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Sleep apnea (SA) is recognized as a predictor of incident atrial fibrillation (AF) and AF recurrence after treatment. However, data on the prevalence of SA phenotypes in patients with AF and the effect of widespread SA screening on AF outcomes are scarce. We conducted a retrospective study of patients with AF referred for SA testing between March 2018 and April 2020. The screening was performed using home sleep testing or polysomnography. AF outcomes were examined by assessment of AF progression as defined by a change from paroxysmal AF to persistent AF, change in antiarrhythmic drug, having an ablation or cardioversion. Of 321 patients evaluated for AF, 251 patients (78%) completed SA testing. A total of 185 patients with complete follow-up data and SA testing were included in our analysis: 172 patients (93%) had SA; 90 of those (49%) had primarily obstructive sleep apnea, 77 patients (42%) had mixed apnea, and 5 patients (3%) had pure central apnea. Time from AF diagnosis to SA testing was associated with AF progression; after 2 years, the risk of AF progression increased (p <0.008). Continuous positive airway pressure treatment did not affect AF progression (p = 0.99). In conclusion, SA is highly prevalent in an unselected population of patients with AF, with mixed apnea being present in over 40% of the population. Early SA testing was associated with decreased rates of AF progression, likely because of earlier and potentially more aggressive pursuit of rhythm control.

Background: Prior studies have examined the association between fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5 )] and fetal growth with either limited spatial or temporal resolution. Objectives: In this study, we examined the association between PM2.5 exposure during pregnancy and fetal growth measures (ultrasound parameters and birth weight) in a pregnancy cohort using spatiotemporally resolved PM2.5 in Eastern Massachusetts, USA. Methods: We used ultrasound measures of biparietal diameter (BPD), head circumference, femur length, and abdominal circumference (AC), in addition to birth weight, from 9,446 pregnancies that were delivered at the Beth Israel Deaconess Medical Center from 2011–2016. We used linear mixed-effects models to examine the associations of PM2.5 in two exposure windows (the first 16 wk of pregnancy and the cumulative exposure up until the assessment of fetal growth) with anatomic scans (ultrasound measures at<24 wk ), growth scans (ultrasound measures at≥24wk ), and birth weight. All models were adjusted for sociodemographic characteristics, long-term trends, and temperature. Results: Higher PM2.5 exposure in the first 16 wk was associated with smaller fetal growth measures, where associations were particularly strong for BPD, AC, and birth weight. For example, a 5-μg/m3 increase in PM2.5 was associated with a lower mean BPD z -score of −0.19 (95% CI: −0.31 , −0.06 ) before 24 wk, a lower mean AC z -score of −0.15 (95% CI: −0.28 , −0.01 ) after 24 wk, and a lower mean birth weight z -score of −0.11 (95% CI: −0.20 , −0.01 ). Analyses examining the associations with cumulative PM2.5 exposure up until the assessment of fetal growth produced attenuated associations. Conclusions: Higher gestational exposure to PM2.5 was associated with smaller fetal growth measures at levels below the current national standards.

Abstract Introduction Delays in surgery and their impact on survival in papillary thyroid cancer (PTC) is unclear. We sought to investigate the association between time to surgery and survival in patients with PTC. Methods A total of 8170 Medicare beneficiaries with PTC who underwent thyroidectomy were identified within the Surveillance, Epidemiology, and End Results-Medicare linked data files between 1999 and 2018. Disease-specific survival (DSS) and overall survival (OS) were estimated using Kaplan-Meir analysis, and Cox proportional hazards models were specified to estimate the association between time to surgery and survival. Results Among 8170 patients with PTC, mean age 69.3 (SD+/− 11.4), 89.8% had surgery within the first 90 days, 7.8% had surgery 91 to 180 days from diagnosis, and 2.4% had surgery after 180 days. Increasing time to surgery was associated with increased mortality for OS in the >180-day group [adjusted hazard ratio (aHR) 1.24; 95% CI, 1.01-1.53]. Moreover, on stratification by summary stage, those with localized disease in the 91- to 180-day group increased risk by 25% (aHR 1.25; 95%CI, 1.05-1.51), and delaying over 180 days increased risk by 61% (aHR 1.61; 95%CI, 1.19-2.18) in OS. Those with localized disease in the >180-day group had almost 4 times the estimated rate of DSS mortality (aHR3.51; 95%CI, 1.68-7.32). When stratified by T stage, those with T2 disease in the >180 days group had double the estimated rate of all-cause mortality (aHR 2.0; 95% CI, 1.1-3.3) and almost triple the estimated rate of disease-specific mortality (aHR 2.7; 95% CI, 1.05-6.8). Conclusions Delays in surgery for PTC may impact OS and DSS in localized disease, prior to nodal metastasis.

ObjectiveCompare the incidence of hypoglycemia in neonates born to patients with diabetes, based on last maternal glucose before delivery.Study designCohort of singleton births from individuals with pregestational and gestational diabetes (GDM) from 2017 to 2019.ResultsWe included 853 deliveries. Maternal hyperglycemia before delivery was associated with 1.8-fold greater risk of neonatal hypoglycemia (glucose < 45 mg/dL) in patients with GDM on medication (adjusted risk ratio (aRR): 1.8; 95% CI: 1.1–2.7), compared with euglycemia. This association was not seen in diet-controlled GDM (0.5; 0.23–1.1), nor in Type 1 (1.1; 0.88–1.4), or Type 2 pregestational diabetes (1.1; 0.61–1.9). Further, pregestational diabetes, compared to GDM, regardless of intrapartum maternal glucose control, was associated with neonatal hypoglycemia and NICU admission.ConclusionMaternal hyperglycemia before delivery only carried a higher risk of neonatal hypoglycemia in those with GDM on medications. Other interventions to reduce neonatal hypoglycemia are needed.

Background: Parametric g-computation is an attractive analytic framework to study the health effects of air pollution. Yet, the ability to explore bio-logically relevant exposure windows within this framework is underdeveloped.Objectives: We outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach \"g-survival-DLM\" and illustrate its use examining the association between PM2:5 during pregnancy and the risk of preterm birth (PTB).Methods: We applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average PM2:5 in each gestational week by 20% on the risk of PTB among 9,403 deliveries from Beth Israel Deaconess Medical Center, Boston, Massachusetts, 2011-2016. Daily PM2:5 was taken from a 1-km grid model and assigned to address at birth. Models were adjusted for sociodemographics, time trends, nitrogen dioxide, and temperature. To facilitate implementation, we provide a detailed description of the procedure and accompanying R syntax.Results: There were 762 (8.1%) PTBs in this cohort. The gestational week-specific median PM2:5 concentration was relatively stable across pregnancy at ~ 7lg=m3. We found that our hypothetical intervention strategy changed the cumulative risk of PTB at week 36 (i.e., the end of the preterm period) by -0:009 (95% confidence interval: -0:034, 0.007) in comparison with the scenario had we not intervened, which translates to about 86 fewer PTBs in this cohort. We also observed that the critical exposure window appeared to be weeks 5-20.Discussion: We demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter <2:5 lm (PM2:5)] during gestational weeks 5-20 could potentially lower the risk of PTB.

Parametric g-computation is an attractive analytic framework to study the health effects of air pollution. Yet, the ability to explore biologically relevant exposure windows within this framework is underdeveloped.BACKGROUNDParametric g-computation is an attractive analytic framework to study the health effects of air pollution. Yet, the ability to explore biologically relevant exposure windows within this framework is underdeveloped.We outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach \"g-survival-DLM\" and illustrate its use examining the association between PM2.5 during pregnancy and the risk of preterm birth (PTB).OBJECTIVESWe outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach \"g-survival-DLM\" and illustrate its use examining the association between PM2.5 during pregnancy and the risk of preterm birth (PTB).We applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average PM2.5 in each gestational week by 20% on the risk of PTB among 9,403 deliveries from Beth Israel Deaconess Medical Center, Boston, Massachusetts, 2011-2016. Daily PM2.5 was taken from a 1-km grid model and assigned to address at birth. Models were adjusted for sociodemographics, time trends, nitrogen dioxide, and temperature. To facilitate implementation, we provide a detailed description of the procedure and accompanying R syntax.METHODSWe applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average PM2.5 in each gestational week by 20% on the risk of PTB among 9,403 deliveries from Beth Israel Deaconess Medical Center, Boston, Massachusetts, 2011-2016. Daily PM2.5 was taken from a 1-km grid model and assigned to address at birth. Models were adjusted for sociodemographics, time trends, nitrogen dioxide, and temperature. To facilitate implementation, we provide a detailed description of the procedure and accompanying R syntax.There were 762 (8.1%) PTBs in this cohort. The gestational week-specific median PM2.5 concentration was relatively stable across pregnancy at ∼7μg/m3. We found that our hypothetical intervention strategy changed the cumulative risk of PTB at week 36 (i.e., the end of the preterm period) by -0.009 (95% confidence interval: -0.034, 0.007) in comparison with the scenario had we not intervened, which translates to about 86 fewer PTBs in this cohort. We also observed that the critical exposure window appeared to be weeks 5-20.RESULTSThere were 762 (8.1%) PTBs in this cohort. The gestational week-specific median PM2.5 concentration was relatively stable across pregnancy at ∼7μg/m3. We found that our hypothetical intervention strategy changed the cumulative risk of PTB at week 36 (i.e., the end of the preterm period) by -0.009 (95% confidence interval: -0.034, 0.007) in comparison with the scenario had we not intervened, which translates to about 86 fewer PTBs in this cohort. We also observed that the critical exposure window appeared to be weeks 5-20.We demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter ≤2.5μm (PM2.5)] during gestational weeks 5-20 could potentially lower the risk of PTB. https://doi.org/10.1289/EHP13891.DISCUSSIONWe demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter ≤2.5μm (PM2.5)] during gestational weeks 5-20 could potentially lower the risk of PTB. https://doi.org/10.1289/EHP13891.

BACKGROUND: Parametric g-computation is an attractive analytic framework to study the health effects of air pollution. Yet, the ability to explore biologically relevant exposure windows within this framework is underdeveloped. OBJECTIVES: We outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach \"g-survival-DLM\" and illustrate its use examining the association between [PM.sub.2.5] during pregnancy and the risk of preterm birth (PTB). METHODS: We applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average [PM.sub.2.5] in each gestational week by 20% on the risk of PTB among 9,403 deliveries from Beth Israel Deaconess Medical Center, Boston, Massachusetts, 2011-2016. Daily [PM.sub.2.5] was taken from a 1-km grid model and assigned to address at birth. Models were adjusted for sociodemographics, time trends, nitrogen dioxide, and temperature. To facilitate implementation, we provide a detailed description of the procedure and accompanying R syntax. RESULTS: There were 762 (8.1%) PTBs in this cohort. The gestational week-specific median [PM.sub.2.5] concentration was relatively stable across pregnancy at ~7[micro]g/[m.sup.3]. We found that our hypothetical intervention strategy changed the cumulative risk of PTB at week 36 (i.e., the end of the preterm period) by -0.009 (95% confidence interval: -0.034, 0.007) in comparison with the scenario had we not intervened, which translates to about 86 fewer PTBs in this cohort. We also observed that the critical exposure window appeared to be weeks 5-20. DISCUSSION: We demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter <2.5 [micro]m ([PM.sub.2.5])] during gestational weeks 5-20 could potentially lower the risk of PTB.

Introduction and hypothesis Many providers recommend concurrent estrogen therapy with pessary use to limit complications; however, limited data exist to support this practice. We hypothesized that vaginal estrogen supplementation decreases incidence of pessary-related complications and discontinuation. Methods We performed a retrospective cohort study of women who underwent a pessary fitting from 1 January 2007 through 1 September 2013 at one institution; participants were identified by billing code and were eligible if they were postmenopausal and had at least 3 months of pessary use and 6 months of follow-up. All tests were two sided, and P values < 0.05 were considered statistically significant. Results Data from 199 women were included; 134 used vaginal estrogen and 65 did not. Women who used vaginal estrogen had a longer median follow-up time (29.5 months) compared with women who did not (15.4 months) and were more likely to have at least one pessary check (98.5 % vs 86.2 %, P  < 0.001). Those in the estrogen group were less likely to discontinue using their pessary (30.6 % vs 58.5 %, P  < 0.001) and less likely to develop increased vaginal discharge than women who did not [hazard ratio (HR) 0.31, 95 % confidence interval (CI) 0.17–0.58]. Vaginal estrogen was not protective against erosions (HR 0.93, 95 % CI 0.54–1.6) or vaginal bleeding (HR 0.78, 95 % CI 0.36–1.7). Conclusions Women who used vaginal estrogen exhibited a higher incidence of continued pessary use and lower incidence of increased vaginal discharge than women who did not.

Purpose The incidence of placenta accreta spectrum (PAS; pathologic diagnosis of placenta accreta, increta or percreta) continues to rise in the USA. The purpose of this study is to compare the hemorrhagic morbidity associated with PAS with and without a placenta previa. Methods This was a retrospective cohort study of 105 deliveries from 1997 to 2017 with histologically confirmed PAS comparing outcomes in women with and without a coexisting placenta previa. We used the Wilcoxon rank sum test to compare continuous data and Chi-square or Fisher’s exact test for categorical data. We also performed log-binomial regression to calculate risk ratios adjusted for depth of invasion (aRR) and 95% confidence intervals (CI). Results We identified 105 pregnancies with PAS. Antenatal diagnosis of PAS was higher in women with coexisting placenta previa (72.3%) than those without (6.9%, p  < 0.001). Women with coexisting placenta previa had greater median estimated blood loss and more units of packed red blood cells transfused (both p  ≤ 0.03). Women with placenta previa were more likely to undergo a hysterectomy (RR 2.7; 95% CI 1.8–3.8) and be admitted to the intensive care unit (aRR 3.3; 95% CI 1.1–9.6). Conclusions Among women with PAS, those with a coexisting placenta previa experienced greater hemorrhagic morbidity compared to those without. In addition, PAS without placenta previa typically was not diagnosed prior to delivery. This study further supports the recommendation for multi-disciplinary planning and assurance of resources for pregnancies complicated by PAS. In addition, our results highlight the need for mobilization of resources for those pregnancies where PAS is not diagnosed until delivery.