Background Nursing workload is a critical factor in ensuring safe, high-quality patient care and maintaining nurse well-being. While high workload has been linked to adverse outcomes for both patients and staff, its assessment remains challenging. Common approaches rely on activity- or dependency-based measures or professional judgement, yet these often overlook important subjective and contextual factors. Nurse-perceived workload, particularly at the shift level, offers valuable insights but has received limited attention in research and is rarely used to guide staffing decisions. Objective This retrospective longitudinal study explored nurse-perceived workload on shift level and its associations with nurse staffing, patient comorbidity score and turnover. Design Retrospective observational study using longitudinal shift-level routine data. Setting(s) Five internal medicine units in a large Swiss acute care hospital. Participants 131 registered nurses (RN). Methods We analysed five years (2015-2019) of routinely collected data from five internal medicine units of a Swiss tertiary hospital. Nurse-perceived workload was documented at the end of each shift using a single-item rating. Staffing system data (e.g., shift assignments, working time) were merged with patient discharge data (e.g., diagnoses, demographics, admission). Descriptive statistics, multilevel logistic regression, and a gradient boosting model were applied. Results High workload was classified in 9,646 of 42,035 nurse-shift observations (22.9%). The number of incoming patients showed the strongest positive association with high workload perception (OR 1.30; 95% CI: 1.26-1.33). Higher RN staffing relative to expected staffing was associated with lower odds of high workload (OR 0.84; 95% CI: 0.78-0.91). Patient comorbidities also showed a positive association (OR 1.07; 95% CI: 1.06-1.08), as well as the number of outgoing patients (OR 1.02; 95% CI: 1.004-1.04). The gradient boosting model showed limited predictive performance. Conclusions This study is among the first to investigate nurse-perceived workload using extensive longitudinal routine data. The findings suggest that single-item ratings are a practical means of capturing workload perception at scale. High workload perception was common and was positively associated with patient inflow and comorbidity, while higher staffing levels were associated with lower odds of high workload. These results underscore the potential of integrating subjective workload data into staffing policy and workforce planning to better support nurses and maintain care quality. Registration not registered. Social media abstract Nurse workload isn’t just personal. It’s part of the system: 23% of nurse-shift observations were classified as high workload; odds were higher with admissions, complexity, and lower with better staffing.

The overlap coefficient (OVL) quantifies the similarity between two distributions through the overlapping area of their distribution functions. It has been discussed in the literature in a variety of different contexts. One approach for testing the bioequivalence of treatments is to measure the overlap of the distributions of individual responses to therapy. In some situations, covariates can significantly influence distributional overlap. This paper develops a covariate-specific OVL estimator using linear regression with a possible Box-Cox transformation. Bootstrap-based confidence intervals for the covariate-specific OVL are proposed and evaluated through extensive simulations. The methodology is illustrated using fingerstick post-prandial blood glucose measurements as a biomarker for diabetes patients adjusted for age.

PURPOSE:This study aims to evaluate metabolic alterations in blood and urine samples from breast cancer patients undergoing adjuvant radiotherapy (RT) to identify potential biomarkers for radiation exposure and contribute to the development of biodosimetry tools, such as for use in nuclear incidents. MATERIALS AND METHODS:Postmenopausal breast cancer patients (n = 20) undergoing postoperative RT were included in this prospective observational study. Blood and urine samples were collected at a total of six time points before, during, and after RT. Metabolic analysis was performed using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry and multivariable analyses, including partial least squares-discriminant analysis (PLS-DA) and random forest methodology, were used to identify discriminating metabolites. All analyses were performed using R version 4.1.2. RESULTS:Univariate analysis of blood samples showed significant downregulation of five metabolites during RT (week 5 + 6) compared to pre-RT: Hypoxanthine, 3-hydroxyisobutyric acid, L-lactic acid, pyruvic acid and xanthine (all p < .05). No statistically significant changes were found in urine samples. Multivariate analysis using PLS-DA identified a bundle of metabolites associated with radiation exposure, including diverse amino acids, purines, and bile acids. Extreme gradient boosting demonstrated moderate model performance in discriminating irradiated subjects with an AUC of 0.669 in blood samples. CONCLUSIONS:This study identified several metabolites altered by RT in blood, providing insight into the metabolic impact of radiation exposure. These findings could provide a basis for developing diagnostic tools to detect radiation exposure. Further studies with larger and more diverse cohorts are needed to validate these biomarkers.

Background:Image-based automated meal analysis using smartphones has the potential to facilitate meal management in type 1 diabetes. We evaluated the glycaemic efficacy of SNAQ-an image-based automated meal analysis app-in adults using hybrid automated insulin delivery (AID) systems requiring carbohydrate entry for prandial insulin dosing. Methods:In this single-centre trial (NCT05671679) adults with type 1 diabetes on AID therapy were randomly assigned to using SNAQ-a commercial mobile app recognizing and quantifying food from images for meal management support-or continuing their usual meal management (control group) for 3 weeks. The primary endpoint was the change in the %time in range (TIR, 3.9-10.0 mmol/L). Following the first three weeks, SNAQ was also provided to the control group for evaluating the sustainability of benefits and usage across all participants. Findings:Twenty-two participants were randomized between March 14 and November 23 2023 to using SNAQ and 22 to control. At baseline, TIR was 75.4 ± 13.7% and 74.3 ± 12.7% in the intervention and control group, respectively. After three weeks, the baseline-adjusted difference in TIR between SNAQ (used 1.6 ± 0.8 per day) and control was 6.6 percentage points in favour of SNAQ (95% CI 2.9 to 10.3, P < 0.001). SNAQ further improved mean glucose (-0.54 mmol/L, CI -0.9 to -0.2, P = 0.004) and time above range (-6.3%, CI -10 to -2.7, P = 0.001). Time below range, total daily insulin dose, bolus frequency, nor carbohydrate entered into the pump did not significantly differ between groups. Post-discontinuation, the glycaemic benefits of SNAQ were not sustained. No study-related serious adverse events occurred. Interpretation:Short-term use of the automated meal analysis app SNAQ improved glucose control in adults with type 1 diabetes treated with AID. Funding:The study was supported by the EFSD/EUDF Digitalisation on Diabetes Care Research Grant and by the Diabetes Center Berne.

Background: Traditional methods for estimating reference intervals (RIs) using patient's blood test results from the clinical routine, typically remove outliers without considering the nuanced health statuses of patients. This removes a vast majority of test results for reference interval estimation without considering the actual health status of the patient. Methods: We introduce the Differential Distribution Method (DDM) which uses laboratory routine data coded with ICD-10 to approximate an underlying non-diseased age and sex stratified population from mixed clinical data. By removing test results that stem from subpopulations significantly different from the general population, reference intervals can be generated stratified by sex and age, taking into account the associated health conditions of the patients as derived by the ICD-10 coding system. Results: Applying the DDM to blood plasma potassium levels demonstrated its ability to adjust RIs dynamically across different patient groups. The method effectively differentiated RIs in a decade-based stratification, showing significant variability and tighter confidence intervals, particularly in older (above 60 years old) adults. The RIs were slightly wider with advancing age in both males and females, while their standard deviation was reduced by removing large portions of test results differing significantly, grouped by either their individual ICD-10 code or clusters of ICD-10 codes. Conclusions: This DDM data mining approach offers a robust framework for RI inference by generating adjusted RIs that incorporate clinical nuances reflected in ICD-10 codes. This approach not only enhances the accuracy of patient diagnostics but also facilitates the identification of potential multimorbidities affecting laboratory results.

Background: Following the current guidelines, immunoassays for the diagnosis of heparin-induced thrombocytopenia (HIT) are interpreted dichotomously, with test results categorized as either positive or negative. However, the extent to which test results hold diagnostic significance across the entire dynamic range remains unclear. Objectives: We utilized data from the prospective towards precise and rapid diagnosis of heparin-induced thrombocytopenia study, comprising 1393 consecutive patients with suspected HIT, to assess the diagnostic significance of 2 heparin/platelet factor 4 immunoassay test results across their respective dynamic ranges: HemoSil Acustar HIT IgG (chemiluminescence immunoassay [CLIA]) and Lifecodes PF4 immunoglobulin G (enzyme-linked immunosorbent assay [ELISA]). Methods: HIT diagnosis was determined by a washed platelet heparin-induced platelet activation assay. For each measurement point in the dataset, we computed likelihood ratios (LRs), sensitivities, and specificities. To provide posttest probabilities for indi- vidual test results, we calculated interval-specific LRs and integrated them into a web- based calculator. Results: The prevalence of HIT was 8.5% (n = 119). An LR of >= 10 was first achieved at 0.3% of the dynamic range (0.4 U/mL; CLIA) and then at 16% (0.64 optical density; ELISA). An LR of >= 100 was present at 9.4% (12 U/mL; CLIA) and 75.0% (3.0 optical density; ELISA). The slope of the linear regression line (LR ti dynamic range) was 9.5 (CLIA) and 0.9 (ELISA). Conclusion: Despite both immunoassays showing an association between results and diagnostic significance, the strength of the association varies by assay. CLIA has a larger increase per measurement unit. Posttest probabilities for individual patients can be estimated using a web-based calculator: https://pcd-research.shinyapps.io/Bayesian Calculator/.

Phosphine is a widely utilized fumigant insecticide in stored-product facilities; however, its excessive application and improper usage have contributed to the emergence of resistance in insect populations. Although phosphine resistance is globally recognized, limited studies address its effects across different developmental stages. This research investigates the efficacy of phosphine on the eggs of major stored-product pests, including Oryzaephilus surinamensis (L.) (Coleoptera: Silvanidae), Rhyzopertha dominica (F.) (Coleoptera: Bostrichidae), Tribolium castaneum, and T. confusum (Coleoptera: Tenebrionidae), focusing on strains with varying levels of phosphine susceptibility. Egg hatching rates were analyzed following exposure to phosphine concentrations ranging from 50 to 1000 ppm for durations of 1 to 7 days. Predictive models were constructed to correlate phosphine concentration and exposure time, facilitating the determination of optimal pest control strategies. The findings reveal significant differences in efficacy among species and strains, with eggs from susceptible strains experiencing complete hatch failure at 50 ppm, while resistant strains showed higher tolerance. Lethal time (LT50 and LT99) values were determined, with LT99 ranging from 6.91 to 12.95 days at 50 ppm, highlighting species-specific and age-related differences in phosphine susceptibility. Lethal concentration (LC50 and LC99) values could only be estimated for T. castaneum, with LC99 for 1-day-old eggs ranging from 773.67 ppm to 923.03 ppm after 2.5 days of exposure. Furthermore, egg age influenced susceptibility, with 2-day-old eggs exhibiting greater mortality compared to 1-day-old eggs. This study underscores the critical role of phosphine concentration, exposure duration, and developmental stage in resistance management, providing valuable insights for enhancing fumigation protocols and improving the control of stored-product pests.

(1) Background: “Kidney Disease: Improving Global Outcomes” (KDIGO) provides guidelines for identifying the stages of acute kidney injury (AKI) and chronic kidney disease (CKD). A data-driven rule-based engine was developed to determine KDIGO staging compared to KD-related keywords in discharge letters. (2) Methods: To assess potential differences in outcomes, we compare the patient subgroups with exact KDIGO staging to imprecise or missing staging for all-cause mortality, in-hospital mortality, selection bias and costs by applying Kaplan–Meier analysis and the Cox proportional hazards regression model. We analysed 63,105 in-patient cases from 2016 to 2023 at a tertiary hospital with AKI, CKD and acute-on-chronic KD. (3) Results: Imprecise and missing CKD staging were associated with an 85% higher risk of all-cause and in-hospital mortality (CI: 1.7 to 2.0 and 1.66 to 2.03, respectively) compared to exact staging for any given disease status; imprecise or missing AKI staging increased in-hospital mortality risk by 56% and 57% (CI: 1.43 to 1.70 and 1.37 to 1.81, respectively) in patients with AKI. (4) Conclusions: Exact staging is associated with better outcomes in KD management. Our study provides valuable insight into potential quality and outcome improvements and lower costs, considering elderly patients, women and patients with acute-on-chronic KD as the most vulnerable.

Fusarium oxysporum f. sp. lentis (Fol) is considered the most destructive disease for lentil (Lens culinaris Medik.) worldwide. Despite the extensive studies elucidating plants’ metabolic response to fungal agents, there is a knowledge gap in the biochemical mechanisms governing Fol-resistance in lentil. Τhis study aimed at comparatively evaluating the metabolic response of two lentil genotypes, with contrasting phenotypes for Fol-resistance, to Fol-inoculation. Apart from gaining insights into the metabolic reprogramming in response to Fol-inoculation, the study focused on discovering novel biomarkers to improve early selection for Fol-resistance. GC-MS-mediated metabolic profiling of leaves and roots was employed to monitor changes across genotypes and treatments as well as their interaction. In total, the analysis yielded 178 quantifiable compounds, of which the vast majority belonged to the groups of carbohydrates, amino acids, polyols and organic acids. Despite the magnitude of metabolic fluctuations in response to Fol-inoculation in both genotypes under study, significant alterations were noted in the content of 18 compounds, of which 10 and 8 compounds referred to roots and shoots, respectively. Overall data underline the crucial contribution of palatinitol and L-proline in the metabolic response of roots and shoots, respectively, thus offering possibilities for their exploitation as metabolic biomarkers for Fol-resistance in lentil. To the best of our knowledge, this is the first metabolomics-based approach to unraveling the effects of Fol-inoculation on lentil’s metabolome, thus providing crucial information related to key aspects of lentil–Fol interaction. Future investigations in metabolic aspects of lentil–Fol interactions will undoubtedly revolutionize the search for metabolites underlying Fol-resistance, thus paving the way towards upgrading breeding efforts to combat fusarium wilt in lentil.

We assessed the diagnostic potential of erythroferrone as a biomarker for iron homeostasis comparing iron deficiency cases with anaemia of inflammation and controls. The dysregulation of the hepcidin axis was observed by Latour et al. in a mouse model of malarial anaemia induced by prolonged Plasmodium infection leading to increased erythroferrone concentrations. In line with that, we found significantly higher erythroferrone levels in cases with malaria and anaemia in an African population, compared to asymptomatic controls. Therefore, our findings extend the previous ones of the mouse model, suggesting also a dysregulation of the hepcidin axis in humans, which should be further corroborated in prospective studies and may lay the basis for the development of improved treatment strategies according to ERFE concentrations in such patients.

Objectives Reference intervals for the general clinical practice are expected to cover non-pathological values, but also reflect the underlying biological variation present in age- and gender-specific patient populations. Reference intervals can be inferred from routine patient data measured in high capacity using parametric approaches. Stratified reference distributions are obtained which may be transformed to normality via e.g. a Yeo-Johnson transformation. The estimation of the optimal transformation parameter for Yeo-Johnson through maximum likelihood can be highly influenced by the presence of outlying observations, resulting in biased reference interval estimates.Methods To reduce the influence of outlying observations on parametric reference interval estimation, a reweighted M-estimator approach for the Yeo-Johnson (YJ) transformation was utilised to achieve central normality in stratified reference populations for a variety of laboratory test results. The reweighted M-estimator for the YJ transformation offers a robust parametric approach to infer relevant reference intervals.Results The proposed method showcases robustness up to 15 % of outliers present in routine patient data, highlighting the applicability of the reweighted M-estimator in laboratory medicine. Furthermore, reference intervals are personalised based on the patients' age and gender for a variety of analytes from routine patient data collected in a tertiary hospital, robustly reducing the dimensionality of the data for more data-driven approaches.Conclusions The method shows the advantages for estimating reference intervals directly and parametrically from routine patient data in order to provide expected reference ranges. This approach to locally inferred reference intervals allows a more nuanced comparison of patients' test results.

Abstract Background An observational hospital-based cohort study, where the kidney disease staging is identified as a digital marker, and the association between the standardised staging of Kidney Disease (KD) and the outcomes is assessed along the treatment process. Methods The clinical and administrative data for all patients are stored in the Clinical Data Warehouse. A specific digital marker for an exact KD staging is applied. Three retrospective patient cohorts for exact and imprecise staging and missing diagnosis are defined and compared regarding the in-hospital mortality, all-cause mortality and disease progression. Results In total, 83146 hospitalisations of 40421 patients with KD, treated in 2014-2023 (2016-2023 will be updated) were identified; the number of the first hospitalisations with exact staging was 847 (2%), unprecise 29934 (72%) and missing diagnosis (26%). The demographic and baseline factors distribution between groups was tested with no association (chi-squared test statistic). The median survival time with a 95% CI was 7.94 (7.66, 8.81) years for the exact and 7.47 (7.42, 7.53) for the imprecise staging group. A log-rank and loglikelihood tests indicated a significant difference in survival between the groups, p < 0.0001. The Cox regression model shows a statistically significant difference in Hazard Ratio; KD staging, age, Elixhauser van Walraven index, treatment, nephrotoxic medication, contrast agent application, and AKI were identified as the significant covariates, C-statistic 0.730, Likelihood ratio test, Wald test, log-rank score on 42 df, p < 2e-16. ANOVA analysis confirmed a significant difference in cost distribution between the groups. An economic decision model proposal integrates the findings, combining a profile-level Markov cohort with a Discreet Time-To-Event Simulation DICE. Conclusions The study demonstrates a better outcome regarding all-cause mortality, in-hospital mortality, and costs if a standardised KD staging was applied. Key messages • Intuition (clinical judgement) and standardised kidney disease staging perform differently regarding clinical outcomes and costs. • The study demonstrates a better outcome regarding all-cause mortality, in-hospital mortality, and costs if a standardised KD staging was applied.

Open-source devices are nowadays used in a vast number of research fields like medicine, education, agriculture, and sports, among others. In this work, an open-source, portable, low-cost pH logger, appropriate for in situ measurements, was designed and developed to assist in experiments on agricultural produce manufacturing. Τhe device was calibrated manually using pH buffers for values of 4.01 and 7.01. Then, it was tested by manually measuring the pH from the juice of citrus fruits. A waterproof temperature sensor was added to the device for temperature compensation when measuring the pH. A formal method comparison process between the open-source device and a Hanna HI9024 Waterproof pH Meter was designed to assess their agreement. We derived indices of agreement and graphical assessment tools using mixed-effects models. The advantages and disadvantages of interpreting agreement through the proposed procedure are discussed. In our illustration, the indices reported mediocre agreement and the subsequent similarity analysis revealed a fixed bias of 0.22 pH units. After recalibration, agreement between the devices improved to excellent levels. The process can be followed in general to avoid misleading or over-simplistic results of studies reporting solely correlation coefficients for formal comparison purposes.

Receiver operating characteristic (ROC) curve analysis is widely used in evaluating the effectiveness of a diagnostic test/biomarker or classifier score. A parametric approach for statistical inference on ROC curves based on a Box-Cox transformation to normality has frequently been discussed in the literature. Many investigators have highlighted the difficulty of taking into account the variability of the estimated transformation parameter when carrying out such an analysis. This variability is often ignored and inferences are made by considering the estimated transformation parameter as fixed and known. In this paper, we will review the literature discussing the use of the Box-Cox transformation for ROC curves and the methodology for accounting for the estimation of the Box-Cox transformation parameter in the context of ROC analysis, and detail its application to a number of problems. We present a general framework for inference on any functional of interest, including common measures such as the AUC, the Youden index, and the sensitivity at a given specificity (and vice versa). We further developed a new R package (named 'rocbc') that carries out all discussed approaches and is available in CRAN.

Background: Despite remarkable progress in diabetes technology, most systems still require estimating meal carbohydrate (CHO) content for meal-time insulin delivery. Emerging smartphone applications may obviate this need, but performance data in relation to patient estimates remain scarce. Objective: The objective is to assess the accuracy of two commercial CHO estimation applications, SNAQ and Calorie Mama, and compare their performance with the estimation accuracy of people with type 1 diabetes (T1D). Methods: Carbohydrate estimates of 53 individuals with T1D (aged ≥16 years) were compared with those of SNAQ (food recognition + quantification) and Calorie Mama (food recognition + adjustable standard portion size). Twenty-six cooked meals were prepared at the hospital kitchen. Each participant estimated the CHO content of two meals in three different sizes without assistance. Participants then used SNAQ for CHO quantification in one meal and Calorie Mama for the other (all three sizes). Accuracy was the estimate’s deviation from ground-truth CHO content (weight multiplied by nutritional facts from recipe database). Furthermore, the applications were rated using the Mars-G questionnaire. Results: Participants’ mean ± standard deviation (SD) absolute error was 21 ± 21.5 g (71 ± 72.7%). Calorie Mama had a mean absolute error of 24 ± 36.5 g (81.2 ± 123.4%). With a mean absolute error of 13.1 ± 11.3 g (44.3 ± 38.2%), SNAQ outperformed the estimation accuracy of patients and Calorie Mama (both P > .05). Error consistency (quantified by the within-participant SD) did not significantly differ between the methods. Conclusions: SNAQ may provide effective CHO estimation support for people with T1D, particularly those with large or inconsistent CHO estimation errors. Its impact on glucose control remains to be evaluated.