The value of blood-based measures of liver function and urate in lung cancer risk prediction: A cohort study and health economic analysis

BACKGROUND: Several studies have reported associations between low-cost blood-based measurements and lung cancer but their role in risk prediction is unclear. We examined the value of expanding lung cancer risk models for targeting low-dose computed tomography (LDCT), including blood measurements of liver function and urate. METHODS: We analysed a cohort of 388,199 UK Biobank participants with 1873 events and calculated the c-index and fraction of new information (FNI) for models expanded to include combinations of blood measurements, lung function (forced expiratory volume in 1 s - FEV1), alcohol status and waist circumference. We calculated the hypothetical cost per lung cancer case detected by LDCT for different scenarios using a threshold of ≥ 1.51 % risk at 6 years. RESULTS: The c-index was 0.805 (95 %CI:0.794-0.816) for the model containing conventional predictors. Expanding to include blood measurements increased the c-index to 0.815 (95 %CI: 0.804-0.826;p < 0.0001;FNI:0.06). Expanding to include FEV1, alcohol status, and waist circumference increased the c-index to 0.811 (95 %CI: 0.800-0.822;p < 0.0001;FNI: 0.04). The c-index for the fully expanded model containing all variables was 0.819 (95 %CI:0.808-0.830;p < 0.0001;FNI:0.09). Model expansion had a greater impact on the c-index and FNI for people with a history of smoking cigarettes relative to the full cohort. Compared with the conventional risk model, the expanded models reduced the number of participants meeting the criteria for LDCT screening by 15-21 %, and lung cancer cases detected by 7-8 %. The additional cost per lung cancer case detected relative to the conventional model was £ 1018 for adding blood tests and £ 9775 for the fully expanded model. CONCLUSION: Blood measurements of liver function and urate made a modest improvement to lung cancer risk prediction compared with a model containing conventional risk factors. There was no evidence that model expansion would improve the cost per lung cancer case detected in UK healthcare settings

Business intelligence and contribution of entrepreneurial information architecture

We are witnessing the need for a quick and intelligent reaction from organizations to the level and speed of change in business processes. The arising problems can be: from wrong lasting information; systems not fully used or explored; slow reaction to change; etc. This requires two main confluent action methods: people to synchronize their visions, ideas and strategies in the whole organization; and, in that context, select the information that strictly answers to the performance factors at the right moment. The proposed methodology turns to the potential of approach to the entrepreneurial architecture as well as to the potential of the information system in order to integrate the data and resources needed for that performance. The modeling of an information architecture of the company and its business helps in the identification of critical information, the one which is according to the mission, prospects and business success factors

Nuclear Spin Relaxation for Higher Spin

We study the relaxation of a spin I that is weakly coupled to a quantum mechanical environment. Starting from the microscopic description, we derive a system of coupled relaxation equations within the adiabatic approximation. These are valid for arbitrary I and also for a general stationary non--equilibrium state of the environment. In the case of equilibrium, the stationary solution of the equations becomes the correct Boltzmannian equilibrium distribution for given spin I. The relaxation towards the stationary solution is characterized by a set of relaxation times, the longest of which can be shorter, by a factor of up to 2I, than the relaxation time in the corresponding Bloch equations calculated in the standard perturbative way.Comment: 4 pages, Latex, 2 figure

Historical roots of Agile methods: where did “Agile thinking” come from?

The appearance of Agile methods has been the most noticeable change to software process thinking in the last fifteen years [16], but in fact many of the “Agile ideas” have been around since 70’s or even before. Many studies and reviews have been conducted about Agile methods which ascribe their emergence as a reaction against traditional methods. In this paper, we argue that although Agile methods are new as a whole, they have strong roots in the history of software engineering. In addition to the iterative and incremental approaches that have been in use since 1957 [21], people who criticised the traditional methods suggested alternative approaches which were actually Agile ideas such as the response to change, customer involvement, and working software over documentation. The authors of this paper believe that education about the history of Agile thinking will help to develop better understanding as well as promoting the use of Agile methods. We therefore present and discuss the reasons behind the development and introduction of Agile methods, as a reaction to traditional methods, as a result of people's experience, and in particular focusing on reusing ideas from histor

Proteogenomic analysis of mycobacterium smegmatis using high resolution mass spectrometry

Biochemical evidence is vital for accurate genome annotation. The integration of experimental data collected at the proteome level using high resolution mass spectrometry allows for improvements in genome annotation by providing evidence for novel gene models, while validating or modifying others. Here, we report the results of a proteogenomic analysis of a reference strain of Mycobacterium smegmatis (mc2155), a fast growing model organism for the pathogenic Mycobacterium tuberculosis—the causative agent for Tuberculosis. By integrating high throughput LC/MS/MS proteomic data with genomic six frame translation and ab initio gene prediction databases, a total of 2887 ORFs were identified, including 2810 ORFs annotated to a Reference protein, and 63 ORFs not previously annotated to a Reference protein. Further, the translational start site (TSS) was validated for 558 Reference proteome gene models, while upstream translational evidence was identified for 81. In addition, N-terminus derived peptide identifications allowed for downstream TSS modification of a further 24 gene models. We validated the existence of six previously described interrupted coding sequences at the peptide level, and provide evidence for four novel frameshift positions. Analysis of peptide posterior error probability (PEP) scores indicates high-confidence novel peptide identifications and shows that the genome of M. smegmatis mc2155 is not yet fully annotated. Data are available via ProteomeXchange with identifier PXD003500

Acute corticospinal tract diffusion tensor imaging predicts 6-month functional outcome after intracerebral haemorrhage

INTRODUCTION: Diffusion tensor imaging (DTI) can assess the structural integrity of the corticospinal tract (CST) in vivo. We aimed to investigate whether CST DTI metrics after intracerebral haemorrhage (ICH) are associated with 6-month functional outcome and can improve the predictive performance of the existing ICH score. METHODS: We retrospectively included 42 patients with DTI performed within 5 days after deep supratentorial spontaneous ICH. Ipsilesional-to-contralesional ratios were calculated for fractional anisotropy (rFA) and mean diffusivity (rMD) in the pontine segment (PS) of the CST. We determined the most predictive variables for poor 6-month functional outcome [modified Rankin Scale (mRS) > 2] using the least absolute shrinkage and selection operator (LASSO) method. We calculated discrimination using optimism-adjusted estimation of the area under the curve (AUC). RESULTS: Patients with 6-month mRS > 2 had lower rFA (0.945 [± 0.139] vs 1.045 [± 0.130]; OR 0.004 [95% CI 0.00-0.77]; p =  0.04) and higher rMD (1.233 [± 0.418] vs 0.963 [± 0.211]; OR 22.5 [95% CI 1.46-519.68]; p = 0.02). Discrimination (AUC) values were: 0.76 (95% CI 0.61-0.91) for the ICH score, 0.71 (95% CI 0.54-0.89) for rFA, and 0.72 (95% CI 0.61-0.91) for rMD. Combined models with DTI and non-DTI variables offer an improvement in discrimination: for the best model, the AUC was 0.82 ([95% CI 0.68-0.95]; p = 0.15). CONCLUSION: In our exploratory study, PS-CST rFA and rMD had comparable predictive ability to the ICH score for 6-month functional outcome. Adding DTI metrics to clinical-radiological scores might improve discrimination, but this needs to be investigated in larger studies

Association between Triglyceride-Glucose Index and Early Neurological Outcomes after Thrombolysis in Patients with Acute Ischemic Stroke

Background: The triglyceride-glucose (TyG) index is a novel biomarker of insulin resistance which might plausibly influence endogenous fibrinolysis and thus early neurological outcomes in patients with acute ischemic stroke (AIS) treated with intravenous thrombolysis using recombinant tissue-plasminogen activator. Methods: We included consecutive AIS patients within 4.5 h of symptom onset undergoing intravenous thrombolysis between January 2015 and June 2022 in this multi-center retrospective observational study. Our primary outcome was early neurological deterioration (END), defined as ≥2 (END2) or ≥ 4 (END4) National Institutes of Health Stroke Scale (NIHSS) score worsening compared to the initial NIHSS score within 24 h of intravenous thrombolysis. Our secondary outcome was early neurological improvement (ENI), defined as a lower NIHSS score at discharge. TyG index was calculated using the log scale of fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2. We evaluated the association of END and ENI with TyG index using a logistic regression model. Results: A total of 676 patients with AIS were evaluated. The median age was 68 (Interquartile range, IQR (60–76) years old), and 432 (63.9%) were males. A total of 89 (13.2%) patients developed END2, 61 (9.0%) patients developed END4, and 492 (72.7%) experienced ENI. In multivariable logistic regression analysis, after adjustment for confounding factors, TyG index was significantly associated with increased risks of END2 (categorical variable, vs. lowest tertile, medium tertile odds ratio [OR] 1.05, 95% confidence interval, CI 0.54–2.02, highest tertile OR 2.94, 95%CI 1.64–5.27, overall p < 0.001) and END4 (categorical variable, vs. lowest tertile, medium tertile OR 1.21, 95%CI 0.54–2.74, highest tertile OR 3.80, 95%CI 1.85–7.79, overall p < 0.001), and a lower probability of ENI (categorical variable, vs. lowest tertile, medium tertile OR 1.00, 95%CI 0.63–1.58, highest tertile OR 0.59, 95%CI 0.38–0.93, overall p = 0.022). Conclusions: Increasing TyG index was associated with a higher risk of END and a lower probability of ENI in patients with acute ischemic stroke treated with intravenous thrombolysis

The impact of the UK COVID-19 pandemic on patient-reported health outcomes after stroke: a retrospective sequential comparison

BACKGROUND AND PURPOSE: The COVID-19 pandemic and related social isolation measures are likely to have adverse consequences on community healthcare provision and outcome after acute illnesses treated in hospital, including stroke. We aimed to evaluate the impact of the COVID-19 pandemic on patient-reported health outcomes after hospital admission for acute stroke. METHODS: This retrospective study included adults with acute stroke admitted to the University College Hospital NHS Foundation Trust Hyperacute Stroke Unit. We included two separate cohorts of consecutively enrolled patients from the same geographical population at two time points: 16th March-16th May 2018 (pre-COVID-19 pandemic); and 16th March-16th May 2020 (during the COVID-19 pandemic). Patients in both cohorts completed the validated Patient Reported Outcomes Measurement Information System-29 (PROMIS-29 version 2.0) at 30 days after stroke. RESULTS: We included 205 patients who were alive at 30 days (106 admitted before and 99 admitted during the COVID-19 pandemic), of whom 201/205 (98%) provided patient-reported health outcomes. After adjustment for confounding factors, admission with acute stroke during the COVID-19 pandemic was independently associated with increased anxiety (β = 28.0, p < 0.001), fatigue (β = 9.3, p < 0.001), depression (β = 4.5, p = 0.002), sleep disturbance (β = 2.3, p = 0.018), pain interference (β = 10.8, p < 0.001); and reduced physical function (β = 5.2, p < 0.001) and participation in social roles and activities (β = 6.9, p < 0.001). CONCLUSION: Compared with the pre-pandemic cohort, patients admitted with acute stroke during the first wave of the COVID-19 pandemic reported poorer health outcomes at 30 day follow-up in all domains. Stroke service planning for any future pandemic should include measures to mitigate this major adverse impact on patient health

Improving Risk Adjustment for Mortality After Pediatric Cardiac Surgery: The UK PRAiS2 Model

BACKGROUND: Partial Risk Adjustment in Surgery (PRAiS), a risk model for 30-day mortality after children's heart surgery, has been used by the UK National Congenital Heart Disease Audit to report expected risk-adjusted survival since 2013. This study aimed to improve the model by incorporating additional comorbidity and diagnostic information. METHODS: The model development dataset was all procedures performed between 2009 and 2014 in all UK and Ireland congenital cardiac centers. The outcome measure was death within each 30-day surgical episode. Model development followed an iterative process of clinical discussion and development and assessment of models using logistic regression under 25 × 5 cross-validation. Performance was measured using Akaike information criterion, the area under the receiver-operating characteristic curve (AUC), and calibration. The final model was assessed in an external 2014 to 2015 validation dataset. RESULTS: The development dataset comprised 21,838 30-day surgical episodes, with 539 deaths (mortality, 2.5%). The validation dataset comprised 4,207 episodes, with 97 deaths (mortality, 2.3%). The updated risk model included 15 procedural, 11 diagnostic, and 4 comorbidity groupings, and nonlinear functions of age and weight. Performance under cross-validation was: median AUC of 0.83 (range, 0.82 to 0.83), median calibration slope and intercept of 0.92 (range, 0.64 to 1.25) and -0.23 (range, -1.08 to 0.85) respectively. In the validation dataset, the AUC was 0.86 (95% confidence interval [CI], 0.82 to 0.89), and the calibration slope and intercept were 1.01 (95% CI, 0.83 to 1.18) and 0.11 (95% CI, -0.45 to 0.67), respectively, showing excellent performance. CONCLUSIONS: A more sophisticated PRAiS2 risk model for UK use was developed with additional comorbidity and diagnostic information, alongside age and weight as nonlinear variables