Clinical Prediction Models for Cardiovascular Disease

Wessler, Benjamin S.; Yh, Lana Lai; Kramer, Whitney; Cangelosi, Michael J.; Raman, Gowri; Lutz, Jennifer S.; Kent, David M.

doi:10.1161/circoutcomes.115.001693

Cited by 80 publications

(41 citation statements)

References 37 publications

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“…To aid physicians and patients in individualizing decisions, clinical predictive models (CPMs) are now widely available to estimate the likelihood of important outcomes (prognostic models) or diagnoses (diagnostic models) based on patient‐specific characteristics 3. In the case of heart failure, CPMs have been proposed to inform decisions for advanced therapies and palliative care4 and also the common and costly admission decision for patients with acute heart failure (AHF) in the emergency department 5.…”

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confidence: 99%

Regional Validation and Recalibration of Clinical Predictive Models for Patients With Acute Heart Failure

Wessler

Ruthazer

Udelson

et al. 2017

JAHA

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BackgroundHeart failure clinical practice guidelines recommend applying validated clinical predictive models (CPMs) to support decision making. While CPMs are now widely available, the generalizability of heart failure CPMs is largely unknown.Methods and ResultsWe identified CPMs derived in North America that predict mortality for patients with acute heart failure and validated these models in different world regions to assess performance in a contemporary international clinical trial (N=4133) of patients with acute heart failure treated with guideline‐directed medical therapy. We performed independent external validations of 3 CPMs predicting in‐hospital mortality, 60‐day mortality, and 1‐year mortality, respectively. CPM discrimination decreased in all regional validation cohorts. The median change in area under the receiver operating curve was −0.09 (range −0.05 to −0.23). Regional calibration was highly variable (90th percentile of absolute difference between smoothed observed and predicted values range <1% to >50%). Calibration remained poor after global recalibrations; however, region‐specific recalibration procedures significantly improved regional performance (recalibrated 90th percentile of absolute difference range <1% to 5% across all regions and all models).ConclusionsAcute heart failure CPM discrimination and calibration vary substantially across different world regions; region‐specific (as opposed to global) recalibration techniques are needed to improve CPM calibration.

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mentioning

confidence: 99%

Regional Validation and Recalibration of Clinical Predictive Models for Patients With Acute Heart Failure

Wessler

Ruthazer

Udelson

et al. 2017

JAHA

Self Cite

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“…Detailed descriptions of article inclusion and exclusion criteria and construction of the registry are described elsewhere 21. CVD included coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease.…”

Section: Methodsmentioning

confidence: 99%

“…The number of CPMs for cardiovascular disease (CVD) reported in the literature has steadily increased over the last 2 decades,21 reflecting their promise as tools to improve decision making, individualize care, and support patient‐centered outcomes research. One so far unexplored implication of the dissemination of risk models into clinical practice is their potential to support appropriate sex‐specific care decisions in sexually dimorphic conditions such as stroke 22.…”

Section: Introductionmentioning

confidence: 99%

Field Synopsis of the Role of Sex in Stroke Prediction Models

Paulus

Lai

Lundquist

et al. 2016

JAHA

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BackgroundGuidelines for stroke prevention recommend development of sex‐specific stroke risk scores. Incorporating sex in Clinical Prediction Models (CPMs) may support sex‐specific clinical decision making. To better understand their potential to guide sex‐specific care, we conducted a field synopsis of the role of sex in stroke‐related CPMs.Methods and ResultsWe identified stroke‐related CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Database, a systematic summary of cardiovascular CPMs published from January 1990 to May 2012. We report the proportion of models including the effect of sex on stroke incidence or prognosis, summarize the directionality of the predictive effects of sex, and explore factors influencing the inclusion of sex. Of 92 stroke‐related CPMs, 30 (33%) contained a coefficient for sex or presented sex‐stratified models. Only 12/58 (21%) CPMs predicting outcomes in patients included sex, compared to 18/30 (60%) models predicting first stroke (P<0.0001). Sex was most commonly included in models predicting stroke among a general population (69%). Female sex was consistently associated with reduced mortality after ischemic stroke (n=4) and higher risk of stroke from arrhythmias or coronary revascularization (n=5). Models predicting first stroke versus outcomes among patients with stroke (odds ratio=5.75, 95% CI 2.18–15.14, P<0.001) and those developed from larger versus smaller sample sizes (odds ratio=4.58, 95% CI 1.73–12.13, P=0.002) were significantly more likely to include sex.ConclusionsSex is included in a minority of published CPMs, but more frequently in models predicting incidence of first stroke. The importance of sex‐specific care may be especially well established for primary prevention.

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“…In this issue of Circulation: Cardiovascular Quality and Outcomes, Wessler et al 5 report findings from a rigorous review of clinical prediction models in cardiovascular disease. Examining over 20 years of contemporary literature, they found ≈800 models focusing on conditions across the entire spectrum of cardiovascular disease.…”

Section: Article See P 368mentioning

confidence: 99%

“…This will substantially increase the value of healthcare and is a priority for our field. Although the work of Wessler et al 5 to assemble and critically evaluate existing models is an important first step, defining how best to disseminate, to implement and sustain the best models is a top priority.…”

Section: Clinical Risk Prediction Models Editorial 333mentioning

confidence: 99%

Realizing the Potential of Clinical Risk Prediction Models

Salisbury

Spertus

2015

Circ: Cardiovascular Quality and Outcomes

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A t the turn of the last century, physicians were largely guided by lessons passed down in training, their own personal experience, and the experiences of their colleagues. Although this approach produced thoughtful clinicians, a key limitation remained-even the busiest, most experienced providers could see only so many patients, experience only a limited number of outcomes, and often struggled to ascertain the accuracy of diagnoses or the effectiveness of treatment. These challenges to delivery of safe and effective patient care were subsequently addressed by a growing focus on progressively larger and better designed cohort studies and randomized clinical trials and later by distilling these insights into clinical practice guidelines and appropriate use criteria to help summarize the rapidly evolving medical literature. Article see p 368Despite this exponential growth in well-conducted clinical research, a barrier in applying these studies into clinical care is that an individual patient may not obtain the average benefit observed in a clinical trial. It is well recognized that the heterogeneity of treatment effect across a population can be obscured by focusing only on the mean treatment effect in a population.1-3 Depending on a patient's age, sex, comorbidities, and other characteristics, that patient may benefit greatly from the same treatment that poses a significant risk for another. 4 Providers, after all, are concerned with delivering the safest and most effective treatment for a particular individual, rather than a population of patients. Accordingly, a growing focus has been placed on developing and implementing tools to identify which patients are likely to benefit from a particular treatment or strategy, those who may be harmed, and those for whom balanced risks and benefits exist that should prompt discussions between patients and providers about that patient's goals and preferences for care. To address this need, there has been a proliferation of published clinical prediction models to help tailor treatment to risk.In this issue of Circulation: Cardiovascular Quality and Outcomes, Wessler et al 5 report findings from a rigorous review of clinical prediction models in cardiovascular disease. Examining over 20 years of contemporary literature, they found ≈800 models focusing on conditions across the entire spectrum of cardiovascular disease. In fact, they found that the number of new clinical prediction models has doubled each decade. The growth of prediction models is encouraging, as the entire profession seeks to better understand the outcomes of their patients and how best to optimize these outcomes. Importantly, however, they highlight many important challenges, including the design, development, and testing of prediction models.With respect to the design of clinical prediction models, it is critically important that the results of the model would alter a clinical decision. If a physician will not treat a low-or high-risk patient differently, then what value is there in using a model? With respe...

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Clinical Prediction Models for Cardiovascular Disease

Cited by 80 publications

References 37 publications

Regional Validation and Recalibration of Clinical Predictive Models for Patients With Acute Heart Failure

Regional Validation and Recalibration of Clinical Predictive Models for Patients With Acute Heart Failure

Field Synopsis of the Role of Sex in Stroke Prediction Models

Realizing the Potential of Clinical Risk Prediction Models

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