Background Bleeding is a significant complication in cardiac surgery and is associated with substantial morbidity and mortality. This study evaluated the impact of bleeding on length of stay (LOS) and critical care utilization in a nationwide sample of cardiac surgery patients treated at English hospitals. Methods Retrospective, observational cohort study using linked English Hospital Episode Statistics (HES) and Clinical Practice Research Datalink (CPRD) records for a nationwide sample of patients aged ≥18 years who underwent coronary artery bypass graft (CABG), valve repair/replacement, or aortic operations from January 2010 through February 2016. The primary independent variables were in-hospital bleeding complications and reoperation for bleeding before discharge. Generalized linear models were used to quantify the adjusted mean incremental difference [MID] in post-procedure LOS and critical care days associated with bleeding complications, independent of measured baseline characteristics. Results The study included 7774 cardiac surgery patients (3963 CABG; 2363 valve replacement/repair; 160 aortic procedures; 1288 multiple procedures, primarily CABG+valve). Mean LOS was 10.7d, including a mean of 4.2d in critical care. Incidences of in-hospital bleeding complications and reoperation for bleeding were 6.7 and 0.3%, respectively. Patients with bleeding had longer LOS (MID: 3.1d; p < 0.0001) and spent more days in critical care (MID: 2.4d; p < 0.0001). Reoperation for bleeding was associated with larger increases in LOS (MID = 4.0d; p = 0.002) and days in critical care (MID = 3.2d; p = 0.001). Conclusions Among English cardiac surgery patients, in-hospital bleeding complications were associated with substantial increases in healthcare utilization. Increased use of evidence-based strategies to prevent and manage bleeding may reduce the clinical and economic burden associated with bleeding complications in cardiac surgery. Electronic supplementary material The online version of this article (10.1186/s13019-019-0881-3) contains supplementary material, which is available to authorized users.

To support the global restart of elective surgery, data from an international prospective cohort study of 8492 patients (69 countries) was analysed using artificial intelligence (machine learning techniques) to develop a predictive score for mortality in surgical patients with SARS-CoV-2. We found that patient rather than operation factors were the best predictors and used these to create the COVIDsurg Mortality Score (https://covidsurgrisk.app). Our data demonstrates that it is safe to restart a wide range of surgical services for selected patients.

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median age of patients with MCID was higher than those without MCID. Compared to patients without MCID, a higher proportion of patients that developed MCID were women, non-white, and had a Charlson score $ 2 at NHL diagnosis. Similar proportions of patients with and without MCID received treatment with chemoimmunotherapy. In unadjusted Fine-Gray regression models, exposure to any chemo-immunotherapy was associated with a null risk of MCID (sHR: 1.01; 95% CI: 0.77-1.33); findings were similar in multivariable models (sHR: 0.77, 95% CI: 0.57, 1.03) adjusting for potential confounders. In stratified analyses, chemo-immunotherapy exposure was consistently associated with a decreased risk of MCID regardless of age groups, race, or lymphoma subtype. Conclusions: This study suggests chemo-immunotherapy exposure in older patients with NHL is not associated with an increased risk of MCID.

2006 through 2015 were used to construct a cohort of prescription opioid users (N = 1,246,642). A total of 278 features (potential predictors) were derived from baseline data, including demographics, pain and mental conditions, physical comorbidities, exposure to other medications, concomitant medication uses with opioids, and opioid using behaviors. Opioid overdoses were defined as opioid poisoning diagnoses or naloxone administration, and for persons older than 50 without a history of heart or lung disease, a respiratory depression diagnosis recorded in the emergency department. We used stratified 10-fold cross-validation to choose the classifier with the best performance. Model performance was evaluated using sensitivity, specificity, and discrimination. As an alternative, traditional approaches based on logistic regression were also explored. Results: A total of 2,274 opioid overdose cases were identified. The boosted tree classifier outperformed other learning algorithms with a sensitivity of 0.70, a specificity of 0.77, and a c-statistic of 0.77. Logistic regressions achieved similar levels of performance. While the list and rank of the top prognostic features were not the same from the two approaches, early refills, total days' supply, concomitant use of antidepressants, concomitant use of antipsychotics, and total opioid claims were defined as the most significant prognostic features by both approaches. ConClusions: Given the nature of the extremely imbalanced data, even the best classifier produces moderate performance. However, identifying key prognostic features will help identify high risk patients. The prediction tool enumerates the risks of opioid overdoses and provides a potential explicit standard for clinicians when making individual patient prescribing and dispensing decisions.