A bootstrap approach for assessing the uncertainty of outcome probabilities when using a scoring system

Background: Scoring systems are a very attractive family of clinical predictive models, because the patient score can be calculated without using any data processing system. Their weakness lies in the difficulty of associating a reliable prognostic probability with each score. In this study a bootstrap approach for estimating confidence intervals of outcome probabilities is described and applied to design and optimize the performance of a scoring system for morbidity in intensive care units after heart surgery. Methods: The bias-corrected and accelerated bootstrap method was used to estimate the 95% confidence intervals of outcome probabilities associated with a scoring system. These confidence intervals were calculated for each score and each step of the scoring-system design by means of one thousand bootstrapped samples. 1090 consecutive adult patients who underwent coronary artery bypass graft were assigned at random to two groups of equal size, so as to define random training and testing sets with equal percentage morbidities. A collection of 78 preoperative, intraoperative and postoperative variables were considered as likely morbidity predictors. Results: Several competing scoring systems were compared on the basis of discrimination, generalization and uncertainty associated with the prognostic probabilities. The results showed that confidence intervals corresponding to different scores often overlapped, making it convenient to unite and thus reduce the score classes. After uniting two adjacent classes, a model with six score groups not only gave a satisfactory trade-off between discrimination and generalization, but also enabled patients to be allocated to classes, most of which were characterized by well separated confidence intervals of prognostic probabilities. Conclusions: Scoring systems are often designed solely on the basis of discrimination and generalization characteristics, to the detriment of prediction of a trustworthy outcome probability. The present example demonstrates that using a bootstrap method for the estimation of outcome-probability confidence intervals provides useful additional information about score-class statistics, guiding physicians towards the most convenient model for predicting morbidity outcomes in their clinical context

Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility

Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: r(g) = 0.098, p = 2.3 x 10(-8)) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: r(g) = 0.137, p = 2.0 x 10(-12)). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR) = 1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR = 1.17, 1.01-1.35) and lung cancer in never smokers (OR = 1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis

IMACCS: A Progress Report on NASA/GSFC's COTS-Based Ground Data Systems, and Their Extension into New Domains

International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, CaliforniaThe Integrated Monitoring, Analysis, and Control COTS System (IMACCS), a system providing real time satellite command and telemetry support, orbit and attitude determination, events prediction, and data trending, was implemented in 90 days at NASA Goddard Space Flight Center (GSFC) in 1995. This paper describes upgrades made to the original commercial, off-the-shelf (COTS)-based prototype. These upgrades include automation capability and spacecraft Integration and Testing (I&T) capability. A further extension to the prototype is the establishment of a direct RF interface to a spacecraft. As with the original prototype, all of these enhancements required lower staffing levels and reduced schedules compared to custom system development approaches. The team's approach to system development, including taking advantage of COTS and legacy software, is also described.International Foundation for TelemeteringProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection

Dronedarone Versus Sotalol in Antiarrhythmic Drug-Naive Veterans With Atrial Fibrillation.

BACKGROUND: Sotalol and dronedarone are both used for maintenance of sinus rhythm for patients with atrial fibrillation. However, while sotalol requires initial monitoring for QT prolongation and proarrhythmia, dronedarone does not. These treatments can be used in comparable patients, but their safety and effectiveness have not been compared head to head. Therefore, we retrospectively evaluated the effectiveness and safety using data from a large health care system. METHODS: Using Veterans Health Administration data, we identified 11 296 antiarrhythmic drug-naive patients with atrial fibrillation prescribed dronedarone or sotalol in 2012 or later. We excluded patients with prior conduction disease, pacemakers or implantable cardioverter-defibrillators, ventricular arrhythmia, cancer, renal failure, liver disease, or heart failure. We used natural language processing to identify and compare baseline left ventricular ejection fraction between treatment arms. We used 1:1 propensity score matching, based on patient demographics, comorbidities, and medications, and Cox regression to compare strategies. To evaluate residual confounding, we performed falsification analysis with nonplausible outcomes. RESULTS: The matched cohort comprised 6212 patients (3106 dronedarone and 3106 sotalol; mean [±SD] age, 71±10 years; 2.5% female; mean [±SD] CHA2DS2-VASC, 2±1.3). The mean (±SD) left ventricular ejection fraction was 55±11 and 58±10 for dronedarone and sotalol users, correspondingly. Dronedarone, compared with sotalol, did not demonstrate a significant association with risk of cardiovascular hospitalization (hazard ratio, 1.03 [95% CI, 0.88-1.21]) or all-cause mortality (hazard ratio, 0.89 [95% CI, 0.68-1.16]). However, dronedarone was associated with significantly lower risk of ventricular proarrhythmic events (hazard ratio, 0.53 [95% CI, 0.38-0.74]) and symptomatic bradycardia (hazard ratio, 0.56 [95% CI, 0.37-0.87]). The primary findings were stable across sensitivity analyses. Falsification analyses were not significant. CONCLUSIONS: Dronedarone, compared with sotalol, was associated with a lower risk of ventricular proarrhythmic events and conduction disorders while having no difference in risk of incident cardiovascular hospitalization and mortality. These observational data provide the basis for prospective efficacy and safety trials