Rapid diagnostic tests for dengue would reduce hospitalizations, healthcare costs and antibiotic prescriptions in Spain: A cost-effectiveness analysis.

BACKGROUND: Current gold standard diagnostic techniques for dengue are expensive and time-consuming. Rapid diagnostic tests (RDTs) have been proposed as alternatives, although data about their potential impact in non-endemic areas is scarce. METHODS: We performed a cost-effectiveness analysis comparing the costs of dengue RDTs to the current standard of care for the management of febrile returning travelers in Spain. Effectiveness was measured in terms of potential averted hospital admissions and reduction of empirical antibiotics, based on 2015-2020 dengue admissions at Hospital Clinic Barcelona (Spain). RESULTS: Dengue RDTs were associated with 53.6% (95% CI: 33.9-72.5) reduction of hospital admissions and were estimated to save 289.08-389.31€ per traveler tested. Moreover, RDTs would have avoided the use of antibiotics in 46.4% (95% CI: 27.5-66.1) of dengue patients. DISCUSSION: Implementation of dengue RDTs for the management of febrile travelers is a cost-saving strategy that would lead to a reduction of half of dengue admissions and a reduction of inappropriate antibiotics in Spain.

MALrisk: a machine-learning­based tool to predict imported malaria in returned travellers with fever

BACKGROUND: Early diagnosis is key to reducing the morbi-mortality associated with P. falciparum malaria among international travellers. However, access to microbiological tests can be challenging for some healthcare settings. Artificial Intelligence could improve the management of febrile travellers. METHODS: Data from a multicentric prospective study of febrile travellers was obtained to build a machine-learning model to predict malaria cases among travellers presenting with fever. Demographic characteristics, clinical and laboratory variables were leveraged as features. Eleven machine-learning classification models were evaluated by 50-fold cross-validation in a Training set. Then, the model with the best performance, defined by the Area Under the Curve (AUC), was chosen for parameter optimization and evaluation in the Test set. Finally, a reduced model was elaborated with those features that contributed most to the model. RESULTS: Out of eleven machine-learning models, XGBoost presented the best performance (mean AUC of 0.98 and a mean F1 score of 0.78). A reduced model (MALrisk) was developed using only six features: Africa as a travel destination, platelet count, rash, respiratory symptoms, hyperbilirubinemia and chemoprophylaxis intake. MALrisk predicted malaria cases with 100% (95%CI 96-100) sensitivity and 72% (95%CI 68-75) specificity. CONCLUSIONS: The MALrisk can aid in the timely identification of malaria in non-endemic settings, allowing the initiation of empiric antimalarials and reinforcing the need for urgent transfer in healthcare facilities with no access to malaria diagnostic tests. This resource could be easily scalable to a digital application and could reduce the morbidity associated with late diagnosis.

Assessing viral metagenomics for the diagnosis of acute undifferentiated fever in returned travellers: a multicenter cohort study.

BACKGROUND: Up to 45% of febrile returning travellers remain undiagnosed after a thorough diagnostic work-up, even at referral centres. Although metagenomic next-generation sequencing (mNGS) has emerged as a promising tool, evidence of its usefulness in imported fever is very limited. METHODS: Travellers returning with fever were prospectively recruited in three referral clinics from November 2017 to November 2019. Unbiased mNGS optimised for virus detection was performed on serum samples of participants with acute undifferentiated febrile illness (AUFI), and results were compared to those obtained by reference diagnostic methods (RDM). RESULTS: Among 507 returned febrile travellers, 433(85.4%) presented with AUFI. Dengue virus (n = 86) and Plasmodium spp. (n = 83) were the most common causes of fever. 103/433(23.8%) AUFI remained undiagnosed at the end of the follow-up.Metagenomic next-generation sequencing unveiled potentially pathogenic microorganisms in 196/433(38.7%) AUFI. mNGS identifications were more common in patients with a shorter duration of fever (42.3% in ≤5 days vs 28.7% in >5 days, P = 0.005). Potential causes of fever were revealed in 25/103(24.2%) undiagnosed AUFI and 5/23(21.7%) travellers with severe undiagnosed AUFI. Missed severe aetiologies included eight bacterial identifications and one co-infection of B19 parvovirus and Aspergillus spp.Additional identifications indicating possible co-infections occurred in 29/316(9.2%) travellers with AUFI, and in 11/128(8.6%) travellers with severe AUFI, who had received a diagnosis through RDM. The most common co-infections detected in severe AUFI were caused by Gram-negative bacteria. Serum mNGS was unable to detect >50% of infectious diagnoses achieved by RDM and also yielded 607 non-pathogenic identifications. DISCUSSION: mNGS of serum can be a valuable diagnostic tool for selected travellers with undiagnosed AUFI or severe disease in addition to reference diagnostic techniques, especially during the first days of symptoms. Nevertheless, mNGS results interpretation presents a great challenge. Further studies evaluating the performance of mNGS using different sample types and protocols tailored to non-viral agents are needed.

Executive Summary of the Spanish Guidelines for the Diagnosis and Management of Imported Febrile Illnesses from the Spanish Society of Tropical Medicine and International Health (SEMTSI), the Imported Pathology Group of the Spanish Society of Infectious Diseases and Clinical Microbiology (GEPI-SEIMC), the Spanish Society of Family and Community Medicine (SEMFYC), the Spanish Society of Primary Care Physicians (SEMERGEN) and the Spanish Society of Emergency Medicine (SEMES).

The Spanish Society of Tropical Medicine and International Health (SEMTSI), the Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC), the Spanish Society of Emergency Medicine (SEMES), the Spanish Society of Primary Care Physicians (SEMERGEN) and the Spanish Society of Family and Community Medicine (SEMFYC) have prepared a consensus statement on the diagnosis and management of patients with imported febrile illnesses. Twenty authors with different backgrounds and representing different healthcare perspectives (ambulatory primary care, travel and tropical medicine specialists, emergency medicine, hospital care, microbiology and parasitology and public health), identified 39 relevant questions, which were organised in 7 thematic blocks. After a systematic review of the literature and a thoughtful discussion, the authors prepared 125 recommendations, as well as several tables and figures to be used as a consulting tool. The present executive summary shows a selection of some of the most relevant questions and recommendations included in the guidelines.

Doxycycline responding illnesses in returning travellers with undifferentiated non-malaria fever: a European multicentre prospective cohort study.

BACKGROUND: Diagnosis of undifferentiated non-malaria fevers (NMF) in returning travellers is a great challenge. Currently, there is no consensus about the use of empirical antibiotics in returning travellers with undifferentiated NMF. Although studies in endemic areas showed that a wide range of pathogens implicated in undifferentiated NMF are treatable with doxycycline, the role of doxycycline in returning travellers with fever still has to be explored. METHODS: Prospective European multicentre cohort study of febrile international travellers (November 2017-November 2019). Immunological and molecular diagnostic techniques for doxycycline responding illnesses (DRI) agents such as Anaplasma phagocytophilum, spotted fever group Rickettsia spp., typhus group Rickettsia spp., Coxiella burnetii, Bartonella spp., Orientia tsutsugamushi, Borrelia miyamotoi, Borrelia recurrentis and Leptospira spp. were systematically performed in all patients with undifferentiated NMF. We estimated the prevalence and predictive factors of DRI in returning travellers with undifferentiated NMF. RESULTS: Among 347 travellers with undifferentiated NMF, 106 (30·5%) were finally diagnosed with DRI. Only 57 (53·8%) of the 106 DRI infections were diagnosed by the standard of care. The main causes of DRI were: 55 (51·9%) Rickettsia spp., 16 (15·1%) C. burnetii; 15 (14·2%) Bartonella spp.; 13 (12·3%) Leptospira spp. and 10 (9·5%) A. phagocytophilum. The only predictive factor associated with DRI was presenting an eschar (aOR 39·52, 95%CI 4·85-322·18). Features of dengue such as retro-orbital pain (aOR 0·40, 95%CI 0·21-0·76) and neutropenia (aOR 0·41, 95%CI 0·21-0·79) were negatively associated with DRI. CONCLUSIONS: Although DRI are responsible for 30% of undifferentiated NMF cases in travellers, those are seldom recognized during the first clinical encounter. Empirical treatment with doxycycline should be considered in returning travellers with undifferentiated fever and negative tests for malaria and dengue, particularly when presenting severe illness, predictive factors for rickettsiosis or no features of dengue.

Clinical evaluation of BioFire® multiplex-PCR panel for acute undifferentiated febrile illnesses in travellers: a prospective multicentre study.

BACKGROUND: Identifying the causes of Acute Undifferentiated Febrile Illness (AUFI) is key to improve the management of returning travellers with fever. We evaluated a BioFire®FilmArray® prototype panel of multiplex nucleic acid amplification tests (NAAT) targeting different relevant pathogens in travellers returning with fever. METHODS: Prospective, multicentre study to evaluate a prototype panel in whole blood samples of adult international travellers presenting with AUFI in three European travel Clinics/Hospitals (November 2017-November 2019). We evaluated 15 target analytes: Plasmodium spp., Plasmodium falciparum, Plasmodium knowlesi, Plasmodium malariae, Plasmodium ovale, Plasmodium vivax, chikungunya virus, dengue virus, Zika virus, Anaplasma phagocytophilum, Borrelia spp., Leptospira spp., Orientia tsutsugamushi, Rickettsia spp. and Salmonella spp. Results were compared with composite reference standards (CRSs) for each target infection, including direct methods [smear microscopy, rapid diagnostic test (RDT), reference NAAT and blood cultures] and indirect methods (paired serology). FINDINGS: Among 455 travellers with AUFI, 229 target infections were diagnosed; the prototype panel detected 143 (overall sensitivity and specificity of 62.5 and 99.8%, respectively). The panel identified all Plasmodium infections (n = 82). Sensitivity for dengue (n = 71) was 92.9, 80.8 and 68.5% compared with RDT, NAAT and CRS, respectively. Compared with direct methods and CRS, respectively, the prototype panel detected 4/4 and 4/6 chikungunya, 2/2 and 4/29 Leptospira spp., 1/1 and 1/6 O. tsutsugamushi and 2/2 and 2/55 Rickettsia spp., but 0/2 and 0/10 Zika, 0/1 and 0/11 A. phagocytophylum and 0/3 Borrelia spp. diagnosed by serology and only 1/7 Salmonella spp. diagnosed by blood cultures. 77/86 (89.5%) infections not detected by the panel were diagnosed by serology. INTERPRETATION: The prototype panel allowed rapid and reliable diagnosis for malaria, dengue and chikungunya. Further improvements are needed to improve its sensitivity for Zika and important travel-related bacterial infections.

Epidemiological and clinical characteristics of patients with monkeypox in the GeoSentinel Network: a cross-sectional study.

BACKGROUND: The early epidemiology of the 2022 monkeypox epidemic in non-endemic countries differs substantially from the epidemiology previously reported from endemic countries. We aimed to describe the epidemiological and clinical characteristics among individuals with confirmed cases of monkeypox infection. METHODS: We descriptively analysed data for patients with confirmed monkeypox who were included in the GeoSentinel global clinical-care-based surveillance system between May 1 and July 1 2022, across 71 clinical sites in 29 countries. Data collected included demographics, travel history including mass gathering attendance, smallpox vaccination history, social history, sexual history, monkeypox exposure history, medical history, clinical presentation, physical examination, testing results, treatment, and outcomes. We did descriptive analyses of epidemiology and subanalyses of patients with and without HIV, patients with CD4 counts of less than 500 cells per mm3 or 500 cells per mm3 and higher, patients with one sexual partner or ten or more sexual partners, and patients with or without a previous smallpox vaccination. FINDINGS: 226 cases were reported at 18 sites in 15 countries. Of 211 men for whom data were available, 208 (99%) were gay, bisexual, or men who have sex with men (MSM) with a median age of 37 years (range 18-68; IQR 32-43). Of 209 patients for whom HIV status was known, 92 (44%) men had HIV infection with a median CD4 count of 713 cells per mm3 (range 36-1659; IQR 500-885). Of 219 patients for whom data were available, 216 (99%) reported sexual or close intimate contact in the 21 days before symptom onset; MSM reported a median of three partners (IQR 1-8). Of 195 patients for whom data were available, 78 (40%) reported close contact with someone who had confirmed monkeypox. Overall, 30 (13%) of 226 patients were admitted to hospital; 16 (53%) of whom had severe illness, defined as hospital admission for clinical care rather than infection control. No deaths were reported. Compared with patients without HIV, patients with HIV were more likely to have diarrhoea (p=0·002), perianal rash or lesions (p=0·03), and a higher rash burden (median rash burden score 9 [IQR 6-21] for patients with HIV vs median rash burden score 6 [IQR 3-14] for patients without HIV; p<0·0001), but no differences were identified in the proportion of men who had severe illness by HIV status. INTERPRETATION: Clinical manifestations of monkeypox infection differed by HIV status. Recommendations should be expanded to include pre-exposure monkeypox vaccination of groups at high risk of infection who plan to engage in sexual or close intimate contact. FUNDING: US Centers for Disease Control and Prevention, International Society of Travel Medicine.

Causes of fever in returning travelers: a European multicenter prospective cohort study.

BACKGROUND: Etiological diagnosis of febrile illnesses in returning travelers is a great challenge, particularly when presenting with no focal symptoms [acute undifferentiated febrile illnesses (AUFI)], but is crucial to guide clinical decisions and public health policies. In this study, we describe the frequencies and predictors of the main causes of fever in travelers. METHODS: Prospective European multicenter cohort study of febrile international travelers (November 2017-November 2019). A predefined diagnostic algorithm was used ensuring a systematic evaluation of all participants. After ruling out malaria, PCRs and serologies for dengue, chikungunya and Zika viruses were performed in all patients presenting with AUFI ≤ 14 days after return. Clinical suspicion guided further microbiological investigations. RESULTS: Among 765 enrolled participants, 310/765 (40.5%) had a clear source of infection (mainly traveler's diarrhea or respiratory infections), and 455/765 (59.5%) were categorized as AUFI. AUFI presented longer duration of fever (p < 0.001), higher hospitalization (p < 0.001) and ICU admission rates (p < 0.001). Among travelers with AUFI, 132/455 (29.0%) had viral infections, including 108 arboviruses, 96/455 (21.1%) malaria and 82/455 (18.0%) bacterial infections. The majority of arboviral cases (80/108, 74.1%) was diagnosed between May and November. Dengue was the most frequent arbovirosis (92/108, 85.2%). After 1 month of follow-up, 136/455 (29.9%) patients with AUFI remained undiagnosed using standard diagnostic methods. No relevant differences in laboratory presentation were observed between undiagnosed and bacterial AUFI. CONCLUSIONS: Over 40% of returning travelers with AUFI were diagnosed with malaria or dengue, infections that can be easily diagnosed by rapid diagnostic tests. Arboviruses were the most common cause of AUFI (above malaria) and most cases were diagnosed during Aedes spp. high season. This is particularly relevant for those areas at risk of introduction of these pathogens. Empirical antibiotic regimens including doxycycline or azithromycin should be considered in patients with AUFI, after ruling out malaria and arboviruses.