023 - Drug Course Medical Analytics Platform

Medical prescriptions/dispensing information in a pharmacy database can be used in conjunction with an electronic medical record/chart warehouse to predict medical outcomes—incident disease, recurrence and exacerbation of disease, death, etc. Drug studies with medical records may serve in assessing benefit on the target disease, in repurposing of the drug for another disease/outcome, in determining drug interactions—harmful or beneficial, or as time-varying covariates in other studies. It often happens in such studies that preparation and cleaning of the drug information for an individual is time consuming taking more than 50% of the time for the whole project. This proposal to extract, pre-process/clean drug courses from drug prescriptions/dispensing data will make drug prescription data usable as part of an analytics pipeline that facilitates future quality assurance, business analytical or research studies while meeting regulatory and governance issues.

Design of a drug course analytics platform and the rules of access and drug course definitions would be determined by the institution requirements and scientific/medical consultancy. The processing of additional drug courses or modification of existing drug courses can be requested by the user. These requests also contain definitions of cohort selection, of study dates, and choice of options for cleaning the drug courses, etc. Thus, the drug course analytics platform would maintain procedures for data extraction currently written in MS SQL and a number of utilities for data cleaning/pre-possessing currently written in SAS.
Such utilities and rules include
1. Clean the drug course information for a new drug with a rule maintaining courses of at least 30 days duration for chronic conditions/diseases.
2. Pool drugs by drug class; for example, pool pain medications by morphine equivalent doses.
3. Combine 2 (maybe 3) drugs into combinations; for example. 22 = 4 combinations for 2 drugs, or 23 = 8 combinations for 3 drugs which can be used to assess additive or interaction drug effects. The combinations can be computed as a refinement of partitions from the constituent drug courses while maintaining course durations of at least 30 days.
4. Define time-varying covariates; for example, body mass index (BMI) might be defined by courses of BMI that are constant up to a ±5% change.
5. Merge the main outcome of the time to event data with the combined drug courses. Intervening binary, medical events can be presented as only two courses.

The drug course analytical platforms with its pre-processing and rules provide data in a form that meet the requirements of business analytics, quality assurance or research for medical outcomes - many outcomes can be represented as time to event data or number of events data. The drug information can be represented as a sequence of drug courses which as mathematical constructs are both time-varying variables [time series] and partitions of the time interval to event. Cox's proportional hazards ratio, survival model is a powerful analysis tool for outcome data as it includes data from individuals who do not have the event under study during the time to the end of the study—called censored records/events. This semi-parametric model is available in SAS's PHREG procedure and R's survival package, both of which have provision for time varying variables such as drug courses. Other medical outcomes can be represented by Poisson number of events in a fixed time period: e.g. exacerbation of disease for two years following incident disease. The rate of such events occurring can be analyzed by Generalized Linear Modeling with log link and Poisson distribution. This model also allows time varying variables. Both statistical models require time-vary input as flat files such as precisely provided by the Drug Course Medical Analytics Platform proposed here-in.

Both models allow propensity scoring as weights which is an optimal method for reducing selection bias based on variables from the database.

The need for the proposed Platform is well illustrated by our recently published drug study about reducing risk of incident asthma [1]. The review process lead to adding a third drug requiring a refinement (merger) of three (3) drug time-courses as new input for recalculation of Propensity Scoring weights and for the survival analyses.

[1] . Sood A, Qualls C, Murata A, Kroth PJ, Mao J, Schade DS, Murata G. (2022) Potential for repurposing oral hypertension/diabetes drugs to decrease asthma risk in obesity. J Asthma. 2022 Jul
13:1-9. doi: 10.1080/02770903.2022.2097919. Online ahead of print. PMID: 35796615

Pharmacy_Drug_Course_Data

Cox_Proportional_Hazards_Survival_analysis

Partition_refinement

Propensity_Scoring

Generalized_Estimating_Equations