Computerized Advisory Decision Support for Cardiovascular Diseases in Primary Care: A Cluster Randomized Trial

McKie, Paul M.; Kor, Daryl J.; Cook, David A.; Kessler, Maya; Carter, Rickey E.; Wilson, Philip; Pencille, Laurie; Hickey, Branden C.; Chaudhry, Rajeev

doi:10.1016/j.amjmed.2019.10.039

Cited by 23 publications

(13 citation statements)

References 20 publications

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“…Clinical decision support tools in prevalent HF are associated with improved medication adherence and appropriate referrals for advanced therapies, but less is known about their impact on HF prevention. 33 , 34 In addition to the feasibility from an implementation standpoint, WATCH‐DM demonstrated adequate discrimination and calibration for predicting HF risk in a real‐world contemporary clinical cohort of patients with T2D. These observations highlight the potential for wide generalizability and applicability of the WATCH‐DM risk score.…”

Section: Discussionmentioning

confidence: 80%

Validation of the WATCH‐DM and TRS‐HF _DM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis

Segar¹,

Patel

Hellkamp

et al. 2022

JAHA

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Background The WATCH‐DM (weight [body mass index], age, hypertension, creatinine, high‐density lipoprotein cholesterol, diabetes control [fasting plasma glucose], ECG QRS duration, myocardial infarction, and coronary artery bypass grafting) and TRS‐HF DM (Thrombolysis in Myocardial Infarction [TIMI] risk score for heart failure in diabetes) risk scores were developed to predict risk of heart failure (HF) among individuals with type 2 diabetes. WATCH‐DM was developed to predict incident HF, whereas TRS‐HF DM predicts HF hospitalization among patients with and without a prior HF history. We evaluated the model performance of both scores to predict incident HF events among patients with type 2 diabetes and no history of HF hospitalization across different cohorts and clinical settings with varying baseline risk. Methods and Results Incident HF risk was estimated by the integer‐based WATCH‐DM and TRS‐HF DM scores in participants with type 2 diabetes free of baseline HF from 2 randomized clinical trials (TECOS [Trial Evaluating Cardiovascular Outcomes With Sitagliptin], N=12 028; and Look AHEAD [Look Action for Health in Diabetes] trial, N=4867). The integer‐based WATCH‐DM score was also validated in electronic health record data from a single large health care system (N=7475). Model discrimination was assessed by the Harrell concordance index and calibration by the Greenwood‐Nam‐D’Agostino statistic. HF incidence rate was 7.5, 3.9, and 4.1 per 1000 person‐years in the TECOS, Look AHEAD trial, and electronic health record cohorts, respectively. Integer‐based WATCH‐DM and TRS‐HF DM scores had similar discrimination and calibration for predicting 5‐year HF risk in the Look AHEAD trial cohort (concordance indexes=0.70; Greenwood‐Nam‐D’Agostino P >0.30 for both). Both scores had lower discrimination and underpredicted HF risk in the TECOS cohort (concordance indexes=0.65 and 0.66, respectively; Greenwood‐Nam‐D’Agostino P <0.001 for both). In the electronic health record cohort, the integer‐based WATCH‐DM score demonstrated a concordance index of 0.73 with adequate calibration (Greenwood‐Nam‐D’Agostino P =0.96). TRS‐HF DM score could not be validated in the electronic health record because of unavailability of data on urine albumin/creatinine ratio in most patients in the contemporary clinical practice. Conclusions The WATCH‐DM and TRS‐HF DM risk scores can discriminate risk of HF among intermediate‐risk populations with type 2 diabetes.

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Section: Discussionmentioning

confidence: 80%

Validation of the WATCH‐DM and TRS‐HF _DM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis

Segar¹,

Patel

Hellkamp

et al. 2022

JAHA

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“…32 A computerized decision support system for primary care providers improved guideline-based treatment of heart failure, but not atrial fibrillation nor hyperlipidemia. 33 Although there may be a role for these physician-oriented interventions in high-risk patients, current literature has not found a consistent benefit in randomized controlled trials.…”

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

A virtual platform to deliver ambulatory care for patients with atrial fibrillation

Weng

Blanchard

Reed

et al. 2021

Cardiovascular Digital Health Journal

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…A previous systematic review found implementation of CDSS improved provider compliance with clinical practice related to the screening and treatment of cardiovascular related illnesses (Njie et al, 2015). McKie et al (McKie et al, 2020) found CDSS significantly improved guideline recommended treatment of patients with HF in a primary care setting. However, another study in patients with HF in a hospital setting reported roughly 3.6 alerts per patient resulting in provider alert fatigue (Wadhwa et al, 2008).…”

Section: Discussionmentioning

confidence: 97%

“…Despite these publications, little has been done to reduce prescribing of these medications among patients with HF through a systematic approach. Recent research has demonstrated that dedicated clinical decision support systems (CDSS) can improve guideline directed prescribing for the treatment of HF in ambulatory care settings (McKie et al, 2020) and electronic engagement of patients can also positively improve prescribing (Allen et al, 2021). However, a review by Kao et al (Kao et al, 2020) reported on the need for greater innovation within electronic health records (EHRs), such as triggered alerts within a CDSS to reduce prescribing of PHMs for optimal management of patients with HF.…”

Section: Introductionmentioning

confidence: 99%

Implementing Clinical Decision Support Tools and Pharmacovigilance to Reduce the Use of Potentially Harmful Medications and Health Care Costs in Adults With Heart Failure

Almodóvar

Nahata

2021

Front. Pharmacol.

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Heart failure (HF) is associated with significant morbidity, mortality, compromised quality of life and socioeconomic burden worldwide. This chronic condition is becoming an increasingly important concern given the increased prevalence of HF among aging populations. Significant contributors toward escalating health care costs are emergency room visits and hospitalizations associated with HF. An important strategy to improve health care outcomes and reduce unnecessary costs is to identify and reduce the prescribing of potentially harmful medications (PHMs) among adults with HF. Previous studies in patients with HF found roughly 10–50% of them were prescribed at least one PHM in ambulatory care and inpatient health care settings. This opinion highlights recent findings from studies assessing prevalence of PHMs, associations between PHM prescribing and characteristics, and what can be done to improve patient outcomes and reduce the use of PHMs and associated health care costs in adults with HF.

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Computerized Advisory Decision Support for Cardiovascular Diseases in Primary Care: A Cluster Randomized Trial

Cited by 23 publications

References 20 publications

Validation of the WATCH‐DM and TRS‐HF _DM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis

Validation of the WATCH‐DM and TRS‐HF _DM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis

A virtual platform to deliver ambulatory care for patients with atrial fibrillation

Implementing Clinical Decision Support Tools and Pharmacovigilance to Reduce the Use of Potentially Harmful Medications and Health Care Costs in Adults With Heart Failure

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Computerized Advisory Decision Support for Cardiovascular Diseases in Primary Care: A Cluster Randomized Trial

Cited by 23 publications

References 20 publications

Validation of the WATCH‐DM and TRS‐HF DM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis

Validation of the WATCH‐DM and TRS‐HF DM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis

A virtual platform to deliver ambulatory care for patients with atrial fibrillation

Implementing Clinical Decision Support Tools and Pharmacovigilance to Reduce the Use of Potentially Harmful Medications and Health Care Costs in Adults With Heart Failure

Contact Info

Product

Resources

About

Validation of the WATCH‐DM and TRS‐HF _DM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis

Validation of the WATCH‐DM and TRS‐HF _DM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis