Heterogeneous osteoimmune profiles via single-cell transcriptomics in osteoporotic patients who fail bisphosphonate treatment.

Postmenopausal osteoporosis arises from imbalanced osteoclast and osteoblast activity, and mounting evidence suggests a role for the osteoimmune system in bone homeostasis. Bisphosphonate (BP) is an antiresorptive agent, but its treatment failure rate can be as high as 40%. Here, we performed single-cell RNA sequencing on peripheral immune cells from carefully selected postmenopausal women: non-osteoporotic, osteoporosis improved after BP treatment, and BP-failed cases. We found an increase in myeloid cells in patients with osteoporosis (specifically, T cell receptor+ macrophages). Furthermore, lymphoid lineage cells varied significantly, notably elevated natural killer cells (NKs) in the BP-failed group. Moreover, we provide fruitful lists of biomarkers within the immune cells that exhibit condition-dependent differences. The existence of osteoporotic- and BP-failure-specific cellular information flows was revealed by cell-cell interaction analysis. These findings deepen our insight of the osteoporosis pathology enhancing comprehension of the role of immune heterogeneity in postmenopausal osteoporosis and BP treatment failure.

Interleukin-7 Availability Is Maintained by a Hematopoietic Cytokine Sink Comprising Innate Lymphoid Cells and T Cells.

Interleukin-7 (IL-7) availability determines the size and proliferative state of the resting T cell pool. However, the mechanisms that regulate steady-state IL-7 amounts are unclear. Using experimental lymphopenic mouse models and IL-7-induced homeostatic proliferation to measure IL-7 availability in vivo, we found that radioresistant cells were the source of IL-7 for both CD4+ and CD8+ T cells. Hematopoietic lineage cells, although irrelevant as a source of IL-7, were primarily responsible for limiting IL-7 availability via their expression of IL-7R. Unexpectedly, innate lymphoid cells were found to have a potent influence on IL-7 amounts in the primary and secondary lymphoid tissues. These results demonstrate that IL-7 homeostasis is achieved through consumption by multiple subsets of innate and adaptive immune cells.

Prolonged viral shedding in an immunocompromised Korean patient infected with hMPXV, sub-lineage B.1.3, with acquired drug resistant mutations during tecovirimat treatment.

Following the worldwide surge in mpox (monkeypox) in 2022, cases have persisted in Asia, including South Korea, and sexual contact is presumed as the predominant mode of transmission, with a discernible surge in prevalence among immunocompromised patients. Drugs such as tecovirimat can result in drug-resistant mutations, presenting obstacles to treatment. This study aimed to ascertain the presence of tecovirimat-related resistant mutations through genomic analysis of the monkeypox virus isolated from a reported case involving prolonged viral shedding in South Korea. Here, tecovirimat-resistant mutations, previously identified in the B.1 clade, were observed in the B.1.3 clade, predominant in South Korea. These mutations exhibited diverse patterns across different samples from the same patient and reflected the varied distribution of viral subpopulations in different anatomical regions. The A290V and A288P mutant strains we isolated hold promise for elucidating these mechanisms, enabling a comprehensive analysis of viral pathogenesis, replication strategies, and host interactions. Our findings imply that acquired drug-resistant mutations, may present a challenge to individual patient treatment. Moreover, they have the potential to give rise to transmitted drug-resistant mutations, thereby imposing a burden on the public health system. Consequently, the meticulous genomic surveillance among immunocompromised patients, conducted in this research, assumes paramount importance.

Optimization of alert notifications in electronic patient-reported outcome (ePRO) remote symptom monitoring systems (AFT-39).

PURPOSE: Clinical benefits result from electronic patient-reported outcome (ePRO) systems that enable remote symptom monitoring. Although clinically useful, real-time alert notifications for severe or worsening symptoms can overburden nurses. Thus, we aimed to algorithmically identify likely non-urgent alerts that could be suppressed. METHODS: We evaluated alerts from the PRO-TECT trial (Alliance AFT-39) in which oncology practices implemented remote symptom monitoring. Patients completed weekly at-home ePRO symptom surveys, and nurses received real-time alert notifications for severe or worsening symptoms. During parts of the trial, patients and nurses each indicated whether alerts were urgent or could wait until the next visit. We developed an algorithm for suppressing alerts based on patient assessment of urgency and model-based predictions of nurse assessment of urgency. RESULTS: 593 patients participated (median age = 64 years, 61% female, 80% white, 10% reported never using computers/tablets/smartphones). Patients completed 91% of expected weekly surveys. 34% of surveys generated an alert, and 59% of alerts prompted immediate nurse actions. Patients considered 10% of alerts urgent. Of the remaining cases, nurses considered alerts urgent more often when patients reported any worsening symptom compared to the prior week (33% of alerts with versus 26% without any worsening symptom, p = 0.009). The algorithm identified 38% of alerts as likely non-urgent that could be suppressed with acceptable discrimination (sensitivity = 80%, 95% CI [76%, 84%]; specificity = 52%, 95% CI [49%, 55%]). CONCLUSION: An algorithm can identify remote symptom monitoring alerts likely to be considered non-urgent by nurses, and may assist in fostering nurse acceptance and implementation feasibility of ePRO systems.

Genomic Analysis of Monkeypox Virus During the 2023 Epidemic in Korea.

We aimed to characterize the genomes of monkeypox virus isolates from the Far East, providing insights into viral transmission and evolution. Genomic analysis was conducted on 8 isolates obtained from patients with monkeypox virus disease in the Republic of Korea between May 2022 and early 2023. These isolates were classified into Clade IIb. Distinct lineages, including B.1.1, A.2.1, and B.1.3, were observed in 2022 and 2023 isolates, with only the B.1.3 lineage detected in six isolates of 2023. These genetic features were specific to Far East isolates (the Republic of Korea, Japan, and Taiwan), distinguishing them from the diverse lineages found in the Americas, Europe, Africa, and Oceania. In early 2023, the prevalence of the B.1.3 lineage of monkeypox virus identified in six patients with no overseas travel history is considered as an indicator of the potential initiation of local transmission in the Republic of Korea.

A Generalized Approach to Photon Avalanche Upconversion in Luminescent Nanocrystals.

Photon avalanching nanoparticles (ANPs) exhibit extremely nonlinear upconverted emission valuable for subdiffraction imaging, nanoscale sensing, and optical computing. Avalanching has been demonstrated with Tm3+-, Pr3+-, or Nd3+-doped nanocrystals, but their emission is limited to a few wavelengths and materials. Here, we utilize Gd3+-assisted energy migration to tune the emission wavelengths of Tm3+-sensitized ANPs and generate highly nonlinear emission from Eu3+, Tb3+, Ho3+, and Er3+ ions. The upconversion intensities of these spectrally discrete ANPs scale with nonlinearity factor s = 10-17 under 1064 nm excitation at power densities as low as 7 kW cm-2. This strategy for imprinting avalanche behavior on remote emitters can be extended to fluorophores adjacent to ANPs, as we demonstrate with CdS/CdSe/CdS core/shell/shell quantum dots. ANPs with rationally designed energy transfer networks provide the means to transform conventional linear emitters into a highly nonlinear ones, expanding the use of photon avalanching in biological, chemical, and photonic applications.

Impact of the restraint decision tree for physical restraint use in South Korean neurointensive care units.

BACKGROUND: Nurses in neurointensive care units (NCUs) commonly use physical restraint (PR) to prevent adverse events like unplanned removal of devices (URDs) or falls. However, PR use should be based on evidenced decisions as it has drawbacks. Unfortunately, there is a lack of research-based PR protocol to support decision-making for nurses, especially for neurocritical patients. AIMS: This study developed a restraint decision tree for neurocritical patients (RDT-N) to assist nurses in making PR decisions. We assessed its effectiveness in reducing PR use and adverse events. STUDY DESIGN: This study employed a baseline and post-intervention test design at a NCU with 19 beds and 45 nurses in a tertiary hospital in a metropolitan city in South Korea. Two-hundred and thirty-seven adult patients were admitted during the study period. During the intervention, nurses were trained on the RDT-N. PR use and adverse events between the baseline and post-intervention periods were compared. RESULTS: Post-intervention, total number of restrained patients decreased (20.7%-16.3%; χ2 = 7.68, p = .006), and the average number of PR applied per restrained patient decreased (2.42-1.71; t = 5.74, p < .001). The most frequently used PR type changed from extremity cuff to mitten (χ2 = 397.62, p < .001). No falls occurred during the study periods. On the other hand, URDs at baseline were 18.67 cases per 1000 patient days in the high-risk group and 5.78 cases per 1000 patient days in the moderate-risk group; however, no URD cases were reported post-intervention. CONCLUSIONS: The RDT-N effectively reduced PR use and adverse events. Its application can enhance patient-centred care based on individual condition and potential risks in NCUs. RELEVANCE TO CLINICAL PRACTICE: Nurses can use the RDT-N to assess the need for PR in caring for neurocritical patients, reducing PR use and adverse events.

Dirichlet process mixture models for the analysis of repeated attempt designs.

In longitudinal studies, it is not uncommon to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful provides useful information for the purposes of assessing missing data assumptions. This is because measurements from subjects who provide the data after numerous failed attempts may differ from those who provide the measurement after fewer attempts. Previous models for these designs were parametric and/or did not allow sensitivity analysis. For the former, there are always concerns about model misspecification and for the latter, sensitivity analysis is essential when conducting inference in the presence of missing data. Here, we propose a new approach which minimizes issues with model misspecification by using Bayesian nonparametrics for the observed data distribution. We also introduce a novel approach for identification and sensitivity analysis. We re-analyze the repeated attempts data from a clinical trial involving patients with severe mental illness and conduct simulations to better understand the properties of our approach.

Measuring symptoms and toxicities: a 35-year experience.

PURPOSE: When conducting trials aimed at the improvement of cancer-related and/or cancer treatment-related toxicities, it is important to determine the best means of measuring patients' symptoms. METHODS: The authors of this current manuscript have an extensive experience with the conduct of symptom-control clinical trials. This experience is utilized to provide insight into the best means of measuring symptoms caused by cancer and/or cancer therapy. RESULTS: Patient-reported outcome data are preferable for measuring bothersome symptoms, for determining toxicities caused by treatment approaches, and offer more accurate and detailed information compared with health care practitioners recording their impressions of patient experiences. Well-validated patient friendly measures are recommended when they are available. When such are not readily available, face-valid, single-item numerical rating scales are effective instruments to document both treatment trial outcomes and cancer treatment side effects/toxicities. CONCLUSION: The use of numerical rating scales are effective means of measuring symptoms caused by cancer, by cancer treatments, and/or alleviated by symptom control treatment approaches.

Identifying meaningful change on PROMIS short forms in cancer patients: a comparison of item response theory and classic test theory frameworks.

BACKGROUND: This study compares classical test theory and item response theory frameworks to determine reliable change. Reliable change followed by anchoring to the change in categorically distinct responses on a criterion measure is a useful method to detect meaningful change on a target measure. METHODS: Adult cancer patients were recruited from five cancer centers. Baseline and follow-up assessments at 6 weeks were administered. We investigated short forms derived from PROMIS® item banks on anxiety, depression, fatigue, pain intensity, pain interference, and sleep disturbance. We detected reliable change using reliable change index (RCI). We derived the T-scores corresponding to the RCI calculated under IRT and CTT frameworks using PROMIS® short forms. For changes that were reliable, meaningful change was identified using patient-reported change in PRO-CTCAE by at least one level. For both CTT and IRT approaches, we applied one-sided tests to detect reliable improvement or worsening using RCI. We compared the percentages of patients with reliable change and reliable/meaningful change. RESULTS: The amount of change in T score corresponding to RCICTT of 1.65 ranged from 5.1 to 9.2 depending on domains. The amount of change corresponding to RCIIRT of 1.65 varied across the score range, and the minimum change ranged from 3.0 to 8.2 depending on domains. Across domains, the RCICTT and RCIIRT classified 80% to 98% of the patients consistently. When there was disagreement, the RCIIRT tended to identify more patients as having reliably changed compared to RCICTT if scores at both timepoints were in the range of 43 to 78 in anxiety, 45 to 70 in depression, 38 to 80 in fatigue, 35 to 78 in sleep disturbance, and 48 to 74 in pain interference, due to smaller standard errors in these ranges using the IRT method. The CTT method found more changes compared to IRT for the pain intensity domain that was shorter in length. Using RCICTT, 22% to 66% had reliable change in either direction depending on domains, and among these patients, 62% to 83% had meaningful change. Using RCIIRT, 37% to 68% had reliable change in either direction, and among these patients, 62% to 81% had meaningful change. CONCLUSION: Applying the two-step criteria demonstrated in this study, we determined how much change is needed to declare reliable change at different levels of baseline scores. We offer reference values for percentage of patients who meaningfully change for investigators using the PROMIS instruments in oncology.

Mediators and moderator of the effects of early exposure to intimate partner violence on children's mental health.

Childhood intimate partner violence (IPV) exposure increases the likelihood of internalizing and externalizing problems. There is substantial variability in children's outcomes following IPV exposure, but the reasons behind this are unclear, particularly among preschool-age children. The current study aimed to examine the direct and indirect effects of IPV on preschoolers' mental health through parent factors (parenting and parental depression), exploring child temperament as a potential moderator of the relation between IPV and child outcomes. Participants were 186 children (85 girls) and their parents living in the United States. Data were initially collected when children were age three, with follow-up at ages four and six. Both parents' baseline IPV perpetration had adverse effects on child outcomes. Mothers' IPV was associated with greater paternal depression, paternal overractivity, and maternal laxness, whereas fathers' IPV was associated with more paternal overreactivity. Only paternal depression mediated the effect of mothers' IPV on child outcomes. Parenting did not mediate nor did child temperament moderate the relation between IPV and child outcomes. Results shed insight into the need to address parental mental health in families experiencing IPV and underline the need for a further exploration of individual- and family-level mechanisms of adjustment following IPV exposure.

Explainable Artificial Intelligence Warning Model Using an Ensemble Approach for In-Hospital Cardiac Arrest Prediction: Retrospective Cohort Study.

BACKGROUND: Cardiac arrest (CA) is the leading cause of death in critically ill patients. Clinical research has shown that early identification of CA reduces mortality. Algorithms capable of predicting CA with high sensitivity have been developed using multivariate time series data. However, these algorithms suffer from a high rate of false alarms, and their results are not clinically interpretable. OBJECTIVE: We propose an ensemble approach using multiresolution statistical features and cosine similarity-based features for the timely prediction of CA. Furthermore, this approach provides clinically interpretable results that can be adopted by clinicians. METHODS: Patients were retrospectively analyzed using data from the Medical Information Mart for Intensive Care-IV database and the eICU Collaborative Research Database. Based on the multivariate vital signs of a 24-hour time window for adults diagnosed with heart failure, we extracted multiresolution statistical and cosine similarity-based features. These features were used to construct and develop gradient boosting decision trees. Therefore, we adopted cost-sensitive learning as a solution. Then, 10-fold cross-validation was performed to check the consistency of the model performance, and the Shapley additive explanation algorithm was used to capture the overall interpretability of the proposed model. Next, external validation using the eICU Collaborative Research Database was performed to check the generalization ability. RESULTS: The proposed method yielded an overall area under the receiver operating characteristic curve (AUROC) of 0.86 and area under the precision-recall curve (AUPRC) of 0.58. In terms of the timely prediction of CA, the proposed model achieved an AUROC above 0.80 for predicting CA events up to 6 hours in advance. The proposed method simultaneously improved precision and sensitivity to increase the AUPRC, which reduced the number of false alarms while maintaining high sensitivity. This result indicates that the predictive performance of the proposed model is superior to the performances of the models reported in previous studies. Next, we demonstrated the effect of feature importance on the clinical interpretability of the proposed method and inferred the effect between the non-CA and CA groups. Finally, external validation was performed using the eICU Collaborative Research Database, and an AUROC of 0.74 and AUPRC of 0.44 were obtained in a general intensive care unit population. CONCLUSIONS: The proposed framework can provide clinicians with more accurate CA prediction results and reduce false alarm rates through internal and external validation. In addition, clinically interpretable prediction results can facilitate clinician understanding. Furthermore, the similarity of vital sign changes can provide insights into temporal pattern changes in CA prediction in patients with heart failure-related diagnoses. Therefore, our system is sufficiently feasible for routine clinical use. In addition, regarding the proposed CA prediction system, a clinically mature application has been developed and verified in the future digital health field.

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.

BACKGROUND: The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via deep learning is emerging as a decent alternative for their convenience and potential accuracy. However, sound-based sleep staging has only been studied using in-laboratory sound data. In real-world sleep environments (homes), there is abundant background noise, in contrast to quiet, controlled environments such as laboratories. The use of sound-based sleep staging at homes has not been investigated while it is essential for practical use on a daily basis. Challenges are the lack of and the expected huge expense of acquiring a sufficient size of home data annotated with sleep stages to train a large-scale neural network. OBJECTIVE: This study aims to develop and validate a deep learning method to perform sound-based sleep staging using audio recordings achieved from various uncontrolled home environments. METHODS: To overcome the limitation of lacking home data with known sleep stages, we adopted advanced training techniques and combined home data with hospital data. The training of the model consisted of 3 components: (1) the original supervised learning using 812 pairs of hospital polysomnography (PSG) and audio recordings, and the 2 newly adopted components; (2) transfer learning from hospital to home sounds by adding 829 smartphone audio recordings at home; and (3) consistency training using augmented hospital sound data. Augmented data were created by adding 8255 home noise data to hospital audio recordings. Besides, an independent test set was built by collecting 45 pairs of overnight PSG and smartphone audio recording at homes to examine the performance of the trained model. RESULTS: The accuracy of the model was 76.2% (63.4% for wake, 64.9% for rapid-eye movement [REM], and 83.6% for non-REM) for our test set. The macro F1-score and mean per-class sensitivity were 0.714 and 0.706, respectively. The performance was robust across demographic groups such as age, gender, BMI, or sleep apnea severity (accuracy 73.4%-79.4%). In the ablation study, we evaluated the contribution of each component. While the supervised learning alone achieved accuracy of 69.2% on home sound data, adding consistency training to the supervised learning helped increase the accuracy to a larger degree (+4.3%) than adding transfer learning (+0.1%). The best performance was shown when both transfer learning and consistency training were adopted (+7.0%). CONCLUSIONS: This study shows that sound-based sleep staging is feasible for home use. By adopting 2 advanced techniques (transfer learning and consistency training) the deep learning model robustly predicts sleep stages using sounds recorded at various uncontrolled home environments, without using any special equipment but smartphones only.

Nuts and Bolts of Patient-Reported Outcomes in Orthopaedics.

Patient-reported outcomes (PROs) are commonly used in orthopaedic clinical practice, comparative effectiveness research (CER), and label claims. In this paper, we provide an overview of PROs, their development, validation, and use in orthopaedic research with examples and conclude with practical guidelines for researchers and reviewers. We discuss considerations for conceptual framework, validity, reliability, factor analysis, and measurement of change with Knee Injury and Osteoarthritis Outcome score (KOOS), as an example. We also describe advantages of instruments developed based on item response theory and statistical analyses for data collected using PRO measures. Please visit the following (https://www.youtube.com/watch?v=4p-DtZgUHOA&t=354s) for a video that explains the highlights of the paper in practical terms.

RADIOGRAPHIC MEASUREMENT OF VERTEBRAL HEART SCALE IN KOREAN WILD RACCOON DOGS (NYCTEREUTES PROCYONOIDES KOREENSIS).

Raccoon dogs (Nyctereutes procyonoides koreensis), which belong to the Canidae family, are the second most injured wildlife animals rescued by the Gangwon Wildlife Medical Rescue Center. Various imaging evaluation methods including echocardiography have been developed, but thoracic radiography remains essential for the diagnosis and management of heart disease in dogs. In particular, vertebral heart scale (VHS) measurement is usually used to evaluate the dimensions of the heart silhouette on thoracic radiographs and can measure cardiomegaly more objectively. The VHS of 50 raccoon dogs without cardiac diseases were measured using thoracic radiography in right lateral (RL) and ventrodorsal (VD) recumbent positions. The VHS in the RL view of 50 raccoon dogs was 9.03 ± 0.52 vertebrae (v), which was slightly smaller than the VHS measured in the VD view of 46 raccoon dogs (9.79 ± 0.84 v). In addition, the thoracic morphology of raccoon dogs was determined to be intermediate (thoracic depth-to-width ratio, 0.75-1.25), and thoracic morphology, gender, and weight were not significantly correlated with VHS. The VHS of raccoon dogs in this study will help veterinarians diagnose potential cardiac diseases in raccoon dogs.

Effects of patient-reported outcome assessment order.

BACKGROUND: In clinical trials and clinical practice, patient-reported outcomes are almost always assessed using multiple patient-reported outcome measures at the same time. This raises concerns about whether patient responses are affected by the order in which the patient-reported outcome measures are administered. METHODS: This questionnaire-based study of order effects included adult cancer patients from five cancer centers. Patients were randomly assigned to complete questionnaires via paper booklets, interactive voice response system, or tablet web survey. Linear Analogue Self-Assessment, Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events, and Patient-Reported Outcomes Measurement Information System assessment tools were each used to measure general health, physical function, social function, emotional distress/anxiety, emotional distress/depression, fatigue, sleep, and pain. The order in which the three tools, and domains within tools, were presented to patients was randomized. Rates of missing data, scale scores, and Cronbach's alpha coefficients were compared by the order in which they were assessed. Analyses included Cochran-Armitage trend tests and mixed models adjusted for performance score, age, sex, cancer type, and curative intent. RESULTS: A total of 1830 patients provided baseline patient-reported outcome assessments. There were no significant trends in rates of missing values by whether a scale was assessed earlier or later. The largest order effect for scale scores was due to a large mean score at one assessment time point. The largest difference in Cronbach's alpha between the versions for the Patient-Reported Outcomes Measurement Information System scales was 0.106. CONCLUSION: The well-being of a cancer patient has many different aspects such as pain, fatigue, depression, and anxiety. These are assessed using a variety of surveys often collected at the same time. This study shows that the order in which the different aspects are collected from the patient is not important.

Association of Function, Symptoms, and Social Support Reported in Standardized Outpatient Clinic Questionnaires With Subsequent Hospital Discharge Disposition and 30-Day Readmissions.

OBJECTIVE: To determine whether patient-reported information, routinely collected in an outpatient setting, is associated with readmission within 30 days of discharge and/or the need for post-acute care after a subsequent hospital admission. DESIGN: Retrospective cohort study. Six domains of patient-reported information collected in the outpatient setting (psychological distress, respiratory symptoms, musculoskeletal pain, family support, mobility, and activities of daily living [ADLs]) were linked to electronic health record hospitalization data. Mixed effects logistic regression models with random intercepts were used to identify the association between the 6 domains and outcomes. SETTING: Outpatient clinics and hospitals in a Midwestern health system. PARTICIPANTS: 7671 patients who were hospitalized 11,445 times between May 2004 and May 2014 (N=7671). INTERVENTION: None. MAIN OUTCOME MEASURES: 30-day hospital readmission and discharge home vs facility. RESULTS: Domains were significantly associated with 30-day readmission and placement in a facility. Specifically, mobility (odds ratio [OR]=1.30; 95% confidence interval [CI], 1.16, 1.46), ADLs (OR=1.27; 95% CI, 1.13, 1.42), respiratory symptoms (OR=1.26; 95% CI, 1.12, 1.41), and psychological distress (OR=1.20; 95% CI, 1.07, 1.35) had the strongest associations with 30-day readmission. The ADL (OR=2.52; 95% CI, 2.26, 2.81), mobility (OR=2.35; 95% CI, 2.10, 2.63), family support (OR=2.28; 95% CI, 1.98, 2.62), and psychological distress (OR=1.38; 95% CI, 1.25, 1.52) domains had the strongest associations with discharge to an institution. CONCLUSIONS: Patient-reported function, symptoms, and social support routinely collected in outpatient clinics are associated with future 30-day readmission and discharge to an institutional setting. Whether these data can be leveraged to guide interventions to address patient needs and improve outcomes requires further research.

Psychometric Characteristics and Validity of the PROMIS Cancer Function Brief 3D Profile.

OBJECTIVE: To develop an item response theory (IRT)-calibrated, patient-reported outcome measure (the PROMIS Cancer Function Brief 3D Profile) of physical function, including associations with fatigue and social participation, in cancer rehabilitation patients. DESIGN: Large-scale field testing, graded response model IRT analyses, and multivariate regression analysis. SETTING: Six cancer rehabilitation clinics associated with cancer centers across the United States. PARTICIPANTS: Adults (N=616) treated in outpatient cancer rehabilitation medicine clinics. INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: The PROMIS(r) Cancer Function 3D Profile (including existing items from PROMIS(r) item banks). RESULTS: A total of 616 patients completed 21 items in the initial item pool. Nine items were removed because of comparatively lower information that they provide according to the IRT item calibrations, low item-total correlations, or bimodal distributions. The remaining items generated a 12-item short form. Regression analyses determined that the items were responsive to and representative of the patient population across trait ranges and multiple domains and subdomains of function. CONCLUSIONS: This psychometric investigation supports the use of the PROMIS Cancer Function Brief 3D Profile for evaluating function in outpatient cancer rehabilitation patients.

Choosing and Using Patient-Reported Outcome Measures in Clinical Practice.

The increasing use of patient-reported outcome (PRO) measures is forcing clinicians and health care systems to decide which to select and how to incorporate them into their records and clinical workflows. This overview addresses 3 topics related to these concerns. First, a literature review summarizes key psychometric and practical factors (such as reliability, responsiveness, computer adaptive testing, and interpretability) in choosing PROs for clinical practice. Second, 3 clinical decision support issues are highlighted: gathering PROs, electronic health record effect on providers, and incorporating PROs into clinical decision support design and implementation. Lastly, the salience of crosscutting domains as well as 9 key pragmatic decisions are reviewed. Crosscutting domains are those that are relevant across most medical and mental health conditions, such as the SPADE symptom pentad (sleep problems, pain, anxiety, depression, low energy/fatigue) and physical functioning. The 9 pragmatic decisions include (1) generic vs disease-specific scales; (2) single- vs multidomain scales; (3) universal scales vs user-choice selection; (4) number of domains to measure; (5) prioritization of domains when multiple domains are assessed; (6) action thresholds; (7) clinical purpose (screening vs monitoring); as well as the (8) frequency and (9) logistical aspects of PRO administration.

The First Case of Monkeypox in the Republic of Korea.

A rapid outbreak of monkeypox is ongoing in non-endemic countries since May 2022. We report the first case of monkeypox in the Republic of Korea. This occurred in a 34-year-old male patient who traveled to Europe in June 2022. On the day of his return to the Republic of Korea (June 21, 2022), the patient presented with a genital lesion. The results of the monkeypox real-time polymerase chain reaction tests were positive in the penile ulcer, oropharyngeal and nasopharyngeal specimens. The patient subsequently developed fever and skin rash after hospital admission. Careful history taking along physical examination should be conducted in the patients who have epidemiologic risk factors for monkeypox. Moreover, appropriate specimens should be obtained from lesions and tested for the monkeypox virus.