Browsing by Author "Zimmerman, Armand"
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Item Open Access An analysis of emergency care delays experienced by traumatic brain injury patients presenting to a regional referral hospital in a low-income country.(PloS one, 2020-01) Zimmerman, Armand; Fox, Samara; Griffin, Randi; Nelp, Taylor; Thomaz, Erika Bárbara Abreu Fonseca; Mvungi, Mark; Mmbaga, Blandina T; Sakita, Francis; Gerardo, Charles J; Vissoci, Joao Ricardo Nickenig; Staton, Catherine A; Staton, Catherine ABackground
Trauma is a leading cause of death and disability worldwide. In low- and middle-income countries (LMICs), trauma patients have a higher risk of experiencing delays to care due to limited hospital resources and difficulties in reaching a health facility. Reducing delays to care is an effective method for improving trauma outcomes. However, few studies have investigated the variety of care delays experienced by trauma patients in LMICs. The objective of this study was to describe the prevalence of pre- and in-hospital delays to care, and their association with poor outcomes among trauma patients in a low-income setting.Methods
We used a prospective traumatic brain injury (TBI) registry from Kilimanjaro Christian Medical Center in Moshi, Tanzania to model nine unique delays to care. Multiple regression was used to identify delays significantly associated with poor in-hospital outcomes.Results
Our analysis included 3209 TBI patients. The most common delay from injury occurrence to hospital arrival was 1.1 to 4.0 hours (31.9%). Most patients were evaluated by a physician within 15.0 minutes of arrival (69.2%). Nearly all severely injured patients needed and did not receive a brain computed tomography scan (95.0%). A majority of severely injured patients needed and did not receive oxygen (80.8%). Predictors of a poor outcome included delays to lab tests, fluids, oxygen, and non-TBI surgery.Conclusions
Time to care data is informative, easy to collect, and available in any setting. Our time to care data revealed significant constraints to non-personnel related hospital resources. Severely injured patients with the greatest need for care lacked access to medical imaging, oxygen, and surgery. Insights from our study and future studies will help optimize resource allocation in low-income hospitals thereby reducing delays to care and improving trauma outcomes in LMICs.Item Open Access Injury characteristics and their association with clinical complications among emergency care patients in Tanzania.(African journal of emergency medicine : Revue africaine de la medecine d'urgence, 2022-12) Zimmerman, Armand; Barcenas, Loren K; Pesambili, Msafiri; Sakita, Francis; Mallya, Simon; Vissoci, Joao Ricardo Nickenig; Park, Lawrence; Mmbaga, Blandina T; Bettger, Janet Prvu; Staton, Catherine ABackground
Over 5 million people annually die from injuries and millions more sustain non-fatal injuries requiring medical care. Ninety percent of injury deaths occur in low- and middle-income countries (LMICs). This study describes the characteristics, predictors and outcomes of adult acute injury patients presenting to a tertiary referral hospital in a low-income country in sub-Saharan Africa.Methods
This secondary analysis uses an adult acute injury registry from Kilimanjaro Christian Medical Centre (KCMC) in Moshi, Tanzania. We describe this patient sample in terms of socio-demographics, clinical indicators, injury patterns, treatments, and outcomes at hospital discharge. Outcomes include mortality, length of hospital stay, and functional independence. Associations between patient characteristics and patient outcomes are quantified using Cox proportional hazards models, negative binomial regression, and multivariable logistic regression.Results
Of all injury patients (n=1365), 39.0% were aged 30 to 49 years and 81.5% were men. Most patients had at least a primary school education (89.6%) and were employed (89.3%). A majority of injuries were road traffic (63.2%), fall (16.8%), or assault (14.0%) related. Self-reported comorbidities included hypertension (5.8%), HIV (3.1%), and diabetes (2.3%). Performed surgeries were classified as orthopedic (32.3%), general (4.1%), neurological (3.7%), or other (59.8%). Most patients reached the hospital at least four hours after injury occurred (53.9%). Mortality was 5.3%, median length of hospital stay was 6.1 days (IQR: 3.1, 15.0), self-care dependence was 54.2%, and locomotion dependence was 41.5%.Conclusions
Our study sample included primarily young men suffering road traffic crashes with delayed hospital presentations and prolonged hospital stays. Being older, male, and requiring non-orthopedic surgeries or having HIV portends a worse prognosis. Prevention and treatment focused interventions to reduce the burden of injury mortality and morbidity at KCMC are needed to lower injury rates and improve injury outcomes.Item Open Access The Impact of Care Delays on Traumatic Brain Injury Outcomes in Tanzania: Descriptive Analytics and Machine Learning(2020) Zimmerman, ArmandBackground: Traumatic brain injury (TBI) is the leading cause of trauma related death and disability worldwide. Poor TBI outcomes disproportionately affect low- and middle-income countries (LMICs). Treatment delays may contribute to poor TBI outcomes in LMIC emergency departments (EDs). A prognostic model is a low-cost, user-friendly solution to optimizing patient care in low-resource hospitals. The aim of this study was twofold: (1) assess associations between care delays and TBI patient outcomes, and (2) build a prognostic model that uses care delays to predict TBI patient outcomes.
Methods: This study uses a 3209 de-identified TBI patient registry from Kilimanjaro Christian Medical Center (KCMC) ED in Moshi, Tanzania. We created nine variables representing delays to care and assessed their association with poor outcomes (Glasgow Coma Score (GCS) < 4) using logistic regression. We then constructed a prognostic model that predicts TBI patient outcomes dichotomized as good (GCS ≥ 4) and poor (GCS < 4). Predictors included socio-demographics, injury characteristics, vital signs, and care delays.
Results: Associations between care delays and TBI outcomes were not significant. However, care delays were top predictors of a poor outcome in our prognostic model. Our model achieved an area under the receiver operating curve of 89.5% (95% CI: 88.8, 90.3).
Conclusion: Our TBI prognostic model demonstrates the predictive value of care delay information. Time to care data is easy to collect. A prognostic model that uses time to care data allows healthcare providers to update patient prognosis as patients progress through their hospital stay.