Precision medicine to improve use of bleeding avoidance strategies and reduce bleeding in patients undergoing percutaneous coronary intervention: prospective cohort study before and after implementation of personalized bleeding risks

Spertus, John A.; Decker, Carole; Gialde, Elizabeth; Jones, Philip G.; McNulty, Edward; Bach, Richard G.; Chhatriwalla, Adnan K.

doi:10.1136/bmj.h1302

Cited by 68 publications

(45 citation statements)

References 35 publications

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“…For example, in a program for patients undergoing percutaneous coronary interventions, which was implemented in nine large US centers, informing physicians of a patient's bleeding risk led to a reduction in the occurrence of bleeding (from 1.7% to 1%, -44%). 9 Regardless of whether the reduction was truly brought about by the precision of the prediction, or by raising the general awareness of the risk, implementation of personalized bleeding risks helped doctors to identify subjects truly at risk of bleeding and to use techniques to avoid hemorrhage appropriately.…”

Section: When Guidelines Cannot Be Relied Onmentioning

confidence: 99%

Tailoring of medical treatment: hemostasis and thrombosis towards precision medicine

Minno

Tremoli

2017

Haematologica

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Section: When Guidelines Cannot Be Relied Onmentioning

confidence: 99%

Tailoring of medical treatment: hemostasis and thrombosis towards precision medicine

Minno

Tremoli

2017

Haematologica

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“…Moreover, despite the increased risk for MB associated with current therapies, the absolute risk for MB has decreased [3]. This downward trend in MB complications may be partially related to the development of accurate risk scores for predicting bleeding that lead to refine the antithrombotic therapy and the optimal management of these patients [4]. Along this line, current ACS guidelines [5,6] recommend the use of the Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA guidelines (CRUSADE) score to identify patients at higher risk for in-hospital MB complications [7].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the CRUSADE Risk Score for Predicting Major Bleeding in Patients with Concomitant Kidney Dysfunction and Acute Coronary Syndromes

et al. 2017

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Background: Kidney dysfunction (KD) has been associated with increased risk for major bleeding (MB) in patients with acute coronary syndromes (ACS) and may be in part related to an underuse of evidence-based therapies. Our aim was to assess the predictive ability of the Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA guidelines (CRUSADE) risk score in patients with concomitant ACS and chronic kidney disease. Methods: We conducted a retrospective analysis of a prospective registry including 1,587 ACS patients. In-hospital MB was prospectively recorded according to the CRUSADE and Bleeding Academic Research Consortium (BARC) criteria. KD was defined as an estimated glomerular filtration rate <60 mL/min/1.73 m². Results: The predictive ability of the CRUSADE risk score was assessed by discrimination and calibration analyses. A total of 465 (29%) subjects had KD. In multivariate logistic regression analyses, we found high CRUSADE risk score values to be associated with a higher rate of in-hospital MB; however, among patients with KD, it was not associated with BARC MB. Regardless of the MB definition, the predictive ability of the CRUSADE score in patients with KD was lower: area under the curve (AUC) 0.71 versus 0.79, p = 0.03 for CRUSADE MB and AUC 0.65 versus 0.75, p = 0.02 for BARC MB. Hosmer-Lemeshow analyses showed a good calibration in all renal function subgroups for both MB definitions (all p values >0.3). Conclusions: The CRUSADE risk score shows a lower accuracy for predicting in-hospital MB in KD patients compared to those without KD.

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“…One of the few examples of prospectively using a risk prediction model in clinical care, that we are aware of, is the use of the American College of Cardiology's prediction model for peri-percutaneous coronary intervention bleeding, which was associated with a 44% reduction in the odds of bleeding. 6,7 Implementing the best prediction models and demonstrating improvements in care are clearly a high priority for the profession and an important step toward precision medicine.Although …”

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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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Precision medicine to improve use of bleeding avoidance strategies and reduce bleeding in patients undergoing percutaneous coronary intervention: prospective cohort study before and after implementation of personalized bleeding risks

Cited by 68 publications

References 35 publications

Tailoring of medical treatment: hemostasis and thrombosis towards precision medicine

Tailoring of medical treatment: hemostasis and thrombosis towards precision medicine

Evaluation of the CRUSADE Risk Score for Predicting Major Bleeding in Patients with Concomitant Kidney Dysfunction and Acute Coronary Syndromes

Realizing the Potential of Clinical Risk Prediction Models

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