Comparative Study of Chemosensory Dysfunction in COVID-19 in 2 Geographically Distinct Regions.

OBJECTIVE: To directly compare the prevalence of chemosensory dysfunction (smell and taste) in geographically distinct regions with the same questionnaires. METHODS: A cross-sectional study was performed to evaluate the self-reported symptoms among adults (older than 18 years) who underwent COVID-19 testing at an ambulatory assessment center in Canada and at a hospital in Israel between March 16, 2020, and August 19, 2020. The primary outcome was the prevalence of self-reported chemosensory dysfunction (anosmia/hypomsia and dysgeusia/ageusia). Subgroup analysis was performed to evaluate the prevalence of chemosensory deficits among the outpatients. RESULTS: We identified a total of 350 COVID-19-positive patients (138 Canadians and 212 Israelis). The overall prevalence of chemosensory dysfunction was 47.1%. There was a higher proportion of chemosensory deficits among Canadians compared to Israelis (66.7% vs 34.4%, P < .01). A subgroup analysis for outpatients (never hospitalized) still identified a higher prevalence of chemosensory dysfunction among Canadians compared to Israelis (68.2% vs 36.1%, P < 0.01). A majority of patients recovered their sense of smell after 4 weeks of symptom onset. CONCLUSION: Although the prevalence of chemosensory deficit in COVID-19 was found to be similar to previously published reports, the prevalence can vary significantly across different geographical regions. Therefore, it is important to obtain regionally specific data so that the symptom of anosmia/dysgeusia can be used as a guide for screening for the clinical diagnosis of COVID-19.

Machine learning for optimal test admission in the presence of resource constraints.

Developing rapid tools for early detection of viral infection is crucial for pandemic containment. This is particularly crucial when testing resources are constrained and/or there are significant delays until the test results are available - as was quite common in the early days of Covid-19 pandemic. We show how predictive analytics methods using machine learning algorithms can be combined with optimal pre-test screening mechanisms, greatly increasing test efficiency (i.e., rate of true positives identified per test), as well as to allow doctors to initiate treatment before the test results are available. Our optimal test admission policies account for imperfect accuracy of both the medical test and the model prediction mechanism. We derive the accuracy required for the optimized admission policies to be effective. We also show how our policies can be extended to re-testing high-risk patients, as well as combined with pool testing approaches. We illustrate our techniques by applying them to a large data reported by the Israeli Ministry of Health for RT-PCR tests from March to September 2020. Our results demonstrate that in the context of the Covid-19 pandemic a pre-test probability screening tool with conventional RT-PCR testing could have potentially increased efficiency by several times, compared to random admission control.

COVID-19 lockdown impact on quality of treatment and outcomes of STEMI and stroke patients in a large tertiary medical center: an observational study.

BACKGROUND: The coronavirus 2019 (COVID-19) pandemic affected health-care systems worldwide, leading to fewer admissions and raising concerns about the quality of care. The objective of this study was to investigate the early effects of the COVID-19 pandemic on quality of care among stroke and ST-elevation myocardial infarction (STEMI) patients, focusing on clinical outcomes and direct treatment costs. METHOD: This retrospective, observational study was based on the 10-week period that included the first wave of the COVID-19 pandemic in Israel (15 February 2020-30 April 2020). Emergency department admissions for stroke and STEMI were compared with parallel periods in 2017-2019, focusing on demographics, risk and severity scores, and the effect of clinical outcomes on hospitalization costs. RESULTS: The 634 stroke and 186 STEMI cases comprised 16% and 19% fewer admissions, respectively, compared to 2019. No significant changes were detected in demographics, most disease management parameters, readmission and mortality outcomes. The mean door-to-balloon time increased insignificantly by 33%, lowering the health quality indicator (HQI) for treatment in <90 min from 94.7% in 2017-2019 to 83% in 2020 (P = 0.022). Among suspected stroke patients, 97.2% underwent imaging, with 28% longer median time from admission (P = 0.05). Consequently, only 24.3% met the HQI of imaging in <29 min, compared to 45.5% in 2017-2019 (P < 0.01). Increased length of stay and more intensive care unit admissions were the leading causes of 6.5% increased mean cost of STEMI patients' initial hospitalization, which totaled $29 300 in the COVID-19 period (P = 0.008). CONCLUSION: The initial pandemic period caused a decline in HQIs linked to diagnostic and treatment protocols, without changes in outcomes, but with increased hospitalization costs. Medical information and awareness of life-threatening conditions among patients and caregivers should be increased to enable proper diagnosis and management.

Machine learning to predict in-hospital mortality among patients with severe obesity: Proof of concept study.

Objectives: Hospitalized patients with severe obesity require adapted hospital management. The aim of this study was to evaluate a machine learning model to predict in-hospital mortality among this population. Methods: Data of unselected consecutive emergency department admissions of hospitalized patients with severe obesity (BMI ≥ 40 kg/m2) was analyzed. Data was retrieved from five hospitals from the Mount Sinai health system, New York. The study time frame was between January 2011 and December 2019. Data was used to train a gradient-boosting machine learning model to identify in-hospital mortality. The model was trained and evaluated based on the data from four hospitals and externally validated on held-out data from the fifth hospital. Results: A total of 14,078 hospital admissions of inpatients with severe obesity were included. The in-hospital mortality rate was 297/14,078 (2.1%). In univariate analysis, albumin (area under the curve [AUC] = 0.77), blood urea nitrogen (AUC = 0.76), acuity level (AUC = 0.73), lactate (AUC = 0.72), and chief complaint (AUC = 0.72) were the best single predictors. For Youden's index, the model had a sensitivity of 0.77 (95% CI: 0.67-0.86) with a false positive rate of 1:9. Conclusion: A machine learning model trained on clinical measures provides proof of concept performance in predicting mortality in patients with severe obesity. This implies that such models may help to adopt specific decision support tools for this population.

A Practical Clinical Score Predicting Respiratory Failure in COVID-19 Patients.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic resulted in repeated surges of patients, sometimes challenging triage protocols and appropriate control of patient flow. Available models, such as the National Early Warning Score (NEWS), have shown significant limitations. Still, they are used by some centers to triage COVID-19 patients due to the lack of better tools. OBJECTIVES: To establish a practical and automated triage tool based on readily available clinical data to rapidly determine a distinction between patients who are prone to respiratory failure. METHODS: The electronic medical records of COVID-19 patients admitted to the Sheba Medical Center March-April 2020 were analyzed. Population data extraction and exploration were conducted using a MDClone (Israel) big data platform. Patients were divided into three groups: non-intubated, intubated within 24 hours, and intubated after 24 hours. The NEWS and our model where applied to all three groups and a best fit prediction model for the prediction of respiratory failure was established. RESULTS: The cohort included 385 patients, 42 of whom were eventually intubated, 15 within 24 hours or less. The NEWS score was significantly lower for the non-intubated patients compared to the two other groups. Our improved model, which included NEWS elements combined with other clinical data elements, showed significantly better performance. The model's receiver operating characteristic curve had area under curve (AUC) of 0.92 with of sensitivity 0.81, specificity 0.89, and negative predictive value (NPV) 98.4% compared to AUC of 0.63 with NEWS. As patients deteriorate and require further support with supplemental O2, the need for re-triage emerges. Our model was able to identify those patients on supplementary O2 prone to respiratory failure with an AUC of 0.86 sensitivity 0.95, and specificity 0.7 NPV 98.9%, whereas NEWS had an AUC of 0.76. For both groups positive predictive value was approximately 35. CONCLUSIONS: Our model, based on readily available and simple clinical parameters, showed an excellent ability to predict negative outcome among patients with COVID-19 and therefore might be used as an initial screening tool for patient triage in emergency departments and other COVID-19 specific areas of the hospital.

Obesity as a mortality risk factor in the medical ward: a case control study.

BACKGROUND: Research regarding the association between severe obesity and in-hospital mortality is inconsistent. We evaluated the impact of body mass index (BMI) levels on mortality in the medical wards. The analysis was performed separately before and during the COVID-19 pandemic. METHODS: We retrospectively retrieved data of adult patients admitted to the medical wards at the Mount Sinai Health System in New York City. The study was conducted between January 1, 2011, to March 23, 2021. Patients were divided into two sub-cohorts: pre-COVID-19 and during-COVID-19. Patients were then clustered into groups based on BMI ranges. A multivariate logistic regression analysis compared the mortality rate among the BMI groups, before and during the pandemic. RESULTS: Overall, 179,288 patients were admitted to the medical wards and had a recorded BMI measurement. 149,098 were admitted before the COVID-19 pandemic and 30,190 during the pandemic. Pre-pandemic, multivariate analysis showed a "J curve" between BMI and mortality. Severe obesity (BMI > 40) had an aOR of 0.8 (95% CI:0.7-1.0, p = 0.018) compared to the normal BMI group. In contrast, during the pandemic, the analysis showed a "U curve" between BMI and mortality. Severe obesity had an aOR of 1.7 (95% CI:1.3-2.4, p < 0.001) compared to the normal BMI group. CONCLUSIONS: Medical ward patients with severe obesity have a lower risk for mortality compared to patients with normal BMI. However, this does not apply during COVID-19, where obesity was a leading risk factor for mortality in the medical wards. It is important for the internal medicine physician to understand the intricacies of the association between obesity and medical ward mortality.

Which outcomes are most important to measure in patients with COVID-19 and how and when should these be measured? Development of an international standard set of outcomes measures for clinical use in patients with COVID-19: a report of the International Consortium for Health Outcomes Measurement (ICHOM) COVID-19 Working Group.

OBJECTIVES: The COVID-19 pandemic has resulted in widespread morbidity and mortality with the consequences expected to be felt for many years. Significant variation exists in the care even of similar patients with COVID-19, including treatment practices within and between institutions. Outcome measures vary among clinical trials on the same therapies. Understanding which therapies are of most value is not possible unless consensus can be reached on which outcomes are most important to measure. Furthermore, consensus on the most important outcomes may enable patients to monitor and track their care, and may help providers to improve the care they offer through quality improvement. To develop a standardised minimum set of outcomes for clinical care, the International Consortium for Health Outcomes Measurement (ICHOM) assembled a working group (WG) of 28 volunteers, including health professionals, patients and patient representatives. DESIGN: A list of outcomes important to patients and professionals was generated from a systematic review of the published literature using the MEDLINE database, from review of outcomes being measured in ongoing clinical trials, from a survey distributed to patients and patient networks, and from previously published ICHOM standard sets in other disease areas. Using an online-modified Delphi process, the WG selected outcomes of greatest importance. RESULTS: The outcomes considered by the WG to be most important were selected and categorised into five domains: (1) functional status and quality of life, (2) mental functioning, (3) social functioning, (4) clinical outcomes and (5) symptoms. The WG identified demographic and clinical variables for use as case-mix risk adjusters. These included baseline demographics, clinical factors and treatment-related factors. CONCLUSION: Implementation of these consensus recommendations could help institutions to monitor, compare and improve the quality and delivery of care to patients with COVID-19. Their consistent definition and collection could also broaden the implementation of more patient-centric clinical outcomes research.

Inpatient Telemedicine and New Models of Care during COVID-19: Hospital Design Strategies to Enhance Patient and Staff Safety.

The challenges of the COVID-19 pandemic have led to the development of new hospital design strategies and models of care. To enhance staff safety while preserving patient safety and quality of care, hospitals have created a new model of remote inpatient care using telemedicine technologies. The design of the COVID-19 units divided the space into contaminated and clean zones and integrated a control room with audio-visual technologies to remotely supervise, communicate, and support the care being provided in the contaminated zone. The research is based on semi-structured interviews and observations of care processes that implemented a new model of inpatient telemedicine at Sheba Medical Center in Israel in different COVID-19 units, including an intensive care unit (ICU) and internal medicine unit (IMU). The study examines the impact of the diverse design layouts of the different units associated with the implementation of digital technologies for remote care on patient and staff safety. The results demonstrate the challenges and opportunities of integrating inpatient telemedicine for critical and intermediate care to enhance patient and staff safety. We contribute insights into the design of hospital units to support new models of remote care and suggest implications for Evidence-based Design (EBD), which will guide much needed future research.

Noninvasive Vocal Biomarker is Associated With Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

OBJECTIVE: To investigate the association of voice analysis with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. PATIENTS AND METHODS: A vocal biomarker, a unitless scalar with a value between 0 and 1, was developed based on 434 voice samples. The biomarker training was followed by a prospective, multicenter, observational study. All subjects were tested for SARS-CoV-2, had their voice recorded to a smartphone application, and gave their informed consent to participate in the study. The association of SARS-CoV-2 infection with the vocal biomarker was evaluated. RESULTS: The final study population included 80 subjects with a median age of 29 [range, 23 to 36] years, of whom 68% were men. Forty patients were positive for SARS-CoV-2. Infected patients were 12 times more likely to report at least one symptom (odds ratio, 11.8; P<.001). The vocal biomarker was significantly higher among infected patients (OR, 0.11; 95% CI, 0.06 to 0.17 vs OR, 0.19; 95% CI, 0.12 to 0.3; P=.001). The area under the receiver operating characteristic curve evaluating the association of the vocal biomarker with SARS-CoV-2 status was 72%. With a biomarker threshold of 0.115, the results translated to a sensitivity and specificity of 85% (95% CI, 70% to 94%) and 53% (95% CI, 36% to 69%), respectively. When added to a self-reported symptom classifier, the area under the curve significantly improved from 0.775 to 0.85. CONCLUSION: Voice analysis is associated with SARS-CoV-2 status and holds the potential to improve the accuracy of self-reported symptom-based screening tools. This pilot study suggests a possible role for vocal biomarkers in screening for SARS-CoV-2-infected subjects.

Telemedicine Implementation in COVID-19 ICU: Balancing Physical and Virtual Forms of Visibility.

OBJECTIVE: This case study examines the implementation of inpatient telemedicine in COVID-19 intensive care units (ICUs) and explores the impact of shifting forms of visibility on the management of the unit, staff collaboration, and patient care. BACKGROUND: The COVID-19 crisis drove healthcare institutions to rapidly develop new models of care based on integrating digital technologies for remote care with transformations in the hospital-built environment. The Sheba Medical Center in Israel created COVID-19 ICUs in an underground structure with an open-ward layout and telemedicine control rooms to remotely supervise, communicate, and support the operations in the contaminated zones. One unit had a physical visual connection between the control room and the contaminated zone through a window, while the other had only a virtual connection with digital technologies. METHODS: The findings are based on semistructured interviews with Sheba medical staff, telemedicine companies, and the architectural design team and observations at the COVID-19 units during March-August 2020. RESULTS: The case study illustrates the implications of virtual and physical visibility on the management of the unit, staff collaboration, and patient care. It demonstrates the correlations between patterns of visibility and the users' sense of control, orientation in space, teamwork, safety, quality of care, and well-being. CONCLUSIONS: The case study demonstrates the limitations of current telemedicine technologies that were not designed for inpatient care to account for the spatial perception of the unit and the dynamic use of the space. It presents the potential of a hybrid model that balances virtual and physical forms of visibility and suggests directions for future research and development of inpatient telemedicine.

The association between obesity and peak antibody titer response in COVID-19 infection.

OBJECTIVE: Obesity is associated with severe coronavirus disease 2019 (COVID-19) infection. Disease severity is associated with a higher COVID-19 antibody titer. The COVID-19 antibody titer response of patients with obesity versus patients without obesity was compared. METHODS: The data of individuals tested for COVID-19 serology at the Mount Sinai Health System in New York City between March 1, 2020, and December 14, 2021, were retrospectively retrieved. The primary outcome was peak antibody titer, assessed as a binary variable (1:2,880, which was the highest detected titer, versus lower than 1:2,880). In patients with a positive serology test, peak titer rates were compared between BMI groups (<18.5, 18.5 to 25, 25 to 30, 30 to 40, and ≥40 kg/m2 ). A multivariable logistic regression model was used to analyze the independent association between different BMI groups and peak titer. RESULTS: Overall, 39,342 individuals underwent serology testing and had BMI measurements. A positive serology test was present in 12,314 patients. Peak titer rates were associated with obesity (BMI < 18.5 [34.5%], 18.5 to 25 [29.2%], 25 to 30 [37.7%], 30 to 40 [44.7%], ≥40 [52.0%]; p < 0.001). In a multivariable analysis, severe obesity had the highest adjusted odds ratio for peak titer (95% CI: 2.1-3.0). CONCLUSION: COVID-19 neutralizing antibody titer is associated with obesity. This has implications on the understanding of the role of obesity in COVID-19 severity.

Institutional logics and innovation in times of crisis: Telemedicine as digital ‘PPE’

How do crises shape digital innovation? In this paper we examine the rapid adoption of digital telemedicine technologies in an Israeli hospital with a focus on the role of the institutional logics held by the stakeholders responding to emerging events. With the onset of COVID-19, the need for social distancing and minimal physical contact challenged and interrupted hospital practices. In response, remote audio-visual functionality of digital technologies were appropriated in different ways, as stakeholders – state actors, managers, health professionals, and family members – sought to improvise and enhance the protection of persons concerned. We show how emerging practices were guided by the dominant institutional logics of stakeholders responding to the crisis. Acting for many as a digital form of ‘personal protective equipment’ (PPE), the technologies enabled diverse action possibilities to become manifest in practices. We add to understanding the role of institutional logics in directing the attention of stakeholders to shape digital innovation in times of crisis.

Patient and clinician experience with a rapidly implemented large-scale video consultation program during COVID-19.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has forced health-care providers to find creative ways to allow continuity of care in times of lockdown. Telemedicine enables provision of care when in-person visits are not possible. Sheba Medical Center made a rapid transition of outpatient clinics to video consultations (VC) during the first wave of COVID-19 in Israel. OBJECTIVE: Results of a survey of patient and clinician user experience with VC are reported. METHODS: Satisfaction surveys were sent by text messages to patients, clinicians who practice VC (users) and clinicians who do not practice VC (non-users). Questions referred to general satisfaction, ease of use, technical issues and medical and communication quality. Questions and scales were based on surveys used regularly in outpatient clinics of Sheba Medical Center. RESULTS: More than 1200 clinicians (physicians, psychologists, nurses, social workers, dietitians, speech therapists, genetic consultants and others) provided VC during the study period. Five hundred and forty patients, 162 clinicians who were users and 50 clinicians who were non-users completed the survey. High level of satisfaction was reported by 89.8% of patients and 37.7% of clinician users. Technical problems were experienced by 21% of patients and 80% of clinician users. Almost 70% of patients but only 23.5% of clinicians found the platform very simple to use. Over 90% of patients were very satisfied with clinician's courtesy, expressed a high sense of trust, thought that clinician's explanations and recommendations were clear and estimated that the clinician understood their problems and 86.5% of them would recommend VC to family and friends. Eighty-seven percent of clinician users recognize the benefit of VC for patients during the COVID-19 pandemic but only 68% supported continuation of the service after the pandemic. CONCLUSION: Our study reports high levels of patient satisfaction from outpatient clinics VC during the COVID-19 pandemic. Lower levels of clinician satisfaction can mostly be attributed to technical and administrative challenges related to the newly implemented telemedicine platform. Our findings support the continued future use of VC as a means of providing patient-centered care. Future steps need to be taken to continuously improve the clinical and administrative application of telemedicine services.

Sick Euthyroid Syndrome on Presentation of Patients With COVID-19: A Potential Marker for Disease Severity.

OBJECTIVE: Precise risk stratification and triage of coronavirus disease 2019 (COVID-19) patients are essential in the setting of an overwhelming pandemic burden. Clinical observation has shown a somewhat high prevalence of sick euthyroid syndrome among patients with COVID-19. This study aimed to evaluate the predictive value of free triiodothyronine (FT3) at the clinical presentation of COVID-19 for disease severity and death. METHODS: This retrospective cohort study was based on electronic medical records. The study was conducted at Sheba Medical Centre, a tertiary hospital where several acute and chronic wards have been dedicated to the treatment of patients with COVID-19. The primary outcome measure was death during hospitalization; secondary outcomes included hospitalization in intensive care, mechanical ventilation, and length of hospitalization. RESULTS: Of a total of 577 polymerase chain reaction-positive patients with COVID-19 hospitalized between February 27 and July 30, 2020, 90 had at least 1 measurement of thyroid-stimulating hormone, free thyroxine, and FT3 within 3 days of presentation. After applying strict exclusion criteria, 54 patients were included in the study. Patients in the lowest tertile of FT3 had significantly higher rates of mortality (40%, 5.9%, and 5.9%, P = .008), mechanical ventilation (45%, 29.4%, and 0.0%; P = .007) and intensive care unit admission (55%, 29.4%, and 5.9%, P = .006). In multivariate analyses adjusted for age, Charlson comorbidity index, creatinine, albumin, and white blood cell count. FT3 remained a significant independent predictor of death. CONCLUSION: FT3 levels can serve as a prognostic tool for disease severity in the early presentation of COVID-19.