Association of Maternal Hypertension with Intrauterine Growth Retardation

To find out the association of intrauterine growth retardation (IUGR) with maternal hypertension.Methods: In this case control study 124 cases and 249 controls (thus giving the case to control ratio of 1:2.) were enrolled. All were born full term and delivered in obstetrics department . After taking verbal consent for the study the mothers were interviewed for the presence or absence of hypertension during pregnancy and their antenatal records checked (if available). To rule out the confounders, a study proforma was used to record maternal hypertension, maternal height, weight, BMI, age, anemia, socioeconomic status, preeclampsia, eclampsia, number of children, age of last born, gender, mode and date of delivery. Cases and controls were identified and assigned an identification number. All the study cases were seen by the pediatrician on the first day of life, weighed properly, their length and head circumference noted and plotted on centile charts in accordance with their gender to note the presence or absence of intra uterine growth restriction (IUGR). A p-value of 0.05 or less was used to see the significance of the association.Results: Mothers of small for gestational age (SGA) babies were more than two times likely to have hypertension. Both low maternal BMI and Anemia in mother were significantly associated with IUGR in both univariate and multivariate analysis. Socioeconomic condition was also showing significant association with IUGR. Other factors like gravidity, maternal age, parity were not showing any association with IUGR.Conclusion: Maternal anemia and low BMI are showing strong association with IUGR while maternal hypertension is not showing a strong associatio

Impact of Comorbidities on the Severity of Disease & Outcome in Children with COVID 19 at a Tertiary Care Pediatric Hospital, Rawalpindi.

Introduction: Coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was initially identified in Wuhan, China, in December 2019. The virus affects almost all countries of the world. It has infected humans in all age groups, of all ethnicities, both males and females. It is considered that COVID-19, in those with underlying health conditions or co-morbidities, has an increasingly rapid and severe progression, often leading to death. This study is designed to evaluate the impact of co-morbidities on the severity and outcome of COVID-19 infection in children. Methods: This retrospective observational study was conducted at the pediatric department, Benazir Bhutto Hospital Rawalpindi from March 2020 to September 2021. Every confirmed COVID-19 admitted case according to inclusion criteria was enrolled for the study. Data were retrieved from hospital records; data was recorded on a predesigned study questionnaire, entered and analyzed in SPSS version 24 for descriptive statistics and bivariate analysis. Results: 109 children were enrolled with confirmed COVID-19, of these 64(58.7%) were males and 45(41.2%) were females. The age of the patients ranged from infants to 12 years with a mean age of 27.25 months. Comorbidities were present in 70 patients     (64.2%).CHD was the most common co-morbid condition (n=18, 16.5%). Most of the patients experienced mild to moderate symptoms (n=64.2%) while severe symptoms were found in (n=35.8%) patients. 27 patients (24.8%) required Ventilatory support, and of these 20 patients (18.3%) had underlying co-morbidity. The total number of patients who expired was 27(24.7%), and 30.3% of patients who expired had co-morbid conditions. the patients with co-morbid conditions had a longer stay in the hospital. 35 patients (32.1%)with underlying co-morbidity remained admitted for more than a week and 13 patients(9%)for more than two weeks. Conclusion: Pediatric patients with co-morbidities have a higher risk of severe COVID-19 and associated mortality than children without underlying disease. Children with underlying conditions need to be carefully assessed and closely monitored. Further data are required to define these associations and adequate guidelines to manage high-risk children with COVID-19. Keywords: Coronavirus, COVID-19, Pediatrics, Co-morbidity, Severity, Outcom

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Combining Vehicle Routing Optimization and Container Loading Optimization

Vehicle routing optimization and container loading combined would produce millions of queries for the remaining capacity of the vehicles. In this situation, these approximate methods for finding the remaining capacity of the vehicle’s container are investigated. These methods reduce the time needed to approximate the remaining capacity in vehicles and will hence accelerate the overall optimization process. In this thesis we consider a solution to improve the accuracy of real-world vehicle routing optimization problems. Simple capacitated vehicle routing optimization does not capture any information about the packing of objects except by deducting the volume of the packed objects from the container’s volume. Bin packing during the routing optimization is usually slow. We combine a very fast approximation algorithm for 3D bin packing with vehicle routing optimization to speed up the whole process. Vehicle routing combined with the 3D container loading problem creates new kinds of challenges. The problem was introduced in Gendreau et al. 2006 where 3D loading space replaces the scalar capacity of the vehicles. The container loading problem attempts to obtain the best possible utilization of space, while the vehicle routing problem is concerned with finding the minimum-cost or minimum-distance route in transportation. The combined problem is about loading boxes with different symmetry into rectangular containers of the vehicles used in delivery. This problem is extremely hard because it is a combination of the two problems mentioned above, which are both NP-hard [Gendreau et al. 2006, Pisinger 2002]. Finding an exact solution for this problem is infeasible since even solving a small instance of bin packing problem alone would require more computing resources as feasible (Martello, Pisinger, and Vigo 2000). To handle this situation approximation algorithms are used as it is often not necessary to find the optimal solution for the bin packing problem. An approximate solution that is close to optimal and computed with the help of reasonable resources and time is considered a good solution. When vehicle routing optimization and container loading are combined, a high number of queries for the remaining capacity of the vehicles are performed. In this thesis we exploit this fact and perform experiments with approximate methods for finding the remaining capacity of the vehicle’s container in a fast but approximate way. In our experiments we use a slight modification of the 3D bin packing algorithm called Largest Area First Fit (LAFF) (Gürbüz et al. 2009) as a rough but fast means to determine the remaining capacity in the containers during the vehicle routing optimization process. A bounding box is used for objects which are not rectangular in shape, such as cylindrical shapes. The LAFF algorithm carries the placement of the boxes such that those with the largest surface area are placed first while keeping the height minimum from the floor of the container. The box which covers the largest ground area of the container is placed first followed by subsequent boxes that are stacked in the remaining space at the same level, the boxes with the greatest volume first. Then the level is increased and the process repeated. Boxes are rotated such that they have the largest possible footprint. This algorithm works exceptionally fast when the number and variety of the objects to be packed are small. During the LAFF stage, all real-world bin packing constraints e.g. the weight of the boxes, loading priorities, orientation, stacking, the distribution of weight in different parts of the container, stability, etc. are ignored to gain as much speed as possible