Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States

Breast cancer (BC) and severe COVID-19 (C-19) outcomes: a matched analysis

PURPOSE

Patients with cancer receiving anticancer treatment have a higher risk of severe COVID-19 (C-19) outcomes. We examine the association between breast cancer (BC), recent treatment (systemic therapy, surgery, radiation), and C-19 outcomes.

METHODS

Retrospective matched cohort study using the Optum® de-identified C-19 Electronic Health Record dataset (2007-2022). Patients with C-19 were categorized into: no cancer, BC with recent treatment, and BC without recent treatment and matched based on age, C-19 diagnosis date, and comorbidity score. We evaluated 30-day mortality, mechanical ventilation, intensive care unit (ICU) stay, and hospitalization. A composite outcome including all outcomes was analyzed. Multivariable logistic regression models were used.

RESULTS

2200 matched triplets (1:1:10) of patients with BC recently treated, BC not recently treated, and no cancer were included. Rates of adverse outcomes improved in 2021 compared to 2020. Compared to patients without cancer, those with BC recently treated had a similar risk of adverse outcomes, while patients with BC not recently treated had a lower risk of ICU stay and hospitalization. Using the composite variable, BC recently treated had similar outcomes (OR = 1.02; 95%CI 0.93-1.11) to patients without cancer, while BC patients not recently treated had better outcomes (OR = 0.66; 95%CI 0.59-0.74). Among patients with BC, chemotherapy within 3 months was associated with a higher risk of hospitalization (OR = 2.30; 95%CI 1.76-2.99) and composite outcome (OR = 2.11; 95%CI 1.64-2.72).

CONCLUSION

Patients with BC have a similar risk of adverse C-19 outcomes compared to patients without cancer. Among patients with BC, recent chemotherapy was associated with a higher risk of hospitalization.

Integrating real-world data from Brazil and Pakistan into the OMOP common data model and standardized health analytics framework to characterize COVID-19 in the Global South

OBJECTIVES

The aim of this work is to demonstrate the use of a standardized health informatics framework to generate reliable and reproducible real-world evidence from Latin America and South Asia towards characterizing coronavirus disease 2019 (COVID-19) in the Global South.

MATERIALS AND METHODS

Patient-level COVID-19 records collected in a patient self-reported notification system, hospital in-patient and out-patient records, and community diagnostic labs were harmonized to the Observational Medical Outcomes Partnership common data model and analyzed using a federated network analytics framework. Clinical characteristics of individuals tested for, diagnosed with or tested positive for, hospitalized with, admitted to intensive care unit with, or dying with COVID-19 were estimated.

RESULTS

Two COVID-19 databases covering 8.3 million people from Pakistan and 2.6 million people from Bahia, Brazil were analyzed. 109 504 (Pakistan) and 921 (Brazil) medical concepts were harmonized to Observational Medical Outcomes Partnership common data model. In total, 341 505 (4.1%) people in the Pakistan dataset and 1 312 832 (49.2%) people in the Brazilian dataset were tested for COVID-19 between January 1, 2020 and April 20, 2022, with a median [IQR] age of 36 [25, 76] and 38 (27, 50); 40.3% and 56.5% were female in Pakistan and Brazil, respectively. 1.2% percent individuals in the Pakistan dataset had Afghan ethnicity. In Brazil, 52.3% had mixed ethnicity. In agreement with international findings, COVID-19 outcomes were more severe in men, elderly, and those with underlying health conditions.

CONCLUSIONS

COVID-19 data from 2 large countries in the Global South were harmonized and analyzed using a standardized health informatics framework developed by an international community of health informaticians. This proof-of-concept study demonstrates a potential open science framework for global knowledge mobilization and clinical translation for timely response to healthcare needs in pandemics and beyond.

EpidemiologiCal POpulatioN STudy of SARS-CoV-2 in Lake CounTy, Illinois (CONTACT): Methodology and Baseline Characteristics of a Community-Based Surveillance Study

Introduction

EpidemiologiCal POpulatioN STudy of SARS-CoV-2 in Lake CounTy, Illinois (CONTACT) is an observational, epidemiological study with a 9-month longitudinal follow-up of nonhospitalized persons aged 18 years or older currently living or employed in Lake County, IL. We describe the study design and report baseline characteristics of the study participants, including the proportion of participants with acute or previous SARS-CoV-2 infection at enrollment.

Methods

At enrollment and subsequent timepoints, participants recruited through digital and paper-based advertising campaigns reported their occupational and school-based exposure, risk factors, and behaviors, and provided nasal and serum specimens. Stratified enrichment was used to enhance enrollment into medium- and higher-risk groups within four occupational risk groups for SARS-CoV-2 infection. RT-PCR and serologic (IgG) testing were conducted to detect acute or previous SARS-CoV-2 infection in participants, respectively.

Results

Between November 2020 and January 2021, 1008 participants (female 70.7%, mean age ± SD 51 ± 13.8 years) completed the questionnaire and diagnostic testing. Among participants, 41.8% (n = 421) were considered low risk, 24.6% (n = 248) were medium-to-low risk, 22.3% (n = 225) were medium-to-high risk, and 11.3% (n = 114) were high risk. Of 56 (5.6%) participants with evidence of acute or previous SARS-CoV-2 infection at baseline, 11 (19.6%) were RT-PCR-positive, 36 (64.3%) were IgG-seropositive, and 9 (16.1%) were positive by both assays. Participants who were adherent vs nonadherent to social distancing measures (odds ratio [95% CI] 0.8 [0.4–1.8]) were less likely, while those in higher vs lower occupational risk groups (2.0 [1.0–4.4]) were more likely to have evidence for acute or previous SARS-CoV-2 infection.

Conclusion

In fall/winter 2020/21, 5.6% of adults in a Lake County convenience sample had evidence for acute or previous SARS-CoV-2 infection at baseline. Nonadherence to social distancing measures and high-risk professions were associated with SARS-CoV-2 infection. The study is ongoing and future analyses will assess infection status over time.

Clinical Trial Registration

NCT04611230.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40121-022-00593-0.

Call, chosen, HA2T2, ANDC: validation of four severity scores in COVID-19 patients

Purpose

To externally validate four previously developed severity scores (i.e., CALL, CHOSEN, HA₂T₂ and ANDC) in patients with COVID-19 hospitalised in a tertiary care centre in Switzerland.

Methods

This observational analysis included adult patients with a real-time reverse-transcription polymerase chain reaction or rapid-antigen test confirmed severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection hospitalised consecutively at the Cantonal Hospital Aarau from February to December 2020. The primary endpoint was all-cause in-hospital mortality. The secondary endpoint was disease progression, defined as needing invasive ventilation, ICU admission or death.

Results

From 399 patients (mean age 66.6 years ± 13.4 SD, 68% males), we had complete data for calculating the CALL, CHOSEN, HA₂T₂ and ANDC scores in 297, 380, 151 and 124 cases, respectively. Odds ratios for all four scores showed significant associations with mortality. The discriminative power of the HA₂T₂ score was higher compared to CALL, CHOSEN and ANDC scores [area under the curve (AUC) 0.78 vs. 0.65, 0.69 and 0.66, respectively]. Negative predictive values (NPV) for mortality were high, particularly for the CALL score (≥ 6 points: 100%, ≥ 9 points: 95%). For disease progression, discriminative power was lower, with the CHOSEN score showing the best performance (AUC 0.66).

Conclusion

In this external validation study, the four analysed scores had a lower performance compared to the original cohorts regarding prediction of mortality and disease progression. However, all scores were significantly associated with mortality and the NPV of the CALL and CHOSEN scores in particular allowed reliable identification of patients at low risk, making them suitable for outpatient management.

Supplementary Information

The online version contains supplementary material available at 10.1007/s15010-021-01728-0.

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

BACKGROUND AND OBJECTIVE

As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code).

METHODS

We show step-by-step how to implement the analytics pipeline for the question: 'In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?'. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA.

RESULTS

Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated.

CONCLUSION

Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world.

Evaluation of COVID-19 Mortality and Adverse Outcomes in US Patients With or Without Cancer

Importance

As the COVID-19 pandemic continues, understanding the clinical outcomes of patients with cancer and COVID-19 has become critically important.

Objective

To compare the outcomes of patients with or without cancer who were diagnosed with COVID-19 and to identify the factors associated with mortality, mechanical ventilation, intensive care unit (ICU) stay, and hospitalization.

Design, Setting, and Participants

This cohort study obtained data from the Optum de-identified COVID-19 electronic health record data set. More than 500 000 US adults who were diagnosed with COVID-19 from January 1 to December 31, 2020, were analyzed.

Exposures

The patient groups were (1) patients without cancer, (2) patients with no recent cancer treatment, and (3) patients with recent cancer treatment (within 3 months before COVID-19 diagnosis) consisting of radiation therapy or systemic therapy.

Main Outcomes and Measures

Mortality, mechanical ventilation, ICU stay, and hospitalization within 30 days of COVID-19 diagnosis were the main outcomes. Unadjusted rates and adjusted odds ratios (ORs) of adverse outcomes were presented according to exposure group.

Results

A total of 507 307 patients with COVID-19 were identified (mean [SD] age, 48.4 [18.4] years; 281 165 women [55.4%]), of whom 493 020 (97.2%) did not have cancer. Among the 14 287 (2.8%) patients with cancer, 9991 (69.9%) did not receive recent treatment and 4296 (30.1%) received recent treatment. In unadjusted analyses, patients with cancer, regardless of recent treatment received, were more likely to have adverse outcomes compared with patients without cancer (eg, mortality rate: 1.6% for patients without cancer, 5.0% for patients with no recent cancer treatment, and 7.8% for patients with recent cancer treatment). After adjustment, patients with no recent cancer treatment had similar or better outcomes than patients without cancer (eg, mortality OR, 0.93 [95% CI, 0.84-1.02]; mechanical ventilation OR, 0.61 [95% CI, 0.54-0.68]). In contrast, a higher risk of death (OR, 1.74; 95% CI, 1.54-1.96), ICU stay (OR, 1.69; 95% CI, 1.54-1.87), and hospitalization (OR, 1.19; 95% CI, 1.11-1.27) was observed in patients with recent cancer treatment. Compared with patients with nonmetastatic solid tumors, those with metastatic solid tumors and hematologic malignant neoplasms had worse outcomes (eg, mortality OR, 2.36 [95% CI, 1.96-2.84]; mechanical ventilation OR, 0.87 [95% CI, 0.70-1.08]). Recent chemotherapy and chemoimmunotherapy were also associated with worse outcomes (eg, chemotherapy mortality OR, 1.84 [95% CI, 1.51-2.26]).

Conclusions and Relevance

This cohort study found that patients with recent cancer treatment and COVID-19 had a significantly higher risk of adverse outcomes, and patients with no recent cancer treatment had similar outcomes to those without cancer. The findings have risk stratification and resource use implications for patients, clinicians, and health systems.