Predictor Of Future Events Research Articles

P115: An analysis of current and forecasted patient visits to Ontario’s emergency departments and its effect on hospital admissions

Introduction: The number of emergency department (ED) visits across Ontario has increased annually over the past two decades leading to overcrowding and longer wait times. ED volume forecasting may provide insight to strategic planners regarding future patient volumes and the effects on health care resources. We investigated the pattern of ED use at the local health integration network (LHIN) level and developed forecasts using historical data. The forecasts were then used to examine the effect on acute care hospital bed requirements and the number of full time equivalent physicians needed. Methods: Aggregated data from the Canadian Institute for Health Information for the period 2003 to 2013 was obtained for each of Ontario’s LHINs. The total number of ED visits per year was first quantified by LHIN and then simple linear regression was used to forecast patient volumes in 2018 and 2023. The rate of hospital admission by LHIN was also calculated. We then used the forecasted volume, admission rate and the total number of acute care hospital beds by LHIN to predict the total number of beds needed by LHIN. Based on the forecasted patient volumes and the hours of coverage model, the total number of full-time equivalent physicians needed was calculated. Results: Over the study period, the number of patients increased from 4 to 37% among LHINs. Admission rates generally decreased from 2003 to 2013. Based on historical trends, all EDs across Ontario are expected to experience increased patient visits in the future but at different rates of growth. Depending on the rate of growth in ED visits, the number of acute care beds needed by LHIN is somewhat variable and affected by the proportion of alternate level of care patients. Given, the forecasted increase in patient volume, the hours of coverage model suggests that approximately 320 additional full-time equivalent ED physicians are needed across the province by 2023. Conclusion: Although all forecasts inherently have a degree of error associated with their estimates, strategic planners require some quantitative prediction of future events to develop initial plans. Through research, these predictions can be focused and refined. The results suggest that many hospitals will experience increased demand for services and will have to do resource allocation planning accordingly to ensure patient demand is met appropriately.

Predictive Technologies: Can Smart Tools Augment the Brain's Predictive Abilities?

The ability of “looking into the future”—namely, the capacity of anticipating future states of the environment or of the body—represents a fundamental function of human (and animal) brains. A goalkeeper who tries to guess the ball's direction; a chess player who attempts to anticipate the opponent's next move; or a man-in-love who tries to calculate what are the chances of her saying yes—in all these cases, people are simulating possible future states of the world, in order to maximize the success of their decisions or actions. Research in neuroscience is showing that our ability to predict the behavior of physical or social phenomena is largely dependent on the brain's ability to integrate current and past information to generate (probabilistic) simulations of the future. But could predictive processing be augmented using advanced technologies? In this contribution, we discuss how computational technologies may be used to support, facilitate or enhance the prediction of future events, by considering exemplificative scenarios across different domains, from simpler sensorimotor decisions to more complex cognitive tasks. We also examine the key scientific and technical challenges that must be faced to turn this vision into reality.

Using emotion as information in future-oriented cognition: Individual differences in the context of state negative affect

Predictions about the future are susceptible to mood-congruent influences of emotional state. However, recent work suggests individuals also differ in the degree to which they incorporate emotion into cognition. This study examined the role of such individual differences in the context of state negative emotion. We examined whether trait tendencies to use negative or positive emotion as information affect individuals' predictions of what will happen in the future (likelihood estimation) and how events will feel (affective forecasting), and whether trait influences depend on emotional state. Participants (N=119) reported on tendencies to use emotion as information (“following feelings”), underwent an emotion induction (negative versus neutral), and made likelihood estimates and affective forecasts for future events. Views of the future were predicted by both emotional state and individual differences in following feelings. Whereas following negative feelings affected most future-oriented cognition across emotional states, following positive feelings specifically buffered individuals' views of the future in the negative emotion condition, and specifically for positive future events, a category of future-event prediction especially important in psychological health. Individual differences may confer predisposition toward optimistic or pessimistic expectations of the future in the context of acute negative emotion, with implications for adaptive and maladaptive functioning.

Popper and the Omics.

Popper and the Omics.

Repeat Cardiovascular Risk Assessment after Four Years: Is There Improvement in Risk Prediction?

BackgroundFramingham risk equations are widely used to predict cardiovascular disease based on health information from a single time point. Little is known regarding use of information from repeat risk assessments and temporal change in estimated cardiovascular risk for prediction of future cardiovascular events. This study was aimed to compare the discrimination and risk reclassification of approaches using estimated cardiovascular risk at single and repeat risk assessmentsMethodsUsing data on 12,197 individuals enrolled in EPIC-Norfolk cohort, with 12 years of follow-up, we examined rates of cardiovascular events by levels of estimated absolute risk (Framingham risk score) at the first and second health examination four years later. We calculated the area under the receiver operating characteristic curve (aROC) and risk reclassification, comparing approaches using information from single and repeat risk assessments (i.e., estimated risk at different time points).ResultsThe mean Framingham risk score increased from 15.5% to 17.5% over a mean of 3.7 years from the first to second health examination. Individuals with high estimated risk (≥20%) at both health examinations had considerably higher rates of cardiovascular events than those who remained in the lowest risk category (<10%) in both health examinations (34.0 [95%CI 31.7–36.6] and 2.7 [2.2–3.3] per 1,000 person-years respectively). Using information from the most up-to-date risk assessment resulted in a small non-significant change in risk classification over the previous risk assessment (net reclassification improvement of -4.8%, p>0.05). Using information from both risk assessments slightly improved discrimination compared to information from a single risk assessment (aROC 0.76 and 0.75 respectively, p<0.001).ConclusionsUsing information from repeat risk assessments over a period of four years modestly improved prediction, compared to using data from a single risk assessment. However, this approach did not improve risk classification.

Cardiac Event Risk Stratification Using Nuclear Cardiology in Japanese Patients with Coronary Artery Disease

Myocardial perfusion single photon emission computed tomography (SPECT) is a useful imaging method for prediction of future cardiac events in patients with known or suspected coronary artery disease (CAD) because ischemic indices obtained from myocardial perfusion SPECT provides risk stratification of cardiac events. The cardiac event risk stratification is clinically important for the design of a therapeutic strategy in especially high risk patients with chronic kidney disease or diabetes mellitus as well as patients with CAD. In Japan, the value of myocardial perfusion SPECT for cardiac event risk stratification has also been widely recognized since the initiation of J-ACCESS (the multicenter trial Japanese assessment of cardiac events and survival study). To date, cardiac event risk stratification with myocardial perfusion SPECT has generally been based on conventional semi-quantitative analysis including visual segmental scoring, which requires expert interpretation. However introduction of automated quantification with a total perfusion deficit allows cardiac event risk stratification based on automated quantification without expert interpreters. In addition, recently some equations have been prepared to estimate a cardiac event risk with the scores of multiple predictors. Those equations enable us to predict the risk of cardiac events before treatment. Myocardial perfusion SPECT is a useful modality to stratify the risk of cardiac events and to improve a prognosis in patients with known or suspected CAD.

Review of Data mining (Knowledge discovery) in the Future

Data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user (e.g., association rule mining). Forecasting, or predictive modeling provides predictions of future events and may be transparent and readable in some approaches (e.g., rule-based systems) and opaque in others such as neural networks. Moreover, some data-mining systems such as neural networks are inherently geared towards prediction and pattern recognition, rather than knowledge discovery. In Future different Scope of data mining are. 1. Developing a unifying theory of data mining. 2. Scaling up for high dimensional data and high speed data streams. 3. Data mining in a network setting. 4. Data mining for biological and environmental problems. 5. Security, privacy and data integrity. 6. Dealing with non-static, unbalanced and cost-sensitive data

Estimating the Extreme Financial Risk of the Kenyan Shilling Versus Us Dollar Exchange Rates

In the last decade, world financial markets, including the Kenyan market have been characterized by significant instabilities. This has resulted to criticism on available risk management systems and motivated research on better methods capable of identifying rare events that have resulted in heavy consequences. With the high volatility of the Kenyan Shilling/Us dollar exchange rates, it is important to come up with a more reliable method of evaluating the financial risk associated with such financial data. In the recent past, extensive research has been carried out to analyze extreme variations that financial markets are subject to, mostly because of currency crises, stock market crashes and large credit defaults. We considered the behavior of the tails of financial series. More specially was focus on the use of extreme value theory to assess tail-related risk; we thus aim at providing a modeling tool for modern risk management. Extreme Value Theory provides a theoretical foundation on which we can build statistical models describing extreme events. This will help in predictability of such future rare events.

Objective criteria for septal fibrosis in non-ischemic dilated cardiomyopathy: validation for the prediction of future cardiovascular events

BackgroundExpert subjective reporting of mid-wall septal fibrosis on late gadolinium enhancement (LGE) images has been shown to predict major cardiovascular outcomes in patients with non-ischemic dilated cardiomyopathy (NIDCM). This study aims to establish objective criteria for non-experts to report clinically relevant septal fibrosis and compare its performance by such readers versus experts for the prediction of cardiovascular events.MethodsLGE cardiovascular magnetic resonance (CMR) was performed in 118 consecutive patients with NIDCM (mean age 57 ± 14, 42 % female) and the presence of septal fibrosis scored by expert readers. CMR-naive readers performed signal threshold-based LGE quantification by referencing mean values of remote tissue and applying these to a pre-defined anatomic region to measure septal fibrosis. All patients were followed for the primary composite outcome of cardiac mortality or appropriate implantable cardioverter-defibrillator (ICD) therapy.ResultsThe mean LVEF was 32 ± 12 %. At a median follow-up of 1.9 years, 20 patients (17 %) experienced a primary composite outcome. Expert visual scoring identified 55 patients with septal fibrosis. Non-expert septal fibrosis quantification was highly reproducible and identified mean septal fibrosis burden for three measured thresholds as follows; 5SD: 2.9 ± 3.6 %, 3SD: 6.9 ± 6.3 %, and 2SD: 11.1 ± 7.5 % of the left ventricular (LV) mass, respectively. By ROC analysis, optimal thresholds for prediction of the primary outcome were; 5SD: 2.74 % (HR 8.7, p < 0.001), 3SD: 6.63 % (HR 5.7, p = 0.001) and 2SD: 10.15 % (HR 6.1, p = 0.001). By comparison, expert visual scoring provided a HR of 5.3 (p = 0.001). In adjusted analysis, objective quantification by a novice reader (>5SD threshold) was the strongest independent predictor of the primary outcome (HR 8.7) and provided improved risk reclassification beyond LVEF alone (NRI 0.54, 95 % CI 0.16–0.92, p = 0.005).ConclusionsNovice readers were able to achieve superior risk prediction for future cardiovascular events versus experts using objective criteria for septal fibrosis in patients with NIDCM. Patients with a septal fibrosis burden >2.74 % of the LV mass (>5SD threshold) were at a 9-fold higher risk of cardiac death or appropriate ICD therapy versus those not meeting this criteria. As such, this study validates reproducible criteria applicable to all levels of expertise to identify NIDCM patients at high risk of future cardiovascular events.

Effect of glycemic and lipid achievements on clinical outcomes type 2 diabetic, Chinese patients with stable coronary artery disease

AimsTo describe the effect of glycemic and lipid achievements and their joint roles in future major adverse cardiovascular events (MACEs) prediction. MethodsOne thousand two hundred sixty consecutive, type 2 diabetic patients with stable coronary artery disease (CAD) were identified, they were followed up a median of 24.07months. ResultsAt baseline, 85.4% of patients with blood pressure less than 140/90mmHg, while only a minority of patients met guideline-recommended hemoglobin A1C (HbA1C) (44.2%), low-density lipoprotein cholesterol (LDL-C) (24.7%), non high-density lipoprotein cholesterol (NHDL-C) (36.3%), or apolipoprotein B (apoB) levels (38.6%). After follow-up, patients achieving either glycemic or lipid goals experienced a lower rate of future events (HbA1C 35.4%, LDL-C 19.3%, NHDL-C 30.4%, apoB 26.7%). Dual-goal achievements of HbA1C and lipids showed the lowest event risk (adjusted relative risk, RR: HbA1C, 0.92 vs. LDL-C 0.75 vs. dual 0.27; HbA1C, 0.86 vs. NHDL-C 0.59 vs. dual 0.44; HbA1C, 0.74 vs. apoB 0.64 vs. dual 0.55). Patients with suboptimal goals (LDL-C 1.8–2.5mmol/L, NHDL-C 2.5–3.4mmol/L, or apoB 0.8–1.0g/L) were at risk when compared to those with guideline-recommended goals. ConclusionsDual-achievement of glycemic and lipid goals based on a relative well-controlled condition of blood pressure conferred a better prognosis in type 2 diabetic patients with CAD.

Imaging of atherosclerosis.

Cardiovascular disease (CVD) is a major health concern worldwide and atherosclerosis is the main cause of CVD. Atherosclerosis is a systemic and chronic inflammatory disease, which is characterized by plaque formation and can affect different vascular beds. Imaging of atherosclerosis could guide therapeutic interventions. Ultrasound, computed tomography, magnetic resonance imaging (MRI), positron emission tomography (PET) and interventional angiography are the main imaging modalities available for the assessment of atherosclerotic burden and for potential prediction of future events. In addition, the introduction of new hybrid imaging techniques like PET/MRI allow for the simultaneous evaluation of anatomical and metabolic characteristics tissues. This article provides an overview of coronary and non-coronary atherosclerosis and summarizes the current understanding of different available imaging techniques. The integration of these techniques in clinical practice may allow for superior risk stratification and therapeutic planning as well as monitoring of interventional and medication based treatment strategies.

Increased Plasma S100A12 Levels Are Associated With Diabetic Retinopathy and Prognostic Biomarkers of Macrovascular Events in Type 2 Diabetic Patients.

To explore the association between plasma S100A12 levels and the presence of diabetic retinopathy (DR) and disease outcomes. Fasting venous blood samples were collected from 372 subjects (264 relatively healthy subjects with type 2 diabetes and 108 nondiabetic subjects). The plasma CRP, pentosidine, soluble form of the receptor for advanced glycation end products (sRAGE), and S100A12 levels were analyzed in all of the patients and controls. In total, 264 patients with diabetes were followed through office visits and medical records for an average of 17.6 ± 1.5 months to determine the occurrence of major adverse disease end points. An increase in the plasma levels of S100A12, pentosidine, sRAGE, and hs-CRP from healthy to T2DM to T2DM with DR was observed. Multivariate logistic regression revealed that plasma S100A12 levels were independently associated with the presence of DR in the patients with T2DM (odds ratio, 1.421; 95% confidence interval [CI], 1.036-2.531; P = 0.033). The receiver operating characteristic (ROC) curves for developing DR using the Log S100A12, Log pentosidine, Log sRAGE, and Log hs-CRP values revealed that the area under the receiver operating characteristic curves (AUC) were 0.822 (P < 0.001), 0.561 (P = 0.092), 0.572 (P = 0.051), and 0.566 (P = 0.068), respectively. Increased plasma S100A12 levels might be predictive of future major adverse disease end points in DR patients without a previous diagnosis of macrovascular disease. The plasma levels of S100A12 are closely associated with the presence of DR and might be a better biomarker for DR and the prediction of future macrovascular events.

Incremental prognostic value of kidney function decline over coronary artery disease for cardiovascular event prediction after coronary computed tomography

It is unknown whether mild chronic kidney disease (CKD) is associated with adverse cardiovascular (CV) prognosis after accounting for coronary artery disease (CAD). Here we evaluated the interplay between CKD and CAD in predicting CV death or myocardial infarction (MI) and all-cause death. We included 1541 consecutive patients in the Partners registry (mean age 55 years, 43% female) over 18 years old with no known prior CAD who underwent coronary computed tomography angiography (CCTA). The results of CCTA were categorized as normal, nonobstructive (under half), or obstructive (half and over). Overall, 653 of the patients had no CAD, 583 had nonobstructive CAD, and 305 had obstructive CAD, while 1299 had eGFR over 60 ml/min per 1.73 m(2) and 242 had an eGFR under this value. The presence and severity of CAD was significantly associated with an increased rate of CV death or MI and all-cause death, even after adjustment for age, gender, symptoms, and risk factors. Similarly, reduced eGFR was significantly associated with CV death or MI and all-cause death after similar adjustment. The addition of reduced GFR to a model which included both clinical variables and CCTA findings resulted in significant improvement in the prediction of CV death or MI and all-cause death. Thus, among individuals referred for CCTA to evaluate CAD, renal dysfunction is associated with an increased rate of CV events, mainly driven by an increase in the rate of noncoronary CV events. In this group of patients, both eGFR and the presence and severity of CAD together improve the prediction of future CV events and death.

Independent Assessment of the European Society of Cardiology Sudden Death Risk Model for Hypertrophic Cardiomyopathy

Risk stratification for sudden death (SD) is an essential component of hypertrophic cardiomyopathy (HC) management, given the proven effectiveness of implantable cardioverter-defibrillators (ICD) for preventing SD. Although highly effective in identifying high-risk patients, current stratification algorithms remain incomplete and novel strategies are encouraged. In this regard, reliability of the statistical model to predict SD risk in HC, as recommended by the recent European Society of Cardiology (ESC) guidelines, was retrospectively tested in an independent cohort of 1,629 consecutive patients with HC aged ≥16years. Of the 1,629 patients, 35 incurred SD events, but only 4 of these (11%) had high predictive risk scores >6%/5years consistent with an ICD recommendation, and most (60%; n= 21) had scores <4%/5years that would not justify ICDs. Of 46 high-risk patients with appropriate ICD interventions for ventricular fibrillation/tachycardia, 27 (59%) had low SD risk scores of <4%/5years, regarded by ESC as insufficient to recommend ICDs, and only 12 (26%) had scores >6%/5years, considered an ICD indication; 11 of these 12 had already met conventional criteria warranting implantation with 2 to 3 risk markers. Of 414 patients with ICDs but without appropriate interventions, 258 (62%) had low risk scores (<4%/5years) that would argue against implant. In conclusion, primary risk stratification using the ESC prognostic score applied retrospectively to a large independent HC cohort proved unreliable for prediction of future SD events. Most patients with HC with SD or appropriate ICD interventions were misclassified with low risk scores and therefore would have remained unprotected from arrhythmic SD without ICDs.

Relative Importance of History of Heart Failure Hospitalization and N-Terminal Pro–B-Type Natriuretic Peptide Level as Predictors of Outcomes in Patients With Heart Failure and Preserved Ejection Fraction

ObjectivesThe aim of this study was to investigate N-terminal pro–B-type natriuretic peptide (NT-proBNP) levels and recent heart failure (HF) hospitalization as predictors of future events in heart failure – preserved ejection fraction (HF-PEF). BackgroundRecently, doubt has been expressed about the value of a history of HF hospitalization as a predictor of adverse cardiovascular outcomes in patients with HF and HF-PEF. MethodsWe estimated rates and adjusted hazard ratios (HRs) for the composite endpoint of cardiovascular death or HF hospitalization, according to history of recent HF hospitalization and baseline NT-proBNP level in the I-PRESERVE (Irbesartan in Heart Failure with Preserved systolic function) trial. ResultsRates of composite endpoints in patients with (n = 804) and without (n = 1,963) a recent HF hospitalization were 12.78 (95% confidence interval [CI]: 11.47 to 14.24) and 4.49 (95% CI: 4.04 to 4.99) per 100 person-years, respectively (HR: 2.71; 95% CI: 2.33 to 3.16). For patients with NT-proBNP concentrations >360 pg/ml (n = 1,299), the event rate was 11.51 (95% CI: 10.54 to 12.58) compared to 3.04 (95% CI: 2.63 to 3.52) per 100 person-years in those with a lower level of NT-proBNP (n = 1468) (HR: 3.19; 95% CI: 2.68 to 3.80). In patients with no recent HF hospitalization and NT-proBNP ≤360 pg/ml (n = 1,187), the event rate was 2.43 (95% CI: 2.03 to 2.90) compared with 17.79 (95% CI: 15.77 to 20.07) per 100 person-years when both risk predictors were present (n = 523; HR: 6.18; 95% CI: 4.96 to 7.69). ConclusionsRecent hospitalization for HF or an elevated level of NT-proBNP identified patients at higher risk for cardiovascular events, and this risk was increased further when both factors were present.

Spatiotemporal trends of illegal activities from ranger-collected data in a Ugandan national park.

Within protected areas, biodiversity loss is often a consequence of illegal resource use. Understanding the patterns and extent of illegal activities is therefore essential for effective law enforcement and prevention of biodiversity declines. We used extensive data, commonly collected by ranger patrols in many protected areas, and Bayesian hierarchical models to identify drivers, trends, and distribution of multiple illegal activities within the Queen Elizabeth Conservation Area (QECA), Uganda. Encroachment (e.g., by pastoralists with cattle) and poaching of noncommercial animals (e.g., snaring bushmeat) were the most prevalent illegal activities within the QECA. Illegal activities occurred in different areas of the QECA. Poaching of noncommercial animals was most widely distributed within the national park. Overall, ecological covariates, although significant, were not useful predictors for occurrence of illegal activities. Instead, the location of illegal activities in previous years was more important. There were significant increases in encroachment and noncommercial plant harvesting (nontimber products) during the study period (1999-2012). We also found significant spatiotemporal variation in the occurrence of all activities. Our results show the need to explicitly model ranger patrol effort to reduce biases from existing uncorrected or capture per unit effort analyses. Prioritization of ranger patrol strategies is needed to target illegal activities; these strategies are determined by protected area managers, and therefore changes at a site-level can be implemented quickly. These strategies should also be informed by the location of past occurrences of illegal activity: the most useful predictor of future events. However, because spatial and temporal changes in illegal activities occurred, regular patrols throughout the protected area, even in areas of low occurrence, are also required.

Doing the Right Thing at the Right Time: Assessing Responses to Patient Deterioration in Electronic Simulation Scenarios Using Course-of-Action Analysis.

International studies indicate that the recognition and management of deteriorating patients in hospitals are poor and that patient assessment is often inadequate. Face-to-face simulation programs have been shown to have an impact on educational and clinical outcomes; however, little is known about performance in contemporary healthcare e-simulation approaches. Using data from an open-access Web-based patient deterioration program (FIRSTACTWeb), the performance of 367 Australian nursing students in identification of treatment priorities and clinical actions was analyzed using a military model of Course of Action Simulation Analysis. Participants' performance in the whole program demonstrated a significant improvement in knowledge and skills (P ≤ .001) with high levels of participant satisfaction. Course of Action Simulation Analysis modeling identified three key participant groupings within which only 18% took the "best course of action" (the right actions and timing), with most (70%) completing the right actions but in the wrong order. The remaining 12% produced incomplete assessments and actions in an incorrect sequence. Contemporary approaches such as e-simulation do enhance educational outcomes. Measurement of performance when combined with Course of Action Simulation Analysis becomes a useful tool in the description of outcomes, an understanding of decision making, and the prediction of future events.

Identifying and Cultivating Superforecasters as a Method of Improving Probabilistic Predictions

Across a wide range of tasks, research has shown that people make poor probabilistic predictions of future events. Recently, the U.S. Intelligence Community sponsored a series of forecasting tournaments designed to explore the best strategies for generating accurate subjective probability estimates of geopolitical events. In this article, we describe the winning strategy: culling off top performers each year and assigning them into elite teams of superforecasters. Defying expectations of regression toward the mean 2 years in a row, superforecasters maintained high accuracy across hundreds of questions and a wide array of topics. We find support for four mutually reinforcing explanations of superforecaster performance: (a) cognitive abilities and styles, (b) task-specific skills, (c) motivation and commitment, and (d) enriched environments. These findings suggest that superforecasters are partly discovered and partly created—and that the high-performance incentives of tournaments highlight aspects of human judgment that would not come to light in laboratory paradigms focused on typical performance.

Association between trajectories of statin adherence and subsequent cardiovascular events

Trajectory models have been shown to (1) identify groups of patients with similar patterns of medication filling behavior and (2) summarize the trajectory, the average adherence in each group over time. However, the association between adherence trajectories and clinical outcomes remains unclear. This study investigated the association between 12-month statin trajectories and subsequent cardiovascular events. We identified patients with insurance coverage from a large national insurer who initiated a statin during January 1, 2007 to December 31, 2010. We assessed medication adherence during the 360 days following initiation and grouped patients based on the proportion of days covered (PDC) and trajectory models. We then measured cardiovascular events during the year after adherence assessment. Cox proportional hazards models were used to evaluate the association between adherence measures and cardiovascular outcomes; strength of association was quantified by the hazard ratio, the increase in model C-statistic, and the net reclassification index (NRI). Among 519 842 statin initiators, 8777 (1.7%) had a cardiovascular event during follow-up. More consistent medication use was associated with a lower likelihood of clinical events, whether adherence was measured through trajectory groups or PDC. When evaluating the prediction of future cardiovascular events by including a measure of adherence in the model, the best model reclassification was observed when adherence was measured using three or four trajectory groups (NRI = 0.189; 95% confidence interval: [0.171, 0.210]). Statin adherence trajectory predicted future cardiovascular events better than measures categorizing PDC. Thus, adherence trajectories may be useful for targeting adherence interventions.

How can development and plasticity contribute to understanding evolution of the human brain?

How can development and plasticity contribute to understanding evolution of the human brain?