Anaerobic Antibiotic Coverage in Aspiration Pneumonia and the Associated Benefits and Harms: A Retrospective Cohort Study

BACKGROUND

Antibiotics with extended anaerobic coverage are used commonly to treat aspiration pneumonia, which is not recommended by current guidelines.

RESEARCH QUESTION

In patients admitted to hospital for community-acquired aspiration pneumonia, does a difference exist between antibiotic therapy with limited anaerobic coverage (LAC) vs antibiotic therapy with extended anaerobic coverage (EAC) in terms of in-hospital mortality and risk of Clostridioides difficile colitis?

STUDY DESIGN AND METHODS

We conducted a multicenter retrospective cohort study across 18 hospitals in Ontario, Canada, from January 1, 2015, to January 1, 2022. Patients were included if the physician diagnosed aspiration pneumonia and prescribed guideline-concordant first-line community-acquired pneumonia parenteral antibiotic therapy to the patient within 48 h of admission. Patients then were categorized into the LAC group if they received ceftriaxone, cefotaxime, or levofloxacin. Patients were categorized into the EAC group if they received amoxicillin-clavulanate, moxifloxacin, or any of ceftriaxone, cefotaxime, or levofloxacin in combination with clindamycin or metronidazole. The primary outcome was all-cause in-hospital mortality. Secondary outcomes included incident C difficile colitis occurring after admission. Overlap weighting of propensity scores was used to balance baseline prognostic factors.

RESULTS

The LAC and EAC groups included 2,683 and 1,316 patients, respectively. In hospital, 814 patients (30.3%) and 422 patients (32.1%) in the LAC and EAC groups died, respectively. C difficile colitis occurred in 5 or fewer patients (≤ 0.2%) and 11 to 15 patients (0.8%-1.1%) in the LAC and EAC groups, respectively. After overlap weighting of propensity scores, the adjusted risk difference of EAC minus LAC was 1.6% (95% CI, -1.7% to 4.9%) for in-hospital mortality and 1.0% (95% CI, 0.3%-1.7%) for C difficile colitis.

INTERPRETATION

Extended anaerobic coverage likely is unnecessary in aspiration pneumonia because it is associated with no additional mortality benefit, only an increased risk of C difficile colitis.

Young adults with a 22q11.2 microdeletion and the cost of aging with complexity in a population-based context

PURPOSE

Information about the impact on the adult health care system is limited for complex rare pediatric diseases, despite their increasing collective prevalence that has paralleled advances in clinical care of children. Within a population-based health care context, we examined costs and multimorbidity in adults with an exemplar of contemporary genetic diagnostics.

METHODS

We estimated direct health care costs over an 18-year period for adults with molecularly confirmed 22q11.2 microdeletion (cases) and matched controls (total 60,459 person-years of data) by linking the case cohort to health administrative data for the Ontario population (∼15 million people). We used linear regression to compare the relative ratio (RR) of costs and to identify baseline predictors of higher costs.

RESULTS

Total adult (age ≥ 18) health care costs were significantly higher for cases compared with population-based (RR 8.5, 95% CI 6.5-11.1) controls, and involved all health care sectors. At study end, when median age was <30 years, case costs were comparable to population-based individuals aged 72 years, likelihood of being within the top 1st percentile of health care costs for the entire (any age) population was significantly greater for cases than controls (odds ratio [OR], for adults 17.90, 95% CI 7.43-43.14), and just 8 (2.19%) cases had a multimorbidity score of zero (vs 1483 (40.63%) controls). The 22q11.2 microdeletion was a significant predictor of higher overall health care costs after adjustment for baseline variables (RR 6.9, 95% CI 4.6-10.5).

CONCLUSION

The findings support the possible extension of integrative models of complex care used in pediatrics to adult medicine and the potential value of genetic diagnostics in adult clinical medicine.

Validation of the Diagnostic Accuracy Levels of International Classification of Diseases, 10th Revision Codes for Diabetic Ketoacidosis: A Multicentre, Cross-sectional Study of Adults

OBJECTIVES

International Classification of Diseases (ICD) codes are commonly used to identify cases of diabetic ketoacidosis (DKA) in health services research, but they have not been validated. Our aim in this study was to assess the accuracy of ICD, 10th revision (ICD-10) diagnosis codes for DKA.

METHODS

We conducted a multicentre, cross-sectional study using data from 5 hospitals in Ontario, Canada. Each hospitalization event has a single most responsible diagnosis code. We identified all hospitalizations assigned diagnosis codes for DKA. A true case of DKA was defined using laboratory values (serum bicarbonate ≤18 mmol/L, arterial pH ≤7.3, anion gap ≥14 mEq/L, and presence of ketones in urine or blood). Chart review was conducted to validate DKA if laboratory values were missing or the diagnosis of DKA was unclear. Outcome measures included positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity of ICD-10 codes in patients with laboratory-defined DKA.

RESULTS

We identified 316,517 hospitalizations. Among these, 312,948 did not have an ICD-10 diagnosis code for DKA and 3,569 had an ICD-10 diagnosis code for DKA. Using a combination of laboratory and chart review, we identified that the overall PPV was 67.0%, the NPV was 99.7%, specificity was 99.6%, and sensitivity was 74.9%. When we restricted our analysis to hospitalizations in which DKA was the most responsible discharge diagnosis (n=3,374 [94.5%]), the test characteristics were PPV 69.8%, NPV 99.7%, specificity 99.7%, and sensitivity 71.9%.

CONCLUSION

ICD-10 codes can identify patients with DKA among those admitted to general internal medicine.

Grand rounds in methodology: key considerations for implementing machine learning solutions in quality improvement initiatives

Machine learning (ML) solutions are increasingly entering healthcare. They are complex, sociotechnical systems that include data inputs, ML models, technical infrastructure and human interactions. They have promise for improving care across a wide range of clinical applications but if poorly implemented, they may disrupt clinical workflows, exacerbate inequities in care and harm patients. Many aspects of ML solutions are similar to other digital technologies, which have well-established approaches to implementation. However, ML applications present distinct implementation challenges, given that their predictions are often complex and difficult to understand, they can be influenced by biases in the data sets used to develop them, and their impacts on human behaviour are poorly understood. This manuscript summarises the current state of knowledge about implementing ML solutions in clinical care and offers practical guidance for implementation. We propose three overarching questions for potential users to consider when deploying ML solutions in clinical care: (1) Is a clinical or operational problem likely to be addressed by an ML solution? (2) How can an ML solution be evaluated to determine its readiness for deployment? (3) How can an ML solution be deployed and maintained optimally? The Quality Improvement community has an essential role to play in ensuring that ML solutions are translated into clinical practice safely, effectively, and ethically.

Comparative Effectiveness of First-Line and Alternative Antibiotic Regimens in Hospitalized Patients With Nonsevere Community-Acquired Pneumonia: A Multicenter Retrospective Cohort Study

BACKGROUND

There are several antibiotic regimens to treat community-acquired pneumonia (CAP).

RESEARCH QUESTION

In patients hospitalized to a non-ICU ward setting with CAP, is there a difference between first-line and alternative antibiotic regimens (β-lactam plus macrolide [BL+M], β-lactam [BL] alone, respiratory fluoroquinolone [FQ], or β-lactam plus doxycycline [BL+D]) in terms of in-hospital mortality?

STUDY DESIGN AND METHODS

This retrospective cohort study included consecutive patients admitted with CAP at 19 Canadian hospitals from 2015 to 2021. Taking a target trial approach, patients were categorized into the four antibiotic groups based on the initial antibiotic treatment within 48 h of admission. Patients with severe CAP requiring ICU admission in the first 48 h were excluded. The primary outcome was all-cause in-hospital mortality. Secondary outcome included time to being discharged alive. Propensity score and overlap weighting were used to balance covariates.

RESULTS

Of 23,512 patients, 9,340 patients (39.7%) received BL+M, 9,146 (38.9%) received BL, 4,510 (19.2%) received FQ, and 516 (2.2%) received BL+D. The number of in-hospital deaths was 703 (7.5%) for the BL+M group, 888 (9.7%) for the BL group, 302 (6.7%) for the FQ group, and 31 (6.0%) for the BL+D group. The adjusted risk difference for in-hospital mortality when compared with BL+M was 1.5% (95% CI, -0.3% to 3.3%) for BL, -0.9% (95% CI, -2.9% to 1.1%) for FQ, and -1.9% (95% CI, -4.8% to 0.9%) for BL+D. Compared with BL+M, the subdistribution hazard ratio for being discharged alive was 0.90 (95% CI, 0.84-0.96) for BL, 1.07 (95% CI, 0.99-1.16) for FQ, and 1.04 (95% CI, 0.93-1.17) for BL+D.

INTERPRETATION

BL+M, FQ, and BL+D had similar outcomes and can be considered effective regimens for nonsevere CAP. Compared with BL+M, BL was associated with longer time to discharge and the CI for mortality cannot exclude a small but clinically important increase in risk.

An Update to the Kaiser Permanente Inpatient Risk Adjustment Methodology Accurately Predicts In-Hospital Mortality: a Retrospective Cohort Study

BACKGROUND

Methods to accurately predict the risk of in-hospital mortality are important for applications including quality assessment of healthcare institutions and research.

OBJECTIVE

To update and validate the Kaiser Permanente inpatient risk adjustment methodology (KP method) to predict in-hospital mortality, using open-source tools to measure comorbidity and diagnosis groups, and removing troponin which is difficult to standardize across modern clinical assays.

DESIGN

Retrospective cohort study using electronic health record data from GEMINI. GEMINI is a research collaborative that collects administrative and clinical data from hospital information systems.

PARTICIPANTS

Adult general medicine inpatients at 28 hospitals in Ontario, Canada, between April 2010 and December 2022.

MAIN MEASURES

The outcome was in-hospital mortality, modeled by diagnosis group using 56 logistic regressions. We compared models with and without troponin as an input to the laboratory-based acute physiology score. We fit and validated the updated method using internal-external cross-validation at 28 hospitals from April 2015 to December 2022.

KEY RESULTS

In 938,103 hospitalizations with 7.2% in-hospital mortality, the updated KP method accurately predicted the risk of mortality. The c-statistic at the median hospital was 0.866 (see Fig. 3) (25th-75th 0.848-0.876, range 0.816-0.927) and calibration was strong for nearly all patients at all hospitals. The 95th percentile absolute difference between predicted and observed probabilities was 0.038 at the median hospital (25th-75th 0.024-0.057, range 0.006-0.118). Model performance was very similar with and without troponin in a subset of 7 hospitals, and performance was similar with and without troponin for patients hospitalized for heart failure and acute myocardial infarction.

CONCLUSIONS

An update to the KP method accurately predicted in-hospital mortality for general medicine inpatients in 28 hospitals in Ontario, Canada. This updated method can be implemented in a wider range of settings using common open-source tools.

Ceftriaxone and the Risk of Ventricular Arrhythmia, Cardiac Arrest, and Death Among Patients Receiving Lansoprazole

Importance

The combination of ceftriaxone and lansoprazole has been shown to prolong the corrected QT interval on electrocardiogram. However, it is unknown whether this translates to clinically important patient outcomes.

Objective

To compare lansoprazole with another proton pump inhibitor (PPI) during ceftriaxone treatment in terms of risk for ventricular arrhythmia, cardiac arrest, and in-hospital mortality.

Design, Setting, and Participants

A retrospective cohort study including adult medical inpatients receiving ceftriaxone with lansoprazole or another PPI in 13 hospitals in Ontario, Canada, was conducted from January 1, 2015, to December 31, 2021.

Exposure

Lansoprazole during ceftriaxone treatment vs other PPIs during ceftriaxone treatment.

Main Outcomes and Measures

The primary outcome was a composite of ventricular arrhythmia or cardiac arrest that occurred after hospital admission. The secondary outcome was all-cause in-hospital mortality. Propensity-score weighting was used to adjust for covariates including hospital site, demographic characteristics, comorbidities, risk factors for ventricular arrhythmia, illness severity, admitting diagnoses, and concomitant medications.

Results

Of the 31 152 patients hospitalized on internal medicine wards who were treated with ceftriaxone while receiving a PPI, 16 135 patients (51.8%) were male, and the mean (SD) age was 71.7 (16.0) years. The study included 3747 patients in the lansoprazole group and 27 405 patients in the other PPI group. Ventricular arrhythmia or cardiac arrest occurred in 126 patients (3.4%) within the lansoprazole group and 319 patients (1.2%) within the other PPI group. In-hospital mortality occurred in 746 patients (19.9%) within the lansoprazole group and 2762 patients (10.1%) in the other PPI group. After weighting using propensity scores, the adjusted risk difference for the lansoprazole group minus other PPI group was 1.7% (95% CI, 1.1%-2.3%) for ventricular arrhythmia or cardiac arrest and 7.4% (95% CI, 6.1%-8.8%) for in-hospital mortality.

Conclusions and Relevance

The findings of this cohort study suggest that combination therapy with lansoprazole and ceftriaxone should be avoided. More studies are needed to determine whether these findings could be replicated in other populations and settings.

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

Objective

Patient data repositories often assemble medication data from multiple sources, necessitating standardization prior to analysis. We implemented and evaluated a medication standardization procedure for use with a wide range of pharmacy data inputs across all drug categories, which supports research queries at multiple levels of granularity.

Methods

The GEMINI-RxNorm system automates the use of multiple RxNorm tools in tandem with other datasets to identify drug concepts from pharmacy orders. GEMINI-RxNorm was used to process 2 090 155 pharmacy orders from 245 258 hospitalizations between 2010 and 2017 at 7 hospitals in Ontario, Canada. The GEMINI-RxNorm system matches drug-identifying information from pharmacy data (including free-text fields) to RxNorm concept identifiers. A user interface allows researchers to search for drug terms and returns the relevant original pharmacy data through the matched RxNorm concepts. Users can then manually validate the predicted matches and discard false positives. We designed the system to maximize recall (sensitivity) and enable excellent precision (positive predictive value) with efficient manual validation. We compared the performance of this system to manual coding (by a physician and pharmacist) of 13 medication classes.

Results

Manual coding was performed for 1 948 817 pharmacy orders and GEMINI-RxNorm successfully returned 1 941 389 (99.6%) orders. Recall was greater than 0.985 in all 13 drug classes, and the F1-score and precision remained above 0.90 in all drug classes, facilitating efficient manual review to achieve 100% precision. GEMINI-RxNorm saved time substantially compared with manual standardization, reducing the time taken to review a pharmacy order row from an estimated 30 to 5 s and reducing the number of rows needed to be reviewed by up to 99.99%.

Discussion and Conclusion

GEMINI-RxNorm presents a novel combination of RxNorm tools and other datasets to enable accurate, efficient, flexible, and scalable standardization of pharmacy data. By facilitating efficient manual validation, the GEMINI-RxNorm system can allow researchers to achieve near-perfect accuracy in medication data standardization.

Essential requirements for the governance and management of data trusts, data repositories, and other data collaborations

Introduction

Around the world, many organisations are working on ways to increase the use, sharing, and reuse of person-level data for research, evaluation, planning, and innovation while ensuring that data are secure and privacy is protected. As a contribution to broader efforts to improve data governance and management, in 2020 members of our team published 12 minimum specification essential requirements (min specs) to provide practical guidance for organisations establishing or operating data trusts and other forms of data infrastructure.

Approach and Aims

We convened an international team, consisting mostly of participants from Canada and the United States of America, to test and refine the original 12 min specs. Twenty-three (23) data-focused organisations and initiatives recorded the various ways they address the min specs. Sub-teams analysed the results, used the findings to make improvements to the min specs, and identified materials to support organisations/initiatives in addressing the min specs.

Results

Analyses and discussion led to an updated set of 15 min specs covering five categories: one min spec for Legal, five for Governance, four for Management, two for Data Users, and three for Stakeholder & Public Engagement. Multiple changes were made to make the min specs language more technically complete and precise. The updated set of 15 min specs has been integrated into a Canadian national standard that, to our knowledge, is the first to include requirements for public engagement and Indigenous Data Sovereignty.

Conclusions

The testing and refinement of the min specs led to significant additions and improvements. The min specs helped the 23 organisations/initiatives involved in this project communicate and compare how they achieve responsible and trustworthy data governance and management. By extension, the min specs, and the Canadian national standard based on them, are likely to be useful for other data-focused organisations and initiatives.

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.

Validity of hospital diagnostic codes to identify SARS-CoV-2 infections in reference to polymerase chain reaction results: a descriptive study

BACKGROUND

In 2020, International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes were created for laboratory-confirmed SARS-CoV-2 infections. We assessed the operating characteristics of ICD-10 discharge diagnostic code U07.1 within the General Medicine Inpatient Initiative (GEMINI).

METHODS

GEMINI assembles hospitalization data (including administrative ICD-10 discharge diagnostic codes, laboratory results and demographic data) from hospitals in Ontario, Canada. We studied adults (age ≥ 18 yr) admitted during 2020 and tested at least once for SARS-CoV-2 via polymerase chain reaction (PCR) during (or within 48 h before) hospitalization. With PCR results as the reference standard, we calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for ICD-10 code U07.1 hospital discharge diagnostic codes. Analyses were stratified by demographic data, calendar period and timing of the first test (within or after 48 h of hospital admission).

RESULTS

In 11 852 hospitalizations with at least 1 SARS-CoV-2 PCR test, 444 (3.7%) were positive. The sensitivity of code U07.1 to identify SARS-CoV-2 infection was 97.8%, specificity was 99.5%, PPV was 88.2% and NPV was 99.9%. Operating characteristics were similar in most stratified analyses, but the specificity and PPV were lower if the first SARS-CoV-2 test was done more than 48 hours after admission.

INTERPRETATION

The sensitivity, specificity, PPV and NPV of code U07.1 were high. This supports using code U07.1 to identify SARS-CoV-2 infection in hospitalization data.

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.

Comparison of Medical and Mental Health Sequelae Following Hospitalization for COVID-19, Influenza, and Sepsis

Importance

People who survive hospitalization for COVID-19 are at risk for developing new cardiovascular, neurological, mental health, and inflammatory autoimmune conditions. It is unclear how posthospitalization risks for COVID-19 compare with those for other serious infectious illnesses.

Objective

To compare risks of incident cardiovascular, neurological, and mental health conditions and rheumatoid arthritis in 1 year following COVID-19 hospitalization against 3 comparator groups: prepandemic hospitalization for influenza and hospitalization for sepsis before and during the COVID-19 pandemic.

Design, Setting, and Participants

This population-based cohort study included all adults hospitalized for COVID-19 between April 1, 2020, and October 31, 2021, historical comparator groups of people hospitalized for influenza or sepsis, and a contemporary comparator group of people hospitalized for sepsis in Ontario, Canada.

Exposure

Hospitalization for COVID-19, influenza, or sepsis.

Main Outcome and Measures

New occurrence of 13 prespecified conditions, including cardiovascular, neurological, and mental health conditions and rheumatoid arthritis, within 1 year of hospitalization.

Results

Of 379 366 included adults (median [IQR] age, 75 [63-85] years; 54% female), there were 26 499 people who survived hospitalization for COVID-19, 299 989 historical controls (17 516 for influenza and 282 473 for sepsis), and 52 878 contemporary controls hospitalized for sepsis. Hospitalization for COVID-19 was associated with an increased 1-year risk of venous thromboembolic disease compared with influenza (adjusted hazard ratio, 1.77; 95% CI, 1.36-2.31) but with no increased risks of developing selected ischemic and nonischemic cerebrovascular and cardiovascular disorders, neurological disorders, rheumatoid arthritis, or mental health conditions compared with influenza or sepsis cohorts.

Conclusions and Relevance

In this cohort study, apart from an elevated risk of venous thromboembolism within 1 year, the burden of postacute medical and mental health conditions among those who survived hospitalization for COVID-19 was comparable with other acute infectious illnesses. This suggests that many of the postacute consequences of COVID-19 may be related to the severity of infectious illness necessitating hospitalization rather than being direct consequences of infection with SARS-CoV-2.

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.

Developing and Validating a Prediction Model For Death or Critical Illness in Hospitalized Adults, an Opportunity for Human-Computer Collaboration

Hospital early warning systems that use machine learning (ML) to predict clinical deterioration are increasingly being used to aid clinical decision-making. However, it is not known how ML predictions complement physician and nurse judgment. Our objective was to train and validate a ML model to predict patient deterioration and compare model predictions with real-world physician and nurse predictions.

DESIGN

Retrospective and prospective cohort study.

SETTING

Academic tertiary care hospital.

PATIENTS

Adult general internal medicine hospitalizations.

MEASUREMENTS AND MAIN RESULTS

We developed and validated a neural network model to predict in-hospital death and ICU admission in 23,528 hospitalizations between April 2011 and April 2019. We then compared model predictions with 3,374 prospectively collected predictions from nurses, residents, and attending physicians about their own patients in 960 hospitalizations between April 30, and August 28, 2019. ML model predictions achieved clinician-level accuracy for predicting ICU admission or death (ML median F1 score 0.32 [interquartile range (IQR) 0.30-0.34], AUC 0.77 [IQ 0.76-0.78]; clinicians median F1-score 0.33 [IQR 0.30-0.35], AUC 0.64 [IQR 0.63-0.66]). ML predictions were more accurate than clinicians for ICU admission. Of all ICU admissions and deaths, 36% occurred in hospitalizations where the model and clinicians disagreed. Combining human and model predictions detected 49% of clinical deterioration events, improving sensitivity by 16% compared with clinicians alone and 24% compared with the model alone while maintaining a positive predictive value of 33%, thus keeping false alarms at a clinically acceptable level.

CONCLUSIONS

ML models can complement clinician judgment to predict clinical deterioration in hospital. These findings demonstrate important opportunities for human-computer collaboration to improve prognostication and personalized medicine in hospital.

Osmotic Demyelination Syndrome in Patients Hospitalized with Hyponatremia

Osmotic Demyelination Syndrome and HyponatremiaThe authors estimated the frequency of occurrence of osmotic demyelination syndrome (ODS) after correction of hyponatremia (i.e., serum sodium <130 mmol/l). Among 22,858 patients hospitalized for hyponatremia, there were 12 cases of ODS (0.05%). Correction of the serum sodium occurred in 3632 (17.7%) patients, less than one-half of the ODS cases.

Analysis of Resident and Attending Physician End-of-Rotation Changeover Days and Association With Patient Length of Stay

Importance

End-of-rotation resident physician changeover is a key part of postgraduate training but could lead to discontinuity in patient care.

Objective

To test whether patients exposed to end-of-rotation resident changeover have longer hospital stays and whether this association is mitigated by separating resident and attending changeover days.

Design, Setting, and Participants

This retrospective cohort analysis included adult patients admitted to general internal medicine. The changeover day was the same day (first Monday of month) for both resident and attending physicians until June 30, 2013 (preseparation period), and then intentionally staggered by 1 or more days after July 1, 2013 (postseparation period). This was a multicenter analysis at 4 teaching hospitals in Ontario, Canada, from July 1, 2010, to June 30, 2019. Data analysis was conducted from July 2022 to January 2023.

Exposures

Patients were classified as changeover patients if the first Monday was a resident changeover day and as control patients if the first Monday was not a resident changeover day.

Main Outcomes and Measures

The primary outcome was length of hospital stay. Secondary outcomes were transfer to critical care, in-hospital death, and rate of discharge per 100 patients on the index day.

Results

Of 95 282 patients. 22 773 (24%; mean [SD] age, 67.8 [18.8] years; 11 156 [49%] female patients) were exposed to resident changeover, and 72 509 (76%; mean [SD] age, 67.8 [18.7] years; 35 293 [49%] female patients) were not exposed to resident changeover. Exposure to resident changeover day was associated with a slightly longer hospital stay compared with control days (0.20 [95% CI, 0.09-0.30] days; P < .001) and decreased relative risk of patient discharge on the index day (relative risk, 0.92; 95% CI, 0.86-1.00; P = .047). These associations were similar in the preseparation and postseparation periods. Resident changeover was not associated with an increased risk of transfer to critical care or in-hospital death.

Conclusions and Relevance

In this study, a small positive association between exposure to resident physician changeover and length of hospital stay as well as reduced rate of discharge was found. These findings suggest that separating changeover days for resident and attending physicians may not significantly change these associations.

Imaging delays among medical inpatients in Toronto, Ontario: A cohort study

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.

Complications Following Elective Major Noncardiac Surgery Among Patients With Prior SARS-CoV-2 Infection

IMPORTANCE

There is an urgent need for evidence to inform preoperative risk assessment for the millions of people who have had SARS-CoV-2 infection and are awaiting elective surgery, which is critical to surgical care planning and informed consent.

OBJECTIVE

To assess the association of prior SARS-CoV-2 infection with death, major adverse cardiovascular events, and rehospitalization after elective major noncardiac surgery.

DESIGN, SETTING, AND PARTICIPANTS

This population-based cohort study included adults who had received a polymerase chain reaction test for SARS-CoV-2 infection within 6 months prior to elective major noncardiac surgery in Ontario, Canada, between April 2020 and October 2021, with 30 days follow-up.

EXPOSURES

Positive SARS-CoV-2 polymerase chain reaction test result.

MAIN OUTCOMES AND MEASURES

The main outcome was the composite of death, major adverse cardiovascular events, and all-cause rehospitalization within 30 days after surgery.

RESULTS

Of 71 144 patients who underwent elective major noncardiac surgery (median age, 66 years [IQR, 57-73 years]; 59.8% female), 960 had prior SARS-CoV-2 infection (1.3%) and 70 184 had negative test results (98.7%). Prior infection was not associated with the composite risk of death, major adverse cardiovascular events, and rehospitalization within 30 days of elective major noncardiac surgery (5.3% absolute event rate [n = 3770]; 960 patients with a positive test result; adjusted relative risk [aRR], 0.91; 95% CI, 0.68-1.21). There was also no association between prior infection with SARS-CoV-2 and postoperative outcomes when the time between infection and surgery was less than 4 weeks (aRR, 1.15; 95% CI, 0.64-2.09) or less than 7 weeks (aRR, 0.95; 95% CI, 0.56-1.61) and among those who were previously vaccinated (aRR, 0.81; 95% CI, 0.52-1.26).

CONCLUSIONS AND RELEVANCE

In this study, prior infection with SARS-CoV-2 was not associated with death, major adverse cardiovascular events, or rehospitalization following elective major noncardiac surgery, although low event rates and wide 95% CIs do not preclude a potentially meaningful increase in overall risk.

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

OBJECTIVES

To examine how the COVID-19 pandemic affected the demographic and clinical characteristics, in-hospital care, and outcomes of long-term care residents admitted to general medicine wards for non-COVID-19 reasons.

METHODS

We conducted a retrospective cohort study of long-term care residents admitted to general medicine wards, for reasons other than COVID-19, in four hospitals in Toronto, Ontario between January 1, 2018 and December 31, 2020. We used an autoregressive linear model to estimate the change in monthly admission volumes during the pandemic period (March-December 2020) compared to the previous two years, adjusting for any secular trend. We summarized and compared differences in the demographics, comorbidities, interventions, diagnoses, imaging, psychoactive medications, and outcomes of residents before and during the pandemic.

RESULTS

Our study included 2,654 long-term care residents who were hospitalized for non-COVID-19 reasons between January 2018 and December 2020. The crude rate of hospitalizations was 79.3 per month between March-December of 2018-2019 and 56.5 per month between March-December of 2020. The was an adjusted absolute difference of 27.0 (95% CI: 10.0, 43.9) fewer hospital admissions during the pandemic period, corresponding to a relative drop of 34%. Residents admitted during the pandemic period had similar demographics and clinical characteristics but were more likely to be admitted for delirium (pandemic: 7% pre-pandemic: 5%, p = 0.01) and were less likely to be admitted for pneumonia (pandemic: 3% pre-pandemic: 6%, p = 0.004). Residents admitted during the pandemic were more likely to be prescribed antipsychotics (pandemic: 37%, pre-pandemic: 29%, p <0.001) and more likely to die in-hospital (pandemic:14% pre-pandemic: 10%, p = 0.04).

CONCLUSIONS AND IMPLICATIONS

Better integration between long-term care and hospitals systems, including programs to deliver urgent medical care services within long-term care homes, is needed to ensure that long-term care residents maintain equitable access to acute care during current and future public health emergencies.

Predictors of ICU admission associated with gastrointestinal endoscopy in medical inpatients: A retrospective cohort study

BACKGROUND AND AIM

Gastrointestinal (GI) endoscopic procedures are commonly performed in medical inpatients. Limited prior research has examined factors associated with intensive care unit (ICU) admission after GI endoscopy in medical inpatients.

METHODS

This retrospective cohort study was conducted using routinely-collected clinical and administrative data from all general medicine hospitalizations at five academic hospitals in Toronto, Canada between 2010 and 2020. We describe ICU admission and death within 48 h of GI endoscopy in medical inpatients. We examined adjusted associations of patient and procedural factors with ICU admission or death using multivariable logistic regression.

RESULTS

Among 18 290 medical inpatients who underwent endoscopy, 900 (4.9%) required ICU admission or died within 48 h of endoscopy. Following risk adjustment, ICU admission or death were associated with the following procedural factors: endoscopy on the day of hospital admission (aOR 3.16 [2.38-4.21]) or 1 day after admission (aOR 1.92 [1.51-2.44]) and esophagogastroduodenoscopy (EGD) procedures; and the following patient factors: Charlson comorbidity index of two (aOR 1.38 [1.05-1.81]) or three or greater (aOR 1.84 [1.47-2.29]), older age, male sex, lower hemoglobin prior to endoscopy, increased creatinine prior to endoscopy, an admitting diagnosis of liver disease and certain medications (antiplatelet agents and corticosteroids).

CONCLUSIONS

ICU admission or death after endoscopy was associated with procedural factors such as EGD and timing of endoscopy, and patient factors indicative of acute illness and greater comorbidity. These findings can contribute to improved triage and monitoring for patients requiring inpatient endoscopy.

Methodology paper for the General Medicine Inpatient Initiative Medical Education Database (GEMINI MedED): a retrospective cohort study of internal medicine resident case-mix, clinical care and patient outcomes

INTRODUCTION

Unwarranted variation in patient care among physicians is associated with negative patient outcomes and increased healthcare costs. Care variation likely also exists for resident physicians. Despite the global movement towards outcomes-based and competency-based medical education, current assessment strategies in residency do not routinely incorporate clinical outcomes. The widespread use of electronic health records (EHRs) may enable the implementation of in-training assessments that incorporate clinical care and patient outcomes.

METHODS AND ANALYSIS

The General Medicine Inpatient Initiative Medical Education Database (GEMINI MedED) is a retrospective cohort study of senior residents (postgraduate year 2/3) enrolled in the University of Toronto Internal Medicine (IM) programme between 1 April 2010 and 31 December 2020. This study focuses on senior IM residents and patients they admit overnight to four academic hospitals. Senior IM residents are responsible for overseeing all overnight admissions; thus, care processes and outcomes for these clinical encounters can be at least partially attributed to the care they provide. Call schedules from each hospital, which list the date, location and senior resident on-call, will be used to link senior residents to EHR data of patients admitted during their on-call shifts. Patient data will be derived from the GEMINI database, which contains administrative (eg, demographic and disposition) and clinical data (eg, laboratory and radiological investigation results) for patients admitted to IM at the four academic hospitals. Overall, this study will examine three domains of resident practice: (1) case-mix variation across residents, hospitals and academic year, (2) resident-sensitive quality measures (EHR-derived metrics that are partially attributable to resident care) and (3) variations in patient outcomes across residents and factors that contribute to such variation.

ETHICS AND DISSEMINATION

GEMINI MedED was approved by the University of Toronto Ethics Board (RIS#39339). Results from this study will be presented in academic conferences and peer-reviewed journals.

From compute to care: Lessons learned from deploying an early warning system into clinical practice

Background

Deploying safe and effective machine learning models is essential to realize the promise of artificial intelligence for improved healthcare. Yet, there remains a large gap between the number of high-performing ML models trained on healthcare data and the actual deployment of these models. Here, we describe the deployment of CHARTwatch, an artificial intelligence-based early warning system designed to predict patient risk of clinical deterioration.

Methods

We describe the end-to-end infrastructure that was developed to deploy CHARTwatch and outline the process from data extraction to communicating patient risk scores in real-time to physicians and nurses. We then describe the various challenges that were faced in deployment, including technical issues (e.g., unstable database connections), process-related challenges (e.g., changes in how a critical lab is measured), and challenges related to deploying a clinical system in the middle of a pandemic. We report various measures to quantify the success of the deployment: model performance, adherence to workflows, and infrastructure uptime/downtime. Ultimately, success is driven by end-user adoption and impact on relevant clinical outcomes. We assess our deployment process by evaluating how closely we followed existing guidance for good machine learning practice (GMLP) and identify gaps that are not addressed in this guidance.

Results

The model demonstrated strong and consistent performance in real-time in the first 19 months after deployment (AUC 0.76) as in the silent deployment heldout test data (AUC 0.79). The infrastructure remained online for >99% of time in the first year of deployment. Our deployment adhered to all 10 aspects of GMLP guiding principles. Several steps were crucial for deployment but are not mentioned or are missing details in the GMLP principles, including the need for a silent testing period, the creation of robust downtime protocols, and the importance of end-user engagement. Evaluation for impacts on clinical outcomes and adherence to clinical protocols is underway.

Conclusion

We deployed an artificial intelligence-based early warning system to predict clinical deterioration in hospital. Careful attention to data infrastructure, identifying problems in a silent testing period, close monitoring during deployment, and strong engagement with end-users were critical for successful deployment.

Outcomes for upper gastrointestinal bleeding during the first wave of the COVID-19 pandemic in the Toronto area

BACKGROUND AND AIM

Changes to endoscopy service availability during the COVID-19 pandemic may have affected management of upper gastrointestinal bleeding (UGIB). The aim of this study was to describe the impact of the pandemic on UGIB outcomes in the Toronto area in Canada.

METHODS

We described all adults admitted to general medicine wards or intensive care units at six hospitals in Toronto and Mississauga, Canada, with UGIB during the first wave of the COVID-19 pandemic (March 1 to June 30, 2020) and compared them with a historical cohort (March 1 to June 30, 2018 and 2019). We compared clinical outcomes (in-hospital mortality, length of stay, 30-day readmission, intensive care utilization, receipt of endoscopy, persistent bleeding, receipt of second endoscopy, and need for angiographic or surgical intervention) using multivariable regression models, controlling for demographics, comorbidities, and severity of clinical presentation.

RESULTS

There were 82.5 and 215.5 admissions per month for UGIB during the COVID-19 and control periods, respectively. There were no baseline differences between groups for demographic characteristics, comorbidities, or severity of bleeding. Patients in the COVID-19 group did not have significantly different unadjusted (3.9% vs 4.2%, P = 0.983) or adjusted mortality (adjusted odds ratio [OR] = 0.64, 95% confidence interval [CI] = 0.25-1.48, P = 0.322). Patients in COVID-19 group were less likely to receive endoscopy for UGIB in the unadjusted (61.8% vs 71.0%, P = 0.003) and adjusted (adjusted OR = 0.64, 95% CI = 0.49-0.84, P < 0.01) models. There were no differences between groups for other secondary outcomes.

CONCLUSIONS

While patients admitted for UGIB during the first wave of the pandemic were less likely to receive endoscopy, this had no impact on mortality or any secondary outcomes.

Prone positioning of patients with moderate hypoxaemia due to covid-19: multicentre pragmatic randomised trial (COVID-PRONE)

OBJECTIVES

To assess the effectiveness of prone positioning to reduce the risk of death or respiratory failure in non-critically ill patients admitted to hospital with covid-19.

DESIGN

Multicentre pragmatic randomised clinical trial.

SETTING

15 hospitals in Canada and the United States from May 2020 until May 2021.

PARTICIPANTS

Eligible patients had a laboratory confirmed or a clinically highly suspected diagnosis of covid-19, needed supplemental oxygen (up to 50% fraction of inspired oxygen), and were able to independently lie prone with verbal instruction. Of the 570 patients who were assessed for eligibility, 257 were randomised and 248 were included in the analysis.

INTERVENTION

Patients were randomised 1:1 to prone positioning (that is, instructing a patient to lie on their stomach while they are in bed) or standard of care (that is, no instruction to adopt prone position).

MAIN OUTCOME MEASURES

The primary outcome was a composite of in-hospital death, mechanical ventilation, or worsening respiratory failure defined as needing at least 60% fraction of inspired oxygen for at least 24 hours. Secondary outcomes included the change in the ratio of oxygen saturation to fraction of inspired oxygen.

RESULTS

The trial was stopped early on the basis of futility for the pre-specified primary outcome. The median time from hospital admission until randomisation was 1 day, the median age of patients was 56 (interquartile range 45-65) years, 89 (36%) patients were female, and 222 (90%) were receiving oxygen via nasal prongs at the time of randomisation. The median time spent prone in the first 72 hours was 6 (1.5-12.8) hours in total for the prone arm compared with 0 (0-2) hours in the control arm. The risk of the primary outcome was similar between the prone group (18 (14%) events) and the standard care group (17 (14%) events) (odds ratio 0.92, 95% confidence interval 0.44 to 1.92). The change in the ratio of oxygen saturation to fraction of inspired oxygen after 72 hours was similar for patients randomised to prone positioning and standard of care.

CONCLUSION

Among non-critically ill patients with hypoxaemia who were admitted to hospital with covid-19, a multifaceted intervention to increase prone positioning did not improve outcomes. However, wide confidence intervals preclude definitively ruling out benefit or harm. Adherence to prone positioning was poor, despite multiple efforts to increase it. Subsequent trials of prone positioning should aim to develop strategies to improve adherence to awake prone positioning.

STUDY REGISTRATION

ClinicalTrials.gov NCT04383613.

Outcomes in patients with and without disability admitted to hospital with COVID-19: a retrospective cohort study

Background:

Disability-related considerations have largely been absent from the COVID-19 response, despite evidence that people with disabilities are at elevated risk for acquiring COVID-19. We evaluated clinical outcomes in patients who were admitted to hospital with COVID-19 with a disability compared with patients without a disability.

Methods:

We conducted a retrospective cohort study that included adults with COVID-19 who were admitted to hospital and discharged between Jan. 1, 2020, and Nov. 30, 2020, at 7 hospitals in Ontario, Canada. We compared in-hospital death, admission to the intensive care unit (ICU), hospital length of stay and unplanned 30-day readmission among patients with and without a physical disability, hearing or vision impairment, traumatic brain injury, or intellectual or developmental disability, overall and stratified by age (≤ 64 and ≥ 65 yr) using multivariable regression, controlling for sex, residence in a long-term care facility and comorbidity.

Results:

Among 1279 admissions to hospital for COVID-19, 22.3% had a disability. We found that patients with a disability were more likely to die than those without a disability (28.1% v. 17.6%), had longer hospital stays (median 13.9 v. 7.8 d) and more readmissions (17.6% v. 7.9%), but had lower ICU admission rates (22.5% v. 28.3%). After adjustment, there were no statistically significant differences between those with and without disabilities for in-hospital death or admission to ICU. After adjustment, patients with a disability had longer hospital stays (rate ratio 1.36, 95% confidence interval [CI] 1.19–1.56) and greater risk of readmission (relative risk 1.77, 95% CI 1.14–2.75). In age-stratified analyses, we observed longer hospital stays among patients with a disability than in those without, in both younger and older subgroups; readmission risk was driven by younger patients with a disability.

Interpretation:

Patients with a disability who were admitted to hospital with COVID-19 had longer stays and elevated readmission risk than those without disabilities. Disability-related needs should be addressed to support these patients in hospital and after discharge.

Bedspacing and clinical outcomes in general internal medicine: A retrospective, multicenter cohort study

BACKGROUND

Admitting hospitalized patients to off-service wards ("bedspacing") is common and may affect quality of care and patient outcomes.

OBJECTIVE

To compare in-hospital mortality, 30-day readmission to general internal medicine (GIM), and hospital length-of-stay among GIM patients admitted to GIM wards or bedspaced to off-service wards.

DESIGN, PARTICIPANTS, AND MEASURES

Retrospective cohort study including all emergency department admissions to GIM between 2015 and 2017 at six hospitals in Ontario, Canada. We compared patients admitted to GIM wards with those who were bedspaced, using multivariable regression models and propensity score matching to control for patient and situational factors.

KEY RESULTS

Among 40,440 GIM admissions, 10,745 (26.6%) were bedspaced to non-GIM wards and 29,695 (73.4%) were assigned to GIM wards. After multivariable adjustment, bedspacing was associated with no significant difference in mortality (adjusted hazard ratio 0.95, 95% confidence interval [CI]: 0.86-1.05, p = .304), slightly shorter median hospital length-of-stay (-0.10 days, 95% CI:-0.20 to -0.001, p = .047) and lower 30-day readmission to GIM (adjusted OR 0.89, 95% CI: 0.83-0.95, p = .001). Results were consistent when examining each hospital individually and outcomes did not significantly differ between medical or surgical off-service wards. Sensitivity analyses focused on the highest risk patients did not exclude the possibility of harm associated with bedspacing, although adverse outcomes were not significantly greater.

CONCLUSIONS

Overall, bedspacing was associated with no significant difference in mortality, slightly shorter hospital length-of-stay, and fewer 30-day readmissions to GIM, although potential harms in high-risk patients remain uncertain. Given that hospital capacity issues are likely to persist, future research should aim to understand how bedspacing can be achieved safely at all hospitals, perhaps by strengthening the selection of low-risk patients.

Developing and validating natural language processing algorithms for radiology reports compared to ICD-10 codes for identifying venous thromboembolism in hospitalized medical patients

BACKGROUND

Identifying venous thromboembolism (VTE) from large clinical and administrative databases is important for research and quality improvement.

OBJECTIVE

To develop and validate natural language processing (NLP) algorithms to identify VTE from radiology reports among general internal medicine (GIM) inpatients.

METHODS

This cross-sectional study included GIM hospitalizations between April 1, 2010 and March 31, 2017 at 5 hospitals in Toronto, Ontario, Canada. We developed NLP algorithms to identify pulmonary embolism (PE) and deep venous thrombosis (DVT) from radiologist reports of thoracic computed tomography (CT), extremity compression ultrasound (US), and nuclear ventilation-perfusion (VQ) scans in a training dataset of 1551 hospitalizations. We compared the accuracy of our NLP algorithms, the previously-published "simpleNLP" tool, and administrative discharge diagnosis codes (ICD-10-CA) for PE and DVT to the "gold standard" manual review in a separate random sample of 4000 GIM hospitalizations.

RESULTS

Our NLP algorithms were highly accurate for identifying DVT from US, with sensitivity 0.94, positive predictive value (PPV) 0.90, and Area Under the Receiver-Operating-Characteristic Curve (AUC) 0.96; and in identifying PE from CT, with sensitivity 0.91, PPV 0.89, and AUC 0.96. Administrative diagnosis codes and the simple NLP tool were less accurate for DVT (ICD-10-CA sensitivity 0.63, PPV 0.43, AUC 0.81; simpleNLP sensitivity 0.41, PPV 0.36, AUC 0.66) and PE (ICD-10-CA sensitivity 0.83, PPV 0.70, AUC 0.91; simpleNLP sensitivity 0.89, PPV 0.62, AUC 0.92).

CONCLUSIONS

Administrative diagnosis codes are unreliable in identifying VTE in hospitalized patients. We developed highly accurate NLP algorithms to identify VTE from radiology reports in a multicentre sample and have made the algorithms freely available to the academic community with a user-friendly tool (https://lks-chart.github.io/CHARTextract-docs/08-downloads/rulesets.html#venous-thromboembolism-vte-rulesets).

Using machine learning to predict severe hypoglycaemia in hospital

AIM

To predict the risk of hypoglycaemia using machine-learning techniques in hospitalized patients.

METHODS

We conducted a retrospective cohort study of patients hospitalized under general internal medicine (GIM) and cardiovascular surgery (CV) at a tertiary care teaching hospital in Toronto, Ontario. Three models were generated using supervised machine learning: least absolute shrinkage and selection operator (LASSO) logistic regression; gradient-boosted trees; and a recurrent neural network. Each model included baseline patient data and time-varying data. Natural-language processing was used to incorporate text data from physician and nursing notes.

RESULTS

We included 8492 GIM admissions and 8044 CV admissions. Hypoglycaemia occurred in 16% of GIM admissions and 13% of CV admissions. The area under the curve for the models in the held-out validation set was approximately 0.80 on the GIM ward and 0.82 on the CV ward. When the threshold for hypoglycaemia was lowered to 2.9 mmol/L (52 mg/dL), similar results were observed. Among the patients at the highest decile of risk, the positive predictive value was approximately 50% and the sensitivity was 99%.

CONCLUSION

Machine-learning approaches can accurately identify patients at high risk of hypoglycaemia in hospital. Future work will involve evaluating whether implementing this model with targeted clinical interventions can improve clinical outcomes.

Internal medicine hospitalisations and liver disease: a comparative disease burden analysis of a multicentre cohort

BACKGROUND

Liver disease is an increasing burden on population health globally.

AIMS

To characterise burden of liver disease among general internal medicine inpatients at seven Toronto-area hospitals and compare it to other common medical conditions.

METHODS

Data from April 2010 to October 2017 were obtained from hospitals participating in the GEMINI collaborative. Using these cohort data from hospital information systems linked to administrative data, we defined liver disease admissions using most responsible discharge diagnoses categorised according to international classification of diseases, 10th Revision-enhanced Canadian version (ICD-10-CA). We identified admissions for heart failure, chronic obstructive pulmonary disease (COPD) and pneumonia as comparators. We calculated standardised mortality ratios (SMRs) as the ratio of observed to expected deaths.

RESULTS

Among 239 018 discharges, liver disease accounted for 1.7% of most responsible discharge diagnoses. Liver disease was associated with marked premature mortality, with SMR of 8.84 (95% CI 8.06-9.67) compared to 1.06 (95% CI 0.99-1.12) for heart failure, 1.05 (95% CI 0.96-1.15) for COPD and 1.28 (95% CI 1.20-1.37) for pneumonia. The majority of deaths were among patients younger than 65 years (57.7%) compared to 3.3% in heart failure, 5.6% in COPD and 10.7% in pneumonia. Liver disease patients presented with worse Laboratory-Based Acute Physiology Scores, were more frequently admitted to the intensive care unit (14.4%), incurred higher average total costs (median $6723 CAD), had higher in-hospital mortality (11.4%), and were more likely to be a readmission from 30 days prior (19.8%). Non-alcoholic fatty liver disease admissions increased from 120 in 2011-2012 to 215 in 2016-2017 (P < 0.01).

CONCLUSION

In Canada's largest urban centre, liver disease admissions resulted in premature morbidity and mortality with higher resource use compared to common cardio-respiratory conditions. Re-evaluation of approaches to caring for inpatients with liver disease is timely and justified.

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

OBJECTIVE

Large clinical databases are increasingly used for research and quality improvement. We describe an approach to data quality assessment from the General Medicine Inpatient Initiative (GEMINI), which collects and standardizes administrative and clinical data from hospitals.

METHODS

The GEMINI database contained 245 559 patient admissions at 7 hospitals in Ontario, Canada from 2010 to 2017. We performed 7 computational data quality checks and iteratively re-extracted data from hospitals to correct problems. Thereafter, GEMINI data were compared to data that were manually abstracted from the hospital's electronic medical record for 23 419 selected data points on a sample of 7488 patients.

RESULTS

Computational checks flagged 103 potential data quality issues, which were either corrected or documented to inform future analysis. For example, we identified the inclusion of canceled radiology tests, a time shift of transfusion data, and mistakenly processing the chemical symbol for sodium ("Na") as a missing value. Manual validation identified 1 important data quality issue that was not detected by computational checks: transfusion dates and times at 1 site were unreliable. Apart from that single issue, across all data tables, GEMINI data had high overall accuracy (ranging from 98%-100%), sensitivity (95%-100%), specificity (99%-100%), positive predictive value (93%-100%), and negative predictive value (99%-100%) compared to the gold standard.

DISCUSSION AND CONCLUSION

Computational data quality checks with iterative re-extraction facilitated reliable data collection from hospitals but missed 1 critical quality issue. Combining computational and manual approaches may be optimal for assessing the quality of large multisite clinical databases.

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

CONTEXTE:

Les caractéristiques des patients, les soins cliniques, l’utilisation des ressources et les issues cliniques des personnes atteintes de la maladie à coronavirus 2019 (COVID-19) hospitalisées au Canada ne sont pas bien connus.

MÉTHODES:

Nous avons recueilli des données sur tous les adultes hospitalisés atteints de la COVID-19 ou de l’influenza ayant obtenu leur congé d’unités médicales ou d’unités de soins intensifs médicaux et chirurgicaux entre le 1^er novembre 2019 et le 30 juin 2020 dans 7 centres hospitaliers de Toronto et de Mississauga (Ontario). Nous avons comparé les issues cliniques des patients à l’aide de modèles de régression multivariée, en tenant compte des facteurs sociodémographiques et de l’intensité des comorbidités. Nous avons validé le degré d’exactitude de 7 scores de risque mis au point à l’externe pour déterminer leur capacité à prédire le risque de décès chez les patients atteints de la COVID-19.

RÉSULTATS:

Parmi les hospitalisations retenues, 1027 patients étaient atteints de la COVID-19 (âge médian de 65 ans, 59,1 % d’hommes) et 783 étaient atteints de l’influenza (âge médian de 68 ans, 50,8 % d’hommes). Les patients âgés de moins de 50 ans comptaient pour 21,2 % de toutes les hospitalisations dues à la COVID-19 et 24,0 % des séjours aux soins intensifs. Comparativement aux patients atteints de l’influenza, les patients atteints de la COVID-19 présentaient un taux de mortalité perhospitalière (mortalité non ajustée 19,9 % c. 6,1 %; risque relatif [RR] ajusté 3,46 %, intervalle de confiance [IC] à 95 % 2,56–4,68) et un taux d’utilisation des ressources des unités de soins intensifs (taux non ajusté 26,4 % c. 18,0 %; RR ajusté 1,50, IC à 95 % 1,25–1,80) significativement plus élevés, ainsi qu’une durée d’hospitalisation (durée médiane non ajustée 8,7 jours c. 4,8 jours; rapport des taux d’incidence ajusté 1,45; IC à 95 % 1,25–1,69) significativement plus longue. Le taux de réhospitalisation dans les 30 jours n’était pas significativement différent (taux non ajusté 9,3 % c. 9,6 %; RR ajusté 0,98 %, IC à 95 % 0,70–1,39). Trois scores de risque utilisant un pointage pour prédire la mortalité perhospitalière ont montré une bonne discrimination (aire sous la courbe [ASC] de la fonction d’efficacité du récepteur [ROC] 0,72–0,81) et une bonne calibration.

INTERPRÉTATION:

Durant la première vague de la pandémie, l’hospitalisation des patients atteints de la COVID-19 était associée à des taux de mortalité et d’utilisation des ressources des unités de soins intensifs et à une durée d’hospitalisation significativement plus importants que les hospitalisations des patients atteints de l’influenza. De simples scores de risque peuvent prédire avec une bonne exactitude le risque de mortalité perhospitalière des patients atteints de la COVID-19.

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

BACKGROUND:

Patient characteristics, clinical care, resource use and outcomes associated with admission to hospital for coronavirus disease 2019 (COVID-19) in Canada are not well described.

METHODS:

We described all adults with COVID-19 or influenza discharged from inpatient medical services and medical–surgical intensive care units (ICUs) between Nov. 1, 2019, and June 30, 2020, at 7 hospitals in Toronto and Mississauga, Ontario. We compared patient outcomes using multivariable regression models, controlling for patient sociodemographic factors and comorbidity level. We validated the accuracy of 7 externally developed risk scores to predict mortality among patients with COVID-19.

RESULTS:

There were 1027 hospital admissions with COVID-19 (median age 65 yr, 59.1% male) and 783 with influenza (median age 68 yr, 50.8% male). Patients younger than 50 years accounted for 21.2% of all admissions for COVID-19 and 24.0% of ICU admissions. Compared with influenza, patients with COVID-19 had significantly greater in-hospital mortality (unadjusted 19.9% v. 6.1%, adjusted relative risk [RR] 3.46, 95% confidence interval [CI] 2.56–4.68), ICU use (unadjusted 26.4% v. 18.0%, adjusted RR 1.50, 95% CI 1.25–1.80) and hospital length of stay (unadjusted median 8.7 d v. 4.8 d, adjusted rate ratio 1.45, 95% CI 1.25–1.69). Thirty-day readmission was not significantly different (unadjusted 9.3% v. 9.6%, adjusted RR 0.98, 95% CI 0.70–1.39). Three points-based risk scores for predicting in-hospital mortality showed good discrimination (area under the receiver operating characteristic curve [AUC] ranging from 0.72 to 0.81) and calibration.

INTERPRETATION:

During the first wave of the pandemic, admission to hospital for COVID-19 was associated with significantly greater mortality, ICU use and hospital length of stay than influenza. Simple risk scores can predict in-hospital mortality in patients with COVID-19 with good accuracy.

Comparing characteristics and outcomes of cancer to non-cancer patients admitted to general internal medicine (GIM)

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Background: Cancer prevalence is rising and there is a corresponding increase in hospitalizations across the cancer continuum. However, little is known about the patterns of care and outcomes of cancer inpatients as administrative data may not capture in-hospital details including investigations and medications required for characterization. Understanding how cancer inpatients are managed and their outcomes can help to optimize care delivery. Methods: We conducted a multicenter study of all patients admitted to GIM at seven hospitals (Toronto, Canada) from 2010 to 2017 where we deterministically linked administrative data with each hospital’s electronic information (pharmacy, orders, notes, laboratory/imaging and results) at the patient level. Multivariable regression models compared characteristics and outcomes between cancer and non-cancer patients for the top 5 non-cancer patient discharge diagnoses. Results: Among 230,040 hospitalizations, 15% had cancer listed as an ICD-10 comorbidity. The most common cancer disease sites were gastrointestinal (20%), lung (13%) and leukemia (11%). The most common discharge diagnoses for cancer patients were disease progression (9%), palliative care (6%), pneumonia (4%), leukemia (4%) and lung cancer (4%), while for non-cancer patients were: heart failure (5%), pneumonia (5%), stroke (5%), COPD (5%) and urinary tract infections (5%). In general, compared to non-cancer patients, cancer patients were younger (70 vs 72), had greater length of stay (LOS; 6.4 vs 4.6 days), in-hospital mortality (16% vs 5%), ICU use (12% vs 11%), 30 day re-admission rate (17% vs 10%) and were more likely to receive CTs (64% vs 52%), MRIs (14% vs 12%) and interventional procedures (22% vs 8%) (p < 0.001, all comparisons). When evaluating the top 5 non-cancer patient discharge diagnoses, results (adjusted for age, gender, Charlson comorbidity score and hospital) were similar wherein cancer patients had a higher in-hospital mortality (aOR = 2.02 p < 0.001), 30 day re-admission rate (aOR = 1.09 p = 0.08) and were more likely to receive CTs (aOR = 1.88 p < 0.001), MRIs (aOR = 1.66 p < 0.001) or interventional procedures (aOR = 1.78 p < 0.001), despite similar mean LOS (5.7 vs 5.1 days p = 0.35). Results were similar across discharge diagnoses. Conclusions: Cancer patients represent a unique population on GIM and have higher resource use, mortality and LOS compared to non-cancer patients, with similar trends even for the same non-cancer diagnoses. Specialized models of care for hospitalized cancer patients may be warranted.

Accuracy of the Preferred Language Field in the Electronic Health Records of Two Canadian Hospitals

BACKGROUND

The collection of race, ethnicity, and language (REaL) data from patients is advocated as a first step to identify, monitor, and improve health inequities. As a result, many health care institutions collect patients' preferred languages in their electronic health records (EHRs). These data may be used in clinical care, research, and quality improvement. However, the accuracy of EHR language data are rarely assessed.

OBJECTIVES

This study aimed to audit the accuracy of EHR language data at two academic hospitals in Toronto, Ontario, Canada.

METHODS

The EHR language was compared with a patient's stated preferred language by interview. Language was dichotomized to English or non-English. Agreement between language documented in the EHR and patient-reported preferred language was calculated using sensitivity, specificity, and positive predictive value (PPV).

RESULTS

A total of 323 patients were interviewed, including 96 with a stated non-English preferred language. The sensitivity of the EHR for English-language preference was high at both hospitals: 100% at hospital A with a PPV of 88%, and 99% at hospital B with a PPV of 85%. However, the sensitivity of the EHR for non-English preference differed greatly between the two hospitals. The sensitivity was 81% with a PPV of 100% at hospital A and the sensitivity was 12% with a PPV of 60% at hospital B.

CONCLUSION

The accuracy of the EHR for identifying non-English language preference differed greatly between the hospitals studied. Language data must be accurate for it to be used, and regular quality assurance is required.

Empiric Antibiotic Treatment Thresholds for Serious Bacterial Infections: A Scenario-based Survey Study

BACKGROUND

Physicians face competing demands of maximizing pathogen coverage while minimizing unnecessary use of broad-spectrum antibiotics when managing sepsis. We sought to identify physicians' perceived likelihood of coverage achieved by their usual empiric antibiotic regimen, along with minimum thresholds of coverage they would be willing to accept when managing these patients.

METHODS

We conducted a scenario-based survey of internal medicine physicians from across Canada using a 2 × 2 factorial design, varied by infection source (undifferentiated vs genitourinary) and severity (mild vs severe) denoted by the Quick Sequential Organ Failure Assessment (qSOFA) score. For each scenario, participants selected their preferred empiric antibiotic regimen, estimated the likelihood of coverage achieved by that regimen, and considered their minimum threshold of coverage.

RESULTS

We had 238 respondents: 87 (36.6%) residents and 151 attending physicians (63.4%). The perceived likelihood of antibiotic coverage and minimum thresholds of coverage (with interquartile range) for each scenario were as follows: (1) severe undifferentiated, 90% (89.5%-95.0%) and 90% (80%-95%), respectively; (2) mild undifferentiated, 89% (80%-95%) and 80% (70%-89.5%); (3) severe genitourinary, 91% (87.3%-95.0%) and 90% (80.0%-90.0%); and (4) mild genitourinary, 90% (81.8%-91.3%) and 80% (71.8%-90%). Illness severity and infectious disease specialty predicted higher thresholds of coverage whereas less clinical experience and lower self-reported prescribing intensity predicted lower thresholds of coverage.

CONCLUSIONS

Pathogen coverage of 80% and 90% are physician-acceptable thresholds for managing patients with mild and severe sepsis from bacterial infections. These data may inform clinical guidelines and decision-support tools to improve empiric antibiotic prescribing.

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

BACKGROUND

The coronavirus disease 2019 (COVID-19) outbreak increases the importance of strategies to enhance urgent medical care delivery in long-term care (LTC) facilities that could potentially reduce transfers to emergency departments. The study objective was to model resource requirements to deliver virtual urgent medical care in LTC facilities.

METHODS

We used data from all general medicine inpatient admissions at 7 hospitals in the Greater Toronto Area, Ontario, Canada, over a 7.5-year period (Apr. 1, 2010, to Oct. 31, 2017) to estimate historical patterns of hospital resource use by LTC residents. We estimated an upper bound of potentially avoidable transfers by combining data on short admissions (≤ 72 h) with historical data on the proportion of transfers from LTC facilities for which patients were discharged from the emergency department without admission. Regression models were used to extrapolate future resource requirements, and queuing models were used to estimate physician staffing requirements to perform virtual assessments.

RESULTS

There were 235 375 admissions to general medicine wards, and residents of LTC facilities (age 16 yr or older) accounted for 9.3% (n = 21 948) of these admissions. Among the admissions of residents of LTC facilities, short admissions constituted 24.1% (n = 5297), and for 99.8% (n = 5284) of these admissions, the patient received laboratory testing, for 86.9% (n = 4604) the patient received plain radiography, for 41.5% (n = 2197) the patient received computed tomography and for 81.2% (n = 4300) the patient received intravenous medications. If all patients who have short admissions and are transferred from the emergency department were diverted to outpatient care, the average weekly demand for outpatient imaging per hospital would be 2.6 ultrasounds, 11.9 computed tomographic scans and 23.9 radiographs per week. The average daily volume of urgent medical virtual assessments would range from 2.0 to 5.8 per hospital. A single centralized virtual assessment centre staffed by 2 or 3 physicians would provide services similar in efficiency (measured by waiting time for physician assessment) to 7 separate centres staffed by 1 physician each.

INTERPRETATION

The provision of acute medical care to LTC residents at their facility would probably require rapid access to outpatient diagnostic imaging, within-facility access to laboratory services and intravenous medication and virtual consultations with physicians. The results of this study can inform efforts to deliver urgent medical care in LTC facilities in light of a potential surge in COVID-19 cases.

Value of routine echocardiography in the management of stroke

BACKGROUND

Transthoracic echocardiography is routinely performed in patients with stroke or transient ischemic attack (TIA) to help plan secondary stroke management, but recent data evaluating its usefulness in this context are lacking. We sought to evaluate the value of echocardiography for identifying clinically actionable findings for secondary stroke prevention.

METHODS

We conducted a multicentre cohort study of patients admitted to hospital with stroke or TIA between 2010 and 2015 at 2 academic hospitals in Toronto, Ontario, Canada. Clinically actionable echocardiographic findings for secondary stroke prevention included cardiac thrombus, patent foramen ovale, atrial myxoma or valvular vegetation. We identified patient characteristics associated with clinically actionable findings using logistic regression.

RESULTS

Of the 1862 patients with stroke or TIA we identified, 1272 (68%) had at least 1 echocardiogram. Nearly all echocardiograms were transthoracic; 1097 (86%) were normal, 1 (0.08%) had an atrial myxoma, 2 (0.2%) had a valvular vegetation, 11 (0.9%) had a cardiac thrombus and 66 (5.2%) had a PFO. Patent foramen ovale was less likely among patients older than 60 years (adjusted odds ratio [OR] 0.34, 95% confidence interval [CI] 0.20-0.57), with prior stroke or TIA (adjusted OR 0.31, 95% CI 0.09-0.76) or with dyslipidemia (adjusted OR 0.39, 95% CI 0.15-0.84). Among the 130 patients with cryptogenic stroke who had an echocardiogram (n = 110), a PFO was detected in 19 (17%) on transthoracic echocardiogram.

INTERPRETATION

Most patients with stroke or TIA had a normal echocardiogram, with few having clinically actionable findings for secondary stroke prevention. Clinically actionable findings, specifically PFO, were more common in patients with cryptogenic stroke.