Administrative data ICD-10 diagnostic codes identifies most lab-confirmed SARS-CoV-2 admissions but misses many discharged from the Emergency Department.

We estimated the operating characteristics of ICD-10 code U07.1, introduced by the World Health Organization in 2020, to identify lab-confirmed SARS-CoV-2. CCEDRRN is a national research registry of adults (March 2020-August 2021) with suspected/confirmed SARS-CoV-2 identified in Canadian emergency departments (EDs) using chart review (symptoms, clinical information, and lab test results including SARS-CoV-2 polymerase chain reaction, PCR results). CCEDRRN data were linked to administrative hospitalization discharge and ED ICD-10 diagnostic codes (accessed centrally via the Canadian Institute for Health Information). We identified ICD-10 diagnostic codes in CCEDRRN participants. We defined lab-confirmed SARS-CoV-2 based on at least one positive PCR in the 0-14 days before the ED presentation and/or during hospitalization (in those admitted from ED). We performed separate analyses for CCEDRRN participants discharged from ED and those hospitalized from the ED. Additional analyses were stratified by province, sex, age, and (for hospitalized patients) timing of the first PCR test. The sensitivity of ICD-10 code U07.1 for a positive SARS-CoV-2 test was 93.6% (95% CI 93.0-94.1%) in those hospitalized from ED and 83.0% (95% CI 82.1-83.9%) in those discharged from the ED. Sensitivity was similar across provinces and demographics, but in each stratified analysis, values were higher in those hospitalized versus those discharged from ED. The ICD-10 diagnostic code for U07.1 within administrative data identified most lab-confirmed SARS-CoV-2 within persons hospitalized from ED, although a significant number of cases discharged from ED were missed. This should be considered when using administrative data for research and public health planning.

Characteristics and outcomes of patients with COVID-19 who return to the emergency department: a multicentre observational study by the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN).

OBJECTIVE: Unplanned return emergency department (ED) visits can reflect clinical deterioration or unmet need from the original visit. We determined the characteristics and outcomes of patients with COVID-19 who return to the ED for COVID-19-related revisits. METHODS: This retrospective observational study used data for all adult patients visiting 47 Canadian EDs with COVID-19 between 1 March 2020 and 31 March 2022. Multivariable logistic regression assessed the characteristics associated with having a no return visit (SV=single visit group) versus at least one return visit (MV=return visit group) after being discharged alive at the first ED visit. RESULTS: 39 809 patients with COVID-19 had 44 862 COVID-19-related ED visits: 35 468 patients (89%) had one visit (SV group) and 4341 (11%) returned to the ED (MV group) within 30 days (mean 2.2, SD=0.5 ED visit). 40% of SV patients and 16% of MV patients were admitted at their first visit, and 41% of MV patients not admitted at their first ED visit were admitted on their second visit. In the MV group, the median time to return was 4 days, 49% returned within 72 hours. In multivariable modelling, a repeat visit was associated with a variety of factors including older age (OR=1.25 per 10 years, 95% CI (1.22 to 1.28)), pregnancy (1.86 (1.46 to 2.36)) and presence of comorbidities (eg, 1.72 (1.40 to 2.10) for cancer, 2.01 (1.52 to 2.66) for obesity, 2.18 (1.42 to 3.36) for organ transplant), current/prior substance use, higher temperature or WHO severe disease (1.41 (1.29 to 1.54)). Return was less likely for females (0.82 (0.77 to 0.88)) and those boosted or fully vaccinated (0.48 (0.34 to 0.70)). CONCLUSIONS: Return ED visits by patients with COVID-19 within 30 days were common during the first two pandemic years and were associated with multiple factors, many of which reflect known risk for worse outcomes. Future studies should assess reasons for revisit and opportunities to improve ED care and reduce resource use. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov, NCT04702945.

Comparing methods to classify admitted patients with SARS-CoV-2 as admitted for COVID-19 versus with incidental SARS-CoV-2: A cohort study.

INTRODUCTION: Not all patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection develop symptomatic coronavirus disease 2019 (COVID-19), making it challenging to assess the burden of COVID-19-related hospitalizations and mortality. We aimed to determine the proportion, resource utilization, and outcomes of SARS-CoV-2 positive patients admitted for COVID-19, and assess the impact of using the Center for Disease Control's (CDC) discharge diagnosis-based algorithm and the Massachusetts state department's drug administration-based classification system on identifying admissions for COVID-19. METHODS: In this retrospective cohort study, we enrolled consecutive SARS-CoV-2 positive patients admitted to one of five hospitals in British Columbia between December 19, 2021 and May 31,2022. We completed medical record reviews, and classified hospitalizations as being primarily for COVID-19 or with incidental SARS-CoV-2 infection. We applied the CDC algorithm and the Massachusetts classification to estimate the difference in hospital days, intensive care unit (ICU) days and in-hospital mortality and calculated sensitivity and specificity. RESULTS: Of 42,505 Emergency Department patients, 1,651 were admitted and tested positive for SARS-CoV-2, with 858 (52.0%, 95% CI 49.6-54.4) admitted for COVID-19. Patients hospitalized for COVID-19 required ICU admission (14.0% versus 8.2%, p<0.001) and died (12.6% versus 6.4%, p<0.001) more frequently compared with patients with incidental SARS-CoV-2. Compared to case classification by clinicians, the CDC algorithm had a sensitivity of 82.9% (711/858, 95% CI 80.3%, 85.4%) and specificity of 98.1% (778/793, 95% CI 97.2%, 99.1%) for COVID-19-related admissions and underestimated COVID-19 attributable hospital days. The Massachusetts classification had a sensitivity of 60.5% (519/858, 95% CI 57.2%, 63.8%) and specificity of 78.6% (623/793, 95% CI 75.7%, 81.4%) for COVID-19-related admissions, underestimating total number of hospital and ICU bed days while overestimating COVID-19-related intubations, ICU admissions, and deaths. CONCLUSION: Half of SARS-CoV-2 hospitalizations were for COVID-19 during the Omicron wave. The CDC algorithm was more specific and sensitive than the Massachusetts classification, but underestimated the burden of COVID-19 admissions. TRIAL REGISTRATION: Clinicaltrials.gov, NCT04702945.

Accuracy of Self-Reported COVID-19 Vaccination Status Compared With a Public Health Vaccination Registry in Québec: Observational Diagnostic Study.

BACKGROUND: The accuracy of self-reported vaccination status is important to guide real-world vaccine effectiveness studies and policy making in jurisdictions where access to electronic vaccine registries is restricted. OBJECTIVE: This study aimed to determine the accuracy of self-reported vaccination status and reliability of the self-reported number of doses, brand, and time of vaccine administration. METHODS: This diagnostic accuracy study was completed by the Canadian COVID-19 Emergency Department Rapid Response Network. We enrolled consecutive patients presenting to 4 emergency departments (EDs) in Québec between March 24, 2020, and December 25, 2021. We included adult patients who were able to consent, could speak English or French, and had a proven COVID-19 infection. We compared the self-reported vaccination status of the patients with their vaccination status in the electronic Québec Vaccination Registry. Our primary outcome was the accuracy of the self-reported vaccination status (index test) ascertained during telephone follow-up compared with the Québec Vaccination Registry (reference standard). The accuracy was calculated by dividing all correctly self-reported vaccinated and unvaccinated participants by the sum of all correctly and incorrectly self-reported vaccinated and unvaccinated participants. We also reported interrater agreement with the reference standard as measured by unweighted Cohen κ for self-reported vaccination status at telephone follow-up and at the time of their index ED visit, number of vaccine doses, and brand. RESULTS: During the study period, we included 1361 participants. At the time of the follow-up interview, 932 participants reported at least 1 dose of a COVID-19 vaccine. The accuracy of the self-reported vaccination status was 96% (95% CI 95%-97%). Cohen κ for self-reported vaccination status at phone follow-up was 0.91 (95% CI 0.89-0.93) and 0.85 (95% CI 0.77-0.92) at the time of their index ED visit. Cohen κ was 0.89 (95% CI 0.87-0.91) for the number of doses, 0.80 (95% CI 0.75-0.84) for the brand of the first dose, 0.76 (95% CI 0.70-0.83) for the brand of the second dose, and 0.59 (95% CI 0.34-0.83) for the brand of the third dose. CONCLUSIONS: We reported a high accuracy of self-reported vaccination status for adult patients without cognitive disorders who can express themselves in English or French. Researchers can use self-reported COVID-19 vaccination data on the number of doses received, vaccine brand name, and timing of vaccination to guide future research with patients who are capable of self-reporting their vaccination data. However, access to official electronic vaccine registries is still needed to determine the vaccination status in certain susceptible populations where self-reported vaccination data remain missing or impossible to obtain. TRIAL REGISTRATION: Clinicaltrials.gov NCT04702945; https://clinicaltrials.gov/ct2/show/NCT04702945.

The burden of incidental SARS-CoV-2 infections in hospitalized patients across pandemic waves in Canada.

Many health authorities differentiate hospitalizations in patients infected with SARS-CoV-2 as being "for COVID-19" (due to direct manifestations of SARS-CoV-2 infection) versus being an "incidental" finding in someone admitted for an unrelated condition. We conducted a retrospective cohort study of all SARS-CoV-2 infected patients hospitalized via 47 Canadian emergency departments, March 2020-July 2022 to determine whether hospitalizations with "incidental" SARS-CoV-2 infection are less of a burden to patients and the healthcare system. Using a priori standardized definitions applied to hospital discharge diagnoses in 14,290 patients, we characterized COVID-19 as (i) the "Direct" cause for the hospitalization (70%), (ii) a potential "Contributing" factor for the hospitalization (4%), or (iii) an "Incidental" finding that did not influence the need for admission (26%). The proportion of incidental SARS-CoV-2 infections rose from 10% in Wave 1 to 41% during the Omicron wave. Patients with COVID-19 as the direct cause of hospitalization exhibited significantly longer LOS (mean 13.8 versus 12.1 days), were more likely to require critical care (22% versus 11%), receive COVID-19-specific therapies (55% versus 19%), and die (17% versus 9%) compared to patients with Incidental SARS-CoV-2 infections. However, patients hospitalized with incidental SARS-CoV-2 infection still exhibited substantial morbidity/mortality and hospital resource use.

Effect of inpatient antibiotic treatment among older adults with delirium found with a positive urinalysis: a health record review.

BACKGROUND: Among older adults with delirium and positive urinalysis, antibiotic treatment for urinary tract infection is common practice, but unsupported by literature or guidelines. We sought to: i) determine the rate of antibiotic treatment and the proportion of asymptomatic patients (other than delirium) in this patient population, and ii) examine the effect of antibiotic treatment on delirium resolution and adverse outcomes. METHODS: A health record review was conducted at a tertiary academic centre from January to December 2020. Inclusion criteria were age ≥ 65, positive delirium screening assessment, positive urinalysis, and admission to general medical units. Outcomes included rates of antibiotic treatment, delirium on day 7 of admission, and 30-day adverse outcomes. We compared delirium and adverse outcome rates in antibiotic-treated vs. non-treated groups. We conducted subgroup analyses among asymptomatic patients. RESULTS: We included 150 patients (57% female, mean age 85.4 years). Antibiotics were given to 86%. The asymptomatic subgroup (delirium without urinary symptoms or fever) comprised 38% and antibiotic treatment rate in this subgroup was 68%. There was no significant difference in delirium rate on day 7 between antibiotic-treated vs. non-treated groups, (entire cohort RR 0.94 [0.41-2.16] and asymptomatic subgroup RR 0.69 [0.22-2.15]) or in 30-day adverse outcomes. CONCLUSIONS: Older adults with delirium and positive urinalysis in general medical inpatient units were frequently treated with antibiotics - often despite the absence of urinary or other infectious symptoms. We failed to find evidence that antibiotic treatment in this population is associated with delirium resolution on day 7 of admission.

The Effect of Human Supervision on an Electronic Implementation of the Canadian Triage Acuity Scale (CTAS).

BACKGROUND: Most electronic emergency department (ED) triage systems allow nurses to modify computer-generated triage scores. It is currently unclear how this affects triage validity. OBJECTIVE: Are nurse-generated triage scores more strongly associated with rates of admission, intensive care unit (ICU) consultation, and mortality than computer-generated scores? METHODS: Retrospective observational cohort study of all adult visits to a tertiary ED. An electronic implementation of the Canadian Triage Acuity Scale (CTAS) generated a CTAS score for each visit. In some cases, the triage nurse overwrote the computer-generated CTAS score with a score they felt was more appropriate. Among visits with nurse-modified triage scores, we compared the rate of acuity-related outcomes (mortality, ICU consultation, hospital admission) in each CTAS level as categorized by nurse-generated vs. computer-generated scores. RESULTS: In a cohort of 229,744 patients, 19,566 (8.51%) had nurse-modified triage scores. Most modifications consisted of assigning a higher acuity triage score than recommended by the computer. Visits with triage scores 1-2 according to the nurse-generated scores had the same or higher rates of the acuity outcomes than visits that were CTAS 1-2 according to the computer-generated CTAS scores. Conversely, visits with triage scores 4-5 according to the nurse-generated scores had lower rates of the outcomes than visits that were CTAS 4-5 according to the computer-generated CTAS scores. CONCLUSIONS: Nursing supervision of the computer-automated CTAS triage system was associated with fewer hospital admissions, ICU consultations, and deaths in the triage score 4-5 categories, suggesting a safer triage process than the automated CTAS algorithm alone.

Impact of Pain Assessment on Canadian Triage and Acuity Scale Prediction of Patient Outcomes.

STUDY OBJECTIVE: How does the removal of patient-reported pain from the Canadian Triage Acuity Scale (CTAS) affect the scale's ability to predict admission, ICU consultation, and mortality? METHODS: Retrospective observational cohort study of all adult visits to a tertiary emergency department. The standard CTAS algorithm combined patient-reported pain levels with other data to generate a triage score for each visit. We calculated a "pain-free" CTAS for each visit in the cohort, assuming that the patient had not reported any pain. We fit logistic regression models for each outcome (admission, ICU consultation, and mortality) using either the standard or the pain-free CTAS as the predictor. We compared the area under the receiver operator characteristic curves of the standard versus pain-free CTAS models for each outcome. RESULTS: We analyzed a sample of 229,744 patients. The average reported pain level was 5.6/10 (SD, 3.0) among the 60.1% of the cohort who reported pain. Higher pain was slightly negatively correlated with hospital admission, ICU consultation, and 72-hour mortality (r = -0.008, -0.009, and -0.006, respectively). The area under the curve of the pain-free CTAS was higher than that of the standard scores for hospital admission (0.691 versus 0.641), ICU consultation (0.829 versus 0.773), and mortality (0.863 versus 0.810). Differences were statistically but not clinically significant. CONCLUSION: The removal of the pain scale from CTAS did not reduce its ability to predict hospital admission, ICU consultation, or the 72-hour mortality.

Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data.

Artificial Intelligence and machine learning (ML) methods are promising for risk-stratification, but the added benefit over traditional statistical methods remains unclear. We compared predictive models developed using machine learning (ML) methods to the Canadian Syncope Risk Score (CSRS), a risk-tool developed with logistic regression for predicting serious adverse events (SAE) after emergency department (ED) disposition for syncope. We used the prospective multicenter cohort data collected for CSRS development at 11 Canadian EDs over an 8-year period to develop four ML models to predict 30-day SAE (death, arrhythmias, MI, structural heart disease, pulmonary embolism, hemorrhage) after ED disposition. The CSRS derivation and validation cohorts were used for training and testing, respectively, and the 43 variables used included demographics, medical history, vital signs, ECG findings, blood tests and the diagnostic impression of the emergency physician. Performance was assessed using the area under the receiver-operating-characteristics curve (AUC) and calibration curves. Of the 4030 patients in the training set and 3819 patients in the test set overall, 286 (3.6%) patients suffered 30-day SAE. The AUCs for model validation in test data were CSRS 0.902 (0.877-0.926), regularized regression 0.903 (0.877-0.928), gradient boosting 0.914 (0.894-0.934), deep neural network 0.906 (0.883-0.929), simplified gradient boosting 0.904 (0.881-0.927). The AUCs and calibration slopes for the ML models and CSRS were similar. Two ML models used fewer predictors than the CSRS but matched its performance. Overall, the ML models matched the CSRS in performance, with some models using fewer predictors.

The bacterial chemotactic response reflects a compromise between transient and steady-state behavior.

Swimming bacteria detect chemical gradients by performing temporal comparisons of recent measurements of chemical concentration. These comparisons are described quantitatively by the chemotactic response function, which we expect to optimize chemotactic behavioral performance. We identify two independent chemotactic performance criteria: In the short run, a favorable response function should move bacteria up chemoattractant gradients; in the long run, bacteria should aggregate at peaks of chemoattractant concentration. Surprisingly, these two criteria conflict, so that when one performance criterion is most favorable, the other is unfavorable. Because both types of behavior are biologically relevant, we include both behaviors in a composite optimization that yields a response function that closely resembles experimental measurements. Our work suggests that the bacterial chemotactic response function can be derived from simple behavioral considerations and sheds light on how the response function contributes to chemotactic performance.