A Nationwide Analysis of Aneurysmal Subarachnoid Hemorrhage Mortality, Complications, and Health Economics in the USA.

Aneurysmal subarachnoid hemorrhage (aSAH) is a devastating neurological condition. Endovascular coiling or surgical clipping have equivocal success rates, but relatively little is known regarding the health economics and complications of these procedures at the population level. We aimed to analyze the complication profiles and healthcare resource utilization (HRCU) associated with the treatment of aSAH in the USA. We performed a retrospective analysis utilizing the IBM MarketScan database between 2008 and 2015. Primary outcomes included economic analysis stratified by post-operative complication; determination of the effect of several factors on total cost by multivariable regression; and analysis of the incidence, timing, and associated HCRU of aSAH-related post-operative complications. Of the 2374 patients meeting inclusion criteria for economic analysis, 1783 (75.1%) patients had at least one of the ten complications. The most common complications included hydrocephalus (43.8%), transient cerebral ischemia (including vasospasm) (30.6%), ischemic stroke (29.1%), syndrome of inappropriate antidiuretic hormone (SIADH)/hyposmolarity/hyponatremia (22.1%), and seizures (14.9%). Patients who experienced complications had higher median 90-day total costs [$161,127 (Q1 to Q3, $101,411 to $257,662)] than those who did not [$97,376 (Q1 to Q3, $55,692 to $147,447)]. Length of stay was longest for those with pulmonary embolism and pneumonia (27 days) and shortest for those with SIADH/hyposmolarity/hyponatremia (16 days). Brain compression/herniation had the highest mortality rate (19.5%). In total, 14.6% of all patients experienced a readmission within 30 days. In conclusion, patients with aSAH have high post-operative complication rates and costs. Development of novel interventions to reduce complications and improve outcomes is crucial.

Treatment Patterns and Health Care Resource Utilization of Iatrogenic Spinal Cerebrospinal Fluid Leaks in the United States.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: We aimed to characterize the treatment patterns and the associated costs in patients with cerebrospinal fluid (CSF) leak after spine procedures in the United States. BACKGROUND: CSF leak is a common complication after spinal procedures. However, there is a little data regarding the national patterns of treatment choice and the associated health care resource utilization. METHODS: We utilized the IBM MarketScan Research databases to retrospectively analyze adult US patients diagnosed with CSF leak within 30 days of spine procedures between 2001 and 2018. Treatment prevalence, treatment failure, and health care resource utilization data within 30 days of the CSF leak were collected. A subanalysis was performed on patients who received epidural blood patches (EBP) to better understand health care utilization attributable to this treatment modality. RESULTS: Twenty one thousand four hundred fourteen patients were identified. The most common causes of CSF leak were diagnostic spinal tap (59.2%) and laminectomy/discectomy (18.7%). With regard to treatment prevalence, 40.4% of the patients (n=8651) had conservative medical management, 46.6% (n=9987) received epidural blood patch repair, 9.6% required surgical repair (n=2066), and 3.3% (n=710) had lumbar drain/puncture. Nine hundred sixty-seven (9.7%), 150 (21.1%), and 280 (13.5%) patients failed initial EBP, lumbar drain, and surgery, respectively, and the overall failure rate was 10.9% (n=1397). The median 30-day total cost across all groups was $5,101. Patients who received lumbar drain ($22,341) and surgical repair ($30,199) had higher 30-day median total costs than EBP ($8,140) or conservative management ($17,012). The median 30-day total cost for patients whose EBP failed ($8,179) was substantially greater than those with a successful EBP repair ($3,439). CONCLUSIONS: National treatment patterns and costs for CSF leaks were described. When used in the correct patient cohort, EBP has the lower failure rates and costs than comparable alternatives. EBP may be considered more often in situations where conservative management or lumbar drains are currently being used.

Machine Learning to Predict Successful Opioid Dose Reduction or Stabilization After Spinal Cord Stimulation.

BACKGROUND: Spinal cord stimulation (SCS) effectively reduces opioid usage in some patients, but preoperatively, there is no objective measure to predict who will most benefit. OBJECTIVE: To predict successful reduction or stabilization of opioid usage after SCS using machine learning models we developed and to assess if deep learning provides a significant benefit over logistic regression (LR). METHODS: We used the IBM MarketScan national databases to identify patients undergoing SCS from 2010 to 2015. Our models predict surgical success as defined by opioid dose stability or reduction 1 year after SCS. We incorporated 30 predictors, primarily regarding medication patterns and comorbidities. Two machine learning algorithms were applied: LR with recursive feature elimination and deep neural networks (DNNs). To compare model performances, we used nested 5-fold cross-validation to calculate area under the receiver operating characteristic curve (AUROC). RESULTS: The final cohort included 7022 patients, of whom 66.9% had successful surgery. Our 5-variable LR performed comparably with the full 30-variable version (AUROC difference <0.01). The DNN and 5-variable LR models demonstrated similar AUROCs of 0.740 (95% CI, 0.727-0.753) and 0.737 (95% CI, 0.728-0.746) ( P = .25), respectively. The simplified model can be accessed at SurgicalML.com . CONCLUSION: We present the first machine learning-based models for predicting reduction or stabilization of opioid usage after SCS. The DNN and 5-variable LR models demonstrated comparable performances, with the latter revealing significant associations with patients' pre-SCS pharmacologic patterns. This simplified, interpretable LR model may augment patient and surgeon decision making regarding SCS.

Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting.

OBJECTIVE: Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of enhancing TBI triage in LMICs, we aim to develop the first deep learning model to predict outcomes after TBI and compare its performance with that of less complex algorithms. METHODS: TBI patients' data were prospectively collected in Kampala, Uganda, from 2016 to 2020. To predict good versus poor outcome at hospital discharge, we created deep neural network, shallow neural network, and elastic-net regularized logistic regression models. Predictors included 13 easily acquirable clinical variables. We assessed model performance with 5-fold cross-validation to calculate areas under both the receiver operating characteristic curve and precision-recall curve (AUPRC), in addition to standardized partial AUPRC to focus on comparisons at clinically relevant operating points. RESULTS: We included 2164 patients for model training, of which 12% had poor outcomes. The deep neural network performed best as measured by the area under the receiver operating characteristic curve (0.941) and standardized partial AUPRC in region maximizing recall (0.291), whereas the shallow neural network was best by the area under the precision-recall curve (0.770). In several other comparisons, the elastic-net regularized logistic regression was noninferior to the neural networks. CONCLUSIONS: We present the first use of deep learning for TBI prognostication, with an emphasis on LMICs, where there is great need for decision support to allocate limited resources. Optimal algorithm selection depends on the specific clinical setting; deep learning is not a panacea, though it may have a role in these efforts.

Machine Learning for Predicting In-Hospital Mortality After Traumatic Brain Injury in Both High-Income and Low- and Middle-Income Countries.

BACKGROUND: Machine learning (ML) holds promise as a tool to guide clinical decision making by predicting in-hospital mortality for patients with traumatic brain injury (TBI). Previous models such as the international mission for prognosis and clinical trials in TBI (IMPACT) and the corticosteroid randomization after significant head injury (CRASH) prognosis calculators can potentially be improved with expanded clinical features and newer ML approaches. OBJECTIVE: To develop ML models to predict in-hospital mortality for both the high-income country (HIC) and the low- and middle-income country (LMIC) settings. METHODS: We used the Duke University Medical Center National Trauma Data Bank and Mulago National Referral Hospital (MNRH) registry to predict in-hospital mortality for the HIC and LMIC settings, respectively. Six ML models were built on each data set, and the best model was chosen through nested cross-validation. The CRASH and IMPACT models were externally validated on the MNRH database. RESULTS: ML models built on National Trauma Data Bank (n = 5393, 84 predictors) demonstrated an area under the receiver operating curve (AUROC) of 0.91 (95% CI: 0.85-0.97) while models constructed on MNRH (n = 877, 31 predictors) demonstrated an AUROC of 0.89 (95% CI: 0.81-0.97). Direct comparison with CRASH and IMPACT models showed significant improvement of the proposed LMIC models regarding AUROC (P = .038). CONCLUSION: We developed high-performing well-calibrated ML models for predicting in-hospital mortality for both the HIC and LMIC settings that have the potential to influence clinical management and traumatic brain injury patient trajectories.

Palliative Care Consultations in Patients with Severe Traumatic Brain Injury: Who Receives Palliative Care Consultations and What Does that Mean for Utilization?

BACKGROUND: Palliative care has the potential to improve goal-concordant care in severe traumatic brain injury (sTBI). Our primary objective was to illuminate the demographic profiles of patients with sTBI who receive palliative care encounters (PCEs), with an emphasis on the role of race. Secondary objectives were to analyze PCE usage over time and compare health care resource utilization between patients with or without PCEs. METHODS: The National Inpatient Sample database was queried for patients age ≥ 18 who had a diagnosis of sTBI, defined by using International Classification of Diseases, 9th Revision codes. PCEs were defined by using International Classification of Diseases, 9th Revision code V66.7 and trended from 2001 to 2015. To assess factors associated with PCE in patients with sTBI, we performed unweighted generalized estimating equations regression. PCE association with decision making was modeled via its effect on rate of percutaneous endoscopic gastrostomy (PEG) tube placement. To quantify differences in PCE-related decisions by race, race was modeled as an effect modifier. RESULTS: From 2001 to 2015, the proportion of palliative care usage in patients with sTBI increased from 1.5 to 36.3%, with 41.6% White, 22.3% Black, and 25% Hispanic patients with sTBI having a palliative care consultation in 2015, respectively. From 2008 to 2015, we identified 17,673 sTBI admissions. White and affluent patients were more likely to have a PCE than Black, Hispanic, and low socioeconomic status patients. Across all races, patients receiving a PCE resulted in a lower rate of PEG tube placement; however, White patients exhibited a larger reduction of PEG tube placement than Black patients. Patients using palliative care had lower total hospital costs (median $16,368 vs. $26,442, respectively). CONCLUSIONS: Palliative care usage for sTBI has increased dramatically this century and it reduces resource utilization. This is true across races, however, its usage rate and associated effect on decision making are race-dependent, with White patients receiving more PCE and being more likely to decline the use of a PEG tube if they have had a PCE.

Corticosteroid Randomization after Significant Head Injury and International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury Models Compared with a Machine Learning-Based Predictive Model from Tanzania.

Hospitals in low- and middle-income countries (LMICs) could benefit from decision support technologies to reduce time to triage, diagnosis, and surgery for patients with traumatic brain injury (TBI). Corticosteroid Randomization after Significant Head Injury (CRASH) and International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury (IMPACT) are robust examples of TBI prognostic models, although they have yet to be validated in Sub-Saharan Africa (SSA). Moreover, machine learning and improved data quality in LMICs provide an opportunity to develop context-specific, and potentially more accurate, prognostic models. We aim to externally validate CRASH and IMPACT on our TBI registry and compare their performances to that of the locally derived model (from the Kilimanjaro Christian Medical Center [KCMC]). We developed a machine learning-based prognostic model from a TBI registry collected at a regional referral hospital in Moshi, Tanzania. We also used the core CRASH and IMPACT online risk calculators to generate risk scores for each patient. We compared the discrimination (area under the curve [AUC]) and calibration before and after Platt scaling (Brier, Hosmer-Lemeshow Test, and calibration plots) for CRASH, IMPACT, and the KCMC model. The outcome of interest was unfavorable in-hospital outcome defined as a Glasgow Outcome Scale score of 1-3. There were 2972 patients included in the TBI registry, of whom 11% had an unfavorable outcome. The AUCs for the KCMC model, CRASH, and IMPACT were 0.919, 0.876, and 0.821, respectively. Prior to Platt scaling, CRASH was the best calibrated model (χ2 = 68.1) followed by IMPACT (χ2 = 380.9) and KCMC (χ2 = 1025.6). We provide the first SSA validation of the core CRASH and IMPACT models. The KCMC model had better discrimination than either of these. CRASH had the best calibration, although all model predictions could be successfully calibrated. The top performing models, KCMC and CRASH, were both developed using LMIC data, suggesting that locally derived models may outperform imported ones from different contexts of care. Further work is needed to externally validate the KCMC model.

Mobile Health Application for Patients Undergoing Breast Cancer Surgery: Feasibility Study.

PURPOSE: Contemporary breast cancer surgery often requires hospital stays of 1 day or less, presenting challenges to delivery of high-quality care. Without sufficient time for proper education and guidance, patients may delay seeking care, experience anxiety, or seek unnecessary care, leading to poorer outcomes and increased costs. To address this, we evaluated the feasibility of a planning-, outcomes-, and analytics-based mobile health application called Manage My Surgery (MMS) for patients undergoing elective breast cancer surgery. METHODS: Patients undergoing breast cancer surgery at an academic health center were invited to use MMS. Those who used the application received pre- and postoperative surveys, which recorded and reported patient satisfaction and outcomes related to the application. RESULTS: Thirty-three female patients undergoing elective breast cancer surgery used MMS. The median age was 58 years. Nineteen patients underwent lumpectomy, and 14 underwent mastectomy. Users logged on to the application an average of 3.5 times. The median number of questions viewed was 12 (range 2-35). Of 17 patients who completed the feedback survey, 100% said that MMS was helpful during preparation for surgery, 82.3% said that MMS was helpful postoperatively, and 94.1% would recommend MMS to others. Preliminary data on patient-reported outcomes collected by MMS suggest improvements in anxiety and depression over time. CONCLUSION: Implementation of a digital care navigation tool in breast cancer surgery patients is feasible. Patients found the tool helpful in both the pre- and postoperative period. Additional ongoing work will focus on patients' self-management skills, long-term outcomes, and health system costs.

A high-resolution interactive atlas of the human brainstem using magnetic resonance imaging.

Conventional atlases of the human brainstem are limited by the inflexible, sparsely-sampled, two-dimensional nature of histology, or the low spatial resolution of conventional magnetic resonance imaging (MRI). Postmortem high-resolution MRI circumvents the challenges associated with both modalities. A single human brainstem specimen extending from the rostral diencephalon through the caudal medulla was prepared for imaging after the brain was removed from a 65-year-old male within 24 h of death. The specimen was formalin-fixed for two weeks, then rehydrated and placed in a custom-made MRI compatible tube and immersed in liquid fluorocarbon. MRI was performed in a 7-Tesla scanner with 120 unique diffusion directions. Acquisition time for anatomic and diffusion images were 14 h and 208 h, respectively. Segmentation was performed manually. Deterministic fiber tractography was done using strategically chosen regions of interest and avoidance, with manual editing using expert knowledge of human neuroanatomy. Anatomic and diffusion images were rendered with isotropic resolutions of 50 µm and 200 µm, respectively. Ninety different structures were segmented and labeled, and 11 different fiber bundles were rendered with tractography. The complete atlas is available online for interactive use at https://www.civmvoxport.vm.duke.edu/voxbase/login.php?return_url=%2Fvoxbase%2F. This atlas presents multiple contrasting datasets and selected tract reconstruction with unprecedented resolution for MR imaging of the human brainstem. There are immediate applications in neuroanatomical education, with the potential to serve future applications for neuroanatomical research and enhanced neurosurgical planning through "safe" zones of entry into the human brainstem.

Complementary and alternative medicine in children with diffuse intrinsic pontine glioma-A SIOPE DIPG Network and Registry study.

INTRODUCTION: Diffuse intrinsic pontine glioma (DIPG) is a rare and aggressive childhood brainstem malignancy with a 2-year survival rate of <10%. This international survey study aims to evaluate the use of complementary and alternative medicine (CAM) in this patient population. METHODS: Parents and physicians of patients with DIPG were asked to participate in a retrospective online survey regarding CAM use during time of illness. RESULTS: Between January and May 2020, 120 parents and 75 physicians contributed to the online survey. Most physicians estimated that <50% of their patients used CAM, whereas 69% of the parents reported using CAM to treat their child during time of illness. Cannabis was the most frequently used form of CAM, followed by vitamins and minerals, melatonin, curcumin, and boswellic acid. CAM was mainly used with the intention of direct antitumor effect. Other motivations were to treat side effects of chemotherapy or to increase comfort of the child. Children diagnosed from 2016 onwards were more likely to use CAM (χ2  = 6.08, p = .014). No significant difference was found between CAM users and nonusers based on ethnicity (χ2  = 4.18, p = .382) or country of residence (χ2  = 9.37, p = .154). Almost 50% of the physicians do not frequently ask their patients about possible CAM use. CONCLUSION: This survey demonstrates that worldwide, a considerable number of patients with DIPG use CAM. Physicians should be more aware of potential CAM use and actively discuss the topic. In addition, more research is needed to gain knowledge about possible anticancer effects of CAM and (positive/negative) interactions with conventional therapies.

Outcomes and Health Care Resource Utilization of Adult Bacterial Meningitis in the United States.

OBJECTIVE: To examine the longitudinal health care resource utilization, in-hospital mortality, and incidence of downstream complications of bacterial meningitis in the United States. METHODS: Using IBM MarketScan, we retrieved data on adult patients with a diagnosis of bacterial meningitis admitted to a US hospital between 2008 and 2015. Patients were stratified into groups (1) with/without prior head trauma/neurosurgical complications, (2) nosocomial/community acquisition, and (3) Gram-negative/positive bacteria. Cost data were collected for up to 2 years and analyzed with descriptive statistics and longitudinal modeling. RESULTS: Among 4,496 patients with bacterial meningitis, 16.5% and 4.6% had preceding neurosurgical complications and head injuries, respectively. Lumbar punctures were performed in 37.3% of patients without prior trauma/complications who went on to develop nosocomial meningitis, and those with prior head injuries or complications had longer initial hospital stays (17.0 days vs 8.0 days). Within a month of diagnosis, 29.2% of patients with bacterial meningitis had experienced downstream complications, most commonly hydrocephalus (12.7%). The worst 30-day mortality was due to tuberculous (12.3%) and streptococcal meningitis (7.2%). Overall, prior head trauma and complications were associated with higher costs. Community-acquired bacterial meningitis had lower median baseline costs relative to the nosocomial group (no head trauma/complication: $17,152 vs $82,778; head trauma/complication: $92,428 vs $168,309) but higher median costs within 3 months of diagnosis (no head trauma/complication: $47,911 vs $34,202; head trauma/complication: $89,207 vs $58,947). All costs demonstrated a sharp decline thereafter. CONCLUSIONS: Bacterial meningitis remains costly and devastating, especially for those who experience traumatic head injuries or have a complicated progress after neurosurgery.

Surgical intervention and patient factors associated with poor outcomes in patients with traumatic brain injury at a tertiary care hospital in Uganda.

OBJECTIVE: The purpose of this study was to investigate whether neurosurgical intervention for traumatic brain injury (TBI) is associated with reduced risks of death and clinical deterioration in a low-income country with a relatively high neurosurgical capacity. The authors further aimed to assess whether the association between surgical intervention and acute poor outcomes differs according to TBI severity and various patient factors. METHODS: Using TBI registry data collected from a national referral hospital in Uganda between July 2016 and April 2020, the authors performed Cox regression analyses of poor outcomes in admitted patients who did and did not undergo surgery for TBI, with surgery as a time-varying treatment variable. Patients were further stratified by TBI severity using the admission Glasgow Coma Scale (GCS) score: mild TBI (mTBI; GCS scores 13-15), moderate TBI (moTBI; GCS scores 9-12), and severe TBI (sTBI; GCS scores 3-8). Poor outcomes constituted Glasgow Outcome Scale scores 2-3, deterioration in TBI severity between admission and discharge (e.g., mTBI to sTBI), and death. Several clinical and demographic variables were included as covariates. Patients were observed for outcomes from admission through hospital day 10. RESULTS: Of 1544 patients included in the cohort, 369 (24%) had undergone surgery. Rates of poor outcomes were 4% (n = 13) for surgical patients and 12% (n = 144) among nonsurgical patients (n = 1175). Surgery was associated with a 59% reduction in the hazard for a poor outcome (HR 0.41, 95% CI 0.23-0.72). Age, pupillary nonreactivity, fall injury, and TBI severity at admission were significant covariates. In models stratifying by TBI severity at admission, patients with mTBI had an 80% reduction in the hazard for a poor outcome with surgery (HR 0.20, 95% CI 0.04-0.90), whereas those with sTBI had a 65% reduction (HR 0.35, 95% CI 0.14-0.89). Patients with moTBI had a statistically nonsignificant 56% reduction in hazard (HR 0.44, 95% CI 0.17-1.17). CONCLUSIONS: In this setting, the association between surgery and rates of poor outcomes varied with TBI severity and was influenced by several factors. Patients presenting with mTBI had the greatest reduction in the hazard for a poor outcome, followed by those presenting with sTBI. However, patients with moTBI had a nonsignificant reduction in the hazard, indicating greater variability in outcomes and underscoring the need for closer monitoring of this population. These results highlight the importance of accurate, timely clinical evaluation throughout a patient's admission and can inform decisions about whether and when to perform surgery for TBI when resources are limited.

Inpatient mortality and healthcare resource utilization of nontraumatic intracerebral hemorrhage complications in the US.

OBJECTIVE: Nontraumatic, primary intracerebral hemorrhage (ICH) accounts for 2 million strokes worldwide annually and has a 1-year survival rate of 50%. Recent studies examining functional outcomes from ICH evacuation have been performed, but limited work has been done quantifying the incidence of subsequent complications and their healthcare economic impact. The purpose of this study was to quantify the incidence and healthcare resource utilization (HCRU) for major complications that can arise from ICH. METHODS: The IBM MarketScan Research databases were used to retrospectively identify patients with ICH from 2010 to 2015. Complications examined included cerebral edema, hydrocephalus, venous thromboembolic events (VTEs), pneumonia, urinary tract infections (UTIs), and seizures. For each complication, inpatient mortality and HCRU were assessed. RESULTS: Of 25,322 adult patients included, 10,619 (42%) developed complications during the initial admission of ICH: 22% had cerebral edema, 11% hydrocephalus, 10% pneumonia, 6% UTIs, 5% seizures, and 5% VTEs. The inpatient mortality rates at 7 and 30 days for each complication of ICH ranked from highest to lowest were hydrocephalus (24% and 32%), cerebral edema (15% and 20%), pneumonia (8% and 18%), seizure (7% and 13%), VTE (4% and 11%), and UTI (4% and 8%). Hydrocephalus had the highest total cost (median $92,776, IQR $39,308-$180,716) at 7 days post-ICH diagnosis and the highest cumulative total cost (median $170,839, IQR $91,462-$330,673) at 1 year post-ICH diagnosis. CONCLUSIONS: This study characterizes one of the largest cohorts of patients with nontraumatic ICH in the US. More than 42% of the patients with ICH developed complications during initial admission, which resulted in high inpatient mortality and considerable HCRU.

Predicting the Individual Treatment Effect of Neurosurgery for Patients with Traumatic Brain Injury in the Low-Resource Setting: A Machine Learning Approach in Uganda.

Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these low-resource settings, effective triage of patients with TBI-including the decision of whether or not to perform neurosurgery-is critical in optimizing patient outcomes and healthcare resource utilization. Machine learning may allow for effective predictions of patient outcomes both with and without surgery. Data from patients with TBI was collected prospectively at Mulago National Referral Hospital in Kampala, Uganda, from 2016 to 2019. One linear and six non-linear machine learning models were designed to predict good versus poor outcome near hospital discharge and internally validated using nested five-fold cross-validation. The 13 predictors included clinical variables easily acquired on admission and whether or not the patient received surgery. Using an elastic-net regularized logistic regression model (GLMnet), with predictions calibrated using Platt scaling, the probability of poor outcome was calculated for each patient both with and without surgery (with the difference quantifying the "individual treatment effect," ITE). Relative ITE represents the percent reduction in chance of poor outcome, equaling this ITE divided by the probability of poor outcome with no surgery. Ultimately, 1766 patients were included. Areas under the receiver operating characteristic curve (AUROCs) ranged from 83.1% (single C5.0 ruleset) to 88.5% (random forest), with the GLMnet at 87.5%. The two variables promoting good outcomes in the GLMnet model were high Glasgow Coma Scale score and receiving surgery. For the subgroup not receiving surgery, the median relative ITE was 42.9% (interquartile range [IQR], 32.7% to 53.5%); similarly, in those receiving surgery, it was 43.2% (IQR, 32.9% to 54.3%). We provide the first machine learning-based model to predict TBI outcomes with and without surgery in LMICs, thus enabling more effective surgical decision making in the resource-limited setting. Predicted ITE similarity between surgical and non-surgical groups suggests that, currently, patients are not being chosen optimally for neurosurgical intervention. Our clinical decision aid has the potential to improve outcomes.

Paediatric bacterial meningitis in the USA: outcomes and healthcare resource utilization of nosocomial versus community-acquired infection.

Introduction. Paediatric bacterial meningitis remains a costly disease, both financially and clinically.Hypothesis/Gap Statement. Previous epidemiological and cost studies of bacterial meningitis (BM) have largely focused on adult populations or single pathogens. There have been few recent, large-scale studies of pediatric BM in the USA.Aim. We examined healthcare resource utilization (HCRU) and associated morbidity and mortality of community-acquired versus nosocomial bacterial infections in children across the USA.Methodology. The IBM MarketScan Research databases were used to identify patients <18 years old admitted to USA hospitals from 2008 to 2015 with a primary diagnosis of BM. Cases were categorized as either community-acquired or nosocomial. HCRU, post-diagnosis neurosurgical procedures, 30-day in-hospital mortality, and complications were compared between groups. Multivariable regression adjusted for sex, age and Gram staining was used to compare costs of nosocomial versus community-acquired infections over time.Results. We identified 1928 cases of paediatric BM without prior head trauma or neurological/systemic complications. Of these, 15.4 % were nosocomial and 84.6 % were community-acquired infections. After diagnostic lumbar puncture (37.1 %), the most common neurosurgical procedure was placement of ventricular catheter (12.6 %). The 30-day complication rates for nosocomial and community-acquired infections were 40.5 and 45.9 %, respectively. The most common complications were hydrocephalus (20.8 %), intracranial abscess (8.8 %) and cerebral oedema (8.1 %). The 30-day in-hospital mortality rates for nosocomial and community-acquired infections were 2.7 and 2.8 %, respectively.Median length of admission was 14.0 days (Q1: 7 days, Q3: 26 days). Median 90-day cost was $40 861 (Q1: $11 988, Q3: $114,499) for the nosocomial group and $56 569 (Q1: $26 127, Q3: $142 780) for the community-acquired group. In multivariable regression, the 90-day post-diagnosis total costs were comparable between groups (cost ratio: 0.89; 95 % CI: 0.70 to 1.13), but at 2 years post-diagnosis, the nosocomial group was associated with 137 % higher costs (CR: 2.37, 95 % CI: 1.51 to 3.70).Conclusion. In multivariable analysis, nosocomial infections were associated with significantly higher long-term costs up to 2 years post-infection. Hydrocephalus, intracranial epidural abscess and cerebral oedema were the most common complications, and lumbar punctures and ventricular catheter placement were the most common neurosurgical procedures. This study represents the first nation-wide, longitudinal comparison of the outcomes and considerable HCRU of nosocomial versus community-acquired paediatric BM, including characterization of complications and procedures contributing to the high costs of these infections.

Health care resource utilization and treatment of leptomeningeal carcinomatosis in the United States.

BACKGROUND: The economic burden of cancer in the United States is substantial, and better understanding it is essential in informing health care policy and innovation. Leptomeningeal carcinomatosis (LC) represents a late complication of primary cancer spreading to the leptomeninges. METHODS: The IBM MarketScan Research databases were queried for adults diagnosed with LC from 2001 to 2015, secondary to 4 primary cancers (breast, lung, gastrointestinal, and melanoma). Health care resource utilization (HCRU) and treatment utilization were quantified at baseline (1-year pre-LC diagnosis) and 30, 90, and 365 days post-LC diagnosis. RESULTS: We identified 4961 cases of LC (46.3% breast cancer, 34.8% lung cancer, 13.5% gastrointestinal cancer, and 5.4% melanoma). The median age was 57.0 years, with 69.7% female and 31.1% residing in the South. Insurance status included commercial (71.1%), Medicare (19.8%), and Medicaid (9.1%). Median follow-up was 66.0 days (25th percentile: 24.0, 75th percentile: 186.0) and total cumulative costs were highest for the gastrointestinal subgroup ($167 768) and lowest for the lung cancer subgroup ($145 244). There was considerable variation in the 89.6% of patients who used adjunctive treatments at 1 year, including chemotherapy (64.3%), radiotherapy (57.6%), therapeutic lumbar puncture (31.5%), and Ommaya reservoir (14.5%). The main cost drivers at 1 year were chemotherapy ($62 026), radiation therapy ($37 076), and specialty drugs ($29 330). The prevalence of neurologic impairments was 46.9%, including radiculopathy (15.0%), paresthesia (12.3%), seizure episode/convulsive disorder not otherwise specified (11.0%), and ataxia (8.0%). CONCLUSIONS: LC is a devastating condition with an overall poor prognosis. We present the largest study of LC in this real-world study, including current treatments, with an emphasis on HCRU. There is considerable variation in the treatment of LC and significant health care costs.

Impact of Spinal Cord Stimulation on Opioid Dose Reduction: A Nationwide Analysis.

BACKGROUND: Opioid misuse in the USA is an epidemic. Utilization of neuromodulation for refractory chronic pain may reduce opioid-related morbidity and mortality, and associated economic costs. OBJECTIVE: To assess the impact of spinal cord stimulation (SCS) on opioid dose reduction. METHODS: The IBM MarketScan® database was retrospectively queried for all US patients with a chronic pain diagnosis undergoing SCS between 2010 and 2015. Opioid usage before and after the procedure was quantified as morphine milligram equivalents (MME). RESULTS: A total of 8497 adult patients undergoing SCS were included. Within 1 yr of the procedure, 60.4% had some reduction in their opioid use, 34.2% moved to a clinically important lower dosage group, and 17.0% weaned off opioids entirely. The proportion of patients who completely weaned off opioids increased with decreasing preprocedure dose, ranging from 5.1% in the >90 MME group to 34.2% in the ≤20 MME group. The following variables were associated with reduced odds of weaning off opioids post procedure: long-term opioid use (odds ratio [OR]: 0.26; 95% CI: 0.21-0.30; P < .001), use of other pain medications (OR: 0.75; 95% CI: 0.65-0.87; P < .001), and obesity (OR: 0.75; 95% CI: 0.60-0.94; P = .01). CONCLUSION: Patients undergoing SCS were able to reduce opioid usage. Given the potential to reduce the risks of long-term opioid therapy, this study lays the groundwork for efforts that may ultimately push stakeholders to reduce payment and policy barriers to SCS as part of an evidence-based, patient-centered approach to nonopioid solutions for chronic pain.

Painful diabetic peripheral neuropathy: Health care costs and complications from 2010 to 2015.

OBJECTIVE: To quantify health care resource utilization and risk of complications in painful diabetic peripheral neuropathy (pDPN). METHODS: Adult patients diagnosed with diabetes mellitus or diabetic peripheral neuropathy (DPN) were identified in MarketScan from January 2010 to December 2015. Subgroups (pDPN and nonpainful DPN) were based on the use of pain medications 6 months before a new indexed diagnosis and 1 year thereafter. Health care costs were collected for up to 5 years, and complications charted for those with at least 1 and 2 years of follow-up. Complication comparisons were made using χ2 or Fisher exact tests, and a multivariable regression cost model was fit with log link function using generalized estimating equations. RESULTS: Among 360,559 patients with diabetes (62 ± 14 years; 54.3% female), 84,069 (23.3%) developed pDPN, 17,267 (4.8%) experienced nonpainful DPN, and the majority (259,223, 71.9%) were controls with diabetes without neuropathy. At baseline, costs associated with pDPN patients were 20% higher than diabetic controls (95% confidence interval [CI] [1.19, 1.21], p < 0.001), which increased to 31% in the 5th year (95% CI [1.27, 1.34], p < 0.001). Patients with pDPN had 200%, 356%, and 224% of the odds of using opioids, anticonvulsants, and antidepressants, respectively, compared with diabetic controls. The amputation risk in the pDPN subgroup was 16.24 times that of diabetic controls (95% CI [2.15, 122.72], p = 0.0003), and 87% more patients with pDPN experienced lower extremity infections (95% CI [1.43, 2.46], p < 0.0001) within a year. Within 2 years, 2.2% of patients with pDPN had falls and fall-related injuries compared with 1.1% of diabetic controls (p < 0.0001). CONCLUSIONS: Our study characterizes a substantial pDPN cohort in the United States, demonstrating considerable morbidity and economic costs.

The longitudinal health economic impact of viral encephalitis in the United States.

Introduction. Previous studies of viral encephalitis have focused on acute costs, estimating incidence at 7.3 per 100 000 and total US annual charges at $2 billion in 2010.Aim. We aim to quantify the most updated longitudinal health economic impact of viral encephalitis in the USA from 2008 to 2015.Methodology. Data on patients diagnosed with viral encephalitis were obtained from the Truven Health Analytics MarketScan database. Patients with a primary diagnosis of viral encephalitis, from herpetic viruses and other viral aetiologies (e.g. West Nile fever) were included in the analysis. Data concerning healthcare resource utilization, inpatient mortality, length of stay and accrued healthcare costs were collected for up to 5 years.Results. Among 3985 patients with continuous enrolment for 13 months prior to the encephalitis diagnosis, more non-herpes simplex encephalitis (61.7 %) than herpes simplex encephalitis (HSE; 38.3 %) cases were recorded, with the majority concentrated in the southern USA (29.2 %). One-year inpatient mortality was 6.2 %, which over a 5-year period rose to 8.9 % for HSE and 5.8 % for all other viral encephalitides. HSE resulted in longer cumulative stays in the hospital (11 days vs. 4 days; P=0.0025), and accrued 37 % higher first-year costs, after adjusting for known confounders [P<0.001, cost ratio=1.37, 95 % confidence interval (1.20, 1.57)]. Additionally, HSE was associated with greater 5-year cumulative median charges ($125 338 vs. $82 317, P=0.0015).Conclusion. The health economic impact and long-term morbidity of viral encephalitis in the USA are substantial.

Independent Association Between Type of Intraoperative Blood Transfusion and Postoperative Delirium After Complex Spinal Fusion for Adult Deformity Correction.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: To determine whether type of intraoperative blood transfusion used is associated with increased incidence of postoperative delirium after complex spine fusion involving five levels or greater. SUMMARY OF BACKGROUND DATA: Postoperative delirium after spine surgery has been associated with age, cognitive status, and several comorbidities. Intraoperative allogenic blood transfusions have previously been linked to greater complication risks and length of hospital stay. However, whether type of intraoperative blood transfusion used increases the risk for postoperative delirium after complex spinal fusion remains relatively unknown. METHODS: The medical records of 130 adult (≥18 years old) spine deformity patients undergoing elective, primary complex spinal fusion (more than or equal to five levels) for deformity correction at a major academic institution from 2010 to 2015 were reviewed. We identified 104 patients who encountered an intraoperative blood transfusion. Of the 104, 15 (11.5%) had Allogenic-only, 23 (17.7%) had Autologous-only, and 66 (50.8%) had Combined transfusions. The primary outcome investigated was the rate of postoperative delirium. RESULTS: There were significant differences in estimated blood loss (Combined: 2155.5 ±â€Š1900.7 mL vs. Autologous: 1396.5 ±â€Š790.0 mL vs. Allogenic: 1071.3 ±â€Š577.8 mL vs. None: 506.9 ±â€Š427.3 mL, P < 0.0001) and amount transfused (Combined: 1739.7 ±â€Š1127.6 mL vs. Autologous: 465.7 ±â€Š289.7 mL vs. Allogenic: 986.9 ±â€Š512.9 mL, P < 0.0001). The Allogenic cohort had a significantly higher proportion of patients experiencing delirium (Combined: 7.6% vs. Autologous: 17.4% vs. Allogenic: 46.7% vs. None: 11.5%, P = 0.002). In multivariate nominal-logistic regression analysis, Allogenic (odds ratio [OR]: 24.81, 95% confidence interval [CI] [3.930, 156.702], P = 0.0002) and Autologous (OR: 6.43, 95% CI [1.156, 35.772], P = 0.0335) transfusions were independently associated with postoperative delirium. CONCLUSION: Our study suggests that there may be an independent association between intraoperative autologous and allogenic blood transfusions and postoperative delirium after complex spinal fusion. Further studies are necessary to identify the physiological effect of blood transfusions to better overall patient care and reduce healthcare expenditures. LEVEL OF EVIDENCE: 3.