Methodological issues for measuring pharmacotherapy treatment and its calibration with patient outcomes using real-world data

Introduction Health system and administrative data sources, although collected for process/procedural healthcare use, have recently been leveraged for research purposes. Complex methodologies such as data linkages, defined algorithms, and proxy measures are needed to validate the use of these data. Alberta has exceptionally rich health system data to be explored. Objectives and Approach Our objectives were to explore data sources in Alberta regarding pharmacotherapy treatments, patient outcomes, and develop methods to calibrate associations. We scoped data sources for pharmacotherapy treatment and patient outcomes in Alberta, Canada. Using a cohort of patients with multiple sclerosis (MS) as an example, we developed algorithms to measure medication possession ratio (MPR), proportion of days covered (PDC), and treatment discontinuation (TD). Patient outcomes included: ambulatory care visits, physician claims and hospitalizations. We explored different algorithms for statistical modeling of patient treatments and outcomes. Optimal cut-points and statistical model performance of MPR/PDC and TD with patient outcomes were evaluated. Results We utilized six Albertan data sources to examine treatment patterns and patient outcomes. From the year 2000, the pharmaceutical information network (PIN) and Alberta Blue Cross drug datasets collected: service dates, dosages, drug identification numbers, anatomical therapeutic chemical classification codes, and days of supplies; which were used to calculate MPR, PDC and TD. MPR and PDC were calculated using three different methods: (1) fixed follow-up period; (2) with and without last fill; and (3) adjustment for hospitalization periods. No significant differences between methods were found. In addition, TD using 60- and 90-day gaps, while considering the medication day supplies, were calculated. Adherence with optimal cut-point 0.8 of MPR/PDC and TD were significantly associated with ambulatory care visits, physician claims, and hospitalizations (p=0.000; C-statistic range: 0.78-0.89). Conclusion/Implications The PIN data can be utilized for measuring pharmacotherapy treatment in patient outcomes research. To understand the impact of therapies on outcomes in a real-world setting, however, comprehensive methods for measuring treatment indicators as well as patient outcomes should be developed to control for inherent biases in observational data

Prediction of vertical distribution of SPAD values within maize canopy based on unmanned aerial vehicles multispectral imagery

Real-time monitoring of canopy chlorophyll content is significant in understanding crop growth status and guiding precision agricultural management. Remote sensing methods have demonstrated great potential in this regard. However, the spatiotemporal heterogeneity of chlorophyll content within crop canopies poses challenges to the accuracy and stability of remote sensing estimation models. Hence, this study aimed to develop a novel method for estimating canopy chlorophyll content (represented by SPAD values) in maize (Zea mays L.) canopies. Firstly, we investigated the spatiotemporal distribution patterns of maize canopy SPAD values under varying nitrogen application rates and different growth stages. The results revealed a non-uniform, “bell-shaped” curve distribution of maize canopy SPAD values in the vertical direction. Nitrogen application significantly influenced the distribution structure of SPAD values within the canopy. Secondly, we achieved satisfactory results by fitting the Lorentz peak distribution function to the SPAD values of different leaf positions in maize. The fitting performance, evaluated using R2 and RMSE, ranged from 0.69 to 0.98 and 0.45 to 3.59, respectively, for the year 2021, and from 0.69 to 0.77 and 2.38 to 6.51, respectively, for the year 2022.Finally, based on the correlation between canopy SPAD values and vegetation indices (VIs) at different growth stages, we identified the sensitive leaf positions for the selected CCCI (Canopy Chlorophyll Index) in each growth stage. The 6th (r = 0.662), 4th (r = 0.816), 12th (r = 0.722), and 12th (r = 0.874) leaf positions exhibited the highest correlations. Compared to the estimation model using canopy wide SPAD values, the model based on sensitive leaf positions showed improved accuracy, with increases of 34%, 3%, 20%, and 3% for each growth stage, respectively. In conclusion, the findings of this study contribute to the enhancement of chlorophyll content estimation models in crop canopies and provide valuable insights for the integration of crop growth models with remote sensing methods

Linking Clinical and Administrative Data to Inform Performance Measures Regarding Access to Specialist Care for Patients with Rheumatoid Arthritis

Introduction Rheumatoid arthritis (RA) is the most prevalent type of chronic adult inflammatory arthritis and requires timely diagnosis and subsequent access to specialist care and treatment from a rheumatologist. We developed a set of key performance indicators (KPIs) to evaluate access, effectiveness, acceptability, appropriateness and efficiency of care. Objectives and Approach The overall objective was to measure performance of a central intake system for referral to rheumatology against the KPIs. We report on one accessibility KPIs: the percentage of patients with new onset RA with at least one visit to a rheumatologist in the first 365 days since diagnosis.  We identified a cohort of RA patients using a validated case definition: >16 years, at least 1 RA related hospitalization (ICD-10-CA:M05.x-M06.x) or two RA related physician visits ≥ eight weeks apart within two years (ICD-9: 714.x).  The incident case date was date of hospitalization or second physician visit (whichever came first). Results This KPI assessed the proportion of patients seen by a rheumatologist within one year of first RA visit by patients in the RA cohort. 13,914 cases of RA were diagnosed between April 1 2010 and March 31 2016. The percentage of patients with new onset RA with at least one visit to a rheumatologist in the first 365 days since diagnosis increased between fiscal years 2011 and 2015. Of the 2851 incident RA cases in fiscal year 2011, 1490 (53%) met the performance measure compared to 1710 of 2710 (63%) who met the definition in fiscal year 2015. Other KPIs, including wait times, are being evaluated using both clinical and administrative data. Conclusion/Implications By linking multiple administrative datasets, we are able to measure system performance against a defined KPI and identify opportunities for system improvement. This is the first initiative in Alberta for patients with RA where data from different multi-custodial data repositories have been extracted, linked and analyzed for this purpose

Identifying Cases of Sleep Disorders through International Classification of Diseases (ICD) Codes in Administrative Data

Objectives Prevalence, and associated morbidity and mortality of chronic sleep disorders have been limited to small cohort studies, however, administrative data may be used to provide representation of larger population estimates of disease. With no guidelines to inform the identification of cases of sleep disorders in administrative data, the objective of this study was to develop and validate a set of ICD-codes used to define sleep disorders including narcolepsy, insomnia, and obstructive sleep apnea (OSA) in administrative data. Methods A cohort of adult patients, with medical records reviewed by two independent board-certified sleep physicians from a sleep clinic in Calgary, Alberta between January 1, 2009 and December 31, 2011, was used as the reference standard. We developed a general ICD-coded case definition for sleep disorders which included conditions of narcolepsy, insomnia, and OSA using: 1) physician claims data, 2) inpatient visit data, 3) emergency department (ED) and ambulatory care data. We linked the reference standard data and administrative data to examine the validity of different case definitions, calculating estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).  Results From a total of 1186 patients from the sleep clinic, 1045 (88.1%) were classified as sleep disorder positive, with 606 (51.1%) diagnosed with OSA, 407 (34.4%) with insomnia, and 59 (5.0%) with narcolepsy. The most frequently used ICD-9 codes were general codes of 307.4 (Nonorganic sleep disorder, unspecified), 780.5 (unspecified sleep disturbance) and ICD-10 codes of G47.8 (other sleep disorders), G47.9 (sleep disorder, unspecified). The best definition for identifying a sleep disorder was an ICD code (from physician claims) 2 years prior and 1 year post sleep clinic visit: sensitivity 79.2%, specificity 28.4%, PPV 89.1%, and NPV 15.6%. ICD codes from ED/ambulatory care data provided similar diagnostic performance when at least 2 codes appeared in a time period of 2 years prior and 1 year post sleep clinic visit: sensitivity 71.9%, specificity 54.6%, PPV 92.1%, and NPV 20.8%. The inpatient data yielded poor results in all tested ICD code combinations. Conclusion Sleep disorders in administrative data can be identified mainly through physician claims data and with some being determined through outpatient/ambulatory care data ICD codes, however these are poorly coded within inpatient data sources. This may be a function of how sleep disorders are diagnosed and/or reported by physicians in inpatient and outpatient settings within medical records. Future work to optimize administrative data case definitions through data linkage are needed

Measuring agreement of administrative data with chart data using prevalence unadjusted and adjusted kappa

YesFunding provided by the Open Access Authors Fund

Home care utilization and outcomes among Asian and other Canadian patients with heart failure

Article deposited according to publisher policy posted on SHERPA/RoMEO, 16/08/2010.YesFunding provided by the Open Access Authors Fund

Numerical Simulation on Interfacial Characteristics in Supersonic Steam–water Injector Using Particle Model Method

Steam–water injectors have been widely applied in various industrial fields because of their compact and passive features. Despite its straightforward mechanical design, the internal two-phase condensing flow phenomena are extremely complicated. In present study, a numerical model has been developed to simulate steam–water interfacial characteristics in the injectors based on Eulerian–Eulerian multiphase model in ANSYS CFX software. A particle model is available for the interphase transfer between steam and water, in which a thermal phase change model was inserted into the model as a CFX Expression Language (CEL) to calculate interphase heat and mass transfer. The developed model is validated against a test case under a typical operating condition. The numerical results are consistent with experimental data both in terms of axial pressure and temperature profiles, which preliminarily demonstrates the feasibility and accuracy of particle model on simulation of gas–liquid interfacial characteristics in the mixing chamber of injector. Based on the dynamic equilibrium of steam supply and its condensation, interfacial characteristics including the variation of steam plume penetration length and steam–water interface have been discussed under different operating conditions. The numerical results show that steam plume expands with steam inlet mass flow rate and water inlet temperature increasing, while it contracts with the increase of water inlet mass flow rate and backpressure. Besides this, the condensation shock position moves upstream with the backpressure increasing