Identifying clusters of coexisting conditions and outcomes among adults admitted to hospital with community-acquired pneumonia: a multicentre cohort study.

BACKGROUND: Little is known about patterns of coexisting conditions and their influence on clinical care or outcomes in adults admitted to hospital for community-acquired pneumonia (CAP). We sought to evaluate how coexisting conditions cluster in this population to advance understanding of how multimorbidity affects CAP. METHODS: We studied 11 085 adults admitted to hospital with CAP at 7 hospitals in Ontario, Canada. Using cluster analysis, we identified patient subgroups based on clustering of comorbidities in the Charlson Comorbidity Index. We derived and replicated cluster analyses in independent cohorts (derivation sample 2010-2015, replication sample 2015-2017), then combined these into a total cohort for final cluster analyses. We described differences in medications, imaging and outcomes. RESULTS: Patients clustered into 7 subgroups. The low comorbidity subgroup (n = 3052, 27.5%) had no comorbidities. The DM-HF-Pulm subgroup had prevalent diabetes, heart failure and chronic lung disease (n = 1710, 15.4%). One disease category defined each remaining subgroup, as follows: pulmonary (n = 1621, 14.6%), diabetes (n = 1281, 11.6%), heart failure (n = 1370, 12.4%), dementia (n = 1038, 9.4%) and cancer (n = 1013, 9.1%). Corticosteroid use ranged from 11.5% to 64.9% in the dementia and pulmonary subgroups, respectively. Piperacillin-tazobactam use ranged from 9.1% to 28.0% in the pulmonary and cancer subgroups, respectively. The use of thoracic computed tomography ranged from 5.7% to 36.3% in the dementia and cancer subgroups, respectively. Adjusting for patient factors, the risk of in-hospital death was greater in the cancer (adjusted odds ratio [OR] 3.12, 95% confidence interval [CI] 2.44-3.99), dementia (adjusted OR 1.57, 95% CI 1.05-2.35), heart failure (adjusted OR 1.66, 95% CI 1.35-2.03) and DM-HF-Pulm subgroups (adjusted OR 1.35, 95% CI 1.12-1.61), and lower in the diabetes subgroup (adjusted OR 0.67, 95% CI 0.50-0.89), compared with the low comorbidity group. INTERPRETATION: Patients admitted to hospital with CAP cluster into clinically recognizable subgroups based on coexisting conditions. Clinical care and outcomes vary among these subgroups with little evidence to guide decision-making, highlighting opportunities for research to personalize care.

Automated identification of unstandardized medication data: a scalable and flexible data standardization pipeline using RxNorm on GEMINI multicenter hospital data.

Objective: Patient 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. Methods: The 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 efficient manual validation. We compared the performance of this system to manual coding (by a physician and pharmacist) of 13 medication classes. Results: Manual coding was performed for 1 948 817 pharmacy orders and GEMINI-RxNorm successfully returned 1 941 389 (99.6%) orders. Recall was greater than 0.985 in all 13 drug classes, and the F1-score and precision remained above 0.90 in all drug classes, facilitating efficient manual review to achieve 100% precision. GEMINI-RxNorm saved time substantially compared with manual standardization, reducing the time taken to review a pharmacy order row from an estimated 30 to 5 s and reducing the number of rows needed to be reviewed by up to 99.99%. Discussion and Conclusion: GEMINI-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 manual validation, the GEMINI-RxNorm system can allow researchers to achieve near-perfect accuracy in medication data standardization.

Variations in long-term care home resident hospitalizations before and during the COVID-19 pandemic in Ontario.

OBJECTIVES: To examine how the COVID-19 pandemic affected the demographic and clinical characteristics, in-hospital care, and outcomes of long-term care residents admitted to general medicine wards for non-COVID-19 reasons. METHODS: We conducted a retrospective cohort study of long-term care residents admitted to general medicine wards, for reasons other than COVID-19, in four hospitals in Toronto, Ontario between January 1, 2018 and December 31, 2020. We used an autoregressive linear model to estimate the change in monthly admission volumes during the pandemic period (March-December 2020) compared to the previous two years, adjusting for any secular trend. We summarized and compared differences in the demographics, comorbidities, interventions, diagnoses, imaging, psychoactive medications, and outcomes of residents before and during the pandemic. RESULTS: Our study included 2,654 long-term care residents who were hospitalized for non-COVID-19 reasons between January 2018 and December 2020. The crude rate of hospitalizations was 79.3 per month between March-December of 2018-2019 and 56.5 per month between March-December of 2020. The was an adjusted absolute difference of 27.0 (95% CI: 10.0, 43.9) fewer hospital admissions during the pandemic period, corresponding to a relative drop of 34%. Residents admitted during the pandemic period had similar demographics and clinical characteristics but were more likely to be admitted for delirium (pandemic: 7% pre-pandemic: 5%, p = 0.01) and were less likely to be admitted for pneumonia (pandemic: 3% pre-pandemic: 6%, p = 0.004). Residents admitted during the pandemic were more likely to be prescribed antipsychotics (pandemic: 37%, pre-pandemic: 29%, p <0.001) and more likely to die in-hospital (pandemic:14% pre-pandemic: 10%, p = 0.04). CONCLUSIONS AND IMPLICATIONS: Better integration between long-term care and hospitals systems, including programs to deliver urgent medical care services within long-term care homes, is needed to ensure that long-term care residents maintain equitable access to acute care during current and future public health emergencies.

Caractéristiques et issues des hospitalisations pour les cas de COVID-19 et d'influenza dans la région de Toronto.

CONTEXTE: Les caractéristiques des patients, les soins cliniques, l'utilisation des ressources et les issues cliniques des personnes atteintes de la maladie à coronavirus 2019 (COVID-19) hospitalisées au Canada ne sont pas bien connus. MÉTHODES: Nous avons recueilli des données sur tous les adultes hospitalisés atteints de la COVID-19 ou de l'influenza ayant obtenu leur congé d'unités médicales ou d'unités de soins intensifs médicaux et chirurgicaux entre le 1er novembre 2019 et le 30 juin 2020 dans 7 centres hospitaliers de Toronto et de Mississauga (Ontario). Nous avons comparé les issues cliniques des patients à l'aide de modèles de régression multivariée, en tenant compte des facteurs sociodémographiques et de l'intensité des comorbidités. Nous avons validé le degré d'exactitude de 7 scores de risque mis au point à l'externe pour déterminer leur capacité à prédire le risque de décès chez les patients atteints de la COVID-19. RÉSULTATS: Parmi les hospitalisations retenues, 1027 patients étaient atteints de la COVID-19 (âge médian de 65 ans, 59,1 % d'hommes) et 783 étaient atteints de l'influenza (âge médian de 68 ans, 50,8 % d'hommes). Les patients âgés de moins de 50 ans comptaient pour 21,2 % de toutes les hospitalisations dues à la COVID-19 et 24,0 % des séjours aux soins intensifs. Comparativement aux patients atteints de l'influenza, les patients atteints de la COVID-19 présentaient un taux de mortalité perhospitalière (mortalité non ajustée 19,9 % c. 6,1 %; risque relatif [RR] ajusté 3,46 %, intervalle de confiance [IC] à 95 % 2,56­4,68) et un taux d'utilisation des ressources des unités de soins intensifs (taux non ajusté 26,4 % c. 18,0 %; RR ajusté 1,50, IC à 95 % 1,25­1,80) significativement plus élevés, ainsi qu'une durée d'hospitalisation (durée médiane non ajustée 8,7 jours c. 4,8 jours; rapport des taux d'incidence ajusté 1,45; IC à 95 % 1,25­1,69) significativement plus longue. Le taux de réhospitalisation dans les 30 jours n'était pas significativement différent (taux non ajusté 9,3 % c. 9,6 %; RR ajusté 0,98 %, IC à 95 % 0,70­1,39). Trois scores de risque utilisant un pointage pour prédire la mortalité perhospitalière ont montré une bonne discrimination (aire sous la courbe [ASC] de la fonction d'efficacité du récepteur [ROC] 0,72­0,81) et une bonne calibration. INTERPRÉTATION: Durant la première vague de la pandémie, l'hospitalisation des patients atteints de la COVID-19 était associée à des taux de mortalité et d'utilisation des ressources des unités de soins intensifs et à une durée d'hospitalisation significativement plus importants que les hospitalisations des patients atteints de l'influenza. De simples scores de risque peuvent prédire avec une bonne exactitude le risque de mortalité perhospitalière des patients atteints de la COVID-19.

Association of diabetes with frequency and cost of hospital admissions: a retrospective cohort study.

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.

Characteristics and outcomes of hospital admissions for COVID-19 and influenza in the Toronto area.

BACKGROUND: Patient characteristics, clinical care, resource use and outcomes associated with admission to hospital for coronavirus disease 2019 (COVID-19) in Canada are not well described. METHODS: We described all adults with COVID-19 or influenza discharged from inpatient medical services and medical-surgical intensive care units (ICUs) between Nov. 1, 2019, and June 30, 2020, at 7 hospitals in Toronto and Mississauga, Ontario. We compared patient outcomes using multivariable regression models, controlling for patient sociodemographic factors and comorbidity level. We validated the accuracy of 7 externally developed risk scores to predict mortality among patients with COVID-19. RESULTS: There were 1027 hospital admissions with COVID-19 (median age 65 yr, 59.1% male) and 783 with influenza (median age 68 yr, 50.8% male). Patients younger than 50 years accounted for 21.2% of all admissions for COVID-19 and 24.0% of ICU admissions. Compared with influenza, patients with COVID-19 had significantly greater in-hospital mortality (unadjusted 19.9% v. 6.1%, adjusted relative risk [RR] 3.46, 95% confidence interval [CI] 2.56-4.68), ICU use (unadjusted 26.4% v. 18.0%, adjusted RR 1.50, 95% CI 1.25-1.80) and hospital length of stay (unadjusted median 8.7 d v. 4.8 d, adjusted rate ratio 1.45, 95% CI 1.25-1.69). Thirty-day readmission was not significantly different (unadjusted 9.3% v. 9.6%, adjusted RR 0.98, 95% CI 0.70-1.39). Three points-based risk scores for predicting in-hospital mortality showed good discrimination (area under the receiver operating characteristic curve [AUC] ranging from 0.72 to 0.81) and calibration. INTERPRETATION: During the first wave of the pandemic, admission to hospital for COVID-19 was associated with significantly greater mortality, ICU use and hospital length of stay than influenza. Simple risk scores can predict in-hospital mortality in patients with COVID-19 with good accuracy.

Physician-level variation in clinical outcomes and resource use in inpatient general internal medicine: an observational study.

BACKGROUND: Variations 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). METHODS: This 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. RESULTS: The 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%, p<0.01); for readmission was 3.3% (95% CI 0.7% to 5.9%, p<0.01); for advanced imaging was 0.32 tests per admission (95% CI 0.12 to 0.52, p<0.01); and for hospital length of stay was 1.2 additional days per admission (95% CI 0.5 to 1.9, p<0.01). Physician-level differences in length of stay and imaging use were consistent across numerous sensitivity analyses and stable over time. Differences in mortality and readmission were consistent across most sensitivity analyses but were not stable over time and estimates were limited by sample size. CONCLUSIONS: Patient 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.

Assessing the quality of clinical and administrative data extracted from hospitals: the General Medicine Inpatient Initiative (GEMINI) experience.

OBJECTIVE: Large clinical databases are increasingly used for research and quality improvement. We describe an approach to data quality assessment from the General Medicine Inpatient Initiative (GEMINI), which collects and standardizes administrative and clinical data from hospitals. METHODS: The GEMINI database contained 245 559 patient admissions at 7 hospitals in Ontario, Canada from 2010 to 2017. We performed 7 computational data quality checks and iteratively re-extracted data from hospitals to correct problems. Thereafter, GEMINI data were compared to data that were manually abstracted from the hospital's electronic medical record for 23 419 selected data points on a sample of 7488 patients. RESULTS: Computational checks flagged 103 potential data quality issues, which were either corrected or documented to inform future analysis. For example, we identified the inclusion of canceled radiology tests, a time shift of transfusion data, and mistakenly processing the chemical symbol for sodium ("Na") as a missing value. Manual validation identified 1 important data quality issue that was not detected by computational checks: transfusion dates and times at 1 site were unreliable. Apart from that single issue, across all data tables, GEMINI data had 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 CONCLUSION: Computational data quality checks with iterative re-extraction facilitated reliable data collection from hospitals but missed 1 critical quality issue. Combining computational and manual approaches may be optimal for assessing the quality of large multisite clinical databases.

Modelling resource requirements and physician staffing to provide virtual urgent medical care for residents of long-term care homes: a cross-sectional study.

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.

Appropriateness of peripherally inserted central catheter use among general medical inpatients: an observational study using routinely collected data.

BACKGROUND: Peripherally inserted central catheters (PICC) are among the most commonly used medical devices in hospital. This study sought to determine the appropriateness of inpatient PICC use in general medicine at five academic hospitals in Toronto, Ontario, Canada, based on the Michigan Appropriateness Guide for Intravenous Catheters (MAGIC). METHODS: This was a retrospective, cross-sectional study of general internal medicine patients discharged between 1 April 2010 and 31 March 2015 who received a PICC during hospitalisation. The primary outcomes were the proportions of appropriate and inappropriate inpatient PICC use based on MAGIC recommendations. Hospital administrative data and electronic clinical data were used to determine appropriateness of each PICC placement. Multivariable regression models were fit to explore patient predictors of inappropriate use. RESULTS: Among 3479 PICC placements, 1848 (53%, 95% CI 51% to 55%) were appropriate, 573 (16%, 95% CI 15% to 18%) were inappropriate and 1058 (30%, 95% CI 29% to 32%) were of uncertain appropriateness. The proportion of appropriate and inappropriate PICCs ranged from 44% to 61% (p<0.001) and 13% to 21% (p<0.001) across hospitals, respectively. The most common reasons for inappropriate PICC use were placement in patients with advanced chronic kidney disease (n=500, 14%) and use for fewer than 15 days in patients who are critically ill (n=53), which represented 14% of all PICC placements in the intensive care unit. Patients who were older, female, had a Charlson Comorbidity Index score greater than 0 and more severe illness based on the Laboratory-based Acute Physiology Score were more likely to receive an inappropriate PICC. CONCLUSIONS: Clinical practice recommendations can be operationalised into measurable domains to estimate the appropriateness of PICC insertions using routinely collected hospital data. Inappropriate PICC use was common and varied substantially across hospitals in this study, suggesting that there are important opportunities to improve care.

Continuous renal replacement therapy for the treatment of acute kidney injury.

BACKGROUND/AIMS: Continuous renal replacement therapy (CRRT) has been widely used for treating critically ill patients with acute kidney injury (AKI). Whether CRRT is better than intermittent hemodialysis for the treatment of AKI remains controversial. We sought to identify the clinical features that can predict survival for the patients who are treated with CRRT. METHODS: We analyzed the data of 125 patients who received CRRT between 2005 and 2007. We identified the demographic variables, the underlying diagnoses, the duration of CRRT, the mean arterial blood pressure (ABP) and the Simplified Acute Physiology Score (SAPS) II. The classification/staging system for acute kidney injury (AKI) was applied to all the patients, who were then divided into stage 1-3 subgroups. RESULTS: The average age of the patients was 61.414.3 years and the mortality rate was 60% (75 of 125 patients). The survivors had a significantly higher mean ABP and a higher mean serum bicarbonate level, which were measured the day after CRRT, than the nonsurvivors (86.723.7 vs. 69.224.6 mm Hg, respectively, 21.43.5 vs. 16.45.4 mmol/L, respectively,; p<0.05 for each). The stage 3 AKI patients showed the worst parameters for the SAPS II score and the serum levels of creatinine and blood urea nitrogen. The mortality rate was higher for the stage 3 subgroup than the other groups (70.5%, p<0.05). CONCLUSIONS: The patients with AKI and who require CRRT continue to have a high mortality rate. A higher mean ABP and a higher serum bicarbonate level measured the day after CRRT may predict a more favorable prognosis. The staging system for AKI can improve the ability to predict the outcomes of CRRT patients.