Use of expert consensus to develop a shared list of procedures with potential for aerosol generation during the coronavirus disease 2019 (COVID-19) pandemic.

The coronavirus disease 2019 (COVID-19) pandemic highlighted the lack of agreement regarding the definition of aerosol-generating procedures and potential risk to healthcare personnel. We convened a group of Massachusetts healthcare epidemiologists to develop consensus through expert opinion in an area where broader guidance was lacking at the time.

Automating surveillance for healthcare-associated infections: Rationale and current realities (Part I/III).

Infection surveillance is one of the cornerstones of infection prevention and control. Measurement of process metrics and clinical outcomes, such as detection of healthcare-associated infections (HAIs), can be used to support continuous quality improvement. HAI metrics are reported as part of the CMS Hospital-Acquired Conditions Program, and they influence facility reputation and financial outcomes.

The future of automated infection detection: Innovation to transform practice (Part III/III).

Current methods of emergency-room-based syndromic surveillance were insufficient to detect early community spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the United States, which slowed the infection prevention and control response to the novel pathogen. Emerging technologies and automated infection surveillance have the potential to improve upon current practice standards and to revolutionize the practice of infection detection, prevention and control both inside and outside of healthcare settings. Genomics, natural language processing, and machine learning can be leveraged to improve identification of transmission events and aid and evaluate outbreak response. In the near future, automated infection detection strategies can be used to advance a true "Learning Healthcare System" that will support near-real-time quality improvement efforts and advance the scientific basis for the practice of infection control.

Distinguishing SARS-CoV-2 persistence and reinfection: A retrospective cohort study.

BACKGROUND: SARS-CoV-2 reinfection is poorly understood, partly because few studies have systematically applied genomic analysis to distinguish reinfection from persistent RNA detection related to initial infection. We aimed to evaluate the characteristics of SARS-CoV-2 reinfection and persistent RNA detection using independent genomic, clinical, and laboratory assessments. METHODS: All individuals at a large academic medical center who underwent a SARS-CoV-2 nucleic acid amplification test (NAAT) ≥ 45 days after an initial positive test, with both tests between March 14th and December 30th, 2020, were analyzed for potential reinfection. Inclusion criteria required having ≥2 positive NAATs collected ≥45 days apart with a cycle threshold (Ct) value <35 at repeat testing. For each included subject, likelihood of reinfection was assessed by viral genomic analysis of all available specimens with a Ct value <35, structured Ct trajectory criteria, and case-by-case review by infectious diseases physicians. RESULTS: Among 1,569 individuals with repeat SARS-CoV-2 testing ≥45 days after an initial positive NAAT, 65 (4%) met cohort inclusion criteria. Viral genomic analysis characterized mutations present, and was successful for 14/65 (22%) subjects. Six subjects had genomically-supported reinfection and eight subjects had genomically-supported persistent RNA detection. Compared to viral genomic analysis, clinical and laboratory assessments correctly distinguished reinfection from persistent RNA detection in 12/14 (86%) subjects but missed 2/6 (33%) genomically-supported reinfections. CONCLUSION: Despite good overall concordance with viral genomic analysis, clinical and Ct value-based assessments failed to identify 33% of genomically-supported reinfections. Scaling-up genomic analysis for clinical use would improve detection of SARS-CoV-2 reinfections.

Infection Prevention and Control of Severe Acute Respiratory Syndrome Coronavirus 2 in Health Care Settings.

The authors describe infection prevention and control approaches to severe acute respiratory syndrome coronavirus 2 in the health care setting, including a review of the chain of transmission and the hierarchy of controls, which are cornerstones of infection control and prevention. The authors also discuss lessons learned from nosocomial transmission events.

Applying the Plan-Do-Study-Act Approach to Support Healthcare Facilities in the Early COVID-19 Pandemic Response.

The identification of a novel respiratory pathogen in late December 2019 and the escalation in the number of infections in January 2020 required healthcare facilities to rapidly assess their planning and preparations to identify and manage suspected or confirmed cases. As a Regional Emerging Special Pathogens Treatment Center, many of the policies, resources, and tools Massachusetts General Hospital had developed before the COVID-19 pandemic were based on the Identify-Isolate-Inform concept to enable rapid identification of persons under investigation; isolation from other patients, visitors, and staff; and appropriate information sharing with internal and external parties to ensure continued safety of the facility and community. Our team sought to leverage these existing resources to support other healthcare facilities and implemented a modified Plan-Do-Study-Act approach to develop, refine, and disseminate a novel coronavirus toolkit. The toolkit underwent 3 Plan-Do-Study-Act cycles resulting in revisions of specific products, and the addition of new products to the toolkit. The toolkit provided access to templated algorithms, policies and procedures, signage, and educational materials, which could be customized for local needs and implemented immediately. There was broad dissemination and use of the resources provided in the toolkit and response to end-user feedback was provided in subsequent revisions. This project demonstrates the role that Regional Emerging Special Pathogens Treatment Centers can play in supporting the sharing of resources and best practices, and the utility of a Plan-Do-Study-Act approach in meeting needs.

Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach.

Side effects of COVID-19 or other vaccinations may affect an individual's safety, ability to work or care for self or others, and/or willingness to be vaccinated. Identifying modifiable factors that influence these side effects may increase the number of people vaccinated. In this observational study, data were from individuals who received an mRNA COVID-19 vaccine between December 2020 and April 2021 and responded to at least one post-vaccination symptoms survey that was sent daily for three days after each vaccination. We excluded those with a COVID-19 diagnosis or positive SARS-CoV2 test within one week after their vaccination because of the overlap of symptoms. We used machine learning techniques to analyze the data after the first vaccination. Data from 50,484 individuals (73% female, 18 to 95 years old) were included in the primary analysis. Demographics, history of an epinephrine autoinjector prescription, allergy history category (e.g., food, vaccine, medication, insect sting, seasonal), prior COVID-19 diagnosis or positive test, and vaccine manufacturer were identified as factors associated with allergic and non-allergic side effects; vaccination time 6:00-10:59 was associated with more non-allergic side effects. Randomized controlled trials should be conducted to quantify the relative effect of modifiable factors, such as time of vaccination.

COVID-19 Seroprevalence in ED Health Care Professionals Study: A Cross-Sectional Study.

INTRODUCTION: ED health care professionals are at the frontline of evaluation and management of patients with acute, and often undifferentiated, illness. During the initial phase of the SARS-CoV-2 outbreak, there were concerns that ED health care professionals may have been at increased risk of exposure to SARS-CoV-2 due to difficulty in early identification of patients. This study assessed the seroprevalence of SARS-CoV-2 antibodies among ED health care professionals without confirmed history of COVID-19 infection at a quaternary academic medical center. METHODS: This study used a cross-sectional design. An ED health care professional was deemed eligible if they had worked at least 4 shifts in the adult emergency department from April 1, 2020, through May 31, 2020, were asymptomatic on the day of blood draw, and were not known to have had prior documented COVID-19 infection. The study period was December 17, 2020, to January 27, 2021. Eligible participants completed a questionnaire and had a blood sample drawn. Samples were run on the Roche Cobas Elecsys Anti-SARS-CoV-2 antibody assay. RESULTS: Of 103 health care professionals (16 attending physicians, 4 emergency residents, 16 advanced practice professionals, and 67 full-time emergency nurses), only 3 (2.9%; exact 95% CI, 0.6%-8.3%) were seropositive for SARS-CoV-2 antibodies. DISCUSSION: At this quaternary academic medical center, among those who volunteered to take an antibody test, there was a low seroprevalence of SARS-CoV-2 antibodies among ED clinicians who were asymptomatic at the time of blood draw and not known to have had prior COVID-19 infection.

Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study.

OBJECTIVE: To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing. DESIGN: Retrospective cohort study. SETTING: One US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21. PARTICIPANTS: 33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19. MAIN OUTCOME MEASURES: An ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early. RESULTS: 9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge. CONCLUSION: A model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.

Coronavirus Disease 2019 (COVID-19) Diagnostic Clinical Decision Support: A Pre-Post Implementation Study of CORAL (COvid Risk cALculator).

BACKGROUND: Isolation of hospitalized persons under investigation (PUIs) for coronavirus disease 2019 (COVID-19) reduces nosocomial transmission risk. Efficient evaluation of PUIs is needed to preserve scarce healthcare resources. We describe the development, implementation, and outcomes of an inpatient diagnostic algorithm and clinical decision support system (CDSS) to evaluate PUIs. METHODS: We conducted a pre-post study of CORAL (COvid Risk cALculator), a CDSS that guides frontline clinicians through a risk-stratified COVID-19 diagnostic workup, removes transmission-based precautions when workup is complete and negative, and triages complex cases to infectious diseases (ID) physician review. Before CORAL, ID physicians reviewed all PUI records to guide workup and precautions. After CORAL, frontline clinicians evaluated PUIs directly using CORAL. We compared pre- and post-CORAL frequency of repeated severe acute respiratory syndrome coronavirus 2 nucleic acid amplification tests (NAATs), time from NAAT result to PUI status discontinuation, total duration of PUI status, and ID physician work hours, using linear and logistic regression, adjusted for COVID-19 incidence. RESULTS: Fewer PUIs underwent repeated testing after an initial negative NAAT after CORAL than before CORAL (54% vs 67%, respectively; adjusted odd ratio, 0.53 [95% confidence interval, .44-.63]; P < .01). CORAL significantly reduced average time to PUI status discontinuation (adjusted difference [standard error], -7.4 [0.8] hours per patient), total duration of PUI status (-19.5 [1.9] hours per patient), and average ID physician work-hours (-57.4 [2.0] hours per day) (all P < .01). No patients had a positive NAAT result within 7 days after discontinuation of precautions via CORAL. CONCLUSIONS: CORAL is an efficient and effective CDSS to guide frontline clinicians through the diagnostic evaluation of PUIs and safe discontinuation of precautions.

Synthetic DNA spike-ins (SDSIs) enable sample tracking and detection of inter-sample contamination in SARS-CoV-2 sequencing workflows.

The global spread and continued evolution of SARS-CoV-2 has driven an unprecedented surge in viral genomic surveillance. Amplicon-based sequencing methods provide a sensitive, low-cost and rapid approach but suffer a high potential for contamination, which can undermine laboratory processes and results. This challenge will increase with the expanding global production of sequences across a variety of laboratories for epidemiological and clinical interpretation, as well as for genomic surveillance of emerging diseases in future outbreaks. We present SDSI + AmpSeq, an approach that uses 96 synthetic DNA spike-ins (SDSIs) to track samples and detect inter-sample contamination throughout the sequencing workflow. We apply SDSIs to the ARTIC Consortium's amplicon design, demonstrate their utility and efficiency in a real-time investigation of a suspected hospital cluster of SARS-CoV-2 cases and validate them across 6,676 diagnostic samples at multiple laboratories. We establish that SDSI + AmpSeq provides increased confidence in genomic data by detecting and correcting for relatively common, yet previously unobserved modes of error, including spillover and sample swaps, without impacting genome recovery.

Symptom monitoring after coronavirus disease 2019 (COVID-19) vaccination in a large integrated healthcare system: Separating symptoms from severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection.

OBJECTIVE: To describe the incidence of systemic overlap and typical coronavirus disease 2019 (COVID-19) symptoms in healthcare personnel (HCP) following COVID-19 vaccination and association of reported symptoms with diagnosis of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection in the context of public health recommendations regarding work exclusion. DESIGN: This prospective cohort study was conducted between December 16, 2020, and March 14, 2021, with HCP who had received at least 1 dose of either the Pfizer-BioNTech or Moderna COVID-19 vaccine. SETTING: Large healthcare system in New England. INTERVENTIONS: HCP were prompted to complete a symptom survey for 3 days after each vaccination. Reported symptoms generated automated guidance regarding symptom management, SARS-CoV-2 testing requirements, and work restrictions. Overlap symptoms (ie, fever, fatigue, myalgias, arthralgias, or headache) were categorized as either lower or higher severity. Typical COVID-19 symptoms included sore throat, cough, nasal congestion or rhinorrhea, shortness of breath, ageusia and anosmia. RESULTS: Among 64,187 HCP, a postvaccination electronic survey had response rates of 83% after dose 1 and 77% after dose 2. Report of ≥3 lower-severity overlap symptoms, ≥1 higher-severity overlap symptoms, or at least 1 typical COVID-19 symptom after dose 1 was associated with increased likelihood of testing positive. HCP with prior COVID-19 infection were significantly more likely to report severe overlap symptoms after dose 1. CONCLUSIONS: Reported overlap symptoms were common; however, only report of ≥3 low-severity overlap symptoms, at least 1 higher-severity overlap symptom, or any typical COVID-19 symptom were associated with infection. Work-related restrictions for overlap symptoms should be reconsidered.

Association of Self-reported High-Risk Allergy History With Allergy Symptoms After COVID-19 Vaccination.

Importance: Allergic history in individuals with confirmed anaphylaxis to a messenger RNA (mRNA) COVID-19 vaccine is common. However, the risk factors for allergy symptoms after receiving the vaccine are unknown. Objective: To assess the association between self-reported history of high-risk allergy and self-reported allergic reactions after mRNA COVID-19 vaccination of health care employees. Design, Setting, and Participants: This cohort study obtained demographic, medical, and vaccine administration data of employees of Mass General Brigham from the institutional electronic health record. Employees who received at least 1 dose of an mRNA COVID-19 vaccine between December 14, 2020, and February 1, 2021, and who completed at least 1 postvaccination symptom survey in the 3 days after vaccination were included. Exposures: Self-reported history of high-risk allergy, defined as a previous severe allergic reaction to a vaccine, an injectable medication, or other allergen. Main Outcomes and Measures: The primary outcome was 1 or more self-reported allergic reactions in the first 3 days after dose 1 or dose 2 of an mRNA COVID-19 vaccine. Multivariable log binomial regression was used to assess the association between allergic reactions and high-risk allergy status. Results: A total of 52 998 health care employees (mean [SD] age, 42 [14] years; 38 167 women [72.0%]) were included in the cohort, of whom 51 706 (97.6%) received 2 doses of an mRNA COVID-19 vaccine and 474 (0.9%) reported a history of high-risk allergy. Individuals with vs without a history of high-risk allergy reported more allergic reactions after receiving dose 1 or 2 of the vaccine (11.6% [n = 55] vs 4.7% [n = 2461]). In the adjusted model, a history of high-risk allergy was associated with an increased risk of allergic reactions (adjusted relative risk [aRR], 2.46; 95% CI, 1.92-3.16), with risk being highest for hives (aRR, 3.81; 95% CI, 2.33-6.22) and angioedema (aRR, 4.36; 95% CI, 2.52-7.54). Conclusions and Relevance: This cohort study found that self-reported history of high-risk allergy was associated with an increased risk of self-reported allergic reactions within 3 days of mRNA COVID-19 vaccination. However, reported allergy symptoms did not impede the completion of the 2-dose vaccine protocol among a cohort of eligible health care employees, supporting the overall safety of mRNA COVID-19 vaccine.

Multisociety statement on coronavirus disease 2019 (COVID-19) vaccination as a condition of employment for healthcare personnel.

This consensus statement by the Society for Healthcare Epidemiology of America (SHEA) and the Society for Post-Acute and Long-Term Care Medicine (AMDA), the Association for Professionals in Epidemiology and Infection Control (APIC), the HIV Medicine Association (HIVMA), the Infectious Diseases Society of America (IDSA), the Pediatric Infectious Diseases Society (PIDS), and the Society of Infectious Diseases Pharmacists (SIDP) recommends that coronavirus disease 2019 (COVID-19) vaccination should be a condition of employment for all healthcare personnel in facilities in the United States. Exemptions from this policy apply to those with medical contraindications to all COVID-19 vaccines available in the United States and other exemptions as specified by federal or state law. The consensus statement also supports COVID-19 vaccination of nonemployees functioning at a healthcare facility (eg, students, contract workers, volunteers, etc).

Yield of Severe Acute Respiratory Syndrome Coronavirus 2 Lower Respiratory Tract Testing After a Negative Nasopharyngeal Test Among Hospitalized Persons Under Investigation for Coronavirus Disease 2019.

Among hospitalized persons under investigation for coronavirus disease 2019 (COVID-19), more repeated severe acute respiratory syndrome coronavirus 2 nucleic acid amplification tests (NAATs) after a negative NAAT were positive from lower than from upper respiratory tract specimens (1.9% vs 1.0%, P = .033). Lower respiratory testing should be prioritized among patients displaying respiratory symptoms with moderate-to-high suspicion for COVID-19 after 1 negative upper respiratory NAAT.