We investigated stroke risks in a very large prospective cohort of patients with multimorbidity, using two common clinical rules, a clinical multimorbid index and a machine-learning (ML) approach accounting for the complex relationships among variables, including the dynamic nature of changing risk factors. Methods We studied a prospective US cohort of 3435224 patients from medical databases in a 2-year investigation. Stroke outcomes were examined in relationship to diverse multi-morbid conditions, demographic variables and other inputs, with ML accounting for the dynamic nature of changing multimorbidity risk factors, 2 clinical risk scores and a clinical multimorbid index. Results Common clinical risk scores had moderate and comparable c indices with stroke outcomes in the training and external validation samples (validation – CHADS2: c index 0.812; CHA2DS2-VASc: c index 0.809). A clinical multimorbid index had higher discriminant validity values for both the training/external validation samples (validation: c-index 0.850). The machine learning (ML) based algorithms yielded the highest discriminant validity values for the gradient boosting/neural network logistic regression formulations with no significant differences among the ML approaches (validation for logistic regression: c index 0.866). Calibration of the ML based formulation was satisfactory across a wide range of predicted probabilities. Decision curve analysis demonstrated that clinical utility for the ML based formulation was best. Also, ML models and clinical stroke risk scores were more clinically useful than the ‘treat all’ strategy. Conclusion Complex relationships of various comorbidities uncovered using a ML approach for diverse(and dynamic) multimorbidity changes have major consequences for stroke risk prediction.
Background: The elderly multi-morbid patient is at high risk of adverse outcomes with COVID-19 complications, and in the general population, the development of incident AF is associated with worse outcomes in such patients. There is therefore the need to identify those patients with COVID-19 who are at highest risk of developing incident AF. We therefore investigated incident AF risks in a large prospective population of elderly patients with/without incident COVID-19 cases and baseline cardiovascular/non-cardiovascular multi-morbidities. We used two approaches: main effect modeling and secondly, a machine-learning (ML) approach, accounting for the complex dynamic relationships among comorbidity variables. Methods: We studied a prospective elderly US cohort of 280,592 patients from medical databases in an 8-month investigation of with/without newly incident COVID19 cases. Incident AF outcomes were examined in relationship to diverse multi-morbid conditions, COVID-19 status and demographic variables, with ML accounting for the dynamic nature of changing multimorbidity risk factors. Results: Multi-morbidity contributed to the onset of confirmed COVID-19 cases with cognitive impairment (OR 1.69; 95%CI 1.52-1.88), anemia (OR 1.41; 95%CI 1.32-1.50), diabetes mellitus (OR 1.35; 95%CI 1.27-1.44) and vascular disease (OR 1.30; 95%CI 1.21-1.39) having the highest associations. A main effect model (C-index value 0.718) showed that COVID-19 had the highest association with incident AF cases (OR 3.12; 95%CI 2.61-3.710, followed by congestive heart failure (1.72; 95%CI 1.50-1.96), then coronary artery disease (OR 1.43; 95%CI 1.27-1.60) and valvular disease (1.42; 95%CI 1.26-1.60). The ML algorithm demonstrated improved discriminatory validity incrementally over the statistical main effect model (training: C-index 0.729, 95%CI 0.718-0.740; validation: C-index 0.704, 95%CI 0.687-0.72). Calibration of the ML based formulation was satisfactory and better than the main-effect model. Decision curve analysis demonstrated that the clinical utility for the ML based formulation was better than the 'treat all' strategy and the main effect model. Conclusion: COVID-19 status has major implications for incident AF in a cohort with diverse cardiovascular/noncardiovascular multi-morbidities. Our ML approach accounting for dynamic multimorbidity changes had good prediction for new onset AF amongst incident COVID19 cases.
Background Diversified cardiovascular/non-cardiovascular multimorbid risk and efficient machine learning algorithms may facilitate improvements in stroke risk prediction, especially in newly diagnosed non-anticoagulated atrial fibrillation (AF) patients where initial decision-making on stroke prevention is needed. Objective sTo update common clinical risk assessment for stroke risk prediction in AF/non-AF cohorts with cardiovascular/non-cardiovascular multimorbid conditions; second, to improve stroke risk prediction using machine learning approaches; and third, to compare the improved clinical prediction rules for multi-morbid conditions using machine learning algorithms. Data design We used cohort data from two health plans with 6,457,412 males/females contributing 14,188,679 person-years of data. Predictive modeling The model inputs consisted of diversified list of comorbidities/demographic/temporal exposure variables, with the outcome capturing stroke event incidences. Machine learning algorithms used two parametric and two non-parametric techniques. Results The best prediction model was derived on the basis of non-linear formulations using machine learning criteria, with the highest c-index was obtained for logistic regression (0.892; 95%CI 0.886-0.898), with consistency on external validation (0.891; 95%CI 0.882-0.9). These were significantly higher than those based on the conventional stroke risk scores (CHADS2: 0.7488, 95% CI 0.746-0.7516; CHA2DS2-VASc: 0.7801, 95% CI 0.7772-0.7831) and multimorbid index (0.8508, 95% CI 0.8483-0.8532). The machine learning algorithm had good internal and external calibration, and net benefit values. Conclusion In this large cohort of newly diagnosed non-anticoagulated AF/non-AF patients, large improvements in stroke risk prediction can be shown with a cardiovascular/non-cardiovascular multimorbid index and a machine learning approach incorporating changes in risk related to ageing and incident comorbidities.
Background Patients with atrial fibrillation (AF) usually have a heterogeneous co‐morbid history, with dynamic changes in risk factors impacting on multiple adverse outcomes. We investigated a large prospective cohort of patients with multimorbidity, using a machine‐learning approach, accounting for the dynamic nature of comorbidity risks and incident AF. Methods Using machine‐learning, we studied a prospective US cohort using medical/pharmacy databases of 1 091 911 patients, with an incident AF cohort of 14 078 and non‐AF cohort of 1 077 833 enrolled in the 4‐year study. Five incident clinical outcomes (heart failure, stroke, myocardial infarction, major bleeding, and cognitive impairment) were examined in relationship to AF status (AF vs non‐AF), diverse multi‐morbid (conditions and medications) history, and demographic parameters (age and gender), with supervised machine‐learning techniques. Results Complex inter‐relationships of various comorbidities were uncovered for AF cases, leading to 6‐fold higher risk of heart failure relative to the non‐AF cohort (OR 6.02, 95% CI 5.72‐6.33), followed by myocardial infarction (OR=2.68), stroke (OR=2.19), and major bleeding (OR=1.36). Supervised machine learning algorithms on the original populations yielded comparable results for both neural network and logistic regression algorithms in terms of discriminant validity, with c‐indexes for incident adverse outcomes: heart failure (0.924, 95%CI 0.923‐0.925), stroke (0.871, 95%CI 0.869‐0.873), myocardial infarction (0.901, 95% CI 0.899‐0.903), major bleeding (0.700, 95%CI 0.697‐0.703), and cognitive impairment (0.919, 95% CI 0.9170.921). External calibration of all models demonstrated a good fit between the predicted probabilities and observed events. Decision curve analysis demonstrated that the obtained models were much more clinically useful than the “treat all” strategy. Conclusions Complex multimorbidity relationships uncovered using a machine learning approach for incident AF cases have major consequences for integrated care management, with implications for risk stratification and adverse clinical outcomes. This approach may facilitate automated approaches in the presence of multimorbidity, potentially helping decision making.
Background: Identification of published data on prevalent/incidence of atrial fibrillation/flutter (AF) often relies on inpatient/outpatient claims, without consideration to other types of healthcare services and pharmacy claims. Accurate, populationlevel data that can enable the ongoing monitoring of AF epidemiology, quality of care at affordable cost, and complications are needed. We hypothesised that prevalent/ incidence data would vary via the use of integrated medical/pharmacy claims, and associated comorbidities would vary accordingly.Purpose: To examine AF prevalence/incidence and associated individual comorbidity and multi-morbidity profiles for a large US adult cohort spanning across a wide age range for both males/females based on both integrated criteria from both medical/ pharmacy claims. Methods:We studied a population of 8 343 992 persons across many geographical areas in the US continent from 1 January/2016 to 31 October 2019. The prevalence and incidence of AF were comparatively analysed for different healthcare parameters (eg, emergency room visit, anticoagulant medication, heart rhythm control medication) and for integrated criteria based on medical/pharmacy claims.Results: Based on integrated medical and pharmacy claims, AF prevalence was 12.7% in the elderly population (≥65 years) and 0.9% in the younger population (<65 years).These prevalence rates are different from estimates provided by the US CDC for those aged ≥65 years (9%) and age <65 years (2%); thus, the prevalence is underestimated in the elderly population and over-estimated in the younger population.The incidence ratios for elderly females relative to younger females was 15.07 (95%CI 14.47-15.70), a value that is about 50% higher than for elderly males (10.57 (95%CI 10.24-10.92)). Comorbidity risk profile for AF identified on the basis of medical and pharmacy criteria varied by age and gender. The proportion with multi-morbidity (defined as ≥2 long term comorbidities) was 10%-12%. Conclusion: Continued reliance only on outpatient and inpatient claims greatly un-derestimates AF prevalence and incidence in the general population by over 100%.Multi-morbidity is common amongst AF patients, affecting approximately 1 in 10 patients. AF patients with four or more co-morbidities captured 20%-40% of the AF cohorts depending on age groups and prevalent or incident cases. 2 of 13 | LIP et aL. | INTRODUC TI ONAtrial fibrillation (AF) is the commonest cardiac rhythm disorder globally, and confers a large healthcare burden from mortality and morbidity from stroke/systemic thromboembolism, heart failure, dementia, and hospitalisations. 1 Accurate, population-level data that can enable ongoing monitoring of AF epidemiology, quality of care at affordable cost, and complications are paramount.In the United States, data on AF prevalence/incidence are typically identified in administrative medical databases using inpatient/ outpatient/physician claims with ICD 9/10 codes. 2,3 Nonetheless, identification does not include claims from other healthcare se...
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