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.

Improving acute kidney injury alerts in tertiary care by linking primary care data: An observational cohort using routine care data.

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.

A novel composition of endogenous metabolic modulators improves red blood cell properties in sickle cell disease.

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.

Metabolic blood profile and response to treatment with the pyruvate kinase activator mitapivat in patients with sickle cell disease.

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.