Balancing access to antibiotics with control of antibiotic resistance is a global public health priority. Currently, antibiotic stewardship is informed by a 'use it and lose it' principle, in which population antibiotic use is linearly related to resistance rates. However, theoretical and mathematical models suggest use-resistance relationships are non-linear. One explanation is that resistance genes are commonly associated with 'fitness costs', impairing pathogen replication or transmissibility. Therefore, resistant genes and pathogens may only gain a survival advantage where antibiotic selection pressures exceed critical thresholds. These thresholds may provide quantitative targets for stewardship: optimising control of resistance while avoiding over-restriction of antibiotics. We evaluated the generalisability of a nonlinear time-series analysis approach for identifying thresholds using historical prescribing and microbiological data from five populations in Europe. We identified minimum thresholds in temporal relationships between use of selected antibiotics and rates of carbapenem-resistant Acinetobacter baumannii (in Hungary), extended spectrum β-lactamase producing Escherichia coli (Spain), cefepime-resistant Escherichia coli (Spain), gentamicin-resistant Pseudomonas aeruginosa (France), and methicillin-resistant Staphylococcus aureus (Northern Ireland) in different epidemiological phases. Using routinely generated data, our approach can identify context-specific quantitative targets for rationalising population antibiotic use and controlling resistance. Prospective intervention studies restricting antibiotic consumption are needed to validate Results Identifying non-linear temporal relationships: from experiment to applicationIn a Monte Carlo experiment we compared the ability of linear and non-linear time-series analysis (Multivariate Adaptive Regression Splines, MARS) to identify pre-defined relationships between simulated explanatory and outcome time-series (Supplementary Figure 1). Non-linear time-series analysis (NL-TSA) accurately identified both truly linear and nonlinear associations. However, linear time-series analysis provided biased estimations and overall poorer data-fit if relationships were non-linear. NL-TSA models applied to retrospective time-series data from five European study populations (examples 1-5), frequently identified minimum thresholds in antibiotic useresistance relationships, (figures 1-5 and Supplementary Table 1). 'Ceiling effects', in which further increases in explanatory variables did not affect resistance rates, were found at highlevels of use of some antibiotics and hand hygiene. Non-linearities in autoregression and population interaction terms further indicated the complexity of transmission dynamics within and between clinical populations. Example 1: Carbapenem-resistant Acinetobacter baumannii (Debrecen, Hungary) We examined ecological determinants of carbapenem-resistant A. baumannii (CRAb) in a tertiary hospital population in Debrecen, Hungary (figure 1). Betwee...

Very young SGA children without spontaneous catch-up growth could benefit from GH treatment because growth was accelerated and no negative side effects were observed.

A questionnaire-based cross-sectional study was conducted to gather information on current microbiological practices for active surveillance of carriage of multidrug-resistant (MDR) bacteria in hospitals from 14 health departments of the Autonomous Community of Valencia (ACV), Spain, which together provided medical attention to 3,271,077 inhabitants in 2017, approximately 70% of the population of the ACV. The survey consisted of 35 questions on MDR bacteria screening policies, surveillance approach chosen (universal vs. targeted), and microbiological methods and processes in use for routine detection and reporting of colonization by MDR bacteria, including the anatomical sites scheduled to be sampled for each MDR bacterial species, and the methodology employed (culture-based, molecular-based, or both). Our study revealed striking differences across centers, likely attributable to the lack of consensus on optimal protocols for sampling, body sites for screening, and microbiological testing, thus underscoring the need for consensus guidelines on these issues.

Background The COVID-19 pandemic has put tremendous pressure on hospital resources around the world. Forecasting demand for healthcare services is important generally, but crucial in epidemic contexts, both to facilitate resource planning and to inform situational awareness. There is abundant research on methods for predicting the spread of COVID-19 and even the arrival of COVID-19 patients to hospitals emergency departments. This study builds on that work to propose a hybrid tool, combining a stochastic Markov model and a discrete event simulation model to dynamically predict hospital admissions and total daily occupancy of hospital and ICU beds. Methods The model was developed and validated at San Juan de Alicante University Hospital from 10 July 2020 to 10 January 2022 and externally validated at Hospital Vega Baja. An admissions generator was developed using a stochastic Markov model that feeds a discrete event simulation model in R. Positive microbiological SARS-COV-2 results from the health department’s catchment population were stratified by patient age to calculate the probabilities of hospital admission. Admitted patients follow distinct pathways through the hospital, which are simulated by the discrete event simulation model, allowing administrators to estimate the bed occupancy for the next week. The median absolute difference (MAD) between predicted and actual demand was used as a model performance measure. Results With respect to the San Juan hospital data, the admissions generator yielded a MAD of 6 admissions/week (interquartile range [IQR] 2-11). The MAD between the tool’s predictions and actual bed occupancy was 20 beds/day (IQR  5-43), or 5% of the hospital beds. The MAD between the intensive care unit (ICU)’s predicted and actual occupancy was 4 beds/day (IQR 2-7), or 25% of the beds. When the model was further evaluated with data from Hospital Vega Baja, the admissions generator showed a MAD of 2.42 admissions/week (IQR 1.02-7.41). The MAD between the tools’ predictions and the actual bed occupancy was 18 beds/day (IQR 19.57-38.89), or 5.1% of the hospital beds. For ICU beds, the MAD was 3 beds/day (IQR 1-5), or 21.4% of the ICU beds. Conclusion Predictions of hospital admissions, ward beds, and ICU occupancy for COVID-19 patients were very useful to hospital managers, allowing early planning of hospital resource allocation.

A 3-year, 8-month-old male was admitted into hospital for nephrologic assessment. He had had frequent episodes of diarrhea and vomiting since he was 4 months old. He had normal intellectual and neurological development. His weight was 9.5 kg (p<3; average for 10 months) and his stature was 71 cm (p<3; average for 8 months), with short trunk ( Fig. 1; body upper/lower segment measurements: 38.7/32.3 cm), short neck and triangular face with a bulbous nose. Growth velocity was 1.6 cm/year. The abdomen was prominent and there were multiple lentigines on the face and trunk. There were microdontia, a high-pitched voice, and a staggering gait. He had a non-affected older sister, and his parents were not consanguineous.The hemogram showed ferropenic anemia (hemoglobin 10.4 g/dl, normal 11.5-13.5; MCV 74.2 fl, normal 75-87; hematocrit 31.5%, normal 42-54; and serum iron 30 µg/dl, normal 59-158), lymphopenia (1.49×10 9 lymphocytes/l, normal 1.5×10 9 -6.5×10 9 ) and thrombocytosis (600×10 9 platelets/l, normal 184×10 9 -465×10 9 ). Clinical chemistry demonstrated elevated total cholesterol (769 mg/dl, normal 50-230) and hypertryglyceridemia (1022 mg/dl, normal 50-200), and reduced albumin (1.6 g/dl, normal 3.5-5.3) and total protein in serum (4.7 g/dl, normal 6-8), while glucose, electrolytes and creatinine (0.4 mg/dl, normal 0.4-0.8) were normal. The 24-h urine specimen demonstrated features of nephrotic syndrome with severe proteinuria (118.9 mg/m 2 /h = 2.85 g/24 h, which evolved to 9.9 g/24 h 3 months later). The cross-linked N-terminal telopeptides of type I collagen (NTx) were high in the 2-h fasting morning urine sample (499 nM ECO/mM creatinine, normal 3-63), indicating an abnormally high bone turnover. There were no mucopolysaccharides in the urine. The albumin/β 2 -microglobulin index confirmed glomerular proteinuria. Immunologic analysis demonstrated an inverse CD4/CD8