Hepatic artery thrombosis and use of anticoagulants and antiplatelet agents in pediatric liver transplantation.

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.

Utilization of geospatial distribution in the measurement of study cohort representativeness.

OBJECTIVE: The ability to apply results from a study to a broader population remains a primary objective in translational science. Distinct from intrinsic elements of scientific rigor, the extrinsic concept of generalization requires there be alignment between a study cohort and population in which results are expected to be applied. Widespread efforts have been made to quantify representativeness of study cohorts. These techniques, however, often consider the study and target cohorts as monolithic collections that can be directly compared. Overlooking known impacts to health from socio-demographic and environmental factors tied to individual's geographical location, and potentially obfuscating misalignment in underrepresented population subgroups. This manuscript introduces several measures to account for geographic information in the assessment of cohort representation. METHODS: Metrics were defined across two themes: First, measures of recruitment, to assess if a study cohort is drawn at an expected rate and in an expected geographical pattern with respect to individuals in a reference cohort. Second, measures of individual characteristics, to assess if the individuals in the study cohort accurately reflect the sociodemographic, clinical, and geographic diversity observed across a reference cohort while accounting for the geospatial proximity of individuals. RESULTS: As an empirical demonstration, methods are applied to an active clinical study examining asthma in Black/African American patients at a US Midwestern pediatric hospital. Results illustrate how areas of over- and under-recruitment can be identified and contextualized in light of study recruitment patterns at an individual-level, highlighting the ability to identify a subset of features for which the study cohort closely resembled the broader population. In addition they provide an opportunity to dive deeper into misalignments, to identify study cohort members that are in some way distinct from the communities for which they are expected to represent. CONCLUSION: Together, these metrics provide a comprehensive spatial assessment of a study cohort with respect to a broader target population. Such an approach offers researchers a toolset by which to target expected generalization of results derived from a given study.

Characterization of a Vigorous sucking style in early infancy and its predictive value for weight gain and eating behaviors at 12 months.

This study sought to identify sucking profiles among healthy, full-term infants and assess their predictive value for future weight gain and eating behaviors. Pressure waves of infant sucking were captured during a typical feeding at age 4 months and quantified via 14 metrics. Anthropometry was measured at 4 and 12 months, and eating behaviors were measured by parent report via the Children's Eating Behavior Questionnaire-Toddler (CEBQ-T) at 12 months. Sucking profiles were created using a clustering approach on the pressure wave metrics, and utility of these profiles was assessed for predicting which infants will have weight-for-age (WFA) percentile changes from ages 4-12 months that exceed thresholds of 5, 10, and 15 percentiles, and for estimating each CEBQ-T subscale score. Among 114 infants, three sucking profiles were identified: Vigorous (51%), Capable (28%), and Leisurely (21%). Sucking profiles were found to improve estimation of change in WFA from 4 to 12 months and 12-month maternal-reported eating behaviors above infant sex, race/ethnicity, birthweight, gestational age, and pre-pregnancy body mass index alone. Infants with a Vigorous sucking profile gained significantly more weight during the study period than infants with a Leisurely profile. Infant sucking characteristics may aid in predicting which infants may be at greater risk of obesity, and therefore sucking profiles deserve more investigation.

Associations of Home Monitoring Data to Interventional Catheterization for Infants with Recurrent Coarctation of the Aorta and Hypoplastic Left Heart Syndrome.

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.

SSSH: Responsive soothing bassinet feasibility study for infants with congenital heart disease after cardiac surgery.

PURPOSE: For infants with congenital heart disease (CHD) requiring surgery, prolonged hospital stays, intermittent caregiver visitation, and constrained unit staffing ratios present barriers to adequately address post-operative stressors and associated need to retain cognitive and physiological reserves. Similar patients requiring high-engagement interventions, such as hospitalized infants with neonatal abstinence syndrome, have found success in utilizing responsive bassinets to soothe infants and save floor nurses' time. However, it remains unclear if such technology can be leveraged in the CHD population given their complex hemodynamics, feeding intolerance, and monitoring requirements. METHODS: This multidisciplinary feasibility study evaluated responsive bassinet use in a cohort of infants with CHD <6 months of age in a medical-surgical unit at a midwestern children's hospital. Specifically assessing 1) implementation requirements, challenges, and potential of utilizing the device, together with 2) ability to perform bedside monitoring (monitoring) and 3) measuring physiologic trends during use. RESULTS: Between 11/2020-1/2022, nine infants utilized a responsive bassinet over 599 h (mean 13, range 4-26 days per infant). No increase in monitoring alarms and accurate vital signs monitoring during bassinet activity were noted with appropriate physiologic responses for infants with single ventricle and biventricular surgeries. CONCLUSIONS: Feasibility of introducing new technology into care, and successful use of its functionality for soothing was found to be plausible for infants with CHD. PRACTICE IMPLICATIONS: After cardiac surgery, infants with CHD have need for interventions to reduce stress. Use of a soothing bassinet has the potential to aid in doing so without interference with monitoring requirements.

Vaping, Perceptions of Vaping, and Plans to Quit Among E-cigarette Users in the United States and the United Kingdom.

INTRODUCTION: Government and health organizations in the United States and the United Kingdom have taken different stances on e-cigarettes policy. To explore the potential effects of these policies, we describe e-cigarette user characteristics, intentions to quit, and perceived attitudes toward vaping. METHODS: We used the online crowdsourcing platform Prolific to conduct a cross-sectional survey of current vapers in both countries. Measures were drawn from international surveys. RESULTS: The sample included 1044 vapers (524 United Kingdom; 520 United States) with a mean age of 34. Samples differed by gender (United States: 57% male vs 45% in United Kingdom), race (United States: 79% White vs 90% in United Kingdom) and employment (United States: 73% employed vs 79% in United Kingdom). UK respondents were more likely than US respondents to be ever smokers (89% vs 71%, p < .0001); be daily vapers (69% vs 53%, p < .0001) and to use e-cigarettes to quit smoking (75% vs 65%, p < .0007). Most vapers in the United Kingdom and the United States want to stop vaping (62% vs 61%; p < .9493), but US respondents plan to quit significantly sooner (odds ratio 0.47, p < .0004). Attitudes differed as well. Over half (56%) of UK respondents reported their government-approved e-cigarette use, and 24% felt health care providers had positive views on e-cigarettes versus 29% and 13% from the United States, respectively (p < .0004 for both). CONCLUSIONS: Plans for quitting and perceptions regarding e-cigarettes differ markedly between demographically similar groups of vapers in the two countries. Future research should determine whether e-cigarette cessation for adults should be a public health goal, and if so, identify effective ways to stop. IMPLICATIONS: The contribution of this study is that it describes differences in behaviors and attitudes of vapers recruited through the same research platform and adjusted to account for minor demographic differences across country samples. For clinicians, these findings suggest that most vapers would welcome assistance in quitting. For researchers and policymakers, findings suggest that government policy regarding nicotine devices might influence behaviors and attitudes related to use and also that future research is needed to determine effective ways to quit.

Heart Rate Recovery Following Exercise Testing in Pediatric Patients with Acyanotic Repaired Congenital Heart Disease.

Attenuated heart rate recovery (HRR) following peak exercise has been shown to be a predictor of mortality in populations of adults with Fontan palliation, coronary artery disease, heart failure, and heart transplantation. However, few have studied HRR in children and adolescents with congenital heart disease (CHD). This case-control study compared HRR patterns from exercise stress testing in children and adolescents with and without repaired acyanotic CHD (raCHD). Retrospective analysis included patients aged 10-18 years who had exercise testing between 2007 and 2017. The raCHD cohort included patients with Tetralogy of Fallot, transposition of the great arteries, coarctation, truncus arteriosus, atrioventricular septal defect, pulmonary outflow obstruction, aortic stenosis and/or insufficiency, or septal defects. Those in the control cohort were matched for age, sex, BMI, peak METs achieved, and peak heart rate (HR). HR at 1-min intervals throughout the 10-min recovery period and HRR patterns were analyzed. The study included n = 584 individuals (raCHD: n = 146), median age 14 years old, 67.1% male. The cohorts had similar resting and peak HRs. Linear mixed-effects models (LMM) suggested statistically significant cohort-by-time interaction for HR in exercise recovery, with the largest mean difference at minute-6 (2.9 bpm, p = 0.008). When comparing lesion types, LMM found no cohort or cohort-by-time interaction. While minute-6 of exercise recovery was statistically significant, the difference was 2.9 bpm and may not have clinical significance. These results suggest that HRR in pediatric raCHD patients should not vary from their healthy peers, and an attenuated HRR may not be directly attributed to underlying raCHD.

Identifying HbA1c trajectories and modifiable risk factors of trajectories in 5- to 9-year-olds with recent-onset type 1 diabetes from the United States.

OBJECTIVE: To explore glycated haemoglobin (HbA1c) patterns in 5- to 9-year-olds in the recent-onset period of type 1 diabetes and identify parent psychosocial factors that may predict children's HbA1c trajectory using a prospective, longitudinal design. RESEARCH DESIGN AND METHODS: We measured family demographics and parent psychosocial factors at baseline. We collected HbA1c levels from children every 3 months for up to 30 months. Deriving several features around HbA1c trends, we used k-means clustering to group trajectories and linear and logistic regressions to identify parent psychosocial predictors of children's HbA1c trajectories. RESULTS: The final cohort included 106 families (48 boys, mean child age 7.50 ± 1.35 years and mean diabetes duration 4.71 ± 3.19 months). We identified four unique HbA1c trajectories in children: high increasing, high stable, intermediate increasing and low stable. Compared to a low stable trajectory, increasing parent-reported hypoglycaemia fear total score was associated with decreased odds of having a high stable or intermediate increasing trajectory. Increasing parent-reported diabetes-specific family conflict total score was associated with increased odds of having a high stable or intermediate increasing trajectory. CONCLUSIONS: We are the first to identify distinct HbA1c trajectories in 5- to 9-year-olds with recent-onset type 1 diabetes as well as parent psychosocial factors that may predict high stable or increasing trajectories and could represent future treatment targets.

Evolving Role and Future Directions of Natural Language Processing in Gastroenterology.

In line with the current trajectory of healthcare reform, significant emphasis has been placed on improving the utilization of data collected during a clinical encounter. Although the structured fields of electronic health records have provided a convenient foundation on which to begin such efforts, it was well understood that a substantial portion of relevant information is confined in the free-text narratives documenting care. Unfortunately, extracting meaningful information from such narratives is a non-trivial task, traditionally requiring significant manual effort. Today, computational approaches from a field known as Natural Language Processing (NLP) are poised to make a transformational impact in the analysis and utilization of these documents across healthcare practice and research, particularly in procedure-heavy sub-disciplines such as gastroenterology (GI). As such, this manuscript provides a clinically focused review of NLP systems in GI practice. It begins with a detailed synopsis around the state of NLP techniques, presenting state-of-the-art methods and typical use cases in both clinical settings and across other domains. Next, it will present a robust literature review around current applications of NLP within four prominent areas of gastroenterology including endoscopy, inflammatory bowel disease, pancreaticobiliary, and liver diseases. Finally, it concludes with a discussion of open problems and future opportunities of this technology in the field of gastroenterology and health care as a whole.

Discrete Heart Rate Values or Continuous Streams? Representation, Variability, and Meaningful Use of Vital Sign Data.

Documentation and review of patient heart rate are a fundamental process across a myriad of clinical settings. While historically recorded manually, bedside monitors now provide for the automated collection of such data. Despite the availability of continuous streaming data, patients' charts continue to reflect only a subset of this information as snapshots recorded throughout a hospitalization. Over the past decade, prominent works have explored the implications of such practices and established fundamental differences in the alignment of discrete charted vitals and steaming data captured by monitoring systems. Limited work has examined the temporal properties of these differences, how they manifest, and their relation to clinical applications. The work presented in this article addresses this disparity, providing evidence that differences between charting techniques extend to measures of variability. Our results demonstrate how variability manifests with respect to temporal elements of charting timing and how it can facilitate personalized care by contextualizing deviations in magnitude. This work also highlights the utility of variability metrics with relation to clinical measures including associations to severity scores and a case study utilizing complex variability metrics derived from the complete set of monitor data.

Examining the weekend effect across ICU performance metrics.

BACKGROUND: Known colloquially as the "weekend effect," the association between weekend admissions and increased mortality within hospital settings has become a highly contested topic over the last two decades. Drawing interest from practitioners and researchers alike, a sundry of works have emerged arguing for and against the presence of the effect across various patient cohorts. However, it has become evident that simply studying population characteristics is insufficient for understanding how the effect manifests. Rather, to truly understand the effect, investigations into its underlying factors must be considered. As such, the work presented in this manuscript serves to address this consideration by moving beyond identification of patient cohorts to examining the role of ICU performance. METHODS: Employing a comprehensive, publicly available database of electronic medical records (EMR), we began by utilizing multiple logistic regression to identify and isolate a specific cohort in which the weekend effect was present. Next, we leveraged the highly detailed nature of the EMR to evaluate ICU performance using well-established ICU quality scorecards to assess differences in clinical factors among patients admitted to an ICU on the weekend versus weekday. RESULTS: Our results demonstrate the weekend effect to be most prevalent among emergency surgery patients (OR 1.53; 95% CI 1.19, 1.96), specifically those diagnosed with circulatory diseases (P<.001). Differences between weekday and weekend admissions for this cohort included a variety of clinical factors such as ventilatory support and night-time discharges. CONCLUSIONS: This work reinforces the importance of accounting for differences in clinical factors as well as patient cohorts in studies investigating the weekend effect.

Nursing networks in the NICU and their association with maternal stress: A pilot study.

AIMS: This pilot study explored how maternal stress experienced in the neonatal intensive care unit (NICU) is affected by the individual nursing structure and the network that provides care to extremely preterm infants. BACKGROUND: Mothers experience high stress when their extremely preterm infants are hospitalized in the NICU. This often translates into maladaptive parenting behaviours that negatively affect the long-term cognitive, social, and emotional development of the infant. Efforts to identify modifiable sources of maternal stress in the NICU could lead to improvement in maternal engagement and, ultimately, long-term neurodevelopmental outcomes. METHOD: Time- and date-stamped nursing shift data were extracted from the medical record and transformed into five structural nursing metrics with resultant nurse data networks. These were then analysed for associations with maternal stress outcomes on the Parental Stressor Scale (PSS: NICU). RESULTS: Infants experienced highly variable nursing care and networks of nurses throughout their hospitalization. This variability is associated with the PSS: NICU (a) Sights and Sounds and (b) Altered Parental Role subscales. CONCLUSION: Nursing structure and the resultant caregiving network have an impact on maternal stress. IMPLICATIONS FOR NURSING MANAGEMENT: Changing the pattern of nurse staffing may be a modifiable intervention target for reducing maternal stress in the NICU.

Scaling and contextualizing personalized healthcare: A case study of disease prediction algorithm integration.

Today, advances in medical informatics brought on by the increasing availability of electronic medical records (EMR) have allowed for the proliferation of data-centric tools, especially in the context of personalized healthcare. While these tools have the potential to greatly improve the quality of patient care, the effective utilization of their techniques within clinical practice may encounter two significant challenges. First, the increasing amount of electronic data generated by clinical processes can impose scalability challenges for current computational tools, requiring parallel or distributed implementations of such tools to scale. Secondly, as technology becomes increasingly intertwined in clinical workflows these tools must not only operate efficiently, but also in an interpretable manner. Failure to identity areas of uncertainty or provide appropriate context creates a potentially complex situation for both physicians and patients. This paper will present a case study investigating the issues associated with first scaling a disease prediction algorithm to accommodate dataset sizes expected in large medical practices. It will then provide an analysis on the diagnoses predictions, attempting to provide contextual information to convey the certainty of the results to a physician. Finally it will investigate latent demographic features of the patient's themselves, which may have an impact on the accuracy of the diagnosis predictions.

Clarification of adverse drug reactions by a pharmacovigilance team results in increased antibiotic re-prescribing at a freestanding United States children's hospital.

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.

Complex trait associations in rare diseases and impacts on Mendelian variant interpretation.

Emerging evidence implicates common genetic variation - aggregated into polygenic scores (PGS) - impacting the onset and phenotypic presentation of rare diseases. In this study, we quantified individual polygenic liability for 1,151 previously published PGS in a cohort of 2,374 probands enrolled in the Genomic Answers for Kids (GA4K) rare disease study, revealing widespread associations between rare disease phenotypes and PGSs for common complex diseases and traits, blood protein levels, and brain and other organ morphological measurements. We observed increased polygenic burden in probands with variants of unknown significance (VUS) compared to unaffected carrier parents. We further observed an enrichment in overlap between diagnostic and candidate rare disease genes and large-effect PGS genes. Overall, our study supports and expands on previous findings of complex trait associations in rare disease phenotypes and provides a framework for identifying novel candidate rare disease genes and in understanding variable penetrance of candidate Mendelian disease variants.

Machine Learning Algorithm Improves the Prediction of Transplant Hepatic Artery Stenosis or Occlusion: A Single-Center Study.

ABSTRACT: The aim of this study was to determine if machine learning can improve the specificity of detecting transplant hepatic artery pathology over conventional quantitative measures while maintaining a high sensitivity.This study presents a retrospective review of 129 patients with transplanted hepatic arteries. We illustrate how beyond common clinical metrics such as stenosis and resistive index, a more comprehensive set of waveform data (including flow half-lives and Fourier transformed waveforms) can be integrated into machine learning models to obtain more accurate screening of stenosis and occlusion. We present a novel framework of Extremely Randomized Trees and Shapley values, we allow for explainability at the individual level.The proposed framework identified cases of clinically significant stenosis and occlusion in hepatic arteries with a state-of-the-art specificity of 65%, while maintaining sensitivity at the current standard of 94%. Moreover, through 3 case studies of correct and mispredictions, we demonstrate examples of how specific features can be elucidated to aid in interpreting driving factors in a prediction.This work demonstrated that by utilizing a more complete set of waveform data and machine learning methodologies, it is possible to reduce the rate of false-positive results in using ultrasounds to screen for transplant hepatic artery pathology compared with conventional quantitative measures. An advantage of such techniques is explainability measures at the patient level, which allow for increased radiologists' confidence in the predictions.

Association between congenital heart disease and parenteral nutrition-associated liver disease in neonates: A retrospective cohort study.

OBJECTIVE: Infants receiving parenteral nutrition (PN) are at increased risk of PN-associated liver disease (PNALD), which can lead to hepatic fibrosis. Congenital heart disease (CHD) represents a risk factor for hepatic fibrosis, so this study sought to better understand whether infants with CHD were at elevated risk of PNALD when receiving long-term PN. STUDY DESIGN: This study includes a retrospective cohort of infants at a level IV neonatal intensive care unit from 2010 to 2020 who received long-term PN during the first 8 weeks of life. A time-varying Cox survival model was used to model risk of PNALD between infants with and without CHD, adjusted for demographics, surgical intervention, and PN exposure, using a 5000-iteration bootstrap estimation. Secondary analyses evaluated risk against discrete CHD diagnoses, and sensitivity analysis was performed on the magnitude and quantity of direct bilirubin laboratory measurements making up the PNALD definition. RESULTS: Neonates with CHD were found to be at higher risk for PNALD during or soon after long-term PN exposure. A pattern of increasing association strength with increasing bilirubin threshold suggests infants with CHD may also experience higher degrees of injury. CONCLUSIONS: This work offers a step in understanding how diagnoses can be factored into neonate PN prescription. Future work will explore modifications in lipid profiles and timing to mitigate risk in patients with CHD.

Rehabilitation Outcomes in Children With Acute Flaccid Myelitis From 2014 to 2019: A Multicenter Retrospective Review.

BACKGROUND: Acute flaccid myelitis (AFM) is a childhood illness characterized by sudden-onset weakness impairing function. The primary goal was to compare the motor recovery patterns of patients with AFM who were discharged home or to inpatient rehabilitation. Secondary analyses focused on recovery of respiratory status, nutritional status, and neurogenic bowel and bladder in both cohorts. METHODS: Eleven tertiary care centers in the United States performed a retrospective chart review of children with AFM between January 1, 2014, and October 1, 2019. Data included demographics, treatments, and outcomes on admission, discharge, and follow-up visits. RESULTS: Medical records of 109 children met inclusion criteria; 67 children required inpatient rehabilitation, whereas 42 children were discharged directly home. The median age was 5 years (range 4 months to 17 years), and the median time observed was 417 days (interquartile range = 645 days). Distal upper extremities recovered better than the proximal upper extremities. At acute presentation, children who needed inpatient rehabilitation had significantly higher rates of respiratory support (P < 0.001), nutritional support (P < 0.001), and neurogenic bowel (P = 0.004) and bladder (P = 0.002). At follow-up, those who attended inpatient rehabilitation continued to have higher rates of respiratory support (28% vs 12%, P = 0.043); however, the nutritional status and bowel/bladder function were no longer statistically different. CONCLUSIONS: All children made improvements in strength. Proximal muscles remained weaker than distal muscles in the upper extremities. Children who qualified for inpatient rehabilitation had ongoing respiratory needs at follow-up; however, recovery of nutritional status and bowel/bladder were similar.

Data-Driven Nurse Staffing in the Neonatal Intensive Care Unit.

ABSTRACT: The challenge of nurse staffing is amplified in the acute care neonatal intensive care unit (NICU) setting, where a wide range of highly variable factors affect staffing. A comprehensive overview of infant factors (severity, intensity), nurse factors (education, experience, preferences, team dynamics), and unit factors (structure, layout, shift length, care model) influencing pre-shift NICU staffing is presented, along with how intra-shift variability of these and other factors must be accounted for to maintain effective and efficient assignments. There is opportunity to improve workload estimations and acuity measures for pre-shift staffing using technology and predictive analytics. Nurse staffing decisions affected by intra-shift factor variability can be enhanced using novel care models that decentralize decision-making. Improving NICU staffing requires a deliberate, systematic, data-driven approach, with commitment from nurses, resources from the management team, and an institutional culture prioritizing patient safety.