Intensive Blood Pressure Lowering in Patients With Renal Impairment and Lacunar Stroke

Peer reviewedPublisher PD

Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation

Diagnostic or procedural coding of clinical notes aims to derive a coded summary of disease-related information about patients. Such coding is usually done manually in hospitals but could potentially be automated to improve the efficiency and accuracy of medical coding. Recent studies on deep learning for automated medical coding achieved promising performances. However, the explainability of these models is usually poor, preventing them to be used confidently in supporting clinical practice. Another limitation is that these models mostly assume independence among labels, ignoring the complex correlation among medical codes which can potentially be exploited to improve the performance. We propose a Hierarchical Label-wise Attention Network (HLAN), which aimed to interpret the model by quantifying importance (as attention weights) of words and sentences related to each of the labels. Secondly, we propose to enhance the major deep learning models with a label embedding (LE) initialisation approach, which learns a dense, continuous vector representation and then injects the representation into the final layers and the label-wise attention layers in the models. We evaluated the methods using three settings on the MIMIC-III discharge summaries: full codes, top-50 codes, and the UK NHS COVID-19 shielding codes. Experiments were conducted to compare HLAN and LE initialisation to the state-of-the-art neural network based methods. HLAN achieved the best Micro-level AUC and $F_1$ on the top-50 code prediction and comparable results on the NHS COVID-19 shielding code prediction to other models. By highlighting the most salient words and sentences for each label, HLAN showed more meaningful and comprehensive model interpretation compared to its downgraded baselines and the CNN-based models. LE initialisation consistently boosted most deep learning models for automated medical coding.Comment: Accepted to Journal of Biomedical Informatics, structured abstract in full text, 21 pages, 5 figures, 4 supplementary materials (4 extra pages

New Insights into Stroke from Continuous Passively Collected Temperature and Sleep Data Using Wrist-Worn Wearables

Actigraphy may provide new insights into clinical outcomes and symptom management of patients through passive, continuous data collection. We used the GENEActiv smartwatch to passively collect actigraphy, wrist temperature, and ambient light data from 27 participants after stroke or probable brain transient ischemic attack (TIA) over 42 periods of device wear. We computed 323 features using established algorithms and proposed 25 novel features to characterize sleep and temperature. We investigated statistical associations between the extracted features and clinical outcomes evaluated using clinically validated questionnaires to gain insight into post-stroke recovery. We subsequently fitted logistic regression models to replicate clinical diagnosis (stroke or TIA) and disability due to stroke. The model generalization performance was assessed using a leave-one-subject-out cross validation method with the selected feature subsets, reporting the area under the curve (AUC). We found that several novel features were strongly correlated (|r|>0.3) with stroke symptoms and mental health measures. Using selected novel features, we obtained an AUC of 0.766 to estimate diagnosis and an AUC of 0.749 to estimate whether disability due to stroke was present. Collectively, these findings suggest that features extracted from the temperature smartwatch sensor may reveal additional clinically useful information over and above existing actigraphy-based features

Post-trial monitoring of a randomised controlled trial of intensive glycaemic control in type 2 diabetes extended from 10 years to 24 years (UKPDS 91)

BACKGROUND: The 20-year UK Prospective Diabetes Study showed major clinical benefits for people with newly diagnosed type 2 diabetes randomly allocated to intensive glycaemic control with sulfonylurea or insulin therapy or metformin therapy, compared with conventional glycaemic control. 10-year post-trial follow-up identified enduring and emerging glycaemic and metformin legacy treatment effects. We aimed to determine whether these effects would wane by extending follow-up for another 14 years.METHODS: 5102 patients enrolled between 1977 and 1991, of whom 4209 (82·5%) participants were originally randomly allocated to receive either intensive glycaemic control (sulfonylurea or insulin, or if overweight, metformin) or conventional glycaemic control (primarily diet). At the end of the 20-year interventional trial, 3277 surviving participants entered a 10-year post-trial monitoring period, which ran until Sept 30, 2007. Eligible participants for this study were all surviving participants at the end of the 10-year post-trial monitoring period. An extended follow-up of these participants was done by linking them to their routinely collected National Health Service (NHS) data for another 14 years. Clinical outcomes were derived from records of deaths, hospital admissions, outpatient visits, and accident and emergency unit attendances. We examined seven prespecified aggregate clinical outcomes (ie, any diabetes-related endpoint, diabetes-related death, death from any cause, myocardial infarction, stroke, peripheral vascular disease, and microvascular disease) by the randomised glycaemic control strategy on an intention-to-treat basis using Kaplan-Meier time-to-event and log-rank analyses. This study is registered with the ISRCTN registry, number ISRCTN75451837.FINDINGS: Between Oct 1, 2007, and Sept 30, 2021, 1489 (97·6%) of 1525 participants could be linked to routinely collected NHS administrative data. Their mean age at baseline was 50·2 years (SD 8·0), and 41·3% were female. The mean age of those still alive as of Sept 30, 2021, was 79·9 years (SD 8·0). Individual follow-up from baseline ranged from 0 to 42 years, median 17·5 years (IQR 12·3-26·8). Overall follow-up increased by 21%, from 66 972 to 80 724 person-years. For up to 24 years after trial end, the glycaemic and metformin legacy effects showed no sign of waning. Early intensive glycaemic control with sulfonylurea or insulin therapy, compared with conventional glycaemic control, showed overall relative risk reductions of 10% (95% CI 2-17; p=0·015) for death from any cause, 17% (6-26; p=0·002) for myocardial infarction, and 26% (14-36; p&lt;0·0001) for microvascular disease. Corresponding absolute risk reductions were 2·7%, 3·3%, and 3·5%, respectively. Early intensive glycaemic control with metformin therapy, compared with conventional glycaemic control, showed overall relative risk reductions of 20% (95% CI 5-32; p=0·010) for death from any cause and 31% (12-46; p=0·003) for myocardial infarction. Corresponding absolute risk reductions were 4·9% and 6·2%, respectively. No significant risk reductions during or after the trial for stroke or peripheral vascular disease were observed for both intensive glycaemic control groups, and no significant risk reduction for microvascular disease was observed for metformin therapy.INTERPRETATION: Early intensive glycaemic control with sulfonylurea or insulin, or with metformin, compared with conventional glycaemic control, appears to confer a near-lifelong reduced risk of death and myocardial infarction. Achieving near normoglycaemia immediately following diagnosis might be essential to minimise the lifetime risk of diabetes-related complications to the greatest extent possible.FUNDING: University of Oxford Nuffield Department of Population Health Pump Priming.</p

Visit-to-visit variability in multiple biological measurements and cognitive performance and risk of cardiovascular disease:A cohort study

BACKGROUND: Visit-to-visit variability in single biological measurements has been associated with cognitive decline and an elevated risk of cardiovascular diseases (CVD). However, the effect of visit-to-visit variability in multiple biological measures is underexplored. We investigated the effect of visit-to-visit variability in blood pressure (BP), heart rate (HR), weight, fasting plasma glucose, cholesterol, and triglycerides on cognitive performance and CVD.METHODS: Data on BP, HR, weight, glucose, cholesterol, and triglycerides from study visits in the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial were used to estimate the association between visit-to-visit variability, cognitive performance (Mini Mental State Examination (MMSE) score) and CVD (non-fatal stroke, non-fatal myocardial infarction, or cardiovascular death). Visit-to-visit variation for each measurement was estimated by calculating each individuals visit-to-visit standard deviation for that measurement. Participants whose standard deviation was in the highest quarter were classified as having high variation. Participants were grouped into those having 0, 1, 2, 3, or ≥ 4 high variation measurements. Regression and survival models were used to estimate the association between biological measures with MMSE and CVD with adjustment for confounders and mean measurement value.RESULTS: After adjustment for covariates, higher visit-to-visit variability in BP, HR, weight, and FPG were associated with poorer MMSE and a higher risk of CVD. Effect sizes did not vary greatly by measurement. The effects of high visit-to-visit variability were additive; compared to participants who had no measurements with high visit-to-visit variability, those who had high visit-to-visit variability in ≥4 measurements had poorer MMSE scores (-0.63 (95 % CI -0.96 to -0·31). Participants with ≥4 measurements with high visit-to-visit variability compared to participants with none had higher risk of CVD (hazard ratio 2.46 (95 % CI 1.63 to 3.70).CONCLUSION: Visit-to-visit variability in several measurements were associated with cumulatively poorer cognitive performance and a greater risk of CVD.</p

Formal and informal prediction of recurrent stroke and myocardial infarction after stroke:a systematic review and evaluation of clinical prediction models in a new cohort

BACKGROUND: The objective of this study was to: (1) systematically review the reporting and methods used in the development of clinical prediction models for recurrent stroke or myocardial infarction (MI) after ischemic stroke; (2) to meta-analyze their external performance; and (3) to compare clinical prediction models to informal clinicians’ prediction in the Edinburgh Stroke Study (ESS). METHODS: We searched Medline, EMBASE, reference lists and forward citations of relevant articles from 1980 to 19 April 2013. We included articles which developed multivariable clinical prediction models for the prediction of recurrent stroke and/or MI following ischemic stroke. We extracted information to assess aspects of model development as well as metrics of performance to determine predictive ability. Model quality was assessed against a pre-defined set of criteria. We used random-effects meta-analysis to pool performance metrics. RESULTS: We identified twelve model development studies and eleven evaluation studies. Investigators often did not report effective sample size, regression coefficients, handling of missing data; typically categorized continuous predictors; and used data dependent methods to build models. A meta-analysis of the area under the receiver operating characteristic curve (AUROCC) was possible for the Essen Stroke Risk Score (ESRS) and for the Stroke Prognosis Instrument II (SPI-II); the pooled AUROCCs were 0.60 (95% CI 0.59 to 0.62) and 0.62 (95% CI 0.60 to 0.64), respectively. An evaluation among minor stroke patients in the ESS demonstrated that clinicians discriminated poorly between those with and those without recurrent events and that this was similar to clinical prediction models. CONCLUSIONS: The available models for recurrent stroke discriminate poorly between patients with and without a recurrent stroke or MI after stroke. Models had a similar discrimination to informal clinicians' predictions. Formal prediction may be improved by addressing commonly encountered methodological problems

Clinical Diagnosis and Magnetic Resonance Imaging in Patients With Transient and Minor Neurological Symptoms: A Prospective Cohort Study

The utility of magnetic resonance imaging (MRI) brain in patients with transient or minor neurological symptoms is uncertain. We sought to determine the proportion of participants with transient or minor neurological symptoms who had MRI evidence of acute ischemia at different clinical probabilities of transient ischemic attack (TIA) or minor stroke. METHODS: Cohort of participants with transient or minor neurological symptoms from emergency and outpatient settings. Clinicians at different levels of training gave each participant a diagnostic probability (probable when TIA/stroke was the most likely differential diagnosis; possible when TIA/stroke was not the most likely differential diagnosis; or uncertain when diagnostic probability could not be given) before 1.5 or 3T brain MRI ≤5 days from onset. Post hoc, each clinical syndrome was defined blind to MRI findings as National Institute of Neurological Disorders and Stroke criteria TIA/stroke; International Headache Society criteria migraine aura; non-TIA focal symptoms; or nonfocal symptoms. MRI evidence of acute ischemia was defined by 2 reads of MRI. Stroke was ascertained for at least 90 days and up to 18 months after recruitment. RESULTS: Two hundred seventy-two participated (47% female, mean age 60, SD 14), 58% with MRI ≤2 days of onset. Most (92%) reported focal symptoms. MR evidence of acute ischemia was found, for stroke/TIA clinical probabilities of probable 23 out of 75 (31% [95% CI, 21%–42%]); possible 26 out of 151 (17% [12%–24%]); and uncertain 9 out of 43, (20% [10%–36%]). MRI evidence of acute ischemia was found in National Institute of Neurological Disorders and Stroke criteria TIA/stroke 40 out of 95 (42% [32%–53%]); migraine aura 4 out of 38 (11% [3%–25%]); non-TIA focal symptoms 16 out of 99 (16% [10%–25%]); and no focal features 1 out of 29 (3% [0%–18%]). After MRI, a further 14 (5% [95% CI, 3–8]) would be treated with an antiplatelet drug compared with treatment plan before MRI. By 18 months, a new ischemic stroke occurred in 9 out of 61 (18%) patients with MRI evidence of acute ischemia and 2 out of 211 (1%) without (age-adjusted hazard ratio, 13 [95% CI, 3–62]; P<0.0001). CONCLUSIONS: MRI evidence of acute brain ischemia was found in about 1 in 6 transient or minor neurological symptoms patients with a nonstroke/TIA initial diagnosis or uncertain diagnosis. Methods to determine the clinical and cost-effectiveness of MRI are needed in this population