Faculty Bibliography

BACKGROUND AND OBJECTIVES/OBJECTIVE:AKI is a common complication of coronavirus disease 2019 (COVID-19) and is associated with high mortality. Palliative care, a specialty that supports patients with serious illness, is valuable for these patients but is historically underutilized in AKI. The objectives of this paper are to describe the use of palliative care in patients with AKI and COVID-19 and their subsequent health care utilization. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS/METHODS:We conducted a retrospective analysis of New York University Langone Health electronic health data of COVID-19 hospitalizations between March 2, 2020 and August 25, 2020. Regression models were used to examine characteristics associated with receiving a palliative care consult. RESULTS:=0.002). Despite greater use of palliative care, patients with AKI had a significantly longer length of stay, more intensive care unit admissions, and more use of mechanical ventilation. Those with AKI did have a higher frequency of discharges to inpatient hospice (6% versus 3%) and change in code status (34% versus 7%) than those without AKI. CONCLUSIONS:Palliative care was utilized more frequently for patients with AKI and COVID-19 than historically reported in AKI. Despite high mortality, consultation occurred late in the hospital course and was not associated with reduced initiation of life-sustaining interventions. PODCAST/UNASSIGNED:This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_02_24_CJN11030821.mp3.

When New York City (NYC) became an epicenter of the COVID-19 pandemic in the spring of 2020, statistical consulting centers at academic medical institutions in the area were immediately inundated with requests from hospital leadership and researchers for methodological support to address different aspects of the outbreak. Statisticians suddenly had to pivot from their usual responsibilities to focus entirely on COVID-19 work, and consulting centers had to devise innovative strategies to restructure their workflow and develop new infrastructure to address the acute demand for support. As statisticians from seven NYC-area institutions, we share our experiences and lessons learned during the pandemic, with the hope that this will lead not only to better preparedness for future public health crises when the skills and expertise of statisticians are critically needed, but also to lasting improvements to the structure and practice of statistical consulting centers.

BACKGROUND:Multiple system atrophy (MSA) is a fatal neurodegenerative disease characterized by the aggregation of α-synuclein in glia and neurons. Sirolimus (rapamycin) is an mTOR inhibitor that promotes α-synuclein autophagy and reduces its associated neurotoxicity in preclinical models. OBJECTIVE:To investigate the efficacy and safety of sirolimus in patients with MSA using a futility design. We also analyzed 1-year biomarker trajectories in the trial participants. METHODS:Randomized, double-blind, parallel group, placebo-controlled clinical trial at the New York University of patients with probable MSA randomly assigned (3:1) to sirolimus (2-6 mg daily) for 48 weeks or placebo. Primary endpoint was change in the Unified MSA Rating Scale (UMSARS) total score from baseline to 48 weeks. (ClinicalTrials.gov NCT03589976). RESULTS:The trial was stopped after a pre-planned interim analysis met futility criteria. Between August 15, 2018 and November 15, 2020, 54 participants were screened, and 47 enrolled and randomly assigned (35 sirolimus, 12 placebo). Of those randomized, 34 were included in the intention-to-treat analysis. There was no difference in change from baseline to week 48 between the sirolimus and placebo in UMSARS total score (mean difference, 2.66; 95% CI, -7.35-6.91; P = 0.648). There was no difference in UMSARS-1 and UMSARS-2 scores either. UMSARS scores changes were similar to those reported in natural history studies. Neuroimaging and blood biomarker results were similar in the sirolimus and placebo groups. Adverse events were more frequent with sirolimus. Analysis of 1-year biomarker trajectories in all participants showed that increases in blood neurofilament light chain (NfL) and reductions in whole brain volume correlated best with UMSARS progression. CONCLUSIONS:Sirolimus for 48 weeks was futile to slow the progression of MSA and had no effect on biomarkers compared to placebo. One-year change in blood NfL and whole brain atrophy are promising biomarkers of disease progression for future clinical trials. © 2022 International Parkinson and Movement Disorder Society.

Dynamic survival analysis is a variant of traditional survival analysis where time-to-event predictions are updated as new information arrives about an individual over time. In this paper we propose a new approach to dynamic survival analysis based on learning a global parametric distribution, followed by individualization via truncating and renormalizing that distribution at different locations over time. We combine this approach with a likelihood-based loss that includes predictions at every time step within an individual's history, rather than just including one term per individual. The combination of this loss and model results in an interpretable approach to dynamic survival, requiring less fine tuning than existing methods, while still achieving good predictive performance. We evaluate the approach on the problem of predicting hospital mortality for a dataset with over 6900 COVID-19 patients.

The widespread availability of high-dimensional electronic healthcare record (EHR) datasets has led to significant interest in using such data to derive clinical insights and make risk predictions. More specifically, techniques from machine learning are being increasingly applied to the problem of dynamic survival analysis, where updated time-to-event risk predictions are learned as a function of the full covariate trajectory from EHR datasets. EHR data presents unique challenges in the context of dynamic survival analysis, involving a variety of decisions about data representation, modeling, interpretability, and clinically meaningful evaluation. In this paper we propose a new approach to dynamic survival analysis which addresses some of these challenges. Our modeling approach is based on learning a global parametric distribution to represent population characteristics and then dynamically locating individuals on the time-axis of this distribution conditioned on their histories. For evaluation we also propose a new version of the dynamic C-Index for clinically meaningful evaluation of dynamic survival models. To validate our approach we conduct dynamic risk prediction on three real-world datasets, involving COVID-19 severe outcomes, cardiovascular disease (CVD) onset, and primary biliary cirrhosis (PBC) time-to-transplant. We find that our proposed modeling approach is competitive with other well-known statistical and machine learning approaches for dynamic risk prediction, while offering potential advantages in terms of interepretability of predictions at the individual level.

BACKGROUND:Patients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critically ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice. METHODS:We modeled the oxygen flow trajectory of COVID-19 patients and their health outcomes as a Markov decision process. Based on individual patient characteristics and health status, an optimal oxygen control policy is learned by using deep deterministic policy gradient (DDPG) and real-time recommends the oxygen flow rate to reduce the mortality rate. We assessed the performance of proposed methods through cross validation by using a retrospective cohort of 1372 critically ill patients with COVID-19 from New York University Langone Health ambulatory care with electronic health records from April 2020 to January 2021. RESULTS:The mean mortality rate under the RL algorithm is lower than the standard of care by 2.57% (95% CI: 2.08-3.06) reduction (P < 0.001) from 7.94% under the standard of care to 5.37% under our proposed algorithm. The averaged recommended oxygen flow rate is 1.28 L/min (95% CI: 1.14-1.42) lower than the rate delivered to patients. Thus, the RL algorithm could potentially lead to better intensive care treatment that can reduce the mortality rate, while saving the oxygen scarce resources. It can reduce the oxygen shortage issue and improve public health during the COVID-19 pandemic. CONCLUSIONS:A personalized reinforcement learning oxygen flow control algorithm for COVID-19 patients under intensive care showed a substantial reduction in 7-day mortality rate as compared to the standard of care. In the overall cross validation cohort independent of the training data, mortality was lowest in patients for whom intensivists' actual flow rate matched the RL decisions.

BACKGROUND:Early reports on cancer patients with coronavirus disease 2019 (COVID-19) corroborated speculation that cancer patients are at increased risk for becoming infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and developing severe COVID-19. However, cancer patients are a heterogeneous population and their corresponding risk may be different. AIM/OBJECTIVE:To compare COVID-19 presentation in patients with active malignancy to those with a history of cancer to determine the impact of cancer status on COVID-19 outcomes in the two groups. METHODS AND RESULTS/RESULTS:Of the 6724 patients who were hospitalized at NYU Langone Health (3/16/20-7/31/20) and tested positive for SARS-CoV-2, 580 had either active cancer (n = 221) or a history of cancer (n = 359). We compared the baseline clinicodemographic characteristics and hospital courses of the two groups. We studied the relationship between cancer status and the rate of admission to the intensive care unit (ICU), use of invasive mechanical ventilation (IMV), and all-cause mortality. The two groups had similar laboratory results associated with COVID-19 infection, incidence of venous thromboembolism, and incidence of severe COVID-19. Active cancer status was not associated with the rate of ICU admission (p = .307) or use of IMV (p = .236), but was significantly associated with worse all-cause mortality in both univariate and multivariate analysis with odds ratios of 1.48 (95% confidence interval [CI]: 1.04-2.09; p = .028) and 1.71 (95% CI: 1.12-2.63; p = .014), respectively. CONCLUSION/CONCLUSIONS:Active cancer patients had worse survival outcomes compared to patients with a history of cancer despite similar COVID-19 disease characteristics in the two groups. Our data suggest that cancer care should continue with minimal interruptions during the pandemic to bring about response and remission as soon as possible.

BACKGROUND:Immune-related adverse events (irAEs) are common, clinically significant autoinflammatory toxicities observed with immune checkpoint inhibitors (ICI). Preexisting immune-mediated inflammatory disease (pre-IMID) is considered a relative contraindication to ICI due to the risk of inciting flares. Improved understanding of the risks and benefits of treating pre-IMID patients with ICI is needed. METHODS:We studied melanoma patients treated with ICI and enrolled in a prospective clinicopathological database. We compiled a list of 23 immune-mediated inflammatory diseases and evaluated their presence prior to ICI. We tested the associations between pre-IMID and progression-free survival (PFS), overall survival (OS), and irAEs. RESULTS:In total, 483 melanoma patients were included in the study; 74 had pre-IMID and 409 did not. In patients receiving ICI as a standard of care (SoC), pre-IMID was significantly associated with irAEs (p = 0.04) as well as improved PFS (p = 0.024) and OS (p = 0.007). There was no significant association between pre-IMID and irAEs (p = 0.54), PFS (p = 0.197), or OS (p = 0.746) in patients treated through a clinical trial. Pre-IMID was significantly associated with improved OS in females (p = 0.012), but not in males (p = 0.35). CONCLUSIONS:The dichotomy of the impact of pre-IMID on survival and irAEs in SoC versus clinical trial patients may reflect the inherit selection bias in patients accrued in clinical trials. Future mechanistic work is required to better understand the differences in outcomes between female and male pre-IMID patients. Our data challenge the notion that clinicians should avoid ICI in pre-IMID patients, although close monitoring and prospective clinical trials evaluating ICI in this population are warranted.