TJ Chen David D Shah Nilay D ND Pruinelli Lisiane L Steinbach Michael M Simon Gyorgy G

JAMA network open 20200701 7

<h4>Importance</h4>Clinical domain knowledge about diseases and their comorbidities, severity, treatment pathways, and outcomes can facilitate diagnosis, enhance preventive strategies, and help create smart evidence-based practice guidelines.<h4>Objective</h4>To introduce a new representation of patient data called disease severity hierarchy that leverages domain knowledge in a nested fashion to create subpopulations that share increasing amounts of clinical details suitable for risk prediction. ...[more]

PMID: 32678448

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Project description:BackgroundLong-term childhood cancer survivors (CCS) are at increased risk of adverse cardiovascular events; however, there is a paucity of risk-stratification tools to identify those at higher-than-normal risk.Subjects, materials, and methodsThis was a population-based study using data from the Surveillance, Epidemiology, and End Results Program (1973-2013). Long-term CCS (age at diagnosis ≤19 years, survival ≥5 years) were followed up over a median time period of 12.3 (5-40.9) years. Independent predictors of cardiovascular mortality (CVM) were combined into a risk score, which was developed in a derivation set (n = 22,374), and validated in separate patient registries (n = 6,437).ResultsIn the derivation registries, older age at diagnosis (≥10 years vs. reference group of 1-5 years), male sex, non-white race, a history of lymphoma, and a history of radiation were independently associated with an increased risk of CVM among long-term CCS (p < .05). A risk score derived from this model (Childhood and Adolescence Cancer Survivor CardioVascular score [CHACS-CV], range: 0-8) showed good discrimination for CVM (Harrell's C-index [95% confidence interval (CI)]: 0.73 [0.68-0.78], p < .001) and identified a high-risk group (CHACS-CV ≥6), with cumulative CVM incidence over 30 years of 6.0% (95% CI: 4.3%-8.1%) versus 2.6% (95% CI: 1.8%-3.7%), and 0.7% (95% CI: 0.5%-1.0%) in the mid- (CHACS-CV = 4-5) and low-risk groups (CHACS-CV ≤3), respectively (plog-rank < .001). In the validation set, the respective cumulative incidence rates were 4.7%, 3.1%, and 0.8% (plog-rank < .001).ConclusionWe propose a simple risk score that can be applied in everyday clinical practice to identify long-term CCS at increased cardiovascular risk, who may benefit from early cardiovascular screening, and risk-reduction strategies.Implications for practiceChildhood cancer survivors (CCS) are known to be at increased cardiovascular risk. Currently available prognostic tools focus on treatment-related adverse events and late development of congestive heart failure, but there is no prognostic model to date to estimate the risk of cardiovascular mortality among long-term CCS. A simple clinical tool is proposed for cardiovascular risk stratification of long-term CCS based on easily obtainable information from their medical history. This scoring system may be used as a first-line screening tool to assist health care providers in identifying those who may benefit from closer follow-up and enable timely deployment of preventive strategies.

Project description:ObjectivesPatients admitted to hospital with acute myocardial infarction (AMI) have considerable variability in in-hospital risks, resulting in higher demands on healthcare resources. Simple risk-assessment tools are important for the identification of patients with higher risk to inform clinical decisions. However, few risk assessment tools have been built that are suitable for populations with AMI in China. We aim to develop and validate a risk prediction model, and further build a risk scoring system.DesignData from a nationally representative retrospective study was used to develop the model. Patients from a prospective study and another nationally representative retrospective study were both used for external validation.Setting161 nationally representative hospitals, and 53 and 157 other hospitals were involved in the above three studies, respectively.Participants8010 patients hospitalised for AMI were included as development sample, and 4485 and 11 223 other patients were included as validation samples in their corresponding studies.Primary and secondary outcome measuresThe in-hospital major adverse cardiovascular events (MACE) was defined as death from any cause, recurrent AMI, or ischaemic stroke.ResultsThe proportion of in-hospital MACE was 11.7%, 8.8% and 11.4% among the development sample and two external-validation samples, respectively. Nine predictors (ie, age, sex, left ventricular ejection fraction, Killip class, systolic blood pressure, creatinine, white blood cell count, heart rate and blood glucose) were independently associated with in-hospital MACE. The model performed well on both discrimination and calibration capability, with areas under the Receiver Operating Characteristic Curve (ROC) curve of 0.85, 0.74 and 0.80, and calibration slopes of 0.98, 0.84 and 0.97 in the development sample and two external validation samples, respectively. A point-based risk scoring system was built with good discrimination and reclassification ability.ConclusionsA prediction model using readily available clinical parameters was developed and externally validated to estimate risks of in-hospital MACE among patients with AMI, thereby better informing decision-making in improving clinical care.

Dataset Information

Development and Validation of a Risk Stratification Model Using Disease Severity Hierarchy for Mortality or Major Cardiovascular Event.

Publications

Development and Validation of a Risk Stratification Model Using Disease Severity Hierarchy for Mortality or Major Cardiovascular Event.

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