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.

Evaluating the positive predictive value of code-based identification of cirrhosis and its complications utilizing GPT-4.

BACKGROUND: Diagnosis code classification is a common method for cohort identification in cirrhosis research, but it is often inaccurate and augmented by labor-intensive chart review. Natural language processing (NLP) using large language models (LLMs) is a potentially more accurate method. To assess LLMs' potential for cirrhosis cohort identification, we compared code-based versus LLM-based classification with chart review as a "gold standard." METHODS: We extracted and conducted a limited chart review of 3,788 discharge summaries of cirrhosis admissions. We engineered zero-shot prompts using Generative Pre-trained Transformer (GPT)-4 to determine whether cirrhosis and its complications were active hospitalization problems. We calculated positive predictive values (PPVs) of LLM-based classification versus limited chart review, and PPVs of code-based versus LLM-based classification as a "silver standard" in all 3,788 summaries. RESULTS: Versus gold standard chart review, code-based classification achieved PPVs of 82.2% for identifying cirrhosis, 41.7% hepatic encephalopathy, 72.8% ascites, 59.8% gastrointestinal bleeding, and 48.8% spontaneous bacterial peritonitis. Compared to chart review, GPT-4 achieved 87.8-98.8% accuracies for identifying cirrhosis and its complications. Using LLM as a silver standard, code-based classification achieved PPVs of 79.8% for identifying cirrhosis, 53.9% hepatic encephalopathy, 55.3% ascites, 67.6% gastrointestinal bleeding, and 65.5% spontaneous bacterial peritonitis. CONCLUSIONS: LLM-based classification was highly accurate versus manual chart review in identifying cirrhosis and its complications - this allowed us to assess the performance of code-based classification at scale using LLMs as a silver standard. These results suggest LLMs could augment or replace code-based cohort classification and raise questions regarding the necessity of chart review.

Liver transplantation provides survival benefit at all levels of frailty: From the Multicenter Functional Assessment in Liver Transplantation Study.

BACKGROUND AND AIMS: Offering LT to frail patients may reduce waitlist mortality but may increase post-LT mortality. LT survival benefit is the concept of balancing these risks. We sought to quantify the net survival benefit with LT by liver frailty index (LFI). APPROACH AND RESULTS: We analyzed data in the multicenter Functional Assessment in LT (FrAILT) study from 2012 to 2021. Pre-LT cohort included ambulatory patients with cirrhosis awaiting LT, without HCC; the post-LT cohort included those who underwent LT. Primary outcomes were pre-LT and post-LT mortality. We computed 1-, 3-, and 5-year restricted mean survival times (RMSTs) from adjusted Cox models. The survival benefit was calculated as a net gain in life-years with LT. Pre-LT cohort included 2628 patients: median Model for End-Stage Liver Disease-Sodium was 18 (IQR: 14-22); 731 (28%) were frail; 440 (17%) died before LT. Post-LT cohort included 1335 patients: median Model for End-Stage Liver Disease-Sodium was 20 (IQR: 14-24); 325 (24%) were frail; 103 (8%) died after LT. Pre-LT RMST decreased substantially as LFI increased. Post-LT RMST also decreased as LFI increased but only modestly. There was no LFI threshold at which pre-LT and post-LT RMST intersected-patients had net survival benefits at all LFI values. CONCLUSIONS: Pre-LT and, to a lesser degree, post-LT mortality increased as LFI increased. Transplant offered a survival benefit at all LFI values, driven by a reduction in pre-LT mortality. No threshold of LFI was identified at which the risk of post-LT mortality exceeded pre-LT mortality. LT offers net survival benefits even in the presence of advanced frailty among those selected for LT.

Association of state Medicaid expansion policies with pediatric liver transplant outcomes.

Children from minoritized/socioeconomically deprived backgrounds suffer disproportionately high rates of uninsurance and graft failure/death after liver transplant. Medicaid expansion was developed to expand access to public insurance. Our objective was to characterize the impact of Medicaid expansion policies on long-term graft/patient survival after pediatric liver transplantation. All pediatric patients (<19 years) who received a liver transplant between January 1, 2005, and December 31, 2020 in the US were identified in the Scientific Registry of Transplant Recipients (N = 8489). Medicaid expansion was modeled as a time-varying exposure based on transplant and expansion dates. We used Cox proportional hazards models to evaluate the impact of Medicaid expansion on a composite outcome of graft failure/death over 10 years. As a sensitivity analysis, we conducted an intention-to-treat analysis from time of waitlisting to death (N = 1 1901). In multivariable analysis, Medicaid expansion was associated with a 30% decreased hazard of graft failure/death (hazard ratio, 0.70; 95% confidence interval, 0.62, 0.79; P < .001) after adjusting for Black race, public insurance, neighborhood deprivation, and living in a primary care shortage area. In intention-to-treat analyses, Medicaid expansion was associated with a 72% decreased hazard of patient death (hazard ratio, 0.28; 95% confidence interval, 0.23-0.35; P < .001). Policies that enable broader health insurance access may help improve outcomes and reduce disparities for children undergoing liver transplantation.

Clinically relevant cut-points for changes in the Liver Frailty Index are associated with waitlist mortality in patients with cirrhosis.

Physical frailty is a critical determinant of mortality in patients with cirrhosis and can be objectively measured using the Liver Frailty Index (LFI), which is potentially modifiable. We aimed to identify LFI cut-points associated with waitlist mortality. Ambulatory adults with cirrhosis without HCC awaiting liver transplantation from 9 centers from 2012 to 2021 for ≥3 months with ≥2 pre-liver transplantation LFI assessments were included. The primary explanatory variable was the change in LFI from first to second assessments per 3 months (∆LFI); we evaluated clinically relevant ∆LFI cut-points at 0.1, 0.2, 0.3, and 0.5. The primary outcome was waitlist mortality (death or delisting for being too sick), with transplant considered as a competing event. Among 1029 patients, the median (IQR) age was 58 (51-63) years; 42% were female; and the median lab Model for End-Stage Liver Disease-Sodium at first assessment was 18 (15-22). For each 0.1 improvement in ∆LFI, the risk of overall mortality decreased by 6% (cause-specific hazard ratio: 0.94, 95% CI: 0.92-0.97, p < 0.001). ∆LFI was associated with waitlist mortality at cut-points as low as 0.1 (cause-specific hazard ratio: 0.63, 95% CI: 0.46-0.87) and 0.2 (HR: 0.61, 95% CI: 0.42-0.87). An improvement in LFI per 3 months as small as 0.1 in the pre-liver transplantation period is associated with a clinically meaningful reduction in waitlist mortality. These data provide estimates of the reduction in mortality risk associated with improvements in LFI that can be used to assess the effectiveness of interventions targeting physical frailty in patients with cirrhosis.

Neighborhood Income Is Associated with Health Care Use in Pediatric Short Bowel Syndrome.

OBJECTIVE: To evaluate associations between neighborhood income and burden of hospitalizations for children with short bowel syndrome (SBS). STUDY DESIGN: We used the Pediatric Health Information System (PHIS) database to evaluate associations between neighborhood income and hospital readmissions, readmissions for central line-associated bloodstream infections (CLABSI), and hospital length of stay (LOS) for patients <18 years with SBS hospitalized between January 1, 2006, and October 1, 2015. We analyzed readmissions with recurrent event analysis and analyzed LOS with linear mixed effects modeling. We used a conceptual model to guide our multivariable analyses, adjusting for race, ethnicity, and insurance status. RESULTS: We included 4289 children with 16 347 hospitalizations from 43 institutions. Fifty-seven percent of the children were male, 21% were Black, 19% were Hispanic, and 67% had public insurance. In univariable analysis, children from low-income neighborhoods had a 38% increased risk for all-cause hospitalizations (rate ratio [RR] 1.38, 95% CI 1.10-1.72, P = .01), an 83% increased risk for CLABSI hospitalizations (RR 1.83, 95% CI 1.37-2.44, P < .001), and increased hospital LOS (ß 0.15, 95% CI 0.01-0.29, P = .04). In multivariable analysis, the association between low-income neighborhoods and elevated risk for CLABSI hospitalizations persisted (RR 1.70, 95% CI 1.23-2.35, P < .01, respectively). CONCLUSIONS: Children with SBS from low-income neighborhoods are at increased risk for hospitalizations due to CLABSI. Examination of specific household- and neighborhood-level factors contributing to this disparity may inform equity-based interventions.

Efficient data integration under prior probability shift.

Conventional supervised learning usually operates under the premise that data are collected from the same underlying population. However, challenges may arise when integrating new data from different populations, resulting in a phenomenon known as dataset shift. This paper focuses on prior probability shift, where the distribution of the outcome varies across datasets but the conditional distribution of features given the outcome remains the same. To tackle the challenges posed by such shift, we propose an estimation algorithm that can efficiently combine information from multiple sources. Unlike existing methods that are restricted to discrete outcomes, the proposed approach accommodates both discrete and continuous outcomes. It also handles high-dimensional covariate vectors through variable selection using an adaptive least absolute shrinkage and selection operator penalty, producing efficient estimates that possess the oracle property. Moreover, a novel semiparametric likelihood ratio test is proposed to check the validity of prior probability shift assumptions by embedding the null conditional density function into Neyman's smooth alternatives (Neyman, 1937) and testing study-specific parameters. We demonstrate the effectiveness of our proposed method through extensive simulations and a real data example. The proposed methods serve as a useful addition to the repertoire of tools for dealing dataset shifts.

The liver frailty index is a predictor of healthcare utilization after liver transplantation in older adults.

BACKGROUND: Older adults have higher healthcare utilization after liver transplantation (LT), yet objective risk stratification tools in this population are lacking. We evaluated the Liver Frailty Index (LFI) as one potential tool. METHODS: Ambulatory LT candidates ≥65 years without hepatocellular carcinoma (HCC) who underwent LT from 1/2012 to 6/2022 at 8 U.S. centers were included. Estimates of the difference in median using quantile regression were used to assess the adjusted association between LFI and hospitalized days within 90 days post-LT. RESULTS: Of 131 LT recipients, median (interquartile range [IQR]) (1st -3rd quartiles) age was 68 years (66-70); median pre-LT MELD-Na was 19 (15-24). Median LFI was 4.1 (3.6-4.7); 27% were frail (LFI≥4.5). Median hospitalized days within 90 days post-LT was 11 (7-20). Compared with non-frail patients, frail patients were hospitalized for a median of 5 days longer post-LT (95% CI .30-9.7, p = .04). Each .5 unit increase in pre-LT LFI was associated with an increase of 1.16 days (95%CI .42-2.69, p = .02) in hospitalized days post-LT. CONCLUSION: Among older adults undergoing LT, frailty was associated with more hospitalized days within 90 days after LT. The LFI can identify older adults who might benefit from pre-LT or early post-LT programs which may reduce post-LT healthcare utilization, such as early rehabilitation or post-hospital discharge programs.

Clinical Characteristics Associated With Posttransplant Survival Among Adults 70 Years Old or Older Undergoing Liver Transplantation.

GOALS: We sought to identify pre-liver transplantation (LT) characteristics among older adults associated with post-LT survival. BACKGROUND: The proportion of older patients undergoing deceased-donor liver transplantation (DDLT) has increased over time. STUDY: We analyzed adult DDLT recipients in the United Network for Organ Sharing registry from 2016 through 2020, excluding patients listed as status 1 or with a model of end-stage liver disease exceptions for hepatocellular carcinoma. Kaplan-Meier methods were used to estimate post-LT survival probabilities among older recipients (age ≥70 y). Associations between clinical covariates and post-LT mortality were assessed using Cox regressions. RESULTS: Of 22,862 DDLT recipients, 897 (4%) were 70 years old or older. Compared with younger recipients, older recipients had worse overall survival ( P < 0.01) (1 y: 88% vs 92%, 3 y: 77% vs 86%, and 5 y: 67% vs 78%). Among older adults, in univariate Cox regressions, dialysis [hazards ratio (HR): 1.96, 95% CI: 1.38-2.77] and poor functional status [defined as Karnofsky Performance Score (KPS) <40] (HR: 1.82, 95% CI: 1.31-2.53) were each associated with mortality, remaining significant on multivariable Cox regressions. The effect of dialysis and KPS <40 at LT on post-LT survival (HR: 2.67, 95% CI: 1.77-4.01) was worse than the effects of either KPS <40 (HR: 1.52, 95% CI: 1.03-2.23) or dialysis alone (HR: 1.44, 95% CI: 0.62-3.36). Older recipients with KPS >40 without dialysis had comparable survival rates compared with younger recipients ( P = 0.30). CONCLUSIONS: While older DDLT recipients had worse overall post-LT survival compared with younger recipients, favorable survival rates were observed among older adults who did not require dialysis and had poor functional status. Poor functional status and dialysis at LT may be useful to stratify older adults at higher risk for poor post-LT outcomes.

Diabetes mellitus and risk of breast cancer: a large-scale, prospective, population-based study.

BACKGROUND: The objective of this study was to evaluate associations of diabetes overall, type 1 diabetes (T1D), and type 2 diabetes (T2D) with breast cancer (BCa) risk. METHODS: We included 250,312 women aged 40-69 years between 2006 and 2010 from the UK Biobank cohort. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were calculated for associations of diabetes and its two major types with the time from enrollment to incident BCa. RESULTS: We identified 8182 BCa cases during a median follow-up of 11.1 years. We found no overall association between diabetes and BCa risk (aHR = 1.02, 95% CI = 0.92-1.14). When accounting for diabetes subtype, women with T1D had a higher risk of BCa than women without diabetes (aHR = 1.52, 95% CI = 1.03-2.23). T2D was not associated with BCa risk overall (aHR = 1.00, 95% CI = 0.90-1.12). However, there was a significantly increased risk of BCa in the short time window after T2D diagnosis. CONCLUSIONS: Though we did not find an association between diabetes and BCa risk overall, an increased risk of BCa was observed shortly after T2D diagnosis. In addition, our data suggest that women with T1D may have an increased risk of BCa.

The tele-liver frailty index (TeLeFI): development of a novel frailty tool in patients with cirrhosis via telemedicine.

Frailty is a critical determinant of outcomes in cirrhosis patients. The increasing use of telemedicine has created an unmet need for virtual frailty assessment. We aimed to develop a telemedicine-enabled frailty tool (tele-liver frailty index). Adults with cirrhosis in the liver transplant setting underwent ambulatory frailty testing with the liver frailty index (LFI) in-person, then virtual administration of (1) validated surveys (eg, SARC-F and Duke Activity Status Index [DASI]), (2) chair stands, and (3) balance. Two models were selected and internally validated for predicting LFI ≥4.4 using: (1) Bayesian information criterion (BIC), (2) C-statistics, and (3) ease of use. Of 145 patients, the median (interquartile range) LFI was 3.7 (3.3-4.2); 15% were frail. Frail (vs not frail) patients reported significantly greater impairment on all virtually assessed instruments. We selected 2 parsimonious models: (1) DASI + chair/bed transfer (SARC-F) (BIC 255, C-statistics 0.78), and (2) DASI + chair/bed transfer (SARC-F) + virtually assessed chair stands (BIC 244, C-statistics 0.79). Both models had high C-statistics (0.76-0.78) for predicting frailty. In conclusion, the tele-liver frailty index is a novel tool to screen frailty in liver transplant patients via telemedicine pragmatically and may be used to identify patients who require in-person frailty assessment, more frequent follow-up, or frailty intervention.

Cytomegalovirus seropositivity is associated with improved responses to CD38 monoclonal antibody therapies in multiple myeloma: A single-centre retrospective study.

The CD38-targeting monoclonal antibodies (CD38 mAbs) are well-established therapies in multiple myeloma (MM), but responses to treatment are not always deep or durable. Natural killer (NK) cells deficient in Fc epsilon receptor gamma subunits, known as g-NK cells, are found in higher numbers among individuals exposed to cytomegalovirus (CMV) and are able to potentiate the efficacy of daratumumab in vivo. Here, we present a single-centre, retrospective analysis of 136 patients with MM with known CMV serostatus who received a regimen containing a CD38 mAb (93.4% daratumumab and 6.6% isatuximab). CMV seropositivity was associated with an increased overall response rate to treatment regimens containing a CD38 mAb (odds ratio 2.65, 95% confidence interval [CI] 1.17-6.02). However, CMV serostatus was associated with shorter time to treatment failure in a multivariate Cox model (7.8 vs. 8.8 months in the CMV-seropositive vs. CMV-seronegative groups respectively, log-rank p = 0.18, hazard ratio 1.98, 95% CI 1.25-3.12). Our data suggest that CMV seropositivity may predict better response to CD38 mAbs, although this did not correspond to longer time to treatment failure. Larger studies directly quantitating g-NK cells are required to fully understand their effect on CD38 mAb efficacy in MM.

CT-quantified sarcopenic visceral obesity is associated with poor transplant waitlist mortality in patients with cirrhosis.

Sarcopenic obesity is associated with higher rates of morbidity and mortality than seen with either sarcopenia or obesity alone. We aimed to define sarcopenic visceral obesity (SVO) using CT-quantified skeletal muscle index and visceral-to-subcutaneous adipose tissue ratio and to examine its association with waitlist mortality in patients with cirrhosis. Included were 326 adults with cirrhosis awaiting liver transplantation in the ambulatory setting with available abdominal CT within 6 months from enrollment between February 2015 and January 2018. SVO was defined as patients with sarcopenia (skeletal muscle index <50 cm 2 /m 2 in men and <39 cm 2 /m 2 in women) and visceral obesity (visceral-to-subcutaneous adipose tissue ratio ≥1.21 in men and ≥0.48 in women). The percentage who met criteria for sarcopenia, visceral obesity, and SVO were 44%, 29%, and 13%, respectively. Cumulative incidence of waitlist mortality was higher in patients with SVO compared to patients with sarcopenia without visceral obesity or visceral obesity without sarcopenia at 12 months (40% vs. 21% vs. 12%) (overall logrank p =0.003). In univariable Cox regression, SVO was associated with waitlist mortality (HR: 3.42, 95% CI: 1.58-7.39), which remained significant after adjusting for age, sex, diabetes, ascites, encephalopathy, MELDNa, liver frailty index, and different body compositions (HR: 2.64, 95% CI: 1.11-6.30). SVO was associated with increase waitlist mortality in patients with cirrhosis in the ambulatory setting awaiting liver transplantation. Concurrent loss of skeletal muscle and gain of adipose tissue seen in SVO quantified by CT may be a useful and objective measurement to identify patients at risk for suboptimal pretransplant outcomes.

Serum and plasma protein biomarkers associated with frailty in patients with cirrhosis.

Frailty, a clinical phenotype of decreased physiological reserve, is a strong determinant of adverse health outcomes in patients with cirrhosis. The only cirrhosis-specific frailty metric is the Liver Frailty Index (LFI), which must be administered in person and may not be feasible for every clinical scenario. We sought to discover candidate serum/plasma protein biomarkers that could differentiate frail from robust patients with cirrhosis. A total of 140 adults with cirrhosis awaiting liver transplantation in the ambulatory setting with LFI assessments and available serum/plasma samples were included. We selected 70 pairs of patients on opposite ends of the frailty spectrum (LFI>4.4 for frail and LFI<3.2 for robust) who were matched by age, sex, etiology, HCC, and Model for End-Stage Liver Disease-Sodium. Twenty-five biomarkers with biologically plausible associations with frailty were analyzed using ELISA by a single laboratory. Conditional logistic regression was used to examine their association with frailty. Of the 25 biomarkers analyzed, we identified 7 proteins that were differentially expressed between frail and robust patients. We observed differences in 6 of the 7 proteins in the expected direction: (a) higher median values in frail versus robust with growth differentiation factor-15 (3682 vs. 2249 pg/mL), IL-6 (17.4 vs. 6.4 pg/mL), TNF-alpha receptor 1 (2062 vs. 1627 pg/mL), leucine-rich alpha-2 glycoprotein (44.0 vs. 38.6 µg/mL), and myostatin (4066 vs. 6006 ng/mL) and (b) lower median values in frail versus robust with alpha-2-Heremans-Schmid glycoprotein (0.11 vs. 0.13 mg/mL) and free total testosterone (1.2 vs. 2.4 ng/mL). These biomarkers represent inflammatory, musculoskeletal, and endocrine/metabolic systems, reflecting the multiple physiological derangements observed in frailty. These data lay the foundation for confirmatory work and development of a laboratory frailty index for patients with cirrhosis to improve diagnosis and prognostication.

Frailty, mortality, and health care utilization after liver transplantation: From the Multicenter Functional Assessment in Liver Transplantation (FrAILT) Study.

BACKGROUND AND AIMS: Frailty is a well-established risk factor for poor outcomes in patients with cirrhosis awaiting liver transplantation (LT), but whether it predicts outcomes among those who have undergone LT is unknown. APPROACH AND RESULTS: Adult LT recipients from 8 US centers (2012-2019) were included. Pre-LT frailty was assessed in the ambulatory setting using the Liver Frailty Index (LFI). "Frail" was defined by an optimal cut point of LFI ≥ 4.5. We used the 75th percentile to define "prolonged" post-LT length of stay (LOS; ≥12 days), intensive care unit (ICU) days (≥4 days), and inpatient days within 90 post-LT days (≥17 days). Of 1166 LT recipients, 21% were frail pre-LT. Cumulative incidence of death at 1 and 5 years was 6% and 16% for frail and 4% and 10% for nonfrail patients (overall log-rank p = 0.02). Pre-LT frailty was associated with an unadjusted 62% increased risk of post-LT mortality (95% CI, 1.08-2.44); after adjustment for body mass index, HCC, donor age, and donation after cardiac death status, the HR was 2.13 (95% CI, 1.39-3.26). Patients who were frail versus nonfrail experienced a higher adjusted odds of prolonged LT LOS (OR, 2.00; 95% CI, 1.47-2.73), ICU stay (OR, 1.56; 95% CI, 1.12-2.14), inpatient days within 90 post-LT days (OR, 1.72; 95% CI, 1.25-2.37), and nonhome discharge (OR, 2.50; 95% CI, 1.58-3.97). CONCLUSIONS: Compared with nonfrail patients, frail LT recipients had a higher risk of post-LT death and greater post-LT health care utilization, although overall post-LT survival was acceptable. These data lay the foundation to investigate whether targeting pre-LT frailty will improve post-LT outcomes and reduce resource utilization.

Hypoxia-specific imaging in patients with lymphoma undergoing CAR-T therapy.

PURPOSE: Intratumoral hypoxia in non-Hodgkin's Lymphoma (NHL) may interfere with chimeric antigen receptor T-cell (CAR-T) function. We conducted a single-center pilot study (clinicaltrials.gov ID NCT04409314) of [18F]fluoroazomycin arabinoside, a hypoxia-specific radiotracer abbreviated as [18F]FAZA, to assess the feasibility of this positron emission tomography (PET) imaging modality in this population. METHODS: Patients with relapsed NHL being evaluated for CAR-T therapy received a one-time [18F]FAZA PET scan before pre-CAR-T lymphodepletion. A tumor to mediastinum (T/M) ratio of 1.2 or higher with regard to [18F]FAZA uptake was defined as positive for intratumoral hypoxia. We planned to enroll 30 patients with an interim futility analysis after 16 scans. RESULTS: Of 16 scanned patients, 3 had no evidence of disease by standard [18F]fluorodeoxyglucose PET imaging before CAR-T therapy. Six patients (38%) had any [18F]FAZA uptake above background. Using a T/M cutoff of 1.20, only one patient (a 68-year-old male with relapsed diffuse large B-cell lymphoma) demonstrated intratumoral hypoxia in an extranodal chest wall lesion (T/M 1.35). Interestingly, of all 16 scanned patients, he was the only patient with progressive disease within 1 month of CAR-T therapy. However, because of our low overall proportion of positive scans, our study was stopped for futility. CONCLUSIONS: Our pilot study identified low-level [18F]FAZA uptake in a small number of patients with NHL receiving CAR-T therapy. The only patient who met our pre-specified threshold for intratumoral hypoxia was also the only patient with early CAR-T failure. Future plans include exploration of [18F]FAZA in a more selected patient population.

Improved semiparametric estimation of the proportional rate model with recurrent event data.

Owing to its robustness properties, marginal interpretations, and ease of implementation, the pseudo-partial likelihood method proposed in the seminal papers of Pepe and Cai and Lin et al. has become the default approach for analyzing recurrent event data with Cox-type proportional rate models. However, the construction of the pseudo-partial score function ignores the dependency among recurrent events and thus can be inefficient. An attempt to investigate the asymptotic efficiency of weighted pseudo-partial likelihood estimation found that the optimal weight function involves the unknown variance-covariance process of the recurrent event process and may not have closed-form expression. Thus, instead of deriving the optimal weights, we propose to combine a system of pre-specified weighted pseudo-partial score equations via the generalized method of moments and empirical likelihood estimation. We show that a substantial efficiency gain can be easily achieved without imposing additional model assumptions. More importantly, the proposed estimation procedures can be implemented with existing software. Theoretical and numerical analyses show that the empirical likelihood estimator is more appealing than the generalized method of moments estimator when the sample size is sufficiently large. An analysis of readmission risk in colorectal cancer patients is presented to illustrate the proposed methodology.

Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity.

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood-based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t-year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancer study.

Simultaneous selection and incorporation of consistent external aggregate information.

Interest has grown in synthesizing participant level data of a study with relevant external aggregate information. Several efficient and flexible procedures have been developed under the assumption that the internal study and the external sources concern the same population. This homogeneity condition, albeit commonly being imposed, is hard to check due to limitedly available external information in aggregate data forms. Bias may be introduced when the assumption is violated. In this article, we propose a penalized likelihood approach that avoids undesirable bias by simultaneously selecting and synthesizing consistent external aggregate information. The proposed approach provides a general framework which incorporate consistent external information from heterogeneous study populations as long as the conditional distribution of the dependent variable under investigation is same and differences in the independent variable distributions are properly accounted for via a semi-parametric density ratio model. The proposed approach also properly accounts for the sampling errors in the external information. A two-step estimator and an optimization algorithm are proposed for computation. We establish the selection and estimation consistency and the asymptotic normality of the two-step estimator. The proposed approach is illustrated with an analysis of gestational weight gain management studies.

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.