Abstract Background Incidence of blood stream infections (BSI) among NICU admissions remains high, with associated mortality and morbidity. Due to COVID-19, there are increased infection prevention (IP) measures in NICUs including universal masking for all healthcare workers and families, social distancing, visitation restrictions, and increased attention to hand hygiene. These measures may also affect late-onset infection rates and offer understanding of novel interventions for prevention. Methods We examined infection rates during the 24 months prior to implementation of COVID-19 IP measures (PRE-period) compared to the months after implementation from April 2020 (POST-period). Late-onset infections were defined as culture-confirmed infection of the blood, urine, or identification of respiratory viral pathogens. An interrupted time series analysis of infection per 1000 patient days was performed based on a change-point Poisson regression with a lagged dependent variable and the number of patient days used as offsets. Each month was treated as independent with additional analysis using an observation-driven model to account for serial dependence. Results Multicenter analysis to date included all infants cared for at three centers (Level 3 and 4) from 2018-2020. Monthly BSI rates decreased in the POST-period at the three centers (Figure 1). At all centers actual BSI rate was lower than the expected rate in the POST-period (Figure 2). The combined BSI rate per 1000 patient days was 41% lower compared to the rate prior to implementation (95% CI, 0.42 to 0.84, P=0.004) (Table 1). In subgroup analysis by birthweight, infants< 1000g had a 39% reduction in BSI (P=0.023), for1000-1500g patients there was a 44% reduction (P=0.292) and in those > 1500g there was a 53% reduction (0.083). Figure 1. PRE and POST MASKING and other COVID Infection Prevention Measures and Monthly BSI Rates. Figure 2. PRE and POST MASKING and other COVID infection prevention measures and BSI Trends. At all centers actual BSI rate was lower than the expected rate for that center in the POST period. UVA and Duke showed a baseline decrease and Pennsylvania Hospital showed a downward trend in infection rates. There was an approximate decrease in expected bloodstream infection events at Pennsylvania Hospital by 7 events, at UVA by 22 events and at Duke by 23 events. Overall, all three centers saw a decrease in their expected infections after COVID-19 infection prevention measures were implemented. Table 1. Percent reduction in Bloodstream Infection at each center. Conclusion In this preliminary analysis, we found a reduction of BSI after the implementation of COVID-19 infection prevention measures. Additionally, there were fewer viral infections, though there were a limited number of episodes. Further analyses of multicenter data and a larger number of patients will elucidate the significance of these findings and the role some of these IP measures such as universal masking may have in infection prevention in the NICU. Disclosures All Authors: No reported disclosures
Abstract Background Pulmonary artery acceleration time measured by echocardiography inversely correlates with pulmonary artery pressures in adults and children older than 1 year of age. There is a paucity of data investigating this relationship in young children, particularly among preterm infants. Objective To characterize the relationship between pulmonary artery acceleration time ( PAAT ) and pulmonary artery pressures in infants. Design/Methods Patients ≤ 1 year of age at Children's Hospital of Philadelphia between 2011 and 2017 were reviewed. Infants with congenital heart disease were excluded, except those with a patent ductus arteriosus (PDA), atrial septal defect (ASD), or ventricular septal defect (VSD). Linear regression analysis was used to assess the correlation between PAAT measured by echocardiography and systolic pulmonary artery pressure, mean pulmonary artery pressure, and indexed pulmonary vascular resistance from cardiac catheterization. Results Fifty‐seven infants were included, of which 61% were preterm and 49% had a diagnosis of bronchopulmonary dysplasia. The median postmenstrual age and weight at catheterization were 51.1 weeks ( IQR 35.8–67.9 weeks) and 4400 g ( IQR 3100–6500 g), respectively. Forty‐four infants (77%) had a patent ductus arteriosus ( PDA ). There was a weak inverse correlation between PAAT with mPAP ( r = −0.35, P = 0.01), sPAP ( r = −0.29, P = 0.03), and PVR i ( r = −0.29, P = 0.03). Conclusion There is a weak inverse relationship between PAAT and pulmonary artery pressures. This relationship is less robust in our population of infants with a high incidence of PDA s compared to previous studies in older children. Thus, PAAT may be less clinically meaningful for diagnosing pulmonary arterial hypertension in infants, particularly those with PDA s.
We sought to evaluate whether there was variability in language used on social media across different time points of pregnancy (before, during, and after pregnancy, as well as by trimester and parity). Consenting patients shared access to their individual Facebook posts and electronic medical records. Random forest models trained on Facebook posts could differentiate first trimester of pregnancy from 3 months before pregnancy (F1 score = .63) and from a random 3-month time period (F1 score = .64). Posts during pregnancy were more likely to include themes about family (β = .22), food craving (β = .14), and date/times (β = .13), while posts 3 months prior to pregnancy included themes about social life (β = .30), sleep (β = .31), and curse words (β = .27), and 3 months post-pregnancy included themes of gratitude (β = .17), health appointments (β = .21), and religiosity (β = .18). Users who were pregnant for the first time were more likely to post about lack of sleep (β = .15), activities of daily living (β = .09), and communication (β = .08) compared with those who were pregnant after having a child who posted about others’ birthdays (β = .16) and life events (.12). A better understanding about social media timelines can provide insight into lifestyle choices that are specific to pregnancy.
Forecasting healthcare utilization has the potential to anticipate care needs, either accelerating needed care or redirecting patients toward care most appropriate to their needs. While prior research has utilized clinical information to forecast readmissions, analyzing digital footprints from social media can inform our understanding of individuals' behaviors, thoughts, and motivations preceding a healthcare visit. We evaluate how language patterns on social media change prior to emergency department (ED) visits and inpatient hospital admissions in this case-crossover study of adult patients visiting a large urban academic hospital system who consented to share access to their history of Facebook statuses and electronic medical records. An ensemble machine learning model forecasted ED visits and inpatient admissions with out-of-sample cross-validated AUCs of 0.64 and 0.70 respectively. Prior to an ED visit, there was a significant increase in depressed language (Cohen's d = 0.238), and a decrease in informal language (d = 0.345). Facebook posts prior to an inpatient admission showed significant increase in expressions of somatic pain (d = 0.267) and decrease in extraverted/social language (d = 0.357). These results are a first step in developing methods to utilize user-generated content to characterize patient care-seeking context which could ultimately enable better allocation of resources and potentially early interventions to reduce unplanned visits.
The year was 1987, and a bold experiment was under way—the US-based National Demonstration Project in Quality Improvement in Health Care (NDP). This effort brought together 21 companies recognised for excellence in quality manufacturing with 21 healthcare organisations to test whether revolutionary practices from quality improvement (QI) could be applied to healthcare. The partnership succeeded and NDP was extended for another 3 years, eventually becoming the Institute for Healthcare Improvement.1 Since then, QI principles and methods have spread broadly across healthcare.
Around the same time the NDP was under way, another quiet revolution in systems thinking was taking shape in the emerging field of human-centred design. In 1988, Norman authored the landmark book ‘The Design of Everyday Things’, an explanation of how human-centred design—‘an approach that puts human needs, capabilities, and behavior first, then designs to accommodate those needs, capabilities, and ways of behaving2’—could dramatically improve products and services. Human-centred design methods, broadly referred to as design thinking (DT), are now widely used across diverse industries.3 Companies that have implemented design practices outperform their peers,4 and leading organisations like Google, Apple and General Electric use DT to create world-class products and services.5
Although healthcare has invested heavily in systems improvement using QI, far fewer in healthcare are familiar with the improvement methodology of DT. A tremendous opportunity exists to further enhance contemporary healthcare improvement efforts by integrating the human-centred methods of DT that have revolutionised other industries. However, a knowledge gap remains on how to practically implement core methods from DT into QI practice.
Here we explain fundamental DT methods and how they can integrate into existing improvement efforts, providing a starting point for organisations and leaders to leverage this human-centred approach and harness the powerful emotional perspectives of ‘users’, the patients, families, caregivers and …
