Characterizing medical patients with delirium: A cohort study comparing ICD-10 codes and a validated chart review method.

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.

Social and demographic factors associated with receipt of a COVID-19 vaccine initial booster dose and with interval between primary series completion and initial booster dose uptake among persons aged ≥ 12 years, United States, August 2021-October 2022.

COVID-19 booster dose vaccination has been crucial in ensuring protection against COVID-19 including recently predominant Omicron variants. Because vaccines against newer SARS-CoV- 2 variants are likely to be recommended in future, it will be valuable to understand past booster dose uptake among different demographic groups. Using U.S. vaccination data, this study examined intervals between primary series completion and receipt of first booster dose (monovalent or bivalent) during August 2021 - October 2022 among persons ≥12 years of age who had completed a COVID-19 vaccine primary series by October 2021. Sub-populations who were late booster recipients (received a booster dose ≥12 months after the primary series) or received no booster dose included persons <35 years old, Johnson & Johnson/Janssen vaccine primary dose recipients, persons in certain racial and ethnic groups, and persons living in rural and more socially vulnerable areas, and in the South region of the United States; these groups may benefit the most from public health outreach efforts to achieve timely COVID-19 vaccination completion in future.

Validation of case-ascertainment algorithms using health administrative data to identify people who inject drugs in Ontario, Canada.

OBJECTIVES: Health administrative data can be used to improve the health of people who inject drugs by informing public health surveillance and program planning, monitoring, and evaluation. However, methodological gaps in the use of these data persist due to challenges in accurately identifying injection drug use (IDU) at the population level. In this study, we validated case-ascertainment algorithms for identifying people who inject drugs using health administrative data in Ontario, Canada. STUDY DESIGN AND SETTING: Data from cohorts of people with recent (past 12 months) IDU, including those participating in community-based research studies or seeking drug treatment, were linked to health administrative data in Ontario from 1992 to 2020. We assessed the validity of algorithms to identify IDU over varying look-back periods (ie, all years of data [1992 onwards] or within the past 1-5 years), including inpatient and outpatient physician billing claims for drug use, emergency department (ED) visits or hospitalizations for drug use or injection-related infections, and opioid agonist treatment (OAT). RESULTS: Algorithms were validated using data from 15,241 people with recent IDU (918 in community cohorts and 14,323 seeking drug treatment). An algorithm consisting of ≥1 physician visit, ED visit, or hospitalization for drug use, or OAT record could effectively identify IDU history (91.6% sensitivity and 94.2% specificity) and recent IDU (using 3-year look back: 80.4% sensitivity, 99% specificity) among community cohorts. Algorithms were generally more sensitive among people who inject drugs seeking drug treatment. CONCLUSION: Validated algorithms using health administrative data performed well in identifying people who inject drugs. Despite their high sensitivity and specificity, the positive predictive value of these algorithms will vary depending on the underlying prevalence of IDU in the population in which they are applied.

Advanced imaging use and delays among inpatients with psychiatric comorbidity.

OBJECTIVE: To determine whether presence of a psychiatric comorbidity impacts use of inpatient imaging tests and subsequent wait times. METHODS: This was a retrospective cohort study of all patients admitted to General Internal Medicine (GIM) at five academic hospitals in Toronto, Ontario from 2010 to 2019. Exposure was presence of a coded psychiatric comorbidity on admission. Primary outcome was time to test, as calculated from the time of test ordering to time of test completion, for computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, or peripherally inserted central catheter (PICC) insertion. Multilevel mixed-effects models were used to identify predictors of time to test, and marginal effects were used to calculate differences in absolute units (h). Secondary outcome was the rate of each type of test included. Subgroup analyses were performed according to type of psychiatric comorbidity: psychotic, mood/anxiety, or substance use disorder. RESULTS: There were 196,819 GIM admissions from 2010to 2019. In 77,562 admissions, ≥1 advanced imaging test was performed. After adjusting for all covariates, presence of any psychiatric comorbidity was associated with increased time to test for MRI (adjusted difference: 5.3 h, 95% confidence interval [CI]: 3.9-6.8), PICC (adjusted difference: 3.7 h, 95% CI: 1.6-5.8), and ultrasound (adjusted difference: 3.0 h, 95% CI: 2.3-3.8), but not for CT (adjusted difference: 0.1 h, 95% CI: -0.3 to 0.5). Presence of any psychiatric comorbidity was associated with lower rate of ordering for all test types (adjusted difference: -17.2 tests per 100 days hospitalization, interquartile range: -18.0 to -16.3). CONCLUSIONS: There was a lower rate of ordering of advanced imaging among patients with psychiatric comorbidity. Once ordered, time to test completion was longer for MRI, ultrasound, and PICC. Further exploration, such as quantifying rates of cancelled tests and qualitative studies evaluating hospital, provider, and patient barriers to timely advanced imaging, will be helpful in elucidating causes for these disparities.

Short-term mortality and palliative care use after delayed hospital discharge: a population-based retrospective cohort study.

OBJECTIVES: In Canada, patients whose acute medical issues have been resolved but are awaiting discharge from hospital are designated as alternate level of care (ALC). We investigated short-term mortality and palliative care use following ALC designation in Ontario, Canada. METHODS: We conducted a population-based retrospective cohort study of adult, acute care hospital admissions in Ontario with an ALC designation between January and December 2021. Our follow-up window was until 90 days post-ALC designation or death. Setting of discharge and death was determined using admission and discharge dates from multiple databases. We measured palliative care using physician billings, inpatient palliative care records and palliative home care records. We compared the characteristics of ALC patients by 90-day survival status and compared palliative care use across settings of discharge and death. RESULTS: We included 54 839 ALC patients with a median age of 80 years. Nearly one-fifth (18.4%) of patients died within 90 days. Patients who died were older, had more comorbid conditions and were more likely to be male. Among those who died, 35.1% were never discharged from hospital and 20.3% were discharged but ultimately died in the hospital. The majority of people who died received palliative care following their ALC designation (68.1%). CONCLUSIONS: A significant proportion of patients experiencing delayed discharge die within 3 months, with the majority dying in hospitals despite being identified as ready to be discharged. Future research should examine the adequacy of palliative care provision for this population.

COVID-19 pandemic impact on the potential exacerbation of screening mammography disparities: A population-based study in Ontario, Canada.

Strategies to ramp up breast cancer screening after COVID-19 require data on the influence of the pandemic on groups of women with historically low screening uptake. Using data from Ontario, Canada, our objectives were to 1) quantify the overall pandemic impact on weekly bilateral screening mammography rates (per 100,000) of average-risk women aged 50-74 and 2) examine if COVID-19 has shifted any mammography inequalities according to age, immigration status, rurality, and access to material resources. Using a segmented negative binomial regression model, we estimated the mean change in rate at the start of the pandemic (the week of March 15, 2020) and changes in weekly trend of rates during the pandemic period (March 15-December 26, 2020) compared to the pre-pandemic period (January 3, 2016-March 14, 2020) for all women and for each subgroup. A 3-way interaction term (COVID-19*week*subgroup variable) was added to the model to detect any pandemic impact on screening disparities. Of the 3,481,283 mammograms, 8.6 % (n = 300,064) occurred during the pandemic period. Overall, the mean weekly rate dropped by 93.4 % (95 % CI 91.7 % - 94.8 %) at the beginning of COVID-19, followed by a weekly increase of 8.4 % (95 % CI 7.4 % - 9.4 %) until December 26, 2020. The pandemic did not shift any disparities (all interactions p > 0.05) and that women who were under 60 or over 70, immigrants, or with a limited access to material resources had persistently low screening rate in both periods. Interventions should proactively target these underserved populations with the goals of reducing advanced-stage breast cancer presentations and mortality.

Monitoring and reporting the US COVID-19 vaccination effort.

Immunizations are an important tool to reduce the burden of vaccine preventable diseases and improve population health.1 High-quality immunization data is essential to inform clinical and public health interventions and respond to outbreaks of vaccine-preventable diseases. To track COVID-19 vaccines and vaccinations, CDC established an integrated network that included vaccination provider systems, health information exchange systems, immunization information systems, pharmacy and dialysis systems, vaccine ordering systems, electronic health records, and tools to support mass vaccination clinics. All these systems reported data to CDC's COVID-19 response system (either directly or indirectly) where it was processed, analyzed, and disseminated. This unprecedented vaccine tracking effort provided essential information for public health officials that was used to monitor the COVID-19 response and guide decisions. This paper will describe systems, processes, and policies that enabled monitoring and reporting of COVID-19 vaccination efforts and share challenges and lessons learned for future public health emergency responses.

Virtual Visits With Own Family Physician vs Outside Family Physician and Emergency Department Use.

Importance: Virtual visits became more common after the COVID-19 pandemic, but it is unclear in what context they are best used. Objective: To investigate whether there was a difference in subsequent emergency department use between patients who had a virtual visit with their own family physician vs those who had virtual visits with an outside physician. Design, Setting, and Participants: This propensity score-matched cohort study was conducted among all Ontario residents attached to a family physician as of April 1, 2021, who had a virtual family physician visit in the subsequent year (to March 31, 2022). Exposure: The type of virtual family physician visit, with own or outside physician, was determined. In a secondary analysis, own physician visits were compared with visits with a physician working in direct-to-consumer telemedicine. Main Outcome and Measure: The primary outcome was an emergency department visit within 7 days after the virtual visit. Results: Among 5 229 240 Ontario residents with a family physician and virtual visit, 4 173 869 patients (79.8%) had a virtual encounter with their own physician (mean [SD] age, 49.3 [21.5] years; 2 420 712 females [58.0%]) and 1 055 371 patients (20.2%) had an encounter with an outside physician (mean [SD] age, 41.8 [20.9] years; 605 614 females [57.4%]). In the matched cohort of 1 885 966 patients, those who saw an outside physician were 66% more likely to visit an emergency department within 7 days than those who had a virtual visit with their own physician (30 748 of 942 983 patients [3.3%] vs 18 519 of 942 983 patients [2.0%]; risk difference, 1.3% [95% CI, 1.2%-1.3%]; relative risk, 1.66 [95% CI, 1.63-1.69]). The increase in the risk of emergency department visits was greater when comparing 30 216 patients with definite direct-to-consumer telemedicine visits with 30 216 patients with own physician visits (risk difference, 4.1% [95% CI, 3.8%-4.5%]; relative risk, 2.99 [95% CI, 2.74-3.27]). Conclusions and Relevance: In this study, patients whose virtual visit was with an outside physician were more likely to visit an emergency department in the next 7 days than those whose virtual visit was with their own family physician. These findings suggest that primary care virtual visits may be best used within an existing clinical relationship.

Association of Neighborhood Deprivation With Thrombolysis and Thrombectomy for Acute Stroke in a Health System With Universal Access.

BACKGROUND AND OBJECTIVES: The association between socioeconomic status and acute ischemic stroke treatments remain uncertain, particularly in countries with universal health care systems. This study aimed to investigate the association between neighborhood-level material deprivation and the odds of receiving IV thrombolysis or thrombectomy for acute ischemic stroke within a single-payer, government-funded health care system. METHODS: We conducted a population-based cohort study using linked administrative data from Ontario, Canada. This study involved all community-dwelling adult Ontario residents hospitalized with acute ischemic stroke between 2017 and 2022. Neighborhood-level material deprivation, measured in quintiles from least to most deprived, was our main exposure. We considered the receipt of thrombolysis or thrombectomy as the primary outcome. We used multivariable logistic regression models adjusted for baseline differences to estimate the association between material deprivation and outcomes. We performed a sensitivity analysis by additionally adjusting for hospital type at initial assessment. Furthermore, we tested whether hospital type modified the associations between deprivation and outcomes. RESULTS: Among 57,704 patients, those in the most materially deprived group (quintile 5) were less likely to be treated with thrombolysis or thrombectomy compared with those in the least deprived group (quintile 1) (16.6% vs 19.6%, adjusted odds ratio [aOR] 0.76, 95% CI 0.63-0.93). The association was consistent when evaluating thrombolysis (13.0% vs 15.3%, aOR 0.78, 95% CI 0.64-0.96) and thrombectomy (6.4 vs 7.8%, aOR 0.73, 95% CI 0.59-0.90) separately. There were no statistically significant differences between the middle 3 quintiles and the least deprived group. These associations persisted after additional adjustment for hospital type, and there was no interaction between material deprivation and hospital type (p interaction >0.1). DISCUSSION: We observed disparities in the use of thrombolysis or thrombectomy for acute ischemic stroke by socioeconomic status despite access to universal health care. Targeted health care policies, public health messaging, and resource allocation are needed to ensure equitable access to acute stroke treatments for all patients.

The association between patients' timely access to their usual primary care physician and use of walk-in clinics in Ontario, Canada: a cross-sectional study.

BACKGROUND: Challenges in timely access to one's usual primary care physician and the ongoing use of walk-in clinics have been major health policy issues in Ontario for over a decade. We sought to determine the association between patient-reported timely access to their usual primary care physician or clinic and their use of walk-in clinics. METHODS: We conducted a cross-sectional study of Ontario residents who had a primary care physician by linking population-based administrative data to Ontario's Health Care Experience Survey, collected between 2013 and 2020. We described sociodemographic characteristics and health care use for users of walk-in clinics and nonusers. We measured the adjusted association between self-reported same-day or next-day access and after-hours access to usual primary care physicians or clinics and the use of walk-in clinics in the previous 12 months. RESULTS: Of the 60 935 total responses from people who had a primary care physician, 16 166 (weighted 28.6%, unweighted 26.5%) reported visiting a walk-in clinic in the previous 12 months. Compared with nonusers, those who used walk-in clinics were predominantly younger, lived in large and medium-sized urban areas and reported a tight, very tight or poor financial situation. Respondents who reported poor same-day or next-day access to their primary care physician or clinic were more likely to report having attended a walk-in clinic in the previous 12 months than those with better access (adjusted odds ratio [OR] 1.23, 95% confidence interval [Cl] 1.13-1.34). Those who reported being unaware that their primary care physician offered after-hours care had a higher likelihood of going to a walk-in clinic (adjusted OR 1.14, 95% Cl 1.07-1.21). INTERPRETATION: In this population-based health survey, patient-reported use of walk-in clinics was associated with a reported lack of access to same-day or next-day care and unawareness of after-hours care by respondents' usual primary care physicians. These findings could inform policies to improve access to primary care, while preserving care continuity.

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.

The impact of the early COVID-19 pandemic on healthcare system resource use and costs in two provinces in Canada: An interrupted time series analysis.

INTRODUCTION: The aim of our study was to assess the initial impact of COVID-19 on total publicly-funded direct healthcare costs and health services use in two Canadian provinces, Ontario and British Columbia (BC). METHODS: This retrospective repeated cross-sectional study used population-based administrative datasets, linked within each province, from January 1, 2018 to December 27, 2020. Interrupted time series analysis was used to estimate changes in the level and trends of weekly resource use and costs, with March 16-22, 2020 as the first pandemic week. Also, in each week of 2020, we identified cases with their first positive SARS-CoV-2 test and estimated their healthcare costs until death or December 27, 2020. RESULTS: The resources with the largest level declines (95% confidence interval) in use in the first pandemic week compared to the previous week were physician services [Ontario: -43% (-49%,-37%); BC: -24% (-30%,-19%) (both p<0.001)] and emergency department visits [Ontario: -41% (-47%,-35%); BC: -29% (-35%,-23%) (both p<0.001)]. Hospital admissions declined by 27% (-32%,-23%) in Ontario and 21% (-26%,-16%) in BC (both p<0.001). Resource use subsequently rose but did not return to pre-pandemic levels. Only home care and dialysis clinic visits did not significantly decrease compared to pre-pandemic. Costs for COVID-19 cases represented 1.3% and 0.7% of total direct healthcare costs in 2020 in Ontario and BC, respectively. CONCLUSIONS: Reduced utilization of healthcare services in the overall population outweighed utilization by COVID-19 patients in 2020. Meeting the needs of all patients across all services is essential to maintain resilient healthcare systems.

Recent functional decline and outpatient follow-up after hospital discharge: a cohort study.

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.

Medication clusters at hospital discharge and risk of adverse drug events at 30 days postdischarge: A population-based cohort study of older adults.

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.

Physician antimicrobial prescribing and patient outcomes on general medical wards: a multicentre retrospective cohort study.

BACKGROUND: Variability in antimicrobial prescribing may indicate an opportunity for improvement in antimicrobial use. We sought to measure physician-level antimicrobial prescribing in adult general medical wards, assess the contribution of patient-level factors to antimicrobial prescribing and evaluate the association between antimicrobial prescribing and clinical outcomes. METHODS: Using the General Medicine Inpatient Initiative (GEMINI) database, we conducted a retrospective cohort study of physician-level volume and spectrum of antimicrobial prescribing in adult general medical wards in 4 academic teaching hospitals in Toronto, Ontario, between April 2010 and December 2019. We stratified physicians into quartiles by hospital site based on volume of antimicrobial prescribing (days of therapy per 100 patient-days and antimicrobial-free days) and antibacterial spectrum (modified spectrum score). The modified spectrum score assigns a value to each antibacterial agent based on the breadth of coverage. We assessed patient-level differences among physician quartiles using age, sex, Laboratory-based Acute Physiology Score, discharge diagnosis and Charlson Comorbidity Index. We evaluated the association of clinical outcomes (in-hospital 30-day mortality, length of stay, intensive care unit [ICU] transfer and hospital readmission) with antimicrobial volume and spectrum using multilevel modelling. RESULTS: The cohort consisted of 124 physicians responsible for 124 158 hospital admissions. The median physician-level volume of antimicrobial prescribing was 56.1 (interquartile range 51.7-67.5) days of therapy per 100 patient-days. We did not find any differences in baseline patient characteristics by physician prescribing quartile. The difference in mean prescribing between quartile 4 and quartile 1 was 15.8 days of therapy per 100 patient-days (95% confidence interval [CI] 9.6-22.0), representing 30% higher antimicrobial prescribing in the fourth quartile than the first quartile. Patient in-hospital deaths, length of stay, ICU transfer and hospital readmission did not differ by physician quartile. In-hospital mortality was higher among patients cared for by prescribers with higher modified spectrum scores (odds ratio 1.13, 95% CI 1.04-1.24). INTERPRETATION: We found that physician-level variability in antimicrobial prescribing was not associated with differences in patient characteristics or outcomes in academic general medicine wards. These findings provide support for considering the lowest quartile of physician antimicrobial prescribing within each hospital as a target for antimicrobial stewardship.

Development of Multivariable Prediction Models for the Identification of Patients Admitted to Hospital with an Exacerbation of COPD and the Prediction of Risk of Readmission: A Retrospective Cohort Study using Electronic Medical Record Data.

BACKGROUND: Approximately 20% of patients who are discharged from hospital for an acute exacerbation of COPD (AECOPD) are readmitted within 30 days. To reduce this, it is important both to identify all individuals admitted with AECOPD and to predict those who are at higher risk for readmission. OBJECTIVES: To develop two clinical prediction models using data available in electronic medical records: 1) identifying patients admitted with AECOPD and 2) predicting 30-day readmission in patients discharged after AECOPD. METHODS: Two datasets were created using all admissions to General Internal Medicine from 2012 to 2018 at two hospitals: one cohort to identify AECOPD and a second cohort to predict 30-day readmissions. We fit and internally validated models with four algorithms. RESULTS: Of the 64,609 admissions, 3,620 (5.6%) were diagnosed with an AECOPD. Of those discharged, 518 (15.4%) had a readmission to hospital within 30 days. For identification of patients with a diagnosis of an AECOPD, the top-performing models were LASSO and a four-variable regression model that consisted of specific medications ordered within the first 72 hours of admission. For 30-day readmission prediction, a two-variable regression model was the top performing model consisting of number of COPD admissions in the previous year and the number of non-COPD admissions in the previous year. CONCLUSION: We generated clinical prediction models to identify AECOPDs during hospitalization and to predict 30-day readmissions after an acute exacerbation from a dataset derived from available EMR data. Further work is needed to improve and externally validate these models.

Trends in Delirium and New Antipsychotic and Benzodiazepine Use Among Hospitalized Older Adults Before and After the Onset of the COVID-19 Pandemic.

Importance: The COVID-19 pandemic caused large disruptions to health care for hospitalized older adults. The incidence and management of delirium may have been affected by high rates of COVID-19 infection, staffing shortages, overwhelmed hospital capacity, and changes to visitor policies. Objective: To measure changes in rates of delirium and related medication prescribing during the COVID-19 pandemic among hospitalized older adults. Design, Setting, and Participants: This population-based, repeated cross-sectional study used linked databases to measure rates of delirium and related medication prescriptions among adults aged 66 years or older hospitalized before and during the COVID-19 pandemic (January 1, 2017, to March 31, 2022) in Ontario, Canada. Exposure: The first 2 years of the COVID-19 pandemic (March 1, 2020, to March 31, 2022). Main Outcomes and Measures: The main outcomes were weekly rates of delirium per 1000 admitted population and monthly rates of new antipsychotic and benzodiazepine prescriptions per 1000 discharged population. Observed rates were compared with projected rates based on modeling from 3 years before pandemic onset. Results: Among 2 128 411 hospitalizations of older adults over the 5-year study period (50.7% female; mean [SD] age, 78.9 [8.3] years), absolute rates of delirium increased from 35.9 per 1000 admitted population during the prepandemic period to 41.5 per 1000 admitted population throughout the pandemic. The adjusted rate ratio (ARR) of delirium during the pandemic compared with the projected rate was 1.15 (95% CI, 1.11-1.19). Monthly rates of new antipsychotic prescriptions increased from 6.9 to 8.8 per 1000 discharged population and new benzodiazepine prescriptions from 4.4 to 6.0 per 1000 discharged population and were significantly higher during the pandemic compared with projected rates (antipsychotics: ARR, 1.28; 95% CI, 1.19-1.38; benzodiazepines: ARR, 1.37; 95% CI, 1.20-1.57). Rates were highest during pandemic waves 1 (March to June 2020), 3 (March to June 2021), and 5 (December 2021 to February 2022) and remained elevated above projected levels throughout the first 2 years of the pandemic. Conclusions and Relevance: In this repeated cross-sectional study of hospitalized older adults, there was a temporal association between COVID-19 pandemic onset and significant increases in rates of delirium in the hospital and new antipsychotic and benzodiazepine prescriptions after hospital discharge. Rates remained elevated over 2 years. Pandemic-related changes such as visitor restrictions, staff shortages, isolation practices, and reduced staff time at the bedside may have contributed to these trends.

Using the Hospital Frailty Risk Score to assess mortality risk in older medical patients admitted to the intensive care unit.

BACKGROUND: Prognostic information at the time of hospital discharge can help guide goals-of-care discussions for future care. We sought to assess the association between the Hospital Frailty Risk Score (HFRS), which may highlight patients' risk of adverse outcomes at the time of hospital discharge, and in-hospital death among patients admitted to the intensive care unit (ICU) within 12 months of a previous hospital discharge. METHODS: We conducted a multicentre retrospective cohort study that included patients aged 75 years or older admitted at least twice over a 12-month period to the general medicine service at 7 academic centres and large community-based teaching hospitals in Toronto and Mississauga, Ontario, Canada, from Apr. 1, 2010, to Dec. 31, 2019. The HFRS (categorized as low, moderate or high frailty risk) was calculated at the time of discharge from the first hospital admission. Outcomes included ICU admission and death during the second hospital admission. RESULTS: The cohort included 22 178 patients, of whom 1767 (8.0%) were categorized as having high frailty risk, 9464 (42.7%) as having moderate frailty risk, and 10 947 (49.4%) as having low frailty risk. One hundred patients (5.7%) with high frailty risk were admitted to the ICU, compared to 566 (6.0%) of those with moderate risk and 790 (7.2%) of those with low risk. After adjustment for age, sex, hospital, day of admission, time of admission and Laboratory-based Acute Physiology Score, the odds of ICU admission were not significantly different for patients with high (adjusted odds ratio [OR] 0.99, 95% confidence interval [CI] 0.78 to 1.23) or moderate (adjusted OR 0.97, 95% CI 0.86 to 1.09) frailty risk compared to those with low frailty risk. Among patients admitted to the ICU, 75 (75.0%) of those with high frailty risk died, compared to 317 (56.0%) of those with moderate risk and 416 (52.7%) of those with low risk. After multivariable adjustment, the risk of death after ICU admission was higher for patients with high frailty risk than for those with low frailty risk (adjusted OR 2.86, 95% CI 1.77 to 4.77). INTERPRETATION: Among patients readmitted to hospital within 12 months, patients with high frailty risk were similarly likely as those with lower frailty risk to be admitted to the ICU but were more likely to die if admitted to ICU. The HFRS at hospital discharge can inform prognosis, which can help guide discussions for preferences for ICU care during future hospital stays.

Data-driven approach to identifying potential laboratory overuse in general internal medicine (GIM) inpatients.

BACKGROUND: Reducing laboratory test overuse is important for high quality, patient-centred care. Identifying priorities to reduce low value testing remains a challenge. OBJECTIVE: To develop a simple, data-driven approach to identify potential sources of laboratory overuse by combining the total cost, proportion of abnormal results and physician-level variation in use of laboratory tests. DESIGN, SETTING AND PARTICIPANTS: A multicentre, retrospective study at three academic hospitals in Toronto, Canada. All general internal medicine (GIM) hospitalisations between 1 April 2010 and 31 October 2017. RESULTS: There were 106 813 GIM hospitalisations during the study period, with median hospital length-of-stay of 4.6 days (IQR: 2.33-9.19). There were 21 tests which had a cumulative cost >US$15 400 at all three sites. The costliest test was plasma electrolytes (US$4 907 775), the test with the lowest proportion of abnormal results was red cell folate (0.2%) and the test with the greatest physician-level variation in use was antiphospholipid antibodies (coefficient of variation 3.08). The five tests with the highest cumulative rank based on greatest cost, lowest proportion of abnormal results and highest physician-level variation were: (1) lactate, (2) antiphospholipid antibodies, (3) magnesium, (4) troponin and (5) partial thromboplastin time. In addition, this method identified unique tests that may be a potential source of laboratory overuse at each hospital. CONCLUSIONS: A simple multidimensional, data-driven approach combining cost, proportion of abnormal results and physician-level variation can inform interventions to reduce laboratory test overuse. Reducing low value laboratory testing is important to promote high value, patient-centred care.

Temporal Clustering of Critical Illness Events on Medical Wards.

Importance: Recognizing and preventing patient deterioration is important for hospital safety. Objective: To investigate whether critical illness events (in-hospital death or intensive care unit [ICU] transfer) are associated with greater risk of subsequent critical illness events for other patients on the same medical ward. Design, Setting, and Participants: Retrospective cohort study in 5 hospitals in Toronto, Canada, including 118 529 hospitalizations. Patients were admitted to general internal medicine wards between April 1, 2010, and October 31, 2017. Data were analyzed between January 1, 2020, and April 10, 2023. Exposures: Critical illness events (in-hospital death or ICU transfer). Main Outcomes and Measures: The primary outcome was the composite of in-hospital death or ICU transfer. The association between critical illness events on the same ward across 6-hour intervals was studied using discrete-time survival analysis, adjusting for patient and situational factors. The association between critical illness events on different comparable wards in the same hospital was measured as a negative control. Results: The cohort included 118 529 hospitalizations (median age, 72 years [IQR, 56-83 years]; 50.7% male). Death or ICU transfer occurred in 8785 hospitalizations (7.4%). Patients were more likely to experience the primary outcome after exposure to 1 prior event (adjusted odds ratio [AOR], 1.39; 95% CI, 1.30-1.48) and more than 1 prior event (AOR, 1.49; 95% CI, 1.33-1.68) in the prior 6-hour interval compared with no exposure. The exposure was associated with increased odds of subsequent ICU transfer (1 event: AOR, 1.67; 95% CI, 1.54-1.81; >1 event: AOR, 2.05; 95% CI, 1.79-2.36) but not death alone (1 event: AOR, 1.08; 95% CI, 0.97-1.19; >1 event: AOR, 0.88; 95% CI, 0.71-1.09). There was no significant association between critical illness events on different wards within the same hospital. Conclusions and Relevance: Findings of this cohort study suggest that patients are more likely to be transferred to the ICU in the hours after another patient's critical illness event on the same ward. This phenomenon could have several explanations, including increased recognition of critical illness and preemptive ICU transfers, resource diversion to the first event, or fluctuations in ward or ICU capacity. Patient safety may be improved by better understanding the clustering of ICU transfers on medical wards.