A modified Hospital Frailty Risk Score for patients with cirrhosis undergoing abdominal operations.

BACKGROUND AND AIMS: Existing tools for perioperative risk stratification in patients with cirrhosis do not incorporate measures of comorbidity. The Hospital Frailty Risk Score (HFRS) is a widely used measure of comorbidity burden in administrative dataset analyses. However, it is not specific to patients with cirrhosis, and application of this index is limited by its complexity. APPROACH AND RESULTS: Adult patients with cirrhosis who underwent nontransplant abdominal operations were identified from the National Inpatient Sample, 2016-2018. Adjusted associations between HFRS and in-hospital mortality and length of stay were computed with logistic and Poisson regression. Lasso regularization was used to identify the components of the HFRS most predictive of mortality and develop a simplified index, the cirrhosis-HFRS. Of 10,714 patients with cirrhosis, the majority were male, the median age was 62 years, and 32% of operations were performed electively. HFRS was associated with an increased risk of both in-hospital mortality (OR=6.42; 95% CI: 4.93, 8.36) and length of stay (incidence rate ratio [IRR]=1.79; 95% CI: 1.72, 1.88), with adjustment. Using lasso, we found that a subset of 12 of the 109 ICD-10 codes within the HFRS resulted in superior prediction of mortality in this patient population (AUC = 0.89 vs. 0.79, p < 0.001). CONCLUSIONS: While the 109-component HFRS was associated with adverse surgical outcomes, 12 components accounted for much of the association between the HFRS and mortality. We developed the cirrhosis-HFRS, a tool that demonstrates superior predictive accuracy for in-hospital mortality and more precisely reflects the specific comorbidity pattern of hospitalized patients with cirrhosis undergoing general surgery procedures.

Outpatient mean arterial pressure: A potentially modifiable risk for acute kidney injury and death among patients with cirrhosis.

Mean arterial blood pressure (MAP), which decreases as portal hypertension progresses, may be a modifiable risk factor among patients with cirrhosis. We included adults enrolled in the Functional Assessment in Liver Transplantation study. We completed latent class trajectory analyses to define MAP trajectories. We completed time-dependent Cox-regression analyses to test the association between outpatient MAP and 3 cirrhosis-related outcomes: (1) stage 2 acute kidney injury (AKI), defined as a ≥200% increase in serum creatinine from baseline; (2) a 5-point increase in the MELD-Na score, defined as the incidence of increase from initial MELD-Na; (3) waitlist mortality, defined as death on the waitlist. For each outcome, we defined MAP cut points by determining the maximally selected Log-rank statistic after univariable Cox-regression analyses. Among the 1786 patients included in this analysis, our latent class trajectory analyses identified 3 specific outpatient MAP trajectories: "stable-low," "stable-high," and "increasing-to-decreasing." However, >80% of patients were in a "stable-low" trajectory. We found in adjusted analyses that outpatient MAP was associated with each of our outcomes: Stage 2 AKI (adjusted hazard ratio 0.88 per 10 mm Hg increase in MAP [95% CI: 0.79-0.99]); 5-point increase in MELD-Na (adjusted hazard ratio: 0.91 [95% CI: 0.86-0.96]; waitlist mortality (adjusted hazard ratio: 0.89 [95% CI: 0.81-0.96]). For each outcome, we found that an outpatient MAP of 82 mm Hg was most associated with outcomes ( p <0.05 for all). Our study informs the association between outpatient MAP and cirrhosis-related outcomes. These findings, coupled with the identification of specific thresholds, lay the foundation for the trial of targeted outpatient MAP modulation in patients with cirrhosis.

The influence of neighborhood income on healthcare utilization in pediatric liver transplant.

OBJECTIVES: Neighborhood contextual factors are associated with liver transplant outcomes. We analyzed associations between neighborhood-level socioeconomic status and healthcare utilization for pediatric liver transplant recipients. METHODS: We merged the Pediatric Health Information System and Scientific Registry of Transplant Recipients databases and included liver transplant recipients ≤21 years hospitalized between January 2004 and May 2022. Outcomes were annual inpatient bed-days, risk of hospitalizations, and risk of liver biopsies. The primary exposure was zip code-based neighborhood income at transplant. We applied causal inference for variable selection in multivariable analysis. We modeled annual inpatient bed-days with mixed-effect zero-inflated Poisson regression, and rates of hospitalization and liver biopsy with a Cox-type proportional rate model. RESULTS: We included 1006 participants from 29 institutions. Children from low-income neighborhoods were more likely to be publicly insured (67% vs. 46%), Black (20% vs. 12%), Hispanic (30% vs. 17%), and have higher model for end-stage liver disease/pediatric end-stage liver disease model scores at transplant (17 vs. 13) than the remaining cohort. We found no differences in inpatient bed-days or rates of hospitalization across neighborhood groups. In univariable analysis, low-income neighborhoods were associated with increased rates of liver biopsy (rate ratio [RR]: 1.57, 95% confidence interval [CI]: 1.04-2.34, p = 0.03). These findings persisted after adjusting for insurance, race, and ethnicity (RR: 1.86, 95% CI: 1.23-2.83, p < 0.01). CONCLUSIONS: Children from low-income neighborhoods undergo more liver biopsies than other children. These procedures are invasive and potentially preventable. In addition to improving outcomes, interventions to mitigate health inequities among liver transplant recipients may reduce resource utilization.

Exploring the complexity and spectrum of racial/ethnic disparities in colon cancer management.

BACKGROUND: Colorectal cancer is a leading cause of morbidity and mortality across U.S. racial/ethnic groups. Existing studies often focus on a particular race/ethnicity or single domain within the care continuum. Granular exploration of disparities among different racial/ethnic groups across the entire colon cancer care continuum is needed. We aimed to characterize differences in colon cancer outcomes by race/ethnicity across each stage of the care continuum. METHODS: We used the 2010-2017 National Cancer Database to examine differences in outcomes by race/ethnicity across six domains: clinical stage at presentation; timing of surgery; access to minimally invasive surgery; post-operative outcomes; utilization of chemotherapy; and cumulative incidence of death. Analysis was via multivariable logistic or median regression, with select demographics, hospital factors, and treatment details as covariates. RESULTS: 326,003 patients (49.6% female, 24.0% non-White, including 12.7% Black, 6.1% Hispanic/Spanish, 1.3% East Asian, 0.9% Southeast Asian, 0.4% South Asian, 0.3% AIAE, and 0.2% NHOPI) met inclusion criteria. Relative to non-Hispanic White patients: Southeast Asian (OR 1.39, p < 0.01), Hispanic/Spanish (OR 1.11 p < 0.01), and Black (OR 1.09, p < 0.01) patients had increased odds of presenting with advanced clinical stage. Southeast Asian (OR 1.37, p < 0.01), East Asian (OR 1.27, p = 0.05), Hispanic/Spanish (OR 1.05 p = 0.02), and Black (OR 1.05, p < 0.01) patients had increased odds of advanced pathologic stage. Black patients had increased odds of experiencing a surgical delay (OR 1.33, p < 0.01); receiving non-robotic surgery (OR 1.12, p < 0.01); having post-surgical complications (OR 1.29, p < 0.01); initiating chemotherapy more than 90 days post-surgery (OR 1.24, p < 0.01); and omitting chemotherapy altogether (OR 1.12, p = 0.05). Black patients had significantly higher cumulative incidence of death at every pathologic stage relative to non-Hispanic White patients when adjusting for non-modifiable patient factors (p < 0.05, all stages), but these differences were no longer statistically significant when also adjusting for modifiable factors such as insurance status and income. CONCLUSIONS: Non-White patients disproportionately experience advanced stage at presentation. Disparities for Black patients are seen across the entire colon cancer care continuum. Targeted interventions may be appropriate for some groups; however, major system-level transformation is needed to address disparities experienced by Black patients.

Regression Modeling for Recurrent Events Possibly with an Informative Terminal Event Using R Package reReg.

Recurrent event analyses have found a wide range of applications in biomedicine, public health, and engineering, among others, where study subjects may experience a sequence of event of interest during follow-up. The R package reReg offers a comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, possibly with the presence of an informative terminal event. The regression framework is a general scale-change model which encompasses the popular Cox-type model, the accelerated rate model, and the accelerated mean model as special cases. Informative censoring is accommodated through a subject-specific frailty without any need for parametric specification. Different regression models are allowed for the recurrent event process and the terminal event. Also included are visualization and simulation tools.

Nonparametric estimation of the survival distribution under covariate-induced dependent truncation.

There is often delayed entry into observational studies, which results in left truncation. In the estimation of the distribution of time-to-event from left-truncated data, standard survival analysis methods require quasi-independence between the truncation time and event time. Incorrectly assuming quasi-independence may lead to biased estimation. We address the problem of estimation of the survival distribution when dependence between the event time and its left truncation time is induced by shared covariates. We introduce propensity scores for truncated data and propose two inverse probability weighting methods that adjust for both truncation and dependence, if all of the shared covariates are measured. The proposed methods additionally allow for right censoring. We evaluate the proposed methods in simulations, conduct sensitivity analyses, and provide guidelines for use in practice. We illustrate our approach in application to data from a central nervous system lymphoma study. The proposed methods are implemented in the R package, depLT.

SAFARI: shape analysis for AI-segmented images.

BACKGROUND: Recent developments to segment and characterize the regions of interest (ROI) within medical images have led to promising shape analysis studies. However, the procedures to analyze the ROI are arbitrary and vary by study. A tool to translate the ROI to analyzable shape representations and features is greatly needed. RESULTS: We developed SAFARI (shape analysis for AI-segmented images), an open-source R package with a user-friendly online tool kit for ROI labelling and shape feature extraction of segmented maps, provided by AI-algorithms or manual segmentation. We demonstrated that half of the shape features extracted by SAFARI were significantly associated with survival outcomes in a case study on 143 consecutive patients with stage I-IV lung cancer and another case study on 61 glioblastoma patients. CONCLUSIONS: SAFARI is an efficient and easy-to-use toolkit for segmenting and analyzing ROI in medical images. It can be downloaded from the comprehensive R archive network (CRAN) and accessed at https://lce.biohpc.swmed.edu/safari/ .

ROC-guided survival trees and ensembles.

Tree-based methods are popular nonparametric tools in studying time-to-event outcomes. In this article, we introduce a novel framework for survival trees and ensembles, where the trees partition the dynamic survivor population and can handle time-dependent covariates. Using the idea of randomized tests, we develop generalized time-dependent receiver operating characteristic (ROC) curves for evaluating the performance of survival trees. The tree-building algorithm is guided by decision-theoretic criteria based on ROC, targeting specifically for prediction accuracy. To address the instability issue of a single tree, we propose a novel ensemble procedure based on averaging martingale estimating equations, which is different from existing methods that average the predicted survival or cumulative hazard functions from individual trees. Extensive simulation studies are conducted to examine the performance of the proposed methods. We apply the methods to a study on AIDS for illustration.

High-Density Lipoprotein, Mean Platelet Volume, and Uric Acid as Biomarkers for Outcomes in Patients With Sepsis: An Observational Study.

BACKGROUND: We conducted an observational study evaluating the association between uric acid, mean platelet volume (MPV), and high-density lipoprotein (HDL) with complications and outcomes of patients with sepsis in a critical care setting. METHODS: We followed patients with a diagnosis of severe sepsis and septic shock for a maximum of 28 days. Main outcomes assessed included length of stay (LOS), the need for renal replacement therapy (RRT), assisted mechanical ventilation (AMV), and vasopressor support as well as in-unit mortality. RESULTS: The overall average age of the 37 patients enrolled was 48.1 (19.8) years; among them, 37.8% were male. Abdominal related (43.2%) and pulmonary (29.7%) were the main sites of infection. The overall Acute Physiology and Chronic Health Evaluation 2 (APACHE-2) median score was 19 (9-24). Acute kidney injury (AKI) was observed in 46.9% of the sample. In all, 54.1% required vasopressor support, 54.1% AMV, and 35.1% RRT. Patients with bacteremia were significantly more likely to require vasopressor support and those with urinary tract infections were significantly younger. We found increasing ΔMPV levels, higher APACHE-2 scores, lower HDL values, and a reduced age to be associated with a longer LOS. Higher scores on the APACHE-2 scale and lower levels of HDL significantly associated with higher odds for developing AKI. The need for vasopressor support was significantly associated with higher values of 72-hour MPV and with higher levels of baseline uric acid and lower values of initial HCO3. Initial and 72-hour levels of MPV and higher scores in the APACHE-2 were all significantly correlated with the need for AMV. An increased probability of dying during follow-up was significantly correlated with increasing age. CONCLUSION: We were able to establish significant associations between our candidate biomarkers and relevant outcomes for patients with sepsis. Our results support the use of these low-cost biomarkers in the assessment of prognosis of patients with sepsis.

Generalized scale-change models for recurrent event processes under informative censoring.

Two major challenges arise in regression analyses of recurrent event data: first, popular existing models, such as the Cox proportional rates model, may not fully capture the covariate effects on the underlying recurrent event process; second, the censoring time remains informative about the risk of experiencing recurrent events after accounting for covariates. We tackle both challenges by a general class of semiparametric scale-change models that allow a scale-change covariate effect as well as a multiplicative covariate effect. The proposed model is flexible and includes several existing models as special cases, such as the popular proportional rates model, the accelerated mean model, and the accelerated rate model. Moreover, it accommodates informative censoring through a subject-level latent frailty whose distribution is left unspecified. A robust estimation procedure which requires neither a parametric assumption on the distribution of the frailty nor a Poisson assumption on the recurrent event process is proposed to estimate the model parameters. The asymptotic properties of the resulting estimator are established, with the asymptotic variance estimated from a novel resampling approach. As a byproduct, the structure of the model provides a model selection approach among the submodels via hypothesis testing of model parameters. Numerical studies show that the proposed estimator and the model selection procedure perform well under both noninformative and informative censoring scenarios. The methods are applied to data from two transplant cohorts to study the risk of infections after transplantation.

Wide Range of Clinical Outcomes in Patients with Gliomatosis Cerebri Growth Pattern: A Clinical, Radiographic, and Histopathologic Study.

BACKGROUND: The 2016 World Health Organization Classification of Central Nervous System Tumors categorizes gliomatosis cerebri growth pattern (GC) as a subgroup of diffuse infiltrating gliomas, defined by extent of brain involvement on magnetic resonance imaging (MRI). Clinical and radiographic features in GC patients are highly heterogeneous; however, prognosis has historically been considered poor. SUBJECTS, MATERIALS, AND METHODS: We performed a retrospective search for patients at our institution meeting radiographic criteria of primary, type I GC (defined as diffuse tumor infiltration without associated tumor mass and contrast enhancement on MRI) and analyzed their clinical, imaging, and histopathologic features. RESULTS: A total of 34 patients met radiographic criteria of primary, type I GC, and 33 had a confirmed histologic diagnosis of an infiltrating glial neoplasm. Age >47 years at diagnosis was associated with worse overall survival (OS) compared with age ≤47 years (hazard ratio [HR] 1.04, 95% confidence interval [CI] 1.01-1.07, p = .003). Patients with grade 2 tumors demonstrated a trend for improved OS compared with those with grade 3 tumors (HR 2.65, 95% CI 0.99-7.08, p = .051). Except for brainstem involvement, extent or location of radiographic involvement did not detectably affect clinical outcome. IDH mutation status identified a subgroup of GC patients with particularly long survival up to 25 years and was associated with longer time to progression (HR 4.81, 95% CI 0.99-23.47, p = .052). CONCLUSION: Patients with primary, type I GC do not uniformly carry a poor prognosis, even in the presence of widespread radiographic involvement. Consistent with other reports, IDH mutation status may identify patients with improved clinical outcome. Molecular characterization, rather than MRI features, may be most valuable for prognostication and management of GC patients. IMPLICATIONS FOR PRACTICE: Patients with gliomatosis cerebri growth pattern (GC) constitute a challenge to clinicians, given their wide range of clinical, histologic, and radiographic presentation, heterogeneous outcome patterns, and the lack of consensus on a standardized treatment approach. This study highlights that radiographic extent of disease-albeit category-defining-does not detectably influence survival and that IDH mutations may impact clinical outcome. Practicing oncologists should be aware that select GC patients may demonstrate exceptionally favorable survival times and prognosticate patients based on molecular markers, rather than imaging features alone.

Semiparametric Regression Analysis of Panel Count Data: A Practical Review.

Panel count data arise in many applications when the event history of a recurrent event process is only examined at a sequence of discrete time points. In spite of the recent methodological developments, the availability of their software implementations has been rather limited. Focusing on a practical setting where the effects of some time-independent covariates on the recurrent events are of primary interest, we review semiparametric regression modelling approaches for panel count data that have been implemented in R package spef. The methods are grouped into two categories depending on whether the examination times are associated with the recurrent event process after conditioning on covariates. The reviewed methods are illustrated with a subset of the data from a skin cancer clinical trial.

Semiparametric estimation of the accelerated mean model with panel count data under informative examination times.

Panel count data arise when the number of recurrent events experienced by each subject is observed intermittently at discrete examination times. The examination time process can be informative about the underlying recurrent event process even after conditioning on covariates. We consider a semiparametric accelerated mean model for the recurrent event process and allow the two processes to be correlated through a shared frailty. The regression parameters have a simple marginal interpretation of modifying the time scale of the cumulative mean function of the event process. A novel estimation procedure for the regression parameters and the baseline rate function is proposed based on a conditioning technique. In contrast to existing methods, the proposed method is robust in the sense that it requires neither the strong Poisson-type assumption for the underlying recurrent event process nor a parametric assumption on the distribution of the unobserved frailty. Moreover, the distribution of the examination time process is left unspecified, allowing for arbitrary dependence between the two processes. Asymptotic consistency of the estimator is established, and the variance of the estimator is estimated by a model-based smoothed bootstrap procedure. Numerical studies demonstrated that the proposed point estimator and variance estimator perform well with practical sample sizes. The methods are applied to data from a skin cancer chemoprevention trial.

Threshold regression to accommodate a censored covariate.

In several common study designs, regression modeling is complicated by the presence of censored covariates. Examples of such covariates include maternal age of onset of dementia that may be right censored in an Alzheimer's amyloid imaging study of healthy subjects, metabolite measurements that are subject to limit of detection censoring in a case-control study of cardiovascular disease, and progressive biomarkers whose baseline values are of interest, but are measured post-baseline in longitudinal neuropsychological studies of Alzheimer's disease. We propose threshold regression approaches for linear regression models with a covariate that is subject to random censoring. Threshold regression methods allow for immediate testing of the significance of the effect of a censored covariate. In addition, they provide for unbiased estimation of the regression coefficient of the censored covariate. We derive the asymptotic properties of the resulting estimators under mild regularity conditions. Simulations demonstrate that the proposed estimators have good finite-sample performance, and often offer improved efficiency over existing methods. We also derive a principled method for selection of the threshold. We illustrate the approach in application to an Alzheimer's disease study that investigated brain amyloid levels in older individuals, as measured through positron emission tomography scans, as a function of maternal age of dementia onset, with adjustment for other covariates. We have developed an R package, censCov, for implementation of our method, available at CRAN.

Permutation Tests for General Dependent Truncation.

Truncated survival data arise when the event time is observed only if it falls within a subject-specific region, known as the truncation set. Left-truncated data arise when there is delayed entry into a study, such that subjects are included only if their event time exceeds some other time. Quasi-independence of truncation and failure refers to factorization of their joint density in the observable region. Under quasi-independence, standard methods for survival data such as the Kaplan-Meier estimator and Cox regression can be applied after simple adjustments to the risk sets. Unlike the requisite assumption of independent censoring, quasi-independence can be tested, e.g., using a conditional Kendall's tau test. Current methods for testing for quasi-independence are powerful for monotone alternatives. Nonetheless, it is essential to detect any kind of deviation from quasi-independence so as not to report a biased Kaplan-Meier estimator or regression effect, which would arise from applying the simple risk set adjustment when dependence holds. Nonparametric, minimum p-value tests that are powerful against non-monotone alternatives are developed to offer protection against erroneous assumptions of quasi-independence. The use of conditional and unconditional methods of permutation for evaluation of the proposed tests are investigated in simulation studies. The proposed tests are applied to a study on the cognitive and functional decline in aging.

Epidemiology of Stroke in Costa Rica: A 7-Year Hospital-Based Acute Stroke Registry of 1319 Consecutive Patients.

BACKGROUND: Limited data on stroke exist for Costa Rica. Therefore, we created a stroke registry out of patients with stroke seen in the Acute Stroke Unit of the Hospital Calderon Guardia. METHODS: We analyzed 1319 patients enrolled over a 7-year period, which incorporated demographic, clinical, laboratory, and neuroimaging data. RESULTS: The mean age of patients with stroke was 68.0 ± 15.5 years. Seven hundred twenty-five were men and the age range was 13-104 years. The most prevalent risk factors were hypertension (78.8%), dyslipidemia (36.3%), and diabetes (31.9%). Fifteen percent had atrial fibrillation and 24.7% had a previous stroke or transient ischemic attack. Prevalence of hypertension and atrial fibrillation increased with age; however, younger patients were more associated with thrombophilia. We documented 962 (72.9%) ischemic and 270 (20.5%) hemorrhagic strokes. Of the ischemic strokes, 174 (18.1%) were considered secondary to large-artery atherothrombosis, 175 (18.2%) were due to cardiac embolism, 19 (2.0%) were due to lacunar infarcts, and 25 (2.6%) were due to other determined causes. Five hundred sixty-nine (59.1%) remained undetermined. Atherothrombotic strokes were mostly associated with dyslipidemia, diabetes, metabolic syndrome, and obesity, whereas lacunar infarcts were associated with hypertension, smoking, sedentary lifestyle, and previous stroke or transient ischemic attack. Of our patients, 69.9% scored between 0 and 9 in the initial National Institutes of Health Stroke Scale (NIHSS). CONCLUSIONS: We found differences in sociodemographic features, risk factors, and stroke severity among stroke subtypes. Risk factor prevalence was similar to other registries involving Hispanic populations.

Onset of persistent pseudomonas aeruginosa infection in children with cystic fibrosis with interval censored data.

BACKGROUND: Persistent Pseudomonas aeruginosa (PPA) infection promotes lung function deterioration in children with cystic fibrosis (CF). Although early CF diagnosis through newborn screening (NBS) has been shown to provide nutritional/growth benefit, it is unclear whether NBS lowers the risk of PPA infection and how the effect of NBS vary with age. Modeling the onset age of PPA infection is challenging because 1) the onset age of PPA infection is interval censored in patient registry data; and 2) some risk factors such as NBS may have time-varying effects. METHODS: This problem fits into the framework of a recently developed Bayesian dynamic Cox model for interval censored data, where each regression coefficient is allowed to be time-varying to an extent determined by the data. RESULTS: Application of the methodology to data from the CF Foundation Patient Registry revealed interesting findings. Compared with patients with meconium ileus or diagnosed through signs or symptoms, patients diagnosed through NBS had significantly lower risks of acquiring PPA infection between age 1 and 2 years, and the benefit in survival rate was found to last up to age 4 years. Two cohorts of five years apart were compared. Patients born in cohort 2003-2004 had significantly lower risks of the PPA infections at any age up to 4 years than those born in 1998-1999. CONCLUSIONS: The study supports benefits of NBS on PPA infection in early childhood. In addition, our analyses demonstrate that patients in the more recent cohort had significantly lower risks of acquiring PPA infection up to age 4 years, which suggests improved CF treatment and care over time.

Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations.

The semiparametric accelerated failure time (AFT) model is not as widely used as the Cox relative risk model due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations for censored data provide promising tools to make the AFT models more attractive in practice. For multivariate AFT models, we propose a generalized estimating equations (GEE) approach, extending the GEE to censored data. The consistency of the regression coefficient estimator is robust to misspecification of working covariance, and the efficiency is higher when the working covariance structure is closer to the truth. The marginal error distributions and regression coefficients are allowed to be unique for each margin or partially shared across margins as needed. The initial estimator is a rank-based estimator with Gehan's weight, but obtained from an induced smoothing approach with computational ease. The resulting estimator is consistent and asymptotically normal, with variance estimated through a multiplier resampling method. In a large scale simulation study, our estimator was up to three times as efficient as the estimateor that ignores the within-cluster dependence, especially when the within-cluster dependence was strong. The methods were applied to the bivariate failure times data from a diabetic retinopathy study.

Associations between hospital-level socioeconomic patient mix and rates of central line-associated bloodstream infections in short bowel syndrome: A retrospective cohort study.

BACKGROUND: Low neighborhood income is linked with increased hospitalizations for central line-associated bloodstream infections (CLABSIs) in pediatric short bowel syndrome (SBS). We assessed whether this relationship varies by hospital center. METHODS: We performed a retrospective cohort study using the Pediatric Health Information System (2018-2023) database for patients <18 years old with SBS (N = 1210) at 24 hospitals in the United States. Using 2015 US Census data, we determined the estimated median household income of each patient's zip code. Hospital-level neighborhood income was defined as the median of the estimated median household income among patients at each hospital. We applied an extension of Cox regression to assess risk for CLABSI hospitalization. RESULTS: Among 1210 children with 5255 hospitalizations, most were <1 year on initial admission (53%), male (58%), and publicly insured (69%). Hospitals serving low-income neighborhoods served more female (46% vs 39%), Black (29% vs 22%), and Hispanic (22% vs 16%) patients with public insurance (72% vs 65%) residing in the southern United States (47% vs 21%). In univariate analysis, low hospital-level neighborhood income was associated with increased risk of CLABSI hospitalization (rate ratio [RR], 1.48; 95% CI, 1.21-1.83; P < 0.001). These findings persisted in multivariate analysis (RR, 1.43; 95% CI, 1.10-1.84; P < 0.01) after adjusting for race, ethnicity, insurance, region, and patient-level neighborhood income. CONCLUSION: Hospitals serving predominantly low-income neighborhoods bear a heavier burden of CLABSI hospitalizations for all their patients across the socioeconomic spectrum. Hospital initiatives focused on CLABSI prevention may be pivotal in addressing this disparity.

Misstatements, misperceptions, and mistakes in controlling for covariates in observational research.

We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.