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

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

Targeted use of heparin, heparinoids, or low-molecular-weight heparin to improve outcome after acute ischaemic stroke:an individual patient data meta-analysis of randomised controlled trials

SummaryBackgroundMany international guidelines on the prevention of venous thromboembolism recommend targeting heparin treatment at patients with stroke who have a high risk of venous thrombotic events or a low risk of haemorrhagic events. We sought to identify reliable methods to target anticoagulant treatment and so improve the chance of avoiding death or dependence after stroke.MethodsWe obtained individual patient data from the five largest randomised controlled trials in acute ischaemic stroke that compared heparins (unfractionated heparin, heparinoids, or low-molecular-weight heparin) with aspirin or placebo. We developed and evaluated statistical models for the prediction of thrombotic events (myocardial infarction, stroke, deep vein thrombosis, or pulmonary embolism) and haemorrhagic events (symptomatic intracranial or significant extracranial) in the first 14 days after stroke. We calculated the absolute risk difference for the outcome “dead or dependent” in patients grouped by quartiles of predicted risk of thrombotic and haemorrhagic events with random effect meta-analysis.FindingsPatients with ischaemic stroke who were of advanced age, had increased neurological impairment, or had atrial fibrillation had a high risk of both thrombotic and haemorrhagic events after stroke. Additionally, patients with CT-visible evidence of recent cerebral ischaemia were at increased risk of thrombotic events. In evaluation datasets, the area under a receiver operating curve for prediction models for thrombotic events was 0·63 (95% CI 0·59–0·67) and for haemorrhagic events was 0·60 (0·55–0·64). We found no evidence that the net benefit from heparins increased with either increasing risk of thrombotic events or decreasing risk of haemorrhagic events.InterpretationThere was no evidence that patients with ischaemic stroke who were at higher risk of thrombotic events or lower risk of haemorrhagic events benefited from heparins. We were therefore unable to define a targeted approach to select the patients who would benefit from treatment with early anticoagulant therapy. We recommend that guidelines for routine or selective use of heparin in stroke should be revised.FundingMRC

The reporting quality of natural language processing studies - systematic review of studies of radiology reports

Abstract Background Automated language analysis of radiology reports using natural language processing (NLP) can provide valuable information on patients’ health and disease. With its rapid development, NLP studies should have transparent methodology to allow comparison of approaches and reproducibility. This systematic review aims to summarise the characteristics and reporting quality of studies applying NLP to radiology reports. Methods We searched Google Scholar for studies published in English that applied NLP to radiology reports of any imaging modality between January 2015 and October 2019. At least two reviewers independently performed screening and completed data extraction. We specified 15 criteria relating to data source, datasets, ground truth, outcomes, and reproducibility for quality assessment. The primary NLP performance measures were precision, recall and F1 score. Results Of the 4,836 records retrieved, we included 164 studies that used NLP on radiology reports. The commonest clinical applications of NLP were disease information or classification (28%) and diagnostic surveillance (27.4%). Most studies used English radiology reports (86%). Reports from mixed imaging modalities were used in 28% of the studies. Oncology (24%) was the most frequent disease area. Most studies had dataset size > 200 (85.4%) but the proportion of studies that described their annotated, training, validation, and test set were 67.1%, 63.4%, 45.7%, and 67.7% respectively. About half of the studies reported precision (48.8%) and recall (53.7%). Few studies reported external validation performed (10.8%), data availability (8.5%) and code availability (9.1%). There was no pattern of performance associated with the overall reporting quality. Conclusions There is a range of potential clinical applications for NLP of radiology reports in health services and research. However, we found suboptimal reporting quality that precludes comparison, reproducibility, and replication. Our results support the need for development of reporting standards specific to clinical NLP studies