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I've had Nina Simone's “sinnerman” on repeat for months. The propulsive force of Simone's 1965 live version of this gospel song drives its ten-minute ferocity straight into the contemporary American zeitgeist. As she tells her audience in the lead-up to a lesser-known performance of the song, recorded in 1961, Simone learned “Sinnerman” when she was a “little bitty girl in revival meetings. It happened when my mother and lots more like her tried to save souls.” The song's judgment-day tale of redemption's refusal is told doubly, both by the sinner—“I cried rock / don't you see I need you, rock”—and by those from whom the sinner begs, if not forgiveness, then simply some measure of mercy from the divine justice to come: “Oh sinnerman, where you gonna run to?” The break in the middle of the 1965 recording strips the song down to Simone's handclaps on the second and fourth beats. All that remains is the tenuous intensity of the time neither of redemption nor of damnation but merely of “accompaniment” in the in-between (Tomlinson and Lipsitz). Called forth from that time, in all of Simone's live recordings, and missing from those of Les Baxter or the Weavers just a few years earlier, comes the insurgent cry for “Power!” over and over, to the point of near exhaustion.

Background: Hepatic artery thrombosis (HAT) is a reported complication of 5%–10% of pediatric liver transplantations, rates 3–4 times that seen in adults. Early HAT (seen within 14 days after transplant) can lead to severe allograft damage and possible urgent re‐transplantation. In this report, we present our analysis of HAT in pediatric liver transplant from a national clinical database and examine the association of HAT with anticoagulant or antiplatelet medication administered in the post‐operative period. Methods: Data were obtained from the Pediatric Health Information System database maintained by the Children's Hospital Association. For each liver transplant recipient identified in a 10‐year period, diagnosis, demographic, and medication data were collected and analyzed. Results: Our findings showed an average rate of HAT of 6.3% across 31 centers. Anticoagulant and antiplatelet medication strategies varied distinctly among and even within centers, likely due to the fact there are no consensus guidelines. Notably, in centers with similar medication usage, HAT rates continue to vary. At the patient level, use of aspirin within the first 72 h of transplantation was associated with a decreased risk of HAT, consistent with other reports in the literature. Conclusion: We suggest that concerted efforts to standardize anticoagulation approaches in pediatric liver transplant may be of benefit in the prevention of HAT. A prospective multi‐institutional study of regimen—possibly including aspirin—following transplantation could have significant value. [ABSTRACT FROM AUTHOR]

Documentation of adverse drug reactions (ADRs) is a key factor in guiding future prescribing. However, incomplete documentation is common and often fails to distinguish implicated drugs as true allergies. This in turn leads to unnecessary avoidance of implicated drug classes and may result in sub-optimal prescribing. Pharmacovigilance (PV) programs utilize a systematic approach to clarify ADR documentation and are known to improve patient safety. Yet it remains unclear if PV alters prescribing. Or, if the existence of the ADR documentation itself continues to prompt avoidance of implicated drugs. To address this, our work presents a retrospective cohort study assessing if clarification of antibiotic ADRs by a hospital-wide PV team was associated with future, safe, re-prescribing at a freestanding pediatric hospital in the midwestern United States. First, we compared the likelihood of future prescribing in an antibiotic class with an active ADR, as compared to alternative drug classes, between PV-clarified and non-clarified patients. Second, we assessed differences in adverse event rates 30-days after future prescribing based on PV clarification status. For robustness, analyses were performed on patients with ADRs in four antibiotic classes: penicillin-based beta-lactams (n = 45,642), sulfonamides/trimethoprim (n = 5,329), macrolides (n = 3,959), and glycopeptides (n = 622). Results illustrate that clarification of an ADR by PV was associated with an increased odds of future prescribing in the same drug class (Odds Ratio [95%-CI]): penicillin-based beta-lactams (1.59 [1.36–1.89]), sulfonamides/trimethoprim (2.29 [0.89–4.91]), macrolides (0.77 [0.33–1.61]), and glycopeptide (1.85 [1.12–3.20]). Notably, patients clarified by PV experienced no increase in the rate of adverse events within 30-days following the prescribing of antibiotics in the same class as an active ADR. Overall, this study provides strong evidence that PV reviews safely increase the rate of re-prescribing antibiotics even in the presence of an existing implicated drug ADR. [ABSTRACT FROM AUTHOR]

Clustered count data, common in health-related research, are routinely analyzed using generalized linear mixed models. There are two well-known challenges in small-sample inference in mixed modeling: bias in the naïve standard error approximation for the empirical best linear unbiased estimator, and lack of clearly defined denominator degrees of freedom. The Kenward–Roger method was designed to address these issues in linear mixed modeling, but neither it nor the simpler option of using between-within denominator degrees of freedom has been thoroughly examined in generalized linear mixed modeling. We compared the Kenward–Roger and between-within methods in two simulation studies. For simulated cluster-randomized trial data, coverage rates for both methods were generally close to the nominal 95% level and never outside 93-97%, even for 5 clusters with an average of 3 observations each. For autocorrelated longitudinal data, between-within intervals were more accurate overall, and under some conditions both the original and improved Kenward–Roger methods behaved erratically. Overall, coverage for Kenward–Roger and between-within intervals was generally adequate, if often conservative. Based on the scenarios examined here, use of between-within degrees of freedom may be a suitable or even preferable alternative to the Kenward–Roger method in some analyses of clustered count data with simple covariance structures. [ABSTRACT FROM AUTHOR]

The post-Norwood interstage period for infants with hypoplastic left heart syndrome is a high-risk time with 10–20% of infants having a complication of recurrent coarctation of the aorta (RCoA). Many interstage programs utilize mobile applications allowing caregivers to submit home physiologic data and videos to the clinical team. This study aimed to investigate if caregiver-entered data resulted in earlier identification of patients requiring interventional catheterization for RCoA. Retrospective home monitoring data were extracted from five high-volume Children's High Acuity Monitoring Program®-affiliated centers (defined as contributing > 20 patients to the registry) between 2014 and 2021 after IRB approval. Demographics and caregiver-recorded data evaluated include weight, heart rate (HR), oxygen saturation (SpO2), video recordings, and 'red flag' concerns prior to interstage readmissions. 27% (44/161) of infants required interventional catheterization for RCoA. In the 7 days prior to readmission, associations with higher odds of RCoA included (mean bootstrap coefficient, [90% CI]) increased number of total recorded videos (1.65, [1.07–2.62]) and days of recorded video (1.62, [1.03–2.59]); increased number of total recorded weights (1.66, [1.09–2.70]) and days of weights (1.56, [1.02–2.44]); increasing mean SpO2 (1.55, [1.02–2.44]); and increased variation and range of HR (1.59, [1.04–2.51]) and (1.71, [1.10–2.80]), respectively. Interstage patients with RCoA had increased caregiver-entered home monitoring data including weight and video recordings, as well as changes in HR and SpO2trends. Identifying these items by home monitoring teams may be beneficial in clinical decision-making for evaluation of RCoA in this high-risk population. [ABSTRACT FROM AUTHOR]