Search

OBJECTIVE-Recently, sleep-disordered breathing (SDB) has been reported to be associated with the development of gestational diabetes mellitus (GDM). Accordingly, as this is emergent area of research that has significant clinical [...]

Evidence regarding the dose-related impact of doxorubicin on subsequent cardiovascular diseases (CVDs) in Asian patients with diffuse large B-cell lymphoma (DLBCL) without preexisting CVDs is lacking. From a territory-wide electronic database in Hong Kong, we identified adults who were diagnosed with DLBCL and treated with chemotherapy between 2000 and 2018. We evaluated the patients for incident CVDs (including ischemic heart disease, heart failure, and cardiomyopathy). We evaluated the cause-specific cumulative incidence (csCI) of CVD with levels of doxorubicin exposure by using flexible parametric competing risk analysis and adjusting for demographics, comorbidities, therapeutic exposure, cardiovascular risk factors, and lifestyle factors. Controls were age- and sex-matched to DLBCL patients. We analyzed 2600 patients and 13 000 controls. The adjusted cause-specific hazard ratio (HR) for CVD in patients treated with >500 mg doxorubicin compared with non-doxorubicin regimens was 2.65 (95% confidence interval [CI], 1.23-5.74; P = .013). The 5-, 10-, and 15-year csCIs were 8.2%, 11.3%, and 12.8% in patients vs 3.1%, 4.4%, and 5.2% in controls, respectively. Hypertension (HR, 6.20; 95% CI, 0.79-48.44; P = .082) and use of aspirin/angiotensin-converting enzyme inhibitor/beta-blocker at baseline (HR, 2.13-4.63; P < .001 to .002) might confer a higher risk of subsequent CVDs. In this Hong Kong population-based study, doxorubicin exposure (absolute dose >500 mg), together with hypertension or baseline use of medication for cardiovascular risk factors, was found to be associated with an increase in csCIs of CVDs. Tailoring therapeutic strategies to underlying CVD risk factors and risk-adapted monitoring and follow-up of susceptible DLBCL patients are advisable.

Receiver operating characteristic (ROC) analysis is used for comparing predictive models in both model selection and model evaluation. ROC analysis is often applied in clinical medicine and social science to assess the tradeoff between model sensitivity and specificity. After fitting a binary logistic or probit regression model with a set of independent variables, the predictive performance of this set of variables can be assessed by the area under the curve (AUC) from an ROC curve. An important aspect of predictive modeling (regardless of model type) is the ability of a model to generalize to new cases. Evaluating the predictive performance (AUC) of a set of independent variables using all cases from the original analysis sample often results in an overly optimistic estimate of predictive performance. One can use K-fold cross-validation to generate a more realistic estimate of predictive performance in situations with a small number of observations. AUC is estimated iteratively for ksamples (the “test” samples) that are independent of the sample used to predict the dependent variable (the “training” sample). cvaurocimplements k-fold cross-validation for the AUC for a binary outcome after fitting a logit or probit regression model, averaging the AUCs corresponding to each fold, and bootstrapping the cross-validated AUC to obtain statistical inference and 95% confidence intervals. Furthermore, cvaurocoptionally provides the cross-validated fitted probabilities for the dependent variable or outcome, contained in a new variable named _fit; the sensitivity and specificity for each of the levels of the predicted outcome, contained in two new variables named _senand _spe; and the plot of the mean cross-validated AUC and k-fold ROC curves.

IntroductionWe investigated socioeconomic disparities and the role of the main prognostic factors in receiving major surgical treatment in patients with lung cancer in England.MethodsOur study comprised 31 351 patients diagnosed with non-small cell lung cancer in England in 2012. Data from the national population-based cancer registry were linked to Hospital Episode Statistics and National Lung Cancer Audit data to obtain information on stage, performance status and comorbidities, and to identify patients receiving major surgical treatment. To describe the association between prognostic factors and surgery, we performed two different analyses: one using multivariable logistic regression and one estimating cause-specific hazards for death and surgery. In both analyses, we used multiple imputation to deal with missing data.ResultsWe showed strong evidence that the comorbidities ‘congestive heart failure’, ‘cerebrovascular disease’ and ‘chronic obstructive pulmonary disease’ reduced the receipt of surgery in early stage patients. We also observed gender differences and substantial age differences in the receipt of surgery. Despite accounting for sex, age at diagnosis, comorbidities, stage at diagnosis, performance status and indication of having had a PET-CT scan, the socioeconomic differences persisted in both analyses: more deprived people had lower odds and lower rates of receiving surgery in early stage lung cancer.DiscussionComorbidities play an important role in whether patients undergo surgery, but do not completely explain the socioeconomic difference observed in early stage patients. Future work investigating access to and distance from specialist hospitals, as well as patient perceptions and patient choice in receiving surgery, could help disentangle these persistent socioeconomic inequalities.