Effectiveness of extended postpartum inpatient monitoring for hypertensive disorders of pregnancy to reduce the risk of readmission for preeclampsia with severe features.

BACKGROUND: Preeclampsia is an obstetrical disorder, which complicates 3% to 6% of pregnancies and contributes to 21.6% of readmissions in the postpartum period. The optimal strategy for inpatient monitoring of blood pressures to minimize readmissions for postpartum patients with hypertensive disorders is not known. We hypothesized that extended monitoring of postpartum patients with hypertensive disorders of pregnancy for at least 36 hours after the last blood pressure that was ≥150/100 mm Hg would result in decreased readmission rates for preeclampsia with severe features compared with those who were not observed by these blood pressure goals. OBJECTIVE: This study aimed to evaluate whether extended inpatient monitoring of postpartum patients with hypertensive disorders of pregnancy for at least 36 hours after their last blood pressure that was ≥150/100 mm Hg would improve readmission rates for preeclampsia with severe features within 6 weeks of delivery. STUDY DESIGN: This was a retrospective cohort study in patients with a singleton pregnancy and a diagnosis of a hypertensive disorder of pregnancy at their delivery admission or at any point during pregnancy who delivered 1 year before and 1 year after the implementation of extended inpatient monitoring of postpartum hypertension. The primary outcome was readmission for preeclampsia with severe features within 6 weeks of delivery. The secondary outcomes were length of stay during first admission, number of readmissions for any indication, intensive care unit admission, postpartum day at readmission, median systolic blood pressure in the 24-hour period before discharge, median diastolic blood pressure in the 24-hour period before discharge, intravenous antihypertensive medication required during first admission, and intravenous antihypertensive medication required during second admission. Univariable analysis was performed for the association between baseline maternal characteristics and the primary outcome. Multivariable analysis was performed, adjusting for baseline maternal characteristic differences between exposure groups. RESULTS: A total of 567 patients met the inclusion criteria of which 248 patients delivered before and 319 delivered after the implementation of extended monitoring. For baseline characteristics, the extended monitoring group had a significantly higher proportion of patients who were non-Hispanic Black and Hispanic, more diagnoses of hypertensive disorders and/or diabetes mellitus at the time of admission for delivery, a difference in the distribution of hypertensive diagnoses at the time of discharge from the first admission, and fewer discharged patients from their first admission on labetalol than the preintervention group. In a univariable analysis of the primary outcome, there was a significantly increased risk of readmission for preeclampsia with severe features in the extended monitoring group (62.5% vs 96.2% of total readmissions; P=.004). In multivariable analysis, patients in the extended monitoring group were more likely to be readmitted for preeclampsia with severe features than patients in the preintervention group (adjusted odds ratio, 3.45; 95% confidence interval, 1.03-11.5; P=.044). CONCLUSION: Extended monitoring with a strict blood pressure goal of <150/<100 mm Hg did not decrease readmissions for preeclampsia with severe features in patients with a previous diagnosis of a hypertensive disorder of pregnancy.

Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature.

The "learning health system" (LHS) concept has been defined in broad terms, which makes it challenging for health system leaders to determine exactly what is required to transform their organization into an LHS. This study provides a conceptual map of the LHS landscape by identifying the activities, principles, tools, and conditions that LHS researchers have associated with the concept. Through a multi-step screening process, two researchers identified 79 publications from PubMed (published before January 2020) that contained information relevant to the question, "What work is required of a healthcare organization that is operating as an LHS?" Those publications were coded as to whether or not they referenced each of 94 LHS elements in the taxonomy developed by the study team. This taxonomy, named the Learning Health Systems Consolidated Framework (LHS-CF), organizes the elements into five "bodies of work" (organizational learning, translation of evidence into practice, building knowledge, analyzing clinical data, and engaging stakeholders) and four "enabling conditions" (workforce skilled for LHS work, data systems and informatics technology in place, organization invests resources in LHS work, and supportive organizational culture). We report the frequency that each of the 94 elements was referenced across the 79 publications. The four most referenced elements were: "organization builds knowledge or evidence," "quality improvement practices are standard practice," "patients and family members are actively engaged," and "organizational culture emphasizes and supports learning." By dissecting the LHS construct into its component elements, the LHS-CF taxonomy can serve as a useful tool for LHS researchers and practitioners in defining the aspects of LHS they are addressing. By assessing how often each element is referenced in the literature, the study provides guidance to health system leaders as to how their organization needs to evolve in order to become an LHS - while also recognizing that each organization should emphasize elements that are most aligned with their mission and goals.

Hidden Markov Models Reveal Tactical Adjustment of Temporally Clustered Courtship Displays in Response to the Behaviors of a Robotic Female.

We present a statistical approach-a custom-built hidden Markov model (HMM)-that is broadly applicable to the analysis of temporally clustered display events, as found in many animals, including birds, orthopterans, and anurans. This HMM can simultaneously estimate both the expected lengths of each animal's display bouts and their within-bout display rates. We highlight the HMM's ability to estimate changes in animals' display effort over time and across different social contexts, using data from male greater sage grouse (Centrocercus urophasianus). Male display effort was modeled across three sites in two experimental treatments (robotic female simulating interested or uninterested behavior) and in the presence or absence of live females. Across contexts, we show that sage grouse males primarily adjust their bout lengths rather than their within-bout display rates. Males' responses to female behavior were correlated with male mating success: males with more matings showed high display persistence regardless of female behavior, while males with fewer matings tended to invest selectively in females that were already showing interest in mating. Additionally, males with higher mating success responded more to the presence of a female than males with fewer matings did. We conclude with suggestions for adapting our HMM approach for use in other animal systems.