Clinical evaluation and determinants of response to HBI0101 (BCMA CART) therapy in relapsed/refractory multiple myeloma.

HBI0101 is an academic chimeric antigen receptor T (CART) targeted to BCMA for the treatment of relapsed and refractory multiple myeloma (RRMM) and light chain amyloidosis. Herein, we present the Phase Ib/II results of fifty heavily pre-treated RRMM patients dosed with 800x106 CART cells (NCT04720313). Inclusion criteria were relatively permissive (i.e., performance status and baseline organ function) and consequently, about half of the enrolled patients would have been ineligible for pivotal clinical trials. The median time elapsed from patient enrolment until CART delivery was 25 days (range, 14-65). HBI0101-related toxicities included grade 1-3 cytokine-release syndrome, grade 3-4 hematologic toxicities and grade 1-2 immune effector cell-associated neurotoxicity syndrome. Responses were achieved in 90% of the patients, 56% achieved stringent and complete response (sCR/CR), and 70% reached a minimal residual disease negativity. Within a median follow-up of 12.3 months, the median progression-free survival (PFS) was 11.0 months; (95% CI, 6.2-14.6), and the overall survival was not reached (95% CI, 13.3-not reached). Multivariable analysis on patient/disease and CART cell-related characteristics revealed that high-risk cytogenetic, extramedullary disease, and increased number of effector-memory T-cells in CART products were independently associated with inferior PFS. In conclusion, comprehensive analyses of the parameters affecting the response to CART therapy are essential for improving patients' outcome.

Fear of cancer recurrence in breast cancer: A moderated serial mediation analysis of a prospective international study.

OBJECTIVE: Women dealing with breast cancer (BC) face many challenges, one of which is the fear of cancer recurrence (FCR). This study examined whether disease severity predicts FCR 6 months after cancer diagnosis through psychological distress and whether cognitive-emotion regulation moderates this effect. METHOD: The study sample included 656 women from Italy (27.5%), Finland (31.9%), Israel (19.8%), and Portugal (20.8%) diagnosed with Stages I-III of BC. Participants' age ranged between 40 and 70 years (M = 54.92, SD = 8.22). Participants were tracked following BC diagnosis and at 3 and 6 months follow-up. Participants filled out self-report questionnaires, including the FCR inventory-short form, the Hospital Anxiety and Depression Scale, and the cognitive-emotion regulation questionnaire along with medical-social-demographic data. RESULTS: Greater disease severity at baseline indicated by higher cancer stage predicted greater psychological distress, which in turn predicted greater psychological distress at 3 months. The latter predicted greater FCR at 6 months. This serial mediation model was moderated by negative cognitive-emotion regulation. The mediating effect of disease severity on FCR through psychological distress was significant only in women with mean or higher levels of negative cognitive-emotion regulation. CONCLUSION: This study suggests that facilitating psychological well-being and effective cognitive-emotion regulation in the early stages after a cancer diagnosis may protect women from FCR. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Well-being trajectories in breast cancer and their predictors: A machine-learning approach.

OBJECTIVE: This study aimed to describe distinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months following a breast cancer diagnosis, and identify the medical, socio-demographic, lifestyle, and psychological factors that predict these trajectories. METHODS: 474 females (mean age = 55.79 years) were enrolled in the first weeks after surgery or biopsy. Data from seven assessment points over 18 months, at 3-month intervals, were used. The two outcomes were assessed at all points. Potential predictors were assessed at baseline and the first follow-up. Machine-Learning techniques were used to detect latent patterns of change and identify the most important predictors. RESULTS: Five trajectories were identified for each outcome: stably high, high with fluctuations, recovery, deteriorating/delayed response, and stably poor well-being (chronic distress). Psychological factors (i.e., negative affect, coping, sense of control, social support), age, and a few medical variables (e.g., symptoms, immune-related inflammation) predicted patients' participation in the delayed response and the chronic distress trajectories versus all other trajectories. CONCLUSIONS: There is a strong possibility that resilience does not always reflect a stable response pattern, as there might be some interim fluctuations. The use of machine-learning techniques provides a unique opportunity for the identification of illness trajectories and a shortlist of major bio/behavioral predictors. This will facilitate the development of early interventions to prevent a significant deterioration in patient well-being.

Personalized Risk Analysis to Improve the Psychological Resilience of Women Undergoing Treatment for Breast Cancer: Development of a Machine Learning-Driven Clinical Decision Support Tool.

BACKGROUND: Health professionals are often faced with the need to identify women at risk of manifesting poor psychological resilience following the diagnosis and treatment of breast cancer. Machine learning algorithms are increasingly used to support clinical decision support (CDS) tools in helping health professionals identify women who are at risk of adverse well-being outcomes and plan customized psychological interventions for women at risk. Clinical flexibility, cross-validated performance accuracy, and model explainability permitting person-specific identification of risk factors are highly desirable features of such tools. OBJECTIVE: This study aimed to develop and cross-validate machine learning models designed to identify breast cancer survivors at risk of poor overall mental health and global quality of life and identify potential targets of personalized psychological interventions according to an extensive set of clinical recommendations. METHODS: A set of 12 alternative models was developed to improve the clinical flexibility of the CDS tool. All models were validated using longitudinal data from a prospective, multicenter clinical pilot at 5 major oncology centers in 4 countries (Italy, Finland, Israel, and Portugal; the Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back [BOUNCE] project). A total of 706 patients with highly treatable breast cancer were enrolled shortly after diagnosis and before the onset of oncological treatments and were followed up for 18 months. An extensive set of demographic, lifestyle, clinical, psychological, and biological variables measured within 3 months after enrollment served as predictors. Rigorous feature selection isolated key psychological resilience outcomes that could be incorporated into future clinical practice. RESULTS: Balanced random forest classifiers were successful at predicting well-being outcomes, with accuracies ranging between 78% and 82% (for 12-month end points after diagnosis) and between 74% and 83% (for 18-month end points after diagnosis). Explainability and interpretability analyses built on the best-performing models were used to identify potentially modifiable psychological and lifestyle characteristics that, if addressed systematically in the context of personalized psychological interventions, would be most likely to promote resilience for a given patient. CONCLUSIONS: Our results highlight the clinical utility of the BOUNCE modeling approach by focusing on resilience predictors that can be readily available to practicing clinicians at major oncology centers. The BOUNCE CDS tool paves the way for personalized risk assessment methods to identify patients at high risk of adverse well-being outcomes and direct valuable resources toward those most in need of specialized psychological interventions.

Trajectories of Quality of Life among an International Sample of Women during the First Year after the Diagnosis of Early Breast Cancer: A Latent Growth Curve Analysis.

The current study aimed to track the trajectory of quality of life (QoL) among subgroups of women with breast cancer in the first 12 months post-diagnosis. We also aimed to assess the number and portion of women classified into each distinct trajectory and the sociodemographic, clinical, and psychosocial factors associated with these trajectories. The international sample included 699 participants who were recruited soon after being diagnosed with breast cancer as part of the BOUNCE Project. QoL was assessed at baseline and after 3, 6, 9, and 12 months, and we used Latent Class Growth Analysis to identify trajectory subgroups. Sociodemographic, clinical, and psychosocial factors at baseline were used to predict latent class membership. Four distinct QoL trajectories were identified in the first 12 months after a breast cancer diagnosis: medium and stable (26% of participants); medium and improving (47%); high and improving (18%); and low and stable (9%). Thus, most women experienced improvements in QoL during the first year post-diagnosis. However, approximately one-third of women experienced consistently low-to-medium QoL. Cancer stage was the only variable which was related to the QoL trajectory in the multivariate analysis. Early interventions which specifically target women who are at risk of ongoing low QoL are needed.

Personalized prediction of one-year mental health deterioration using adaptive learning algorithms: a multicenter breast cancer prospective study.

Identifying individual patient characteristics that contribute to long-term mental health deterioration following diagnosis of breast cancer (BC) is critical in clinical practice. The present study employed a supervised machine learning pipeline to address this issue in a subset of data from a prospective, multinational cohort of women diagnosed with stage I-III BC with a curative treatment intention. Patients were classified as displaying stable HADS scores (Stable Group; n = 328) or reporting a significant increase in symptomatology between BC diagnosis and 12 months later (Deteriorated Group; n = 50). Sociodemographic, life-style, psychosocial, and medical variables collected on the first visit to their oncologist and three months later served as potential predictors of patient risk stratification. The flexible and comprehensive machine learning (ML) pipeline used entailed feature selection, model training, validation and testing. Model-agnostic analyses aided interpretation of model results at the variable- and patient-level. The two groups were discriminated with a high degree of accuracy (Area Under the Curve = 0.864) and a fair balance of sensitivity (0.85) and specificity (0.87). Both psychological (negative affect, certain coping with cancer reactions, lack of sense of control/positive expectations, and difficulties in regulating negative emotions) and biological variables (baseline percentage of neutrophils, thrombocyte count) emerged as important predictors of mental health deterioration in the long run. Personalized break-down profiles revealed the relative impact of specific variables toward successful model predictions for each patient. Identifying key risk factors for mental health deterioration is an essential first step toward prevention. Supervised ML models may guide clinical recommendations toward successful illness adaptation.

Psychological and somatic symptoms among breast cancer patients in four European countries: A cross-lagged panel model.

Psychological and physical health among women with breast cancer are linked. However, more research is needed to test the interrelations between psychological and somatic symptoms, over time and throughout the different phases of breast cancer treatment, to determine when and which interventions should be prioritized. Six hundred and eighty nine women from four countries (Finland, Israel, Italy and Portugal) completed questionnaires during their first clinical consultation following diagnosis with breast cancer, and again after 3 and 6 months. The questionnaires included self-reported measures of psychological symptoms (Hospital Anxiety and Depression Scale; the Positive and Negative Affect Schedule Short Form) and somatic symptoms [selected items from the International European Organization for Research and Treatment of Cancer (EORTC) questionnaires]. Psychological and somatic symptoms were relatively stable across the three time-points. Cross-lagged paths leading from somatic to psychological symptoms (beta coefficients of 0.08-0.10), as well as vice-versa (beta 0.11-0.12), were found to be significant. No evidence was found for cross-cultural differences in mutual effects of psychological and somatic symptoms. The findings of this study call for tailoring personal interventions for breast cancer patients-either from a somatic perspective or a psychological perspective-and adjust them to the specific experiences of the individual patient.

Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back: Protocol for a Multicenter Clinical Pilot Study.

BACKGROUND: Despite the continued progress of medicine, dealing with breast cancer is becoming a major socioeconomic challenge, particularly due to its increasing incidence. The ability to better manage and adapt to the entire care process depends not only on the type of cancer but also on the patient's sociodemographic and psychological characteristics as well as on the social environment in which a person lives and interacts. Therefore, it is important to understand which factors may contribute to successful adaptation to breast cancer. To our knowledge, no studies have been performed on the combination effect of multiple psychological, biological, and functional variables in predicting the patient's ability to bounce back from a stressful life event, such as a breast cancer diagnosis. Here we describe the study protocol of a multicenter clinical study entitled "Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back" or, in short, BOUNCE. OBJECTIVE: The aim of the study is to build a quantitative mathematical model of factors associated with the capacity for optimal adjustment to cancer and to study resilience through the cancer continuum in a population of patients with breast cancer. METHODS: A total of 660 women with breast cancer will be recruited from five European cancer centers in Italy, Finland, Israel, and Portugal. Biomedical and psychosocial variables will be collected using the Noona Healthcare platform. Psychosocial, sociodemographic, lifestyle, and clinical variables will be measured every 3 months, starting from presurgery assessment (ie, baseline) to 18 months after surgery. Temporal data mining, time-series prediction, sequence classification methods, clustering time-series data, and temporal association rules will be used to develop the predictive model. RESULTS: The recruitment process stared in January 2019 and ended in November 2021. Preliminary results have been published in a scientific journal and are available for consultation on the BOUNCE project website. Data analysis and dissemination of the study results will be performed in 2022. CONCLUSIONS: This study will develop a predictive model that is able to describe individual resilience and identify different resilience trajectories along the care process. The results will allow the implementation of tailored interventions according to patients' needs, supported by eHealth technologies. TRIAL REGISTRATION: ClinicalTrials.gov NCT05095675; https://clinicaltrials.gov/ct2/show/NCT05095675. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34564.

Trajectories and Predictors of Depression After Breast Cancer Diagnosis: A 1-year longitudinal study.

Being diagnosed with breast cancer (BC) can be a traumatic experience for patients who may experience symptoms of depression. In order to facilitate the prevention of such symptoms, it is crucial to understand how and why depressive symptoms emerge and evolve for each individual, from diagnosis through treatment and recovery. In the present work, data from a multicentric study of 706 BC patients followed for 12 months are analyzed. First, a trajectory-based unsupervised clustering based on K-means is performed to capture the dynamic patterns of change in patients' depressive symptoms after BC diagnosis and to identify distinct trajectory clusters. Then a supervised learning approach was employed to build a classification model of depression progression and to identify potential predictors. Patients were clustered into 4 groups: stable low, stable high, improving, and worsening depressive symptoms. In a nested cross-validation pipeline, the performance of the Support Vector Machine model for discriminating between "good" and "poor" progression was 0.78±0.05 in terms of AUC. Several psychological variables emerged as highly predictive of the evolution of depressive symptoms with the most important ones being negative affectivity and anxious preoccupation. Clinical Relevance-The findings of the present study may help clinicians tailor individualized psychological interventions aiming at alleviating the burden of these symptoms in women with breast cancer and improving their overall well-being.

The moderating role of coping flexibility in reports of somatic symptoms among early breast cancer patients.

OBJECTIVE: The current study assessed breast cancer patients' somatic symptoms during the first six months post diagnosis and examined the moderating role of coping flexibility (i.e., trauma-focused and forward-focused coping strategies) on the association between reported somatic symptoms three months after breast cancer diagnosis and somatic symptoms six months after diagnosis. METHOD AND MEASURES: An international sample of 702 women diagnosed with breast cancer from four countries (Finland, Israel, Italy, Portugal) completed self-reported questionnaires at three time points: at the time of diagnosis (M0), three months post diagnosis (M3), and six months post diagnosis (M6). The questionnaires included the coping flexibility scale and questions about demographics, medical data, and somatic symptoms. RESULTS: The highest level of somatic symptoms was reported after three months post diagnosis (M3), as compared to M0 and M6. Both trauma-focused and forward-focused coping strategies moderated the relationship between somatic symptoms at M3 and somatic symptoms at M6. CONCLUSION: The findings highlight the importance of assessing somatic symptoms soon after breast cancer diagnosis and throughout the early phase of treatment. Coping flexibility can buffer the stability of the somatic symptoms during this initial phase.

Two-dimensional Scheme for Arranging Voice Disorders.

Importance: Prevalent schemes that have been used for arranging voice pathologies have shaped theoretical and clinical views and the conceptualization of the pathologies and of the field as a whole. However, these available schemes contain inconsistencies and categorical overlaps. Objective: To develop and evaluate a new approach for arranging voice pathologies, using 2 continuous scales, organicity and tonicity, which were used to construct a 2-dimensional plane. Design, Setting, and Participants: This survey study was conducted among experts in the fields of laryngology and/or voice disorders from 10 countries. The survey was conducted using an online platform from March to May 2021. The data were analyzed in June 2021. Of the 45 experts who were initially approached, 39 (86.7%) completed the survey. Main Outcomes and Measures: The primary outcome measures were group ratings on 2 rating scales: organicity and tonicity. On the organicity scale, 0 represented nonorganic and 10 organic. On the tonicity scale, 0 represented hypotonic and 10 hypertonic. Results: Participants included 16 laryngologists and 23 speech-language pathologists, of whom 27 (69.2%) were women and 12 (30.8%) men with a mean age of 55 years. The Cronbach α was high for organicity and tonicity (0.98 and 0.97, respectively). Interrater agreement (rwg) was moderate to very strong (rwg≥0.50) for most pathologies. The correlation between the 2 scales was moderate and negative (r = -0.38; P = .03). The pathologies were scattered across the full range of both scales and the 4 quadrants of the 2-dimensional plane, suggesting the continuity and bidimensionality of the new arrangement scheme. In addition, a latent profile analysis suggested that the 4-cluster solution is valid and roughly corresponded to the 4 quadrants of the constructed plane. Conclusions and Relevance: The findings of this survey study suggest the potential use of a 2-dimensional plane that was based on 2 continuous scales as a new arrangement scheme for voice disorders. The results suggest that this approach provides a valid representation of the field based on 2 basic measures beyond the specific etiology of each laryngeal pathology or condition. This simple and comprehensive organization scheme has the potential to facilitate new insights on the nature of voice pathologies, considering the interpathology similarities and differences.

Prediction of Poor Mental Health Following Breast Cancer Diagnosis Using Random Forests1.

Breast cancer diagnosis has been associated with poor mental health, with significant impairment of quality of life. In order to ensure support for successful adaptation to this illness, it is of paramount importance to identify the most prominent factors affecting well-being that allow for accurate prediction of mental health status across time. Here we exploit a rich set of clinical, psychological, socio-demographic and lifestyle data from a large multicentre study of patients recently diagnosed with breast cancer, in order to classify patients based on their mental health status and further identify potential predictors of such status. For this purpose, a supervised learning pipeline using cross-sectional data was implemented for the formulation of a classification scheme of mental health status 6 months after diagnosis. Model performance in terms of AUC ranged from 0.81± 0.04 to 0.90± 0.03. Several psychological variables, including initial levels of anxiety and depression, emerged as highly predictive of short-term mental health status of women diagnosed with breast cancer.

Cognitive, emotional, and behavioral mediators of the impact of coping self-efficacy on adaptation to breast cancer: An international prospective study.

OBJECTIVE: The main objective of this prospective multicenter study was to examine whether illness representations of control, affect, and coping behaviors mediate the effects of self-efficacy to cope with cancer on psychological symptoms and overall quality of life, in breast cancer patients. METHOD: Data from 413 women (Mean age = 54.87; SD = 8.01), coming from four countries (i.e., Finland, Israel, Italy, Portugal), who received medical therapy for their early breast cancer, were analyzed. Coping self-efficacy was assessed at baseline. Potential mediators were assessed three months later, and outcomes after six months. RESULTS: Coping self-efficacy was related to all mediators and outcomes. Illness representations of treatment control, positive and negative affect, and certain coping behaviors (mostly, anxiety preoccupation) mediated the effects of coping self-efficacy. Coping self-efficacy was related to each outcome through a different combination of mediators. CONCLUSIONS: Coping self-efficacy is a major self-regulation factor which is linked to well-being through multiple cognitive, emotional, and behavioral pathways. Enhancement of coping self-efficacy should be a central intervention goal for patients with breast cancer, towards promotion of their well-being.

How face masks influence the sinonasal quality of life during the COVID-19 pandemic.

PURPOSE: To study different mask types' impact on a sinonasal quality of life. METHODS: For this observational cross-sectional study, a web-based survey was distributed via social media forums. We used the validated Hebrew version of the Sinonasal Outcome Test-22 followed by a questionnaire developed specifically for the present study, focusing on the time of the COVID-19 pandemic (Mask Sinonasal Outcome Test), and questions regarding general health issues. The participants' mask-wearing routine was also studied. RESULTS: Seventy percent of 351 participants had experienced a change in their breathing during the time of the pandemic. The median total Sinonasal Outcome Test-22 score was 13, and 10% of the participants reported a significantly impaired quality of life. According to multivariate analyses, the only subject-related variables significantly associated with the reduced sinonasal quality of life were female gender, younger age, a background of chronic rhinitis and sinusitis, and the mask-wearing average daily duration. The mask sinonasal outcome test convergent validity was confirmed. CONCLUSION: The majority of our survey's responders, predominantly female and younger participants, reported reduced sinonasal quality of life in the COVID-19 pandemic period. It can be attributed to mask-wearing, especially for a prolonged time, irrespective of the existing mask type. These findings should encourage medical companies to produce more "airway-minded" personal protection equipment.

A machine learning-based pipeline for modeling medical, socio-demographic, lifestyle and self-reported psychological traits as predictors of mental health outcomes after breast cancer diagnosis: An initial effort to define resilience effects.

Displaying resilience following a diagnosis of breast cancer is crucial for successful adaptation to illness, well-being, and health outcomes. Several theoretical and computational models have been proposed toward understanding the complex process of illness adaptation, involving a large variety of patient sociodemographic, lifestyle, medical, and psychological characteristics. To date, conventional multivariate statistical methods have been used extensively to model resilience. In the present work we describe a computational pipeline designed to identify the most prominent predictors of mental health outcomes following breast cancer diagnosis. A machine learning framework was developed and tested on the baseline data (recorded immediately post diagnosis) from an ongoing prospective, multinational study. This fully annotated dataset includes socio-demographic, lifestyle, medical and self-reported psychological characteristics of women recently diagnosed with breast cancer (N = 609). Nine different feature selection and cross-validated classification schemes were compared on their performance in classifying patients into low vs high depression symptom severity. Best-performing approaches involved a meta-estimator combined with a Support Vector Machines (SVMs) classification algorithm, exhibiting balanced accuracy of 0.825, and a fair balance between sensitivity (90%) and specificity (74%). These models consistently identified a set of psychological traits (optimism, perceived ability to cope with trauma, resilience as trait, ability to comprehend the illness), and subjective perceptions of personal functionality (physical, social, cognitive) as key factors accounting for concurrent depression symptoms. A comprehensive supervised learning pipeline is proposed for the identification of predictors of depression symptoms which could severely impede adaptation to illness.

Can posttraumatic growth after breast cancer promote positive coping?-A cross-lagged study.

OBJECTIVES: The diagnosis of breast cancer can be associated with significant emotional distress, yet, over time, cancer survivors also may experience positive psychological changes labeled posttraumatic growth (PTG). Two alternative paths between coping strategies and PTG were tested among breast cancer patients, employing longitudinal design: Would PTG after medical treatment of breast cancer lead to an increase in reporting positive coping strategies; alternatively, would positive coping strategies after medical treatment of breast cancer lead to increased PTG. METHODS: A longitudinal study was conducted among 198 breast cancer patients who completed validated self-report scales, at five time points: at 3 months after completing active medical treatment (T1); after 6 months (T2); after 1 year (T3); after 2 years (T4); and after 7 years (T5). The current study analyzes three time points: T1, T3, and T5 using structural equation modeling. RESULTS: Our results support the path that describes the first alternative, that higher report of PTG after medical treatment of breast cancer patients would lead to increased report of positive coping strategies. In addition, positive coping strategies were found to be positively linked to PTG after 6 months, and the link was slightly stronger after 2 years. Nevertheless, 7 years after diagnosis, no association was found between positive coping strategies and PTG measurements. CONCLUSIONS: Finding positive meaning after coping with breast cancer, as reflected in PTG, may be a significant resource in coping with cancer, and it may lead to increase of positive coping strategies.

Physical and Visual Characteristics of the Neck Predicting Gender Perception.

The association between neck characteristics (physical and visual) and the perception of gender is unclear. This association is critical, especially when the perception of the speakers' gender is of interest, such as in transgender patients. This study was the first to provide basic empirical data on this association among cisgender men and women. METHODS: The necks of 30 adult men and women were measured physically and then photographed and evaluated visually by a group of 10 judges. These judges also evaluated voice recordings of the same speakers. Another group of 124 judges rated the visual and auditory masculinity/femininity of the necks and the voices. RESULTS: While most physical measures of the neck were larger for men, neck-length did not significantly differ between genders. A stepwise multiple regression model revealed that the single physical measure that consistently differed between genders was neck-girth (P < 0.0001). The single visual-appearance measure that consistently differed between genders was thyroid-protrusion (P = 0.0003). Neck-girth was the only physical characteristic that significantly correlated with gender differences in voice. Furthermore, the size of the thyroid prominence (ie, Adam's apple) was not associated with gender differences in voice. CONCLUSIONS: Neck characteristics (both physical and visual) are significantly associated with the perception of gender. While larger necks are typically perceived as masculine, neck-length is neither associated with gender nor with the speaker's voice characteristics. These findings highlight the importance of examining various physical and visual characteristics of the neck, when considering a feminization confirmation procedure for transgender patients.

Substance Abuse and Other Adverse Outcomes for Military-Connected Youth in California: Results From a Large-Scale Normative Population Survey.

IMPORTANCE: Military families and military-connected youth exhibit significant strengths; however, a sizeable proportion of these families appear to be struggling in the face of war-related stressors. Understanding the consequences of war is critical as a public health concern and because additional resources may be needed to support military families. OBJECTIVE: To determine whether rates of adverse outcomes are higher for military-connected adolescents during war compared with nonmilitary peers. DESIGN, SETTING, AND PARTICIPANTS: This study is a secondary data analysis of a large, normative, and geographically comprehensive administrative data set (2013 California Healthy Kids Survey) to determine whether military-connected youth are at risk for adverse outcomes, including substance use, experiencing violence and harassment, and weapon carrying, during wartime. These outcomes are of particular concern because they affect socioemotional adjustment and academic success. Data were collected in March and April 2013 and participants included 54,679 military-connected and 634,034 nonmilitary-connected secondary school students from public civilian schools in every county and almost all school districts in California. MAIN OUTCOMES AND MEASURES: Outcomes included lifetime and recent use of alcohol, tobacco, marijuana, prescription medications, and other drugs, as well as experiences of physical and nonphysical violence and harassment and weapon carrying during the last year. RESULTS: Multivariable logistic regression models indicated that military-connected youth had greater odds of substance use, experience of physical violence and nonphysical harassment, and weapon carrying. For example, military-connected youth had 73% greater odds of recent other drug use (eg, cocaine and lysergic acid diethylamide; odds ratio [OR], 1.73; 95% CI, 1.66-1.80) and twice the odds of bringing a gun to school (OR, 2.20; 95% CI, 2.10-2.30) compared with nonmilitary-connected peers. Their odds of being threatened with a weapon or being in a fight were also significantly higher than their civilian counterparts (OR, 1.87; 95% CI, 1.80-1.95 and OR, 1.67; 95% CI, 1.62-1.71, respectively). CONCLUSIONS AND RELEVANCE: Most military-connected youth demonstrate resilience. However, results suggest that during wartime, military-connected youth are at increased risk for adverse outcomes. Further, when compared with data from 2011, the rates of these negative outcomes appear to be increasing. These findings suggest a need to identify and intervene with military-connected adolescents and reflect a larger concern regarding the well-being of military families during wartime.

Outcome of tracheostomy in patients over 85 years old (oldest-old patients).

OBJECTIVE: To investigate morbidity, complication rate, and mortality in oldest-old patients who undergo tracheostomy. STUDY DESIGN: Historical cohort study. SUBJECT AND METHODS: The medical records for 64 patients (>85 years) who underwent standard or percutaneous tracheostomy between 2001 and 2005 in a tertiary care hospital were reviewed for in-hospital and out-of-hospital mortality, complications, and decannulation rate. RESULTS: Twenty-eight (43.8%) patients were discharged from the hospital and all remained tracheotomized at that time. Postoperative mortality had not been related to the procedure itself and the mortality rate reached 75 percent within the first three postoperative months and 93.8 percent within the first year post-tracheostomy. The post-tracheostomy course was complicated in three (4.7%) patients. There was no significant correlation between the length of hospital stay or survival and demographic parameters, pneumonia as the reason for mechanical ventilation, or performance of surgery before tracheostomy. CONCLUSION: Tracheostomy is a safe surgical procedure in the oldest-old patients. The high rate of the postoperative mortality is not related to the procedure itself. The possibility of permanent stoma should be considered and discussed with the patients and their families during the preoperative counseling.