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Background: Delirium is a major cause of preventable mortality and morbidity in hospitalized adults, but accurately determining rates of delirium remains a challenge. Objective: To characterize and compare medical inpatients identified as having delirium using two common methods, administrative data and retrospective chart review. Methods: We conducted a retrospective study of 3881 randomly selected internal medicine hospital admissions from six acute care hospitals in Toronto and Mississauga, Ontario, Canada. Delirium status was determined using ICD-10-CA codes from hospital administrative data and through a previously validated chart review method. Baseline sociodemographic and clinical characteristics, processes of care and outcomes were compared across those without delirium in hospital and those with delirium as determined by administrative data and chart review. Results: Delirium was identified in 6.3% of admissions by ICD-10-CA codes compared to 25.7% by chart review. Using chart review as the reference standard, ICD-10-CA codes for delirium had sensitivity 24.1% (95%CI: 21.5–26.8%), specificity 99.8% (95%CI: 99.5–99.9%), positive predictive value 97.6% (95%CI: 94.6–98.9%), and negative predictive value 79.2% (95%CI: 78.6–79.7%). Age over 80, male gender, and Charlson comorbidity index greater than 2 were associated with misclassification of delirium. Inpatient mortality and median costs of care were greater in patients determined to have delirium by ICD-10-CA codes (5.8% greater mortality, 95% CI: 2.0–9.5 and $6824 greater cost, 95%CI: 4713–9264) and by chart review (11.9% greater mortality, 95%CI: 9.5–14.2% and $4967 greater cost, 95%CI: 4415–5701), compared to patients without delirium. Conclusions: Administrative data are specific but highly insensitive, missing most cases of delirium in hospital. Mortality and costs of care were greater for both the delirium cases that were detected and missed by administrative data. Better methods of routinely measuring delirium in hospital are needed. [ABSTRACT FROM AUTHOR]

Aims: Certain combinations of medications can be harmful and may lead to serious adverse drug events (ADEs). Identifying potentially problematic medication clusters could help guide prescribing and/or deprescribing decisions in hospital. The aim of this study is to characterize medication prescribing patterns at hospital discharge and determine which medication clusters were associated with an increased risk of ADEs in the 30‐day posthospital discharge. Methods: All residents of the province of Ontario in Canada aged 66 years or older admitted to hospital between March 2016 and February 2017 were included. Identification of medication clusters prescribed at hospital discharge was conducted using latent class analysis. Cluster identification and categorization were based on medications dispensed up to 30‐day posthospitalization. Multivariable logistic regression was used to assess the potential association between membership to a particular medication cluster and ADEs postdischarge, while also evaluating other patient characteristics. Results: In total, 188 354 patients were included in the study cohort. Median age (interquartile range) was 77 (71–84) years, and patients had a median (IQR) (interquartile range [IQR]) of 9 (6–13) medications dispensed prior to admission. Within the study population, 6 separate clusters of dispensing patterns were identified: cardiovascular (14%), respiratory (26%), complex care needs (12%), cardiovascular and metabolic (15%), infection (10%), and surgical (24%). Overall, 12 680 (7%) patients had an ADE in the 30 days following discharge. After considering other patient characteristics, those belonging to the respiratory cluster had the highest risk of ADEs (adjusted odds ratio: 1.12, 95% confidence interval: 1.08–1.17) compared with all the other clusters, while those in the complex care needs cluster had the lowest risk (adjusted odds ratio: 0.82, 95% confidence interval: 0.77–0.87). Conclusion: This study suggests that ADEs post hospital discharge can be linked with identifiable medication clusters. This information may help clinicians and researchers better understand patient populations that are more or less likely to benefit from peri‐hospital discharge interventions aimed at reducing ADEs. PLAIN LANGUAGE SUMMARY: Certain combinations of medications prescribed to patients when they are being discharged from hospital can increase the risk of adverse events after hospital discharge. [ABSTRACT FROM AUTHOR]

Background: Functional decline is common following acute hospitalization and is associated with hospital readmission, institutionalization, and mortality. People with functional decline may have difficulty accessing post-discharge medical care, even though early physician follow-up has the potential to prevent poor outcomes and is integral to high-quality transitional care. We sought to determine whether recent functional decline was associated with lower rates of post-discharge physician follow-up, and whether this association changed during the COVID-19 pandemic, given that both functional decline and COVID-19 may affect access to post-discharge care. Method: We conducted a retrospective cohort study using health administrative data from Ontario, Canada. We included patients over 65 who were discharged from an acute care facility during March 1st, 2019 – January 31st, 2020 (pre-COVID-19 period), and March 1st, 2020 – January 31st, 2021 (COVID-19 period), and who were assessed for home care while in hospital. Patients with and without functional decline were compared. Our primary outcome was any physician follow-up visit within 7 days of discharge. We used propensity score weighting to compare outcomes between those with and without functional decline. Results: Our study included 21,771 (pre-COVID) and 17,248 (COVID) hospitalized patients, of whom 15,637 (71.8%) and 12,965 (75.2%) had recent functional decline. Pre-COVID, there was no difference in physician follow-up within 7 days of discharge (Functional decline 45.0% vs. No functional decline 44.0%; RR = 1.02, 95% CI 0.98–1.06). These results did not change in the COVID-19 period (Functional decline 51.1% vs. No functional decline 49.4%; RR = 1.03, 95% CI 0.99–1.08, Z-test for interaction p = 0.72). In the COVID-19 cohort, functional decline was associated with having a 7-day physician virtual visit (RR 1.15; 95% CI 1.08–1.24) and a 7-day physician home visit (RR 1.64; 95% CI 1.10–2.43). Conclusions: Functional decline was not associated with reduced 7-day post-discharge physician follow-up in either the pre-COVID-19 or COVID-19 periods. In the COVID-19 period, functional decline was positively associated with 7-day virtual and home-visit follow-up. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Chaim M. Bell and Lauren Lapointe-Shaw discuss the meaning of the "weekend effect" in outcomes for hospital admissions and surgeries, and comment on surprising new results published in PLOS Medicine this week. [ABSTRACT FROM AUTHOR]

To determine which nursing home (NH) resident-level admission characteristics are associated with potentially preventable emergency department (PPED) transfers. We conducted a population-level retrospective cohort study on NH resident data collected using the Resident Assessment Instrument-Minimum Data Set Version 2.0 and linked to the National Ambulatory Care Reporting System for ED transfers. We used all NH resident admission assessments from January 1, 2017, to December 31, 2018, in Ontario. The cohort included the admission assessment of 56,433 NH residents. PPED transfers were defined based on the International Classification of Disease, Version 10 (Canadian) We used logistic regression with 10-fold cross-validation and computed average marginal effects to identify the association between resident characteristics at NH admission and PPED transfers within 92 days after admission. Overall, 6.2% of residents had at least 1 PPED transfer within 92 days of NH admission. After adjustment, variables that had a prevalence of 10% or more that were associated with a 1% or more absolute increase in the risk of a PPED transfer included polypharmacy [of cohort (OC) 84.4%, risk difference (RD) 2.0%], congestive heart failure (OC 29.0%, RD 3.0%), and renal failure (OC 11.6%, RD 1.2%). Female sex (OC 63.2%, RD -1.3%), a do not hospitalize directive (OC 24.4%, RD -2.6%), change in mood (OC 66.9%, RD -1.2%), and Alzheimer's or dementia (OC 62.1%, RD -1.2%) were more than 10% prevalent and associated with a 1% or more absolute decrease in the risk of a PPED. Though many routinely collected resident characteristics were associated with a PPED transfer, the absence of sufficiently discriminating characteristics suggests that emergency department visits by NH residents are multifactorial and difficult to predict. Future studies should assess the clinical utility of risk factor identification to prevent transfers. [ABSTRACT FROM AUTHOR]