Protocol: identifying policy, system, and environment change interventions to enhance availability of blood for transfusion in Kenya, a mixed-methods study.

BACKGROUND: Safe blood is essential for the care of patients with life-threatening anemia and hemorrhage. Low blood donation rates, inefficient testing procedures, and other supply chain disruptions in blood administration affect patients in low-resource settings across Sub-Saharan countries, including Kenya. Most efforts to improve access to transfusion have been unidimensional, usually focusing on only point along the blood system continuum, and have excluded community stakeholders from early stages of intervention development. Context-appropriate interventions to improve the availability of safe blood at the point of use in low-resource settings are of paramount importance. Thus, this protocol proposes a multifaceted approach to characterize the Kenyan blood supply chain through quantitative and qualitative analyses as well as an industrial engineering approach. METHODS: This study will use a mixed-methods approach in addition to engineering process mapping, modeling and simulation of blood availability in Kenya. It will be guided by a multidimensional three-by-three-by-three matrix: three socioeconomic settings, three components of the blood system continuum, and three levels of urgency of blood transfusion. Qualitative data collection includes one-on-one interviews and focus group discussions with stakeholders across the continuum to characterize ground-level deficits and potential policy, systems, and environment (PSE) interventions. Prospectively-collected quantitative data will be used to estimate blood collection and transfusion of blood. We will create a process map of the blood system continuum to model the response to PSE changes proposed by stakeholders. Lastly, we will identify those PSE changes that may have the greatest impact on blood transfusion availability, accounting for differences across socioeconomic settings and levels of urgency. DISCUSSION: Identifying and prioritizing community-driven interventions to improve blood supply in low-resource settings are of utmost importance. Varied constraints in blood collection, processing, delivery, and use make each socioeconomic setting unique. Using a multifaceted approach to understand the Kenyan blood supply and model the response to stakeholder-proposed PSE changes may lead to identification of contextually appropriate intervention targets to meet the transfusion needs of the population.

The BLOODSAFE program: Building the future of access to safe blood in Sub-Saharan Africa.

BACKGROUND: The supply of blood in many low- and middle-income nations in Sub-Saharan Africa (SSA) does not meet the patient care needs. Lack and delay of blood transfusion cause harm to patients and slow the rate of progress in other parts of the health system. Recognizing the power of implementation science, the BLOODSAFE Program was initiated which supports three SSA research study teams and one data coordinating center (DCC) with the goal to improve access to safe blood transfusion in SSA. STUDY DESIGN AND METHODS: The study team in Ghana is focusing on studying and decreasing iron deficiency in blood donors and evaluating social engagement of blood donors through different approaches. The study team in Kenya is building a "vein to vein" workflow model to elucidate and devise strategies to overcome barriers to blood donation and improve infrastructural components of blood product production and use. The Malawi team is studying the infectious disease ramifications of blood donation as well as blood donor retention strategies aimed at blood donors who commence their donation career in secondary schools. RESULTS AND DISCUSSION: Together the project teams and the DCC work as a consortium to support each other through a shared study protocol that will study donor motivations, outcomes, and adverse events across all three countries. The BLOODSAFE Program has the potential to lead to generalizable improvement approaches for increasing access to safe blood in SSA as well as mentoring and building the research capacity and careers of many investigators.

A 2-Biomarker Model Augments Clinical Prediction of Mortality in Melioidosis.

BACKGROUND: Melioidosis, infection caused by Burkholderia pseudomallei, is a common cause of sepsis with high associated mortality in Southeast Asia. Identification of patients at high likelihood of clinical deterioration is important for guiding decisions about resource allocation and management. We sought to develop a biomarker-based model for 28-day mortality prediction in melioidosis. METHODS: In a derivation set (N = 113) of prospectively enrolled, hospitalized Thai patients with melioidosis, we measured concentrations of interferon-γ, interleukin-1ß, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-ɑ, granulocyte-colony stimulating factor, and interleukin-17A. We used least absolute shrinkage and selection operator (LASSO) regression to identify a subset of predictive biomarkers and performed logistic regression and receiver operating characteristic curve analysis to evaluate biomarker-based prediction of 28-day mortality compared with clinical variables. We repeated select analyses in an internal validation set (N = 78) and in a prospectively enrolled external validation set (N = 161) of hospitalized adults with melioidosis. RESULTS: All 8 cytokines were positively associated with 28-day mortality. Of these, interleukin-6 and interleukin-8 were selected by LASSO regression. A model consisting of interleukin-6, interleukin-8, and clinical variables significantly improved 28-day mortality prediction over a model of only clinical variables [AUC (95% confidence interval [CI]): 0.86 (.79-.92) vs 0.78 (.69-.87); P = .01]. In both the internal validation set (0.91 [0.84-0.97]) and the external validation set (0.81 [0.74-0.88]), the combined model including biomarkers significantly improved 28-day mortality prediction over a model limited to clinical variables. CONCLUSIONS: A 2-biomarker model augments clinical prediction of 28-day mortality in melioidosis.

Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study.

BACKGROUND: Sepsis is life-threatening organ dysfunction due to a dysregulated host response to infection. It is considered a major cause of health loss, but data for the global burden of sepsis are limited. As a syndrome caused by underlying infection, sepsis is not part of standard Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimates. Accurate estimates are important to inform and monitor health policy interventions, allocation of resources, and clinical treatment initiatives. We estimated the global, regional, and national incidence of sepsis and mortality from this disorder using data from GBD 2017. METHODS: We used multiple cause-of-death data from 109 million individual death records to calculate mortality related to sepsis among each of the 282 underlying causes of death in GBD 2017. The percentage of sepsis-related deaths by underlying GBD cause in each location worldwide was modelled using mixed-effects linear regression. Sepsis-related mortality for each age group, sex, location, GBD cause, and year (1990-2017) was estimated by applying modelled cause-specific fractions to GBD 2017 cause-of-death estimates. We used data for 8·7 million individual hospital records to calculate in-hospital sepsis-associated case-fatality, stratified by underlying GBD cause. In-hospital sepsis-associated case-fatality was modelled for each location using linear regression, and sepsis incidence was estimated by applying modelled case-fatality to sepsis-related mortality estimates. FINDINGS: In 2017, an estimated 48·9 million (95% uncertainty interval [UI] 38·9-62·9) incident cases of sepsis were recorded worldwide and 11·0 million (10·1-12·0) sepsis-related deaths were reported, representing 19·7% (18·2-21·4) of all global deaths. Age-standardised sepsis incidence fell by 37·0% (95% UI 11·8-54·5) and mortality decreased by 52·8% (47·7-57·5) from 1990 to 2017. Sepsis incidence and mortality varied substantially across regions, with the highest burden in sub-Saharan Africa, Oceania, south Asia, east Asia, and southeast Asia. INTERPRETATION: Despite declining age-standardised incidence and mortality, sepsis remains a major cause of health loss worldwide and has an especially high health-related burden in sub-Saharan Africa. FUNDING: The Bill & Melinda Gates Foundation, the National Institutes of Health, the University of Pittsburgh, the British Columbia Children's Hospital Foundation, the Wellcome Trust, and the Fleming Fund.

sTREM-1 predicts mortality in hospitalized patients with infection in a tropical, middle-income country.

BACKGROUND: Few studies of biomarkers as predictors of outcome in infection have been performed in tropical, low- and middle-income countries where the burden of sepsis is highest. We evaluated whether selected biomarkers could predict 28-day mortality in infected patients in rural Thailand. METHODS: Four thousand nine hundred eighty-nine adult patients admitted with suspected infection to a referral hospital in northeast Thailand were prospectively enrolled within 24 h of admission. In a secondary analysis of 760 patients, interleukin-8 (IL-8), soluble tumor necrosis factor receptor 1 (sTNFR-1), angiopoietin-1 (Ang-1), angiopoietin-2 (Ang-2), and soluble triggering receptor expressed by myeloid cells 1 (sTREM-1) were measured in the plasma. Association with 28-day mortality was evaluated using regression; a parsimonious biomarker model was selected using the least absolute shrinkage and selection operator (LASSO) method. Discrimination of mortality was assessed by receiver operating characteristic curve analysis and verified by multiple methods. RESULTS: IL-8, sTNFR-1, Ang-2, and sTREM-1 concentrations were strongly associated with death. LASSO identified a three-biomarker model of sTREM-1, Ang-2, and IL-8, but sTREM-1 alone provided comparable mortality discrimination (p = 0.07). sTREM-1 alone was comparable to a model of clinical variables (area under receiver operating characteristic curve [AUC] 0.81, 95% confidence interval [CI] 0.77-0.85 vs AUC 0.79, 95% CI 0.74-0.84; p = 0.43). The combination of sTREM-1 and clinical variables yielded greater mortality discrimination than clinical variables alone (AUC 0.83, 95% CI 0.79-0.87; p = 0.004). CONCLUSIONS: sTREM-1 predicts mortality from infection in a tropical, middle-income country comparably to a model derived from clinical variables and, when combined with clinical variables, can further augment mortality prediction. TRIAL REGISTRATION: The Ubon-sepsis study was registered on ClinicalTrials.gov ( NCT02217592 ), 2014.

Sepsis Epidemiology Across the International Classification of Diseases, 9th Edition, to International Classification of Diseases, 10th Edition, Chasm-A Direct Application of the Institute for Health Metrics and Evaluation Case Definition to Hospital Discharge Data.

OBJECTIVES: Describe the epidemiology of sepsis across the transition from the International Classification of Diseases, 9th Edition, and International Classification of Diseases, 10th Edition, coding systems, evaluating estimates of two previously published International Classification of Diseases, 10th Edition, coding strategies. DESIGN: Serial cross-sectional analysis. SETTING: Healthcare Utilization Project's annual Nationwide Inpatient Sample of U.S. hospital discharges, 2012-2017. PATIENTS: Discharges greater than or equal to 18 years old, which met one of the three case definitions for sepsis. For the records using International Classification of Diseases, 9th Edition, we used previously published modified Angus criteria, and for records using International Classification of Diseases, 10th Edition, we deployed a case definition used by the Centers for Medicare & Medicaid Services and a case definition developed by the Institute for Health Metrics and Evaluation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: During the study period, there were discontinuities in the sepsis incidence estimates using the modified Angus International Classification of Diseases, 9th Edition, criteria in 2014 and either Centers for Medicare & Medicaid Services or Institute for Health Metrics and Evaluation International Classification of Diseases, 10th Edition, criteria in 2016. In 2014, there were an estimated 1,009 cases (95% CI, 989-1,030) of modified Angus sepsis per 100,000 persons, whereas in 2016, there were 709 cases (95% CI, 694-724) of Centers for Medicare & Medicaid Services sepsis and 1,498 cases (95% CI, 1,471-1,092) of Institute for Health Metrics and Evaluation sepsis per 100,000 persons. Furthermore, the Institute for Health Metrics and Evaluation definition identified a sepsis cohort with similar hospital characteristics but a younger age distribution, higher proportion of women, lower severity of illness, and lower hospital mortality. CONCLUSIONS: The Institute for Health Metrics and Evaluation International Classification of Diseases, 10th Edition, coding strategy for identifying sepsis may capture a larger patient population within administrative datasets that are different from those identified with previously deployed International Classification of Diseases-based methods. Further work is required to determine the optimal International Classification of Diseases, 10th Edition, coding strategy for use in hospital discharge data.

The global burden of sepsis: barriers and potential solutions.

Sepsis is a major contributor to the global burden of disease. The majority of sepsis cases and deaths are estimated to occur in low and middle-income countries. Barriers to reducing the global burden of sepsis include difficulty quantifying attributable morbidity and mortality, low awareness, poverty and health inequity, and under-resourced and low-resilience public health and acute health care delivery systems. Important differences in the populations at risk, infecting pathogens, and clinical capacity to manage sepsis in high and low-resource settings necessitate context-specific approaches to this significant problem. We review these challenges and propose strategies to overcome them. These strategies include strengthening health systems, accurately identifying and quantifying sepsis cases, conducting inclusive research, establishing data-driven and context-specific management guidelines, promoting creative clinical interventions, and advocacy.

Mortality outcomes based on ED qSOFA score and HIV status in a developing low income country.

OBJECTIVE: To evaluate the utility of the quick Sepsis-related Organ Failure Assessment (qSOFA) score to predict risks for emergency department (ED) and hospital mortality among patients in a sub-Saharan Africa (SSA) setting. METHODS: This retrospective cohort study was carried out at a tertiary-care hospital, in Kigali, Rwanda and included patients ≥15years, presenting for ED care during 2013 with an infectious disease (ID). ED and overall hospital mortality were evaluated using multivariable regression, with qSOFA scores as the primary predictor (reference: qSOFA=0), to yield adjusted relative risks (aRR) with 95% confidence intervals (CI). Analyses were performed for the overall population and stratified by HIV status. RESULTS: Among 15,748 cases, 760 met inclusion (HIV infected 197). The most common diagnoses were malaria and intra-abdominal infections. Prevalence of ED and hospital mortality were 12.5% and 25.4% respectively. In the overall population, ED mortality aRR was 4.8 (95% CI 1.9-12.0) for qSOFA scores equal to 1 and 7.8 (95% CI 3.1-19.7) for qSOFA scores ≥2. The aRR for hospital mortality in the overall cohort was 2.6 (95% 1.6-4.1) for qSOFA scores equal to 1 and 3.8 (95% 2.4-6.0) for qSOFA scores ≥2. For HIV infected cases, although proportional mortality increased with greater qSOFA score, statistically significant risk differences were not identified. CONCLUSION: The qSOFA score provided risk stratification for both ED and hospital mortality outcomes in the setting studied, indicating utility in sepsis care in SSA, however, further prospective study in high-burden HIV populations is needed.

Association of the Quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA) Score With Excess Hospital Mortality in Adults With Suspected Infection in Low- and Middle-Income Countries.

Importance: The quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA) score has not been well-evaluated in low- and middle-income countries (LMICs). Objective: To assess the association of qSOFA with excess hospital death among patients with suspected infection in LMICs and to compare qSOFA with the systemic inflammatory response syndrome (SIRS) criteria. Design, Settings, and Participants: Retrospective secondary analysis of 8 cohort studies and 1 randomized clinical trial from 2003 to 2017. This study included 6569 hospitalized adults with suspected infection in emergency departments, inpatient wards, and intensive care units of 17 hospitals in 10 LMICs across sub-Saharan Africa, Asia, and the Americas. Exposures: Low (0), moderate (1), or high (≥2) qSOFA score (range, 0 [best] to 3 [worst]) or SIRS criteria (range, 0 [best] to 4 [worst]) within 24 hours of presentation to study hospital. Main Outcomes and Measures: Predictive validity (measured as incremental hospital mortality beyond that predicted by baseline risk factors, as a marker of sepsis or analogous severe infectious course) of the qSOFA score (primary) and SIRS criteria (secondary). Results: The cohorts were diverse in enrollment criteria, demographics (median ages, 29-54 years; males range, 36%-76%), HIV prevalence (range, 2%-43%), cause of infection, and hospital mortality (range, 1%-39%). Among 6218 patients with nonmissing outcome status in the combined cohort, 643 (10%) died. Compared with a low or moderate score, a high qSOFA score was associated with increased risk of death overall (19% vs 6%; difference, 13% [95% CI, 11%-14%]; odds ratio, 3.6 [95% CI, 3.0-4.2]) and across cohorts (P < .05 for 8 of 9 cohorts). Compared with a low qSOFA score, a moderate qSOFA score was also associated with increased risk of death overall (8% vs 3%; difference, 5% [95% CI, 4%-6%]; odds ratio, 2.8 [95% CI, 2.0-3.9]), but not in every cohort (P < .05 in 2 of 7 cohorts). High, vs low or moderate, SIRS criteria were associated with a smaller increase in risk of death overall (13% vs 8%; difference, 5% [95% CI, 3%-6%]; odds ratio, 1.7 [95% CI, 1.4-2.0]) and across cohorts (P < .05 for 4 of 9 cohorts). qSOFA discrimination (area under the receiver operating characteristic curve [AUROC], 0.70 [95% CI, 0.68-0.72]) was superior to that of both the baseline model (AUROC, 0.56 [95% CI, 0.53-0.58; P < .001) and SIRS (AUROC, 0.59 [95% CI, 0.57-0.62]; P < .001). Conclusions and Relevance: When assessed among hospitalized adults with suspected infection in 9 LMIC cohorts, the qSOFA score identified infected patients at risk of death beyond that explained by baseline factors. However, the predictive validity varied among cohorts and settings, and further research is needed to better understand potential generalizability.

Physician Variation in Time to Antimicrobial Treatment for Septic Patients Presenting to the Emergency Department.

OBJECTIVES: Delayed initiation of appropriate antimicrobials is linked to higher sepsis mortality. We investigated interphysician variation in septic patients' door-to-antimicrobial time. DESIGN: Retrospective cohort study. SETTING: Emergency department of an academic medical center. SUBJECTS: Adult patients treated with antimicrobials in the emergency department between 2009 and 2015 for fluid-refractory severe sepsis or septic shock. Patients who were transferred, received antimicrobials prior to emergency department arrival, or were treated by an attending physician who cared for less than five study patients were excluded. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We employed multivariable linear regression to evaluate the association between treating attending physician and door-to-antimicrobial time after adjustment for illness severity (Acute Physiology and Chronic Health Evaluation II score), patient age, prehospital or arrival hypotension, admission from a long-term care facility, mode of arrival, weekend or nighttime admission, source of infection, and trainee involvement in care. Among 421 eligible patients, 74% received antimicrobials within 3 hours of emergency department arrival. After covariate adjustment, attending physicians' (n = 40) median door-to-antimicrobial times varied significantly, ranging from 71 to 359 minutes (p = 0.002). The percentage of each physician's patients whose antimicrobials began within 3 hours of emergency department arrival ranged from 0% to 100%. Overall, 12% of variability in antimicrobial timing was explained by the attending physician compared with 4% attributable to illness severity as measured by the Acute Physiology and Chronic Health Evaluation II score (p < 0.001). Some but not all physicians started antimicrobials later for patients who were normotensive on presentation (p = 0.017) or who had a source of infection other than pneumonia (p = 0.006). The adjusted odds of in-hospital mortality increased by 20% for each 1 hour increase in door-to-antimicrobial time (p = 0.046). CONCLUSIONS: Among patients with severe sepsis or septic shock receiving antimicrobials in the emergency department, door-to-antimicrobial times varied five-fold among treating physicians. Given the association between antimicrobial delay and mortality, interventions to reduce physician variation in antimicrobial initiation are likely indicated.

Prognostic Accuracy of Screening Tools for Clinical Deterioration in Adults With Suspected Sepsis in Northeastern Thailand: A Cohort Validation Study.

Background: We sought to assess the performance of commonly used clinical scoring systems to predict imminent clinical deterioration in patients hospitalized with suspected infection in rural Thailand. Methods: Patients with suspected infection were prospectively enrolled within 24 hours of admission to a referral hospital in northeastern Thailand between 2013 and 2017. In patients not requiring intensive medical interventions, multiple enrollment scores were calculated including the National Early Warning Score (NEWS), the Modified Early Warning Score, Between the Flags, and the quick Sequential Organ Failure Assessment score. Scores were tested for predictive accuracy of clinical deterioration, defined as a new requirement of mechanical ventilation, vasoactive medications, intensive care unit admission, and/or death approximately 1 day after enrollment. The association of each score with clinical deterioration was evaluated by means of logistic regression, and discrimination was assessed by generating area under the receiver operating characteristic curve. Results: Of 4989 enrolled patients, 2680 met criteria for secondary analysis, and 100 of 2680 (4%) experienced clinical deterioration within 1 day after enrollment. NEWS had the highest discrimination for predicting clinical deterioration (area under the receiver operating characteristic curve, 0.78 [95% confidence interval, .74-.83]) compared with the Modified Early Warning Score (0.67 [.63-.73]; P < .001), quick Sequential Organ Failure Assessment (0.65 [.60-.70]; P < .001), and Between the Flags (0.69 [.64-.75]; P < .001). NEWS ≥5 yielded optimal sensitivity and specificity for clinical deterioration prediction. Conclusions: In patients hospitalized with suspected infection in a resource-limited setting in Southeast Asia, NEWS can identify patients at risk of imminent clinical deterioration with greater accuracy than other clinical scoring systems.

Clinical research during a public health emergency: a systematic review of severe pandemic influenza management.

OBJECTIVE: Rigorous evaluation of clinical interventions in the setting of a public health emergency is necessary to identify best practices, to develop clinical management guidelines, and to inform resource allocation. The 2009 influenza A (H1N1) pandemic necessitated care of critically ill patients around the world. To inform the World Health Organization Public Health Research Agenda for Influenza, we conducted a systematic review to identify clinical interventions other than antiviral therapies that would benefit severely ill 2009 H1N1 influenza patients (adults and children) in both high- and low-resource settings. DATA SOURCES: PubMed, EMBASE, Cochrane Central Register of Clinical Trials, and Cochrane Database of Systematic Reviews; hand search of abstracts from six professional society annual conferences and bibliographies of clinical review articles; and personal communication with leaders in the field. STUDY SELECTION: English language; human studies; citations added to databases from January 1, 2009 (Cochrane databases) or March 15, 2009 (PubMed and EMBASE) through January 31, 2012; randomized controlled trials, prospective cohort studies, or systematic reviews/meta-analyses of non-antiviral clinical interventions in hospitalized 2009 influenza A (H1N1) patients. DATA EXTRACTION: The search identified 2,452 articles. Thirty-six potentially relevant articles were read. Seven articles met criteria. All were observational studies. DATA SYNTHESIS: One study found benefit of convalescent plasma infusion, three studies found no benefit of corticosteroids, and three studies had mixed results on the benefit of extracorporeal lung support. No study was applicable to health care delivery in low-resource settings. CONCLUSIONS: There is a paucity of high quality clinical research to inform clinical care of severe H1N1 influenza, and we found no beneficial interventions appropriate for low-resource settings. This may be due to the logistical difficulties of conducting clinical research in response to a public health emergency. Our investigation underscores the need for the development of outbreak-ready research capacity in both high- and low-resource settings.

Association of clinical prediction scores with hospital mortality in an adult medical and surgical intensive care unit in Kenya.

Importance: Mortality prediction among critically ill patients in resource limited settings is difficult. Identifying the best mortality prediction tool is important for counseling patients and families, benchmarking quality improvement efforts, and defining severity of illness for clinical research studies. Objective: Compare predictive capacity of the Modified Early Warning Score (MEWS), Universal Vital Assessment (UVA), Tropical Intensive Care Score (TropICS), Rwanda Mortality Probability Model (R-MPM), and quick Sequential Organ Failure Assessment (qSOFA) for hospital mortality among adults admitted to a medical-surgical intensive care unit (ICU) in rural Kenya. We performed a pre-planned subgroup analysis among ICU patients with suspected infection. Design setting and participants: Prospective single-center cohort study at a tertiary care, academic hospital in Kenya. All adults 18 years and older admitted to the ICU January 2018-June 2019 were included. Main outcomes and measures: The primary outcome was association of clinical prediction tool score with hospital mortality, as defined by area under the receiver operating characteristic curve (AUROC). Demographic, physiologic, laboratory, therapeutic, and mortality data were collected. 338 patients were included, none were excluded. Median age was 42 years (IQR 33-62) and 61% (n = 207) were male. Fifty-nine percent (n = 199) required mechanical ventilation and 35% (n = 118) received vasopressors upon ICU admission. Overall hospital mortality was 31% (n = 104). 323 patients had all component variables recorded for R-MPM, 261 for MEWS, and 253 for UVA. The AUROC was highest for MEWS (0.76), followed by R-MPM (0.75), qSOFA (0.70), and UVA (0.69) (p < 0.001). Predictive capacity was similar among patients with suspected infection. Conclusion and relevance: All tools had acceptable predictive capacity for hospital mortality, with variable observed availability of the component data. R-MPM and MEWS had high rates of variable availability as well as good AUROC, suggesting these tools may prove useful in low resource ICUs.

Prevalence of Antimicrobial Resistance and Association with Patient Outcomes in a Rural Kenyan Hospital.

Data on antimicrobial resistance (AMR) and association with outcomes in resource-variable intensive care units (ICU) are lacking. Data currently available are limited to large, urban centers. We attempted to understand this locally through a dual-purpose, retrospective study. Cohort A consisted of adult and pediatric patients who had blood, urine, or cerebrospinal fluid cultures obtained from 2016 to 2020. A total of 3,013 isolates were used to create the Kijabe Hospital's first antibiogram. Gram-negative organisms were found to be less than 50% susceptible to third- and fourth-generation cephalosporins, 67% susceptible to piperacillin-tazobactam, 87% susceptible to amikacin, and 93% susceptible to meropenem. We then evaluated the association between AMR and clinical characteristics, management, and outcomes among ICU patients (Cohort B). Demographics, vital signs, laboratory results, management data, and outcomes were obtained. Antimicrobial resistance was defined as resistance to one or more antimicrobials. Seventy-six patients were admitted to the ICU with bacteremia during this time. Forty complete paper charts were found for review. Median age was 34 years (interquartile range, 9-51), 26 patients were male (65%), and 28 patients were older than 18 years (70%). Septic shock was the most common diagnosis (n = 22, 55%). Six patients had AMR bacteremia; Escherichia coli was most common (n = 3, 50%). There was not a difference in mortality between patients with AMR versus non-AMR infections (P = 0.54). This study found a prevalence of AMR. There was no association between AMR and outcomes among ICU patients. More studies are needed to understand the impact of AMR in resource-variable settings.