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Background: Between 2002 and 2011, the incidence of severe primary postpartum hemorrhage (PPH) in Dutch women with von Willebrand disease (VWD) and hemophilia carriers (HCs) was 8% vs 4.5% in the general population. Objectives: To determine the contemporary incidence of severe primary PPH in women with VWD and HCs. Methods: All women with VWD or HCs who delivered between 2012 and 2017 were selected from all 6 Dutch hemophilia treatment centers. Data on patient and disease characteristics, peripartum hematologic and obstetric management, and outcomes were retrospectively collected. Incidence of severe primary (≥1000 mL of blood loss ≤24 hours after childbirth) and primary (≥500 mL within ≤24 hours after childbirth) PPH was compared with the (1) previous cohort and (2) general Dutch population and between (3) women with VWD and HCs with third-trimester coagulation activity levels <50 international units (IU)/dL vs ≥50 IU/dL and (4) women treated with vs without peripartum hemostatic prophylaxis. Results: Three-hundred forty-eight deliveries (151 VWD, 167 hemophilia A, and 30 hemophilia B carriers) were included. The severe primary PPH incidence was 10% (36/348) and remained stable over time, whereas this incidence has increased in the general population (to 8%), leading to a similar risk (P = .17). Severe primary PPH risk was comparable between women with coagulation activity levels <50 and ≥50 IU/dL (11% [7/66] vs 10% [29/279]; odds ratio, 1.02; 95% CI, 0.43-2.44) and comparable between those with and those without prophylaxis (12% [11/91] vs 10% [25/254]; odds ratio, 1.26; 95% CI, 0.59-2.68). Conclusion: Severe primary PPH in women with VWD and HCs remained stable and is comparable with the increasing prevalence in the general population. More research is needed to find the optimal pregnancy management strategy for safe delivery in VWD and HC.
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Objective: Acute kidney injury (AKI) is easily missed and underdiagnosed in routine clinical care. Timely AKI management is important to decrease morbidity and mortality risks. We recently implemented an AKI e-alert at the University Medical Center Utrecht, comparing plasma creatinine concentrations with historical creatinine baselines, thereby identifying patients with AKI. This alert is limited to data from tertiary care, and primary care data can increase diagnostic accuracy for AKI. We assessed the added value of linking primary care data to tertiary care data, in terms of timely diagnosis or excluding AKI. Methods: With plasma creatinine tests for 84,984 emergency department (ED) visits, we applied the Kidney Disease Improving Global Outcome guidelines in both tertiary care-only data and linked data and compared AKI cases. Results: Using linked data, the presence of AKI could be evaluated in an additional 7886 ED visits. Sex- and age-stratified analyses identified the largest added value for women (an increase of 4095 possible diagnoses) and patients ≥60 years (an increase of 5190 possible diagnoses). We observed 398 additional visits where AKI was diagnosed, as well as 185 cases where AKI could be excluded. We observed no overall decrease in time between baseline and AKI diagnosis (28.4 days vs. 28.0 days). For cases where AKI was diagnosed in both data sets, we observed a decrease of 2.8 days after linkage, indicating a timelier diagnosis of AKI. Conclusions: Combining primary and tertiary care data improves AKI diagnostic accuracy in routine clinical care and enables timelier AKI diagnosis.
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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.
Subject(s)
Informed Consent , Humans , Informed Consent/statistics & numerical data , Male , Female , Middle Aged , Aged , Netherlands , Electronic Health Records , Pilot ProjectsABSTRACT
Mitapivat is an investigational, oral, small-molecule allosteric activator of pyruvate kinase (PK). PK is a regulatory glycolytic enzyme that is key in providing the red blood cell (RBC) with sufficient amounts of adenosine triphosphate (ATP). In sickle cell disease (SCD), decreased 2,3-DPG levels increase the oxygen affinity of hemoglobin, thereby preventing deoxygenation and polymerization of sickle hemoglobin. The PK activator mitapivat has been shown to decrease levels of 2,3-DPG and increase levels of ATP in RBCs in patients with SCD. In this phase 2, investigator-initiated, open-label study (https://www.clinicaltrialsregister.eu/ NL8517; EudraCT 2019-003438-18), untargeted metabolomics was used to explore the overall metabolic effects of 8-week treatment with mitapivat in the dose-finding period. In total, 1773 unique metabolites were identified in dried blood spots of whole blood from ten patients with SCD and 42 healthy controls (HCs). The metabolic phenotype of patients with SCD revealed alterations in 139/1773 (7.8%) metabolites at baseline when compared to HCs (false discovery rate-adjusted p < 0.05), including increases of (derivatives of) polyamines, purines, and acyl carnitines. Eight-week treatment with mitapivat in nine patients with SCD altered 85/1773 (4.8%) of the total metabolites and 18/139 (12.9%) of the previously identified altered metabolites in SCD (unadjusted p < 0.05). Effects were observed on a broad spectrum of metabolites and were not limited to glycolytic intermediates. Our results show the relevance of metabolic profiling in SCD, not only to unravel potential pathophysiological pathways and biomarkers in multisystem diseases but also to determine the effect of treatment.
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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.
Subject(s)
Artificial Intelligence , Physician-Patient Relations , Trust , Adult , Aged , Female , Humans , Male , Middle Aged , Cross-Sectional Studies , Decision Support Systems, Clinical , Netherlands , Surveys and QuestionnairesABSTRACT
The most common forms of sickle cell disease (SCD) are sickle cell anemia (SCA; HbSS) and HbSC disease. In both, especially the more dense, dehydrated and adherent red blood cells (RBCs) with reduced deformability are prone to hemolysis and sickling, and thereby vaso-occlusion. Based on plasma amino acid profiling in SCD, a composition of 10 amino acids and derivatives (RCitNacQCarLKHVS; Axcella Therapeutics, USA), referred to as endogenous metabolic modulators (EMMs), was designed to target RBC metabolism. The effects of ex vivo treatment with the EMM composition on different RBC properties were studied in SCD (n = 9 SCA, n = 5 HbSC disease). Dose-dependent improvements were observed in RBC hydration assessed by hemocytometry (MCV, MCHC, dense RBCs) and osmotic gradient ektacytometry (Ohyper). Median (interquartile range [IQR]) increase in Ohyper compared to vehicle was 4.9% (4.0%-5.5%), 7.5% (6.9%-9.4%), and 12.8% (11.5%-14.0%) with increasing 20×, 40×, and 80X concentrations, respectively (all p < 0.0001). RBC deformability (EImax using oxygen gradient ektacytometry) increased by 8.1% (2.2%-12.1%; p = 0.0012), 9.6% (2.9%-15.1%; p = 0.0013), and 13.3% (5.7%-25.5%; p = 0.0007), respectively. Besides, RBC adhesion to subendothelial laminin decreased by 43% (6%-68%; p = 0.4324), 58% (48%-72%; p = 0.0185), and 71% (49%-82%; p = 0.0016), respectively. Together, these results provide a rationale for further studies with the EMM composition targeting multiple RBC properties in SCD.
ABSTRACT
Targeting the primary pathogenic event of sickle cell disease (SCD), the polymerization of sickle hemoglobin (HbS), may prevent downstream clinical events. Mitapivat, an oral pyruvate kinase (PK) activator, has therapeutic potential by increasing adenosine triphosphate (ATP) and decreasing 2,3-diphosphoglycerate (2,3-DPG), a glycolytic red blood cell (RBC) intermediate. In the previously reported 8-week dose-finding period of this phase 2, investigator-initiated, open-label study, mitapivat was well tolerated and showed efficacy in SCD. Here, the 1-year fixed-dose extension period is reported in which 9 of 10 included patients (90%) aged ≥16 years with SCD (HbSS, HbS/ß0, or HbS/ß+) continued with mitapivat. Mostly mild treatment-emergent adverse events (AEs) (most commonly, transaminase increase and headache) were still reported. Apart from the reported nontreatment-related serious AE (SAE) of a urinary tract infection in the dose-finding period, 1 nontreatment-related SAE occurred in the fixed-dose extension period in a patient who died of massive pulmonary embolism due to COVID-19. Importantly, sustained improvement in Hb level (mean increase, 1.1 ± 0.7 g/dL; P = .0014) was seen, which was accompanied by decreases in markers of hemolysis. In addition, the annualized rate of vaso-occlusive events reduced significantly from a historic baseline of 1.33 ± 1.32 to 0.64 ± 0.87 (P = .0489) when combining the dose-finding period and fixed-dose extension period. Cellularly, the ATP:2,3-DPG ratio and Hb-oxygen affinity significantly increased and RBC sickling (point of sickling) nonsignificantly reduced. Overall, this study demonstrated 1-year safety and efficacy of treatment with mitapivat in SCD, supporting further evaluation in ongoing phase 2/3 study (RISE UP, NCT05031780). This trial was registered at https://www.clinicaltrialsregister.eu/ as NL8517 and EudraCT 2019-003438-18.
Subject(s)
Anemia, Sickle Cell , Humans , 2,3-Diphosphoglycerate , Adenosine Triphosphate , Anemia, Sickle Cell/complications , Follow-Up Studies , Hemoglobin, Sickle , Adolescent , AdultABSTRACT
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.
Subject(s)
Atherosclerosis , Brain Ischemia , Endovascular Procedures , Ischemic Stroke , Stroke , Humans , Male , Female , Ischemic Stroke/surgery , Ischemic Stroke/etiology , Brain Ischemia/etiology , Sex Characteristics , Treatment Outcome , Retrospective Studies , Thrombectomy/adverse effects , Stroke/etiology , Blood Cells , Atherosclerosis/etiologyABSTRACT
Biological processes underlying decreased cerebral blood flow (CBF) in patients with cardiovascular disease (CVD) are largely unknown. We hypothesized that identification of protein clusters associated with lower CBF in patients with CVD may explain underlying processes. In 428 participants (74% cardiovascular diseases; 26% reference participants) from the Heart-Brain Connection Study, we assessed the relationship between 92 plasma proteins from the Olink® cardiovascular III panel and normal-appearing grey matter CBF, using affinity propagation and hierarchical clustering algorithms, and generated a Biomarker Compound Score (BCS). The BCS was related to cardiovascular risk and observed cardiovascular events within 2-year follow-up using Spearman correlation and logistic regression. Thirteen proteins were associated with CBF (ρSpearman range: -0.10 to -0.19, pFDR-corrected <0.05), and formed one cluster. The cluster primarily reflected extracellular matrix organization processes. The BCS was higher in patients with CVD compared to reference participants (pFDR-corrected <0.05) and was associated with cardiovascular risk (ρSpearman 0.42, p < 0.001) and cardiovascular events (OR 2.05, p < 0.01). In conclusion, we identified a cluster of plasma proteins related to CBF, reflecting extracellular matrix organization processes, that is also related to future cardiovascular events in patients with CVD, representing potential targets to preserve CBF and mitigate cardiovascular risk in patients with CVD.
Subject(s)
Cardiovascular Diseases , Humans , Brain , Blood Proteins , Biomarkers , Cerebrovascular Circulation/physiologyABSTRACT
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.
Subject(s)
Acute Kidney Injury , Humans , Pilot Projects , Early Diagnosis , Acute Kidney Injury/therapy , Kidney Function Tests , Academic Medical CentersABSTRACT
Background and aims: Patients who underwent carotid endarterectomy (CEA) still have a residual risk of 13% of developing a major adverse cardiovascular event (MACE) within 3 years. Inflammatory processes leading up to MACE are not fully understood. Therefore, we examined blood cell characteristics (BCCs), possibly reflecting inflammatory processes, in relation to MACE to identify BCCs that may contribute to an increased risk. Methods: We analyzed 75 pretreatment BCCs from the Sapphire analyzer, and clinical data from the Athero-Express biobank in relation to MACE after CEA using Random Survival Forests, and a Generalized Additive Survival Model. To understand biological mechanisms, we related the identified variables to intraplaque hemorrhage (IPH). Results: Of 783 patients, 97 (12%) developed MACE within 3 years after CEA. Red blood cell distribution width (RDW) (HR 1.23 [1.02, 1.68], p = 0.022), CV of lymphocyte size (LACV) (HR 0.78 [0.63, 0.99], p = 0.043), neutrophil complexity of the intracellular structure (NIMN) (HR 0.80 [0.64, 0.98], p = 0.033), mean neutrophil size (NAMN) (HR 0.67 [0.55, 0.83], p < 0.001), mean corpuscular volume (MCV) (HR 1.35 [1.09, 1.66], p = 0.005), eGFR (HR 0.65 [0.52, 0.80], p < 0.001); and HDL-cholesterol (HR 0.62 [0.45, 0.85], p = 0.003) were related to MACE. NAMN was related to IPH (OR 0.83 [0.71-0.98], p = 0.02). Conclusions: This is the first study to present a higher RDW and MCV and lower LACV, NIMN and NAMN as biomarkers reflecting inflammatory processes that may contribute to an increased risk of MACE after CEA.
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Red blood cell distribution width (RDW) is a biomarker associated with a variety of clinical outcomes. While anemia and subclinical inflammation have been posed as underlying pathophysiology, it is unclear what mechanisms underlie these assocations. Hence, we aimed to unravel the mechanisms in silico using a large clinical dataset and validate our findings in vitro. We retrieved complete blood counts (CBC) from 1,403,663 measurements from the Utrecht Patient Oriented Database, to model RDW using gradient boosting regression. We performed (sex-stratified) analyses in patients with anemia, patients younger/older than 50 and validation across platforms and care settings. We then validated our hypothesis regarding oxidative stress using an in vitro approach. Only percentage microcytic (pMIC) and macrocytic (pMAC) erythrocytes and mean corpuscular volume were most important in modelling RDW (RMSE = 0.40, R2 = 0.96). Subgroup analyses and validation confirmed our findings. In vitro induction of oxidative stress underscored our results, namely increased RDW and decreased erythrocyte volume, yet no vesiculation was observed. We found that erythrocyte size, especially pMIC, is most informative in predicting RDW, but no role for anemia or inflammation. Oxidative stress affecting the size of the erythrocytes may play a role in the association between RDW and clinical outcomes.
Subject(s)
Anemia , Erythrocytes , Humans , Erythrocyte Indices , Inflammation , Oxidative StressABSTRACT
This commentary discusses the prevalence and causes of anemia in primary care in the Netherlands and the role of laboratory diagnostics in determining the cause of anemia. There are indications that guidelines in primary care regarding anemia are insufficiently followed; there are also indications that the correct laboratory measurements are requested too limited (under-diagnosis). A possible solution lies in the introduction of reflective testing, in which the laboratory specialist has additional diagnostic laboratory tests performed on the basis of the laboratory results and specific characteristics of the patient. Reflective testing is in contrast to reflex testing; in reflex testing, laboratory measurements are added automatically using a simple flowchart. In the future, Artificial Intelligence solutions could play a role in determining the most optimal laboratory diagnostic strategy for the diagnosis of anemia in primary care.
Subject(s)
Anemia , Artificial Intelligence , Humans , Anemia/diagnosis , Anemia/etiology , Reflex , Algorithms , Primary Health CareABSTRACT
It is of paramount importance that healthcare professionals can participate in the academic and societal debate surrounding medical AI. To realise this critical-constructive guidance of AI, it is necessary to be able to distinguish between different types of AI, different applications of AI and to paint the different shades of grey in the current black-and-white debate. This article describes and nuances eight misconceptions that currently dominate the public debate surrounding AI in healthcare. By asking ourselves as healthcare professionals 'what specifically defines our line of work?' we must define what aspects of our occupation we want to have AI either carry out or support, and in what way.
Subject(s)
Artificial Intelligence , Health Personnel , Humans , Delivery of Health CareABSTRACT
BACKGROUND: Immune checkpoint inhibitors (ICI) show remarkable results in cancer treatment, but at the cost of immune-related adverse events (irAE). irAE can be difficult to differentiate from infections or tumor progression, thereby challenging treatment, especially in the emergency department (ED) where time and clinical information are limited. As infections are traceable in blood, we were interested in the added diagnostic value of routinely measured hematological blood cell characteristics in addition to standard diagnostic practice in the ED to aid irAE assessment. METHODS: Hematological variables routinely measured with our hematological analyzer (Abbott CELL-DYN Sapphire) were retrieved from Utrecht Patient Oriented Database (UPOD) for all patients treated with ICI who visited the ED between 2013 and 2020. To assess the added diagnostic value, we developed and compared two models; a base logistic regression model trained on the preliminary diagnosis at the ED, sex, and gender, and an extended model trained with lasso that also assessed the hematology variables. RESULTS: A total of 413 ED visits were used in this analysis. The extended model showed an improvement in performance (area under the receiver operator characteristic curve) over the base model, 0.79 (95% CI 0.75-0.84), and 0.67 (95% CI 0.60-0.73), respectively. Two standard blood count variables (eosinophil granulocyte count and red blood cell count) and two advanced variables (coefficient of variance of neutrophil depolarization and red blood cell distribution width) were associated with irAE. CONCLUSION: Hematological variables are a valuable and inexpensive aid for irAE diagnosis in the ED. Further exploration of the predictive hematological variables could yield new insights into the pathophysiology underlying irAE and in distinguishing irAE from other inflammatory conditions.
Subject(s)
Hematology , Immune Checkpoint Inhibitors , Humans , Immune Checkpoint Inhibitors/adverse effects , Emergency Service, Hospital , Retrospective StudiesABSTRACT
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.
Subject(s)
Cardiovascular Diseases , Learning Health System , Humans , Risk Factors , Heart Disease Risk Factors , Informed ConsentABSTRACT
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.
Subject(s)
Artificial Intelligence , Medicine , Infant, Newborn , Humans , Neonatologists , Computers , Delivery of Health CareABSTRACT
In Diamond-Blackfan anaemia (DBA), iron overload (IO) is common in transfusion-dependent patients, yet has also been reported in non-transfusion-dependent patients. We explored the incidence of IO in transfusion-dependent and non-transfusion-dependent DBA patients. We observed hepatic IO in 65% of patients analysed with MRI, including three patients that were only treated with transfusions in the past. Whereas overall ferritin levels and liver iron content correlated, ferritin levels did not reflect total body iron adequately. Our data suggest that transfusion burden in the past plays an important role in IO in DBA, and should be taken into account during follow up.
ABSTRACT
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.
Subject(s)
Emergency Service, Hospital , Sepsis , Humans , Machine Learning , Algorithms , Sepsis/diagnosis , Social Group , Retrospective StudiesABSTRACT
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.