Predict Infection Risk Research Articles

OP0274 HOSPITALISATION FOR TUBERCULOSIS AND OTHER OPPORTUNISTIC INFECTIONS IN PATIENTS WITH INFLAMMATORY JOINT DISEASES BEFORE AND AFTER THE INTRODUCTION OF BIOLOGICAL THERAPY.

BackgroundOver the last 20 years aggressive therapy for inflammatory joint diseases (IJD) has become standard of care following the acceptance of Methotrexate as anchor drug and extensive of use biologic therapy since 2003. Immunosuppression increases the risk for common and uncommon infections (1).ObjectivesTo compare the temporal rates and associated mortality of hospitalisation with opportunistic infections (OI) (Table 1) for IJD patients in Western Australia between 1985 and 2015.MethodsAll patients hospitalized in Western Australia in the period 1980-2015 with ≥ 2 diagnostic codes for rheumatoid arthritis (RA, n=8490), psoriatic arthritis (PsA, n=601), axial spondylarthritis (AS, n=1419). Overall incidence rates (IR) with microbiologically confirmed OI (Mycobacterial, Fungal and viral infections)(Table 1) during 306.514 person years were expressed per 1000 person years and compared across IJD by incidence rate ratios (IRR) with 95% CI. IR trend rates across 5-year periods for each IJD were analyzed by least square regression (R2). Mortality rate ratio (MRR) was number of deaths per 1000 observation years in patients with OI compared to patients in same disease category hospitalized without OI.Table 1.Diagnostic codes applied to define conditions and opportunistic infections in hospital discharge database.ICD9CMICD10AMRA714.0-714.9M05.0-M06.9AS720.0M45, M08.10-M08.19PsA696.0M07.0-M07.3, L40.5 Tuberculosis010.x-018.xA15–A19 Non-tuberculous mycobacteria031.xA30-A31 Cryptococcosis117.5B45 Aspergillosis117.3B44 Histoplasmosis115B39 All mycosis114.0 - 118.9B35.0 - B49.9 Pneumocystosis136.3B59 Cytomegaloviral disease078.5B25 Influenza487.x, 488.xJ09, J10 Herpes zoster053.xB02 Varicella052.xB01ResultsThe IR for all OI in RA patients (5.19, CI 4.8-5.6) was significantly higher than for PsA (IRR 0.56, CI 0.41-0.76 and AS (IRR 0.64, CI 0.53-0.79) with lower IRR observed especially for tuberculosis and H Zoster in PsA (0,49 and 0,47) and AS patients (0,43 and 0,49). H Zoster, TBC and other mycobacteria were the most frequent cause of OI with cryptococcal and pneumocystis only seen in RA. The IR for TBC decreased over time in RA (R2 0.51, p=0.08), and AS (R2 0.47, p=0.09) while the IR for H. zoster decreased in RA only (R2 0.46, p=0.09) (Figure 1). In-hospital mortality rate in patients with OI was 4.9 % for RA, 2.6 % for PsA and 2.2% for AS, but MRR for RA (1.15; 0.71-1.97), PsA (1.64; 0.82-3.57) and AS patients (1.35; 0.68-2.89) was not significantly increased.Figure 1.Incidence rate per 1000 personyears over time for hospitalisation with opportunistic infections in RA patients.ConclusionThe IR for hospitalization with OI is twice as high for RA patients compared to AS and PsA patients. Admission rates for most OI including have decreased in RA patients over the two decades where more intensive treatment became standard of care. This suggests efficacy of preventative measures. Hospital admission with OI associated with a moderate risk of death, but did not incur a higher risk of death than admission for other medical complications in IJD patientsReferences[1]Wang D, Yeo AL, Dendle C, Morton S, Morand E, Leech M. Severe infections remain common in a real-world rheumatoid arthritis cohort: A simple clinical model to predict infection risk. Eur J Rheumatol 2021; 8(3): 133-8.AcknowledgementsSupported by an unrestricted grant from the Arthritis Foundation of Western Australia.Disclosure of InterestsNone declared

Predicting infection risk in iatrogenic immune deficiency

For many years, conventional synthetic disease modifying anti-rheumatic drugs (csDMARDS) such as methotrexate and salazopyrine have been standard therapy for treatment of inflammatory arthritis. The advent of biologic DMARDS, such as adalimumab and tocilizumab, have revolutionised therapy and improved patient outcomes. More recently, the increasing use of oral targeted synthetic DMARDS which inhibit Janus Kinases (JAKi), such as tofacitinib and upadacitinib, have further broadened therapeutic options.

Predicting fungal infection rate and severity with skin-associated microbial communities on amphibians.

Pathogen success (risk and severity) is influenced by host-associated microbiota, but the degree to which variation in microbial community traits predict future infection presence/absence (risk) and load (severity) for the host is unknown. We conducted a time-series experiment by sampling the skin-associated bacterial communities of five amphibian species before and after exposure to the fungal pathogen, Batrachochytrium dendrobaditis (Bd). We sought to determine whether microbial community traits are predictors of, or are affected by, Bd infection risk and intensity. Our results show that richness of putative Bd-inhibitory bacteria strongly predicts infection risk, while the proportion of putative Bd-inhibitory bacteria predicts future infection intensity. Variation in microbial community composition is high across time and individual, and bacterial prevalence is low. Our findings demonstrate how ecological community traits of host-associated microbiota may be used to predict infection risk by pathogenic microbes.

Life-threatening infections during treatment for acute lymphoblastic leukemia on the Malaysia-Singapore 2003 and 2010 clinical trials: A risk prediction model.

Life-threatening infections significantly impact the care of children undergoing therapy for acute lymphoblastic leukemia (ALL) who are at risk of severe sepsis due to both host and treatment factors. Our aim was to develop a life-threatening infection risk prediction model that would allow remote rapid triage of patients to reduce time to first dose of antibiotics and sepsis-related mortality. A retrospective analysis of 2068 fever episodes during ALL therapy was used for model building and subsequent internal validation. Three hundred and seventy-seven patients were treated for ALL in two institutions with comparable critical and supportive care resources. A total of 55 patients accounted for 71 admissions to the critical care unit for sepsis that led to eight septic deaths during a 16-year study period. A retrospective analysis of risk factors for sepsis enabled us to build a model focused on 13 variables that discriminated admissions requiring critical care well: area under the receiver operating characteristic curve of .82; 95% CI .76-.87, p<.001, and Brier score of .033. Significant univariate predictors included neutropenia, presence of symptoms of abdominal pain, diarrhea, fever during induction or steroid-based phases, and the lack of any localizing source of infection at time of presentation. We have developed a risk prediction model that can reliably identify ALL patients undergoing treatment who are at a higher risk of life-threatening sepsis. Clinical applicability can potentially be extended to low-middle income settings, and its utility should be further studied in real-world settings.

Prediction of post-stroke urinary tract infection risk in immobile patients using machine learning: an observational cohort study

Urinary tract infection (UTI) is one of major nosocomial infections significantly affecting the outcomes of immobile stroke patients. Previous studies have identified several risk factors, but it is still challenging to accurately estimate personal UTI risk. To develop predictive models for UTI risk identification for immobile stroke patients. Research data were collected from our previous multicentre study. Derivation cohort included 3982 immobile stroke patients collected from November 1st, 2015 to June 30th, 2016; external validation cohort included 3837 patients collected from November 1st, 2016 to July 30th, 2017. Six machine learning models and an ensemble learning model were derived, based on 80% of derivation cohort, and effectiveness was evaluated with the remaining 20%. Shapley additive explanation values were used to determine feature importance and examine the clinical significance of prediction models. In all, 2.59% (103/3982) patients were diagnosed with UTI in derivation cohort, 1.38% (53/3837) in external cohort. The ensemble learning model performed the best in area under the receiver operating characteristic (ROC) curve in internal validation (82.2%); second best in external validation (80.8%). In addition, the ensemble learning model performed the best sensitivity in both internal and external validation sets (80.9% and 81.1%, respectively). Seven UTI risk factors (pneumonia, glucocorticoid use, female sex, mixed cerebrovascular disease, increased age, prolonged length of stay, and duration of catheterization) were also identified. This ensemble learning model demonstrated promising performance. Future work should continue to develop a more concise scoring tool based on machine learning models and prospectively examining the model in practical use, thus improving clinical outcomes.

The bacterial density of clinical rectal swabs is highly variable, correlates with sequencing contamination, and predicts patient risk of extraintestinal infection

BackgroundIn ecology, population density is a key feature of community analysis. Yet in studies of the gut microbiome, bacterial density is rarely reported. Studies of hospitalized patients commonly use rectal swabs for microbiome analysis, yet variation in their bacterial density—and the clinical and methodologic significance of this variation—remains undetermined. We used an ultra-sensitive quantification approach—droplet digital PCR (ddPCR)—to quantify bacterial density in rectal swabs from 118 hospitalized patients. We compared bacterial density with bacterial community composition (via 16S rRNA amplicon sequencing) and clinical data to determine if variation in bacterial density has methodological, clinical, and prognostic significance.ResultsBacterial density in rectal swab specimens was highly variable, spanning five orders of magnitude (1.2 × 104–3.2 × 109 16S rRNA gene copies/sample). Low bacterial density was strongly correlated with the detection of sequencing contamination (Spearman ρ = − 0.95, p < 10−16). Low-density rectal swab communities were dominated by peri-rectal skin bacteria and sequencing contaminants (p < 0.01), suggesting that some variation in bacterial density is explained by sampling variation. Yet bacterial density was also associated with important clinical exposures, conditions, and outcomes. Bacterial density was lower among patients who had received piperacillin-tazobactam (p = 0.017) and increased among patients with multiple medical comorbidities (Charlson score, p = 0.0040) and advanced age (p = 0.043). Bacterial density at the time of hospital admission was independently associated with subsequent extraintestinal infection (p = 0.0028), even when controlled for severity of illness and comorbidities.ConclusionsThe bacterial density of rectal swabs is highly variable, and this variability is of methodological, clinical, and prognostic significance. Microbiome studies using rectal swabs are vulnerable to sequencing contamination and should include appropriate negative sequencing controls. Among hospitalized patients, gut bacterial density is associated with clinical exposures (antibiotics, comorbidities) and independently predicts infection risk. Bacterial density is an important and under-studied feature of gut microbiome community analysis.Av9k92WP_5xRVzWB_rRVwHVideo abstract

Infection Risk Prediction Model for COVID-19 Based on an Analysis of the Settlement of Particles Generated during Dental Procedures in Dental Clinics.

Background The health emergency declaration owing to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has drawn attention toward nosocomial transmission. The transmission of the disease varies depending on the environmental conditions. Saliva is a recognized SARS-CoV-2 reservoir in infected individuals. Therefore, exposure to fluids during dental procedures leads to a high risk of contagion. Objective This study aimed to develop an infection risk prediction model for COVID-19 based on an analysis of the settlement of the aerosolized particles generated during dental procedures. Materials and Methods The settlement of aerosolized particles during dental aerosol-generating procedures (AGPs) performed on phantoms was evaluated using colored saliva. The gravity-deposited particles were registered using a filter paper within the perimeter of the phantom head, and the settled particles were recorded in standardized photographs. Digital images were processed to analyze the stained area. A logistic regression model was built with the variables ventilation, distance from the mouth, instrument used, area of the mouth treated, and location within the perimeter area. Results The largest percentage of the areas stained by settled particles ranged from 1 to 5 µm. The maximum settlement range from the mouth of the phantom head was 320 cm, with a high-risk cutoff distance of 78 cm. Ventilation, distance, instrument used, area of the mouth being treated, and location within the perimeter showed association with the amount of settled particles. These variables were used for constructing a scale to determine the risk of exposure to settled particles in dentistry within an infection risk prediction model. Conclusion The greatest risk of particle settlement occurs at a distance up to 78 cm from the phantom mouth, with inadequate ventilation, and when working with a high-speed handpiece. The majority of the settled particles generated during the AGPs presented stained areas ranging from 1 to 5 µm. This model was useful for predicting the risk of exposure to COVID-19 in dental practice.

Numerical Model for Prediction of Indoor COVID-19 Infection Risk Based on Sensor Data

In addition to infection with SARS-CoV-2 via direct droplet transmission or contact with contaminated surfaces, infection via aerosol transport is a predominant pathway in indoor environments. The developed numerical model evaluates the risk of a COVID-19 infection in a particular room based on measurements of temperature, humidity, CO2 and particle concentration, the number of people and instances of speech, coughs and sneezing using a dedicated low-cost sensor system. The model can dynamically provide the predicted risk of infection to the building management system or people in the room. The effect of temperature, humidity and ventilation intensity on the infection risk is shown. Coughing and especially sneezing greatly increase the probability of infection in the room; therefore distinguishing these events is crucial for the applied measurement system.

Severe infections remain common in a real-world rheumatoid arthritis cohort: A simple clinical model to predict infection risk.

This study aimed to investigate the incidence of severe infections in patients of a dedicated rheumatoid arthritis (RA) clinic, identify the associated risk factors, and derive an infection risk screening tool. Between January and July 2019, 263 eligible patients with a diagnosis of RA were recruited retrospectively and consecutively from an RA clinic of an Australian tertiary hospital. The primary outcome was severe infection (requiring hospital admission) between January 2018 and July 2019. We collected data from medical records and pathology results. We used validated scores, such as the disease activity score of 28 joints (DAS28) and the Charlson comorbidity index, to assess the disease activity and comorbidity burden. Multivariable logistic regression was used for statistical analysis. A total of 45 severe infection episodes occurred in 34 (13%) patients, corresponding to 10.8 infections per 100 patient-years. Respiratory (53%) and urinary (13%) tract infections were the most common. In the multivariable analysis, significant risk factors included low lymphocyte count (odds ratio [OR], 4.08; 95% confidence interval [CI], 1.16-14.29), severe infection in the past 3 years (OR, 3.58; 95% CI, 1.28-9.97), Charlson comorbidity index >2 (OR, 2.69; 95% CI, 1.03-7.00), and higher DAS28 (OR, 1.35/0.5-unit increment; 95% CI, 1.10-1.67). A model incorporating these factors and age had an area under receiver operating characteristic curve of 0.82. To the best of our knowledge, this was one of the first Australian studies to evaluate severe infection rates in a real-world RA cohort. The rates remained high and comparable with those of the older studies. Lymphopenia, disease activity, comorbidity burden, and previous severe infection were the independent risk factors for infection. A model comprising easily assessable clinical and biological parameters has an excellent predictive potential for severe infection. Once validated, it may be developed into a screening tool to help clinicians rapidly identify the high-risk patients and inform the tailored clinical decision making.

Study on ventilation rates and assessment of infection risks of COVID-19 in an outpatient building

A modified Wells-Riley model combining the airborne route and close contact route was proposed to predict the infection risks of coronavirus disease 2019 (COVID-19) in main functional spaces of an outpatient building in Shenzhen, China. The personnel densities and ventilation rates in the 20 waiting rooms, outpatient hall and hospital street were on-site measured. The average fresh air volume per person and occupant area per person in the 20 waiting rooms were 77.6 m3/h and 6.47 m2/per, satisfied with the Chinese standard. The average waiting time of the occupants was 0.69 h. Thus, assuming the proportion of infected people in the outpatient building was 2%, the daily average infection probabilities of COVID-19 in the 20 waiting rooms were 0.19–1.88% with a reasonable setting of the quanta produced by an infector (q = 45 quanta/h) and the effective exposure dose of pathogen per unit close contact time (β = 0.05 h−1). The design of the semi-closed hospital street with a height of 24 m improved its natural ventilation with a fresh air volume per person of 70–185 m3/h and further dilute the viral aerosol and decreased the infection risk to a negligible level (i.e., below 0.04% with an infector proportion of 2%). The assessment method provides real-time prediction of indoor infection risk and good assist in spread control of COVID-19.

Variability in expiratory trajectory angles during consonant production by one human subject and from a physical mouth model: Application to respiratory droplet emission.

The COVID‐19 pandemic has highlighted the need to improve understanding of droplet transport during expiratory emissions. While historical emphasis has been placed on violent events such as coughing and sneezing, the recognition of asymptomatic and presymptomatic spread has identified the need to consider other modalities, such as speaking. Accurate prediction of infection risk produced by speaking requires knowledge of both the droplet size distributions that are produced, as well as the expiratory flow fields that transport the droplets into the surroundings. This work demonstrates that the expiratory flow field produced by consonant productions is highly unsteady, exhibiting extremely broad inter‐ and intra‐consonant variability, with mean ejection angles varying from ≈+30° to −30°. Furthermore, implementation of a physical mouth model to quantify the expiratory flow fields for fricative pronunciation of [f] and [θ] demonstrates that flow velocities at the lips are higher than previously predicted, reaching 20–30 m/s, and that the resultant trajectories are unstable. Because both large and small droplet transport are directly influenced by the magnitude and trajectory of the expirated air stream, these findings indicate that prior investigations of the flow dynamics during speech have largely underestimated the fluid penetration distances that can be achieved for particular consonant utterances.

Clinical outcomes of patients with multiple sclerosis treated with ocrelizumab in a US community MS center: an observational study

BackgroundTo monitor long-term outcomes of ocrelizumab treatment.ObjectiveTo evaluate safety and treatment outcomes of ocrelizumab in a community-based multiple sclerosis (MS) population.MethodsAdult patients with MS prescribed ocrelizumab were eligible. Chart reviews...

Epidemiology of cardiac implantable electronic device infections: incidence and risk factors.

Cardiac implantable electronic device (CIED) infection is a potentially devastating complication of CIED procedures, causing significant morbidity and mortality for patients. Of all CIED complications, infection has the greatest impact on mortality, requirement for re-intervention and additional hospital treatment days. Based on large prospective studies, the infection rate at 12-months after a CIED procedure is approximately 1%. The risk of CIED infection may be related to several factors which should be considered with regards to risk minimization. These include technical factors, patient factors, and periprocedural factors. Technical factors include the number of leads and size of generator, the absolute number of interventions which have been performed for the patient, and the operative approach. Patient factors include various non-modifiable underlying comorbidities and potentially modifiable transient conditions. Procedural factors include both peri-operative and post-operative factors. The contemporary PADIT score, derived from a large cohort of CIED patients, is useful for the prediction of infection risk. In this review, we summarize the key information regarding epidemiology, incidence and risk factors for CIED infection.

Firefighters and COVID-19: An Occupational Health Perspective.

Firefighters and COVID-19: An Occupational Health Perspective.

Mucus, Microbiomes and Pulmonary Disease.

The respiratory tract harbors a stable and diverse microbial population within an extracellular mucus layer. Mucus provides a formidable defense against infection and maintaining healthy mucus is essential to normal pulmonary physiology, promoting immune tolerance and facilitating a healthy, commensal lung microbiome that can be altered in association with chronic respiratory disease. How one maintains a specialized (healthy) microbiome that resists significant fluctuation remains unknown, although smoking, diet, antimicrobial therapy, and infection have all been observed to influence microbial lung homeostasis. In this review, we outline the specific role of polymerizing mucin, a key functional component of the mucus layer that changes during pulmonary disease. We discuss strategies by which mucin feed and spatial orientation directly influence microbial behavior and highlight how a compromised mucus layer gives rise to inflammation and microbial dysbiosis. This emerging field of respiratory research provides fresh opportunities to examine mucus, and its function as predictors of infection risk or disease progression and severity across a range of chronic pulmonary disease states and consider new perspectives in the development of mucolytic treatments.

Assessing the geographic range of classical swine fever vaccinations by spatiotemporal modelling in Japan

A classical swine fever (CSF) epidemic has been ongoing in Japan since September 2018. The outbreak started in Gifu Prefecture and involved 21 prefectures by the end of October 2020, posing a serious threat to pork industries. The present study was conducted to capture the spatiotemporal dynamics of CSF in Japan and assess the geographic range of the CSF vaccination on pig farms. First infection dates were collected for wild boars and on swine farms by prefecture. A simple statistical model was used to describe the spatiotemporal dynamics of CSF, describing the infection risk in wild boars and the subsequent transmission hazards to swine farms for 47 prefectures. Because the spatial transmission mechanisms and wild boar population dynamics involved substantial uncertainties, 16 models were applied to the empirical data. Estimated hazard parameters were used to predict the risk of infection on swine farms by 15 December 2020 to explicitly evaluate the governmental recommendation for vaccinations on pig farms by prefecture in light of the predicted infection risk in domestic pigs. The best‐fit model for the wild boars indicated that transmission occurred via neighbouring prefectures and involved seasonality. The estimated conditional hazard was 0.008 (95% confidence interval [CI]: 0.001–0.014) per day for infections transmitted from wild boars to swine farms, and the median time from wild boar infection to swine farm infection was 129.4 days (95% CI: 69.5–935.0). Our prediction indicated that prefectures connected by land to those with wild boar infections had a higher risk of infection on swine farms. CSF transmission in Japan likely progressed diffusively via wild boar movement, and tracking wild boar infections may help determine the risk of infection on swine farms. Our risk map highlights the importance of deciding vaccination policies according to predicted risk.

Phenotyping preterm infants at birth to predict infection risk.

Phenotyping preterm infants at birth to predict infection risk.

Predicting Infection Risk in Multiple Sclerosis Patients Treated with Ocrelizumab: A Retrospective Cohort Study.

BackgroundOcrelizumab safety outcomes have been well evaluated in clinical trials and open-label extension (OLE) studies. However, risk factors for infection in patients with multiple sclerosis (MS) receiving ocrelizumab have not been extensively studied in the real-world setting.ObjectiveThe aim of this study was to examine factors determining risk of self-reported infections and antimicrobial use in patients receiving ocrelizumab for MS.MethodsA retrospective, observational cohort study was conducted in patients receiving ocrelizumab at the Royal Melbourne Hospital. Infection type and number were reported by patients, and the associations of potential clinical and laboratory risk factors with self-reported infection and antimicrobial use were estimated using univariate and multivariable logistic regression models.ResultsA total of 185 patients were included in the study; a total of 176 infections were reported in 89 patients (46.1%), and antimicrobial use was identified in 47 patients (25.3%). In univariate analyses, a higher serum IgA was associated with reduced odds of infection (OR 0.44, 95% CI 0.25–0.76). In multivariable analyses, older age (OR 0.94, 95% CI 0.88–0.99), higher serum IgA (OR 0.37, 95% CI 0.17–0.80) and higher serum IgG (OR 0.81, 95% CI 0.67–0.99) were associated with reduced odds of infection. Older age (OR 0.85, 95% CI 0.75–0.96) and higher serum IgA (OR 0.23, 95% CI 0.07–0.79) were associated with reduced odds of antimicrobial use, whilst longer MS disease duration (OR 1.22, 95% CI 1.06–1.41) and higher Expanded Disability Status Scale (EDSS) score (OR 1.99, 95% CI 1.02–3.86) were associated with increased odds of antimicrobial use.ConclusionsHigher serum IgA and IgG and older age were associated with reduced odds of infection. Our findings highlight that infection risk is not uniform in patients with MS receiving ocrelizumab and substantiate the need to monitor immunoglobulin levels pre-treatment and whilst on therapy.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40263-021-00810-3.

Successful identification of predictive profiles for infection utilising systems-level immune analysis: a pilot study in patients with relapsed and refractory multiple myeloma.

ObjectivesPatients with multiple myeloma (MM) are at increased risk for infection. Clinical assessment of infection risk is increasingly challenging in the era of immune‐based therapy. A pilot systems‐level immune analysis study to identify predictive markers for infection was conducted.MethodsPatients with relapsed and/or refractory MM (RRMM) who participated in a treatment trial of lenalidomide and dexamethasone were evaluated. Data on patient demographics, disease and episodes of infection were extracted from clinical records. Peripheral blood mononuclear cells (PBMCs) collected at defined intervals were analysed, with or without mitogen re‐stimulation, using RNA sequencing and mass cytometry (CyTOF). CyTOF‐derived cell subsets and RNAseq gene expression profiles were compared between patients that did and did not develop infection to identify immune signatures that predict infection over a 3‐month period.ResultsTwenty‐three patients participated in the original treatment trial, and we were able to access samples from 17 RRMM patients for further evaluation in our study. Nearly half the patients developed an infection (8/17) within 3 months of sample collection. Infections were mostly clinically diagnosed (62.5%), and the majority involved the respiratory tract (87.5%). We did not detect phenotypic or numerical differences in immune cell populations between patients that did and did not develop infections. Transcriptional profiling of stimulated PBMCs revealed distinct Th2 immune pathway signatures in patients that developed infection.ConclusionImmune cell counts were not useful predictors of infection risk. Functional assessment of stimulated PBMCs has identified potential immune profiles that may predict future infection risk in patients with RRMM.

Does Angiotensin II receptor blockers increase the risk of SARS-CoV-2 infection? A real-life experience.

Since the start of the COVID-19 pandemic, millions of people have been infected with thousands of deaths. Few data regarding factors that increase the risk of infection are available. Our study aimed to evaluate all people living in retirement homes (PLRNH) and identify factors that could increase infection risk in a close community. We conducted a retrospective study enrolling all PLRNH, where at least one SARS-CoV-2 infected person was present. Variables were compared with Student's t-test or Pearson chi-square test as appropriate. Uni- and multivariate analyses were conducted to evaluate variables' influence on the infection. We included 452 PLRNH; 144 (31.7%) were male, with a mean age of 82.2±8.6 years. People with a positive swab for SARS-CoV-2 were 306 (67.4%). A significant difference between SARS-CoV-2 infected and not infected was observed in the percentage of those receiving chronic treatment with Angiotensin II receptor blockers (ARBs) (18.6% vs. 9.5%, p=0.012). On the contrary, there was no difference in the proportion of those receiving ACE inhibitors (ACE-I) (21.2% vs. 23.6%, p=0.562). At multivariate analysis, people with mental illness and cancer had an increased risk of being infected. Furthermore, receiving ARBs as a chronic treatment was an independent predictor of infection risk [OR 1.95 (95% CI 1.03-3.72) p=0.041]. Our data suggest that, in close communities, such as retirement nursing homes, the receipt of ARBs increased the risk of acquiring SARS-CoV-2 infection. However, before changing an important chronic treatment in a fragile population, such as the elderly living in retirement nursing homes, clinicians should carefully evaluate the risk-benefit ratio.