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Item does not contain fulltext, BACKGROUND: Recognising patients who need immediate hospital treatment for sepsis while simultaneously limiting unnecessary referrals is challenging for GPs. AIM: To develop and validate a sepsis prediction model for adult patients in primary care. DESIGN AND SETTING: This was a prospective cohort study in four out-of-hours primary care services in the Netherlands, conducted between June 2018 and March 2020. METHOD: Adult patients who were acutely ill and received home visits were included. A total of nine clinical variables were selected as candidate predictors, next to the biomarkers C-reactive protein, procalcitonin, and lactate. The primary endpoint was sepsis within 72 hours of inclusion, as established by an expert panel. Multivariable logistic regression with backwards selection was used to design an optimal model with continuous clinical variables. The added value of the biomarkers was evaluated. Subsequently, a simple model using single cut-off points of continuous variables was developed and externally validated in two emergency department populations. RESULTS: A total of 357 patients were included with a median age of 80 years (interquartile range 71-86), of which 151 (42%) were diagnosed with sepsis. A model based on a simple count of one point for each of six variables (aged >65 years; temperature >38°C; systolic blood pressure ≤110 mmHg; heart rate >110/min; saturation ≤95%; and altered mental status) had good discrimination and calibration (C-statistic of 0.80 [95% confidence interval = 0.75 to 0.84]; Brier score 0.175). Biomarkers did not improve the performance of the model and were therefore not included. The model was robust during external validation. CONCLUSION: Based on this study's GP out-of-hours population, a simple model can accurately predict sepsis in acutely ill adult patients using readily available clinical parameters.

Kort na de uitbraak van de covid-19-pandemie kregen huisartsen in een korte periode te maken met een forse en onvoorspelbare toename aan ziektegevallen. Ze stonden voor de vraag hoe de zorg optimaal en veilig geleverd moest worden. Limburg was op dat moment een van de zwaarst getroffen gebieden. Daarom werd in een bovenregionale samenwerking het coronacentrum urmond opgezet. Het plan van aanpak kan als voorbeeld dienen bij een toekomstige infectieziekte-uitbraak.

Subclinical mastitis, an economically challenging disease of dairy cattle, is associated with an increased use of antimicrobials which reduces milk quantity and quality. It is more common than clinical mastitis and far more difficult to detect. Recently, much attention has been paid to the development of machine-learning expert systems for early detection of subclinical mastitis from milking features. However, differences between animals within a farm as well as between farms, particularly across multiple years, are major obstacles to the generalisation of machine learning models. Here, for the first time, we integrated scaling by quartiling with classification based on associations in a multi-year study to deal with farm heterogeneity by discovery of multiple patterns towards mastitis. The data were obtained from one farm comprising Holstein Friesian cows in Ongaonga, New Zealand, using an electronic automated monitoring system. The data collection was repeated annually over 3 consecutive years. Some discovered rules, such as when the milking peak flow is low, electrical conductivity (EC) of milk is low, milk lactose is low, milk fat is high, and milk volume is low, the cow has subclinical mastitis, reached high confidence (>70%) in multiple years. On averages, over 3 years, low level of milk lactose and high value of milk EC were part of 93% and 83.8% of all subclinical mastitis detecting rules, offering a reproducible pattern of subclinical mastitis detection. The scaled year-independent combinational rules provide an easy-to-apply and cost-effective machine-learning expert system for early detection of hidden mastitis using milking parameters.

Digital dermatitis (DD) is the leading infectious cause of lameness in dairy cattle, and it affects their welfare and productivity worldwide. At the herd level, DD is often assessed while cows are standing in a milking parlor, and lesions are most commonly evaluated using the M-score. The objective of this study was to examine the interobserver agreement for M-scores of the feet of standing cattle, based on digital color photographs of dairy cattle hind feet. A total of 88 photographs and written descriptors of the M-score were sent to 11 scorers working at 10 different institutions in 5 countries. The scorers received no formal training immediately before scoring the photographs; however, all regularly used the M-score to score DD. The answers for 36 photographs were excluded from the analysis because the photograph either had more than 1 M-stage as mode or not all scorers assigned an M-score to it. The M-scores of the 11 scorers from 52 photographs were available for analysis. Interobserver agreement was tested using Gwet's agreement coefficient (AC1) and the mode was assumed correct. Overall, moderate agreement emerged for the M-score (AC1 = 0.48). For the individual M-stages, almost perfect agreement existed for M0 (AC1 = 0.99), M1 (AC1 = 0.92), and M3 (AC1 = 0.82), and substantial agreement for M2 (AC1 = 0.61), M4 (AC1 = 0.65), and M4.1 (AC1 = 0.71). This outcome indicates the degree of individual variation in M-scoring in this context by unstandardized, experienced European observers, particularly for the M2, M4, and M4.1 stages. Standardized training is likely to improve the consistency of M-scoring and thus the generalizability of future DD research results on this important endemic disease.