Xiaochun Li

Importance: Combinations of VEGFR tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), such as antibodies to programmed cell death-1 (PD-1), or to its ligand PD-L1, are now first-line standard of care for renal cell carcinoma (RCC), but the pivotal clinical trials excluded patients with common comorbidities, leaving their real-world effectiveness uncertain. Objective: To determine whether adding PD-1/PD-L1 inhibitors to VEGFR-TKIs therapy is associated with improved overall survival in a real-world RCC cohort. Design, Setting, and Participants: This retrospective cohort study used a target trial emulation framework and real-world electronic health records data from the University of Florida Health Integrated Data Repository (IDR). Data was analyzed from September 2009 through June 2023. Adult patients (≥18 years) with confirmed RCC and at least one VEGFR-TKIs prescription were eligible. The date of the first VEGFR-TKIs prescription was defined as the index date, and patients were followed for up to 24 months. Variable-ratio propensity score matching (up to 2:1) across 13 baseline covariates was used to emulate randomized treatment assignments. Of 107,783 patients screened, 387 met eligibility criteria, and 319 remained in the matched cohort. Exposures: VEGFR-TKIs monotherapy (control group) versus VEGFR-TKIs combined with PD-1/PD-L1 inhibitors (experimental group). Main Outcomes and Measures: Overall survival (OS), analyzed by weighted Kaplan-Meier estimation, cluster-robust Cox regression, and restricted mean survival time (RMST) at τ = 24 months, prespecified given anticipated non-proportional hazards. Results: Among 319 matched patients (mean [SD] age, 62 [12] years; 76% male), 107 deaths occurred (33.5%). Twelve-month OS was higher in the combination arm (81.8%; 95% CI, 74.7—89.6%) than VEGFR-TKIs monotherapy (68.1%; 95% CI, 61.1—76.0%), converging by 24 months (61.1% vs 56.7%). The Cox hazard ratio was 0.718 (95% CI, 0.484— 1.064; P = 0.0986). RMST was 2.79 months greater with combination therapy (95% CI, 0.93— 4.65; P = 0.0033). Conclusions: Adding PD-1/PD-L1 inhibitors to VEGFR-TKIs therapy was associated with a statistically significant and clinically meaningful gain in restricted mean survival, supporting the real-world generalizability of combination therapy and the importance of appropriate treatment effect measures under non-proportional hazards.

Purpose: Evaluate operational and clinical outcomes of stocking continuous albuterol syringes in the emergency department (ED) automatic dispensing cabinet (ADC) for pediatric patients with moderate to severe asthma exacerbations. Methods: Retrospective chart review evaluating patients who received continuous albuterol 10 mg/hr in a pediatric ED before and after stocking in the ED ADC. The primary outcome is the time to first dose initiation from pharmacist verification. The secondary outcomes include hospital length of stay (LOS), duration of continuous albuterol, ED disposition, change in Pediatric Asthma Scores (PAS) from baseline, or rates of treatment failure. Results: Baseline demographics were well balanced between the two groups, with the exception that the pre-implementation group had a higher rate of Hispanic/Latino ethnicity (20.7% vs 8.9%, p=0.04). For the primary outcome, the post-implementation group had a decreased time for initiation of first dose of continuous albuterol from verification (47.3 vs 14.7 minutes, p<0.001). There were no differences were seen between hospital LOS, duration of continuous albuterol, ED disposition, rates of treatment failure, the reason for treatment failure, or change in PAS from baseline. No issues were documented with stocking the continuous albuterol syringes in the ED ADC in the post-implementation group. Conclusion: Proactively stocking continuous albuterol syringes in a pediatric ED ADC reduced the time from pharmacist verification to dose initiation by almost 70%. There were no changes to other operational or clinical outcomes. Further studies are needed to determine the clinical and operational impacts of stocking continuous albuterol syringes or other emergent medications in an ED ADC.

Background: Accurate patient record linkage is essential for clinical care, health information exchange, research, and public health surveillance. However, linkage accuracy may vary across demographic groups due to differences in data completeness, quality, and the structural factors underlying how demographic information is captured. Objective: This study aimed to explore whether probabilistic patient matching accuracy varies by age, sex, race, and ethnicity and to identify potential sources of bias that may influence matching performance. Methods: We used 4 Indiana data sources—the Indiana Network for Patient Care, Newborn Screening, Social Security Administration Death Master File, and Marion County Public Health Department—and applied a modified Fellegi-Sunter probabilistic linkage algorithm accommodating missing data under a missing at random assumption. Gold standard match status was established through dual manual review with adjudication. For each dataset, matching sensitivity, positive predictive value, and F ₁ -scores were estimated and stratified by age, sex, race, and ethnicity. Data completeness, distinct value ratio, and Shannon entropy were assessed to characterize data quality. Ninety-five percent bootstrap CIs were used to assess significance. Results: The algorithm-matching F ₁ -score was greater than 0.82 for all age strata, ranging from 0.88 to 0.97 for sex, 0.85 to 0.99 for race, and 0.88 to 0.99 for ethnicity. Sensitivity ranged from 0.70 to 0.97 across age strata, 0.76 to 0.97 across sex, 0.85 to 0.99 across race, and 0.85 to 0.989 across ethnicity. Lower sensitivity and F ₁ -scores were consistently observed in strata with greater missingness or discordance, particularly in Newborn Screening and Social Security Administration Death Master File. Race and ethnicity exhibited the highest missingness and lowest informational diversity, coinciding with the largest declines in accuracy. Shannon entropy and distinct value ratio varied across demographic groups and were strongly associated with performance, indicating that both low and excessively high informational diversity can impair matching. Conclusions: Probabilistic patient matching accuracy is not uniform across demographics and is strongly influenced by data quality and completeness. Although overall matching performance, as assessed by the F ₁ -score, remained above 0.8, it varied across datasets when stratified by sociodemographic characteristics. Sociodemographic data missingness is associated with lower matching accuracy, raising equity and ethical concerns for clinical, research, and public health applications. Routine demographic-stratified evaluations of matching accuracy, improved standardization of sociodemographic data, and fairness-aware linkage methods are essential to prevent the amplification of structural inequities in linked health datasets.

Heterozygosity for missense mutations in one of 3 seemingly redundant calmodulin (CALM)-encoding genes can cause life-threatening arrhythmias, suggesting that small fractions of mutant CALM protein suffice to cause a severe phenotype. However, the exact molar ratios of wildtype to mutant CALM protein in calmodulinopathy hearts remain unknown. The aim of the present study was to quantitate mutant versus wildtype CALM transcript and protein levels in hearts of knock-in mice harboring the p.N98S mutation in the Calm1 gene. We found that the transcripts from the mutant Calm1 allele were the least abundantly expressed Calm transcripts in both hetero- and homozygous mutant hearts, while mutant hearts accumulate high levels of N98S-CALM protein in a Calm1^N98S allele dosage-dependent manner, exceeding those of wildtype CALM protein. We further show that the severity of the electrophysiological phenotype incrementally increases with the graded increase in the mutant-to-wildtype CALM protein expression ratio seen in homozygous versus heterozygous mutant mice. We finally show a decrease in N98S-CALM protein degradation, suggesting that mutant CALM stabilization contributed to its enrichment in the heart. Our results support what we believe to be a novel mechanism by which a mutation in a single Calm gene can give rise to a severe phenotype.

Key Points: Body fluid volumes are significantly higher at the end of the 3-day interdialytic interval compared with the 2-day interdialytic interval. V_E/VCO₂ slope, a noninvasive index of exercise pulmonary vascular resistance, is elevated after the 3-day interdialytic interval. VO₂Peak is similar across interdialytic intervals but was limited following dialysis by postdialysis fatigue. Background: The long interdialytic interval in thrice-weekly hemodialysis is associated with volume accumulation and excess cardiovascular risk. Few studies have evaluated cardiovascular functional changes after interdialytic intervals. This study sought to evaluate cardiovascular functional response patterns, as assessed by cardiopulmonary exercise testing, after the short and long interdialytic intervals. Methods: We conducted an exploratory single-center, quasi-experimental 2-arm study involving crossover of treatment sequences in patients on conventional thrice-weekly maintenance hemodialysis. All patients underwent cardiopulmonary exercise testing, bioelectrical impedance spectroscopy, and physical function testing on three study visits: posthemodialysis and prehemodialysis at the end of a 2-day and the 3-day interdialytic intervals. Results: Thirty-one patients (n=19 men, mean [SD] age 55 [12] years, median [interquartile range] dialysis vintage 56 [24–96] months) completed all study visits. Interdialytic weight gain and total body fluid were greater after the 3-day compared with the 2-day interval (P < 0.001). Ventilation/carbon dioxide production (V_E/VCO₂) slope, a noninvasive index of exercise pulmonary vascular resistance and right ventricular (RV) dysfunction, was significantly elevated at the 3-day (31.2 [4.8] L V_E/L VCO₂; P = 0.002) compared with the 2-day interval (29.5 [3.9] L V_E/L VCO₂). No significant differences between interdialytic intervals were observed for peak oxygen uptake (VO₂Peak) or VO₂ at the anaerobic threshold. Change (Δ) in V_E/VCO₂ slope (ΔV_E/VCO₂) the 2-day to the 3-day interval was correlated with Δbody weight (r=0.36, P = 0.04) and left ventricular mass index (r=0.44, P = 0.02). Conclusions: The long interdialytic interval is associated with abnormal exercise-derived ventilatory and hemodynamic responses, including ventilation-perfusion mismatch and potentially RV dysfunction and RV-pulmonary artery uncoupling during exercise as suggested by elevated V_E/VCO₂ slope.

Objective: To assess the health disparities across social determinants of health (SDoH) domains for the risk of severe acidosis independent of demographical and clinical factors. Materials and Methods: A retrospective case-control study (n = 13 310, 1:4 matching) is performed using electronic health records (EHRs), SDoH surveys, and genomics data from the All of Us participants. The propensity score matching controls confounding effects due to EHR data availability. Conditional logistic regressions are used to estimate odds ratios describing associations between SDoHs and the risk of acidosis events, adjusted for demographic features, and clinical conditions. Results: Those with employer-provided insurance and those with Medicaid plans show dramatically different risks [adjusted odds ratio (AOR): 0.761 vs 1.41]. Low-income groups demonstrate higher risk (household income less than $25k, AOR: 1.3-1.57) than high-income groups ($100-$200k, AOR: 0.597-0.867). Other high-risk factors include impaired mobility (AOR: 1.32), unemployment (AOR: 1.32), renters (AOR: 1.41), other non-house-owners (AOR: 1.7), and house instability (AOR: 1.25). Education was negatively associated with acidosis risk. Discussion: Our work provides real-world evidence of the comprehensive health disparities due to socioeconomic and behavioral contributors in a cohort enriched in minority groups or underrepresented populations. Conclusions: SDoHs are strongly associated with systematic health disparities in the risk of severe metabolic acidosis. Types of health insurance, household income levels, housing status and stability, employment status, educational level, and mobility disability play significant roles after being adjusted for demographic features and clinical conditions. Comprehensive solutions are needed to improve equity in healthcare and reduce the risk of severe acidosis.

Key Points: Mild-to-moderate CKD is associated with impairment in cardiovascular functional capacity as assessed by oxygen uptake at peak exercise (VO ₂ Peak). Cardiac output is significantly reduced in patients with mild-to-moderate CKD and is associated with impaired VO ₂ Peak. Assessment of VO ₂ Peak by cardiopulmonary exercise testing can detect decrements in cardiovascular function during early stages of kidney function decline that may not be captured using resting left ventricular geometric indices alone. Background: Traditional diagnostic tools that assess resting cardiac function and structure fail to accurately reflect cardiovascular alterations in patients with CKD. This study sought to determine whether multidimensional exercise response patterns related to cardiovascular functional capacity can detect abnormalities in mild-to-moderate CKD. Methods: In a cross-sectional study, we examined 3075 participants from the Framingham Heart Study (FHS) and 451 participants from the Massachusetts General Hospital Exercise Study (MGH-ExS) who underwent cardiopulmonary exercise testing. Participants were stratified by eGFR: eGFR ≥90, eGFR 60–89, and eGFR 30–59. Our primary outcomes of interest were peak oxygen uptake (VO ₂ Peak), VO ₂ at anaerobic threshold (VO ₂ AT), and ratio of minute ventilation to carbon dioxide production (VE/VCO ₂ ). Multiple linear regression models were fitted to evaluate the associations between eGFR group and each outcome variable adjusted for covariates. Results: In the FHS cohort, 1712 participants (56%) had an eGFR ≥90 ml/min per 1.73 m ² , 1271 (41%) had an eGFR of 60–89 ml/min per 1.73 m ² , and 92 (3%) had an eGFR of 30–59 ml/min per 1.73 m ² . In the MGH-ExS cohort, 247 participants (55%) had an eGFR ≥90 ml/min per 1.73 m ² , 154 (34%) had an eGFR of 60–89 ml/min per 1.73 m ² , and 50 (11%) had an eGFR of 30–59 ml/min per 1.73 m ² . In FHS, VO ₂ Peak and VO ₂ AT were incrementally impaired with declining kidney function ( P < 0.001); however, this pattern was attenuated after adjustment for age. Percent-predicted VO ₂ Peak at AT was higher in the lower eGFR groups ( P < 0.001). In MGH-ExS, VO ₂ Peak and VO ₂ AT were incrementally impaired with declining kidney function in unadjusted and adjusted models ( P < 0.05). VO ₂ Peak was associated with eGFR ( P < 0.05) in all models even after adjusting for age. On further mechanistic analysis, we directly measured cardiac output (CO) at peak exercise by right heart catheterization and found impaired CO in the lower eGFR groups ( P ≤ 0.007). Conclusions: Cardiopulmonary exercise testing–derived indices may detect impairment in cardiovascular functional capacity and track CO declines in mild-to-moderate CKD.

The Fellegi-Sunter model is a latent class model widely used in probabilistic linkage to identify records that belong to the same entity. Record linkage practitioners typically employ all available matching fields in the model with the premise that more fields convey greater information about the true match status and hence result in improved match performance. In the context of model-based clustering, it is well known that such a premise is incorrect and the inclusion of noisy variables could compromise the clustering. Variable selection procedures have therefore been developed to remove noisy variables. Although these procedures have the potential to improve record matching, they cannot be applied directly due to the ubiquity of the missing data in record linkage applications. In this paper, we modify the stepwise variable selection procedure proposed by Fop, Smart, and Murphy and extend it to account for missing data common in record linkage. Through simulation studies, our proposed method is shown to select the correct set of matching fields across various settings, leading to better-performing algorithms. The improved match performance is also seen in a real-world application. We therefore recommend the use of our proposed selection procedure to identify informative matching fields for probabilistic record linkage algorithms.

Healthcare systems are hampered by incomplete and fragmented patient health records. Record linkage is widely accepted as a solution to improve the quality and completeness of patient records. However, there does not exist a systematic approach for manually reviewing patient records to create gold standard record linkage data sets. We propose a robust framework for creating and evaluating manually reviewed gold standard data sets for measuring the performance of patient matching algorithms. Our 8-point approach covers data preprocessing, blocking, record adjudication, linkage evaluation, and reviewer characteristics. This framework can help record linkage method developers provide necessary transparency when creating and validating gold standard reference matching data sets. In turn, this transparency will support both the internal and external validity of recording linkage studies and improve the robustness of new record linkage strategies.

Background: Quality patient care requires comprehensive health care data from a broad set of sources. However, missing data in medical records and matching field selection are 2 real-world challenges in patient-record linkage. Objective: In this study, we aimed to evaluate the extent to which incorporating the missing at random (MAR)–assumption in the Fellegi-Sunter model and using data-driven selected fields improve patient-matching accuracy using real-world use cases. Methods: We adapted the Fellegi-Sunter model to accommodate missing data using the MAR assumption and compared the adaptation to the common strategy of treating missing values as disagreement with matching fields specified by experts or selected by data-driven methods. We used 4 use cases, each containing a random sample of record pairs with match statuses ascertained by manual reviews. Use cases included health information exchange (HIE) record deduplication, linkage of public health registry records to HIE, linkage of Social Security Death Master File records to HIE, and deduplication of newborn screening records, which represent real-world clinical and public health scenarios. Matching performance was evaluated using the sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results: Incorporating the MAR assumption in the Fellegi-Sunter model maintained or improved F1-scores, regardless of whether matching fields were expert-specified or selected by data-driven methods. Combining the MAR assumption and data-driven fields optimized the F1-scores in the 4 use cases. Conclusions: MAR is a reasonable assumption in real-world record linkage applications: it maintains or improves F1-scores regardless of whether matching fields are expert-specified or data-driven. Data-driven selection of fields coupled with MAR achieves the best overall performance, which can be especially useful in privacy-preserving record linkage.