Weekends at risk: Uncovering the impact of the weekend effect on emergency general surgery.

Background: Weekend admissions have been proposed to be associated with the worst outcomes across varied patient populations and health care systems. However, there is a dearth of literature on weekend effects in low- and middle-income countries like Pakistan. This study aimed to assess the role of the "weekend effect" on the outcomes of emergency general surgery patients admitted to a tertiary care center in Pakistan.

A Step Beyond Mortality: Identifying Factors of Prolonged Hospital Stay for Emergency General Surgery Conditions in a Low- and Middle-Income Country.

Introduction: While various factors leading to prolonged length of stay (LOS) have been identified for emergency general surgery (EGS), there is limited literature on specific factors for individual emergent specialties. This study aimed to identify patient factors and in-hospital complications associated with prolonged LOS for gastrointestinal (GI) and non-GI-related EGS presentations in a low-resource setting.

Methods: Data from 2010 to 2019 were retrieved from one of the largest tertiary care centers in Pakistan.

No healthcare coverage, big problem: lack of insurance for older population associated with worse emergency general surgery outcomes.

Introduction: Older populations, being a unique subset of patients, have poor outcomes for emergency general surgery (EGS). In regions lacking specialized medical coverage for older patients, disparities in healthcare provision lead to poor clinical outcomes. We aimed to identify factors predicting index admission inpatient mortality from EGS among sexagenarians, septuagenarians, and octogenarians.

The algorithm journey map: a tangible approach to implementing AI solutions in healthcare.

When integrating AI tools in healthcare settings, complex interactions between technologies and primary users are not always fully understood or visible. This deficient and ambiguous understanding hampers attempts by healthcare organizations to adopt AI/ML, and it also creates new challenges for researchers to identify opportunities for simplifying adoption and developing best practices for the use of AI-based solutions. Our study fills this gap by documenting the process of designing, building, and maintaining an AI solution called SepsisWatch at Duke University Health System.

Prevalence of familial hypercholesterolemia in a country-wide laboratory network in Pakistan: 10-year data from 988, 306 patients.

Introduction: Familial hypercholesterolemia (FH) is a modifiable risk factor for premature coronary heart disease but is poorly diagnosed and treated. We leveraged a large laboratory network in Pakistan to study the prevalence, gender and geographic distribution of FH.

Methodology: Data were curated from the Aga Khan University Hospital clinical laboratories, which comprises of 289 laboratories and collection points spread over 94 districts.

Leveraging Clinical Digitized Data to Understand Temporal Characteristics and Outcomes of Acute Myocardial Infarctions at a Tertiary Care Medical Centre in Pakistan from 1988-2018 - Methods and Results.

Background And Objective: Few data exist on trends in acute myocardial infarction (AMI) patterns spanning recent epidemiological shifts in low middle-income countries (LMICs). To understand temporal disease patterns of AMI characteristics and outcomes between 1988-2018, we used digitized legacy clinical data at a large tertiary care centre in Pakistan.

Methods: We reviewed digital health information capture systems maintained across the Aga Khan University Hospital and obtained structured elements to create a master dataset.