Objective To assess the association between specific heart diseases and suicide. Design Nationwide retrospective cohort study. Participants A total of 7 298 002 individuals (3 640 632 males and 3 657 370 females) aged ≥15 years and living in Denmark during 1980–2016. Main outcome measures Incidence rate ratios (IRR) with 95% confidence intervals. In multivariate analysis, we adjust for sex, period, age group, living status, income level, Charlson Comorbidity Index, psychiatric disorders prior to heart disease and self‐harm prior to heart disease. Results Excess suicide rate ratios were found for following disorders: heart failure (IRR: 1.48; 95% CI: 1.38–1.58); cardiomyopathy (IRR: 1.41; 95% CI: 1.16–1.70); acute myocardial infarction (IRR: 1.28; 95% CI: 1.21–1.36); cardiac arrest with successful resuscitation (IRR: 4.75; 95% CI: 3.57–6.33); atrial fibrillation and flutter (IRR: 1.42; 95% CI: 1.32–1.52); angina pectoris (IRR: 1.19; 95% CI: 1.12–1.26); and ventricular tachycardia (IRR: 1.53; 95% CI: 1.20–1.94). A higher rate of suicide was noted during the first 6 months after the diagnosis of heart failure (IRR: 2.38; 95% CI: 2.04–2.79); acute myocardial infarction (IRR: 2.24; 95% CI: 1.89–2.66); atrial fibrillation and flutter (IRR: 2.70; 95% CI: 2.30–3.18); and angina pectoris (IRR: 1.83; 95% CI: 1.53–2.19) when compared to later. Conclusion Several specific disorders were found to be associated with elevated rates of suicide. Additionally, we found temporal associations with higher suicide rates in the first time after diagnosis. Our results underscore the importance of being attentive towards psychological distress in individuals with heart disease.

Unanswered questions remain in determining which high-risk node-negative colon cancer (CC) cohorts benefit from adjuvant therapy and how it may differ in an equal access population. Machine-learned Bayesian Belief Networks (ml-BBNs) accurately estimate outcomes in CC, providing clinicians with Clinical Decision Support System (CDSS) tools to facilitate treatment planning. We evaluated ml-BBNs ability to estimate survival and recurrence in CC. We performed a retrospective analysis of registry data of patients with CC to train–test–crossvalidate ml-BBNs using the Department of Defense Automated Central Tumor Registry (January 1993 to December 2004). Cases with events or follow-up that passed quality control were stratified into 1-, 2-, 3-, and 5-year survival cohorts. ml-BBNs were trained using machine-learning algorithms and k-fold crossvalidation and receiver operating characteristic curve analysis used for validation. BBNs were comprised of 5301 patients and areas under the curve ranged from 0.85 to 0.90. Positive predictive values for recurrence and mortality ranged from 78 to 84 per cent and negative predictive values from 74 to 90 per cent by survival cohort. In the 12-month model alone, 1,132,462,080 unique rule sets allow physicians to predict individual recurrence/mortality estimates. Patients with Stage II (N0M0) CC benefit from chemotherapy at different rates. At one year, all patients older than 73 years of age with T2–4 tumors and abnormal carcinoembryonic antigen levels benefited, whereas at five years, all had relative reduction in mortality with the largest benefit amongst elderly, highest T-stage patients. ml-BBN can readily predict which high-risk patients benefit from adjuvant therapy. CDSS tools yield individualized, clinically relevant estimates of outcomes to assist clinicians in treatment planning.

e14500 Background: Unanswered questions remain regarding treatment efficacy in colon cancer (CC), especially those determining high-risk node-negative cohorts that may benefit from adjuvant therapy. We sought to evaluate the use of machine learning and classification modeling to estimate survival and recurrence in CC. Methods: We used the Department of Defense Automated Central Tumor Registry (ACTUR) to identify primary CC patients treated between January 1993 and December 2004. Cases with events or follow-up that passed quality control were stratified into one-, two-, three-, and five-year survival cohorts. ml-BBNs were trained using machine-learning algorithms and k-fold cross-validation, and receiver operating characteristic (ROC) curve analysis used for validation. Results: There were 5,301 cases stratified into cohorts. Survival cohort Areas-Under-the-Curve (AUCs) ranged from 0.85–0.90, positive-predictive-values (PPVs) for recurrence and mortality ranged from 78-84% and negative-predictive-values (NPVs) from 74-90%. Cross-validation showed that the ml-BBNs produce robust individual estimates of recurrence (p<0.001) and mortality (p<0.001) based on readily available clinical-pathological information in the context of adjuvant chemotherapy. Conclusions: Tumor registry data and machine-learned Bayesian Belief Networks produce robust classifiers. These Clinical Decision Support System tools yield clinically relevant estimates of outcomes that may assist clinicians in treatment planning.

We thank Dr. HJ Aubin et al.[1] for their insightful comments to our study on the association between heart diseases and death by suicide [2]. Dr Aubin and colleagues suggested that smoking might moderate the studied association. This is a highly interesting question, as smoking might increase the likelihood of developing heart diseases. Furthermore, an increasing amount of evidence supports an association between smoking and suicide, although causation is yet to be proven [3].