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 \u3e18 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 \u3c 0.001). In-hospital mortality rates declined significantly between 1988-1997 and 2008-2018 (13.8% to 9.2%, p \u3c 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

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 \u3c 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

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\u27s 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 \u27lessons learned\u27 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

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

Background: South Asian region contributes 59 % to the global mortality due to burns. However, we find a paucity of literature on the outcomes of burns from low- and middle-income countries (LMICs). South Asian Burn Registry (SABR) is a facility-based burns registry that collected data on in-patient burn care. This study assesses factors associated with mortality, length of hospital stay at the burns center, and functional status of burn patients.Methods: Prospective data was collected from two specialized public sector burn centers between September 2014 - January 2015 from Bangladesh and Pakistan. Multivariable logistic, linear, and ordinal logistic regression was conducted to assess factors associated with inpatient-mortality, length of hospital stay, and functional status at discharge, respectively.Results: Data on 883 patients was analyzed. Increased association with mortality was observed with administration of blood product (OR:3, 95 % CI:1.18-7.58) and nutritional support (OR:4.32, 95 % CI:1.55-12.02). Conversely, antibiotic regimens greater than 8 days was associated with decreased mortality (OR:0.1, 95 % CI:0.03-0.41). Associated increase in length of hospital stay was observed in patients with trauma associated with their burn injury, history of seizures (CE:47.93, 95 % CI 12.05-83.80), blood product (CE:22.09, 95 % CI:0.83-43.35) and oxygen administration (CE:23.7, 95 % CI:7.34-40.06). Patients who developed sepsis (OR:6.89, 95 % CI:1.92-24.73) and received blood products during hospitalization (OR:2.55, 95 % CI:1.38- 4.73) were more likely to have poor functional status at discharge.Conclusion: This study identified multiple factors associated with worse clinical outcomes for burn patients in South Asia. Understanding these parameters can guide targeted efforts to improve the process and quality of burn care in LMICs