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Background How to improve the high mortality rate of sepsis? The prompt identification of at-risk patients, and the interdisciplinary sepsis treatment protocol implementation are interventions that can reverse such unacceptable outcome. The objective of our study is to summarize main results of the protocol for the management of sepsis and septic shock, implemented at Biocor Instituto, a general hospital in Belo Horizonte, a 3,000,000 inhabitants city from Brazil. Methods Prospective cohort study of patients with sepsis, evaluated between May/2018-Apr/2020. Univariate and multivariate analysis by logistic regression to identify risk factors for hospital death. Results Over 28 months, 220 patients were included in sepsis protocol: 121 hospital deaths, a crude mortality = 121/220 = 55% (95%C.I. = [48%;62%]). 136 patients (62%) came from the emergency room. In 97 cases (44%) it was possible to isolate 111 microorganisms, with a predominance of Klebsiella, E.coli, and S.aureus. 75% of the cases (165) had definition of APACHE, with the absolute majority of these (88%) having APACHE between 25 and 40. Most patients (52%) received antibiotic (ATB) in 15 minutes and only 4% received ATB after 60 minutes of waiting time. In 198 patients (90%) it was possible to identify the focus of sepsis, with a predominance of pulmonary (47%), urinary (21%) and abdominal (15%). Hospital mortality varied from 30 to 62%, when the focus was pulmonary (p-value = 0.045). In univariate analysis (Figure 1), pulmonary sepsis, creatinine, lactate, and APACHE were significantly associated with hospital death. The time for ATB administration was typically close to 20 minutes, and time to receive the therapeutic antibiotic were not associated with the patient’s death. By using the logistic model (Figure 2) to assign cases of predicted hospital death for probabilities &amp;gt;= 0.5 and controls for probabilities &amp;lt; 0.5, the prediction model had a sensitivity of 0.68 (0.59–0.76), a specificity of 0.58 (0.48–0.67), an area under the curve of the receiver operating characteristic curve of 0.75 (0.68–0.82). There was no significant difference between observed versus expected mortality by APACHE (Figure 3). Figure 1. Univariate analysis to identify risk factors for hospital death. Figure 2. Logistic model for predicting hospital death. Figure 3. Observed X Expected/severity-adjusted mortality (APACHE). Conclusion The logistic model developed uses only creatinine and lactate data to predict suspected sepsis patients with high death risk. Disclosures All Authors: No reported disclosures

197. A Predictive Model for the Prompt Identification of Suspected Sepsis Patients with High Death Risk