Producing personalized statin treatment plans to optimize clinical outcomes using big data and machine learning.

Almost half of Americans 65 years of age and older take statins, which are highly effective in lowering low-density lipoprotein cholesterol, preventing atherosclerotic cardiovascular disease (ASCVD), and reducing all-cause mortality. Unfortunately, ∼50% of patients prescribed statins do not obtain these critical benefits because they discontinue use within one year of treatment initiation. Therefore, statin discontinuation has been identified as a major public health concern due to the increased morbidity, mortality, and healthcare costs associated with ASCVD. In clinical practice, statin-associated symptoms (SAS) often result in dose reduction or discontinuation of these life-saving medications. Currently, physician decision-making in statin prescribing typically relies on only a few patient data elements. Physicians then employ reactive strategies to manage SAS concerns after they manifest (e.g., offering an alternative statin treatment plan or a statin holiday). A preferred approach would be a proactive strategy to identify the optimal treatment plan (statin agent + dosage) to prevent/minimize SAS and statin discontinuation risks for a particular individual prior to initiating treatment. Given that using a single patient's data to identify the optimal statin regimen is inadequate to ensure that the harms of statin use are minimized, alternative tactics must be used to address this problem. In this proof-of-concept study, we explore the use of a machine-learning personalized statin treatment plan (PSTP) platform to assess the numerous statin treatment plans available and identify the optimal treatment plan to prevent/minimize harms (SAS and statin discontinuation) for an individual. Our study leveraged de-identified administrative insurance claims data from the OptumLabs® Data Warehouse, which includes medical and pharmacy claims, laboratory results, and enrollment records for more than 130 million commercial and Medicare Advantage (MA) enrollees, to successfully develop the PSTP platform. In this study, we found three results: (1) the PSTP platform recommends statin prescription with significantly lower risks of SAS and discontinuation compared with standard-practice, (2) because machine learning can consider many more dimensions of data, the performance of the proactive prescription strategy with machine-learning support is better, especially the artificial neural network approach, and (3) we demonstrate a method of incorporating optimization constraints for individualized patient-centered medicine and shared decision making. However, more research into its clinical use is needed. These promising results show the feasibility of using machine learning and big data approaches to produce personalized healthcare treatment plans and support the precision-health agenda.

Draft genome sequences of Brucella melitensis from human and animal sources in India.

We present the draft genome sequences of 23 Brucella melitensis isolates derived from human and animal sources across India with genome size predominantly at 3.207 M and uniform GC content (57.24%) across isolates. The accession numbers and detailed sequencing data enhance the utility of this resource for further genomic studies.

Waste to energy: A review of biochar production with emphasis on mathematical modelling and its applications.

United Nations charter to build a sustainable future has paved the way for the introduction of the Sustainability Development Goals (SDGs) at a global forum. In particular, SDG 11 is aligned with the idea of developing cities and communities that provide quality human life, by attaining net-zero discharge and self-sustainability. In line with the efforts of the global community, biochar has emerged as a viable solution due to its ability to convert waste into value. Finding applications in a spectrum of domains, biochar is being studied for use as an adsorbent, a co-catalyst to promote industrial-grade reactions and as a feed for fuel cells. Moreover, the inclusion of biochar as a soil enhancement material advocates the implementation of closed-loop nutrient cycles. Hence, it is imperative to have a proper understanding of the biomass characteristics, the hydrothermal treatment and the process parameters to be adopted for the production of char in order to identify biomass feedstock based on the application. The current work provides insight into the key factors and conditions employed for the production of biochar based on the plethora of applications. In order build a basic framework to aid in the production of char, the development of a statistical correlation was undertaken to determine the feed and optimum process parameters for the production of biochar based on its applications.

Out-of-pocket spending on hypertension and diabetes among patients reporting in a health -care teaching institute of the Western Rajasthan.

Background: Across the globe, morbidity and mortality due to non-communicable diseases (NCDs) are major public health issues. The resulting concern is not just epidemiological but also about the economic consequences at the household level. Objective: To assess the various facets of out-of-pocket spending (OOPs) incurring on NCDs, namely hypertension and diabetes on patients attending a healthcare teaching institute in Rajasthan. Methodology: This cross-sectional study involves patients older than 18 years attending either out-patient clinics or who were admitted in the wards in a healthcare teaching institute for seeking care for diabetes or hypertension. Four hundred patients were chosen purposively and a pretested questionnaire was used to elicit information on incurring OOPs for NCDs. Descriptive statistics (like percentage, mean, median, and standard deviation) were calculated. Results: The study shows a significant expenditure other than out-patient, in-patient admissions, in the form of personal expenditure and loss of employment, amounting to 31.86 and 34.07%, respectively, of the mean total expenditure. In a quarter (3 months), the mean total expenditure is ₹ 9014.37 ± 6452.37. On average, the OOP expenditure per visit for an out-patient visit was ₹370.54 ± 237, while for the patients admitted to the hospital, the average OOPs was ₹1564.72 ± 1310.5. Conclusions: Health expenditures can contribute toward the impoverishment of many segments of the community. Undoubtedly, numerous people may tend to neglect the needed care for NCDs due to financial hurdles. Thus, there is a need to develop NCD care management centers with health insurance packages and make them accessible for all.