Implementation of Systematic Financial Screening in an Outpatient Breast Oncology Setting.

PURPOSE: Implementation of routine financial screening is a critical step toward mitigating financial toxicity. We evaluated the feasibility, sustainability, and acceptability of systematic financial screening in the outpatient breast oncology clinic at a large, urban cancer center. METHODS: We developed and implemented a stakeholder-informed process to systematically screen for financial hardship and worry. A 2-item assessment in English or Spanish was administered to patients through the electronic medical record portal or using paper forms. We evaluated completion rates and mode of completion. Through feedback from patients, clinicians, and staff, we identified strategies to improve completion rates and acceptability. RESULTS: From March, 2021, to February, 2022, 3,500 patients were seen in the breast oncology clinic. Of them, 39% (n = 1,349) responded to the screening items, either by paper or portal, 12% (n = 437) preferred not to answer, and the remaining 49% (n = 1,714) did not have data in their electronic health record, meaning they were not offered screening or did not complete the paper forms. Young adults (18-39 years) were more likely to respond compared with patients 70 years or older (61% v 30%, P < .01). English-preferring patients were more likely to complete the screening compared with those who preferred Spanish (46% v 28%, P < .01). Non-Hispanic White patients were more likely to respond compared with Non-Hispanic Black patients and with Hispanic patients (46% v 39% v 32%, P < .01). Strategies to improve completion rates included partnering with staff to facilitate paper form administration, optimizing patient engagement with the portal, and clearly communicating the purpose of the screening. CONCLUSION: Systematic financial screening is feasible, and electronic data capture facilitates successful implementation. However, inclusive procedures that address language and technology preferences are needed to optimize screening.

Structured deep embedding model to generate composite clinical indices from electronic health records for early detection of pancreatic cancer.

The high-dimensionality, complexity, and irregularity of electronic health records (EHR) data create significant challenges for both simplified and comprehensive health assessments, prohibiting an efficient extraction of actionable insights by clinicians. If we can provide human decision-makers with a simplified set of interpretable composite indices (i.e., combining information about groups of related measures into single representative values), it will facilitate effective clinical decision-making. In this study, we built a structured deep embedding model aimed at reducing the dimensionality of the input variables by grouping related measurements as determined by domain experts (e.g., clinicians). Our results suggest that composite indices representing liver function may consistently be the most important factor in the early detection of pancreatic cancer (PC). We propose our model as a basis for leveraging deep learning toward developing composite indices from EHR for predicting health outcomes, including but not limited to various cancers, with clinically meaningful interpretations.

Association between neighborhood socioeconomic status, built environment and SARS-CoV-2 infection among cancer patients treated at a Tertiary Cancer Center in New York City.

BACKGROUND: Racial and ethnic minority groups experience a disproportionate burden of SARS-CoV-2 illness and studies suggest that cancer patients are at a particular risk for severe SARS-CoV-2 infection. AIMS: The objective of this study was examine the association between neighborhood characteristics and SARS-CoV-2 infection among patients with cancer. METHODS AND RESULTS: We performed a cross-sectional study of New York City residents receiving treatment for cancer at a tertiary cancer center. Patients were linked by their address to data from the US Census Bureau's American Community Survey and to real estate tax data from New York's Department of City Planning. Models were used to both to estimate odds ratios (ORs) per unit increase and to predict probabilities (and 95% CI) of SARS-CoV2 infection. We identified 2350 New York City residents with cancer receiving treatment. Overall, 214 (9.1%) were infected with SARS-CoV-2. In adjusted models, the percentage of Hispanic/Latino population (aOR = 1.01; 95% CI, 1.005-1.02), unemployment rate (aOR = 1.10; 95% CI, 1.05-1.16), poverty rates (aOR = 1.02; 95% CI, 1.0002-1.03), rate of >1 person per room (aOR = 1.04; 95% CI, 1.01-1.07), average household size (aOR = 1.79; 95% CI, 1.23-2.59) and population density (aOR = 1.86; 95% CI, 1.27-2.72) were associated with SARS-CoV-2 infection. CONCLUSION: Among cancer patients in New York City receiving anti-cancer therapy, SARS-CoV-2 infection was associated with neighborhood- and building-level markers of larger household membership, household crowding, and low socioeconomic status. NOVELTY AND IMPACT: We performed a cross-sectional analysis of residents of New York City receiving treatment for cancer in which we linked subjects to census and real estate date. This linkage is a novel way to examine the neighborhood characteristics that influence SARS-COV-2 infection. We found that among patients receiving anti-cancer therapy, SARS-CoV-2 infection was associated with building and neighborhood-level markers of household crowding, larger household membership, and low socioeconomic status. With ongoing surges of SARS-CoV-2 infections, these data may help in the development of interventions to decrease the morbidity and mortality associated with SARS-CoV-2 among cancer patients.

Breast Cancer Risk and Screening Mammography Frequency Among Multiethnic Women.

INTRODUCTION: In 2009, the U.S. Preventive Services Task Force updated recommended mammography screening frequency from annual to biennial for average-risk women aged 50-74 years. The association between estimated breast cancer risk and mammography screening frequency was evaluated. METHODS: A single-center retrospective cohort study was conducted among racially/ethnically diverse women, aged 50-74 years, who underwent screening mammography from 2014 to 2018. Data on age, race/ethnicity, first-degree family history of breast cancer, previous benign breast biopsies, and mammographic density were extracted from the electronic health record to calculate Breast Cancer Surveillance Consortium 5-year risk of invasive breast cancer, with a 5-year risk ≥1.67% defined as high risk. Multivariable analyses were conducted to determine the association between breast cancer risk factors and mammography screening frequency (annual versus biennial). Data were analyzed from 2020 to 2022. RESULTS: Among 12,929 women with a mean age of 61±6.9 years, 82.7% underwent annual screening mammography, and 30.7% met high-risk criteria for breast cancer. Hispanic women were more likely to screen annually than non-Hispanic Whites (85.0% vs 79.8%, respectively), despite fewer meeting high-risk criteria. In multivariable analyses adjusting for breast cancer risk factors, high- versus low/average-risk women (OR=1.17; 95% CI=1.04, 1.32) and Hispanic versus non-Hispanic White women (OR=1.46; 95% CI=1.29, 1.65) were more likely to undergo annual mammography. CONCLUSIONS: A majority of women continue to undergo annual screening mammography despite only a minority meeting high-risk criteria, and Hispanic women were more likely to screen annually despite lower overall breast cancer risk. Future studies should focus on the implementation of risk-stratified breast cancer screening strategies.

Deep learning on time series laboratory test results from electronic health records for early detection of pancreatic cancer.

The multi-modal and unstructured nature of observational data in Electronic Health Records (EHR) is currently a significant obstacle for the application of machine learning towards risk stratification. In this study, we develop a deep learning framework for incorporating longitudinal clinical data from EHR to infer risk for pancreatic cancer (PC). This framework includes a novel training protocol, which enforces an emphasis on early detection by applying an independent Poisson-random mask on proximal-time measurements for each variable. Data fusion for irregular multivariate time-series features is enabled by a "grouped" neural network (GrpNN) architecture, which uses representation learning to generate a dimensionally reduced vector for each measurement set before making a final prediction. These models were evaluated using EHR data from Columbia University Irving Medical Center-New York Presbyterian Hospital. Our framework demonstrated better performance on early detection (AUROC 0.671, CI 95% 0.667 - 0.675, p < 0.001) at 12 months prior to diagnosis compared to a logistic regression, xgboost, and a feedforward neural network baseline. We demonstrate that our masking strategy results greater improvements at distal times prior to diagnosis, and that our GrpNN model improves generalizability by reducing overfitting relative to the feedforward baseline. The results were consistent across reported race. Our proposed algorithm is potentially generalizable to other diseases including but not limited to cancer where early detection can improve survival.

Immunogenetics of gastrointestinal cancers: A systematic review and retrospective survey of inborn errors of immunity in humans.

BACKGROUND AND AIM: Humans with inborn errors of immunity (IEI), or primary immunodeficiencies, may be associated with a potential risk factor for early-onset gastrointestinal (GI) cancer. METHODS: We systematically reviewed all cases with clinical diagnoses of both an IEI and a GI cancer in three databases (MEDLINE, SCOPUS, and EMBASE). In total, 76 publications satisfying our inclusion criteria were identified, and data for 149 cases were analyzed. We also searched our institutional cancer registry for such cases. RESULTS: We identified 149 patients with both an IEI and a GI cancer, 95 presented gastric cancer, 13 small bowel cancer, 35 colorectal cancer, and 6 had an unspecified cancer or cancer at another site. Gastric and colon adenocarcinomas were the most common. For both gastric and colorectal cancers, age at onset was significantly earlier in patients with IEIs than in the general population, based on the SEER database. Common variable immunodeficiency (CVID) was the most common IEI associated with gastrointestinal cancer. About 12% of patients had molecular genetic diagnoses, the three most frequently implicated genes being ATM, CARMIL2, and CTLA4. Impaired humoral immunity and Epstein-Barr virus (EBV) infection were frequently reported as factors potentially underlying early-onset GI cancers in patients with IEIs. We identified one patient with CVID and early-onset gastric adenocarcinoma, recurrent diarrhea, and gastrointestinal CMV infection from a retrospective survey. CONCLUSION: Patients with IEIs should be considered at risk of early-onset GI cancers and should therefore undergo cancer screening at an earlier age.

Where Have All the Emergencies Gone? The Impact of the COVID-19 Pandemic on Obstetric and Gynecologic Procedures and Consults at a New York City Hospital.

STUDY OBJECTIVE: The purpose of this study was to assess the impact of the coronavirus disease 2019 (COVID-19) pandemic on surgical volume and emergency department (ED) consults across obstetrics-gynecology (OB-GYN) services at a New York City hospital. DESIGN: Retrospective cohort study. SETTING: Tertiary care academic medical center in New York City. PATIENTS: Women undergoing OB-GYN ED consults or surgeries between February 1, 2020 and April 15, 2020. INTERVENTIONS: March 16 institutional moratorium on elective surgeries. MEASUREMENTS AND MAIN RESULTS: The volume and types of surgeries and ED consults were compared before and after the COVID-19 moratorium. During the pandemic, the average weekly volume of ED consults and gynecology (GYN) surgeries decreased, whereas obstetric (OB) surgeries remained stable. The proportions of OB-GYN ED consults, GYN surgeries, and OB surgeries relative to all ED consults, all surgeries, and all labor and delivery patients were 1.87%, 13.8%, 54.6% in the pre-COVID-19 time frame (February 1-March 15) vs 1.53%, 21.3%, 79.7% in the COVID-19 time frame (March 16-April 15), representing no significant difference in proportions of OB-GYN ED consults (p = .464) and GYN surgeries (p = .310) before and during COVID-19, with a proportionate increase in OB surgeries (p <.002). The distribution of GYN surgical case types changed significantly during the pandemic with higher proportions of emergent surgeries for ectopic pregnancies, miscarriages, and concern for cancer (p <.001). Alternatively, the OB surgery distribution of case types remained relatively constant. CONCLUSION: This study highlights how the pandemic has affected the ways that patients in OB-GYN access and receive care. Institutional policies suspending elective surgeries during the pandemic decreased GYN surgical volume and affected the types of cases performed. This decrease was not appreciated for OB surgical volume, reflecting the nonelective and time-sensitive nature of obstetric care. A decrease in ED consults was noted during the pandemic begging the question "Where have all the emergencies gone?" Although the moratorium on elective procedures was necessary, "elective" GYN surgeries remain medically indicated to address symptoms such as pain and bleeding and to prevent serious medical sequelae such as severe anemia requiring transfusion. As we continue to battle COVID-19, we must not lose sight of those patients whose care has been deferred.