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

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.

Indicators of patient deterioration in poorly resourced private hospitals: Which vital sign to watch? A retrospective case-control study

BACKGROUND

Patient vital signs are a measure of wellness if monitored regularly and accurately. Staff shortages in poorly resourced regional hospitals often result in inadequate patient monitoring, putting patients at risk of undetected deterioration.

OBJECTIVE

This study aims to explore the pattern and completeness of vital sign monitoring and the contribution of each vital sign in predicting clinical deterioration events in resource-poor regional/rural hospitals.

METHOD

Using a retrospective case-control study design, we compared 24 h of vital sign data from deteriorating and nondeteriorating patients from two poorly-resourced regional hospitals. Descriptive statistics, t-tests, and analysis of variance are used to compare patient-monitoring frequency and completeness. The contribution of each vital sign in predicting patient deterioration was determined using the Area Under the Receiver Operator Characteristic curve and binary logistical regression analysis.

RESULTS

Deteriorating patients were monitored more frequently (9.58 [7.02] times) in the 24-h period than nondeteriorating patients (4.93 [2.66] times). However, the completeness of vital sign documentation was higher in nondeteriorating (85.2%) than in deteriorating patients (57.7%). Body temperature was the most frequently omitted vital sign. Patient deterioration was positively linked to the frequency of abnormal vital signs and the number of abnormal vital signs per set (Area Under the Receiver Operator Characteristic curve: 0.872 and 0.867, respectively). No single vital sign strongly predicts patient outcomes. However, a supplementary oxygen value of >3 L/min and a heart rate of >139 beats/min were the best predictors of patient deterioration.

CONCLUSION

Given the poor resourcing and often geographical remoteness of small regional hospitals, it is prudent that the nursing staff are made aware of the vital signs that best indicate deterioration for the cohort of patients in their care. Tachycardic patients on supplementary oxygen are at high risk of deterioration.

Performance of the Universal Vital Assessment (UVA) mortality risk score in hospitalized adults with infection in Rwanda: A retrospective external validation study

Background

We previously derived a Universal Vital Assessment (UVA) score to better risk-stratify hospitalized patients in sub-Saharan Africa, including those with infection. Here, we aimed to externally validate the performance of the UVA score using previously collected data from patients hospitalized with acute infection in Rwanda.

Methods

We performed a secondary analysis of data collected from adults ≥18 years with acute infection admitted to Gitwe District Hospital in Rwanda from 2016 until 2017. We calculated the UVA score from the time of admission and at 72 hours after admission. We also calculated quick sepsis-related organ failure assessment (qSOFA) and modified early warning scores (MEWS). We calculated amalgamated qSOFA scores by inserting UVA cut-offs into the qSOFA score, and modified UVA scores by removing the HIV criterion. The performance of each score determined by the area under the receiver operator characteristic curve (AUC) was the primary outcome measure.

Results

We included 573 hospitalized adult patients with acute infection of whom 40 (7%) died in-hospital. The admission AUCs (95% confidence interval [CI]) for the prediction of mortality by the scores were: UVA, 0.77 (0.68–0.85); modified UVA, 0.77 (0.68–0.85); qSOFA, 0.66 (0.56–0.75), amalgamated qSOFA, 0.71 (0.61–0.80); and MEWS, 0.74 (0.64, 0.83). The positive predictive values (95% CI) of the scores at commonly used cut-offs were: UVA >4, 0.35 (0.15–0.59); modified UVA >4, 0.35 (0.15–0.59); qSOFA >1, 0.14 (0.07–0.24); amalgamated qSOFA >1, 0.44 (0.20–0.70); and MEWS >5, 0.14 (0.08–0.22). The 72 hour (N = 236) AUC (95% CI) for the prediction of mortality by UVA was 0.59 (0.43–0.74). The Chi-Square test for linear trend did not identify an association between mortality and delta UVA score at 72 hours (p = 0.82).

Conclusions

The admission UVA score and amalgamated qSOFA score had good predictive ability for mortality in adult patients admitted to hospital with acute infection in Rwanda. The UVA score could be used to assist with triage decisions and clinical interventions, for baseline risk stratification in clinical studies, and in a clinical definition of sepsis in 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.

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.