Use of a Telemedicine Risk Assessment Tool to Predict the Risk of Hospitalization of 496 Outpatients With COVID-19: Retrospective Analysis.

BACKGROUND: Risk assessment of patients with acute COVID-19 in a telemedicine context is not well described. In settings of large numbers of patients, a risk assessment tool may guide resource allocation not only for patient care but also for maximum health care and public health benefit. OBJECTIVE: The goal of this study was to determine whether a COVID-19 telemedicine risk assessment tool accurately predicts hospitalizations. METHODS: We conducted a retrospective study of a COVID-19 telemedicine home monitoring program serving health care workers and the community in Atlanta, Georgia, with enrollment from March 24 to May 26, 2020; the final call range was from March 27 to June 19, 2020. All patients were assessed by medical providers using an institutional COVID-19 risk assessment tool designating patients as Tier 1 (low risk for hospitalization), Tier 2 (intermediate risk for hospitalization), or Tier 3 (high risk for hospitalization). Patients were followed with regular telephone calls to an endpoint of improvement or hospitalization. Using survival analysis by Cox regression with days to hospitalization as the metric, we analyzed the performance of the risk tiers and explored individual patient factors associated with risk of hospitalization. RESULTS: Providers using the risk assessment rubric assigned 496 outpatients to tiers: Tier 1, 237 out of 496 (47.8%); Tier 2, 185 out of 496 (37.3%); and Tier 3, 74 out of 496 (14.9%). Subsequent hospitalizations numbered 3 out of 237 (1.3%) for Tier 1, 15 out of 185 (8.1%) for Tier 2, and 17 out of 74 (23%) for Tier 3. From a Cox regression model with age of 60 years or older, gender, and reported obesity as covariates, the adjusted hazard ratios for hospitalization using Tier 1 as reference were 3.74 (95% CI 1.06-13.27; P=.04) for Tier 2 and 10.87 (95% CI 3.09-38.27; P<.001) for Tier 3. CONCLUSIONS: A telemedicine risk assessment tool prospectively applied to an outpatient population with COVID-19 identified populations with low, intermediate, and high risk of hospitalization.

Subcutaneous sarilumab for the treatment of hospitalized patients with moderate to severe COVID19 disease: A pragmatic, embedded randomized clinical trial.

IMPORTANCE AND OBJECTIVE: The aim of this pragmatic, embedded, adaptive trial was to measure the effectiveness of the subcutaneous anti-IL-6R antibody sarilumab, when added to an evolving standard of care (SOC), for clinical management of inpatients with moderate to severe COVID-19 disease. DESIGN: Two-arm, randomized, open-label controlled trial comparing SOC alone to SOC plus sarilumab. The trial used a randomized play-the-winner design and was fully embedded within the electronic health record (EHR) system. SETTING: 5 VA Medical Centers. PARTICIPANTS: Hospitalized patients with clinical criteria for moderate to severe COVID-19 but not requiring mechanical ventilation, and a diagnostic test positive for SARS-CoV-2. INTERVENTIONS: Sarilumab, 200 or 400 mg subcutaneous injection. SOC was not pre-specified and could vary over time, e.g., to include antiviral or other anti-inflammatory drugs. MAIN OUTCOMES AND MEASURES: The primary outcome was intubation or death within 14 days of randomization. All data were extracted remotely from the EHR. RESULTS: Among 162 eligible patients, 53 consented, and 50 were evaluated for the primary endpoint of intubation or death. This occurred in 5/20 and 1/30 of participants in the sarilumab and SOC arms respectively, with the majority occurring in the initial 9 participants (3/4 in the sarilumab and 1/5 in the SOC) before the sarilumab dose was increased to 400 mg and before remdesivir and dexamethasone were widely adopted. After interim review, the unblinded Data Monitoring Committee recommended that the study be stopped due to concern for safety: a high probability that rates of intubation or death were higher with addition of sarilumab to SOC (92.6%), and a very low probability (3.4%) that sarilumab would be found to be superior. CONCLUSIONS AND RELEVANCE: This randomized trial of patients hospitalized due to respiratory compromise from COVID-19 but not mechanical ventilation found no benefit from subcutaneous sarilumab when added to an evolving SOC. The numbers of patients and events were too low to allow definitive conclusions to be drawn, but this study contributes valuable information about the role of subcutaneous IL-6R inhibition in the treatment of hospitalized COVID-19 patients. Methods developed and piloted during this trial will be useful in conducting future studies more efficiently. TRIAL REGISTRATION: Clinicaltrials.gov-NCT04359901; https://clinicaltrials.gov/ct2/show/NCT04359901?cond=NCT04359901&draw=2&rank=1.

Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.

BACKGROUND: Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine learning methods at identifying and ranking the real-time readiness of individual patients for discharge, with the goal of improving patient flow within hospitals during periods of crisis. METHODS AND PERFORMANCE: Electronic Health Record data from Oxford University Hospitals was used to train independent models to classify and rank patients' real-time readiness for discharge within 24 hours, for patient subsets according to the nature of their admission (planned or emergency) and the number of days elapsed since their admission. A strategy for the use of the models' inference is proposed, by which the model makes predictions for all patients in hospital and ranks them in order of likelihood of discharge within the following 24 hours. The 20% of patients with the highest ranking are considered as candidates for discharge and would therefore expect to have a further screening by a clinician to confirm whether they are ready for discharge or not. Performance was evaluated in terms of positive predictive value (PPV), i.e., the proportion of these patients who would have been correctly deemed as 'ready for discharge' after having the second screening by a clinician. Performance was high for patients on their first day of admission (PPV = 0.96/0.94 for planned/emergency patients respectively) but dropped for patients further into a longer admission (PPV = 0.66/0.71 for planned/emergency patients still in hospital after 7 days). CONCLUSION: We demonstrate the efficacy of machine learning methods at making operationally focused, next-day discharge readiness predictions for all individual patients in hospital at any given moment and propose a strategy for their use within a decision-support tool during crisis periods.

Development and validation of early warning score systems for COVID-19 patients.

COVID-19 is a major, urgent, and ongoing threat to global health. Globally more than 24 million have been infected and the disease has claimed more than a million lives as of November 2020. Predicting which patients will need respiratory support is important to guiding individual patient treatment and also to ensuring sufficient resources are available. The ability of six common Early Warning Scores (EWS) to identify respiratory deterioration defined as the need for advanced respiratory support (high-flow nasal oxygen, continuous positive airways pressure, non-invasive ventilation, intubation) within a prediction window of 24 h is evaluated. It is shown that these scores perform sub-optimally at this specific task. Therefore, an alternative EWS based on the Gradient Boosting Trees (GBT) algorithm is developed that is able to predict deterioration within the next 24 h with high AUROC 94% and an accuracy, sensitivity, and specificity of 70%, 96%, 70%, respectively. The GBT model outperformed the best EWS (LDTEWS:NEWS), increasing the AUROC by 14%. Our GBT model makes the prediction based on the current and baseline measures of routinely available vital signs and blood tests.

A Survey of Provider-Reported Use and Perceived Effectiveness of Medications for Symptom Management in Telemedicine and Outpatient Visits for Mild COVID-19.

INTRODUCTION: Many patients with mild coronavirus disease 2019 (COVID-19) have symptoms requiring acute and follow-up care. The aims of this study were to assess (1) provider-reported use of medications and their perceived effectiveness and (2) degree of difficulty managing specific symptoms at episodic COVID-19 care sites and in a longitudinal monitoring program. METHODS: We sent an online survey to physicians, advanced practice providers, and registered nurses redeployed to COVID-19 care sites at an academic medical center from March to May 2020. We asked about the use of medications and perceived effectiveness of medications to treat symptoms of COVID-19 and the perceived challenge of symptom management. Comparison was made by provider type (episodic or longitudinal site of care). RESULTS: Responses from 64 providers were included. The most frequently used medications were acetaminophen (87.1% of respondents), benzonatate (83.9%), and albuterol metered dose inhalers (MDI) (80.6%). Therapies for lower respiratory tract symptoms were reported as more commonly used by longitudinal follow-up providers compared to episodic providers including guaifenesin (90.6% vs 60.0%, p = 0.007), benzonatate (93.8% vs 73.3%, p = 0.04), nebulized albuterol for patients with asthma (75.0% vs 43.3%, p = 0.019), and albuterol MDIs for patients without asthma (90.6% vs 66.7%, p = 0.029). Medications found to have the highest perceived efficacy by respondents using the therapy (> 80% reporting "very efficacious") included albuterol, acetaminophen for fever, non-sedating antihistamines, nasal steroid spray, and non-steroidal anti-inflammatory drugs (NSAIDs) for myalgia, arthralgia, or headache. Lower respiratory symptoms and anxiety were rated as the most challenging symptoms to manage. CONCLUSIONS: Providers reported that clinical care of mild COVID-19 with medications in common use for other respiratory infections is effective, both at episodic care and longitudinal sites of care, but that specific symptoms are still challenging to manage.

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

BACKGROUND: The early clinical course of COVID-19 can be difficult to distinguish from other illnesses driving presentation to hospital. However, viral-specific PCR testing has limited sensitivity and results can take up to 72 h for operational reasons. We aimed to develop and validate two early-detection models for COVID-19, screening for the disease among patients attending the emergency department and the subset being admitted to hospital, using routinely collected health-care data (laboratory tests, blood gas measurements, and vital signs). These data are typically available within the first hour of presentation to hospitals in high-income and middle-income countries, within the existing laboratory infrastructure. METHODS: We trained linear and non-linear machine learning classifiers to distinguish patients with COVID-19 from pre-pandemic controls, using electronic health record data for patients presenting to the emergency department and admitted across a group of four teaching hospitals in Oxfordshire, UK (Oxford University Hospitals). Data extracted included presentation blood tests, blood gas testing, vital signs, and results of PCR testing for respiratory viruses. Adult patients (>18 years) presenting to hospital before Dec 1, 2019 (before the first COVID-19 outbreak), were included in the COVID-19-negative cohort; those presenting to hospital between Dec 1, 2019, and April 19, 2020, with PCR-confirmed severe acute respiratory syndrome coronavirus 2 infection were included in the COVID-19-positive cohort. Patients who were subsequently admitted to hospital were included in their respective COVID-19-negative or COVID-19-positive admissions cohorts. Models were calibrated to sensitivities of 70%, 80%, and 90% during training, and performance was initially assessed on a held-out test set generated by an 80:20 split stratified by patients with COVID-19 and balanced equally with pre-pandemic controls. To simulate real-world performance at different stages of an epidemic, we generated test sets with varying prevalences of COVID-19 and assessed predictive values for our models. We prospectively validated our 80% sensitivity models for all patients presenting or admitted to the Oxford University Hospitals between April 20 and May 6, 2020, comparing model predictions with PCR test results. FINDINGS: We assessed 155 689 adult patients presenting to hospital between Dec 1, 2017, and April 19, 2020. 114 957 patients were included in the COVID-negative cohort and 437 in the COVID-positive cohort, for a full study population of 115 394 patients, with 72 310 admitted to hospital. With a sensitive configuration of 80%, our emergency department (ED) model achieved 77·4% sensitivity and 95·7% specificity (area under the receiver operating characteristic curve [AUROC] 0·939) for COVID-19 among all patients attending hospital, and the admissions model achieved 77·4% sensitivity and 94·8% specificity (AUROC 0·940) for the subset of patients admitted to hospital. Both models achieved high negative predictive values (NPV; >98·5%) across a range of prevalences (≤5%). We prospectively validated our models for all patients presenting and admitted to Oxford University Hospitals in a 2-week test period. The ED model (3326 patients) achieved 92·3% accuracy (NPV 97·6%, AUROC 0·881), and the admissions model (1715 patients) achieved 92·5% accuracy (97·7%, 0·871) in comparison with PCR results. Sensitivity analyses to account for uncertainty in negative PCR results improved apparent accuracy (ED model 95·1%, admissions model 94·1%) and NPV (ED model 99·0%, admissions model 98·5%). INTERPRETATION: Our models performed effectively as a screening test for COVID-19, excluding the illness with high-confidence by use of clinical data routinely available within 1 h of presentation to hospital. Our approach is rapidly scalable, fitting within the existing laboratory testing infrastructure and standard of care of hospitals in high-income and middle-income countries. FUNDING: Wellcome Trust, University of Oxford, Engineering and Physical Sciences Research Council, National Institute for Health Research Oxford Biomedical Research Centre.

COVID-19 Life Events Spill-Over on Family Functioning and Adolescent Adjustment (preprint)

In this longitudinal study, we examined parent and youth perceptions of how life events, both positive and negative, associated with the COVID-19 pandemic resulted in changes in family functioning as well as youth functioning. We tested both direct effects of parent- and youth-reported negative and positive events as well as indirect or spillover effects that have their effects on parent functioning and marital relationships. Families (n=101 parent-youth dyads, 80% European American, 48% boys, and 87% mothers) completed surveys during the pandemic (May to July 2020) and 1.5 years prior.  We conducted multivariate path analyses predicting residualized change in family and youth functioning. According to child-report of family functioning, open family communication, parent-child relationship quality and family satisfaction all decreased during this time, although no changes were found in parent-report of family functioning. Several forms of parent-reported negative life events and child-reports of school-related stress during the pandemic predicted changes in family functioning. Moreover, positive life events predicted child reports of family functioning directly and evidence for spill-over effects of parent-reported positive life events on family functioning were also found. In addition, the receipt of social support by parents during the pandemic protected against decrements in family functioning and, indirectly, increases in child symptomatology. School-related stress also predicted increases in child-reported symptomatology. Moreover, several aspects of family functioning pre-pandemic impacted the extent to which parents and children experienced both positive and negative life events during the pandemic. The current findings thus shed light on how experiences of the pandemic are linked with family functioning and have implications for how to support families during this time.

Coping and Mental Health in Early Adolescence during COVID-19 (preprint)

The current longitudinal study examines changes in overall mental health symptomatology from before to after the COVID-19 outbreak in youth from the southeastern United States as well as the potential mitigating effects of self-efficacy, optimism, and coping. A sample of 105 parent-child dyads participated in the study (49% boys; 81% European American, 1% Alaska Native/American Indian, 9% Asian/Asian American; 4% Black/African American; 4% Latinx; and 4% other; 87% mothers; 25% high school graduate without college education; 30% degree from 4-year college; 45% graduate or professional school). Parents completed surveys when children were aged 6-9, 8-12, 9-13, and 12-16, with the last assessments occurring between May 13, 2020 and July 1, 2020 during the COVID-19 outbreak. Children also completed online surveys at ages 11-16 assessing self-efficacy, optimism, and coping. Multi-level modeling analyses showed a within-person increase in mental health symptoms from before to after the outbreak after controlling for changes associated with maturation. Symptom increases were mitigated in youth with greater self-efficacy and (to some extent) problem-focused engaged coping, and exacerbated in youth with greater emotion-focused engaged and disengaged coping. Implications of this work include the importance of reinforcing self-efficacy in youth during times of crisis, such as the pandemic, and the potential downsides of emotion-focused coping as an early response to the crisis for youth.