Search

Infective endocarditis continues to represent a serious disease worldwide with high morbidity and mortality rates despite advances in diagnosis and treatment. Risk assessment plays a pivotal role in determining the appropriate course of treatment for patients diagnosed with infective endocarditis. In this context, specific endocarditis risk scores have been developed trying to help in the risk assessment process. During the last 15 years, 19 specific endocarditis scores have been published. These newly created scores are very heterogenous in their characteristics, factors included, and validation strategies. The purpose of this review is to analyze the published specific infective endocarditis risk scores and discuss their advantages, limitations, and usefulness. [ABSTRACT FROM AUTHOR]

Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care. [ABSTRACT FROM AUTHOR]

Purpose: Bariatric surgery has proven to be the most efficient treatment for obesity and type 2 diabetes mellitus (T2DM). Despite detailed qualification, desirable outcome after an intervention is not achieved by every patient. Various risk prediction models of diabetes remission after metabolic surgery have been established to facilitate the decision-making process. The purpose of the study is to validate the performance of available risk prediction scores for diabetes remission a year after surgical treatment and to determine the optimal model. Methods: A retrospective analysis comprised 252 patients who underwent Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) between 2009 and 2017 and completed 1-year follow-up. The literature review revealed 5 models, which were subsequently explored in our study. Each score relationship with diabetes remission was assessed using logistic regression. Discrimination was evaluated by area under the receiver operating characteristic (AUROC) curve, whereas calibration by the Hosmer–Lemeshow test and predicted versus observed remission ratio. Results: One year after surgery, 68.7% partial and 21.8% complete diabetes remission and 53.4% excessive weight loss were observed. DiaBetter demonstrated the best predictive performance (AUROC 0.81; 95% confidence interval (CI) 0.71–0.90; p-value > 0.05 in the Hosmer–Lemeshow test; predicted-to-observed ratio 1.09). The majority of models showed acceptable discrimination power. In calibration, only the DiaBetter score did not lose goodness-of-fit in all analyzed groups. Conclusion: The DiaBetter score seems to be the most appropriate tool to predict diabetes remission after metabolic surgery since it presents adequate accuracy and is convenient to use in clinical practice. There are no accurate models to predict T2DM remission in a patient with advanced diabetes. [ABSTRACT FROM AUTHOR]

Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.

OBJECTIVES: We assessed the prognostic utility of risk scores in surgery for infective endocarditis (IE) to evaluate their reliability in mortality risk prediction. METHODS: An observational retrospective study was developed to include all patients who underwent surgery for active IE from 2002 to 2016. Classical and endocarditis-specific risk scores were calculated. RESULTS: A total of 180 patients were included in the study. The 30-day mortality rate was 26.82% [95% confidence interval (CI) 20.26– 33.20%]. Classical risk scores were confirmed to have a suboptimal prognostic ability. Therefore, 4 IE-specific risk scores were calculated. Discrimination was evaluated using the area under the receiver operating characteristic curve. It was 0.76 (95% CI 0.68–0.82) for the Society of Thoracic Surgeons-IE (STS-IE) score; 0.68 (95% CI 0.58–0.76) for the De Feo–Cotrufo score; 0.73 (95% CI 0.66–0.79) for the PALSUSE score and 0.65 (95% CI 0.57–0.72) for the Costa score. The STS-IE score had higher discrimination when compared with the De Feo–Cotrufo score (P = 0.055) and the Costa score (P = 0.024); however, there was no significant difference when we compared the STS-IE score with the PALSUSE score (P = 0.58). Calibration was assessed using the Hosmer–Lemeshow test; an adequate calibration was confirmed in all 4 scores. CONCLUSIONS: Specific risk scores had better prognostic performance than classical risk scores. The STS-IE score had the highest discrimination and was adequately calibrated. The PALSUSE score also showed optimal discrimination and calibration. The De Feo–Cotrufo score had a lower discrimination in our sample; however, the De Feo–Cotrufo score is recommended in the current guidelines. The Costa score had the lowest discrimination. [ABSTRACT FROM AUTHOR]