Natural History of Keloids: A Sociodemographic Analysis Using Structured and Unstructured Data.

INTRODUCTION: Keloids are lesions characterized by the growth of dense fibrous tissue extending beyond original wound boundaries. Research into the natural history of keloids and potential differences by sociodemographic factors in the USA is limited. This real-world, retrospective cohort study aimed to characterize a population of patients with keloids compared with matched dermatology and general cohorts. METHODS: Patients with ≥ 2 International Classification of Diseases codes for keloid ≥ 30 days apart and a confirmed keloid diagnosis from clinical notes enrolled in the OM1 Real-World Data Cloud between 1 January 2013 and 18 March 2022 were age- and sex-matched 1:1:1 to patients without keloids who visited dermatologists ("dermatology cohort") and those who did not ("general cohort"). Results are presented using descriptive statistics and analysis stratified by cohort, race, ethnicity, household income, and education. RESULTS: Overall, 24,453 patients with keloids were matched to 23,936 dermatology and 24,088 general patients. A numerically higher proportion of patients with keloids were Asian or Black. Among available data for patients with keloids, 67.7% had 1 keloid lesion, and 68.3% had keloids sized 0.5 to < 3 cm. Black patients tended to have larger keloids. Asian and Black patients more frequently had > 1 keloid than did white patients (30.6% vs. 32.5% vs. 20.5%). Among all patients with keloids who had available data, 56.4% had major keloid severity, with major severity more frequent in Black patients. Progression was not significantly associated with race, ethnicity, income, or education level; 29%, 25%, and 20% of the dermatology, keloid, and general cohorts were in the highest income bracket (≥ US$75,000). The proportion of patients with income below the federal poverty line (< US$22,000) and patterns of education level were similar across cohorts. CONCLUSION: A large population of patients in the USA with keloids was identified and characterized using structured/unstructured sources. A numerically higher proportion of patients with keloids were non-white; Black patients had larger, more severe keloids at diagnosis.

The Association between Direct Oral Anticoagulants Prescribing Behavior and Non-Valvular Atrial Fibrillation Outcomes: An Instrumental Variable Analysis of Real-World Data.

Several observational studies have compared apixaban with rivaroxaban in patients with non-valvular atrial fibrillation (NVAF), but these analyses may be confounded by unmeasured characteristics. This study used provider prescribing preference (PPP) as an instrumental variable (IV) to assess the association between prescriber choice of rivaroxaban vs. apixaban and the study outcomes of stroke/systemic embolism (SE), major bleeding, and death in a retrospective cohort of NVAF patients in the US. Initiators of either medication were linked to their prescribers and followed until the first of the study outcome, the end of rivaroxaban/apixaban use, or 365 days after initiation. PPP for each patient was the percent of rivaroxaban initiations issued by the provider for the prior 10 NVAF patients. Cox regression models tested associations between quintiles of PPP and each outcome. A total of 61,155 patients and 1726 providers were included. The IV was a strong predictor of rivaroxaban prescription (OR = 17.9; 95% CI: 16.6, 19.3). There were statistically significant associations between increasing preference for rivaroxaban and rates of major bleeding (ptrend = 0.041) and death (ptrend = 0.031), but not stroke/SE (ptrend = 0.398). This analysis provides evidence of the relative safety of apixaban over rivaroxaban for the risk of major bleeding and death.

Mammographic Screening in Routine Practice: Multisite Study of Digital Breast Tomosynthesis and Digital Mammography Screenings.

Background The use of digital breast tomosynthesis (DBT) is increasing over digital mammography (DM) following studies demonstrating lower recall rates (RRs) and higher cancer detection rates (CDRs). However, inconsistent interpretation of evidence on the risks and benefits of mammography has resulted in varying screening mammography recommendations. Purpose To evaluate screening outcomes among women in the United States who underwent routine DM or DBT mammographic screening. Materials and Methods This retrospective cohort study included women aged 40-79 years who underwent DM or DBT screening mammograms between January 2014 and December 2020. Outcomes of RR, CDR, positive predictive value of recall (PPV1), biopsy rate, and positive predictive value of biopsy (PPV3) were compared between DM and DBT with use of adjusted multivariable logistic regression models. Results A total of 2 528 063 screening mammograms from 1 100 447 women (mean age, 57 years ± 10 [SD]) were included. In crude analyses, DBT (1 693 727 screening mammograms vs 834 336 DM screening mammograms) demonstrated lower RR (10.3% [95% CI: 10.3, 10.4] for DM vs 8.9% [95% CI: 8.9, 9.0] for DBT; P < .001) and higher CDR (4.5 of 1000 screening mammograms [95% CI: 4.3, 4.6] vs 5.3 of 1000 [95% CI: 5.2, 5.5]; P < .001), PPV1 (4.3% [95% CI: 4.2, 4.5] vs 5.9% [95% CI: 5.7, 6.0]; P < .001), and biopsy rates (14.5 of 1000 screening mammograms [95% CI: 14.2, 14.7] vs 17.6 of 1000 [95% CI: 17.4, 17.8]; P < .001). PPV3 was similar between cohorts (30.0% [95% CI: 29.2, 30.9] for DM vs 29.3% [95% CI: 28.7, 29.9] for DBT; P = .16). After adjustment for age, breast density, site, and index year, associations remained stable with respect to statistical significance. Conclusion Women undergoing digital breast tomosynthesis had improved screening mammography outcomes compared with women who underwent digital mammography. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae and Seo in this issue.

External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature.

BACKGROUND: There are many clinical prediction models (CPMs) available to inform treatment decisions for patients with cardiovascular disease. However, the extent to which they have been externally tested, and how well they generally perform has not been broadly evaluated. METHODS: A SCOPUS citation search was run on March 22, 2017 to identify external validations of cardiovascular CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Registry. We assessed the extent of external validation, performance heterogeneity across databases, and explored factors associated with model performance, including a global assessment of the clinical relatedness between the derivation and validation data. RESULTS: We identified 2030 external validations of 1382 CPMs. Eight hundred seven (58%) of the CPMs in the Registry have never been externally validated. On average, there were 1.5 validations per CPM (range, 0-94). The median external validation area under the receiver operating characteristic curve was 0.73 (25th-75th percentile [interquartile range (IQR)], 0.66-0.79), representing a median percent decrease in discrimination of -11.1% (IQR, -32.4% to +2.7%) compared with performance on derivation data. 81% (n=1333) of validations reporting area under the receiver operating characteristic curve showed discrimination below that reported in the derivation dataset. 53% (n=983) of the validations report some measure of CPM calibration. For CPMs evaluated more than once, there was typically a large range of performance. Of 1702 validations classified by relatedness, the percent change in discrimination was -3.7% (IQR, -13.2 to 3.1) for closely related validations (n=123), -9.0 (IQR, -27.6 to 3.9) for related validations (n=862), and -17.2% (IQR, -42.3 to 0) for distantly related validations (n=717; P<0.001). CONCLUSIONS: Many published cardiovascular CPMs have never been externally validated, and for those that have, apparent performance during development is often overly optimistic. A single external validation appears insufficient to broadly understand the performance heterogeneity across different settings.

Healthcare workers' perspectives on coronavirus testing availability: a cross sectional survey.

BACKGROUND: Studies on the impact of the novel SARS-CoV-2 virus (COVID) for healthcare workers (HCWs) rarely include the full spectrum of hospital workers, including less visible patient support roles. In the early days of the pandemic, COVID testing was preferentially available to HCWs. The objective of this study was to understand how individual experiences for all HCWs during the pandemic were associated with perceptions of access to, and receipt of COVID testing . METHODS: All hospital employees (n = 6736) in a single academic medical center in Boston, Massachusetts were invited to participate in a cross-sectional survey regarding perceived access to, and receipt of COVID testing during the first wave of the pandemic (March - August 2020). Responses were linked to human resources data. Log binomial univariate and multivariable models were used to estimate associations between individual and employment variables and COVID testing. RESULTS: A total of 2543 employees responded to the survey (38 %). The mean age was 40 years (± 14). Respondents were female (76 %), white (55 %), worked as nurses (27 %), administrators (22 %) and patient support roles (22 %); 56 % of respondents wanted COVID testing. Age (RR 0.91, CI 0.88-0.93), full time status (RR 0.85, CI 0.79-0.92), employment tenure (RR 0.96, CI 0.94-0.98), changes in quality of life (RR 0.94, CI 0.91-0.96), changes in job duties (RR 1.19, CI 1.03-1.37), and worry about enough paid sick leave (RR 1.21, CI 1.12-1.30) were associated with interest in testing. Administrators (RR 0.64, CI 0.58-0.72) and patient support staff (RR 0.85, CI 0.78-0.92) were less likely than nurses to want testing. Age (RR 1.04, CI 1.01-1.07), material hardships (RR 0.87, CI 0.79-0.96), and employer sponsored insurance (RR 1.10, CI 1.00-1.22) were associated with receiving a COVID test. Among all employees, only administrative/facilities staff were less likely to receive COVID testing (RR 0.69, CI 0.59-0.79). CONCLUSIONS: This study adds to our understanding of how hospital employees view availability of COVID testing. Hazard pay or other supports for hospital workers may increase COVID testing rates. These findings may be applicable to perceived barriers towards vaccination receipt.

Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination.

OBJECTIVE: To assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at external validation. STUDY DESIGN AND SETTING: We evaluated risk of bias (ROB) on 102 CPMs from the Tufts CPM Registry, comparing PROBAST to a short form consisting of six PROBAST items anticipated to best identify high ROB. We then applied the short form to all CPMs in the Registry with at least 1 validation (n=556) and assessed the change in discrimination (dAUC) in external validation cohorts (n=1,147). RESULTS: PROBAST classified 98/102 CPMS as high ROB. The short form identified 96 of these 98 as high ROB (98% sensitivity), with perfect specificity. In the full CPM registry, 527 of 556 CPMs (95%) were classified as high ROB, 20 (3.6%) low ROB, and 9 (1.6%) unclear ROB. Only one model with unclear ROB was reclassified to high ROB after full PROBAST assessment of all low and unclear ROB models. Median change in discrimination was significantly smaller in low ROB models (dAUC -0.9%, IQR -6.2-4.2%) compared to high ROB models (dAUC -11.7%, IQR -33.3-2.6%; P<0.001). CONCLUSION: High ROB is pervasive among published CPMs. It is associated with poor discriminative performance at validation, supporting the application of PROBAST or a shorter version in CPM reviews.

Surgical Predictive Model for Breast Cancer Patients Assessing Acute Postoperative Complications: The Breast Cancer Surgery Risk Calculator.

BACKGROUND: Prognostic tools, such as risk calculators, improve the patient-physician informed decision-making process. These tools are limited for breast cancer patients when assessing surgical complication risk preoperatively. OBJECTIVE: In this study, we aimed to assess predictors associated with acute postoperative complications for breast cancer patients and then develop a predictive model that calculates a complication probability using patient risk factors. METHODS: We performed a retrospective cohort study using the National Surgical Quality Improvement Program (NSQIP) database from 2005 to 2017. Women diagnosed with ductal carcinoma in situ or invasive breast cancer who underwent either breast conservation or mastectomy procedures were included in this predictive modeling scheme. Four models were built using logistic regression methods to predict the following composite outcomes: overall, infectious, hematologic, and internal organ complications. Model performance, accuracy and calibration measures during internal/external validation included area under the curve, Brier score, and Hosmer-Lemeshow statistic, respectively. RESULTS: A total of 163,613 women met the inclusion criteria. The area under the curve for each model was as follows: overall, 0.70; infectious, 0.67; hematologic, 0.84; and internal organ, 0.74. Brier scores were all between 0.04 and 0.003. Model calibration using the Hosmer-Lemeshow statistic found all p-values to be > 0.05. Using model coefficients, individualized risk can be calculated on the web-based Breast Cancer Surgery Risk Calculator (BCSRc) platform ( www.breastcalc.org ). CONCLUSION: We developed an internally and externally validated risk calculator that estimates a breast cancer patient's unique risk of acute complications following each surgical intervention. Preoperative use of the BCSRc can potentially help stratify patients with an increased complication risk and improve expectations during the decision-making process.

Association Between Lymphopenia at 1 Month Posttransplant and Infectious Outcomes or Death in Heart Transplant Recipients.

BACKGROUND: Cell-mediated immunity is a specific target of several medications used to prevent or treat rejection in orthotopic heart transplantation. Low absolute lymphocyte count (ALC) has potential to be a useful and accessible clinical indicator of overall infection risk. Though some studies have demonstrated this association in other transplant populations, it has not been assessed in heart transplant recipients. METHODS: A single-center retrospective cohort study examined adult heart transplant recipients transplanted between 2000 and 2018. The exposure of interest was ALC ≤0.75 × 103 cells/µL at 1 month posttransplant, and the primary endpoint was a composite outcome of infection (including cytomegalovirus [CMV], herpes simplex I/II or varicella zoster virus [HSV/VZV], bloodstream infection [BSI], invasive fungal infection [IFI]) or death occurring after 1 month and before 1 year posttransplant. A multivariable Cox proportional hazards model was created to control for confounders identified using clinical judgment and statistical criteria. RESULTS: Of 375 subjects analyzed, 101 (27%) developed the composite outcome (61 CMV, 3 HSV/VZV, 19 BSI, 10 IFI, 8 deaths). Lymphopenia (ALC ≤0.75 × 103 cells/µL) at 1 month was associated with a >2-fold higher rate of the composite outcome (hazard ratio [HR], 2.26 [95% confidence interval {CI}, 1.47-3.46]; P < .001) compared to patients without lymphopenia at 1 month. After adjustment for confounding variables, the presence of lymphopenia remained statistically significantly associated with the composite outcome (HR, 1.72 [95% CI, 1.08-2.75]; P = .02). CONCLUSIONS: ALC measured at 1 month after heart transplant is associated with an increased risk of infectious outcomes or death in the ensuing 11 months. This is a simple, accessible laboratory measure.

Predictive approaches to heterogeneous treatment effects: a scoping review.

BACKGROUND: Recent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial. METHODS: We performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel. RESULTS: The approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups, relying on the mathematical dependency of the absolute risk difference on baseline risk; 2) Treatment effect modeling methods (9 papers) use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy on a relative scale. These methods couple data-driven subgroup identification with approaches to prevent overfitting, such as penalization or use of separate data sets for subgroup identification and effect estimation. 3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers to classify the trial population into those who benefit from treatment and those who do not. Finally, we also identified papers which describe model evaluation methods (4 papers). CONCLUSIONS: Three classes of approaches were identified to assess heterogeneity of treatment effect. Methodological research, including both simulations and empirical evaluations, is required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis.

Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities.

The machine learning community has become alert to the ways that predictive algorithms can inadvertently introduce unfairness in decision-making. Herein, we discuss how concepts of algorithmic fairness might apply in healthcare, where predictive algorithms are being increasingly used to support decision-making. Central to our discussion is the distinction between algorithmic fairness and algorithmic bias. Fairness concerns apply specifically when algorithms are used to support polar decisions (i.e., where one pole of prediction leads to decisions that are generally more desired than the other), such as when predictions are used to allocate scarce health care resources to a group of patients that could all benefit. We review different fairness criteria and demonstrate their mutual incompatibility. Even when models are used to balance benefits-harms to make optimal decisions for individuals (i.e., for non-polar decisions)-and fairness concerns are not germane-model, data or sampling issues can lead to biased predictions that support decisions that are differentially harmful/beneficial across groups. We review these potential sources of bias, and also discuss ways to diagnose and remedy algorithmic bias. We note that remedies for algorithmic fairness may be more problematic, since we lack agreed upon definitions of fairness. Finally, we propose a provisional framework for the evaluation of clinical prediction models offered for further elaboration and refinement. Given the proliferation of prediction models used to guide clinical decisions, developing consensus for how these concerns can be addressed should be prioritized.

Why clinical trials may not help patients make treatment decisions: results from focus group discussions with 22 patients.

Aim: Despite broad interest in advancing personalized medicine, most evidence is currently derived from average results of clinical trials that may obscure heterogeneity of trial participants. Little is known currently about how patients view heterogeneity in trials and whether they can participate in methodological discussions about this concept. Materials & methods: In structured discussions with three focus groups involving 22 participants, we assessed how representatives of patient communities have used research to guide individual treatment decisions. Discussion themes were organized into a framework describing patient decision-making in four steps: decisions patients make in the course of care; information used to make decisions; sources for information; and quality of information. Results/conclusion: Patients prioritize information that reflects their own characteristics, preferences and values. They struggle applying clinical research to their own case.

When predictions are used to allocate scarce health care resources: three considerations for models in the era of Covid-19.

BACKGROUND: The need for life-saving interventions such as mechanical ventilation may threaten to outstrip resources during the Covid-19 pandemic. Allocation of these resources to those most likely to benefit can be supported by clinical prediction models. The ethical and practical considerations relevant to predictions supporting decisions about microallocation are distinct from those that inform shared decision-making in ways important for model design. MAIN BODY: We review three issues of importance for microallocation: (1) Prediction of benefit (or of medical futility) may be technically very challenging; (2) When resources are scarce, calibration is less important for microallocation than is ranking to prioritize patients, since capacity determines thresholds for resource utilization; (3) The concept of group fairness, which is not germane in shared decision-making, is of central importance in microallocation. Therefore, model transparency is important. CONCLUSION: Prediction supporting allocation of life-saving interventions should be explicit, data-driven, frequently updated and open to public scrutiny. This implies a preference for simple, easily understood and easily applied prognostic models.

Impact of early surveillance on safety signal identification in the CathPCI DELTA study.

OBJECTIVES: The CathPCI Data Extraction and Longitudinal Trend Analysis study was designed to determine the feasibility of conducting prospective surveillance of a large national registry to perform comparative safety analyses of medical devices. We sought to determine whether the complementary use of retrospective case data could improve safety signal detection time. DESIGN: We performed a simulated surveillance study of the comparative safety of the Mynx vascular closure device (VCD) with propensity score matched alternate VCD recipients, using both retrospective and prospective cohort data. SETTING: Centers within the USA using the National Cardiovascular Data Registry (NCDR) CathPCI Registry. PARTICIPANTS: Percutaneous coronary intervention cases captured within the NCDR CathPCI Registry from July 1, 2009 to September 30, 2013 were included in the analysis. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Absolute and relative risk (RR) of any vascular complication (a composite of bleeding at access site, hematoma at access site, retroperitoneal bleeding, and other vascular complications requiring treatment); time to signal detection. RESULTS: A safety alert was detected for the primary outcome of "any vascular complication" after 15 months of surveillance and was sustained for the study duration (absolute risk of any vascular complication, 1.20% vs 0.73%, RR, 1.63; 95% CI 1.50 to 1.79; p<0.001). The safety signal was identified 12 months earlier with the use of retrospective case data than during the initial study. CONCLUSIONS: Prospective, active surveillance of cardiovascular registries is feasible to perform comparative analyses of medical devices. Retrospective data may complement prospective surveillance to improve time to signal detection, indicating the need for earlier prospective application of safety surveillance for devices new to the market.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration.

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed to promote the conduct of, and provide guidance for, predictive analyses of heterogeneity of treatment effects (HTE) in clinical trials. The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risk with versus without the intervention, taking into account all relevant patient attributes simultaneously, to support more personalized clinical decision making than can be made on the basis of only an overall average treatment effect. The authors distinguished 2 categories of predictive HTE approaches (a "risk-modeling" and an "effect-modeling" approach) and developed 4 sets of guidance statements: criteria to determine when risk-modeling approaches are likely to identify clinically meaningful HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. They discuss limitations of these methods and enumerate research priorities for advancing methods designed to generate more personalized evidence. This explanation and elaboration document describes the intent and rationale of each recommendation and discusses related analytic considerations, caveats, and reservations.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement.

Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined "1 variable at a time" (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, "predictive HTE analysis." The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs.

Clinical Prediction Models for Valvular Heart Disease.

Background While many clinical prediction models (CPMs) exist to guide valvular heart disease treatment decisions, the relative performance of these CPMs is largely unknown. We systematically describe the CPMs available for patients with valvular heart disease with specific attention to performance in external validations. Methods and Results A systematic review identified 49 CPMs for patients with valvular heart disease treated with surgery (n=34), percutaneous interventions (n=12), or no intervention (n=3). There were 204 external validations of these CPMs. Only 35 (71%) CPMs have been externally validated. Sixty-five percent (n=133) of the external validations were performed on distantly related populations. There was substantial heterogeneity in model performance and a median percentage change in discrimination of -27.1% (interquartile range, -49.4%--5.7%). Nearly two-thirds of validations (n=129) demonstrate at least a 10% relative decline in discrimination. Discriminatory performance of EuroSCORE II and Society of Thoracic Surgeons (2009) models (accounting for 73% of external validations) varied widely: EuroSCORE II validation c-statistic range 0.50 to 0.95; Society of Thoracic Surgeons (2009) Models validation c-statistic range 0.50 to 0.86. These models performed well when tested on related populations (median related validation c-statistics: EuroSCORE II, 0.82 [0.76, 0.85]; Society of Thoracic Surgeons [2009], 0.72 [0.67, 0.79]). There remain few (n=9) external validations of transcatheter aortic valve replacement CPMs. Conclusions Many CPMs for patients with valvular heart disease have never been externally validated and isolated external validations appear insufficient to assess the trustworthiness of predictions. For surgical valve interventions, there are existing predictive models that perform reasonably well on related populations. For transcatheter aortic valve replacement (CPMs additional external validations are needed to broadly understand the trustworthiness of predictions.

Comparative analysis of traumatic esophageal injury in pediatric and adult populations.

PURPOSE: Distribution and outcomes of traumatic injury of the esophagus (TIE) in pediatric versus adult populations are unknown. Our study sought to perform a descriptive analysis of TIE in children and adults. METHODS: We reviewed the National Trauma Data Bank (NTDB) for the years 2010-2015. Demographics, characteristics, and outcomes of pediatric (age < 16 years) and adult TIE patients were described and compared. RESULTS: Among 526,850 pediatric and 3,838,895 adult trauma patients, 90 pediatric (0.02%) and 1,411 (0.04%) adult TIE patients were identified. Demographics and esophageal injury severity did not differ. Children were more likely to sustain blunt trauma (63% versus 37%), with the most common mechanism being transportation-related accidents, were less-severely injured (median ISS 14 versus 22), and had fewer associated injuries (79% versus 95%) and complications (30% versus 51%) (all p < 0.001). Children had shorter hospitalizations (median 5 versus 10 days) and were more likely to be discharged home (84% versus 64%) (both p = 0.01). In-hospital mortality did not differ significantly between children and adults (10% versus 19%, p = 0.09). CONCLUSION: TIE in the pediatric population has unique characteristics compared to adults: it is more likely to be a result of blunt trauma, has lower injury burden, and has more favorable clinical outcomes.

Epidemiology of Traumatic Esophageal Injury: An Analysis of the National Trauma Data Bank.

Existing literature on traumatic injury of the esophagus (TIE) is limited. We aimed to describe the clinical characteristics and outcomes of TIE. We reviewed the National Trauma Data Bank for the years 2010-2015. We described the demographics, characteristics, and outcomes of adult (age ≥16 years) TIE patients and also compared those factors in blunt versus penetrating TIE. The association between TIE and mortality was analyzed using multivariable logistic regression. Thousand four hundred eleven adult TIE patients were identified (37 per 100,000 trauma patients, 95% confidence intervals (CI): 35, 39). TIE patients were younger (38 vs 52 years), more likely to be male (81% vs 62%), and more severely injured (Injury Severity Score ≥ 25: 45% vs 7%) than patients without TIE (all P < 0.001). TIE was observed 16 times more frequently with penetrating injuries (257 per 100,000, 95% CI: 240, 270) than with blunt injuries (16 per 100,000, 95% CI: 15, 18). Inhospital TIE mortality was 19 per cent. TIE patients had greater risk of mortality than other trauma patients, after adjusting for age, gender, and Injury Severity Score (odds ratio = 1.4, 95% CI: 1.1, 1.7). Mortality in blunt and penetrating TIE did not differ. Although extremely rare, TIE is independently associated with a marked increase in mortality, even after adjusting for other risk factors.

Qualitative and quantitative variations in liver function thresholds among clinical trials in cancer: a need for harmonization.

PURPOSE: The liver is critically involved in drug metabolism pathways and the potential for hepatic toxicity is significant with specific cancer therapeutics. Variations in the definition of liver function thresholds that may generate heterogeneity of toxicity and efficacy outcomes across therapeutics trials in cancer require assessment. METHODS: A random sample of therapeutic trials in cancer (n = 500, general category), trials using hepatotoxic drugs (abiraterone, pazopanib: n = 181), trials using drugs metabolized by the liver (doxorubicin, vincristine: n = 606), and therapeutic trials in hepatic dysfunction (n = 49) were each identified on clinicaltrials.gov. Definitions of liver function thresholds and their distribution were collated and categorized in each group. RESULTS: A third of all trials listed on clinicaltrials.gov across the four categories failed to provide an explicit definition of liver function. Among trials with an explicit definition, a combination of bilirubin and transaminase levels was used in 33-64%, whereas a miscellaneous combination of definitions (in the general category consisting of 11 unique liver function parameters creating 17 unique combinations) was used 29-58% of the time across the four categories of studies. The Child-Pugh or National Cancer Institute Organ Dysfunction Working Group (NCI-ODWG) criteria were rarely employed (0-12% studies). Allowance for Gilbert's disease in bilirubin thresholds was identified in only 6-23% studies and for liver metastases in 2-15% of studies. CONCLUSIONS: There is a marked heterogeneity in the liver function definitions used across cancer clinical trials even when the potential for drug toxicity and altered drug metabolism is significant. Harmonization of criteria will streamline eligibility and mitigate variations in key outcomes across trials.