The latest medical research on Medical Organisation

The research magnet gathers the latest research from around the web, based on your specialty area. Below you will find a sample of some of the most recent articles from reputable medical journals about medical organisation gathered by our medical AI research bot.

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Performance of universal early warning scores in different patient subgroups and clinical settings: a systematic review.

BMJ Open

To assess predictive performance of universal early warning scores (EWS) in disease subgroups and clinical settings.

Randomised trials and observational studies of internal or external validation of EWS to predict deterioration (mortality, intensive care unit (ICU) transfer and cardiac arrest) in disease subgroups or clinical settings.

We identified 770 studies, of which 103 were included. Study designs and methods were inconsistent, with significant risk of bias (high: n=16 and unclear: n=64 and low risk: n=28). There were only two randomised trials. There was a high degree of heterogeneity in all subgroups and in national early warning score (I2=72%-99%). Predictive accuracy (mean area under the curve; 95% CI) was highest in medical (0.74; 0.74 to 0.75) and surgical (0.77; 0.75 to 0.80) settings and respiratory diseases (0.77; 0.75 to 0.80). Few studies evaluated EWS in specific diseases, for example, cardiology (n=1) and respiratory (n=7). Mortality and ICU transfer were most frequently studied outcomes, and cardiac arrest was least examined (n=8). Integration with electronic health records was uncommon (n=9).

Methodology and quality of validation studies of EWS are insufficient to recommend their use in all diseases and all clinical settings despite good performance of EWS in some subgroups. There is urgent need for consistency in methods and study design, following consensus guidelines for predictive risk scores. Further research should consider specific diseases and settings, using electronic health record data, prior to large-scale implementation.

PROSPERO CRD42019143141.

Mental health conditions and use of rhythm control therapies in patients with atrial fibrillation: a nationwide cohort study.

BMJ Open

Mental health conditions (MHCs) have been associated with undertreatment of unrelated medical conditions, but whether patients with MHCs face disparities in receiving rhythm control therapies for atrial fibrillation (AF) is currently unknown. We assessed the hypothesis that MHCs are associated with a lower use of antiarrhythmic therapies (AATs).

We identified 239 222 patients (mean age 72.6±13.2 years; 49.8% women) with incident AF, in whom the prevalence of any MHC was 19.9%.

Lower overall use of any AAT emerged in patients with any MHC than in those without MHC (16.9% vs 22.9%, p<0.001). Any MHC, depression, bipolar disorder, anxiety disorder and schizophrenia were all associated with lower incidence of any AAT with adjusted subdistribution HRs of 0.790 (95% CI 0.771 to 0.809), 0.817 (0.796 to 0.838), 0.811 (0.789 to 0.835), 0.807 (0.785 to 0.830) and 0.795 (0.773 to 0.818), respectively. Adjusted rates of AAD, cardioversion and catheter ablation use were lower in all MHC groups compared with patients without MHC. The findings in patients with any MHC were confirmed in propensity score matching analysis.

Among patients with AF, a clear disparity exists in AAT use between those with and without MHCs.

ClinicalTrials Identifier: NCT04645537; ENCePP Identifier: EUPAS29845.

Is self-rated health associated with cardiovascular risk factors and disease in a low-income setting? A cross-sectional study from the Amazon Basin of Brazil.

BMJ Open

Prior studies have suggested that self-rated health may be a useful indicator of cardiovascular disease. Consequently, we aimed to assess the relationship between self-rated health, cardiovascular risk factors and subclinical cardiac disease in the Amazon Basin.

Cardiovascular risk factors and subclincial cardiac disease by echocardiography.

In participants from the Amazon Basin of Brazil we obtained self-rated health according to a Visual Analogue Scale, ranging from 0 (poor) to 100 (excellent). We performed questionnaires, physical examination and echocardiography. Logistic and linear regression models were applied to assess self-rated health, cardiac risk factors and cardiac disease by echocardiography. Multivariable models were mutually adjusted for other cardiovascular risk factors, clinical and socioeconomic data, and known cardiac disease.

A total of 574 participants (mean age 41 years, 61% female) provided information on self-rated health (mean 75±21 (IQR 60-90) points). Self-rated health (per 10-point increase) was negatively associated with hypertension (OR 0.87 (95% CI 0.78 to 0.97), p=0.01), hypercholesterolaemia (OR 0.89 (95%CI 0.80 to 0.99), p=0.04) and positively with healthy diet (OR 1.13 (95%CI 1.04 to 1.24), p=0.004). Sex modified these associations (p-interaction <0.05) such that higher self-rated health was associated with healthy diet and physical activity in men, and lower odds of hypertension and hypercholesterolaemia in women. No relationship was found with left ventricular ejection fraction <45% (OR 0.97 (95% CI 0.77 to 1.23), p=0.8), left ventricular hypertrophy (OR 0.97 (95% CI 0.76 to 1.24), p=0.81) or diastolic dysfunction (OR 1.09 (95% CI 0.85 to 1.40), p=0.51).

Self-rated health was positively associated with health parameters in the Amazon Basin, but not with subclinical cardiac disease by echocardiography. Our findings are of hypothesis generating nature and future studies should aim to determine whether assessment of self-rated health may be useful for screening related to policy-making or lifestyle interventions. NCT04445103; Post-results.

Mortality of Puerto Ricans in the USA post Hurricane Maria: an interrupted time series analysis.

BMJ Open

To determine death occurrences of Puerto Ricans on the mainland USA following the arrival of Hurricane Maria in Puerto Rico in September 2017.

Hurricane Maria.

We found an increase in mortality for persons of Puerto Rican origin during the 6-month period following the hurricane (October 2017 through March 2018), suggesting that deaths among these persons were 3.7% (95% CI 0.025 to 0.049) higher than would have otherwise been expected. In absolute terms, we estimated 514 excess deaths (95% CI 346 to 681) of persons of Puerto Rican origin that occurred on the mainland USA, concentrated in those aged 65 years or older.

Our findings suggest an undercounting of previous deaths as a result of the hurricane due to the systematic effects on the displaced and resident populations in the mainland USA. Displaced populations are frequently overlooked in disaster relief and subsequent research. Ignoring these populations provides an incomplete understanding of the damages and loss of life.

Describing, predicting and explaining adherence to total skin self-examination (TSSE) in people with melanoma: a 12-month longitudinal study.

BMJ Open

To describe trajectories in melanoma survivors' adherence to monthly total skin self-examination (TSSE) over 12 months, and to investigate whether adherence trajectories can be predicted from demographic, cognitive or emotional factors at baseline.

The primary outcome was adherence to guideline recommended (monthly) TSSE over 12 months. This was determined from time-stamped TSSE data recorded by the ASICA intervention app.

Latent growth mixture models identified three TSSE adherence trajectories (adherent -41%; drop-off -35%; non-adherent -24%). People who were non-adherent were less likely to intend to perform TSSE as recommended, intending to do it more frequently (OR=0.21, 95% CI 0.06 to 0.81, p=0.023) and were more depressed (OR=1.31, 95% CI 1.06 to 1.61, p=0.011) than people who were adherent. People whose adherence dropped off over time had less well-developed action plans (OR=0.78, 95% CI 0.63 to 0.96, p=0.016) and lower self-efficacy about TSSE (OR=0.92, 95% CI 0.86 to 0.99, p=0.028) than people who were adherent.

Adherence to monthly TSSE in people treated for melanoma can be differentiated into adherent, drop-off and non-adherent trajectories. Collecting information about intentions to engage in TSSE, depression, self-efficacy and/or action planning at outset may help to identify those who would benefit from additional intervention. Registry (NCT03328247).

Associations between social fragmentation, socioeconomic deprivation and suicide risk across 1887 municipalities in Japan, 2009-2017: a spatial analysis using the Bayesian hierarchical model.

BMJ Open

Previous studies have indicated that spatial variation in suicide mortality is associated with area-specific socioeconomic characteristics, such as socioeconomic deprivation and social fragmentation. However, most of these studies have been conducted in the West and findings from Asian countries are limited. This study aims to investigate associations between socioeconomic characteristics and suicide mortality rates across 1887 municipalities in Japan between 2009 and 2017. We also assessed these associations by gender and age group.

Suicide data were obtained from the suicide statistics of the Ministry of Health, Labour and Welfare in Japan and included information on the number of suicides by gender, age and municipality location. Social fragmentation, socioeconomic deprivation and urbanicity were used as socioeconomic characteristics in this study and were created from survey data obtained from the 2010 census. Bayesian hierarchical models were used to examine associations between socioeconomic characteristics and suicide risk.

Suicide rates were significantly higher in municipalities with higher levels of deprivation, with a rate ratio of 1.13 (95% credible interval: 1.10 to 1.17) in the highest quartile compared with the lowest. Higher levels of urbanicity had significantly lower suicide rates, with a rate ratio of 0.79 (95% credible interval: 0.77 to 0.82) in the highest quartile compared with the lowest. However, associations between exposures and suicide varied considerably by gender and age. Among both men and women aged 0-39 years, fragmentation was significantly associated with suicide, with rate ratios of 1.07 and 1.15 for men and women, respectively, in the highest quartile compared with the lowest.

Suicide prevention in Japan should particularly focus on areas with high levels of deprivation or low levels of urbanicity. Furthermore, young Japanese people residing in the most fragmented municipalities were also at high risk of suicide, and appropriate measures need to be taken.

Study protocol for a real-world evaluation of an integrated child and family health hub for migrant and refugee women.

BMJ Open


Our study will evaluate the impact, implementation and cost-benefit of the First 2000 Days Care Connect (FDCC) integrated hub model for pregnant migrant and refugee women and their infants.

This study has three components. Component 1 is a non-randomised controlled trial to compare the FDCC model of care with usual care. This trial will allocate eligible women to intervention and control groups based on their proximity to the Hub sites. Outcome measures include: the proportion of children attending child and family health (CFH) nurse services and completing their CFH checks to 12 months of age; improved surveillance of growth and development in children up to 12 months, post partum; improved breastfeeding rates; reduced emergency department presentations; and improved maternal well-being. These will be measured using linked medical record data and surveys. Component 2 will involve a mixed-method implementation evaluation to clarify how and why FDCC was implemented within the sites to inform future roll-out. Component 3 is a within-trial economic evaluation from a healthcare perspective to assess the cost-effectiveness of the Hubs relative to usual care and the implementation costs if Hubs were scaled and replicated.

Development and validation of automated computer-aided risk scores to predict in-hospital mortality for emergency medical admissions with COVID-19: a retrospective cohort development and validation study.

BMJ Open

There are no established mortality risk equations specifically for unplanned emergency medical admissions which include patients with SARS-19 (COVID-19). We aim to develop and validate a computer-aided risk score (CARMc19) for predicting mortality risk by combining COVID-19 status, the first electronically recorded blood test results and the National Early Warning Score (NEWS2).

Adult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic NEWS and blood test results recorded on admission. We used logistic regression modelling to predict the risk of in-hospital mortality using two models: (1) CARMc19_N: age+sex+NEWS2 including subcomponents+COVID19; (2) CARMc19_NB: CARMc19_N in conjunction with seven blood test results and acute kidney injury score. Model performance was evaluated according to discrimination (c-statistic), calibration (graphically) and clinical usefulness at NEWS2 thresholds of 4+, 5+, 6+.

The risk of in-hospital mortality following emergency medical admission was similar in development and validation datasets (8.4% vs 8.2%). The c-statistics for predicting mortality for CARMc19_NB is better than CARMc19_N in the validation dataset (CARMc19_NB=0.88 (95% CI 0.86 to 0.90) vs CARMc19_N=0.86 (95% CI 0.83 to 0.88)). Both models had good calibration (CARMc19_NB=1.01 (95% CI 0.88 to 1.14) and CARMc19_N:0.95 (95% CI 0.83 to 1.06)). At all NEWS2 thresholds (4+, 5+, 6+) model, CARMc19_NB had better sensitivity and similar specificity.

We have developed a validated CARMc19 scores with good performance characteristics for predicting the risk of in-hospital mortality. Since the CARMc19 scores place no additional data collection burden on clinicians, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

Protocol for an observational study investigating hormones triggering the onset of sustained lactation: the INSIGHT study.

BMJ Open


The primary objective of the investigating hormones triggering the onset of sustained lactation study is to establish reference intervals for the circulating hormone concentrations initiating postpartum milk secretion. The study will also assess how maternal factors such as parity, pregnancy comorbidities and complications during labour and delivery, which are known to delay lactation, may affect hormone concentrations. This single-centre observational study will recruit up to 1068 pregnant women over a 3-year period. A baseline blood sample will be obtained at 36 weeks' gestation. Participants will be monitored during postpartum days 1-4. Lactation onset will be reported using a validated breast fullness scale. Blood samples will be collected before and after a breastfeed on up to two occasions per day during postpartum days 1-4. Colostrum, milk and spot urine samples will be obtained on a single occasion. Serum hormone reference intervals will be calculated as mean±1.96 SD, with 90% CIs determined for the upper and lower reference limits. Differences in hormone values between healthy breastfeeding women and those at risk of delayed onset of lactation will be assessed by repeated measures two-way analysis of variance or a mixed linear model. Correlations between serum hormone concentrations and milk composition and volume will provide insights into the endocrine regulation of milk synthesis.

Analysis of wheelchair falls in team sports at the Paralympic Games: video-based descriptive comparison between the Rio 2016 and Tokyo 2020 games.

BMJ Open

To identify the fall characteristics of athletes in wheelchair rugby and wheelchair basketball during the Tokyo 2020 Paralympic Games and descriptively compare these with those of the Rio 2016 Paralympic Games.

We obtained video footage from the International Paralympic Committee of the Tokyo 2020 Paralympic Games that included 8 teams from each of the 18 wheelchair rugby and 10 wheelchair basketball games (men and women). The data were analysed to evaluate the number of falls, class difference (low or high pointer), time of play during the fall, phase of play, contact with other athletes, fall direction, fall location and the body part that first contacted the floor during the fall. These data from the Rio 2016 and Tokyo 2020 games were compared.

Overall, 430 falls (rugby, 104; men's basketball, 230 and women's basketball, 96) occurred (average per game ±SD: 5.8±3.1, 23.0±5.4 and 9.6±5.0, respectively). Significant differences in class, direction, fall location and body part point of contact between the three sports were observed. In wheelchair rugby, falls occurred mainly in high pointers and tended to be more lateral due to contact. In wheelchair basketball, falls occurred more in female high-pointers and in male low pointers, with more forward falls due to forward contact. Unlike in the Rio 2016 games, no difference between the events based on the presence or absence of contact was observed in the Tokyo 2020 games.

The number of falls increased in Tokyo 2020 compared with Rio 2016, with no significant difference in the characteristics of falls between the Rio 2016 and Tokyo 2020 games. Only in men's wheelchair basketball, the number of falls in low pointers significantly increased in the Tokyo 2020 games when compared with that in the Rio 2016 games.

Association between use of systemic and inhaled glucocorticoids and changes in brain volume and white matter microstructure: a cross-sectional study using data from the UK Biobank.

BMJ Open

To test the hypothesis that systemic and inhaled glucocorticoid use is associated with changes in grey matter volume (GMV) and white matter microstructure.

Primary outcomes were differences in 22 volumetric and 14 diffusion imaging parameters between glucocorticoid users and controls, determined using linear regression analyses adjusted for potential confounders. Secondary outcomes included cognitive functioning (six tests) and emotional symptoms (four questions).

Both systemic and inhaled glucocorticoid use were associated with reduced white matter integrity (lower fractional anisotropy (FA) and higher mean diffusivity (MD)) compared with controls, with larger effect sizes in systemic users (FA: adjusted mean difference (AMD)=-3.7e-3, 95% CI=-6.4e-3 to 1.0e-3; MD: AMD=7.2e-6, 95% CI=3.2e-6 to 1.1e-5) than inhaled users (FA: AMD=-2.3e-3, 95% CI=-4.0e-3 to -5.7e-4; MD: AMD=2.7e-6, 95% CI=1.7e-7 to 5.2e-6). Systemic use was also associated with larger caudate GMV (AMD=178.7 mm3, 95% CI=82.2 to 275.0), while inhaled users had smaller amygdala GMV (AMD=-23.9 mm3, 95% CI=-41.5 to -6.2) than controls. As for secondary outcomes, systemic users performed worse on the symbol digit substitution task (AMD=-0.17 SD, 95% CI=-0.34 to -0.01), and reported more depressive symptoms (OR=1.76, 95% CI=1.25 to 2.43), disinterest (OR=1.84, 95% CI=1.29 to 2.56), tenseness/restlessness (OR=1.78, 95% CI=1.29 to 2.41), and tiredness/lethargy (OR=1.90, 95% CI=1.45 to 2.50) compared with controls. Inhaled users only reported more tiredness/lethargy (OR=1.35, 95% CI=1.14 to 1.60).

Both systemic and inhaled glucocorticoid use are associated with decreased white matter integrity and limited changes in GMV. This association may contribute to the neuropsychiatric side effects of glucocorticoid medication, especially with chronic use.

Prediction of heart failure 1 year before diagnosis in general practitioner patients using machine learning algorithms: a retrospective case-control study.

BMJ Open

Heart failure (HF) is a commonly occurring health problem with high mortality and morbidity. If potential cases could be detected earlier, it may be possible to intervene earlier, which may slow progression in some patients. Preferably, it is desired to reuse already measured data for screening of all persons in an age group, such as general practitioner (GP) data. Furthermore, it is essential to evaluate the number of people needed to screen to find one patient using true incidence rates, as this indicates the generalisability in the true population. Therefore, we aim to create a machine learning model for the prediction of HF using GP data and evaluate the number needed to screen with true incidence rates.

GP data from 8543 patients (-2 to -1 year before diagnosis) and controls aged 70+ years were obtained retrospectively from 01 January 2012 to 31 December 2019 from the Nivel Primary Care Database. Codes about chronic illness, complaints, diagnostics and medication were obtained. Data were split in a train/test set. Datasets describing demographics, the presence of codes (non-sequential) and upon each other following codes (sequential) were created. Logistic regression, random forest and XGBoost models were trained. Predicted outcome was the presence of HF after 1 year. The ratio case:control in the test set matched true incidence rates (1:45).

Sole demographics performed average (area under the curve (AUC) 0.692, CI 0.677 to 0.706). Adding non-sequential information combined with a logistic regression model performed best and significantly improved performance (AUC 0.772, CI 0.759 to 0.785, p<0.001). Further adding sequential information did not alter performance significantly (AUC 0.767, CI 0.754 to 0.780, p=0.07). The number needed to screen dropped from 14.11 to 5.99 false positives per true positive.

This study created a model able to identify patients with pending HF a year before diagnosis.