Search

Background: Electronic informed consent (eIC) is increasingly used in clinical research due to several benefits including increased enrollment and improved efficiency. Within a learning health care system, a pilot was conducted with an eIC for linking data from electronic health records with national registries, general practitioners, and other hospitals. Objective: We evaluated the eIC pilot by comparing the response to the eIC with the former traditional paper-based informed consent (IC). We assessed whether the use of eIC resulted in a different study population by comparing the clinical patient characteristics between the response categories of the eIC and former face-to-face IC procedure. Methods: All patients with increased cardiovascular risk visiting the University Medical Center Utrecht, the Netherlands, were eligible for the learning health care system. From November 2021 to August 2022, an eIC was piloted at the cardiology outpatient clinic. Prior to the pilot, a traditional face-to-face paper-based IC approach was used. Responses (ie, consent, no consent, or nonresponse) were assessed and compared between the eIC and face-to-face IC cohorts. Clinical characteristics of consenting and nonresponding patients were compared between and within the eIC and the face-to-face cohorts using multivariable regression analyses. Results: A total of 2254 patients were included in the face-to-face IC cohort and 885 patients in the eIC cohort. Full consent was more often obtained in the eIC than in the face-to-face cohort (415/885, 46.9% vs 876/2254, 38.9%, respectively). Apart from lower mean hemoglobin in the full consent group of the eIC cohort (8.5 vs 8.8; P =.0021), the characteristics of the full consenting patients did not differ between the eIC and face-to-face IC cohorts. In the eIC cohort, only age differed between the full consent and the nonresponse group (median 60 vs 56; P =.0002, respectively), whereas in the face-to-face IC cohort, the full consent group seemed healthier (ie, higher hemoglobin, lower glycated hemoglobin [HbA1c], lower C-reactive protein levels) than the nonresponse group. Conclusions: More patients provided full consent using an eIC. In addition, the study population remained broadly similar. The face-to-face IC approach seemed to result in a healthier study population (ie, full consenting patients) than the patients without IC, while in the eIC cohort, the characteristics between consent groups were comparable. Thus, an eIC may lead to a better representation of the target population, increasing the generalizability of results. [ABSTRACT FROM AUTHOR]

Background: Acute kidney injury (AKI) is defined as a sudden episode of kidney failure but is known to be under-recognized by healthcare professionals. The Kidney Disease Improving Global Outcome (KDIGO) guidelines have formulated criteria to facilitate AKI diagnosis by comparing changes in plasma creatinine measurements (PCr). To improve AKI awareness, we implemented these criteria as an electronic alert (e-alert), in our electronic health record (EHR) system. Methods: For every new PCr measurement measured in the University Medical Center Utrecht that triggered the e-alert, we provided the physician with actionable insights in the form of a memo, to improve or stabilize kidney function. Since e-alerts qualify for software as a medical device (SaMD), we designed, implemented and validated the e-alert according to the European Union In Vitro Diagnostic Regulation (IVDR). Results: We evaluated the impact of the e-alert using pilot data six months before and after implementation. 2,053 e-alerts of 866 patients were triggered in the before implementation, and 1,970 e-alerts of 853 patients were triggered after implementation. We found improvements in AKI awareness as measured by (1) 2 days PCr follow up (56.6–65.8%, p-value: 0.003), and (2) stop of nephrotoxic medication within 7 days of the e-alert (59.2–63.2%, p-value: 0.002). Conclusion: Here, we describe the design and implementation of the e-alert in line with the IVDR, leveraging a multi-disciplinary team consisting of physicians, clinical chemists, data managers and data scientists, and share our firsts results that indicate an improved awareness among treating physicians. [ABSTRACT FROM AUTHOR]

Purpose: Although standard-of-care has been defined for the treatment of glioblastoma patients, substantial practice variation exists in the day-to-day clinical management. This study aims to compare the use of laboratory tests in the perioperative care of glioblastoma patients between two tertiary academic centers—Brigham and Women's Hospital (BWH), Boston, USA, and University Medical Center Utrecht (UMCU), Utrecht, the Netherlands. Methods: All glioblastoma patients treated according to standard-of-care between 2005 and 2013 were included. We compared the number of blood drawings and laboratory tests performed during the 70-day perioperative period using a Poisson regression model, as well as the estimated laboratory costs per patient. Additionally, we compared the likelihood of an abnormal test result using a generalized linear mixed effects model. Results: After correction for age, sex, IDH1 status, postoperative KPS score, length of stay, and survival status, the number of blood drawings and laboratory tests during the perioperative period were 3.7-fold (p < 0.001) and 4.7-fold (p < 0.001) higher, respectively, in BWH compared to UMCU patients. The estimated median laboratory costs per patient were 82 euros in UMCU and 256 euros in BWH. Furthermore, the likelihood of an abnormal test result was lower in BWH (odds ratio [OR] 0.75, p < 0.001), except when the prior test result was abnormal as well (OR 2.09, p < 0.001). Conclusions: Our results suggest a substantially lower clinical threshold for ordering laboratory tests in BWH compared to UMCU. Further investigating the clinical consequences of laboratory testing could identify over and underuse, decrease healthcare costs, and reduce unnecessary discomfort that patients are exposed to. [ABSTRACT FROM AUTHOR]

Background and purpose—Nonunion is common in femoral fractures. Previous studies suggested that the systemic immune response after trauma can interfere with fracture healing. Therefore, we investigated whether there is a relation between peripheral blood cell counts and healing of femur fractures. Patients and methods—62 multi-trauma patients with a femoral fracture presenting at the University Medical Centre Utrecht between 2007 and 2013 were retrospectively included. Peripheral blood cell counts from hematological analyzers were recorded from the 1st through the 14th day of the hospital stay. Generalized estimating equations were used to compare outcome groups. Results—12 of the 62 patients developed nonunion of the femoral fracture. The peripheral blood-count curves of total leukocytes, neutrophils, monocytes, lymphocytes, and platelets were all statistically significantly lower in patients with nonunion, coinciding with significantly higher CRP levels during the first 2 weeks after trauma in these patients. Interpretation—Patients who developed femoral nonunion after major trauma demonstrated lower numbers of myeloid cells in the peripheral blood than patients with normal fracture healing. This absent rise of myeloid cells seems to be related to a more severe post-traumatic immune response. [ABSTRACT FROM AUTHOR]

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Drug–drug interactions (DDIs) may lead to often preventable adverse drug events and health damage. • In Dutch community pharmacies approximately 6% of all prescriptions generate a DDI alert. • Hospitalized patients may be especially at risk, as they are more severely ill and multiple medications may be prescribed simultaneously; however, only limited data are available on the frequency and nature of DDIs during hospitalization. WHAT THIS STUDY ADDS • In a Dutch university hospital 10% of all prescriptions generated a DDI alert; overall 25% of patients encountered at least one potential DDI. • Besides the risk of decreased effectiveness (25% of the DDIs), the most frequently occurring potential clinical consequence of the DDIs was an increased risk of side-effects, such as an increased bleeding risk (22% of DDIs), hypotension (15%) and nephrotoxicity (13%). • Almost half of the DDIs could be managed by monitoring laboratory values. AIM Drug–drug interactions (DDIs) may lead to often preventable adverse drug events and health damage. Especially within hospitals, this might be an important factor, as patients are severely ill and multiple medications may be prescribed simultaneously. The objective of this study was to measure the frequency and nature of DDI alerts in a Dutch university hospital. METHODS All patients hospitalized in the University Medical Centre Utrecht in 2006 who were prescribed at least one medication were included. The frequency of DDIs was calculated as: (i) the percentage of patients experiencing at least one DDI, and (ii) the percentage of prescriptions generating a DDI alert. Based on the national professional guideline, DDIs were classified into categories of potential clinical outcome, management advice, clinical relevance (A–F) and available evidence (0–4). RESULTS Of the 21 277 admissions included, 5909 (27.8%) encountered at least one DDI. Overall, the prescribing physician received a DDI alert in 9.6% of all prescriptions. The most frequently occurring potential clinical consequence of the DDIs was an increased risk of side-effects such as increased bleeding risk (22.0%), hypotension (14.9%), nephrotoxicity (12.6%) and electrolyte disturbances (10.5%). Almost half (48.6%) of the DDIs could be managed by monitoring laboratory values. CONCLUSIONS Computerized DDI alerts may be a useful tool to prevent adverse drug events within hospitals, but they may also result in ‘alert fatigue’. The specificity of alerts could significantly improve by the use of more sophisticated clinical decision support systems taking into account, for example, laboratory values. [ABSTRACT FROM AUTHOR]

Background: There is an increasing demand for easy to measure biomarkers in clinical practice. We created the relational database Utrecht Patient Oriented Database (UPOD) to develop tools for identifying new biomarkers for disease. In this study, we used UPOD to identify better biomarkers for discriminating different asthma phenotypes. Methods: We performed a prospective study at the University Medical Center (UMC) Utrecht using blood from patients with asthma and a healthy reference group. Since asthma is an inflammatory disease, absolute leukocyte counts and leukocyte differential parameters were analyzed using raw data files and a logistic regression model. Results: We compared 17 difficult-to-treat asthma (DTA) cases, 13 non-difficult-to-treat asthma cases, and 19 healthy volunteers. Absolute leukocyte counts and differential parameters for leukocytes were able to discriminate asthma patients from healthy volunteers. However, among patients with asthma, difficult-to-treat cases could be more accurately defined with a neutrophil morphology change (OR 8.0; 95% CI 1.5–42.0), compared to the absolute neutrophil count (OR 4.0; 95% CI 0.8–21.0). Conclusions: In this asthma patient population, we were able to define asthma phenotypes more precisely using neutrophil morphology parameters, compared to absolute counts. Using UPOD with differential parameters, it is possible to conduct larger scale biomarker studies, combining clinical, laboratory medicine, and epidemiological techniques. Clin Chem Lab Med 2009;47:573–8. [ABSTRACT FROM AUTHOR]