Best practices to evaluate the impact of biomedical research software-metric collection beyond citations.

MOTIVATION: Software is vital for the advancement of biology and medicine. Impact evaluations of scientific software have primarily emphasized traditional citation metrics of associated papers, despite these metrics inadequately capturing the dynamic picture of impact and despite challenges with improper citation. RESULTS: To understand how software developers evaluate their tools, we conducted a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We found that although developers realize the value of more extensive metric collection, they find a lack of funding and time hindering. We also investigated software among this community for how often infrastructure that supports more nontraditional metrics were implemented and how this impacted rates of papers describing usage of the software. We found that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seemed to be associated with increased mention rates. Analysing more diverse metrics can enable developers to better understand user engagement, justify continued funding, identify novel use cases, pinpoint improvement areas, and ultimately amplify their software's impact. Challenges are associated, including distorted or misleading metrics, as well as ethical and security concerns. More attention to nuances involved in capturing impact across the spectrum of biomedical software is needed. For funders and developers, we outline guidance based on experience from our community. By considering how we evaluate software, we can empower developers to create tools that more effectively accelerate biological and medical research progress. AVAILABILITY AND IMPLEMENTATION: More information about the analysis, as well as access to data and code is available at https://github.com/fhdsl/ITCR_Metrics_manuscript_website.

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

BACKGROUND: In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients. METHODS: We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern. RESULTS: With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors. CONCLUSION: In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.

SurvMaximin: Robust federated approach to transporting survival risk prediction models.

OBJECTIVE: For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. MATERIALS AND METHODS: For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning. RESULTS: Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. CONCLUSIONS: The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

Tocilizumab for Treatment of Mechanically Ventilated Patients With COVID-19.

BACKGROUND: Severe coronavirus disease 2019 (COVID-19) can manifest in rapid decompensation and respiratory failure with elevated inflammatory markers, consistent with cytokine release syndrome for which IL-6 blockade is an approved treatment. METHODS: We assessed effectiveness and safety of IL-6 blockade with tocilizumab in a single-center cohort of patients with COVID-19 requiring mechanical ventilation. The primary endpoint was survival probability postintubation; secondary analyses included an ordinal illness severity scale integrating superinfections. Outcomes in patients who received tocilizumab compared with tocilizumab-untreated controls were evaluated using multivariable Cox regression with propensity score inverse probability of treatment weighting (IPTW). RESULTS: 154 patients were included, of whom 78 received tocilizumab and 76 did not. Median follow-up was 47 days (range, 28-67). Baseline characteristics were similar between groups, although tocilizumab-treated patients were younger (mean: 55 vs 60 years), less likely to have chronic pulmonary disease (10% vs 28%), and had lower D-dimer values at time of intubation (median: 2.4 vs 6.5 mg/dL). In IPTW-adjusted models, tocilizumab was associated with a 45% reduction in hazard of death (HR, .55; 95% CI, .33-.90) and improved status on the ordinal outcome scale [OR per 1-level increase, .58; .36-.94). Although tocilizumab was associated with an increased proportion of patients with superinfections (54% vs 26%; P < .001), there was no difference in 28-day case fatality rate among tocilizumab-treated patients with versus without superinfection (22% vs 15%; P = .42). Staphylococcus aureus accounted for ~50% of bacterial pneumonia. CONCLUSIONS: In this cohort of mechanically ventilated COVID-19 patients, tocilizumab was associated with lower mortality despite higher superinfection occurrence.

Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks.

BACKGROUND: Traditional methods for disease risk prediction and assessment, such as diagnostic tests using serum, urine, blood, saliva or imaging biomarkers, have been important for identifying high-risk individuals for many diseases, leading to early detection and improved survival. For pancreatic cancer, traditional methods for screening have been largely unsuccessful in identifying high-risk individuals in advance of disease progression leading to high mortality and poor survival. Electronic health records (EHR) linked to genetic profiles provide an opportunity to integrate multiple sources of patient information for risk prediction and stratification. We leverage a constellation of temporally associated diagnoses available in the EHR to construct a summary risk score, called a phenotype risk score (PheRS), for identifying individuals at high-risk for having pancreatic cancer. The proposed PheRS approach incorporates the time with respect to disease onset into the prediction framework. We combine and contrast the PheRS with more well-known measures of inherited susceptibility, namely, the polygenic risk scores (PRS) for prediction of pancreatic cancer. METHODOLOGY: We first calculated pairwise, unadjusted associations between pancreatic cancer diagnosis and all possible other diagnoses across the medical phenome. We call these pairwise associations co-occurrences. After accounting for cross-phenotype correlations, the multivariable association estimates from a subset of relatively independent diagnoses were used to create a weighted sum PheRS. We constructed time-restricted risk scores using data from 38,359 participants in the Michigan Genomics Initiative (MGI) based on the diagnoses contained in the EHR at 0, 1, 2, and 5 years prior to the target pancreatic cancer diagnosis. The PheRS was assessed for predictability in the UK Biobank (UKB). We tested the relative contribution of PheRS when added to a model containing a summary measure of inherited genetic susceptibility (PRS) plus other covariates like age, sex, smoking status, drinking status, and body mass index (BMI). RESULTS: Our exploration of co-occurrence patterns identified expected associations while also revealing unexpected relationships that may warrant closer attention. Solely using the pancreatic cancer PheRS at 5 years before the target diagnoses yielded an AUC of 0.60 (95% CI = [0.58, 0.62]) in UKB. A larger predictive model including PheRS, PRS, and the covariates at the 5-year threshold achieved an AUC of 0.74 (95% CI = [0.72, 0.76]) in UKB. We note that PheRS does contribute independently in the joint model. Finally, scores at the top percentiles of the PheRS distribution demonstrated promise in terms of risk stratification. Scores in the top 2% were 10.20 (95% CI = [9.34, 12.99]) times more likely to identify cases than those in the bottom 98% in UKB at the 5-year threshold prior to pancreatic cancer diagnosis. CONCLUSIONS: We developed a framework for creating a time-restricted PheRS from EHR data for pancreatic cancer using the rich information content of a medical phenome. In addition to identifying hypothesis-generating associations for future research, this PheRS demonstrates a potentially important contribution in identifying high-risk individuals, even after adjusting for PRS for pancreatic cancer and other traditional epidemiologic covariates. The methods are generalizable to other phenotypic traits.

Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/31400.].

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

BACKGROUND: Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. OBJECTIVE: In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. METHODS: Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. RESULTS: Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. CONCLUSIONS: Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

An inclusive Research Education Community (iREC): Impact of the SEA-PHAGES program on research outcomes and student learning.

Engaging undergraduate students in scientific research promises substantial benefits, but it is not accessible to all students and is rarely implemented early in college education, when it will have the greatest impact. An inclusive Research Education Community (iREC) provides a centralized scientific and administrative infrastructure enabling engagement of large numbers of students at different types of institutions. The Science Education Alliance-Phage Hunters Advancing Genomics and Evolutionary Science (SEA-PHAGES) is an iREC that promotes engagement and continued involvement in science among beginning undergraduate students. The SEA-PHAGES students show strong gains correlated with persistence relative to those in traditional laboratory courses regardless of academic, ethnic, gender, and socioeconomic profiles. This persistent involvement in science is reflected in key measures, including project ownership, scientific community values, science identity, and scientific networking.

Impact of a health information technology tool addressing information needs of caregivers of adult and pediatric hematopoietic stem cell transplantation patients.

PURPOSE: We developed BMT Roadmap, a health information technology (HIT) application on a tablet, to address caregivers' unmet needs with patient-specific information from the electronic health record. We conducted a preliminary feasibility study of BMT Roadmap in caregivers of adult and pediatric HSCT patients. The study was registered on ClinicalTrials.gov (NCT03161665; NCT02409121). METHODS: BMT Roadmap was delivered to 39 caregivers of adult and pediatric patients undergoing first-time HSCT at a single study site. We assessed person-reported outcome measures (PROMs) at baseline (hospital admission), discharge, and day 100: usefulness of BMT Roadmap (Perceived Usefulness); activation (Patient Activation Measure-Caregiver version [PAM-C]); mental health ([POMS-2®]: depression, distress, vigor, and fatigue); anxiety (State-Trait Anxiety Inventory); and quality of life (Caregiver Quality of Life Index-Cancer [CQOLC]). To identify determinants of caregiver activation and quality of life, we used linear mixed models. RESULTS: BMT Roadmap was perceived useful and activation increased from baseline to discharge (p = 0.001). Further, burden decreased through discharge (p = 0.007). Overall, a pattern of increasing vigor and decreasing depression, distress, fatigue, and anxiety was apparent from baseline to discharge. However, overall quality of life lowered at discharge after accounting for BMT Roadmap use, depression, anxiety, and fatigue (p = 0.04). CONCLUSIONS: BMT Roadmap was a feasible HIT intervention to implement in HSCT caregivers. BMT Roadmap was associated with increased activation and decreased burden, but quality of life lowered across hospitalization. Findings support the need to further develop caregiver-specific self-directed resources and provide them both inpatient and outpatient across the HSCT trajectory.

Complexities, variations, and errors of numbering within clinical notes: the potential impact on information extraction and cohort-identification.

BACKGROUND: Numbers and numerical concepts appear frequently in free text clinical notes from electronic health records. Knowledge of the frequent lexical variations of these numerical concepts, and their accurate identification, is important for many information extraction tasks. This paper describes an analysis of the variation in how numbers and numerical concepts are represented in clinical notes. METHODS: We used an inverted index of approximately 100 million notes to obtain the frequency of various permutations of numbers and numerical concepts, including the use of Roman numerals, numbers spelled as English words, and invalid dates, among others. Overall, twelve types of lexical variants were analyzed. RESULTS: We found substantial variation in how these concepts were represented in the notes, including multiple data quality issues. We also demonstrate that not considering these variations could have substantial real-world implications for cohort identification tasks, with one case missing > 80% of potential patients. CONCLUSIONS: Numbering within clinical notes can be variable, and not taking these variations into account could result in missing or inaccurate information for natural language processing and information retrieval tasks.

BMT Roadmap: A User-Centered Design Health Information Technology Tool to Promote Patient-Centered Care in Pediatric Hematopoietic Cell Transplantation.

Health information technology (HIT) has great potential for increasing patient engagement. Pediatric hematopoietic cell transplantation (HCT) is a setting ripe for using HIT but in which little research exists. "BMT Roadmap" is a web-based application that integrates patient-specific information and includes several domains: laboratory results, medications, clinical trial details, photos of the healthcare team, trajectory of transplant process, and discharge checklist. BMT Roadmap was provided to 10 caregivers of patients undergoing first-time HCT. Research assistants performed weekly qualitative interviews throughout the patient's hospitalization and at discharge and day 100 to assess the impact of BMT Roadmap. Rigorous thematic analysis revealed 5 recurrent themes: emotional impact of the HCT process itself; critical importance of communication among patients, caregivers, and healthcare providers; ways in which BMT Roadmap was helpful during inpatient setting; suggestions for improving BMT Roadmap; and other strategies for organization and management of complex healthcare needs that could be incorporated into BMT Roadmap. Caregivers found the tool useful and easy to use, leading them to want even greater access to information. BMT Roadmap was feasible, with no disruption to inpatient care. Although this initial study is limited by the small sample size and single-institution experience, these initial findings are encouraging and support further investigation.

Development and empirical user-centered evaluation of semantically-based query recommendation for an electronic health record search engine.

OBJECTIVE: The utility of biomedical information retrieval environments can be severely limited when users lack expertise in constructing effective search queries. To address this issue, we developed a computer-based query recommendation algorithm that suggests semantically interchangeable terms based on an initial user-entered query. In this study, we assessed the value of this approach, which has broad applicability in biomedical information retrieval, by demonstrating its application as part of a search engine that facilitates retrieval of information from electronic health records (EHRs). MATERIALS AND METHODS: The query recommendation algorithm utilizes MetaMap to identify medical concepts from search queries and indexed EHR documents. Synonym variants from UMLS are used to expand the concepts along with a synonym set curated from historical EHR search logs. The empirical study involved 33 clinicians and staff who evaluated the system through a set of simulated EHR search tasks. User acceptance was assessed using the widely used technology acceptance model. RESULTS: The search engine's performance was rated consistently higher with the query recommendation feature turned on vs. off. The relevance of computer-recommended search terms was also rated high, and in most cases the participants had not thought of these terms on their own. The questions on perceived usefulness and perceived ease of use received overwhelmingly positive responses. A vast majority of the participants wanted the query recommendation feature to be available to assist in their day-to-day EHR search tasks. DISCUSSION AND CONCLUSION: Challenges persist for users to construct effective search queries when retrieving information from biomedical documents including those from EHRs. This study demonstrates that semantically-based query recommendation is a viable solution to addressing this challenge.

User-Centered Design Groups to Engage Patients and Caregivers with a Personalized Health Information Technology Tool.

Health information technology (IT) has opened exciting avenues for capturing, delivering and sharing data, and offers the potential to develop cost-effective, patient-focused applications. In recent years, there has been a proliferation of health IT applications such as outpatient portals. Rigorous evaluation is fundamental to ensure effectiveness and sustainability, as resistance to more widespread adoption of outpatient portals may be due to lack of user friendliness. Health IT applications that integrate with the existing electronic health record and present information in a condensed, user-friendly format could improve coordination of care and communication. Importantly, these applications should be developed systematically with appropriate methodological design and testing to ensure usefulness, adoption, and sustainability. Based on our prior work that identified numerous information needs and challenges of HCT, we developed an experimental prototype of a health IT tool, the BMT Roadmap. Our goal was to develop a tool that could be used in the real-world, daily practice of HCT patients and caregivers (users) in the inpatient setting. Herein, we examined the views, needs, and wants of users in the design and development process of the BMT Roadmap through user-centered Design Groups. Three important themes emerged: 1) perception of core features as beneficial (views), 2) alerting the design team to potential issues with the user interface (needs); and 3) providing a deeper understanding of the user experience in terms of wider psychosocial requirements (wants). These findings resulted in changes that led to an improved, functional BMT Roadmap product, which will be tested as an intervention in the pediatric HCT population in the fall of 2015 (ClinicalTrials.govNCT02409121).

DREAM: Classification scheme for dialog acts in clinical research query mediation.

Clinical data access involves complex but opaque communication between medical researchers and query analysts. Understanding such communication is indispensable for designing intelligent human-machine dialog systems that automate query formulation. This study investigates email communication and proposes a novel scheme for classifying dialog acts in clinical research query mediation. We analyzed 315 email messages exchanged in the communication for 20 data requests obtained from three institutions. The messages were segmented into 1333 utterance units. Through a rigorous process, we developed a classification scheme and applied it for dialog act annotation of the extracted utterances. Evaluation results with high inter-annotator agreement demonstrate the reliability of this scheme. This dataset is used to contribute preliminary understanding of dialog acts distribution and conversation flow in this dialog space.

Time dependent patient no-show predictive modelling development.

Purpose - The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach - A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings - The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications - The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value - This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.

Supporting information retrieval from electronic health records: A report of University of Michigan's nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE).

OBJECTIVE: This paper describes the University of Michigan's nine-year experience in developing and using a full-text search engine designed to facilitate information retrieval (IR) from narrative documents stored in electronic health records (EHRs). The system, called the Electronic Medical Record Search Engine (EMERSE), functions similar to Google but is equipped with special functionalities for handling challenges unique to retrieving information from medical text. MATERIALS AND METHODS: Key features that distinguish EMERSE from general-purpose search engines are discussed, with an emphasis on functions crucial to (1) improving medical IR performance and (2) assuring search quality and results consistency regardless of users' medical background, stage of training, or level of technical expertise. RESULTS: Since its initial deployment, EMERSE has been enthusiastically embraced by clinicians, administrators, and clinical and translational researchers. To date, the system has been used in supporting more than 750 research projects yielding 80 peer-reviewed publications. In several evaluation studies, EMERSE demonstrated very high levels of sensitivity and specificity in addition to greatly improved chart review efficiency. DISCUSSION: Increased availability of electronic data in healthcare does not automatically warrant increased availability of information. The success of EMERSE at our institution illustrates that free-text EHR search engines can be a valuable tool to help practitioners and researchers retrieve information from EHRs more effectively and efficiently, enabling critical tasks such as patient case synthesis and research data abstraction. CONCLUSION: EMERSE, available free of charge for academic use, represents a state-of-the-art medical IR tool with proven effectiveness and user acceptance.

Engraftment syndrome after allogeneic hematopoietic cell transplantation predicts poor outcomes.

Engraftment syndrome (ES), characterized by fever, rash, pulmonary edema, weight gain, liver and renal dysfunction, and/or encephalopathy, occurs at the time of neutrophil recovery after hematopoietic cell transplantation (HCT). In this study, we evaluated the incidence, clinical features, risk factors, and outcomes of ES in children and adults undergoing first-time allogeneic HCT. Among 927 patients, 119 (13%) developed ES at a median of 10 days (interquartile range 9 to 12) after HCT. ES patients experienced significantly higher cumulative incidence of grade 2 to 4 acute GVHD at day 100 (75% versus 34%, P < .001) and higher nonrelapse mortality at 2 years (38% versus 19%, P < .001) compared with non-ES patients, resulting in lower overall survival at 2 years (38% versus 54%, P < .001). There was no significant difference in relapse at 2 years (26% versus 31%, P = .772). Suppression of tumorigenicity 2, interleukin 2 receptor alpha, and tumor necrosis factor receptor 1 plasma biomarker levels were significantly elevated in ES patients. Our results illustrate the clinical significance and prognostic impact of ES on allogeneic HCT outcomes. Despite early recognition of the syndrome and prompt institution of corticosteroid therapy, outcomes in ES patients were uniformly poor. This study suggests the need for a prospective approach of collecting clinical features combined with correlative laboratory analyses to better characterize ES.

Osteochondromas after radiation for pediatric malignancies: a role for expanded counseling for skeletal side effects.

BACKGROUND: A relationship has been reported between total body irradiation (TBI) and later development of osteochondromas in children who receive radiation therapy as conditioning before hematopoietic stem cell transplantation (HSCT). The goal of this study was to better characterize osteochondromas occurring in these children. METHODS: We identified all children (0 to 18 y) who received an allogeneic HSCT and TBI from 2000 to 2012 from a blood and marrow transplant (BMT) database. Thereafter, we identified those who developed osteochondromas through a chart review. In addition, we searched for diagnosis and operative codes from 1996 to 2012 in our pediatric orthopaedic clinical records, isolating osteochondroma patients with a history of radiation exposure. RESULTS: Four patients who underwent allogeneic HSCT and were later diagnosed with osteochondromas were identified from the BMT database (N=233 children); all 4 were among a group of 72 patients who received TBI. Three patients were identified from orthopaedic records. The cohort included 5 boys and 2 girls with acute lymphoblastic leukemia (N=5) or neuroblastoma (N=2), diagnosed at a median age of 2.0 years. Therapy for all patients included chemotherapy, radiation therapy (TBI, N=5; abdominal, N=2), and HSCT. A diagnosis of osteochondroma was made at a median age of 11.7 years (range, 5 to 16 y), on average 8.6 years after radiation therapy. Diagnosis was incidental in 2 patients and secondary to symptoms (pain or genu valgum) in 5. Locations of osteochondromas were the proximal tibia (N=3), distal tibia, distal femur, distal ulna, and the distal phalanx (N=1 each). Three patients underwent surgical resection. CONCLUSIONS: Children may be more likely to develop osteochondromas after early exposure to radiation therapy, which may cause pain and require surgical resection. To the best of our knowledge, this is the first reported case of a radiation-induced osteochondroma causing lower extremity malalignment. Patients typically present to the pediatric orthopaedist's attention when symptomatic, but there may be an expanded role for counseling for potential for long-term skeletal effects in this group. LEVEL OF EVIDENCE: Level IV, case series.