Unknown Outcomes Research Articles

Effort Expenditure Increases Risk-Taking for Improbable Rewards.

Previous studies have found that exerting effort can lead people to engage in risk-taking behaviors. While effort can be either cognitive or physical, risk-taking can take place in either a risky context with known outcome probabilities or an ambiguous context with unknown outcome probabilities. The goal of the current research is to investigate how effort type and decision context influence risk-taking after effort exertion. Across three experiments, we find evidence that investing effort increases risk-taking at a short timescale. Importantly, this effect is particularly noticeable when the chance of winning is low, rather than when it is uncertain. Furthermore, the increase in risk-taking happens regardless of whether the effort is cognitive or physical. These findings suggest the existence of a cost-invariant but decision context-variant mechanism for the risk-taking after-effect of effort expenditure, which helps to bring the negative emotions caused by effort exertion back to a state of emotional homeostasis.

Gene Therapy for Genetic Syndromes: Understanding the Current State to Guide Future Care.

Gene therapy holds promise as a life-changing option for individuals with genetic variants that give rise to disease. FDA-approved gene therapies for Spinal Muscular Atrophy (SMA), cerebral adrenoleukodystrophy, β-Thalassemia, hemophilia A/B, retinal dystrophy, and Duchenne Muscular Dystrophy have generated buzz around the ability to change the course of genetic syndromes. However, this excitement risks over-expansion into areas of genetic disease that may not fit the current state of gene therapy. While in situ (targeted to an area) and ex vivo (removal of cells, delivery, and administration of cells) approaches show promise, they have a limited target ability. Broader in vivo gene therapy trials have shown various continued challenges, including immune response, use of immune suppressants correlating to secondary infections, unknown outcomes of overexpression, and challenges in driving tissue-specific corrections. Viral delivery systems can be associated with adverse outcomes such as hepatotoxicity and lethality if uncontrolled. In some cases, these risks are far outweighed by the potentially lethal syndromes for which these systems are being developed. Therefore, it is critical to evaluate the field of genetic diseases to perform cost-benefit analyses for gene therapy. In this work, we present the current state while setting forth tools and resources to guide informed directions to avoid foreseeable issues in gene therapy that could prevent the field from continued success.

Attitudes Towards Unknown Outcomes

Attitudes Towards Unknown Outcomes

Reproductive Safety Issues of Novel Small Molecules for the Treatment of Inflammatory Bowel Disease: A Systematic Review.

Maintenance of remission during pregnancy is vital for women with inflammatory bowel disease (IBD). The antenatal safety of novel small molecules for IBD is yet to be ascertained. We aimed to describe the current evidence on reproductive data regarding small-molecule drugs. We performed a systematic review searching Embase Classic + Embase and Ovid MEDLINE for reproductive outcomes for tofacitinib, filgotinib, upadacitininb, and ozanimod. Additionally, we asked the manufacturers for available data on file regarding reproduction. We analysed data from 10 sources; six studies and four manufacturer reports were identified from our search. Significant malformation risks were reported for tofacitinib, filgotinib, upadacitininb, and ozanimod in animal studies. In 126 tofacitinib-exposed pregnancies, there were 55 live births with 2 congenital malformations and 1 serious infant infection, 14 terminations, 15 miscarriages, and 42 outcomes unknown. In 50 filgotinib-exposed pregnancies, there were 20 healthy babies, 1 congenital malformation, 9 terminations, 10 miscarriages, and 10 outcomes unknown. In 78 upadacitinib-exposed pregnancies, there were 30 healthy babies, 15 terminations, 15 miscarriages, and 18 outcomes unknown. In 60 ozanimod-exposed pregnancies, there were 31 live births with 1 congenital malformation, 1 case of intra-uterine growth restriction, 1 case of neonatal icterus, 13 terminations, 9 miscarriages, and 8 unknown outcomes. Animal data suggest significant risks of malformations for tofacitinib, filgotinib, upadacitininb, and ozanimod. Human data from clinical trials and real-world observations do not show concerning data so far, but these are very limited. Currently, alternative treatments should be used for IBD during pregnancy.

Mortality of hospitalized patients with COVID-19: Effects of treatment options (vitamin D, anticoagulation, isoprinosine, ivermectin) assessed by propensity score matching, retrospective analysis.

SARS-CoV-2 respiratory infection is associated with significant morbidity and mortality, especially in hospitalized high-risk patients. We aimed to evaluate the effects of treatment options (vitamin D, anticoagulation, isoprinosine, ivermectin) on hospital mortality in non-vaccinated patients during the 2021 spring wave in the Czech Republic. Initially, 991 patients hospitalized in the period January 1, 2021, to March 31, 2021, with PCR-confirmed SARS-CoV-2 acute respiratory infection in two university and five rural hospitals were included in the study. After exclusion of patients with an unknown outcome, a total of 790 patients entered the final analysis. The effects of different treatments were assessed in this cohort by means of propensity score matching. Of the 790 patients, 282 patients died in the hospital; 37.7% were male and 33.3% were female. Age, sex, state of the disease, pneumonia, therapy, and several comorbidities were matched to simulate a case-control study. For anticoagulation treatment, 233 cases (full-dose) vs. 233 controls (prophylactic dose) were matched. The difference in mortality was significant in 16 of the 50 runs. For the treatment with isoprinosine, ivermectin, and vitamin D, none of the 50 runs led to a significant difference in hospital mortality. Prophylactic-dose anticoagulation treatment in our study was found to be beneficial in comparison with the full dose. Supplementation with vitamin D did not show any meaningful benefit in terms of lowering the hospital mortality. Neither ivermectin nor, isoprinosine was found to significantly decrease hospital mortality.

Test negative case–control study of COVID-19 vaccine effectiveness for symptomatic SARS-CoV-2 infection among healthcare workers: Zambia, 2021–2022

ObjectivesThe study aim was to evaluate vaccine effectiveness (VE) of COVID-19 vaccines in preventing symptomatic COVID-19 among healthcare workers (HCWs) in Zambia. We sought to answer the question, ‘What is...

Вплив перенесених паразитарних інфекцій під час вагітності на імунну систему плода (огляд літератури)

Transmission of infections during pregnancy is known to alter the development and functioning of the fetus immune system, resulting in an inadequate immune response to common childhood infections and immunizations. Although it is a developing and actively researched topic, maternal parasitic infections remain poorly understood. Millions of women of reproductive age are currently at risk of parasite infection, while many pregnant, chronically infected women are excluded from mass drug administration due in part to a lack of resources, as well as fear of unknown adverse fetal outcomes. infection, there is no clear mechanism for understanding how maternal infection reprograms offspring immunity. Purpose - to determine the currently studied mechanisms of the influence of parasitic infections transmitted during pregnancy on the immune system of the fetus and to describe the possible effects of these influence in the long term on the health of children. Conclusions. The study of maternal parasitic infections is a rapidly developing field. Although infant mortality rates are declining, areas with endemic parasites still have the highest infant mortality rates. As antiparasitic therapy is not always curable, there is a critical need to understand the effects of maternal infection on offspring immune development. Due to the increased risk of immune alteration due to maternal inflammation and reduced passive antibody transmission, maternal parasitic infections and their mechanism of adverse immunomodulation must be uncovered to improve vaccination regimens and reduce childhood morbidity in endemic regions. No conflict of interests was declared by the author.

Impact of villitis of unknown etiology and adverse acute neonatal outcomes in Eastern Ontario

IntroductionVillitis of unknown etiology (VUE) is a histopathological lesion associated with adverse neonatal outcomes. We seek to define the obscure relationship between the severity and distribution of VUE and adverse neonatal outcomes. MethodsA retrospective chart review was conducted of pathologic findings from singleton placentas diagnosed with VUE between 2013 and 2019. Control placentas were matched 1:1 for gestational age and presence/absence of fetal IUGR. Neonatal outcomes of interest included: newborn resuscitation, NICU admission, Apgar scores and cord blood acidosis. Odds ratio and 95 % confidence intervals were calculated with controls as the reference. Results452 placentas were included. 35 % of pregnancies were complicated by IUGR. When analyzed by severity (low-grade: OR = 4.75 [2.86–8.14]; high-grade: OR = 4.76 [2.71–8.79]) and distribution (focal: OR = 5.24 [2.87–10.17]; multifocal: OR = 4.90 [2.90–8.59]), VUE was significantly associated with need for newborn resuscitation. No other neonatal outcomes of interest were significantly associated with VUE diagnosis. DiscussionWe determined a statistically significant association between VUE severity and distribution and the need for newborn resuscitation. VUE lesions were not associated with any additional neonatal outcomes of interest. Further studies with larger sample sizes are required to confirm these associations for obstetric and neonatal case management.

Prognostic impact of lymph node involvement and loss of heterozygosity of 1p or 16q in stage III favorable histology Wilms tumor: A report from Children's Oncology Group Studies AREN03B2 and AREN0532.

The prognostic impact of positive lymph nodes (LN+) and/or singular loss of heterozygosity (LOH) of 1p or 16q were assessed in children with stage III favorable histology Wilms tumor (FHWT) enrolled on AREN0532 or AREN03B2 alone. A total of 635 stage III FHWT vincristine/dactinomycin/doxorubicin (DD4A)-treated patients met inclusion criteria. Event-free survival (EFS) and overall survival are reported overall and by LN sampling, LN status, LOH 1p, LOH 16q, and a combination of LN status and singular LOH. Patients with unknown or positive combined LOH of 1p and 16q status and AREN03B2-only patients with unknown outcomes or treatment other than DD4A were excluded. EFS did not differ by study, supporting pooling. Lack of LN sampling (hazard ratio [HR],2.12; p=.0037), LN positivity (HR,2.78; p=.0002), LOH 1p (HR,2.18; p=.0067), and LOH 16q (HR,1.72; p=.042) were associated with worse EFS. Compared with patients with both LN- and LOH-, those with negative nodes but positive LOH 1p or 16q and those with LN+but LOH- for 1p or 16q had significantly worse EFS (HR,3.05 and 3.57, respectively). Patients positive for both LN and LOH had the worst EFS (HR,6.33; overall group factor, p<.0001). Findings confirm LN+status as an adverse prognostic factor amplified by presence of singular LOH 1p or 16q, supporting study of intensified therapy for patients with LN+ in combination with singular LOH in a prospective clinical trial.

LBA100 CUP-ONE trial: A prospective double-blind validation of molecular classifiers in the diagnosis of cancer of unknown primary and clinical outcomes

LBA100 CUP-ONE trial: A prospective double-blind validation of molecular classifiers in the diagnosis of cancer of unknown primary and clinical outcomes

Comparison of threshold tuning methods for predictive monitoring

AbstractPredictive monitoring techniques produce signals in case of a high predicted probability of an undesirable event, such as mortality, heart attacks, or machine failure. When using these predicted probabilities to classify the unknown outcome, a decision threshold needs to be chosen in statistical and machine learning models. In many cases, this is set to 0.5 by default. However, this may not lead to an acceptable model performance. To mitigate this issue, different threshold optimization approaches have been proposed in the literature. In this paper, we compare existing thresholding techniques to achieve a desired false alarm rate, and also evaluate the corresponding precision and recall performance metrics. A simulation study is conducted and a real‐world example on a medical dataset is provided.

Health workers’ perspectives of hepatitis B-related stigma among Aboriginal and Torres Strait Islander people in New South Wales, Australia

BackgroundExperiences of stigma and discrimination can act as a significant barrier to testing, monitoring, and treatment for hepatitis B virus (HBV). Aboriginal and Torres Strait Islander Australians are a population disproportionately impacted by HBV and yet limited research has explored HBV-related stigma in these communities. To begin preliminary explorations of HBV-related stigma among Aboriginal and Torres Strait Islander people, we interviewed health workers about their perceptions regarding HBV infection and HBV-related stigma.MethodsParticipants were recruited from staff involved in the Deadly Liver Mob (DLM) program which is a health promotion program that offers incentives for Aboriginal and Torres Strait Islander clients to be educated on viral hepatitis, recruit and educate peers, and receive screening and treatment for blood-borne viruses (BBVs) and sexually transmissible infections (STIs), and vaccination. Semi-structured interviews were conducted with 11 Aboriginal and Torres Strait Islander and non-Aboriginal or Torres Strait Islander health workers who have been involved in the development, implementation, and/or management of the DLM program within participating services in New South Wales, Australia.ResultsFindings suggest that stigma is a barrier to accessing mainstream health care among Aboriginal and Torres Strait Islander clients, with stigma being complex and multi-layered. Aboriginal and Torres Strait Islander people contend with multiple and intersecting layers of stigma and discrimination in their lives, and thus HBV is just one dimension of those experiences. Health workers perceived that stigma is fuelled by multiple factors, including poor HBV health literacy within the health workforce broadly and among Aboriginal and Torres Strait Islander clients, shame about social practices associated with viral hepatitis, and fear of unknown transmission risks and health outcomes. The DLM program was viewed as helping to resist and reject stigma, improve health literacy among both health workers and clients, and build trust and confidence in mainstream health services.ConclusionsHealth promotion programs have the potential to reduce stigma by acting as a ‘one stop shop’ for BBVs and STIs through one-on-one support, yarning, and promotion of the HBV vaccine, monitoring for chronic HBV, and treatment (where required).

Clinical characteristics and predictors of human mpox outcome during the 2022 outbreak in Nigeria: a cohort study

Research from sub-Saharan Africa that contributes to our understanding of the 2022 mpox (formerly known as monkeypox) global outbreak is insufficient. Here, we describe the clinical presentation and predictors of severe disease among patients with mpox diagnosed between Feb 1, 2022, and Jan 30, 2023 in Nigeria. We did a cohort study among laboratory-confirmed and probable mpox cases seen in 22 mpox-treatment centres and outpatient clinics across Nigeria. All individuals with confirmed and probable mpox were eligible for inclusion. Exclusion criteria were individuals who could not be examined for clinical characterisation and those who had unknown mortality outcomes. Skin lesion swabs or crust samples were collected from each patient for mpox diagnosis by PCR. A structured questionnaire was used to document sociodemographic and clinical data, including HIV status, complications, and treatment outcomes from the time of diagnosis to discharge or death. Severe disease was defined as mpox associated with death or with a life-threatening complication. Two logistic regression models were used to identify clinical characteristics associated with severe disease and potential risk factors for severe disease. The primary outcome was the clinical characteristics of mpox and disease severity. We enrolled 160 people with mpox from 22 states in Nigeria, including 134 (84%) adults, 114 (71%) males, 46 (29%) females, and 25 (16%) people with HIV. Of the 160 patients, distinct febrile prodrome (n=94, 59%), rash count greater than 250 (90, 56%), concomitant varicella zoster virus infection (n=48, 30%), and hospital admission (n=70, 48%) were observed. Nine (6%) of the 160 patients died, including seven (78%) deaths attributable to sepsis. The clinical features independently associated with severe disease were a rash count greater than 10 000 (adjusted odds ratio 26·1, 95% CI 5·2-135·0, p<0·0001) and confluent or semi-confluent rash (6·7, 95% CI 1·9-23·9). Independent risk factors for severe disease were concomitant varicella zoster virus infection (3·6, 95% CI 1·1-11·5) and advanced HIV disease (35·9, 95% CI 4·1-252·9). During the 2022 global outbreak, mpox in Nigeria was more severe among those with advanced HIV disease and concomitant varicella zoster virus infection. Proactive screening, management of co-infections, the integration and strengthening of mpox and HIV surveillance, and preventive and treatment services should be prioritised in Nigeria and across Africa. None.

Using Unlabeled Information of Embryo Siblings from the Same Cohort Cycle to Enhance In Vitro Fertilization Implantation Prediction.

High-content time-lapse embryo imaging assessed by machine learning is revolutionizing the field of in vitro fertilization (IVF). However, the vast majority of IVF embryos are not transferred to the uterus, and these masses of embryos with unknown implantation outcomes are ignored in current efforts that aim to predict implantation. Here, whether, and to what extent the information encoded within "sibling" embryos from the same IVF cohort contributes to the performance of machine learning-based implantation prediction is explored. First, it is shown that the implantation outcome is correlated with attributes derived from the cohort siblings. Second, it is demonstrated that this unlabeled data boosts implantation prediction performance. Third, the cohort properties driving embryo prediction, especially those that rescued erroneous predictions, are characterized. The results suggest that predictive models for embryo implantation can benefit from the overlooked, widely available unlabeled data of sibling embryos by reducing the inherent noise of the individual transferred embryo.

Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (January 1, 2018-April 7, 2022). HIPPS combines: (1) an extension of a previously published pregnancy episode algorithm, (2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and (3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics. We identified 628 165 pregnant persons with 816 471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within 1 week of precision for 475 433 (58.2%) episodes. 62 540 (7.7%) episodes had incident COVID-19 during pregnancy. HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence. We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data.

Exploring the transfer of risks

Societal safety is often centred on handling or managing a single risk from the perspective of a particular at-risk group or individual, in a specific time and place. However, such linear and single-facetted processes are rare and the current approaches in societal risk management fail to comprehensively include and discuss the full range of outcomes and its inherent uncertainty and complexity. By combining different examples of known and unknown outcomes of societal risk management in the scientific literature, this paper aim to contribute to the risk and safety research field by presenting a conceptual model of risk transfer. The conceptual model shows how traditional societal risk management strategies often aim at reducing a targeted risk, considering the original actors affected by this risk, in a defined geographical area and for a decided time frame despite risk management also altering the landscape of risks for other actors, in other places, and at other times, as well as for the original actors, in the original place, and during the original time. Combining these aspects in a conceptual model that accepts and incorporates complexity, the underlying intention is to initiate a discussion regarding the current approaches and understandings of societal risk management and societal safety.

O-087 Patient-centric, machine learning (ML)-based personalised prognostics supports fertility specialists to improve access to assisted reproductive technology (ART) and increase overall live birth (LB) outcomes

Abstract Study question Does the use of patient-centric, ML-prognostics counselling report (Univfy® PreIVF Report) affect assisted reproductive technology (ART) conversion (first ART cycle usage) and LB rate (LBR)? Summary answer The use of patient-centric, ML-prognostics counselling report (Univfy® PreIVF Report) by fertility specialists is associated with higher ART conversion and LBR among new patients. What is known already ART is a highly effective and safe treatment for clinical infertility. However, ART remains vastly underutilised resulting in missed opportunities to help more people build families. Commonly used age-based trends often do not address patients' perceived risks including their own ART success probability and ART cost burden as related to their personalised LB probabilities. We previously reported the use of artificial intelligence (AI)/ML to generate patient-centric counselling reports based on ART success prediction models developed and validated for each fertility centre to address their local patient populations in ways that are personalised, relevant and actionable. Study design, size, duration Retrospective cohort analysis. Eight fertility centres from 22 locations across 9 states (US) and Ontario, Canada contributed to the research design, compilation of outcomes data, and interpretation of results. Five centres provided ART utilisation and outcomes data for 15,289 new patients seen in each centre's study period when the Univfy® PreIVF Report was available and data were submitted for aggregated research analysis. Each centre provided 4-6 years of data within the period 2016-2022. Participants/materials, setting, methods The effect of Univfy or No-Univfy Group on ART conversion was analyzed by Chi square tests using aggregated data and separately for each centre's data, for 3 timed analyses, 180-Day, 360-Day and “Ever” (no restriction) after new patient visit. Patients who received the Univfy® PreIVF Report prior to IUI or ART conversion, or had no such conversion after receiving it were placed into the Univfy Group. The No-Univfy Group comprises patients who did not receive a report. Main results and the role of chance Univfy report usage was associated with higher conversions to Direct-ART (by 2.6-, 2.4-, 1.9-folds) and Any-ART (by 2.9-, 3.0-, 2.4-folds) in the aggregated data when analyzed for 180-Day, 360-Day and Ever, respectively; p-value &amp;lt; 0.001. Direct-ART is ART conversion without prior IUI(s); Any-ART conversion includes ART conversion with or without prior IUI(s). In the centre-specific analyses, the fold increase in Direct-ART and Any-ART conversions ranged from 1.8 to 4.5 and 2.2 to 4.7, respectively, in the 360-Day period; p-value &amp;lt;0.001. Univfy® PreIVF Report usage was associated with an increase in estimated LBR ranging from 2.1 to 1.3 folds for the Univfy Group compared to No-Univfy Group (360-Day analysis, p &amp;lt; 0.001) based on conservative versus liberal scenarios. Similar ART conversion and LBR results were observed for 180-Day and Ever analyses, p &amp;lt; 0.001. We used conservative to liberal assumptions for IUI-LBR and NC-LBR because IUI and NC outcomes were not readily available. (Conservative: IUI-LBR 15%, natural conception (NC)-LBR 5%; Liberal: IUI-LBR 25%, NC-LBR 20%). Estimated LBR for ART used clinical ongoing pregnancies and documented live births as LBs and the following LBR assumptions: freeze-all with no transfers yet (50%); gestational carrier ART (45%); ART with unknown outcomes (0%). Limitations, reasons for caution This study was not prospective or randomised. The intended report usage was to support physicians when counselling patients. Although we observed comparable report and ART usage across predicted ART-LB probabilities, there is potential unintentional bias towards higher report or ART utilisation among patients with more favorable clinical characteristics. Wider implications of the findings These results represent our retrospective experience in diverse geographies in North America. We endeavor to collaborate with additional centres to test the reproducibility of AI/ML-driven, validated personalized IVF prognostics on improved overall live birth outcomes and ART access when counselling patients about treatment options. Trial registration number not applicable

Accounting for uncertainty in training data to improve machine learning performance in predicting new disease activity in early multiple sclerosis.

Machine learning (ML) has great potential for using health data to predict clinical outcomes in individual patients. Missing data are a common challenge in training ML algorithms, such as when subjects withdraw from a clinical study, leaving some samples with missing outcome labels. In this study, we have compared three ML models to determine whether accounting for label uncertainty can improve a model's predictions. We used a dataset from a completed phase-III clinical trial that evaluated the efficacy of minocycline for delaying the conversion from clinically isolated syndrome to multiple sclerosis (MS), using the McDonald 2005 diagnostic criteria. There were a total of 142 participants, and at the 2-year follow-up 81 had converted to MS, 29 remained stable, and 32 had uncertain outcomes. In a stratified 7-fold cross-validation, we trained three random forest (RF) ML models using MRI volumetric features and clinical variables to predict the conversion outcome, which represented new disease activity within 2 years of a first clinical demyelinating event. One RF was trained using subjects with the uncertain labels excluded (RFexclude), another RF was trained using the entire dataset but with assumed labels for the uncertain group (RFnaive), and a third, a probabilistic RF (PRF, a type of RF that can model label uncertainty) was trained on the entire dataset, with probabilistic labels assigned to the uncertain group. Probabilistic random forest outperformed both the RF models with the highest AUC (0.76, compared to 0.69 for RFexclude and 0.71 for RFnaive) and F1-score (86.6% compared to 82.6% for RFexclude and 76.8% for RFnaive). Machine learning algorithms capable of modeling label uncertainty can improve predictive performance in datasets in which a substantial number of subjects have unknown outcomes.

Chest CT opportunistic biomarkers for phenotyping high-risk COVID-19 patients: a retrospective multicentre study

ObjectiveTo assess the value of opportunistic biomarkers derived from chest CT performed at hospital admission of COVID-19 patients for the phenotypization of high-risk patients.MethodsIn this multicentre retrospective study, 1845 consecutive COVID-19 patients with chest CT performed within 72 h from hospital admission were analysed. Clinical and outcome data were collected by each center 30 and 80 days after hospital admission. Patients with unknown outcomes were excluded. Chest CT was analysed in a single core lab and behind pneumonia CT scores were extracted opportunistic data about atherosclerotic profile (calcium score according to Agatston method), liver steatosis (≤ 40 HU), myosteatosis (paraspinal muscle F < 31.3 HU, M < 37.5 HU), and osteoporosis (D12 bone attenuation < 134 HU). Differences according to treatment and outcome were assessed with ANOVA. Prediction models were obtained using multivariate binary logistic regression and their AUCs were compared with the DeLong test.ResultsThe final cohort included 1669 patients (age 67.5 [58.5–77.4] yo) mainly men 1105/1669, 66.2%) and with reduced oxygen saturation (92% [88–95%]). Pneumonia severity, high Agatston score, myosteatosis, liver steatosis, and osteoporosis derived from CT were more prevalent in patients with more aggressive treatment, access to ICU, and in-hospital death (always p < 0.05). A multivariable model including clinical and CT variables improved the capability to predict non-critical pneumonia compared to a model including only clinical variables (AUC 0.801 vs 0.789; p = 0.0198) to predict patient death (AUC 0.815 vs 0.800; p = 0.001).ConclusionOpportunistic biomarkers derived from chest CT can improve the characterization of COVID-19 high-risk patients.Clinical relevance statementIn COVID-19 patients, opportunistic biomarkers of cardiometabolic risk extracted from chest CT improve patient risk stratification.Key Points•In COVID-19 patients, several information about patient comorbidities can be quantitatively extracted from chest CT, resulting associated with the severity of oxygen treatment, access to ICU, and death.•A prediction model based on multiparametric opportunistic biomarkers derived from chest CT resulted superior to a model including only clinical variables in a large cohort of 1669 patients suffering from SARS- CoV2 infection.•Opportunistic biomarkers of cardiometabolic comorbidities derived from chest CT may improve COVID-19 patients’ risk stratification also in absence of detailed clinical data and laboratory tests identifying subclinical and previously unknown conditions.

Bridges-Round 2: A study protocol to examine the longitudinal HIV risk prevention and care continuum outcomes among orphaned youth transitioning to young adulthood.

Youth orphaned by HIV in sub-Saharan Africa experience immense hardships including social disadvantage, adverse childhood events and limited economic prospects. These adversities disrupt the normative developmental milestones and can gravely compromise their health and emotional wellbeing. The Bridges to the Future study (2012-2018) prospectively followed 1,383 adolescents, between 10-16 years, to evaluate the efficacy and cost-effectiveness of a family-based economic empowerment intervention comprising of child development accounts, financial literacy training, family income generating activities and peer mentorship. Study findings show efficacy of this contextually-driven intervention significantly improving mental health, school retention and performance and sexual health. However, critical questions, such as those related to the longitudinal impact of economic empowerment on HIV prevention and engagement in care remain. This paper presents a protocol for the follow-up phase titled, Bridges Round 2. The Original Bridges study participants will be tracked for an additional four years (2022-2026) to examine the longitudinal developmental and behavioral health outcomes and potential mechanisms of the effect of protective health behaviors of the Bridges cohort. The study will include a new qualitative component to examine participants' experiences with the intervention, the use of biomedical data to provide the most precise results of the highly relevant, but currently unknown sexual health outcomes among study participants, as well as a cost-benefit analysis to inform policy and scale-up. Study findings may contribute to the scientific knowledge for low-resource communities on the potential value of providing modest economic resources to vulnerable boys and girls during childhood and early adolescence and how these resources may offer long-term protection against known HIV risks, poor mental health functioning and improve treatment among the HIV treatment care continuum.