Assessment of Health Care Costs and Total Baclofen Use Associated With Targeted Drug Delivery for Spasticity.

BACKGROUND: Chronic spasticity causes significant impairment and financial burden. Oral baclofen, the first-line therapy, can have intolerable, dose-dependent side effects. Targeted drug delivery (TDD) through intrathecal baclofen delivers smaller amounts of baclofen into the thecal sac via an implanted infusion system. However, the health care resource utilization of patients with spasticity receiving TDD has not been studied extensively. MATERIALS AND METHODS: Adult patients who received TDD for spasticity between 2009 and 2017 were identified using the IBM MarketScan® data bases. Patients' use of oral baclofen and health care costs were examined at baseline (one year before implantation) and three years after implantation. A multivariable regression model using the generalized estimating equations method and a log link function was used to compare postimplantation costs with those at baseline. RESULTS: The study identified 771 patients with TDD for medication analysis and 576 for cost analysis. At baseline, the median costs were $39,326 (interquartile range [IQR]: $19,526-$80,679), which increased to $75,728 (IQR: $44,199-$122,676) in year 1, decreased to $27,160 (IQR: $11,896-$62,427) in year 2, and increased slightly to $28,008 (IQR: $11,771-$61,885) in year 3. In multivariable analysis, the cost was 47% higher than at baseline (cost ratio [CR] 1.47, 95% CI: 1.32-1.63) in year 1 but was 25% lower (CR 0.75, 95% CI: 0.66-0.86) in year 2 and 32% lower (CR 0.68, 95% CI: 0.59-0.79) in year 3. Before implant, 58% of patients took oral baclofen, which decreased to 24% by year 3. The median daily baclofen dose decreased from 61.8 mg (IQR: 40-86.4) before TDD to 32.8 mg (IQR: 30-65.7) three years later. CONCLUSIONS: Our findings indicate that patients who undergo TDD use less oral baclofen, potentially reducing the risk of side effects. Although total health care costs increased immediately after TDD, most likely owing to device and implantation costs, they decreased below baseline after one year. The costs of TDD reach cost neutrality approximately three years after implant, indicating its potential for long-term cost savings.

Management of unruptured incidentally found intracranial saccular aneurysms.

Unruptured intracranial saccular aneurysms occur in 3-5% of the general population. As the use of diagnostic medical imaging has steadily increased over the past few decades with the increased availability of computed tomography (CT) and magnetic resonance imaging (MRI), so has the detection of incidental aneurysms. The management of an unruptured intracranial saccular aneurysm is challenging for both patients and physicians, as the decision to intervene must weigh the risk of rupture and resultant subarachnoid hemorrhage against the risk inherent to the surgical or endovascular procedure. The purpose of this paper is to provide an overview of factors to be considered in the decision to offer treatment for unruptured intracranial aneurysms in adults. In addition, we review aneurysm and patient characteristics that favor surgical clipping over endovascular intervention and vice versa. Finally, the authors propose a novel, simple, and clinically relevant algorithm for observation versus intervention in unruptured intracranial aneurysms based on the PHASES scoring system.

Mesocorticolimbic hemodynamic response in Parkinson's disease patients with compulsive behaviors.

BACKGROUND: PD patients treated with dopamine therapy can develop maladaptive impulsive and compulsive behaviors, manifesting as repetitive participation in reward-driven activities. This behavioral phenotype implicates aberrant mesocorticolimbic network function, a concept supported by past literature. However, no study has investigated the acute hemodynamic response to dopamine agonists in this subpopulation. OBJECTIVES: We tested the hypothesis that dopamine agonists differentially alter mesocortical and mesolimbic network activity in patients with impulsive-compulsive behaviors. METHODS: Dopamine agonist effects on neuronal metabolism were quantified using arterial-spin-labeling MRI measures of cerebral blood flow in the on-dopamine agonist and off-dopamine states. The within-subject design included 34 PD patients, 17 with active impulsive compulsive behavior symptoms, matched for age, sex, disease duration, and PD severity. RESULTS: Patients with impulsive-compulsive behaviors have a significant increase in ventral striatal cerebral blood flow in response to dopamine agonists. Across all patients, ventral striatal cerebral blood flow on-dopamine agonist is significantly correlated with impulsive-compulsive behavior severity (Questionnaire for Impulsive Compulsive Disorders in Parkinson's Disease- Rating Scale). Voxel-wise analysis of dopamine agonist-induced cerebral blood flow revealed group differences in mesocortical (ventromedial prefrontal cortex; insular cortex), mesolimbic (ventral striatum), and midbrain (SN; periaqueductal gray) regions. CONCLUSIONS: These results indicate that dopamine agonist therapy can augment mesocorticolimbic and striato-nigro-striatal network activity in patients susceptible to impulsive-compulsive behaviors. Our findings reinforce a wider literature linking studies of maladaptive behaviors to mesocorticolimbic networks and extend our understanding of biological mechanisms of impulsive compulsive behaviors in PD. © 2017 International Parkinson and Movement Disorder Society.

Dissociable Effects of Dopamine on the Initial Capture and the Reactive Inhibition of Impulsive Actions in Parkinson's Disease.

Dopamine plays a key role in a range of action control processes. Here, we investigate how dopamine depletion caused by Parkinson disease (PD) and how dopamine restoring medication modulate the expression and suppression of unintended action impulses. Fifty-five PD patients and 56 healthy controls (HCs) performed an action control task (Simon task). PD patients completed the task twice, once withdrawn from dopamine medications and once while taking their medications. PD patients experienced similar susceptibility to making fast errors in conflict trials as HCs, but PD patients were less proficient compared with HCs at suppressing incorrect responses. Administration of dopaminergic medications had no effect on impulsive error rates but significantly improved the proficiency of inhibitory control in PD patients. We found no evidence that dopamine precursors and agonists affected action control in PD differently. Additionally, there was no clear evidence that individual differences in baseline action control (off dopamine medications) differentially responded to dopamine medications (i.e., no evidence for an inverted U-shaped performance curve). Together, these results indicate that dopamine depletion and restoration therapies directly modulate the reactive inhibitory control processes engaged to suppress interference from the spontaneously activated response impulses but exert no effect on an individual's susceptibility to act on impulses.

Nationwide Analysis of Risk Factors Related to Opioid Weaning Following Lumbar Decompression Surgery - A Retrospective Database Study.

BACKGROUND: Opioids are often prescribed for patients who eventually undergo lumbar decompression. Given the potential for opioid-related morbidity and mortality, postoperative weaning is often a goal of surgery. The purpose of this study was to examine the relationship between preoperative opioid use and postoperative complete opioid weaning among lumbar decompression patients. METHODS: We surveyed the IBM Marketscan Databases for patients who underwent lumbar decompression during 2008-2017, had >30 days of opioid use in the year preceding surgery, and consumed a daily average of >0 morphine milligram equivalents in the 3 months preceding surgery. We used multivariable logistic regression and marginal standardization to examine the association between preoperative opioid use duration, average daily dose, and their interactions with complete opioid weaning in the 10-12 months after surgery. RESULTS: Of the 11,114 patients who met inclusion criteria, most (54.7%, n = 6083) had a preoperative average daily dose of 1-20 morphine milligram equivalents. Postoperatively, 6144 patients (55.3%) remained on opioids. For patients with >180 days of preoperative use, the adjusted probability of weaning increased as the preoperative dose decreased. Obesity increased the likelihood of weaning, whereas older age, several comorbidities, female sex, and Medicaid decreased the odds of weaning. CONCLUSIONS: Patients who used opioids for longer preoperatively were less likely to completely wean following surgery. Among patients with >180 days of preoperative use, those with lower preoperative doses were more likely to wean. Weaning was also associated with several clinical and demographic factors. These findings may help shape expectations regarding opioid use following lumbar decompression.

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.

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.

Influence of Caretakers' Health Literacy on Delays to Traumatic Brain Injury Care in Uganda.

Background: Traumatic brain injury (TBI) is a life-altering condition, and delays to care can significantly impact outcomes. In Uganda, where nurse shortages are prevalent, patients' family members are the primary caretakers of these patients and play an important role in ensuring patients' access to timely care. However, caretakers often have little or no knowledge of appropriate patient care. Caretakers' ability to navigate the healthcare system and find and use health information to support their patients can impact delays in seeking, reaching, and receiving care. Objectives: This study seeks to determine the factors that impact TBI patient caretakers' health literacy and examine how these factors influence delays in care. Methods: This study was carried out in the Mulago National Referral Hospital neurosurgical ward, where 27 adult caretakers were interviewed using semi-structured, in-depth, qualitative interviews. "The Three Delay Framework" was utilized to understand participants' experiences in seeking, reaching, and receiving care for TBI patients. Thematic content analysis and manual coding was used to analyze interview transcripts and identify overarching themes in participant responses. Findings: The main health literacy themes identified were Extrinsic, Intrinsic and Health System Factors. Nine sub-themes were identified: Government Support, Community Support, Financial Burdens, Lack of Medical Resources, Access to Health Information, Physician Support, Emotional Challenges, Navigational Skills, and Understanding of Health Information. These components were found to influence the delays to care to varying degrees. Financial Burdens, Government Support, Emotional Challenges, Physician Support and Lack of Medical Resources were recurring factors across the three delays. Conclusion: The health literacy factors identified in this study influence caretakers' functional health literacy and delays to care in a co-dependent manner. A better understanding of how these factors impact patient outcomes is necessary for the development of interventions targeted at improving a caretaker's ability to maneuver the healthcare system and support patients in resource-poor settings.

Withdrawal of Life-supporting Treatment in Severe Traumatic Brain Injury.

Importance: There are limited data on which factors affect the critical and complex decision to withdraw life-supporting treatment (LST) in patients with severe traumatic brain injury (sTBI). Objective: To determine demographic and clinical factors associated with the decision to withdraw LST in patients with sTBI. Design, Setting, and Participants: This retrospective analysis of inpatient data from more than 825 trauma centers across the US in the American College of Surgeons Trauma Quality Improvement Program database from January 2013 to December 2015 included adult patients with sTBI and documentation of a decision regarding withdrawal of LST (WLST). Data analysis was conducted in September 2019. Main Outcomes and Measures: Factors associated with WLST in sTBI. Results: A total of 37931 patients (9817 women [25.9%]) were included in the multivariable analysis; 7864 (20.7%) had WLST. Black patients (4806 [13.2%]; odds ratio [OR], 0.66; 95% CI, 0.59-0.72; P < .001) and patients of other race (4798 [13.2%]; OR, 0.83; 95% CI, 0.76-0.91; P < .001) were less likely than white patients (26 864 [73.7%]) to have WLST. Patients from hospitals in the Midwest (OR, 1.12; 95% CI, 1.04-1.20; P = .002) or Northeast (OR, 1.23; 95% CI, 1.13-1.34; P < .001) were more likely to have WLST than patients from hospitals in the South. Patients with Medicare (OR, 1.55; 95% CI, 1.43-1.69; P < .001) and self-pay patients (OR, 1.36; 95% CI, 1.25-1.47; P < .001) were more likely to have WLST than patients with private insurance. Older patients and those with lower Glasgow Coma Scale scores, higher Injury Severity Scores, or craniotomy were generally more likely to have WLST. Withdrawal of LST was more likely for patients with functionally dependent health status (OR, 1.30; 95% CI, 1.08-1.58; P = .01), hematoma (OR, 1.19; 95% CI, 1.12-1.27; P < .001), dementia (OR, 1.29; 95% CI, 1.08-1.53; P = .004), and disseminated cancer (OR, 2.82; 95% CI, 2.07-3.82; P < .001) than for patients without these conditions. Conclusions and Relevance: Withdrawal of LST is common in sTBI and socioeconomic factors are associated with the decision to withdraw LST. These results highlight the many factors that contribute to decision-making in sTBI and demonstrate that in a complex and variable disease process, variation based on race, payment, and region presents as a potential challenge.

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.

An Attitude Survey and Assessment of the Feasibility, Acceptability, and Usability of a Traumatic Brain Injury Decision Support Tool in Uganda.

BACKGROUND: Traumatic brain injury (TBI) prognostic models are potential solutions to severe human and technical shortages. Although numerous TBI prognostic models have been developed, none are widely used in clinical practice, largely because of a lack of feasibility research to inform implementation. We previously developed a prognostic model and Web-based application for in-hospital TBI care in low-resource settings. In this study, we tested the feasibility, acceptability, and usability of the application with potential end-users. METHODS: We performed our feasibility assessment with providers involved in TBI care at both a regional and national referral hospital in Uganda. We collected qualitative and quantitative data on decision support needs, application ease of use, and implementation design. RESULTS: We completed 25 questionnaires on potential uses of the app and 11 semistructured feasibility interviews. Top-cited uses were informing the decision to operate, informing the decision to send the patient to intensive care, and counseling patients and relatives. Participants affirmed the potential of the application to support difficult triage situations, particularly in the setting of limited access to diagnostics and interventions, but were hesitant to use this technology with end-of-life decisions. Although all participants were satisfied with the application and agreed that it was easy to use, several expressed a need for this technology to be accessible by smartphone and offline. CONCLUSIONS: We elucidated several potential uses for our app and important contextual factors that will support future implementation. This investigation helps address an unmet need to determine the feasibility of TBI clinical decision support systems in low-resource settings.

Estimating prognosis for traumatic brain injury patients in a low-resource setting: how do providers compare to the CRASH risk calculator?

OBJECTIVE: Traumatic brain injury (TBI), a burgeoning global health concern, is one condition that could benefit from prognostic modeling. Risk stratification of TBI patients on presentation to a health facility can support the prudent use of limited resources. The CRASH (Corticosteroid Randomisation After Significant Head Injury) model is a well-established prognostic model developed to augment complex decision-making. The authors' current study objective was to better understand in-hospital decision-making for TBI patients and determine whether data from the CRASH risk calculator influenced provider assessment of prognosis. METHODS: The authors performed a choice experiment using a simulated TBI case. All participant doctors received the same case, which included a patient history, vitals, and physical examination findings. Half the participants also received the CRASH risk score. Participants were asked to estimate the patient prognosis and decide the best next treatment step. The authors recruited a convenience sample of 28 doctors involved in TBI care at both a regional and a national referral hospital in Uganda. RESULTS: For the simulated case, the CRASH risk scores for 14-day mortality and an unfavorable outcome at 6 months were 51.4% (95% CI 42.8%, 59.8%) and 89.8% (95% CI 86.0%, 92.6%), respectively. Overall, participants were overoptimistic when estimating the patient prognosis. Risk estimates by doctors provided with the CRASH risk score were closer to that score than estimates made by doctors in the control group; this effect was more pronounced for inexperienced doctors. Surgery was selected as the best next step by 86% of respondents. CONCLUSIONS: This study was a novel assessment of a TBI prognostic model's influence on provider estimation of risk in a low-resource setting. Exposure to CRASH risk score data reduced overoptimistic prognostication by doctors, particularly among inexperienced providers.