Background Delirium is a neurobehavioral syndrome, which is characterized by a fluctuation of mental status, disorientation, confusion and inappropriate behavior, and it is prevalent among hospitalized patients. Recognizing modifiable risk factors of delirium is the key point for improving our preventive strategies and restraining its devastating consequences. This study aimed to identify and investigate various factors predisposing hospitalized patients to develop delirium, focusing mostly on underlying diseases and medications. Method In a prospective, observational trial, we investigated 220 patients who had been admitted to the internal, emergency, surgery and hematology-oncology departments. We employed the Confusion Assessment Method (CAM) questionnaire, The Richmond Agitation Sedation Scale (RASS), the General Practitioner Assessment of Cognition (GPCOG), demographic questionnaire, patient interviews and medical records. Multivariate logistic regression models were used to analyze the predictive value of medications and underlying diseases for daily transition to delirium.; demographics were analyzed using univariate analysis to identify those independently associated with delirium. Results Two hundred twenty patients were enrolled; the emergency department had the most incident delirium (31.3%), and the surgery section had the least (2.4%); delirium was significantly correlated with older ages and sleep disturbance. Among multiple underlying diseases and the medications evaluated in this study, we found that a history of dementia, neurological diseases and malignancies increases the odds of transition to delirium and the use of anticoagulants decreases the incident delirium. Conclusion Approximately 1 out of 10 overall patients developed delirium; It is important to evaluate underlying diseases and medications more thoroughly in hospitalized patients to assess the risk of delirium.

Précis: We developed a deep learning-based classifier that can discriminate primary angle closure suspects (PACS), primary angle closure (PAC)/primary angle closure glaucoma (PACG), and also control eyes with open angle with acceptable accuracy. Purpose: To develop a deep learning-based classifier for differentiating subtypes of primary angle closure disease, including PACS and PAC/PACG, and also normal control eyes. Materials and Methods: Anterior segment optical coherence tomography images were used for analysis with 5 different networks including MnasNet, MobileNet, ResNet18, ResNet50, and EfficientNet. The data set was split with randomization performed at the patient level into a training plus validation set (85%), and a test data set (15%). Then 4-fold cross-validation was used to train the model. In each mentioned architecture, the networks were trained with original and cropped images. Also, the analyses were carried out for single images and images grouped on the patient level (case-based). Then majority voting was applied to the determination of the final prediction. Results: A total of 1616 images of normal eyes (87 eyes), 1055 images of PACS (66 eyes), and 1076 images of PAC/PACG (66 eyes) eyes were included in the analysis. The mean ± SD age was 51.76 ± 15.15 years and 48.3% were males. MobileNet had the best performance in the model, in which both original and cropped images were used. The accuracy of MobileNet for detecting normal, PACS, and PAC/PACG eyes was 0.99 ± 0.00, 0.77 ± 0.02, and 0.77 ± 0.03, respectively. By running MobileNet in a case-based classification approach, the accuracy improved and reached 0.95 ± 0.03, 0.83 ± 0.06, and 0.81 ± 0.05, respectively. For detecting the open angle, PACS, and PAC/PACG, the MobileNet classifier achieved an area under the curve of 1, 0.906, and 0.872, respectively, on the test data set. Conclusion: The MobileNet-based classifier can detect normal, PACS, and PAC/PACG eyes with acceptable accuracy based on anterior segment optical coherence tomography images.

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Background: Delirium is a neurobehavioral syndrome, which is characterized by fluctuation of mental status, disorientation, confusion and inappropriate behavior and it is prevalent among hospitalized patients. Recognizing modifiable risk factors of delirium is the key point for improving our preventive strategies and restraining its devastating consequences. The present study investigated a wide range of possible predisposing factors of delirium, mainly focused on underlying diseases and mediations, of hospitalized patients in the different wards of a general hospital.Method: In a prospective, observational trial, we investigated 220 patients who had been admitted to the internal, emergency, surgery and hematology-oncology departments. We employed the Confusion Assessment Method (CAM) questionnaire, The Richmond Agitation Sedation Scale (RASS), the General Practitioner Assessment of Cognition (GPCOG), demographic questionnaire, patient interviews and medical records. Multivariate logistic regression models were used to analyze predictive value of medications and underlying diseases for daily transition to delirium.; demographics were analyzed using univariate analysis to identify those independently associated with delirium.Results: 220 patients were enrolled; the emergency department had the most incident delirium (%31.3) and the surgery section had the least (%2.4); delirium was significantly correlated with older ages and sleep disturbance. Among multiple underlying diseases and the medications evaluated in this study, we found that history of dementia, neurological diseases and malignancies increase the odds of transition to delirium and the use of anticoagulants decreases the incident delirium.Conclusion: Approximately, 1 out of 10 overall patients developed delirium; Considering underlying diseases and the medications as the predisposing factors of delirium would help to better predict those at risk.

Background: Thoracic disc herniation is a rare illness and is mainly asymptomatic. There are some surgical approaches to treat symptomatic patients, and none has absolute dominance over the others. For this reason, there is a debate between spine surgeons to decide which method could help these patients with better efficacy and safety. Objectives: To seek the potential differences between the two of these methods, the conventional anterior transthoracic and the more recent modified transfacet approaches, we conducted this study. Methods: This is a retrospective case-series study comparing the anterior transthoracic and the modified transfacet method; each of these approaches was preferred and performed by one surgery team. Patients were divided into two groups based on the procedure and assessed using Frankel’s score, visual along scale (VAS) score, short-form health survey questionnaire (SF-36), and the spine functional index (SFI). Results: Eleven patients underwent a transthoracic approach, and eight patients had a posterior transfacet pedicle-sparing approach. The Frankel’s score improved at least one score in ten patients from the transthoracic group and seven patients from the transfacet pedicle-sparing group. No major difference was found between the two groups concerning SFI and SF-36 questionnaire. Conclusions: This study exhibited satisfying efficacy and safety of the modified transfacet pedicle-sparing method compared to the transthoracic approach. Both improved Frankel’s scores, SFI, and patients’ quality of life. Despite encountering some limitations, especially a small number of subjects, our study suggests that these surgical methods could be used efficiently considering the patient’s comorbidities, location of the herniated disc and its calcification, and experience and skill of the surgeon.