The impact of age and parity on the experience of relief and regret in women who have undergone hysterectomy for benign disease: A cross sectional survey.

OBJECTIVE: To investigate the impact of age and parity on the experience on relief and regret following elective hysterectomy for benign disease, and to explore the factors that impact relief and regret. DESIGN: Retrospective cross-sectional survey of a cohort. SETTING: Single-centre tertiary hospital in Melbourne, Australia. POPULATION: Patients who underwent elective hysterectomy for benign indications from 01 January 2008 - 31 July 2015 (inclusive) with age <51 years at time of admission. METHODS: Eligible participants completed a retrospective survey regarding their experience of relief and regret following hysterectomy. MAIN OUTCOME MEASURES: Regret was defined as a positive response to "Do you regret the decision to have a hysterectomy?". Relief was defined as responding "agree/strongly agree" to "I feel relieved I had a hysterectomy". RESULTS: 268 of 1285 (21%) eligible participants completed the study questionnaire. Of these, 29 were aged <36 years at the time of hysterectomy. Seven percent (n=18/262) reported regretting having a hysterectomy and 88% (n=230/262) reported experiencing relief. We did not observe associations between age at hysterectomy and regret (aOR 0.93; 95% CI 0.85, 1.03), age at hysterectomy and relief (aOR 1.01; 95% CI 0.93, 1.09), nulliparity and regret (aOR 0.32; 95% CI 0.06, 1.59) or nulliparity and relief (aOR 2.37; 95% CI 0.75, 7.51). Desire for future pregnancy at the time of hysterectomy was more frequently reported in those who experienced regret vs no regret (46.7% vs 12.1%, OR: 6.33; 95% CI: 2.12, 18.90; p=0.001). CONCLUSIONS: Age and parity are not associated with relief nor regret following elective hysterectomy for benign disease.

ROCK inhibitors in ophthalmology: A critical review of the existing clinical evidence.

Rho kinase (ROCK) inhibitors have emerged as a key therapeutic class of interest in ophthalmology over the last decade. Promising in vitro studies laid the foundations for the development of novel therapeutic agents that target the ROCK signalling pathway in ocular disease, with subsequent clinical trials supporting their use. Corneal endothelial disease, glaucoma, and vitreoretinal disease are the major pathologies in which ROCK inhibitors have been investigated to date. Ripasudil and netarsudil represent the current leaders in this pharmaceutical group, having been extensively validated and approved for use in glaucoma in some countries. Less substantial evidence exists for fasudil in ophthalmic use. ROCK inhibitors are also increasingly used in cultured endothelial cell grafting and as an adjunct to aid in endothelial cell migration and replication in Descemet's stripping procedures or Descemet's membrane injuries. This review has synthesised both established and emerging research to provide a practical guide to prescribing in this drug class. Drug efficacies, side effect profiles, and the demographic and clinical characteristics of appropriate drug candidates are discussed.

Machine Learning Outperforms Existing Clinical Scoring Tools in the Prediction of Postoperative Atrial Fibrillation During Intensive Care Unit Admission After Cardiac Surgery.

OBJECTIVE(S): Using the Medical Information Mart for Intensive Care III (MIMIC-III) database, we compared the performance of machine learning (ML) to the to the established gold standard scoring tool (POAF Score) in predicting postoperative atrial fibrillation (POAF) during intensive care unit (ICU) admission after cardiac surgery. METHODS: Random forest classifier (RF), decision tree classifier (DT), logistic regression (LR), K neighbours classifier (KNN), support vector machine (SVM), and gradient boosted machine (GBM) were compared to the POAF Score. Cross-validation was used to assess the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of ML models. POAF Score performance confidence intervals were generated using 1,000 bootstraps. Risk profiles for GBM were generated using Shapley additive values. RESULTS: A total of 6,349 ICU admissions encompassing 6,040 patients were included. POAF occurred in 1,364 of the 6,349 admissions (21.5%). For predicting POAF during ICU admission after cardiac surgery, GBM, LR, RF, KNN, SVM and DT achieved an AUC of 0.74 (0.71-0.77), 0.73 (0.71-0.75), 0.72 (0.69-0.75), 0.68 (0.67-0.69), 0.67 (0.66-0.68) and 0.59 (0.55-0.63) respectively. The POAF Score AUC was 0.63 (0.62-0.64). Shapley additive values analysis of GBM generated patient level explanations for each prediction. CONCLUSION: Machine learning models based on readily available preoperative data can outperform clinical scoring tools for predicting POAF during ICU admission after cardiac surgery. Explanatory models are shown to have potential in personalising POAF risk profiles for patients by illustrating probabilistic input variable contributions. Future research is required to evaluate the clinical utility and safety of implementing ML-driven tools for POAF prediction.

Exercise Parameters and Outcome Measures Used in Cardiac Rehabilitation Programs Following Median Sternotomy in the Elderly: A Systematic Review and Meta-Analysis.

OBJECTIVE: The aim of this systematic review was to identify exercise parameters and outcome measures used in cardiac rehabilitation programs following median sternotomy, in the elderly cardiac population. DATA SOURCES: Five (5) electronic databases were searched for relevant studies published in English after 1997. STUDY SELECTION: The screening process was completed by two independent researchers, with a third independent reviewer for overall agreement. Studies were selected if they included only cardiac patients aged ≥65 years who had undergone valve surgery and/or coronary artery bypass grafting via median sternotomy, and who had undertaken a postoperative cardiac rehabilitation exercise intervention assessing physical function and/or cognitive recovery as outcomes. DATA EXTRACTION: Two researchers independently completed the data extraction and quality assessment. Quality was assessed using a modified Downs and Black tool. DATA SYNTHESIS: In total, 11 articles were included for appraisal with respect to the quality of the study. Only two randomised controlled trials were suitable for meta-analysis. A higher volume of exercise was shown to have a positive effect on functional recovery, assessed using the 6-minute walk test (6MWT) (mean difference=26.97m; 95% confidence interval [CI], 6.96-46.97; p=0.008; I2=0%). No significant improvement was shown between additional exercise compared to standard care in improving VO2peak, maximal power output or quality of life. No studies evaluated the effect of exercise on cognitive recovery. CONCLUSIONS: Exercise significantly improves functional recovery in the post-surgical elderly cardiac population, however uncertainty still exists with regard to which modes of exercise and their specific parameters are most effective in improving cognitive recovery.

Using machine learning to predict health-related quality of life outcomes in patients with low grade glioma, meningioma, and acoustic neuroma.

BACKGROUND: Predicting reduced health-related quality of life (HRQoL) after resection of a benign or low-grade brain tumour provides the opportunity for early intervention, and targeted expenditure of scarce supportive care resources. We aimed to develop, and evaluate the performance of, machine learning (ML) algorithms to predict HRQoL outcomes in this patient group. METHODS: Using a large prospective dataset of HRQoL outcomes in patients surgically treated for low grade glioma, acoustic neuroma and meningioma, we investigated the capability of ML to predict a) HRQoL-impacting symptoms persisting between 12 and 60 months from tumour resection and b) a decline in global HRQoL by more than the minimum clinically important difference below a normative population mean within 12 and 60 months after resection. Ten-fold cross-validation was used to measure the area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (PR-AUC), sensitivity, and specificity of models. Six ML algorithms were explored per outcome: Random Forest Classifier, Decision Tree Classifier, Logistic Regression, K Neighbours Classifier, Support Vector Machine, and Gradient Boosting Machine. RESULTS: The final cohort included 262 patients. Outcome measures for which AUC>0.9 were Appetite loss, Constipation, Nausea and vomiting, Diarrhoea, Dyspnoea and Fatigue. AUC was between 0.8 and 0.9 for global HRQoL and Financial difficulty. Pain and Insomnia achieved AUCs below 0.8. PR-AUCs were similar overall to the AUC of each respective classifier. CONCLUSIONS: ML algorithms based on routine demographic and perioperative data show promise in their ability to predict HRQoL outcomes in patients with low grade and benign brain tumours between 12 and 60 months after surgery.

Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients.

OBJECTIVE(S): To use machine learning (ML) to predict short-term requirements for invasive ventilation in patients with COVID-19 admitted to Australian intensive care units (ICUs). DESIGN: A machine learning study within a national ICU COVID-19 registry in Australia. PARTICIPANTS: Adult patients who were spontaneously breathing and admitted to participating ICUs with laboratory-confirmed COVID-19 from 20 February 2020 to 7 March 2021. Patients intubated on day one of their ICU admission were excluded. MAIN OUTCOME MEASURES: Six machine learning models predicted the requirement for invasive ventilation by day three of ICU admission from variables recorded on the first calendar day of ICU admission; (1) random forest classifier (RF), (2) decision tree classifier (DT), (3) logistic regression (LR), (4) K neighbours classifier (KNN), (5) support vector machine (SVM), and (6) gradient boosted machine (GBM). Cross-validation was used to assess the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of machine learning models. RESULTS: 300 ICU admissions collected from 53 ICUs across Australia were included. The median [IQR] age of patients was 59 [50-69] years, 109 (36%) were female and 60 (20%) required invasive ventilation on day two or three. Random forest and Gradient boosted machine were the best performing algorithms, achieving mean (SD) AUCs of 0.69 (0.06) and 0.68 (0.07), and mean sensitivities of 77 (19%) and 81 (17%), respectively. CONCLUSION: Machine learning can be used to predict subsequent ventilation in patients with COVID-19 who were spontaneously breathing and admitted to Australian ICUs.

A normative database of A-scan data using the Heidelberg Spectralis Spectral Domain Optical Coherence Tomography machine.

PURPOSE: To develop the first normative database of macular and circumpapillary scans with reference values at the level of the A-scan using the Heidelberg Spectralis Optical Coherence Tomography (OCT) machine. METHODS: This study is a retrospective cross sectional analysis of macular and circumpapillary OCT scans of healthy individuals. All participants had a full ophthalmic examination, including best corrected visual acuity, intraocular pressure, biomicroscopy, posterior segment examination and OCT scan. The volume and thickness of each of the nine Early Treatment Diabetic Retinopathy zones at the macula were analysed for the total retinal thickness, retinal nerve fibre layer (RNFL), ganglion cell layer (GCL) and inner plexiform layer (IPL). The thickness of the circumpapillary RNFL was analysed at the disc. Associations between age, gender, refractive error and OCT measurements were explored. De-identified A-scans were extracted from the OCT machine as separate tab-separated text file and made available according to the data sharing statement. RESULTS: Two-hundred eyes from 146 participants were included of which 69 (47%) were female. The mean age (SD) was 48.52 (17.52). Participants were evenly distributed across four age groups and represented nine broad ethnic groups in proportions comparable to the local distribution. All the macular scans were 20° x 20° (5.9 mm x 5.9 mm), with a total scan density between 12,800 and 49,152 A-scans. The peripapillary scans were all 12° (3.5 mm), at a scan density of 768 A-scans. The mean retinal, GCL and IPL volumes were significantly greater in males than females. Mean peripapillary RNFL thickness did not differ significantly between males and females. Age and total retinal volume (r = -0.2561, P = 0.0003), GCL volume (-0.2911, P < 0.0001) and IPL volume (-0.3194, P < 0.0001) were negatively correlated. The IPL had the strongest three significant negatively associated segments; superior inner IPL (r = -0.3444, P < 0.0001), nasal outer IPL (r = -0.3217, P < 0.0001) and inferior inner IPL (r = -0.3179, P < 0.0001). The temporal inner macular RNFL showed a statistically significant positive correlation (r = 0.1929, P = 0.0062) with age. The only significant association between age and thickness at the peripapillary disc scan was the superior temporal sector (r = -0.1910, P = 0.0067). All retinal layers were negatively correlated for refractive error, except for the central RNFL which was positively correlated (r = 0.1426, P = 0.044). CONCLUSION: This study provides a normative database of macular and circumpapillary scans with reference values at the level of the A-scan using the Heidelberg Spectralis Optical Coherence Tomography (OCT) machine.