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Background People with schizophrenia are less likely than those without to be treated for cardiovascular disease. We aimed to evaluate the association between schizophrenia and secondary preventive care after ischemic stroke. Methods and Results In this retrospective cohort study, we used linked population‐based administrative data to identify adults who survived 1 year after ischemic stroke hospitalization in Ontario, Canada between 2004 and 2017. Outcomes were screening, treatment, and control of risk factors, and receipt of outpatient physician services. We used modified Poisson regression to model the relative risk of each outcome among people with and without schizophrenia, adjusting for age and other factors. Among 81 163 people with ischemic stroke, 844 (1.04%) had schizophrenia. Schizophrenia was associated with lower rates of screening for hyperlipidemia (60.5% versus 66.0%, adjusted relative risk [aRR] 0.88 [95% CI, 0.84–0.93]) and diabetes (69.4% versus 73.9%, aRR 0.93 [95% CI, 0.89–0.97]), prescription of antihypertensive medications (91.2% versus 94.7%, aRR 0.96 [95% CI, 0.93–0.99]), achievement of target lipid levels (low‐density lipoprotein

BackgroundStroke incidence among young adults of working age (under 65 years of age) has significantly increased in the past decade, with major individual, social, and economic implications. There is a paucity of research exploring the needs of this patient population. This study assessed: (1) young adult stroke patients' physical, psychological, and occupational functioning and health-related quality of life (HRQoL); and (2) post-stroke care preferences using patient-reported outcome measures (PROMs), with attention to gender differences.MethodsA cross-sectional pilot study was conducted. Sociodemographic and clinical characteristics were collected through chart review and data on occupational function, physical, psychological, and social wellbeing >90 days post-stroke through a self-reported survey. Descriptive statistics, gender-based, and regression analyses were conducted.ResultsThe sample included 85 participants. Participants reported impairments in both, occupational functioning, with 58.7% not returned to work (RTW), and HRQoL, specifically with social activities (37%), anxiety (34%), and cognitive function (34%). Women had significantly (p < 0.05) worse physical symptoms (sleep disturbance and fatigue), emotional health (depression, stigma, emotional dyscontrol) scores, and sense of self-identify post-stroke. Over 70% of participants preferred in-person post-stroke care led by health care providers and felt they would have benefited from receiving information on physical health (71.4%), emotional and psychological health (56.0%), RTW (38.1%), and self-identity (26.2%) post-stroke. Women preferred cognitive behavioral therapy (p = 0.018) and mindfulness-based stress reduction therapy (p = 0.016), while men preferred pharmacotherapy (p = 0.02) for psychological symptoms.ConclusionThis is the first study to report impaired HRQoL, psychological and occupational functioning using PROMs, with significant gender differences and preferences for post-stroke care delivery among young adult stroke patients at >90 days after stroke. The findings highlight the importance of needs, gender, and age-specific post-stroke education and interventions.

Background Schizophrenia is associated with an increased risk of death following stroke; however, the magnitude and underlying reasons for this are not well understood.Objective To determine the association between schizophrenia and stroke case fatality, adjusting for baseline characteristics, stroke severity and processes of care.Design Retrospective cohort study used linked clinical and administrative databases.Setting All acute care institutions (N=152) in the province of Ontario, Canada.Participants All patients (N=52 473) hospitalised with stroke between 1 April 2002 and 31 March 2013 and included in the Ontario Stroke Registry. Those with schizophrenia (n=612) were identified using validated algorithms.Main outcomes and measures We compared acute stroke care in those with and without schizophrenia and used Cox proportional hazards models to examine the association between schizophrenia and mortality, adjusting for demographics, comorbidity, stroke severity and processes of care.Results Compared with those without schizophrenia, people with schizophrenia were less likely to undergo thrombolysis (10.1% vs 13.4%), carotid imaging (66.3% vs 74.0%), rehabilitation (36.6% vs 46.6% among those with disability at discharge) or be treated with antihypertensive, lipid-lowering or anticoagulant therapies. After adjustment for age and other factors, schizophrenia was associated with death from any cause at 1 year (adjusted HR (aHR) 1.33, 95% CI 1.14 to 1.54). This was mainly attributable to early deaths from stroke (aHR 1.47, 95% CI 1.20 to 1.80, with survival curves separating in the first 30 days), and the survival disadvantage was particularly marked in those aged over 70 years (1-year mortality 46.9% vs 35.0%).Conclusions Schizophrenia is associated with increased stroke case fatality, which is not fully explained by stroke severity, measurable comorbid conditions or processes of care. Future work should focus on understanding this mortality gap and on improving acute stroke and secondary preventive care in people with schizophrenia.

We compared the performance of two 96-well multiplex immunoassay platforms in assessing plasma cytokine concentrations in patients with glioblastoma (GBM; n = 27), individuals with melanoma, breast or lung cancer metastases to the brain (n = 17), and healthy volunteers (n = 11). Assays included a bead-based fluorescence MILLIPLEX ® assay/Luminex (LMX) platform and 4 planar electrochemiluminescence kits from Meso Scale Discovery (MSD). The LMX kit evaluated 21 cytokines and the 3 MSD kits evaluated 20 cytokines in total, with 19 overlapping human cytokines between platforms (GM-CSF, IFNγ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12p70, IL-13, IL-17A, IL-21, IL-23, MIP-1α, MIP-1β, MIP-3α, TNFα). The MSD platform had lower LLoQs (lower limits of quantification) than LMX for 17/19 cytokines, and higher LLoQs for IFN-γ and IL-21. The ULoQs were higher in LMX versus MSD assays for 17/19 shared analytes, but lower than MSD for IL-17A and IL-21. With LMX, all 19 shared analytes were quantifiable in each of 55 samples. Although MSD recombinant protein standard curves indicated lower LLoQs than LMX for most cytokines, MSD detected 7/19 (37%) native analytes in

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

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.

BackgroundThere is increasing interest in collecting sociodemographic and social needs data in hospital settings to inform patient care and health equity. However, few studies have examined inpatients' views on this data collection and what should be done to address social needs. This study describes internal medicine inpatients' perspectives on the collection and use of sociodemographic and social needs information.MethodsA qualitative interpretive description methodology was used. Semi-structured interviews were conducted with 18 patients admitted to a large academic hospital in Toronto, Canada. Participants were recruited using maximum variation sampling for diverse genders, races, and those with and without social needs. Interviews were coded using a predominantly inductive approach and a thematic analysis was conducted.ResultsPatients expressed that sociodemographic and social needs data collection is important to offer actionable solutions to address their needs. Patients described a gap between their ideal care which would attend to social needs, versus the reality that hospital-based teams are faced with competing priorities and pressures that make it unfeasible to provide such care. They also believed that this data collection could facilitate more holistic, integrated care. Patients conveyed a need to have a trusting and transparent relationship with their provider to alleviate concerns surrounding bias, discrimination, and confidentiality. Lastly, they indicated that sociodemographic and social needs data could be useful to inform care, support research to inspire social change, and assist them with navigating community resources or creating in-hospital programs to address unmet social needs.ConclusionsWhile the collection of sociodemographic and social needs information in hospital settings is generally acceptable, there were varied views on whether hospital staff should intervene, as their priority is medical care. The results can inform the implementation of social data collection and interventions in hospital settings.

BackgroundDelirium is an acute neurocognitive disorder that affects up to half of older hospitalized medical patients and can lead to dementia, longer hospital stays, increased health costs, and death. Although delirium can be prevented and treated, it is difficult to identify and predict. ObjectiveThis study aimed to improve machine learning models that retrospectively identify the presence of delirium during hospital stays (eg, to measure the effectiveness of delirium prevention interventions) by using the natural language processing (NLP) technique of sentiment analysis (in this case a feature that identifies sentiment toward, or away from, a delirium diagnosis). MethodsUsing data from the General Medicine Inpatient Initiative, a Canadian hospital data and analytics network, a detailed manual review of medical records was conducted from nearly 4000 admissions at 6 Toronto area hospitals. Furthermore, 25.74% (994/3862) of the eligible hospital admissions were labeled as having delirium. Using the data set collected from this study, we developed machine learning models with, and without, the benefit of NLP methods applied to diagnostic imaging reports, and we asked the question “can NLP improve machine learning identification of delirium?” ResultsAmong the eligible 3862 hospital admissions, 994 (25.74%) admissions were labeled as having delirium. Identification and calibration of the models were satisfactory. The accuracy and area under the receiver operating characteristic curve of the main model with NLP in the independent testing data set were 0.807 and 0.930, respectively. The accuracy and area under the receiver operating characteristic curve of the main model without NLP in the independent testing data set were 0.811 and 0.869, respectively. Model performance was also found to be stable over the 5-year period used in the experiment, with identification for a likely future holdout test set being no worse than identification for retrospective holdout test sets. ConclusionsOur machine learning model that included NLP (ie, sentiment analysis in medical image description text mining) produced valid identification of delirium with the sentiment analysis, providing significant additional benefit over the model without NLP.