The optimal duration of continuous respiratory rate monitoring to predict in-hospital mortality within seven days of admission - A pilot study in a low resource setting.

Background: Currently there are no established benefits from the continuous monitoring of vital signs, and the optimal time period for respiratory rate measurement is unknown. Setting: Low resource Ugandan hospital. Methods: Prospective observational study. Respiratory rates of acutely ill patients were continuously measured by a piezoelectric device for up to seven hours after admission to hospital. Results: 22 (5.5%) out of 402 patients died within 7 days of hospital admission. The highest c-statistic of discrimination for 7-day mortality (0.737 SE 0.078) was obtained after four hours of continuously measured respiratory rates transformed into a weighted respiratory rate score (wRRS). After seven hours of measurement the c-statistic of the wRRS fell to 0.535 SE 0.078. 20% the patients who died within seven days did not have an elevated National Early Warning Score (NEWS) on admission but were identified by the 4-hour wRRS. None of the 88 patients whose average respiratory rate remained between 12 and 20 bpm throughout four hours of observation died within 7 days of admission. A simple predictive model that included the four-hour wRRS, Shock Index and altered mental status had a c-statistic for 7-day in-hospital mortality of 0.843 SE. 0.057. Conclusion: Four hours of continuously measured respiratory rates was the observation period that best predicted 7-day in-hospital mortality. After four hours the discrimination of a weighted respiratory rate score deteriorated rapidly.

Implementation of a fast triage score for patients arriving to a low resource hospital in Uganda.

Background: The Kitovu Fast Triage (KFT) score predicts imminent mortality from mental status, gait and either respiratory rate or oxygen status. As some non-life-threatening conditions require immediate attention, the South African Triage System (SATS) assigns arbitrary rankings of urgency for specific patient presentations. Aim: Establish the feasibility of determining and then comparing the KFT score and explicitly defined SATS urgency rankings. Methods: A computerized proforma used standardized methods of assessing and measuring mental status and gait, and respiratory rate and collected explicitly defined clinical presentations and SATS urgency rankings on 4,842 patients at the time of their arrival to the hospital. Results: 75 % of patients were awake and able to count the months backwards from December to September. Respiratory rates measured by a computer application had no clustering of values or digit preference; however, oximetry failed in 14 % of patients, making the score based on respiratory rate the most practical in our setting. Determining the SATS acuity ranking and both KFT scores usually took <90 s; the commonest complaints were pain, dyspnoea, and fever, which often occurred together; overall 3574 (73.8 %) patients had at least one of these symptoms as did 96.4 % of those with the highest KFT score based on respiratory rate. 12 % of patients with the lowest KFT score based on respiratory rate had one or more very urgent SATS rankings, 52 % of whom had non-severe chest pain. Only 5.7 % of patients complaining of fever had a temperature >38 °C. Conclusion: Whilst the KFT score based on respiratory rate could be rapidly determined in all patients, it identified some patients as low acuity who had very urgent SATS rankings. However, most of these patients had non-severe chest pain, which may not be a very urgent presentation in our setting as ischaemic heart disease remains uncommon in sub-Saharan Africa.

Two simple replacements for the Triage Early Warning Score to facilitate the South African Triage Scale in low resource settings.

BACKGROUND: The South African Triage Scale (SATS) requires the calculation of the Triage Early Warning Score (TEWS), which takes time and is prone to error. AIM: to derive and validate triage scores from a clinical database collected in a low-resource hospital in sub-Saharan Africa over four years and compare them with the ability of TEWS to triage patients. METHODS: A retrospective observational study carried out in Kitovu Hospital, Masaka, Uganda as part of an ongoing quality improvement project. Data collected on 4482 patients was divided into two equal cohorts: one for the derivation of scores by logistic regression and the other for their validation. RESULTS: Two scores identified the largest number of patients with the lowest in-hospital mortality. A score based on oxygen saturation, mental status and mobility had a c statistic for discrimination of 0.83 (95% CI 0.079-0.87) in the derivation, and 0.81 (95% CI 0.77-0.86) in the validation cohort. Another score based on respiratory rate, mental status and mobility had a c statistic of 0.82 (95% CI 0.078-0.87) in the derivation, and 0.81 (95% CI 0.77-0.86) in the validation cohort. The oxygen saturation-based score of zero points identified 51% of patients in the derivation cohort who had in-hospital mortality rate of 0.5%, and 49% of patients in the validation cohort who had in-hospital mortality of 1.0%. A respiratory rate-based score of zero points identified 45% in the derivation cohort who had in-hospital mortality rate of 0.5%, and 44% of patients in the validation cohort who had in-hospital mortality of 0.8%. Both scores had comparable performance to TEWS. CONCLUSION: Two easy to calculate scores have comparable performance to TEWS and, therefore, could replace it to facilitate the adoption of SATS in low-resource settings.

Do different patient populations need different early warning scores? The performance of nine different early warning scores used on acutely ill patients admitted to a low-resource hospital in sub-Saharan Africa.

BACKGROUND: Early warning scores (EWS) generated in a developed healthcare setting may not perform as well in low-resource settings in sub-Saharan Africa. METHOD: The performance of EWS used in developed world was compared with those generated in low-resource settings in sub-Saharan Africa. RESULTS: When tested on 1,266 acutely ill patients consecutively admitted to a low-resource Ugandan hospital there was no statistical difference in the performance of any of the EWS tested. The performance of all the scores appeared to be improved by the addition of mobility assessment. Although statistically insignificant, the National Early Warning Score with extra points added for impaired mobility had the highest discrimination and sensitivity. CONCLUSION: There were only marginal and no statistical differences in the performance of EWS generated in low- and high-resource healthcare settings in a cohort of unselected acutely ill medical patients admitted to a low-resource hospital in sub-Saharan Africa.

Oxygen use in low-resource settings: An intervention still triggered by intuition.

BACKGROUND: Although hypoxic patients attending low-resource hospitals have a high mortality, many are not given supplemental oxygen. If oximetry is not available, then the decision to provide oxygen must be based on other factors. METHODS: The variables associated with the decision to provide supplemental oxygen made by an emergency department staff, without access to oximetry, in a low resource Ugandan hospital were determined from data collected within 16 h of admission to the hospital's medical and surgical wards. RESULTS: Of 2,599 patients, 731 (28.1%) had an oxygen saturation <95%, and 164 (6.3%) an oxygen saturation <90%. Of the 731 patients with oxygen levels below 95% 573 (83%) were not given oxygen; oxygen was only given to 63 (38%) of the 164 patients with oxygen saturation <90%. On average, a patient given oxygen was more likely to die than one not given oxygen, regardless of their oxygen saturation (odds ratio 13.4, 95%CI 9.1-19.6). After multivariate analysis weakness, dyspnoea, low oxygen saturation, high heart rate, high respiratory rate, low temperature, alertness, gait, and a medical illness were all significantly associated with the use of supplemental oxygen and in-hospital mortality. Logistic regression modelling of these variables had comparable discrimination for both oxygen use (c statistic 0.88 SE 0.02) and in-hospital mortality (c statistic 0.84 SE 0.02). CONCLUSION: The intuitive decision to provide oxygen was strongly associated with in-hospital mortality, suggesting that oxygen was given to those considered the sickest patients. In the future, oximetry may guide oxygen therapy more efficiently.