Recommending treatments for comorbid patients using word-based and phrase-based alignment methods

The problem of finding treatments for patients diagnosed with multiple diseases (i.e.~a comorbidity) is an important research topic in the medical literature. In this paper, we propose a new data driven approach to recommend treatments for these comorbidities using word-based and phrase-based alignment methods. The most popular methods currently rely on combining specific information from individual diseases (e.g.~procedures, tests, etc.), then aim to detect and repair the conflicts that arise in the combined treatments. This proves to be a challenge especially in the cases where the studied comorbidities contain large numbers of diseases. In contrast, our methods rely on training a translation model using previous medical records to find treatments for newly diagnosed comorbidities. We also explore the use of additional criteria in the form of a drug interactions penalty and a treatment popularity score to select the best treatment in the case where multiple valid translations for a single comorbidity are available

A Review of Generalizability and Transportability

When assessing causal effects, determining the target population to which the results are intended to generalize is a critical decision. Randomized and observational studies each have strengths and limitations for estimating causal effects in a target population. Estimates from randomized data may have internal validity but are often not representative of the target population. Observational data may better reflect the target population, and hence be more likely to have external validity, but are subject to potential bias due to unmeasured confounding. While much of the causal inference literature has focused on addressing internal validity bias, both internal and external validity are necessary for unbiased estimates in a target population. This paper presents a framework for addressing external validity bias, including a synthesis of approaches for generalizability and transportability, the assumptions they require, as well as tests for the heterogeneity of treatment effects and differences between study and target populations.Comment: 30 pages, 3 figure

Effect on smoking quit rate of telling patients their lung age: the Step2quit randomised controlled trial

Objective To evaluate the impact of telling patients their estimated spirometric lung age as an incentive to quit smoking.Design Randomised controlled trial.Setting Five general practices in Hertfordshire, England.Participants 561 current smokers aged over 35.Intervention All participants were offered spirometric assessment of lung function. Participants in intervention group received their results in terms of "lung age" (the age of the average healthy individual who would perform similar to them on spirometry). Those in the control group received a raw figure for forced expiratory volume at one second (FEV1). Both groups were advised to quit and offered referral to local NHS smoking cessation services.Main outcome measures The primary outcome measure was verified cessation of smoking by salivary cotinine testing 12 months after recruitment. Secondary outcomes were reported changes in daily consumption of cigarettes and identification of new diagnoses of chronic obstructive lung disease.Results Follow-up was 89%. Independently verified quit rates at 12 months in the intervention and control groups, respectively, were 13.6% and 6.4% (difference 7.2%, P=0.005, 95% confidence interval 2.2% to 12.1%; number needed to treat 14). People with worse spirometric lung age were no more likely to have quit than those with normal lung age in either group. Cost per successful quitter was estimated at 280 pound ((euro) 365, $556). A new diagnosis of obstructive lung disease was made in 17% in the intervention group and 14% in the control group; a total of 16% (89/561) of participants.Conclusion Telling smokers their lung age significantly improves the likelihood of them quitting smoking, but the mechanism by which this intervention achieves its effect is unclear.Trial registration National Research Register N0096173751

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 204

This bibliography lists 140 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

Insufficient sleep, impaired sleep, and injury in Canadian adolescents: a cross-sectional analysis of the 2017/18 Health Behaviour in School-aged Children study

Background. Insufficient and impaired sleep are common in adolescents, and can impact their health and safety. One negative consequence of poor sleep is risks for unintentional injuries, yet evidence addressing this relationship among Canadian adolescents is limited in scope. In this study, we documented contemporary sleeping behaviours of Canadian adolescents and examined their relationship with risks for injuries. Methods. A cross-sectional study was employed, using records from the 2017/18 Canadian Health Behaviour in School-aged Children study (n=21,745). Participants’ usual sleep patterns, including insufficient sleep (on school and non-school days), impaired sleep, and daytime sleepiness, and annual reports of any and serious medically treated injury were obtained. Descriptive and hierarchical multivariable Poisson regression analyses were performed, with adjustment for potential confounders, and tests of interaction by age and gender. Results. Insufficient sleep, impaired sleep, and daytime sleepiness affected 11.3% to 35.3% of adolescents; these estimates varied by age and gender. Sleep indicators displayed modest, but consistent associations with risks for “any injury”, whereas sleep impairment and daytime sleepiness were the only meaningful and statistically significant risk factors for “serious injuries”, after adjustment for potential confounders. The analysis of interactions showed that boys with insufficient sleep on non-school days and impaired sleep had significantly higher injury risks compared to girls without poor sleep in these domains. Conclusion. Indicators of poor sleep affected up to one-third of Canadian adolescents, and were associated with risks for various types of injury. Sleep hygiene may act as a plausible focus for public health and prevention initiatives to mitigate injury risks

Bacteria isolated from milk of dairy cows with and without clinical mastitis in different regions of Australia and their AMR profiles

Mastitis is the most common disease in dairy cattle worldwide. The objectives of this study were to estimate the prevalence of different bacterial species associated with mastitis from dairy herds located in geographically and climatically distinct zones in Australia, and to evaluate the antimicrobial susceptibility of the isolated bacteria. Quarter-level milk samples (n = 419) were collected from 151 mastitis cases and 268 healthy controls originating from 18 dairy herds located in tropical (Northern Queensland), subtropical (Southeast Queensland) and temperate zones (Victoria) between March and June 2019. Milk samples were cultured, and the isolated bacteria were grouped into six groups: Enterobacteriaceae spp.; Streptococcus spp.; Staphylococcus aureus, non-aureus staphylococci (NAS); Bacillus spp.; and Others. Mixed effects conditional logistic regression models were applied to quantify the association between the prevalence of each bacterial group and the herd zone and bulk milk tank somatic cell counts (BMTSCC). Of the 205 isolates, 102 (50%) originated from mastitis cases, and 103 (50%) from controls. Staphylococci were the most prevalent (NAS 32% and S. aureus 11%). Contagious mastitis bacteria were more prevalent in Victoria compared to Queensland dairy herds. NAS species (P 300,000 cells/mL compared with herds with low BMTSCC ≤150,000 cells/mL. Enterobacteriaceae and Streptococcus spp. groups showed high resistance rates to 1 (51 and 47%, respectively), and 2 (11 and 23%, respectively), antimicrobials. More than one third of the Enterobacteriaceae (48%) and Others (43%) groups spp. were resistant to at least three antimicrobials. This study provided a unique opportunity to investigate the prevalence of mastitis-associated bacteria in clinical cases and in apparently healthy controls. The findings of this study help inform mastitis control and antimicrobial stewardship programs aimed to reduce the prevalence of mastitis and antimicrobial resistance in dairy herds

Prediction of coronary artery disease using urinary proteomics

Aims: Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD. Methods and results: Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78–0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66–0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47–0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80–0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26–1.89, P \u3c 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25–0.95, P = 0.001; 0.64, 95% CI: 0.28–0.98, P = 0.001, correspondingly). Conclusion: A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention