Flexible Approaches Based on Multistate Models and Microsimulation to Perform Real-World Cost-Effectiveness Analyses: An Application to Proprotein Convertase Subtilisin-Kexin Type 9 Inhibitors.

OBJECTIVES: This study aims to show the application of flexible statistical methods in real-world cost-effectiveness analyses applied in the cardiovascular field, focusing specifically on the use of proprotein convertase subtilisin-kexin type 9 inhibitors for hyperlipidemia. METHODS: The proposed method allowed us to use an electronic health database to emulate a target trial for cost-effectiveness analysis using multistate modeling and microsimulation. We formally established the study design and provided precise definitions of the causal measures of interest while also outlining the assumptions necessary for accurately estimating these measures using the available data. Additionally, we thoroughly considered goodness-of-fit assessments and sensitivity analyses of the decision model, which are crucial to capture the complexity of individuals' healthcare pathway and to enhance the validity of this type of health economic models. RESULTS: In the disease model, the Markov assumption was found to be inadequate, and a "time-reset" timescale was implemented together with the use of a time-dependent variable to incorporate past hospitalization history. Furthermore, the microsimulation decision model demonstrated a satisfying goodness of fit, as evidenced by the consistent results obtained in the short-term horizon compared with a nonmodel-based approach. Notably, proprotein convertase subtilisin-kexin type 9 inhibitors revealed their favorable cost-effectiveness only in the long-term follow-up, with a minimum willingness to pay of 39 000 Euro/life years gained. CONCLUSIONS: The approach demonstrated its significant utility in several ways. Unlike nonmodel-based or alternative model-based methods, it enabled to (1) investigate long-term cost-effectiveness comprehensively, (2) use an appropriate disease model that aligns with the specific problem under study, and (3) conduct subgroup-specific cost-effectiveness analyses to gain more targeted insights.

Comparison of discrimination and calibration performance of ECG-based machine learning models for prediction of new-onset atrial fibrillation.

BACKGROUND: Machine learning (ML) methods to build prediction models starting from electrocardiogram (ECG) signals are an emerging research field. The aim of the present study is to investigate the performances of two ML approaches based on ECGs for the prediction of new-onset atrial fibrillation (AF), in terms of discrimination, calibration and sample size dependence. METHODS: We trained two models to predict new-onset AF: a convolutional neural network (CNN), that takes as input the raw ECG signals, and an eXtreme Gradient Boosting model (XGB), that uses the signal's extracted features. A penalized logistic regression model (LR) was used as a benchmark. Discrimination was evaluated with the area under the ROC curve, while calibration with the integrated calibration index. We investigated the dependence of models' performances on the sample size and on class imbalance corrections introduced with random under-sampling. RESULTS: CNN's discrimination was the most affected by the sample size, outperforming XGB and LR only around n = 10.000 observations. Calibration showed only a small dependence on the sample size for all the models considered. Balancing the training set with random undersampling did not improve discrimination in any of the models. Instead, the main effect of imbalance corrections was to worsen the models' calibration (for CNN, integrated calibration index from 0.014 [0.01, 0.018] to 0.17 [0.16, 0.19]). The sample size emerged as a fundamental point for developing the CNN model, especially in terms of discrimination (AUC = 0.75 [0.73, 0.77] when n = 10.000, AUC = 0.80 [0.79, 0.81] when n = 150.000). The effect of the sample size on the other two models was weaker. Imbalance corrections led to poorly calibrated models, for all the approaches considered, reducing the clinical utility of the models. CONCLUSIONS: Our results suggest that the choice of approach in the analysis of ECG should be based on the amount of data available, preferring more standard models for small datasets. Moreover, imbalance correction methods should be avoided when developing clinical prediction models, where calibration is crucial.

Deep-learning-based prognostic modeling for incident heart failure in patients with diabetes using electronic health records: A retrospective cohort study.

Patients with type 2 diabetes mellitus (T2DM) have more than twice the risk of developing heart failure (HF) compared to patients without diabetes. The present study is aimed to build an artificial intelligence (AI) prognostic model that takes in account a large and heterogeneous set of clinical factors and investigates the risk of developing HF in diabetic patients. We carried out an electronic health records- (EHR-) based retrospective cohort study that included patients with cardiological clinical evaluation and no previous diagnosis of HF. Information consists of features extracted from clinical and administrative data obtained as part of routine medical care. The primary endpoint was diagnosis of HF (during out-of-hospital clinical examination or hospitalization). We developed two prognostic models using (1) elastic net regularization for Cox proportional hazard model (COX) and (2) a deep neural network survival method (PHNN), in which a neural network was used to represent a non-linear hazard function and explainability strategies are applied to estimate the influence of predictors on the risk function. Over a median follow-up of 65 months, 17.3% of the 10,614 patients developed HF. The PHNN model outperformed COX both in terms of discrimination (c-index 0.768 vs 0.734) and calibration (2-year integrated calibration index 0.008 vs 0.018). The AI approach led to the identification of 20 predictors of different domains (age, body mass index, echocardiographic and electrocardiographic features, laboratory measurements, comorbidities, therapies) whose relationship with the predicted risk correspond to known trends in the clinical practice. Our results suggest that prognostic models for HF in diabetic patients may improve using EHRs in combination with AI techniques for survival analysis, which provide high flexibility and better performance with respect to standard approaches.

Interpretability of time-series deep learning models: A study in cardiovascular patients admitted to Intensive care unit.

Interpretability is fundamental in healthcare problems and the lack of it in deep learning models is currently the major barrier in the usage of such powerful algorithms in the field. The study describes the implementation of an attention layer for Long Short-Term Memory (LSTM) neural network that provides a useful picture on the influence of the several input variables included in the model. A cohort of 10,616 patients with cardiovascular diseases is selected from the MIMIC III dataset, an openly available database of electronic health records (EHRs) including all patients admitted to an ICU at Boston's Medical Centre. For each patient, we consider a 10-length sequence of 1-hour windows in which 48 clinical parameters are extracted to predict the occurrence of death in the next 7 days. Inspired from the recent developments in the field of attention mechanisms for sequential data, we implement a recurrent neural network with LSTM cells incorporating an attention mechanism to identify features driving model's decisions over time. The performance of the LSTM model, measured in terms of AUC, is 0.790 (SD = 0.015). Regard our primary objective, i.e. model interpretability, we investigate the role of attention weights. We find good correspondence with driving predictors of a transparent model (r = 0.611, 95% CI [0.395, 0.763]). Moreover, most influential features identified at the cohort-level emerge as known risk factors in the clinical context. Despite the limitations of study dataset, this work brings further evidence of the potential of attention mechanisms in making deep learning model more interpretable and suggests the application of this strategy for the sequential analysis of EHRs.

Sex-related differences in chronic heart failure: a community-based study.

AIMS: To evaluate sex-related differences among real-life outpatients with chronic heart failure across the ejection fraction spectrum and to evaluate whether these differences might impact therapy and outcomes. METHODS: A total of 2528 heart failure patients were examined between 2009 and 2015 [mean age 76, 42% females; 59% with heart failure with preserved ejection fraction (HFpEF), 17% with heart failure with mid-range ejection fraction (HFmrEF) and 24% with heart failure with reduced ejection fraction (HFrEF)]. Females showed a higher prevalence of HFpEF than males. RESULTS: Females were older, less obese and with less ischaemic heart disease. They have renal failure and anaemia more frequently than males. There were no differences in terms of heart failure therapy in the HFrEF group, but a lower prescription rate of angiotensin-converting enzyme-I/AT1 blockers in HFmrEF and HFpEF and a higher prescription of mineralocorticoid receptor antagonists in the female group with HFpEF were observed. Crude rate mortality and composite outcome (death/heart failure progression) run similarly across sexes regardless of the ejection fraction categories. After adjustment, risk of mortality was significantly lower in females than males in the HFmrEF and HFpEF groups, whereas similar risk was confirmed across sexes in the HFrEF group. Considering prognostic risk factors, noncardiac comorbidities emerged in the HFpEF group. CONCLUSION: In a community-based heart failure cohort, females were differently distributed within heart failure phenotypes and they presented some different characteristics across ejection fraction categories. Although in an unadjusted model there was no significant difference for adverse outcomes, in an adjusted model females showed a lower risk of mortality in HFpEF and HFmrEF. Concerning sex-related prognostic risk factors, noncardiac comorbidities significantly affected adverse prognosis in females with HFpEF.

CHA2DS2-VASc Score Predicts Adverse Outcome in Patients with Simple Congenital Heart Disease Regardless of Cardiac Rhythm.

Adult patients with simple congenital heart disease (sACHD) represent an expanding population vulnerable to atrial arrhythmias (AA). CHA2DS2-VASc score estimates thromboembolic risk in non-valvular atrial fibrillation patients. We investigated the prognostic role of CHA2DS2-VASc score in a non-selected sACHD population regardless of cardiac rhythm. Between November 2009 and June 2018, 427 sACHD patients (377 in sinus rhythm, 50 in AA) were consecutively referred to our ACHD service. Cardiovascular hospitalization and/or all-cause death were considered as composite primary end-point. Patients were divided into group A with CHA2DS2-VASc score = 0 or 1 point, and group B with a score greater than 1 point. Group B included 197 patients (46%) who were older with larger prevalence of cardiovascular risk factors than group A. During a mean follow-up of 70 months (IQR 40-93), primary end-point occurred in 94 patients (22%): 72 (37%) in group B and 22 (10%, p < 0.001) in group A. Rate of death for all causes was also significantly higher in the group B than A (22% vs 2%, respectively, p < 0.001). Multivariable Cox regression analysis revealed that CHA2DS2-VASc score was independently related to the primary end-point (HR 1.84 [1.22-2.77], p = 0.004) together with retrospective AA, stroke/TIA/peripheral thromboembolism and diabetes. Furthermore, CHA2DS2-VASc score independently predicted primary end-point in the large subgroup of 377 patients with sinus rhythm (HR 2.79 [1.54-5.07], p = 0.01). In conclusion, CHA2DS2-VASc score accurately stratifies sACHD patients with different risk for adverse clinical events in the long term regardless of cardiac rhythm.

Adherence to Disease-Modifying Therapy in Patients Hospitalized for HF: Findings from a Community-Based Study.

BACKGROUND: Much data about prescription adherence in patients with heart failure (HF) are available, but few exist about the evaluation of true patient adherence. Further, methods for analyzing this issue are poorly known. OBJECTIVES: Our objective was to evaluate the impact of patient adherence to disease-modifying drugs after HF hospitalization in a community-based cohort. METHODS AND RESULTS: Patients hospitalized with first diagnostic HF code and at least one post-discharge purchase of evidence-based drugs for HF between 2009 and 2015 were included (12,938 patients). A new method for measuring adherence to polypharmacy (patient adherence indicator [PAI]) was introduced, based on proportion of days covered (PDC) and medication possession ratio (MPR). The investigated drugs were ß-blockers (BBs), angiotensin-converting enzyme inhibitors (ACEIs), angiotensin-receptor blockers (ARBs), and anti-aldosterone agents (AAs). Regional administrative databases were analyzed. RESULTS: The mean age of the cohort was 80 years; 53% was female; the median Charlson Comorbidity Index score was 2, and the overall death rate was 60%. PAI based on PDC estimated a nonadherence rate of 47%. Median daily dosages were well below target dosages for all drugs considered. A good PAI significantly lowered the mortality risk, irrespective of the computational method used: PDC (PAI adjusted hazard ratio [HR] 0.93; 95% confidence interval [CI] 0.88-0.97; p = 0.001) or MPR (PAI adjusted HR 0.93; 95% CI 0.89-0.98; p = 0.004). CONCLUSIONS: In a real-world setting, medication adherence of patients with HF remains unsatisfactory, especially when in a polypharmacy setting. Irrespective of PDC and MPR, good patient adherence to polypharmacy was associated with a lower death rate.

HF progression among outpatients with HF in a community setting.

BACKGROUND: Incidence and prognostic impact of heart failure (HF) progression has been not well addressed. METHODS: From 2009 until 2015, consecutive ambulatory HF patients were recruited. HF progression was defined by the presence of at least two of the following criteria: step up of ≥1 New York Heart Association (NYHA) class; decrease LVEF ≥ 10 points; association of diuretics or increase ≥ 50% of furosemide dosage, or HF hospitalization. RESULTS: 2528 met study criteria (mean age 76; 42% women). Of these, 48% had ischemic heart disease, 18% patients with LVEF ≤ 35%. During a median follow-up of 2.4 years, overall mortality was 31% (95% CI: 29%-33%), whereas rate of HF progression or death was 57% (95% CI: 55%-59%). The 4-year incidence of HF progression was 39% (95% CI: 37%-41%) whereas the competing mortality rate was 18% (95% CI: 16%-19%). Rates of HF progression and death were higher in HF patients with LVEF ≤ 35% vs >35% (HF progression: 42% vs 38%, p = 0.012; death as a competing risk: 22% vs 17%, p = 0.002). HF progression identified HF patients with a worse survival (HR = 3.16, 95% CI: 2.75-3.72). In cause-specific Cox models, age, previous HF hospitalization, chronic obstructive pulmonary disease, chronic kidney disease, anemia, sex, LVEF ≤ 35% emerged as prognostic factors of HF progression. CONCLUSIONS: Among outpatients with HF, at 4 years 39% presented a HF progression, while 18% died before any sign of HF progression. This trend was higher in patients with LVEF ≤ 35%. These findings may have implications for healthcare planning and resource allocation.

Multi-state modelling of heart failure care path: A population-based investigation from Italy.

BACKGROUND: How different risk profiles of heart failure (HF) patients can influence multiple readmissions and outpatient management is largely unknown. We propose the application of two multi-state models in real world setting to jointly evaluate the impact of different risk factors on multiple hospital admissions, Integrated Home Care (IHC) activations, Intermediate Care Unit (ICU) admissions and death. METHODS AND FINDINGS: The first model (model 1) concerns only hospitalizations as possible events and aims at detecting the determinants of repeated hospitalizations. The second model (model 2) considers both hospitalizations and ICU/IHC events and aims at evaluating which profiles are associated with transitions in intermediate care with respect to repeated hospitalizations or death. Both are characterized by transition specific covariates, adjusting for risk factors. We identified 4,904 patients (4,129 de novo and 775 worsening heart failure, WHF) hospitalized for HF from 2009 to 2014. 2,714 (55%) patients died. Advanced age and higher morbidity load increased the rate of dying and of being rehospitalized (model 1), decreased the rate of being discharged from hospital (models 1 and 2) and increased the rate of inactivation of IHC (model 2). WHF was an important risk factor associated with hospital readmission. CONCLUSION: Multi-state models enable a better identification of two patterns of HF patients. Once adjusted for age and comorbidity load, the WHF condition identifies patients who are more likely to be readmitted to hospital, but does not represent an increasing risk factor for activating ICU/IHC. This highlights different ways to manage specific patients' patterns of care. These results provide useful healthcare support to patients' management in real world context. Our study suggests that the epidemiology of the considered clinical characteristics is more nuanced than traditionally presented through a single event.