Home Oxygen After Hospitalization for COVID-19: Results From the Multi-Center OXFORD Study.

BACKGROUND: In the first months of the pandemic, prior to the introduction of proven-effective treatments, 15-37% of patients hospitalized with COVID-19 were discharged on home oxygen. After proven-effective treatments for acute COVID-19 were established by evidence-based guidelines, little remains known about home oxygen requirements following hospitalization for COVID-19. METHODS: This was a retrospective, multi-center cohort study of subjects hospitalized for COVID-19 between October 2020-September 2021 at 3 academic health centers. Information was abstracted from electronic health records at the index hospitalization and for 60 d after discharge. The World Health Organization COVID-19 Clinical Progression Scale score was used to identify patients with severe COVID-19. RESULTS: Of 517 subjects (mean age 58 y, 47% female, 42% Black, 36% Hispanic, 22% with severe COVID-19), 81% were treated with systemic corticosteroids, 61% with remdesivir, and 2.5% with tocilizumab. About one quarter of subjects were discharged on home oxygen (26% [95% CI 22-29]). Older age (adjusted odds ratio [aOR] 1.02 per 5 y [95% CI 1.02-1.02]), higher body mass index (aOR 1.02 per kg/m2 [1.00-1.04]), diabetes (yes vs no, aOR 1.73 [1.46-2.02]), severe COVID-19 (vs moderate, aOR 3.19 [2.19-4.64]), and treatment with systemic corticosteroids (yes vs no, aOR 30.63 [4.51-208.17]) were associated with an increased odds of discharge on home oxygen. Comorbid hypertension (yes vs no, aOR 0.71 [0.66-0.77) was associated with a decreased odds of home oxygen. Within 60 d of hospital discharge, 50% had documentation of pulse oximetry; in this group, home oxygen was discontinued in 46%. CONCLUSIONS: About one in 41 subjects were prescribed home oxygen after hospitalization for COVID-19, even after guidelines established proven-effective treatments for acute illness. Evidence-based strategies to reduce the requirement for home oxygen in patients hospitalized for COVID-19 are needed.

Bayesian Method of Borrowing Study-Level Historical Longitudinal Control Data for Mixed-Effects Models with Repeated Measures.

Bringing historical control information into a new trial appropriately holds the promise of more efficient trial design with more accurate estimates, increased power, and fewer patients allocated to inefficacious control group, provided the historical control data are sufficiently similar to the concurrent control. Interest has been growing over the past few decades in leveraging historical clinical trial on the control arm. However, most of the current historical borrowing methods focus on incorporating patient-level historical control information at only one time point. In this work, we propose a Bayesian hierarchical Mixed effect Models for Repeated Measures to incorporate aggregated study-level longitudinal historical control estimates into the concurrent trial that collected repeated longitudinal data. The simulation study demonstrates that, as compared to one time point data analysis approach, leveraging longitudinal historical control data produces greater power enhancement and mitigates the power loss when the missing data under missing at random mechanism is present. Our work also helps fill the gap of lack of methods borrowing historical longitudinal control data from the published summarized estimates when patient-level control data are not available.

Recruitment in a research study via chatbot versus telephone outreach: a randomized trial at a minority-serving institution.

Chatbots are software applications to simulate a conversation with a person. The effectiveness of chatbots in facilitating the recruitment of study participants in research, specifically among racial and ethnic minorities, is unknown. The objective of this study is to compare a chatbot versus telephone-based recruitment in enrolling research participants from a predominantly minority patient population at an urban institution. We randomly allocated adults to receive either chatbot or telephone-based outreach regarding a study about vaccine hesitancy. The primary outcome was the proportion of participants who provided consent to participate in the study. In 935 participants, the proportion who answered contact attempts was significantly lower in the chatbot versus telephone group (absolute difference -21.8%; 95% confidence interval [CI] -27.0%, -16.5%; P < 0.001). The consent rate was also significantly lower in the chatbot group (absolute difference -3.4%; 95% CI -5.7%, -1.1%; P = 0.004). However, among participants who answered a contact attempt, the difference in consent rates was not significant. In conclusion, the consent rate was lower with chatbot compared to telephone-based outreach. The difference in consent rates was due to a lower proportion of participants in the chatbot group who answered a contact attempt.

Hepatic Toxicity After Selective Chemoembolization Is Associated With Decreased Survival Among Patients With Hepatocellular Carcinoma.

OBJECTIVE. The purpose of this study was to identify risk factors for and outcomes of hepatotoxicity after selective chemoembolization of hepatocellular carcinoma. MATERIALS AND METHODS. This retrospective study included 182 patients (136 men and 46 women; median age, 63 years [interquartile range, 57-70 years]) who underwent 338 consecutive doxorubicin drug-eluting bead (DEB) chemoembolization procedures between 2011 and 2014. Outcomes were assessed until November 2019. In 97% of procedures, two or fewer segments were targeted. The Barcelona Clinic Liver Cancer (BCLC) stage was 0 or A for 77 procedures (22.8%), B for 75 (22.2%), C for 122 (36.1%), and D for 64 (18.9%). Hepatotoxicity was defined as worsened ascites or encephalopathy or as grade 3 or 4 elevations in liver function test results, creatinine levels, or the international normalized ratio within 30 days. Risk factors were assessed by univariate and multivariable generalized estimating equations. Transplant-free survival was assessed using Cox proportional hazard models. RESULTS. Hepatotoxicity was observed after 84 of 338 procedures (24.9%) performed for 70 of 182 patients (38.5%) and was irreversible for 40 procedures (11.8%). On multivariable analysis, risk factors for irreversible toxicity included Child-Pugh class C liver function (odds ratio [OR], 4.4; 95% CI, 1.0-19.0; p = .04), BCLC stage C (OR, 5.0; 95% CI, 1.6-16.0; p = .006) or D (OR, 7.4; 95% CI, 2.1-25.5; p = .002) disease, TIPS or hepatofugal portal venous flow (OR, 6.3; 95% CI, 2.3-17.0; p < .001), and a serum α-fetoprotein level of 200 ng/mL or greater (OR, 2.6; 95% CI, 1.1-6.1; p = .03). Irreversible toxicity was associated with reduced transplant-free survival among patients who were ineligible for liver transplant (hazard ratio, 2.5; standard error, 0.42; p = .03). CONCLUSION. Irreversible hepatotoxicity was common after selective chemoembolization in patients with advanced stage disease, an elevated serum α-fetoprotein level, or reduced hepatic portal venous perfusion, and it may hasten death among patients who are ineligible for liver transplant.

Differentiating between borderline and invasive malignancies in ovarian tumors using a multivariate logistic regression model.

OBJECTIVE: The objective of this study was to build a model to differentiate between borderline and invasive ovarian tumors. MATERIALS AND METHODS: We performed a retrospective study involving 148 patients with borderline or invasive ovarian tumors in our institute between 1997 and 2012. Clinical and pathologic data were collected. Logistic regression was used to build the model. RESULTS: The model was created based on the following variables (p < 0.05): menopausal status; preoperative serum level of cancer antigen 125; the greatest diameter of the tumor; and the presence of solid parts on ultrasound imaging. The sensitivity and specificity of the model were 94.6% [95% confidence interval (CI), 0.887-1] and 78.3% (95% CI, 0.614-0.952) for patients aged ≥ 50 years, and 76.0% (95% CI, 0.622-0.903) and 60.0% (95% CI, 0.438-0.762) for those aged < 50 years, respectively. The performance of the model was tested using cross-validation. CONCLUSION: Differentiation between borderline and invasive ovarian tumors can be achieved using a model based on the following criteria: menopausal status; cancer antigen 125 level; and ultrasound parameters. The model is helpful to oncologists and patients in the initial evaluation phase of ovarian tumors.

A longitudinal analysis with CA-125 to predict overall survival in patients with ovarian cancer.

OBJECTIVE: The objective of this study was to explore the association of longitudinal CA-125 measurements with overall survival (OS) time by developing a flexible model for patient-specific CA-125 profiles, and to provide a simple and reliable prediction of OS. METHODS: A retrospective study was performed on 275 patients with ovarian cancer who underwent at least one cycle of primary chemotherapy in our institute. Serial measurements of patients' CA-125 levels were performed at different frequencies according to their clinical plans. A statistical model coupling the Cox proportional hazards and the mixed-effects models was applied to determine the association of OS with patient-specific longitudinal CA-125 values. Stage and residual tumor size were additional variables included in the analysis. RESULTS: A total of 1,601 values of CA-125 were included. Longitudinal CA-125 levels, stage, and the residual tumor size were all significantly associated with OS. A patient-specific survival probability could be calculated. Validation showed that, in average, 85.4% patients were correctly predicted to have a high or low risk of death at a given time point. Comparison with a traditional model using CA-125 half-life and time to reach CA-125 nadir showed that the longitudinal CA-125 model had an improved predicative value. CONCLUSION: Longitudinal CA-125 values, measured from the diagnosis of ovarian cancer to the completion of primary chemotherapy, could be used to reliably predict OS after adjusting for the stage and residual tumor disease. This model could be potentially useful in clinical counseling of patients with ovarian cancer.