Tree-based survival analysis improves mortality prediction in cardiac surgery

Objectives: Machine learning (ML) classification tools are known to accurately predict many cardiac surgical outcomes. A novel approach, ML-based survival analysis, remains unstudied for predicting mortality after cardiac surgery. We aimed to benchmark performance, as measured by the concordance index (C-index), of tree-based survival models against Cox proportional hazards (CPH) modeling and explore risk factors using the best-performing model. Methods: 144,536 patients with 147,301 surgery events from the Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) national database were used to train and validate models. Univariate analysis was performed using Student’s T-test for continuous variables, Chi-squared test for categorical variables, and stratified Kaplan-Meier estimation of the survival function. Three ML models were tested, a decision tree (DT), random forest (RF), and gradient boosting machine (GBM). Hyperparameter tuning was performed using a Bayesian search strategy. Performance was assessed using 2-fold cross-validation repeated 5 times. Results: The highest performing model was the GBM with a C-index of 0.803 (0.002), followed by RF with 0.791 (0.003), DT with 0.729 (0.014), and finally CPH with 0.596 (0.042). The 5 most predictive features were age, type of procedure, length of hospital stay, drain output in the first 4 h (ml), and inotrope use greater than 4 h postoperatively. Conclusion: Tree-based learning for survival analysis is a non-parametric and performant alternative to CPH modeling. GBMs offer interpretable modeling of non-linear relationships, promising to expose the most relevant risk factors and uncover new questions to guide future research.The ANZSCTS National Cardiac Surgery Database Program is funded by the Department of Health (Victoria)the Clinical Excellence Commission (NSW)Queensland Health (QLD)Cardiac surgical units participating in the registry. ANZSCTS Database Research activities are supported through a National Health and Medical Research Council Principal Research Fellowship (APP 1136372)Program Grant (APP 1092642

Paying attention to cardiac surgical risk: An interpretable machine learning approach using an uncertainty-aware attentive neural network

Machine learning (ML) is increasingly applied to predict adverse postoperative outcomes in cardiac surgery. Commonly used ML models fail to translate to clinical practice due to absent model explainability, limited uncertainty quantification, and no flexibility to missing data. We aimed to develop and benchmark a novel ML approach, the uncertainty-aware attention network (UAN), to overcome these common limitations. Two Bayesian uncertainty quantification methods were tested, generalized variational inference (GVI) or a posterior network (PN). The UAN models were compared with an ensemble of XGBoost models and a Bayesian logistic regression model (LR) with imputation. The derivation datasets consisted of 153,932 surgery events from the Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) Cardiac Surgery Database. An external validation consisted of 7343 surgery events which were extracted from the Medical Information Mart for Intensive Care (MIMIC) III critical care dataset. The highest performing model on the external validation dataset was a UAN-GVI with an area under the receiver operating characteristic curve (AUC) of 0.78 (0.01). Model performance improved on high confidence samples with an AUC of 0.81 (0.01). Confidence calibration for aleatoric uncertainty was excellent for all models. Calibration for epistemic uncertainty was more variable, with an ensemble of XGBoost models performing the best with an AUC of 0.84 (0.08). Epistemic uncertainty was improved using the PN approach, compared to GVI. UAN is able to use an interpretable and flexible deep learning approach to provide estimates of model uncertainty alongside stateof- the-art predictions. The model has been made freely available as an easy-to-use web application demonstrating that by designing uncertainty-aware models with innately explainable predictions deep learning may become more suitable for routine clinical use.The ANZSCTS Cardiac Surgery Database Program is funded by the Department of Health (Victoria), the Clinical Excellence Commission (NSW)Queensland Health (QLD)ANZSCTS Database Research activities are supported through a National Health and Medical Research Council Principal Research Fellowship (APP 1136372)Program Grant (APP 1092642

Topical cystic fibrosis transmembrane conductance regulator gene replacement for cystic fibrosis-related lung disease

BACKGROUND: Cystic fibrosis is caused by a defective gene encoding a protein called the cystic fibrosis transmembrane conductance regulator (CFTR), and is characterised by chronic lung infection resulting in inflammation and progressive lung damage that results in a reduced life expectancy. OBJECTIVES: To determine whether topical CFTR gene replacement therapy to the lungs in people with cystic fibrosis is associated with improvements in clinical outcomes, and to assess any adverse effects. SEARCH METHODS: We searched the Cochrane Cystic Fibrosis and Genetic Disorders Group Trials Register comprising references identified from comprehensive electronic database searches, handsearching relevant journals and abstract books of conference proceedings. Date of most recent search: 05 May 2016. An additional search of the National Institutes for Health (NIH) Genetic Modification Clinical Research Information System (GeMCRIS) was also performed for the years 1992 to 2015. Date of most recent search: 20 April 2016. SELECTION CRITERIA: Randomised controlled studies comparing topical CFTR gene delivery to the lung, using either viral or non‐viral delivery systems, with placebo or an alternative delivery system in people with confirmed cystic fibrosis. DATA COLLECTION AND ANALYSIS: The authors independently extracted data and assessed study quality. Authors of included studies were contacted and asked for any available additional data. Meta‐analysis was limited due to differing study designs. MAIN RESULTS: Four randomised controlled studies met the inclusion criteria for this review, involving a total of 302 participants lasting from 29 days to 13 months; 14 studies were excluded. The included studies differed in terms of CFTR gene replacement agent and study design, which limited the meta‐analysis. One study only enrolled adult males, the remaining studies included both males and females aged 12 years and over. Risk of bias in the studies was moderate. Random sequence generation and allocation concealment was only described in the more recent study; the remaining three studies were judged to have an unclear risk of bias. All four studies documented double‐blinding to the intervention, but there is some uncertainty with regards to participant blinding in one study. Some outcome data were missing from all four studies. There were no differences in either the number of respiratory exacerbations or the number of participants with an exacerbation between replacement therapy or placebo groups at any time point. Meta‐analysis of most respiratory function tests showed no difference between treatment and placebo groups, but the smallest study (n = 16) reported forced vital capacity (litres) increased more in the placebo group at up to 24 hours. A further study reported a significant improvement in forced expiratory volume at one second (litres) at 30 days after participants had received their first dose of favouring the gene therapy agent, but this finding was not confirmed when combined with at second study in the meta‐analysis. The more recent study (n = 140) demonstrated a small improvement in forced vital capacity (per cent predicted) at two and three months and again at 11 and 12 months for participants receiving CFTR gene replacement therapy compared to those receiving placebo. The same study reported a significant difference in the relative change in forced expiratory volume at one second (per cent predicted) at two months, three months and 12 months. One small study reported significant concerns with "influenza‐like" symptoms in participants treated with CFTR gene replacement therapy; this was not reported on repeated use of the same agent in a larger recent study. There was no other evidence of positive impact on outcomes, in particular improved quality of life or reduced treatment burden. Two studies measured ion transport in the lower airways; one (n = 16) demonstrated significant changes toward normal values in the participants who received gene transfer agents (P < 0.0001), mean difference 6.86 (95% confidence interval 3.77 to 9.95). The second study (n = 140) also reported significant changes toward normal values (P = 0.032); however, aggregate data were not available for analysis. In the most recent study, there was also evidence of increased salt transport in cells obtained by brushing the lower airway. These outcomes, whilst important, are not of direct clinical relevance. AUTHORS' CONCLUSIONS: One study of liposome‐based CFTR gene transfer therapy demonstrated some improvements in respiratory function in people with CF, but this limited evidence of efficacy does not support this treatment as a routine therapy at present. There was no evidence of efficacy for viral‐mediated gene delivery. Future studies need to investigate clinically important outcome measures

Paying attention to cardiac surgical risk: An interpretable machine learning approach using an uncertainty-aware attentive neural network

Machine learning (ML) is increasingly applied to predict adverse postoperative outcomes in cardiac surgery. Commonly used ML models fail to translate to clinical practice due to absent model explainability, limited uncertainty quantification, and no flexibility to missing data. We aimed to develop and benchmark a novel ML approach, the uncertainty-aware attention network (UAN), to overcome these common limitations. Two Bayesian uncertainty quantification methods were tested, generalized variational inference (GVI) or a posterior network (PN). The UAN models were compared with an ensemble of XGBoost models and a Bayesian logistic regression model (LR) with imputation. The derivation datasets consisted of 153,932 surgery events from the Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) Cardiac Surgery Database. An external validation consisted of 7343 surgery events which were extracted from the Medical Information Mart for Intensive Care (MIMIC) III critical care dataset. The highest performing model on the external validation dataset was a UAN-GVI with an area under the receiver operating characteristic curve (AUC) of 0.78 (0.01). Model performance improved on high confidence samples with an AUC of 0.81 (0.01). Confidence calibration for aleatoric uncertainty was excellent for all models. Calibration for epistemic uncertainty was more variable, with an ensemble of XGBoost models performing the best with an AUC of 0.84 (0.08). Epistemic uncertainty was improved using the PN approach, compared to GVI. UAN is able to use an interpretable and flexible deep learning approach to provide estimates of model uncertainty alongside state-of-the-art predictions. The model has been made freely available as an easy-to-use web application demonstrating that by designing uncertainty-aware models with innately explainable predictions deep learning may become more suitable for routine clinical use

Cost-effectiveness and budget impact of lung cancer immunotherapy in South America: strategies to improve access

Aim: Immune checkpoint inhibitors revolutionized the treatment of non-small-cell lung cancer, although their costs are a limitation. Methods: The number of patients with non-small-cell lung cancer eligible for immunotherapy was estimated using local epidemiology data. We extracted survival data from RCTs to estimate the life-years saved in a 5-year time horizon. All costs were in local prices converted to US dollars. Results: In the first-line, the budget impact of pembrolizumab decreased by 35% through risk-sharing. In the second-line, patient selection by programmed-death receptor ligand 1 expression decreased the budgetary impact by 45%, and improved cost-effectiveness. Immunotherapy was more cost-effective in the first-line. Conclusion: Given current pricing, Immune checkpoint inhibitors are cost-prohibitive in the majority of South American health services. Nevertheless, several strategies should improve access to immunotherapy