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Objectives To describe the prevalence and associated risk factors of new onset anisocoria (new pupil size difference of at least 1 mm) and its subtypes: new onset anisocoria accompanied by abnormal and normal pupil reactivities in patients with acute neurologic injuries. Design We tested the association of patients who experienced new onset anisocoria subtypes with degree of midline shift using linear regression. We further explored differences between quantitative pupil characteristics associated with first-time new onset anisocoria and nonnew onset anisocoria at preceding observations using mixed effects logistic regression, adjusting for possible confounders. Setting All quantitative pupil observations were collected at two neuro-ICUs by nursing staff as standard of care. Patients We conducted a retrospective two-center study of adult patients with intracranial pathology in the ICU with at least a 24-hour stay and three or more quantitative pupil measurements between 2016 and 2018. Measurements and main results We studied 221 patients (mean age 58, 41% women). Sixty-three percent experienced new onset anisocoria. New onset anisocoria accompanied by objective evidence of abnormal pupil reactivity occurring at any point during hospitalization was significantly associated with maximum midline shift (β = 2.27 per mm; p = 0.01). The occurrence of new onset anisocoria accompanied by objective evidence of normal pupil reactivity was inversely associated with death (odds ratio, 0.34; 95% CI, 0.16-0.71; p = 0.01) in adjusted analyses. Subclinical continuous pupil size difference distinguished first-time new onset anisocoria from nonnew onset anisocoria in up to four preceding pupil observations (or up to 8 hr prior). Minimum pupil reactivity between eyes also distinguished new onset anisocoria accompanied by objective evidence of abnormal pupil reactivity from new onset anisocoria accompanied by objective evidence of normal pupil reactivity prior to first-time new onset anisocoria occurrence. Conclusions New onset anisocoria occurs in over 60% of patients with neurologic emergencies. Pupil reactivity may be an important distinguishing characteristic of clinically relevant new onset anisocoria phenotypes. New onset anisocoria accompanied by objective evidence of abnormal pupil reactivity was associated with midline shift, and new onset anisocoria accompanied by objective evidence of normal pupil reactivity had an inverse relationship with death. Distinct quantitative pupil characteristics precede new onset anisocoria occurrence and may allow for earlier prediction of neurologic decline. Further work is needed to determine whether quantitative pupillometry sensitively/specifically predicts clinically relevant anisocoria, enabling possible earlier treatments.

Qiu et al. present a novel deep learning strategy to generate high-resolution visualizations of Alzheimer’s disease risk in humans that are highly interpretable and can accurately predict Alzheimer’s disease status. They then test the model by comparing its performance to that of neurologists and neuropathological data., Alzheimer’s disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer’s disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer’s disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer’s disease and cognitively normal subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer’s Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer’s disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

Background There is a paucity of prognostic factors for patients with stage I non-small cell lung cancer (NSCLC) undergoing operations. We investigated the prognostic role of preoperative complete blood count values in patients with stage I NSCLC patients undergoing operations. Methods A retrospective medical record review was performed of patients who underwent operations for stage I NSCLC between 2000 and 2015. Patients who died within 30 days of the operations were excluded. The primary end point was recurrence. Preoperative complete blood count values were analyzed, and a median value was used as the cutoff. Statistical analysis used χ2 and t tests along with univariate and multivariate analyses by Cox regression modeling. Results The study included 103 patients. A high lymphocyte count was significantly associated with recurrence (5-year recurrence-free survival [RFS] of 69.8% for high vs 95.7% for low, P = .003), as well as high platelet (5-year RFS of 72.0% for high vs 91.8% for low, P = .02). Independent prognostic factors on multivariate analysis were high lymphocyte (hazard ratio [HR], 7.27; P = .005) and platelet counts (HR, 7.49; P = .003) as well as tumor (HR, 5.40; P = .008) and treatment characteristics (HR, 4.59; P = .01). Among patients with pT1 lesions, high lymphocyte (HR, 8.41; P = .03) and high platelet counts (HR, 19.78; P = .004) remained independent prognostic factors. Neither NLR nor PLR were significantly associated with recurrence. Conclusions In patients with pathologic stage I NSCLC undergoing surgical resection, the preoperative blood count from peripheral blood may provide prognostic value. Of significance, in patients with pT1 N0 NSCLC, high lymphocyte count and high platelet count were associated with higher recurrence.

Introduction : Automated processing of electronic health data to classify complications of ischemic stroke serves numerous purposes, including improved electronic phenotyping for clinical research. Here, we present a natural language processing (NLP) approach to identify critical findings in acute ischemic stroke from unstructured radiology reports of computed tomography (CT) and magnetic resonance imaging (MRI). Methods : Text reports of CT and MRI scans taken from 2292 patients admitted for large (>1/2 middle cerebral artery territory), acute anterior circulation ischemic stroke were gathered from a single‐institution retrospective cohort. Reports were reviewed and labelled for the presence of hemorrhagic conversion, intracerebral edema, midline shift, intraventricular hemorrhage and parenchymal hematoma as defined by European Cooperative Acute Stroke Study PH1 and PH2 categories. For binary classifications, we quantified co‐occurrence of individual words within reports using two separate NLP methods: Bag‐of‐Words (BOW) and Term Frequency‐Inverse Document Frequency (TF‐IDF). We then trained Lasso regression, random forest, and neural network classifiers to predict all complications based on word co‐occurrence. Classifier performance was measured by area under receiver operating characteristic curves (AUC) using five separate folds of an internal test dataset. To predict midline shift as a continuous outcome, we developed a semantic rule‐based system (RBS) based on regular radiographic report expressions. This system was tested using an external validation dataset of 1472 acute large anterior circulation stroke reports from a separate hospital. Results : 2292 reports were fully labelled for the presence of all stroke complications. Lasso regression consistently displayed the best discrimination among all models. For BOW and TF‐IDF, Lasso yielded respective AUCs of 0.894 and 0.919 (hemorrhagic conversion), 0.935 and 0.950 (intracerebral edema), 0.968 and 0.963 (midline shift), 0.933 and 0.904 (intraventricular hemorrhage), and 0.873 and 0.879 (parenchymal hematoma). All models were well‐calibrated to underlying complication rates. The RBS also achieved strong performance in quantifying midline shift, achieving a mean absolute error (MAE) of 0.103 mm, sensitivity of 99.1% and specificity of 97.5% in the original cohort. In the external validation set of 1472 additional stroke reports, this same system achieved a MAE of 0.126 mm, sensitivity of 99.5% and specificity of 97.5% for midline shift. Wilcoxon rank sum testing on bootstrapped samples confirmed no statistically‐significant differences in RBS performance between institutions when comparing MAE (p = 0.918), sensitivity (p = 0.152), and specificity (p = 0.929). Conclusions : A machine learning pipeline based on Lasso regression successfully identified critical complications of large anterior circulation ischemic stroke from unstructured radiology reports, while our RBS quantified midline shift with a high degree of generalized accuracy between different institutions. We propose that these systems may warrant prospective validation in care settings and data mining for stroke research.

Background Violent trauma has lasting psychological impacts. Our institution’s Community Violence Response Team (CVRT) offers mental health services to trauma victims. We characterized implementation and determined factors associated with utilization by pediatric survivors of interpersonal violence-related penetrating trauma. Methods Analysis included survivors (0–21 years) of violent penetrating injury at our institution (2011–2017). Injury and demographic data were collected. Nonparametric regression models determined factors associated with utilization. Results There was initial rapid uptake of CVRT (2011–2013) after which it plateaued, serving >80% of eligible patients (2017). White race and higher injury severity were associated with receipt and duration of services. In post-hoc analysis, race was found to be associated with continued treatment but not with initial consultation. Conclusion Successful implementation required three years, aiding >80% of patients. CVRT is a blueprint to strengthen existing violence intervention programs. Efforts should be made to ensure that barriers to providing care, including those related to race, are overcome.

Accurate, automated extraction of clinical stroke information from unstructured text has several important applications. ICD-9/10 codes can misclassify ischemic stroke events and do not distinguish acuity or location. Expeditious, accurate data extraction could provide considerable improvement in identifying stroke in large datasets, triaging critical clinical reports, and quality improvement efforts. In this study, we developed and report a comprehensive framework studying the performance of simple and complex stroke-specific Natural Language Processing (NLP) and Machine Learning (ML) methods to determine presence, location, and acuity of ischemic stroke from radiographic text. We collected 60,564 Computed Tomography and Magnetic Resonance Imaging Radiology reports from 17,864 patients from two large academic medical centers. We used standard techniques to featurize unstructured text and developed neurovascular specific word GloVe embeddings. We trained various binary classification algorithms to identify stroke presence, location, and acuity using 75% of 1,359 expert-labeled reports. We validated our methods internally on the remaining 25% of reports and externally on 500 radiology reports from an entirely separate academic institution. In our internal population, GloVe word embeddings paired with deep learning (Recurrent Neural Networks) had the best discrimination of all methods for our three tasks (AUCs of 0.96, 0.98, 0.93 respectively). Simpler NLP approaches (Bag of Words) performed best with interpretable algorithms (Logistic Regression) for identifying ischemic stroke (AUC of 0.95), MCA location (AUC 0.96), and acuity (AUC of 0.90). Similarly, GloVe and Recurrent Neural Networks (AUC 0.92, 0.89, 0.93) generalized better in our external test set than BOW and Logistic Regression for stroke presence, location and acuity, respectively (AUC 0.89, 0.86, 0.80). Our study demonstrates a comprehensive assessment of NLP techniques for unstructured radiographic text. Our findings are suggestive that NLP/ML methods can be used to discriminate stroke features from large data cohorts for both clinical and research-related investigations.

Alzheimer’s disease (AD) is the primary cause of dementia worldwide (1), with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and magnetic resonance imaging (MRI) to identify likely cases, yet effective practices remain variably-applied and lacking in sensitivity and specificity (2). Here we report an explainable deep learning strategy that delineates unique AD signatures from multimodal inputs of MRI, age, gender, and mini-mental state examination (MMSE) score. Our framework linked a fully convolutional network (FCN) to a multilayer perceptron (MLP) to construct high resolution maps of disease probability from local brain structure. This enabled precise, intuitive visualization of individual AD risk en route to accurate diagnosis. The model was trained using clinically-diagnosed AD and cognitively normal (NC) subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n=417) (3), and validated on three independent cohorts: the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n=382) (4), the Framingham Heart Study (FHS, n=102) (5), and the National Alzheimer’s Coordinating Center (NACC, n=582) (6). Model performance was consistent across datasets, with mean accuracy values of 0.966, 0.948, 0.815, and 0.916 for ADNI, AIBL, FHS and NACC, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n=11), and high-risk cerebral regions predicted by the model closely tracked postmortem histopathological findings. This framework provides a clinically-adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for AD diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

Herein, we report the case of a 35-year-old female with a trapped right lung secondary to catamenial haemothorax. Following surgical decortication, re-expansion of the lung was not observed until postoperative day 81. This delay represents a heretofore unencountered complication that should be considered in the surgical management of catamenial haemothorax due to thoracic endometriosis syndrome.

A steerable catheter with a compact fiber optic sensor for temperature-insensitive contact force measurement is demonstrated. A fiber Bragg grating (FBG) is used as the sensor, which is very small and can be fixed on the outer arc of a steerable catheter, such that it does not occupy any space inside the catheter. Contact force sensing performance under various curve diameters during steering is studied and the results show high repeatability. Since the curve diameter during steering is predetermined, accurate contact force measurement can be performed through calibrating the measurement with various steered catheter positions. Different from most conventional FBG contact force sensors, this approach does not require the analysis of FBG reflection spectra. Instead, contact force applied on the catheter tip is determined by the direct measurement of total FBG reflection power, enabling real time, low cost, and compact sensing. Furthermore, the proposed catheter is tested under different temperatures, and temperature-insensitive performance is also obtained.