Patients undergoing abdominal surgery often develop delirium, which greatly influences their postoperative course of clinical recovery and length of hospital stay (LOS).^1-3 Delirium affects 13% to 50% of patients undergoing noncardiac surgery,⁴ and the health care costs attributable to delirium are more than $164 billion per year in the United States.^5,6 Older surgical patients (≥65 years of age) have a particularly high risk for developing delirium, with detrimental effects on their recovery.⁶ Delirium has been associated with alterations in cholinergic activity, inflammatory processes induced by neural signaling, and excessive depth of anesthesia and sedation.^7,8 Delirium may also be precipitated by factors such as infection, malnutrition, electrolyte and fluid imbalances, anemia, and social isolation.^4,9,10 Nevertheless, 30% to 40% of cases of delirium are preventable¹¹; thus, implementing effective interventions to prevent incident delirium and reduce LOS is a clinical priority.

(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants’ exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction.

Care for type 1 diabetes mellitus should both alleviate the physical complications of the disease and improve overall quality of life. The Wisconsin Diabetes Registry, comprising a population-based cohort that is followed longitudinally from diagnosis of type 1 diabetes, provided the authors with a unique opportunity to examine quality of life in children, adolescents, and young adults with type 1 diabetes and its relation to both sociodemographic and clinical risk factors. The authors analyzed data from 1987–2002 with a mean of 10.2 years’ duration of diabetes (n = 569). They used ordinal-scaled self-rated global health as a measure of quality of life. In this paper, they propose a random-effects model for drawing inferences on individuals regarding the relation of longitudinally measured quality of life to multiple risk factors. Results showed that male gender, higher parental socioeconomic level, younger age at diabetes diagnosis, shorter diabetes duration, no hospitalization in the preceding 6 months, lower glycosylated hemoglobin level, and questionnaire responses by a person other than the subject were independently associated with better reported health. The authors found that individuals varied in their reported health even after adjustment for all identified risk factors. This could imply either that there is variability in perception even with the same health status or that there are independent unmeasured risk factors for poor health in persons with type 1 diabetes.

This paper focuses on analyzing data collected in situations where investigators use multiple discrete indicators as surrogates, for example, a set of questionnaires. A very flexible latent class model is used for analysis. We propose a Bayesian framework to perform the joint estimation of the number of latent classes and model parameters. The proposed approach applies the reversible jump Markov chain Monte Carlo to analyze finite mixtures of multivariate multinomial distributions. In the paper, we also develop a procedure for the unique labeling of the classes. We have carried out a detailed sensitivity analysis for various hyperparameter specifications, which leads us to make standard default recommendations for the choice of priors. The usefulness of the proposed method is demonstrated through computer simulations and a study on subtypes of schizophrenia using the Positive and Negative Syndrome Scale (PANSS).