Rapid real‐world data analysis of patients with cancer, with and without COVID‐19, across distinct health systems

Abstract

Background The understanding of the impact of COVID‐19 in patients with cancer is evolving, with need for rapid analysis. Aims This study aims to compare the clinical and demographic characteristics of patients with cancer (with and without COVID‐19) and characterize the clinical outcomes of patients with COVID‐19 and cancer. Methods and Results Real‐world data (RWD) from two health systems were used to identify 146 702 adults diagnosed with cancer between 2015 and 2020; 1267 COVID‐19 cases were identified between February 1 and July 30, 2020. Demographic, clinical, and socioeconomic characteristics were extracted. Incidence of all‐cause mortality, hospitalizations, and invasive respiratory support was assessed between February 1 and August 14, 2020. Among patients with cancer, patients with COVID‐19 were more likely to be Non‐Hispanic black (NHB), have active cancer, have comorbidities, and/or live in zip codes with median household income <$30 000. Patients with COVID‐19 living in lower‐income areas and NHB patients were at greatest risk for hospitalization from pneumonia, fluid and electrolyte disorders, cough, respiratory failure, and acute renal failure and were more likely to receive hydroxychloroquine. All‐cause mortality, hospital admission, and invasive respiratory support were more frequent among patients with cancer and COVID‐19. Male sex, increasing age, living in zip codes with median household income <$30 000, history of pulmonary circulation disorders, and recent treatment with immune checkpoint inhibitors or chemotherapy were associated with greater odds of all‐cause mortality in multivariable logistic regression models. Conclusion RWD can be rapidly leveraged to understand urgent healthcare challenges. Patients with cancer are more vulnerable to COVID‐19 effects, especially in the setting of active cancer and comorbidities, with additional risk observed in NHB patients and those living in zip codes with median household income <$30 000.

Real-World Treatment Patterns and Clinical Effectiveness of Palbociclib Plus an Aromatase Inhibitor as First-Line Therapy in Advanced/Metastatic Breast Cancer: Analysis from the US Syapse Learning Health Network

“…This single arm, retrospective study used the US Syapse Learning Health Network (LHN) to evaluate patient characteristics, treatment patterns, and clinical outcomes among patients receiving first-line therapy with palbociclib plus an aromatase inhibitor for A/MBC. The Syapse LHN is a longitudinal database capturing cancer care and noncancer care data and has previously been used by the US Food and Drug Administration [ 21 , 22 , 23 ]. It includes patients from large community health systems based in the United States that include managed care delivery networks across 25 states, 457 hospitals, and more than 1300 oncologists.…”

Quantifying mortality burden in patients with cancer due to COVID‐19 in the US: A national cross‐sectional analysis

“…In addition, patients with cancer during the pandemic experienced more problems with access to care due to a variety of reasons, such as difficulty in obtaining timely appointments due to the healthcare shifts in focusing on COVID‐19 specific care, leading to potential delayed diagnosis and treatments 4 . Sociodemographic factors such as zip code of residency, non‐Hispanic (NH) Black race/ethnicity, and male sex have also been associated with hospitalization and mortality, however, their study has been limited in the pandemic 5 . Han et al quantified deaths due to COVID‐19 and cancer using data from 2020, prior to the widespread availability of vaccination, and found that factors such as American Indian or Alaskan Native, Black, and Hispanic race/ethnicity, age greater than or equal to 85, and residence in a large central metropolitan area were significant risk factors for mortality in patients with cancer and COVID‐19 6 .…”

COVID-19 Outcomes in Patients with Cancer Receiving Immune Checkpoint Inhibitors: A Systematic Review

“…Mortality was reported in nine studies including 393 patients on ICIs and 1025 controls. [10,11,[13][14][15]17,22,23,26] Retrospective cohorts Albiges 2020 [10] 178 19 (10.7) Chemotherapy, targeted therapy, hormone therapy Hospital admission, clinical worsening, ICU admission Assaad 2020 [11] 55 3 (5.5) Chemotherapy, no treatment Mortality Dai 2020 [13] 105 6 (5.7) Chemotherapy, targeted therapy, chemotherapy þ immunotherapy, healthy controls…”

“…Gonzalez-Cao 2020 [24] 50 22 (44) Targeted therapy, no treatment Mortality Grivas 2021 [15]a 4966 238 (4.8) Chemotherapy, targeted therapy, no treatment, hormone therapy Mortality, ICU admission, hospital admission, mechanical ventilation Gulati 2021 [16] 4217 199 (4.7) Targeted therapy, hormone therapy Thrombotic complications, ICU admission, mortality Hwang 2021 [17]b 1267 12 (1) Chemotherapy, targeted therapy, no treatment…”

“…Mortality was reported in eight studies including 364 patients on ICIs and 851 controls. [10,[13][14][15]17,22,24,33] No statistically significant differences were observed (RR 1.28; 95% CI 0.93-1.75; I 2 ¼ 6%). Certainty of evidence was very low (rated down for imprecision, risk of bias).…”