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. Methods: Data of patients aged >60 years with EGS conditions defined by the American Association for the Surgery of Trauma at primary index admission from 2010 to 2019 operated and non-operated at a large South Asian tertiary care hospital were analyzed. The primary outcome was primary index admission inpatient 30-day mortality. Parametric survival regression using Weibull distribution was performed. Factors such as patients' insurance status and surgical intervention were assessed using adjusted HR and 95% CI with a p-value of <0.05 considered statistically significant. Results: We included 9551 primary index admissions of patients diagnosed with the nine most common primary EGS conditions. The mean patient age was 69.55±7.59 years. Overall mortality and complication rates were 3.94% and 42.29%, respectively. Primary index admission inpatient mortality was associated with complications including cardiac arrest and septic shock. Multivariable survival analysis showed that insurance status was not associated with mortality (HR 1.13; 95% CI 0.79, 1.61) after adjusting for other variables. The odds of developing complications among self-paid individuals were higher (adjusted OR 1.17; 95% CI 1.02, 1.35). Conclusion: Lack of healthcare coverage for older adults can result in delayed presentation, leading to increased morbidity. Close attention should be paid to such patients for timely provision of treatment. There is a need to expand primary care access and proper management of comorbidities for overall patient well-being. Government initiatives for expanding insurance coverage for older population can further enhance their healthcare access, mitigating the risk of essential treatments being withheld due to financial limitations. Level of evidence: III.

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. We conducted 20 interviews with the team of engineers and scientists that led the multi-year effort to build the tool, integrate it into practice, and maintain the solution. This "Algorithm Journey Map" enumerates all social and technical activities throughout the AI solution's procurement, development, integration, and full lifecycle management. In addition to mapping the "who?" and "what?" of the adoption of the AI tool, we also show several 'lessons learned' throughout the algorithm journey maps including modeling assumptions, stakeholder inclusion, and organizational structure. In doing so, we identify generalizable insights about how to recognize and navigate barriers to AI/ML adoption in healthcare settings. We expect that this effort will further the development of best practices for operationalizing and sustaining ethical principles-in algorithmic systems.

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. Clinically ordered lipid profiles from 1st January 2009 to 30th June 2018 were included and data on 1,542,281 LDL-C values was extracted. We used the Make Early Diagnosis to Prevent Early Death (MEDPED) criteria to classify patients as FH and reported data on patients with low-density liporotein -cholesterol (LDL-C) ≥ 190 mg/dL. FH cases were also examined by their spatial distribution. RESULTS: After applying exclusions, the final sample included 988,306 unique individuals, of which 24,273 individuals (1:40) had LDL-C values of ≥190 mg/dL. Based on the MEDPED criteria, 2416 individuals (1:409) had FH. FH prevalence was highest in individuals 10-19 years (1:40) and decreased as the patient age increased. Among individuals ≥40 years, the prevalence of FH was higher for females compared with males (1:755 vs 1:1037, p < 0.001). Median LDL-C for the overall population was 112 mg/dL (IQR = 88-136 mg/dL). The highest prevalence after removing outliers was observed in Rajan Pur district (1.23% [0.70-2.10%]) in Punjab province, followed by Mardan (1.18% [0.80-1.70%]) in Khyber Pakhtunkhwa province, and Okara (0.99% [0.50-1.80%]) in Punjab province. CONCLUSION: There is high prevalence of actionable LDL-C values in lipid samples across a large network of laboratories in Pakistan. Variable FH prevalence across geographic locations in Pakistan may need to be explored at the population level for intervention and management of contributory factors. Efforts at early diagnosis and treatment of FH are urgently needed.

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. We included index admissions of patients >18 years that were discharged between January 1, 1988, and December 31, 2018, with a primary discharge diagnosis of AMI (using ICD-9 diagnoses). The outcome evaluated was in-hospital mortality.Clinical characteristics derived from the electronic database were validated against chart review in a random sample of cases (k 0.53-1.00). Results: The final population consisted of 14,601 patients of which 30.6% (n = 4,470) were female, 52.4% (n = 7,651) had ST elevation MI and 47.6% (n = 6,950) had non-ST elevation MI. The median (IQR) age at presentation was 61 (52-70) years. Overall unadjusted in-hospital mortality was 10.3%. Across the time period, increasing trends were noted for the following characteristics: age, proportion of women, prevalence of hypertension, diabetes, proportion with NSTEMI (all ptrend < 0.001). In-hospital mortality rates declined significantly between 1988-1997 and 2008-2018 (13.8% to 9.2%, p < 0.001). Conclusions: The patterns of AMI have changed over the last three decades with a concomitant decline in in-hospital mortality at a tertiary care centre in Pakistan. Clinical digitized data presents a unique opportunity for gaining insights into disease patterns in LMICs.