We evaluated the impact of introducing a mandatory indication field into electronic order entry for targeted antibiotics in adult inpatients.Retrospective, before-and-after trial.A 400-bed community hospital.All adult electronic intravenous (IV) and enteral orders for targeted antibiotics  had a mandatory indication field added. Control antibiotics  were chosen to track shifts in antibiotic prescribing due to the introduction of mandatory indication field.Descriptive statistics were used to summarize the primary outcome, measured in Defined Daily Doses (DDD) per 1000 patient days (PD). Interrupted time-series (ITS) analysis was performed to compare levels and trends in antibiotic usage of targeted and control antibiotics during 24 months before and after the intervention. Additionally, a descriptive analysis of mandatory indication fields for targeted antibiotics in the postintervention period was conducted.In total, 4,572 study antibiotic orders were evaluated after the intervention. Preset mandatory indications were selected for 30%–55% of orders. There was decreased usage of targeted antibiotics  with increased usage of control antibiotics . ITS analysis showed no statistically significant difference in overall antibiotic usage before and after the intervention for all targeted antibiotics.This study showed moderate use of preset mandatory indications, suggesting that the preset list of indications can be optimized. There was no impact on overall antibiotic usage with the use of mandatory indications. More prospective research is needed to study the utility of this intervention in different contexts. Unnecessary usage of antibiotics can lead to selection for antimicrobial resistant pathogens, Clostridioides difficile infection, and other adverse events such as nephrotoxity. Documentation of indications for antibiotics is recommended to facilitate antimicrobial stewardship interventions such as prospective audit and feedback. Some studies have shown to reduce rates of inappropriate prescribing and improve medication safety; however, more studies are needed to confirm the impact on overall antibiotic usage and shifts in antibiotic prescribing.Recent evidence suggests that 30%–40% of prescribed antibiotics in hospital are inappropriate.In October 2015, St. Joseph’s Health Centre introduced mandatory indication fields into electronic order entry for selected antibiotics. These fields acted as a force function, requiring prescribers to provide a reason for prescribing at the time of order entry before the order can be processed. We evaluated the impact of introducing this intervention on antibiotic utilization and characterized the use of predefined indications by prescribers.The study was conducted at a 400-bed community hospital in Toronto, Canada. An antimicrobial stewardship program (ASP) was established at our institution in 2011, with a multimodal approach including prospective audit and feedback, development of guidelines and order sets, microbiology laboratory report optimization and education. Staffing consisted of a lead ASP physician and 2 full-time equivalent ASP-trained pharmacists during the study period of October 1, 2013, to October 31, 2017.Clostridioides difficile infection and being not first line agents for most infections in our setting so there is higher potential for inappropriate use. The latter two antibiotics (metronidazole and vancomycin) were selected as they had more clear-cut indications. Each mandatory indication field had predefined indications tailored to the selected antibiotics as determined by the ASP team and a free-text field (Table All adult electronic intravenous (IV) and enteral orders for targeted antibiotics  had a mandatory indication field added on October 22, 2015. The first three antibiotics  were selected based of risk for causing Control antibiotics  were chosen to track shifts in antibiotic prescribing due to the introduction of mandatory indication field because there were no specific initiatives addressing these control antibiotics during the study period.The preintervention period was defined as October 1, 2013, to October 31, 2015, and the postintervention period was from November 1, 2015, to October 31, 2017. October 2015 was assigned to the preintervention period because the intervention started 1 week before the end of October and the assumption was that effect on antibiotic usage would be small after only a week of intervention. We used Stata version 15 software  for these analyses. ITS analysis was used to estimate regression parameters by ordinary least-squares regression-based models which accommodated ITS data. These models estimate ordinary least-squares regression coefficients with Newey-West standard errors, which handle autocorrelation and heteroscedasticity. The command “actest” was used to perform the Cumby-Huizinga tests for autocorrelation and the specific lag order up to 12.Data on antibiotics were collected monthly as Defined Daily Doses (DDD) per 1000 patient days (PD). DDD was the available metric at the time and provided a longer history of antibiotic usage prior to the intervention. Descriptive statistics were used to summarize the antibiotic data. Interrupted time-series (ITS) analysis was performed to compare changes in level and slope with regard to the primary outcome. We used the regression model proposed by Wagner et al.This study was approved by the research ethics board at St. Joseph’s Health Centre on January 20, 2017.In total, 8,399 orders were evaluated in the 1-year postintervention period, of which 4,572 orders were for targeted antibiotics and 3,287 were for control antibiotics. The preset mandatory indications were selected 30%–55% of the time, depending on targeted antibiotic , driven by decreased usage of metronidazole , ciprofloxacin  and moxifloxacin . We noted increased usage of control antibiotics  driven by increased usage of amoxicillin-clavulanate  and ceftriaxone .P = .538). Similarly, the preintervention slope (−0.92 per month) was not different from the postintervention slope (−1.00 per month)  (Table P = .013). There was no significant change in levels or slopes for other targeted antibiotics. Similarly, ITS analysis for control antibiotics showed that levels were not different before and after the intervention. The difference between postintervention levels  and preintervention levels  was 8.60 . However, the preintervention slope (−0.04 per month) was different from postintervention slope . Nonetheless, given the small change in DDD/1000-PD, this is likely not clinically significant (Table P = .013).The ITS analysis showed levels were not different before and after the intervention for targeted antibiotics with mandatory indications. The difference in antibiotic usage DDD/1000-PD) before the intervention  and after the intervention  was 3.51  at our institution comprise of twice weekly interdisciplinary rounds on our four internal medicine wards with a review of all internal medicine patients receiving any antimicrobial agent. High-intensity PAF was associated with a reduction in antibiotic use compared to our previous low-intensity PAF which consisted of ad-hoc review of patients on targeted antimicrobials.Although the use of prescriber-entered indications to track antibiotic prescribing has been described in literature and has been shown to improve appropriate antibiotic prescribing, for antimicrobials. We infer, based on these studies, that there was moderate to high accuracy and clinical appropriateness in the selection and use of our predefined mandatory indications. This finding was supported by the observations of specific infections noted when the free-text field was used and almost no incomprehensible rationale provided by prescribers.This study had several limitations. Given its retrospective design, unaccounted confounding factors may have mitigated the change in antibiotic usage. However, our time-series analysis accounted for seasonal and secular (consistent) trends in antibiotic use to reduce the impact of any confounder. Secondly, we did not evaluate the accuracy of indication selection nor appropriateness of therapy. However, previous studies have shown high accuracy of selected indication (74%–100%)Clostridioides difficile infection from IV and oral metronidazole given the Infectious Diseases Society of America guideline update in 2017.Our next steps based on our findings include (1) optimizing the predefined list of indications to reflect the most commonly used free-form indications and (2) removing mandatory indications from oral vancomycin and indications pertaining to


Antimicrobial resistance is a pressing concern, and while antibiotic stewardship interventions are intended to limit unnecessary antibiotic exposures, including to asymptomatically carried pathogens , the frequency and characteristics of these bystander exposures have not been well described. We quantified the frequency that bacterial enteric pathogens were exposed to antibiotics when not the target of treatment in a study of children in low-resource settings. Our analysis demonstrated that almost all enteropathogen exposures to antibiotics occurred when the bacteria were carried asymptomatically, and respiratory infections were responsible for the largest proportion of exposures. Interventions to reduce antibiotic use and the illnesses that prompt treatment could have the ancillary benefit of reducing selection pressure for antimicrobial resistance among pathogens carried asymptomatically. Escherichia coli was the most frequently exposed pathogen, with 229.6 exposures per 100 child-years. Almost all antibiotic exposures for Campylobacter (98.8%), enterotoxigenic E. coli (95.6%), and typical enteropathogenic E. coli (99.4%), and the majority for Shigella (77.6%), occurred when the pathogens were not the target of treatment. Respiratory infections accounted for half (49.9%) and diarrheal illnesses accounted for one-fourth (24.6%) of subclinical enteric bacteria exposures to antibiotics. Bystander exposure of E. coli to class-specific antibiotics was associated with the prevalence of phenotypic resistance at the community level. Antimicrobial stewardship and illness-prevention interventions among children in low-resource settings would have a large ancillary benefit of reducing bystander selection that may contribute to antimicrobial resistance.Children in low-resource settings carry enteric pathogens asymptomatically and are frequently treated with antibiotics, resulting in opportunities for pathogens to be exposed to antibiotics when not the target of treatment . We quantified the frequency of bystander antibiotic exposures for enteric pathogens and estimated associations with resistance among children in eight low-resource settings. We analyzed 15,697 antibiotic courses from 1,715 children aged 0 to 2 y from the MAL-ED birth cohort. We calculated the incidence of bystander exposures and attributed exposures to respiratory and diarrheal illnesses. We associated bystander exposure with phenotypic susceptibility of E. coli isolates in the 30 d following exposure and at the level of the study site. There were 744.1 subclinical pathogen exposures to antibiotics per 100 child-years. Enteroaggregative  Antibiotic use causes selection pressure for antimicrobial resistance (AMR), a growing global public health crisis that threatens to render antibiotics ineffective against many high-burden infections . Most ofEscherichia coli (EAEC), for example, was detected in nearly half (49%) of nondiarrheal stools collected in the first 2 y of life in the Etiology, Risk Factors and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development Project (MAL-ED) birth cohort study conducted in South America, South Asia, and sub-Saharan Africa  of antibiotic courses had a stool sample collected and tested by qPCR with valid results within the previous 30 d  of exposures were to drug classes that are of particular concern for AMR  . As a subclinical bystander, EAEC was exposed to antibiotics more frequently than any other pathogen, with an incidence rate of 229.6 exposures to any antibiotic per 100 child-years  for ETEC, 104.0 exposures per 100 child-years (95% CI 99.8 to 108.1) for atypical enteropathogenic E. coli (aEPEC), 70.3 exposures per 100 child-years (95% CI 67.0 to 73.6) for typical enteropathogenic E. coli (tEPEC), and 46.1 exposures per 100 child-years (95% CI 43.2 to 48.8) for Shigella. The results were similar when antibiotic courses were matched to the most recent stool within 7 d  or 21 d  instead of 30 d in a sensitivity analysis.Overall, for any subclinical bacterial pathogen analyzed, there were 744.1 exposures to antibiotics per 100 child-years (95% CI 729.1 to 760.6) . Overall, macrolides and cephalosporins were the most frequently used antibiotics for all indications, with 149.3 (95% CI 137.6 to 160.7) and 145.9 (95% CI 137.5 to 154.6) courses per 100 child-years, respectively . Macrolides were overwhelmingly used in the Bangladesh and Peru sites and cephalosporins in the Pakistan, Bangladesh, and India sites. The median duration of antibiotic courses was 5 d (interquartile range [IQR] 3 to 7 d), such that the total days of antibiotic exposure per 100 child-years for each subclinical pathogen was approximately 5 times the incidence rate .The sites in Bangladesh  and Pakistan  had the highest incidence rates of subclinical pathogen exposure to antibiotics, while those in Brazil  and South Africa  had the lowest incidence rates (Campylobacter (98.8%), ETEC (95.6%), and tEPEC (99.4%), and the majority for Shigella (77.6%) occurred when they were subclinical infections and not when they were the cause of treatment . A sensitivity analysis, in which antibiotic courses were matched to the nearest stool within 21 d, instead of 30 d, yielded similar results . In an additional sensitivity analysis in which diarrhea etiology was more liberally assigned if the pathogen was detected at any quantity (quantification cycle [Cq] <35) during diarrhea, the percentage of antibiotic exposure that occurred when the pathogen was the cause of diarrhea increased. However, the majority of exposures still occurred during subclinical infections: Campylobacter (86.0%), ETEC (77.8%), tEPEC (80.9%), and Shigella (68.1%) .Nearly all antibiotic exposures for reatment . When exSI Appendix, Table S3). Bystander pathogen exposure to antibiotics due to diarrheal and dysentery illnesses combined only accounted for approximately one-fourth (24.6%) of antibiotic exposures (with dysentery accounting for <3% of illness for each bystander pathogen). When subset to bystander pathogen exposures to fluoroquinolones and macrolides only, URIs and ALRIs were still attributed as the reason for antibiotic use in nearly half (45.5%) of all antibiotic courses; however, the proportion of diarrheal and dysentery illnesses as the reason for antibiotic use increased to more than one-third (40.7%) . Upon further examination of fluoroquinolone and macrolide drug classes individually, the incidence rates of exposure due to URIs and diarrheal illnesses were nearly equivalent across bystander pathogens .Together, upper respiratory infections (URIs) (37.6%) and acute lower respiratory infections (ALRIs) (12.3%) accounted for half (49.9%) of antibiotic courses in which bystander pathogens were exposed . For every 0.5-U increase in WAMI  score, a measure of socioeconomic status, there were 10% fewer total exposures of subclinical bacterial pathogens to antibiotics . In addition, female sex , exclusive breastfeeding , treated water , and access to an improved latrine  were associated with fewer total exposures of subclinical bacterial pathogens to antibiotics. However, there was no association with WAMI score  and treated water , and the associations with improved source of drinking water  and access to improved latrine  strengthened when subset to exposures to fluoroquinolones or macrolides.Certain childhood characteristics were associated with lower rates of bystander exposure to antibiotics, which may be due to either less frequent antibiotic receipt or carriage of pathogens or both . Recent bystander exposures to cephalosporins, fluoroquinolones, and sulfonamides were associated with smaller (<10%), nonsignificant increases in resistance to the corresponding drug class. Conversely, the incidence of class-specific antibiotic exposures at the site level were strongly correlated with the prevalence of class-specific resistance among all isolates from that site, particularly for fluoroquinolones (R = 0.91) and macrolides , although such treatment accounted for only 22% of exposures of Shigella to antibiotics. Shigella is the leading cause of dysentery , com, comE. cl  and the use of cephalosporins, fluoroquinolones, macrolides, and sulfonamides, specifically, which are used to treat bacterial diarrhea and are relevant for the development of resistance , 39. DruCampylobacter spp., Shigella, enterotoxigenic E. coli (ETEC), tEPEC, aEPEC, and EAEC.The QIAamp Fast DNA Stool Mini Kit (Qiagen) was used to extract total nucleic acid from stool samples, described elsewhere . QuantitE. coli) from all of the stool samples were cultured, pooled, and stored at −80 °C. We randomly selected 50 children from each site who had E. coli isolates cultured from nondiarrheal stools collected at 3, 6, 9, 12, 15, 18, 21, and 24 mo of age and conducted antibiotic susceptibility testing on those isolates. Because a subset of pooled E. coli isolates was archived at the Peru site, fewer than 8 isolates from more than 50 children (n = 157) were included. Antibiotic susceptibility testing was performed using the E-test for azithromycin and disk diffusion for all of the other antibiotics. Susceptibility cutoffs were derived from the literature for azithromycin. For all other antibiotics, susceptibility cutoffs were defined by the Clinical Laboratory Standards Institute Guidelines . Ceftriaxone, ciprofloxacin, azithromycin, and trimethoprim-sulfamethoxazole resistance were used as indicators of class-specific resistance to cephalosporins, fluoroquinolones, macrolides, and sulfonamides, respectively.Five indole-positive, lactose-fermenting colonies , unless that pathogen was the target pathogen of treatment . For courses in which a stool sample with valid pathogen data were not available in the past 30 d, we extrapolated from courses in which data were available by site and drug class for class-specific analyses. In sensitivity analyses, we restricted ourselves to courses with samples collected within 21 d before starting the antibiotic course based on an analysis of the average duration of pathogen carriage after a diarrheal episode  and alsoIndicating illnesses for antibiotic treatment were determined by the overlap between antibiotic use and reported symptoms, as previously outlined , 10. If Campylobacter jenjui/Campylobacter coli, Cryptosporidium, norovirus, rotavirus, sapovirus, Shigella, heat-stable enterotoxigenic E. coli (ST-ETEC), and tEPEC. A pathogen was presumed to be the causative agent of diarrhea if the AFe was >0.5. For antibiotic courses in which a pathogen was identified as the cause of diarrhea prompting treatment, the antibiotic course was not considered a cause of bystander exposure to that pathogen, even if it was also detected in the stool collected in the prior 30 d. In a sensitivity analysis, we considered a pathogen to be the causative agent of diarrhea if detected at any quantity (Ct < 35).Target pathogens of antibiotic use for diarrhea were determined by attributing etiology to diarrheal episodes that were identified as responsible for antibiotic treatment. If multiple diarrhea episodes occurred during an antibiotic course, then we used pathogen data from the first diarrheal episode to define etiology. To determine which specific enteric pathogens caused the antibiotic-treated diarrhea, we constructed mixed-effects models using diarrheal and nondiarrheal stools that associated pathogen quantity with diarrhea to calculate pathogen-specific attributable fractions for each episode (AFe), adjusting for other pathogens, age, sex, test batch, site, and individual. Etiology-specific incidence estimates for all diarrhea episodes using these methods in comparison with pathogen prevalence during diarrhea have been published previously . We inclTo quantify bystander exposure or exposures of subclinical infections to antibiotics, we calculated the incidence rate per 100 child-years as the number of subclinical pathogen exposures divided by the observed person-time, divided by the proportion of antibiotic courses that could be linked to a stool within the prior 30 d . We also calculated the total number of days of antibiotic exposure per 100 child-years for each subclinical pathogen by summing the duration of each course. These rates were calculated for each bacterial pathogen of interest and for any bacterial pathogen. For the latter, if multiple subclinical pathogens were exposed to the same course of antibiotics, then the exposure was counted for each pathogen.We then calculated the proportion of antibiotic exposures for each pathogen that occurred as bystander exposure  and the proportion of antibiotic exposures that occurred when the pathogen was the cause of treatment .We quantified the incidence per 100 child-years and proportion of bystander exposure events that were attributed to the treatment of diarrhea, dysentery, URI, ALRI, and other causes for all courses of antibiotics, fluoroquinolone, and macrolide courses specifically.Finally, we estimated the associations between child characteristics and the frequency of bystander exposure to characterize the population in which this exposure occurs. We used Poisson regression to estimate IRRs for the total number of times that bacterial subclinical pathogens were exposed to any antibiotic. Separately, we restricted the outcome to instances in which bacterial subclinical pathogens were exposed to fluoroquinolones or macrolides. In both instances, we ran individual models assessing each child characteristic alone while adjusting for site and multivariable models that included all child characteristics and site.All of the analyses were performed overall, at each site, and for specific pathogens and drug classes. We estimated the 95% CIs for all of the estimates except those from the Poisson models by bootstrap with 1,000 resamples. Wald intervals were used for the Poisson models.E. coli isolates. Because E. coli are ubiquitously present in stool, commensal, and therefore not the target of antibiotic treatment, all of the observed antibiotic exposures were considered bystander exposures to E. coli. To estimate risk ratios, we used the Poisson approximation for log-binomial regression with generalized estimating equations to account for multiple isolates cultured from the same child, adjusting for site, age, sex, WAMI index, and hospitalization in the last 90 d. To compare rates of bystander exposures with antibiotic resistance at the community level, we associated site-level incidence of class-specific antibiotic exposures with the prevalence of class-specific resistance among all of the isolates from that site using linear regression, and we estimated the correlation between these two variables using the Pearson correlation coefficient (R). All of the statistical analyses were performed via R software, version 4.0.2 .To associate bystander antibiotic exposure with antibiotic resistance at the individual level, we estimated the effect of class-specific antibiotic exposure in the last 30 d on the prevalence of resistance to the same drug class in the cultured https://osf.io/3asxh. Deidentified participant data from the MAL-ED study is publicly available at ClinEpiDB.org after approval of a proposal by the study’s principal investigators (PIs).This study involves human participants. For the parent study, ethical approval was obtained from the institutional review boards at the University of Virginia School of Medicine  (14595), and at each of the participating research sites: Ethical Review Committee, International Centre for Diarrhoeal Disease Research, Bangladesh (Bangladesh); Committee for Ethics in Research, Universidade Federal do Ceara, and National Ethical Research Committee, Health Ministry, Council of National Health (Brazil); Institutional Review Board, Christian Medical College, Vellore, and Health Ministry Screening Committee, Indian Council of Medical Research (India); Institutional Review Board, Institute of Medicine, Tribhuvan University, Ethical Review Board, Nepal Health Research Council, and Institutional Review Board, Walter Reed Army Institute of Research ; Institutional Review Board, Johns Hopkins University, and PRISMA Ethics Committee; Health Ministry, Loreto (Peru); Ethical Review Committee, Aga Khan University (Pakistan); Health, Safety and Research Ethics Committee, University of Venda, and Department of Health and Social Development, Limpopo Provincial Government (South Africa); and Medical Research Coordinating Committee, National Institute for Medical Research, and Chief Medical Officer, Ministry of Health and Social Welfare (Tanzania). For the present study, we obtained ethical approval at the University of Virginia School of Medicine  (22398) and Emory University  (STUDY00003285). Participants gave informed consent to participate in the study before taking part. The statistical analysis plan is available at Supplementary File


The addition of Superabsorbent Polymer (SAP) decreases the effect of autogenous shrinkage present in pastes, mortars, and concretes. In this study we investigated the influence of the addition of SAP in self-compacting cement paste mixtures. Eighteen 5 × 10 cylindrical specimens were molded in all, three for each mixture . Two types of cement were tested, CP II-Z and CP V-ARI with 0.15% of weight replaced per two diameters of SAP (600 μm and 800 μm). The samples followed the standards required. Mini slump tests were carried out in the fresh state, and uniaxial compressive strength, elastic modulus, specific mass, absorption, and air content in the hardened state after 28 days. The results obtained show the SAP is high indicated to replaced cement in small % of weight i/to fresh and hardened paste. Likewise, the group mix n° 3 composed of CPII 0.15% of SAP with 800 μm diameter presented the best result. The construction industry’s technology has been updating and implementing new concepts, such as the usage of numerous alternative material such as superabsorbent polymer (SAP), in pastes, concretes, and mortars. These polymers, also known as hydrogels, are cross-linked polymers that have the ability to absorb a large amount of liquid, expanding to form an insoluble gel.According to Souza et al.  the numbAccording to Gomes and Barros , self-coInitially, the great interest in the use of SAP was in the area of polymer chemistry capable of absorbing large volumes of water. Known as “superabsorbents”, they have been used mainly for agricultural and forestry soil preparation . SAP is Recently, the usage of superabsorbent polymers has sparked attention in civil engineering, notably in the manufacture of concrete. Research on the production of concrete with the addition of SAP has primarily focused on its use as an internal curing agent, because it has been demonstrated to be able to release water in a controlled amount according to the demand of the concrete during the curing process, thereby mitigating shrinkage during hydration reactions in low weight/mass mixtures .The curing process is very important for the production of cement paste, however, during this process a very common problem occurs in works with large volumes of concrete: autogenous shrinkage. With it, cracks appear in building elements  which can compromise the structural integrity of the work by reducing its service life. Paying attention to this fact, the study of SAP incorporation in pastes, mortars and concretes is very promising because it can mitigate this process, helping in the curing process of the mixture. Autogenous shrinkage is a phenomenon that occurs in high-performance concretes because they have a very low water/cement ratio, which reduces the amount of water needed for cement-hydration reactions .Studies have shown that SAP can significantly influence the amount of superplasticizer used in the mixture, increasing its consumption. Moreover, depending on the curing method adopted, the polymer can improve the hydration reactions and increase the mixture strength, or it can induce voids in the matrix and impair its performance in compression tests. Finally, a relationship was also observed between the amount of SAP added and performance in mechanical compression tests of the mixtures, where it was found that a higher addition rate of the polymer can lead to a decrease in mechanical strength in the tests of specimens .Many studies on the impact of SAP on conventional concretes have been published, but there is still a significant demand for investigations on the behavior of SAP in self-compacting concretes.In order to achieve the required fluidity and high strength concrete, a low water-to-cementitious ratio is essential for self-compacting cementitious materials mixture. The purpose of internal curing is to prevent autogenous stresses and deformations, which can lead to early cracking, by maintaining saturated conditions within a hydrated cement paste .Tests with self-compacting high performance concrete, using the cement CEM II/A-D 52.5R, were performed for two mixtures, both containing polypropylene fibers, and one of them with the addition of SAP. The samples were submitted to a high temperature, around 1000 °C, for 90 min, and a visual analysis showed that the concrete with SAP did not suffer fragmentation, unlike the reference concrete. It is assumed that the mechanisms in which the SAP acts when exposed to high temperatures unite the polypropylene fibers, allowing their percolation at a low volume content, around 0.2% of the volume of concrete .Al-Hubboubi et al.  studied In the scope of self-compacting cement, Baloch et al.  presenteChindasipiphan et al.  developeAnother interesting alternative for the use of SAP in cementitious materials is in the control of shrinkage in self-leveling cementitious mortar. YANG et al.  evaluateThe study of the microstructure of concrete samples with addition of SAP is a very interesting way to identify the influence of the superabsorbent polymer in the curing process. Through X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), mercury intrusion porosimetry (MIP), and compressive strength tests, a concrete sample with 0.3% of SAP incorporation was evaluated. The incorporation of SAP resulted in a refinement of the porous structure of the paste, despite increasing the total porosity. In addition, the paste containing 0.3% SAP resulted in an intermediate calcium hydroxide content compared to the reference pastes, thus demonstrating that internal SAP curing water participates in the hydration reactions of the cementitious material .This study aims to explore the use of alternative resources in the production of self-compacting cementitious materials by analyzing the mechanical and physical properties of pastes with the addition of SAP (in the fresh and hardened state).The aim of this study is to analyze the behavior of self-compacting paste under the addition of superabsorbent polymer (SAP). Therefore, the cement used must have a low heat of hydration, because a high heat of hydration would cause shrinkage . For this reason, two different types of cement were used: CPII-F and CPV-ARI, one with low and the other with high heat of hydration. Characterization tests of CPII-F and CPV-ARI were performed, being specific mass according to (ABNT NBR 16605:2017)  and laseTo improve the self-compactability of the paste, silica fume was added, a pozzolan  . For theThe use of pozzolans, however, influences the water demand in the mixture, which has a direct effect on the mechanical properties of the material. In this study we chose to use a type II superplasticizer  because it significantly reduces the use of water without modifying the consistency of the mixture; such an additive aims to increase the workability of concrete, obtaining high values in the slump test and a good fluidity of the paste .The business IGTPAN provided the requirements for the particular mass and absorption of the superabsorbent polymer that were required for this investigation.In general, there are several dosage methods for the production of concrete, mortar, and, in the case of this test, self-compacting cement paste specimens. We chose to use the values used in the Silva  dosage, Additions were made in place of cement, at a content of 0.15% of its weight, for two different maximum diameters: 600 μm and 800 μm, also varying the type of cement used: CP II-F and CP V-ARI.The mixing process adopted consisted of mixing the dry components first, and then the incorporation of the liquid components gradually, as shown in To ensure the self-compacting properties, the slump test was used, which allows observing if the mixture presents fluidity and cohesion. The paste should present values within the limit of the data presented in the literature . Figure The time it took the mixture to reach 115 mm , after the paste-containing container had been raised, should then be noted.Curing requires regulating the temperature and flow of moisture to enhance the hydration of the cement. As the strength of the concrete section increases, it is vital to keep the temperature constant in order to prevent cracks produced by the strength diminishing .To ensure the development of concrete’s physical attributes, such as mechanical strength and durability shown resistance, cool the material to temperature and humidity, particularly in the state and early ages. The curing process’ purpose is to enhance cement hydration and decrease shrinkage.The continuity of curing is critical, and it must begin soon after molding. Curing late does not compensate for the lack of curing in the early moments after molding, committing the concrete to a limited performance. Proper curing may significantly minimize concrete shrinkage, and the consequences of not curing can be disastrous .After the tests in the fresh state, the molded specimens remained in submerged curing in water solution with calcium hydroxide for 28 days.With the hardened paste, experiments must be performed to verify its mechanical properties. In the case of this work, the following tests were performed: uniaxial compressive strength, dynamic modulus of elasticity, specific mass, absorption by immersion, and voids index.s), after immersion in water (mi), and after drying the specimen with the help of a wet cloth (msat). Mathematical relations are used to obtain the results of each property cited above, as shown in The compressive strength test basically consists of applying successive force values, at a constant speed, to the specimen until its rupture . The tesIn this work, 18 cylindrical specimens of 5 cm in diameter and 10 cm in height were molded, as shown in The characterization results for specific mass according to (ABNT NBR 16605:2017)  and aver3) and absorption of 200 to 400 (g of water/g of SAP). For the superabsorbent polymer, the specifications are specific mass (g/cm3) = 1.1 and absorption (g of water/g of SAP) = 200–400 according to IGTPAN, 2022 , staAl-Habboubi et.al (2018)  studied The last two articles cited here did not bring information about the diameter of the SAP, but the percentage of SAP used in the mixture is an important factor in determining the strengths.The incorporation of SAP in cement pastes influenced the self-compacting properties due to the diameter of the SAP and the type of cement, whose slump tests showed greater inconsistency for the diameter of 600 μm.The mechanical properties were also influenced by the incorporation of SAP in the pastes tested. The results of the compressive strength test presented very interesting values, demonstrating an increase in strength in relation to the base mixture. The best performances were observed for the mixtures that had in their composition CPII cement with in-corporation of SAP of 600 μm diameter and for the 800 μm diameter, presenting an increase of 35.2% and 34.3%, respectively, in relation to the reference mixtures.The results of absorption and air content corroborate the analysis of the influence of the addition of SAP on the mechanical strengths. The CPV mixture with addition of SAP with maximum diameter showed results with the highest rates of absorption and air content, with an increase of 29.2% for the absorption and 33.6% for the content, compared with the reference mixture, substantiating the reduction in compressive strength of mixture 6.In general, cementitious paste mixtures with the addition of SAP improve the properties analyzed. However, due to the results found for mixture 6, future works can be studied for the dry curing processes.In the current study, the effects of using superabsorbent polymer (SAP) on the production of self-compacting cement paste in both their fresh and hardened states were examined. The main conclusions follow:


Antibiotic overuse is common in ambulatory care settings, underscoring the importance of outpatient antibiotic stewardship to ensure safe and effective antibiotic prescription. In response to this need, the Agency for Healthcare Research and Quality (AHRQ) developed the AHRQ Safety Program for Improving Antibiotic Use in Ambulatory Care. The Safety Program successfully assisted 389 outpatient practices across the United States to establish ambulatory antibiotic stewardship. Herein, we have used lessons learned from the AHRQ Safety Program to describe a step-by-step framework to assist practices with establishing antibiotic stewardship in the outpatient setting. Steps include obtaining support from practice leadership; establishing an antibiotic stewardship team; garnering support from practice members; determining how to access antibiotic prescribing data; building communication skills around antibiotic use in the practice; implementing educational content around an infectious syndrome; monitoring antibiotic prescription data; and implementing a sustainability plan. In the United States, sufficient antibiotic prescriptions are written in the outpatient setting that half of all Americans would receive a prescription in any given year. Moreover, one-third of antibiotic prescriptions in ambulatory care are considered unnecessary.In the United States, antibiotic-resistant organisms cause 2.8 million infections and 35,000 deaths each year. They may be unaware of the potential harm caused by antibiotics and may not weigh the risks versus benefits associated with antibiotic use. From the clinician viewpoint, interactions with patients occur during time-constrained office or telemedicine visits, sometimes precluding the ability to collect adequate clinical and diagnostic data prior to the decision to prescribe antibiotics. These brief encounters also pose challenges with balancing competing priorities related to the screening, diagnosis, treatment, and prevention of disease with adequate counseling about antibiotics. For clinicians caring for patients where there may be concerns about their readiness to return to medical care if the clinical status worsens, antibiotics may be prescribed as an attempt to avoid a subsequent complication. In addition, clinician compensation is partially tied to patient satisfaction scores, and many clinicians trying to establish and grow their practices depend on favorable internet and social media reviews from patients.Several challenges specific to ambulatory care make judicious antibiotic prescribing difficult. Patient and family preferences have an impact on antibiotic prescribing decisions. Patients may expect antibiotic prescriptions based on prior experiences when presenting with similar symptoms. The CDC Core Elements include (1) a commitment to antibiotic stewardship, (2) action for policy and practice to promote appropriate antibiotic prescribing, (3) tracking and reporting to assess progress improving antibiotic prescribing, and (4) education on appropriate antibiotic use for clinicians and patients, with access to colleagues and consultants with additional expertise for specialty care as needed. The Joint Commission developed antibiotic stewardship requirements for accredited ambulatory practices in 2020 that closely resemble the CDC Core Elements. These include (1) identifying an antibiotic stewardship leader; (2) establishing an annual antibiotic stewardship goal; (3) implementing evidence-based practice guidelines related to the antibiotic stewardship goal; (4) providing clinical staff with educational resources related to the antibiotic stewardship goal; and (5) collecting, analyzing, and reporting data related to the antibiotic stewardship goal. Real-world experience on how to successfully implement antibiotic stewardship in ambulatory care consistent with the criteria outlined by the CDC and The Joint Commission remains limited. In response to this need, the Agency for Healthcare Research and Quality (AHRQ) developed the AHRQ Safety Program for Improving Antibiotic Use in Ambulatory Care .Understanding the need for antibiotic stewardship in ambulatory settings, the Centers for Disease Control and Prevention (CDC) established the Core Elements of Outpatient Antibiotic Stewardship, which are evidence-based strategies to conduct stewardship in ambulatory settings. The AHRQ Safety Program was associated with an overall 50% reduction in antibiotic prescribing and a 36% decrease in antibiotic prescribing for acute respiratory tract infections across participating sites. The content of the Safety Program will soon be publicly available to assist outpatient practices with implementing antibiotic stewardship. The diversity of participating practice types, regional diversity, and types of clinicians observed across practices has provided insight into a framework for implementing antibiotic stewardship in the ambulatory setting  to assist ambulatory practices with implementing antibiotic stewardship, (2) to provide frontline clinicians with tools to incorporate antibiotic stewardship principles into routine antibiotic decision making through improving teamwork and communication among practices and between healthcare providers and patients, (3) to provide education on best practices for the diagnosis and treatment of common ambulatory infectious conditions, and (4) to establish the science of safety as an integral component of antibiotic prescribing. In total, 389 outpatient practices including both children and adults from across the United States completed the 1-year AHRQ Safety Program: 162 primary care practices, 160 urgent care practices, 49 federally supported practices, and 18 other practice types. to assisInternational Classification of Disease, Tenth Revision (ICD-10) codes, and other necessary data for the antibiotic stewardship team to measure success over time. Senior leadership can set expectations for other clinic faculty and staff that safe antibiotic prescribing is a priority of the practice. Additionally, obtaining support from senior leadership may be necessary to decouple patient satisfaction scores associated with visits for antibiotic-inappropriate infectious conditions  from bonuses and other rewards.Support from senior leadership is an essential first step in implementing antibiotic stewardship in any setting but especially ambulatory care. Senior leadership may include practice owners , medical directors, or the most senior clinician in the group, depending on the practice organizational structure. Senior leadership will likely be able to assist with identifying financial or other resources necessary to develop, implement, and sustain antibiotic stewardship interventions. This step may include some protected time for antibiotic stewardship leaders to focus on improving antibiotic prescribing across the practice. Support from senior leadership enables prioritizing information technology (IT) support for electronic data extraction for antibiotic prescriptions, After obtaining support from senior leadership, the antibiotic stewardship team leaders should be identified. One antibiotic stewardship team leader should be a prescribing clinician, such as a physician or an advanced practitioner. Necessary skill sets for clinician antibiotic stewardship leaders include an interest and knowledge in the diagnosis and treatment of common outpatient infections, ability to garner respect from colleagues, ability to be diplomatic and rally members of the practice around antibiotic stewardship goals, ability to communicate clearly and concisely, comfort in the role of educating other practice members, and ability and interest in reviewing data and identifying new approaches to improve antibiotic use.The other antibiotic stewardship team leader should be an administrative leader who ensures that antibiotic stewardship activities occur in a timely fashion. The administrative leader should have organizational skills that allow for extraction of the necessary electronic data to measure progress of the practice (or the ability to identify someone who can assist with electronic data extraction), ensuring that progress with antibiotic stewardship activities is discussed routinely at practice-wide meetings and that meeting minutes are distributed. Depending on the structure of the practice, the administrative leader may be an office manager, a nurse, a pharmacist, or another practice member.To improve antibiotic stewardship in ambulatory care, all members of the practice must prioritize safe antibiotic prescribing. After obtaining support from senior leadership and developing the antibiotic stewardship team, it is important to make the case for antibiotic stewardship to the rest of the practice because antibiotic stewardship activities are more likely to succeed if everyone adheres to them. Understanding of antibiotic stewardship principles should not be limited to practice clinicians but should also include other staff members such as nurses, medical assistants, front-desk staff, and pharmacists. All of these staff members may interact with patients presenting with potentially infectious symptoms and may inadvertently set expectations with patients regarding the likelihood of receiving an antibiotic prescription. Although this tradeoff may be acceptable for antibiotics that are necessary, this is not acceptable for antibiotics that are not necessary. Second, remind members of the practice that important downstream consequences of unnecessary antibiotic use may not be immediately apparent, such as increasing antibiotic resistance. Reductions in the diversity of the microbiome and the killing of healthy bacteria increases the likelihood of antibiotic-resistant bacteria that can cause future infections that become increasingly challenging to treat. Third, some of the challenging and dissatisfying conversations with patients who really prefer antibiotics can be avoided if the practice reaches consensus on indications for antibiotic use. By standardizing practices and developing communication tools , patients will be less likely to seek antibiotics for viral infections from any members of the practice. It is important to recognize that the antibiotic prescribing approach of one clinician can impact that of future clinicians. In one study of urgent care practices, receipt of antibiotics for an acute respiratory infection increased the likelihood that both the patient and their spouse would receive an antibiotic for another acute respiratory infection over the subsequent year.When making the case for antibiotic stewardship to practice members, several points are important to convey. First, emphasize that antibiotic stewardship is a patient safety issue. Remind members of your practice that 19% of all emergency department visits for drug-related adverse events are due to antibiotics.One approach that may help engage practice members in appropriate antibiotic prescribing is an antibiotic commitment poster. Commitment posters require the clinician (by signing or adding their picture to the poster) to make a commitment to prescribe antibiotics judiciously. In this way, the poster not only informs patients and families but also reminds clinicians that they should only prescribe antibiotics for indicated reasons. Necessary data include antibiotic prescription data (numerator) and the number of clinic visits  can streamline the process of electronic data extraction. Practices need access to an IT expert to assist with extracting and tabulating data. An IT expert may be able to develop automated reports for the antibiotic stewardship team to access on an “as needed” basis. Senior leadership may be able to assist with financial resources and with identifying an IT expert. Another option is to contact the EHR vendor to see if they have resources already available to assist with data extraction. The antibiotic stewardship team can lead the practice in communication skills training that will reduce some of the pressure around unnecessary antibiotic prescribing during routine clinic meetings.Communication (1) between practice members and patients and families and (2) among practice members is essential in implementing antibiotic stewardship. First, communication skills are important in explaining antibiotic decision making to patients and families. Providing specific statements about why inappropriate antibiotic use can cause harm may help patients and families understand the risks and benefits the clinician is considering in determining whether antibiotics are necessary. The antibiotic stewardship team should consider providing clinicians with sample responses to common patient questions about antibiotics.Communication skills training that can reduce unnecessary antibiotic prescribing include the following: (1) encourage use of language that ensures the patient feels heard and that the provider acknowledges the patient is suffering , (2) communicate negative findings during the physical exam , and (3) state the lack of need of an antibiotic as a positive outcome .Second, communication among practice members about antibiotic prescribing is essential. Members of the practice must agree on how to manage common infectious syndromes. The antibiotic stewardship team should schedule time to discuss antibiotic stewardship on a regular basis. Finding a time to meet (with full or overbooked clinician schedules) may be difficult, but it is essential in building antibiotic stewardship in ambulatory care. Information provided asynchronously does not replace intrapractice communication about approaches to managing common conditions. Reserving a few moments in a previously scheduled, regular practice meeting  for which patient visits are already blocked to discuss antibiotic stewardship may be preferred. Practice discussions will facilitate communication and ensure that antibiotic stewardship changes progress.The antibiotic stewardship team should consider focusing their educational efforts around specific clinical syndromes. In the AHRQ Safety Program, infectious conditions were stratified by those for which antibiotics are always indicated, sometimes indicated, and never indicated. The antibiotic stewardship team may first elect to focus on conditions for which antibiotics are never indicated, such as upper respiratory tract infections, acute bronchitis, influenza without pneumonia, respiratory syncytial virus infections, or COVID-19, because the appropriate antibiotic prescribing rate for these conditions is close to zero. Plan to take several months to focus on education and implementing interventions to improve appropriateness of antibiotic prescribing for the first syndrome selected for focus by the practice, prior to addressing a second high-impact syndrome. As described in step 5, reserve time in a previously scheduled, regular practice meeting to educate clinicians on best practices for the management of the infectious syndrome.After selecting the first syndrome to focus on, the clinician antibiotic stewardship leader (as well as a pharmacist if available) should review available evidence-based data on the diagnosis and management of the syndrome. The Safety Program tool kit synthesizes evidence on clinical syndromes  through a variety of content, including slide presentations with accompanying facilitator guides that the antibiotic stewardship team can review with the practice, audio presentations, which members of the practice can listen to on their own time, and 1-page documents that summarize each syndrome with sample discussion guides Fig. . and has since been adapted to the ambulatory setting. The Four Moments have been outlined as follows: First, does my patient have an infection that requires antibiotics? Second, do I need to order any diagnostic tests? Third, if antibiotics are indicated, what is the narrowest, safest, and shortest regimen I can prescribe? And fourth, does my patient understand what to expect and the follow-up plan?For each clinical syndrome, the safety program content is organized using the “Four Moments of Antibiotic Decision Making” framework. This framework was originally developed in the acute-care settingNext, after educational material on a syndrome has been reviewed either by a group presentation, listening to audio presentations, or reviewing 1-page documents, the antibiotic stewardship clinician should lead a practice-wide discussion on that syndrome during a regularly scheduled staff meeting to determine what steps the practice can take to improve and standardize their approach to diagnosing and managing that infection. This process may be an iterative one across several monthly meetings and may require reviewing antibiotic prescribing data to ensure changes are occurring (step 7). Once the antibiotic stewardship team and practice members believe sufficient progress has been made on a syndrome, it is reasonable to move on to the next syndrome. as part of a regularly scheduled practice meeting so other members can observe the progress achieved over time.After a process has been developed for the antibiotic stewardship team to periodically (monthly or at most quarterly) access antibiotic prescription data, they should review it to determine whether improvements in antibiotic prescribing are occurring and to determine whether additional education or further intervention should be considered. It is helpful to review antibiotic prescription data with the rest of the practice either quarterly or monthly if possible In peer comparison, antibiotic prescribing feedback is delivered to clinicians so that they can compare their antibiotic prescribing practices with those of their colleagues. Consider informing clinicians whether they are “top performers”  or “not top performers” . This approach may incentivize clinicians who are just above the mean to continue to improve in addition to low performers.Along with evaluating practice-wide antibiotic prescription data, peer comparison can be an especially impactful antibiotic stewardship tool in ambulatory care. Therefore, open communication among the practice members at standing meetings about interpreting the reports and how to continue to make improvements in antibiotic prescribing is helpful. Discuss as a practice whether individualized reports should include names of practice members or only the name of the individual for whom the report is intended. Reports should be concise, easy to access, and easy to interpret, or they are unlikely to be reviewed by clinicians. Use graphics where possible. Recommendations for feedback reports are available  to encourage clinician and staff-level ownership of antibiotic stewardship activities? The antibiotic stewardship team should continue to closely oversee all antibiotic stewardship activities. The sustainability plan should also include some general next steps after the 2-year period regarding what the antibiotic stewardship team plans to do to continue to keep the program engaging for both themselves and the rest of the practice. The sustainability plan should be reviewed with senior leadership.The antibiotic stewardship team should develop a sustainability plan to ensure the longevity of antibiotic stewardship activities. The plan should include the primary goals the antibiotic stewardship team plans to achieve over a designated period  as well as an approximate outline of the month to month plans during this time . The following aspects should be considered: What processes can become automated , integrated into the electronic health record system , In these triage protocols, patients calling with symptoms not concerning for severe illness or a condition requiring an antibiotic can be provided symptomatic management over the phone via a nurse. In preventing a visit for a condition for which antibiotics would not be appropriate, patients will be less likely to receive an inappropriate antibiotic. In addition, practices can require written justifications for inappropriate antibiotic prescribing, in which clinicians prescribing an antibiotic for a nonappropriate condition would be required to enter a note in the EHR explaining their decision making. Alternatively, practices can implement clinical decision support at the time of documentation or ordering antibiotics. Other interventions are available, for example, with the CDC Core Elements of Outpatient Antibiotic Stewardship and with the AHRQ Safety Program Toolkit for Improving Antibiotic Use in Ambulatory Care.Once a practice and antibiotic stewardship team has gone through the above steps, a particular set of initial interventions have been implemented, and a sustainability plan is in place, the practice and the antibiotic stewardship team may consider choosing additional interventions to improve antibiotic prescribing for conditions of interest. For example, practices can implement triage protocols to prevent unnecessary visits for antibiotic-inappropriate conditions through call centers, nurse-managed triage hotlines, or pharmacist consultations.In conclusion, antibiotic stewardship in ambulatory care can be successful, but it requires resources and both a dedicated antibiotic stewardship team and dedicated providers. To assist ambulatory practices in developing and sustaining antibiotic stewardship activities and in following the CDC and The Joint Commission criteria for establishing successful stewardship approaches, we encourage using the tool kit developed through the AHRQ Safety Program for Improving Antibiotic Use in the Ambulatory Care Setting.


The containment measures following COVID-19 pandemic drastically reduced airway infections, but they also limited the access of patients to healthcare services. We aimed to assess the antibiotic prescription patterns in the Italian paediatric primary care setting before and after the containment measures implementation. For this retrospective analysis, we used a population database, Pedianet, collecting data of patients aged 0–14 years enrolled with family paediatricians (FP) from March 2019 to March 2021. Antibiotic prescriptions were classified according to WHO AWaRe classification. An interrupted time series evaluating the impact of the containment measures implementation on the monthly antibiotic index, on the access to watch index, and on the amoxicillin to co-amoxiclav index stratified by diagnosis was performed. Overall, 121,304 antibiotic prescriptions were retrieved from 134 FP, for a total of 162,260 children. From March 2020, the antibiotic index dropped by more than 80% for respiratory infections. The Access to Watch trend did not change after the containment measures, reflecting the propensity to prescribe more broad-spectrum antibiotics for respiratory infections even during the pandemic. Similarly, co-amoxiclav was prescribed more often than amoxicillin alone for all the diagnoses, with a significant variation in the trend slope for upper respiratory tract infections prescriptions. Antibiotics are among the most prescribed medicines worldwide, especially in pre-school-aged children . OutpatiSince the beginning of the COVID-19 pandemic in March 2020, the implementation of containment measures  drastically reduced the incidence of respiratory tract infections in the cold season globally. Consequently, differently from prescriptions for chronic diseases medication , a reducThis study aimed to assess the impact of COVID-19 containment measure implementation on antibiotic prescriptions in the Italian paediatric primary care setting. To have our results more generalisable and comparable with data from other countries, we used the AWaRe classifications and metrics developed in 2017 by the WHO Expert Committee on Selection and Use of Essential Medicines (and further updated) , as wellOverall, 121,304 antibiotics were prescribed from 134 family paediatricians (FPs) to 162,260 children, with 278,909 person-years of follow-up during the study period. Half of the patients were males (51.9%), and almost 60% of the children resided in the North of Italy . The mean age of the population in the period before the COVID-19 containment measure implementation was 6.2 years, while in the period after, it was 6.4 years . The monp < 0.001) following the containment measures implementation in March 2020 from a maximum of 1.2 antibiotics per child in the pre-period to a maximum of 0.23 antibiotics per child in the post-period. The increase in the Access to Watch index in the months immediately after March 2020 (from 1.22 to 1.66 prescriptions per child) did not reach statistical significance (The antibiotic index decreased by 80% (relative rate (RR) 0.27 (95%CI: 0.15–0.46), ificance .The amoxicillin to co-amoxiclav index decreased in the months after the implementation of containment measures, following the increase in the co-amoxiclav prescription rate. However, it was not significant. The peak was registered between June and July, with 1.66 Access prescriptions per every Watch prescription .The number of telemedicine consultations increased significantly by five times following the containment measures implementation, with a change in the trend. On the contrary, the number of well-child visits decreased significantly by 80% from March 2020 .In the study period, 20,357 (16.8%) antibiotics were prescribed for a diagnosis of lower respiratory tract infection (LRTI), 3704 (3.1%) for skin and soft tissue infection (SSTI), 58,793 (48.5%) for upper respiratory tract infection (URTI), and 2302 (1.9%) for urinary infection (UTI). Before the containment measures implementation, the diagnosis most frequently associated with an antibiotic prescription was URTI, while in the post-implementation period, especially after August 2020, most of the prescriptions were not associated with a diagnosis (from a minimum of 33.1% in March 2020 to a maximum of 58.8% in December 2020 of prescriptions prescribed without a diagnosis) . The monthly Access prescriptions rate for LRTI, constituted mainly by co-amoxiclav, was above the 35% before the COVID-19 containment measures implementation, while in the post-period was below 30% . The monthly Access rate for SSTI and URTI was higher  than LRTI rates, and co-amoxiclav rates accounted for 70% and 60% of Access prescriptions, respectively .Regarding UTI, the Watch rate was above 50% for most of the months, and among Access prescriptions, co-amoxiclav was the most prescribed antibiotic, accounting for around 90% of the Access prescriptions .The amoxicillin index trend did not vary in level after implementing COVID-19 containment measures for any of the diagnosis classes; however, the trend slope for URTI varied significantly .The number of antibiotics prescribed for a specific diagnosis class per child decreased significantly after implementing the containment measures for all the diagnoses except for UTIs . The higAs regarding the Access to Watch trend, the index was above two just for SSTIs prescriptions. The lowest index was observed for LRTIs in November 2020 (0.33 Access antibiotics prescribed for every Watch), where it was observed the only variation in the trend slope after the COVID-19 containment measures implementation .Co-amoxiclav was prescribed more often than amoxicillin alone for all the diagnoses, with no variation in the trend after the containment measures implementation, but a variation in the trend slope for URTI prescriptions .Our study showed an immediate 80% decrease in the overall monthly antibiotic prescriptions per child, but no variation was noticed in the Access to Watch index and co-amoxiclav to amoxicillin index. In general, the decrease was noticed for all diagnoses except for UTIs. Still, the Watch monthly antibiotic prescription rates remained higher than 30% for most of the respiratory tract infection.To our knowledge, this is the first study assessing antibiotic prescription patterns before and after the COVID-19 pandemic according to the AWaRe classifications in the paediatric outpatient population in Italy. Providing information from a national electronic Healthcare Record database allowed us to have a likely picture of antibiotic prescriptions and infections trend (by proxy) and to explore variations in prescribing appropriateness, using as indicators the antibiotic index and the ratio of amoxicillin on co-amoxiclav, in line with national indicators . In the The results of our study are consistent with previous findings in the general and paediatric populations in other countries, including the USA, South Korea, Canada, Finland, and the UK. The number of antibiotics prescribed per child decreased after the COVID-19 containment measures implementation in March 2020 by 80%, and the fall was more evident for respiratory tract infections (URTIs and LRTIs), the classes for which were prescribed most of the antibiotics. In general, the reduction in antibiotic prescriptions follows the decline in primary care and ED visits related to infections, especially acute respiratory infections (ARIs) ,16,17,18In our study, overall Access and Watch antibiotics prescriptions fell just after the beginning of the COVID-19 pandemic in March 2020. In the same way, a national report from Portugal analysed the overall antibiotic prescriptions in the general population in the outpatient setting after the pandemic outbreak, finding a significant reduction in the Watch group antibiotics; this is explained by the authors as a possible result of the decrease of infections with the containment measures and cancellation of appointments ; howeverTo better describe the antibiotic prescription drivers in our country, we stratified the data according to the diagnosis, adopting four categories. Overall, in our report, prescriptions for RTIs dropped significantly; on the contrary, the antibiotic prescription rate for UTIs did not change after the pandemic began, reflecting the different aetiologies and pathogeneses, not being related to interpersonal transmission. Differently from our findings, a study using general practice data in the Netherlands showed a reduction in episodes treated with antibiotics both for ARIs and UTIs in adults after implementing anti-covid measures. Authors postulated a general attitude to avoid medical visits for uncomplicated infections during the pandemic .Interestingly, oppositely to the reduction in the trend of infections and overall prescriptions, antibiotic prescription appropriateness did not improve the same way. Access antibiotics should always be the first-line therapy, with amoxicillin for URTIs and LRTIs and co-amoxiclav for UTIs and SSTIs. In our study, the ratio of Watch on Access prescriptions, as well as the ratio of amoxicillin on co-amoxiclav, did not change after the containment measures implementation, as would be expected, assuming that the overall incidence of severe or non-responding to first-line treatment infections is dramatically decreased . As prevAmoxicillin to co-amoxiclav index is considered a strong indicator of antibiotic prescription appropriateness in the context of antimicrobial surveillance in the outpatient setting. The amoxicillin to co-amoxiclav index should be used by regulators as a proxy to measure prescribing appropriateness in the general outpatient population and as a stewardship metric. It is not indicated to measure the appropriateness of a single diagnosis class. Amoxicillin is recommended as a first-line therapy for different respiratory tract infections ; however, the treatment of choice can vary on the basis of the child’s age, history of immunisation, comorbidities, and allergies. In 2016, the European Commission indicated a value of >=4 as the target ratio to achieve an appropriate control of broad-spectrum prescriptions . Our repAware indicators were for the first time used in an international report analysing the 2015 consumption data of oral antibiotic formulations for children from a global database . On averAmoxicillin Index and Access to Watch Index were stratified according to the diagnostic categories in our report. Independently from the spread of COVID-19, in our research, the most critical point remains the treatment of LRTI, for which Watch antibiotics (including macrolides) are the most prevalent. From a recent WHO survey, the use of macrolides is reported to be increased after the COVID-19 pandemic with around half of the countries included reporting a rise in azithromycin prescription for patients with respiratory-like symptoms in healthcare facilities .Streptococcus pneumoniae resistance to macrolides over the past two decades, up to 30% in Europe and 30–35% in USA [S. pneumoniae to penicillin decreased after the introduction of universal infant immunisation with the pneumococcal conjugate vaccine and was estimated to be about 12% in 2019 in Europe [Strengthening empirical therapy for CAP guidelines diffusion is urgent in our outpatient setting in order to improve prescribing behaviour and reduce the inappropriate broad-spectrum prescriptions  . Indeed,% in USA , is a man Europe . Still, A study from the UK reported stratified antibiotic prescriptions for CAP in the paediatric population before and after the COVID-19 pandemic: for children up to four years, overall amoxicillin prescription rate per 1000 population decreased from March 2020 by 62.5% . HoweverS. pyogenes represents the leading cause of pharyngitis, while S. pneumoniae is one of the leading causes of acute otitis media (AOM). Amoxicillin alone should still be considered the first choice in most of the cases.In our report, Access prescription rates for URTI were high. However, co-amoxiclav was still predominantly prescribed, especially just after March 2020, determining a variation in the amoxicillin to co-amoxiclav slope. It should be addressed that most URTIs have a viral aetiology, and if bacterial, We can speculate that the lack of specific surveillance data on antibiotic resistance in primary care, particularly after the COVID-19 pandemic, directly impacts prescribers’ behaviour, which might prefer antibiotics with a higher coverage, although this is not supported by evidence and guidelines.After the containment measures implementation, telemedicine was employed worldwide in different fields and situations ,35,36. AAntimicrobial stewardship (AMS) in the outpatient setting is a well-known topic that needs to be addressed and implemented, as family paediatricians’ prescriptions represent the majority of antibiotics dispensed to the paediatric population. During the pandemic, AMS programs had to cope with several problems, as reported from a WHO survey: decreased in national and local funding for antimicrobial resistance (AMR) surveillance activities (more frequently reported in low- and middle-income countries), decreased ability to work in partnerships, reduction in the staff available for AMR activities, and reduction in laboratory supplies and equipment for AMR . MoreoveConsidering the important impact on AMS on AMR, outpatient AMS implementation should be continued as the pandemic continues. Continuous education and guidelines supplied to family paediatricians may raise antibiotic prescription consciousness and improve prescription appropriateness. The CDC in 2017 outlined the core elements of outpatient AMS. Apart from education and expertise, the other elements include a public commitment for AMS, action for policy and practice , and tracking and reporting systems .The strengths of our study are its size, generalisability, and representative coverage of paediatric patients in Italy. It is particularly relevant because, to the best of our knowledge, there are no other studies assessing antibiotic prescription patterns before and after the COVID-19 pandemic according to the AWaRe classifications in the same population.Our study has several limitations. First, it is retrospective in nature, and there is significant heterogeneity in how FP reported data on the electronic medical chart, not always following the standardised list of diagnoses codes. Different diagnoses were categorised a posteriori by the authors. Second, we cannot say with certainty that every prescribed medicine was administered. Indeed, some paediatricians might have instructed parents to follow a “wait and see” approach, administering antibiotics only if needed. Third, some antibiotic indications were not considered in the analysis, such as odontoiatric and ophthalmological prescriptions. Fourth, prescribing behaviours among the different family paediatricians vary. However, we included only prescribers participating for the whole study period. Moreover, significant heterogeneity is not properly a limit of our study but a characteristic of the outpatient management, and therefore we can say it is a close portrait of the Italian paediatric primary care situation.® software. This database contains information on patient demographic characteristics, prescriptions, vaccinations, diagnoses, and referrals to specialists and hospitalisations. The Pedianet database contains patient-level data from children followed by around 160 FPs distributed throughout several Italian regions, including Friuli-Venezia Giulia, Liguria, Lombardia, Piemonte, Veneto, Lazio, Marche, Toscana, Abruzzo, Campania, Sardegna, and Sicilia. The Pedianet database has been widely used since 2004 to conduct pharmacoepidemiological and pharmacovigilance studies [This is a retrospective analysis of antibiotic prescribing from 9 March 2019 to 9 March 2021 using Pedianet, a paediatric primary case database. Pedianet captures data from the routine patient care using JuniorBit studies . Accordi studies . Only faAntibiotic prescriptions were classified on the basis of Anatomical Therapeutic Chemical Classification (ATC) and route of administration in Access, Watch, and Other according to the Italian Medical competent Authority for Italy  AWaRe classification adapted from the WHO AWaRe classification . Diagnoses were retrieved using ICD9-CM codes and classified as follows in line with previously published studies  and confThe primary outcomes were the rates of prescriptions of amoxicillin (ATC: J01CA04), co-amoxiclav (ATC: J01CR02), Access and Watch, the antibiotic index (defined as the number of antibiotic prescriptions per person-years), the Access to Watch index, and the amoxicillin to co-amoxiclav index. The rate of telemedicine consultation and the rate of well-child visits were assessed to account for variation in the use of primary healthcare services.Results were summarised using numbers and percentages and stratified by diagnosis class. p < 0.05 was considered statistically significant. The analysis was performed using R statistical software—v. 4.0.3  [An interrupted time series analysis supposing an abrupt step change and a slope change in monthly outcomes using quasi-Poisson regression models was performed to determine the impact of COVID-19 containment measures implementation . The totAustria) .These data highlight the tendency in prescribing broad-spectrum antibiotics in the Italian paediatric primary care setting during the pandemic (Watch antibiotics for LRTI and co-amoxiclav for URTI), possibly reflecting the diagnostic uncertainty. Efforts are needed to improve outpatient antimicrobial stewardship programmes, reinforcing the ones already existing and implementing newly available tools for telemedicine.


Currently, there are no effective approaches for differentiating ovarian fibrothecoma (OF) from broad ligament myoma (BLM). This retrospective study aimed to construct a nomogram prediction model based on MRI to differentiate OF from BLM. The quantitative and qualitative MRI features of 41 OFs and 51 BLMs were compared. Three models were established based on the combination of these features. The ability of the models to differentiate between the two cancers was assessed by ROC analysis. A nomogram based on the best model was constructed for clinical application. The three models showed good performance in differentiating between OF and BLM. The areas under the curve (AUC) of the models based on quantitative and qualitative variables were 0.88 (95% CI: 0.79–0.96) and 0.85 (95% CI: 0.76–0.93), respectively. The combined model designed from the significant variables exhibited the best diagnostic performance with the highest AUC of 0.92 (95% CI: 0.86–0.98). Calibration of the nomogram showed that the predicted probability matched the actual probability well. Analysis of the decision curve demonstrated that the nomogram was clinically useful. Relative T1 value, stone paving sign, enhancement patterns, and ascites were identified as valuable predictors for identifying OF or BLM. The MRI-based nomogram can serve as a preoperative tool to differentiate OF from BLM. Although most OFs are benign2, surgery is recommended for tumor removal. Ovarian fibrothecoma is the most common solid ovarian tumor3, with similar imaging features to those of uterine leiomyomas.Ovarian fibrothecoma (OF) is a rare type of ovarian tumor that originates from the sex-cord stroma. There are several types of OFs, including fibroma, fibrothecoma, and thecoma, each with a different proportion of fibroblasts and theca cells4. Notably, extrauterine leiomyomas may originate from the fallopian tubes of the uterus or broad ligament (5). Leiomyoma, particularly broad ligament myoma (BLM), is often misdiagnosed as OF preoperatively owing to its solid nature. It is also misdiagnosed as originating from the ovaries5. Pharmacotherapy or hormonal therapy is recommended for symptomatic patients.Uterine leiomyomas are the most common benign gynecologic tumors6 found that combining conventional MRI and diffusion-weighted imaging (DWI) could effectively differentiate OF from malignant solid pelvic tumors. However, owing to the overlap of tumor components and imaging characteristics, it is challenging to distinguish OF from BLM preoperatively. Moreover, it is difficult to differentiate between OF and BLM based on preoperative quantitative and qualitative MRI features.Yin et al.This retrospective study aimed to develop quantitative and qualitative MRI-based nomogram for preoperative differentiation between OF and BLMs.This study protocol was performed in accordance with the guidelines outlined in the Declaration of Helsinki and was approved by the Ethics Committee of the Affiliated Hospital of Nanjing University of Chinese Medicine, and the requirement for written informed consent was waived due to the retrospective nature of the study.n = 24); (2) the tumor lesions were too small(< 1 cm)7to be measure on MRI (n = 3); (3) the myoma was not located in the broad ligament (n = 35); and (4) poor image quality to allow proper assessment (n = 7). Ultimately, 91 patients were enrolled in this study.A total of 53 patients with histologically proven OF and 97 patients with histologically confirmed extrauterine fibroids were enrolled from May 2016 to May 2020 and May 2017 to May 2020, respectively. Overall, 12 patients with OF and 57 patients with BLM were excluded based on the following criteria: (1) the preoperative MRI scan was absent or lacked one of the following sequences: DWI, apparent diffusion coefficient (ADC) map, or dynamic contrast-enhanced (DCE)  using VIBE sequence. .All MRI scans were performed using a 1.5-T MRI equipment  with an abdominal phased-array coil. The MRI scanning parameters are listed in Table Two experienced radiologists, Gu and Zhang  independently reviewed the MRI images. Discrepancies between the results were resolved through discussion.Tumor size, ADC value, relative T1 value (r-T1), and relative T2 value (r-T2) were measured. Regions of interest (ROIs) were drawn on the solid part of the tumor as large as possible to cover the entire area. During the measurements, cystic and hemorrhagic areas were avoided. The r-T1 and r-T2 values were calculated by dividing the signal intensity by that of the iliopsoas muscle on T1WI and T2WI, respectively.The following qualitative MRI features were recorded: tumor margin (well-defined or ill-defined); tumor component ; shape (smooth or lobulated); signal intensity on DWI (hyperintensity or isointensity compared with the adjacent myometrium); tumor enhancement ; associated myoma (present or absent); stone paving sign (present or absent); ovary sign (present or absent); and pelvic-free ascites (present or absent). The tumor component was defined as solid (80–100% solid component), cystic (80–100% cystic component), or solid-cystic (others). The tumor enhancement type was graded as follows: (1) mild enhancement (less than the myometrium), (2) moderate enhancement (similar to the myometrium), or (3) avid enhancement (more than the myometrium). The stone paving sign was defined as a non-specific sign of tumor characterized by diffuse pebble-like hypo-intensity associated with thickened septa and mimicking a paving stone. An ovarian sign was defined as the presence of an ipsilateral ovary on any of the MR images.http://www.r-project.org/). The inter-observer agreement between the two readers was compared using the kappa test. Univariate analysis was conducted to compare clinical and imaging features between the groups. Data normality was evaluated using the Shapiro–Wilk test for quantitative variables. Continuous data were compared using the Student’s t-test or Mann–Whitney U-test. Categorical variables were compared using the chi-squared test or Fisher’s exact test. Multivariable logistic regression analyses were used to identify factors that could independently differentiate OFs from BLMs. Three differentiation models were built based on the quantitative and qualitative variables. Specifically, Model 1 was based on preoperative quantitative variables . Model 2 was established based on qualitative features . Model 3 was constructed from a combination of all quantitative and qualitative variables that were found to be significantly different between the two groups. Given the small sample size, fivefold cross-validation method9 was used for internal validation to avoid overfitting models and improve accuracy. Logistic regression models were established using fivefold cross-validation, and the area under the ROC curve (AUC) was calculated. Based on the AUC, the optimal training and validation sets were selected, and a logistic regression model was established. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of each model were determined based on the threshold that maximized Youden index of the cross-validation results. Subsequently, a nomogram was established to visualize the model based on the results of multivariable logistic analysis. The Hosmer–Lemeshow test was performed to assess the calibration capability of the nomogram. Decision curve analysis10 (DCA) was performed to assess the clinical usefulness of the nomogram by calculating net benefits. Statistical significance was set at P < 0.05.Statistical analysis was performed using IBM SPSS  and R software . A significant difference was found in age and menopausal status between the two groups . According to histopathological findings, 25 patients were classified as having fibromas, 13 as having fibrothecomas, and 3 as having thecomas. Pathological characteristics are shown in Tables A total of 91 patients were included in the final analysis after excluding 69 patients who met the exclusion criteria Fig. n = 9, 70.7%.p < 0.01). Mild enhancement and moderate enhancement were found in 19 (46.3%) and 16 (39.1%) patients with OF, respectively, whereas avid enhancement was found in 39 (78%) patients with BLMs.The interobserver agreement was excellent (0.8–1) for all quantitative and qualitative measurements. The MRI findings of the OF and BLM groups are listed in Table Regarding the tumor component, BLMs mostly appeared as solid component (96%), whereas OFs showed a partial cystic component (43.9%). Notably, there was a higher tendency for stone paving sign and ovary sign in tumors of the BLM group than in those of the OF group. Pelvic free ascites was commonly seen in OF patients (85.4%), whereas only 21 (42%) cases were observed in BLMs.Multivariable logistic regression analyses were used to construct models to discriminate OF patients from BLM patients Table . Among tThe AUC of Model 1, 2 and 3 was 0.88 (95% CI: 0.79–0.96), 0.85 (0.76–0.93), and 0.92 (0.86–0.98), respectively. The cutoff value, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the three models are shown in Table A nomogram was constructed based on the multivariate logistic regression results of Model 3 , while only 38% were postmenopausal in the BLM group. Patients in the BLM group showed significantly lower r-T1 and r-T2 values than those in the OF group (p < 0.05). Previous studies have qualitatively compared the signal characteristics of OF or BLM with adjacent myometrium19. OF and BLM are all fibrous-containing tumors; hence, they are mostly isointense to hypointense on T1WI and T2WI compared to the adjacent myometrium .In the present study, Model 1, based on preoperative quantitative variables, showed the potential to preoperatively differentiate OF from BLM, with an AUC of 0.88, and an ACC of 80.2%. Both the AUC and ACC of Model 2, based on qualitative MRI features, were lower than those of Model 1 were. Model 3 showed the best performance of the three models with an AUC of 0.92 and ACC of 84.6%, indicating that MRI features can preoperatively differentiate OF from BLM. The results of Model 3 were used to construct a nomogram for the preoperative differentiation of OF from BLM. In the present study, age and r-T1 and r-T2 values were identified as valuable quantitative factors that could differentiate OF from BLM. Patients with OF were significantly older than those with BLM were. As a uterine fibroid, BLM usually affects women of reproductive age20. In the present study, T1WI and T2WI signal intensities were quantitatively and objectively evaluated. We found that r-T1 and r-T2 were significantly different between the two groups. Model 1, which included age, r-T1, and r-T2, showed good performance in differentiating between the two tumors. The study findings suggest that the patients' quantitative features play a significant role in differentiating OFs from BLMs.No significant difference in signal intensity was observed in most previous studies5 evaluated the ability of dynamic contrast-enhanced (DCE) MRI to distinguish between OF and subserous uterine leiomyomas. They found that the enhancement measurements were lower for OF than for uterine leiomyomas with respect to the maximal enhancement or enhancement rate. This feature reflects the differential blood supply between the two tumors. The vasculature of uterine leiomyomas is characterized by sizable branches of the uterine artery and dilated capsular vessels. In contrast, OFs tend to have very few or no arterial supply. In the present study, 14.6% of the OFs showed avid enhancement and were proven to be thecomas and fibrothecomas with a sizeable number of theca cells. These features indicated that it was more likely to be a fibrothecoma with more theca cell components or thecoma when an ovarian lesion showed marked enhancement. Moreover, the stone-paving sign was an independent predictor of BLM. This imaging manifestation is in line with the morphology of the gross specimen and arrangement of smooth muscle cells.Moreover, we evaluated qualitative parameters for differential diagnosis. Menopause status, tumor component, enhancement pattern, stone-paving sign, ovary sign, and ascites were significantly different between the OF and BLM groups. Multivariate logistic regression analysis showed that paving signs, ovarian signs, and ascites could differentiate between the two tumors. Further analysis revealed that the enhancement of the OF was significantly lower than that of the BLM. Thomassin-Naggara et al.19. Ovarian signs were identified in 86% and 46% of patients by15, indicating that the female pelvic mass did not originate from the ipsilateral ovary. Our results showed that the detection rate of ovarian signs was lower in the OF group than in the BLM group. This implies that the ovaries were obscured in patients with masses of ovary-origin. We speculate that some exogenous growth patterns of OFs also contribute to the typical appearance of the ipsilateral ovaries. Many OFs with peritoneal and pleural effusions are considered Meigs syndrome, which mimics malignant pelvic masses21. As reported by Iyer et al.22, ascites disappear after removal of the primary tumor. Although the syndrome is present in a small proportion of ovarian tumors, pelvic effusion is commonly observed in benign or malignant gynecological tumors. However, pelvic fluid is rarely observed in uterine leiomyomas24, indicating that ascites is also an important sign in the differential diagnosis.Notably, OF contained a cystic portion or stromal edema, whereas most BLM tumors contained a pure solid mass. We presume that this discrepancy explains the difference in stone-paving sign. Furthermore, the ipsilateral ovary was present in 66% of the BLM cases and in only 26.8% of the patients. Several studies have evaluated ovarian signs in patients with OFAs previously mentioned, MRI features are useful for preoperative differentiation between OF and BLM. In this context, we assessed the predictive efficacy of Model 3 by combining all the qualitative and quantitative features. The results revealed that Model 3 had the highest AUC and ACC among the three models. These outcomes indicate that the combined model could correctly differentiate OF from BLM. Owing to its noninvasive nature, the preoperative performance of this model is clinically acceptable and meaningful for patients in helping to make clinical decisions. Based on Model 3, a nomogram was established for clinical application. Furthermore, the nomogram is a practical tool for describing the value of an individual factor in the scoring system. To the best of our knowledge, the present study is the first to establish a nomogram to differentiate OF from BLM preoperatively.Study limitations: First, only imaging features were used to construct the model, without including a single laboratory feature. Further studies, including serum biomarkers, should be performed to explore the effects of laboratory tests. Second, the sample size enrolled in this study was small; hence, a prospective study with a larger sample size is required. Third, the performance of the constructed model was not clinically verified. Further studies with more samples and validation cohorts should be performed to validate our results. Fourth, the predictive model obtained in this study was not externally verified, and external data should be collected for external validation.In summary, preoperative MRI parameters can be used to differentiate OFs from BLMs. The nomogram, based on Model 3, showed good discriminatory ability between OF and BLM.