Presenting symptoms, diagnoses and in-hospital mortality in a low resource hospital environment.

BACKGROUND: The relationship between symptoms, signs and discharge diagnoses with in-hospital mortality is poorly defined in low-resource settings. AIM: To explore the prevalence of presenting symptoms, signs and discharge diagnoses of medical patients admitted to a low-resource sub-Saharan hospital and their association with in-hospital mortality. METHODS: In this prospective observational study, the presenting symptoms and signs of all medical patients admitted to a low-resource hospital in sub-Saharan Africa, their discharge diagnoses and in-hospital mortality were recorded. RESULTS: Pain, gastro-intestinal complaints and feverishness were the commonest presenting symptoms, but none were associated with in-hospital mortality. Only headache was associated with decreased mortality, and no symptom was associated with increased in-hospital mortality. Malaria was the commonest diagnosis. Vital signs, mobility, mental alertness and mid-upper arm circumference (MUAC) had the strongest association with in-hospital mortality. Tuberculosis and cancer were the only diagnoses associated with in-hospital mortality after adjustment for these signs. CONCLUSION: Vital signs, mobility, mental alertness and MUAC had the strongest association with in-hospital mortality. All these signs can easily be determined at the bedside at no additional cost and, after adjustment for them by logistic regression the only diagnoses that remain statistically associated with in-hospital mortality are tuberculosis and cancer.

Respiratory rates observed over 15 seconds compared with rates measured using the RRate app. Practice-based evidence from an observational study of acutely ill adult medical patients during their hospital admission.

BACKGROUND: counting respiratory rate over 60 seconds can be impractical in a busy clinical setting. METHODS: 870 respiratory rates of 272 acutely ill medical patients estimated from observations over 15 seconds and those calculated by a computer algorithm were compared. RESULTS: The bias of 15 seconds of observations was 1.85 breaths per minute and 0.11 breaths per minute for the algorithm derived rate, which took 16.2 SD 8.1 seconds. The algorithm assigned 88% of respiratory rates their correct National Early Warning Score points, compared with 80% for rates from 15 seconds of observation. CONCLUSION: The respiratory rates of acutely ill patients are measured nearly as quickly and more reliably by a computer algorithm than by observations over 15 seconds.