Objectives: Emergency medicine in low- and middle-income countries (LMICs) is hindered by lack of research into patient outcomes. Chief complaints (CCs) are fundamental to emergency care but have only recently been uniquely codified for an LMIC setting in Uganda. It is not known whether CCs independently predict emergency unit patient outcomes. Methods: Patient data collected in a Ugandan emergency unit between 2009 and 2018 were randomized into validation and derivation data sets. A recursive partitioning algorithm stratified CCs by 3-day mortality risk in each group. The process was repeated in 10,000 bootstrap samples to create an averaged risk ranking. Based on this ranking, CCs were categorized as “high-risk” (>2× baseline mortality), “medium-risk” (between 2 and 0.5× baseline mortality), and “low-risk” (<0.5× baseline mortality). Risk categories were then included in a logistic regression model to determine if CCs independently predicted 3-day mortality. Results: Overall, the derivation data set included 21,953 individuals with 7,313 in the validation data set. In total, 43 complaints were categorized, and 12 CCs were identified as high-risk. When controlled for triage data including age, sex, HIV status, vital signs, level of consciousness, and number of complaints, high-risk CCs significantly increased 3-day mortality odds ratio (OR = 2.39, 95% confidence interval [CI] = 1.95 to 2.93, p < 0.001) while low-risk CCs significantly decreased 3-day mortality odds (OR = 0.16, 95% CI = 0.09 to 0.29, p < 0.001). Conclusions: High-risk CCs were identified and found to predict increased 3-day mortality independent of vital signs and other data available at triage. This list can be used to expand local triage systems and inform emergency training programs. The methodology can be reproduced in other LMIC settings to reflect their local disease patterns.
Global Emergency Care (GEC) is a U.S.‐ and‐Uganda‐based nongovernmental organization dedicated to providing emergency care training in Uganda. In collaboration with Karoli Lwanga Hospital, GEC developed a 2‐year training program through which nurses earn an emergency care practitioner (ECP) certification and independently provide emergency care in a dedicated emergency unit at the Hospital. The program has since been adopted by Mbarara University of Science and Technology (MUST), and MUST and GEC are working collaboratively with multiple Ugandan stakeholders to expand the training capacity. Karoli Lwanga Hospital is located in the rural Rukungiri District of southwest Uganda, has a 6‐bed emergency unit that is staffed by ECPs from 8am until midnight, and cares for patients with medical and surgical emergencies (maternal emergencies are triaged to a separate labor and delivery ward). The annual census of the emergency unit has been relatively stable since 2009, seeing approximately 3,000–3,500 adults and 1,500 children less than 18 years old every year. Karoli Lwanga Hospital is not a referral hospital so very few patients present with diagnoses from other facilities. Emergency unit care is provided by ECPs trained in medical management, resuscitation, trauma, and minor surgical and orthopedic procedures and who have access to a limited number of blood tests, intravenous and oral medications, and extremely limited and inconsistent medical imaging. They admit to a hospital with separate medical and surgical wards managed by nurses and physicians. Further details about the setting, resource availability, and outcomes of the training program are comprehensively described elsewhere. 19 , 20 , 21 Global Emergency Care has maintained a prospectively collected quality assurance database of all emergency unit visits since 2009. The database captures data including demographics, CCs, vital signs, laboratory and radiology results, treatments given, procedures, diagnoses, disposition, and patient outcomes. Data were collected by trained research assistants at the time patients presented for care. Follow‐up data were collected in person for admitted patients and by structured telephone interview within 3 days for patients who were discharged from the emergency unit. If a patient could not be reached on the initial attempt, calls were made daily until 10 days had elapsed since the initial visit. CCs were entered as unstructured free text with the ability to enter multiple CCs for each patient visit. The study population included all adults 18 years of age and older who presented to the Karoli Lwanga Hospital emergency unit between November 2009 and December 2018. All patients who were “dead on arrival” were excluded. This exclusion applied only to patients who arrived at triage clearly dead; with no vital signs; who received no treatments; and who, by definition, were unable to provide any CC. Patients who arrived at triage with signs of life but died while in the emergency unit were included in analysis. All patients with “pregnancy‐related” complaints were also excluded. This exclusion applied only to patients who presented for emergencies associated with a known pregnancy. Hospital policy dictated that patients with pregnancy‐related emergencies were supposed to be evaluated and treated in a maternity ward housed in a separate building from the main emergency department. Since those patients were explicitly not intended to be seen at the study site they were excluded from analysis. Patients for whom a pregnancy was discovered during evaluation (e.g., for vaginal bleeding or abdominal pain) were included in analysis. Patient visit data were abstracted from the quality assurance database and randomly split into two data sets: one that included 75% of cases for derivation and one with 25% of cases for validation. The derivation data set was subsequently split into two groups: patients with a single CC and patients with multiple CCs. Each shortlist CC from Rice et al. 13 was analyzed independently and ranked by mortality risk using a recursive partitioning algorithm. This was performed in both the single and the multiple CC groups. Crude mortality was calculated for each remaining CC and the highest mortality CC was recorded. Every patient record that included that highest‐risk complaint was then removed from the database for the next round of analysis. This analysis also expressly omitted interaction terms. The above procedure was repeated until all complaints with associated mortality had been ranked as a list. The remaining CCs were not associated with any mortality and were then preserved as a separate list. Finally, any CC that occurred in fewer than 0.5% of total patient visits was removed and not included in subsequent analysis. Both the “associated mortality” and the “no mortality” lists were subject to this exclusion criteria. Since some CCs occurred relatively infrequently in the total data set, their distribution and inclusion for analysis was susceptible to the randomization process above. This variability was addressed by using bootstrap aggregation. The above randomization and risk ranking algorithm were run in 10,000 bootstrap samples with results averaged to generate risk lists for single and multiple CCs based on average risk categories. A final list was formed by combining the two lists, placing each CC into the highest‐risk category in which it appeared during at least 10% of the iterations of the algorithm. In this way, the final list categorized CCs as high (greater than twice baseline mortality), medium (half to twice baseline mortality), low (less than half baseline mortality), and zero risk (no associated mortality). Sensitivity analysis was performed using alternative thresholds for high‐risk (both one‐and‐a‐half and triple baseline mortality) with the aim of identifying at least 10% of the population as high‐risk. Univariate analysis was conducted for all variables available at triage using Student’s t‐test for continuous variables and the chi‐square test for categorical variables. Physiologic variables were defined as follows: for blood pressure, hypotension was defined as systolic blood pressure ≤ 90 and hypertension as systolic blood pressure ≥180; for heart rate, bradycardia was defined as heart rate < 60 beats/min and tachycardia as heart rate ≥ 120 beats/min; for temperature, hypothermia was defined as ≤ 35°C and febrile as ≥ 37.5°C; altered mental status was defined as AVPU score of “verbal,” “pain,” or “unresponsive”; and hypoxia was defined as oxygen saturation on pulse oximeter (SpO2) < 92%. Missing values for physiologic variables were coded as “normal.” Patients missing age were excluded from analysis. Sensitivity analysis of patients discharged from the emergency unit showed that those who answered follow‐up phone calls and those who did not (including those who did not answer the phone, provided a wrong number or had no phone) were very similar in terms of demographics, CCs, and indicators of disease severity. These similarities supported an assumption that missingness was completely at random. Given the low mortality rate for patients discharged from the emergency unit, all missing mortality outcomes were coded as alive for analysis and multiple imputation was not used. Multivariable logistic regression, controlling for demographic and clinical factors, was performed to test the association of an ordered categorical variable of CC “riskiness” (high, medium, and low) with 3‐day mortality. This multivariable logistic regression model was applied to two data sets as a sensitivity analysis to verify the assumptions about the handling of missing outcome data: one assumed patients with missing outcomes to be alive and a second excluded all patients missing confirmed outcomes. All univariate variables were added in a stepwise manner, with CC riskiness added as the final variable. A likelihood ratio test was performed to test the significance for including CC in the model. The coefficients from the logistic regression model built in the derivation data set were then applied to the validation data set. The sensitivity and specificity obtained from those coefficients were used for a net benefit calculation. The area under the receiver operating characteristics curve (AUROC), Hosmer‐Lemeshow goodness of fit, Brier score, and mean bias were all calculated for this data set. All analyses were performed using Stata Statistical Software version 15.1. Approval for the study was sought by local hospital administration in conjunction with U.S.‐based researchers and was granted by the institutional review board at the Makerere School of Public Health (Kampala, Uganda), the Uganda National Council of Science and Technology (Kampala, Uganda), and the University of Massachusetts (Amherst, MA). The ECP training program was originally developed in response to several years of clinical emergency medicine experience in Uganda. The positive response of patients, staff, and administrators at Karoli Lwanga Hospital to the training programs and their interest in improving patient care led to ongoing research and program evaluation. Patients and the public were not involved in the design of the study; however, outcome measures are explicitly patient oriented. Results will be disseminated through open‐access publication.
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