Association of proteomic markers with nutritional risk and response to nutritional support: A secondary pilot study of the EFFORT trial using an untargeted proteomics approach

Background: By means of a structured nutritional support intervention, EFFORT showed a risk reduction for adverse events in medical in-patients. We were interested in the prognostic and therapeutic potential of an untargeted proteomics approach to understand response to nutritional support, risk of 30-day mortality, and distinct patterns in severity of malnutrition risk as assessed by the Nutritional Risk screening (NRS 2002), respectively. Methods: From 2,088 patients, we randomly took 120 blood samples drawn before treatment initiation on day 1 after hospital admission. Cases were selected by treatment allocation (nutritional support vs. usual nutrition), NRS 2002, and mortality at 30 days, but not on disease type. We measured proteins by untargeted liquid chromatography mass spectrometry (LC-MS/MS). Results: We found 242 distinct proteins in 120 patients of which 81 (67.5%) survived until day 30. Between group analysis revealed a slight difference between the treatment groups in patients with a NRS 3, but not in those with a higher NRS. C-statistic between non-survivors and survivors at day 30 ranged from 0.60 (95% confidence interval 0.34-0.78) for a combination of 3 proteins/predictors to 0.65 (95% CI 0.53-0.78) for a combination of 32 proteins/predictors. In nutritional support non-survivors, pathway analysis found significant enrichment in pathways for signal transduction, platelet function, immune system regulation, extracellular matrix organization, and integrin cell surface interactions compared to survivors. Conclusion: Within this pilot study using an untargeted proteomics approach, there was only little prognostic and therapeutic potential of proteomics for phenotyping the risk of malnutrition and response to nutritional therapy. The small sample size and high heterogeneity of our population regarding comorbidity burden calls for more targeted approaches in more homogenous populations to understand the true potential of proteomics for individualizing nutritional care. Trial registration: This is a pre-planned secondary analysis of the EFFORT trial (ClinicalTrials.gov NCT02517476)

The association between urinary phytoestrogen excretion and components of the metabolic syndrome in NHANES

Background: Metabolic syndrome is a major risk factor for cardiovascular diseases, which are still the major cause of death in developed countries. Methods: We cross-sectionally studied the association between urinary phytoestrogen excretion and metabolic cardiovascular risk factors. Hence, we used data from the National Health and Nutrition Examination Survey from 1999 to 2004 with 1,748 participants, who had urine levels of isoflavones and lignans measured. Geometric means of waist circumference, blood pressure, fasting glucose, HDL cholesterol, and triglyceride levels were computed by quartiles of isoflavone or lignan urinary excretion. Outcome was assessed as the presence of metabolic syndrome according to NCEP-ATP III criteria. The association between phytoestrogen concentration and the metabolic syndrome was calculated using logistic regression analyses. Results: Plasma triglyceride and HDL cholesterol levels were lower in participants in the highest quartile of lignan excretion compared with the lowest (both P<0.01). However, blood pressure, waist circumference, and plasma glucose levels did not differ significantly between extreme quartiles. The presence of metabolic syndrome was lower with increasing levels of urinary lignans (OR 0.48, 95 % CI 0.28; 0.80 top vs. bottom quartile), especially when separately computed for the excretion of enterolactone (OR 0.47, 95 % CI 0.28; 0.78). There was no significant association between isoflavone excretion and any component of the metabolic syndrome. Conclusions: Our study shows that an increasing excretion of lignans, especially enterolactone, might be associated with a decreased presence of the metabolic syndrome

Metabolomics for Prediction of Relapse in Graves' Disease: Observational Pilot Study

Background: There is a lack of biochemical markers for early prediction of relapse in patients with Graves' disease [GD], which may help to direct treatment decisions. We assessed the prognostic ability of a high-throughput proton NMR metabolomic profile to predict relapse in a well characterized cohort of GD patients.Methods: Observational study investigating patients presenting with GD at a Swiss hospital endocrine referral center and an associated endocrine outpatient clinic. We measured 227 metabolic markers in the blood of patients before treatment initiation. Main outcome was relapse of hyperthyroidism within 18 months of stopping anti-thyroid drugs. We used ROC analysis with AUC to assess discrimination.Results: Of 69 included patients 18 (26%) patients had a relapse of disease. The clinical GREAT score had an AUC of 0.68 (95% CI 0.63–0.70) to predict relapse. When looking at the metabolomic markers, univariate analysis revealed pyruvate and triglycerides in medium VLDL as predictors with AUCs of 0.73 (95% CI 0.58–0.84) and 0.67 (95% CI 0.53–0.80), respectively. All other metabolomic markers had lower AUCs.Conclusion: Overall, metabolomic markers in our pilot study had low to moderate prognostic potential for prediction of relapse of GD, with pyruvate and triglycerides being candidates with acceptable discriminatory abilities. Our data need validation in future larger trials

The association between urinary phytoestrogen excretion and components of the metabolic syndrome in NHANES

BACKGROUND: Metabolic syndrome is a major risk factor for cardiovascular diseases, which are still the major cause of death in developed countries. METHODS: We cross-sectionally studied the association between urinary phytoestrogen excretion and metabolic cardiovascular risk factors. Hence, we used data from the National Health and Nutrition Examination Survey from 1999 to 2004 with 1,748 participants, who had urine levels of isoflavones and lignans measured. Geometric means of waist circumference, blood pressure, fasting glucose, HDL cholesterol, and triglyceride levels were computed by quartiles of isoflavone or lignan urinary excretion. Outcome was assessed as the presence of metabolic syndrome according to NCEP-ATP III criteria. The association between phytoestrogen concentration and the metabolic syndrome was calculated using logistic regression analyses. RESULTS: Plasma triglyceride and HDL cholesterol levels were lower in participants in the highest quartile of lignan excretion compared with the lowest (both P < 0.01). However, blood pressure, waist circumference, and plasma glucose levels did not differ significantly between extreme quartiles. The presence of metabolic syndrome was lower with increasing levels of urinary lignans (OR 0.48, 95% CI 0.28; 0.80 top vs. bottom quartile), especially when separately computed for the excretion of enterolactone (OR 0.47, 95% CI 0.28; 0.78). There was no significant association between isoflavone excretion and any component of the metabolic syndrome. CONCLUSIONS: Our study shows that an increasing excretion of lignans, especially enterolactone, might be associated with a decreased presence of the metabolic syndrome

Comparison of Cardiovascular Procedure Rates in Patients With Supplementary vs Basic Insurance in Switzerland

IMPORTANCE Switzerland's mandatory health insurance provides universal coverage, but residents can opt for supplementary private insurance for nonessential, nonvital amenities. It is debated whether people with supplementary private insurance receive overtreatment due to financial incentives. OBJECTIVE To assess whether incidence rates of cardiovascular procedures in people with supplementary private insurance are higher than in those with basic insurance only. DESIGN, SETTING, AND PARTICIPANTS A population-based weighted cohort comparative effectiveness study, using administrative claims data from Switzerland assessing incidence rates (IRs), was conducted in adults undergoing a nonemergency cardiovascular inpatient procedure from January 1, 2012, to December 31, 2020. Analysis included primary or secondary discharge procedure codes for 1 of the following: percutaneous transluminal coronary angioplasty (PTCA), left atrial appendage (LAA) occlusion, patent foramen ovale (PFO) closure, transcatheter aortic valve replacement (TAVR), mitral valve clip implantation, cardiac pacemaker implantation, and atrial fibrillation/atrial flutter ablation. EXPOSURES Supplementary private health insurance. MAIN OUTCOMES AND MEASURES Incidence rates of cardiovascular procedures between insurance groups calculated by negative binomial regression adjusted by inverse probability weights. RESULTS Of 590 919 admissions (median age, 68 years; IQR, 57-77 years), 55.5% male, 15.7% non-Swiss nationality), 70.1% had basic insurance only. Independent of insurance status, IR for all cardiovascular procedures steadily increased over the study years. In general, people with supplementary private insurance received cardiovascular procedures more frequently (IR ratio [IRR], 1.11; 99% CI, 1.10-1.11) than people with basic insurance only. There was also an increase for every procedure: PTCA (IRR, 1.12; 99% CI, 1.12-1.13), LAA closure (IRR, 1.15; 99% CI, 1.13-1.16), mitral valve clip implantation (IRR, 1.08; 99% CI, 1.07-1.09), TAVR (IRR, 1.04; 99% CI, 1.03-1.06), PFO closure (IRR, 1.01; 99% CI, 1.00-1.02), pacemaker implantation (IRR, 1.08; 99% CI, 1.07-1.09), and atrial fibrillation/atrial flutter ablation (IRR, 1.12; 99% CI, 1.11-1.12). Sensitivity analyses, including side procedures, stratification by length of stay, and propensity score matching, suggested robustness of the results. CONCLUSIONS AND RELEVANCE This study found an association between supplementary private insurance and a higher likelihood of receiving nonemergency cardiovascular procedures. Whether this higher rate of procedures in people with supplementary private insurance is based on clinical reasoning or due to financial incentives warrants further exploration

Association of metabolomic markers and response to nutritional support: A secondary analysis of the EFFORT trial using an untargeted metabolomics approach

Background & aims The EFFORT trial reported a substantial risk reduction for adverse events and mortality in medical in-patients receiving a nutritional support intervention. With the use of an untargeted metabolomics approach, we investigated the prognostic and therapeutic potential of metabolomic markers to understand, whether there are distinct metabolic patterns associated with malnutrition risk as assessed by the Nutritional Risk screening (NRS 2002) score, the risk of 30-day mortality and the response to nutritional support, respectively. Methods Out of the 2088 samples we randomly selected 120 blood samples drawn on day 1 after hospital admission and before treatment initiation. Samples were stratified by NRS 2002, treatment allocation (intervention vs. control), and mortality at 30 days, but not on the type of medical illness. We performed untargeted analysis by liquid chromatography mass spectrometry (LC-MS/MS). Results We measured 1389 metabolites in 120 patients of which 81 (67.5%) survived until day 30. After filtering, 371 metabolites remained, and 200 were matched to one or more Human Metabolome Data Base (HMDB) entries. Between group analysis showed a slight distinction between the treatment groups for patients with a NRS 3, but not for those with NRS 4 and ≥ 5. C-statistic between those who died and survived at day 30 ranged from 0.49 (95% confidence interval 0.35–0.68) for a combination of 5 metabolites/predictors to 0.66 (95% confidence interval 0.53–0.79) for a combination of 100 metabolites. Pathway analysis found significant enrichment in the pathways for nitrogen, vitamin B3 (nicotinate and nicotinamide), leukotriene, and arachidonic acid metabolisms in nutritional support responders compared to non-responders. Conclusion In our heterogenous population of medical inpatients with different illnesses and comorbidities, metabolomic markers showed little prognostic and therapeutic potential for better phenotyping malnutrition and response to nutritional therapy. Future studies should focus on more selected patient populations to understand whether a metabolomic approach can advance the nutritional care of patients

Cadre causal pour l'aide à la décision à partir de dossiers médicaux électroniques : Pourquoi et comment

Accurate predictions, as with machine learning, may not suffice to provide optimal healthcare for every patient. Indeed, prediction can be driven by shortcuts in the data, such as racial biases. Causal thinking is needed for data-driven decisions. Here, we give an introduction to the key elements, focusing on routinely-collected data, electronic health records (EHRs) and claims data. Using such data to assess the value of an intervention requires care: temporal dependencies and existing practices easily confound the causal effect. We present a step-by-step framework to help build valid decision making from real-life patient records by emulating a randomized trial before individualizing decisions, eg with machine learning. Our framework highlights the most important pitfalls and considerations in analysing EHRs or claims data to draw causal conclusions. We illustrate the various choices in studying the effect of albumin on sepsis mortality in the Medical Information Mart for Intensive Care database (MIMIC-IV). We study the impact of various choices at every step, from feature extraction to causal-estimator selection. In a tutorial spirit, the code and the data are openly available.Des prédictions précises, par exemple à partir d'apprentissage automatique, peuvent ne pas suffire à fournir des soins optimaux à chaque patient. En effet, la prédiction peut être motivée par des raccourcis dans les données, tels que des préjugés raciaux. Le cadre causal est nécessaire pour les construire des système d'aide à la décision fondés sur les données. Ici, nous donnons une introduction aux éléments clés, en mettant l'accent sur les données recueillies routièrement, les dossiers médicaux électroniques (DME) et les données médico-administratives. L'utilisation de telles données pour évaluer la valeur d'une intervention requiert des précautions: les dépendances temporelles et les pratiques locales confondent facilement l'effet causal visé. Nous présentons un cadre étape par étape pour aider à construire un système d'aide à la décision valide à partir des dossiers de patients réels en émulant un essai randomisé avant d'individualiser les décisions, par exemple avec l'apprentissage automatique. Notre cadre met en évidence les pièges et les considérations les plus importants dans l'analyse des données DME ou des données de facturation afin de tirer des conclusions causales.Nous illustrons les différents choix à partir de l'étude de l'effet de l'albumine sur la mortalité due à la septicémie dans la base de données Medical Information Mart for Intensive Care (MIMIC-IV). Nous étudions l'impact de divers choix à chaque étape, de l'extraction des caractéristiques de la population à la sélection de l'estimateur causal. Dans un esprit tutoriel, le code et les données sont librement disponibles

Prevalence, Risk Factors and Outcomes Associated with Physical Restraint in Acute Medical Inpatients over 4 Years&mdash;A Retrospective Cohort Study

Background: Physical restraints are frequently used in acute care hospitals. Their application is associated with negative outcomes, while their intended preventive effect is debated. Objectives: To determine the prevalence of physical restraints and associated outcomes on medical wards in a tertiary care hospital. Methods: Retrospective cohort study (January 2018 to December 2021). We included all adult medical in-patients and excluded patients with admission to the intensive care unit, short stays (length of stay (LOS) &lt; 48 h), and patients declining informed consent. Results: Of 11,979 admissions, the prevalence of patients with at least one restraint was 6.4% (n = 772). Sensor mats were used most frequently (73.0%, n = 666), followed by blanket restrictions (14.5%, n = 132), bedrails (8.8%, n = 80) and belts (3.7%, n = 34). On average, restraints were applied 19 h (standard deviation (SD) &plusmn; 161) before a fall. Average restraint duration was 42 h (SD &plusmn; 57). Patients with a restraint had longer LOS 8 days (IQR 5&ndash;14) vs. 5 days (IQR 3&ndash;9). Median nurses&rsquo; time expenditure was 309 h (IQR 242&ndash;402) vs. 182 h (IQR 136&ndash;243) for non-restrained patients. Patients with restraints fell more often (22.5% vs. 2.7%) and were more likely to die (13.3% vs. 5.1%). These differences persisted after adjusting a regression model for important clinical confounders. We saw a decline in the duration of restraints over the years, but no variation between wards. Conclusion: Approximately 6% of medical patients, mostly older and severely ill, were affected by restraint use. For the first time, we report data over 4 years up to ward-level granularity