Association of Remote Patient-Reported Outcomes and Step Counts With Hospitalization or Death Among Patients With Advanced Cancer Undergoing Chemotherapy: Secondary Analysis of the PROStep Randomized Trial.

BACKGROUND: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death. OBJECTIVE: The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death. METHODS: The PROStep randomized trial enrolled 108 patients with advanced gastrointestinal or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs plus continuous step count monitoring via Fitbit (Google) versus usual care. This preplanned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step count data. We analyzed the associations between PROs and mean daily step counts and the associations of PROs and step counts with the composite outcome of hospitalization or death using bootstrapped generalized linear models to account for longitudinal data. RESULTS: Among 57 patients, the mean age was 57 (SD 10.9) years, 24 (42%) were female, 43 (75%) had advanced gastrointestinal cancer, 14 (25%) had advanced lung cancer, and 25 (44%) were hospitalized or died during follow-up. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI -277 to -213; P<.001). PROs most strongly associated with step count decline were patient-reported activity (daily step change -892), nausea score (-677), and constipation score (524). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P=.01). PROs most strongly associated with hospitalization or death were pain (aOR 3.2, 95% CI 1.6-6.5; P<.001), decreased activity (aOR 3.2, 95% CI 1.4-7.1; P=.01), dyspnea (aOR 2.6, 95% CI 1.2-5.5; P=.02), and sadness (aOR 2.1, 95% CI 1.1-4.3; P=.03). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0-1.3; P=.03). Compared with baseline, mean daily step count decreased 7% (n=274 steps), 9% (n=351 steps), and 16% (n=667 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively. CONCLUSIONS: In this secondary analysis of a randomized trial among patients with advanced cancer, higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step count data to target interventions toward patients at risk for poor outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-054675.

Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates.

In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The model partitions individuals' data into two phases, known as regimes, with: (1) a zero-inflation regime that is used to accommodate high instances of zeros (non-drinking) and (2) a multilevel Poisson regression regime in which variations in individuals' log-transformed average rates of alcohol use are captured by means of an autoregressive process with exogenous predictors and a person-specific intercept. The times at which individuals are in each regime are unknown, but may be estimated from the data. We assume that the regime indicator follows a first-order Markov process as related to exogenous predictors of interest. The forecast performance of the proposed model was evaluated using a Monte Carlo simulation study and further demonstrated using substance use and spatial covariate data from the Colorado Online Twin Study (CoTwins). Results showed that the proposed model yielded better forecast performance compared to a baseline model which predicted all cases as non-drinking and a reduced ZIMLP model without the RS structure, as indicated by higher AUC (the area under the receiver operating characteristic (ROC) curve) scores, and lower mean absolute errors (MAEs) and root-mean-square errors (RMSEs). The improvements in forecast performance were even more pronounced when we limited the comparisons to participants who showed at least one instance of transition to drinking.

Comparison of 6 Injectable Anesthetic Regimens and Isoflurane in Gray Short-tailed Opossums (Monodelphis domestica).

Gray short-tailed opossums are used in a wide variety of research in the areas of developmental biology, oncology, immunology, and comparative biology. Despite many frequent experimental manipulations of these animals under anesthesia, few studies to date have characterized the effects of anesthesia in this species. Our aim was to identify safe and effective injectable anesthetic combinations using ketamine and xylazine or ketamine and dexmedetomidine at doses of 40 mg/kg to 100 mg/kg for ketamine, 5 mg/kg to 10 mg/kg for xylazine, and 0.05 mg/kg to 0.1 mg/kg for dexmedetomidine. Effects of the proposed regimens ranged from light sedation to surgical anesthesia, but only 100 mg/kg ketamine + 0.1 mg/kg dexmedetomidine induced surgical anesthesia in all opossums, with a mean duration of 25.4 min. The 2 lowest doses of ketamine and xylazine (40 mg/kg ketamine + 5 mg/kg xylazine and 40 mg/kg ketamine + 10 mg/kg xylazine) achieved sedation to light anesthesia in all animals but did not produce a surgical plane of anesthesia in any animal. All regimens that induced a surgical plane of anesthesia caused bradycardia and bradypnea, and 75 mg/kg ketamine + 10 mg/kg xylazine and 100 mg/kg ketamine + 0.1 mg/kg dexmedetomidine caused the greatest decreases in SpO2. Except for one opossum that died of unknown causes, all animals remained healthy and apparently free of anesthetic complications. Among all treatments, isoflurane delivered by a precision vaporizer provided the most consistent and reliable anesthesia; therefore, we recommend inhalant anesthesia over the injectable combinations used in this study.

Heart rate variability and DNA methylation levels are altered after short-term metal fume exposure among occupational welders: a repeated-measures panel study.

BACKGROUND: In occupational settings, boilermakers are exposed to high levels of metallic fine particulate matter (PM2.5) generated during the welding process. The effect of welding PM2.5 on heart rate variability (HRV) has been described, but the relationship between PM2.5, DNA methylation, and HRV is not known. METHODS: In this repeated-measures panel study, we recorded resting HRV and measured DNA methylation levels in transposable elements Alu and long interspersed nuclear element-1 (LINE-1) in peripheral blood leukocytes under ambient conditions (pre-shift) and right after a welding task (post-shift) among 66 welders. We also monitored personal PM2.5 level in the ambient environment and during the welding procedure. RESULTS: The concentration of welding PM2.5 was significantly higher than background levels in the union hall (0.43 mg/m3 vs. 0.11 mg/m3, p < 0.0001). The natural log of transformed power in the high frequency range (ln HF) had a significantly negative association with PM2.5 exposure (ß = -0.76, p = 0.035). pNN10 and pNN20 also had a negative association with PM2.5 exposure (ß = -0.16%, p = 0.006 and ß = -0.13%, p = 0.030, respectively). PM2.5 was positively associated with LINE-1 methylation [ß = 0.79%, 5-methylcytosince (%mC), p = 0.013]; adjusted for covariates. LINE-1 methylation did not show an independent association with HRV. CONCLUSIONS: Acute decline of HRV was observed following exposure to welding PM2.5 and evidence for an epigenetic response of transposable elements to short-term exposure to high-level metal-rich particulates was reported.

Analytic P-value calculation for the higher criticism test in finite d problems.

The higher criticism is effective for testing a joint null hypothesis against a sparse alternative, e.g., for testing the effect of a gene or a genetic pathway that consists of d genetic markers. Accurate p-value calculations for the higher criticism based on the asymptotic distribution require a very large d, which is not the case for the number of genetic variants in a gene or a pathway. In this paper we propose an analytic method that accurately computes the p-value of the higher criticism test for finite d problems. Unlike previous treatments, this method does not rely on asymptotics in d or simulation, and is exact for arbitrary d when the test statistics are normally distributed. The method is particularly computationally advantageous when d is not large. We illustrate the proposed method with a case-control genome-wide association study of lung cancer and compare its power to competing methods through simulations.

Detecting rare variant effects using extreme phenotype sampling in sequencing association studies.

In the increasing number of sequencing studies aimed at identifying rare variants associated with complex traits, the power of the test can be improved by guided sampling procedures. We confirm both analytically and numerically that sampling individuals with extreme phenotypes can enrich the presence of causal rare variants and can therefore lead to an increase in power compared to random sampling. Although application of traditional rare variant association tests to these extreme phenotype samples requires dichotomizing the continuous phenotypes before analysis, the dichotomization procedure can decrease the power by reducing the information in the phenotypes. To avoid this, we propose a novel statistical method based on the optimal Sequence Kernel Association Test that allows us to test for rare variant effects using continuous phenotypes in the analysis of extreme phenotype samples. The increase in power of this method is demonstrated through simulation of a wide range of scenarios as well as in the triglyceride data of the Dallas Heart Study.