Juhn, Young

Abstract Models that estimate the probability of an adverse clinical outcome require an operational cutoff to translate continuous estimated probabilities into discrete labels that can trigger clinical action. Although statistical methods identify ‘optimal’ cut-offs, threshold selection ultimately reflects value judgments regarding harm tolerance, resource allocation, and workflow feasibility. We describe a governance-informed approach to selecting a deployment threshold for an asthma exacerbation (AE) prediction model integrated into clinical workflows. Using prevalence-adjusted performance metrics and real-world provider capacity modeling, we evaluated multiple candidate thresholds and quantified downstream workload and missed-event trade-offs. We demonstrate that statistically optimal thresholds may produce operationally infeasible alert volumes or unacceptable miss rates. We propose a structured threshold governance framework integrating statistical performance, clinical utility, stakeholder input, and human oversight safeguards. This case illustrates how threshold decisions should be treated as organizational governance processes rather than purely technical optimizations.

Key Points Pediatric preemptive kidney transplantation rates in the United States vary by socioeconomic status (SES). The HOUsing-based index of SES, an individual-level SES measure derived from residential addresses, effectively predicts pediatric preemptive kidney transplant rates. Background Preemptive kidney transplantation in children is associated with better patient and graft survival compared with transplants after dialysis. However, preemptive kidney transplantation rates in children remain low. This study aimed to evaluate the effect of socioeconomic status (SES) on preemptive transplantation in children. Methods Our study included 173 Minnesota-resident pediatric kidney transplant recipients (younger than 18 years) transplanted at the University of Minnesota from 2010 to 2020. Using the HOUsing-based index of SES (HOUSES) index, a validated, individual SES measure based on housing units categorized into quartiles (Q1: lower SES; Q2–Q4: higher SES), we applied mixed-effects multivariable logistic models to examine the effects of HOUSES on preemptive kidney transplants and pretransplant dialysis duration. Results Of 173 pediatric kidney transplant recipients, 46 (26.6%) received a preemptive transplant, and of 109 recipients with dialysis duration data, 39 (35.8%) received dialysis for >1 year. After adjusting for age at kidney failure, sex, donor type, insurance type, and underlying cause of kidney failure, we observed significantly lower odds of preemptive transplantation among Q1 recipients compared with Q2–4 recipients (adjusted odds ratio, 0.31; 95% confidence interval, 0.11 to 0.90; P = 0.03). Conclusions Using the HOUSES index, we found significant socioeconomic disparities in preemptive kidney transplantation rates among children.

Background. The limitations of conventional measures of socioeconomic status (SES) limit our ability to elucidate the role of SES as a key element of social determinants of health in kidney transplantation. This study’s objective was to use an innovative SES measure, the HOUsing-based SES measure (HOUSES) index, to examine the effects of social determinants of health on access to and outcomes of kidney transplantation. Methods. Our study included residents of Minnesota (age older than 18 y) who underwent kidney transplantation at a single center between 2010 and 2020. SES was determined using the HOUSES index, categorized into quartiles (Q1 for lower, Q2–Q4 for higher SES). We used mixed-effects multivariable logistic and Cox models to examine the effects of HOUSES on preemptive transplants, pretransplant dialysis duration, and death-censored graft loss, adjusting for covariates. Results. Among 1975 eligible patients, 29.4% received preemptive transplants, 34.9% underwent pretransplant dialysis for >3 y, and 15.1% experienced death-censored graft loss for a median follow-up of 7.15 (interquartile range, 4.25–11.38) y. Lower SES recipients (Q1) demonstrated decreased preemptive transplant likelihood (adjusted odds ratio [aOR]: 0.74; 95% confidence interval [CI], 0.57-0.97; P = 0.03), longer dialysis duration (>3 y; aOR: 1.43; 95% CI, 1.01-2.03; P = 0.046), and higher death-censored graft loss (adjusted hazard ratio 1.36; 95% CI, 1.02-1.12; P = 0.036) versus higher SES recipients (Q2–Q4). Conclusions. We observed significant socioeconomic disparities in kidney transplant access, dialysis duration, and graft survival. The HOUSES index may be a promising tool for individual-based targeted interventions as it identifies SES on an individual rather than an area-level basis.

AbstractBackground and AimsInfluenza is a challenging infectious illness for older adults. It is not completely clear whether influenza is associated with frailty or functional decline. We sought to determine the association between incident influenza infection and frailty and prefrailty in community patients over 50 years of age. We also investigated the association between influenza vaccination and frailty and prefrailty as a secondary aim.MethodsThis was a prospective community cohort study from October 2019 to November 2020 in participants over 50 years. The primary outcome was the development of frailty as defined by three of five frailty criteria (slow gait speed, low grip strength, 5% weight loss, low energy, and low physical functioning). The primary predictor was a positive polymerase chain reaction (PCR) for influenza infection. Influenza vaccination was based on electronic health record reviewing 1 year before enrollment. We reported the relationship between influenza and frailty by calculating odds ratios (OR) with 95% confidence intervals (CI) after adjustment for age, sex, socioeconomic status, Charlson Comorbidity Index (CCI), influenza vaccine, and previous self‐rated frailty from multinomial logistic regression model comparing frail and prefrail to nonfrail subjects.ResultsIn 1135 participants, the median age was 67 years (interquartile range 60−74), with 41% men. Eighty‐one participants had PCR‐confirmed influenza (7.1%). Frailty was not associated with influenza, with an OR of 0.50 (95% CI 0.17−1.43) for frail participants compared to nonfrail participants. Influenza vaccination is associated with frailty, with an OR of 1.69 (95% CI 1.09−2.63) for frail compared to nonfrail. Frailty was associated with a higher CCI with an OR of 1.52 (95% CI 1.31−1.76).ConclusionWe did not find a relationship between influenza infection and frailty. We found higher vaccination rates in participants with frailty compared to nonfrail participants While influenza was not associated with frailty, future work may involve longer follow‐up.

Introduction The lack of effective, consistent, reproducible and efficient asthma ascertainment methods results in inconsistent asthma cohorts and study results for clinical trials or other studies. We aimed to assess whether application of expert artificial intelligence (AI)-based natural language processing (NLP) algorithms for two existing asthma criteria to electronic health records of a paediatric population systematically identifies childhood asthma and its subgroups with distinctive characteristics. Methods Using the 1997–2007 Olmsted County Birth Cohort, we applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) as well as Asthma Predictive Index (NLP-API). We categorised subjects into four groups (both criteria positive (NLP-PAC+/NLP-API+); PAC positive only (NLP-PAC+ only); API positive only (NLP-API+ only); and both criteria negative (NLP-PAC−/NLP-API−)) and characterised them. Results were replicated in unsupervised cluster analysis for asthmatics and a random sample of 300 children using laboratory and pulmonary function tests (PFTs). Results Of the 8196 subjects (51% male, 80% white), we identified 1614 (20%), NLP-PAC+/NLP-API+; 954 (12%), NLP-PAC+ only; 105 (1%), NLP-API+ only; and 5523 (67%), NLP-PAC−/NLP-API−. Asthmatic children classified as NLP-PAC+/NLP-API+ showed earlier onset asthma, more Th2-high profile, poorer lung function, higher asthma exacerbation and higher risk of asthma-associated comorbidities compared with other groups. These results were consistent with those based on unsupervised cluster analysis and lab and PFT data of a random sample of study subjects. Conclusion Expert AI-based NLP algorithms for two asthma criteria systematically identify childhood asthma with distinctive characteristics. This approach may improve precision, reproducibility, consistency and efficiency of large-scale clinical studies for asthma and enable population management.

Objectives Literature suggests an inconsistent, but largely inverse, association between asthma and risk of glioma, which is primarily due to methodological inconsistency in sampling frame and ascertainment of asthma. The objective of the study was to clarify the association between asthma and risk of glioma by minimising methodological biases (eg, recall and detection bias). Design A population-based case–control study. Setting General population in Olmsted County, Minnesota, USA. Participants All eligible biopsy-proven incident glioma cases (1995–2014) and two sets of controls among residents matched to age and sex (first set: community controls without glioma; second set: MRI-negative controls from the same community). Methods The predetermined asthma criteria via medical record review were applied to ascertain asthma status of cases and controls. History of asthma prior to index date was compared between glioma cases and their matched controls using conditional logistic regression models. Propensity score for asthma status was adjusted for multivariate analysis. Results We enrolled 135 glioma cases (median age at index date: 53 years) and 270 controls. Of the cases, 21 had a history of asthma (16%), compared with 36 of MRI controls (27%) (OR (95% CI) 0.48 (0.26 to 0.91), p=0.03). With MRI controls, an inverse association between asthma and risk of glioma persisted after adjusting for the propensity score for asthma status, but did not reach statistical significance probably due to the lack of statistical power (OR (95% CI) 0.48 (0.21 to 1.09); p=0.08). Based on comparison of characteristics of controls and cases, community controls seem to be more susceptible to a detection bias. Conclusions While differential detection might account for the association between asthma and risk of glioma, asthma may potentially pose a protective effect on risk of glioma. Our study results need to be replicated by a larger study.

Objective Two pertussis outbreaks occurred in Olmsted County, Minnesota, during 2004–2005 and 2012 (5–10 times higher than other years), with significantly higher incidence than for the State. We aimed to assess whether there were similar spatio-temporal patterns between the two outbreaks. Setting Olmsted County, Minnesota, USA Participants We conducted a population-based retrospective cohort study of all Olmsted County residents during the 2004–2005 and 2012 outbreaks, including laboratory-positive pertussis cases. Primary outcome measure For each outbreak, we estimated (1) age-specific incidence rate using laboratory-positive pertussis cases (numerator) and the Rochester Epidemiology Project Census (denominator), a medical record-linkage system for virtually all Olmsted County residents, and (2) pertussis case density using kernel density estimation to identify areas with high case density. To account for population size, we calculated relative difference of observed density and expected density based on age-specific incidence. Results We identified 157 and 195 geocoded cases in 2004–2005 and 2012, respectively. Incidence was the highest among adolescents (ages 11 to <14 years) for both outbreaks (9.6 and 7.9 per 1000). The 2004–2005 pertussis outbreak had higher incidence in winter (52% of cases) versus summer in 2012 (53%). We identified a consistent area with higher incidence at the beginning (ie, first quartile) of two outbreaks, but it was inconsistent for later quartiles. The relative difference maps for the two outbreaks suggest a greater role of neighbourhood population size in 2012 compared with 2004–2005. Conclusions Comparing spatio-temporal patterns between two pertussis outbreaks identified a consistent geographical area with higher incidence of pertussis at the beginning of outbreaks in this community. This finding can be tested in future outbreaks, and, if confirmed, can be used for identifying epidemiological risk factors clustered in such areas for geographically targeted intervention.