Background: The COVID-19 pandemic rapidly strained healthcare systems worldwide. The reference standard for diagnosis is a positive reverse transcription polymerase chain reaction (RT-PCR) test, but results are not immediate and sensibility is variable. Aim: To evaluate the diagnostic accuracy of lung ultrasound compared to chest X-ray for COVID-19 pneumonia. Design and Setting: A retrospective analysis of symptomatic patients admitted into one primary care centre in Spain between March and September 2020. Method: Patients’ chest X-rays and lung ultrasounds were categorized as normal or pathologic. RT-PCR confirmed COVID-19 infection. Pathologic lung ultrasound images were further categorized as showing either local or diffuse interstitial disease. McNemar and Fisher tests were used to compare diagnostic accuracy. Results: Most of the 212 patients presented fever at admission, either as a standalone symptom (37.74% of patients) or together with others (72.17% of patients). The positive predictive value of the lung ultrasound was 90% for the diffuse interstitial pattern and 46.92% for local pattern. The lung ultrasound had a significantly higher sensitivity (82.75%) (p < 0.001), but lower specificity (71%) than the chest X-ray (54.02% and 86%, respectively) (p = 0.008) for identifying interstitial lung disease. Moreover, sensitivity of the lung ultrasound for severe interstitial disease was 100%, and was significantly higher than the chest X-ray (58.33%) (p = 0.002). Conclusion: The lung ultrasound is more accurate than the chest X-ray for identifying patients with COVID-19 pneumonia and it is especially useful for those presenting diffuse interstitial disease.

Background: The usefulness of Lung Ultrasound (LUS) for the diagnosis of interstitial syndrome caused by COVID-19 has been broadly described. The aim of this study was to evaluate if LUS may predict the complications (hospital admission) of COVID-19 pneumonia in primary care patients. Methods: This observational study collects data from a cohort of 279 patients with clinical symptoms of COVID-19 pneumonia who attended the Balaguer Primary Health Care Area between 16 March 2020 and 30 September 2020. We collected the results of LUS scans reported by one general practitioner. We created a database and analysed the absolute and relative frequencies of LUS findings and their association with hospital admission. We found that different LUS patterns (diffuse, attenuated diffuse, and predominantly unilateral) were risk factors for hospital admission (p < 0.05). Additionally, an evolutionary pattern during the acute phase represented a risk factor (p = 0.0019). On the contrary, a normal ultrasound pattern was a protective factor (p = 0.0037). Finally, the presence of focal interstitial pattern was not associated with hospital admission (p = 0.4918). Conclusion: The lung ultrasound was useful to predict complications in COVID-19 pneumonia and to diagnose other lung diseases such as cancer, tuberculosis, pulmonary embolism, chronic interstitial pneumopathy, pleuropericarditis, pneumonia or heart failure.

Background: Lower respiratory tract infections (LRTIs) are a significant cause of primary care consultations. Differentiating between viral and bacterial infections is critical for effective treatment and to minimize unnecessary antibiotic use. This study investigates the impact of combining lung ultrasound (LUS) with capillary blood C-reactive protein (CRP) rapid testing on clinical decision-making for patients with LRTIs. Objectives: The primary objective is to assess how the integration of CRP testing and LUS influences antibiotic prescription decisions. The study aims to quantify the percentage change in antibiotic prescriptions before and after performing LUS, following history taking, clinical examination, and CRP testing. Methods: This analytical observational study will be conducted in six primary care centers within the Health Region of Lleida, Catalonia, serving a mixed urban and rural population of approximately 105,000 residents. The study will recruit 196 patients aged 18 and over, presenting with LRTI symptoms and not treated with antibiotics in the preceding 14 days. Participants will undergo CRP testing followed by LUS during their consultation. Statistical analyses, including linear regression, bivariate analysis, Pearson or Spearman correlation, and logistic regression, will be used to evaluate the impact of LUS on clinical decisions and its predictive value in diagnosing bacterial infections. Results and Conclusions: This study will provide insights into the role of LUS combined with CRP testing in improving diagnostic accuracy and guiding antibiotic prescription decisions in primary care. The findings aim to enhance treatment protocols for LRTIs, reducing unnecessary antibiotic use and improving patient outcomes.

Background: During the COVID-19 pandemia, the imaging test of choice to diagnose COVID-19 pneumonia as chest computed tomography (CT). However, access was limited in the hospital setting and patients treated in Primary Care (PC) could only access the chest x-ray as an imaging test. Several scientific articles that demonstrated the sensitivity of lung ultrasound, being superior to chest x-ray [Cleverley J et al., BMJ 370, 202013] and comparable to CT scan [Tung-Chen Y et al., Ultrasound Med Biol 46:2918-2926, 2020], promoted the incorporation of this technique in the assessment of COVID-19 patients in PC. [Pérez J et al., Arch. Bronconeumol 56:27-30, 2020; Gargani L et al., Eur Heart J Cardiovasc Imaging 21:941-8, 2020, Soldati G et al., J Ultrasound Med 39:1459, 2020] A prior study in our territory (Lleida, Spain) was designed to predict complications (hospital admission) of COVID-19 pneumonia in PC patients, being different patterns of Lung ultrasounds (LUS) risk factors for hospital admission. [Martínez Redondo J et al., Int J Environ Res Public Health 18:3481, 2021] The rationale for conducting this study lies in the urgent need to understand the determinants of severity and prognosis in COVID-19 patients with interstitial pneumonia, according to its lung ultrasound patterns. This research is crucial to provide a deeper understanding of how these pre-existing ultrasound patterns related to disease progression influence the medical treatment. Methods: The objective of the study is to generate predictive models of lung ultrasound patterns for the prediction of lung areas characteristics associated with hospitalizations and admissions to the Intensive Care Unit (ICU) associated with COVID-19 disease, using ultrasound, sociodemographic and medical data obtained through the computerized medical history. Results: A single relevant variable has been found for the prediction of hospitalization (number of total regions with potentially pathological presence of B lines) and one for the prediction of ICU admission (number of regions of the right lung with potentially pathological presence of B lines). In both cases it has been determined that the optimal point for classification was 2 or more lung affected areas. Those areas under the curve have been obtained with good predictive capacity and consistency in both cohorts. Conclusions: The results of this study will contribute to the determination of the ultrasound prognostic value based on the number of lung areas affected, the presence of pulmonary condensation or the irregularity of pleural effusion patterns in COVID-19 patients, being able to be extended to other lung viral infections with similar patterns.

Background/Objectives: Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome with substantial variability in clinical progression and survival. Although neutrophil-to-lymphocyte ratio has been widely studied, evidence regarding neutrophil-to-monocyte plus lymphocyte ratio (NMLR) remains limited. This study evaluated whether baseline peripheral blood neutrophil-to-monocyte plus lymphocyte ratio (NMLR), a composite marker of systemic inflammation, was independently associated with long-term all-cause mortality in patients with COPD managed in primary care. Methods: We conducted a retrospective population-based cohort study using electronic health records from the Lleida Health Region (Spain) between 1 January 2014 and 31 December 2023. Patients with spirometry-confirmed COPD and available baseline complete blood counts were included. Baseline NMLR was calculated as (absolute neutrophil count + absolute monocyte count)/absolute lymphocyte count using the first valid blood sample obtained after cohort entry. Patients were categorized into NMLR quartiles. Associations with all-cause mortality were assessed using multivariable Cox proportional hazards regression models adjusted for age, sex, smoking status, lung function, and baseline comorbidity burden. Results: A total of 2644 patients were included, contributing 19,231 person-years of follow-up. During a median follow-up of 8.8 years, 1085 deaths occurred. Mortality increased progressively across NMLR quartiles, from 28.3% in the lowest quartile to 60.2% in the highest quartile. In fully adjusted analyses, patients in the highest quartile had higher all-cause mortality than those in the lowest quartile (hazard ratio [HR] 2.00; 95% confidence interval [CI] 1.66–2.40; p < 0.001). When modeled continuously, each doubling of baseline NMLR was associated with a 34% increase in mortality risk (HR 1.34; 95% CI 1.26–1.42). Conclusions: Elevated baseline NMLR was independently associated with increased long-term mortality in COPD. As an inexpensive and routinely available inflammatory biomarker, NMLR may improve mortality risk stratification and support future evaluation of risk-stratified follow-up strategies in primary care. External validation is required before implementation in routine clinical practice.

Chest X-rays and lung ultrasounds are extensively utilized in primary care to diagnose pneumonia, a primary clinical manifestation of COVID-19. Employing chest X-rays for the diagnostic management of suspected COVID-19 pneumonia is significantly more costly than utilizing lung ultrasound. Since the pandemic swiftly overwhelmed healthcare systems globally, identifying the most cost-effective diagnostic tools is essential to optimize resource allocation.