A Multivariate Regression Model for Predicting the 28days Flexural Strength of Lime-Cement Concrete Beam

In this study, a multivariate regression model for predicting the 28days flexural strength of lime-cement concrete prototype beam was developed. The response function is a multivariate function of the proportions of the component materials of concrete. A total of twenty mix ratios, consisting of water, Portland cement, hydrated lime, river sand and granite chipping were used in the prediction process. The first ten mix ratios were used for model development while the remaining ten mix ratios were used as check points for model validation. The model developed was tested for adequacy at 95% level of confidence using the t-statistic. Calculated t-value was -1.3342 and this was less than the critical t-value of 2.2622. Thus, the model was found to be adequate. An average percentage difference of 14.303% was observed between the model prediction and the experimental values. A visual basic program using the Visual studio 2015 software was developed based on the regression model. It was invoked to quicken the process of selecting the mix ratios of the component materials corresponding to any desired flexural strength value that falls within the region of experimentation and vice versa Keywords— Concrete , flexural strength, multivariate regression model, response function.

Factors Influencing Change in MCV and Age at Transplantation in the Belgian Sickle Cell Disease Registry

The Belgian sickle cell disease registry (BCR) was initiated in 2008 and aims to evaluate mortality, morbidity as well as clinicals practices in participating centers. The current analysis focuses on criteria influencing age at transplantation (HSCT) and on the management of Hydroxyurea (HU) therapy across centers. The methodology of the registry has already been published (Le PQ et al., Pediatric Blood and Cancer, 2015) . Data are recorded prospectively from neonatal screening or first contact until last annual follow-up (FU) or death. The data collected included diagnosis, demography, treatment and outcome data as well as a minimal set of biological values. Data were extracted from the database in May 2021. There are 1029 patients registered by 14 different centers (2 centers exclusively treating adult patients). The median FU is 9 y (1-53 y). Median age at last FU is 13 y (0-61 y). 890 patients (86,5%) have a severe phenotype (SS or Sβ°) and 52% are female. Among them, 561 (55%) are born in Belgium of whom 379 (68%) are diagnosed by neonatal screening. In the absence of neonatal screening, median age at diagnosis is 1 year (range 0-18). 131 patients have been transplanted (126 successfully), 68 HSCT were performed before 2005. At last FU, 646 patients (76%) received at least 1 disease-modifying treatment (DMT) : 598 patients receive HU, 65 are chronically transfused, 8 participate in a study with crizanlizumab. The prescribed HU dose is known for 572 patients. 179 patients (31.3%) receive less than 20 mg/kg/day, 217 (37.9%) less than 25 mg/kg/day, 148 (25.9%) less than 30 mg/kg/day and 28 (4.9%) were prescribed more than 30 mg/kg/day. The majority of HSCT were performed in two centers (68 and 59, respectively). Median age at HSCT was significantly different between both centers (8y (2-15) versus 5y (0-19); p=0.002) (figure 1). Variables associated with a lower age at HSCT are detailed in table 1. In a linear multivariate regression model, birth in Belgium (p=0.002), no treatment with HU (p=0.009) and shorter duration of FU (p<0.001) but not the center are independent factors correlated with younger age at transplantation. This linear model explains 90.60% of the variance (adjusted R²) of age at HSCT. Among not transplanted patients, the proportion of those receiving HU is different between centers (50% to 91%; p=0.050). The median age at which HU was initiated was also significantly different between centers (4y to 21y; p<0.001) as was the management of HU treatment in a multiple comparison model measured by ΔMCV (difference in MCV before start of HU versus at last FU). In a linear univariate regression model, other variables are significant predictors for the variance of ΔMCV (table 2). In the linear multivariate regression model, the variance of ΔMCV between centers is controlled by the duration of FU (p<0.001), neonatal screening (p=0.046), HU dose between 25-30mg/kg/day (p<0.001), all resulting in a higher ΔMCV, while patients not born in Belgium (p=0.033) have a lower ΔMCV. Age at diagnosis, severity of the disease (assessed by the number of VOC/FU year) and HU dose <20 mg/kg/day are not correlated with the variance in ΔMCV. Twenty-seven (2.6%) patients died which accounted for a mortality rate of 0.24/100 patients-years (PY) which increases significantly with age (0.18/100PY <18 years, 0.35/100PY 18-40 years and 1.43/100PY >40 years; p=0.001). Conclusions: BSR has an excellent registration activity from participating centers and represents a reliable tool to evaluate the Belgian SCD population. Mortality remains low with a significant trend to increase with age. Regarding treatment practices, the age at start of HU is significantly different between centers as the approach to further HU treatment, evaluated by ΔMCV. A higher dose of HU resulted in a higher ΔMCV. However, the policy to increase HU to maximal tolerated dose seems not implemented in most centers, as 2/3 of the patients are prescribed less than 25 mg/kg/day. Being born in Belgium and no treatment with HU are associated with younger age at HSCT. Nevertheless since 2005, almost all patients were treated with HU prior HSCT, reflecting the wider implementation of HU in SCD patients living in Belgium. Figure 1 Figure 1. Disclosures Benghiat: Novartis: Consultancy; BMS: Consultancy. Labarque: Bayer: Consultancy; Sobi: Consultancy; NovoNordisk: Consultancy; Octapharma: Consultancy; Novartis: Consultancy.

231. Retrospective Comparison of Intravenous Therapy, Oral Therapy, and Lipoglycopeptides for the Treatment of Osteomyelitis

Background The use of oral (PO) antibiotics and lipoglycopeptides are challenging the previous standard of osteomyelitis (OM) treatment, but there is currently a paucity of comparative data between these approaches. Methods This retrospective study included patients diagnosed with OM treated with intravenous (IV) antibiotics, PO antibiotics, or lipoglycopeptides between January 1, 2010 and June 1, 2020. Patients in the PO group could receive no more than 14 days of IV antibiotics prior to the PO course, and inclusion into the lipoglycopeptide group required at least 2 doses of drug to be administered. The primary outcome was occurrence of clinical failure within six months of completion of therapy, which was defined as new antibiotics or unplanned surgical intervention for an infection at the same site. Secondary outcomes included in-hospital length of stay (LOS), amputation within 6 months of therapy completion, and incidence of drug and line-related adverse effects. Previous osteomyelitis at index site, surgical intervention as a part of initial management, presence of Staphylococcus aureus on culture, utilization of outpatient parenteral antibiotic therapy (OPAT) services (IV group only), and concomitant PO therapy (lipoglycopeptide group only) were included in a bivariate analysis and variables with a p-value < 0.2 were included in a multivariate regression model. Results The IV group included 257 patients, while the PO and lipoglycopeptide groups included 20 and 15 patients respectively. In the IV group, 89 (35%) of the patients experienced clinical treatment failure compared to 5 (25%) in the PO group and 5 (33%) in the lipoglycopeptide group (p=0.71). Median LOS was significantly shorter in the PO group compared to the IV and LGP groups [1 day (IQR 0-2.5) vs. 7 days (IQR 4-10) and 4 days (IQR 4-9), p=0.003]. No difference between groups was observed for amputation within 6 months or incidence of adverse effects. The only variable included in the multivariate regression model was previous osteomyelitis at index site [OR 1.75, 95% CI (1.07 – 2.87)]. Conclusion PO and lipoglycopeptide therapy resulted in similar outcomes compared to IV antibiotics. Only previous OM at the same site was identified as an independent risk factor for failure. Disclosures Ryan P. Moenster, Pharm.D., FIDSA, AbbVie (Speaker’s Bureau)Melinta (Consultant, Speaker’s Bureau)

Aerosol box for intubation of patients with suspected COVID-19: simulation study

Introduction. Endotracheal intubation is a procedure associated with a high level of exposure to the COVID-19 virus. This has led to the search of alternatives to reduce the risk of contamination, including the so-called aerosol box. Objective: To compare time and difficulty of orotracheal intubation when using the aerosol box in a simulated setting. Methodology: Observational study conducted with the participation of 33 anesthetist physicians and anesthesia residents; groups were compared in terms of time and intubation difficulty using a conventional Macintosh laryngoscope and the McGRATH™ MAC (Medtronic) videolaryngoscope with or without aerosol box. In order to determine performance with the intubation maneuver, crude hazard ratios were estimated, and a Cox multivariate regression model was built, adjusted by anesthetist years of experience and difficulties during the procedure. Results: On average, the aerosol box increased intubation time by 7.57 seconds (SD 8.33) when the videolaryngoscope was used, and by 6.62 (SD 5.74) with the Macintosh. Overall, 132 intubations were performed, with 121 successful and 6 failed first-time attempts (4 with the use of the aerosol box); 16 participants (48.48%) reported difficulty handling the box. With the use of the Macintosh, intubation was found to be faster than with the videolaryngoscope (cHR: 1.36 [95% CI 0.64-2.88]; adjusted HR: 2.20 [95% CI 0.73-6.62]). Conclusions: The use of the aerosol box and personal protective equipment in a simulation setting hinders the intubation maneuver and may result in protracted execution time.

Estimation of Heart Rate Using Regression Models and Artificial Neural Network in Middle-Aged Adults

Purpose: Heart rate is the most commonly used indicator in clinical medicine to assess the functionality of the cardiovascular system. Most studies have focused on age-based equations to estimate the maximal heart rate, neglecting multiple factors that affect the accuracy of the prediction.Methods: We studied 121 middle-aged adults at an average age of 57.2years with an average body mass index (BMI) of 25.9. The participants performed on a power bike with a starting wattage of 0W that was increased by 25W every 3min until the experiment terminated. Ambulatory blood pressure and electrocardiography were monitored through gas metabolic analyzers for safety concerns. Six descriptive characteristics of participants were observed, which were further analyzed using a multivariate regression model and an artificial neural network (ANN).Results: The input variables for the multivariate regression model and ANN were selected by correlation for the reduction of dimension. The accuracy of estimation by multivariate regression model and ANN was 9.74 and 9.42%, respectively, which outperformed the traditional age-based model (with an accuracy of 10.31%).Conclusion: This study provides comprehensive approaches to estimate the maximal heart rate using multiple indicators, revealing that both the multivariate regression model and ANN incorporated with age, resting heart rate (RHR), and second-order heart rate (SOHR) are more accurate than univariate models.

Impact of Wind and Solar Generation on the Italian Zonal Electricity Price

This paper assesses the impact of increasing wind and solar power generation on zonal market prices in the Italian electricity market from 2015 to 2019, employing a multivariate regression model. A significant aspect to be considered is how the additional wind and solar generation brings changes in the inter-zonal export and import flows. We constructed a zonal dataset consisting of electricity price, demand, wind and solar generation, net input flow, and gas price. In the first and second steps of this study, the impact of additional wind and solar generation that is distributed across zonal borders is calculated separately based on an empirical approach. Then, the Merit Order Effect of the intermittent renewable energy sources is quantified in every six geographical zones of the Italian day-ahead market. The results generated by the multivariate regression model reveal that increasing wind and solar generation decreases the daily zonal electricity price. Therefore, the Merit Order Effect in each zonal market is confirmed. These findings also suggest that the Italian electricity market operator can reduce the National Single Price by accelerating wind and solar generation development. Moreover, these results allow to generate knowledge advantageous for decision-makers and market planners to predict the future market structure.

A Novel Nomogram for Individualized Gonadotropin Starting Dose in GnRH Antagonist Protocol

Controlled ovarian stimulation (COS) is one of the most vital parts of in vitro fertilization-embryo transfer (IVF-ET). At present, no matter what kinds of COS protocols are used, clinicians have to face the challenge of selection of gonadotropin starting dose. Although several nomograms have been developed to calculate the appropriate gonadotropin starting dose in gonadotropin releasing hormone (GnRH) agonist protocol, no nomogram was suitable for GnRH antagonist protocol. This study aimed to develop a predictive nomogram for individualized gonadotropin starting dose in GnRH antagonist protocol. Single-center prospective cohort study was conducted, with 198 women aged 20-45 years underwent IVF/intracytoplasmic sperm injection (ICSI)-ET cycles. Blood samples were collected on the second day of the menstrual cycle. All women received ovarian stimulation using GnRH antagonist protocol. Univariate and multivariate analysis were performed to identify predictive factors of ovarian sensitivity (OS). A nomogram for gonadotropin starting dose was developed based on the multivariate regression model. Validation was performed using concordance statistics and bootstrap resampling. A multivariate regression model based on serum anti-Müllerian hormone (AMH) level, antral follicle count (AFC), and body mass index (BMI) was developed and accounted for 59% of the variability of OS. An easy-to-use predictive nomogram for gonadotropin starting dose was established with excellent accuracy. The concordance index (C-index) of the nomogram was 0.833 (95% CI, 0.829-0.837). Internal validation using bootstrap resampling further showed the good performance of the nomogram. In conclusion, gonadotropin starting dose in antagonist protocol can be predicted precisely by a novel nomogram.