Developing and validating a natural language processing algorithm to extract preoperative cannabis use status documentation from unstructured narrative clinical notes.

OBJECTIVE: This study aimed to develop a natural language processing algorithm (NLP) using machine learning (ML) techniques to identify and classify documentation of preoperative cannabis use status. MATERIALS AND METHODS: We developed and applied a keyword search strategy to identify documentation of preoperative cannabis use status in clinical documentation within 60 days of surgery. We manually reviewed matching notes to classify each documentation into 8 different categories based on context, time, and certainty of cannabis use documentation. We applied 2 conventional ML and 3 deep learning models against manual annotation. We externally validated our model using the MIMIC-III dataset. RESULTS: The tested classifiers achieved classification results close to human performance with up to 93% and 94% precision and 95% recall of preoperative cannabis use status documentation. External validation showed consistent results with up to 94% precision and recall. DISCUSSION: Our NLP model successfully replicated human annotation of preoperative cannabis use documentation, providing a baseline framework for identifying and classifying documentation of cannabis use. We add to NLP methods applied in healthcare for clinical concept extraction and classification, mainly concerning social determinants of health and substance use. Our systematically developed lexicon provides a comprehensive knowledge-based resource covering a wide range of cannabis-related concepts for future NLP applications. CONCLUSION: We demonstrated that documentation of preoperative cannabis use status could be accurately identified using an NLP algorithm. This approach can be employed to identify comparison groups based on cannabis exposure for growing research efforts aiming to guide cannabis-related clinical practices and policies.

Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible.

Established guidelines describe minimum requirements for reporting algorithms in healthcare; it is equally important to objectify the characteristics of ideal algorithms that confer maximum potential benefits to patients, clinicians, and investigators. We propose a framework for ideal algorithms, including 6 desiderata: explainable (convey the relative importance of features in determining outputs), dynamic (capture temporal changes in physiologic signals and clinical events), precise (use high-resolution, multimodal data and aptly complex architecture), autonomous (learn with minimal supervision and execute without human input), fair (evaluate and mitigate implicit bias and social inequity), and reproducible (validated externally and prospectively and shared with academic communities). We present an ideal algorithms checklist and apply it to highly cited algorithms. Strategies and tools such as the predictive, descriptive, relevant (PDR) framework, the Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence (SPIRIT-AI) extension, sparse regression methods, and minimizing concept drift can help healthcare algorithms achieve these objectives, toward ideal algorithms in healthcare.

Perioperative Use of Gabapentinoids and Risk for Postoperative Long-Term Opioid Use in Older Adults Undergoing Total Knee or Hip Arthroplasty.

BACKGROUND: Gabapentinoids are recommended by guidelines as a component of multimodal analgesia to manage postoperative pain and reduce opioid use. It remains unknown whether perioperative use of gabapentinoids is associated with a reduced or increased risk of postoperative long-term opioid use (LTOU) after total knee or hip arthroplasty (TKA/THA). METHODS: Using Medicare claims data from 2011 to 2018, we identified fee-for-service beneficiaries aged ≥ 65 years who were hospitalized for a primary TKA/THA and had no LTOU before the surgery. Perioperative use of gabapentinoids was measured from 7 days preadmission through 7 days postdischarge. Patients were required to receive opioids during the perioperative period and were followed from day 7 postdischarge for 180 days to assess postoperative LTOU (ie, ≥90 consecutive days). A modified Poisson regression was used to estimate the relative risk (RR) of postoperative LTOU in patients with versus without perioperative use of gabapentinoids, adjusting for confounders through propensity score weighting. RESULTS: Of 52,788 eligible Medicare older beneficiaries (mean standard deviation [SD] age 72.7 [5.3]; 62.5% females; 89.7% White), 3,967 (7.5%) received gabapentinoids during the perioperative period. Postoperative LTOU was 3.8% in patients with and 4.0% in those without perioperative gabapentinoids. After adjusting for confounders, the risk of postoperative LTOU was similar comparing patients with versus without perioperative gabapentinoids (RR = 1.07; 95% confidence interval [CI] = 0.91-1.26, P = .408). Sensitivity and bias analyses yielded consistent results. CONCLUSION: Among older Medicare beneficiaries undergoing a primary TKA/THA, perioperative use of gabapentinoids was not associated with a reduced or increased risk for postoperative LTOU.

Concurrent use of prescription gabapentinoids with opioids and risk for fall-related injury among older US Medicare beneficiaries with chronic noncancer pain: A population-based cohort study.

BACKGROUND: Gabapentinoids are increasingly prescribed to manage chronic noncancer pain (CNCP) in older adults. When used concurrently with opioids, gabapentinoids may potentiate central nervous system (CNS) depression and increase the risks for fall. We aimed to investigate whether concurrent use of gabapentinoids with opioids compared with use of opioids alone is associated with an increased risk of fall-related injury among older adults with CNCP. METHODS AND FINDINGS: We conducted a population-based cohort study using a 5% national sample of Medicare beneficiaries in the United States between 2011 and 2018. Study sample consisted of fee-for-service (FFS) beneficiaries aged ≥65 years with CNCP diagnosis who initiated opioids. We identified concurrent users with gabapentinoids and opioids days' supply overlapping for ≥1 day and designated first day of concurrency as the index date. We created 2 cohorts based on whether concurrent users initiated gabapentinoids on the day of opioid initiation (Cohort 1) or after opioid initiation (Cohort 2). Each concurrent user was matched to up to 4 opioid-only users on opioid initiation date and index date using risk set sampling. We followed patients from index date to first fall-related injury event ascertained using a validated claims-based algorithm, treatment discontinuation or switching, death, Medicare disenrollment, hospitalization or nursing home admission, or end of study, whichever occurred first. In each cohort, we used propensity score (PS) weighted Cox models to estimate the adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) of fall-related injury, adjusting for year of the index date, sociodemographics, types of chronic pain, comorbidities, frailty, polypharmacy, healthcare utilization, use of nonopioid medications, and opioid use on and before the index date. We identified 6,733 concurrent users and 27,092 matched opioid-only users in Cohort 1 and 5,709 concurrent users and 22,388 matched opioid-only users in Cohort 2. The incidence rate of fall-related injury was 24.5 per 100 person-years during follow-up (median, 9 days; interquartile range [IQR], 5 to 18 days) in Cohort 1 and was 18.0 per 100 person-years during follow-up (median, 9 days; IQR, 4 to 22 days) in Cohort 2. Concurrent users had similar risk of fall-related injury as opioid-only users in Cohort 1(aHR = 0.97, 95% CI 0.71 to 1.34, p = 0.874), but had higher risk for fall-related injury than opioid-only users in Cohort 2 (aHR = 1.69, 95% CI 1.17 to 2.44, p = 0.005). Limitations of this study included confounding due to unmeasured factors, unavailable information on gabapentinoids' indication, potential misclassification, and limited generalizability beyond older adults insured by Medicare FFS program. CONCLUSIONS: In this sample of older Medicare beneficiaries with CNCP, initiating gabapentinoids and opioids simultaneously compared with initiating opioids only was not significantly associated with risk for fall-related injury. However, addition of gabapentinoids to an existing opioid regimen was associated with increased risks for fall. Mechanisms for the observed excess risk, whether pharmacological or because of channeling of combination therapy to high-risk patients, require further investigation. Clinicians should consider the risk-benefit of combination therapy when prescribing gabapentinoids concurrently with opioids.

Fairness in the prediction of acute postoperative pain using machine learning models.

Introduction: Overall performance of machine learning-based prediction models is promising; however, their generalizability and fairness must be vigorously investigated to ensure they perform sufficiently well for all patients. Objective: This study aimed to evaluate prediction bias in machine learning models used for predicting acute postoperative pain. Method: We conducted a retrospective review of electronic health records for patients undergoing orthopedic surgery from June 1, 2011, to June 30, 2019, at the University of Florida Health system/Shands Hospital. CatBoost machine learning models were trained for predicting the binary outcome of low (≤4) and high pain (>4). Model biases were assessed against seven protected attributes of age, sex, race, area deprivation index (ADI), speaking language, health literacy, and insurance type. Reweighing of protected attributes was investigated for reducing model bias compared with base models. Fairness metrics of equal opportunity, predictive parity, predictive equality, statistical parity, and overall accuracy equality were examined. Results: The final dataset included 14,263 patients [age: 60.72 (16.03) years, 53.87% female, 39.13% low acute postoperative pain]. The machine learning model (area under the curve, 0.71) was biased in terms of age, race, ADI, and insurance type, but not in terms of sex, language, and health literacy. Despite promising overall performance in predicting acute postoperative pain, machine learning-based prediction models may be biased with respect to protected attributes. Conclusion: These findings show the need to evaluate fairness in machine learning models involved in perioperative pain before they are implemented as clinical decision support tools.

Association of Postoperative Undertriage to Hospital Wards With Mortality and Morbidity.

Importance: Undertriaging patients who are at increased risk for postoperative complications after surgical procedures to low-acuity hospital wards (ie, floors) rather than highly vigilant intensive care units (ICUs) may be associated with risk of unrecognized decompensation and worse patient outcomes, but evidence for these associations is lacking. Objective: To test the hypothesis that postoperative undertriage is associated with increased mortality and morbidity compared with risk-matched ICU admission. Design, Setting, and Participants: This longitudinal cross-sectional study was conducted using data from the University of Florida Integrated Data Repository on admissions to a university hospital. Included patients were individuals aged 18 years or older who were admitted after a surgical procedure from June 1, 2014, to August 20, 2020. Data were analyzed from April through August 2021. Exposures: Ward admissions were considered undertriaged if their estimated risk for hospital mortality or prolonged ICU stay (ie, ≥48 hours) was in the top quartile among all inpatient surgical procedures according to a validated machine-learning model using preoperative and intraoperative electronic health record features available at surgical procedure end time. A nearest neighbors algorithm was used to identify a risk-matched control group of ICU admissions. Main Outcomes and Measures: The primary outcomes of hospital mortality and morbidity were compared among appropriately triaged ward admissions, undertriaged wards admissions, and a risk-matched control group of ICU admissions. Results: Among 12 348 postoperative ward admissions, 11 042 admissions (89.4%) were appropriately triaged (5927 [53.7%] women; median [IQR] age, 59 [44-70] years) and 1306 admissions (10.6%) were undertriaged and matched with a control group of 2452 ICU admissions. The undertriaged group, compared with the control group, had increased median [IQR] age (64 [54-74] years vs 62 [50-73] years; P = .001) and increased proportions of women (649 [49.7%] women vs 1080 [44.0%] women; P < .001) and admitted patients with do not resuscitate orders before first surgical procedure (53 admissions [4.1%] vs 27 admissions [1.1%]); P < .001); 207 admissions that were undertriaged (15.8%) had subsequent ICU admission. In the validation cohort, hospital mortality and prolonged ICU stay estimations had areas under the receiver operating characteristic curve of 0.92 (95% CI, 0.91-0.93) and 0.92 (95% CI, 0.92-0.92), respectively. The undertriaged group, compared with the control group, had similar incidence of prolonged mechanical ventilation (32 admissions [2.5%] vs 53 admissions [2.2%]; P = .60), decreased median (IQR) total costs for admission ($26 900 [$18 400-$42 300] vs $32 700 [$22 700-$48 500]; P < .001), increased median (IQR) hospital length of stay (8.1 [5.1-13.6] days vs 6.0 [3.3-9.3] days, P < .001), and increased incidence of hospital mortality (19 admissions [1.5%] vs 17 admissions [0.7%]; P = .04), discharge to hospice (23 admissions [1.8%] vs 14 admissions [0.6%]; P < .001), unplanned intubation (45 admissions [3.4%] vs 49 admissions [2.0%]; P = .01), and acute kidney injury (341 admissions [26.1%] vs 477 admissions [19.5%]; P < .001). Conclusions and Relevance: This study found that admitted patients at increased risk for postoperative complications who were undertriaged to hospital wards had increased mortality and morbidity compared with a risk-matched control group of admissions to ICUs. Postoperative undertriage was identifiable using automated preoperative and intraoperative data as features in real-time machine-learning models.

Cannabis use assessment and documentation in healthcare: Priorities for closing the gap.

Several factors, including the lack of a systematic cannabis use assessment within healthcare systems, have led to significant under-documentation of cannabis use and its correlates in medical records, the unpreparedness of clinicians, and poor quality of cannabis-related electronic health record data, limiting its utilization in research. Multiple steps are required to overcome the existing knowledge gaps and accommodate the health needs implied by the increasing cannabis use prevalence. These steps include (1) enhancing clinician and patient education on the importance of cannabis use assessment and documentation, (2) implementing a standardized approach for comprehensive cannabis use assessment within and across healthcare systems, (3) improving documentation of cannabis use and its correlates in medical records and electronic health records by building in prompts, (4) developing and validating reliable computable phenotypes of cannabis use, (5) conducting research utilizing electronic health data to study a wide array of related health outcomes, (6) and establishing evidence-based guidelines to inform clinical practices and policies. Integrating comprehensive cannabis use assessment and documentation within healthcare systems is necessary to enhance patient care and improve the quality of electronic health databases. Employing electronic health record data in cannabis-related research is crucial to accelerate research in light of the existing knowledge gaps on a wide array of health outcomes. Thus, improving and modernizing cannabis use assessment and documentation in healthcare is an integral step on which research conduct and evidence generation primarily rely.

Association Between Dual Trajectories of Opioid and Gabapentinoid Use and Healthcare Expenditures Among US Medicare Beneficiaries.

OBJECTIVES: Little is known about relationships between opioid- and gabapentinoid-use patterns and healthcare expenditures that may be affected by pain management and risk of adverse outcomes. This study examined the association between patients' opioid and gabapentinoid prescription filling/refilling trajectories and direct medical expenditures in US Medicare. METHODS: This cross-sectional study included a 5% national sample (2011-2016) of fee-for-service beneficiaries with fibromyalgia, low back pain, neuropathy, or osteoarthritis newly initiating opioids or gabapentinoids. Using group-based multitrajectory modeling, this study identified patients' distinct opioid and gabapentinoid (OPI-GABA) dose and duration patterns, based on standardized daily doses, within a year of initiating opioids and/or gabapentinoids. Concurrent direct medical expenditures within the same year were estimated using inverse probability of treatment weighted multivariable generalized linear regression, adjusting for sociodemographic and health status factors. RESULTS: Among 67 827 eligible beneficiaries (mean age ± SD = 63.6 ± 14.8 years, female = 65.8%, white = 77.1%), 11 distinct trajectories were identified (3 opioid-only, 4 gabapentinoid-only, and 4 concurrent OPI-GABA trajectories). Compared with opioid-only early discontinuers ($13 830, 95% confidence interval = $13 643-14 019), gabapentinoid-only early discontinuers and consistent low-dose and moderate-dose gabapentinoid-only users were associated with 11% to 23% lower health expenditures (adjusted mean expenditure = $10 607-$11 713). Consistent low-dose opioid-only users, consistent high-dose opioid-only users, consistent low-dose OPI-GABA users, consistent low-dose opioid and high-dose gabapentinoid users, and consistent high-dose opioid and moderate-dose gabapentinoid users were associated with 14% to 106% higher healthcare expenditures (adjusted mean expenditure = $15 721-$28 464). CONCLUSIONS: Dose and duration patterns of concurrent OPI-GABA varied substantially among fee-for-service Medicare beneficiaries. Consistent opioid-only users and all concurrent OPI-GABA users were associated with higher healthcare expenditures compared to opioid-only discontinuers.

Dual-trajectories of opioid and gabapentinoid use and risk of subsequent drug overdose among Medicare beneficiaries in the United States: a retrospective cohort study.

BACKGROUND AND AIMS: Little is known about opioid and gabapentinoid (OPI-GABA) use duration and dose patterns' associations with adverse outcome risks. We examined associations between OPI-GABA dose and duration trajectories and subsequent drug overdose. DESIGN: Retrospective cohort study. SETTING: US Medicare. PARTICIPANTS: Using a 5% sample (2011-16), we identified 71 005 fee-for-service Medicare beneficiaries with fibromyalgia, low back pain, neuropathy and/or osteoarthritis initiating OPIs and/or GABAs [mean age ± standard deviation (SD) = 65.5 ± 14.5 years, female = 68.1%, white = 76.8%]. MEASUREMENTS: Group-based multi-trajectory models identified distinct OPI-GABA use patterns during the year of OPI and/or GABA initiation, based on weekly average standardized daily dose (i.e. OPIs = morphine milligram equivalent, GABAs = minimum effective daily dose). We estimated models with three to 12 trajectories and selected the best model based on Bayesian information criterion (BIC) and Nagin's criteria. We estimated risk of time to first drug overdose diagnosis within 12 months following the index year, adjusting for socio-demographic and health factors using inverse probability of treatment weighted multivariable Cox proportional hazards models. FINDINGS: We identified 10 distinct trajectories (BIC = -1 176 954; OPI-only = 3, GABA-only = 3, OPI-GABA = 4). Compared with OPI-only early discontinuers (40.6% of the cohort), 1-year drug overdose risk varied by trajectory group: consistent low-dose OPI-only users [16.6%; hazard ratio (HR) = 1.47, 95% confidence interval (CI) = 1.19-1.82], consistent high-dose OPI-only users (1.8%; HR = 4.57, 95% CI = 2.99-6.98), GABA-only early discontinuers (12.5%; HR = 1.39, 95% CI = 1.09-1.77), consistent low-dose GABA-only users (11.0%; HR = 1.44, 95% CI = 1.12-1.85), consistent high-dose GABA-only users (3.1%; HR = 1.43, 95% CI = 0.94-2.17), early discontinuation of OPIs and consistent low-dose GABA users (6.9%; HR = 1.24, 95% CI = 0.90-1.69), consistent low-dose OPI-GABA users (3.4%; HR = 2.49, 95% CI = 1.76-3.52), consistent low-dose OPI and high-dose GABA users (3.2%; HR = 2.46, 95% CI = 1.71-3.53) and consistent high-dose OPI and moderate-dose GABA users (0.9%; HR = 7.22, 95% CI = 4.46-11.69). CONCLUSIONS: Risk of drug overdose varied substantially among US Medicare beneficiaries on different use trajectories of opioids and gabapentinoids. High-dose opioid-only users and all consistent opioid and gabapentinoid users (regardless of doses) had more than double the risk of subsequent drug overdose compared with opioid-only early discontinuers.

Decision analysis and reinforcement learning in surgical decision-making.

BACKGROUND: Surgical patients incur preventable harm from cognitive and judgment errors made under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges but are poorly understood and rarely used clinically. This review seeks to promote an understanding of decision analysis and reinforcement learning by describing their use in the context of surgical decision-making. METHODS: Cochrane, EMBASE, and PubMed databases were searched from their inception to June 2019. Included were 41 articles about cognitive and diagnostic errors, decision-making, decision analysis, and machine-learning. The articles were assimilated into relevant categories according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. RESULTS: Requirements for time-consuming manual data entry and crude representations of individual patients and clinical context compromise many traditional decision-support tools. Decision analysis methods for calculating probability thresholds can inform population-based recommendations that jointly consider risks, benefits, costs, and patient values but lack precision for individual patient-centered decisions. Reinforcement learning, a machine-learning method that mimics human learning, can use a large set of patient-specific input data to identify actions yielding the greatest probability of achieving a goal. This methodology follows a sequence of events with uncertain conditions, offering potential advantages for personalized, patient-centered decision-making. Clinical application would require secure integration of multiple data sources and attention to ethical considerations regarding liability for errors and individual patient preferences. CONCLUSION: Traditional decision-support tools are ill-equipped to accommodate time constraints and uncertainty regarding diagnoses and the predicted response to treatment, both of which often impair surgical decision-making. Decision analysis and reinforcement learning have the potential to play complementary roles in delivering high-value surgical care through sound judgment and optimal decision-making.

Intelligent, Autonomous Machines in Surgery.

Surgeons perform two primary tasks: operating and engaging patients and caregivers in shared decision-making. Human dexterity and decision-making are biologically limited. Intelligent, autonomous machines have the potential to augment or replace surgeons. Rather than regarding this possibility with denial, ire, or indifference, surgeons should understand and steer these technologies. Closer examination of surgical innovations and lessons learned from the automotive industry can inform this process. Innovations in minimally invasive surgery and surgical decision-making follow classic S-shaped curves with three phases: (1) introduction of a new technology, (2) achievement of a performance advantage relative to existing standards, and (3) arrival at a performance plateau, followed by replacement with an innovation featuring greater machine autonomy and less human influence. There is currently no level I evidence demonstrating improved patient outcomes using intelligent, autonomous machines for performing operations or surgical decision-making tasks. History suggests that if such evidence emerges and if the machines are cost effective, then they will augment or replace humans, initially for simple, common, rote tasks under close human supervision and later for complex tasks with minimal human supervision. This process poses ethical challenges in assigning liability for errors, matching decisions to patient values, and displacing human workers, but may allow surgeons to spend less time gathering and analyzing data and more time interacting with patients and tending to urgent, critical-and potentially more valuable-aspects of patient care. Surgeons should steer these technologies toward optimal patient care and net social benefit using the uniquely human traits of creativity, altruism, and moral deliberation.

Economic Considerations of Acute Pain Medicine Programs.

Acute pain medicine services influence many different aspects of postoperative recovery and function. Here, we discuss the various stakeholders of an acute pain medicine service, review the direct and indirect impact on said stakeholders, review the shared and competing interests between acute pain medicine programs and various payer systems, and discuss how APM services can help service lines align with the interests of the recent CMS Innovations Comprehensive Care for Joint Replacement Model.

Markov chain evaluation of acute postoperative pain transition states.

Previous investigations on acute postoperative pain dynamicity have focused on daily pain assessments, and so were unable to examine intraday variations in acute pain intensity. We analyzed 476,108 postoperative acute pain intensity ratings, which were clinically documented on postoperative days 1 to 7 from 8346 surgical patients using Markov chain modeling to describe how patients are likely to transition from one pain state to another in a probabilistic fashion. The Markov chain was found to be irreducible and positive recurrent, with no absorbing states. Transition probabilities ranged from 0.0031, for the transition from state 10 to state 1, to 0.69 for the transition from state 0 to state 0. The greatest density of transitions was noted in the diagonal region of the transition matrix, suggesting that patients were generally most likely to transition to the same pain state as their current state. There were also slightly increased probability densities in transitioning to a state of asleep or 0 from the current state. An examination of the number of steps required to traverse from a particular first pain score to a target state suggested that overall, fewer steps were required to reach a state of 0 (range 6.1-8.8 steps) or asleep (range 9.1-11) than were required to reach a mild pain intensity state. Our results suggest that using Markov chains is a feasible method for describing probabilistic postoperative pain trajectories, pointing toward the possibility of using Markov decision processes to model sequential interactions between pain intensity ratings, and postoperative analgesic interventions.

Geospatial analysis of hospital consumer assessment of healthcare providers and systems pain management experience scores in U.S. hospitals.

Although prior work has investigated the interplay between demographic and intrasurvey correlations of Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores, these prior studies have not included geospatial analyses, or analyses that take into account location effects. Here, we report the results of a geospatial analysis (not equivalent to simple geographical analysis) of patient experience scores pertaining to pain. HCAHPS data collected in 2011 were examined to test the hypothesis that HCAHPS patient experience with pain management (PEPM) scores were geospatially distributed throughout the United States using Moran's Index, which measures the association between PEPM scores and hospital location. After limiting the dataset to hospitals in the continental United States with nonzero HCAHPS response rates, 3645 hospitals were included in the analyses. "Always" responses were geospatially clustered amongst the analyzed hospitals. Clustering was significant in all distances tested from 10 to 5000km (P<0.0001). We identified 6 demarcated groups of hospitals. Taken together, these results strongly suggest a regional geographic effect on PEPM scores. These results may carry policy implications for U.S. hospitals with regard to acute pain outcomes. Further analyses will be necessary to evaluate policy explanations and implications of the regional geographic differences in PEPM results.

Primary payer status is associated with the use of nerve block placement for ambulatory orthopedic surgery.

INTRODUCTION: Although more than 30 million patients in the United States undergo ambulatory surgery each year, it remains unclear what percentage of these patients receive a perioperative nerve block. We reviewed data from the 2006 National Survey of Ambulatory Surgery to determine the demographic, socioeconomic, geographic, and clinical factors associated with the likelihood of nerve block placement for ambulatory orthopedic surgery. The primary outcome of interest was the association between primary method of payment and likelihood of nerve block placement. In addition, we examined the association between type of surgical procedures, patient demographics, and hospital characteristics with the likelihood of receiving a nerve block. METHODS: This cross-sectional study reviewed 6000 orthopedic anesthetics from the 2006 National Survey of Ambulatory Surgery data set, which accounted for more than 3.9 million orthopedic anesthetics when weighted. The primary outcome of this study addressed the likelihood of receiving a nerve block for orthopedic ambulatory surgery according to the patient's primary method of payment. Secondary end points included differences in demographics, surgical procedures, adverse effects, complications, recovery profile, anesthesia staffing model, and total perioperative charges in those with and without nerve block. RESULTS: Overall, 14.9% of anesthetics in this sample involved a peripheral nerve block. Length of time in postoperative recovery, total perioperative time, and total charges were less for those receiving nerve blocks. Patients were more likely to receive a nerve block if their procedures were performed in metropolitan service areas (odds ratio [OR], 1.86; 95% confidence interval [CI], 1.19-2.91; P = 0.007) or in freestanding surgical facilities (OR, 2.27; 95% CI, 1.74-2.96; P < 0.0001) and if payment for their surgery was supported by government programs (OR, 2.5; 95% CI, 1.01-6.21; P = 0.048) or private insurance (OR, 2.62; 95% CI, 1.12-6.13; P = 0.03) versus self-pay or charity care. CONCLUSIONS: For patients receiving ambulatory orthopedic surgery in the United States, our results suggest that geographic and socioeconomic factors are associated with different likelihoods of perioperative peripheral nerve block placement.

Role of interleukin-6 in a patient with tumor necrosis factor receptor-associated periodic syndrome: assessment of outcomes following treatment with the anti-interleukin-6 receptor monoclonal antibody tocilizumab.

In this report, we describe treatment outcomes in the first case of a patient with tumor necrosis factor receptor-associated periodic syndrome (TRAPS) treated with the anti-interleukin-6 (anti-IL-6) receptor monoclonal antibody tocilizumab. Since IL-6 levels are elevated in TRAPS, we hypothesized that tocilizumab might be effective. The patient, a 52-year-old man with lifelong TRAPS in whom treatment with etanercept and anakinra had failed, was administered tocilizumab for 6 months, and the therapeutic response was assessed by measurement of monocyte CD16 expression and cytokine levels. Following treatment, the evolving acute attack was aborted and further attacks of TRAPS were prevented. The patient did not require corticosteroids and showed significant clinical improvement in scores for pain, stiffness, and well-being. Moreover, the acute-phase response diminished significantly with treatment. Monocyte CD16 expression was reduced and the numbers of circulating CD14+CD16+ and CD14++CD16- monocytes were transiently decreased. However, cytokine levels were not reduced. This case supports the notion of a prominent role for IL-6 in mediating the inflammatory attacks in TRAPS, but blockade of IL-6 did not affect the underlying pathogenesis. These preliminary findings require confirmation.

Apoptosis in proliferative vitreoretinopathy.

PURPOSE: To study the involvement of apoptosis using different apoptosis markers in PVR pathogenesis. METHODS: The presence of mRNA coding for Fas, Fas ligand (FasL), and TNF-related apoptosis inducing ligand (TRAIL) was investigated in vitreous samples from 46 consecutive patients-25 with PVR, 11 with retinal detachment (RD) not complicated by PVR, and 10 with macular hole (MH)-using RT-PCR. From previously examined vitreous samples, 21 PVR, 9 RD, and 10 MH were examined for their levels of TGF-beta2 protein with sandwich ELISA kits. Five epiretinal membranes excised from five patients with PVR were also examined for apoptotic cell death using the terminal deoxytransferase (TdT) mediated dUTP-biotin nick end labeling (TUNEL) technique. RESULTS: FAS mRNA was detected in 72% of patients with PVR, 55% of patients with RD and 20% of patients with MH. TRAIL mRNA was detected in 67% of patients with PVR, 89% of patients with RD, and 20% of patients with MH. FasL mRNA was detected in 20% of patients with PVR, 9% of patients with RD, and 10% of patients with MH. The median levels of Fas and TRAIL mRNA were significantly higher (P < 0.05) in patients with PVR than in those with MH hole but between patients with PVR and those with RD the difference was not significant (P > 0.05). A significant difference was detected between RD and MH for TRAIL mRNA levels (P = 0.008). For FasL, no significant difference between groups was found. TGF-beta2 was detected in all investigated vitreous samples. A significant difference was found between the PVR and MH groups (P = 0.001) and between the RD and MH groups (P = 0.004), but not between the PVR and RD groups (P < 0.05). The level of TGF-beta2 was significantly correlated to the level of TRAIL mRNA (r = 0.86), but no correlation was found between TGF-beta2 and Fas mRNA levels (r = 0.21). Four of five examined PVR epiretinal membranes showed positive staining for apoptotic cells using the TUNEL technique. CONCLUSIONS: Apoptosis is one of the mechanisms that is involved in PVR pathogenesis. Different apoptosis markers suggest different pathways occur in PVR, including Fas/FasL, TRAIL, and TGF-beta2 mediated processes.