Hubbardkoenig9494

s and policy makers who aim at improving hospital discharge.

ISRCTN18427377.

ISRCTN18427377.

The objectives of this study were to (1) document violent and controlling behaviours within intimate partnerships during the perinatal period; and (2) determine individual, interpersonal and household-level factors influencing the risk of perinatal intimate partner violence (IPV).

Cross-sectional survey.

The Ottawa Hospital, Department of Obstetrics and Gynecology, Ottawa, Ontario, Canada.

Patients who gave birth at The Ottawa Hospital and were >20 days post partum between 17 March and 16 June 2020.

Perinatal IPV was defined as regular controlling behaviours or act-based forms of emotional/physical/sexual abuse in the 12 months before pregnancy, during pregnancy and/or post partum. Log-binomial multivariable regression models were used to compute adjusted risk ratios (aRRs) and 95% CIs to identify potential risk factors for IPV maternal age, postpartum depression, parity, increase in partner substance use and household income.

Among 216 participants, the median maternal age was 33 years (IQR 30erinatal IPV. U0126 order Household income was the strongest risk factor, and surprisingly, many hypothesised risk factors (eg, mental health, partner substance use, etc) were not significantly associated with perinatal IPV in this sample. This highlights the challenges in both measuring IPV and identifying individuals exposed to perinatal IPV during the high stress of the COVID-19 pandemic.

A previous study reported that food consumption is useful to rule out bacteraemia in hospitalised patients. We aimed to validate the diagnostic performance of (1) food consumption and (2) a previously reported algorithm using food consumption and shaking chills for bacteraemia in patients admitted to hospital with suspected infection.

Prospective cohort study.

Department of General Medicine in two acute care hospitals in Japan.

A total of 2009 adult patients who underwent at least two blood cultures on admission.

The reference standard for bacteraemia was judgement by two independent specialists of infectious diseases. Food consumption was evaluated by the physician in charge asking the patient or their caregivers the following question on admission 'What percentage of usual food intake were you able to eat during the past 24 hours?'

Among 2009 patients, 326 patients were diagnosed with bacteraemia (16.2%). Diagnostic performance of food consumption was sensitivity of 84.4% (95% CI 80.1 to 88), specificity of 19.8% (95% CI 18 to 21.8), positive predictive value (PPV) of 16.9% (95% CI 15.2 to 18.9) and negative predictive value (NPV) of 86.8% (95% CI 83.1 to 89.8). The discriminative performance was an area under the curve of 0.53 (95% CI 0.50 to 0.56). The performance of the algorithm using food consumption and shaking chills was sensitivity of 89% (95% CI 85.1 to 91.9), specificity of 18.8% (95% CI 17 to 20.7), PPV of 17.5% (95% CI 15.7 to 19.4) and NPV of 89.8% (95% CI 86.2 to 92.5).

Our results did not show the usefulness of food consumption and the algorithm using food consumption and shaking chills for the diagnosis of bacteraemia in patients admitted to hospital with suspected infection.

Our results did not show the usefulness of food consumption and the algorithm using food consumption and shaking chills for the diagnosis of bacteraemia in patients admitted to hospital with suspected infection.

There is a global interest in cancer immunotherapy. Clinical trials have found that one group, immune checkpoint inhibitors (ICIs), has demonstrated clinical benefits across various cancers. However, research focused on the experiences of people affected by cancer who have undergone this treatment using qualitative methodology is currently limited. Moreover, little is known about the experiences and education needs of the healthcare staff supporting the people receiving these immunotherapies. This study therefore seeks to explore the experiences of using ICIs by both the people affected by cancer and the healthcare professionals who support those people, and use the findings to make recommendations for ICI supportive care guidance development, cancer immunotherapy education materials for healthcare professionals, cancer policy and further research.

Patient participants (n

up to 30) will be recruited within the UK. The sample will incorporate a range of perspectives, sociodemographic factors, diagnoses an, disseminated at relevant national and international conferences and presented via a webinar. The study is listed on the National Institute for Health Research (NIHR) Clinical Research Network Central Portfolio.

The research will be performed in accordance with the UK Policy for Health and Social Care Research and Cardiff University's Research Integrity and Governance Code of Practice (2018). The study received ethical approval from the West Midlands and Black Country Research Ethics Committee in October 2019. Health Research Authority and Health and Care Research Wales approvals were confirmed in December 2019. All participants will provide informed consent. Findings will be published in peer-reviewed journals, non-academic platforms, the Macmillan Cancer Support website, disseminated at relevant national and international conferences and presented via a webinar. The study is listed on the National Institute for Health Research (NIHR) Clinical Research Network Central Portfolio.

Patients admitted to hospital with acute myocardial infarction (AMI) have considerable variability in in-hospital risks, resulting in higher demands on healthcare resources. Simple risk-assessment tools are important for the identification of patients with higher risk to inform clinical decisions. However, few risk assessment tools have been built that are suitable for populations with AMI in China. We aim to develop and validate a risk prediction model, and further build a risk scoring system.

Data from a nationally representative retrospective study was used to develop the model. Patients from a prospective study and another nationally representative retrospective study were both used for external validation.

161 nationally representative hospitals, and 53 and 157 other hospitals were involved in the above three studies, respectively.

8010 patients hospitalised for AMI were included as development sample, and 4485 and 11 223 other patients were included as validation samples in their corresponding sks of in-hospital MACE among patients with AMI, thereby better informing decision-making in improving clinical care.

A prediction model using readily available clinical parameters was developed and externally validated to estimate risks of in-hospital MACE among patients with AMI, thereby better informing decision-making in improving clinical care.

Self-rated health (SRH) is a strong predictor for healthcare utilisation among chronically ill patients. However, its association with acute hospitalisation is unclear. Individuals' perception of urgency in acute illness expressed as degree-of-worry (DOW) is however associated with acute hospitalisation. This study examines DOW and SRH, respectively, and their association with acute hospitalisation within 48 hours after calling a medical helpline.

A prospective cohort study.

The Medical Helpline 1813 (MH1813) in the Capital Region of Denmark, Copenhagen.

Adult (≥18 years of age) patients and relatives/close friends calling the MH1813 between 24 January and 9 February 2017. A total of 6812 callers were included.

The primary outcome measure was acute hospitalisation. Callers rated their DOW (1=minimum worry, 5=maximum worry) and SRH (1=excellent, 5=poor). Covariates included age, sex, Charlson Comorbidity Score and reason for calling. Logistic regression was conducted to measure the associations in three models (1) crude; (2) age-and-sex-adjusted; (3) full fitted model (age, sex, comorbidity, reason for calling, DOW/SRH).

Of 6812 callers, 492 (7.2%) were acutely hospitalised. Most callers rated their health as being excellent to good (65.3%) and 61% rated their worry to be low (DOW 1-3). Both the association between DOW and acute hospitalisation and SRH and acute hospitalisation indicated a dose-response relationship DOW 1=ref, 3=1.8 (1.1;3.1), 5=3.5 (2.0;5.9) and SRH 1=ref, 3=0.8 (0.6;1.4), 5=1.6 (1.1;2.4). The association between DOW and acute hospitalisation decreased slightly, when further adjusting for SRH, whereas the estimates for SRH weakened markedly when including DOW.

DOW and poor SRH were associated with acute hospitalisation. However, DOW had a stronger association with hospitalisation than SRH. This suggests that DOW may capture acutely ill patients' perception of urgency better than SRH in relation to acute hospitalisation after calling a medical helpline.

NCT02979457.

NCT02979457.

Combinations of unhealthy lifestyle factors are strongly associated with mortality, cardiovascular disease (CVD) and cancer. It is unclear how socioeconomic status (SES) affects those associations. Lower SES groups may be disproportionately vulnerable to the effects of unhealthy lifestyle factors compared with higher SES groups via interactions with other factors associated with low SES (eg, stress) or via accelerated biological ageing. This systematic review aims to synthesise studies that examine how SES moderates the association between lifestyle factor combinations and adverse health outcomes. Greater understanding of how lifestyle risk varies across socioeconomic spectra could reduce adverse health by (1) identifying novel high-risk groups or targets for future interventions and (2) informing research, policy and interventions that aim to support healthy lifestyles in socioeconomically deprived communities.

Three databases will be searched (PubMed, EMBASE, CINAHL) from inception to March 2020. Referewed publication, professional networks, social media and conference presentations.

CRD42020172588.

CRD42020172588.Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring "virtual FC" from empirical SC or "virtual SC" from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.