Ibrahim, Sufyan

BACKGROUND AND OBJECTIVES: Enhanced recovery after surgery (ERAS) protocols aim to optimize patient outcomes by reducing the surgical stress response, expediting recovery, and reducing care costs. We aimed to evaluate the impact of implementing ERAS protocols on the perioperative surgical outcomes and financial implications associated with spine surgeries. METHODS: A systematic review and meta-analysis of peer-reviewed studies directly comparing outcome differences between spine surgeries performed with and without utilization of ERAS pathways was conducted along Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS: Of 676 unique articles identified, 59 with 15 198 aggregate patients (7748 ERAS; 7450 non-ERAS) were included. ERAS-treated patients had shorter operative times (mean difference [MD]: 10.2 mins; P < .01), shorter hospitalizations (MD: 1.41 days, P < .01), fewer perioperative complications (relative risk [RR] = 0.64, P < .01), lower postoperative opioid use (MD of morphine equivalent dose: 164.36 mg; P < .01), and more rapid mobilization/time to first out-of-bed ambulation (MD: 0.92 days; P < .01). Spine surgeries employing ERAS were also associated with lower total costs (MD: $1140.26/patient; P < .01), especially in the United States (MD: $2869.11/patient, P < .01) and lower postoperative visual analog pain scores (MD = 0.56, P < .01), without any change in odds of 30-day readmission (RR: 0.80, P = .13) or reoperation (RR: 0.88, P = .60). Subanalyses based on the region of spine showed significantly lower length of stay in both cervical and lumbar surgeries implementing ERAS. Type of procedure showed a significantly lesser time-to-initiate mobilization in fusion surgeries using ERAS protocols compared with decompression. CONCLUSION: The present meta-analysis indicates that current literature supports ERAS implementation as a means of reducing care costs and safely accelerating hospital discharge for patients undergoing spine surgery.

Emerging evidence indicates that synaptic plasticity is significantly involved in the pathophysiology and treatment of perinatal depression. Animal models have demonstrated the effects of overstimulated or weakened synapses in various circuits of the brain in causing affective disturbances. GABAergic theory of depression, stress, and the neuroplasticity model of depression indicate the role of synaptic plasticity in the pathogenesis of depression. Multiple factors related to perinatal depression like hormonal shifts, newer antidepressants, mood stabilizers, monoamine systems, biomarkers, neurotrophins, cytokines, psychotherapy and electroconvulsive therapy have demonstrated direct and indirect effects on synaptic plasticity. In this review, we discuss and summarize the various patho-physiology-related effects of synaptic plasticity in depression. We also discuss the association of treatment-related aspects related to psychotropics, electroconvulsive therapy, neuromodulation, psychotherapy, physical exercise and yoga with synaptic plasticity in perinatal depression. Future insights into newer methods of treatment directed towards the modulation of neuroplasticity for perinatal depression will be discussed.

Artificial intelligence (AI) is an emerging technological system that provides a platform to manage and analyze data by emulating human cognitive functions with greater accuracy, revolutionizing patient care and introducing a paradigm shift to the healthcare industry. The purpose of this study is to identify the underlying factors that affect the adoption of artificial intelligence in healthcare (AIH) by healthcare providers and to understand “What are the factors that influence healthcare providers’ behavioral intentions to adopt AIH in their routine practice?” An integrated survey was conducted among healthcare providers, including consultants, residents/students, and nurses. The survey included items related to performance expectancy, effort expectancy, initial trust, personal innovativeness, task complexity, and technology characteristics. The collected data were analyzed using structural equation modeling. A total of 392 healthcare professionals participated in the survey, with 72.4% being male and 50.7% being 30 years old or younger. The results showed that performance expectancy, effort expectancy, and initial trust have a positive influence on the behavioral intentions of healthcare providers to use AIH. Personal innovativeness was found to have a positive influence on effort expectancy, while task complexity and technology characteristics have a positive influence on effort expectancy for AIH. The study’s empirically validated model sheds light on healthcare providers’ intention to adopt AIH, while the study’s findings can be used to develop strategies to encourage this adoption. However, further investigation is necessary to understand the individual factors affecting the adoption of AIH by healthcare providers.

OBJECTIVE Patient-reported outcomes (PROs) have become the standard means to measure surgical outcomes. Insurers and policy makers are also increasingly utilizing PROs to assess the value of care and measure different aspects of a patient’s condition. For cervical myelopathy, it is currently unclear which outcome measure best reflects patient satisfaction. In this investigation, the authors evaluated patients treated for cervical myelopathy to determine which outcome questionnaires best correlate with patient satisfaction. METHODS The Quality Outcomes Database (QOD), a prospectively collected multi-institutional database, was used to retrospectively analyze patients undergoing surgery for cervical myelopathy. The North American Spine Society (NASS) satisfaction index, Neck Disability Index (NDI), numeric rating scales for neck pain (NP-NRS) and arm pain (AP-NRS), EQ-5D, and modified Japanese Orthopaedic Association (mJOA) scale were evaluated. RESULTS The analysis included 1141 patients diagnosed with myelopathy, of whom 1099 had an NASS satisfaction index recorded at any of the follow-up time points. Concomitant radiculopathy was an indication for surgery in 368 (33.5%) patients, and severe neck pain (NP-NRS ≥ 7) was present in 471 (42.8%) patients. At the 3-month follow-up, NASS patient satisfaction index scores were positively correlated with scores for the NP-NRS (r = 0.30), AP-NRS (r = 0.32), and NDI (r = 0.36) and negatively correlated with EQ-5D (r = −0.38) and mJOA (r = −0.29) scores (all p < 0.001). At the 12-month follow-up, scores for the NASS index were positively correlated with scores for the NP-NRS (r = 0.44), AP-NRS (r = 0.38), and NDI (r = 0.46) and negatively correlated with scores for the EQ-5D (r = −0.40) and mJOA (r = −0.36) (all p < 0.001). At the 24-month follow-up, NASS index scores were positively correlated with NP-NRS (r = 0.49), AP-NRS (r = 0.36), and NDI (r = 0.49) scores and negatively correlated with EQ-5D (r = −0.44) and mJOA (r = −0.38) scores (all p < 0.001). CONCLUSIONS Neck pain was highly prevalent in patients with myelopathy. Notably, improvement in neck pain–associated disability rather than improvement in myelopathy was the most prominent PRO factor for patients. This finding may reflect greater patient concern for active pain symptoms than for neurological symptoms caused by myelopathy. As commercial payers begin to examine novel remuneration strategies for surgical interventions, thoughtful analysis of PRO measurements will have increasing relevance.

Spinal cord tumors constitute a diverse group of rare neoplasms associated with significant mortality and morbidity that pose unique clinical and surgical challenges. Diagnostic accuracy and outcome prediction are critical for informed decision making and can promote personalized medicine and facilitate optimal patient management. Machine learning has the ability to analyze and combine vast amounts of data, allowing the identification of patterns and the establishment of clinical associations, which can ultimately enhance patient care. Although artificial intelligence techniques have been explored in other areas of spine surgery, such as spinal deformity surgery, precise machine learning models for spinal tumors are lagging behind. Current applications of machine learning in spinal cord tumors include algorithms that improve diagnostic precision by predicting genetic, molecular, and histopathological profiles. Furthermore, artificial intelligence-based systems can assist surgeons with preoperative planning and surgical resection, potentially reducing the risk of recurrence and consequently improving clinical outcomes. Machine learning algorithms promote personalized medicine by enabling prognostication and risk stratification based on accurate predictions of treatment response, survival, and postoperative complications. Despite their promising potential, machine learning models require extensive validation processes and quality assessments to ensure safe and effective translation to clinical practice.

Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

Background: Wearable device technology has recently been involved in the healthcare industry substantially. India is the world’s third largest market for wearable devices and is projected to expand at a compound annual growth rate of ~26.33%. However, there is a paucity of literature analyzing the factors determining the acceptance of wearable healthcare device technology among low-middle-income countries. Methods: This cross-sectional, web-based survey aims to analyze the perceptions affecting the adoption and usage of wearable devices among the Indian population aged 16 years and above. Results: A total of 495 responses were obtained. In all, 50.3% were aged between 25–50 years and 51.3% belonged to the lower-income group. While 62.2% of the participants reported using wearable devices for managing their health, 29.3% were using them daily. technology and task fitness (TTF) showed a significant positive correlation with connectivity (r = 0.716), health care (r = 0.780), communication (r = 0.637), infotainment (r = 0.598), perceived usefulness (PU) (r = 0.792), and perceived ease of use (PEOU) (r = 0.800). Behavioral intention (BI) to use wearable devices positively correlated with PEOU (r = 0.644) and PU (r = 0.711). All factors affecting the use of wearable devices studied had higher mean scores among participants who were already using wearable devices. Male respondents had significantly higher mean scores for BI (p = 0.034) and PEOU (p = 0.009). Respondents older than 25 years of age had higher mean scores for BI (p = 0.027) and Infotainment (p = 0.032). Conclusions: This study found a significant correlation with the adoption and acceptance of wearable devices for healthcare management in the Indian context.