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There is wide variation in mortality among patients hospitalized with COVID-19. Whether this is related to patient or hospital factors is unknown.To compare the risk of mortality for patients hospitalized with COVID-19 and to determine whether the majority of that variation was explained by differences in patient characteristics across sites.An international multicenter cohort study of hospitalized adults with laboratory-confirmed COVID-19 enrolled from 10 hospitals in Ontario, Canada and 8 hospitals in Copenhagen, Denmark between January 1, 2020 and November 11, 2020.Inpatient mortality. We used a multivariable multilevel regression model to compare the in-hospital mortality risk across hospitals and quantify the variation attributable to patient-level factors.There were 1364 adults hospitalized with COVID-19 in Ontario (n = 1149) and in Denmark (n = 215). In Ontario, the absolute risk of in-hospital mortality ranged from 12.0% to 39.8% across hospitals. Ninety-eight percent of the variation in mortality in Ontario was explained by differences in the characteristics of the patients. In Denmark, the absolute risk of inpatients ranged from 13.8% to 20.6%. One hundred percent of the variation in mortality in Denmark was explained by differences in the characteristics of the inpatients.There was wide variation in inpatient COVID-19 mortality across hospitals, which was largely explained by patient-level factors, such as age and severity of presenting illness. However, hospital-level factors that could have affected care, including resource availability and capacity, were not taken into account. These findings highlight potential limitations in comparing crude mortality rates across hospitals for the purposes of reporting on the quality of care.

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site’s data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare. Federated learning, a method for training artificial intelligence algorithms that protects data privacy, was used to predict future oxygen requirements of symptomatic patients with COVID-19 using data from 20 different institutes across the globe.

The role of remdesivir in the treatment of hospitalized patients with COVID-19 remains ill-defined. We conducted a cost-effectiveness analysis alongside the Canadian Treatments for COVID-19 (CATCO) open-label, randomized clinical trial evaluating remdesivir.Patients with COVID-19 in Canadian hospitals from Aug. 14, 2020, to Apr. 1, 2021, were randomly assigned to receive remdesivir plus usual care versus usual care alone. Taking a public health care payer's perspective, we collected in-hospital outcomes and health care resource utilization alongside estimated unit costs in 2020 Canadian dollars over a time horizon from randomization to hospital discharge or death. Data from 1281 adults admitted to 52 hospitals in 6 Canadian provinces were analyzed.The total mean cost per patient was $37 918 (standard deviation [SD] $42 413; 95% confidence interval [CI] $34 617 to $41 220) for patients randomly assigned to the remdesivir group and $38 026 (SD $46 021; 95% CI $34 480 to $41 573) for patients receiving usual care (incremental cost -$108 [95% CI -$4953 to $4737],From a health care payer perspective, treating patients hospitalized with COVID-19 with remdesivir and usual care appears to be preferrable to treating with usual care alone, albeit with marginal incremental cost and small clinical effects. The added cost of remdesivir was offset by shorter lengths of stay in the intensive care unit and less need for ventilation.ClinicalTrials. gov, no. NCT04330690.

ImportanceDiabetes has been reported to be associated with an increased risk of death among patients with COVID-19. However, available studies lack detail on COVID illness severity and measurement of relevant comorbidities.Design, Setting, and ParticipantsWe conducted a multicenter, retrospective cohort study of patients over the age of 18 years who were hospitalized with COVID-19 between January 1, 2020 and November 30, 2020 in Ontario, Canada and Copenhagen, Denmark. Chart abstraction emphasizing co-morbidities and disease severity was performed by trained research personnel. The association between diabetes and death was measured using Poissson regression.Main Outcomes and Measureswithin hospital 30-day risk of death.ResultsOur study included 1018 hospitalized patients with COVID-19 in Ontario and 305 in Denmark, of whom 405 and 75 patients respectively had pre-existing diabetes. In both Ontario and Denmark, patients with diabetes were more likely to be older, have chronic kidney disease, cardiovascular disease, higher troponin levels, and to receive antibiotics compared with adults who did not have diabetes. In Ontario, the crude mortality rate ratio among patients with diabetes was 1.60 [1.24 – 2.07 95% CI] and in the adjusted regression model was 1.19 [0.86 – 1.66 95% CI]. In Denmark, the crude mortality rate ratio among patients with diabetes was 1.27 (0.68 – 2.36 95% CI) and in the adjusted model was 0.87 (0.49 – 1.54 95% CI)]. Meta-analyzing the two rate ratios from each region resulted in a crude mortality rate ratio of 1.55 (95% CI 1.22,1.96) and an adjusted mortality rate ratio of 1.11 (95% CI 0.84, 1.47).ConclusionsPresence of diabetes was not strongly associated with in-hospital COVID mortality independent of illness severity and other comorbidities.

ObjectivesTo assess the effectiveness of prone positioning to reduce the risk of death or respiratory failure in non-critically ill patients hospitalized with COVID-19DesignPragmatic randomized clinical trial of prone positioning of patients hospitalized with COVID-19 across 15 hospitals in Canada and the United States from May 2020 until May 2021.SettingsPatients were eligible is they had a laboratory-confirmed or a clinically highly suspected diagnosis of COVID-19, required supplemental oxygen (up to 50% fraction of inspired oxygen [FiO2]), and were able to independently prone with verbal instruction. (NCT04383613).Main Outcome MeasuresThe primary outcome was a composite of in-hospital death, mechanical ventilation, or worsening respiratory failure defined as requiring at least 60% FiO2 for at least 24 hours. Secondary outcomes included the change in the ratio of oxygen saturation to FiO2 (S/F ratio).ResultsA total of 248 patients were included. The trial was stopped early on the basis of futility for the pre-specified primary outcome. The median time from hospital admission until randomization was 1 day, the median age of patients was 56 years (interquartile range [IQR] 45,65), 36% were female, and 90% of patients were receiving oxygen via nasal prongs at the time of randomization. The median time spent prone in the first 72 hours was 6 hours total (IQR 1.5,12.8) for the prone arm compared to 0 hours (0,2) in the control arm. The risk of the primary outcome was similar between the prone group (18 [14.3%] events) and the standard care group (17 [13.9%] events), odds ratio 0.92 (95% CI 0.44 to 1.92). The change in the S/F ratio after 72 hours was similar for patients randomized to prone compared to standard of care.ConclusionAmong hypoxic but not critically patients with COVID-19 in hospital, a multifaceted intervention to increase prone positioning did not improve outcomes. Adherence to prone positioning was poor, despite multiple efforts. Subsequent trials of prone positioning should aim to develop strategies to improve adherence to awake prone positioning.What is already known on this topicProne positioning is considered standard of care for mechanically ventilated patients who have severe acute respiratory distress syndrome. Recent data suggest prone positioning is beneficial for patients with COVID-19 who are requiring high flow oxygen. It is unknown of prone positioning is beneficial for patients not on high flow oxygen.What this study addsProne positioning is generally not well tolerated and innovative approaches are needed to improve adherence. Clinical and physiologic outcomes were not improved with prone positioning among hypoxic but not critically ill patients hospitalized with COVID-19.

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.

BackgroundCOronaVirus Disease 2019 (COVID-19) can be challenging to diagnose, because symptoms are non-specific, clinical presentations are heterogeneous, and false negative tests can occur. Our objective was to assess the utility of lymphocyte count to differentiate COVID-19 from influenza or community-acquired pneumonia (CAP).MethodsWe conducted a cohort study of adults hospitalized with COVID-19 or another respiratory infection (i.e., influenza, CAP) at seven hospitals in Ontario, Canada.The first available lymphocyte count during the hospitalization was used. Standard test characteristics for lymphocyte count (×109/L) were calculated (i.e., sensitivity, specificity, area under the receiver operating curve [AUC]). All analyses were conducting using R.ResultsThere were 869 hospitalizations for COVID-19, 669 for influenza, and 3009 for CAP. The mean age across the three groups was 67 and patients with pneumonia were older than those with influenza or COVID19, and approximately 46% were woman. The median lymphocyte count was nearly identical for the three groups of patients: 1.0 ×109/L (interquartile range [IQR]:0.7,2.0) for COVID-19, 0.9 ×109/L (IQR 0.6,1.0) for influenza, and 1.0 ×109/L (IQR 0.6,2.0) for CAP. At a lymphocyte threshold of less than 2.0 ×109/L, the sensitivity was 87% and the specificity was approximately 10%. As the lymphocyte threshold increased, the sensitivity of diagnosing COVID-19 increased while the specificity decreased. The AUC for lymphocyte count was approximately 50%.InterpretationLymphocyte count has poor diagnostic discrimination to differentiate between COVID-19 and other respiratory illnesses. The lymphopenia we consistently observed across the three illnesses in our study may reflect a non-specific sign of illness severity. However, lymphocyte count above 2.0 ×109/L may be useful in ruling out COVID-19 (sensitivity = 87%).

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC)0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

The COVID-19 pandemic has posed novel infection-control challenges for hospitals around the globe. One infection-control strategy that has been widely used in the context of other outbreaks is patient cohorting. This strategy refers to the placement of all patients exposed to the same laboratory-confirmed infectious agent in one location within the hospital. Little is known about the current utilization of this strategy with non-critically ill COVID-19 patients. An international multicenter, survey study was conducted to identify what strategies are planned or in place for patients with COVID-19 who are not critically ill. The survey was distributed from March 23-29th, 2020 to GIM physicians in Canada, USA, Denmark, Singapore, Hong Kong, and England. Of the 31 hospitals, 29 (94%) indicated that they plan on cohorting all GIM patients with COVID-19 to one location in the hospital. Among these 29 hospitals, 23 (79%) had implemented the plan at the time of the survey. The primary reasons for this decision were to limit the spread of COVID-19 and conserve PPE use. In conclusion, in the face of a novel virus there is near unanimity in the practice of patient cohorting as a potential mitigation strategy.

Clinical trials are underpowered to detect serious but rare adverse events (AEs). We applied a novel data mining approach to detect potential AEs for canagliflozin (CANA), the first marketed SGLT2 inhibitor, prior to public awareness of its potential safety concerns. In a U.S. commercial claims dataset (3/2013-9/2015), two pair-wise cohorts of patients with T2DM initiating CANA or a comparator, i.e., a DPP-4i or a GLP-1RA, were identified and propensity score matched. We used variable ratio matching with up to 4 comparators for each CANA initiator (44,733 CANA vs. 99,458 DPP-4i; and 55,974 CANA vs. 74,727 GLP-1RA). Using a tree-based scan statistic data mining method, we assessed thousands of incident outcomes in CANA vs. comparator initiators, scanning for statistical AE alerts after adjusting for multiple testing. Incident outcomes were defined by hierarchical groupings of clinically related ICD-9 codes. For inpatient and emergency room diagnoses, diabetic ketoacidosis was the only severe AE associated with CANA (p Disclosure M. Fralick: None. M. Kulldorff: None. S. Wang: Research Support; Self; Boehringer Ingelheim International GmbH, Johnson & Johnson, Novartis Pharmaceuticals Corporation. S. Schneeweiss: None. D. Redelmeier: None. E. Patorno: Other Relationship; Self; Boehringer Ingelheim International GmbH. Funding National Institute on Aging (K08AG055670)

Source Citation Etminan M, Sodhi M, Ganjizadeh-Zavareh S, et al. Oral fluoroquinolones and risk of mitral and aortic regurgitation. J Am Coll Cardiol. 2019;74:1444-50. 31514945

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.