Mixed Higgsino Dark Matter from a Large SU(2) Gaugino Mass

We observe that in SUSY models with non-universal GUT scale gaugino mass parameters, raising the GUT scale SU(2) gaugino mass |M_2| from its unified value results in a smaller value of -m_{H_u}^2 at the weak scale. By the electroweak symmetry breaking conditions, this implies a reduced value of \mu^2 {\it vis \`a vis} models with gaugino mass unification. The lightest neutralino can then be mixed Higgsino dark matter with a relic density in agreement with the measured abundance of cold dark matter (DM). We explore the phenomenology of this high |M_2| DM model. The spectrum is characterized by a very large wino mass and a concomitantly large splitting between left- and right- sfermion masses. In addition, the lighter chargino and three light neutralinos are relatively light with substantial higgsino components. The higgsino content of the LSP implies large rates for direct detection of neutralino dark matter, and enhanced rates for its indirect detection relative to mSUGRA. We find that experiments at the LHC should be able to discover SUSY over the portion of parameter space where m_{\tg} \alt 2350-2750 ~GeV, depending on the squark mass, while a 1 TeV electron-positron collider has a reach comparable to that of the LHC. The dilepton mass spectrum in multi-jet + \ell^+\ell^- + \eslt events at the LHC will likely show more than one mass edge, while its shape should provide indirect evidence for the large higgsino content of the decaying neutralinos.Comment: 36 pages with 26 eps figure

Mixed Higgsino Dark Matter from a Reduced SU(3) Gaugino Mass: Consequences for Dark Matter and Collider Searches

In gravity-mediated SUSY breaking models with non-universal gaugino masses, lowering the SU(3) gaugino mass |M_3| leads to a reduction in the squark and gluino masses. Lower third generation squark masses, in turn, diminish the effect of a large top quark Yukawa coupling in the running of the higgs mass parameter m_{H_u}^2, leading to a reduction in the magnitude of the superpotential mu parameter (relative to M_1 and M_2). A low | mu | parameter gives rise to mixed higgsino dark matter (MHDM), which can efficiently annihilate in the early universe to give a dark matter relic density in accord with WMAP measurements. We explore the phenomenology of the low |M_3| scenario, and find for the case of MHDM increased rates for direct and indirect detection of neutralino dark matter relative to the mSUGRA model. The sparticle mass spectrum is characterized by relatively light gluinos, frequently with m(gl)<<m(sq). If scalar masses are large, then gluinos can be very light, with gl->Z_i+g loop decays dominating the gluino branching fraction. Top squarks can be much lighter than sbottom and first/second generation squarks. The presence of low mass higgsino-like charginos and neutralinos is expected at the CERN LHC. The small m(Z2)-m(Z1) mass gap should give rise to a visible opposite-sign/same flavor dilepton mass edge. At a TeV scale linear e^+e^- collider, the region of MHDM will mean that the entire spectrum of charginos and neutralinos are amongst the lightest sparticles, and are most likely to be produced at observable rates, allowing for a complete reconstruction of the gaugino-higgsino sector.Comment: 35 pages, including 26 EPS figure

Changing trends in mortality among solid organ transplant recipients hospitalized for COVID-19 during the course of the pandemic

Mortality among patients hospitalized for COVID-19 has declined over the course of the pandemic. Mortality trends specifically in solid organ transplant recipients (SOTR) are unknown. Using data from a multicenter registry of SOTR hospitalized for COVID-19, we compared 28-day mortality between early 2020 (March 1, 2020–June 19, 2020) and late 2020 (June 20, 2020–December 31, 2020). Multivariable logistic regression was used to assess comorbidity-adjusted mortality. Time period of diagnosis was available for 1435/1616 (88.8%) SOTR and 971/1435 (67.7%) were hospitalized: 571/753 (75.8%) in early 2020 and 402/682 (58.9%) in late 2020 (p <.001). Crude 28-day mortality decreased between the early and late periods (112/571 [19.6%] vs. 55/402 [13.7%]) and remained lower in the late period even after adjusting for baseline comorbidities (aOR 0.67, 95% CI 0.46–0.98, p =.016). Between the early and late periods, the use of corticosteroids (≥6 mg dexamethasone/day) and remdesivir increased (62/571 [10.9%] vs. 243/402 [61.5%], p <.001 and 50/571 [8.8%] vs. 213/402 [52.2%], p <.001, respectively), and the use of hydroxychloroquine and IL-6/IL-6 receptor inhibitor decreased (329/571 [60.0%] vs. 4/492 [1.0%], p <.001 and 73/571 [12.8%] vs. 5/402 [1.2%], p <.001, respectively). Mortality among SOTR hospitalized for COVID-19 declined between early and late 2020, consistent with trends reported in the general population. The mechanism(s) underlying improved survival require further study

COVID-19 in hospitalized lung and non-lung solid organ transplant recipients: A comparative analysis from a multicenter study

Lung transplant recipients (LTR) with coronavirus disease 2019 (COVID-19) may have higher mortality than non-lung solid organ transplant recipients (SOTR), but direct comparisons are limited. Risk factors for mortality specifically in LTR have not been explored. We performed a multicenter cohort study of adult SOTR with COVID-19 to compare mortality by 28 days between hospitalized LTR and non-lung SOTR. Multivariable logistic regression models were used to assess comorbidity-adjusted mortality among LTR vs. non-lung SOTR and to determine risk factors for death in LTR. Of 1,616 SOTR with COVID-19, 1,081 (66%) were hospitalized including 120/159 (75%) LTR and 961/1457 (66%) non-lung SOTR (p =.02). Mortality was higher among LTR compared to non-lung SOTR (24% vs. 16%, respectively, p =.032), and lung transplant was independently associated with death after adjusting for age and comorbidities (aOR 1.7, 95% CI 1.0–2.6, p =.04). Among LTR, chronic lung allograft dysfunction (aOR 3.3, 95% CI 1.0–11.3, p =.05) was the only independent risk factor for mortality and age >65 years, heart failure and obesity were not independently associated with death. Among SOTR hospitalized for COVID-19, LTR had higher mortality than non-lung SOTR. In LTR, chronic allograft dysfunction was independently associated with mortality

A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well

The diagnostic accuracy of a screening tool is often characterized by its sensitivity and specificity. An analysis of these measures must consider their intrinsic correlation. In the context of an individual participant data meta-analysis, heterogeneity is one of the main components of the analysis. When using a random-effects meta-analytic model, prediction regions provide deeper insight into the effect of heterogeneity on the variability of estimated accuracy measures across the entire studied population, not just the average. This study aimed to investigate heterogeneity via prediction regions in an individual participant data meta-analysis of the sensitivity and specificity of the Patient Health Questionnaire-9 for screening to detect major depression. From the total number of studies in the pool, four dates were selected containing roughly 25%, 50%, 75% and 100% of the total number of participants. A bivariate random-effects model was fitted to studies up to and including each of these dates to jointly estimate sensitivity and specificity. Two-dimensional prediction regions were plotted in ROC-space. Subgroup analyses were carried out on sex and age, regardless of the date of the study. The dataset comprised 17,436 participants from 58 primary studies of which 2322 (13.3%) presented cases of major depression. Point estimates of sensitivity and specificity did not differ importantly as more studies were added to the model. However, correlation of the measures increased. As expected, standard errors of the logit pooled TPR and FPR consistently decreased as more studies were used, while standard deviations of the random-effects did not decrease monotonically. Subgroup analysis by sex did not reveal important contributions for observed heterogeneity; however, the shape of the prediction regions differed. Subgroup analysis by age did not reveal meaningful contributions to the heterogeneity and the prediction regions were similar in shape. Prediction intervals and regions reveal previously unseen trends in a dataset. In the context of a meta-analysis of diagnostic test accuracy, prediction regions can display the range of accuracy measures in different populations and settings

Comparison of the Accuracy of the 7-Item HADS Depression Subscale and 14-Item Total HADS for Screening for Major Depression: A Systematic Review and Individual Participant Data Meta-Analysis

The seven-item Hospital Anxiety and Depression Scale Depression subscale (HADS-D) and the total score of the 14-item HADS (HADS-T) are both used for major depression screening. Compared to the HADS-D, the HADS-T includes anxiety items and requires more time to complete. We compared the screening accuracy of the HADS-D and HADS-T for major depression detection. We conducted an individual participant data meta-analysis and fit bivariate random effects models to assess diagnostic accuracy among participants with both HADS-D and HADS-T scores. We identified optimal cutoffs, estimated sensitivity and specificity with 95% confidence intervals, and compared screening accuracy across paired cutoffs via two-stage and individual-level models. We used a 0.05 equivalence margin to assess equivalency in sensitivity and specificity. 20,700 participants (2,285 major depression cases) from 98 studies were included. Cutoffs of >= 7 for the HADS-D (sensitivity 0.79 [0.75, 0.83], specificity 0.78 [0.75, 0.80]) and >= 15 for the HADS-T (sensitivity 0.79 [0.76, 0.82], specificity 0.81 [0.78, 0.83]) minimized the distance to the top-left corner of the receiver operating characteristic curve. Across all sets of paired cutoffs evaluated, differences of sensitivity between HADS-T and HADS-D ranged from -0.05 to 0.01 (0.00 at paired optimal cutoffs), and differences of specificity were within 0.03 for all cutoffs (0.02-0.03). The pattern was similar among outpatients, although the HADS-T was slightly (not nonequivalently) more specific among inpatients. The accuracy of HADS-T was equivalent to the HADS-D for detecting major depression. In most settings, the shorter HADS-D would be preferred.Public Significance Statement The present study suggests that the accuracy of 14-item Hospital Anxiety and Depression Scale (HADS-D) and the seven-item HADS Depression subscale (HADS-D) are equivalent for detecting major depression. Using the seven-item HADS-D for depression screening instead of the full 14-item HADS-T has minimal influence on performance of the measure but would reduce patient and participant burden in most clinical and research settings

Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis

Objectives: Validated diagnostic interviews are required to classify depression status and estimate prevalence of disorder, but screening tools are often used instead. We used individual participant data meta-analysis to compare prevalence based on standard Hospital Anxiety and Depression Scale – depression subscale (HADS-D) cutoffs of ≥8 and ≥11 versus Structured Clinical Interview for DSM (SCID) major depression and determined if an alternative HADS-D cutoff could more accurately estimate prevalence. Methods: We searched Medline, Medline In-Process &amp; Other Non-Indexed Citations via Ovid, PsycINFO, and Web of Science (inception-July 11, 2016) for studies comparing HADS-D scores to SCID major depression status. Pooled prevalence and pooled differences in prevalence for HADS-D cutoffs versus SCID major depression were estimated. Results: 6005 participants (689 SCID major depression cases) from 41 primary studies were included. Pooled prevalence was 24.5% (95% Confidence Interval (CI): 20.5%, 29.0%) for HADS-D ≥8, 10.7% (95% CI: 8.3%, 13.8%) for HADS-D ≥11, and 11.6% (95% CI: 9.2%, 14.6%) for SCID major depression. HADS-D ≥11 was closest to SCID major depression prevalence, but the 95% prediction interval for the difference that could be expected for HADS-D ≥11 versus SCID in a new study was −21.1% to 19.5%. Conclusions: HADS-D ≥8 substantially overestimates depression prevalence. Of all possible cutoff thresholds, HADS-D ≥11 was closest to the SCID, but there was substantial heterogeneity in the difference between HADS-D ≥11 and SCID-based estimates. HADS-D should not be used as a substitute for a validated diagnostic interview. © 2020 Elsevier Inc