Predictive model of blood transfusion during CABG surgery in Pakistan

OBJECTIVE: To determine predictors of need for transfusion of blood and blood products and create a clinical predictive model to reduce indiscriminate use of blood products during surgery. METHOD: We conducted a retrospective chart review of 485 patients who underwent coronary artery bypass surgery from January 2004 to December 2004 at a Tertiary Care Hospital in Karachi, Pakistan. Independent predictors associated with transfusion were identified and a clinical prediction model developed. RESULTS: The transfusion rate was 37.1%. A predictive model was created based on the presence of pulmonary disease, diabetes mellitus, low ejection fraction and recent/ongoing myocardial infarction. CONCLUSION: The study identifies some predictors of need for blood transfusion in patients undergoing Coronary Artery Bypass Grafting. However, prospective studies with a larger sample of patients are needed to determine other predictors and their applicability in patient selection across institutions

Etiology, clinical, radiological, and microbiological profile of patients with non-cystic fibrosis bronchiectasis at a tertiary care hospital of Pakistan

Objectives: To identify the etiology of non-cystic fibrosis bronchiectasis (NCFB), to assess the clinical presentation, radiological findings, and microbiological profile of patients presenting with a diagnosis of bronchiectasis in a tertiary care center of Pakistan.Methods: This was a prospective observational cohort study where patients with a diagnosis of bronchiectasis proven by high-resolution computed tomography (HRCT) were evaluated for etiology, clinical characteristics, microbiology, radiology, spirometric profile, and in-hospital outcomes.Results: During the study period, 196 patients were diagnosed with NCFB. The majority of the patients were men 76.5% (n = 150) and 83.6% (n = 163) of the total patients were younger than 60 years of age. The majority of these patients (58.7%, n = 111) had a duration of symptoms between 5-10 years. The etiology of bronchiectasis was identified in 92.9% of cases. Post-infectious bronchiectasis was the most common cause (67.8%, n = 133), followed by chronic obstructive pulmonary disease (COPD) (9.2%, n = 18), and allergic bronchopulmonary aspergillosis (ABPA) (7.1%, n = 14). Among the post infectious causes, a history of TB was present in 85% (n = 114/133) of patients. Obstructive impairment was the most common spirometric pattern, observed in 68.9% (n = 135) of patients. Pseudomonas aeruginosa was the most commonly isolated organism (36.2%, n = 71). Hemoptysis was the most frequent complication found in 20.9% of patients (n = 41). Out of these 196 patients, 94.4% (n = 185) received medical management and were discharged from the hospital. Respiratory failure was significantly associated with the Pseudomonas group as compared to non-pseudomonas group [(n = 21 (29%) vs n = 18 (14.4%) p = 0.01]. During hospitalization seven patients (3.6%) were died because of respiratory failure.Conclusions: Post TB bronchiectasis was the leading cause of non-cystic fibrosis (CF) bronchiectasis in this cohort, with Pseudomonas was the commonest pathogen isolated from the respiratory specimen, which was significantly associated with respiratory failure. On spirometry, obstructive impairment was found in the majority of patients and hemoptysis was the most frequent complication

Multi-objective resource optimization in space-aerial-ground-sea integrated networks

Space-air-ground-sea integrated (SAGSI) networks are envisioned to connect satellite, aerial, ground, and sea networks to provide connectivity everywhere and all the time in sixth-generation (6G) networks. However, the success of SAGSI networks is constrained by several challenges including resource optimization when the users have diverse requirements and applications. We present a comprehensive review of SAGSI networks from a resource optimization perspective. We discuss use case scenarios and possible applications of SAGSI networks. The resource optimization discussion considers the challenges associated with SAGSI networks. In our review, we categorized resource optimization techniques based on throughput and capacity maximization, delay minimization, energy consumption, task offloading, task scheduling, resource allocation or utilization, network operation cost, outage probability, and the average age of information, joint optimization (data rate difference, storage or caching, CPU cycle frequency), the overall performance of network and performance degradation, software-defined networking, and intelligent surveillance and relay communication. We then formulate a mathematical framework for maximizing energy efficiency, resource utilization, and user association. We optimize user association while satisfying the constraints of transmit power, data rate, and user association with priority. The binary decision variable is used to associate users with system resources. Since the decision variable is binary and constraints are linear, the formulated problem is a binary linear programming problem. Based on our formulated framework, we simulate and analyze the performance of three different algorithms (branch and bound algorithm, interior point method, and barrier simplex algorithm) and compare the results. Simulation results show that the branch and bound algorithm shows the best results, so this is our benchmark algorithm. The complexity of branch and bound increases exponentially as the number of users and stations increases in the SAGSI network. We got comparable results for the interior point method and barrier simplex algorithm to the benchmark algorithm with low complexity. Finally, we discuss future research directions and challenges of resource optimization in SAGSI networks

Resource Optimization in UAV-assisted IoT Networks: The Role of Generative AI

We investigate how generative Artificial Intelligence (AI) can be used to optimize resources in Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) networks. In particular, generative AI models for real-time decision-making have been used in public safety scenarios. This work describes how generative AI models can improve resource management within UAV-assisted networks. Furthermore, this work presents generative AI in UAV-assisted networks to demonstrate its practical applications and highlight its broader capabilities. We demonstrate a real-life case study for public safety, demonstrating how generative AI can enhance real-time decision-making and improve training datasets. By leveraging generative AI in UAV- assisted networks, we can design more intelligent, adaptive, and efficient ecosystems to meet the evolving demands of wireless networks and diverse applications. Finally, we discuss challenges and future research directions associated with generative AI for resource optimization in UAV-assisted networks.Comment: Accepted - IEEE Internet of Things Magazin

Bringing efficiency into practice: a quality improvement initiative to reduce operating room turnaround time

Abstract Operating room (OR) turnaround time (TAT) is the minimal essential time required for cleaning of OR and preparation for the next case. The TAT inversely affects OR efficiency. Several factors related to personnel, equipment and scheduling have been identified as causes of increased TAT. We conducted the study to identify factors that affect OR TAT and to propose recommendations for its reduction. The retrospective study, conducted at Aga Khan University Hospital, Karachi, comprised TAT records related to March 2014. Of the 88 cases, 22(25%) showed a delay. Upon Pareto analysis it was found that in 8(36.6%) cases there was a delay of 70% related to scheduling of OR list and 5(22.7%) related to movement of patients from wards to OR. As such, improvement in these two broad areas can take care of majority of delays. We also recommend documentation of all processes as part of continuous improvement

Anisotropic Durgapal-Fuloria Neutron Stars in f(R,T2)f(\mathcal{R},\mathrm{T}^{2}) Gravity

The main purpose of this paper is to obtain physically stable stellar models coupled with anisotropic matter distribution in the context of $f(\mathcal{R},\mathrm{T}^{2})$ theory. For this, we consider a static spherical geometry and formulate modified field equations containing various unknowns such as matter determinants and metric potentials. We then obtain a unique solution to these equations by employing Durgapal-Fuloria ansatz possessing a constant doublet. We also use matching criteria to calculate the values of these constants by considering the Schwarzschild exterior spacetime. Two different viable models of this modified theory are adopted to analyze the behavior of effective matter variables, anisotropy, energy conditions, compactness and redshift in the interiors of Her X-1, PSR J0348-0432, LMC X-4, SMC X-1, Cen X-3, and SAX J 1808.4-3658 star candidates. We also check the stability of these models by using three different physical tests. It is concluded that our considered stars satisfy all the physical requirements and are stable in this modified gravity for the considered parametric values.Comment: 28 pages, 10 figure

Evaluating the effectiveness of text messaging and phone call reminders to minimize no show at pediatric outpatient clinics in Pakistan: protocol for a mixed-methods study

Background: Missing health care appointments without canceling in advance results in a no show, a vacant appointment slot that cannot be offered to others. No show can be reduced by reminding patients about their appointment in advance. In this regard, mobile health (mHealth) strategy is to use text messaging (short message service, SMS), which is available on all cellular phones, including cheap low-end handsets. Nonattendance for appointments in health care results in wasted resources and disturbs the planned work schedules.Objectives: The purpose of this study is to evaluate the efficacy of the current text messaging (SMS) and call-based reminder system and further explore how to improve the attendance at the pediatric outpatient clinics. The primary objectives are to (1) determine the efficacy of the current clinic appointment reminder service at pediatric outpatient clinics at Aga Khan University Hospital, (2) assess the mobile phone access and usage among caregivers visiting pediatrics consultant clinics, and (3) explore the perception and barriers of parents regarding the current clinic appointment reminder service at the pediatric outpatient clinics at Aga Khan University Hospital.Methods: The study uses a mixed-method design that consists of 3 components: (1) retrospective study (component A) which aims to determine the efficacy of text messaging (SMS) and phone call–based reminder service on patient’s clinic attendance during January to June 2017 (N=58,517); (2) quantitative (component B) in which a baseline survey will be conducted to assess the mobile phone access and usage among parents/caregivers of children visiting pediatrics consultant clinics (n=300); and (3) qualitative (component C) includes in-depth interviews and focus group discussion with parents/caregivers of children visiting the pediatric consultancy clinic and with health care providers and administrative staff. Main constructs will be to explore perceptions and barriers related to existing clinic appointment reminder service. Ethics approval has been obtained from the Ethical Review Committee, Aga Khan University, Pakistan (4770-Ped-ERC-17).Results: Results will be disseminated to pediatric quality public health and mHealth communities through scientific meetings and through publications, nationally and internationally.Conclusions: This study will provide insight regarding efficacy of using mHealth-based reminder services for patient’s appointments in low- and middle-income countries setup. The finding of this study will be used to recommend further enhanced mHealth-based solutions to improve patient appointments and decrease no show

Community pharmacists’ perception of and practice with drug package inserts in UAE

Background: Written information in drug package inserts (DPIs) is important source of information for doctors and pharmacists. Objectives: The present study was undertaken to evaluate the knowledge, perception and practice of community pharmacists with information in the DPIs, and their views on their usefulness.Methods: Seventy five pre-piloted questionnaires were distributed to community pharmacists in the United Arab Emirates. The questionnaire included questions covering demographics of pharmacists and whether they read and rely on DPI as a source of information. Pharmacists were also asked to evaluate and categorize DPI information with respect to the ease for patient use. The data were analyzed and are expressed as frequency and percentage.Results: The response rate was 90.7%. The majority (52, 76.5%) of pharmacists were in the age range of 20-39 years, with bachelor of pharmacy degree (50, 73.5%) and having 1-10 years of practice experience (48, 70.6%). Two thirds of the respondents obtained their degree outside the UAE. The majority (60, 88.2%) of pharmacists read the DPIs of prescription and OTC drugs, for all the information and think it is useful (67, 98.5%). Most participants think DPI is useful to patients and advise them to read it. The majority (49, 72.1%) of pharmacists believe that DPI are clear to read but their content should be shorter (46, 67.6%) and limited to the most important information (51, 75%). More pharmacists with a degree from outside UAE do not read the DPIs (p < 0.003), and find DPIs easily understood (P <0.008). More pharmacists with 1-5 years’ experience advise their patients to read the DPIs (P<0.033).Conclusions: Our findings suggest that there is a need for improving the content of drug package inserts to provide the necessary information required not only for health care professionals but also patients to further enhance their acceptance of and compliance with their medications

The dynamic impact of renewable energy sources on environmental economic growth: evidence from selected Asian economies

The linkage between renewable energy resources and environmental influences on economic growth among selected Asian economies play a vital role in sustainable economic development. This study encompasses the panel data sets for eight selected Asian countries, and the period starts from 1990 to 2018. This research relies on the panel vector error correction model (PVECM) for data estimation. The overall findings indicate that biomass, geothermal, and wind power sources of energy have a positive and significant impact on the economic advancement of Asian economies. Besides that, as opposed to the other two renewable energy sources, windpower has a greater impact on economic development. Furthermore, the empirical findings of current research have significant implications towards selected Asian countries’ energy policy related to both private and public sector enterprises as it helps in identifying the industrial sectors which have greater contribution towards the economy and their energy requirements in long term