Efficient measurement of multiple ventricular assist device patient-reported outcomes: Creation of a 20-item profile from the MCS A-QOL study.

BACKGROUND: Patient-reported outcome (PRO) measures of distinct concepts are often put together into patient profile assessments. When brief, profile assessments can decrease respondent burden and increase measure completion rates. In this report, we describe the creation of 5 self-reported 4-item short forms and the Mechanical Circulatory Support: Measures of Adjustment and Quality of Life (MCS A-QOL) 20-item profile to assess PROs specific to adjustment and health-related quality of life (HRQOL) among patients who undergo left ventricular assist device (LVAD) implantation. METHODS: Using a cross-sectional sample of patients (n = 620) who underwent LVAD implantation at 12 U.S. sites or participated in the MyLVAD.com support group, we created 5 4-item short forms: Satisfaction with Treatment, ventricular assist device (VAD) Team Communication, Being Bothered by VAD Self-care and Limitations, Self-efficacy Regarding VAD self-care, and Stigma, which we combined into a 20-item profile. Analyses included intercorrelations among measures, Cronbach's alpha (i.e., internal consistency reliability)/score-level-specific reliability, and construct validity. RESULTS: The 620 patients were mean age = 57 years, 78% male, 70% White, and 56% on destination therapy LVADs. Intercorrelations among the 5 4-item measures were low to moderate (≤0.50), indicating they are associated yet largely distinct, and correlations with calibrated measures and 6-item short forms were ≥0.76, indicating their ability to reflect full-item bank scores. Internal consistency reliability for the 5 4-item short forms ranged from acceptable (≥0.70) to good (≥0.80). Construct validity was demonstrated for these measures. CONCLUSIONS: Our 5 4-item short forms are reliable and valid and may be used individually or together as a 20-item profile to assess adjustment and HRQOL in patients who undergo LVAD implantation.

Novel measures to assess ventricular assist device patient-reported outcomes: Findings from the MCS A-QOL study.

BACKGROUND: Generic and heart failure-specific measures do not capture unique aspects of living with a ventricular assist device (VAD). Using state-of-the-science psychometric measurement methods, we developed a measurement system to assess post-ventricular assist device adjustment and health-related quality of life (HRQOL). METHODS: Patients were recruited from 10/26/16-2/29/20 from 12 U.S. VAD programs. We created a dataset of participants (n = 620) enrolled before left (L)VAD implantation, with data at 3- or 6- months post-implantation (group1 [n = 154]), and participants enrolled after LVAD implantation, with data at one timepoint (group 2 [n = 466]). We constructed 5 item banks: 3 modified from existing measures and 2 new measures. Analyses included item response theory (IRT) modeling, differential item functioning tests for systematic measurement bias, and indicators of reliability and validity. RESULTS: Of 620 participants, 56% (n = 345) were implanted as destination therapy, 51% (n = 316) were <12 months post-implantation, mean age = 57.3 years, 78% (n = 485) male, 70% (n = 433) White, 58% (n = 353) married/partnered, and 58% (n = 357) with >high school education. We developed 5 new VAD item banks/measures: 6-item VAD Team Communication; 12-item Self-efficacy Regarding VAD Self-care; 11-item Being Bothered by VAD Self-care and Limitations; 7-item Satisfaction with Treatment; and 11-item Stigma. Cronbach's alpha reliability ranged from good (≥0.80) to excellent (≥0.90) for item banks/measures. All measures, except VAD Team Communication, demonstrated at least moderate correlations (≥0.30) with construct validity indicators. CONCLUSIONS: These measures meet IRT modeling assumptions and requirements; scores demonstrate reliability and validity. Use of these measures may assist VAD clinicians to inform patients about VADs as a treatment option and guide post-VAD interventions.

Effectiveness of a bivalent mRNA vaccine dose against symptomatic SARS-CoV-2 infection among U.S. Healthcare personnel, September 2022-May 2023.

BACKGROUND: Bivalent mRNA vaccines were recommended since September 2022. However, coverage with a recent vaccine dose has been limited, and there are few robust estimates of bivalent VE against symptomatic SARS-CoV-2 infection (COVID-19). We estimated VE of a bivalent mRNA vaccine dose against COVID-19 among eligible U.S. healthcare personnel who had previously received monovalent mRNA vaccine doses. METHODS: We conducted a case-control study in 22 U.S. states, and enrolled healthcare personnel with COVID-19 (case-participants) or without COVID-19 (control-participants) during September 2022-May 2023. Participants were considered eligible for a bivalent mRNA dose if they had received 2-4 monovalent (ancestral-strain) mRNA vaccine doses, and were ≥67 days after the most recent vaccine dose. We estimated VE of a bivalent mRNA dose using conditional logistic regression, accounting for matching by region and four-week calendar period. We adjusted estimates for age group, sex, race and ethnicity, educational level, underlying health conditions, community COVID-19 exposure, prior SARS-CoV-2 infection, and days since the last monovalent mRNA dose. RESULTS: Among 3,647 healthcare personnel, 1,528 were included as case-participants and 2,119 as control-participants. Participants received their last monovalent mRNA dose a median of 404 days previously; 1,234 (33.8%) also received a bivalent mRNA dose a median of 93 days previously. Overall, VE of a bivalent dose was 34.1% (95% CI, 22.6%-43.9%) against COVID-19 and was similar by product, days since last monovalent dose, number of prior doses, age group, and presence of underlying health conditions. However, VE declined from 54.8% (95% CI, 40.7%-65.6%) after 7-59 days to 21.6% (95% CI 5.6%-34.9%) after ≥60 days. CONCLUSIONS: Bivalent mRNA COVID-19 vaccines initially conferred approximately 55% protection against COVID-19 among U.S. healthcare personnel. However, protection waned after two months. These findings indicate moderate initial protection against symptomatic SARS-CoV-2 infection by remaining up-to-date with COVID-19 vaccines.

Effectiveness of a Messenger RNA Vaccine Booster Dose Against Coronavirus Disease 2019 Among US Healthcare Personnel, October 2021-July 2022.

Background: Protection against symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease 2019 [COVID-19]) can limit transmission and the risk of post-COVID conditions, and is particularly important among healthcare personnel. However, lower vaccine effectiveness (VE) has been reported since predominance of the Omicron SARS-CoV-2 variant. Methods: We evaluated the VE of a monovalent messenger RNA (mRNA) booster dose against COVID-19 from October 2021 to June 2022 among US healthcare personnel. After matching case-participants with COVID-19 to control-participants by 2-week period and site, we used conditional logistic regression to estimate the VE of a booster dose compared with completing only 2 mRNA doses >150 days previously, adjusted for multiple covariates. Results: Among 3279 case-participants and 3998 control-participants who had completed 2 mRNA doses, we estimated that the VE of a booster dose against COVID-19 declined from 86% (95% confidence interval, 81%-90%) during Delta predominance to 65% (58%-70%) during Omicron predominance. During Omicron predominance, VE declined from 73% (95% confidence interval, 67%-79%) 14-60 days after the booster dose, to 32% (4%-52%) ≥120 days after a booster dose. We found that VE was similar by age group, presence of underlying health conditions, and pregnancy status on the test date, as well as among immunocompromised participants. Conclusions: A booster dose conferred substantial protection against COVID-19 among healthcare personnel. However, VE was lower during Omicron predominance, and waning effectiveness was observed 4 months after booster dose receipt during this period. Our findings support recommendations to stay up to date on recommended doses of COVID-19 vaccines for all those eligible.

PROMIS: Physical, Mental and Social Health Outcomes Improve From Before to Early After LVAD Implant: Findings From the Mechanical Circulatory Support: Measures of Adjustment and Quality of Life (MCS A-QOL) Study.

Study participants (n = 272) completed 12 Patient-Reported Outcomes Measurement Information System (PROMIS) physical, mental and social health measures (questionnaires) prior to implantation of a left ventricular assist device (LVAD) and again at 3 and 6 months postimplant. All but 1 PROMIS measure demonstrated significant improvement from pre-implant to 3 months; there was little change between 3 and 6 months. Because PROMIS measures were developed in the general population, patients with an LVAD, their caregivers and their clinicians can interpret the meaning of PROMIS scores in relation to the general population, helping them to monitor a return to normalcy in everyday life.

Validity of Patient-Reported Outcomes Measurement Information System Physical, Mental, and Social Health Measures After Left Ventricular Assist Device Implantation and Implications for Patient Care.

BACKGROUND: A better understanding is needed of the burdens and benefits of left ventricular assist device (LVAD) implantation on patients' physical, mental, and social well-being. The purpose of this report was to evaluate the validity of Patient-Reported Outcomes Measurement Information System (PROMIS) measures for LVAD patients and to estimate clinically important score differences likely to have implications for patient treatment or care. METHODS: Adults from 12 sites across all US geographic regions completed PROMIS measures ≥3 months post-LVAD implantation. Other patient-reported outcomes (eg, Kansas City Cardiomyopathy Questionnaire-12 item), clinician ratings, performance tests, and clinical adverse events were used as validity indicators. Criterion and construct validity and clinically important differences were estimated with Pearson correlations, ANOVA methods, and Cohen d effect sizes. RESULTS: Participants' (n=648) mean age was 58 years, and the majority were men (78%), non-Hispanic White people (68%), with dilated cardiomyopathy (55%), long-term implantation strategy (57%), and New York Heart Association classes I and II (54%). Most correlations between validity indicators and PROMIS measures were medium to large (≥0.3; p<0.01). Most validity analyses demonstrated medium-to-large effect sizes (≥0.5) and clinically important differences in mean PROMIS scores (up to 14.8 points). Ranges of minimally important differences for 4 PROMIS measures were as follows: fatigue (3-5 points), physical function (2-3), ability to participate in social roles and activities (3), and satisfaction with social roles and activities (3-5). CONCLUSIONS: The findings provide convincing evidence for the relevance and validity of PROMIS physical, mental, and social health measures in patients from early-to-late post-LVAD implantation. Findings may inform shared decision-making when patients consider treatment options. Patients with an LVAD, their caregivers, and their clinicians should find it useful to interpret the meaning of their PROMIS scores in relation to the general population, that is, PROMIS may help to monitor a return to normalcy in everyday life.

Computer adaptive testing to assess impairing behavioral health problems in emergency department patients with somatic complaints.

Objectives: To assess: (1) the prevalence of mental health and substance use in patients presenting to the emergency department (ED) through use of a computer adaptive test (CAT-MH), (2) the correlation among CAT-MH scores and self- and clinician-reported assessments, and (3) the association between CAT-MH scores and ED utilization in the year prior and 30 days after enrollment. Methods: This was a single-center observational study of adult patients presenting to the ED for somatic complaints (97%) from May 2019 to March 2020. The main outcomes were computer-adaptive-assessed domains of suicidality, depression, anxiety, post-traumatic stress disorder (PTSD), and substance use. We conducted Pearson correlations and logistic regression for objectives 2 and 3, respectively. Results: From a sample of 794 patients, the proportion of those at moderate/severe risk was: 24.1% (suicidality), 8.3% (depression), 16.5% (anxiety), 12.3% (PTSD), and 20.4% (substance use). CAT-MH domains were highly correlated with self-report assessments (r = 0.49-0.79). Individuals who had 2 or more ED visits in the prior year had 62% increased odds of being in the intermediate-high suicide risk category (odds ratio [OR], 1.62; 95% confidence interval [CI], 1.07-2.44) compared to those with zero prior ED visits. Individuals who scored in the intermediate-high-suicide risk group had 63% greater odds of an ED visit within 30 days after enrollment compared to those who scored as low risk (OR, 1.63; 95% CI, 1.09, 2.44). Conclusion: The CAT-MH documented that a considerable proportion of ED patients presenting for somatic problems had mental health conditions, even if mild. Mental health problems were also associated with ED utilization.

Estimation of Respiratory Rate from Breathing Audio.

The COVID-19 pandemic has fueled exponential growth in the adoption of remote delivery of primary, specialty, and urgent health care services. One major challenge is the lack of access to physical exam including accurate and inexpensive measurement of remote vital signs. Here we present a novel method for machine learning-based estimation of patient respiratory rate from audio. There exist non-learning methods but their accuracy is limited and work using machine learning known to us is either not directly useful or uses non-public datasets. We are aware of only one publicly available dataset which is small and which we use to evaluate our algorithm. However, to avoid the overfitting problem, we expand its effective size by proposing a new data augmentation method. Our algorithm uses the spectrogram representation and requires labels for breathing cycles, which are used to train a recurrent neural network for recognizing the cycles. Our augmentation method exploits the independence property of the most periodic frequency components of the spectrogram and permutes their order to create multiple signal representations. Our experiments show that our method almost halves the errors obtained by the existing (non-learning) methods. Clinical Relevance- We achieve a Mean Absolute Error (MAE) of 1.0 for the respiratory rate while relying only on an audio signal of a patient breathing. This signal can be collected from a smartphone such that physicians can automatically and reliably determine respiratory rate in a remote setting.

Preoperative Assessment of Anxiety and Depression Using Computerized Adaptive Screening Tools: A Pilot Prospective Cohort Study.

BACKGROUND: Serious mental health conditions (eg, anxiety and depression) are common in surgical patients, yet likely underassessed due to the time-consuming and cumbersome traditional screening process. A recently developed computerized adaptive mental health assessment tool (computerized adaptive test-mental health [CAT-MH]) allows rapid, precise, and accurate assessment of numerous mental health disorders, including anxiety and depression, without the need for a trained interviewer. The goal of this investigation was to determine the feasibility of administering CAT-MH for anxiety and depression in the preoperative setting and to obtain preliminary evidence of the prevalence of anxiety and depression in preoperative patients. METHODS: In this prospective cohort study, 100 adult patients scheduled for elective surgery were enrolled and asked to complete the CAT-MH in the preoperative clinic. Urgent and emergency surgeries were excluded as were pregnant patients. Primary feasibility outcomes were completion rate and time to completion. Secondary outcomes were prevalence estimates of anxiety and depression. RESULTS: All 100 enrolled patients completed the study. All patients were able to complete the mental health assessment (mean time: 3.6 ± 1.8 minutes standard deviation). Sixteen patients (16%) screened positive for anxiety (severity: mild, n = 7 [7%]; moderate, n = 7 [7%]); severe, n = 2 [2%]); 12 of 16 (75%) did not have a previous diagnosis of anxiety disorder. Twenty-eight (28%) patients screened positive for depression (severity: mild, n = 26 [26%]; moderate and severe, n = 1 each [1%]); 23 of 28 (82%) had no previous diagnosis of depressive disorder. Nineteen patients (19%) met the screening criteria for major depressive disorder; 14 of 19 (74%) of which had no previous diagnosis of major depressive disorder. CONCLUSIONS: The results of this pilot study support the feasibility of using CAT-MH in a preoperative evaluation and indicate that there is a substantial prevalence of undiagnosed anxiety and depression in surgical patients.

Building and experimenting with an agent-based model to study the population-level impact of CommunityRx, a clinic-based community resource referral intervention.

CommunityRx (CRx), an information technology intervention, provides patients with a personalized list of healthful community resources (HealtheRx). In repeated clinical studies, nearly half of those who received clinical "doses" of the HealtheRx shared their information with others ("social doses"). Clinical trial design cannot fully capture the impact of information diffusion, which can act as a force multiplier for the intervention. Furthermore, experimentation is needed to understand how intervention delivery can optimize social spread under varying circumstances. To study information diffusion from CRx under varying conditions, we built an agent-based model (ABM). This study describes the model building process and illustrates how an ABM provides insight about information diffusion through in silico experimentation. To build the ABM, we constructed a synthetic population ("agents") using publicly-available data sources. Using clinical trial data, we developed empirically-informed processes simulating agent activities, resource knowledge evolution and information sharing. Using RepastHPC and chiSIM software, we replicated the intervention in silico, simulated information diffusion processes, and generated emergent information diffusion networks. The CRx ABM was calibrated using empirical data to replicate the CRx intervention in silico. We used the ABM to quantify information spread via social versus clinical dosing then conducted information diffusion experiments, comparing the social dosing effect of the intervention when delivered by physicians, nurses or clinical clerks. The synthetic population (N = 802,191) exhibited diverse behavioral characteristics, including activity and knowledge evolution patterns. In silico delivery of the intervention was replicated with high fidelity. Large-scale information diffusion networks emerged among agents exchanging resource information. Varying the propensity for information exchange resulted in networks with different topological characteristics. Community resource information spread via social dosing was nearly 4 fold that from clinical dosing alone and did not vary by delivery mode. This study, using CRx as an example, demonstrates the process of building and experimenting with an ABM to study information diffusion from, and the population-level impact of, a clinical information-based intervention. While the focus of the CRx ABM is to recreate the CRx intervention in silico, the general process of model building, and computational experimentation presented is generalizable to other large-scale ABMs of information diffusion.

Effectiveness of mRNA Covid-19 Vaccine among U.S. Health Care Personnel.

BACKGROUND: The prioritization of U.S. health care personnel for early receipt of messenger RNA (mRNA) vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (Covid-19), allowed for the evaluation of the effectiveness of these new vaccines in a real-world setting. METHODS: We conducted a test-negative case-control study involving health care personnel across 25 U.S. states. Cases were defined on the basis of a positive polymerase-chain-reaction (PCR) or antigen-based test for SARS-CoV-2 and at least one Covid-19-like symptom. Controls were defined on the basis of a negative PCR test for SARS-CoV-2, regardless of symptoms, and were matched to cases according to the week of the test date and site. Using conditional logistic regression with adjustment for age, race and ethnic group, underlying conditions, and exposures to persons with Covid-19, we estimated vaccine effectiveness for partial vaccination (assessed 14 days after receipt of the first dose through 6 days after receipt of the second dose) and complete vaccination (assessed ≥7 days after receipt of the second dose). RESULTS: The study included 1482 case participants and 3449 control participants. Vaccine effectiveness for partial vaccination was 77.6% (95% confidence interval [CI], 70.9 to 82.7) with the BNT162b2 vaccine (Pfizer-BioNTech) and 88.9% (95% CI, 78.7 to 94.2) with the mRNA-1273 vaccine (Moderna); for complete vaccination, vaccine effectiveness was 88.8% (95% CI, 84.6 to 91.8) and 96.3% (95% CI, 91.3 to 98.4), respectively. Vaccine effectiveness was similar in subgroups defined according to age (<50 years or ≥50 years), race and ethnic group, presence of underlying conditions, and level of patient contact. Estimates of vaccine effectiveness were lower during weeks 9 through 14 than during weeks 3 through 8 after receipt of the second dose, but confidence intervals overlapped widely. CONCLUSIONS: The BNT162b2 and mRNA-1273 vaccines were highly effective under real-world conditions in preventing symptomatic Covid-19 in health care personnel, including those at risk for severe Covid-19 and those in racial and ethnic groups that have been disproportionately affected by the pandemic. (Funded by the Centers for Disease Control and Prevention.).

Early Convalescent Plasma for High-Risk Outpatients with Covid-19.

BACKGROUND: Early administration of convalescent plasma obtained from blood donors who have recovered from coronavirus disease 2019 (Covid-19) may prevent disease progression in acutely ill, high-risk patients with Covid-19. METHODS: In this randomized, multicenter, single-blind trial, we assigned patients who were being treated in an emergency department for Covid-19 symptoms to receive either one unit of convalescent plasma with a high titer of antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or placebo. All the patients were either 50 years of age or older or had one or more risk factors for disease progression. In addition, all the patients presented to the emergency department within 7 days after symptom onset and were in stable condition for outpatient management. The primary outcome was disease progression within 15 days after randomization, which was a composite of hospital admission for any reason, seeking emergency or urgent care, or death without hospitalization. Secondary outcomes included the worst severity of illness on an 8-category ordinal scale, hospital-free days within 30 days after randomization, and death from any cause. RESULTS: A total of 511 patients were enrolled in the trial (257 in the convalescent-plasma group and 254 in the placebo group). The median age of the patients was 54 years; the median symptom duration was 4 days. In the donor plasma samples, the median titer of SARS-CoV-2 neutralizing antibodies was 1:641. Disease progression occurred in 77 patients (30.0%) in the convalescent-plasma group and in 81 patients (31.9%) in the placebo group (risk difference, 1.9 percentage points; 95% credible interval, -6.0 to 9.8; posterior probability of superiority of convalescent plasma, 0.68). Five patients in the plasma group and 1 patient in the placebo group died. Outcomes regarding worst illness severity and hospital-free days were similar in the two groups. CONCLUSIONS: The administration of Covid-19 convalescent plasma to high-risk outpatients within 1 week after the onset of symptoms of Covid-19 did not prevent disease progression. (SIREN-C3PO ClinicalTrials.gov number, NCT04355767.).

Interim Estimates of Vaccine Effectiveness of Pfizer-BioNTech and Moderna COVID-19 Vaccines Among Health Care Personnel - 33 U.S. Sites, January-March 2021.

Throughout the COVID-19 pandemic, health care personnel (HCP) have been at high risk for exposure to SARS-CoV-2, the virus that causes COVID-19, through patient interactions and community exposure (1). The Advisory Committee on Immunization Practices recommended prioritization of HCP for COVID-19 vaccination to maintain provision of critical services and reduce spread of infection in health care settings (2). Early distribution of two mRNA COVID-19 vaccines (Pfizer-BioNTech and Moderna) to HCP allowed assessment of the effectiveness of these vaccines in a real-world setting. A test-negative case-control study is underway to evaluate mRNA COVID-19 vaccine effectiveness (VE) against symptomatic illness among HCP at 33 U.S. sites across 25 U.S. states. Interim analyses indicated that the VE of a single dose (measured 14 days after the first dose through 6 days after the second dose) was 82% (95% confidence interval [CI] = 74%-87%), adjusted for age, race/ethnicity, and underlying medical conditions. The adjusted VE of 2 doses (measured ≥7 days after the second dose) was 94% (95% CI = 87%-97%). VE of partial (1-dose) and complete (2-dose) vaccination in this population is comparable to that reported from clinical trials and recent observational studies, supporting the effectiveness of mRNA COVID-19 vaccines against symptomatic disease in adults, with strong 2-dose protection.

Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021.

OBJECTIVES: Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. METHODS: Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. RESULTS: Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79-0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8-96.3%), specificity of 20.0% (19.0-21.0%), negative likelihood ratio of 0.22 (0.19-0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points). CONCLUSION: Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.

Predicting 30-day return hospital admissions in patients with COVID-19 discharged from the emergency department: A national retrospective cohort study.

OBJECTIVES: Identification of patients with coronavirus disease 2019 (COVID-19) at risk for deterioration after discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies patients with COVID-19 at risk for return and hospital admission within 30 days of ED discharge. METHODS: We performed a retrospective cohort study of discharged adult ED patients (n = 7529) with SARS-CoV-2 infection from 116 unique hospitals contributing to the National Registry of Suspected COVID-19 in Emergency Care. The primary outcome was return hospital admission within 30 days. Models were developed using classification and regression tree (CART), gradient boosted machine (GBM), random forest (RF), and least absolute shrinkage and selection (LASSO) approaches. RESULTS: Among patients with COVID-19 discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine-learning (ML) models (GBM, RF, and LASSO) performed similarly. The RF model yielded a test area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI], 0.71-0.78), with a sensitivity of 0.46 (95% CI, 0.39-0.54) and a specificity of 0.84 (95% CI, 0.82-0.85). Predictive variables, including lowest oxygen saturation, temperature, or history of hypertension, diabetes, hyperlipidemia, or obesity, were common to all ML models. CONCLUSIONS: A predictive model identifying adult ED patients with COVID-19 at risk for return for return hospital admission within 30 days is feasible. Ensemble/boot-strapped classification methods (eg, GBM, RF, and LASSO) outperform the single-tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize the allocation of follow-up resources.

Emergency Department-initiated High-flow Nasal Cannula for COVID-19 Respiratory Distress.

INTRODUCTION: Patients with coronavirus disease 2019 (COVID-19) can develop rapidly progressive respiratory failure. Ventilation strategies during the COVID-19 pandemic seek to minimize patient mortality. In this study we examine associations between the availability of emergency department (ED)-initiated high-flow nasal cannula (HFNC) for patients presenting with COVID-19 respiratory distress and outcomes, including rates of endotracheal intubation (ETT), mortality, and hospital length of stay. METHODS: We performed a retrospective, non-concurrent cohort study of patients with COVID-19 respiratory distress presenting to the ED who required HFNC or ETT in the ED or within 24 hours following ED departure. Comparisons were made between patients presenting before and after the introduction of an ED-HFNC protocol. RESULTS: Use of HFNC was associated with a reduced rate of ETT in the ED (46.4% vs 26.3%, P <0.001) and decreased the cumulative proportion of patients who required ETT within 24 hours of ED departure (85.7% vs 32.6%, P <0.001) or during their entire hospitalization (89.3% vs 48.4%, P <0.001). Using HFNC was also associated with a trend toward increased survival to hospital discharge; however, this was not statistically significant (50.0% vs 68.4%, P = 0.115). There was no impact on intensive care unit or hospital length of stay. Demographics, comorbidities, and illness severity were similar in both cohorts. CONCLUSIONS: The institution of an ED-HFNC protocol for patients with COVID-19 respiratory distress was associated with reductions in the rate of ETT. Early initiation of HFNC is a promising strategy for avoiding ETT and improving outcomes in patients with COVID-19.

The utility of computed tomography to evaluate thoracic complications after cardiopulmonary resuscitation.

BACKGROUND: Cardiopulmonary resuscitation (CPR) in adults following non-traumatic out of hospital cardiac arrest (OHCA) can cause thoracic complications including rib fractures, sternal fractures, and pneumothorax. Post-CPR complication rates are poorly studied and the optimum imaging modality to detect these complications post-resuscitation has not been established. METHODS: We performed a retrospective review of adult patients transported to a single, urban, academic hospital following atraumatic OHCA between September 2015 and January 2020. Patients who achieved sustained return of spontaneous circulation (ROSC) and who underwent computed tomography (CT) imaging of the chest following radiographic chest x-ray were included in the analyses. Patient demographics and prehospital data were collected. Descriptive statistics and multivariate logistic regression analysis were performed. Sensitivity and specificity of chest x-ray for the detection of thoracic injury in this population were estimated. RESULTS: 786 non-traumatic OHCA patients were transported to the ED, 417 of whom obtained sustained ROSC and were admitted to the hospital (53%). 137 (32.9%) admitted patients underwent CT imaging of the chest in the ED. Of these imaged patients median age was 62 years old (IQR 53-70) with 54.0% female and 38.0% of patients having received bystander CPR. 40/137 (29.2%) patients had skeletal fractures noted on CT imaging and 12/137 (8.8%) had pneumothorax present on CT imaging. X-ray yielded a sensitivity of 7.5% for rib fracture and 50% for pneumothorax with a specificity of 100% for both. Logistic regression analysis revealed no significant association between age, sex, bystander CPR, or resuscitation length with thoracic fractures or pneumothorax. CONCLUSIONS: Complications from OHCA CPR were high with 29.2% of CT imaged patients having rib fractures and 8.8% having pneumothoraces. X-ray had poor sensitivity for these post-resuscitation complications. Post-CPR CT imaging of the chest should be considered for detecting post-CPR complications.

Impact of a Low-Intensity Resource Referral Intervention on Patients' Knowledge, Beliefs, and Use of Community Resources: Results from the CommunityRx Trial.

BACKGROUND: Connecting patients to community-based resources is now a cornerstone of modern healthcare that supports self-management of health. The mechanisms that link resource information to behavior change, however, remain poorly understood. OBJECTIVE: To evaluate the impact of CommunityRx, an automated, low-intensity resource referral intervention, on patients' knowledge, beliefs, and use of community resources. DESIGN: Real-world controlled clinical trial at an urban academic medical center in 2015-2016; participants were assigned by alternating week to receive the CommunityRx intervention or usual care. Surveys were administered at baseline, 1 week, 1 month, and 3 months. PARTICIPANTS: Publicly insured adults, ages 45-74 years. INTERVENTION: CommunityRx generated an automated, personalized list of resources, known as HealtheRx, near each participant's home using condition-specific, evidence-based algorithms. Algorithms used patient demographic and health characteristics documented in the electronic health record to identify relevant resources from a comprehensive, regularly updated database of health-related resources in the study area. MAIN MEASURES: Using intent-to-treat analysis, we examined the impact of HealtheRx referrals on (1) knowledge of the most commonly referred resource types, including healthy eating classes, individual counseling, mortgage assistance, smoking cessation, stress management, and weight loss classes or groups, and (2) beliefs about having resources in the community to manage health. KEY RESULTS: In a real-world controlled trial of 374 adults, intervention recipients improved knowledge (AOR = 2.15; 95% CI, 1.29-3.58) and beliefs (AOR = 1.68; 95% CI, 1.07-2.64) about common resources in the community to manage health, specifically gaining knowledge about smoking cessation (AOR = 2.76; 95% CI, 1.07-7.12) and weight loss resources (AOR = 2.26; 95% CI 1.05-4.84). Positive changes in both knowledge and beliefs about community resources were associated with higher resource use (P = 0.02). CONCLUSIONS: In a middle-age and older population with high morbidity, a low-intensity health IT intervention to deliver resource referrals promoted behavior change by increasing knowledge and positive beliefs about community resources for self-management of health. NIH TRIAL REGISTRY: NCT02435511.