Predicting Out-of-Hospital Cardiac Arrest in the General Population Using Electronic Health Records.

BACKGROUND: The majority of out-of-hospital cardiac arrests (OHCAs) occur among individuals in the general population, for whom there is no established strategy to identify risk. In this study, we assess the use of electronic health record (EHR) data to identify OHCA in the general population and define salient factors contributing to OHCA risk. METHODS: The analytical cohort included 2366 individuals with OHCA and 23 660 age- and sex-matched controls receiving health care at the University of Washington. Comorbidities, electrocardiographic measures, vital signs, and medication prescription were abstracted from the EHR. The primary outcome was OHCA. Secondary outcomes included shockable and nonshockable OHCA. Model performance including area under the receiver operating characteristic curve and positive predictive value were assessed and adjusted for observed rate of OHCA across the health system. RESULTS: There were significant differences in demographic characteristics, vital signs, electrocardiographic measures, comorbidities, and medication distribution between individuals with OHCA and controls. In external validation, discrimination in machine learning models (area under the receiver operating characteristic curve 0.80-0.85) was superior to a baseline model with conventional cardiovascular risk factors (area under the receiver operating characteristic curve 0.66). At a specificity threshold of 99%, correcting for baseline OHCA incidence across the health system, positive predictive value was 2.5% to 3.1% in machine learning models compared with 0.8% for the baseline model. Longer corrected QT interval, substance abuse disorder, fluid and electrolyte disorder, alcohol abuse, and higher heart rate were identified as salient predictors of OHCA risk across all machine learning models. Established cardiovascular risk factors retained predictive importance for shockable OHCA, but demographic characteristics (minority race, single marital status) and noncardiovascular comorbidities (substance abuse disorder) also contributed to risk prediction. For nonshockable OHCA, a range of salient predictors, including comorbidities, habits, vital signs, demographic characteristics, and electrocardiographic measures, were identified. CONCLUSIONS: In a population-based case-control study, machine learning models incorporating readily available EHR data showed reasonable discrimination and risk enrichment for OHCA in the general population. Salient factors associated with OCHA risk were myriad across the cardiovascular and noncardiovascular spectrum. Public health and tailored strategies for OHCA prediction and prevention will require incorporation of this complexity.

Selective prediction-set models with coverage rate guarantees.

The current approach to using machine learning (ML) algorithms in healthcare is to either require clinician oversight for every use case or use their predictions without any human oversight. We explore a middle ground that lets ML algorithms abstain from making a prediction to simultaneously improve their reliability and reduce the burden placed on human experts. To this end, we present a general penalized loss minimization framework for training selective prediction-set (SPS) models, which choose to either output a prediction set or abstain. The resulting models abstain when the outcome is difficult to predict accurately, such as on subjects who are too different from the training data, and achieve higher accuracy on those they do give predictions for. We then introduce a model-agnostic, statistical inference procedure for the coverage rate of an SPS model that ensembles individual models trained using K-fold cross-validation. We find that SPS ensembles attain prediction-set coverage rates closer to the nominal level and have narrower confidence intervals for its marginal coverage rate. We apply our method to train neural networks that abstain more for out-of-sample images on the MNIST digit prediction task and achieve higher predictive accuracy for ICU patients compared to existing approaches.

Unveiling genetic diversity and forensic utility of SureID® human DNA identification kits: a comprehensive analysis of 44 autosomal STR loci in English and Irish populations.

Background: Human identification and kinship testing in forensic science rely on Short Tandem Repeat (STR) multiplex kits, typically containing loci recommended by standard sets. However, complementary kits with additional STR loci can be valuable in complex cases. Allele frequency databases specific to the population are essential for accurate forensic analysis.Aim: This study aimed to generate allele frequencies and population genetic data for 44 autosomal STR loci from SureID® PanGlobal and 27comp kits in English and Irish populations for forensic casework, human identification, and kinship testing.Subjects and methods: Buccal swab samples from 645 White Caucasians (365 English, 280 Irish) were collected. DNA was extracted and amplified using the mentioned kits. Quality control, statistical analysis, and genetic distance calculations were performed.Results: Both kits demonstrated robustness with no significant deviations from Hardy-Weinberg Equilibrium (HWE). Variant alleles and minor discordances between kits were observed. Syntenic STR pairs were identified but showed no significant linkage. A close genetic relationship was found between English and Irish populations, allowing for combined databases.Conclusions: The SureID® PanGlobal and 27comp kits showed high discriminatory power and reliability in the English and Irish populations. Care is needed when handling variant alleles, discordances, and syntenic loci. Combining data from both populations is feasible for a comprehensive database. Further studies are required to explore their effectiveness in diverse populations.

Sperm granulomas: Predictive factors and impacts on patency post vasectomy reversal.

The objective of this study was to identify factors that predict for sperm granuloma formation and the impact of sperm granuloma presence and quantity on vasectomy reversal (VR) outcomes. A cross sectional retrospective review of prospectively collected data, on the impact of granuloma on VR outcomes from a single academic center was performed. The impact of age, obstructive interval, intraoperative vasal fluid findings, anastomosis type, body mass index, tobacco use and total motile count (TMC) was determined. A total of 1550 men underwent VR between January 2000 and August 2019. Granulomas were present unilaterally in 23.3% (n = 361) and bilaterally in 14.2% (n = 220). On univariate analysis, increasing patient age negatively correlated with a larger number of granulomas (p = .011). Granuloma presence was associated with finding intact and motile sperm from the vasal stump intraoperatively (p = .001), and vasoepididymostomy anastomosis (p < .001). However, granuloma presence (and quantity) did not correlate with obstructive interval or maximum TMC. Tobacco use and body mass index (BMI) were not associated with granuloma presence. On multivariate analysis, granuloma quantity was not associated with TMC. Obstructive interval and vasovasostomy anastomosis were associated with higher TMC, while BMI was negatively associated with TMC. In conclusion, increasing age was negatively correlated with granuloma formation. Granuloma presence was associated with more favourable intraoperative fluid findings and anastomosis type, but not post-VR TMC, suggesting men with and without granulomas undergoing skilled microsurgery will have similar patency rates. Heavier men should be encouraged for weight loss prior to vasectomy reversal as increasing BMI was associated with lower TMC.

Pediatric Metastatic Cardiac Angiosarcoma Successfully Treated With Multimodal Therapy: Case Report and Review of Literature.

Cardiac angiosarcoma (AS) is an extremely rare, malignant vascular tumor with <10 cases reported in the pediatric literature. Prognosis is dismal with overall survival often <1 year from initial diagnosis. In this report, we present the case of a 10-year-old boy with metastatic cardiac AS who is currently alive and is the longest pediatric survivor of metastatic cardiac AS reported in the literature. This is the only published pediatric case to successfully use a combination of surgical resection, conventional chemotherapy, radiation and targeted therapies including bevacizumab and pazopanib for metastatic cardiac AS.

Autoimmune cytopenias following allogeneic hematopoietic stem cell transplant in pediatric patients: Response to therapy and late effects.

BACKGROUND: Autoimmune cytopenias (AICs) are rare, but serious complications of allogeneic hematopoietic cell transplantation (allo-HSCT). PROCEDURE: We performed a case-control study using 20 pediatric AIC cases and 40 controls, matched by stem cell source and primary indication comparing clinical and transplant characteristics, treatment, outcomes, and late effects. RESULTS: Cases were more likely to be human leukocyte antigen mismatched (P = 0.04). There was no difference in conditioning regimen, serotherapy use, graft-versus-host disease (GVHD) prophylaxis, incidence of acute or chronic GVHD, ABO compatibility, infections, and donor engraftment. The median time to AIC onset was 219 days (range, 97-1205 days) and AIC resolution was 365 days (range, 10 days to 2737.5 days). First-line therapies for AIC patients most commonly included corticosteroids (75%) and rituximab (55%). Only 25% of patients responded to first-line treatment. At a median of 611.5 days from last rituximab dose, 82.5% patients were still receiving intravenous immune globulin for hypogammaglobulinemia compared with 2.5% of controls (P < 0.0001). Iron overload was higher in AIC patients (P = 0.0004), as was avascular necrosis (P = 0.04). There was no difference in overall survival at one year after HSCT (85% vs 82.5%). Two patients with refractory autoimmune hemolytic anemia responded to daratumumab and had resolution of B-cell aplasia. CONCLUSIONS: In this study, we find poor initial responses to AIC-directed therapies and significant late effects.

Organophosphate exposure and the chronic effects on farmers: a narrative review.

INTRODUCTION: Organophosphates are a class of insecticides used globally by the agricultural industry for insect control. Acute consequences of organophosphate exposures are well known, while there has been limited research on their long-term effects. The objective of this review was to discuss the health effects of chronic organophosphate exposure in farmers. METHODS: Medline, Scopus and Web of Science were searched to find the relevant articles. Articles published only in English and until December 2018 were reviewed. The selected articles were then categorised as neurological (neurobehaviour, neurodevelopmental, neurological signs and symptoms) or non-neurological subheadings. RESULTS: A total of 53 articles for neurological effects and 17 articles for non-neurological effects were identified. Chronic organophosphates exposure was associated with deficits in the neurobehaviour subsets of attention and short-term memory, increased incidence of neurodegenerative diseases and effects on peripheral nerves and neurodevelopment. However, research to support non-neurological effects such as respiratory symptoms, increased cancer risk, endocrine disruption, cardiac issues, chronic fatigue and infertility was limited. CONCLUSION: Chronic organophosphate exposure was found to affect four of the five areas of described neurological effects in the literature. A large proportion of the research in this area was not methodologically strong, therefore few recommendations can be conclusively made. Future research is warranted to investigate the non-neurological effects of chronic exposure to ensure the occupational risks of low-level chronic exposure are clearly communicated to farmers and farm workers.

Cellular Immune Responses against Simian T-Lymphotropic Virus Type 1 Target Tax in Infected Baboons.

UNLABELLED: There are currently 5 million to 10 million human T-lymphotropic virus type 1 (HTLV-1)-infected people, and many of them will develop severe complications resulting from this infection. A vaccine is urgently needed in areas where HTLV-1 is endemic. Many vaccines are best tested in nonhuman primate animal models. As a first step in designing an effective HTLV-1 vaccine, we defined the CD8(+) and CD4(+) T cell response against simian T-lymphotropic virus type 1 (STLV-1), a virus closely related to HTLV-1, in olive baboons (Papio anubis). Consistent with persistent antigenic exposure, we observed that STLV-1-specific CD8(+) T cells displayed an effector memory phenotype and usually expressed CD107a, gamma interferon (IFN-γ), and tumor necrosis factor alpha (TNF-α). To assess the viral targets of the cellular immune response in STLV-1-infected animals, we used intracellular cytokine staining to detect responses against overlapping peptides covering the entire STLV-1 proteome. Our results show that, similarly to humans, the baboon CD8(+) T cell response narrowly targeted the Tax protein. Our findings suggest that the STLV-1-infected baboon model may recapitulate some of the important aspects of the human response against HTLV-1 and could be an important tool for the development of immune-based therapy and prophylaxis. IMPORTANCE: HTLV-1 infection can lead to many different and often fatal conditions. A vaccine deployed in areas of high prevalence might reduce the incidence of HTLV-1-induced disease. Unfortunately, there are very few animal models of HTLV-1 infection useful for testing vaccine approaches. Here we describe cellular immune responses in baboons against a closely related virus, STLV-1. We show for the first time that the immune response against STLV-1 in naturally infected baboons is largely directed against the Tax protein. Similar findings in humans and the sequence similarity between the human and baboon viruses suggest that the STLV-1-infected baboon model might be useful for developing a vaccine against HTLV-1.

Subject-level spinal osteoporotic fracture prediction combining deep learning vertebral outputs and limited demographic data.

Automated screening for vertebral fractures could improve outcomes. We achieved an AUC-ROC = 0.968 for the prediction of moderate to severe fracture using a GAM with age and three maximal vertebral body scores of fracture from a convolutional neural network. Maximal fracture scores resulted in a performant model for subject-level fracture prediction. Combining individual deep learning vertebral body fracture scores and demographic covariates for subject-level classification of osteoporotic fracture achieved excellent performance (AUC-ROC of 0.968) on a large dataset of radiographs with basic demographic data. PURPOSE: Osteoporotic vertebral fractures are common and morbid. Automated opportunistic screening for incidental vertebral fractures from radiographs, the highest volume imaging modality, could improve osteoporosis detection and management. We consider how to form patient-level fracture predictions and summarization to guide management, using our previously developed vertebral fracture classifier on segmented radiographs from a prospective cohort study of US men (MrOS). We compare the performance of logistic regression (LR) and generalized additive models (GAM) with combinations of individual vertebral scores and basic demographic covariates. METHODS: Subject-level LR and GAM models were created retrospectively using all fracture predictions or summary variables such as order statistics, adjacent vertebral interactions, and demographic covariates (age, race/ethnicity). The classifier outputs for 8663 vertebrae from 1176 thoracic and lumbar radiographs in 669 subjects were divided by subject to perform stratified fivefold cross-validation. Models were assessed using multiple metrics, including receiver operating characteristic (ROC) and precision-recall (PR) curves. RESULTS: The best model (AUC-ROC = 0.968) was a GAM using the top three maximum vertebral fracture scores and age. Using top-ranked scores only, rather than all vertebral scores, improved performance for both model classes. Adding age, but not ethnicity, to the GAMs improved performance slightly. CONCLUSION: Maximal vertebral fracture scores resulted in the highest-performing models. While combining multiple vertebral body predictions risks decreasing specificity, our results demonstrate that subject-level models maintain good predictive performance. Thresholding strategies can be used to control sensitivity and specificity as clinically appropriate.

Using an Ensemble of Segmentation Methods to Detect Vertebral Bodies on Radiographs.

BACKGROUND AND PURPOSE: Vertebral compression fractures may indicate osteoporosis but are underdiagnosed and underreported by radiologists. We have developed an ensemble of vertebral body (VB) segmentation models for lateral radiographs as a critical component of an automated, opportunistic screening tool. Our goal is to detect the approximate location of thoracic and lumbar VBs, including fractured vertebra, on lateral radiographs. MATERIALS AND METHODS: The Osteoporotic Fractures in Men Study (MrOS) data set includes spine radiographs of 5994 men aged ≥65 years from 6 clinical centers. Two segmentation models, U-Net and Mask-RCNN (Region-based Convolutional Neural Network), were independently trained on the MrOS data set retrospectively, and an ensemble was created by combining them. Primary performance metrics for VB detection success included precision, recall, and F1 score for object detection on a held-out test set. Intersection over union (IoU) and Dice coefficient were also calculated as secondary metrics of performance for the test set. A separate external data set from a quaternary health care enterprise was acquired to test generalizability, comprising diagnostic clinical radiographs from men and women aged ≥65 years. RESULTS: The trained models achieved F1 score of U-Net = 83.42%, Mask-RCNN = 86.30%, and ensemble = 88.34% in detecting all VBs, and F1 score of U-Net = 87.88%, Mask-RCNN = 92.31%, and ensemble = 97.14% in detecting severely fractured vertebrae. The trained models achieved an average IoU per VB of 0.759 for U-Net and 0.709 for Mask-RCNN. The trained models achieved F1 score of U-Net = 81.11%, Mask-RCNN = 79.24%, and ensemble = 87.72% in detecting all VBs in the external data set. CONCLUSIONS: An ensemble model combining predictions from U-Net and Mask-RCNN resulted in the best performance in detecting VBs on lateral radiographs and generalized well to an external data set. This model could be a key component of a pipeline to detect fractures on all vertebrae in a radiograph in an automated, opportunistic screening tool under development.

Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria.

RATIONALE AND OBJECTIVES: Spinal osteoporotic compression fractures (OCFs) can be an early biomarker for osteoporosis but are often subtle, incidental, and underreported. To ensure early diagnosis and treatment of osteoporosis, we aimed to build a deep learning vertebral body classifier for OCFs as a critical component of our future automated opportunistic screening tool. MATERIALS AND METHODS: We retrospectively assembled a local dataset, including 1790 subjects and 15,050 vertebral bodies (thoracic and lumbar). Each vertebral body was annotated using an adaption of the modified-2 algorithm-based qualitative criteria. The Osteoporotic Fractures in Men (MrOS) Study dataset provided thoracic and lumbar spine radiographs of 5994 men from six clinical centers. Using both datasets, five deep learning algorithms were trained to classify each individual vertebral body of the spine radiographs. Classification performance was compared for these models using multiple metrics, including the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and positive predictive value (PPV). RESULTS: Our best model, built with ensemble averaging, achieved an AUC-ROC of 0.948 and 0.936 on the local dataset's test set and the MrOS dataset's test set, respectively. After setting the cutoff threshold to prioritize PPV, this model achieved a sensitivity of 54.5% and 47.8%, a specificity of 99.7% and 99.6%, and a PPV of 89.8% and 94.8%. CONCLUSION: Our model achieved an AUC-ROC>0.90 on both datasets. This testing shows some generalizability to real-world clinical datasets and a suitable performance for a future opportunistic osteoporosis screening tool.

Validation of reduced volume VeriFiler™ Express PCR Amplification Kit for buccal swab samples extracted using Prep-n-Go™ Buffer.

The efficiency of reduced volume PCR amplification was studied using the VeriFiler™ Express PCR Amplification Kit. Full (25 µL) and reduced (5 µL) volumes were tested in parallel to identify any differences in template DNA sensitivity and other electropherogram parameters. Both volumes produced full DNA profiles down to 0.08 ng/µL DNA concentration at 26 PCR cycles; however, reduced volume produced higher peak heights due to increased signal intensities. Significant difference (p-value ≤ 0.05) in heterozygote peak height ratios was observed between both volumes, where the reduced volume threshold was lowered to 0.6 to accommodate all data points. However, no significant difference (p-value > 0.05) was identified in the stutter ratios between both volumes. The analytical threshold for reduced volume was also determined to be 150 RFU with the presence of template DNA in PCR amplification. When the optimized reduced volume parameters were tested on DNA extracted from buccal swab samples using Prep-n-Go™ Buffer, good quality DNA profiles were produced. Overall, the reduced volume not only showed better results compared to the full volume, but also enable more samples to be processed with a PCR amplification kit, thus reduced the cost.

Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria.

RATIONALE AND OBJECTIVES: Osteoporosis affects 9% of individuals over 50 in the United States and 200 million women globally. Spinal osteoporotic compression fractures (OCFs), an osteoporosis biomarker, are often incidental and under-reported. Accurate automated opportunistic OCF screening can increase the diagnosis rate and ensure adequate treatment. We aimed to develop a deep learning classifier for OCFs, a critical component of our future automated opportunistic screening tool. MATERIALS AND METHODS: The dataset from the Osteoporotic Fractures in Men Study comprised 4461 subjects and 15,524 spine radiographs. This dataset was split by subject: 76.5% training, 8.5% validation, and 15% testing. From the radiographs, 100,409 vertebral bodies were extracted, each assigned one of two labels adapted from the Genant semiquantitative system: moderate to severe fracture vs. normal/trace/mild fracture. GoogLeNet, a deep learning model, was trained to classify the vertebral bodies. The classification threshold on the predicted probability of OCF outputted by GoogLeNet was set to prioritize the positive predictive value (PPV) while balancing it with the sensitivity. Vertebral bodies with the top 0.75% predicted probabilities were classified as moderate to severe fracture. RESULTS: Our model yielded a sensitivity of 59.8%, a PPV of 91.2%, and an F1 score of 0.72. The areas under the receiver operating characteristic curve (AUC-ROC) and the precision-recall curve were 0.99 and 0.82, respectively. CONCLUSION: Our model classified vertebral bodies with an AUC-ROC of 0.99, providing a critical component for our future automated opportunistic screening tool. This could lead to earlier detection and treatment of OCFs.

What's Race Got to Do With It? CRP Levels in Immune Mediated Skin Diseases: Considerations for Hidradenitis Suppurativa.

Currently, there is a lack of racial/ethnic heterogeneity in research databases, exposing a systematic issue in studies exploring inflammation-mediated diseases, such as hidradenitis suppurativa (HS). HS is a chronic inflammatory skin condition that disrupts normal structure and functioning of terminal hair follicles, resulting in the formation of recurrent abscesses, nodules, and sinus tracts within intertriginous regions. Studies have described higher serum levels of inflammation-mediated C-reactive protein (CRP) in patients with HS, a disease that predominantly affects skin of color (SOC) populations. Herein, we explore the role of CRP levels in the context of HS disease presentation, management, and psychosocial implications in SOC patients to determine existing disparities in research studies.

Extra-articular arthroscopic release of lateral epicondylitis: a prospective study.

Background: Operative management of lateral epicondylitis can be managed with percutaneous, arthroscopic, or open surgical release. Extraarticular arthroscopic release is a new technique, and no study has compared its outcomes and risk profile. Methods: A 26-patient cohort was reviewed before and after extraarticular arthroscopic release, which was performed by the senior author. The Mayo Elbow Performance Scores were used as a functional outcome score and obtained via a phone interview. Results were analyzed using a paired t-test with a statistical significance set at P < .05. Results: Of the 26 patients, 10 were being treated under workers compensation. Preoperative Mayo Elbow Performance Score was 47.5, and the postoperative score was 90.2 with a significant difference of 42.7 (P value = .05). The workers compensation group scored 13.3 points lower postoperatively than the remainder of patients, which was shown to also be significant with a P value of .002. Discussion and Conclusion: The advantage of extraarticular arthroscopic release was better visualization of affected structures, which improved accuracy of debridement, and a small capsulotomy, which decreased the risk of a transient radial nerve palsy. Overall, extraarticular arthroscopic results were found to be good and comparable to the results of other operative techniques with the added advantage of a lower risk profile.

Characterizing Immune-Mediated Cytopenias After Allogeneic Hematopoietic Cell Transplantation for Pediatric Nonmalignant Disorders.

Immune-mediated cytopenias (IMC)-isolated or combined hemolytic anemia, thrombocytopenia, or neutropenia-are increasingly recognized as serious complications after allogeneic hematopoietic cell transplantation (HCT) for nonmalignant disorders (NMD). However, IMC incidence, duration, response to therapy, and risk factors are not well defined. This retrospective chart review identified cases of IMC with serologic confirmation among patients who underwent HCT for NMD at a single institution between 2010 and 2017. IMC after HCT for NMD in a large pediatric cohort (n = 271) was common with a cumulative incidence of 18%, identified at a median of 136 days after HCT. Treatment included prolonged immune suppression (>3 months) in 58% of all IMC cases, 91% when multiple cell lines were affected. Multiple therapeutic agents were used for the majority affected, and median time to resolution of IMC was 118 days from diagnosis. Fine-Gray competing risk multivariate regression analysis identified a combined risk factor of younger age (<3 years) and inherited metabolic disorder, as well as hemoglobinopathy (at any age) associated with 1-year incidence of IMC (P < .01). We expand these findings with the observation of declining donor T-lymphoid chimerism from day 60 to 100 and lower absolute CD4+ counts at day 100 (P < .01), before median onset of IMC, for patients with IMC compared to those without. In this cohort, 4 deaths (8%) were associated with IMC, including 2 requiring second transplantation for secondary graft failure. Although the pathogenesis of IMC post-HCT for NMD remains elusive, further research may identify approaches to prevent and better treat this HCT complication.

Standardizing Definitions of Hematopoietic Recovery, Graft Rejection, Graft Failure, Poor Graft Function, and Donor Chimerism in Allogeneic Hematopoietic Cell Transplantation: A Report on Behalf of the American Society for Transplantation and Cellular Therapy.

Allogeneic hematopoietic cell transplantation (allo-HCT) is potentially curative for certain hematologic malignancies and nonmalignant diseases. The field of allo-HCT has witnessed significant advances, including broadening indications for transplantation, availability of alternative donor sources, less toxic preparative regimens, new cell manipulation techniques, and novel GVHD prevention methods, all of which have expanded the applicability of the procedure. These advances have led to clinical practice conundrums when applying traditional definitions of hematopoietic recovery, graft rejection, graft failure, poor graft function, and donor chimerism, because these may vary based on donor type, cell source, cell dose, primary disease, graft-versus-host disease (GVHD) prophylaxis, and conditioning intensity, among other variables. To address these contemporary challenges, we surveyed a panel of allo-HCT experts in an attempt to standardize these definitions. We analyzed survey responses from adult and pediatric transplantation physicians separately. Consensus was achieved for definitions of neutrophil and platelet recovery, graft rejection, graft failure, poor graft function, and donor chimerism, but not for delayed engraftment. Here we highlight the complexities associated with the management of mixed donor chimerism in malignant and nonmalignant hematologic diseases, which remains an area for future research. We recognize that there are multiple other specific, and at times complex, clinical scenarios for which clinical management must be individualized.