To Örebro University

OBJECTIVES: To i) compare baseline clinical and demographic characteristics of post-acute stroke inpatients who were diagnosed with first-time urinary tract infection (UTI) vs inpatients who were not ii) compare rehabilitation outcomes between both groups and iii) examine associations between time to UTI event and risk factors. DESIGN: Retrospective observational cohort study SETTING: Institution for inpatient neurological rehabilitation.

PARTICIPANTS: Inpatients (n=1683) admitted within 3 months post-stroke to a rehabilitation facility between 2005 and 2023.

INTERVENTIONS: Not applicable.

MAIN OUTCOME MEASURES: Functional Independence Measure (FIM), Functional Ambulation Categories (FAC) at admission. Cox proportional hazards models analyzed the association between UTI event timing and risk factors.

RESULTS: Of the (n=1683) included patients, 196 (11.6%) experienced a UTI. In 32.1% of cases, the UTI occurred during the first week after admission to rehabilitation and 47.9% of UTIs occurred during the first two weeks. Median (IQR) time to UTI was 16 (5-37) days since admission. Most common germs were Escherichia coli (40.5%), Klebsiella pneumoniae (23.7%) and Pseudomonas aeruginosa (6.4%). Patients who acquired a UTI had older age, higher stroke severity, higher proportion of dysphagia, hypertension, neglect, bilateral affectation, atrial fibrillation, hemiplegia, lower levels of functional independence and lower FAC. We identified no differences in sex, type of stroke (ischemic or hemorrhagic), time to admission, aphasia, diabetes, dyslipidemia, chronic obstructive pulmonary disease, dominant side affected and educational level between both groups. Patients with UTI presented significantly poorer rehabilitation outcomes including lower discharge FIM and FAC, larger LOS, lower FIM efficiency and decreased FIM effectiveness. Multivariable Cox proportional hazards identified hypertension HR=1.60(1.13-2.27), admission FIM HR=0.98(0.97-0.99), admission BMI HR=0.96(0.93-0.99) and admitted with catheter HR= 1.80(1.22-2.64) as significant predictors of time to first UTI event (Concordance-index = 0.754).

CONCLUSIONS: UTIs identification, characterization, and predictive factors can support post-acute stroke mitigation strategies to minimize UTI-related complications and optimize rehabilitation outcomes.

Background: Stroke now represents the condition with the highest need for physical rehabilitation worldwide, with only low or moderate-level evidence testing telerehabilitation compared to in-person care. We compared functional ambulation in subacute patients with stroke following telerehabilitation and matched in-person controls with no biopsychosocial differences at baseline.

Methods: We conducted a matched case-control study to compare functional ambulation between individuals with stroke following telerehabilitation and in-person rehabilitation, assessed using the Functional Ambulation Categories (FAC) and the Functional Independence Measure™ (FIM).

Results: The telerehabilitation group (n =38) achieved significantly higher FAC gains (1.5 (1.3) vs 1.0 (1.0)) than the in-person rehabilitation group, with no differences in ambulation efficiency, in individuals: admitted to rehabilitation within 60 days after stroke onset; aged 49.8 (±11.4) years at admission; 55.3% female sex; moderate stroke severity; 42.1% with 'good' motor FIM at baseline; mostly living with sentimental partner (73.7%); with 21.1% holding an university education degree.

Conclusions: The groups showed no significant differences in ambulation efficiency, though the telerehabilitation group achieved higher FAC gains. Our results suggest that home telerehabilitation can be considered a good alternative to in-person rehabilitation when addressing ambulation in patients with moderate stroke severity and whose home situation mostly includes a cohabiting partner.

Functional magnetic resonance imaging (fMRI) is a pivotal tool for mapping neuronal activity in the brain. Traditionally, the observed hemodynamic changes are assumed to reflect the activity of the most common neuronal type: excitatory neurons. In contrast, recent experiments, using optogenetic techniques, suggest that the fMRI-signal could reflect the activity of inhibitory interneurons. However, these data paint a complex picture, with numerous regulatory interactions, and with responses that sometimes seem to point in different directions. It is therefore not trivial how to quantify the relative contributions of the different cell types into a consensus view compatible with the considered data. To address this, we present a new model-driven meta-analysis, which provides a unified and quantitative explanation for the considered data. This model-driven analysis allows for quantification of the relative contribution of different cell types: the contribution to the BOLD-signal from the excitatory cells is <20 % and 50-80 % comes from the interneurons. Our analysis also provides a mechanistic explanation for the observed experiment-to-experiment differences. For instance, one of the reasons that data seem to point in different directions is a biphasic vascular response, with a transient increase and a subsequent decrease. Our model-based data analysis explains why this biphasic response appears only for high-intensity stimulations and not for low-intensity stimulations. In other words, our meta-analysis goes beyond a simple vote-by-majority and provides a single unified explanation for the considered data. This explanation provides a consensus view that constitutes a paradigm shift in how fMRI can, and cannot, be used to interpret neuronal activity.

Subject-specific parameters in lumped hemodynamic models of the cardiovascular system can be estimated using data from experimental measurements, but the parameter estimation may be hampered by the variability in the input data. In this study, we investigate the influence of inter-sequence, intra-observer, and inter-observer variability in input parameters on estimation of subject-specific model parameters using a previously developed approach for model-based analysis of data from 4D Flow MRI acquisitions and cuff pressure measurements. The investigated parameters describe left ventricular time-varying elastance and aortic compliance. Parameter reproducibility with respect to variability in the MRI input measurements was assessed in a group of ten healthy subjects. The subject-specific parameters had coefficient of variations between 2.6 and 35% in the intra- and inter-observer analysis. In comparing parameters estimated using data from the two MRI sequences, the coefficients of variation ranged between 3.3 and 41%. The diastolic time constant of the left ventricle and the compliance of the ascending aorta were the parameters with the lowest and the highest variability, respectively. In conclusion, the modeling approach allows for estimating left ventricular elastance parameters and aortic compliance from non-invasive measurements with good to moderate reproducibility concerning intra-user, inter-user, and inter-sequence variability in healthy subjects.

Cardiovascular digital twins and mechanistic models can be used to obtain new biomarkers from patient-specific hemodynamic data. However, such model-derived biomarkers are only clinically relevant if the uncertainty of the biomarkers is smaller than the variation between timepoints/patients. Unfortunately, this uncertainty is challenging to calculate, as the uncertainty of the underlying hemodynamic data is largely unknown and has several sources that are not additive or normally distributed. This violates normality assumptions of current methods; implying that also biomarkers have an unknown uncertainty. To remedy these problems, we herein present a method, with attached code, for uncertainty calculation of model-derived biomarkers using non-normal data. First, we estimated all sources of uncertainty, both normal and non-normal, in hemodynamic data used to personalize an existing model; the errors in 4D flow MRI-derived stroke volumes were 5-20 % and the blood pressure errors were 0 ± 8 mmHg. Second, we estimated the resulting model-derived biomarker uncertainty for 100 simulated datasets, sampled from the data distributions, by: 1) combining data uncertainties 2) parameter estimation, 3) profile-likelihood. The true biomarker values were found within a 95 % confidence interval in 98 % (median) of the cases. This shows both that our estimated data uncertainty is reasonable, and that we can use profile-likelihood despite the non-normality. Finally, we demonstrated that e.g. ventricular relaxation rate has a smaller uncertainty (∼10 %) than the variation across a clinical cohort (∼40 %), meaning that these biomarkers have clinical potential. Our results take us one step closer to the usage of model-derived biomarkers for cardiovascular patient characterization.

Functional magnetic resonance imaging (fMRI) is a pivotal tool for mapping neuronal activity in the brain. Traditionally, the observed hemodynamic changes are assumed to reflect the activity of the most common neuronal type: excitatory neurons. In contrast, recent experiments, using optogenetic techniques, suggest that the fMRI-signal instead reflects the activity of inhibitory interneurons. However, these data paint a complex picture, with numerous regulatory interactions, and where the different experiments display many qualitative differences. It is therefore not trivial how to quantify the relative contributions of the different cell types and to combine all observations into a unified theory. To address this, we present a new model-driven meta-analysis, which provides a unified and quantitative explanation for all data. This model-driven analysis allows for quantification of the relative contribution of different cell types: the contribution to the BOLD-signal from the excitatory cells is <20 % and 50-80 % comes from the interneurons. Our analysis also provides a mechanistic explanation for the observed experiment-to-experiment differences, e.g. a biphasic vascular response dependent on different stimulation intensities and an emerging secondary post-stimulation peak during longer stimulations. In summary, our study provides a new, emerging consensus-view supporting the larger role of interneurons in fMRI.

Alcohol consumption is associated with a wide variety of preventable health complications and is a major risk factor for all-cause mortality in the age group 15-47 years. To reduce dangerous drinking behavior, eHealth applications have shown promise. A particularly interesting potential lies in the combination of eHealth apps with mathematical models. However, existing mathematical models do not consider real-life situations, such as combined intake of meals and beverages, and do not connect drinking to clinical markers, such as phosphatidylethanol (PEth). Herein, we present such a model which can simulate real-life situations and connect drinking to long-term markers. The new model can accurately describe both estimation data according to a χ2 -test (187.0 < Tχ2 = 226.4) and independent validation data (70.8 < Tχ2 = 93.5). The model can also be personalized using anthropometric data from a specific individual and can thus be used as a physiologically-based digital twin. This twin is also able to connect short-term consumption of alcohol to the long-term dynamics of PEth levels in the blood, a clinical biomarker of alcohol consumption. Here we illustrate how connecting short-term consumption to long-term markers allows for a new way to determine patient alcohol consumption from measured PEth levels. An additional use case of the twin could include the combined evaluation of patient-reported AUDIT forms and measured PEth levels. Finally, we integrated the new model into an eHealth application, which could help guide individual users or clinicians to help reduce dangerous drinking.

Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70-90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes.

BACKGROUND: The increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as the development of type 2 diabetes. Insulin resistance also occurs on different physiological levels, ranging from disease phenotypes to organ-organ communication and intracellular signaling. To better understand the progression of insulin resistance, an analysis method is needed that can combine different timescales and physiological levels. One such method is digital twins, consisting of combined mechanistic mathematical models. We have previously developed a model for short-term glucose homeostasis and intracellular insulin signaling, and there exist long-term weight regulation models. Herein, we combine these models into a first interconnected digital twin for the progression of insulin resistance in humans.

METHODS: The model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox. RESULTS: The interconnected twin correctly predicts independent data from a weight increase study, both for weight-changes, fasting plasma insulin and glucose levels, and intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study with the weight loss drug topiramate. The model can also predict non-measured variables.

CONCLUSIONS: The model presented herein constitutes the basis for a new digital twin technology, which in the future could be used to aid medical pedagogy and increase motivation and compliance and thus aid in the prevention and treatment of insulin resistance.

BACKGROUND: Liver cirrhosis, the advanced stage of many chronic liver diseases, is associated with escalated risks of liver-related complications like decompensation and hepatocellular carcinoma (HCC). Morbidity and mortality in cirrhosis patients are linked to portal hypertension, sarcopenia, and hepatocellular carcinoma. Although conventional cirrhosis management centered on treating complications, contemporary approaches prioritize preemptive measures. This study aims to formulate novel blood- and imaging-centric methodologies for monitoring liver cirrhosis patients.

METHODS: In this prospective study, 150 liver cirrhosis patients will be enrolled from three Swedish liver clinics. Their conditions will be assessed through extensive blood-based markers and magnetic resonance imaging (MRI). The MRI protocol encompasses body composition profile with Muscle Assement Score, portal flow assessment, magnet resonance elastography, and a abbreviated MRI for HCC screening. Evaluation of lifestyle, muscular strength, physical performance, body composition, and quality of life will be conducted. Additionally, DNA, serum, and plasma biobanking will facilitate future investigations.

DISCUSSION: The anticipated outcomes involve the identification and validation of non-invasive blood- and imaging-oriented biomarkers, enhancing the care paradigm for liver cirrhosis patients. Notably, the temporal evolution of these biomarkers will be crucial for understanding dynamic changes.

TRIAL REGISTRATION: Clinicaltrials.gov, registration identifier NCT05502198. Registered on 16 August 2022. Link: https://classic. CLINICALTRIALS: gov/ct2/show/NCT05502198 .