Factors associated with cardiac implantable electronic device-related infections, New South Wales, 2016-21: a retrospective cohort study.

OBJECTIVES: To quantify the rate of cardiac implantable electronic device (CIED)-related infections and to identify risk factors for such infections. DESIGN: Retrospective cohort study; analysis of linked hospital admissions and mortality data. SETTING, PARTICIPANTS: All adults who underwent CIED procedures in New South Wales between 1 January 2016 and 30 June 2021 (public hospitals) or 30 June 2020 (private hospitals). MAIN OUTCOME MEASURES: Proportions of patients hospitalised with CIED-related infections (identified by hospital record diagnosis codes); risk of CIED-related infection by patient, device, and procedural factors. RESULTS: Of 37 675 CIED procedures (23 194 men, 63.5%), 500 were followed by CIED-related infections (median follow-up, 24.9 months; interquartile range, 11.2-40.8 months), including 397 people (1.1%) within twelve months of their procedures, and 186 of 10 540 people (2.5%) at high risk of such infections (replacement or upgrade procedures; new cardiac resynchronisation therapy with defibrillator, CRT-D). The overall infection rate was 0.50 (95% confidence interval [CI], 0.45-0.54) per 1000 person-months; it was highest during the first month after the procedure (5.60 [95% CI, 4.89-6.42] per 1000 person-months). The risk of CIED-related infection was greater for people under 65 years of age than for those aged 65-74 years (adjusted hazard ratio [aHR], 1.71; 95% CI, 1.32-2.23), for people with CRT-D devices than for those with permanent pacemakers (aHR, 1.46; 95% CI, 1.02-2.08), for people who had previously undergone CIED procedures (two or more v none: aHR, 1.51; 95% CI, 1.02-2.25) or had CIED-related infections (aHR, 11.4; 95% CI, 8.34-15.7), or had undergone concomitant cardiac surgery (aHR, 1.62; 95% CI, 1.10-2.39), and for people with atrial fibrillation (aHR, 1.33; 95% CI, 1.11-1.60), chronic kidney disease (aHR, 1.54; 95% CI, 1.27-1.87), chronic obstructive pulmonary disease (aHR, 1.37; 95% CI, 1.10-1.69), or cardiomyopathy (aHR 1.60; 95% CI, 1.25-2.05). CONCLUSIONS: Knowledge of risk factors for CIED-related infections can help clinicians discuss them with their patients, identify people at particular risk, and inform decisions about device type, upgrades and replacements, and prophylactic interventions.

Incidence and Predictors of Readmissions to Non-Index Hospitals After Transcatheter Aortic Valve Implantation in the Contemporary Era in New South Wales, Australia.

BACKGROUND: In Australia, transcatheter aortic valve implantation (TAVI) is only performed in a limited number of specialised metropolitan centres, many of which are private hospitals, making it likely that TAVI patients who require readmission will present to another (non-index) hospital. It is important to understand the impact of non-index readmission on patient outcomes and healthcare resource utilisation. METHOD: We analysed linked hospital and death records for residents of New South Wales, Australia, aged ≥18 years, who had an emergency readmission within 90 days following a TAVI procedure in 2013-2022. Mixed-effect, multi-level logistic regression models were used to evaluate predictors of non-index readmission, and associations between non-index readmission and readmission length of stay, 90-day mortality, and 1-year mortality. RESULTS: Of 4,198 patients (mean age, 82.7 years; 40.6% female) discharged alive following TAVI, 933 (22.2%) were readmitted within 90 days of discharge. Over three-quarters (76.0%) of those readmitted returned to a non-index hospital, with no significant difference in readmission principal diagnosis between index hospital and non-index hospital readmissions. Among readmitted patients, independent predictors of non-index readmission included: residence in regional or remote areas, lower socio-economic status, having a pre-procedure transfer, and a private index hospital. Readmission length of stay (median, 4 days), 90-day mortality (adjusted odds ratio [OR] 1.04, 95% confidence interval [CI] 0.56-1.96) and 1-year mortality (adjusted OR 1.01, 95% CI 0.64-1.58) were similar between index and non-index readmissions. CONCLUSIONS: Non-index readmission following TAVI was highly prevalent but not associated with increased mortality or healthcare utilisation. Our results are reassuring for TAVI patients in regional and remote areas with limited access to return to index TAVI hospitals.

Identifying prehospital trauma patients from ambulance patient care records; comparing two methods using linked data in New South Wales, Australia.

BACKGROUND: Linked datasets for trauma system monitoring should ideally follow patients from the prehospital scene to hospital admission and post-discharge. Having a well-defined cohort when using administrative datasets is essential because they must capture the representative population. Unlike hospital electronic health records (EHR), ambulance patient-care records lack access to sources beyond immediate clinical notes. Relying on a limited set of variables to define a study population might result in missed patient inclusion. We aimed to compare two methods of identifying prehospital trauma patients: one using only those documented under a trauma protocol and another incorporating additional data elements from ambulance patient care records. METHODS: We analyzed data from six routinely collected administrative datasets from 2015 to 2018, including ambulance patient-care records, aeromedical data, emergency department visits, hospitalizations, rehabilitation outcomes, and death records. Three prehospital trauma cohorts were created: an Extended-T-protocol cohort (patients transported under a trauma protocol and/or patients with prespecified criteria from structured data fields), T-protocol cohort (only patients documented as transported under a trauma protocol) and non-T-protocol (extended-T-protocol population not in the T-protocol cohort). Patient-encounter characteristics, mortality, clinical and post-hospital discharge outcomes were compared. A conservative p-value of 0.01 was considered significant RESULTS: Of 1 038 263 patient-encounters included in the extended-T-population 814 729 (78.5 %) were transported, with 438 893 (53.9 %) documented as a T-protocol patient. Half (49.6 %) of the non-T-protocol sub-cohort had an International Classification of Disease 10th edition injury or external cause code, indicating 79644 missed patients when a T-protocol-only definition was used. The non-T-protocol sub-cohort also identified additional patients with intubation, prehospital blood transfusion and positive eFAST. A higher proportion of non-T protocol patients than T-protocol patients were admitted to the ICU (4.6% vs 3.6 %), ventilated (1.8% vs 1.3 %), received in-hospital transfusion (7.9 vs 6.8 %) or died (1.8% vs 1.3 %). Urgent trauma surgery was similar between groups (1.3% vs 1.4 %). CONCLUSION: The extended-T-population definition identified 50 % more admitted patients with an ICD-10-AM code consistent with an injury, including patients with severe trauma. Developing an EHR phenotype incorporating multiple data fields of ambulance-transported trauma patients for use with linked data may avoid missing these patients.

Predictive analytics for cardiovascular patient readmission and mortality: An explainable approach.

BACKGROUND: Cardiovascular patients experience high rates of adverse outcomes following discharge from hospital, which may be preventable through early identification and targeted action. This study aimed to investigate the effectiveness and explainability of machine learning algorithms in predicting unplanned readmission and death in cardiovascular patients at 30 days and 180 days from discharge. METHODS: Gradient boosting machines were trained and evaluated using data from hospital electronic medical records linked to hospital administrative and mortality data for 39,255 patients admitted to four hospitals in New South Wales, Australia between 2017 and 2021. Sociodemographic variables, admission history, and clinical information were used as potential predictors. The performance was compared to LASSO regression, as well as the HOSPITAL and LACE risk score indices. Important risk factors identified by the gradient-boosting machine model were explored using Shapley values. RESULTS: The models performed well, especially for the mortality outcomes. Area under the receiver operating characteristic curve values were 0.70 for readmission and 0.87-0.90 for mortality using the full gradient boosting machine algorithms. Among the top predictors for 30-day and 180-day readmission were increased red cell distribution width, old age (especially above 80 years), high measured troponin and urea levels, not being married or in a relationship, and low albumin levels. For mortality, these included increased red cell distribution width, old age (especially older than 70 years), high measured troponin and urea levels, high neutrophil and monocyte counts, and low eosinophil and lymphocyte counts. The Shapley values gave clear insight into the dynamics of decision-tree-based models. CONCLUSIONS: We demonstrated an explainable predictive algorithm to identify cardiovascular patients who are at high risk of readmission or death at discharge from the hospital and identified key risk factors.

The Australian Health Care Homes trial: quality of care and patient outcomes. A propensity score-matched cohort study.

OBJECTIVE: To assess the impact of the Health Care Homes (HCH) primary health care initiative on quality of care and patient outcomes. DESIGN, SETTING: Quasi-experimental, matched cohort study; analysis of general practice data extracts and linked administrative data from ten Australian primary health networks, 1 October 2017 - 30 June 2021. PARTICIPANTS: People with chronic health conditions (practice data extracts: 9811; linked administrative data: 10 682) enrolled in the HCH 1 October 2017 - 30 June 2019; comparison groups of patients receiving usual care (1:1 propensity score-matched). INTERVENTION: Participants were involved in shared care planning, provided enhanced access to team care, and encouraged to seek chronic condition care at the HCH practice where they were enrolled. Participating practices received bundled payments based on clinical risk tier. MAIN OUTCOME MEASURES: Access to care, processes of care, diabetes-related outcomes, hospital service use, risk of death. RESULTS: During the first twelve months after enrolment, the mean numbers of general practitioner encounters (rate ratio, 1.14; 95% confidence interval [CI], 1.11-1.17) and Medicare Benefits Schedule claims for allied health services (rate ratio, 1.28; 95% CI, 1.24-1.33) were higher for the HCH than the usual care group. Annual influenza vaccinations (relative risk, 1.20; 95% CI, 1.17-1.22) and measurements of blood pressure (relative risk, 1.09; 95% CI, 1.08-1.11), blood lipids (relative risk, 1.19; 95% CI, 1.16-1.21), glycated haemoglobin (relative risk, 1.06; 95% CI, 1.03-1.08), and kidney function (relative risk, 1.13; 95% CI, 1.11-1.15) were more likely in the HCH than the usual care group during the twelve months after enrolment. Similar rate ratios and relative risks applied in the second year. The numbers of emergency department presentations (rate ratio, 1.09; 95% CI, 1.02-1.18) and emergency admissions (rate ratio, 1.13; 95% CI, 1.04-1.22) were higher for the HCH group during the first year; other differences in hospital use were not statistically significant. Differences in glycaemic and blood pressure control in people with diabetes in the second year were not statistically significant. By 30 June 2021, 689 people in the HCH group (6.5%) and 646 in the usual care group (6.1%) had died (hazard ratio, 1.07; 95% CI, 0.96-1.20). CONCLUSIONS: The HCH program was associated with greater access to care and improved processes of care for people with chronic diseases, but not changes in diabetes-related outcomes, most measures of hospital use, or risk of death.

Estimating the impact of bias in causal epidemiological studies: the case of health outcomes following assisted reproduction.

Researchers interested in causal questions must deal with two sources of error: random error (random deviation from the true mean value of a distribution), and bias (systematic deviance from the true mean value due to extraneous factors). For some causal questions, randomization is not feasible, and observational studies are necessary. Bias poses a substantial threat to the validity of observational research and can have important consequences for health policy developed from the findings. The current piece describes bias and its sources, outlines proposed methods to estimate its impacts in an observational study, and demonstrates how these methods may be used to inform debate on the causal relationship between medically assisted reproduction (MAR) and health outcomes, using cancer as an example. In doing so, we aim to enlighten researchers who work with observational data, especially regarding the health effects of MAR and infertility, on the pitfalls of bias, and how to address them. We hope that, in combination with the provided example, we can convince readers that estimating the impact of bias in causal epidemiologic research is not only important but necessary to inform the development of robust health policy and clinical practice recommendations.

Predicting Adverse Outcomes Following Catheter Ablation Treatment for Atrial Flutter/Fibrillation.

BACKGROUND & AIM: To develop prognostic survival models for predicting adverse outcomes after catheter ablation treatment for non-valvular atrial fibrillation (AF) and/or atrial flutter (AFL). METHODS: We used a linked dataset including hospital administrative data, prescription medicine claims, emergency department presentations, and death registrations of patients in New South Wales, Australia. The cohort included patients who received catheter ablation for AF and/or AFL. Traditional and deep survival models were trained to predict major bleeding events and a composite of heart failure, stroke, cardiac arrest, and death. RESULTS: Out of a total of 3,285 patients in the cohort, 177 (5.3%) experienced the composite outcome-heart failure, stroke, cardiac arrest, death-and 167 (5.1%) experienced major bleeding events after catheter ablation treatment. Models predicting the composite outcome had high-risk discrimination accuracy, with the best model having a concordance index >0.79 at the evaluated time horizons. Models for predicting major bleeding events had poor risk discrimination performance, with all models having a concordance index <0.66. The most impactful features for the models predicting higher risk were comorbidities indicative of poor health, older age, and therapies commonly used in sicker patients to treat heart failure and AF and AFL. DISCUSSION: Diagnosis and medication history did not contain sufficient information for precise risk prediction of experiencing major bleeding events. Predicting the composite outcome yielded promising results, but future research is needed to validate the usefulness of these models in clinical practice. CONCLUSIONS: Machine learning models for predicting the composite outcome have the potential to enable clinicians to identify and manage high-risk patients following catheter ablation for AF and AFL proactively.

Overcoming silos in health care systems through meso-level organisations - a case study of health reforms in New South Wales, Australia.

Fragmented care delivery is a barrier to improving health system performance worldwide. Investment in meso-level organisations is a potential strategy to improve health system integration, however, its effectiveness remains unclear. In this paper, we provide an overview of key international and Australian integrated care policies. We then describe Collaborative Commissioning - a novel health reform policy to integrate primary and hospital care sectors in New South Wales (NSW), Australia and provide a case study of a model focussed on older person's care. The policy is theorised to achieve greater integration through improved governance (local stakeholders identifying as part of one health system), service delivery (communities perceive new services as preferable to status quo) and incentives (efficiency gains are reinvested locally with progressively higher value care achieved). If effectively implemented at scale, Collaborative Commissioning has potential to improve health system performance in Australia and will be of relevance to similar reform initiatives in other countries.

Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project.

Large-scale medical data sets are vital for hands-on education in health data science but are often inaccessible due to privacy concerns. Addressing this gap, we developed the Health Gym project, a free and open-source platform designed to generate synthetic health data sets applicable to various areas of data science education, including machine learning, data visualization, and traditional statistical models. Initially, we generated 3 synthetic data sets for sepsis, acute hypotension, and antiretroviral therapy for HIV infection. This paper discusses the educational applications of Health Gym's synthetic data sets. We illustrate this through their use in postgraduate health data science courses delivered by the University of New South Wales, Australia, and a Datathon event, involving academics, students, clinicians, and local health district professionals. We also include adaptable worked examples using our synthetic data sets, designed to enrich hands-on tutorial and workshop experiences. Although we highlight the potential of these data sets in advancing data science education and health care artificial intelligence, we also emphasize the need for continued research into the inherent limitations of synthetic data.

Six-minute walk test distance at time of hospital discharge is strongly and independently associated with all-cause mortality following cardiac surgery.

We investigated the impact of distance covered in the six-minute walk test (6mWT) before being discharged from the hospital after cardiac surgery on the risk of all-cause mortality. Our study included 1127 patients who underwent cardiac surgery and then took part in a standardised physiotherapist-supervised inpatient rehabilitation programme during 2007-2017. The percentage of the predicted 6mWT distance, and the lower limit of normal distance was calculated based on individual patients' age, sex, and body mass index. We used Cox regression with adjustment for confounders to determine multivariable-adjusted hazard ratios (HRs) for mortality. Over a median follow-up period of 6.4 (IQR: 3.5-9.2) years, 15% (n = 169) patients died. We observed a strong and independent inverse association between 6mWT distance and mortality, with every 10 m increase in distance associated to a 4% reduction in mortality (HR: 0.96, 95% CI 0.94-0.98, P < 0.001). Those in the top tertile for predicted 6mWT performance had a 49% reduced risk of mortality (HR: 0.51, 95% CI 0.33-0.79) compared to those in the bottom tertile. Patients who met or exceeded the minimum normal 6mWT distance had 36% lower mortality risk (HR: 0.64, 95% CI 0.45-0.92) compared to those who did not meet this benchmark. Subgroup analysis showed that combined CABG and valve surgery patients walked less in the 6mWT compared to those undergoing isolated CABG or valve surgeries, with a significant association between 6mWT and mortality observed in the isolated procedure groups only. In conclusion, the longer the distance covered in the 6mWT before leaving the hospital, the lower the risk of mortality.

Does use of GP and specialist services vary across areas and according to individual socioeconomic position? A multilevel analysis using linked data in Australia.

OBJECTIVE: Timely access to primary care and supporting specialist care relative to need is essential for health equity. However, use of services can vary according to an individual's socioeconomic circumstances or where they live. This study aimed to quantify individual socioeconomic variation in general practitioner (GP) and specialist use in New South Wales (NSW), accounting for area-level variation in use. DESIGN: Outcomes were GP use and quality-of-care and specialist use. Multilevel logistic regression was used to estimate: (1) median ORs (MORs) to quantify small area variation in outcomes, which gives the median increased risk of moving to an area of higher risk of an outcome, and (2) ORs to quantify associations between outcomes and individual education level, our main exposure variable. Analyses were adjusted for individual sociodemographic and health characteristics and performed separately by remoteness categories. SETTING: Baseline data (2006-2009) from the 45 and Up Study, NSW, Australia, linked to Medicare Benefits Schedule and death data (to December 2012). PARTICIPANTS: 267 153 adults aged 45 years and older. RESULTS: GP (MOR=1.32-1.35) and specialist use (1.16-1.18) varied between areas, accounting for individual characteristics. For a given level of need and accounting for area variation, low education-level individuals were more likely to be frequent users of GP services (no school certificate vs university, OR=1.63-1.91, depending on remoteness category) and have continuity of care (OR=1.14-1.24), but were less likely to see a specialist (OR=0.85-0.95). CONCLUSION: GP and specialist use varied across small areas in NSW, independent of individual characteristics. Use of GP care was equitable, but specialist care was not. Failure to address inequitable specialist use may undermine equity gains within the primary care system. Policies should also focus on local variation.

Readmission to a non-index hospital following total joint replacement.

Aims: It is unclear whether mortality outcomes differ for patients undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA) surgery who are readmitted to the index hospital where their surgery was performed, or to another hospital. Methods: We analyzed linked hospital and death records for residents of New South Wales, Australia, aged ≥ 18 years who had an emergency readmission within 90 days following THA or TKA surgery between 2003 and 2022. Multivariable modelling was used to identify factors associated with non-index readmission and to evaluate associations of readmission destination (non-index vs index) with 90-day and one-year mortality. Results: Of 394,248 joint arthroplasty patients (THA = 149,456; TKA = 244,792), 9.5% (n = 37,431) were readmitted within 90 days, and 53.7% of these were admitted to a non-index hospital. Non-index readmission was more prevalent among patients who underwent surgery in private hospitals (60%). Patients who were readmitted for non-orthopaedic conditions (62.8%), were more likely to return to a non-index hospital compared to those readmitted for orthopaedic complications (39.5%). Factors associated with non-index readmission included older age, higher socioeconomic status, private health insurance, and residence in a rural or remote area. Non-index readmission was significantly associated with 90-day (adjusted odds ratio (aOR) 1.69; 95% confidence interval (CI) 1.39 to 2.05) and one-year mortality (aOR 1.31; 95% CI 1.16 to 1.47). Associations between non-index readmission and mortality were similar for patients readmitted with orthopaedic and non-orthopaedic complications (90-day mortality aOR 1.61; 95% CI 0.98 to 2.64, and aOR 1.67; 95% CI 1.35 to 2.06, respectively). Conclusion: Non-index readmission was associated with increased mortality, irrespective of whether the readmission was for orthopaedic complications or other conditions.

Automated ICD coding using extreme multi-label long text transformer-based models.

Encouraged by the success of pretrained Transformer models in many natural language processing tasks, their use for International Classification of Diseases (ICD) coding tasks is now actively being explored. In this study, we investigated two existing Transformer-based models (PLM-ICD and XR-Transformer) and proposed a novel Transformer-based model (XR-LAT), aiming to address the extreme label set and long text classification challenges that are posed by automated ICD coding tasks. The Transformer-based model PLM-ICD, which currently holds the state-of-the-art (SOTA) performance on the ICD coding benchmark datasets MIMIC-III and MIMIC-II, was selected as our baseline model for further optimisation on both datasets. In addition, we extended the capabilities of the leading model in the general extreme multi-label text classification domain, XR-Transformer, to support longer sequences and trained it on both datasets. Moreover, we proposed a novel model, XR-LAT, which was also trained on both datasets. XR-LAT is a recursively trained model chain on a predefined hierarchical code tree with label-wise attention, knowledge transferring and dynamic negative sampling mechanisms. Our optimised PLM-ICD models, which were trained with longer total and chunk sequence lengths, significantly outperformed the current SOTA PLM-ICD models, and achieved the highest micro-F1 scores of 60.8 % and 50.9 % on MIMIC-III and MIMIC-II, respectively. The XR-Transformer model, although SOTA in the general domain, did not perform well across all metrics. The best XR-LAT based models obtained results that were competitive with the current SOTA PLM-ICD models, including improving the macro-AUC by 2.1 % and 5.1 % on MIMIC-III and MIMIC-II, respectively. Our optimised PLM-ICD models are the new SOTA models for automated ICD coding on both datasets, while our novel XR-LAT models perform competitively with the previous SOTA PLM-ICD models.

In-hospital outcomes by insurance type among patients undergoing percutaneous coronary interventions for acute myocardial infarction in New South Wales public hospitals.

BACKGROUND: International evidence suggests patients receiving cardiac interventions experience differential outcomes by their insurance status. We investigated outcomes of in-hospital care according to insurance status among patients admitted in public hospitals with acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI). METHODS: We conducted a cohort study within the Australian universal health care system with supplemental private insurance. Using linked hospital and mortality data, we included patients aged 18 + years admitted to New South Wales public hospitals with AMI and undergoing their first PCI from 2017-2020. We measured hospital-acquired complications (HACs), length of stay (LOS) and in-hospital mortality among propensity score-matched private and publicly funded patients. Matching was based on socio-demographic, clinical, admission and hospital-related factors. RESULTS: Of 18,237 inpatients, 30.0% were privately funded. In the propensity-matched cohort (n = 10,630), private patients had lower rates of in-hospital mortality than public patients (odds ratio: 0.59, 95% CI: 0.45-0.77; approximately 11 deaths avoided per 1,000 people undergoing PCI procedures). Mortality differences were mostly driven by STEMI patients and those from major cities. There were no significant differences in rates of HACs or average LOS in private, compared to public, patients. CONCLUSION: Our findings suggest patients undergoing PCI in Australian public hospitals with private health insurance experience lower in-hospital mortality compared with their publicly insured counterparts, but in-hospital complications are not related to patient health insurance status. Our findings are likely due to unmeasured confounding of broader patient selection, socioeconomic differences and pathways of care (e.g. access to emergency and ambulatory care; delays in treatment) that should be investigated to improve equity in health outcomes.

Risk for Congenital Anomalies in Children Conceived With Medically Assisted Fertility Treatment : A Population-Based Cohort Study.

BACKGROUND: More than 2 million children are conceived annually using assisted reproductive technologies (ARTs), with a similar number conceived using ovulation induction and intrauterine insemination (OI/IUI). Previous studies suggest that ART-conceived children are at increased risk for congenital anomalies (CAs). However, the role of underlying infertility in this risk remains unclear, and ART clinical and laboratory practices have changed drastically over time, particularly there has been an increase in intracytoplasmic sperm injection (ICSI) and cryopreservation. OBJECTIVE: To investigate the role of underlying infertility and fertility treatment on CA risks in the first 2 years of life. DESIGN: Propensity score-weighted population-based cohort study. SETTING: New South Wales, Australia. PARTICIPANTS: 851 984 infants (828 099 singletons and 23 885 plural children) delivered between 2009 and 2017. MEASUREMENTS: Adjusted risk difference (aRD) in CAs of infants conceived through fertility treatment compared with 2 naturally conceived (NC) control groups-those with and without a parental history of infertility (NC-infertile and NC-fertile). RESULTS: The overall incidence of CAs was 459 per 10 000 singleton births and 757 per 10 000 plural births. Compared with NC-fertile singleton control infants (n = 747 018), ART-conceived singleton infants (n = 31 256) had an elevated risk for major genitourinary abnormalities (aRD, 19.0 cases per 10 000 births [95% CI, 2.3 to 35.6]); the risk remained unchanged (aRD, 22 cases per 10 000 births [CI, 4.6 to 39.4]) when compared with NC-infertile singleton control infants (n = 36 251) (that is, after accounting for parental infertility), indicating that ART remained an independent risk. After accounting for parental infertility, ICSI in couples without male infertility was associated with an increased risk for major genitourinary abnormalities (aRD, 47.8 cases per 10 000 singleton births [CI, 12.6 to 83.1]). There was some suggestion of increased risk for CAs after fresh embryo transfer, although estimates were imprecise and inconsistent. There were no increased risks for CAs among OI/IUI-conceived infants (n = 13 574). LIMITATIONS: This study measured the risk for CAs only in those children who were born at or after 20 weeks' gestation. Observational study design precludes causal inference. Many estimates were imprecise. CONCLUSION: Patients should be counseled on the small increased risk for genitourinary abnormalities after ART, particularly after ICSI, which should be avoided in couples without problems of male infertility. PRIMARY FUNDING SOURCE: Australian National Health and Medical Research Council.

Study protocol for development and validation of a single tool to assess risks of stroke, diabetes mellitus, myocardial infarction and dementia: DemNCD-Risk.

INTRODUCTION: Current efforts to reduce dementia focus on prevention and risk reduction by targeting modifiable risk factors. As dementia and cardiometabolic non-communicable diseases (NCDs) share risk factors, a single risk-estimating tool for dementia and multiple NCDs could be cost-effective and facilitate concurrent assessments as compared with a conventional single approach. The aim of this study is to develop and validate a new risk tool that estimates an individual's risk of developing dementia and other NCDs including diabetes mellitus, stroke and myocardial infarction. Once validated, it could be used by the public and general practitioners. METHODS AND ANALYSIS: Ten high-quality cohort studies from multiple countries were identified, which met eligibility criteria, including large representative samples, long-term follow-up, data on clinical diagnoses of dementia and NCDs, recognised modifiable risk factors for the four NCDs and mortality data. Pooled harmonised data from the cohorts will be used, with 65% randomly allocated for development of the predictive model and 35% for testing. Predictors include sociodemographic characteristics, general health risk factors and lifestyle/behavioural risk factors. A subdistribution hazard model will assess the risk factors' contribution to the outcome, adjusting for competing mortality risks. Point-based scoring algorithms will be built using predictor weights, internally validated and the discriminative ability and calibration of the model will be assessed for the outcomes. Sensitivity analyses will include recalculating risk scores using logistic regression. ETHICS AND DISSEMINATION: Ethics approval is provided by the University of New South Wales Human Research Ethics Committee (UNSW HREC; protocol numbers HC200515, HC3413). All data are deidentified and securely stored on servers at Neuroscience Research Australia. Study findings will be presented at conferences and published in peer-reviewed journals. The tool will be accessible as a public health resource. Knowledge translation and implementation work will explore strategies to apply the tool in clinical practice.

The relationship between hyperglycaemia on admission and patient outcome is modified by hyperlactatemia and diabetic status: a retrospective analysis of the eICU collaborative research database.

Both blood glucose and lactate are well-known predictors of organ dysfunction and mortality in critically ill patients. Previous research has shown that concurrent adjustment for glucose and lactate modifies the relationship between these variables and patient outcomes, including blunting of the association between blood glucose and patient outcome. We aim to investigate the relationship between ICU admission blood glucose and hospital mortality while accounting for lactate and diabetic status. Across 43,250 ICU admissions, weighted to account for missing data, we assessed the predictive ability of several logistic regression and generalised additive models that included blood glucose, blood lactate and diabetic status. We found that inclusion of blood glucose marginally improved predictive performance in all patients: AUC-ROC 0.665 versus 0.659 (p = 0.005), with a greater degree of improvement seen in non-diabetics: AUC-ROC 0.675 versus 0.663 (p < 0.001). Inspection of the estimated risk profiles revealed the standard U-shaped risk profile for blood glucose was only present in non-diabetic patients after controlling for blood lactate levels. Future research should aim to utilise observational data to estimate whether interventions such as insulin further modify this effect, with the goal of informing future RCTs of interventions targeting glycaemic control in the ICU.

Continuous time recurrent neural networks: Overview and benchmarking at forecasting blood glucose in the intensive care unit.

OBJECTIVE: Blood glucose measurements in the intensive care unit (ICU) are typically made at irregular intervals. This presents a challenge in choice of forecasting model. This article gives an overview of continuous time autoregressive recurrent neural networks (CTRNNs) and evaluates how they compare to autoregressive gradient boosted trees (GBT) in forecasting blood glucose in the ICU. METHODS: Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations through incorporating continuous evolution of the hidden states between observations. This is achieved using a neural ordinary differential equation (ODE) or neural flow layer. In this manuscript, we give an overview of these models, including the varying architectures that have been proposed to account for issues such as ongoing medical interventions. Further, we demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting using electronic medical record and simulated data and compare with GBT and linear models. RESULTS: The experiments confirm that addition of a neural ODE or neural flow layer generally improves the performance of autoregressive recurrent neural networks in the irregular measurement setting. However, several CTRNN architecture are outperformed by a GBT model (Catboost), with only a long short-term memory (LSTM) and neural ODE based architecture (ODE-LSTM) achieving comparable performance on probabilistic forecasting metrics such as the continuous ranked probability score (ODE-LSTM: 0.118 ± 0.001; Catboost: 0.118 ± 0.001), ignorance score (0.152 ± 0.008; 0.149 ± 0.002) and interval score (175 ± 1; 176 ± 1). CONCLUSION: The application of deep learning methods for forecasting in situations with irregularly measured time series such as blood glucose shows promise. However, appropriate benchmarking by methods such as GBT approaches (plus feature transformation) are key in highlighting whether novel methodologies are truly state of the art in tabular data settings.

Real-world evidence on the association between cardiac implantable electronic device infection and all-cause mortality.

AIMS: An infection following cardiac implantable electronic device (CIED) procedure is a serious complication, but its association with all-cause mortality is inconsistent across observational studies. To quantify the association between CIED infection and all-cause mortality in a large, contemporary cohort from New South Wales, Australia. METHODS AND RESULTS: This retrospective cohort study used linked hospital and mortality data and included all patients aged >18 years who underwent a CIED procedure between July 2017 and September 2022. Cardiac implantable electronic device infection was defined by the presence of relevant diagnosis codes. Cox regression to estimate adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) for the association of CIED infection with mortality, at 1-year, and at the end of follow-up, with CIED infection included as a time-dependent variable, and other potential risk factors for mortality included as fixed covariates. We followed 37,750 patients with CIED procedures {36% female, mean age [standard deviation (SD)] 75.8 [12.7] years}, and 487 (1.3%) CIED infections were identified. We observed 5771 (15.3%) deaths during an average follow-up of 25.2 (SD 16.8) months. Compared with no infection group, patients with CIED infection had a higher Kaplan-Meier mortality rate (19.4 vs. 6.8%) and adjusted hazard of mortality (aHR 2.73, 95% CI 2.10-3.54) at 12 months post-procedure. These differences were attenuated but still remained significant at the end of follow-up (aHR 1.83, 95% CI 1.52-2.19). CONCLUSION: In a complete, state-wide cohort of CIED patients, infection was associated with higher risks of both short-term and long-term mortality.