ABSTRACT
Background: The COVID-19 pandemic exploits existing inequalities in the social determinants of health (SDOH) that influence disease burden and access to healthcare. The role of health behaviours and socioeconomic status in genitourinary (GU) malignancy has also been highlighted. Our aim was to evaluate predictors of patient-level and neighbourhood-level factors contributing to disparities in COVID-19 outcomes in GU cancer patients. Methods: Demographic information and co-morbidities for patients screened for COVID-19 across the Mount Sinai Health System (MSHS) up to 10 June 2020 were included. Descriptive analyses and ensemble feature selection were performed to describe the relationships between these predictors and the outcomes of positive SARS-CoV-2 RT-PCR test, COVID-19-related hospitalisation, intubation and death. Results: Out of 47,379 tested individuals, 1094 had a history of GU cancer diagnosis;of these, 192 tested positive for SARS-CoV-2. Ensemble feature selection identified social determinants including zip code, race/ethnicity, age, smoking status and English as the preferred first language-being the majority of significant predictors for each of this study's four COVID-19-related outcomes: a positive test, hospitalisation, intubation and death. Patient and neighbourhood level SDOH including zip code/ NYC borough, age, race/ethnicity, smoking status, and English as preferred language are amongst the most significant predictors of these clinically relevant outcomes for COVID-19 patients. Conclusion: Our results highlight the importance of these SDOH and the need to integrate SDOH in patient electronic medical records (EMR) with the goal to identify at-risk groups. This study's results have implications for COVID-19 research priorities, public health goals, and policy implementations.
ABSTRACT
Objectives: To determine the best way to intervene for ureteric stones which still require treatment during the COVID-19 pandemic, with respect to infection control. In this setting, in which resources are constrained, extracorporeal shockwave lithotripsy (SWL) has prima facie advantages over ureteroscopy (URS). It is also necessary to also consider posttreatment resource consumption in regards to complications and repeat procedures. Subjects and methods: The ideal ureteric stone treatment during a pandemic such as COVID-19 would involve minimum resource consumption and a minimum number of patient attendances. We compared all patients initially treated with SWL to those initially treated with URS for acute ureteral colic within the state of Victoria, Australia in 2017. Results: A total of 2724 ureteric stones were analyzed, a cumulative "3-month exposure and burden on the healthcare system" was calculated for each patient by their initial procedure type. The readmission rate for URS was significantly higher than for SWL, 0.92 readmissions/patient for URS versus 0.54 readmissions/patient for SWL (P < .001). The cumulative hospital stay per patient for these two procedures was 2.35 days for SWL versus 3.21 days for URS (P < .001). The number of procedures per patient was 1.52 for SWL versus 1.89 for URS (P = .0213). Conclusions: Patients with ureteric stones treated initially by SWL have shorter length of stay with fewer overall attendances and procedures at 3 months than those treated with URS. During a pandemic such as COVID-19, SWL may have benefits in preserving hospital resources and limiting opportunity for virus transmission, compared to URS.
ABSTRACT
Background: Detecting and isolating cases of COVID-19 are amongst the key elements listed by the WHO to reduce transmission. This approach has been reported to reduce those symptomatic with COVID-19 in the population by over 90%. Testing is part of a strategy that will save lives. Testing everyone maybe ideal, but it is not practical. A risk tool based on patient demographics and clinical parameters has the potential to help identify patients most likely to test negative for SARS-CoV-2. If effective it could be used to aide clinical decision making and reduce the testing burden. Methods: At the time of this analysis, a total of 9,516 patients with symptoms suggestive of Covid-19, were assessed and tested at Mount Sinai Institutions in New York. Patient demographics, clinical parameters and test results were collected. A robust prediction pipeline was used to develop a risk tool to predict the likelihood of a positive test for Covid-19. The risk tool was analyzed in a holdout dataset from the cohort and its discriminative ability, calibration and net benefit assessed. Results: Over 48% of those tested in this cohort, had a positive result. The derived model had an AUC of 0.77, provided reliable risk prediction, and demonstrated a superior net benefit than a strategy of testing everybody. When a risk cut-off of 70% was applied, the model had a negative predictive value of 96%. Conclusion: Such a tool could be used to help aide but not replace clinical decision making and conserve vital resources needed to effectively tackle this pandemic.
ABSTRACT
Treatment decisions for both early and advanced genitourinary (GU) malignancies take into account the risk of dying from the malignancy as well as the risk of death due to other causes such as other co-morbidities. COVID-19 is a new additional and immediate risk to a patient's morbidity and mortality and there is a need for an accurate assessment as to the potential impact on of this syndrome on GU cancer patients. The aim of this work was to develop a risk tool to identify GU cancer patients at risk of diagnosis, hospitalization, intubation, and mortality from COVID-19. A retrospective case showed a series of GU cancer patients screened for COVID-19 across the Mount Sinai Health System (MSHS). Four hundred eighty-four had a GU malignancy and 149 tested positive for SARS-CoV-2. Demographic and clinical variables of >38,000 patients were available in the institutional database and were utilized to develop decision aides to predict a positive SARS-CoV-2 test, as well as COVID-19-related hospitalization, intubation, and death. A risk tool was developed using a combination of machine learning methods and utilized BMI, temperature, heart rate, respiratory rate, blood pressure, and oxygen saturation. The risk tool for predicting a diagnosis of SARS-CoV-2 had an AUC of 0.83, predicting hospitalization for management of COVID-19 had an AUC of 0.95, predicting patients requiring intubation had an AUC of 0.97, and for predicting COVID-19-related death, the risk tool had an AUC of 0.79. The models had an acceptable calibration and provided a superior net benefit over other common strategies across the entire range of threshold probabilities.