Generalizability of an Automatic Explanation Method for Machine Learning Prediction Results on Asthma-Related Hospital Visits in Patients With Asthma: Quantitative Analysis.

BACKGROUND: Asthma exerts a substantial burden on patients and health care systems. To facilitate preventive care for asthma management and improve patient outcomes, we recently developed two machine learning models, one on Intermountain Healthcare data and the other on Kaiser Permanente Southern California (KPSC) data, to forecast asthma-related hospital visits, including emergency department visits and hospitalizations, in the succeeding 12 months among patients with asthma. As is typical for machine learning approaches, these two models do not explain their forecasting results. To address the interpretability issue of black-box models, we designed an automatic method to offer rule format explanations for the forecasting results of any machine learning model on imbalanced tabular data and to suggest customized interventions with no accuracy loss. Our method worked well for explaining the forecasting results of our Intermountain Healthcare model, but its generalizability to other health care systems remains unknown. OBJECTIVE: The objective of this study is to evaluate the generalizability of our automatic explanation method to KPSC for forecasting asthma-related hospital visits. METHODS: Through a secondary analysis of 987,506 data instances from 2012 to 2017 at KPSC, we used our method to explain the forecasting results of our KPSC model and to suggest customized interventions. The patient cohort covered a random sample of 70% of patients with asthma who had a KPSC health plan for any period between 2015 and 2018. RESULTS: Our method explained the forecasting results for 97.57% (2204/2259) of the patients with asthma who were correctly forecasted to undergo asthma-related hospital visits in the succeeding 12 months. CONCLUSIONS: For forecasting asthma-related hospital visits, our automatic explanation method exhibited an acceptable generalizability to KPSC. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.5039.

Evaluation of a Paradigm to Investigate Detection of Road Hazards when Using a Bioptic Telescope.

SIGNIFICANCE: A new driving simulator paradigm was developed and evaluated that will enable future investigations of the effects of the ring scotoma in bioptic drivers with diverse vision impairments and different telescope designs. PURPOSE: The ring scotoma may impair detection of peripheral hazards when viewing through a bioptic telescope. To investigate this question, we developed and tested a sign-reading and pedestrian-detection paradigm in a driving simulator. METHODS: Twelve normally sighted subjects with simulated acuity loss (median 20/120) used a 3.0× monocular bioptic to read 36 road signs while driving in a simulator. Thirteen of 21 pedestrian hazards appeared and ran on the road for 1 second within the ring scotoma while participants were reading signs through the bioptic. Head movements were analyzed to determine whether the pedestrian appeared before or only while using the bioptic. Six subjects viewed binocularly, and six viewed monocularly (fellow eye patched). Two patients with real visual acuity loss in one eye and no light perception in the other performed the same tasks using their own telescopes. RESULTS: For the monocular simulated acuity loss group, detection rates were significantly higher when the pedestrian appeared before using the bioptic than when it appeared while using the bioptic and was likely within the area of the ring scotoma (77% vs. 28%, P < .001). For the binocular simulated acuity loss group, there was no significant difference in detection rates for pedestrians that appeared before or while using the bioptic (80% vs. 91%, P = .20). The two monocular patients detected only 17% of pedestrians that appeared while looking through the bioptic. CONCLUSIONS: Our results confirm the utility of the testing paradigm and suggest that the fellow eye of normally sighted observers with simulated acuity loss was able to compensate for the ring scotoma when using a monocular bioptic telescope in a realistic driving task.

A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma.

In the United States, ~9% of people have asthma. Each year, asthma incurs high health care cost and many hospital encounters covering 1.8 million emergency room visits and 439,000 hospitalizations. A small percentage of patients with asthma use most health care resources. To improve outcomes and cut resource use, many health care systems use predictive models to prospectively find high-risk patients and enroll them in care management for preventive care. For maximal benefit from costly care management with limited service capacity, only patients at the highest risk should be enrolled. However, prior models built by others miss >50% of true highest-risk patients and mislabel many low-risk patients as high risk, leading to suboptimal care and wasted resources. To address this issue, 3 site-specific models were recently built to predict hospital encounters for asthma, gaining up to >11% better performance. However, these models do not generalize well across sites and patient subgroups, creating 2 gaps before translating these models into clinical use. This paper points out these 2 gaps and outlines 2 corresponding solutions: (1) a new machine learning technique to create cross-site generalizable predictive models to accurately find high-risk patients and (2) a new machine learning technique to automatically raise model performance for poorly performing subgroups while maintaining model performance on other subgroups. This gives a roadmap for future research.

Error and Timeliness Analysis for Using Machine Learning to Predict Asthma Hospital Visits: Retrospective Cohort Study.

BACKGROUND: Asthma hospital visits, including emergency department visits and inpatient stays, are a significant burden on health care. To leverage preventive care more effectively in managing asthma, we previously employed machine learning and data from the University of Washington Medicine (UWM) to build the world's most accurate model to forecast which asthma patients will have asthma hospital visits during the following 12 months. OBJECTIVE: Currently, two questions remain regarding our model's performance. First, for a patient who will have asthma hospital visits in the future, how far in advance can our model make an initial identification of risk? Second, if our model erroneously predicts a patient to have asthma hospital visits at the UWM during the following 12 months, how likely will the patient have ≥1 asthma hospital visit somewhere else or ≥1 surrogate indicator of a poor outcome? This work aims to answer these two questions. METHODS: Our patient cohort included every adult asthma patient who received care at the UWM between 2011 and 2018. Using the UWM data, our model made predictions on the asthma patients in 2018. For every such patient with ≥1 asthma hospital visit at the UWM in 2019, we computed the number of days in advance that our model gave an initial warning. For every such patient erroneously predicted to have ≥1 asthma hospital visit at the UWM in 2019, we used PreManage and the UWM data to check whether the patient had ≥1 asthma hospital visit outside of the UWM in 2019 or any surrogate indicators of poor outcomes. Such surrogate indicators included a prescription for systemic corticosteroids during the following 12 months, any type of visit for asthma exacerbation during the following 12 months, and asthma hospital visits between 13 and 24 months later. RESULTS: Among the 218 asthma patients in 2018 with asthma hospital visits at the UWM in 2019, 61.9% (135/218) were given initial warnings of such visits ≥3 months ahead by our model and 84.4% (184/218) were given initial warnings ≥1 day ahead. Among the 1310 asthma patients in 2018 who were erroneously predicted to have asthma hospital visits at the UWM in 2019, 29.01% (380/1310) had asthma hospital visits outside of the UWM in 2019 or surrogate indicators of poor outcomes. CONCLUSIONS: Our model gave timely risk warnings for most asthma patients with poor outcomes. We found that 29.01% (380/1310) of asthma patients for whom our model gave false-positive predictions had asthma hospital visits somewhere else during the following 12 months or surrogate indicators of poor outcomes, and thus were reasonable candidates for preventive interventions. There is still significant room for improving our model to give more accurate and more timely risk warnings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/5039.

Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major cause of death and places a heavy burden on health care. To optimize the allocation of precious preventive care management resources and improve the outcomes for high-risk patients with COPD, we recently built the most accurate model to date to predict severe COPD exacerbations, which need inpatient stays or emergency department visits, in the following 12 months. Our model is a machine learning model. As is the case with most machine learning models, our model does not explain its predictions, forming a barrier for clinical use. Previously, we designed a method to automatically provide rule-type explanations for machine learning predictions and suggest tailored interventions with no loss of model performance. This method has been tested before for asthma outcome prediction but not for COPD outcome prediction. OBJECTIVE: This study aims to assess the generalizability of our automatic explanation method for predicting severe COPD exacerbations. METHODS: The patient cohort included all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019. In a secondary analysis of 43,576 data instances, we used our formerly developed automatic explanation method to automatically explain our model's predictions and suggest tailored interventions. RESULTS: Our method explained the predictions for 97.1% (100/103) of the patients with COPD whom our model correctly predicted to have severe COPD exacerbations in the following 12 months and the predictions for 73.6% (134/182) of the patients with COPD who had ≥1 severe COPD exacerbation in the following 12 months. CONCLUSIONS: Our automatic explanation method worked well for predicting severe COPD exacerbations. After further improving our method, we hope to use it to facilitate future clinical use of our model. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/13783.

Ranking Rule-Based Automatic Explanations for Machine Learning Predictions on Asthma Hospital Encounters in Patients With Asthma: Retrospective Cohort Study.

BACKGROUND: Asthma hospital encounters impose a heavy burden on the health care system. To improve preventive care and outcomes for patients with asthma, we recently developed a black-box machine learning model to predict whether a patient with asthma will have one or more asthma hospital encounters in the succeeding 12 months. Our model is more accurate than previous models. However, black-box machine learning models do not explain their predictions, which forms a barrier to widespread clinical adoption. To solve this issue, we previously developed a method to automatically provide rule-based explanations for the model's predictions and to suggest tailored interventions without sacrificing model performance. For an average patient correctly predicted by our model to have future asthma hospital encounters, our explanation method generated over 5000 rule-based explanations, if any. However, the user of the automated explanation function, often a busy clinician, will want to quickly obtain the most useful information for a patient by viewing only the top few explanations. Therefore, a methodology is required to appropriately rank the explanations generated for a patient. However, this is currently an open problem. OBJECTIVE: The aim of this study is to develop a method to appropriately rank the rule-based explanations that our automated explanation method generates for a patient. METHODS: We developed a ranking method that struck a balance among multiple factors. Through a secondary analysis of 82,888 data instances of adults with asthma from the University of Washington Medicine between 2011 and 2018, we demonstrated our ranking method on the test case of predicting asthma hospital encounters in patients with asthma. RESULTS: For each patient predicted to have asthma hospital encounters in the succeeding 12 months, the top few explanations returned by our ranking method typically have high quality and low redundancy. Many top-ranked explanations provide useful insights on the various aspects of the patient's situation, which cannot be easily obtained by viewing the patient's data in the current electronic health record system. CONCLUSIONS: The explanation ranking module is an essential component of the automated explanation function, and it addresses the interpretability issue that deters the widespread adoption of machine learning predictive models in clinical practice. In the next few years, we plan to test our explanation ranking method on predictive modeling problems addressing other diseases as well as on data from other health care systems. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/5039.

Safety and Efficacy of Anticoagulation in Patients with Cirrhosis: A Meta-Analysis.

Background and Aims: Portal vein thrombosis is a serious adverse event that occurs during liver cirrhosis. We performed a meta-analysis to evaluate the safety and efficacy of anticoagulant therapy and prophylactic anticoagulant therapy in cirrhosis patients with (/without) portal vein thrombosis. Methods: Eligible comparative studies were identified by searching the following electronic databases: PubMed, Embase, Cochrane Library, Web of Science, and CNKI. A meta-analysis was performed to calculate odds ratios and 95% confidence intervals using fixed-effects models. Recanalization and thrombus progression were defined as the primary outcomes. Secondary outcomes included adverse events and death mortality. Results: A total of 3479 patients were included in this analysis. Compared with the control group, the recanalization rate in the anticoagulant therapy group was increased (P < 0.00001) in patients with cirrhosis and portal vein thrombosis without increasing adverse events. Multiple use of enoxaparin in small doses is safer than single large doses (P=0.004). Direct oral anticoagulants are more effective (P < 0.00001) and safer than traditional anticoagulants. Prophylactic anticoagulant therapy can effectively prevent portal vein thrombosis formation (P < 0.00001). Conclusions: Anticoagulation therapy can treat or prevent portal vein thrombosis in patients with liver cirrhosis and is a relatively safe treatment.

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis.

BACKGROUND: Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well. OBJECTIVE: To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions. METHODS: We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians' decisions to use integrated disease management to prevent proneness to exacerbation. RESULTS: We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years. CONCLUSIONS: Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/27065.

[Effectiveness of repairing the deep layer of deltoid ligament by suture anchor repair method in treatment of mixed medial injury associated with ankle fractures].

OBJECTIVE: To explore the necessity of repairing the deep layer of deltoid ligament in the treatment of mixed medial injury associated with ankle fractures. METHODS: Between January 2016 and December 2018, 12 patients with mixed medial injury associated with ankle fractures were treated with the fixation of the lateral malleolus by bone plates, the fixation of the anterior colliculus of medial malleolus by cannulated screws, and the repair of the deltoid ligament by suture anchors. There were 8 males and 4 females, with an average age of 42 years (range, 18-56 years). According to the Lauge-Hansen classification criteria, there were 11 cases of supination-external rotation type and 1 case of pronation-external rotation type. According to the Weber classification criteria, all cases were type B. The time from injury to operation was 3-6 days, with an average of 4.7 days. In each patient, X-ray films of anteroposterior and lateral views and mortise view of ankle were taken postoperatively. The motion range of ankle joints was observed. The function of the ankle and the outcome of the treatment were evaluated according to the American Orthopaedic Foot and Ankle Society (AOFAS) scoring system, Olerud-Molander scoring system, and the visual analogue scale (VAS) score. RESULTS: All cases were followed up 12-42 months (mean, 28 months). The 12 patients returned to their pre-injury jobs. Five patients with sports injury completely recovered to their pre-injury motor function. No patient experienced persistent medial ankle pain or ankle instability. At last follow-up, the ankle range of motion in dorsiflexion was 9°-25° (mean, 17.96°), which was 0°-11° (mean, 4.02°) less than that in normal side; the range of motion in plantar flexion was 38°-50° (mean, 43.90°), which was 0°-7° (mean, 2.53°) less than that in normal side. The AOFAS score was 88-100 (mean, 96.7); the Olerud-Molander score was 90-100 (mean, 96.5); the VAS score was 0-3 (mean, 1.1). CONCLUSION: It is necessary to repair the deep layer of deltoid ligament in the mixed medial injuries associated with ankle fracture, which include anterior colliculus fracture and deep deltoid ligament injury. A better outcome can be achieved by employing the suture anchor repair method.

Testing the Generalizability of an Automated Method for Explaining Machine Learning Predictions on Asthma Patients' Asthma Hospital Visits to an Academic Healthcare System.

Asthma puts a tremendous overhead on healthcare. To enable effective preventive care to improve outcomes in managing asthma, we recently created two machine learning models, one using University of Washington Medicine data and the other using Intermountain Healthcare data, to predict asthma hospital visits in the next 12 months in asthma patients. As is common in machine learning, neither model supplies explanations for its predictions. To tackle this interpretability issue of black-box models, we developed an automated method to produce rule-style explanations for any machine learning model's predictions made on imbalanced tabular data and to recommend customized interventions without lowering the prediction accuracy. Our method exhibited good performance in explaining our Intermountain Healthcare model's predictions. Yet, it stays unknown how well our method generalizes to academic healthcare systems, whose patient composition differs from that of Intermountain Healthcare. This study evaluates our automated explaining method's generalizability to the academic healthcare system University of Washington Medicine on predicting asthma hospital visits. We did a secondary analysis on 82,888 University of Washington Medicine data instances of asthmatic adults between 2011 and 2018, using our method to explain our University of Washington Medicine model's predictions and to recommend customized interventions. Our results showed that for predicting asthma hospital visits, our automated explaining method had satisfactory generalizability to University of Washington Medicine. In particular, our method explained the predictions for 87.6% of the asthma patients whom our University of Washington Medicine model accurately predicted to experience asthma hospital visits in the next 12 months.

Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis.

BACKGROUND: As a major chronic disease, asthma causes many emergency department (ED) visits and hospitalizations each year. Predictive modeling is a key technology to prospectively identify high-risk asthmatic patients and enroll them in care management for preventive care to reduce future hospital encounters, including inpatient stays and ED visits. However, existing models for predicting hospital encounters in asthmatic patients are inaccurate. Usually, they miss over half of the patients who will incur future hospital encounters and incorrectly classify many others who will not. This makes it difficult to match the limited resources of care management to the patients who will incur future hospital encounters, increasing health care costs and degrading patient outcomes. OBJECTIVE: The goal of this study was to develop a more accurate model for predicting hospital encounters in asthmatic patients. METHODS: Secondary analysis of 334,564 data instances from Intermountain Healthcare from 2005 to 2018 was conducted to build a machine learning classification model to predict the hospital encounters for asthma in the following year in asthmatic patients. The patient cohort included all asthmatic patients who resided in Utah or Idaho and visited Intermountain Healthcare facilities during 2005 to 2018. A total of 235 candidate features were considered for model building. RESULTS: The model achieved an area under the receiver operating characteristic curve of 0.859 (95% CI 0.846-0.871). When the cutoff threshold for conducting binary classification was set at the top 10.00% (1926/19,256) of asthmatic patients with the highest predicted risk, the model reached an accuracy of 90.31% (17,391/19,256; 95% CI 89.86-90.70), a sensitivity of 53.7% (436/812; 95% CI 50.12-57.18), and a specificity of 91.93% (16,955/18,444; 95% CI 91.54-92.31). To steer future research on this topic, we pinpointed several potential improvements to our model. CONCLUSIONS: Our model improves the state of the art for predicting hospital encounters for asthma in asthmatic patients. After further refinement, the model could be integrated into a decision support tool to guide asthma care management allocation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.5039.

Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis.

BACKGROUND: Asthma causes numerous hospital encounters annually, including emergency department visits and hospitalizations. To improve patient outcomes and reduce the number of these encounters, predictive models are widely used to prospectively pinpoint high-risk patients with asthma for preventive care via care management. However, previous models do not have adequate accuracy to achieve this goal well. Adopting the modeling guideline for checking extensive candidate features, we recently constructed a machine learning model on Intermountain Healthcare data to predict asthma-related hospital encounters in patients with asthma. Although this model is more accurate than the previous models, whether our modeling guideline is generalizable to other health care systems remains unknown. OBJECTIVE: This study aims to assess the generalizability of our modeling guideline to Kaiser Permanente Southern California (KPSC). METHODS: The patient cohort included a random sample of 70.00% (397,858/568,369) of patients with asthma who were enrolled in a KPSC health plan for any duration between 2015 and 2018. We produced a machine learning model via a secondary analysis of 987,506 KPSC data instances from 2012 to 2017 and by checking 337 candidate features to project asthma-related hospital encounters in the following 12-month period in patients with asthma. RESULTS: Our model reached an area under the receiver operating characteristic curve of 0.820. When the cutoff point for binary classification was placed at the top 10.00% (20,474/204,744) of patients with asthma having the largest predicted risk, our model achieved an accuracy of 90.08% (184,435/204,744), a sensitivity of 51.90% (2259/4353), and a specificity of 90.91% (182,176/200,391). CONCLUSIONS: Our modeling guideline exhibited acceptable generalizability to KPSC and resulted in a model that is more accurate than those formerly built by others. After further enhancement, our model could be used to guide asthma care management. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.5039.

Early blood pressure management for endovascular therapy in acute ischemic stroke: A review of the literature.

The perioperative optimal blood pressure targets during mechanical thrombectomy for acute ischemic stroke are uncertain, and randomized controlled trials addressing this issue are lacking. There is still no consensus on the optimal target for perioperative blood pressure in acute ischemic stroke patients with large vessel occlusion. In addition, there are many confounding factors that can influence the outcome including the patient's clinical history and stroke characteristics. We review the factors that have an impact on perioperative blood pressure change and discuss the influence of perioperative blood pressure on functional outcome after mechanical thrombectomy. In conclusion, we suggest that blood pressure should be carefully and flexibly managed perioperatively in patient-received mechanical thrombectomy. Blood pressure changes during mechanical thrombectomy were independently correlated with poor prognosis, and blood pressure should be maintained in a normal range perioperatively. Postoperative blood pressure control is associated with recanalization status in which successful recanalization requires normal range blood pressure (systolic blood pressure 120-140 mmHg), while non-recanalization requires higher blood pressure (systolic blood pressure 160-180 mmHg). The preoperative blood pressure targets for mechanical thrombectomy should be tailored based on the patient's clinical history (systolic blood pressure ≤185 mmHg). Blood pressure should be carefully and flexibly managed intraoperatively (systolic blood pressure 140-180 mmHg) in patient-received endovascular therapy.

Angiogenic study in Graves' disease treated with thyroid arterial embolization.

PURPOSE: To investigate angiogenesis in the thyroid of Graves' disease (GD) treated with thyroid arterial embolization through analysis of vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF) and microvessel density (MVD). MATERIALS AND METHODS: Forty-two GD patients were treated with thyroid arterial embolization and followed up for 1-68 months after embolization. Before embolization and at 7 days, 3, 6, 12, 36 and 48 months following embolization, TT3, TT4, FT3, FT4, TSH and thyroid stimulating antibody (TSAb) were tested respectively. Thyroid biopsy was performed under the guidance of computed tomography for immunohistochemical staining of VEGF and bFGF, and MVD within the thyroid gland was marked by CD34. RESULTS: VEGF and bFGF were mostly expressed in the cytoplasm and on the cell membrane. The expression of VEGF was increased (P < 0.05) at < or = 6 months compared with before embolization and decreased (P < 0.05) at > or = 1 year compared with either at < or = 6 months or before embolization. The expression of bFGF was not statistically different at < or = 6 months compared with before embolization but was decreased (P < 0.05) at > or = 1 year compared with either at ?6 months or before embolization. Thyroid MVD marked by CD34 had similar changes to those of the VEGF expression after embolization. There was a positive correlation between VEGF and bFGF (P < 0.05) and between VEGF or bFGF and MVD (P < 0.05). Thyroid hormones mostly returned to normal and TSAb was decreased in longer follow-up. CONCLUSION: Thyroid arterial embolization can decrease the expression of VEGF, bFGF and MVD. Consequently, angiogenesis within the GD thyroid will be decreased in the long term after embolization and may serve as the basis for reduced thyroid size and function.

Ebselen protects rat hearts against myocardial ischemia-reperfusion injury.

Ebselen is an organoselenium compound that has demonstrated potent antioxidant and anti-inflammatory effects in previous studies. The present study was conducted to evaluate the effect of ebselen on myocardial ischemia-reperfusion (I/R) injury in a rat model and to elucidate the related mechanisms. Myocardial infarct size was assessed using triphenyltetrazolium chloride staining. Myocardial injury was evaluated according to the histopathological and ultrastructural alterations of rat hearts and the serum activity levels of cardiac enzymes, including creatine kinase (CK), CK-MB isoenzyme and lactate dehydrogenase (LDH). Cardiomyocyte apoptosis was detected using the terminal dUTP nick end-labelling (TUNEL) assay. In addition, the expression of apoptosis-associated proteins was measured using western blot analysis. In heart tissue specimens the activity of superoxide dismutase (SOD) and glutathione peroxidase (GPx), and levels of malondialdehyde (MDA) and protein carbonyl (PC) were also detected. The results indicated that ebselen reduced I/R-induced increase in myocardial infarct size and prevented the I/R-induced decreases in ejection fraction and fractional shortening. Further of note, ebselen improved I/R-induced rat heart injury. This was indicated by attenuation of histological and ultrastructural changes; reduction of serum CK, CK-MB and LDH activity levels; and decreased cell apoptosis on TUNEL staining, which was verified by decreased expression of cleaved (C)-Caspase-8, C-Caspase-3, B-cell lymphoma 2 (Bcl-2)-associated X protein and C-PARP, and increased expression of Bcl-2. Additionally, SOD and GPx activity levels were significantly higher, while MDA and PC levels were significantly lower in the ebselen + I/R group compared with in the I/R group. In conclusion, the present results suggested that ebselen serves an important role in protecting against myocardial I/R injury. The underlying mechanism may involve suppression of cardiomyocyte apoptosis and promotion of antioxidant activity.

Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis.

BACKGROUND: Both chronic obstructive pulmonary disease (COPD) and asthma incur heavy health care burdens. To support tailored preventive care for these 2 diseases, predictive modeling is widely used to give warnings and to identify patients for care management. However, 3 gaps exist in current modeling methods owing to rarely factoring in temporal aspects showing trends and early health change: (1) existing models seldom use temporal features and often give late warnings, making care reactive. A health risk is often found at a relatively late stage of declining health, when the risk of a poor outcome is high and resolving the issue is difficult and costly. A typical model predicts patient outcomes in the next 12 months. This often does not warn early enough. If a patient will actually be hospitalized for COPD next week, intervening now could be too late to avoid the hospitalization. If temporal features were used, this patient could potentially be identified a few weeks earlier to institute preventive therapy; (2) existing models often miss many temporal features with high predictive power and have low accuracy. This makes care management enroll many patients not needing it and overlook over half of the patients needing it the most; (3) existing models often give no information on why a patient is at high risk nor about possible interventions to mitigate risk, causing busy care managers to spend more time reviewing charts and to miss suited interventions. Typical automatic explanation methods cannot handle longitudinal attributes and fully address these issues. OBJECTIVE: To fill these gaps so that more COPD and asthma patients will receive more appropriate and timely care, we will develop comprehensible data-driven methods to provide accurate early warnings of poor outcomes and to suggest tailored interventions, making care more proactive, efficient, and effective. METHODS: By conducting a secondary data analysis and surveys, the study will: (1) use temporal features to provide accurate early warnings of poor outcomes and assess the potential impact on prediction accuracy, risk warning timeliness, and outcomes; (2) automatically identify actionable temporal risk factors for each patient at high risk for future hospital use and assess the impact on prediction accuracy and outcomes; and (3) assess the impact of actionable information on clinicians' acceptance of early warnings and on perceived care plan quality. RESULTS: We are obtaining clinical and administrative datasets from 3 leading health care systems' enterprise data warehouses. We plan to start data analysis in 2020 and finish our study in 2025. CONCLUSIONS: Techniques to be developed in this study can boost risk warning timeliness, model accuracy, and generalizability; improve patient finding for preventive care; help form tailored care plans; advance machine learning for many clinical applications; and be generalized for many other chronic diseases. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/13783.

Identifying Patients Who Are Likely to Receive Most of Their Care From a Specific Health Care System: Demonstration via Secondary Analysis.

BACKGROUND: In the United States, health care is fragmented in numerous distinct health care systems including private, public, and federal organizations like private physician groups and academic medical centers. Many patients have their complete medical data scattered across these several health care systems, with no particular system having complete data on any of them. Several major data analysis tasks such as predictive modeling using historical data are considered impractical on incomplete data. OBJECTIVE: Our objective was to find a way to enable these analysis tasks for a health care system with incomplete data on many of its patients. METHODS: This study presents, to the best of our knowledge, the first method to use a geographic constraint to identify a reasonably large subset of patients who tend to receive most of their care from a given health care system. A data analysis task needing relatively complete data can be conducted on this subset of patients. We demonstrated our method using data from the University of Washington Medicine (UWM) and PreManage data covering the use of all hospitals in Washington State. We compared 10 candidate constraints to optimize the solution. RESULTS: For UWM, the best constraint is that the patient has a UWM primary care physician and lives within 5 miles of at least one UWM hospital. About 16.01% (55,707/348,054) of UWM patients satisfied this constraint. Around 69.38% (10,501/15,135) of their inpatient stays and emergency department visits occurred within UWM in the following 6 months, more than double the corresponding percentage for all UWM patients. CONCLUSIONS: Our method can identify a reasonably large subset of patients who tend to receive most of their care from UWM. This enables several major analysis tasks on incomplete medical data that were previously deemed infeasible.

[The biomechanical change of lumbar unilateral graded facetectomy and strategies of its microsurgical reconstruction: report of 23 cases].

OBJECTIVE: To study the effects of unilateral graded facetectomy on lumbar stability through biomechanical analysis. The primary clinical results of unilateral facetectomy, posterior lumbar interbody fusion and unilateral pedicle screw instrumentation using X-tube system were also evaluated. METHODS: 5 functional spinal units (FSU) from fresh cadavers of 5 adults were made, divided into 5 groups to remain intact, or with the lateral 1/4, 1/2, or 3/4 or the whole of the left L4/5 articular process, and then put in the biomechanical testing apparatus to evaluate the effects of operation on the lumbar motion range of flexion, extension, lateral bending and axial rotation. Twenty-three patients, 16 males and 7 females, aged 47.7 (32 - 74), underwent unilateral facetectomy, posterior lumbar interbody fusion, and unilateral pedicle screw instrumentation using X-tube system. The clinical outcomes of the 23 patients were assessed by use of the visual analog score (VAS), Oswestry disability index (ODI), and Nakai criteria. RESULT: The experiment of the 5 FSUs showed that no significantly negative effects on the change in lumbar motion range of flexion, extension after unilateral graded facetectomy (all P > 0.05), and the stability of lateral bending and axial rotation had been greatly affected with the range of graded facetectomy exceeding 1/2 (P < 0.05). According to the Nakai criteria, the clinical effect was excellent in 15 cases (65.2%), good in 6 cases (26.1%), and fair in 2 cases (8.7%). The excellent and good cases accounted for 91.3% with a fusion rate of 95.6%. CONCLUSION: The lumbar stability is significantly affected if the range of graded facetectomy exceeds 1/2. The use of procedures of unilateral facetectomy, diskectomy, spinal nerve root decompression, autologous bone grafting, and unilateral pedicle screw fixation using X-tube is an optional strategy for minimally invasive spine technique.

An investigation of perceived vehicle speed from a driver's perspective.

PURPOSE: Speed estimation of drivers' own vehicles and other vehicles on the road is an important task for drivers and is also crucial to the roadway safety. The objective of the study was to examine the effects of multiple factors such as image scale, speed, road type, driving experience, and gender on the speed perception of drivers' own vehicles. METHODS: Thirty participants consisted of 17 males and 13 females, including 13 without driving experience. All participants estimated the driving speed of 192 5-second video clips, which were selected from naturalistic driving recordings. The recorded driving speeds were evenly distributed across the entire range from 5mph to 65mph. Half of the selected video clips were recorded on wide roads and another half were recorded on comparatively narrow roads. Video clips were played on a large screen, with each clip shown in one of 4 image scales (100%, 75%, 50%, and 38% of the actual field of view in the real world). RESULTS: Speed estimates were most accurate for the smallest image size (38% of the actual field of view). As the image size increased, the driving speed was increasingly underestimated. Participants with driving experience accurately estimated the driving speed on both wide and narrow roads whereas those without driving experience had greater underestimates on wider roads. Speeds were most accurately estimated within the range 25-35mph, but the speeds slower than the range tend to be overestimated and the speeds faster than the range are more likely to be underestimated. While males and females showed the same pattern across speed groups, females have greater estimation errors at the highest and lowest speed groups. Participants without driving experience showed increasing underestimation of speed as driving speed increased whereas participants with driving experience primarily underestimated the highest speeds. CONCLUSIONS: The present study shows the effect of multidimensional influential factors on perceived vehicle speed from drivers' perspective. The results also have implications for driving simulation scenario design, driving simulator setup, and the assessment of speed control in simulated and naturalistic environments.