Can magnetic resonance imaging findings effectively diagnose the instability observed on radiographs in patients with degenerative lumbar spinal stenosis?

BACKGROUND: Degenerative lumbar spinal stenosis (LSS) is a common condition that involves the narrowing of the spinal canal. Diagnosing instability traditionally requires standing lateral radiographs to detect dynamic translation, but there is debate about relying solely on radiographs due to challenges like patient discomfort and radiation exposure. This study aimed to evaluate if Magnetic Resonance Imaging (MRI) findings could effectively diagnose instability observed on radiographs. METHODS: We reviewed 478 consecutive patients with degenerative LSS who had surgery at our institution. Instability was defined as a sagittal translation exceeding 3 mm on standing lateral radiographs in both extension and flexion. Patients were divided into stable (those with < 3 mm translation) and unstable groups (those with > 3 mm translation). The study assessed potential variables for instability, including MRI findings like facet joint effusion, facet joint angle, disk height index, intradiscal vacuum presence, endplate sclerosis, ligamentum flavum hypertrophy, and multifidus muscle fatty degeneration, comparing these factors between the two groups. RESULTS: A total of 478 consecutive patients diagnosed with degenerative Lumbar Spinal Stenosis (LSS) were included. The average age of the patients was 66.32 years, with 43.3% being male. Approximately 27.6% of the cases exhibited signs of instability on the standing lateral radiograph during extension and flexion. The multivariate analysis using binary logistic regression revealed that facet joint effusion (odds ratio [OR] 2.73; 95% confidence interval [CI] 1.27-3.94; P = 0.002), disk height index (OR 2.22; 95% CI 1.68-3.35; P = 0.009), and the presence of the Vacuum sign (OR 1.77; 95% CI 1.32-2.84; P = 0.021) were identified as factors associated with instability. CONCLUSIONS: Our findings showed thata higher facet joint effusion, the presence of Vacuum sign, and a greater Disk Height Index were associated with the presence of instability on the standing lateral radiograph in extension and flexion in patients with degenerative LSS.

The most important variables associated with death due to COVID-19 disease, based on three data mining models Decision Tree, AdaBoost, and Support Vector Machine: A cross-sectional study.

Introduction: Death due to covid-19 is one of the biggest health challenges in the world. There are many models that can predict death due to COVID-19. This study aimed to fit and compare Decision Tree (DT), Support Vector Machine (SVM), and AdaBoost models to predict death due to COVID-19. Methods: To describe the variables, mean (SD) and frequency (%) were reported. To determine the relationship between the variables and the death caused by COVID-19, chi-square test was performed with a significance level of 0.05. To compare DT, SVM and AdaBoost models for predicting death due to COVID-19 from sensitivity, specificity, accuracy and the area under the rock curve under R software using psych, caTools, random over-sampling examples, rpart, rpartplot packages was done. Results: Out of the total of 23,054 patients studied, 10,935 cases (46.5%) were women, and 12,569 cases (53.5%) were men. Additionally, the mean age of the patients was 54.9 ± 21.0 years. There is a statistically significant relationship between gender, fever, cough, muscle pain, smell and taste, abdominal pain, nausea and vomiting, diarrhea, anorexia, dizziness, chest pain, intubation, cancer, diabetes, chronic blood disease, Violation of immunity, pregnancy, Dialysis, chronic lung disease with the death of covid-19 patients showed (p < 0.05). The results showed that the sensitivity, specificity, accuracy and the area under the receiver operating characteristic curve were respectively 0.60, 0.68, 0.71, and 0.75 in the DT model, 0.54, 0.62, 0.63, and 0.71 in the SVM model, and 0.59, 0.65, 0.69 and 0.74 in the AdaBoost model. Conclusion: The results showed that DT had a high predictive power compared to other data mining models. Therefore, it is suggested to researchers in different fields to use DT to predict the studied variables. Also, it is suggested to use other approaches such as random forest or XGBoost to improve the accuracy in future studies.

Unveiling the potential impact of vitamin C in postoperative spinal pain.

BACKGROUND: Spinal surgery is a common procedure associated with significant postoperative pain, and identifying effective interventions to manage this pain is crucial for optimizing patient outcomes. This review assesses the existing literature to determine the overall impact of vitamin C supplementation on spinal postoperative pain. Vitamin C, also known as ascorbic acid, is an essential nutrient that plays a vital role in numerous physiological processes. It functions as a potent antioxidant, neutralizing free radicals and reducing oxidative stress within the body. Furthermore, vitamin C is a cofactor in collagen synthesis, a crucial component of connective tissues, including those found in the spinal structures. Given its antioxidant and collagen-promoting properties, vitamin C has piqued interest as a potential therapeutic option for postoperative spinal pain. Based on the available evidence, vitamin C may have a beneficial effect on postoperative spinal pain, including reducing pain scores, analgesic consumption, and the incidence of complications such as complex regional pain syndrome. However, more research is needed to fully understand the optimal dosage and duration of vitamin C supplementation for postoperative pain management. CONCLUSION: Vitamin C could be considered a potentially beneficial adjunctive therapy for managing spinal postoperative pain, but its routine use requires further investigation.

Assessing the predictive capability of machine learning models in determining clinical outcomes for patients with cervical spondylotic myelopathy treated with laminectomy and posterior spinal fusion.

BACKGROUND: Cervical spondylotic myelopathy (CSM) is a prevalent degenerative condition resulting from spinal cord compression and injury. Laminectomy with posterior spinal fusion (LPSF) is a commonly employed treatment approach for CSM patients. This study aimed to assess the effectiveness of machine learning models (MLMs) in predicting clinical outcomes in CSM patients undergoing LPSF. METHODS: A retrospective analysis was conducted on 329 CSM patients who underwent LPSF at our institution from Jul 2017 to Jul 2023. Neurological outcomes were evaluated using the modified Japanese Orthopaedic Association (mJOA) scale preoperatively and at the final follow-up. Patients were categorized into two groups based on clinical outcomes: the favorable group (recovery rates ≥ 52.8%) and the unfavorable group (recovery rates < 52.8%). Potential predictors for poor clinical outcomes were compared between the groups. Four MLMs-random forest (RF), logistic regression (LR), support vector machine (SVM), and k-nearest neighborhood (k-NN)-were utilized to predict clinical outcome. RF model was also employed to identify factors associated with poor clinical outcome. RESULTS: Out of the 329 patients, 185 were male (56.2%) and 144 were female (43.4%), with an average follow-up period of 17.86 ± 1.74 months. Among them, 267 patients (81.2%) had favorable clinical outcomes, while 62 patients (18.8%) did not achieve favorable results. Analysis using binary logistic regression indicated that age, preoperative mJOA scale, and symptom duration (p < 0.05) were independent predictors of unfavorable clinical outcomes. All models performed satisfactorily, with RF achieving the highest accuracy of 0.922. RF also displayed superior sensitivity and specificity (sensitivity = 0.851, specificity = 0.944). The Area under the Curve (AUC) values for RF, Logistic LR, SVM, and k-NN were 0.905, 0.827, 0.851, and 0.883, respectively. The RF model identified preoperative mJOA scale, age, symptom duration, and MRI signal changes as the most significant variables associated with poor clinical outcomes in descending order. CONCLUSIONS: This study highlighted the effectiveness of machine learning models in predicting the clinical outcomes of CSM patients undergoing LPSF. These models have the potential to forecast clinical outcomes in this patient population, providing valuable prognostic insights for preoperative counseling and postoperative management.

Red cell distribution width to lymphocyte ratio could serve as a new inflammatory biomarker for predicting hematoma expansion in patients with intracerebral hemorrhage.

BACKGROUND: Hematoma expansion is a critical factor associated with increased mortality and adverse outcomes in patients with intracerebral hemorrhage (ICH). Identifying and preventing hematoma expansion early on is crucial for effective therapeutic intervention. This study aimed to investigate the potential association between the Red cell distribution width to lymphocyte ratio (RDWLR) and hematoma expansion in ICH patients. METHODS: We conducted a retrospective analysis of clinical data from 303 ICH patients treated at our department between May 2018 and May 2023. Demographic, clinical, radiological, and laboratory data, including RDWLR upon admission, were assessed. Binary logistic regression analysis was employed to determine independent associations between various variables and hematoma expansion. RESULTS: The study included 303 ICH patients, comprising 167 (55.1%) males and 136 (44.9%) females, with a mean age of 65.25 ± 7.32 years at admission. Hematoma expansion occurred in 73 (24.1%) cases. Multivariate analysis revealed correlations between hematoma volume at baseline (OR, 2.73; 95% CI: 1.45 -4,78; P < 0.001), admission systolic blood pressure (OR, 2.98 ; 95% CI: 1.54-4.98; P < 0.001), Glasgow Coma Scale (GCS) (OR, 1.58; 95% CI: 1.25-2.46; P = 0.017), and RDWLR (OR, 1.58; 95% CI: 1.13-2.85; P = 0.022) and hematoma expansion in these patients. CONCLUSIONS: Our findings suggest that RDWLR could serve as a new inflammatory biomarker for hematoma expansion in ICH patients. This cost-effective and readily available biomarker has the potential for early prediction of hematoma expansion in these patients.

The efficacy of machine learning models in forecasting treatment failure in thoracolumbar burst fractures treated with short-segment posterior spinal fixation.

BACKGROUND: Although short-segment posterior spinal fixation (SSPSF) has shown promising clinical outcomes in thoracolumbar burst fractures, the treatment may be prone to a relatively high failure rate. This study aimed to assess the effectiveness of machine learning models (MLMs) in predicting factors associated with treatment failure in thoracolumbar burst fractures treated with SSPSF. METHODS: A retrospective review of 332 consecutive patients with traumatic thoracolumbar burst fractures who underwent SSPSF at our institution between May 2016 and May 2023 was conducted. Patients were categorized into two groups based on treatment outcome (failure or non-failure). Potential risk factors for treatment failure were compared between the groups. Four MLMs, including random forest (RF), logistic regression (LR), support vector machine (SVM), and k-nearest neighborhood (k-NN), were employed to predict treatment failure. Additionally, LR and RF models were used to assess factors associated with treatment failure. RESULTS: Of the 332 included patients, 61.4% were male (n = 204), and treatment failure was observed in 44 patients (13.3%). Logistic regression analysis identified Load Sharing Classification (LSC) score, lack of index level instrumentation, and interpedicular distance (IPD) as factors associated with treatment failure (P < 0.05). All models demonstrated satisfactory performance. RF exhibited the highest accuracy in predicting treatment failure (accuracy = 0.948), followed by SVM (0.933), k-NN (0.927), and LR (0.917). Moreover, the RF model outperformed other models in terms of sensitivity and specificity (sensitivity = 0.863, specificity = 0.959). The area under the curve (AUC) for RF, LR, SVM, and k-NN was 0.911, 0.823, 0.844, and 0.877, respectively. CONCLUSIONS: This study demonstrated the utility of machine learning models in predicting treatment failure in thoracolumbar burst fractures treated with SSPSF. The findings support the potential of MLMs to predict treatment failure in this patient population, offering valuable prognostic information for early intervention and cost savings.

The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review.

Machine learning algorithms have the potential to significantly improve patient safety in spine surgeries by providing healthcare professionals with valuable insights and predictive analytics. These algorithms can analyze preoperative data, such as patient demographics, medical history, and imaging studies, to identify potential risk factors and predict postoperative complications. By leveraging machine learning, surgeons can make more informed decisions, personalize treatment plans, and optimize surgical techniques to minimize risks and enhance patient outcomes. Moreover, by harnessing the power of machine learning, healthcare providers can make data-driven decisions, personalize treatment plans, and optimize surgical interventions, ultimately enhancing the quality of care in spine surgery. The findings highlight the potential of integrating artificial intelligence in healthcare settings to mitigate risks and enhance patient safety in surgical practices. The integration of machine learning holds immense potential for enhancing patient safety in spine surgeries. By leveraging advanced algorithms and predictive analytics, healthcare providers can optimize surgical decision-making, mitigate risks, and personalize treatment strategies to improve outcomes and ensure the highest standard of care for patients undergoing spine procedures. As technology continues to evolve, the future of spine surgery lies in harnessing the power of machine learning to transform patient safety and revolutionize surgical practices. The present review article was designed to discuss the available literature in the field of machine learning techniques to enhance patient safety in spine surgery.

Are there any benefits for post-operative splinting after carpal tunnel release? A systematic review and meta-analysis.

BACKGROUND: There is a controversy on the effectiveness of post-operating splinting in patients with carpal tunnel release (CTR) surgery. This study aimed to systematically evaluate various outcomes regarding the effectiveness of post-operating splinting in CTR surgery. METHODS: Multiple databases, including PubMed, EMBASE, CINAHL, Web of Science, and Cochrane, were searched for terms related to carpal tunnel syndrome. A total of eight studies involving 596 patients were included in this meta-analysis. The quality of studies was evaluated, and their risk of bias was calculated using the methodological index for non-randomized studies (MINORS) and Cochrane's collaboration tool for assessing the risk of bias in randomized controlled trials. Data including the visual analogue scale (VAS), pinch strength, grip strength, two-point discrimination, symptom severity score (SSS), and functional status scale (FSS) were extracted. RESULTS: Our analysis showed no significant differences between the splinted and non-splinted groups based on the VAS, SSS, FSS, grip strength, pinch strength, and two-point discrimination. The calculated values of the standardized mean difference (SMD) or the weighted mean difference (WMD) and a 95% confidence interval (CI) for different variables were as follows: VAS [SMD = 0.004, 95% CI (-0.214, 0.222)], pinch strength [WMD = 1.061, 95% CI (-0.559, 2.681)], grip strength [SMD = 0.178, 95% CI (-0.014, 0.369)], SSS [WMD = 0.026, 95% CI (- 0.191, 0.242)], FSS [SMD = 0.089, 95% CI (-0.092, 0.269)], and the two-point discrimination [SMD = 0.557, 95% CI (-0.140, 1.253)]. CONCLUSIONS: Our findings revealed no statistically significant differences between the splinted and non-splinted groups in terms of the VAS, SSS, FSS, grip strength, pinch strength, and two-point discrimination. These results indicate that there is no substantial evidence supporting a significant advantage of post-operative splinting after CTR.

High prognostic nutritional index could be associated with improved survival in patients with brain metastases: a retrospective observational study.

BACKGROUND: Prognostic nutritional index (PNI) is an indicator of immunonutritional status. The present study aimed to evaluate the association between PNI and overall survival in patients with brain metastases. METHODS: A total of 257 patients with histologically confirmed brain metastasis who underwent surgery at our center between April 2011 and April 2021 were investigated retrospectively.Demographic, clinical, and radiologic data of all cases were extracted. Patients were divided into the low PNI group(PNI <45) (malnutrition status) and the high PNI group (PNI ≥45)(normal nutritional status). Survival analyses were performed using the Kaplan-Meier method and compared using the log-rank test. Univariate and multivariate analyses were performed using the Cox proportional hazards method. RESULTS: From the 257 cases of brain metastases, there were 148 (57.6%) males and 109 (42.4%) females. The mean age was 57.02 ± 8.7. The median overall survival was 13 months. The results of the multivariate Cox regression analyses showed primary cancer site (HR 2.196, 95% CI 1.988-6.52605, P = 0.000), the number of metastases (HR 1.829, 95% CI 1.380-2.872, P = 0.037), extracranial involvement (HR 2.186, 95% CI 1.213-4.171, P = 0.026), adjuvant radiotherapy (HR 2.823, 95% CI 1.187-5.415, P = 0.017) and PNI (HR 1.961, 95% CI 1.168-3.515, P = 0.021) as the independent predictors of overall survival in patients with brain metastases. CONCLUSION: Our results suggest that PNI could be associated with survival in patients with brain metastases. Patients with a lower PNI had a significantly worse outcome in comparison with those with a higher PNI. Evaluation of PNI may provide additional prognostic information in patients with brain metastases.

Analysis of factors associated with the failure of treatment in thoracolumbar burst fractures treated with short-segment posterior spinal fixation.

BACKGROUND: The treatment of thoracolumbar burst fractures continues to pose challenges. Although short-segment posterior spinal fixation (SSPSF) has shown satisfactory clinical outcomes, it is accompanied by a relatively high rate of treatment failure. This study aimed to assess factors associated with treatment failure in thoracolumbar burst fractures treated with SSPSF. METHODS: The clinical data of 241 consecutive patients with a traumatic thoracolumbar burst fracture who underwent SSPSF at our center between Apr 2016 and Apr 2021 were retrospectively reviewed. Patients were divided into two groups (failure of the treatment group and non-failure of the treatment group). We compared potential risk factors for the failure of treatment including age, gender, body mass index, smoking, diabetes, vertebral body compression rate, use of crosslinks, percentage of anterior height compression, presence of index level instrumentation, Cobb angle, interpedicular distance (IPD), canal compromise, Load Sharing Classification (LSC) score, use of posterolateral fusion, and pain intensity between the two groups. RESULTS: A sum of 137 (56.8%) males and 104 (43.2%) females were enrolled where the mean age and follow-up of the participants were 48.34 ± 10.23 years and 18.67 ± 5.23 months, respectively. Treatment failure was observed in 34 cases (14.1%). The results of the binary logistic regression analysis revealed that the lack of index level instrumentation (OR 2.21; 95% CI 1.78-3.04; P = 0.014), LSC score (odds ratio [OR] 2.64; 95% confidence interval [95% CI], 1.34-3.77; P = 0.007), and IPD (OR 1.77; 95% CI 1.51-2.67; P = 0.023) were independently associated with a higher rate of failure of treatment. CONCLUSIONS: The findings of this study revealed that increased rates of treatment failure in thoracolumbar burst fractures treated with SSPSF were associated with factors such as the absence of index level instrumentation, higher LSC scores, and larger IPD. These findings could be helpful in the proper management of patients with unstable thoracolumbar burst fractures.

Evaluation of clinical outcomes, complication rate, feasibility, and applicability of transfacet pedicle-sparing approach in thoracic disc herniation: a systematic review and meta-analysis.

OBJECTIVE: This study aimed to evaluate the clinical outcomes, complication rate, feasibility, and applicability of transfacet pedicle-sparing approach for treating thoracic disc herniation. METHODS: We searched three databases including the Cochrane Library, PubMed, and Embase for eligible studies until Dec 2022. The quality of studies and their risk of bias were assessed using the methodological index for non-randomized studies. We evaluated the heterogeneity between studies using the I2 statistic and the P-value for the heterogeneity. RESULTS: A total of 328 patients described in 11 included articles were published from 2009 to 2022. Pain outcomes using the visual analog scale (VAS score) were reported in four studies. The standardized mean difference was reported as 0.749 (CI 95% 0.555-0.943). The obtained result showed the positive effect of the procedure and the improvement of patients' pain after the surgery. Myelopathy outcomes using the Nurick score were reported in five studies. The standardized mean difference was reported as 0.775 (CI 95% 0.479-1.071). The result showed the positive effect of the procedure. Eight studies assessed postoperative complications and neurological deterioration. The pooled overall complication was 12.4% (32/258) and 3.5% (9/258) neurological worsening. CONCLUSION: The results of this study demonstrated a positive effect of the transfacet pedicle-sparing approach on the clinical outcomes of patients with thoracic disc herniation surgery. The technique has been shown to be safe and effective for the right patient. The technique is associated with lower rates of complications and a shorter hospital stay compared to other surgical approaches. This information can assist clinicians in making informed decisions when selecting the most appropriate surgical technique for their patients with thoracic disc herniation.

The experiences of the caregivers caring for the patients in persistent vegetative state due to traumatic brain injury.

Objective: Persistent vegetative state often occurs as a result of traumatic brain injuries; these patients are usually hospitalized for sustained periods, and the family caregivers are the main care providers in Iranian hospitals, especially for chronic and persistent vegetative state patients. The current study was conducted to investigate the family caregivers' experiences of caring for persistent vegetative state patients following traumatic brain injury. Methods: This descriptive phenomenological study was carried out in 2019. Semi-structured interviews were done with 12 family caregivers caring for the patients in persistent vegetative state, hospitalized in a trauma center, after taking informed written consent and assuring about anonymity and confidentiality of their personal information. The interviews were analyzed using the Colaizzis҆ method. Results: After analysis of 12 interviews, 5 themes, and 10 subthemes were extracted from 428 codes. Five themes include "uncountable struggles/challenges," "looking for peace," "therapeutic concerns," "preserving the connection," and "unheard sounds." Conclusion: In this study, the family caregivers of the persistent vegetative state patients in the hospital experienced some challenges, and looked for peace by doing some work, for example, praying. They had some therapeutic concerns and unheard sounds and tried to fulfill them. We recommend, by using the results of this study and other related research, necessary care and facilities would be provided for the family caregivers of persistent vegetative state patients in hospitals.