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.

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.

Proton pump inhibition for secondary hemochromatosis in hereditary anemia: a phase III placebo-controlled randomized cross-over clinical trial.

Iron overload is a severe general complication of hereditary anemias. Treatment with iron chelators is hampered by important side-effects, high costs, and the lack of availability in many countries with a high prevalence of hereditary anemias. In this phase III randomized placebo-controlled trial, we assigned adults with non-transfusion-dependent hereditary anemias with mild-to-moderate iron overload to receive esomeprazole (at a dose of 40 mg twice daily) or placebo for 12 months in a cross-over design. The primary end point was change of liver iron content measured by MRI. A total of 30 participants were enrolled in the trial. Treatment with esomeprazole resulted in a statistically significant reduction in liver iron content that was 0.55 mg Fe/g dw larger than after treatment with placebo (95%CI [0.05 to 1.06]; p = 0.03). Median baseline liver iron content at the start of esomeprazole was 4.99 versus 4.49 mg Fe/g dw at start of placebo. Mean delta liver iron content after esomeprazole treatment was -0.57 (SD 1.20) versus -0.11 mg Fe/g dw (SD 0.75) after placebo treatment. Esomeprazole was well tolerated, reported adverse events were mild and none of the patients withdrew from the study due to side effects. In summary, esomeprazole resulted in a significant reduction in liver iron content when compared to placebo in a heterogeneous group of patients with non-transfusion-dependent hereditary anemias. From an international perspective this result can have major implications given the fact that proton pump inhibitors may frequently be the only realistic therapy for many patients without access to or not tolerating iron chelators.

International practice variation in perioperative laboratory testing in glioblastoma patients-a retrospective cohort study.

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.

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.

Dried blood spot metabolomics reveals a metabolic fingerprint with diagnostic potential for Diamond Blackfan Anaemia.

The diagnostic evaluation of Diamond Blackfan Anaemia (DBA), an inherited bone marrow failure syndrome characterised by erythroid hypoplasia, is challenging because of a broad phenotypic variability and the lack of functional screening tests. In this study, we explored the potential of untargeted metabolomics to diagnose DBA. In dried blood spot samples from 18 DBA patients and 40 healthy controls, a total of 1752 unique metabolite features were identified. This metabolic fingerprint was incorporated into a machine-learning algorithm, and a binary classification model was constructed using a training set. The model showed high performance characteristics (average accuracy 91·9%), and correct prediction of class was observed for all controls (n = 12) and all but one patient (n = 4/5) from the validation or 'test' set (accuracy 94%). Importantly, in patients with congenital dyserythropoietic anaemia (CDA) - an erythroid disorder with overlapping features - we observed a distinct metabolic profile, indicating the disease specificity of the DBA fingerprint and underlining its diagnostic potential. Furthermore, when exploring phenotypic heterogeneity, DBA treatment subgroups yielded discrete differences in metabolic profiles, which could hold future potential in understanding therapy responses. Our data demonstrate that untargeted metabolomics in dried blood spots is a promising new diagnostic tool for DBA.

Untargeted metabolic profiling in dried blood spots identifies disease fingerprint for pyruvate kinase deficiency.

The diagnostic evaluation and clinical characterization of rare hereditary anemia (RHA) is to date still challenging. In particular, there is little knowledge on the broad metabolic impact of many of the molecular defects underlying RHA. In this study we explored the potential of untargeted metabolomics to diagnose a relatively common type of RHA: Pyruvate Kinase Deficiency (PKD). In total, 1903 unique metabolite features were identified in dried blood spot samples from 16 PKD patients and 32 healthy controls. A metabolic fingerprint was identified using a machine learning algorithm, and subsequently a binary classification model was designed. The model showed high performance characteristics (AUC 0.990, 95%CI 0.981-0.999) and an accurate class assignment was achieved for all newly added control (13) and patient samples (6), with the exception of one patient (accuracy 94%). Important metabolites in the metabolic fingerprint included glycolytic intermediates, polyamines and several acyl carnitines. In general, the application of untargeted metabolomics in dried blood spots is a novel functional tool that holds promise for diagnostic stratification and studies on disease pathophysiology in RHA.

AG-348 (Mitapivat), an allosteric activator of red blood cell pyruvate kinase, increases enzymatic activity, protein stability, and ATP levels over a broad range of PKLR genotypes.

Pyruvate kinase (PK) deficiency is a rare hereditary disorder affecting red cell (RBC) glycolysis, causing changes in metabolism including a deficiency in ATP. This affects red cell homeostasis, promoting premature removal of RBCs from the circulation. In this study we characterized and evaluated the effect of AG-348, an allosteric activator of PK that is currently in clinical trials for treatment of PK deficiency, on RBCs and erythroid precursors from PK-deficient patients. In 15 patients ex vivo treatment with AG-348 resulted in increased enzymatic activity in all patient cells after 24 hours (mean increase 1.8-fold, range 1.2-3.4). ATP levels increased (mean increase 1.5-fold, range 1.0-2.2) similar to control cells (mean increase 1.6-fold, range, 1.4-1.8). Generally, PK thermostability was strongly reduced in PK-deficient RBCs. Ex vivo treatment with AG-348 increased residual activity 1.4 to >10-fold than residual activity of vehicle-treated samples. Protein analyses suggests that a sufficient level of PK protein is required for cells to respond to AG-348 treatment ex-vivo, as treatment effects were minimal in patient cells with very low or undetectable levels of PK-R. In half of the patients, ex vivo treatment with AG-348 was associated with an increase in RBC deformability. These data support the hypothesis that drug intervention with AG-348 effectively upregulates PK enzymatic activity and increases stability in PK-deficient RBCs over a broad range of PKLR genotypes. The concomitant increase in ATP levels suggests that glycolytic pathway activity may be restored. AG-348 treatment may represent an attractive way to correct the underlying pathologies of PK deficiency. (AG-348 is currently in clinical trials for the treatment of PK deficiency. ClinicalTrials.gov: NCT02476916, NCT03853798, NCT03548220, NCT03559699).

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.

The Interplay between Drivers of Erythropoiesis and Iron Homeostasis in Rare Hereditary Anemias: Tipping the Balance.

Rare hereditary anemias (RHA) represent a group of disorders characterized by either impaired production of erythrocytes or decreased survival (i.e., hemolysis). In RHA, the regulation of iron metabolism and erythropoiesis is often disturbed, leading to iron overload or worsening of chronic anemia due to unavailability of iron for erythropoiesis. Whereas iron overload generally is a well-recognized complication in patients requiring regular blood transfusions, it is also a significant problem in a large proportion of patients with RHA that are not transfusion dependent. This indicates that RHA share disease-specific defects in erythroid development that are linked to intrinsic defects in iron metabolism. In this review, we discuss the key regulators involved in the interplay between iron and erythropoiesis and their importance in the spectrum of RHA.

Density, heterogeneity and deformability of red cells as markers of clinical severity in hereditary spherocytosis.

Hereditary spherocytosis (HS) originates from defective anchoring of the cytoskeletal network to the transmembrane protein complexes of the red blood cell (RBC). Red cells in HS are characterized by membrane instability and reduced deformability and there is marked heterogeneity in disease severity among patients. To unravel this variability in disease severity, we analyzed blood samples from 21 HS patients with defects in ankyrin, band 3, α-spectrin or ß-spectrin using red cell indices, eosin-5-maleimide binding, microscopy, the osmotic fragility test, Percoll density gradients, vesiculation and ektacytometry to assess cell membrane stability, cellular density and deformability. Reticulocyte counts, CD71 abundance, band 4.1 a:b ratio, and glycated hemoglobin were used as markers of RBC turnover. We observed that patients with moderate/severe spherocytosis have short-living erythrocytes of low density and abnormally high intercellular heterogeneity. These cells show a prominent decrease in membrane stability and deformability and, as a consequence, are quickly removed from the circulation by the spleen. In contrast, in mild spherocytosis less pronounced reduction in deformability results in prolonged RBC lifespan and, hence, cells are subject to progressive loss of membrane. RBC from patients with mild spherocytosis thus become denser before they are taken up by the spleen. Based on our findings, we conclude that RBC membrane loss, cellular heterogeneity and density are strong markers of clinical severity in spherocytosis.

Reticulated Platelets as Predictor of Myocardial Injury and 30 Day Mortality After Non-cardiac Surgery.

OBJECTIVE: A pre-operative marker for identification of patients at risk of peri-operative adverse events and 30 day mortality might be the percentage of young, reticulated platelets (pRP). This study aimed to determine the predictive value of pre-operative pRP on post-operative myocardial injury (PMI) and 30 day mortality, in patients aged ≥ 60 years undergoing moderate to high risk non-cardiac surgery. METHODS: The incidence of PMI (troponin I > 0.06 µg/L) and 30 day mortality was compared for patients with normal and high pRP (≥2.82%) obtained from The Utrecht Patient Orientated Database. The predictive pRP value was assessed using logistic regression. A prediction model for PMI or 30 day mortality with known risk factors was compared with a model including increased pRP using the area under the receiving operator characteristics curve (AUROC). RESULTS: In total, 26.5% (607/2289) patients showed pre-operative increased pRP. Increased pRP was associated with more PMI and 30 day mortality compared with normal pRP (36.1% vs. 28.3%, p < .001 and 8.6% vs. 3.6%, p < .001). The median pRP was higher in patients suffering PMI and 30 day mortality compared with not (2.21 [IQR: 1.57-3.11] vs. 2.07 [IQR: 1.52-1.78], p = .002, and 2.63 [IQR: 1.76-4.15] vs. 2.09 [IQR: 1.52-3.98], p < .001). pRP was independently related to PMI (OR: 1.28 [95% CI: 1.04-1.59], p = .02) and 30 day mortality (OR: 2.35 [95% CI: 1.56-3.55], p < .001). Adding increased pRP to the predictive model of PMI or 30 day mortality did not increase the AUROC 0.71 vs. 0.72, and 0.80 vs. 0.81. CONCLUSION: In patients undergoing major non-cardiac surgery, increased pre-operative pRP is related to 30 day mortality and PMI.

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.

eHealth as the Next-Generation Perinatal Care: An Overview of the Literature.

BACKGROUND: Unrestricted by time and place, electronic health (eHealth) provides solutions for patient empowerment and value-based health care. Women in the reproductive age are particularly frequent users of internet, social media, and smartphone apps. Therefore, the pregnant patient seems to be a prime candidate for eHealth-supported health care with telemedicine for fetal and maternal conditions. OBJECTIVE: This study aims to review the current literature on eHealth developments in pregnancy to assess this new generation of perinatal care. METHODS: We conducted a systematic literature search of studies on eHealth technology in perinatal care in PubMed and EMBASE in June 2017. Studies reporting the use of eHealth during prenatal, perinatal, and postnatal care were included. Given the heterogeneity in study methods, used technologies, and outcome measurements, results were analyzed and presented in a narrative overview of the literature. RESULTS: The literature search provided 71 studies of interest. These studies were categorized in 6 domains: information and eHealth use, lifestyle (gestational weight gain, exercise, and smoking cessation), gestational diabetes, mental health, low- and middle-income countries, and telemonitoring and teleconsulting. Most studies in gestational diabetes and mental health show that eHealth applications are good alternatives to standard practice. Examples are interactive blood glucose management with remote care using smartphones, telephone screening for postnatal depression, and Web-based cognitive behavioral therapy. Apps and exercise programs show a direction toward less gestational weight gain, increase in step count, and increase in smoking abstinence. Multiple studies describe novel systems to enable home fetal monitoring with cardiotocography and uterine activity. However, only few studies assess outcomes in terms of fetal monitoring safety and efficacy in high-risk pregnancy. Patients and clinicians report good overall satisfaction with new strategies that enable the shift from hospital-centered to patient-centered care. CONCLUSIONS: This review showed that eHealth interventions have a very broad, multilevel field of application focused on perinatal care in all its aspects. Most of the reviewed 71 articles were published after 2013, suggesting this novel type of care is an important topic of clinical and scientific relevance. Despite the promising preliminary results as presented, we accentuate the need for evidence for health outcomes, patient satisfaction, and the impact on costs of the possibilities of eHealth interventions in perinatal care. In general, the combination of increased patient empowerment and home pregnancy care could lead to more satisfaction and efficiency. Despite the challenges of privacy, liability, and costs, eHealth is very likely to disperse globally in the next decade, and it has the potential to deliver a revolution in perinatal care.