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Background: Acute kidney injury (AKI) is one of the most common complications after cardiac surgery with cardiopulmonary bypass (CPB). Renal transplant recipients (RTRs) have a higher risk of cardiac surgery-associated AKI (CSA-AKI). A relationship has been strongly suggested between AKI and poor long-term graft survival. The main objective was to evaluate the impact of on-pump cardiac surgery on the 1-year renal allograft survival rate., Methods: The study population consisted of 37 RTRs and 56 non-RTRs who underwent cardiac surgery between 1 January 2010 and 31 December 2019. They were matched according to age, sex, preoperative glomerular function, diabetes and type of surgery. The primary composite outcome was renal survival, defined as patient survival without the requirement for permanent dialysis or new kidney transplantation at 1 year after surgery., Results: The renal survival rate was significantly lower in the RTR group than in the non-RTR group [81% versus 96%; odds ratio 0.16 (95% confidence interval 0.03-0.82), P = .03]. The proportion of patients who returned to permanent dialysis was higher in the RTR group than in the non-RTR group (12% versus 0%; P = .02). The proportion of patients with severe AKI was also higher in the RTR group. At 1 year after surgery, serum creatinine level, glomerular filtration rate and all-cause mortality rates were comparable between both groups., Conclusion: Patients with a functional renal allograft have a low 1-year renal allograft survival rate after cardiac surgery with CPB. In addition, these patients have significant risks of AKI and acute kidney disease after open-heart surgery., (© The Author(s) 2022. Published by Oxford University Press on behalf of the ERA.)

Objective: The European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) initially developed for predicting early postoperative mortality of all types of cardiac surgery, is less able to predict, more specifically, long-term outcomes after aortic valve replacement (AVR). The study authors here evaluated the risk factors for three-year mortality after isolated aortic valve replacement (AVR) for severe calcified tricuspid aortic valve stenosis and compared them with EuroSCORE II to predict long-term outcomes., Design: A retrospective study., Setting: A university teaching hospital., Participants: This study included 1,101 adults who underwent isolated AVR for severe calcified tricuspid aortic valve stenosis between September 2010 to June 2015., Interventions: None., Measurements and Main Results: The primary endpoint was that of three-year all-cause mortality after AVR. By three years, 168 patients (15.3%) had died. Risk factors for all-cause mortality were: male gender (odds ratio [OR] = 1.78; 95% confidence interval [CI] = 1.21-2.62; p < 0.01), peripheral arterial disease (OR = 1.77; 95% CI = 1.08-2.92; p = 0.03), age (OR = 1.06 per year increase; 95% CI =1.04-1.09; p < 0.01), pulmonary artery systolic pressure (OR = 1.02 per mmHg increase; 95% CI = 1.01-1.03; p < 0.01), platelet count (OR = 1.003 per G/L increase; 95% CI = 1.000-1.005; p = 0.04), and valve area (OR = 0.97 per cm²/m² increase; 95% CI= 0.95-0.99; p < 0.01). The area under the receiver operating characteristic curves were 0.67 (95% CI = 0.60-0.75) and 0.60 (95% CI = 0.56-0.65) for the authors' logistic regression model and EuroSCORE II, respectively (p = 0.11)., Conclusions: The study authors identified six independent risk factors for three-year mortality after isolated AVR. The logistic regression model had relatively modest predictive performance for three-year mortality., Competing Interests: Conflict of Interest None., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background Although many anticholinergics are inappropriate in older patients, the prescription of these drugs in a hospital setting has not been extensively studied. Objective To describe prescriptions of anticholinergic drugs in terms of frequency, at risk situations and constipation in hospitalized, older adults. Setting Using a database from a French general hospital (period 2009-2013), we extracted information on 14,090 hospital stays by patients aged 75 and over. Methods Anticholinergic drug prescriptions were automatically detected, with a focus on prescriptions in three well-known at-risk situations: falls, dementia, and benign prostatic hyperplasia. Cases of constipation that might have been causally related to the administration of anticholinergic drugs were screened for and reviewed. Main outcome measure Prescriptions with a high associated risk of anticholinergic related adverse reactions. Results Administration of an anticholinergic drug was detected in 1412 (10.0%) of the hospital stays by older patients. At-risk situations were identified in 413 (36.5%) of these stays: 137 (9.7%) for falls, 243 (17.2%) for dementia, and 114 (8.1%) for benign prostatic hyperplasia; 78 (18.9%) of these 413 stays featured a combination of two or three at-risk situations. Cases of constipation induced by anticholinergic drug administration were identified in 188 (13.3%) patient stays by using validated adjudication rules for adverse drug reactions: 85 and 103 cases were respectively evaluated as "possible" or "probable" adverse drug reactions. Conclusions Anticholinergic drugs prescription was found in 10.0% of hospitalized, older patients. More than one third of these prescriptions occurred in at-risk situations and more than one in ten prescriptions induced constipation.

Introduction: In France, over 20% of hospitalizations of elderly people are a result of adverse drug events, of which 50% are considered preventable. Tools have been developed to detect inappropriate prescriptions. The Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert doctors to Right Treatment (STOPP/START) criteria are innovative and adapted to French prescriptions. This is one of the first French prospective studies to evaluate the impact of acute geriatric care on prescriptions at discharge in elderly patients using the STOPP/START criteria., Method: The evaluation of prescriptions according to STOPP/START was carried out on admission and at discharge of patients in acute geriatric units at three hospitals in the Nord-Pas de Calais region, France. A total of 202 elderly hospitalized patients were included during the 4.5 months of the study (1.5 months per center)., Results: The mean number of drugs was seven on admission and at discharge. Over half of the prescriptions at admission contained at least one potentially inappropriate medication or one potential prescription omission. The prescriptions at discharge contained significantly fewer potentially inappropriate medications than prescriptions on admission (P < 0.001). In contrast, there was no difference between prescriptions at discharge in terms of potential prescription omissions., Conclusion: Acute geriatric hospitalization in France improves prescriptions in terms of potentially inappropriate medication, but has no impact on potential prescription omissions. Further studies must be carried out to see if STOPP/START could be used as a tool in French prescription., (© 2015 Japan Geriatrics Society.)

Although several methods exist for Adverse Drug events (ADE) detection due to past hospitalizations, a tool that could display those ADEs to the physicians does not exist yet. This article presents the ADE Scorecards, a Web tool that enables to screen past hospitalizations extracted from Electronic Health Records (EHR), using a set of ADE detection rules, presently rules discovered by data mining. The tool enables the physicians to (1) get contextualized statistics about the ADEs that happen in their medical department, (2) see the rules that are useful in their department, i.e. the rules that could have enabled to prevent those ADEs and (3) review in detail the ADE cases, through a comprehensive interface displaying the diagnoses, procedures, lab results, administered drugs and anonymized records. The article shows a demonstration of the tool through a use case.

In hospital information systems, laboratory results are stored using specific terminologies which may differ between hospitals. The objective is to create a tool helping to build a mapping between a target terminology (reference dataset) and a new one. Using a training sample consisting of correct and incorrect correspondences between parameters of different hospitals, a match probability score is built. This model also enables to determine the theoretical conversion factor between two parameters. This method is evaluated on a test sample of a new hospital: For each reference parameter, best candidates are returned and sorted in decreasing order using the probability given by the model. The correct correspondent of 14 among 15 reference parameters are ranked in the top five among more than 70. All conversion factors are correct. A mapping webtool is built to present the essential information for best candidates. Using this tool, an expert has found all the correct pairs.