Trajectories of depressive symptoms 10 years after stroke and associated risk factors: a prospective cohort study.

BACKGROUND: Previous studies have investigated the risk factors for post-stroke depression at only one timepoint, neglecting its dynamic nature. We aimed to identify trajectories of post-stroke depression from multiple assessments and explore their risk factors. METHODS: We did a population-based cohort study with the South London Stroke Register (1995-2019). All stroke patients with three or more measurements of the Hospital Anxiety and Depression Scale were included. We identified trajectories of post-stroke depression over a 10-year follow-up using group-based trajectory modelling. We determined the optimal number and shape of trajectories based on the lowest Bayesian information criterion, average posterior probability of assignment of each group over 0·70, and inclusion of at least 5% of participants within each group. We used multinomial logistic regression adjusted for age, sex, ethnicity, comorbidity, physical disability, stroke severity, history of depression and cognitive impairment to explore associations with different trajectories. FINDINGS: The analysis comprised 1968 participants (mean age 64·9 years [SD 13·8], 56·6% male and 43·4% female, 65·1% white ethnicity, 30·7% severe disability and 32·7% severe stroke). We identified four patterns of symptoms: no depressive symptoms (14·1%, n=277), low symptoms (41·7%, n=820), moderate symptoms and symptoms worsening early and then improving (34·6%, n=681), and high and increasing symptoms (9·7%, n=190). Compared with no depressive symptom trajectory, patients with severe disability, severe stroke, pre-stroke depression, and cognitive impairment were more likely to be in the moderate and high symptom groups (adjusted odds ratios [ORs] 2·26 [95% CI 1·56-3·28], 1·75 [1·19-2·57], 2·20 [1·02-4·74], and 2·04 [1·25-3·32], respectively). Female sex was associated with high depression (OR 1·65 [1·13-2·41]), while older age (≥65 years) was associated with moderate depression (OR 1·82 [1·36-2·45]). In men, the ORs for patients with severe disability, severe stroke, pre-stroke depression, and cognitive impairment being in the high depression group were 1·91 (1·01-3·60), 2·41 (1·26-4·60), 2·57 (0·84-7·88), and 2·68 (1·28-5·60), respectively. In women, the ORs were 1·08 (0·52-2·23), 1·30 (0·60-2·79), 19·2 (2·35-156·05), and 3·80 (1·44-10·01), respectively. INTERPRETATION: Female sex and older age were associated with distinct courses of depressive symptoms. In men, high depressive symptom trajectory was associated with severe stroke and severe disability, which was not the case in women. These findings were limited to patients with three or more assessments, who tended to have less severe disabilities than excluded patients and might not generalise to all stroke survivors. FUNDING: National Institute for Health and Care Research (NIHR).

The feasibility, repeatability, validity and responsiveness of the EQ-5D-3L in Krio for patients with stroke in Sierra Leone.

OBJECTIVES: To assess the feasibility, repeatability, validity and responsiveness of the EQ-5D-3L in Krio for patients with stroke in Sierra Leone, the first psychometric assessment of the EQ-5D-3L to be conducted in patients with stroke in Sub Saharan Africa. METHODS: A prospective stroke register at two tertiary government hospitals recruited all patients with the WHO definition of stroke and followed patients up at seven days, 90 days and one year post stroke. The newly translated EQ-5D-3L, Barthel Index (BI), modified Rankin Scale (mRS) and National Institute of Health Stroke Scale (NIHSS), a measure of stroke severity, were collected by trained researchers, face to face during admission and via phone at follow up. Feasibility was assessed by completion rate and proportion of floor/ceiling effects. Internal consistency was assessed by inter item correlations (IIC) and Cronbach's alpha. Repeatability of the EQ-5D-3L was examined using test-retest, EQ-5D-3L utility scores at 90 days were compared to EQ-5D-3L utility scores at one year in the same individuals, whose Barthel Index had remained within the minimally clinical important difference. Known group validity was assessed by stroke severity. Convergent validity was assessed against the BI, using Spearman's rho. Responsiveness was assessed in patients whose BI improved or deteriorated from seven to 90 days. Sensitivity analyses were conducted using the UK and Zimbabwe value sets, to evaluate the effect of value set, in a subgroup of patients with no formal education to evaluate the influence of patient educational attainment, and using the mRS instead of the BI to evaluate the influence of utilising an alternative functional scale. RESULTS: The EQ-5D-3L was completed in 373/460 (81.1%), 360/367 (98.1%) and 299/308 (97.1%) eligible patients at seven days, 90 days and one year post stroke. Missing item data was low overall, but was highest in the anxiety/depression dimension 1.3% (5/373). Alpha was 0.81, 0.88 and 0.86 at seven days, 90 days and one year post stroke and IIC were within pre-specified ranges. Repeatability of the EQ-5D-3L was moderate to poor, weighted Kappa 0.23-0.49. EQ-5D-3L utility was significantly associated with stroke severity at all timepoints. Convergent validity with BI was strong overall and for shared subscales. EQ-5D-3L was moderately responsive to both improvement Cohen's D 0.55 (95% CI:0.15-0.94) and deterioration 0.92 (95% CI:0.29-1.55). Completion rates were similar in patients with no formal education 148/185 (80.0%) vs those with any formal education 225/275 (81.8%), and known group validity for stroke severity in patients with no formal education was strong. Using the Zimbabwe value set instead of the UK value set, and using the mRS instead of the BI did not change the direction or significance of results. CONCLUSIONS: The EQ-5D-3L for stroke in Sierra Leone was feasible, and responsive including in patients with no formal education. However, repeatability was moderate to poor, which may be due to the study design, but should add a degree of caution in the analysis of repeated measures of EQ-5D-3L over time in this population. Known group validity and convergent validity with BI and mRS were strong. Further research should assess the EQ-5D in the general population, examine test-retest reliability over a shorter time period and assess the acceptability and validity of the anxiety/depression dimension against other validated mental health instruments. Development of an EQ-5D value set for West Africa should be a research priority.

A systematic review of the incidence and outcomes of ICD-11 defined stroke.

BACKGROUND: The World Health Organisation has expanded the definition of stroke to include people with symptoms less than 24 h if they have evidence of stroke on neuroimaging. The impact is that people previously diagnosed as having a transient ischaemic attack (TIA) would now be considered to have had a stroke. This change will impact incidence and outcomes of stroke and increase eligibility for secondary prevention. We aimed to evaluate the new ICD-11 criteria retrospectively to previous TIA studies to understand the change in incidence and outcomes of this type of stroke. METHODS: We conducted a systematic review of observational studies of the incidence and outcomes of clinically defined TIA. We searched PubMed, EMBASE, and Google Scholar from inception to 23rd May 2023. Study quality was assessed using a risk of bias tool for prevalence studies. FINDINGS: Our review included 25 studies. The rate of scan positivity for stroke among those with clinically defined TIA was 24 %, (95 % CI, 16-33 %) but with high heterogeneity (I2 = 100 %, p <0.001). Sensitivity analyses provided evidence that heterogeneity could be explained by methodology and recruitment method. The scan positive rate when examining only studies at low risk of bias was substantially lower, at 13 % (95 % CI, 11-15 %, I2 = 0, p = 0.77). We estimate from population-based incidence studies that ICD-11 would result in an increase stroke incidence between 4.8 and 10.5 per 100,000 persons/year. Of those with DWI-MRI evidence of stroke, 6 % (95 % CI, 3-11 %) developed a recurrent stroke in the subsequent 90 days, but with substantial heterogeneity (I2 = 67 %, p = 0.02). CONCLUSION: The impact of the ICD-11 change in stroke definition on incidence and outcomes may have been overestimated by individual studies. Community-based stroke services with access to DWI MRI are likely to accurately diagnose greater numbers of people with mild ICD-11 stroke, increasing access to effective prevention.

Prevalence and natural history of depression after stroke: A systematic review and meta-analysis of observational studies.

BACKGROUND: Depression is the most frequent psychiatric condition after stroke and is associated with negative health outcomes. We aim to undertake a systematic review and meta-analysis of the prevalence and natural history of depression after stroke. METHODS AND FINDINGS: Studies published up to 4 November 2022 on Medline, Embase, PsycINFO, and Web of Science Core Collection were searched. We included studies of adults with stroke, where depression was assessed at a prespecified time point. Studies excluding people with aphasia and history of depression are excluded. Critical Appraisal Skills Programme(CASP) cohort study tool was used to assess risk of bias. A total of 77 studies were included in the pooled estimates of the prevalence of poststroke depression (PSD). The overall prevalence of depression was 27% (95% CI 25 to 30). Prevalence of depression was 24% (95% CI 21 to 28) by clinical interview and 29% (95% CI 25 to 32) by rating scales. Twenty-four studies with more than one assessment time point reported the natural history of PSD. Among people who were depressed within 3 months of stroke, 53% (95% CI 47 to 59) experienced persistent depression, while 44% (95% CI 38 to 50) recovered. The incidence of later depression (3 to 12 months after stroke) was 9% (95% CI 7 to 12). The cumulative incidence during 1 year after stroke was 38% (95% CI 33 to 43), and the majority (71% (95% CI 65 to 76)) of depression had onset within 3 months after stroke. The main limitation of the present study is that excluding people in source studies with severe impairments may produce imprecise estimates of the prevalence of PSD. CONCLUSIONS: In this study, we observed that stroke survivors with early-onset depression (within 3 months after stroke) are at high risks for remaining depressed and make up two-thirds of the incident cases during 1 year after stroke. This highlights the need for ongoing clinical monitoring of patients depressed shortly after stroke. TRIAL REGISTRATION: PROSPERO CRD42022314146.

Accuracy and Efficiency of Machine Learning-Assisted Risk-of-Bias Assessments in "Real-World" Systematic Reviews : A Noninferiority Randomized Controlled Trial.

BACKGROUND: Automation is a proposed solution for the increasing difficulty of maintaining up-to-date, high-quality health evidence. Evidence assessing the effectiveness of semiautomated data synthesis, such as risk-of-bias (RoB) assessments, is lacking. OBJECTIVE: To determine whether RobotReviewer-assisted RoB assessments are noninferior in accuracy and efficiency to assessments conducted with human effort only. DESIGN: Two-group, parallel, noninferiority, randomized trial. (Monash Research Office Project 11256). SETTING: Health-focused systematic reviews using Covidence. PARTICIPANTS: Systematic reviewers, who had not previously used RobotReviewer, completing Cochrane RoB assessments between February 2018 and May 2020. INTERVENTION: In the intervention group, reviewers received an RoB form prepopulated by RobotReviewer; in the comparison group, reviewers received a blank form. Studies were assigned in a 1:1 ratio via simple randomization to receive RobotReviewer assistance for either Reviewer 1 or Reviewer 2. Participants were blinded to study allocation before starting work on each RoB form. MEASUREMENTS: Co-primary outcomes were the accuracy of individual reviewer RoB assessments and the person-time required to complete individual assessments. Domain-level RoB accuracy was a secondary outcome. RESULTS: Of the 15 recruited review teams, 7 completed the trial (145 included studies). Integration of RobotReviewer resulted in noninferior overall RoB assessment accuracy (risk difference, -0.014 [95% CI, -0.093 to 0.065]; intervention group: 88.8% accurate assessments; control group: 90.2% accurate assessments). Data were inconclusive for the person-time outcome (RobotReviewer saved 1.40 minutes [CI, -5.20 to 2.41 minutes]). LIMITATION: Variability in user behavior and a limited number of assessable reviews led to an imprecise estimate of the time outcome. CONCLUSION: In health-related systematic reviews, RoB assessments conducted with RobotReviewer assistance are noninferior in accuracy to those conducted without RobotReviewer assistance. PRIMARY FUNDING SOURCE: University College London and Monash University.

Cohort profile: The South London Stroke Register - a population-based register measuring the incidence and outcomes of stroke.

PURPOSE: The South London Stroke Register (SLSR) is a population-based cohort study, which was established in 1995 to study the causes, incidence, and outcomes of stroke. The SLSR aims to estimate incidence, and acute and long term needs in a multi-ethnic inner-city population, with follow-up durations for some participants exceeding 20 years. PARTICIPANTS: The SLSR aims to recruit residents of a defined area within Lambeth and Southwark who experience a first stroke. More than 7700 people have been registered since inception, and >2750 people continue to be followed up. At the 2011 census, the source population was 357,308. FINDINGS TO DATE: The SLSR was instrumental in highlighting the inequalities in risk and outcomes in the UK, and demonstrating the dramatic improvements in care quality and outcomes in recent decades. Data from the SLSR informed the UK National Audit Office in its 2005 report criticising the poor state of stroke care in England. For people living in the SLSR area the likelihood of being treated in a stroke unit increased from 19% in 1995-7 to 75% in 2007-9. The SLSR has investigated health inequalities in stroke incidence and outcome. SLSR analyses have demonstrated that lower socioeconomic status was associated with poorer outcome, and that Black people and younger people have not experienced the same improvements in stroke incidence as other groups. FUTURE PLANS: As part of an NIHR Programme Grant for Applied Research, from April 2022 the SLSR has expanded to recruit ICD-11 defined stroke (including those with <24 h symptoms where there are neuroimaging findings), and have expanded the follow up interviews to collect more detailed information on quality of life, cognition, and care needs. Additional data items will be added over the Programme based on feedback from patients and other stakeholders.

Blood eosinophil count, a marker of inhaled corticosteroid effectiveness in preventing COPD exacerbations in post-hoc RCT and observational studies: systematic review and meta-analysis.

BACKGROUND: Blood eosinophil count has been proposed as a predictor of response to inhaled corticosteroid (ICS) in the prevention of acute exacerbations of COPD. An optimal threshold of blood eosinophil count for prescribing ICS has not been agreed. Doubt has been cast on the role by observational studies. The role of inhaled corticosteroids in this relationship, independent of long-acting bronchodilators, has not been examined. METHODS: We conducted a systematic review of post-hoc analyses of randomised controlled trials (RCTs) and observational studies examining three blood eosinophil thresholds and the independent role of ICS. Included studies were categorised by the form (relative or absolute count) and cut point of eosinophil threshold used. Thresholds assessed were relative eosinophil count of 2%, and absolute counts of 150 cells/µL and 300 cells/µL. Three meta-analyses of the effect of ICS use in post-hoc analyses of RCTs based on these counts were carried out. Initial analysis included all studies of ICS vs. any non-ICS regimen. Further analysis examined the effect of ICS, independent of the effect of long-acting bronchodilators. RESULTS: Sixteen studies examined the association between blood eosinophil count and response of exacerbation risk to ICS, in COPD patients. Eleven studies (25,881 patients) were post-hoc analyses of RCTs. Five studies (109,704 patients) were retrospective observational studies. The independent effect of ICS on the reduction of exacerbation risk was 20% at ≥2% blood eosinophil threshold (RR, 0.80; 95% CI, 0.74-0.85), 35% at ≥150 cells/µL blood eosinophil threshold (RR, 0.65; 0.52-0.79), and 39% at ≥300 cells/µL blood eosinophil threshold (RR, 0.61; 0.44-0.78). No association was found in four out of five observational studies. CONCLUSION: This is the first systematic review to assess, in post-hoc analyses of RCTs, the independent effect of ICS in reducing the risk of COPD exacerbation across a range of blood eosinophil thresholds. Association between ICS prescription and reduced exacerbation risk at these thresholds was confirmed. The lack of association found in the observational studies questions the relevance of these observations to a "real world" COPD population. To clarify the clinical utility of this biomarker, the association should be tested in prospective effectiveness studies.

Machine learning to help researchers evaluate biases in clinical trials: a prospective, randomized user study.

OBJECTIVE: Assessing risks of bias in randomized controlled trials (RCTs) is an important but laborious task when conducting systematic reviews. RobotReviewer (RR), an open-source machine learning (ML) system, semi-automates bias assessments. We conducted a user study of RobotReviewer, evaluating time saved and usability of the tool. MATERIALS AND METHODS: Systematic reviewers applied the Cochrane Risk of Bias tool to four randomly selected RCT articles. Reviewers judged: whether an RCT was at low, or high/unclear risk of bias for each bias domain in the Cochrane tool (Version 1); and highlighted article text justifying their decision. For a random two of the four articles, the process was semi-automated: users were provided with ML-suggested bias judgments and text highlights. Participants could amend the suggestions if necessary. We measured time taken for the task, ML suggestions, usability via the System Usability Scale (SUS) and collected qualitative feedback. RESULTS: For 41 volunteers, semi-automation was quicker than manual assessment (mean 755 vs. 824 s; relative time 0.75, 95% CI 0.62-0.92). Reviewers accepted 301/328 (91%) of the ML Risk of Bias (RoB) judgments, and 202/328 (62%) of text highlights without change. Overall, ML suggested text highlights had a recall of 0.90 (SD 0.14) and precision of 0.87 (SD 0.21) with respect to the users' final versions. Reviewers assigned the system a mean 77.7 SUS score, corresponding to a rating between "good" and "excellent". CONCLUSIONS: Semi-automation (where humans validate machine learning suggestions) can improve the efficiency of evidence synthesis. Our system was rated highly usable, and expedited bias assessment of RCTs.

A scoping review of stroke registers in Sub-Saharan Africa.

BACKGROUND: Stroke registers are recommended as a key priority by the Lancet Neurology World Stroke Organization Commission for Stroke, 2023, and the African Stroke Leaders' Summit, 2022. AIMS: This scoping review aims to map where stroke registers have been implemented in Sub-Saharan Africa (SSA). The article then compares and critiques the methods and definitions used and summarizes key results from the registers. The scoping review searched EMBASE, MEDLINE, and CABI Global Health databases and included all studies with a prospective longitudinal design in SSA, where adult acute stroke was the primary condition studied. Articles were screened against inclusion and exclusion criteria independently by two authors. SUMMARY: We identified 42 unique stroke registers from 48 individual studies. The registers were located in 19 countries, with 19 from East Africa, 15 West Africa, 6 Central Africa, and 2 from Southern Africa. Cumulatively, the registers recruited 12,345 participants with stroke, the median number of participants was 183 (interquartile range (IQR): 121-312), and the range was 50-1018. Only one study was a population-based register, and 41 were hospital-based registers. Of the hospital-based registers, 29 were single site, 10 were conducted at two sites, and 2 at three sites. Twenty-three (54.7%) of the registers were located in the capital city of their respective country, and only one of the hospital-based registers was in a self-described rural area. Length of recruitment ranged from 4 months to 6 years; the median length of recruitment was 12 months. Methodology and definitions were heterogenous between the registers. Only seven (19.4%) registers referenced the WHO STEPwise approach to implementing stroke registers. Twenty-seven (64.3%) registers used the WHO definition of stroke. The mean neuroimaging rate was 84%, and ranged from 0% to 100%. Stroke severity was measured using the National Institute of Health Stroke Scale (NIHSS) in 22 (52.4%) registers, four registers used the Glasgow Coma Scale (GCS), two registers used the miniNIHSS, one used the Scandinavian Stroke Scale, one modified Rankin Scale (mRS), and 11 registers did not report a stroke severity measure. Seventeen (40.5%) registers used the mRS to measure function, six registers used Barthel Index alone, and three registers used both mRS and Barthel Index. Only two registers included a quality-of-life measure, the EQ-5D. Eight registers included a quality-of-care measure, and 26 (61.9%) registers recorded socioeconomic status or a socioeconomic status proxy, most frequently educational attainment. CONCLUSIONS: This scoping review found high heterogeneity of methods and definitions used by stroke registers, with low uptake of the WHO stepwise method of stroke surveillance. A drive to standardize methodology would improve the comparability of stroke data in SSA. The shared use of educational attainment by registers in our review may enable future meta-analyses of inequities in stroke in SSA. Incorporating health-related quality-of-life measures, such as EQ-5D, into stroke registers should be encouraged, bringing a patient perspective, and allow the estimation of quality-adjusted life years lost to stroke. Agreement on a standardized register methodology or further promotion and uptake of the WHO stepwise method is essential to produce comparable data to improve stroke prevention and care.

Question answering systems for health professionals at the point of care-a systematic review.

OBJECTIVES: Question answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence. However, QA systems have not been widely adopted. This systematic review aims to characterize current medical QA systems, assess their suitability for healthcare, and identify areas of improvement. MATERIALS AND METHODS: We searched PubMed, IEEE Xplore, ACM Digital Library, ACL Anthology, and forward and backward citations on February 7, 2023. We included peer-reviewed journal and conference papers describing the design and evaluation of biomedical QA systems. Two reviewers screened titles, abstracts, and full-text articles. We conducted a narrative synthesis and risk of bias assessment for each study. We assessed the utility of biomedical QA systems. RESULTS: We included 79 studies and identified themes, including question realism, answer reliability, answer utility, clinical specialism, systems, usability, and evaluation methods. Clinicians' questions used to train and evaluate QA systems were restricted to certain sources, types and complexity levels. No system communicated confidence levels in the answers or sources. Many studies suffered from high risks of bias and applicability concerns. Only 8 studies completely satisfied any criterion for clinical utility, and only 7 reported user evaluations. Most systems were built with limited input from clinicians. DISCUSSION: While machine learning methods have led to increased accuracy, most studies imperfectly reflected real-world healthcare information needs. Key research priorities include developing more realistic healthcare QA datasets and considering the reliability of answer sources, rather than merely focusing on accuracy.

Natural history of depression up to 18 years after stroke: a population-based South London Stroke Register study.

Background: Current evidence on the long-term natural history of post-stroke depression (PSD) is limited. We aim to determine the prevalence, incidence, duration and recurrence rates of depression to 18-years after stroke and assess differences by onset-time and depression severity. Methods: Data were from the South London Stroke Register (1995-2019, N = 6641 at registration). Depression was defined using the Hospital Anxiety and Depression scale (scores > 7 = depression) at 3-months, then annually to 18-years after stroke. We compared early- (3-months post-stroke) vs late-onset depression (1-year) and initial mild (HADS scores > 7) vs severe depression (scores > 10). Findings: 3864 patients were assessed for depression at any time-points during the follow-up (male:55.4% (2141), median age: 68.0 (20.4)), with the number ranging from 2293 at 1-year to 145 at 18-years after stroke. Prevalence of PSD ranged from 31.3% (28.9-33.8) to 41.5% (33.6-49.3). The cumulative incidence of depression was 59.4% (95% CI 57.8-60.9), of which 87.9% (86.5-89.2) occurred within 5-years after stroke. Of patients with incident PSD at 3-months after stroke, 46.6% (42.1-51.2) recovered after 1 year. Among those recovered, 66.7% (58.0-74.5) experienced recurrent depression and 94.4% (87.5-98.2) of recurrences occurred within 5-years since recovery. Similar estimates were observed in patients with PSD at 1-year. 34.3% (27.9-41.1) of patients with severe depression had recovered at the next time-point, compared to 56.7% (50.5-62.8) with mild depression. Recurrence rate at 1-year after recovery was higher in patients with severe depression (52.9% (35.1-70.2)) compared to mild depression (23.5% (14.1-35.4)) (difference: 29.4% (7.6-51.2), p = 0.003). Interpretation: Long-term depressive status may be established by 5-years post-onset. Early- and late-onset depression presented similar natural history, while severe depression had a longer duration and quicker recurrence than mild depression. These estimates were limited to alive patients completing the depression assessment, who tended to have less severe stroke than excluded patients, so may be underestimated and not generalizable to all stroke survivors. Funding: National Institute for Health and Care Research (NIHR202339).

Trends in risk factor prevalence and management before first stroke: data from the South London Stroke Register 1995-2011.

BACKGROUND AND PURPOSE: Vascular risk factors are suboptimally managed internationally. This study investigated time trends in risk factors diagnosed before stroke and their treatment, and factors associated with appropriate medication use. METHODS: A total of 4416 patients with a first stroke were registered in the population-based South London Stroke Register from 1995 to 2011. Previously diagnosed risk factors and usual medications were collected from patients' primary care and hospital records. Trends and associations were assessed using multivariate logistic regression. RESULTS: Seventy-two percent of patients were diagnosed previously with 1 or more risk factors; 30% had diagnosed risk factors that were untreated. Hypercholesterolemia increased significantly during the study period; myocardial infarction and transient ischemic attack prevalences decreased. Antiplatelet prescription increased in atrial fibrillation (AF), myocardial infarction, and transient ischemic attack (AF, 37%-51%, P<0.001; myocardial infarction, 48%-69%, P<0.001; transient ischemic attack, 49%-61%, P=0.015). Anticoagulant prescription for AF showed a nonsignificant increase (12%-23%; P=0.059). Fewer older patients with AF were prescribed anticoagulants (age, >85 versus <65 years; adjusted relative risk, 0.19; 95% confidence interval, 0.08-0.41). Black ethnicity (adjusted relative risk, 1.17; 95% confidence interval, 1.10-1.23) and female sex (adjusted relative risk, 1.09; 95% confidence interval, 1.03-1.15) were associated with increased antihypertensive drug prescription; other medications did not vary by ethnicity or sex. CONCLUSIONS: Antiplatelet and cholesterol-lowering treatment prescribing have improved significantly over time; however, only a minority with AF received anticoagulants, and this did not improve significantly. Overall, 30% of strokes occurred in patients with previously diagnosed but untreated risk factors.

Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges.

We present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query. Building on prior work (Marshall et al., 2020), the system retrieves trial publications matching a query specifying a combination of condition, intervention(s), and outcome(s), and ranks these according to sample size and estimated study quality. The top-k such studies are passed through a neural multi-document summarization system, yielding a synopsis of these trials. We consider two architectures: A standard sequence-to-sequence model based on BART (Lewis et al., 2019), and a multi-headed architecture intended to provide greater transparency to end-users. Both models produce fluent and relevant summaries of evidence retrieved for queries, but their tendency to introduce unsupported statements render them inappropriate for use in this domain at present. The proposed architecture may help users verify outputs allowing users to trace generated tokens back to inputs. The demonstration video is available at: https://vimeo.com/735605060 The prototype, source code, and model weights are available at: https://sanjanaramprasad.github.io/trials-summarizer/.

In a pilot study, automated real-time systematic review updates were feasible, accurate, and work-saving.

OBJECTIVES: The aim of this study is to describe and pilot a novel method for continuously identifying newly published trials relevant to a systematic review, enabled by combining artificial intelligence (AI) with human expertise. STUDY DESIGN AND SETTING: We used RobotReviewer LIVE to keep a review of COVID-19 vaccination trials updated from February to August 2021. We compared the papers identified by the system with those found by the conventional manual process by the review team. RESULTS: The manual update searches (last search date July 2021) retrieved 135 abstracts, of which 31 were included after screening (23% precision, 100% recall). By the same date, the automated system retrieved 56 abstracts, of which 31 were included after manual screening (55% precision, 100% recall). Key limitations of the system include that it is limited to searches of PubMed/MEDLINE, and considers only randomized controlled trial reports. We aim to address these limitations in future. The system is available as open-source software for further piloting and evaluation. CONCLUSION: Our system identified all relevant studies, reduced manual screening work, and enabled rolling updates on publication of new primary research.

Stroke in Sierra Leone: Case fatality rate and functional outcome after stroke in Freetown.

BACKGROUND: There is limited information on long-term outcomes after stroke in sub-Saharan Africa (SSA). Current estimates of case fatality rate (CFR) in SSA are based on small sample sizes with varying study design and report heterogeneous results. AIMS: We report CFR and functional outcomes from a large, prospective, longitudinal cohort of stroke patients in Sierra Leone and describe factors associated with mortality and functional outcome. METHODS: A prospective longitudinal stroke register was established at both adult tertiary government hospitals in Freetown, Sierra Leone. It recruited all patients ⩾ 18 years with stroke, using the World Health Organization definition, from May 2019 until October 2021. To reduce selection bias onto the register, all investigations were paid by the funder and outreach conducted to raise awareness of the study. Sociodemographic data, National Institute of Health Stroke Scale (NIHSS), and Barthel Index (BI) were collected on all patients on admission, at 7 days, 90 days, 1 year, and 2 years post stroke. Cox proportional hazards models were constructed to identify factors associated with all-cause mortality. A binomial logistic regression model reports odds ratio (OR) for functional independence at 1 year. RESULTS: A total of 986 patients with stroke were included, of which 857 (87%) received neuroimaging. Follow-up rate was 82% at 1 year, missing item data were <1% for most variables. Stroke cases were equally split by sex and mean age was 58.9 (SD: 14.0) years. About 625 (63%) were ischemic, 206 (21%) primary intracerebral hemorrhage, 25 (3%) subarachnoid hemorrhage, and 130 (13%) were of undetermined stroke type. Median NIHSS was 16 (9-24). CFR at 30 days, 90 days, 1 year, and 2 years was 37%, 44%, 49%, and 53%, respectively. Factors associated with increased fatality at any timepoint were male sex (hazard ratio (HR): 1.28 (1.05-1.56)), previous stroke (HR: 1.34 (1.04-1.71)), atrial fibrillation (HR: 1.58(1.06-2.34)), subarachnoid hemorrhage (HR: 2.31 (1.40-3.81)), undetermined stroke type (HR: 3.18 (2.44-4.14)), and in-hospital complications (HR: 1.65 (1.36-1.98)). About 93% of patients were completely independent prior to their stroke, declining to 19% at 1 year after stroke. Functional improvement was most likely to occur between 7 and 90 days post stroke with 35% patients improving, and 13% improving between 90 days to 1 year. Increasing age (OR: 0.97 (0.95-0.99)), previous stroke (OR: 0.50 (0.26-0.98)), NIHSS (OR: 0.89 (0.86-0.91)), undetermined stroke type (OR: 0.18 (0.05-0.62)), and ⩾1 in-hospital complication (OR: 0.52 (0.34-0.80)) were associated with lower OR of functional independence at 1 year. Hypertension (OR: 1.98 (1.14-3.44)) and being the primary breadwinner of the household (OR: 1.59 (1.01-2.49)) were associated with functional independence at 1 year. CONCLUSION: Stroke affected younger people and resulted in high rates of fatality and functional impairment relative to global averages. Key clinical priorities for reducing fatality include preventing stroke-related complications through evidence-based stroke care, improved detection and management of atrial fibrillation, and increasing coverage of secondary prevention. Further research into care pathways and interventions to encourage care seeking for less severe strokes should be prioritized, including reducing the cost barrier for stroke investigations and care.

The provision of person-centred care for care home residents with stroke: An ethnographic study.

Care home residents with stroke have higher levels of disability and poorer access to health services than those living in their own homes. We undertook observations and semi-structured interviews (n = 28 participants) with managers, staff, residents who had experienced a stroke and their relatives in four homes in London, England, in 2018/2019. Thematic analysis revealed that residents' needs regarding valued activity and stroke-specific care and rehabilitation were not always being met. This resulted from an interplay of factors: staff's lack of recognition of stroke and its effects; gaps in skills; time pressures; and the prioritisation of residents' safety. To improve residential care provision and residents' quality of life, care commissioners, regulators and providers may need to re-examine how care homes balance safety and limits on staff time against residents' valued activity, alongside improving access to specialist healthcare treatment and support.

What Would it Take to get Biomedical QA Systems into Practice?

Medical question answering (QA) systems have the potential to answer clinicians' uncertainties about treatment and diagnosis on-demand, informed by the latest evidence. However, despite the significant progress in general QA made by the NLP community, medical QA systems are still not widely used in clinical environments. One likely reason for this is that clinicians may not readily trust QA system outputs, in part because transparency, trustworthiness, and provenance have not been key considerations in the design of such models. In this paper we discuss a set of criteria that, if met, we argue would likely increase the utility of biomedical QA systems, which may in turn lead to adoption of such systems in practice. We assess existing models, tasks, and datasets with respect to these criteria, highlighting shortcomings of previously proposed approaches and pointing toward what might be more usable QA systems.

Paragraph-level Simplification of Medical Texts.

We consider the problem of learning to simplify medical texts. This is important because most reliable, up-to-date information in biomedicine is dense with jargon and thus practically inaccessible to the lay audience. Furthermore, manual simplification does not scale to the rapidly growing body of biomedical literature, motivating the need for automated approaches. Unfortunately, there are no large-scale resources available for this task. In this work we introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics. We then propose a new metric based on likelihood scores from a masked language model pretrained on scientific texts. We show that this automated measure better differentiates between technical and lay summaries than existing heuristics. We introduce and evaluate baseline encoder-decoder Transformer models for simplification and propose a novel augmentation to these in which we explicitly penalize the decoder for producing 'jargon' terms; we find that this yields improvements over baselines in terms of readability.

Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization.

We consider the problem of automatically generating a narrative biomedical evidence summary from multiple trial reports. We evaluate modern neural models for abstractive summarization of relevant article abstracts from systematic reviews previously conducted by members of the Cochrane collaboration, using the authors conclusions section of the review abstract as our target. We enlist medical professionals to evaluate generated summaries, and we find that summarization systems yield consistently fluent and relevant synopses, but these often contain factual inaccuracies. We propose new approaches that capitalize on domain-specific models to inform summarization, e.g., by explicitly demarcating snippets of inputs that convey key findings, and emphasizing the reports of large and high-quality trials. We find that these strategies modestly improve the factual accuracy of generated summaries. Finally, we propose a new method for automatically evaluating the factuality of generated narrative evidence syntheses using models that infer the directionality of reported findings.

Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations.

The best evidence concerning comparative treatment effectiveness comes from clinical trials, the results of which are reported in unstructured articles. Medical experts must manually extract information from articles to inform decision-making, which is time-consuming and expensive. Here we consider the end-to-end task of both (a) extracting treatments and outcomes from full-text articles describing clinical trials (entity identification) and, (b) inferring the reported results for the former with respect to the latter (relation extraction). We introduce new data for this task, and evaluate models that have recently achieved state-of-the-art results on similar tasks in Natural Language Processing. We then propose a new method motivated by how trial results are typically presented that outperforms these purely data-driven baselines. Finally, we run a fielded evaluation of the model with a non-profit seeking to identify existing drugs that might be re-purposed for cancer, showing the potential utility of end-to-end evidence extraction systems.