Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.

The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e-questionnaire comprised of pre-diagnostic descriptors of first symptoms/sensations was administered to patients referred for suspected LC. Respondents were included in the present analysis only if they later received a primary LC diagnosis or had no cancer; and inclusion of each descriptor required ≥4 observations. Fully-completed data from 506/670 individuals later diagnosed with primary LC (n = 311) or no cancer (n = 195) were modelled with orthogonal projections to latent structures (OPLS). After analysing 145/285 descriptors, meeting inclusion criteria, through randomised seven-fold cross-validation (six-fold training set: n = 433; test set: n = 73), 63 provided best LC prediction. The most-significant LC-positive descriptors included a cough that varied over the day, back pain/aches/discomfort, early satiety, appetite loss, and having less strength. Upon combining the descriptors with the background variables current smoking, a cold/flu or pneumonia within the past two years, female sex, older age, a history of COPD (positive LC-association); antibiotics within the past two years, and a history of pneumonia (negative LC-association); the resulting 70-variable model had accurate cross-validated test set performance: area under the ROC curve = 0.767 (descriptors only: 0.736/background predictors only: 0.652), sensitivity = 84.8% (73.9/76.1%, respectively), specificity = 55.6% (66.7/51.9%, respectively). In conclusion, accurate prediction of LC was found through 63 early symptoms/sensations and seven background factors. Further research and precision in this model may lead to a tool for referral and LC diagnostic decision-making

Meeting the future through bio-behavioral healthcare sciences; Innovations for Healthcare Science

The call for the establishment of an endowed chair in innovative care was motivated by a need to address a multitude of challenges, including the aging population with increasing chronic diseases and multiple comorbidities; continuing breakthroughs in biomedical and medtech research which change care strategies and needs; and the increasing provision of care outside hospital environments. Meeting these challenges calls for investment in healthcare science research, education, and innovation. To meet these challenges, Investor AB donated SEK 10 million in 2013 to the establishment of an endowed Chair in Innovative Care at Karolinska Institutet. This competitive position, which integrates health and social care research, was awarded to Professor Carol Tishelman in October 2014 and is based at the Department of Learning, Informatics, Management and Ethics (LIME) where she leads the Innovative Care research group (ki.se/en/lime/innovative-care) with Assoc. Prof Lars E. Eriksson. Tishelman has a joint position with Karolinska University Hospital, Center for Innovation, as University nurse with responsibility for strategic healthcare innovation

A cross sectional study of ‘care left undone’ on nursing shifts in hospitals

Aims: To determine factors associated with variation in ‘care left undone’ (also referred to as “missed care”) by registered nurses in acute hospital wards in Sweden. Background: ‘Care left undone’ has been examined as a factor mediating the relationship between nurse staffing and patient outcomes. The context has not previously been explored to determine what other factors are associated with variation in ‘care left undone’ by registered nurses. Design: Cross-sectional survey to explore the association of registered nurse staffing and contextual factors such as time of shift, nursing role and patient acuity / dependency on ‘care left undone’ was examined using multi-level logistic regression. Methods: A survey of 10,174 registered nurse working on general medical and surgical wards in 79 acute care hospitals in Sweden (Jan-March 2010). Results: 74% of nurses reported some care was left undone on their last shift. The time of shift, patient mix, nurses’ role, practice environment, and staffing have a significant relationship with care left undone. The odds of care being left undone is halved on shifts where registered nurse care for 6 patients or fewer compared with shifts where they care for 10 or more. Conclusion: The previously observed relationship between registered nurse staffing and care left undone is confirmed. Reports of care left undone is influenced by registered nurse roles. Support worker staffing has little effect. Research is needed to identify how these factors relate to one another and whether care left undone is a predictor of adverse patient outcomes. <br/

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum