Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data.

Forecasts of infectious agents provide public health officials advanced warning about the intensity and timing of the spread of disease. Past work has found that accuracy and calibration of forecasts is weakest when attempting to predict an epidemic peak. Forecasts from a mechanistic model would be improved if there existed accurate information about the timing and intensity of an epidemic.

Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons.

Chimeric forecasting: combining probabilistic predictions from computational models and human judgment.

Forecasts of the trajectory of an infectious agent can help guide public health decision making. A traditional approach to forecasting fits a computational model to structured data and generates a predictive distribution. However, human judgment has access to the same data as computational models plus experience, intuition, and subjective data.

Crowdsourced Perceptions of Human Behavior to Improve Computational Forecasts of US National Incident Cases of COVID-19: Survey Study.

Background: Past research has shown that various signals associated with human behavior (eg, social media engagement) can benefit computational forecasts of COVID-19. One behavior that has been shown to reduce the spread of infectious agents is compliance with nonpharmaceutical interventions (NPIs). However, the extent to which the public adheres to NPIs is difficult to measure and consequently difficult to incorporate into computational forecasts of infectious diseases.

An expert judgment model to predict early stages of the COVID-19 pandemic in the United States.

From February to May 2020, experts in the modeling of infectious disease provided quantitative predictions and estimates of trends in the emerging COVID-19 pandemic in a series of 13 surveys. Data on existing transmission patterns were sparse when the pandemic began, but experts synthesized information available to them to provide quantitative, judgment-based assessments of the current and future state of the pandemic. We aggregated expert predictions into a single "linear pool" by taking an equally weighted average of their probabilistic statements.

Aggregating Human Judgment Probabilistic Predictions of Coronavirus Disease 2019 Transmission, Burden, and Preventive Measures.

Aggregated human judgment forecasts for coronavirus disease 2019 (COVID-19) targets of public health importance are accurate, often outperforming computational models. Our work shows that aggregated human judgment forecasts for infectious agents are timely, accurate, and adaptable, and can be used as a tool to aid public health decision making during outbreaks.

Aggregating human judgment probabilistic predictions of COVID-19 transmission, burden, and preventative measures.

Aggregated human judgment forecasts for COVID-19 targets of public health importance are accurate, often outperforming computational models. Our work shows aggregated human judgment forecasts for infectious agents are timely, accurate, and adaptable, and can be used as tool to aid public health decision making during outbreaks.

Aggregating human judgment probabilistic predictions of the safety, efficacy, and timing of a COVID-19 vaccine.

Safe, efficacious vaccines were developed to reduce the transmission of SARS-CoV-2 during the COVID-19 pandemic. But in the middle of 2020, vaccine effectiveness, safety, and the timeline for when a vaccine would be approved and distributed to the public was uncertain. To support public health decision making, we solicited trained forecasters and experts in vaccinology and infectious disease to provide monthly probabilistic predictions from July to September of 2020 of the efficacy, safety, timing, and delivery of a COVID-19 vaccine.

Adaptively stacking ensembles for influenza forecasting.

Seasonal influenza infects between 10 and 50 million people in the United States every year. Accurate forecasts of influenza and influenza-like illness (ILI) have been named by the CDC as an important tool to fight the damaging effects of these epidemics. Multi-model ensembles make accurate forecasts of seasonal influenza, but current operational ensemble forecasts are static: they require an abundance of past ILI data and assign fixed weights to component models at the beginning of a season, but do not update weights as new data on component model performance is collected.

Aggregating predictions from experts: a review of statistical methods, experiments, and applications.

Forecasts support decision making in a variety of applications. Statistical models can produce accurate forecasts given abundant training data, but when data is sparse or rapidly changing, statistical models may not be able to make accurate predictions. Expert judgmental forecasts-models that combine expert-generated predictions into a single forecast-can make predictions when training data is limited by relying on human intuition.

Impact of Coronary Artery Tortuosity on Outcomes Following Stenting: A Pooled Analysis From 6 Trials.

Objectives: The authors sought to determine whether coronary artery tortuosity negatively affects clinical outcomes after stent implantation.

Background: Coronary artery tortuosity is a common angiographic finding and has been associated with increased rates of early and late major adverse events after balloon angioplasty.

Methods: Individual patient data from 6 prospective, randomized stent trials were pooled.

Relationship between insulin resistance, coronary plaque, and clinical outcomes in patients with acute coronary syndromes: an analysis from the PROSPECT study.

Background: We investigated the association of insulin resistance (IR) with coronary plaque morphology and the risk of cardiovascular events in patients enrolled in the Providing Regional Observations to Study Predictors of Events in Coronary Tree (PROSPECT) study.

Methods: Patients with acute coronary syndromes (ACS) were divided based on DM status. Non-DM patients were further stratified according to homeostasis-model-assessment IR (HOMA-IR) index as insulin sensitive (IS; HOMA-IR ≤ 2), likely-IR (LIR; 2 < HOMA-IR < 5), or diabetic-IR (DIR; HOMA-IR ≥ 5).

Diastolic Function and Clinical Outcomes After Transcatheter Aortic Valve Replacement: PARTNER 2 SAPIEN 3 Registry.

Background: Few studies have evaluated if diastolic function could predict outcomes in patients with aortic stenosis.

Objectives: The authors aimed to assess the association between diastolic dysfunction (DD) and outcomes in patients with aortic stenosis undergoing transcatheter aortic valve replacement (TAVR).

Methods: Baseline, 30-day, and 1- and 2-year transthoracic echocardiograms from the PARTNER (Placement of Aortic Transcatheter Valves) 2 SAPIEN 3 registry were analyzed by a consortium of core laboratories and divided into the American Society of Echocardiography DD groups.

An expert judgment model to predict early stages of the COVID-19 outbreak in the United States.

During early stages of the COVID-19 pandemic, forecasts provided actionable information about disease transmission to public health decision-makers. Between February and May 2020, experts in infectious disease modeling made weekly predictions about the impact of the pandemic in the U.S.

The win ratio approach for composite endpoints: practical guidance based on previous experience.

The win ratio was introduced in 2012 as a new method for examining composite endpoints and has since been widely adopted in cardiovascular (CV) trials. Improving upon conventional methods for analysing composite endpoints, the win ratio accounts for relative priorities of the components and allows the components to be different types of outcomes. For example, the win ratio can combine the time to death with the number of occurrences of a non-fatal outcome such as CV-related hospitalizations (CVHs) in a single hierarchical composite endpoint.

Impact of lesion preparation strategies on outcomes of left main PCI: The EXCEL trial.

Objectives: We examined outcomes according to lesion preparation strategy (LPS) in patients with left main coronary artery (LMCA) percutaneous coronary intervention (PCI) in the EXCEL trial.

Background: The optimal LPS for LMCA PCI is unclear.

Methods: We categorized LPS hierarchically (high to low) as: (a) rotational atherectomy (RA); (b) cutting or scoring balloon (CSB); (c) balloon angioplasty (BAL); and d) direct stenting (DIR).

Effect of Pressure-controlled intermittent Coronary Sinus Occlusion (PiCSO) on infarct size in anterior STEMI: PiCSO in ACS study.

Background: The aim of this clinical research was to investigate the effects of Pressure-controlled intermittent Coronary Sinus Occlusion (PiCSO) on infarct size at 5 days after primary percutaneous coronary intervention (pPCI) in patients with ST-segment elevation myocardial infarction (STEMI).

Methods And Results: This comparative study was carried out in four UK hospitals. Forty-five patients with anterior STEMI presenting within 12 h of symptom onset received pPCI plus PiCSO (initiated after reperfusion; n = 45) and were compared with a propensity score-matched control cohort from INFUSE-AMI (n = 80).

Global Approach to High Bleeding Risk Patients With Polymer-Free Drug-Coated Coronary Stents: The LF II Study.

Background: High bleeding risk (HBR) patients undergoing percutaneous coronary intervention have been widely excluded from randomized device registration trials. The LF study (LEADERS FREE) reported superior outcomes of HBR patients receiving 30-day dual antiplatelet therapy after percutaneous coronary intervention with a polymer-free drug-coated stent (DCS). LFII was designed to assess the reproducibility and generalizability of the benefits of DCS observed in LF to inform the US Food and Drug Administration in a device registration decision.

Stent-Related Adverse Events >1 Year After Percutaneous Coronary Intervention.

Background: The majority of stent-related major adverse cardiovascular events (MACE) after percutaneous coronary intervention (PCI) are believed to occur within the first year. Very-late (>1-year) stent-related MACE have not been well described.

Objectives: The purpose of this study was to assess the frequency and predictors of very-late stent-related events or MACE by stent type.

Mortality After Repeat Revascularization Following PCI or CABG for Left Main Disease: The EXCEL Trial.

Objectives: The aim of this study was to investigate the incidence and impact on mortality of repeat revascularization after index percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) for left main coronary artery disease (LMCAD).

Background: The impact on mortality of the need of repeat revascularization following PCI or CABG in patients with unprotected LMCAD is unknown.

Methods: All patients with LMCAD and site-assessed low or intermediate SYNTAX (Synergy Between PCI With Taxus and Cardiac Surgery) scores randomized to PCI (n = 948) or CABG (n = 957) in the EXCEL (Evaluation of XIENCE Versus Coronary Artery Bypass Surgery for Effectiveness of Left Main Revascularization) trial were included.

Individual Patient Data Pooled Analysis of Randomized Trials of Bivalirudin versus Heparin in Acute Myocardial Infarction: Rationale and Methodology.

Background:  Individual randomized controlled trials (RCTs) of periprocedural anticoagulation with bivalirudin versus heparin during percutaneous coronary intervention (PCI) have reported conflicting results. Study-level meta-analyses lack granularity to adjust for confounders, explore heterogeneity, or identify subgroups that may particularly benefit or be harmed.

Objective:  To overcome these limitations, we sought to develop an individual patient-data pooled database of RCTs comparing bivalirudin versus heparin.

Effect of Baseline Left Ventricular Ejection Fraction on 2-Year Outcomes After Transcatheter Aortic Valve Replacement: Analysis of the PARTNER 2 Trials.

Background: Impaired left ventricular function is associated with worse prognosis among patients with aortic stenosis treated medically or with surgical aortic valve replacement. It is unclear whether reduced left ventricular ejection fraction (LVEF) is an independent predictor of adverse outcomes after transcatheter aortic valve replacement.

Methods And Results: Patients who underwent transcatheter aortic valve replacement in the PARTNER 2 trials (Placement of Aortic Transcatheter Valves) and registries were stratified according to presence of reduced LVEF (<50%) at baseline, and 2-year risk of cardiovascular mortality was compared using Kaplan-Meier methods and multivariable Cox proportional hazards regression.