Diagnostic Delay of Pulmonary Embolism in COVID-19 Patients

The Utilization of Electronic Consultations (eConsults) to Address Emerging Questions Related to Long COVID-19 in Ontario, Canada: Mixed Methods Analysis

Background

Long COVID is an often debilitating condition affecting millions of people. Its diverse clinical presentations make effective diagnosis and management at the primary care level difficult, while specialist services for long COVID face extensive wait times. An electronic consultation (eConsult) program in Ontario developed a long COVID specialist group to allow primary care providers (PCPs) prompt access to specialist advice for patients with long COVID.

Objective

This study aims to assess patterns of service use, response times, impact, and clinical content of eConsult cases submitted to an eConsult long COVID specialist group in Ontario.

Methods

This study is a mixed methods analysis of eConsults submitted by PCPs to the long COVID specialist group of 2 eConsult services (Champlain eConsult BASE and Ontario eConsult) between June 1, 2021, and July 31, 2022. Data sources included the use data collected automatically by the services, responses to a mandatory closeout survey, and the content of PCP questions and specialist responses (Champlain eConsult BASE service only). Clinical questions or responses were analyzed using 2 validated taxonomies. Descriptive statistics were used for survey responses and use data.

Results

A total of 40 PCPs submitted 47 eConsults through Champlain eConsult BASE and 197 PCPs submitted 228 cases through Ontario eConsult. The median specialist response time was 0.6 (IQR 0.19-2.36; mean 1.7, SD 2.29) days. The 5 most common symptoms of long COVID were fatigue (14/47, 30%), dyspnea (7/47, 15%), cough (6/47, 13%), altered sense of smell (ie, anosmia and parosmia; 6/47, 13%), and cognitive changes (6/47, 13%). The five main question categories asked by PCPs were: (1) management of chronic symptoms of COVID-19, (2) need for additional work-up or follow-up testing, (3) community resources to support or manage patients with long COVID, (4) diagnostic clarification, and (5) guidance regarding COVID-19 vaccination.

Conclusions

The long COVID groups provided rapid access to a multispecialty service that facilitated the avoidance of unnecessary face-to-face referrals. An assessment of eConsults highlighted 5 common question types, providing insight into potential gaps in knowledge among PCPs that could help guide medical education and policy.

COVID-19 Coagulopathy

Coronavirus disease of 2019 (COVID-19) is the respiratory viral infection caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite being a primary respiratory illness, it is commonly complicated by systemic involvement of the vasculature leading to arterial and venous thrombosis. In this review, we will focus on the association between COVID-19 and thrombosis. We will highlight the pathophysiology of COVID-19 coagulopathy. The clinical manifestations of COVID-19 vasculopathy will be discussed with a focus on venous and arterial thromboembolic events. COVID-19 vasculopathy and disseminated intravascular coagulation (DIC) are distinguished within, as well as areas of controversy, such as "long COVID". Finally, the current professional guidelines on prevention and treatment of thrombosis associated with SARS-CoV-2 infection will be discussed.

Automatic detection of pulmonary embolism in computed tomography pulmonary angiography using Scaled-YOLOv4

BACKGROUND

Pulmonary embolism (PE) is a common but fatal clinical condition and the gold standard of diagnosis is computed tomography pulmonary angiography (CTPA). Prompt diagnosis and rapid treatment can dramatically reduce mortality in patients. However, the diagnosis of PE is often delayed and missed.

METHODS

In this study, we identified a deep learning model Scaled-YOLOv4 that enables end-to-end automated detection of PE to help solve these problems. A total of 307 CTPA data (Tianjin 142 cases, Linyi 133 cases, and FUMPE 32 cases) were included in this study. The Tianjin dataset was divided 10 times in the ratio of training set: validation set: test set = 7:2:1 for model tuning, and both the Linyi and FUMPE datasets were used as independent external test sets to evaluate the generalization of the model.

RESULTS

Scaled-YOLOv4 was able to process one patient in average 3.55 s [95% CI: 3.51-3.59 s]. It also achieved an average precision (AP) of 83.04 [95% CI: 79.36-86.72] for PE detection on the Tianjin test set, and 75.86 [95% CI: 75.48-76.24] and 72.74 [95% CI: 72.10-73.38] on Linyi and FUMPE, respectively.

CONCLUSIONS

This deep learning algorithm helps detect PE in real time, providing radiologists with aided diagnostic evidence without increasing their workload, and can effectively reduce the probability of delayed patient diagnosis.

Effect of the COVID-19 pandemic on patients with presumed diagnosis of acute appendicitis

BACKGROUND

Acute appendicitis (AAp) is the most frequent cause of acute abdominal pain, and appendectomy is the most frequent emergency procedure that is performed worldwide. The coronavirus disease 2019 (COVID-19) pandemic has caused delays in managing diseases requiring emergency approaches such as AAp and trauma.

AIM

To compare the demographic, clinical, and histopathological outcomes of patients with AAp who underwent appendectomy during pre-COVID-19 and COVID-19 periods.

METHODS

The demographic, clinical, biochemical, and histopathological parameters were evaluated and compared in patients who underwent appendectomy with the presumed diagnosis of AAp in the pre-COVID-19 (October 2018-March 2020) and COVID-19 (March 2020-July 2021) periods.

RESULTS

Admissions to our tertiary care hospital for AAp increased 44.8% in the COVID-19 period. Pre-COVID-19 (n = 154) and COVID-19 (n = 223) periods were compared for various parameters, and we found that there were statistically significant differences in terms of variables such as procedures performed on the weekdays or weekends [odds ratio (OR): 1.76; P = 0.018], presence of AAp findings on ultrasonography (OR: 15.4; P < 0.001), confirmation of AAp in the histopathologic analysis (OR: 2.6; P = 0.003), determination of perforation in the appendectomy specimen (OR: 2.2; P = 0.004), the diameter of the appendix (P < 0.001), and hospital stay (P = 0.003). There was no statistically significant difference in terms of interval between the initiation of symptoms and admission to the hospital between the pre-COVID-19 (median: 24 h; interquartile range: 34) and COVID-19 (median: 36 h; interquartile range: 60) periods (P = 0.348). The interval between the initiation of symptoms until the hospital admission was significantly longer in patients with perforated AAp regardless of the COVID-19 or pre-COVID-19 status (P < 0.001).

CONCLUSION

The present study showed that in the COVID-19 period, the ultrasonographic determination rate of AAp, perforation rate of AAp, and duration of hospital stay increased. On the other hand, negative appendectomy rate decreased. There was no statistically significant delay in hospital admissions that would delay the diagnosis of AAp in the COVID-19 period.

Developing a Nomogram-Based Scoring Tool to Estimate the Risk of Pulmonary Embolism

Background

Pulmonary embolisms (PEs) are clinically challenging because of their high morbidity and mortality. This study aimed to develop a scoring tool for predicting PEs to improve their clinical management.

Methods

Clinical, laboratory, and imaging parameters were retrospectively collected from suspected PE patients who had cough or chest pain and were hospitalized in West China Hospital of Sichuan University from May 2015 to April 2020. The final diagnosis of PE was defined based on findings from computed tomographic pulmonary angiography (CTPA). In this study, patients were randomly divided 2:1 into derivation and validation cohorts, which were used to create and validate, respectively, a nomogram. Model performance was estimated with the area under the receiver operating characteristic curve and a calibration curve.

Results

Our study incorporated data on more than 100 features from 1480 patients (811 non-PE, 669 PE). The nomogram was constructed using important predictive features including D-dimer, APTT, FDP, platelet count, sodium, albumin and cholesterol and achieved AUC values of 0.692 with the derivation cohort (95% CI 0.688–0.696, P < 0.01) and 0.688 with the validation cohort (95% CI 0.653–0.723, P < 0.01). The calibration curve showed good agreement between the probability predicted by the nomogram and the actual probability.

Conclusion

In this study, we successfully developed a nomogram that can predict the risk of PE, which can not only improve the clinical management of PE patients but also decrease unnecessary CTPA scans and their adverse effects.

Obesity and Its Impact on Adverse In-Hospital Outcomes in Hospitalized Patients With COVID-19

BACKGROUND

An increasing level of evidence suggests that obesity not only is a major risk factor for cardiovascular diseases (CVDs) but also has adverse outcomes during COVID-19 infection.

METHODS

We used the German nationwide inpatient sample to analyze all hospitalized patients with confirmed COVID-19 diagnosis in Germany from January to December 2020 and stratified them for diagnosed obesity. Obesity was defined as body mass index ≥30 kg/m² according to the WHO. The impact of obesity on in-hospital case fatality and adverse in-hospital events comprising major adverse cardiovascular and cerebrovascular events (MACCE), acute respiratory distress syndrome (ARDS), venous thromboembolism (VTE), and others was analyzed.

RESULTS

We analyzed data of 176,137 hospitalizations of patients with confirmed COVID-19 infection; among them, 9,383 (5.3%) had an additional obesity diagnosis. Although COVID-19 patients without obesity were older (72.0 [interquartile range (IQR) 56.0/82.0] vs. 66.0 [54.0/76.0] years, p < 0.001), the CVD profile was less favorable in obese COVID-19 patients (Charlson comorbidity index 4.44 ± 3.01 vs. 4.08 ± 2.92, p < 0.001). Obesity was independently associated with increased in-hospital case fatality (OR 1.203 [95% CI 1.131-1.279], p < 0.001) and MACCE (OR 1.168 [95% CI 1.101-1.239], p < 0.001), ARDS (OR 2.605 [95% CI 2.449-2.772], p < 0.001), and VTE (OR 1.780 [95% CI 1.605-1.973], p < 0.001) and also associated with increased necessity of treatment on intensive care unit (OR 2.201 [95% CI 2.097-2.310], p < 0.001), mechanical ventilation (OR 2.277 [95% CI 2.140-2.422], p < 0.001), and extracorporeal membrane oxygenation (OR 3.485 [95% CI 3.023-4.017], p < 0.001).

CONCLUSIONS

Obesity independently affected case fatality, MACCE, ARDS development, VTE, and other adverse in-hospital events in patients with COVID-19 infection. Obesity should be taken into account regarding COVID-19 prevention strategies, risk stratification, and adequate healthcare planning. Maintaining a healthy weight is important not only to prevent cardiometabolic diseases but also for better individual outcomes during COVID-19 infection.