Gender inequality and utilization of ART: an international cross-sectional and longitudinal analysis.

STUDY QUESTION: What is the association between a country's level of gender equality and access to ART, as measured through ART utilization? SUMMARY ANSWER: ART utilization is associated with a country's level of gender equality even after controlling for the level of development. WHAT IS KNOWN ALREADY: Although gender equality is recognized as an important determinant of population health, its association with fertility care, a highly gendered condition, has not been explored. STUDY DESIGN, SIZE, DURATION: A longitudinal cross-national analysis of ART utilization in 69 countries during 2002-2014 was carried out. PARTICPANTS/MATERIALS, SETTING, METHODS: The Gender Inequality Index (GII), Human Development Index (HDI), and their component indicators were modelled against ART utilization using univariate regression models as well as mixed-effects regression methods (adjusted for country, time, and economic/human development) with multiple imputation to account for missing data. MAIN RESULTS AND THE ROLE OF CHANCE: ART utilization is associated with the GII. In an HDI-adjusted analysis, a one standard deviation decrease in the GII (towards greater equality) is associated with a 59% increase in ART utilization. Gross national income per capita, the maternal mortality ratio, and female parliamentary representation were the index components most predictive of ART utilization. LIMITATIONS, REASONS FOR CAUTION: Only ART was used rather than all infertility treatments (including less costly and non-invasive treatments such as ovulation induction). This was a country-level analysis and the results cannot be generalized to smaller groups. Not all modelled variables were available for each country across 2002-2014. WIDER IMPLICATIONS OF THE FINDINGS: Access to fertility care is central to women's sexual and reproductive health, to women's rights, and to human rights. As gender equality improves, so does access to ART. This relation is likely to be reinforcing and bi-directional, with progress towards global, equitable access to fertility care also improving women's status and participation in societies. STUDY FUNDING/COMPETING INTEREST(S): External funding was not provided for this study. G.D.A. declares consulting fees from Labcorp and CooperSurgical. G.D.A. is the founder and CEO of Advanced Reproductive Care, Inc., as well as the Chair of the International Committee for Monitoring Assisted Reproductive Technologies (ICMART) and the World Endometriosis Research Foundation, both of which are unpaid roles. G.M.C. is an ICMART Board Representative, which is an unpaid role, and no payments are received from ICMART to UNSW, Sydney, or to G.M.C. to undertake this study. O.F., S.D., F.Z.-H., and E.K. report no conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.

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.

Does a public online IVF prediction tool help set patient expectations? A mixed methods evaluation study.

STUDY QUESTION: Does a public online IVF success prediction calculator based on real-world data help set patient expectations? SUMMARY ANSWER: The YourIVFSuccess Estimator aided consumer expectations of IVF success: one quarter (24%) of participants were unsure of their estimated IVF success before using the tool; one half changed their prediction of success after using the tool and one quarter (26%) had their expectations of IVF success confirmed. WHAT IS KNOWN ALREADY: Several web-based IVF prediction tools exist worldwide but have not been evaluated for their impact on patient expectations, nor for patient perceptions of usefulness and trustworthiness. STUDY DESIGN, SIZE, DURATION: This is a pre-post evaluation of a convenience sample of 780 online users of the Australian YourIVFSuccess Estimatorhttps://yourivfsuccess.com.au/ between 1 July and 31 November 2021. PARTICIPANTS/MATERIALS, SETTING, METHODS: Participants were eligible if they were over 18 years of age, Australian residents, and considering IVF for themselves or their partner. Participants filled in online surveys before and after using the YourIVFSuccess Estimator. MAIN RESULTS AND THE ROLE OF CHANCE: The response rate of participants who completed both surveys and the YourIVFSuccess Estimator was 56% (n = 439). The YourIVFSuccess Estimator aided consumer expectations of IVF success: one quarter (24%) of participants were unsure of their estimated IVF success before using the tool; one half changed their prediction of success after using the tool (20% increased, 30% decreased), bringing their predictions in line with the YourIVFSuccess Estimator, and one quarter (26%) had their IVF success expectations confirmed. One in five participants claimed they would change the timing of IVF treatment. The majority of participants found the tool to be at least moderately trustworthy (91%), applicable (82%), and helpful (80%), and would recommend it to others (60%). The main reasons given for the positive responses were that the tool is independent (government funded, academic) and based on real-world data. Those who did not find it applicable or helpful were more likely to have had a worse-than-expected prediction, or to have experienced non-medical infertility (e.g. single women, LGBTQIA+), noting that at the time of evaluation the Estimator did not accommodate these patient groups. LIMITATIONS, REASONS FOR CAUTION: Those who dropped out between the pre- and post-surveys tended to have a lower education status or have been born outside of Australia or New Zealand, therefore there may be issues with generalizability. WIDER IMPLICATIONS OF THE FINDINGS: With consumers demanding increasing levels of transparency and participation in decisions around their medical care, public-facing IVF predictor tools based on real-world data are useful for aligning expectations about IVF success rates. Given differences in patient characteristics and IVF practices internationally, national data sources should be used to inform country-specific IVF prediction tools. STUDY FUNDING/COMPETING INTEREST(S): The YourIVFSuccess website and evaluation of the YourIVFSuccess Estimator are supported by the Medical Research Future Fund (MRFF) Emerging Priorities and Consumer Driven Research initiative: EPCD000007. BKB, ND, and OF have no conflicts to declare. DM holds a clinical role at Virtus Health. His role did not influence the analysis plan or interpretation of results in this study. GMC is an employee of the UNSW Sydney, and Director of the UNSW NPESU. UNSW receives research funding on behalf of Prof Chambers from the MRFF to develop and manage the Your IVF Success website. Grant ID: MRFF Emerging Priorities and Consumer Driven Research initiative: EPCD000007. TRIAL REGISTRATION NUMBER: N/A.

Blood pressure lowering and prevention of dementia: an individual patient data meta-analysis.

AIMS: Observational studies indicate U-shaped associations of blood pressure (BP) and incident dementia in older age, but randomized controlled trials of BP-lowering treatment show mixed results on this outcome in hypertensive patients. A pooled individual participant data analysis of five seminal randomized double-blind placebo-controlled trials was undertaken to better define the effects of BP-lowering treatment for the prevention of dementia. METHODS AND RESULTS: Multilevel logistic regression was used to evaluate the treatment effect on incident dementia. Effect modification was assessed for key population characteristics including age, baseline systolic BP, sex, and presence of prior stroke. Mediation analysis was used to quantify the contribution of trial medication and changes in systolic and diastolic BP on risk of dementia. The total sample included 28 008 individuals recruited from 20 countries. After a median follow-up of 4.3 years, there were 861 cases of incident dementia. Multilevel logistic regression reported an adjusted odds ratio 0.87 (95% confidence interval: 0.75, 0.99) in favour of antihypertensive treatment reducing risk of incident dementia with a mean BP lowering of 10/4 mmHg. Further multinomial regression taking account of death as a competing risk found similar results. There was no effect modification by age or sex. Mediation analysis confirmed the greater fall in BP in the actively treated group was associated with a greater reduction in dementia risk. CONCLUSION: The first single-stage individual patient data meta-analysis from randomized double-blind placebo-controlled clinical trials provides evidence to support benefits of antihypertensive treatment in late-mid and later life to lower the risk of dementia. Questions remain as to the potential for additional BP lowering in those with already well-controlled hypertension and of antihypertensive treatment commenced earlier in the life-course to reduce the long-term risk of dementia. CLASSIFICATION OF EVIDENCE: Class I evidence in favour of antihypertensive treatment reducing risk of incident dementia compared with placebo.

Modelling vaccination capacity at mass vaccination hubs and general practice clinics: a simulation study.

BACKGROUND: COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform service delivery. METHODS: We use stochastic queue network models to simulate queue dynamics in larger mass vaccination hubs and smaller general practice (GP) clinics. We estimate waiting times and daily capacity based on a range of assumptions about appointment schedules, service times and staffing and stress-test these models to assess the impact of increased demand and staff shortages. We also provide an interactive applet, allowing users to explore vaccine administration under their own assumptions. RESULTS: Based on our assumed service times, the daily throughput for an eight-hour clinic at a mass vaccination hub ranged from 500 doses for a small hub to 1400 doses for a large hub. For GP clinics, the estimated daily throughput ranged from about 100 doses for a small practice to almost 300 doses for a large practice. What-if scenario analysis showed that sites with higher staff numbers were more robust to system pressures and mass vaccination sites were more robust than GP clinics. CONCLUSIONS: With the requirement for ongoing COVID-19 booster shots, mass vaccination is likely to be a continuing feature of healthcare delivery. Different vaccine sites are useful for reaching different populations and maximising coverage. Stochastic queue networks offer a flexible and computationally efficient approach to simulate vaccination queues and estimate waiting times and daily throughput to inform service delivery.

The impact of re-opening the international border on COVID-19 hospitalisations in Australia: a modelling study.

OBJECTIVE: To estimate the numbers of COVID-19-related hospitalisations in Australia after re-opening the international border. DESIGN: Population-level deterministic compartmental epidemic modelling of eight scenarios applying various assumptions regarding SARS-CoV-2 transmissibility (baseline R0 = 3.5 or 7.0), vaccine rollout speed (slow or fast), and scale of border re-opening (mean of 2500 or 13 000 overseas arrivals per day). SETTING: Simulation population size, age structure, and age-based contact rates based on recent estimates for the Australian population. We assumed that 80% vaccination coverage of people aged 16 years or more was reached in mid-October 2021 (fast rollout) or early January 2022 (slow rollout). MAIN OUTCOME MEASURES: Numbers of people admitted to hospital with COVID-19, December 2021 - December 2022. RESULTS: In scenarios assuming a highly transmissible SARS-CoV-2 variant (R0  = 7.0), opening the international border on either scale was followed by surges in both infections and hospitalisations that would require public health measures beyond mask wearing and social distancing to avoid overwhelming the health system. Reducing the number of hospitalisations to manageable levels required several cycles of additional social and mobility restrictions. CONCLUSIONS: If highly transmissible SARS-CoV-2 variants are circulating locally or overseas, large and disruptive COVID-19 outbreaks will still be possible in Australia after 80% of people aged 16 years or more have been vaccinated. Continuing public health measures to restrict the spread of disease are likely to be necessary throughout 2022.

Vaccinating Australia: How long will it take?

The Australian Government began to roll out the national COVID-19 vaccination program in late February 2021, with the initial aim to vaccinate the Australian adult population by the end of October 2021. The task of vaccinating some 20 million people presents considerable logistic challenges, but a rapid rollout is essential to allow for the reopening of borders and is especially urgent as new more transmissible variants arise. Here, we run a series of projections to estimate how long it will take to vaccinate the Australian population under different assumptions about the rate of vaccine administration, the schedule for vaccine eligibility and prevalence of vaccine hesitancy. Our analysis highlights the number of vaccine doses that can be administered per day as the key factor determining the duration of the vaccine rollout. A rate of 200,000 doses per day would achieve 90% population coverage by the end of 2021; 80,000 doses a day would see the rollout extended until mid-2023. Vaccine hesitancy has the potential to greatly slow down the rollout and becomes the main limiting factor when the supply of vaccine doses is high. Speed is of the essence when it comes vaccinating populations against COVID-19: a rapid rollout will minimise the risk of sporadic and costly lockdowns and the potential for small, local clusters getting out of control and sparking new epidemic waves. In order to achieve rapid population coverage, the Australian government must ramp up vaccine administration to at least 200,000 doses per day as quickly as possible, while also promoting vaccine willingness in the community through clear public health messaging, especially to known hesitant demographics.

Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU.

OBJECTIVE: Glycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care. MATERIALS AND METHODS: Using electronic medical records (EMRs) of 18 961 ICU admissions from the MIMIC-III dataset, including 318 574 blood glucose measurements, we train and validate a gradient boosted tree machine learning (ML) algorithm to forecast patient blood glucose and a 95% prediction interval at 2-hour intervals. The model uses as inputs irregular multivariate time series data relating to recent in-patient medical history and glycemic control, including previous blood glucose, nutrition, and insulin dosing. RESULTS: Our forecasting model using routinely collected EMRs achieves performance comparable to previous models developed in planned research studies using continuous blood glucose monitoring. Model error, expressed as mean absolute percentage error is 16.5%-16.8%, with Clarke error grid analysis demonstrating that 97% of predictions would be clinically acceptable. The 95% prediction intervals achieve near intended coverage at 93%-94%. DISCUSSION: ML algorithms built on observational data sources, such as EMRs, present a promising approach for personalization and automation of glycemic control in critical care. Future research may benefit from applying a combination of methodologies and data sources to develop robust methodologies that account for the variations seen in ICU patients and difficultly in detecting the extremes of observed blood glucose values. CONCLUSION: We demonstrate that EMRs can be used to train ML algorithms that may be suitable for incorporation into ICU decision support systems.

Blood pressure postpartum (BP2) RCT protocol: Follow-up and lifestyle behaviour change strategies in the first 12 months after hypertensive pregnancy.

OBJECTIVES: Women who had hypertensive disorders of pregnancy (HDP) are twice as likely to experience maternal cardiovascular disease later in life. The primary aim of this study (BP2) is to compare outcomes of 3 different management strategies, including lifestyle behaviour change (LBC), in the first 12 months postpartum in women who had HDP in their preceding pregnancy. Secondary aims include assessing the effects on other cardiometabolic parameters. STUDY DESIGN: Three-arm multicentre randomised trial in metropolitan Australian hospitals, (registration: ACTRN12618002004246) target sample size 480. Participants are randomised to one of three groups: 1) Optimised usual care: information package and family doctor follow-up 6 months postpartum 2) Brief intervention: information package as per group 1, plus assessment and brief LBC counselling at a specialised clinic with an obstetric physician and dietitian 6 months postpartum 3) Extended intervention: as per group 2 plus enrolment into a 6 month telephone-based LBC program from 6 to 12 months postpartum. All women have an outcome assessment at 12 months. MAIN OUTCOME MEASURES: Primary outcomes: (a) BP change or (b) weight change and/or waist circumference change. SECONDARY OUTCOMES: maternal health-related quality of life, engagement and retention in LBC program, biochemical markers, vascular function testing, infant weight trajectory, incremental cost-effectiveness ratios. The study is powered to detect a 4 mmHg difference in systolic BP between groups, or a 4 kg weight loss difference/2cm waist circumference change. CONCLUSIONS: BP2 will provide evidence regarding the feasibility and effectiveness of postpartum LBC interventions and structured clinical follow-up in improving cardiovascular health markers after HDP.

Cumulative live birth rates for women returning to ART treatment for a second ART-conceived child.

STUDY QUESTION: What are the success rates for women returning to ART treatment in the hope of having a second ART-conceived child. SUMMARY ANSWER: The cumulative live birth rate (LBR) for women returning to ART treatment was between 50.5% and 88.1% after six cycles depending on whether women commenced with a previously frozen embryo or a new ovarian stimulation cycle and the assumptions made regarding the success rates for women who dropped-out of treatment. WHAT IS KNOWN ALREADY: Previous studies have reported the cumulative LBR for the first ART-conceived child to inform patients about their chances of success. However, most couples plan to have more than one child to complete their family and, for that reason, patients commonly return to ART treatment after the birth of their first ART-conceived child. To our knowledge, there are no published data to facilitate patient counseling and clinical decision-making regarding the success rates for these patients. STUDY DESIGN, SIZE, DURATION: A population-based cohort study with 35 290 women who commenced autologous (using their own oocytes) ART treatment between January 2009 and December 2013 and achieved their first treatment-dependent live birth from treatment performed during this period. These women were then followed up for a further 2 years of treatment to December 2015, providing a minimum of 2 years and a maximum of 7 years of treatment follow-up. PARTICIPANTS/MATERIALS, SETTING, METHODS: Cycle-specific LBR and cumulative LBR were calculated for up to six complete ART cycles (one ovarian stimulation and all associated transfers). Three cumulative LBR were calculated based on the likelihood of success in women who dropped-out of treatment (conservative, optimal and inverse probability-weighted (IPW)). A multivariable logistic regression model was used to predict the chance of returning to ART treatment for a second ART-conceived child, and a discrete time logistic regression model was used to predict the chance of achieving a second ART-conceived child up to a maximum of six complete cycles. The models were adjusted for patient characteristics and previous and current treatment characteristics. MAIN RESULTS AND THE ROLE OF CHANCE: Among the women who had their first ART-conceived live birth, 15 325 (43%) returned to treatment by December 2015. LBRs were consistently better in women who recommenced treatment with a previously frozen embryo, compared to women who underwent a new ovarian stimulation cycle. After six complete cycles, plus any surplus frozen embryos, the cumulative LBR was between 60.9% (95% CI: 60.0-61.8%) (conservative) and 88.1% (95% CI: 86.7-89.5%) (optimal) [IPW 87.2% (95% CI: 86.2-88.2%)] for women who recommenced treatment with a frozen embryo, compared to between 50.5% (95% CI: 49.0-52.0%) and 69.8% (95% CI: 67.5-72.2%) [IPW 68.1% (95% CI: 67.3-68.9%)] for those who underwent a new ovarian stimulation cycle. The adjusted odds of a second ART-conceived live birth decreased for women ≥35 years, who waited at least 3 years before returning to treatment, or who required a higher number of ovarian stimulation cycles or double embryo transfer to achieve their first child. LIMITATIONS, REASONS FOR CAUTION: Our estimates do not fully account for a number of individual prognostic factors, including duration of infertility, BMI and ovarian reserve. WIDER IMPLICATIONS OF THE FINDINGS: This is the first study to report success rates for women returning to ART treatment to have second ART-conceived child. These age-specific success rates can facilitate individualized counseling for the large number of patients hoping to have a second child using ART treatment. STUDY FUNDING/COMPETING INTEREST(S): No funding was received to undertake this study. R. Paul and O. Fitzgerald have nothing to declare. D. Lieberman reports being a fertility specialist and receiving non-financial support from MSD and Merck outside the submitted work. C. Venetis reports being a fertility specialist and receiving personal fees and non-financial support from MSD, personal fees and non-financial support from Merck Serono and Beisins and non-financial support from Ferring outside the submitted work. G.M. Chambers reports being a paid employee of the University of New South Wales, Sydney (UNSW) and Director of the National Perinatal Epidemiology and Statistics Unit (NPESU), UNSW. The Fertility Society of Australia (FSA) contracts UNSW to prepare the Australian and New Zealand Assisted Reproductive Technology Database (ANZARD) annual report series and benchmarking reports. TRIAL REGISTRATION NUMBER: NA.

Vascular measures of atherosclerosis in detrusor overactivity and controls.

AIM: The mechanisms leading to the development of detrusor overactivity (DO) are still relatively poorly understood, however, animal studies suggest that atherosclerosis and reduced blood flow to the bladder may be one etiological pathway. Thus, the aim of this study was to evaluate signs of atherosclerosis in a large cohort of women with detrusor overactivity, using two precise measures of atherosclerotic vascular impairment, Ankle Brachial Index (ABI), and Brachial-ankle Pulse Wave Velocity (baPWV). METHODS: A prospective cohort study measuring ABI and baPWV of women with DO and controls was conducted. The ABI and baPWV were measured using an automated oscillometric blood pressure machine, to evaluate the degree of atherosclerosis in patients with DO and controls. Associations between ABI and baPWV and important confounding variables were assessed by a linear regression model. RESULTS: Ninety-eight women with DO, and 98 controls without any symptoms of DO were studied. Multivariate analysis showed an increase in left baPWV of approximately 96 cm/s units of velocity (95%CI 20.65-172.05, P = 0.01) is predicted significantly by the presence or absence of detrusor overactivity (as well by independent factors of age, diastolic blood pressure and body mass index). A similar effect was seen for right baPWV. CONCLUSIONS: On linear regression modeling, the presence of DO was a strong predictor for an increased PWV when controlling for age, BMI and diastolic blood pressure (DBP), thus supporting the hypothesis that atherosclerosis may contribute to the etiology of DO.

Assisted reproductive technology in Australia and New Zealand: cumulative live birth rates as measures of success.

OBJECTIVES: To estimate cumulative live birth rates (CLBRs) following repeated assisted reproductive technology (ART) ovarian stimulation cycles, including all fresh and frozen/thaw embryo transfers (complete cycles). DESIGN, SETTING AND PARTICIPANTS: Prospective follow-up of 56 652 women commencing ART in Australian and New Zealand during 2009-2012, and followed until 2014 or the first treatment-dependent live birth. MAIN OUTCOME MEASURES: CLBRs and cycle-specific live birth rates were calculated for up to eight cycles, stratified by the age of the women (< 30, 30-34, 35-39, 40-44, > 44 years). Conservative CLBRs assumed that women discontinuing treatment had no chance of achieving a live birth if had they continued treatment; optimal CLBRs assumed that they would have had the same chance as women who continued treatment. RESULTS: The overall CLBR was 32.7% (95% CI, 32.2-33.1%) in the first cycle, rising by the eighth cycle to 54.3% (95% CI, 53.9-54.7%) (conservative) and 77.2% (95% CI, 76.5-77.9%) (optimal). The CLBR decreased with age and number of complete cycles. For women who commenced ART treatment before 30 years of age, the CLBR for the first complete cycle was 43.7% (95% CI, 42.6-44.7%), rising to 69.2% (95% CI, 68.2-70.1%) (conservative) and 92.8% (95% CI, 91.6-94.0) (optimal) for the seventh cycle. For women aged 40-44 years, the CLBR was 10.7% (95% CI, 10.1-11.3%) for the first complete cycle, rising to 21.0% (95% CI, 20.2-21.8%) (conservative) and 37.9% (95% CI, 35.9-39.9%) (optimal) for the eighth cycle. CONCLUSION: CLBRs based on complete cycles are meaningful estimates of ART success, reflecting contemporary clinical practice and encouraging safe practice. These estimates can be used when counselling patients and to inform public policy on ART treatment.