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20th September 2021
Although a formal diagnosis of COVID-19 is based on a positive PCR test, it can take up to 24 hours before the result is available. Given that COVID-19 is a respiratory infection, clinicians have often turned to chest imaging with lung ultrasound, X-rays and CT scans, to diagnose the infection prior to confirmation from a PCR test. In fact, a Cochrane review has concluded that the use of a lung ultrasound correctly diagnoses COVID-19 in 86.4% of infected patients.
With the potential value of lung ultrasound as a diagnostic aid in COVID-19, an Italian team from the Department of Morphology, Surgery and Experimental Medicine, University of Ferrara, Italy, undertook a prospective, observational study to further examine the value of the imaging modality in those with suspected COVID-19. Patients were those consecutively admitted to the emergency department of a single hospital with suspected COVID-19. Each underwent a standard lung ultrasound examination which included 12 thoracic areas. The team then calculated several different measures with a view to determining whether any of these could be used prognostically for COVID-19. The scores for each thoracic areas were added to calculate a regional lung ultrasound severity score (rLUSS) and a lung ultrasound severity score (LUSS) was calculated by summing all rLUSS values. The number of different ultrasound patterns found in each area defined the regional lung ultrasound heterogeneity score (rLUHS). A regional lung ultrasound severity index (rLUSI) was then calculated as rLUSS/rLUHS for each area. Finally, the team computed a lung ultrasound severity index (LUSI), which was the sum of all the rLUSI scores. The team were interested in whether LUSI, rLUSS or LUHS. The outcomes of interest were a diagnosis of COVID-19 pneumonia and in-hospital mortality and the area under the receiver operating curves (AUROC) analysis was used to determine the relationship between LUSS, LUHS, LUSI and the severity of pneumonia.
The study included 159 patients with a mean age of 64.6 years (66% male) of whom, 49% had respiratory failure upon admission. For each of the three lung ultrasound measures (i.e., LUSS, LUHS and LUSI), in relation to the differentiation of COVID-19 positive and negative cases, LUSI offered the greatest sensitivity and specificity with an AUROC of 0.72 (95% CI 0.64 – 0.78), giving a sensitivity of 63% and a specificity of 75%. With respect to overall in-hospital mortality, again LUSI scores provided the best AUROC, at 0.81 (95% CI 0.73 – 0.86) providing a sensitivity of 90.9% and a specificity of 65.6%. Finally, when considering only COVID-19 patients, LUSI also gave the highest AUROC, 0.76 (95% CI 0.66 – 0.84) with a sensitivity of 63.1% and a specificity of 90%.
The authors concluded that their newly developed lung ultrasound severity index provided the highest accuracy with respect to COVID-19 diagnosis and prognosis. They also added that a further advantage was how the lung ultrasound could be performed in under 10 minutes, allowing LUSI scores to quickly identify patients at a higher risk of both COVID-19 and mortality and called for future studies to understand LUSI’s role for different clinical goals such as monitoring of treatment or progression.
Spampinato MD et al. Lung Ultrasound Severity Index: Development and Usefulness in Patients with Suspected SARS-Cov-2 Pneumonia. A Prospective Study. Ultrasound Med Biol 2021
13th September 2021
Symptoms of community-acquired pneumonia (CAP) include shortness of breath, coughing, fever and chest pain some of which such as fever and coughing, overlap with COVID-19. Determining whether the causative agent in CAP is bacterial or viral can be difficult and measurement of procalcitonin levels can serve as an important biomarker for the presence of a bacterial cause. Given that higher procalcitonin levels are more likely to indicate a bacterial rather than viral cause for CAP, a team from the Emergency Department, University Libre Bruxelles, Belgium, wondered if the measurement of procalcitonin levels could help distinguish between viral and bacterial CAP in patients infected with COVID-19 and retrospectively analysed data for a cohort of patients admitted to their emergency department.
All patients who were admitted with a suspicion of CAP had their procalcitonin levels measured. Subsequently, enrolled patients were those with clinical signs of a lower respiratory tract infection and with at least one symptom of acute respiratory illness, e.g., cough, dyspnoea, sputum production, tachypnoea and pleuritic chest pain. Other inclusion criteria were those with signs of an acute infection, e.g., temperature > 38oC, chills, altered mental status and a leucocyte count > 10,000/microL and oxygen saturation < 94%. Only patients who underwent both bacteriological, viral and radiological imaging (CT) within 48 hours of admission were subsequently included. Patients were classified as having bacterial CAP based on both microbiological analysis and the findings from the CT scan. Alternatively, patients were classed as having viral CAP in the absence of positive bacteriological findings and where the CT scan indicated a high suspicion of viral pneumonia.
During the period of the study, 3593 patients visited the emergency department with symptoms potentially related to COVID-19 and 151 were subsequently included in the analysis after applying the inclusion criteria, of whom, 138 had a microbiologically confirmed bacterial pathogen. Among those with diagnosed viral CAP, 112 had COVID-19-related pneumonia. The discriminatory accuracy of procalcitonin levels for bacterial and viral CAP were calculated from receiver operating characteristic (ROC) curves. The median procalcitonin levels were higher in bacterial CAP (0.53ng/ml vs 0.16ng/ml, bacterial vs viral, p = 0.005). Using the ROC curves to discriminate between viral and bacterial CAP generated an area under the curve (AUC) of 0.68 (95% CI 0.53 – 0.83). Based on a threshold procalcitonin level of > 0.5ng/ml, to identify bacterial CAP, gave a sensitivity of 52.2% and a specificity of 82%.
Commenting on their findings, the authors noted that there were no procalcitonin levels which were able to differentiate between bacterial CAP and COVID-19 associated pneumonia. Based on their findings, the authors calculated that the administration of antibiotics to those with procalcitonin levels > 0.5ng/ml would have resulted in the inappropriate treatment of 65.7% of patients with radiological signs of CAP.
They concluded that procalcitonin measurements upon admission in those with suspected CAP cannot accurately differentiate between bacterial or viral CAP.
Malinverni S et al. Is procalcitonin a reliable marker of bacterial community-acquired pneumonia in adults admitted to the emergency department during SARS-CoV-2 pandemic? Eur J Emerg Med 2021
4th August 2021
In 2020, the US Department of Health and Human services launched Ending the HIV Epidemic: A Plan for America, with the aim of diagnosing all people with HIV as early as possible. The receipt of a HIV diagnosis is critical for assessing treatment and prevention services yet, despite recommendations from the World Health Organization (WHO), a study in 2020 concluded that global adherence to the WHO strategy remained low. A targeted HIV screening approach can be either provider-initiated, where a healthcare professional identifies specific risk factors which prompt a test or client-initiated, where an individual feels that they are at risk. There are clear benefits to a targeted approach, for instance, the opportunity for service providers to engage and counsel high-risk individuals as well as delivery of services through a range of different settings. In fact, evidence suggests that community-based targeted HIV screening is more effective than universal screening. One targeted HIV screening tool, the Denver Human Immunodeficiency Virus risk score, includes several factors such as age, ethnicity, receptive anal intercourse and injection drug use and has been shown to accurately categorise patients into groups with increasing probabilities of HIV infection. Whether such a tool could be used to improve HIV screening within an emergency department has been largely unexplored, prompting a team from the Department of Emergency Medicine, Denver Health Medical Centre, Colorado, US, to undertake a randomised trial to compare targeted HIV screening with a non-targeted approach. The team used three arms: non-targeted, i.e., without any risk assessment of HIV; the Denver risk assessment tool and finally, a traditional targeted approach. This latter method involved the use of a behavioural risk screening tool and a positive answer to any risk questions triggered a HIV test. The primary outcome for the study was confirmed new HIV diagnoses.
A total of 76,561 individuals were randomised to non-targeted screening (25,469), Denver risk assessment (25,453) and traditional screening (25,639). The median age of the sample was 40 years (51.2% female) and with 32.6% of black ethnicity. Of those assigned to non-targeted screening, there were 10 (0.15%) new HIV diagnoses compared to 7 (0.16%) for Denver risk assessment and 7 (0.22%) for the traditional targeted screening. When comparing non-targeted with targeted screening (combining both methods), there was no significant difference (risk ratio, RR = 0.70, 95% CI 0.30–1.56, p = 0.38). Furthermore, the enhanced targeted HIV screening (Denver) identified about twice as many people at increased risk compared to traditional targeted screening (54.5% vs 27.7%, enhanced vs traditional screening).
Commenting on their results, the authors noted that while targeted HIV screening did not increase the number of new diagnoses, screening within an emergency department is still important to ensure prompt antiretroviral therapy and to avert further transmission of the virus. They concluded that while targeted HIV screening was not superior to a non-targeted strategy at identifying new patients, a targeted approach did reduce the number of tests performed.
Haukoos JS et al. Comparison of HIV Screening Strategies in the Emergency Department: a randomized clinical trial. JAMA Netw Open 2021
26th July 2021
Sepsis can be defined as is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Furthermore, sepsis is responsible for around 11 million deaths each year, which amounts to approximately 20% of all global deaths. Thus, it is crucial that clinicians have a comprehensive understanding of all the relevant clinical factors that can help with the early identification of those patients for whom a poor outcome is likely. This is particularly important since early use of crystalloid therapy reduces mortality, as does prompt administration of antibiotics. Though several scoring systems for sepsis are available, these are based on the assessment of vital signs but which can sometimes be normal upon admission to an emergency department. While machine learning has been shown to have some level of predictive power for mortality, none of the variables currently used in these models are reflective of the symptoms at first presentation. This led a team from the Department of Medical Sciences, Orebro University, Sweden, to use machine learning in an attempt to identify the variables which were predictive of 7- and 30-day mortality in sepsis patients, based on the clinical presentation at an emergency department. They employed a retrospective design and included patients 18 years and older, admitted to hospital with suspected sepsis. The team input previously identified variables, e.g., abnormal temperature, acute altered mental status, etc into the machine learning algorithm. The sensitivity and specificity of the predictive models generated by the machine learning model, were calculated from the area under the receiver operating curve (AUC).
A total of 445 patients with sepsis and a median age of 73 years (52.6% male) were included in the retrospective analysis. Overall, 234 (49.7%) had severe sepsis and 63 patients died within 7-days of admission and 98 within 30 days. The accuracy of the 7-day predictive model was maximal after the inclusion of only six variables; fever, abnormal verbal response, low oxygen saturation, arrival by emergency services, abnormal behaviour/level of consciousness and chills. Using these variables, the AUC sensitivity was 0.84 (95 CI 0.78–0.89) and the specificity 0.67 (95% CI 0.64 –0.70). For the prediction of 30-day mortality, again, only 6 variables were significant; abnormal verbal response, fever, chills, arrival by emergency services, low oxygen saturation and breathing difficulties. This model gave a sensitivity of 0.87 (95% CI 0.81–0.93) and a specificity of 0.64 (95% CI 0.61–0.67).
In discussing their findings, the authors highlighted how their results revealed the importance of the using a clinical symptom complex that was representative of what an emergency department clinician would be likely to encounter in practice. They also suggested that the 7-day model might be of more use in practice since it would be of assistance to emergency care staff for the likely short-term outcome for patients. They concluded that given how the clinical presentation of sepsis can often be non-specific, the use of a machine learning algorithm, based on symptoms and observations, would be most helpful to staff and that future work should focus on validating the method in other cohorts.
Karlsson A et al. Predicting mortality among septic patients presenting to the emergency department– a cross sectional analysis using machine learning. BMC Emerg Med 2021