BackgroundThe Centers for Disease Control and Prevention (CDC) proposed standard definitions for acquired resistance in bacterias. Resistant bacteria were categorized as multidrug-resistant (MDR), extensively drug-resistant (XDR) and pandrug-resistant (PDR). This study describes the incidence of Gram-negative MDR, XDR and PDR in 12 private and adult intensive care units (ICU’s) from Belo Horizonte, Minas Gerais, the sixth most populated city in Brazil, with approximately 3 million inhabitants.MethodsData were collected between January/2013 to December/2017 from 12 ICU’s. The hospitals used prospective healthcare-associated infections (HAI) surveillance protocols, in accordance to the CDC. Antimicrobial resistance from six Gram-negatives, causing nosocomial infections, were evaluated: Acinetobacter sp., Klebsiella sp., Proteus sp., Enterobacter sp., Escherichia coli, and Pseudomonas sp.. We computed the three categories of drug-resistance (MDR+XDR+PDR) to define benchmarks for the resistance rate of each Gram-negative evaluated. Benchmarks were defined as the superior limits of 95% confidence interval for the resistance rate.ResultsAfter a 5 year surveillance, 6,242 HAI strains were tested: no pandrug-resistant bacteria (PDR) was found. Acinetobacter sp. was the most resistant Gram-negative: 206 strains from 1,858 were XDR (11%), and 1,638 were MDR (88%). Pseudomonas sp.: 41/1,159 = 3.53% XDR; 180/1,159 = 15.53% MDR. Klebsiella sp.: 2/1,566 = 0,1% XDR; 813/1,566 = 52% MDR. Proteus sp.: 0/507 = 0% XDR; 163/507 = 32% MDR. Enterobacter sp.: 0/471 = 0% XDR; 148/471 = 31% MDR. Escherichia coli: 0/681 = 0% XDR; 157/681 = 23% MDR. Benchmarks for the global resistance rate of each Gram-negative (MDR+XDR+PDR): Acinetobacter sp. = 92%; Klebsiella sp. = 62%; Proteus sp. = 40%; Enterobacter sp. = 48%; Escherichia coli = 33%; Pseudomonas sp. = 30%.ConclusionThis study has calculated the incidence of Gram-negative MDR, XDR and PDR, and found a higher incidence of MDR Acinetobacter sp., with an 88% multiresistance rate. Henceforth, developing countries healthcare institutions must be aware of an increased risk of infection by Acinetobacter sp.. Benchmarks have been defined, and can be used as indicators for healthcare assessment. Disclosures All authors: No reported disclosures.

Objective: To determine whether the SEIR model, associated to mobility changes parameters, can determine the likelihood of establishing control over an epidemic in a city, state or country. Study design and setting: The critical step in the prediction of COVID-19 by a SEIR model are the values of the basic reproduction number (R0) and the infectious period, in days. R0 and the infectious periods were calculated by mathematical constrained optimization, and used to determine the numerically minimum SEIR model errors in a country, based on COVID-19 data until April 11th. The Community Mobility Reports from Google Maps (<https://www.google.com/covid19/mobility>) provided mobility changes on April 5th compared to the baseline (Jan 3th to Feb 6th). The data was used to measure the non-pharmacological intervention adherence. The impact of each mobility component was calculated by logistic regression models. COVID-19 control was defined by SEIR model R0<1.0 in a country. Results: The ECDC has registered 1,653,204 COVID-19 worldwide on April 11th. Sixteen countries presented 78% of all cases. Of the six Google Maps mobility parameters, the “Stay at home” parameter was the strongest one to control COVID-19 in a country: an increase of 50% in mobility trends for places of residence has a 99% chance of outbreak control. Conclusions: Residential mobility restriction presented itself as the most effective measure. The SEIR model associated with mobility parameters proved to be a useful tool in determining the chance of COVID-19 outbreak control.

Background: The ventriculoperitoneal shunt is the main procedure used for to treat communicating hydrocephalus. Surgical site infection associated with the shunt device is the most common complication and a cause of morbidity and mortality of related to the treatment. We sought to answer 3 questions: (1) What is the risk of meningitis after ventricular shunt operations? (2) What are the risk factors for meningitis? (3) What are the main microorganisms causing meningitis? Methods: We conducted a retrospective cohort study of patients undergoing ventricular shunt operations between July 2015 and June 2018 from 12 hospitals at Belo Horizonte, Brazil. Data were gathered by standardized methods defined by the CDC NHSN. Our sample size was 926, and we evaluated 26 preoperative and operative variables by univariate and multivariate analysis. Our outcome variables of interest were meningitis and hospital death. Results: In total, 71 cases of meningitis were diagnosed (risk, 7.7%; 95% CI, 6.1%–9.6%). The mortality rate among patients without infection was 10%, whereas hospital mortality of infected patients was 13% (P = .544). The 3 main risk factors for meningitis after ventricular shunt were identified by logistic regression model: age <2 years (OR, 3.20; P < .001), preoperative hospital stay >4 days (OR, 2.02; P = .007) and >1 surgical procedure, in addition to ventricular shunt (OR, 3.23; P = .043). Almost 1 of 3 of all patients was <2 years old (290, 31%). Also, 430 patients had >4 preoperative days (46%). Patients aged ≥2 years who underwent surgery 4 days after hospital admission had an increased risk of meningitis, from 4% to 6% (P = .140). If a patient <2 years old underwent surgery 4 or more days after hospital admission, the risk of meningitis increased from 9% to 18% (P = .026; Fig. 1). We built a risk index using the number of main risk factors based on a logistic regression model (0, 1, 2 or 3; Fig. 2). Conclusions:We identified 2 intrinsic risk factors for meningitis after ventricular shunt, age <2 years and multiple surgical procedures, and 1 extrinsic risk factor, the preoperative length of hospital stay.Funding: NoneDisclosures: None

Background: Digital games play an important role in the learning process, and are used to teach languages and train surgeons. Based on theoretical frameworks that prove the relevance of games in teaching, we began developing a computer game that simulates a hospital, so that medical students could analyze clinical cases from different areas of medical science, including neurology, while playing a game. Objectives: Create a game to teach medicine in a ludic manner. Design and Setting: The game is being developed by Doctors, Programmers, Engineers, students of Medicine, Information Technology (IT), Design and Architecture from Brazil and Peru, in a startup incubator from Centro Universitário de Belo Horizonte, in partnership with the Universidad Científica del Sur, Lima, Peru. Methods: Medical students, under the supervision of Doctors, defined behavioral algorithms, based on Brazilian guidelines, and outcomes (i.e. gain or loss of points, clinical improvement or worsening) addressing different topics in Medicine. Design students created the artistic elements. IT students programmed the prototype of the game using Unity software. Results: An expandable minimum viable product was obtained, with artistic elements of two characters, one being a non-playable character, a scenario, and a dialogue script based on a clinical examination of a patient. Conclusion: The software is running, with the launch of the pre-alpha version in December 2021. A scoring system will be included for qualitative assessment of the player, as well as feedback reports to educate the player. We speculate this game will improve accuracy and clinical skills of medical students.

BackgroundMeningitis after craniotomy can be devastating. The objective of our study is to answer four questions: (a) what is the risk of meningitis after craniotomy? (b) What are the main microorganisms causing meningitis after craniotomy? (c) What is the impact of meningitis in the hospital length of stay and mortality? (d) What are risk factors for meningitis after craniotomy?MethodsSurveillance data based on NHSN/CDC protocols were collected between 2013 and 2017 from nine hospitals at Belo Horizonte, Brazil. Outcome: meningitis, hospital death and total length of hospital stay. Twenty-three independent variables were analyzed using Epi Info and applying statistical two-tailed test hypothesis with significance level of 5%.ResultsA sample of 4,549 patients submitted to craniotomy was analyzed: risk of meningitis = 1.9% (IC 95% = 1.6%; 2.4%). Mortality rate in patients without infection was 9% rising to 33% in infected patients (P < 0.01). Hospital length of stay in non-infected patients (days): mean = 18, median = 7, std. dev. = 36. Hospital stay in infected patients: mean = 56, median = 37, std. dev. = 63 (P < 0.001). The duration of procedure was the main risk factor for meningitis: 1.5% risk of meningitis in surgery less than or equal to 4 hours vs. 2.5% if the duration of procedure was more than 4 hours (relative risk = 1.7; P = 0.041). From 88 meningitis, in 68 (77%) the etiologic agent was identified: Klebsiella pneumoniae (20%), Staphylococcus aureus (16%), Acinetobacter baumannii (13%), Pseudomonas aeruginosa (9%), Staphylococcus sp. (8%), Acinetobacter sp. (7%), Staphylococcus epidermidis (5%), and other (20%).ConclusionThe study showed how much meningitis is devastating, rising the death risk and length of hospital stay.Disclosures All authors: No reported disclosures.