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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.

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.

In December 2020, the US Food and Drug Administration (FDA) issued Emergency Use Authorizations (EUAs) for the Pfizer BioNTech (https://www. pfizer.com) and Moderna (https://www.modernatx. com) 2-dose primary mRNA COVID-19 vaccine [...]

Although more common among adults, severe coronavirus disease (COVID-19) and hospitalization can occur in children. Among >8,300 hospitalized children 5-11 years of age, 1/3 required intensive care (1,2). Children can [...]

Our study aimed to describe patient experience of information coordination between their primary care physician and specialists and to examine the associations between their experience and their personal and primary care characteristics. We conducted a cross-sectional study of Ontario residents rostered to a primary care physician and visited a specialist physician in the previous 12 months by linking population-based health administrative data to the Health Care Experience Survey collected between 2013 and 2020. We described respondents' sociodemographic and health care utilization characteristics and their experience of information coordination between their primary care physician and specialists. We measured the adjusted association between patient-reported measures of information coordination before and after respondents received care from a specialist physician and their type of primary care model. 1,460 out 20,422 (weighted 7.5%) of the respondents reported that their specialist physician did not have basic medical information about their visit from their primary care physician in the previous 12 months. 2,298 out of 16,442 (weighted 14.9%) of the respondents reported that their primary care physician seemed uninformed about the care they received from the specialist. Females, younger individuals, those with a college or undergraduate level of education, and users of walk-in clinics had a higher likelihood of reporting a lack of information coordination between the primary care and specialist physicians. Only respondents rostered to an enhanced fee-for-service model had a higher odds of reporting that the specialist physician did not have basic medical information about their visit compared to those rostered to a Family Health Team (OR 1.22, 95% Cl 1.12–1.40). We found no significant association between respondent's type of primary care model and that their primary care physician was uninformed about the care received from the specialist physician. In this population-based health study, respondents reported high information coordination between their primary care physician and specialists. Except for respondents rostered to an enhanced fee-for-service model of care, we did not find any difference in information coordination across other primary care models. [ABSTRACT FROM AUTHOR]

Objective: Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort. Design: A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors) using a population-based retrospective cohort study. Setting: Internal medicine services across four academic teaching hospitals in Toronto, Canada from April 2010—March 2015 (primary cohort) and 2015–2019 (secondary cohort). Patients: We identified patients admitted with sepsis based upon receipt of antibiotics and positive cultures. Measurements and main results: The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a secondary cohort (2015–2019) of hospitalized patients receiving antibiotics. Our primary cohort included 1,890 patients with a median age of 72 years (IQR: 56–83). 9% died during hospitalization, 18.6% were admitted to ICU, and mean LOS was 12.7 days (SD: 21.5). In the primary and secondary (2015–2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay > 14 days, were similar between scores, in the primary and secondary cohorts. All scores had comparable calibration for predicting mortality. Conclusions: An EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score's simplicity, it may prove a useful tool for clinical and research applications. [ABSTRACT FROM AUTHOR]

Background: High-quality primary care is associated with better health outcomes and more efficient and equitable health system performance. However, the rate of primary care attachment is falling, and timely access to primary care is worsening, driving many patients to use walk-in clinics for their comprehensive primary care needs. This study sought to explore the experiences and perceived roles and responsibilities of walk-in physicians in this current climate. Methods: Qualitative interviews were conducted with nineteen physicians currently providing walk-in care in Ontario, Canada between May and December 2022. Results: Limited capacity for continuity and comprehensiveness of care were identified as major sources of professional tension for walk-in physicians. Divergent perspectives on their roles were anchored in how physicians viewed their professional identity. Some saw providing continuous and comprehensive care as an infringement on their professional role; others saw their professional role as more flexible and responsive to population needs. Regardless of their professional identity, participants reported feeling ill-equipped to manage the swell of unattached patients, citing a lack of time, resources, connectivity to the system, and remuneration flexibility. Conclusions: As practice demands of walk-in clinics change, an evolution in the professional roles and responsibilities of walk-in physicians follows. However, the resources, structure, and incentives of walk-in care have not evolved to reflect this, leaving physicians to set their own professional boundaries with patients. This results in increasing variations in care and confusion across the primary care sector around who is responsible for what, when, and how. [ABSTRACT FROM AUTHOR]

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]

BackgroundIn Canada, 30%-60% of patients presenting to emergency departments are ambulatory. This category has been labeled as a source of emergency department overuse. Acting on the presumption that primary care practices and walk-in clinics offer equivalent care at a lower cost, governments have invested massively in improving access to these alternative settings in the hope that patients would present there instead when possible, thereby reducing the load on emergency departments. Data in support of this approach remain scarce and equivocal. ObjectiveThe aim of this study is to compare the value of care received in emergency departments, walk-in clinics, and primary care practices by ambulatory patients with upper respiratory tract infection, sinusitis, otitis media, tonsillitis, pharyngitis, bronchitis, influenza-like illness, pneumonia, acute asthma, or acute exacerbation of chronic obstructive pulmonary disease. MethodsA multicenter prospective cohort study will be performed in Ontario and Québec. In phase 1, a time-driven activity-based costing method will be applied at each of the 15 study sites. This method uses time as a cost driver to allocate direct costs (eg, medication), consumable expenditures (eg, needles), overhead costs (eg, building maintenance), and physician charges to patient care. Thus, the cost of a care episode will be proportional to the time spent receiving the care. At the end of this phase, a list of care process costs will be generated and used to calculate the cost of each consultation during phase 2, in which a prospective cohort of patients will be monitored to compare the care received in each setting. Patients aged 18 years and older, ambulatory throughout the care episode, and discharged to home with one of the aforementioned targeted diagnoses will be considered. The estimated sample size is 1485 patients. The 3 types of care settings will be compared on the basis of primary outcomes in terms of the proportion of return visits to any site 3 and 7 days after the initial visit and the mean cost of care. The secondary outcomes measured will include scores on patient-reported outcome and experience measures and mean costs borne wholly by patients. We will use multilevel generalized linear models to compare the care settings and an overlap weights approach to adjust for confounding factors related to age, sex, gender, ethnicity, comorbidities, registration with a family physician, socioeconomic status, and severity of illness. ResultsPhase 1 will begin in 2021 and phase 2, in 2023. The results will be available in 2025. ConclusionsThe end point of our program will be for deciders, patients, and care providers to be able to determine the most appropriate care setting for the management of ambulatory emergency respiratory conditions, based on the quality and cost of care associated with each alternative. International Registered Report Identifier (IRRID)PRR1-10.2196/25619

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: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Little is known about the association between the COVID-19 pandemic and early survival among newly diagnosed cancer patients. Methods: This retrospective population-based cohort study used linked administrative datasets from Ontario, Canada. Adults (=18 years) who received a cancer diagnosis between March 15 and December 31, 2020, were included in a pandemic cohort, while those diagnosed during the same dates in 2018/2019 were included in a pre-pandemic cohort. All patients were followed for one full year after the date of diagnosis. Cox proportional hazards regression models were used to assess survival in relation to the pandemic, patient characteristics at diagnosis, and the modality of first cancer treatment as a time-varying covariate. Interaction terms were explored to measure the pandemic association with survival for each cancer type. Results: Among 179,746 patients, 53,387 (29.7%) were in the pandemic cohort and 37,741 (21.0%) died over the first post-diagnosis year. No association between the pandemic and survival was found when adjusting for patient characteristics at diagnosis (HR 0.99 [95% CI 0.96-1.01]), while marginally better survival was found for the pandemic cohort when the modality of treatment was additionally considered (HR 0.97 [95% CI 0.95-0.99]). When examining each cancer type, only a new melanoma diagnosis was associated with a worse survival in the pandemic cohort (HR 1.25 [95% CI 1.05-1.49]). Conclusions: Among patients able to receive a cancer diagnosis during the pandemic, one-year overall survival was not different than those diagnosed in the previous 2 years. This study highlights the complex nature of the COVID-19 pandemic impact on cancer care. [ABSTRACT FROM AUTHOR]

Background: Physician home visits are essential for populations who cannot easily access office-based primary care. The objective of this study was to describe the characteristics, practice patterns and physician-level patient characteristics of Ontario physicians who provide home visits. Methods: This was a retrospective cross-sectional study, based on health administrative data, of Ontario physicians who provided home visits and their patients, between Jan. 1, 2019, and Dec. 31, 2019. We selected family physicians who had at least 1 home visit in 2019. Physician demographic characteristics, practice patterns and aggregated patient characteristics were compared between high-volume home visit physicians (the top 5%) and low-volume home visit physicians (bottom 95%). Results: A total of 6572 family physicians had at least 1 home visit in 2019. The top 5% of home visit physicians (n = 330) performed 58.6% of all home visits (n = 227 321 out of 387 139). Compared with low-volume home visit physicians (n = 6242), the top 5% were more likely to be male and practise in large urban areas, and rarely saw patients who were enrolled to them (median 4% v. 87.5%, standardized mean difference 1.12). High-volume physicians' home visit patients were younger, had greater levels of health care resource utilization, resided in lower-income and large urban neighbourhoods, and were less likely to have a medical home. Interpretation: A small subset of home visit physicians provided a large proportion of home visits in Ontario. These home visits may be addressing a gap in access to primary care for certain patients, but could be contributing to lower continuity of care. [ABSTRACT FROM AUTHOR]

Author(s): Lauren Lapointe-Shaw 1,*, Chaim M. Bell 1,2 Who would have thought that outcomes for elective surgery would differ according to the day of admission? In this issue of PLOS [...]

The article presents the discussion on COVID-19 illness and hospitalization being more common among older adults. Topics include CDC by jurisdictions, pharmacies, and federal entities through immunization information systems (IISs); and tests for statistical significance being notconducted due to the data reflecting the U.S. population not based on population samples.

Background: Imaging procedures are commonly performed on hospitalized patients and waiting for these could increase length-of-stay. The study objective was to quantify delays for imaging procedures in General Internal Medicine and identify contributing patient, physician, and system factors. Methods: This was a retrospective cohort study of medical inpatients admitted to 5 hospitals in Toronto, Ontario (2010–2019), with at least one imaging procedure (CT, MRI, ultrasound, or peripherally-inserted central catheter [PICC] insertion). The primary outcome was time-to-test, and the secondary outcome was acute length-of-stay after test ordering. Results: The study cohort included 73,107 hospitalizations. Time-to-test was longest for MRI (median 22 hours) and shortest for CT (median 7 hours). The greatest contributors to time-to-test were system factors such as hospital site (up to 22 additional hours), location of test ordering (up to 10 additional hours), the timing of test ordering relative to admission (up to 13 additional hours), and ordering during weekends (up to 21 additional hours). Older patient age, having more comorbidities, and residence in a low-income neighborhood were also associated with testing delays. Each additional hour spent waiting for a test was associated with increased acute length-of-stay after test ordering, ranging from 0.4 additional hours for CT to 1.2 hours for MRI. Conclusions: The greatest contributors to testing delays relate to when and where a test was ordered. Wait times affect length-of-stay and the quality of patient care. Hospitals can apply our novel approach to explore opportunities to decrease testing delays locally. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

In this narrative review, we describe what is known about non-pharmacological and pharmacological treatments for insomnia in medical inpatients, with a focus on melatonin. Hospital-acquired insomnia is common, resulting in shortened total sleep time and more nighttime awakenings. Sleep disturbance has been shown to increase systemic inflammation, pain, and the likelihood of developing delirium in hospital. Treatment for insomnia includes both non-pharmacological and pharmacological interventions, the latter of which requires careful consideration of risks and benefits given the known adverse effects. Though benzodiazepines and non-benzodiazepine benzodiazepine receptor agonists are commonly prescribed (i.e., sedative-hypnotics), they are relatively contraindicated for patients over the age of 65 due to the risk of increased falls, cognitive decline, and potential for withdrawal symptoms after long-term use. Exogenous melatonin has a comparatively low likelihood of adverse effects and drug–drug interactions and is at least as effective as other sedative-hypnotics. Though more research is needed on both its effectiveness and relative safety for inpatients, small doses of melatonin before bedtime may be an appropriate choice for inpatients when insomnia persists despite non-pharmacological interventions. [ABSTRACT FROM AUTHOR]

Objectives: In summer 2021, the number of COVID-19–associated hospitalizations in the United States increased with the surge of the SARS-CoV-2 Delta variant. We assessed how COVID-19 vaccine initiation and dose completion changed during the Delta variant surge, based on jurisdictional vaccination coverage before the surge. Methods: We analyzed COVID-19 vaccination data reported to the Centers for Disease Control and Prevention. We classified jurisdictions (50 states and the District of Columbia) into quartiles ranging from high to low first-dose vaccination coverage among people aged ≥12 years as of June 30, 2021. We calculated first-dose vaccination coverage as of June 30 and October 31, 2021, and stratified coverage by quartile, age (12-17, 18-64, ≥65 years), and sex. We assessed dose completion among those who initiated a 2-dose vaccine series. Results: Of 51 jurisdictions, 15 reached at least 70% vaccination coverage before the Delta variant surge (ie, as of June 30, 2021), while 35 reached that goal as of October 31, 2021. Jurisdictions in the lowest quartile of vaccination coverage (44.9%-54.9%) had the greatest absolute (9.7%-17.9%) and relative (18.1%-39.8%) percentage increase in vaccination coverage during July 1–October 31, 2021. Of those who received the first dose during this period across all jurisdictions, nearly 1 in 5 missed the second dose. Conclusions: Although COVID-19 vaccination initiation increased during July 1–October 31, 2021, in jurisdictions in the lowest quartile of vaccination coverage, coverage remained below that of jurisdictions in the highest quartile of vaccination coverage before the Delta variant surge. Efforts are needed to improve access to and increase confidence in COVID-19 vaccines, especially in low-coverage areas. [ABSTRACT FROM AUTHOR]