Eddye Golden

Bio: Eddye Golden is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: Psychological resilience & Health informatics. The author has an hindex of 6, co-authored 17 publications receiving 269 citations. Previous affiliations of Eddye Golden include Hasso Plattner Institute.

Coronavirus 2019 and People Living With Human Immunodeficiency Virus: Outcomes for Hospitalized Patients in New York City

Abstract: BACKGROUND: There have been limited data regarding the clinical impact of COVID-19 disease on people with HIV (PWH) In this study we compared outcomes for PWH with COVID-19 disease to a matched comparison group DESIGN: We identified 88 PWH hospitalized with laboratory confirmed COVID-19 in our hospital system in New York between March 12 and April 23, 2020 We collected data on baseline clinical characteristics, laboratory values, HIV infection status, COVID-19 treatment, and outcomes from this group and matched comparators (one PWH to up to five patients by age, sex, race/ethnicity and calendar week of infection) We compared baseline clinical characteristics and outcomes (death, mechanical ventilation, hospital discharge) for these two groups, as well as cumulative incidence of death by HIV status RESULTS: Patients did not differ significantly by HIV status by age, sex or race/ethnicity due to the matching algorithm PWH hospitalized with COVID-19 had high proportions of HIV virologic control on antiretroviral therapy PWH had greater proportions of smoking (p<0001) and comorbid illness than demographically similar uninfected comparators There was no difference in COVID-19 severity on admission by HIV status (p=015) Poor outcomes for hospitalized PWH were frequent but similar to proportions in comparators; 18% required mechanical ventilation and ultimately 21% died during follow-up (compared with 23% and 20% respectively) There was similar cumulative incidence of death over time by HIV status (p=094) INTERPRETATION: We found no differences in adverse outcomes associated with HIV infection for hospitalized COVID-19 patients compared to a demographically similar patient group

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.

Abstract: Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19–positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.

Abstract: Background: Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. Objective: We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. Methods: Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. Results: Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). Conclusions: Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Machine Learning to Predict Mortality and Critical Events in COVID-19 Positive New York City Patients: A Cohort Study

Abstract: BACKGROUND: Coronavirus disease 2019 (COVID-19) has infected millions of patients worldwide and has been responsible for several hundred thousand fatalities This has necessitated thoughtful resource allocation and early identification of high-risk patients However, effective methods for achieving this are lacking OBJECTIVE: We analyze Electronic Health Records from COVID-19 positive hospitalized patients admitted to the Mount Sinai Health System in New York City (NYC) We present machine learning models for making predictions about the hospital course over clinically meaningful time horizons based on patient characteristics at admission We assess performance of these models at multiple hospitals and time points METHODS: We utilized XGBoost and baseline comparator models, for predicting in-hospital mortality and critical events at time windows of 3, 5, 7 and 10 days from admission Our study population included harmonized electronic health record (EHR) data from five hospitals in NYC for 4,098 COVID-19+ patients admitted from March 15, 2020 to May 22, 2020 Models were first trained on patients from a single hospital (N=1514) before or on May 1, externally validated on patients from four other hospitals (N=2201) before or on May 1, and prospectively validated on all patients after May 1 (N=383) Finally, we establish model interpretability to identify and rank variables that drive model predictions RESULTS: On cross-validation, the XGBoost classifier outperformed baseline models, with area under the receiver operating characteristic curve (AUC-ROC) for mortality at 0 89 at 3 days, 0 85 at 5 and 7 days, and 0 84 at 10 days;XGBoost also performed well for critical event prediction with AUC-ROC of 0 80 at 3 days, 0 79 at 5 days, 0 80 at 7 days, and 0 81 at 10 days In external validation, XGBoost achieved an AUC-ROC of 0 88 at 3 days, 0 86 at 5 days, 0 86 at 7 days, and 0 84 at 10 days for mortality prediction Similarly, XGBoost achieved an AUC-ROC of 0 78 at 3 days, 0 79 at 5 days, 0 80 at 7 days, and 0 81 at 10 days Trends in performance on prospective validation sets were similar At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers for mortality prediction CONCLUSIONS: We trained and validated (both externally and prospectively) machine-learning models for mortality and critical events at different time horizons These models identify at-risk patients, as well as uncover underlying relationships predicting outcomes

Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City.

Abstract: Objective The COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive. Design Demographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive. Setting All patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system. Participants Participants over the age of 18 years were included. Primary outcomes We investigated in-hospital mortality during the study period. Results A total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 μg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 μg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL. Conclusions In our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Abstract: Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

Racial differences in sleep-disordered breathing in African-Americans and Caucasians

Abstract: In this case-control family study of sleep-disordered breathing (SDB), we describe the distributions of SDB and SDB risk factors in African-Americans and Caucasians. A total of 225 African-Americans and 622 Caucasians, ages 2 to 86 yr, recruited as members of families with an individual with known sleep apnea (85 index families) or as members of neighborhood control families (63 families) were studied with an overnight home sleep-study, questionnaires, and physical measurements. A subsample underwent cephalometry. Outcome measures were the respiratory disturbance index (RDI) and a binary variable indicating the presence of increased apneic activity (IAA). In both races, a strong relationship was demonstrated between the (log transformed) RDI and age and age2. African-Americans with SDB were younger than Caucasians with SDB (37.2 +/- 19.5 versus 45.6 +/- 18.7 yr, p < 0.01). In subjects < or = 25 yr, RDI level and IAA prevalence were higher in African-Americans (odds ratio, adjusted for obesity, sex, proban...

Risk factors for COVID-19 death in a population cohort study from the Western Cape Province, South Africa.

Abstract: Background Risk factors for coronavirus disease 2019 (COVID-19) death in sub-Saharan Africa and the effects of human immunodeficiency virus (HIV) and tuberculosis on COVID-19 outcomes are unknown. Methods We conducted a population cohort study using linked data from adults attending public-sector health facilities in the Western Cape, South Africa. We used Cox proportional hazards models, adjusted for age, sex, location, and comorbidities, to examine the associations between HIV, tuberculosis, and COVID-19 death from 1 March to 9 June 2020 among (1) public-sector "active patients" (≥1 visit in the 3 years before March 2020); (2) laboratory-diagnosed COVID-19 cases; and (3) hospitalized COVID-19 cases. We calculated the standardized mortality ratio (SMR) for COVID-19, comparing adults living with and without HIV using modeled population estimates. Results Among 3 460 932 patients (16% living with HIV), 22 308 were diagnosed with COVID-19, of whom 625 died. COVID-19 death was associated with male sex, increasing age, diabetes, hypertension, and chronic kidney disease. HIV was associated with COVID-19 mortality (adjusted hazard ratio [aHR], 2.14; 95% confidence interval [CI], 1.70-2.70), with similar risks across strata of viral loads and immunosuppression. Current and previous diagnoses of tuberculosis were associated with COVID-19 death (aHR, 2.70 [95% CI, 1.81-4.04] and 1.51 [95% CI, 1.18-1.93], respectively). The SMR for COVID-19 death associated with HIV was 2.39 (95% CI, 1.96-2.86); population attributable fraction 8.5% (95% CI, 6.1-11.1). Conclusions While our findings may overestimate HIV- and tuberculosis-associated COVID-19 mortality risks due to residual confounding, both living with HIV and having current tuberculosis were independently associated with increased COVID-19 mortality. The associations between age, sex, and other comorbidities and COVID-19 mortality were similar to those in other settings.