Effect of interpregnancy interval on gestational diabetes: a retrospective matched cohort study

© 2019 The Authors Purpose: To examine the association between interpregnancy interval (IPI) and gestational diabetes using both within-mother and between-mother comparisons. Methods: A retrospective cohort study of 103,909 women who delivered three or more consecutive singleton births (n = 358,046) between 1 January 1980 and 31 December 2015 in Western Australia. The association between IPI and gestational diabetes was estimated using conditional logistic regression, matching pregnancies to the same mother and adjusted for factors that vary within-mother across pregnancies. For comparison with previous studies, we also applied unmatched logistic regression (between-mother analysis). Results: The conventional between-mother analysis resulted in adjusted odds ratios (aOR) of 1.13 (95% CI, 1.06–1.21) for intervals of 24–59 months and 1.51 (95% CI, 1.33–1.70) for intervals of 120 or more months, compared with IPI of 18–23 months. In addition, short IPIs were associated with lower odds of gestational diabetes with (aOR: 0.89; 95% CI, 0.82–0.97) for 6–11 months and (aOR: 0.92; 95% CI, 0.85–0.99) for 12–17-month. In comparison, the adjusted within-mother matched analyses showed no statistically significant association between IPIs and gestational diabetes. All effect estimates were attenuated using the within-mother matched model. Conclusion: Our findings do not support the hypothesis that short IPI (<6 months) increases the risk of gestational diabetes and suggest that observed associations in previous research might be attributable to confounders that vary between mothers

Two-year follow-up of participants in the BreastScreen Victoria pilot trial of tomosynthesis versus mammography: breast density-stratified screening outcomes

This follow-up study of BreastScreen Victoria’s pilot trial of digital breast tomosynthesis aimed to report interval cancer rates, screening sensitivity, and density-stratified outcomes for tomosynthesis vs mammography screening. Prospective pilot trial [ACTRN-12617000947303] in Maroondah BreastScreen recruited females ≥ 40 years presenting for screening (August 2017–November 2018) to DBT; concurrent screening participants who received mammography formed a comparison group. Follow-up of 24 months from screen date was used to ascertain interval cancers; automated breast density was measured

Interpregnancy intervals and adverse birth outcomes in high-income countries: An international cohort study.

BACKGROUND: Most evidence for interpregnancy interval (IPI) and adverse birth outcomes come from studies that are prone to incomplete control for confounders that vary between women. Comparing pregnancies to the same women can address this issue. METHODS: We conducted an international longitudinal cohort study of 5,521,211 births to 3,849,193 women from Australia (1980-2016), Finland (1987-2017), Norway (1980-2016) and the United States (California) (1991-2012). IPI was calculated based on the time difference between two dates-the date of birth of the first pregnancy and the date of conception of the next (index) pregnancy. We estimated associations between IPI and preterm birth (PTB), spontaneous PTB, and small-for-gestational age births (SGA) using logistic regression (between-women analyses). We also used conditional logistic regression comparing IPIs and birth outcomes in the same women (within-women analyses). Random effects meta-analysis was used to calculate pooled adjusted odds ratios (aOR). RESULTS: Compared to an IPI of 18-23 months, there was insufficient evidence for an association between IPI 24 month IPIs. CONCLUSIONS: We found consistently elevated odds of adverse birth outcomes following long IPIs. IPI shorter than 6 months were associated with elevated risk of spontaneous PTB, but there was insufficient evidence for increased risk of other adverse birth outcomes. Current recommendations of waiting at least 24 months to conceive after a previous pregnancy, may be unnecessarily long in high-income countries

Rates of reoperation after breast conserving cancer surgery in Western Australia before and after publication of the SSO-ASTRO margins guideline

Background A 2014 SSO-ASTRO guideline on surgical margins aimed to reduce unnecessary reoperation after breast conserving surgery (BCS). We investigate whether publication of the guideline was associated with a reduction in reoperation in Western Australia (WA). Methods In this retrospective, population-based cohort study, cases of newly-diagnosed breast cancer were identified from the WA Cancer Registry. Linkage to the Hospital Morbidity Data Collection identified index BCS for invasive cancer between January 2009 and June 2018 (N = 8059) and reoperation within 90 days. Pre-guideline (2009–2013) and post-guideline (2014–2018) reoperation proportions were compared, and temporal trends were estimated with generalised linear regression. Results The pre-guideline reoperation proportion was 25.8% compared with 21.7% post-guideline (difference −4.0% [95% CI —5.9, −2.2, p < 0.001], odds ratio [OR] 0.80 [95% CI 0.72, 0.89, p < 0.001]). Absolute reductions were similar for repeat BCS (16.3% versus 14.6%; difference −1.8% [95% CI —3.4, −0.2, p = 0.03]) and conversion to mastectomy (9.4% versus 7.2%; difference −2.2% [95% CI —3.4, −1.0, p < 0.001]). Over the study period, there was an annual absolute change in reoperation of −0.8% (95% CI —1.2, −0.5, p < 0.001). Accounting for this linear trend, the difference in reoperation between time periods was −0.5% (95% CI —4.3, 3.3; p = 0.81), reflecting a non-significant reduction in conversion to mastectomy. Conclusions Comparisons of pre- versus post-guideline time periods in WA showed reductions in reoperation that were similar to international estimates; however, an annual decline in reoperation predated the guideline. Analyses that do not account for temporal trends are likely to overestimate changes in reoperation associated with the guideline

Differential detection by breast density for digital breast tomosynthesis versus digital mammography population screening: a systematic review and meta-analysis

Background We examined whether digital breast tomosynthesis (DBT) detects differentially in high- or low-density screens. Methods We searched six databases (2009–2020) for studies comparing DBT and digital mammography (DM), and reporting cancer detection rate (CDR) and/or recall rate by breast density. Meta-analysis was performed to pool incremental CDR and recall rate for DBT (versus DM) for high- and low-density (dichotomised based on BI-RADS) and within-study differences in incremental estimates between high- and low-density. Screening settings (European/US) were compared. Results Pooled within-study difference in incremental CDR for high- versus low-density was 1.0/1000 screens (95% CI: 0.3, 1.6; p = 0.003). Estimates were not significantly different in US (0.6/1000; 95% CI: 0.0, 1.3; p = 0.05) and European (1.9/1000; 95% CI: 0.3, 3.5; p = 0.02) settings (p for subgroup difference = 0.15). For incremental recall rate, within-study differences between density subgroups differed by setting (p < 0.001). Pooled incremental recall was less in high- versus low-density screens (−0.9%; 95% CI: −1.4%, −0.4%; p < 0.001) in US screening, and greater (0.8%; 95% CI: 0.3%, 1.3%; p = 0.001) in European screening. Conclusions DBT has differential incremental cancer detection and recall by breast density. Although incremental CDR is greater in high-density, a substantial proportion of additional cancers is likely to be detected in low-density screens. Our findings may assist screening programmes considering DBT for density-tailored screening

Evidence from a BreastScreen cohort does not support a longer inter-screen interval in women who have no conventional risk factors for breast cancer

Objectives: To determine screening outcomes in women who have no recorded risk factors for breast cancer. Methods: A retrospective population-based cohort study included all 1,026,137 mammography screening episodes in 323,082 women attending the BreastScreen Western Australia (part of national biennial screening) program between July 2007 and June 2017. Cancer detection rates (CDR) and interval cancer rates (ICR) were calculated in screening episodes with no recorded risk factors for breast cancer versus at least one risk factor stratified by age. CDR was further stratified by timeliness of screening (<27 versus ≥27 months); ICR was stratified by breast density. Results: Amongst 566,948 screens (55.3%) that had no recorded risk factors, 2347 (40.9%) screen-detected cancers were observed. In screens with no risk factors, CDR was 50 (95%CI 48-52) per 10,000 screens and ICR was 7.9 (95%CI 7.4-8.4) per 10,000 women-years, estimates that were lower than screens with at least one risk factor (CDR 83 (95%CI 80-86) per 10,000 screens, ICR 12.2 (95%CI 11.5-13.0) per 10,000 women-years). Compared to timely screens with risk factors, delayed screens with no risk factors had similar CDR across all age groups and a higher proportion of node positive cancers (26.1% vs 20.7%). ICR was lowest in screens that had no risk factors nor dense breasts in all age groups. Conclusions: Majority of screens had no recorded breast cancer risk factors, hence a substantial proportion of screen-detected cancers occur in these screening episodes. Our findings may not justify less frequent screening in women with no risk factors

A systematic review of the impact of the COVID-19 pandemic on breast cancer screening and diagnosis

Background: Breast cancer care has been affected by the COVID-19 pandemic. This systematic review aims to describe the observed pandemic-related changes in clinical and health services outcomes for breast screening and diagnosis. Methods: Seven databases (January 2020–March 2021) were searched to identify studies of breast cancer screening or diagnosis that reported observed outcomes before and related to the pandemic. Findings were presented using a descriptive and narrative approach. Results: Seventy-four studies were included in this systematic review; all compared periods before and after (or fluctuations during) the pandemic. None were assessed as being at low risk of bias. A reduction in screening volumes during the pandemic was found with over half of studies reporting reductions of ≥49%. A majority (66%) of studies reported reductions of ≥25% in the number of breast cancer diagnoses, and there was a higher proportion of symptomatic than screen-detected cancers. The distribution of cancer stage at diagnosis during the pandemic showed lower proportions of early-stage (stage 0–1/I-II, or Tis and T1) and higher proportions of relatively more advanced cases than that in the pre-pandemic period, however population rates were generally not reported. Conclusions: Evidence of substantial reductions in screening volume and number of diagnosed breast cancers, and higher proportions of advanced stage cancer at diagnosis were found during the pandemic. However, these findings reflect short term outcomes, and higher-quality research examining the long-term impact of the pandemic is needed

Independent External Validation of Artificial Intelligence Algorithms for Automated Interpretation of Screening Mammography: A Systematic Review

Purpose: The aim of this study was to describe the current state of science regarding independent external validation of artificial intelligence (AI) technologies for screening mammography. Methods: A systematic review was performed across five databases (Embase, PubMed, IEEE Explore, Engineer Village, and arXiv) through December 10, 2020. Studies that used screening examinations from real-world settings to externally validate AI algorithms for mammographic cancer detection were included. The main outcome was diagnostic accuracy, defined by area under the receiver operating characteristic curve (AUC). Performance was also compared between radiologists and either stand-alone AI or combined radiologist and AI interpretation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Results: After data extraction, 13 studies met the inclusion criteria (148,361 total patients). Most studies (77% [n = 10]) evaluated commercially available AI algorithms. Studies included retrospective reader studies (46% [n = 6]), retrospective simulation studies (38% [n = 5]), or both (15% [n = 2]). Across 5 studies comparing stand-alone AI with radiologists, 60% (n = 3) demonstrated improved accuracy with AI (AUC improvement range, 0.02-0.13). All 5 studies comparing combined radiologist and AI interpretation with radiologists alone demonstrated improved accuracy with AI (AUC improvement range, 0.028-0.115). Most studies had risk for bias or applicability concerns for patient selection (69% [n = 9]) and the reference standard (69% [n = 9]). Only two studies obtained ground-truth cancer outcomes through regional cancer registry linkage. Conclusions: To date, external validation efforts for AI screening mammographic technologies suggest small potential diagnostic accuracy improvements but have been retrospective in nature and suffer from risk for bias and applicability concerns