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Background: Prompt identification of infections is critical for slowing the spread of infectious diseases. However, diagnostic testing shortages are common in emerging diseases, low resource settings, and during outbreaks. This forces difficult decisions regarding who receives a test, often without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions., Methods: Using early severe acute respiratory syndrome coronavirus disease 2 (SARS-CoV-2) as an example, we used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive. To consider the implications of gains in daily case detection at the population level, we incorporated testing using the CPR into a compartmentalized model of SARS-CoV-2., Results: We found that applying this CPR (area under the curve, 0.69; 95% confidence interval, .68-.70) to prioritize testing increased the proportion of those testing positive in settings of limited testing capacity. We found that prioritized testing led to a delayed and lowered infection peak (ie, "flattens the curve"), with the greatest impact at lower values of the effective reproductive number (such as with concurrent community mitigation efforts), and when higher proportions of infectious persons seek testing. In addition, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit burden., Conclusion: We highlight the population-level benefits of evidence-based allocation of limited diagnostic capacity.SummaryWhen the demand for diagnostic tests exceeds capacity, the use of a clinical prediction rule to prioritize diagnostic testing can have meaningful impact on population-level outcomes, including delaying and lowering the infection peak, and reducing healthcare burden., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Aims: Cardiovascular (CV) complications are the leading cause of maternal morbidity and mortality. The objective was to estimate trends in the incidence of peripartum CV complications in the USA between 2010 and 2016., Methods and Results: This was a retrospective analyses using data from the Healthcare Cost and Utilization Project. We included women with delivery codes consistent with delivery, weighted to a national estimate. The primary outcome was the age-adjusted incidence of CV complications among all deliveries, including complications that occurred during re-hospitalizations. Complications were identified using International Classification of Diseases (ICD) codes. Joinpoint regression was used to evaluate time trends and complications were stratified by type. The secondary outcome was in-hospital maternal death among women with a CV complication. We identified a weighted estimate of 27 408 652 women hospitalized for delivery from 2010 to 2016. Including all years, the complication incidence was 7.36/1000 births [95% confidence interval (CI) 7.18-7.54], with an estimated annual percentage change of 5.8% (95% CI 3.7-7.8%). Cardiac dysrhythmia was the most common complication [3.98/1000 births (95% CI 3.88-4.08)] and acute myocardial infarction was the least common complication [0.11/1000 births (95% CI 0.10-0.11)]. The incidence of hypertension, acute myocardial infarction, and cardiac arrest increased over time, the incidence of congestive heart failure and acute cerebrovascular disease remained stable, the incidence of pulmonary heart disease increased from 2015 onward, and the incidence of cardiac dysrhythmia decreased in 2016. Complications during re-hospitalization accounted for 13.6% (95% CI 13.2-14.1%) of all complications and was highest for acute myocardial infarction [28.1% (95% CI 23.2-33.1)]. Among women with any complication, the mortality rate was 1.20 (95% CI 1.11-1.29) per 100 complications., Conclusion: Our analyses suggest the rate of peripartum CV complications are increasing in the USA, which highlights the need for active efforts in research and prevention., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.)

Aims: Incorporating patient-reported outcomes (PROs) into routine care of atrial fibrillation (AF) enables direct integration of symptoms, function, and health-related quality of life (HRQoL) into practice. We report our initial experience with a system-wide PRO initiative among AF patients., Methods and Results: All patients with AF in our practice undergo PRO assessment with the Toronto AF Severity Scale (AFSS), and generic PROs, prior to electrophysiology clinic visits. We describe the implementation, feasibility, and results of clinic-based, electronic AF PRO collection, and compare AF-specific and generic HRQoL assessments. From October 2016 to February 2019, 1586 unique AF patients initiated 2379 PRO assessments, 2145 of which had all PRO measures completed (90%). The median completion time for all PRO measures per visit was 7.3 min (1st, 3rd quartiles: 6, 10). Overall, 38% of patients were female (n = 589), mean age was 68 (SD 12) years, and mean CHA2DS2-VASc score was 3.8 (SD 2.0). The mean AFSS symptom score was 8.6 (SD 6.6, 1st, 3rd quartiles: 3, 13), and the full range of values was observed (0, 35). Generic PROs of physical function, general health, and depression were impacted at the most severe quartiles of AF symptom score (P < 0.0001 for each vs. AFSS quartile)., Conclusion: Routine clinic-based, PRO collection for AF is feasible in clinical practice and patient time investment was acceptable. Disease-specific AF PROs add value to generic HRQoL instruments. Further research into the relationship between PROs, heart rhythm, and AF burden, as well as PRO-guided management, is necessary to optimize PRO utilization., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.)