Search

RESUME CONTEXTE: Les caracteristiques des patients, les soins cliniques, l’utilisation des ressources et les issues cliniques des personnes atteintes de la maladie a coronavirus 2019 (COVID-19) hospitalisees au Canada ne sont pas bien connus. METHODES: Nous avons recueilli des donnees sur tous les adultes hospitalises atteints de la COVID-19 ou de l’influenza ayant obtenu leur conge d’unites medicales ou d’unites de soins intensifs medicaux et chirurgicaux entre le 1er novembre 2019 et le 30 juin 2020 dans 7 centres hospitaliers de Toronto et de Mississauga (Ontario). Nous avons compare les issues cliniques des patients a l’aide de modeles de regression multivariee, en tenant compte des facteurs sociodemographiques et de l’intensite des comorbidites. Nous avons valide le degre d’exactitude de 7 scores de risque mis au point a l’externe pour determiner leur capacite a predire le risque de deces chez les patients atteints de la COVID-19. RESULTATS: Parmi les hospitalisations retenues, 1027 patients etaient atteints de la COVID-19 (âge median de 65 ans, 59,1 % d’hommes) et 783 etaient atteints de l’influenza (âge median de 68 ans, 50,8 % d’hommes). Les patients âges de moins de 50 ans comptaient pour 21,2 % de toutes les hospitalisations dues a la COVID-19 et 24,0 % des sejours aux soins intensifs. Comparativement aux patients atteints de l’influenza, les patients atteints de la COVID-19 presentaient un taux de mortalite perhospitaliere (mortalite non ajustee 19,9 % c. 6,1 %; risque relatif [RR] ajuste 3,46 %, intervalle de confiance [IC] a 95 % 2,56–4,68) et un taux d’utilisation des ressources des unites de soins intensifs (taux non ajuste 26,4 % c. 18,0 %; RR ajuste 1,50, IC a 95 % 1,25–1,80) significativement plus eleves, ainsi qu’une duree d’hospitalisation (duree mediane non ajustee 8,7 jours c. 4,8 jours; rapport des taux d’incidence ajuste 1,45; IC a 95 % 1,25–1,69) significativement plus longue. Le taux de rehospitalisation dans les 30 jours n’etait pas significativement different (taux non ajuste 9,3 % c. 9,6 %; RR ajuste 0,98 %, IC a 95 % 0,70–1,39). Trois scores de risque utilisant un pointage pour predire la mortalite perhospitaliere ont montre une bonne discrimination (aire sous la courbe [ASC] de la fonction d’efficacite du recepteur [ROC] 0,72–0,81) et une bonne calibration. INTERPRETATION: Durant la premiere vague de la pandemie, l’hospitalisation des patients atteints de la COVID-19 etait associee a des taux de mortalite et d’utilisation des ressources des unites de soins intensifs et a une duree d’hospitalisation significativement plus importants que les hospitalisations des patients atteints de l’influenza. De simples scores de risque peuvent predire avec une bonne exactitude le risque de mortalite perhospitaliere des patients atteints de la COVID-19.

BackgroundVariations in inpatient medical care are typically attributed to system, hospital or patient factors. Little is known about variations at the physician level within hospitals. We described the physician-level variation in clinical outcomes and resource use in general internal medicine (GIM).MethodsThis was an observational study of all emergency admissions to GIM at seven hospitals in Ontario, Canada, over a 5-year period between 2010 and 2015. Physician-level variations in inpatient mortality, hospital length of stay, 30-day readmission and use of ‘advanced imaging’ (CT, MRI or ultrasound scans) were measured. Physicians were categorised into quartiles within each hospital for each outcome and then quartiles were pooled across all hospitals (eg, physicians in the highest quartile at each hospital were grouped together). We report absolute differences between physicians in the highest and lowest quartiles after matching admissions based on propensity scores to account for patient-level variation.ResultsThe sample included 103 085 admissions to 135 attending physicians. After propensity score matching, the difference between physicians in the highest and lowest quartiles for in-hospital mortality was 2.4% (95% CI 0.6% to 4.3%, pConclusionsPatient outcomes and resource use in inpatient medical care varied substantially across physicians in this study. Physician-level variations in length of stay and imaging use were unlikely to be explained by patient factors whereas differences in mortality and readmission should be interpreted with caution and could be explained by unmeasured confounders. Physician-level variations may represent practice differences that highlight quality improvement opportunities.

ObjectivePatient data repositories often assemble medication data from multiple sources, necessitating standardization prior to analysis. We implemented and evaluated a medication standardization procedure for use with a wide range of pharmacy data inputs across all drug categories, which supports research queries at multiple levels of granularity.MethodsThe GEMINI-RxNorm system automates the use of multiple RxNorm tools in tandem with other datasets to identify drug concepts from pharmacy orders. GEMINI-RxNorm was used to process 2,090,155 pharmacy orders from 245,258 hospitalizations between 2010 and 2017 at 7 hospitals in Ontario, Canada. The GEMINI-RxNorm system matches drug-identifying information from pharmacy data (including free-text fields) to RxNorm concept identifiers. A user interface allows researchers to search for drug terms and returns the relevant original pharmacy data through the matched RxNorm concepts. Users can then manually validate the predicted matches and discard false positives. We designed the system to maximize recall (sensitivity) and enable excellent precision (positive predictive value) with minimal manual validation. We compared the performance of this system to manual coding (by a physician and pharmacist) of 13 medication classes.ResultsManual coding was performed for 1,948,817 pharmacy orders and GEMINI-RxNorm successfully returned 1,941,389 (99.6%) orders. Recall was greater than 98.5% in all 13 drug classes, and the F-Measure and precision remained above 90.0% in all drug classes, facilitating efficient manual review to achieve 100.0% precision. GEMINI-RxNorm saved time substantially compared to manual standardization, reducing the time taken to review a pharmacy order row from an estimated 30 seconds to 5 seconds and reducing the number of rows needed to be reviewed by up to 99.99%.Discussion and ConclusionGEMINI-RxNorm presents a novel combination of RxNorm tools and other datasets to enable accurate, efficient, flexible, and scalable standardization of pharmacy data. By facilitating efficient minimal manual validation, the GEMINI-RxNorm system can allow researchers to achieve near-perfect accuracy in medication data standardization.

Background: Acute inpatient hospital admissions account for more than half of all health care costs related to diabetes. We sought to identify the most common and costly conditions leading to hospital admission among patients with diabetes compared with patients without diabetes. Methods: We used data from the General Internal Medicine Inpatient Initiative (GEMINI) study, a retrospective cohort study, of all patients admitted to a general internal medicine service at 7 Toronto hospitals between 2010 and 2015. The Canadian Institute for Health Information (CIHI) Most Responsible Diagnosis code was used to identify the 10 most frequent reasons for admission in patients with diabetes. Cost of hospital admission was estimated using the CIHI Resource Intensity Weight. Comparisons were made between patients with or without diabetes using the Pearson χ2 test for frequency and distribution-free confidence intervals (CIs) for median cost. Results: Among the 150 499 hospital admissions in our study, 41 934 (27.8%) involved patients with diabetes. Compared with patients without diabetes, hospital admissions because of soft tissue and bone infections were most frequent (2.5% v. 1.9%; prevalence ratio [PR] 1.28, 95% CI 1.19–1.37) and costly (Can$8794 v. Can$5845; cost ratio [CR] 1.50, 95% CI 1.37–1.65) among patients with diabetes. This was followed by urinary tract infections (PR 1.16, 95% CI 1.11–1.22; CR 1.23, 95% CI 1.17–1.29), stroke (PR 1.13, 95% CI 1.07–1.19; CR 1.19, 95% CI 1.14–1.25) and electrolyte disorders (PR 1.11, 95% CI 1.03–1.20; CR 1.20, 95% CI 1.08–1.34). Interpretation: Soft tissue and bone infections, urinary tract infections, stroke and electrolyte disorders are associated with a greater frequency and cost of hospital admissions in patients with diabetes than in those without diabetes. Preventive strategies focused on reducing hospital admissions secondary to these disorders may be beneficial in patients with diabetes.

BackgroundPatient characteristics, clinical care, resource use, and outcomes associated with hospitalization for coronavirus disease (COVID-19) in Canada are not well described.MethodsWe described all adult discharges from inpatient medical services and medical-surgical intensive care units (ICU) between November 1, 2019 and June 30, 2020 at 7 hospitals in Toronto and Mississauga, Ontario. We compared patients hospitalized with COVID-19, influenza and all other conditions using multivariable regression models controlling for patient age, sex, comorbidity, and residence in long-term-care.ResultsThere were 43,462 discharges in the study period, including 1,027 (3.0%) with COVID-19 and 783 (2.3%) with influenza. Patients with COVID-19 had similar age to patients with influenza and other conditions (median age 65 years vs. 68 years and 68 years, respectively, SDInterpretationAdults hospitalized with COVID-19 during the first wave of the pandemic used substantial hospital resources and suffered high mortality. COVID-19 was associated with significantly greater mortality, ICU use, and hospital length-of-stay than influenza.

Background The coronavirus disease 2019 (COVID-19) outbreak increases the importance of strategies to enhance urgent medical care delivery in long-term care (LTC) facilities that could potentially reduce transfers to emergency departments. The study objective was to model resource requirements to deliver virtual urgent medical care in LTC facilities. Methods We used data from all general medicine inpatient admissions at 7 hospitals in the Greater Toronto Area, Ontario, Canada, over a 7.5-year period (Apr. 1, 2010, to Oct. 31, 2017) to estimate historical patterns of hospital resource use by LTC residents. We estimated an upper bound of potentially avoidable transfers by combining data on short admissions (≤ 72 h) with historical data on the proportion of transfers from LTC facilities for which patients were discharged from the emergency department without admission. Regression models were used to extrapolate future resource requirements, and queuing models were used to estimate physician staffing requirements to perform virtual assessments. Results There were 235 375 admissions to general medicine wards, and residents of LTC facilities (age 16 yr or older) accounted for 9.3% (n = 21 948) of these admissions. Among the admissions of residents of LTC facilities, short admissions constituted 24.1% (n = 5297), and for 99.8% (n = 5284) of these admissions, the patient received laboratory testing, for 86.9% (n = 4604) the patient received plain radiography, for 41.5% (n = 2197) the patient received computed tomography and for 81.2% (n = 4300) the patient received intravenous medications. If all patients who have short admissions and are transferred from the emergency department were diverted to outpatient care, the average weekly demand for outpatient imaging per hospital would be 2.6 ultrasounds, 11.9 computed tomographic scans and 23.9 radiographs per week. The average daily volume of urgent medical virtual assessments would range from 2.0 to 5.8 per hospital. A single centralized virtual assessment centre staffed by 2 or 3 physicians would provide services similar in efficiency (measured by waiting time for physician assessment) to 7 separate centres staffed by 1 physician each. Interpretation The provision of acute medical care to LTC residents at their facility would probably require rapid access to outpatient diagnostic imaging, within-facility access to laboratory services and intravenous medication and virtual consultations with physicians. The results of this study can inform efforts to deliver urgent medical care in LTC facilities in light of a potential surge in COVID-19 cases.

ObjectiveLarge clinical databases are increasingly being used for research and quality improvement, but there remains uncertainty about how computational and manual approaches can be used together to assess and improve the quality of extracted data. The General Medicine Inpatient Initiative (GEMINI) database extracts and standardizes a broad range of data from clinical and administrative hospital data systems, including information about attending physicians, room transfers, laboratory tests, diagnostic imaging reports, and outcomes such as death in-hospital. We describe computational data quality assessment and manual data validation techniques that were used for GEMINI.MethodsThe GEMINI database currently contains 245,559 General Internal Medicine patient admissions at 7 hospital sites in Ontario, Canada from 2010-2017. We performed 7 computational data quality checks followed by manual validation of 23,419 selected data points on a sample of 7,488 patients across participating hospitals. After iteratively re-extracting data as needed based on the computational data quality checks, we manually validated GEMINI data against the data that could be obtained using the hospital’s electronic medical record (i.e. the data clinicians would see when providing care), which we considered the gold standard. We calculated accuracy, sensitivity, specificity, and positive and negative predictive values of GEMINI data.ResultsComputational checks identified multiple data quality issues – for example, the inclusion of cancelled radiology tests, a time shift of transfusion data, and mistakenly processing the symbol for sodium, “Na”, as a missing value. Manual data validation revealed that GEMINI data were ultimately highly reliable compared to the gold standard across nearly all data tables. One important data quality issue was identified by manual validation that was not detected by computational checks, which was that the dates and times of blood transfusion data at one site were not reliable. This resulted in low sensitivity (66%) and positive predictive value (75%) for blood transfusion data at that site. Apart from this single issue, GEMINI data were highly reliable across all data tables, with high overall accuracy (ranging from 98-100%), sensitivity (95-100%), specificity (99-100%), positive predictive value (93-100%), and negative predictive value (99-100%) compared to the gold standard.Discussion and ConclusionIterative assessment and improvement of data quality based primarily on computational checks permitted highly reliable extraction of multisite clinical and administrative data. Computational checks identified nearly all of the data quality issues in this initiative but one critical quality issue was only identified during manual validation. Combining computational checks and manual validation may be the optimal method for assessing and improving the quality of large multi-site clinical databases.

In this article, new Gibbs free energy of mixing is derived when there are specific interactions between sol- vent-solute molecules and between solute-solute molecules in binary solutions. It is asssumed that a probability of specific interactions satisfies a binomial distribution. Using this Gibbs free energy of mixing, we investigate possible types of partial miscibilities and show that Ω-shaped temperature-composition phase diagrams can exist. We calculate Ω-shaped temperature- composition phase diagram of water-2-butanol system and compare that with result calculated by the method of Hino 5 et al.

In some binary solution, closed miscibility loop of temperature-composition phase diagram occurs where both an upper critical solution temperature and a lower critical solution temperature exist. It is known that this phenomena occurs if specific interaction between molecules exists. There are several ways describing the specific interaction. In this work it is assumed that the total number of specific interactions is distributed according to binomial distribution. In this case, exact mathematical conditions for closed miscibility loop phase behavior are derived when the specific interaction is applied to reg- ular solution theory, quasichemical theory and Flory-Huggins lattice theory. And we investigated the effect of parameters on the phase diagram. The phase diagram of water-nicotine is calculated and compared with experimental data.