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1.
Journal of Pain and Symptom Management ; 63(5):862, 2022.
Article in English | ScienceDirect | ID: covidwho-1783541

ABSTRACT

Outcomes 1. Describe disparities in telemedicine utilization for patients with cancer in the ambulatory palliative care setting 2. Identify strategies to address disparities in telemedicine access for patients with cancer in the ambulatory palliative care setting Original Research Background Given a shortage of specialty palliative care clinicians and geographic variation in availability, telemedicine has been proposed as one way to improve access to palliative care services for patients with cancer. However, the enduring digital divide raises questions about whether unequal access will exacerbate healthcare disparities. Research Objectives Examine characteristics associated with utilization of telemedicine as compared to in-person visits by patients with cancer in the ambulatory palliative care setting. Methods We collected data on patients seen in the supportive oncology clinic by palliative care clinicians with an in-person or telemedicine visit from March 1 to December 30, 2020. A logistic regression with generalized estimating equation was fit to assess the association between visit type and patient characteristics. Results A total of 491 patients and 1,783 visits were identified, including 1,061 (60%) in-person visits and 722 (40%) telemedicine visits. Spanish-speaking patients (OR 0.32, 95% CI 0.17-0.61), those without insurance (OR 0.29, 95% CI 0.16-0.53), and those without an activated patient portal (inactivated: OR 0.45, 95% CI 0.26-0.80;pending activation: OR 0.29, 95% CI 0.18-0.47) were less likely to use telemedicine. In a comparison of video to audio-only visits in a secondary analysis, married patients were more likely to engage in video visits (OR 1.89, 95% CI 1.05-3.39). Conclusion Our study reveals disparities in telemedicine utilization in the ambulatory palliative care setting for patients with cancer who are Spanish-speaking, uninsured, or unmarried or do not have an activated patient portal. These findings suggest that the recent shift to telemedicine as a substitute for in-person visits may exacerbate existing disparities in access to disease-directed therapy, symptom management, and serious illness communication. Implications for Research, Policy, or Practice In the wake of the COVID-19 pandemic, we can better meet the palliative care needs of patients with cancer through telemedicine only if equity is kept at the forefront of our discussions.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-321880

ABSTRACT

Background: Clinical biomarkers that accurately predict mortality are needed for the effective management of patients with severe COVID-19 illness. Here, we determine whether changes in D-dimer levels after anticoagulation are predictive of in-hospital mortality.Methods: Adult patients hospitalized for severe COVID-19 who received therapeutic anticoagulation for thromboprophylaxis were identified from a large COVID-19 database of the Mount Sinai Health System in New York City. We studied the ability post-anticoagulant D-dimer levels to predict in-hospital mortality, while taking into consideration 65 other clinically important covariates including patient demographics, comorbidities, vital signs and laboratory tests at baseline.Findings: 1835 adult patients with PCR-confirmed COVID-19 who received therapeutic anticoagulation during hospitalization were included. Overall, 26% of patients died in the hospital. Significantly different in-hospital mortality rates were observed in patient groups based on the mean D-dimer levels and its trend following anticoagulation initiation: 49% for the high mean-increase trend (HI) group;27% for the high-decrease (HD) group;21% for the low-increase (LI) group;and 9% for the low-decrease (LD) group (p<0·001). Using penalized logistic regression models to simultaneously analyze 67 clinical variables, the HI (adjusted odds ratios [ORadj]: 6·58, 95% CI 3·81-11·16), LI (ORadj: 4·06, 95% CI 2·23-7·38) and HD (ORadj: 2·37;95% CI 1·37-4·09) post-anticoagulant D-dimer groups had the highest odds for in-hospital mortality when compared to the LD group.Interpretation: D-dimer levels and its trend following anticoagulation are highly predictive of in-hospital mortality and may help guide resource allocation and identify candidates for studies of emerging treatments for severe COVID-19.Funding: NoneDeclaration of Interests: Authors have no competing interests to declare.Ethics Approval Statement: The Icahn School of Medicine at Mount Sinai Institutional Review Boardconsidered the study exempt.

3.
J Pain Symptom Manage ; 63(3): 423-429, 2022 03.
Article in English | MEDLINE | ID: covidwho-1458612

ABSTRACT

CONTEXT: Given a shortage of specialty palliative care clinicians and geographic variation in availability, telemedicine has been proposed as one way to improve access to palliative care services for patients with cancer. However, the enduring digital divide raises questions about whether unequal access will exacerbate healthcare disparities. OBJECTIVES: To examine factors associated with utilization of telemedicine as compared to in-person visits by patients with cancer in the ambulatory palliative care setting. METHODS: We collected data on patients seen in Supportive Oncology clinic by palliative care clinicians with an in-person or telemedicine visit from March 1 to December 30, 2020. A logistic regression with generalized estimating equation was fit to assess the association between visit type and patient characteristics. RESULTS: A total of 491 patients and 1783 visits were identified, including 1061 (60%) in-person visits and 722 (40%) telemedicine visits. Female patients were significantly more likely to utilize telemedicine than male patients (OR 1.46; 95% CI 1.11-1.90). Spanish-speaking patients (OR 0.32, 95% CI 0.17-0.61), those without insurance (OR 0.28, 95% CI 0.15-0.52), and those without an activated patient portal (Inactivated: OR 0.46, 95% CI 0.26-0.82; Pending Activation: OR 0.29, 95% CI 0.18-0.48) were less likely to utilize telemedicine. CONCLUSION: Our study reveals disparities in telemedicine utilization in the ambulatory palliative care setting for patients with cancer who are male, Spanish-speaking, uninsured, or do not have an activated patient portal. In the wake of the COVID-19 pandemic, we can better meet the palliative care needs of patients with cancer through telemedicine only if equity is kept at the forefront of our discussions.


Subject(s)
COVID-19 , Telemedicine , Ambulatory Care , Female , Humans , Male , Palliative Care , Pandemics , SARS-CoV-2
4.
Med Care ; 59(8): 694-698, 2021 08 01.
Article in English | MEDLINE | ID: covidwho-1393508

ABSTRACT

BACKGROUND: Concerns exist regarding exacerbation of existing disparities in health care access with the rapid implementation of telemedicine during the coronavirus disease 2019 (COVID-19) pandemic. However, data on pre-existing disparities in telemedicine utilization is currently lacking. OBJECTIVE: We aimed to study: (1) the prevalence of outpatient telemedicine visits before the COVID-19 pandemic by patient subgroups based on age, comorbidity burden, residence rurality, and median household income; and (2) associated diagnosis categories. RESEARCH DESIGN: This was a retrospective cohort study. SUBJECT: Commercial claims data from the Truven MarketScan database (2014-2018) representing n=846,461,609 outpatient visits. MEASURES: We studied characteristics and utilization of outpatient telemedicine services before the COVID-19 pandemic by patient subgroups based on age, comorbidity burden, residence rurality, and median household income. Disparities were assessed in unadjusted and adjusted (regression) analyses. RESULTS: With overall telemedicine uptake of 0.12% (n=1,018,092/846,461,609 outpatient visits) we found that pre-COVID-19 disparities in telemedicine use became more pronounced over time with lower use in patients who were older, had more comorbidities, were in rural areas, and had lower median household incomes (all trends and effect estimates P<0.001). CONCLUSION: These results contextualize pre-existing disparities in telemedicine use and are crucial in the monitoring of potential disparities in telemedicine access and subsequent outcomes after the rapid expansion of telemedicine during the COVID-19 pandemic.


Subject(s)
Ambulatory Care/trends , COVID-19/therapy , Health Services Accessibility/trends , Healthcare Disparities/statistics & numerical data , Telemedicine/trends , Adult , COVID-19/epidemiology , Humans , Infection Control/trends , Male , Middle Aged , Patient Satisfaction/statistics & numerical data , Quality Improvement , Retrospective Studies
5.
ERJ Open Res ; 7(3)2021 Jul.
Article in English | MEDLINE | ID: covidwho-1299322

ABSTRACT

Clinical biomarkers that accurately predict mortality are needed for the effective management of patients with severe coronavirus disease 2019 (COVID-19) illness. In this study, we determine whether changes in D-dimer levels after anticoagulation are independently predictive of in-hospital mortality. Adult patients hospitalised for severe COVID-19 who received therapeutic anticoagulation for thromboprophylaxis were identified from a large COVID-19 database of the Mount Sinai Health System in New York City (NY, USA). We studied the ability of post-anticoagulant D-dimer levels to predict in-hospital mortality, while taking into consideration 65 other clinically important covariates including patient demographics, comorbidities, vital signs and several laboratory tests. 1835 adult patients with PCR-confirmed COVID-19 who received therapeutic anticoagulation during hospitalisation were included. Overall, 26% of patients died in the hospital. Significantly different in-hospital mortality rates were observed in patient groups based on mean D-dimer levels and trend following anticoagulation: 49% for the high mean-increase trend group; 27% for the high-decrease group; 21% for the low-increase group; and 9% for the low-decrease group (p<0.001). Using penalised logistic regression models to simultaneously analyse 67 clinical variables, the high increase (adjusted odds ratios (ORadj): 6.58, 95% CI 3.81-11.16), low increase (ORadj: 4.06, 95% CI 2.23-7.38) and high decrease (ORadj: 2.37; 95% CI 1.37-4.09) D-dimer groups (reference: low decrease group) had the highest odds for in-hospital mortality among all clinical features. Changes in D-dimer levels and trend following anticoagulation are highly predictive of in-hospital mortality and may help guide resource allocation and future studies of emerging treatments for severe COVID-19.

6.
Heart Lung ; 50(5): 618-621, 2021.
Article in English | MEDLINE | ID: covidwho-1244741

ABSTRACT

OBJECTIVE: To evaluate the association between pre-hospitalization antiplatelet medication use and COVID-19 disease severity. DESIGN: Retrospective cohort study. SETTING: Inpatient units at The Mount Sinai Hospital. PATIENTS: Adults age ≥18 admitted between March 1, 2020 and April 9, 2020 with confirmed COVID-19 infection with at least 28 days follow-up. MEASUREMENTS: We captured baseline demographic, pre-hospitalization antiplatelet medication use, and clinical encounter data for all patients who met inclusion criteria. The primary endpoint was peak score on a 6-point modified ordinal scale (MOS), which is based on World Health Organization blueprint R&S groups, used to grade severity of illness through clinical outcomes of interest. Scores indicate the following: 1 - COVID-19 infection not requiring hospitalization, 2 - requiring hospitalization but not supplemental oxygen, 3 - hospitalization requiring supplemental oxygen, 4 - hospitalization requiring high-flow nasal cannula (HFNC) or non-invasive positive pressure ventilation (NIPPV), 5 - hospitalization requiring intubation or extracorporeal membrane oxygenation (ECMO), 6 - death. Multivariable adjusted partial proportional odds model (PPOM) was performed to examine the association between pre-hospitalization antiplatelet medication use and likelihood of each MOS score. MAIN RESULTS: Of 762 people admitted with COVID-19, 239 (31.4%) used antiplatelet medications pre-hospitalization while 523 (68.6%) did not. Antiplatelet users were older and had more co-morbidities at baseline. Before adjusting for covariates, patients who used antiplatelet medications pre-hospitalization were more likely than non-users to have peak MOS score 6 (death, OR 1.75, 95% CI 1.21-2.52), peak MOS score ≥5 (intubation/ECMO or death, OR 1.4, 95% CI 1.00-1.98) and peak MOS score ≥4 (HFNC, NIPPV, intubation/ECMO or death, OR 1.40, 95% CI 1.01-1.94). On multivariable adjusted PPOM analysis controlling for 13 covariates, there were no longer any significant differences in peak MOS scores between users and non-users. CONCLUSIONS: After adjusting for covariates, pre-hospital antiplatelet use was not associated with COVID-19 severity in hospitalized patients.


Subject(s)
COVID-19 , Adult , Hospitalization , Hospitals , Humans , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index
7.
BMJ Support Palliat Care ; 2020 Sep 22.
Article in English | MEDLINE | ID: covidwho-788172

ABSTRACT

OBJECTIVES: To develop and validate a model for prediction of near-term in-hospital mortality among patients with COVID-19 by application of a machine learning (ML) algorithm on time-series inpatient data from electronic health records. METHODS: A cohort comprised of 567 patients with COVID-19 at a large acute care healthcare system between 10 February 2020 and 7 April 2020 observed until either death or discharge. Random forest (RF) model was developed on randomly drawn 70% of the cohort (training set) and its performance was evaluated on the rest of 30% (the test set). The outcome variable was in-hospital mortality within 20-84 hours from the time of prediction. Input features included patients' vital signs, laboratory data and ECG results. RESULTS: Patients had a median age of 60.2 years (IQR 26.2 years); 54.1% were men. In-hospital mortality rate was 17.0% and overall median time to death was 6.5 days (range 1.3-23.0 days). In the test set, the RF classifier yielded a sensitivity of 87.8% (95% CI: 78.2% to 94.3%), specificity of 60.6% (95% CI: 55.2% to 65.8%), accuracy of 65.5% (95% CI: 60.7% to 70.0%), area under the receiver operating characteristic curve of 85.5% (95% CI: 80.8% to 90.2%) and area under the precision recall curve of 64.4% (95% CI: 53.5% to 75.3%). CONCLUSIONS: Our ML-based approach can be used to analyse electronic health record data and reliably predict near-term mortality prediction. Using such a model in hospitals could help improve care, thereby better aligning clinical decisions with prognosis in critically ill patients with COVID-19.

8.
Nat Med ; 26(10): 1636-1643, 2020 10.
Article in English | MEDLINE | ID: covidwho-728994

ABSTRACT

Several studies have revealed that the hyper-inflammatory response induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major cause of disease severity and death. However, predictive biomarkers of pathogenic inflammation to help guide targetable immune pathways are critically lacking. We implemented a rapid multiplex cytokine assay to measure serum interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)-α and IL-1ß in hospitalized patients with coronavirus disease 2019 (COVID-19) upon admission to the Mount Sinai Health System in New York. Patients (n = 1,484) were followed up to 41 d after admission (median, 8 d), and clinical information, laboratory test results and patient outcomes were collected. We found that high serum IL-6, IL-8 and TNF-α levels at the time of hospitalization were strong and independent predictors of patient survival (P < 0.0001, P = 0.0205 and P = 0.0140, respectively). Notably, when adjusting for disease severity, common laboratory inflammation markers, hypoxia and other vitals, demographics, and a range of comorbidities, IL-6 and TNF-α serum levels remained independent and significant predictors of disease severity and death. These findings were validated in a second cohort of patients (n = 231). We propose that serum IL-6 and TNF-α levels should be considered in the management and treatment of patients with COVID-19 to stratify prospective clinical trials, guide resource allocation and inform therapeutic options.


Subject(s)
Coronavirus Infections/immunology , Interleukin-1beta/immunology , Interleukin-6/immunology , Interleukin-8/immunology , Pneumonia, Viral/immunology , Tumor Necrosis Factor-alpha/immunology , Aged , Betacoronavirus , COVID-19 , Coronavirus Infections/mortality , Coronavirus Infections/physiopathology , Coronavirus Infections/therapy , Cytokines/immunology , Female , Hospitalization , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/mortality , Pneumonia, Viral/physiopathology , Pneumonia, Viral/therapy , SARS-CoV-2 , Severity of Illness Index , Survival Rate
9.
J Clin Med ; 9(6)2020 Jun 01.
Article in English | MEDLINE | ID: covidwho-457499

ABSTRACT

OBJECTIVES: Approximately 20-30% of patients with COVID-19 require hospitalization, and 5-12% may require critical care in an intensive care unit (ICU). A rapid surge in cases of severe COVID-19 will lead to a corresponding surge in demand for ICU care. Because of constraints on resources, frontline healthcare workers may be unable to provide the frequent monitoring and assessment required for all patients at high risk of clinical deterioration. We developed a machine learning-based risk prioritization tool that predicts ICU transfer within 24 h, seeking to facilitate efficient use of care providers' efforts and help hospitals plan their flow of operations. METHODS: A retrospective cohort was comprised of non-ICU COVID-19 admissions at a large acute care health system between 26 February and 18 April 2020. Time series data, including vital signs, nursing assessments, laboratory data, and electrocardiograms, were used as input variables for training a random forest (RF) model. The cohort was randomly split (70:30) into training and test sets. The RF model was trained using 10-fold cross-validation on the training set, and its predictive performance on the test set was then evaluated. RESULTS: The cohort consisted of 1987 unique patients diagnosed with COVID-19 and admitted to non-ICU units of the hospital. The median time to ICU transfer was 2.45 days from the time of admission. Compared to actual admissions, the tool had 72.8% (95% CI: 63.2-81.1%) sensitivity, 76.3% (95% CI: 74.7-77.9%) specificity, 76.2% (95% CI: 74.6-77.7%) accuracy, and 79.9% (95% CI: 75.2-84.6%) area under the receiver operating characteristics curve. CONCLUSIONS: A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h. This tool could improve the management of hospital resources and patient-throughput planning, thus delivering more effective care to patients hospitalized with COVID-19.

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