A cost-sensitive deep neural network-based prediction model for the mortality in acute myocardial infarction patients with hypertension on imbalanced data.

Background and objectives: Hypertension is one of the most serious risk factors and the leading cause of mortality in patients with cardiovascular diseases (CVDs). It is necessary to accurately predict the mortality of patients suffering from CVDs with hypertension. Therefore, this paper proposes a novel cost-sensitive deep neural network (CSDNN)-based mortality prediction model for out-of-hospital acute myocardial infarction (AMI) patients with hypertension on imbalanced data. Methods: The synopsis of our research is as follows. First, the experimental data is extracted from the Korea Acute Myocardial Infarction Registry-National Institutes of Health (KAMIR-NIH) and preprocessed with several approaches. Then the imbalanced experimental dataset is divided into training data (80%) and test data (20%). After that, we design the proposed CSDNN-based mortality prediction model, which can solve the skewed class distribution between the majority and minority classes in the training data. The threshold moving technique is also employed to enhance the performance of the proposed model. Finally, we evaluate the performance of the proposed model using the test data and compare it with other commonly used machine learning (ML) and data sampling-based ensemble models. Moreover, the hyperparameters of all models are optimized through random search strategies with a 5-fold cross-validation approach. Results and discussion: In the result, the proposed CSDNN model with the threshold moving technique yielded the best results on imbalanced data. Additionally, our proposed model outperformed the best ML model and the classic data sampling-based ensemble model with an AUC of 2.58% and 2.55% improvement, respectively. It aids in decision-making and offers a precise mortality prediction for AMI patients with hypertension.

A Machine Learning-Based Applied Prediction Model for Identification of Acute Coronary Syndrome (ACS) Outcomes and Mortality in Patients during the Hospital Stay.

Nowadays, machine learning (ML) is a revolutionary and cutting-edge technology widely used in the medical domain and health informatics in the diagnosis and prognosis of cardiovascular diseases especially. Therefore, we propose a ML-based soft-voting ensemble classifier (SVEC) for the predictive modeling of acute coronary syndrome (ACS) outcomes such as STEMI and NSTEMI, discharge reasons for the patients admitted in the hospitals, and death types for the affected patients during the hospital stay. We used the Korea Acute Myocardial Infarction Registry (KAMIR-NIH) dataset, which has 13,104 patients' data containing 551 features. After data extraction and preprocessing, we used the 125 useful features and applied the SMOTETomek hybrid sampling technique to oversample the data imbalance of minority classes. Our proposed SVEC applied three ML algorithms, such as random forest, extra tree, and the gradient-boosting machine for predictive modeling of our target variables, and compared with the performances of all base classifiers. The experiments showed that the SVEC outperformed other ML-based predictive models in accuracy (99.0733%), precision (99.0742%), recall (99.0734%), F1-score (99.9719%), and the area under the ROC curve (AUC) (99.9702%). Overall, the performance of the SVEC was better than other applied models, but the AUC was slightly lower than the extra tree classifier for the predictive modeling of ACS outcomes. The proposed predictive model outperformed other ML-based models; hence it can be used practically in hospitals for the diagnosis and prediction of heart problems so that timely detection of proper treatments can be chosen, and the occurrence of disease predicted more accurately.

Association between CHADS2, CHA2DS2-VASc, ATRIA, and Essen Stroke Risk Scores and Functional Outcomes in Acute Ischemic Stroke Patients Who Received Endovascular Thrombectomy.

Background: CHADS2, CHA2DS2-VASc, ATRIA, and Essen stroke risk scores are used to estimate thromboembolism risk. We aimed to investigate the association between unfavorable outcomes and stroke risk scores in patients who received endovascular thrombectomy (EVT). Methods: This study was performed using data from a nationwide, multicenter registry to explore the selection criteria for patients who would benefit from reperfusion therapies. We calculated pre-admission CHADS2, CHA2DS2-VASc, ATRIA, and Essen scores for each patient who received EVT and compared the relationship between these scores and 3-month modified Rankin Scale (mRS) records. Results: Among the 404 patients who received EVT, 213 (52.7%) patients had unfavorable outcomes (mRS 3−6). All scores were significantly higher in patients with unfavorable outcomes than in those with favorable outcomes. Multivariable logistic regression analysis indicated that CHADS2 and the ATRIA score were positively correlated with unfavorable outcomes after adjusting for body mass index and variables with p < 0.1 in the univariable analysis (CHADS2 score: odds ratio [OR], 1.484; 95% confidence interval [CI], 1.290−1.950; p = 0.005, ATRIA score, OR, 1.128; 95% CI, 1.041−1.223; p = 0.004). Conclusions: The CHADS2 and ATRIA scores were positively correlated with unfavorable outcomes and could be used to predict unfavorable outcomes in patients who receive EVT.

Evaluation of genotoxicity of SUNACTIVE Zn-P240 in vitro and in vivo.

We evaluated the potential genotoxic effects of the nutrient supplement SUNACTIVE Zn-P240 in vitro and in vivo. Genotoxicity tests were performed at the Korea Testing and Research Institute, a GLP certification institution. A bacterial reverse mutation test was performed using the pre-incubation method, while the in vitro chromosome aberration test was performed using a cultured Chinese hamster lung cell line in the presence or absence of metabolic activation. The in vivo micronucleus test was performed using ICR mice. The bacterial reverse mutation test revealed that SUNACTIVE Zn-P240 did not induce genetic mutations at the tested doses in Salmonella typhimurium (TA98, TA100, TA1535, and TA1537) and Escherichia coli (WP2uvrA) tester strains. Meanwhile, the results of the in vitro chromosomal aberration and in vivo micronucleus tests revealed that SUNACTIVE Zn-P240 did not induce chromosomal aberrations. These results suggest that SUNACTIVE Zn-P240 did not exhibit mutagenic or clastogenic properties in vitro and in vivo.

Deep learning-based prediction of heart failure rehospitalization during 6, 12, 24-month follow-ups in patients with acute myocardial infarction.

Heart failure is a clinical syndrome that occurs when the heart is too weak or stiff and cannot pump enough blood that our body needs. It is one of the most expensive diseases due to frequent hospitalizations and emergency room visits. Reducing unnecessary rehospitalizations is also an important and challenging task that has the potential of saving healthcare costs, enabling discharge planning, and identifying patients at high risk. Therefore, this paper proposes a deep learning-based prediction model of heart failure rehospitalization during 6, 12, 24-month follow-ups after hospital discharge in patients with acute myocardial infarction (AMI). We used the Korea Acute Myocardial Infarction-National Institutes of Health (KAMIR-NIH) registry which included 13,104 patient records and 551 features. The proposed deep learning-based rehospitalization prediction model outperformed traditional machine learning algorithms such as logistic regression, support vector machine, AdaBoost, gradient boosting machine, and random forest. The performance of the proposed model was accuracy, the area under the curve, precision, recall, specificity, and F1 score of 99.37%, 99.90%, 96.86%, 98.61%, 99.49%, and 97.73%, respectively. This study showed the potential of a deep learning-based model for cardiology, which can be used for decision-making and medical diagnosis tool of heart failure rehospitalization in patients with AMI.

Association between CHADS2, CHA2DS2-VASc, ATRIA, and Essen Stroke Risk Scores and Unsuccessful Recanalization after Endovascular Thrombectomy in Acute Ischemic Stroke Patients.

BACKGROUND: The CHADS2, CHA2DS2-VASc, ATRIA, and Essen scores have been developed for predicting vascular outcomes in stroke patients. We investigated the association between these stroke risk scores and unsuccessful recanalization after endovascular thrombectomy (EVT). METHODS: From the nationwide multicenter registry (Selection Criteria in Endovascular Thrombectomy and Thrombolytic therapy (SECRET)) (Clinicaltrials.gov NCT02964052), we consecutively included 501 patients who underwent EVT. We identified pre-admission stroke risk scores in each included patient. RESULTS: Among 501 patients who underwent EVT, 410 (81.8%) patients achieved successful recanalization (mTICI ≥ 2b). Adjusting for body mass index and p < 0.1 in univariable analysis revealed the association between all stroke risk scores and unsuccessful recanalization (CHADS2 score: odds ratio (OR) 1.551, 95% confidence interval (CI) 1.198-2.009, p = 0.001; CHA2DS2VASc score: OR 1.269, 95% CI 1.080-1.492, p = 0.004; ATRIA score: OR 1.089, 95% CI 1.011-1.174, p = 0.024; and Essen score: OR 1.469, 95% CI 1.167-1.849, p = 0.001). The CHADS2 score had the highest AUC value and differed significantly only from the Essen score (AUC of CHADS2 score; 0.618, 95% CI 0.554-0.681). CONCLUSION: All stroke risk scores were associated with unsuccessful recanalization after EVT. Our study suggests that these stroke risk scores could be used to predict recanalization in stroke patients undergoing EVT.

A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome.

OBJECTIVE: Some researchers have studied about early prediction and diagnosis of major adverse cardiovascular events (MACE), but their accuracies were not high. Therefore, this paper proposes a soft voting ensemble classifier (SVE) using machine learning (ML) algorithms. METHODS: We used the Korea Acute Myocardial Infarction Registry dataset and selected 11,189 subjects among 13,104 with the 2-year follow-up. It was subdivided into two groups (ST-segment elevation myocardial infarction (STEMI), non ST-segment elevation myocardial infarction NSTEMI), and then subdivided into training (70%) and test dataset (30%). Third, we selected the ranges of hyper-parameters to find the best prediction model from random forest (RF), extra tree (ET), gradient boosting machine (GBM), and SVE. We generated each ML-based model with the best hyper-parameters, evaluated by 5-fold stratified cross-validation, and then verified by test dataset. Lastly, we compared the performance in the area under the ROC curve (AUC), accuracy, precision, recall, and F-score. RESULTS: The accuracies for RF, ET, GBM, and SVE were (88.85%, 88.94%, 87.84%, 90.93%) for complete dataset, (84.81%, 85.00%, 83.70%, 89.07%) STEMI, (88.81%, 88.05%, 91.23%, 91.38%) NSTEMI. The AUC values in RF were (98.96%, 98.15%, 98.81%), ET (99.54%, 99.02%, 99.00%), GBM (98.92%, 99.33%, 99.41%), and SVE (99.61%, 99.49%, 99.42%) for complete dataset, STEMI, and NSTEMI, respectively. Consequently, the accuracy and AUC in SVE outperformed other ML models. CONCLUSIONS: The performance of our SVE was significantly higher than other machine learning models (RF, ET, GBM) and its major prognostic factors were different. This paper will lead to the development of early risk prediction and diagnosis tool of MACE in ACS patients.

Immediate and Long-Term Outcomes of Reperfusion Therapy in Patients With Cancer.

Background and Purpose: Patients with acute stroke are often accompanied by comorbidities, such as active cancer. However, adequate treatment guidelines are not available for these patients. The purpose of this study was to evaluate the association between cancer and the outcomes of reperfusion therapy in patients with stroke. Methods: We compared treatment outcomes in patients who underwent reperfusion therapy, using a nationwide reperfusion therapy registry. We divided the patients into 3 groups according to cancer activity: active cancer, nonactive cancer, and without a history of cancer. We investigated reperfusion processes, 24-hour neurological improvement, adverse events, 3-month functional outcome, and 6-month survival and related factors after reperfusion therapy. Results: Among 1338 patients who underwent reperfusion therapy, 62 patients (4.6%) had active cancer, 78 patients (5.8%) had nonactive cancer, and 1198 patients (89.5%) had no history of cancer. Of the enrolled patients, 969 patients received intravenous thrombolysis and 685 patients underwent endovascular treatment (316 patients received combined therapy). Patients with active cancer had more comorbidities and experienced more severe strokes; however, they showed similar 24-hour neurological improvement and adverse events, including cerebral hemorrhage, compared with the other groups. Although the functional outcome at 3 months was poorer than the other groups, 36.4% of patients with active cancer showed functional independence. Additionally, 52.9% of the patients with determined stroke etiology showed functional independence despite active cancer. During the 6-month follow-up, 46.6% of patients with active cancer died, and active cancer was independently associated with poor survival (hazard ratio, 3.973 [95% CI, 2.528­6.245]). Conclusions: In patients with active cancer, reperfusion therapy showed similar adverse events and short-term outcomes to that of other groups. While long-term prognosis was worse in the active cancer group than the nonactive cancer groups, not negligible number of patients had good functional outcomes, especially those with determined stroke mechanisms.

Comorbidity index for predicting mortality at 6 months after reperfusion therapy.

The eligibility of reperfusion therapy has been expanded to increase the number of patients. However, it remains unclear the reperfusion therapy will be beneficial in stroke patients with various comorbidities. We developed a reperfusion comorbidity index for predicting 6-month mortality in patients with acute stroke receiving reperfusion therapy. The 19 comorbidities included in the Charlson comorbidity index were adopted and modified. We developed a statistical model and it was validated using data from a prospective cohort. Among 1026 patients in the retrospective nationwide reperfusion therapy registry, 845 (82.3%) had at least one comorbidity. As the number of comorbidities increased, the likelihood of mortality within 6 months also increased (p < 0.001). Six out of the 19 comorbidities were included for developing the reperfusion comorbidity index on the basis of the odds ratios in the multivariate logistic regression analysis. This index showed good prediction of 6-month mortality in the retrospective cohort (area under the curve [AUC], 0.747; 95% CI, 0.704-0.790) and in 333 patients in the prospective cohort (AUC, 0.784; 95% CI, 0.709-0.859). Consideration of comorbidities might be helpful for the prediction of the 6-month mortality in patients with acute ischemic stroke who receive reperfusion therapy.

Patient characteristics according to rehabilitation and employment status in Korean hemodialysis patients.

BACKGROUND: While the final goal of renal replacement therapy is to achieve normal social adaptation and employment, many studies to date have focused on the analysis of complications and causes of death. In contrast, the analysis of dialysis patients with normal job employment and exhibiting a good medical status can be important for clinical treatment policy. This study assessed various clinical parameters according to rehabilitation and employment status to elucidate characteristics correlated with better rehabilitation status among hemodialysis patients. METHODS: A total of 29,865 hemodialysis patients who were registered with rehabilitation status information in the Korean Society of Nephrology end-stage renal disease registration program from January 2017 to April 2019 were included and divided into five groups according to their rehabilitation status. RESULTS: About half (47%) of enrolled patients belonged to the "independent but without a job" group and 21% of patients belonged to the "employed with a full-time job" group. Analysis revealed significantly younger mean age, longer mean hemodialysis duration, a higher proportion of male sex, and a lower proportion of diabetic nephropathy cases in the full-time job group than in the other hemodialysis patient groups. Also, hemoglobin, albumin, and phosphorus levels were higher but the urea reduction ratio was lower in the full-time job group. CONCLUSION: A better rehabilitation and employment state of hemodialysis patients was associated with younger age, male sex, and underlying chronic glomerulonephritis. Patients with full-time jobs generally have better laboratory data but lower dialysis efficacy.

Clopidogrel preventive effect based on cytochrome P450 2C19 genotype in ischaemic stroke: protocol for multicentre observational study.

INTRODUCTION: Clopidogrel is an antiplatelet agent that is widely used for the secondary prevention of cardiovascular and cerebrovascular events. The genotype of cytochrome P450 2C19 (CYP2C19) differentially affects the liver's metabolism of clopidogrel, which may influence the drug's response and efficacy for cardiovascular event prevention. In contrast to prior studies of patients with coronary artery diseases, little is known about whether the CYP2C19 genotype influences the preventive efficacy of clopidogrel in patients who had a stroke. We hypothesise that, among patients who had an acute ischaemic stroke who are prescribed clopidogrel, the patients with a loss-of-function CYP2C19 genotype (poor and intermediate metabolisers) may be at a higher risk of composite cardiovascular events than those who are non-carriers (extensive metabolisers). METHODS AND ANALYSIS: This prospective observational multicentre study was designed to determine whether composite cardiovascular events would differ among patients who had an ischaemic stroke prescribed clopidogrel according to CYP2C19 genotype (poor or intermediate vs extensive metabolisers). Inclusion criteria were patients who had an acute ischaemic stroke who underwent CYP2C19 genotype evaluation and received clopidogrel within 72 hours of stroke onset. The primary outcome is composite cardiovascular events (stroke, myocardial infarction, or cardiovascular death) within 6 months after acute ischaemic stroke between patients categorised as poor or intermediate metabolisers and those categorised as extensive metabolisers according to their CYP2C19 genotype. ETHICS AND DISSEMINATION: The Institutional Review Board of Severance Hospital, Yonsei University College of Medicine approved this study (3-2019-0195). We received study approval from the institutional review board of each participating hospital. We plan to disseminate our findings at relevant conferences and meetings and through peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04072705.

Effects of human recombinant granulocyte-colony stimulating factor treatment during in vitro culture on porcine pre-implantation embryos.

Granulocyte-colony stimulating factor (G-CSF), a pleiotropic cytokine, belongs to the hematopoietic growth factor family. Recent studies have reported that G-CSF is a predictive biomarker of oocyte and embryo developmental competence in humans. The aim of our study was to determine whether CSF3 and its receptor (CSF3R) were expressed in porcine maternal reproductive tissues (oviduct and uterus), cumulus cells, and embryos and to investigate the effects of human recombinant G-CSF (hrG-CSF) supplementation during in vitro culture (IVC) on the developmental competence of pre-implantation embryos. To do this, we first performed reverse-transcription polymerase chain reaction (RT-PCR). Second, we performed parthenogenetic activation (PA), in vitro fertilization (IVF), and somatic cell nuclear transfer (SCNT) to evaluate the embryonic developmental potential after hrG-CSF supplementation based on various concentrations (0 ng/mL, 10 ng/mL, 50 ng/mL, and 100 ng/mL) and durations (Un-treated, Days 0-3, Days 4-7, and Days 0-7) of IVC. Finally, we examined transcriptional levels of several marker genes in blastocysts. The results of our study showed that CSF3 transcript was present in all samples we assessed. CSF3-R was also detected, except in cumulus cells and blastocysts from PA. Furthermore, 10 ng/mL and Days 0-7 were the optimal concentration and duration for the viability of in vitro embryonic development, especially for SCNT-derived embryos. The rate of blastocyst formation and the total cell number of blastocysts were significantly enhanced, while the number and index of apoptotic nuclei were significantly decreased in optimal condition groups compared to others. Moreover, the transcriptional levels of anti-apoptotis- (BCL2), proliferation- (PCNA), and pluripotency- (POU5F1) related genes were dramatically upregulated. In conclusion, for the first time, we demonstrated that CSF3 and CSF3R were expressed in porcine reproductive organs, cells, and embryos. Additionally, we determined that hrG-CSF treatment improved porcine embryonic development capacity in vitro.

A machine learning-based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome.

Cardiovascular disease is the leading cause of death worldwide so, early prediction and diagnosis of cardiovascular disease is essential for patients affected by this fatal disease. The goal of this article is to propose a machine learning-based 1-year mortality prediction model after discharge in clinical patients with acute coronary syndrome. We used the Korea Acute Myocardial Infarction Registry data set, a cardiovascular disease database registered in 52 hospitals in Korea for 1 November 2005-30 January 2008 and selected 10,813 subjects with 1-year follow-up traceability. The ranges of hyperparameters to find the best prediction model were selected from four different machine learning models. Then, we generated each machine learning-based mortality prediction model with hyperparameters completed the range fitness via grid search using training data and was evaluated by fourfold stratified cross-validation. The best prediction model with the highest performance was found, and its hyperparameters were extracted. Finally, we compared the performance of machine learning-based mortality prediction models with GRACE in area under the receiver operating characteristic curve, precision, recall, accuracy, and F-score. The area under the receiver operating characteristic curve in applied machine learning algorithms was averagely improved up to 0.08 than in GRACE, and their major prognostic factors were different. This implementation would be beneficial for prediction and early detection of major adverse cardiovascular events in acute coronary syndrome patients.

Reconstruction error based deep neural networks for coronary heart disease risk prediction.

Coronary heart disease (CHD) is one of the leading causes of death worldwide; if suffering from CHD and being in its end-stage, the most advanced treatments are required, such as heart surgery and heart transplant. Moreover, it is not easy to diagnose CHD at the earlier stage; hospitals diagnose it based on various types of medical tests. Thus, by predicting high-risk people who are to suffer from CHD, it is significant to reduce the risks of developing CHD. In recent years, some research works have been done using data mining to predict the risk of developing diseases based on medical tests. In this study, we have proposed a reconstruction error (RE) based deep neural networks (DNNs); this approach uses a deep autoencoder (AE) model for estimating RE. Initially, a training dataset is divided into two groups by their RE divergence on the deep AE model that learned from the whole training dataset. Next, two DNN classifiers are trained on each group of datasets separately by combining a RE based new feature with other risk factors to predict the risk of developing CHD. For creating the new feature, we use deep AE model that trained on the only high-risk dataset. We have performed an experiment to prove how the components of our proposed method work together more efficiently. As a result of our experiment, the performance measurements include accuracy, precision, recall, F-measure, and AUC score reached 86.3371%, 91.3716%, 82.9024%, 86.9148%, and 86.6568%, respectively. These results show that the proposed AE-DNNs outperformed regular machine learning-based classifiers for CHD risk prediction.

In vitro and in vivo evaluation of the genotoxicity of Eriobotrya japonica leaf extract.

Eriobotrya japonica leaf is included in the Chinese Pharmacopoeia, and is widely used as a medicinal material in traditional medicine. The present study investigated the potential genotoxic effects of E. japonica leaf extract (EJE) using three standard battery systems. Genotoxicity tests were conducted following the test guidelines of the Organisation for Economic Cooperation and Development (OECD) and Ministry of Food and Drug Safety (MFDS), with application of Good Laboratory Practice. The bacterial reverse mutation test was conducted using the pre-incubation method in the presence or absence of the metabolic activation system (S9 mixture). The in vitro chromosome aberration test was performed using cultured Chinese hamster lung cell line in the presence or absence of the S9 mixture. The in vivo micronucleus test was performed using ICR mice. The bacterial reverse mutation test with Salmonella typhimurium strains TA98, TA100, TA1535, and TA1537 and Escherichia coli strain WP2uvrA showed that EJE did not induce gene mutations at any dose level in all the strains tested. EJE also did not show any chromosomal aberrations in the in vitro chromosomal aberration test and in the in vivo micronucleus test. These results showed that EJE did not induce mutagenicity or clastogenicity in either in vitro or in vivo systems.

Effects of Triflusal and Clopidogrel on the Secondary Prevention of Stroke Based on Cytochrome P450 2C19 Genotyping.

BACKGROUND AND PURPOSE: To compare the efficacy and safety of antiplatelet agents for the secondary prevention of ischemic stroke based on cytochrome P450 2C19 (CYP2C19) polymorphisms. METHODS: This study was a prospective, multicenter, randomized, parallel-group, open-label, blind genotype trial. First time non-cardiogenic ischemic stroke patients were enrolled and screened within 30 days. Participants were randomized to receive either triflusal or clopidogrel for secondary stroke prevention. The primary outcome was the time from randomization to first recurrent ischemic stroke or hemorrhagic stroke. RESULTS: The required sample size was 1,080 but only 784 (73%) participants were recruited. In patients with a poor CYP2C19 genotype for clopidogrel metabolism (n=484), the risk of recurrent stroke among those who received triflusal treatment was 2.9% per year, which was not significantly different from those who received clopidogrel treatment (2.2% per year; hazard ratio [HR], 1.23; 95% confidence interval [CI], 0.60-2.53). In the clopidogrel treatment group (n=393), 38% had good genotypes and 62% poor genotypes for clopidogrel metabolism. The risk of recurrent stroke in patients with a good CYP2C19 genotype was 1.6% per year, which was not significantly different from those with a poor genotype (2.2% per year; HR, 0.69; 95% CI, 0.26-1.79). CONCLUSIONS: Whilst there were no significant differences between the treatment groups in the rates of stroke recurrence, major vascular events, or coronary revascularization, the efficacy of antiplatelet agents for the secondary prevention of stroke according to CYP2C19 genotype status remains unclear.

Cardiovascular Autonomic Dysfunction in Patients with Drug-Induced Parkinsonism.

BACKGROUND AND PURPOSE: Recent studies have shown that several nonmotor symptoms differ between Parkinson's disease (PD) and drug-induced parkinsonism (DIP). However, there have been no reports on cardiovascular autonomic function in DIP, and so this study investigated whether cardiovascular autonomic function differs between PD and DIP patients. METHODS: This study consecutively enrolled 20 DIP patients, 99 drug-naïve PD patients, and 25 age-matched healthy controls who underwent head-up tilt-table testing and 24-h ambulatory blood pressure monitoring. RESULTS: Orthostatic hypotension was more frequent in patients with PD or DIP than in healthy controls. In DIP, orthostatic hypotension was associated with the underlying psychiatric diseases and neuroleptics use, whereas prokinetics were not related to orthostatic hypotension. The supine blood pressure, nighttime blood pressure, and nocturnal blood pressure dipping did not differ significantly between the DIP and control groups. Supine hypertension and nocturnal hypertension were more frequent in PD patients than in controls. CONCLUSIONS: The included DIP patients frequently exhibited orthostatic hypotension that was associated with the underlying diseases as well as the nature of and exposure time to the offending drugs. Clinicians should individualize the manifestations of DIP according to underlying diseases as well as the action mechanism of and exposure time to each offending drug.

Arthroscopic-assisted plate fixation for displaced large-sized comminuted greater tuberosity fractures of proximal humerus: a novel surgical technique.

PURPOSE: The purpose of the present study was to describe the use of a novel hybrid surgical technique-arthroscopic-assisted plate fixation-and evaluate its clinical and anatomical outcomes in the management of large, displaced greater tuberosity (GT) fractures with comminution. METHODS: From 2009 to 2011, this novel technique was performed in 11 patients [2 men and 9 women; median age, 64 years (range 41-83 years)] with large, comminuted GT fractures, with fragment displacements of >5 mm. The preoperative mean posterior and superior migration of the fractured fragment, as measured on computed tomography (CT), was 19.5 and 5.5 mm, respectively. Two patients had shoulder fracture-dislocation, and three had associated undisplaced surgical neck fracture. The mean duration between injury and surgery was 4 days. The mean follow-up duration was 26 months. RESULTS: At the final follow-up, the mean postoperative ASES, UCLA and SST scores were 84, 29, and 8, respectively. The mean range of motion was as follows: forward flexion, 138°; abduction, 135°; external rotation at the side, 19°; and internal rotation, up to the L2 level. The mean posterior and superior displacements of fracture fragments on postoperative CT scan [0.7 ± 0.8 mm (range 0-2.1 mm) and 2.8 ± 0.5 mm (range 3.4-5.3 mm), respectively] were significantly improved (p < 0.05). On arthroscopy, a partial articular-side supraspinatus tendon avulsion lesion was identified in 10 of 11 patients (91 %), and 1 of these patients had a partial tear of the biceps and 1 had a partial subscapularis tear, respectively (9 %). Intraoperatively, 1 anchor pullout and 1 anchor protrusion through the humeral head were noted and corrected. Postoperatively, the loss of reduction in the fracture fragment was noted in 1 patient at 4 weeks, after corrective reduction and fixation surgery. CONCLUSIONS: The novel arthroscopic-assisted anatomical plate fixation technique was found to be effective in reducing large-sized, displaced, comminuted GT fractures and in allowing concurrent management of intra-articular pathologies and early functional rehabilitation. Compared with the conventional plate fixation or arthroscopic suture anchor fixation technique, arthroscopic-assisted plate fixation enabled accurate restoration of the medial footprint of the GT fracture and provided an effective buttress to the large-sized GT fracture fragments. LEVEL OF EVIDENCE: Retrospective clinical study, Level IV.

Serum Uric Acid Is Associated with Cerebral White Matter Hyperintensities in Patients with Acute Lacunar Infarction.

BACKGROUND AND PURPOSE: The Effect of Cilostazol in Acute Lacunar Infarction Based on Pulsatility Index of Transcranial Doppler (ECLIPse) study showed a significant decrease in transcranial Doppler pulsatility index (PI) with cilostazol treatment after 90 days of acute lacunar infarction. The aim of this analysis was to perform a subgroup analysis of the ECLIPse study to explore the relationship between serum uric acid (UA) and the volume of white matter hyperintensities (WMH) in patients with acute lacunar infarction. METHODS: The ECLIPse was a multicenter, randomized, double-blind, placebo-controlled study conducted in Korea. For this subgroup analysis, WMH volume was measured for those subjects for whom FLAIR or T2-weighted images were available using semiautomated computerized software. RESULTS: Of the 203 patients in 8 hospitals in the ECLIPse study, 130 in 6 hospitals were entered for this subgroup analysis. The mean age was 64.7 ± 9.95 years, and 20.8% were women. The mean WMH volume was 11.57 cm(3) (.13 to 68.45, median 4.86) and mean serum UA was 5.2 mg/dL (1.5 to 8.9). Multiple linear regression analysis revealed that age (P < .001) and serum UA (P = .013) were significantly associated with WMH volume. Age-adjusted scatterplots showed that serum UA level was positively related to WMH volume in patients with acute lacunar infarction (r = 0.275, P = .003). CONCLUSIONS: This study showed that serum UA was associated with cerebral WMH in patients with acute lacunar infarction.