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Historically, spine surgeons used expected postoperative survival of 3-months to help select candidates for operative intervention in spinal metastasis. However, this cutoff has been challenged by the development of minimally invasive techniques, novel biologics, and advanced radiotherapy. Recent studies have suggested that a life expectancy of 6 weeks may be enough to achieve significant improvements in postoperative health-related quality of life.The purpose of this study was to develop a model capable of predicting 6-week mortality in patients with spinal metastases treated with radiation or surgery.A retrospective review was conducted at five large tertiary centers in the United States and Taiwan.The development cohort consisted of 3,001 patients undergoing radiotherapy and/or surgery for spinal metastases from one institution. The validation institutional cohort consisted of 1,303 patients from four independent, external institutions.The primary outcome was 6-week mortality.Five models were considered to predict 6-week mortality, and the model with the best performance across discrimination, calibration, decision-curve analysis, and overall performance was integrated into an open access web-based application.The most important variables for prediction of 6-week mortality were albumin, primary tumor histology, absolute lymphocyte, three or more spine metastasis, and ECOG score. The elastic-net penalized logistic model was chosen as the best performing model with AUC 0.84 on evaluation in the independent testing set. On external validation in the 1,303 patients from the four independent institutions, the model retained good discriminative ability with an area under the curve of 0.81. The model is available here: https://sorg-apps.shinyapps.io/spinemetssurvival/.While this study does not advocate for the use of a 6-week life expectancy as criteria for considering operative management, the algorithm developed and externally validated in this study may be helpful for preoperative planning, multidisciplinary management, and shared decision-making in spinal metastasis patients with shorter life expectancy.

Spinal epidural abscess is a rare but severe condition with high rates of postoperative adverse events.The objective of the study was to identify independent prognostic factors for reoperation using two datasets: an institutional and national database.Retrospective Review.Database 1: Review of five medical centers from 1993 to 2016. Database 2: The National Surgical Quality Improvement Program (NSQIP) was queried between 2012 and 2016.Thirty-day and ninety-day reoperation rate.Two independent datasets were reviewed to identify patients with spinal epidural abscesses undergoing spinal surgery. Multivariate analyses were used to determine independent prognostic factors for reoperation while including factors identified in bivariate analyses.Overall, 642 patients underwent surgery for a spinal epidural abscess in the institutional cohort, with a 90-day unplanned reoperation rate of 19.9%. In the NSQIP database, 951 patients were identified with a 30-day unplanned reoperation rate of 12.3%. On multivariate analysis in the NSQIP database, cervical spine abscess was the only factor that reached significance for 30-day reoperation (OR=1.71, 95% CI=1.11-2.63, p=.02, Area under the curve (AUC)=0.61). On multivariate analysis in the institutional cohort, independent prognostic factors for 30-day reoperation were: preoperative urinary incontinence, ventral location of abscess relative to thecal sac, cervical abscess, preoperative wound infection, and leukocytosis (AUC=0.65). Ninety-day reoperation rate also found hypoalbuminemia as a significant predictor (AUC=0.66).Six novel independent prognostic factors were identified for 90-day reoperation after surgery for a spinal epidural abscess. The multivariable analysis fairly predicts reoperation, indicating that there may be additional factors that need to be uncovered in future studies. The risk factors delineated in this study through the use of two large cohorts of spinal epidural abscess patients can be used to improve preoperative risk stratification and patient management.

In the treatment of native shoulder septic arthritis, the optimal irrigation and débridement modality-arthroscopic versus open-is a matter of controversy. We aim to compare revision-free survival (RFS), complications, and resource utilization between these approaches.The National Readmission Database was queried from 2016 to 2019 to identify patients using International Classification of Diseases, 10th revision, diagnostic and procedure codes. Days to revision irrigation and débridement (ID) were calculated for patients during index admission or subsequent readmissions. Multivariate regression was used for healthcare utilization analysis. Survival analysis was done using Kaplan-Meier analysis and Cox proportional hazard regression.A total of 4,113 patients with native shoulder septic arthritis undergoing ID were identified, 2,775 arthroscopic (67.5%) and 1,338 open (32.5%). The median follow-up was 170 days (interquartile range 79 to 265). A total of 341 patients (8.3%) underwent revision ID at a median of 9 days. On multivariate analysis, arthroscopic ID was associated with a reduction in hospital costs of $4,154 ( P0.001) and length of stay of 0.78 days ( P = 0.030). Arthroscopic ID was associated with reduced blood transfusions (odds ratio 0.69, P = 0.001) and wound complications (odds ratio 0.30, P0.001). RFS was 96.4%, 94.9%, 93.3%, and 92.6% for arthroscopic ID and 94.1%, 92.6%, 90.4%, and 89.0% for open ID at 10, 30, 90 and 180 days, respectively ( P = 0.00043). On multivariate Cox modeling, arthroscopic ID was associated with improved survival (hazard ratio 0.67, P = 0.00035). On stratified analysis, arthroscopic ID was associated with improved RFS in patients aged 65 years or older ( P0.001), but RFS was similar in those younger than 65 years ( P = 0.17).Risk of revision ID was markedly lower after arthroscopic ID compared with open, although the protective benefit was limited to patients aged 65 years or older. Arthroscopy was also associated with decreased costs, length of stay, and complications. Although surgeons must consider specific patient factors, our results suggest that arthroscopic ID is superior to open ID.III.

Lumbar fusion (LF) is commonly performed to manage lumbar degenerative disc disease (LDDD) that has failed conservative measures. However, lumbar disc replacement (LDR) procedures are increasingly prevalent and designed to preserve motion in carefully selected patients.A retrospective cohort study was performed using the National Inpatient Sample (NIS), queried from 2010 to 2019 to identify patients undergoing single and double-level LF or LDR with a diagnosis of LDDD using International Classification of Diseases (ICD) 9th (ICD-9) and 10th (ICD-10) revision diagnostic and procedure codes. Propensity score matching (PSM) with a ratio of 2:1 was performed. All cost estimates reflect reported hospital costs adjusted to December 2019 United States Dollars.A total of 1,129,121 LF cases (99.3%) and 8,049 LDR cases (0.7%) were identified, with 364,637 (32.3%) and 712 (8.8%) comprising two-level surgeries, respectively. 1,712 LDRs were performed in 2010 (1.27% of all), decreasing to 565 in 2013 (0.52%), and increased slightly to 870 in 2019 (0.74%). LDR patients were significantly more likely to be younger (mean age 41.2The prevalence of LDR procedures decreased from 2010-2017 but began to increase again in 2018 and 2019. Single-level LDR was associated with reduced costs and length of stay (LOS), and lower rates of blood transfusion compared to LF in patients with LDDD.

In the treatment of native knee bacterial septic arthritis, the optimal irrigation and débridement modality-arthroscopic versus open-is a matter of controversy. We aim to compare revision-free survival, complications, and resource utilization between these approaches.The National Readmission Database was queried from 2016 to 2019 to identify patients using International Classification of Diseases, 10th revision, diagnostic and procedure codes. Days to revision irrigation and débridement (ID), if any, were calculated for patients during index admission or subsequent readmissions. Multivariate regression was used for healthcare utilization analysis. Survival analysis was done using Kaplan-Meier analysis and Cox proportional hazard regression.A total of 14,365 patients with native knee septic arthritis undergoing ID were identified, 8,063 arthroscopic (56.1%) and 6,302 open (43.9%). The mean follow-up was 148 days (interquartile range 53 to 259). A total of 2,156 patients (15.0%) underwent revision ID. On multivariate analysis, arthroscopic ID was associated with a reduction in hospital costs of $5,674 and length of stay of 1.46 days (P0.001 for both). Arthroscopic ID was associated with lower overall complications (odds ratio [OR] 0.63, P0.001), need for blood transfusion (OR 0.58, P0.001), and wound complications (OR 0.32, P0.001). Revision-free survival after index ID was 95.3% at 3 days, 91.0% at 10 days, 88.3% at 30 days, 86.0% at 90 days, and 84.5% at 180 days. No statistically significant difference was observed between surgical approaches on Cox modeling.Risk of revision ID did not differ between arthroscopic and open ID; however, arthroscopy was associated with decreased costs, length of stay, and complications. Additional study is necessary to confirm these findings and characterize which patients require an open ID.III.

Adequate coverage of the soft tissue defects from wide resection of sacropelvic malignancies remains challenging. The vastus lateralis flap has been described for coverage in the setting of trauma and infection. This flap has not been described for coverage of sacropelvic tumor defects.This is a retrospective cohort study of adult patients who underwent wide resection of a primary sacropelvic malignancy with reconstruction employing a pedicled vastus lateralis flap at two tertiary care centers. Patient demographics, tumor staging, and rate of complications were assessed.Twenty-eight patients were included, with a median age of 51 years. The most common primary tumor was chondrosarcoma followed by chondroblastic osteosarcoma. The median follow-up was 1.1 years. There were 10 cases of wound infection requiring re-operation and three cases of flap failure.We describe a pedicled vastus lateralis flap for coverage of defects after wide resection of sacropelvic malignancies. A large proportion of our cohort had independent risk factors for wound complications. Even with a cohort with high baseline risk for wound complications, we show that the use of a pedicled vastus lateralis flap is a safe reconstructive option with a wound complication rate in line with the literature.

In 2-party Circuit-based Private Set Intersection (Circuit-PSI), P 0 and P 1 hold sets S0 and S1 respectively and wish to securely compute a function f over the set S0 ∩ S1 (e.g., cardinality, sum over associated attributes, or threshold intersection). Following a long line of work, Pinkas et al. (PSTY, Eurocrypt 2019) showed how to construct a concretely efficient Circuit-PSI protocol with linear communication complexity. However, their protocol requires super-linear computation. In this work, we construct concretely efficient Circuit-PSI protocols with linear computational and communication cost. Further, our protocols are more performant than the state-of-the-art, PSTY – we are ≈ 2.3× more communication efficient and are up to 2.8× faster. We obtain our improvements through a new primitive called Relaxed Batch Oblivious Programmable Pseudorandom Functions (RB-OPPRF) that can be seen as a strict generalization of Batch OPPRFs that were used in PSTY. This primitive could be of independent interest.

BACKGROUND CONTEXT Surgical decompression and stabilization in the setting of spinal metastasis is performed to relieve pain and preserve functional status. These potential benefits must be weighed against the risks of perioperative morbidity and mortality. Accurate prediction of a patient's postoperative survival is a crucial component of patient counseling. PURPOSE To externally validate the SORG machine learning algorithms for prediction of 90-day and 1-year mortality after surgery for spinal metastasis. STUDY DESIGN/SETTING Retrospective, cohort study PATIENT SAMPLE Patients 18 years or older at a tertiary care medical center treated surgically for spinal metastasis OUTCOME MEASURES Mortality within 90 days of surgery, mortality within 1 year of surgery METHODS This is a retrospective cohort study of 298 adult patients at a tertiary care medical center treated surgically for spinal metastasis between 2004 and 2020. Baseline characteristics of the validation cohort were compared to the derivation cohort for the SORG algorithms. The following metrics were used to assess the performance of the algorithms: discrimination, calibration, overall model performance, and decision curve analysis. RESULTS Sixty-one patients died within 90 days of surgery and 133 died within 1 year of surgery. The validation cohort differed significantly from the derivation cohort. The SORG algorithms for 90-day mortality and 1-year mortality performed excellently with respect to discrimination; the algorithm for 1-year mortality was well-calibrated. At both postoperative time points, the SORG algorithms showed greater net benefit than the default strategies of changing management for no patients or for all patients. CONCLUSIONS With an independent, contemporary, and geographically distinct population, we report successful external validation of SORG algorithms for preoperative risk prediction of 90-day and 1-year mortality after surgery for spinal metastasis. By providing accurate prediction of intermediate and long-term mortality risk, these externally validated algorithms may inform shared decision-making with patients in determining management of spinal metastatic disease.

The purpose of the present pilot study was to estimate T1 and T2 metric values derived simultaneously from a new, rapid Magnetic Resonance Fingerprinting (MRF) technique, as well as to assess their ability to characterize—brain metastases (BM) and normal-appearing brain tissues. Fourteen patients with BM underwent MRI, including prototype MRF, on a 3T scanner. In total, 108 measurements were analyzed: 42 from solid parts of BM’s (21 each on T1 and T2 maps) and 66 from normal-appearing brain tissue (11 ROIs each on T1 and T2 maps for gray matter [GM], white matter [WM], and cerebrospinal fluid [CSF]). The BM’s mean T1 and T2 values differed significantly from normal-appearing WM (p < 0.05). The mean T1 values from normal-appearing GM, WM, and CSF regions were 1205 ms, 840 ms, and 4233 ms, respectively. The mean T2 values were 108 ms, 78 ms, and 442 ms, respectively. The mean T1 and T2 values for untreated BM (n = 4) were 2035 ms and 168 ms, respectively. For treated BM (n = 17) the T1 and T2 values were 2163 ms and 141 ms, respectively. MRF technique appears to be a promising and rapid quantitative method for the characterization of free water content and tumor morphology in BMs.

Posterior cervical fusion is associated with increased rates of complications and readmission when compared to anterior fusion. Machine learning (ML) models for risk stratification of patients undergoing posterior cervical fusion remain limited. We aim to develop a novel ensemble ML algorithm for prediction of major perioperative complications and readmission after posterior cervical fusion and identify factors important to model performance. This is a retrospective cohort study of adults who underwent posterior cervical fusion at non-federal California hospitals between 2015 and 2017. The primary outcome was readmission or major complication. We developed an ensemble model predicting complication risk using an automated ML framework. We compared performance with standard ML models and logistic regression (LR), ranking contribution of included variables to model performance. Of the included 6822 patients, 18.8% suffered a major complication or readmission. The ensemble model demonstrated slightly superior predictive performance compared to LR and standard ML models. The most important features to performance include sex, malignancy, pneumonia, stroke, and teaching hospital status. Seven of the ten most important features for the ensemble model were markedly less important for LR. We report an ensemble ML model for prediction of major complications and readmission after posterior cervical fusion with a modest risk prediction advantage compared to LR and benchmark ML models. Notably, the features most important to the ensemble are markedly different from those for LR, suggesting that advanced ML methods may identify novel prognostic factors for adverse outcomes after posterior cervical fusion.

Background Given the significant cost and morbidity of patients undergoing lumbar fusion, accurate preoperative risk-stratification would be of great utility. We aim to develop a machine learning model for prediction of major complications and readmission after lumbar fusion. We also aim to identify the factors most important to performance of each tested model. Methods We identified 38,788 adult patients who underwent lumbar fusion at any California hospital between 2015 and 2017. The primary outcome was major perioperative complication or readmission within 30 days. We build logistic regression and advanced machine learning models: XGBoost, AdaBoost, Gradient Boosting, and Random Forest. Discrimination and calibration were assessed using area under the receiver operating characteristic curve and Brier score, respectively. Results There were 4470 major complications (11.5%). The XGBoost algorithm demonstrates the highest discrimination of the machine learning models, outperforming regression. The variables most important to XGBoost performance include angina pectoris, metastatic cancer, teaching hospital status, history of concussion, comorbidity burden, and workers’ compensation insurance. Teaching hospital status and concussion history were not found to be important for regression. Conclusions We report a machine learning algorithm for prediction of major complications and readmission after lumbar fusion that outperforms logistic regression. Notably, the predictors most important for XGBoost differed from those for regression. The superior performance of XGBoost may be due to the ability of advanced machine learning methods to capture relationships between variables that regression is unable to detect. This tool may identify and address potentially modifiable risk factors, helping risk-stratify patients and decrease complication rates.

Background There remains a lack of accurate and validated outcome-prediction models in total knee arthroplasty (TKA). While machine learning (ML) is a powerful predictive tool, determining the proper algorithm to apply across diverse data sets is challenging. AutoPrognosis (AP) is a novel method that uses automated ML framework to incorporate the best performing stages of prognostic modeling into a single well-calibrated algorithm. We aimed to compare various ML methods to AP in predictive performance of complications after TKA. Methods Thirty-eight preoperative patient demographics and clinical features from all primary TKAs performed at California-licensed hospitals between 2015 and 2017 were evaluated as predictors of major complications after TKA. Traditional logistic regression (LR), various other ML methods (XGBoost, Gradient Boosting, AdaBoost, and Random Forest), and AP were used for model building to determine discriminative power (area under receiver operating curve), calibration (Brier score), and feature importance. Results Between 2015 and 2017, there were a total of 156,750 TKAs with 1109 (0.7%) total major complications. AP had the highest discriminative performance with area under receiver operating curve 0.679 compared with LR, XGBoost, Gradient Boosting, AdaBoost, and Random Forest (0.617, 0.601, 0.662, 0.657, and 0.545, respectively). AP (Brier score 0.007) had similar calibration as the other ML methods (0.006, 0.006, 0.022, 0.007, and 0.008, respectively). The variables that are most important for AP differ from those that are most important for LR. Conclusion Compared to conventional ML algorithms, AP has superior discriminative ability with similar calibration and suggests nonlinear relationships between variables in outcomes of TKA.

The Biomass is the electricity generation unit and is developed from organic material and renewable and sustainable source of energy. It is used to create the electricity or the other form of power. It is carbon neutral electricity and generated from the renewable organic waste that would otherwise be dumped in the landfills, openly burned and left as fodder for the forest fire. In biomass power station, the wood waste and other waste is burned to produce steam that run a turbine to generated the electricity and provides heat to homes and industries. The main aim of hazard analysis and risk assessment in Biomass power station is to analysis and identified the physical, chemical and biological and environmental hazard and calculate the consequence and the frequency of the hazardous event. The risk level is also help and analysing the hazard and the required safety control to minimize the risk and eliminate the Hazards.

C5 palsy is a common postoperative complication after cervical fusion and is associated with increased health care costs and diminished quality of life. Accurate prediction of C5 palsy may allow for appropriate preoperative counseling and risk stratification. We primarily aim to develop an algorithm for the prediction of C5 palsy after instrumented cervical fusion and identify novel features for risk prediction. Additionally, we aim to build a risk calculator to provide the risk of C5 palsy.We identified adult patients who underwent instrumented cervical fusion at a tertiary care medical center between 2013 and 2020. The primary outcome was postoperative C5 palsy. We developed ensemble machine learning, standard machine learning, and logistic regression models predicting the risk of C5 palsy-assessing discrimination and calibration. Additionally, a web-based risk calculator was built with the best-performing model.A total of 1024 patients were included, with 52 cases of C5 palsy. The ensemble model was well-calibrated and demonstrated excellent discrimination with an area under the receiver-operating characteristic curve of 0.773. The following features were the most important for ensemble model performance: diabetes mellitus, bipolar disorder, C5 or C4 level, surgical approach, preoperative non-motor neurologic symptoms, degenerative disease, number of fused levels, and age.We report a risk calculator that generates patient-specific C5 palsy risk after instrumented cervical fusion. Individualized risk prediction for patients may facilitate improved preoperative patient counseling and risk stratification as well as potential intraoperative mitigating measures. This tool may also aid in addressing potentially modifiable risk factors such as diabetes and obesity.

Background The demand and incidence of anatomic total shoulder arthroplasty (aTSA) procedures is projected to increase substantially over the next decade. There is a paucity of accurate risk prediction models which would be of great utility in minimizing morbidity and costs associated with major post-operative complications. Machine learning is a powerful predictive modeling tool and has become increasingly popular, especially in orthopedics. We aimed to build a ML model for prediction of major complications and readmission following primary aTSA. Methods A large California administrative database was retrospectively reviewed for all adults undergoing primary aTSA between 2015 to 2017. The primary outcome was any major complication or readmission following aTSA. A wide scope of standard ML benchmarks, including Logistic regression (LR), XGBoost, Gradient boosting, AdaBoost and Random Forest were employed to determine their power to predict outcomes. Additionally, important patient features to the prediction models were indentified. Results There were a total of 10,302 aTSAs with 598 (5.8%) having at least one major post-operative complication or readmission. XGBoost had the highest discriminative power (area under receiver operating curve AUROC of 0.689) of the 5 ML benchmarks with an area under precision recall curve AURPC of 0.207. History of implant complication, severe chronic kidney disease, teaching hospital status, coronary artery disease and male sex were the most important features for the performance of XGBoost. In addition, XGBoost identified teaching hospital status and male sex as markedly more important predictors of outcomes compared to LR models. Conclusion We report a well calibrated XGBoost ML algorithm for predicting major complications and 30-day readmission following aTSA. History of prior implant complication was the most important patient feature for XGBoost performance, a novel patient feature that surgeons should consider when counseling patients.

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.

Patients with cancer have been identified in several studies to be at high risk of developing severe COVID-19; however, rates of SARS-CoV-2 IgG seroconversion and its association with cancer types and anti-cancer therapy remain obscure. We conducted a retrospective cohort study in patients with cancer that underwent SARS-CoV-2 IgG testing. Two hundred and sixty-one cancer patients underwent SARS-CoV-2 IgG testing and demonstrated a high rate of seroconversion (92%). However, significantly lower seroconversion was observed in patients with hematologic malignancies (82%), patients that received anti-CD-20 antibody therapy (59%), CAR-T/cellular therapy (33%) and stem cell transplant (60%). Interestingly, all 17 patients that received immunotherapy, including 16 that received anti-PD-1/PD-L1 monoclonal antibodies, developed SARS-Cov-2 IgG antibodies (100% seroconversion). These data show differential rates of seroconversion in specific patient groups and bear importance for clinical monitoring and vaccination strategies that are being developed to mitigate the COVID-19 pandemic

There is currently a paucity of data regarding the potential relationships between preexisting spinal deformity and clinical outcomes following total knee arthroplasty (TKA). We sought to expand upon this deficit. We hypothesize that lumbar sagittal mismatch deformity (MD) will correlate with a decrease in functional outcomes after TKA. This retrospective cohort comparison of 933 TKAs was performed between January 2017 and 2020. TKAs were excluded if they were not performed for primary osteoarthritis (OA) or if preoperative lumbar radiographs were unavailable/inadequate to measure sagittal parameters of interest: pelvic incidence, sacral slope, pelvic tilt, lumbar lordosis, and deformity mismatch. Ninety-four TKAs were subsequently available for inclusion and divided into two groups: those with MD as defined by |PI-LL| > 10 degrees and those without MD. The following clinical outcomes were compared between the groups: total postoperative arc of motion (AOM), incidence of flexion contracture, and need for manipulation under anesthesia (MUA). In total, 53 TKAs met the MD criteria, while 41 did not have MD. There were no significant differences in demographics, body mass index, preoperative knee range of motion (ROM), preoperative AOM, or opiate use between the groups. TKAs with MD were more likely to have MUA (p = 0.026), ROM

BACKGROUND AND PURPOSE: To determine the ability of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict long-term response of brain metastases prior to and within 72 hours of stereotactic radiosurgery (SRS). METHODS: In this prospective pilot study, multiple b-value DWI and T1-weighted DCE-MRI were performed in patients with brain metastases before and within 72 hours following SRS. Diffusion-weighted (DW) images were analyzed using the monoexponential and intravoxel incoherent motion (IVIM) models. DCE-MRI data were analyzed using the extended Tofts pharmacokinetic model. The parameters obtained with these methods were correlated with brain metastasis outcomes according to modified Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. RESULTS: We included 25 lesions from 16 patients; 16 patients underwent pre-SRS MRI and 12/16 patients underwent both pre- and early (within 72 hours) post-SRS MRI. The perfusion fraction (f) derived from IVIM early post-SRS was higher in lesions demonstrating progressive disease than in lesions demonstrating stable disease, partial response, or complete response (q = 0.041). Pre-SRS extracellular extravascular volume fraction, v(e), and volume transfer coefficient, k(trans), derived from DCE-MRI were higher in non-responders versus responders (q = 0.041). CONCLUSIONS: Quantitative DWI and DCE-MRI are feasible imaging methods in the pre- and early (within 72 hours) post-SRS evaluation of brain metastases. DWI- and DCE-MRI-derived parameters demonstrated physiologic changes (tumor cellularity and vascularity) and offer potentially useful biomarkers that can predict treatment response. This allows for initiation of alternate therapies within an effective time window that may help prevent disease progression.

The present preliminary study aims to characterize brain metastases (BM) using T1 and T2 maps generated from newer, rapid, synthetic MRI (MAGnetic resonance image Compilation; MAGiC) in a clinical setting. We acquired synthetic MRI data from 11 BM patients on a 3T scanner. A multiple-dynamic multiple-echo (MDME) sequence was used for data acquisition and synthetic image reconstruction, including post-processing. MDME is a multi-contrast sequence that enables absolute quantification of physical tissue properties, including T1 and T2, independent of the scanner settings. In total, 82 regions of interest (ROIs) were analyzed, which were obtained from both normal-appearing brain tissue and BM lesions. The mean values obtained from the 48 normal-appearing brain tissue regions and 34 ROIs of BM lesions (T1 and T2) were analyzed using standard statistical methods. The mean T1 and T2 values were 1143 ms and 78 ms, respectively, for normal-appearing gray matter, 701 ms and 64 ms for white matter, and 4206 ms and 390 ms for cerebrospinal fluid. For untreated BMs, the mean T1 and T2 values were 1868 ms and 100 ms, respectively, and 2211 ms and 114 ms for the treated group. The quantitative T1 and T2 values generated from synthetic MRI can characterize BM and normal-appearing brain tissues.

BACKGROUND: Sinonasal mucosal melanoma (SNMM) is an aggressive cancer with high mortality. Identifying patients at risk of distant metastasis assists with management and prognostication. We aimed to define the relationship between volume, survival, and risk of distant metastases. METHODS: A retrospective review of all patients with SNMM treated at a single institution over a 21-year period was conducted. Tumor volume was calculated using cross-sectional imaging and survival analysis was performed. RESULTS: Sixty-one patients were included. Tumor volume was predictive of local progression free survival (LPFS) (p=0.032), distant metastases free survival (DMFS) (p=0.002), and overall survival (OS) (p=0.022). It was a better predictor than AJCC stage and T-classification. Tumor volume equal to or greater than 5cm(3) was associated with a significantly worse DMFS and OS (p=0.022 and 0.009, respectively). CONCLUSION: Calculation of tumor volume assists in quantifying the risk of distant metastases and death in SNMM.

Background Context In-hospital and short-term mortality in patients with spinal epidural abscess (SEA) remains unacceptably high despite diagnostic and therapeutic advancements. Forecasting this potentially avoidable consequence at the time of admission could improve patient management and counseling. Few studies exist to meet this need, and none have explored methodologies such as machine learning. Purpose The purpose of this study was to develop machine learning algorithms for prediction of in-hospital and 90-day postdischarge mortality in SEA. Study Design/Setting Retrospective, case-control study at two academic medical centers and three community hospitals from 1993 to 2016. Patients Sample Adult patients with an inpatient admission for radiologically confirmed diagnosis of SEA. Outcome Measures In-hospital and 90-day postdischarge mortality. Methods Five machine learning algorithms (elastic-net penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed and assessed by discrimination, calibration, overall performance, and decision curve analysis. Results Overall, 1,053 SEA patients were identified in the study, with 134 (12.7%) experiencing in-hospital or 90-day postdischarge mortality. The stochastic gradient boosting model achieved the best performance across discrimination, c-statistic=0.89, calibration, and decision curve analysis. The variables used for prediction of 90-day mortality, ranked by importance, were age, albumin, platelet count, neutrophil to lymphocyte ratio, hemodialysis, active malignancy, and diabetes. The final algorithm was incorporated into a web application available here: https://sorg-apps.shinyapps.io/seamortality/ . Conclusions Machine learning algorithms show promise on internal validation for prediction of 90-day mortality in SEA. Future studies are needed to externally validate these algorithms in independent populations.

BACKGROUND CONTEXT Data regarding risk of failure of nonoperative management in spinal epidural abscess (SEA) are limited. Given the potential for deterioration with treatment failure, a tool that predicts the probability of failure would be of great clinical utility. PURPOSE We primarily aim to build a machine learning model using independent predictors of nonoperative management failure. Secondarily, we aim to develop an open-access web-based application that provides a patient-specific probability of treatment failure. STUDY DESIGN/SETTING Retrospective, case-control study. PATIENT SAMPLE Patients 18 years or older diagnosed with SEA at 2 academic medical centers and 3 community hospitals. OUTCOME MEASURES Failure of nonoperative management. METHODS This is a retrospective cohort study of 367 patients with SEA initially managed nonoperatively between 1993 and 2016. The primary outcome was failure of nonoperative management defined as neurologic deterioration, worsened back and/or radicular pain, or persistent symptoms despite initiation of antibiotic therapy. Five machine learning algorithms were developed and assessed by discrimination, calibration, and overall performance. RESULTS Ninety-nine (27%) patients failed nonoperative management. Factors determined for prediction of nonoperative management were: motor deficit, diabetes, ventral component of abscess relative to thecal sac, history of compression or pathologic vertebral fracture, sensory deficit, active malignancy, and involvement of 3 or more vertebral levels. The elastic-net penalized logistic regression model was chosen as the final model given its superior discrimination, calibration, and overall model performance. This model was incorporated into an open access web application. CONCLUSION By building a discriminative and well-calibrated model in a user-friendly and open-access digital interface, we hope to provide a prognostic tool that can be used to inform clinical decision-making in real-time.

Anti-COVID-19 immunity dynamics were assessed in patients with cancer in a prospective clinical trial. Waning of immunity was detected 4-6 months post-vaccination with significant increases in anti-spike IgG titers after booster dosing, and 56% of seronegative patients seroconverted post-booster vaccination. Prior anti-CD20/BTK inhibitor therapy was associated with reduced vaccine efficacy.

Secure inference allows a server holding a machine learning (ML) inference algorithm with private weights, and a client with a private input, to obtain the output of the inference algorithm, without revealing their respective private inputs to one another. While this problem has received plenty of attention, existing systems are not applicable to a large class of ML algorithms (such as in the domain of Natural Language Processing) that perform featurization as their first step. In this work, we address this gap and make the following contributions: We initiate the formal study of secure featurization and its use in conjunction with secure inference protocols. We build secure featurization protocols in the one/two/three-server settings that provide a tradeoff between security and efficiency. Finally, we apply our algorithms in the context of secure phishing detection and evaluate our end-to-end protocol on models that are commonly used for phishing detection.

Multiparty Private Set Intersection (mPSI), enables n parties, each holding private sets (each of size m) to securely compute the intersection of these private sets. While several protocols are known for this task, the only concretely efficient protocol is due to the work of Kolesnikov et al. (KMPRT, CCS 2017), who gave a semi-honest secure protocol with communication complexity O(nmtƛ), where t –First, for the natural adversarial setting of semi-honest honest majority (i.e. t –Second, concretely, our protocol has 6(t+2)/5 times lesser communication than KMPRT and is up to 5× and 6.2× faster than KMPRT in the LAN and WAN setting even for 15 parties. –Finally, we introduce and consider two important variants of mPSI - circuit PSI (that allows the parties to compute a function over the intersection set without revealing the intersection itself) and quorum PSI (that allows P1 to learn all the elements in his/her set that are present in at least k other sets) and provide concretely efficient protocols for these variants.

Imaging plays a vital role in diagnosing, planning, and monitoring treatment, and determining prognosis for head and neck and thyroid cancers. Ultrasound, CT, and MRI are first-line modalities for evaluating these tumors, and are excellent at characterizing tumor anatomy and relationship to surrounding structures. With the rise of advanced MRI in recent years, quantitative MRI has demonstrated promise in providing biomarkers for tumor histology, tumor aggressiveness, and prediction of clinical outcomes.

Ogilvie syndrome (OS) is a rare but serious condition seen in the postoperative period. This was an epidemiologic study using data from the National Inpatient Sample from 2005 to 2014 to look at incidence, risk factors, and outcomes associated with OS after primary spine fusion.International Classification of Diseases, Ninth Revision codes were used to identify patients who underwent spine fusion surgery. Patients were separated into 2 cohorts based on the diagnosis of OS. Outcome measures and risk factors for cohorts were analyzed using multivariate logistic regression and compared.Over the 10-year study period, 3,884,395 patients underwent primary spine fusion surgery. Among these, 0.04% developed OS during the index hospitalization. The greatest incidence seen in primary fusion involved the thoracic spine (0.15%). OS was more common after spine fusion for spine deformity (P0.001). Patients with OS were more likely to be men (P0.001), older (P0.0001), and have more comorbidities (P0.0001). Patients with OS were more likely to require postoperative blood transfusions (odds ratio [OR], 3.39; 95% confidence interval [CI], 2.51-4.59; P0.001) and sustain any complication (OR, 4.20; 95% CI, 3.17-5.57; P0.001). Patients with OS had a longer length of stay (15.7 vs. 3.9 days; P0.001) and increased average hospitalization cost ($63,037.03 vs. $26,792.19; P0.001). The development of OS was associated with fluid electrolyte disorder (OR, 4.06; 95% CI, 2.99-5.51; P0.001).OS is a rare but serious complication of primary spine fusion surgery. Identifying the specific risk factors, symptoms, and potential complications related to OS is critical to aid in decreasing the significant morbidity associated with its development.

The direct anterior approach (DAA) to the hip was initially described in the nineteenth century and has been used sporadically for total hip arthroplasty (THA). However, recent increased interest in tissue-sparing and small incision arthroplasty has given rise to a sharp increase in the utilization of the DAA. Although some previous studies claimed that this approach results in less muscle damage and pain as well as rapid recovery, a paucity in the literature exists to conclusively support these claims. While the DAA may be comparable to other THA approaches, no evidence to date shows improved long-term outcomes for patients compared to other surgical approaches for THA. However, the advent of new surgical instruments and tables designed specifically for use with the DAA has made the approach more feasible for surgeons. In addition, the capacity to utilize fluoroscopy intraoperatively for component positioning is a valuable asset to the approach and can be of particular benefit for surgeons during their learning curve. An understanding of its limitations and challenges is vital for the safe employment of this technique. This review summarizes the pearls and pitfalls of the DAA for THA in order to improve the understanding of this surgical technique for hip replacement surgeons.

Neutrophil to lymphocyte ratio and platelet to lymphocyte ratio have been previously identified as markers for overall survival in oncology but remain heretofore unexplored in spinal epidural abscess (SEA).The purpose of this study was to determine the impact of these routinely collected assessments on 90-day mortality in SEA.Retrospective, case-control study.Patients 18 years or older diagnosed with SEA at 2 academic medical centers and 3 community hospitals.Ninety-day postdischarge and in-hospital mortality.Complete blood count with differential obtained on the day immediately preceding or on the day of admission was used to calculate platelet to lymphocyte and neutrophil to lymphocyte ratios. Multivariate analyses were used to determine if these ratios were independent risk factors for 90-day mortality.For 1,053 SEA patients included in the study, the rate of 90-day mortality was 134 (12.7%). The rate of 90-day mortality with neutrophil to lymphocyte ratio (≥8) was (20.5%) compared to (8.1%) with neutrophil to lymphocyte ratio8. Neutrophil to lymphocyte ratio was positively associated with bacteremia, elevated erythrocyte sedimentation rate, and concurrent systemic infections (endocarditis, meningitis) and negatively associated with duration of symptoms prior to presentation. On multivariate analysis, elevated neutrophil to lymphocyte remained an independent risk factor for 90-day mortality (odds ratio=2.62, 95% confidence interval=1.66-4.17, p.001). Platelet to lymphocyte ratio was not associated with 90-day mortality.Absolute neutrophil to lymphocyte ratio is a routinely collected but overlooked biomarker in patients with spinal epidural abscess that is a novel independent risk factor for 90-day mortality.

None of the existing prognostic scoring systems for spinal epidural abscess (SEA) include albumin despite albumin's established role in inflammation, nutrition, lipid peroxidation, and regulation of apoptosis. The purpose of the present study was to determine the prognostic value of albumin in SEA.We performed a retrospective, case-control study of 2 independent data sets: patients with SEA in an institutional population and patients in the National Surgical Quality Improvement Program (NSQIP). Bivariate analyses and multivariate analyses were used to determine whether albumin is an independent prognostic factor for survival in both data sets.For the 1053 patients with SEA in the institutional cohort, the 90-day postdischarge mortality was 134 (12.7%). Overall, 633 (60.1%) underwent surgery in the initial admission, with a 30-day postoperative mortality rate of 5.5% (n = 35). For the 1154 patients with SEA in the NSQIP database, the 30-day postoperative mortality rate was 3.6% (n = 42). The rate of 90-day postdischarge mortality in the institutional cohort for patients with albumin2.3 g/dL was 25.1%. In contrast, the rate for patients with albumin3.3 g/dL was 4.5%. On multivariate analysis of the NSQIP database, hypoalbuminemia was an independent prognostic factor for 30-day postoperative mortality. On multivariate analysis of the institutional cohort, hypoalbuminemia remained a prognostic factor for 90-day postdischarge mortality.Albumin was validated as an independent prognostic factor in patients with SEA. The lack of this marker in existing scoring systems underscores the need for updated models to optimize risk stratification and shared decision-making before surgery.

BACKGROUND CONTEXT Fungal spinal epidural abscess (FSEA) is a rare entity with high morbidity and mortality. Reports describing the clinical features, diagnosis, treatment, and outcomes of FSEA are scarce in the literature. PURPOSE This study aimed to describe the clinical features, diagnosis, treatment, and outcomes of FSEA. STUDY DESIGN This study is designed as a retrospective clinical case series. PATIENT SAMPLE A continuous series of patients with the diagnosis of FSEA who presented at our institution from 1993 to 2016. METHODS We reviewed the electronic medical records of patients with SEA who were treated within our hospital system from 1993 to 2016. We only included SEA cases that were due to fungi. We also reviewed FSEA cases in the English language literature from 1952 to 2017 to analyze the features of FSEA. RESULTS From a database of 1,053 SEA patients, we identified 9 patients with FSEA. Aspergillus fumigatus was isolated from 2 (22%) patients, and Candida species were isolated from 7 (78%). Focal spine pain, neurologic deficit, and fever were demonstrated in 89%, 50%, and 44% of FSEA cases, respectively. Five of nine cases involved the thoracic spine, and eight were located anterior to the thecal sac. Three cases had fungemia, six had long symptom duration (>2 weeks) prior to presentation, seven had concurrent immunosuppression, and eight had vertebral osteomyelitis. Additionally, one case had residual motor deficit at last follow-up, one had S1 sensory radicular symptoms, two suffered recurrent FSEA, two died within hospitalization, and two died within 90 days after discharge. CONCLUSIONS In summary, the classic diagnostic triad (focal spine pain, neurologic deficit, and fever) is not of great clinical utility for FSEA. Biopsy, intraoperative tissue culture, and blood culture can be used to diagnose FSEA. The most common pathogens of FSEA are Aspergillus and Candida species. Therefore, empiric treatment for FSEA should cover these species while definitive identification is pending. FSEA is found in patients with poor baseline health status, which is the essential reason for its high mortality.

Background Preoperative prognostication of short-term postoperative mortality in patients with spinal metastatic disease can improve shared decision making around end-of-life care. Objective To (1) develop machine learning algorithms for prediction of short-term mortality and (2) deploy these models in an open access web application. Methods The American College of Surgeons, National Surgical Quality Improvement Program was used to identify patients that underwent operative intervention for metastatic disease. Four machine learning algorithms were developed, and the algorithm with the best performance across discrimination, calibration, and overall performance was integrated into an open access web application. Results The 30-d mortality for the 1790 patients undergoing surgery for spinal metastatic disease was 8.49%. Preoperative factors used for prognostication were albumin, functional status, white blood cell count, hematocrit, alkaline phosphatase, spinal location (cervical, thoracic, lumbosacral), and severity of comorbid systemic disease (American Society of Anesthesiologist Class). In this population, machine learning algorithms developed to predict 30-d mortality performed well on discrimination (c-statistic), calibration (assessed by calibration slope and intercept), Brier score, and decision analysis. An open access web application was developed for the best performing model and this web application can be found here: https://sorg-apps.shinyapps.io/spinemets/. Conclusion Machine learning algorithms are promising for prediction of postoperative outcomes in spinal oncology and these algorithms can be integrated into clinically useful decision tools. As the volume of data in oncology continues to grow, creation of learning systems and deployment of these systems as accessible tools may significantly enhance prognostication and management.