Bi-allelic variants in NAE1 cause intellectual disability, ischiopubic hypoplasia, stress-mediated lymphopenia and neurodegeneration.

Neddylation has been implicated in various cellular pathways and in the pathophysiology of numerous diseases. We identified four individuals with bi-allelic variants in NAE1, which encodes the neddylation E1 enzyme. Pathogenicity was supported by decreased NAE1 abundance and overlapping clinical and cellular phenotypes. To delineate how cellular consequences of NAE1 deficiency would lead to the clinical phenotype, we focused primarily on the rarest phenotypic features, based on the assumption that these would best reflect the pathophysiology at stake. Two of the rarest features, neuronal loss and lymphopenia worsening during infections, suggest that NAE1 is required during cellular stress caused by infections to protect against cell death. In support, we found that stressing the proteasome system with MG132-requiring upregulation of neddylation to restore proteasomal function and proteasomal stress-led to increased cell death in fibroblasts of individuals with NAE1 genetic variants. Additionally, we found decreased lymphocyte counts after CD3/CD28 stimulation and decreased NF-κB translocation in individuals with NAE1 variants. The rarest phenotypic feature-delayed closure of the ischiopubic rami-correlated with significant downregulation of RUN2X and SOX9 expression in transcriptomic data of fibroblasts. Both genes are involved in the pathophysiology of ischiopubic hypoplasia. Thus, we show that NAE1 plays a major role in (skeletal) development and cellular homeostasis during stress. Our approach suggests that a focus on rare phenotypic features is able to provide significant pathophysiological insights in diseases caused by mutations in genes with pleiotropic effects.

The Effect of Artificial Intelligence on Patient-Physician Trust: Cross-Sectional Vignette Study.

BACKGROUND: Clinical decision support systems (CDSSs) based on routine care data, using artificial intelligence (AI), are increasingly being developed. Previous studies focused largely on the technical aspects of using AI, but the acceptability of these technologies by patients remains unclear. OBJECTIVE: We aimed to investigate whether patient-physician trust is affected when medical decision-making is supported by a CDSS. METHODS: We conducted a vignette study among the patient panel (N=860) of the University Medical Center Utrecht, the Netherlands. Patients were randomly assigned into 4 groups-either the intervention or control groups of the high-risk or low-risk cases. In both the high-risk and low-risk case groups, a physician made a treatment decision with (intervention groups) or without (control groups) the support of a CDSS. Using a questionnaire with a 7-point Likert scale, with 1 indicating "strongly disagree" and 7 indicating "strongly agree," we collected data on patient-physician trust in 3 dimensions: competence, integrity, and benevolence. We assessed differences in patient-physician trust between the control and intervention groups per case using Mann-Whitney U tests and potential effect modification by the participant's sex, age, education level, general trust in health care, and general trust in technology using multivariate analyses of (co)variance. RESULTS: In total, 398 patients participated. In the high-risk case, median perceived competence and integrity were lower in the intervention group compared to the control group but not statistically significant (5.8 vs 5.6; P=.16 and 6.3 vs 6.0; P=.06, respectively). However, the effect of a CDSS application on the perceived competence of the physician depended on the participant's sex (P=.03). Although no between-group differences were found in men, in women, the perception of the physician's competence and integrity was significantly lower in the intervention compared to the control group (P=.009 and P=.01, respectively). In the low-risk case, no differences in trust between the groups were found. However, increased trust in technology positively influenced the perceived benevolence and integrity in the low-risk case (P=.009 and P=.04, respectively). CONCLUSIONS: We found that, in general, patient-physician trust was high. However, our findings indicate a potentially negative effect of AI applications on the patient-physician relationship, especially among women and in high-risk situations. Trust in technology, in general, might increase the likelihood of embracing the use of CDSSs by treating professionals.

Comparison of the Response to an Electronic Versus a Traditional Informed Consent Procedure in Terms of Clinical Patient Characteristics: Observational Study.

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.

Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods.

When developing models for clinical information retrieval and decision support systems, the discrete outcomes required for training are often missing. These labels need to be extracted from free text in electronic health records. For this extraction process one of the most important contextual properties in clinical text is negation, which indicates the absence of findings. We aimed to improve large scale extraction of labels by comparing three methods for negation detection in Dutch clinical notes. We used the Erasmus Medical Center Dutch Clinical Corpus to compare a rule-based method based on ContextD, a biLSTM model using MedCAT and (finetuned) RoBERTa-based models. We found that both the biLSTM and RoBERTa models consistently outperform the rule-based model in terms of F1 score, precision and recall. In addition, we systematically categorized the classification errors for each model, which can be used to further improve model performance in particular applications. Combining the three models naively was not beneficial in terms of performance. We conclude that the biLSTM and RoBERTa-based models in particular are highly accurate accurate in detecting clinical negations, but that ultimately all three approaches can be viable depending on the use case at hand.

From computer to bedside, involving neonatologists in artificial intelligence models for neonatal medicine.

In recent years, data have become the main driver of medical innovation. With increased availability and decreased price of storage and computing power, the potential for improvement in care is enormous. Many data-driven explorations have started. However, the actual implementation of artificial intelligence in healthcare remains scarce. We describe essential elements during a computer-to-bedside process in a data science project that support the crucial role of the neonatologist. IMPACT: There is a great potential for data science in neonatal medicine. Multidisciplinary teams form the foundation of a data science project. Domain experts will need to play a pivotal role. We need an open learning environment.

Asking informed consent may lead to significant participation bias and suboptimal cardiovascular risk management in learning healthcare systems.

BACKGROUND: The Utrecht Cardiovascular Cohort - CardioVascular Risk Management (UCC-CVRM) was set up as a learning healthcare system (LHS), aiming at guideline based cardiovascular risk factor measurement in all patients in routine clinical care. However, not all patients provided informed consent, which may lead to participation bias. We aimed to study participation bias in a LHS by assessing differences in and completeness of cardiovascular risk management (CVRM) indicators in electronic health records (EHRs) of consenting, non-consenting, and non-responding patients, using the UCC-CVRM as an example. METHODS: All patients visiting the University Medical Center Utrecht for first time evaluation of a(n) (a)symptomatic vascular disease or condition were invited to participate. Routine care data was collected in the EHR and an informed consent was asked. Differences in patient characteristics were compared between consent groups. We performed multivariable logistic regression to identify determinants of non-consent. We used multinomial regression for an exploratory analysis for the determinants of non-response. Presence of CVRM indicators were compared between consent groups. A waiver (19/641) was obtained from our ethics committee. RESULTS: Out of 5730 patients invited, 2378 were consenting, 1907 non-consenting, and 1445 non-responding. Non-consent was related to young and old age, lower education level, lower BMI, physical activity and haemoglobin levels, higher heartrate, cardiovascular disease history and absence of proteinuria. Non-response increased with young and old age, higher education level, physical activity, HbA1c and decreased with lower levels of haemoglobin, BMI, and systolic blood pressure. Presence of CVRM indicators was 5-30% lower in non-consenting patients and even lower in non-responding patients, compared to consenting patients. Non-consent and non-response varied across specialisms. CONCLUSIONS: A traditional informed consent procedure in a LHS may lead to participation bias and potentially to suboptimal CVRM, which is detrimental for feedback on findings in a LHS. This underlines the importance of reassessing the informed consent procedure in a LHS.

Sexual dimorphism in peripheral blood cell characteristics linked to recanalization success of endovascular thrombectomy in acute ischemic stroke.

Endovascular thrombectomy (EVT) success to treat acute ischemic stroke varies with factors like stroke etiology and clot composition, which can differ between sexes. We studied if sex-specific blood cell characteristics (BCCs) are related to recanalization success. We analyzed electronic health records of 333 EVT patients from a single intervention center, and extracted 71 BCCs from the Sapphire flow cytometry analyzer. Through Sparse Partial Least Squares Discriminant Analysis, incorporating cross-validation and stability selection, we identified BCCs associated with successful recanalization (TICI 3) in both sexes. Stroke etiology was considered, while controlling for cardiovascular risk factors. Of the patients, successful recanalization was achieved in 51% of women and 49% of men. 21 of the 71 BCCs showed significant differences between sexes  (pFDR-corrected < 0.05). The female-focused recanalization model had lower error rates than both combined [t(192.4) = 5.9, p < 0.001] and male-only models [t(182.6) = - 15.6, p < 0.001]. In women, successful recanalization and cardioembolism were associated with a higher number of reticulocytes, while unsuccessful recanalization and large artery atherosclerosis (LAA) as cause of stroke were associated with a higher mean corpuscular hemoglobin concentration. In men, unsuccessful recanalization and LAA as cause of stroke were associated with a higher coefficient of variance of lymphocyte complexity of the intracellular structure. Sex-specific BCCs related to recanalization success varied and were linked to stroke etiology. This enhanced understanding may facilitate personalized treatment for acute ischemic stroke.

Design, validation and implementation of an automated e-alert for acute kidney injury: 6-month pilot study shows increased awareness.

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.

Monocyte-Chemoattractant Protein-1 Levels in Human Atherosclerotic Lesions Associate With Plaque Vulnerability.

[Figure: see text].

Recommendations for IVDR compliant in-house software development in clinical practice: a how-to paper with three use cases.

OBJECTIVES: The In Vitro Diagnostics Regulation (IVDR) will be effective in May 2022 by which in-house developed tests need to apply to the general safety and performance requirements defined in Annex I of the IVDR ruling. Yet, article 16 from Annex I about software can be hard to interpret and implement, particularly as laboratories are unfamiliar with quality standards for software development. METHODS: In this paper we provide recommendations on organizational structure, standards to use, and documentation, for IVDR compliant in-house software development. RESULTS: A practical insight is offered into novel standard operating procedures using three examples: an Excel file with a formula to calculate the pharmacokinetics of tacrolimus and to calculate the new dose, a rule for automated diagnosis of acute kidney injury and a bioinformatics pipeline for DNA variant calling. CONCLUSIONS: We recommend multidisciplinary development teams supported by higher management, use of ISO-15189 in synergy with IEC-62304, and concise documentation that includes intended purpose, classification, requirement management, risk management, verification and validation, configuration management and references to clinical or performance evidence.

A Wolf in Sheep's Clothing: Reuse of Routinely Obtained Laboratory Data in Research.

Electronic health records (EHRs) contain valuable data for reuse in science, quality evaluations, and clinical decision support. Because routinely obtained laboratory data are abundantly present, often numeric, generated by certified laboratories, and stored in a structured way, one may assume that they are immediately fit for (re)use in research. However, behind each test result lies an extensive context of choices and considerations, made by both humans and machines, that introduces hidden patterns in the data. If they are unaware, researchers reusing routine laboratory data may eventually draw incorrect conclusions. In this paper, after discussing health care system characteristics on both the macro and micro level, we introduce the reader to hidden aspects of generating structured routine laboratory data in 4 steps (ordering, preanalysis, analysis, and postanalysis) and explain how each of these steps may interfere with the reuse of routine laboratory data. As researchers reusing these data, we underline the importance of domain knowledge of the health care professional, laboratory specialist, data manager, and patient to turn routine laboratory data into meaningful data sets to help obtain relevant insights that create value for clinical care.

Shorter laboratory turnaround time is associated with shorter emergency department length of stay: a retrospective cohort study.

BACKGROUND: A longer emergency department length of stay (EDLOS) is associated with poor outcomes. Shortening EDLOS is difficult, due to its multifactorial nature. A potential way to improve EDLOS is through shorter turnaround times for diagnostic testing. This study aimed to investigate whether a shorter laboratory turnaround time (TAT) and time to testing (TTT) were associated with a shorter EDLOS. METHODS: A retrospective cohort study was performed, including all visits to the emergency department (ED) of an academic teaching hospital from 2017 to 2020 during which a standardized panel of laboratory tests had been ordered. TTT was calculated as the time from arrival in the ED to the ordering of laboratory testing. TAT was calculated as the time from test ordering to the reporting of the results, and was divided into a clinical and a laboratory stage. The outcome was EDLOS in minutes. The effect of TTT and TAT on EDLOS was estimated through a linear regression model. RESULTS: In total, 23,718 ED visits were included in the analysis. Median EDLOS was 199.0 minutes (interquartile range [IQR] 146.0-268.0). Median TTT was 7.0 minutes (IQR 2.0-12.0) and median TAT was 51.1 minutes (IQR 41.1-65.0). Both TTT and TAT were positively associated with EDLOS. The laboratory stage comprised a median of 69% (IQR 59-78%) of total TAT. CONCLUSION: Longer TTT and TAT are independently associated with longer EDLOS. As the laboratory stage predominantly determines TAT, it provides a promising target for interventions to reduce EDLOS and ED crowding.

A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department.

Accurate sepsis diagnosis is paramount for treatment decisions, especially at the emergency department (ED). To improve diagnosis, clinical decision support (CDS) tools are being developed with machine learning (ML) algorithms, using a wide range of variable groups. ML models can find patterns in Electronic Health Record (EHR) data that are unseen by the human eye. A prerequisite for a good model is the use of high-quality labels. Sepsis gold-standard labels are hard to define due to a lack of reliable diagnostic tools for sepsis at the ED. Therefore, standard clinical tools, such as clinical prediction scores (e.g. modified early warning score and quick sequential organ failure assessment), and claims-based methods (e.g. ICD-10) are used to generate suboptimal labels. As a consequence, models trained with these "silver" labels result in ill-trained models. In this study, we trained ML models for sepsis diagnosis at the ED with labels of 375 ED visits assigned by an endpoint adjudication committee (EAC) that consisted of 18 independent experts. Our objective was to evaluate which routinely measured variables show diagnostic value for sepsis. We performed univariate testing and trained multiple ML models with 95 routinely measured variables of three variable groups; demographic and vital, laboratory and advanced haematological variables. Apart from known diagnostic variables, we identified added diagnostic value for less conventional variables such as eosinophil count and platelet distribution width. In this explorative study, we show that the use of an EAC together with ML can identify new targets for future sepsis diagnosis research.

A pan-inflammatory microRNA-cluster is associated with orbital non-Hodgkin lymphoma and idiopathic orbital inflammation.

Non-Hodgkin orbital lymphoma (NHOL) and idiopathic orbital inflammation (IOI) are common orbital conditions with largely unknown pathophysiology that can be difficult to diagnose. In this study we aim to identify serum miRNAs associated with NHOL and IOI. We performed OpenArray® miRNA profiling in 33 patients and controls. Differentially expressed miRNAs were technically validated across technology platforms and replicated in an additional cohort of 32 patients and controls. We identified and independently validated a serum miRNA profile of NHOL that was remarkably similar to IOI and characterized by an increased expression of a cluster of eight miRNAs. Pathway enrichment analysis indicated that the miRNA-cluster is associated with immune-mediated pathways, which we supported by demonstrating the elevated expression of this cluster in serum of patients with other inflammatory conditions. The cluster contained miR-148a, a key driver of B-cell tolerance, and miR-365 that correlated with serum IgG and IgM concentrations. In addition, miR-29a and miR-223 were associated with blood lymphocyte and neutrophil populations, respectively. NHOL and IOI are characterized by an abnormal serum miRNA-cluster associated with immune pathway activation and linked to B cell and neutrophil dysfunction.

Ambiguous definitions for baseline serum creatinine affect acute kidney diagnosis at the emergency department.

BACKGROUND: Acute kidney injury (AKI) incidence is increasing, however AKI is often missed at the emergency department (ED). AKI diagnosis depends on changes in kidney function by comparing a serum creatinine (SCr) measurement to a baseline value. However, it remains unclear to what extent different baseline values may affect AKI diagnosis at ED. METHODS: Routine care data from ED visits between 2012 and 2019 were extracted from the Utrecht Patient Oriented Database. We evaluated baseline definitions with criteria from the RIFLE, AKIN and KDIGO guidelines. We evaluated four baseline SCr definitions (lowest, most recent, mean, median), as well as five different time windows (up to 365 days prior to ED visit) to select a baseline and compared this to the first measured SCr at ED. As an outcome, we assessed AKI prevalence at ED. RESULTS: We included 47,373 ED visits with both SCr-ED and SCr-BL available. Of these, 46,100 visits had a SCr-BL from the - 365/- 7 days time window. Apart from the lowest value, AKI prevalence remained similar for the other definitions when varying the time window. The lowest value with the - 365/- 7 time window resulted in the highest prevalence (21.4%). Importantly, applying the guidelines with all criteria resulted in major differences in prevalence ranging from 5.9 to 24.0%. CONCLUSIONS: AKI prevalence varies with the use of different baseline definitions in ED patients. Clinicians, as well as researchers and developers of automatic diagnostic tools should take these considerations into account when aiming to diagnose AKI in clinical and research settings.

Intra-Operative Hypotension is a Risk Factor for Post-operative Silent Brain Ischaemia in Patients With Pre-operative Hypertension Undergoing Carotid Endarterectomy.

OBJECTIVE: Intra-operative haemodynamic instability during carotid endarterectomy (CEA) has been associated with an increased risk of procedural stroke. Diffusion weighted imaging (DWI) lesions have been proposed as a surrogate marker for peri-operative silent cerebral ischaemia. This study aimed to investigate the relationship between peri-operative blood pressure (BP) and presence of post-operative DWI lesions in patients undergoing CEA. METHODS: A retrospective analysis was performed based on patients with symptomatic CEA included in the MRI substudy of the International Carotid Stenting Study. Relative intra-operative hypotension was defined as a decrease of intra-operative systolic BP ≥ 20% compared with pre-operative ('baseline') BP, absolute hypotension was defined as a drop in systolic BP < 80  mmHg. The primary endpoint was the presence of any new DWI lesions on post-operative MRI (DWI positive). The occurrence and duration of intra-operative hypotension was compared between DWI positive and DWI negative patients as was the magnitude of the difference between pre- and intra-operative BP. RESULTS: Fifty-five patients with symptomatic CEA were included, of whom eight were DWI positive. DWI positive patients had a significantly higher baseline systolic (186 ± 31 vs. 158 ± 27 mmHg, p = .011) and diastolic BP (95 ± 15 vs. 84 ± 13 mmHg, p = .046) compared with DWI negative patients. Other pre-operative characteristics did not differ. Relative intra-operative hypotension compared with baseline occurred in 53/55 patients (median duration 34 min; range 0-174). Duration of hypotension did not differ significantly between the groups (p = .088). Mean systolic intra-operative BP compared with baseline revealed a larger drop in BP (-37 ± 29 mmHg) in DWI positive compared with DWI negative patients (-14 ± 26 mmHg, p = .024). Absolute intra-operative systolic BP values did not differ between the groups. CONCLUSION: In this exploratory study, high pre-operative BP and a larger drop of intra-operative BP were associated with peri-procedural cerebral ischaemia as documented with DWI. These results call for confirmation in an adequately sized prospective study, as they suggest important consequences for peri-operative haemodynamic management in carotid revascularisation.

Cerebral Small Vessel Disease in Standard Pre-operative Imaging Reports Is Independently Associated with Increased Risk of Cardiovascular Death Following Carotid Endarterectomy.

OBJECTIVE: Cerebral white matter lesions (WMLs) and lacunar infarcts are surrogates of cerebral small vessel disease (SVD). WML severity as determined by trained radiologists predicts post-operative stroke or death in patients undergoing carotid endarterectomy (CEA). It is unknown whether routine pre-operative brain imaging reports as part of standard clinical practice also predict short and long term risk of stroke and death after CEA. METHODS: Consecutive patients from the Athero-Express biobank study that underwent CEA for symptomatic high degree stenosis between March 2002 and November 2014 were included. Pre-operative brain imaging (computed tomography [CT] or magnetic resonance imaging [MRI]) reports were reviewed for reporting of SVD, defined as WMLs or any lacunar infarcts. The primary outcome was defined as any stroke or any cardiovascular death over three year follow up. The secondary outcome was defined as the 30 day peri-operative risk of stroke or cardiovascular death. RESULTS: A total of 1038 patients were included (34% women), of whom 659 (63.5%) had CT images and 379 (36.5%) MRI images available. Of all patients, 697 (67%) had SVD reported by radiologists. Patients with SVD had a higher three year risk of cardiovascular death than those without (6.5% vs. 2.1%, adjusted HR 2.52 [95% CI 1.12-5.67]; p = .026) but no association was observed for the three year risk of stroke (9.0% vs. 6.7%, for patients with SVD vs. those without, adjusted HR 1.24 [95% CI 0.76-2.02]; p = .395). No differences in 30 day peri-operative risk were observed for stroke (4.4% vs. 2.9%, for patients with vs. those without SVD; adjusted HR 1.49 [95% CI 0.73-3.05]; p = .28), and for the combined stroke/cardiovascular death risk (4.4% vs. 3.5%, adjusted HR 1.20 [95% CI 0.61-2.35]; p = .59). CONCLUSION: Presence of SVD in pre-operative brain imaging reports can serve as a predictor for the three year risk of cardiovascular death in symptomatic patients undergoing CEA but does not predict peri-operative or long term risk of stroke.

The Atherosclerosis Risk Variant rs2107595 Mediates Allele-Specific Transcriptional Regulation of HDAC9 via E2F3 and Rb1.

Background and Purpose- Genome-wide association studies have identified the HDAC9 (histone deacetylase 9) gene region as a major risk locus for atherosclerotic stroke and coronary artery disease in humans. Previous results suggest a role of altered HDAC9 expression levels as the underlying disease mechanism. rs2107595, the lead single nucleotide polymorphism for stroke and coronary artery disease resides in noncoding DNA and colocalizes with histone modification marks suggestive of enhancer elements. Methods- To determine the mechanisms by which genetic variation at rs2107595 regulates HDAC9 expression and thus vascular risk we employed targeted resequencing, proteome-wide search for allele-specific nuclear binding partners, chromatin immunoprecipitation, genome-editing, reporter assays, circularized chromosome conformation capture, and gain- and loss-of-function experiments in cultured human cell lines and primary immune cells. Results- Targeted resequencing of the HDAC9 locus in patients with atherosclerotic stroke and controls supported candidacy of rs2107595 as the causative single nucleotide polymorphism. A proteomic search for nuclear binding partners revealed preferential binding of the E2F3/TFDP1/Rb1 complex (E2F transcription factor 3/transcription factor Dp-1/Retinoblastoma 1) to the rs2107595 common allele, consistent with the disruption of an E2F3 consensus site by the risk allele. Gain- and loss-of-function studies showed a regulatory effect of E2F/Rb proteins on HDAC9 expression. Compared with the common allele, the rs2107595 risk allele exhibited higher transcriptional capacity in luciferase assays and was associated with higher HDAC9 mRNA levels in primary macrophages and genome-edited Jurkat cells. Circularized chromosome conformation capture revealed a genomic interaction of the rs2107595 region with the HDAC9 promoter, which was stronger for the common allele as was the in vivo interaction with E2F3 and Rb1 determined by chromatin immunoprecipitation. Gain-of-function experiments in isogenic Jurkat cells demonstrated a key role of E2F3 in mediating rs2107595-dependent transcriptional regulation of HDAC9. Conclusions- Collectively, our findings imply allele-specific transcriptional regulation of HDAC9 via E2F3 and Rb1 as a major mechanism mediating vascular risk at rs2107595.

A single preoperative blood test predicts postoperative sepsis and pneumonia after coronary bypass or open aneurysm surgery.

BACKGROUND: Major surgery comes with a high risk for postoperative inflammatory complications. Preoperative risk scores predict mortality risk but fail to identify patients at risk for complications following cardiovascular surgery. We therefore assessed the value of preoperative red cell distribution width (RDW) as a predictor for pneumonia and sepsis after cardiovascular surgery and studied the relation of RDW with hematopoietic tissue activity. METHODS: RDW is an easily accessible, yet seldomly used parameter from routine haematology measurements. RDW was extracted from the Utrecht Patient Orientated Database (UPOD) for preoperative measurements in patients undergoing open abdominal aortic anuerysm repair (AAA)(N = 136) or coronary artery bypass grafting (CABG)(N = 2193). The cohorts were stratified in tertiles to assess effects over the different groups. Generalized Linear Models were used to determine associations between RDW and postoperative inflammatory complications. Hematopoietic tissue activity was scored using fluor-18-(18F)-deoxyglucose positron emission tomography and associated with RDW using linear regression models. RESULTS: In total, 43(31.6%) and 73 patients (3.3%) suffered from inflammatory complications after AAA-repair or CABG, respectively; the majority being pneumonia in both cohorts. Postoperative inflammatory outcome incidence increased from 19.6% in the lowest to 48.9% in the highest RDW tertile with a corresponding risk ratio (RR) of 2.35 ([95%CI:1.08-5.14] P = 0.032) in AAA patients. In the CABG cohort, the incidence of postoperative inflammatory outcomes increased from 1.8% to 5.3% with an adjusted RR of 1.95 ([95%CI:1.02-3.75] P = 0.044) for the highest RDW tertile compared with the lowest RDW tertile. FDG-PET scans showed associations of RDW with tissue activity in the spleen (B = 0.517 [P = 0.001]) and the lumbar bone marrow (B = 0.480 [P = 0.004]). CONCLUSION: Elevated RDW associates with increased risk for postoperative inflammatory complications and hematopoietic tissue activity. RDW likely reflects chronic low-grade inflammation and should be considered to identify patients at risk for postoperative inflammatory complications following cardiovascular surgery.

A Human(e) Factor in Clinical Decision Support Systems.

The overwhelming amount, production speed, multidimensionality, and potential value of data currently available-often simplified and referred to as big data -exceed the limits of understanding of the human brain. At the same time, developments in data analytics and computational power provide the opportunity to obtain new insights and transfer data-provided added value to clinical practice in real time. What is the role of the health care professional in collaboration with the data scientist in the changing landscape of modern care? We discuss how health care professionals should provide expert knowledge in each of the stages of clinical decision support design: data level, algorithm level, and decision support level. Including various ethical considerations, we advocate for health care professionals to responsibly initiate and guide interprofessional teams, including patients, and embrace novel analytic technologies to translate big data into patient benefit driven by human(e) values.