Statin Prescriptions for High-Risk Patients Are Increased by Laboratory-Initiated Framingham Risk Scores: A Quality-Improvement Initiative

2017 ◽  
Vol 33 (5) ◽  
pp. 682-684 ◽  
Author(s):  
Christopher Naugler ◽  
Charles Cook ◽  
Louise Morrin ◽  
James Wesenberg ◽  
Allison A. Venner ◽  
...  
Keyword(s):  
Quality Improvement ◽  
High Risk ◽  
Risk Scores ◽  
Quality Improvement Initiative ◽  
High Risk Patients ◽  
Framingham Risk ◽  
Risk Patients

scholarly journals The impact of the incorporation of a feasible postoperative mortality model at the Post-Anaesthestic Care Unit (PACU) on postoperative clinical deterioration: A pragmatic trial with 5,353 patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0257941
Author(s):  
Claudia de Souza Gutierrez ◽  
Katia Bottega ◽  
Stela Maris de Jezus Castro ◽  
Gabriela Leal Gravina ◽  
Eduardo Kohls Toralles ◽  
...  
Keyword(s):  
Quality Improvement ◽  
High Risk ◽  
Clinical Deterioration ◽  
Surgical Patients ◽  
Predictive Tool ◽  
Postoperative Death ◽  
High Risk Patients ◽  
Death Probability ◽  
Risk Patients ◽  
The Impact

Background Practical use of risk predictive tools and the assessment of their impact on outcome reduction is still a challenge. This pragmatic study of quality improvement (QI) describes the preoperative adoption of a customised postoperative death probability model (SAMPE model) and the evaluation of the impact of a Postoperative Anaesthetic Care Unit (PACU) pathway on the clinical deterioration of high-risk surgical patients. Methods A prospective cohort of 2,533 surgical patients compared with 2,820 historical controls after the adoption of a quality improvement (QI) intervention. We carried out quick postoperative high-risk pathways at PACU when the probability of postoperative death exceeded 5%. As outcome measures, we used the number of rapid response team (RRT) calls within 7 and 30 postoperative days, in-hospital mortality, and non-planned Intensive Care Unit (ICU) admission. Results Not only did the QI succeed in the implementation of a customised risk stratification model, but it also diminished the postoperative deterioration evaluated by RRT calls on very high-risk patients within 30 postoperative days (from 23% before to 14% after the intervention, p = 0.05). We achieved no survival benefits or reduction of non-planned ICU. The small group of high-risk patients (13% of the total) accounted for the highest proportion of RRT calls and postoperative death. Conclusion Employing a risk predictive tool to guide immediate postoperative care may influence postoperative deterioration. It encouraged the design of pragmatic trials focused on feasible, low-technology, and long-term interventions that can be adapted to diverse health systems, especially those that demand more accurate decision making and ask for full engagement in the control of postoperative morbi-mortality.

Cardiovascular risk assessment in diabetes and pre-diabetes

ESC CardioMed ◽  
2018 ◽  
pp. 923-924
Author(s):  
Nikolaus Marx
Keyword(s):  
Risk Assessment ◽  
Cardiovascular Risk ◽  
High Risk ◽  
Target Organ Damage ◽  
Risk Scores ◽  
High Risk Patients ◽  
Patients With Diabetes ◽  
Risk Patients ◽  
Artery Disease ◽  
Very High

Patients with diabetes exhibit an increased propensity to develop cardiovascular disease with an increased mortality. Early risk assessment, especially for coronary artery disease, is important to initiate therapeutic strategies to reduce cardiovascular risk. This chapter reviews the current literature on risk scores in patients with type 1 and type 2 diabetes and summarizes the role of risk assessment based on biomarkers and different imaging strategies. Current guidelines recommend that patients with diabetes are characterized as high-risk or very high-risk patients. In the presence of target organ damage or other risk factors such as smoking, marked hypercholesterolaemia, or hypertension, patients with diabetes are classified as very high-risk patients while most other people with diabetes are categorized as high-risk patients.

scholarly journals Putting veterans with heart failure FIRST improves follow-up and reduces readmissions

2019 ◽  
Vol 8 (1) ◽  
pp. e000386 ◽  
Author(s):  
Serena Michelle Ogunwole ◽  
Jason Phillips ◽  
Amber Gossett ◽  
John Richard Downs
Keyword(s):  
Heart Failure ◽  
High Risk ◽  
Best Practices ◽  
Implementation Strategies ◽  
Quality Improvement Initiative ◽  
Medicine Department ◽  
High Risk Patients ◽  
Resource Limited ◽  
Risk Patients

BackgroundDespite improvements in length of stay and mortality, congestive heart failure (CHF) remains the most common cause of 30-day readmissions to the hospital. Though multiple studies have found that early follow-up after discharge (eg, within 7 days) is critical to improving 30-day readmissions, implementation strategies are challenging in resource-limited settings. Here we present a quality improvement initiative aimed at improving early follow-up while maximising available resources.MethodsThis was a medical resident-driven initiative. A process map of the discharge and follow-up appointment process was created that identified multiple areas for improvement. Based on these findings, a two-part intervention was implemented. First, heart failure discharge education with focus on early follow-up was disseminated to providers throughout the internal medicine department. Subsequently, improved identification of high-risk patients (Failure Intervention Risk StratificationTool) and innovative use of the existing electronic medical record (EMR) were employed to sustain and improve on gains from the first set of interventions.ResultsWe increased our 7-day follow-up rate from 47% to 57% (p=0.429) and decreased the average time to follow-up from 17.6 days to 8.7 days (p=0.016) following the first intervention. The percentage of patients readmitted within 30 days after discharge at baseline (2012–2013) and following the first intervention (education and standardisation of follow-up scheduling) and second intervention (risk stratification, intensive follow-up and EMR change) was 25% and 21%, respectively. Thirty-day mortality rate decreased from 10% in 2011 to 7.16% in December 2015.ConclusionClose hospital discharge follow-up and identification of high-risk patients with CHF are useful approaches to reduce readmissions. Using the existing EMR tool for identifying high-risk patients and improving adherence to best practices is an effective intervention. In patients with CHF these strategies improved time to follow-up and 30-day readmissions while decreasing mortality.

scholarly journals Digital Quality Improvement Approach Reduces the Need for Rescue Antiemetics in High-Risk Patients

2019 ◽  
Vol 128 (5) ◽  
pp. 867-876 ◽  
Author(s):  
Eilon Gabel ◽  
John Shin ◽  
Ira Hofer ◽  
Tristan Grogan ◽  
Keren Ziv ◽  
...  
Keyword(s):  
Quality Improvement ◽  
High Risk ◽  
High Risk Patients ◽  
Risk Patients

A multicenter quality improvement initiative on the impact of pharmacists’ postdischarge follow-up to reduce medication-related acute care episodes

2020 ◽  
Vol 77 (12) ◽  
pp. 938-942
Author(s):  
Lydia Noh ◽  
Kristina Heimerl ◽  
Rita Shane
Keyword(s):  
Quality Improvement ◽  
High Risk ◽  
Acute Care ◽  
Academic Medical Centers ◽  
Quality Improvement Initiative ◽  
Medical Centers ◽  
Academic Medical ◽  
Risk Patients ◽  
The Impact

Abstract Purpose This multicenter quality improvement initiative aims to measure and quantify pharmacists’ impact on reducing medication-related acute care episodes (MACEs) for high-risk patients at an increased risk for readmission due to drug-related problems (DRPs). Methods This was a prospective, multicenter quality improvement initiative conducted at 9 academic medical centers. Each participant implemented a standardized methodology for evaluating MACE likelihood to demonstrate the impact of pharmacist postdischarge follow-up (PDFU). The primary outcome was MACEs prevented, and the secondary outcome was DRPs identified and resolved by pharmacists. During PDFU, pharmacists were responsible for identification and resolution of DRPs, and cases were reviewed by physicians to confirm whether potential MACEs were prevented. Results A total of 840 patients were contacted by 9 participating academic medical centers during a 6-week data collection period. Of these, 328 cases were identified as MACEs prevented during PDFU by pharmacists, and physician reviewers confirmed that pharmacist identification of DRPs during PDFU prevented 27.9% of readmissions. Pharmacist identified 959 DRPs, 2.8% (27) of which were identified as potentially life threatening. Potentially serious or significant DRPs made up 56.6% (543) of the DRPs, and 40.6% (389) were identified as having a low capacity for harm. Conclusion The results demonstrate that PDFU of high-risk patients reduces DRPs and prevents MACEs based on physician confirmation. Implementation of MACE methodology provides health-system pharmacy departments the ability to demonstrate pharmacists’ value in transitions of care and assist in expanding pharmacist services.

scholarly journals Mortality risk prediction in high-risk patients undergoing coronary artery bypass grafting: Are traditional risk scores accurate?

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255662
Author(s):  
Maxim Goncharov ◽  
Omar Asdrúbal Vilca Mejia ◽  
Camila Perez de Souza Arthur ◽  
Bianca Maria Maglia Orlandi ◽  
Alexandre Sousa ◽  
...  
Keyword(s):  
New York ◽  
High Risk ◽  
Artery Bypass ◽  
Nyha Class ◽  
Heart Association ◽  
Receiver Operating Curve ◽  
Risk Scores ◽  
Cross Sectional ◽  
High Risk Patients ◽  
Risk Patients

Background The performance of traditional scores is significantly limited to predict mortality in high-risk cardiac surgery. The aim of this study was to compare the performance of STS, ESII and HiriSCORE models in predicting mortality in high-risk patients undergoing CABG. Methods Cross-sectional analysis in the international prospective database of high-risk patients: HiriSCORE project. We evaluated 248 patients with STS or ESII (5–10%) undergoing CABG in 8 hospitals in Brazil and China. The main outcome was mortality, defined as all deaths occurred during the hospitalization in which the operation was performed, even after 30 days. Five variables were selected as predictors of mortality in this cohort of patients. The model’s performance was evaluated through the calibration-in-the-large and the receiver operating curve (ROC) tests. Results The mean age was 69.90±9.45, with 52.02% being female, 25% of the patients were on New York Heart Association (NYHA) class IV and 49.6% had Canadian Cardiovascular Society (CCS) class 4 angina, and 85.5% had urgency or emergency status. The mortality observed in the sample was 13.31%. The HiriSCORE model showed better calibration (15.0%) compared to ESII (6.6%) and the STS model (2.0%). In the ROC curve, the HiriSCORE model showed better accuracy (ROC = 0.74) than the traditional models STS (ROC = 0.67) and ESII (ROC = 0.50). Conclusion Traditional models were inadequate to predict mortality of high-risk patients undergoing CABG. However, the HiriSCORE model was simple and accurate to predict mortality in high-risk patients.

Abstract 3264: Remarkable Differences in calculating the Cardiovascular Risk according the Framingham-, Procam- and ESC Risk-Scores. First Results of the DETECT Study

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Sigmund Silber ◽  
Barbara M Richartz ◽  
Frauke Jarre ◽  
David Pittrow ◽  
Jens Klotsche ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
High Risk ◽  
Risk Score ◽  
High Risk Group ◽  
Risk Scores ◽  
Individual Risk ◽  
High Risk Patients ◽  
Number Of Patients ◽  
Risk Patients ◽  
The Individual

The identification of high-risk patients is of utmost importance for an intensive and effective primary prevention program. Currently, three different scores are used to identify high-risk patients: In the USA, the Framingham risk score, in Germany the Procam risk score and in Europe the European Society of Cardiology ESC) recommended ESC risk score. There is, however, little knowledge how these three risk scores compare to each other in the same population. Therefore we calculated the individual risk of 7519 pats with no known cardiovascular disease according to these three scoring systems. In the DETECT study, 55 518 patients in 3188 primary care offices were enrolled. A representative subgroup of 7,519 randomly chosen patients participated in a cohort sub-study. According to the Framingham-Procam- and ESC-Score, the individual 10-year-risk was determined and patients were _ategorized into groups of high, medium or low risk. The mean 10-year cardiovascular risk is estimated by the PROCAM score at 4.4%, with the ESC score at 8.8% and with the Framingham-Score at 11.5%. The number of patients assigned to a group differs most for the high risk group (please see table ). Unexpectedly, major discrepancies were observed in the same pats, if the Framingham, Procam- or ESC score was used, especially in the identification of high-risk pats. Follow-up will show, which of these risk scores will best predict the actual occurrence of cardiovascular events. Results:

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