Predicting Mortality in Low-Income Country ICUs: The Rwanda Mortality Probability Model (R-MPM)

Epidemiology and Outcomes of Critical Illness and Novel Predictors of Mortality in an Ethiopian Medical Intensive Care Unit

Low- and middle-income countries (LMICs) bear most of the global burden of critical illness. Managing this burden requires improved understanding of epidemiology and outcomes in LMIC intensive care units (ICUs), including LMIC-specific mortality prediction scores. This study was a retrospective observational study at Tikur Anbessa Specialized Hospital in Addis Ababa, Ethiopia, examining all consecutive medical ICU admissions from June 2014 to April 2015. The primary outcome was ICU mortality; secondary outcomes were prolonged ICU stay and prolonged mechanical ventilation. ICU mortality prediction models were created using multivariable logistic regression and compared with the Mortality Probability Model-II (MPM-II). Associations with secondary outcomes were examined with multivariable logistic regression. There were 198 admissions during the study period; mortality was 35%. Age, shock on admission, mechanical ventilation, human immunodeficiency virus, and Glasgow Coma Scale ≤8 were associated with ICU mortality. The receiver operating characteristic curve for this 5-factor model had an AUC of 0.8205 versus 0.7468 for MPM-II, favoring the simplified new model. Mechanical ventilation and lack of shock were associated with prolonged ICU stays. Mortality in an LMIC medical ICU was high. This study examines an LMIC medical ICU population, showing a simplified prediction model may predict mortality as well as complex models.

Risk Prediction Models for Hospital Mortality in General Medical Patients: A Systematic Review

Objective

To systematically review contemporary prediction models for hospital mortality developed or validated in general medical patients.

Methods

We screened articles in five databases, from January 1, 2010, through April 7, 2022, and the bibliography of articles selected for final inclusion. We assessed the quality for risk of bias and applicability using the Prediction Model Risk of Bias Assessment Tool (PROBAST) and extracted data using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist. Two investigators independently screened each article, assessed quality, and extracted data.

Results

From 20,424 unique articles, we identified 15 models in 8 studies across 10 countries. The studies included 280,793 general medical patients and 19,923 hospital deaths. Models included 7 early warning scores, 2 comorbidities indices, and 6 combination models. Ten models were studied in all general medical patients (general models) and 7 in general medical patients with infection (infection models). Of the 15 models, 13 were developed using logistic or Poisson regression and 2 using machine learning methods. Also, 4 of 15 models reported on handling of missing values. None of the infection models had high discrimination, whereas 4 of 10 general models had high discrimination (area under curve >0.8). Only 1 model appropriately assessed calibration. All models had high risk of bias; 4 of 10 general models and 5 of 7 infection models had low concern for applicability for general medical patients.

Conclusion

Mortality prediction models for general medical patients were sparse and differed in quality, applicability, and discrimination. These models require hospital-level validation and/or recalibration in general medical patients to guide mortality reduction interventions.

Association of clinical prediction scores with hospital mortality in an adult medical and surgical intensive care unit in Kenya

Importance

Mortality prediction among critically ill patients in resource limited settings is difficult. Identifying the best mortality prediction tool is important for counseling patients and families, benchmarking quality improvement efforts, and defining severity of illness for clinical research studies.

Objective

Compare predictive capacity of the Modified Early Warning Score (MEWS), Universal Vital Assessment (UVA), Tropical Intensive Care Score (TropICS), Rwanda Mortality Probability Model (R-MPM), and quick Sequential Organ Failure Assessment (qSOFA) for hospital mortality among adults admitted to a medical-surgical intensive care unit (ICU) in rural Kenya. We performed a pre-planned subgroup analysis among ICU patients with suspected infection.

Design, setting, and participants

Prospective single-center cohort study at a tertiary care, academic hospital in Kenya. All adults 18 years and older admitted to the ICU January 2018–June 2019 were included.

Main outcomes and measures

The primary outcome was association of clinical prediction tool score with hospital mortality, as defined by area under the receiver operating characteristic curve (AUROC). Demographic, physiologic, laboratory, therapeutic, and mortality data were collected. 338 patients were included, none were excluded. Median age was 42 years (IQR 33–62) and 61% (n = 207) were male. Fifty-nine percent (n = 199) required mechanical ventilation and 35% (n = 118) received vasopressors upon ICU admission. Overall hospital mortality was 31% (n = 104). 323 patients had all component variables recorded for R-MPM, 261 for MEWS, and 253 for UVA. The AUROC was highest for MEWS (0.76), followed by R-MPM (0.75), qSOFA (0.70), and UVA (0.69) (p < 0.001). Predictive capacity was similar among patients with suspected infection.

Conclusion and relevance

All tools had acceptable predictive capacity for hospital mortality, with variable observed availability of the component data. R-MPM and MEWS had high rates of variable availability as well as good AUROC, suggesting these tools may prove useful in low resource ICUs.

Pattern of disease and determinants of mortality among ICU patients on mechanical ventilator in Sub-Saharan Africa: a multilevel analysis

BACKGROUND

The global mortality rate of patients with MV is very high, despite a significant variation worldwide. Previous studies conducted in Sub-Saharan Africa among ICU patients focused on the pattern of admission and the incidence of mortality. However, the body of evidence on the clinical outcomes among patients with MV is still uncertain.

OBJECTIVE

The objective of this study was to investigate the pattern of disease and determinants of mortality among patients receiving mechanical ventilation in Southern Ethiopia.

METHODS

Six hundred and thirty patients on mechanical ventilation were followed for 28 days, and multilevel analysis was used to account for the clustering effect of ICU care in the region.

RESULTS

The incidence of 28-day mortality among patients with MV was 49% (95% CI: 36-58). The multilevel multivariate analysis revealed that being diabetic, having GSC < 8, and night time admission (AOR = 7.4; 95% CI: 2.96-18.38), (AOR = 5.9; (5% CI: 3.23, 10.69), and (AOR = 2.5; 95% CI: 1.24, 5.05) were predictors.

CONCLUSION

The higher 28-day mortality among ICU patients on mechanical ventilation in our study might be attributed to factors such as delayed patient presentation, lack of resources, insufficient healthcare infrastructure, lack of trained staff, and financial constraints.

TRIAL REGISTRATION

The protocol was registered retrospectively on ( NCT05303831 ).

Enhanced bedside mortality prediction combining point-of-care lactate and the quick Sequential Organ Failure Assessment (qSOFA) score in patients hospitalised with suspected infection in southeast Asia: a cohort study

Background

Simple, bedside prediction of infection-related mortality in low-resource settings is crucial for triage and resource-utilisation decisions. We aimed to evaluate mortality prediction by combining point-of-care venous lactate with the quick Sequential Organ Failure Assessment (qSOFA) score in adult patients admitted to hospital with suspected infection in southeast Asia.

Methods

We performed a cohort study by prospectively enrolling patients aged 18 years or older who had been admitted to hospital within the previous 24 h for suspected infection (with at least three documented systemic manifestations of infection according to the 2012 Surviving Sepsis Campaign) at Sunpasitthiprasong Hospital in Ubon Ratchathani, Thailand (derivation cohort). Venous lactate concentration was determined by a point-of-care device and multiple scores were developed. We then evaluated candidate 28-day mortality prediction models combining qSOFA and the lactate scores. A final model was compared with the qSOFA score, a lactate score, and a modified Sequential Organ Failure Assessment (SOFA) score for mortality discrimination using the area under the receiver operating characteristic curve (AUROC). Mortality discrimination of the qSOFA-lactate score was then verified in an external, prospectively enrolled, multinational cohort in southeast Asia.

Findings

Between March 1, 2013, and Jan 26, 2017, 5001 patients were enrolled in the derivation cohort; 4980 had point-of-care lactate data available and were eligible for analysis, and 816 died within 28 days of enrolment. The discrimination for 28-day mortality prediction of a qSOFA-lactate score combining the qSOFA score and a lactate score was superior to that of the qSOFA score alone (AUROC 0·78 [95% CI 0·76–0·80] vs 0·68 [0·67–0·70]; p<0·0001) and similar to a modified SOFA score (0·77 [0·75–0·78]; p=0·088). A lactate score alone had superior discrimination compared with the qSOFA score (AUROC 0·76 [95% CI 0.74–0.78]; p<0·0001). 815 patients were enrolled in the external validation cohort and 792 had point-of-care lactate data and were included in the analysis; the qSOFA-lactate score (AUROC 0·77 [95% CI 0·73–0·82]) showed significantly improved 28-day mortality discrimination compared with the qSOFA score alone (0·69 [0·63–0·74]; p<0·0001).

Interpretation

In southeast Asia, rapid, bedside assessments based on point-of-care lactate concentration combined with the qSOFA score can identify patients at risk of sepsis-related mortality with greater accuracy than the qSOFA score alone, and with similar accuracy to a modified SOFA score.

Funding

National Institutes of Health, Wellcome Trust.

Improving sepsis care in Africa: an opportunity for change?

Sepsis is common and represents a major public health burden with significant associated morbidity and mortality. However, despite substantial advances in sepsis recognition and management in well-resourced health systems, there remains a distinct lack of research into sepsis in Africa. The lack of evidence affects all levels of healthcare delivery from individual patient management to strategic planning at health-system level. This is particular pertinent as African countries experience some of the highest global burden of sepsis. The 2017 World Health Assembly resolution on sepsis and the creation of the Africa Sepsis Alliance provided an opportunity for change. However, progress so far has been frustratingly slow. The recurrent Ebola virus disease outbreaks and the COVID-19 pandemic on the African continent further reinforce the need for urgent healthcare system strengthening. We recommend that African countries develop national action plans for sepsis which should address the needs of all critically ill patients.

Admission pattern, treatment outcomes, and associated factors for children admitted to pediatric intensive care unit of Tikur Anbessa specialized hospital, 2021: a retrospective cross-sectional study

Background

Assessement of the pattern of admission and treatment outcomes of critically ill pediatrics admitted to pediatric intensive care units (PICU) in developing countries is crucial. In these countries with resource limitations, it may help to identify priorities for resource mobilization that may improve patient service quality. The PICU mortality rate varies globally, depending on the facilities of the intensive care unit, availability of experties, and admission patterns. This study assessed the admission pattern, treatment outcomes, and associated factors for children admitted to the PICU.

Methods

A retrospective cross-sectional study was implemented on 406 randomly selected pediatrics patients admitted to the PICU of Tikur Anbessa Specialized Hospital from 1-Oct-2018 to 30-Sept-2020. The data were collected with a pretested questionnaire. A normality curve was used to check for data the distribution. Both bivariable and multivariable analyses were used to see association of variables. A variable with a p-value of < 0.2 in the bivariable model was a candidate for multivariate analysis. The strength of association was shown by an adjusted odds ratio (AOR) with a 95% Confidence interval (CI), and a p-value of < 0.05 was considered statistically significant. Frequency, percentage,and tables were used to present the data.

Results

A total of 361 (89% response rate) patient charts were studied, 197 (54.6%) were male, and 164(45.4%) were female. The most common pattern for admission was a septic shock (27.14%), whereas the least common pattern was Asthma 9(2.50%). The mortality rate at the pediatric intensive care unit was 43.8%. Moreover, mechanical ventilation need (AOR = 11.2, 95%CI (4.3–28.9), P < 0.001), need for inotropic agents (AOR = 10.7, 95%CI (4.1–27.8), P < 0.001), comorbidity (AOR =8.4, 95%CI (3.5–20.5), P < 0.001), length of PICU stay from 2 to 7 days (AOR = 7.3, 95%CI (1.7–30.6), P = 0.007) and severe GCS (< 8) (AOR = 10.5, 95%CI (3.8–29.1), P < 0.001) were independent clinical outcome predictors (mortality).

Conclusion

The mortality rate at the PICU was 43.8%. Septic shock, and meningitis were the common cause of death and the largest death has happened in less than 7 days of admission.

Performance in mortality prediction of SAPS 3 And MPM-III scores among adult patients admitted to the ICU of a private tertiary referral hospital in Tanzania: a retrospective cohort study

Background

Illness predictive scoring systems are significant and meaningful adjuncts of patient management in the Intensive Care Unit (ICU). They assist in predicting patient outcomes, improve clinical decision making and provide insight into the effectiveness of care and management of patients while optimizing the use of hospital resources. We evaluated mortality predictive performance of Simplified Acute Physiology Score (SAPS 3) and Mortality Probability Models (MPM₀-III) and compared their performance in predicting outcome as well as identifying disease pattern and factors associated with increased mortality.

Methods

This was a retrospective cohort study of adult patients admitted to the ICU of the Aga Khan Hospital, Dar- es- Salaam, Tanzania between August 2018 and April 2020. Demographics, clinical characteristics, outcomes, source of admission, primary admission category, length of stay and the support provided with the worst physiological data within the first hour of ICU admission were extracted. SAPS 3 and MPM₀-III scores were calculated using an online web-based calculator. The performance of each model was assessed by discrimination and calibration. Discrimination between survivors and non-survivors was assessed by the area under the receiver operator characteristic curve (ROC) and calibration was estimated using the Hosmer-Lemeshow goodness-of-fit test.

Results

A total of 331 patients were enrolled in the study with a median age of 58 years (IQR 43-71), most of whom were male (n = 208, 62.8%), of African origin (n = 178, 53.8%) and admitted from the emergency department (n = 306, 92.4%). In- hospital mortality of critically ill patients was 16.1%. Discrimination was very good for all models, the area under the receiver-operating characteristic (ROC) curve for SAPS 3 and MPM₀-III was 0.89 (95% CI [0.844-0.935]) and 0.90 (95% CI [0.864-0.944]) respectively. Calibration as calculated by Hosmer-Lemeshow goodness-of-fit test showed good calibration for SAPS 3 and MPM₀-III with Chi- square values of 4.61 and 5.08 respectively and P-Value > 0.05.

Conclusion

Both SAPS 3 and MPM₀-III performed well in predicting mortality and outcome in our cohort of patients admitted to the intensive care unit of a private tertiary hospital. The in-hospital mortality of critically ill patients was lower compared to studies done in other intensive care units in tertiary referral hospitals within Tanzania.

What intensive care registries can teach us about outcomes

PURPOSE OF REVIEW

Critical care registries are synonymous with measurement of outcomes following critical illness. Their ability to provide longitudinal data to enable benchmarking of outcomes for comparison within units over time, and between units, both regionally and nationally is a key part of the evaluation of quality of care and ICU performance as well as a better understanding of case-mix. This review aims to summarize literature on outcome measures currently being reported in registries internationally, describe the current strengths and challenges with interpreting existing outcomes and highlight areas where registries may help improve implementation and interpretation of both existing and new outcome measures.

RECENT FINDINGS

Outcomes being widely reported through ICU registries include measures of survival, events of interest, patient-reported outcomes and measures of resource utilization (including cost). Despite its increasing adoption, challenges with quality of reporting of outcomes measures remain. Measures of short-term survival are feasible but those requiring longer follow-ups are increasingly difficult to interpret given the evolving nature of critical care in the context of acute and chronic disease management. Furthermore, heterogeneity in patient populations and in healthcare organisations in different settings makes use of outcome measures for international benchmarking at best complex, requiring substantial advances in their definitions and implementation to support those seeking to improve patient care.

SUMMARY

Digital registries could help overcome some of the current challenges with implementing and interpreting ICU outcome data through standardization of reporting and harmonization of data. In addition, ICU registries could be instrumental in enabling data for feedback as part of improvement in both patient-centred outcomes and in service outcomes; notably resource utilization and efficiency.

Sepsis in two hospitals in Rwanda: A retrospective cohort study of presentation, management, outcomes, and predictors of mortality

Purpose

Few studies have assessed the presentation, management, and outcomes of sepsis in low-income countries (LICs). We sought to characterize these aspects of sepsis and to assess mortality predictors in sepsis in two referral hospitals in Rwanda.

Materials and methods

This was a retrospective cohort study in two public academic referral hospitals in Rwanda. Data was abstracted from paper medical records of adult patients who met our criteria for sepsis.

Results

Of the 181 subjects who met eligibility criteria, 111 (61.3%) met our criteria for sepsis without shock and 70 (38.7%) met our criteria for septic shock. Thirty-five subjects (19.3%) were known to be HIV positive. The vast majority of septic patients (92.7%) received intravenous fluid therapy (median = 1.0 L within 8 hours), and 94.0% received antimicrobials. Vasopressors were administered to 32.0% of the cohort and 46.4% received mechanical ventilation. In-hospital mortality for all patients with sepsis was 51.4%, and it was 82.9% for those with septic shock. Baseline characteristic mortality predictors were respiratory rate, Glasgow Coma Scale score, and known HIV seropositivity.

Conclusions

Septic patients in two public tertiary referral hospitals in Rwanda are young (median age = 40, IQR = 29, 59) and experience high rates of mortality. Predictors of mortality included baseline clinical characteristics and HIV seropositivity status. The majority of subjects were treated with intravenous fluids and antimicrobials. Further work is needed to understand clinical and management factors that may help improve mortality in septic patients in LICs.

High mortality rate of obstetric critically ill women in Rwanda and its predictability

BACKGROUND

Reasons for admission to intensive care units (ICUs) for obstetric patients vary from one setting to another. Outcomes from ICU and prediction models are not well explored in Rwanda owing to lack of appropriate scores. This study aimed to assess reasons for admission and accuracy of prediction models for mortality of obstetric patients admitted to ICUs of two public tertiary hospitals in Rwanda.

METHODS

We prospectively collected data from all obstetric patients admitted to the ICUs of the two public tertiary hospitals in Rwanda from March 2017 to February 2018 to identify reasons for admission, demographic and clinical characteristics, outcome including death and its predictability by both the Modified Early Obstetric Warning Score (MEOWS) and quick Sequential Organ Failure Assessment (qSOFA). We analysed the accuracy of mortality prediction models by MEOWS or qSOFA by using logistic regression adjusting for factors associated with mortality. Area under the Receiver Operating characteristic (AUROC) curves is used to show the predicting capacity for each individual tool.

RESULTS

Obstetric patients (n = 94) represented 12.8 % of all 747 ICU admissions which is 1.8 % of all 4.999 admitted women for pregnancy or labor. Sepsis (n = 30; 31.9 %) and obstetric haemorrhage (n = 24; 25.5 %) were the two commonest reasons for ICU admission. Overall ICU mortality for obstetric patients was 54.3 % (n = 51) with average length of stay of 6.6 ± 7.525 days. MEOWS score was an independent predictor of mortality (adjusted (a)OR 1.25; 95 % CI 1.07-1.46) and so was qSOFA score (aOR 2.81; 95 % CI 1.25-6.30) with an adjusted AUROC of 0.773 (95 % CI 0.67-0.88) and 0.764 (95 % CI 0.65-0.87), indicating fair accuracy for ICU mortality prediction in these settings of both MEOWS and qSOFA scores.

CONCLUSIONS

Sepsis and obstetric haemorrhage were the commonest reasons for obstetric admissions to ICU in Rwanda. MEOWS and qSOFA scores could accurately predict ICU mortality of obstetric patients in resource-limited settings, but larger studies are needed before a recommendation for their use in routine practice in similar settings.

Survival and predictors of mortality among patients admitted to the intensive care units in southern Ethiopia: A multi-center cohort study

BACKGROUND

The burden of life-threatening conditions requiring intensive care units has grown substantially in low-income countries related to an emerging pandemic, urbanization, and hospital expansion. The rate of ICU mortality varied from region to region in Ethiopia. However, the body of evidence on ICU mortality and its predictors is uncertain. This study was designed to investigate the pattern of disease and predictors of mortality in Southern Ethiopia.

METHODS

After obtaining ethical clearance from the Institutional Review Board (IRB), a multi-center cohort study was conducted among three teaching referral hospital ICUs in Ethiopia from June 2018 to May 2020. Five hundred and seventeen Adult ICU patients were selected. Data were entered in Statistical Package for Social Sciences version 22 and STATA version 16 for analysis. Descriptive statistics were run to see the overall distribution of the variables. Chi-square test and odds ratio were determined to identify the association between independent and dependent variables. Multivariate analysis was conducted to control possible confounders and identify independent predictors of ICU mortality.

RESULTS

The mean (±SD) of the patients admitted in ICU was 34.25(±5.25). The overall ICU mortality rate was 46.8%. The study identified different independent predictors of mortality. Patients with cardiac arrest were approximately 12 times more likely to die as compared to those who didn't, AOR = 11.9(95% CI:6.1 to 23.2).

CONCLUSION

The overall mortality rate in ICU was very high as compared to other studies in Ethiopia as well as globally which entails a rigorous activity from different stakeholders.

A 2-Biomarker Model Augments Clinical Prediction of Mortality in Melioidosis

BACKGROUND

Melioidosis, infection caused by Burkholderia pseudomallei, is a common cause of sepsis with high associated mortality in Southeast Asia. Identification of patients at high likelihood of clinical deterioration is important for guiding decisions about resource allocation and management. We sought to develop a biomarker-based model for 28-day mortality prediction in melioidosis.

METHODS

In a derivation set (N = 113) of prospectively enrolled, hospitalized Thai patients with melioidosis, we measured concentrations of interferon-γ, interleukin-1β, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-ɑ, granulocyte-colony stimulating factor, and interleukin-17A. We used least absolute shrinkage and selection operator (LASSO) regression to identify a subset of predictive biomarkers and performed logistic regression and receiver operating characteristic curve analysis to evaluate biomarker-based prediction of 28-day mortality compared with clinical variables. We repeated select analyses in an internal validation set (N = 78) and in a prospectively enrolled external validation set (N = 161) of hospitalized adults with melioidosis.

RESULTS

All 8 cytokines were positively associated with 28-day mortality. Of these, interleukin-6 and interleukin-8 were selected by LASSO regression. A model consisting of interleukin-6, interleukin-8, and clinical variables significantly improved 28-day mortality prediction over a model of only clinical variables [AUC (95% confidence interval [CI]): 0.86 (.79-.92) vs 0.78 (.69-.87); P = .01]. In both the internal validation set (0.91 [0.84-0.97]) and the external validation set (0.81 [0.74-0.88]), the combined model including biomarkers significantly improved 28-day mortality prediction over a model limited to clinical variables.

CONCLUSIONS

A 2-biomarker model augments clinical prediction of 28-day mortality in melioidosis.

The development of critical care nursing education in Zambia

BACKGROUND

Critical care services reflect the healthcare services they support. In many low-to-middle-income countries (LMICs), balancing a sparse workforce, resources and competing demands to fund services, is a significant challenge when providing critical care. In Zambia, critical care has evolved significantly over the past 10 years. This article explores the provision of critical care services and the review and validation of a critical care nursing course.

OBJECTIVES

To review the literature relating to critical care nursing in sub-Saharan Africa to support a review and validation of the current critical care nursing course and to prepare a framework for a Bachelor of Science (BSc) in critical care nursing programme in Zambia.

RESULTS

A search of the published literature identified key themes, including a paucity of evidence, limited educational opportunities, a lack of national and international opportunities, protocols and standards, and the challenges of providing technical services. The subsequent review and validation took account of these themes.

CONCLUSION

This project has had an impact on improving critical care nurses' knowledge and skills and provided the foundations for the BSc in critical care nursing.

Delirium as a predictor of mortality and disability among hospitalized patients in Zambia

OBJECTIVE

To study the epidemiology and outcomes of delirium among hospitalized patients in Zambia.

METHODS

We conducted a prospective cohort study at the University Teaching Hospital in Lusaka, Zambia, from October 2017 to April 2018. The primary exposure was delirium duration over the initial 3 days of hospitalization, assessed daily using the Brief Confusion Assessment Method. The primary outcome was 6-month mortality. Secondary outcomes included 6-month disability, evaluated using the World Health Organization Disability Assessment Schedule 2.0.

FINDINGS

711 adults were included (median age, 39 years; 461 men; 459 medical, 252 surgical; 323 with HIV). Delirium prevalence was 48.5% (95% CI, 44.8%-52.3%). 6-month mortality was higher for delirious participants (44.6% [39.3%-50.1%]) versus non-delirious participants (20.0% [15.4%-25.2%]; P < .001). After adjusting for covariates, delirium duration independently predicted 6-month mortality and disability with a significant dose-response association between number of days with delirium and odds of worse clinical outcome. Compared to no delirium, presence of 1, 2 or 3 days of delirium resulted in odds ratios for 6-month mortality of 1.43 (95% CI, 0.73-2.80), 2.20 (1.07-4.51), and 3.92 (2.24-6.87), respectively (P < .001). Odds of 6-month disability were 1.20 (0.70-2.05), 1.73 (0.95-3.17), and 2.80 (1.78-4.43), respectively (P < .001).

CONCLUSION

Among hospitalized medical and surgical patients in Zambia, delirium prevalence was high and delirium duration independently predicted mortality and disability at 6 months. This work lays the foundation for prevention, detection, and management of delirium in low-income countries. Long-term follow up of outcomes of critical illness in resource-limited settings appears feasible using the WHO Disability Assessment Schedule.

Predicting mortality in adults with suspected infection in a Rwandan hospital: an evaluation of the adapted MEWS, qSOFA and UVA scores

RATIONALE

Mortality prediction scores are increasingly being evaluated in low and middle income countries (LMICs) for research comparisons, quality improvement and clinical decision-making. The modified early warning score (MEWS), quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA), and Universal Vital Assessment (UVA) score use variables that are feasible to obtain, and have demonstrated potential to predict mortality in LMIC cohorts.

OBJECTIVE

To determine the predictive capacity of adapted MEWS, qSOFA and UVA in a Rwandan hospital.

DESIGN, SETTING, PARTICIPANTS AND OUTCOME MEASURES

We prospectively collected data on all adult patients admitted to a tertiary hospital in Rwanda with suspected infection over 7 months. We calculated an adapted MEWS, qSOFA and UVA score for each participant. The predictive capacity of each score was assessed including sensitivity, specificity, positive and negative predictive value, OR, area under the receiver operating curve (AUROC) and performance by underlying risk quartile.

RESULTS

We screened 19 178 patient days, and enrolled 647 unique patients. Median age was 35 years, and in-hospital mortality was 18.1%. The proportion of data missing for each variable ranged from 0% to 11.7%. The sensitivities and specificities of the scores were: adapted MEWS >4, 50.4% and 74.9%, respectively; qSOFA >2, 24.8% and 90.4%, respectively; and UVA >4, 28.2% and 91.1%, respectively. The scores as continuous variables demonstrated the following AUROCs: adapted MEWS 0.69 (95% CI 0.64 to 0.74), qSOFA 0.65 (95% CI 0.60 to 0.70), and UVA 0.71 (95% CI 0.66 to 0.76); there was no statistically significant difference between the discriminative capacities of the scores.

CONCLUSION

Three scores demonstrated a modest ability to predict mortality in a prospective study of inpatients with suspected infection at a Rwandan tertiary hospital. Careful consideration must be given to their adequacy before using them in research comparisons, quality improvement or clinical decision-making.

Characteristics, physiology and mortality of intubated patients in an emergency care population in sub-Saharan Africa: a prospective cohort study from Kigali, Rwanda

BACKGROUND

Formalised emergency departments (ED) are in early development in sub-Saharan Africa and there are limited data on emergency airway management in those settings. This study evaluates characteristics and outcomes of ED endotracheal intubation, as well as risk factors for mortality, at a teaching hospital in Rwanda.

METHODS

This was a prospective observational study of consecutive patients requiring endotracheal intubation at the University Teaching Hospital of Kigali ED conducted between 1 January and 31 December 2017. A standardised data collection tool was used to record patient demographics, preintubation clinical presentation, indication for intubation, vital signs. medications and equipment used, and periintubation complications. The primary outcome was in-hospital mortality. Univariate associations were determined for risks of mortality.

RESULTS

Of 198 intubations were analysed, 72.7% were male and the median age was 35 years (IQR 23-51). Airway protection was the most common indication for intubation (73.7%). Rapid sequence intubation was performed in 74.2% of cases; sedative-only facilitated intubation in 20.6% and non-drug assisted in 5.2%. The most common agents used were Ketamine for sedation (85.4%) and vecuronium for paralysis (65.7%). All patients were successfully intubated within three attempts, 85.4% on the first attempt. During intubation, 23.1% of patients experienced hypoxia, 6.7% aspiration and 3.6% cardiac arrest. Median ED length of stay was 2 days. Outcome data were available for 164 patients of whom 67.7% died. Bonferroni-corrected univariate analysis demonstrated that mortality was associated with higher postintubation shock index (p=0.0007) and lower postintubation systolic blood pressure (SBP) (p=0.0006).

CONCLUSION

The first-attempt and overall success rates for intubation in this ED in Rwanda were comparable to those in high-income countries (HIC). Mortality postintubation is associated with lower postintubation SBP and higher postintubation shock index. The high complication and mortality rates suggest the need for better resources and training to address differences in compared with HIC.

Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country

Background

The use of severity of illness scoring systems such as the Acute Physiology and Chronic Health Evaluation in lower-middle income settings comes with important limitations, primarily due to data burden, missingness of key variables and lack of resources. To overcome these challenges, in Asia, a simplified model, designated as e-TropICS was previously developed. We sought to externally validate this model using data from a multi-centre critical care registry in India.

Methods

Seven ICUs from the Indian Registry of IntenSive care(IRIS) contributed data to this study. Patients > 18 years of age with an ICU length of stay > 6 hours were included. Data including age, gender, co-morbidity, diagnostic category, type of admission, vital signs, laboratory measurements and outcomes were collected for all admissions. e-TropICS was calculated as per original methods. The area under the receiver operator characteristic curve was used to express the model’s power to discriminate between survivors and non-survivors. For all tests of significance, a 2-sided P less than or equal to 0.05 was considered to be significant. AUROC values were considered poor when ≤ to 0.70, adequate between 0.71 to 0.80, good between 0.81 to 0.90, and excellent at 0.91 or higher. Calibration was assessed using Hosmer-Lemeshow C -statistic.

Results

We included data from 2062 consecutive patient episodes. The median age of the cohort was 60 and predominantly male (n = 1350, 65.47%). Mechanical Ventilation and vasopressors were administered at admission in 504 (24.44%) and 423 (20.51%) patients respectively. Overall, mortality at ICU discharge was 10.28% (n = 212). Discrimination (AUC) for the e-TropICS model was 0.83 (95% CI 0.812–0.839) with an HL C statistic p value of < 0.05. The best sensitivity and specificity (84% and 72% respectively) were achieved with the model at an optimal cut-off for probability of 0.29.

Conclusion

e-TropICS has utility in the care of critically unwell patients in the South Asia region with good discriminative capacity. Further refinement of calibration in larger datasets from India and across the South-East Asia region will help in improving model performance.

qSOFA as a predictor of ICU outcomes in a resource-limited setting in KwaZulu-Natal Province, South Africa

Background

Sepsis is a major cause of morbidity and mortality, especially in critical care patients. Developing tools to identify patients who are at risk of poor outcomes and prolonged length of stay in intensive care units (ICUs) is critical, particularly in resource-limited settings.

Objectives

To determine whether the quick sequential organ failure assessment (qSOFA) score based on bedside assessment alone was a promising tool for risk prediction in low-resource settings.

Methods

A retrospective cohort of adult patients admitted to the intensive care unit (ICU) at Edendale Hospital in Pietermaritzburg, South Africa (SA), was recruited into the study between 2014 and 2018. The association of qSOFA with in-ICU mortality was measured using multivariable logistic regression. Discrimination was assessed using the area under the receiver operating characteristic curve and the additive contribution to a baseline model using likelihood ratio testing.

Results

The qSOFA scores of 0, 1 and 2 were not associated with increased odds of in-ICU mortality (adjusted odds ratio (aOR) 1.24, 95% confidence interval (CI) 0.86 - 1.79; p=0.26) in patients with infection, while the qSOFA of 3 was associated with in-ICU mortality in infected patients (aOR 2.82; 95% CI 1.91 - 4.16; p<0.001). On the other hand, the qSOFA scores of 2 (aOR 3.25; 95% CI 1.91 - 5.53; p<0.001) and 3 (aOR 6.26, 95% CI 0.38 - 11.62, p<0.001) were associated with increased odds of in-ICU mortality in patients without infection. Discrimination for mortality was fair to poor and adding qSOFA to a baseline model yielded a statistical improvement in both cases (p<0.001).

Conclusion

qSOFA was associated with, but weakly discriminant, for in-ICU mortality for patients with and without infection in a resource-limited, public hospital in SA. These findings add to the growing body of evidence that support the use of qSOFA to deliver low-cost, high-value critical care in resource-limited settings.

Contributions of the study

This study expanded the data supporting the use of qSOFA in resource-limited settings beyond the emergency department or ward to include patients admitted to the ICU. Additionally, this study demonstrated stronger predictive abilities in a population of patients admitted with trauma without suspected or confirmed infection, thus providing an additional use of qSOFA as a risk-prediction tool for a broader population.

Investigating SOFA, delta-SOFA and MPM-III for mortality prediction among critically ill patients at a private tertiary hospital ICU in Kenya: A retrospective cohort study

Background

Outcomes in well-resourced, intensive care units (ICUs) in Kenya are thought to be comparable to those in high-income countries (HICs) but risk-adjusted mortality data is unavailable. We undertook an evaluation of the Aga Khan University Hospital, Nairobi ICU to analyze patient clinical-demographic characteristics, compare the performance of Sequential Organ Failure Assessment (SOFA), delta-SOFA at 48 hours and Mortality Prediction Model-III (MPM-III) mortality prediction systems, and identify factors associated with increased risk of mortality.

Methods

A retrospective cohort study was conducted of adult patients admitted to the ICU between January 2015 and September 2017. SOFA and MPM-III scores were determined at admission and SOFA repeated at 48 hours.

Results

Approximately 33% of patients did not meet ICU admission criteria. Mortality among the population of critically ill patients in the ICU was 31.7%, most of whom were male (61.4%) with a median age of 53.4 years. High adjusted odds of mortality were found among critically ill patients with leukemia (aOR 6.32, p<0.01), tuberculosis (aOR 3.96, p<0.01), post-cardiac arrest (aOR 3.57, p<0.01), admissions from the step-down unit (aOR 3.13, p<0.001), acute kidney injury (aOR 2.97, p<0.001) and metastatic cancer (aOR 2.45, p = 0.04). The area under the receiver-operating characteristic (ROC) curve of admission SOFA was 0.77 (95% CI, 0.73–0.81), MPM-III 0.74 (95% CI, 0.69–0.79), delta-SOFA 0.69 (95% CI, 0.63–0.75) and 48-hour SOFA 0.83 (95% CI, 0.79–0.87). The difference between SOFA at 48 hours and admission SOFA, MPM-III and delta-SOFA was statistically significant (chi² = 17.1, 24.2 and 26.5 respectively; p<0.001). Admission SOFA, MPM-III and 48-hour SOFA were well calibrated (p >0.05) while delta-SOFA was borderline (p = 0.05).

Conclusion

Mortality among the critically ill was higher than expected in this well-resourced ICU. 48-hour SOFA performed better than admission SOFA, MPM-III and delta-SOFA in our cohort. While a large proportion of patients did not meet admission criteria but were boarded in the ICU, critically ill patients stepped-up from the step-down unit were unlikely to survive. Patients admitted following a cardiac arrest, and those with advanced disease such as leukemia, stage-4 HIV and metastatic cancer, had particularly poor outcomes. Policies for fair allocation of beds, protocol-driven admission criteria and appropriate case selection could contribute to lowering the risk of mortality among the critically ill to a level on par with HICs.

Mortality prediction models in the adult critically ill: A scoping review

BACKGROUND

Mortality prediction models are applied in the intensive care unit (ICU) to stratify patients into different risk categories and to facilitate benchmarking. To ensure that the correct prediction models are applied for these purposes, the best performing models must be identified. As a first step, we aimed to establish a systematic review of mortality prediction models in critically ill patients.

METHODS

Mortality prediction models were searched in four databases using the following criteria: developed for use in adult ICU patients in high-income countries, with mortality as primary or secondary outcome. Characteristics and performance measures of the models were summarized. Performance was presented in terms of discrimination, calibration and overall performance measures presented in the original publication.

RESULTS

In total, 43 mortality prediction models were included in the final analysis. In all, 15 models were only internally validated (35%), 13 externally (30%) and 10 (23%) were both internally and externally validated by the original researchers. Discrimination was assessed in 42 models (98%). Commonly used calibration measures were the Hosmer-Lemeshow test (60%) and the calibration plot (28%). Calibration was not assessed in 11 models (26%). Overall performance was assessed in the Brier score (19%) and the Nagelkerke's R² (4.7%).

CONCLUSIONS

Mortality prediction models have varying methodology, and validation and performance of individual models differ. External validation by the original researchers is often lacking and head-to-head comparisons are urgently needed to identify the best performing mortality prediction models for guiding clinical care and research in different settings and populations.

A cross sectional survey of factors influencing mortality in Rwandan surgical patients in the intensive care unit

BACKGROUND

Management of critically ill patients is a challenge in low resource settings where there is a paucity of trained staff, infrastructure, resources, and drugs. We aimed to study the characteristics of surgical patients admitted in intensive care unit in a limited resource setting and determine factors associated with mortality.

METHODS

This was a cross-sectional observational study of all surgical patients admitted to the intensive care unit of a tertiary referral hospital in Rwanda. Data included demographics, diagnosis, management, and outcomes. Logistic regression was used to determine factors associated with mortality.

RESULTS

Over a 7-month period, there were 126 surgical patients admitted to the intensive care unit. Common diagnoses included head injury (n = 55, 44%), peritonitis (n = 33, 26%), brain tumor (n = 15, 12%), and trauma (n = 15, 12%). The overall mortality was 47% with the highest mortality seen in patients with peritonitis (76%). Factors associated with mortality on intensive care unit admission included hypotension (odds ratio, 12.50; 95% confidence interval, 3.04, 51.32) and having any comorbidity (odds ratio 5.69, 95% confidence interval, 1.58, 20.50).

CONCLUSION

Surgical patients admitted to the intensive care unit bear a significant mortality. Common surgical intensive care unit diagnoses include head injury and peritonitis. We recommend a review of the admission policy to optimize utility of the intensive care unit.

Mortality Prediction in Rural Kenya: A Cohort Study of Mechanical Ventilation in Critically Ill Patients

Critical care is expanding in low- and middle-income countries. Yet, due to factors such as missing data and different disease patterns, predictive scores often fail to adequately predict the high rates of mortality observed.

Intensive Care Outcomes and Mortality Prediction at a National Referral Hospital in Western Kenya

RATIONALE

The burden of critical care is greatest in resource-limited settings. Intensive care unit (ICU) outcomes at public hospitals in Kenya are unknown. The present study is timely, given the Kenyan Ministry of Health initiative to expand ICU capacity.

OBJECTIVES

To identify factors associated with mortality at Moi Teaching and Referral Hospital and validate the Mortality Probability Admission Model II (MPM₀-II).

METHODS

A retrospective cohort of 450 patients from January 1, 2013, to April 5, 2015, was evaluated using demographics, presenting diagnoses, interventions, mortality, and cost data.

RESULTS

ICU mortality was 53.6%, and 30-day mortality was 57.3%. Most patients were male (61%) and at least 18 years old (70%); the median age was 29 years. Factors associated with high adjusted odds of mortality were as follows: age younger than 10 years (adjusted odds ratio [aOR], 3.59; P ≤ 0.001), ages 35-49 years (aOR, 3.13; P = 0.002), and age above 50 years (aOR, 2.86; P = 0.004), with reference age range 10-24 years; sepsis (aOR, 3.39; P = 0.01); acute stroke (aOR, 8.14; P = 0.011); acute respiratory failure or mechanical ventilation (aOR, 6.37; P < 0.001); and vasopressor support (aOR, 7.98; P < 0.001). Drug/alcohol poisoning (aOR, 0.33; P = 0.005) was associated with lower adjusted odds of mortality. MPM₀-II discrimination showed an area under the receiver operating characteristic curve of 0.78 (95% confidence interval, 0.72-0.82). The result of the Hosmer-Lemeshow test for calibration was significant (P < 0.001).

CONCLUSIONS

In a Kenyan public ICU, high mortality was noted despite the use of advanced therapies. MPM₀-II has acceptable discrimination but poor calibration. Modification of MPM₀-II or development of a new model using a prospective multicenter global collaboration is needed. Standardized triage and treatment protocols for high-risk diagnoses are needed to improve ICU outcomes.

THAI-ICU score as a simplified severity score for critically ill patients in a resource limited setting: Result from SEA-AKI study group

PURPOSE

To create a simplified ICU scoring system to predict mortality in critically ill patients that can be feasibly applied in resource limited setting with good performance of predicting hospital mortality.

MATERIALS AND METHODS

A retrospective study from prospective cohort was created consisting of adult patients who were admitted to an ICU of 17 centers across Thailand from 2013 to 2015. A development cohort (n = 3503) and a validation cohort (n = 1909) were randomly selected from the available enrollment data.

RESULTS

In the development cohort, the predictors of the simplified score 6 variable model were low Glasgow coma score (GCS), low mean arterial pressure or need vasopressor, positive net-fluid balance, tachypnea, thrombocytopenia, and high blood urea nitrogen. In the validation study of THAI-ICU, AUC (95%CI) was 0.81(0.78-0.83). At the optimum cutoff value of 9; the sensitivity, specificity, positive likelihood ratio were 72%, 73%, and 2.72 respectively. The Hosmer-Lemeshow - C statistic was 13.5 (p = .2) and the Brier score 95% CI was 0.16 (0.15, 0.17).

CONCLUSIONS

The THAI-ICU score is a new simplified severity score for predicting hospital mortality. The simplicity of the score will increase the possibility to apply in resource limited settings.

ICU Risk Stratification Models Feasible for Use in Sub-Saharan Africa Show Poor Discrimination in Malawi: A Prospective Cohort Study

BACKGROUND

Critical illness disproportionately affects people in low-income countries (LICs). Efforts to improve critical care in LICs must account for differences in demographics and infrastructure compared to high-income settings. Part of this effort includes the development and validation of intensive care unit (ICU) risk stratification models feasible for use in LICs. The purpose of this study was to validate and compare the performance of ICU mortality models developed for use in sub-Saharan Africa.

MATERIALS AND METHODS

This was a prospective, observational cohort study of ICU patients in a referral hospital in Malawi. Models were selected for comparison based on a Medline search for studies which developed ICU mortality models based on cohorts in sub-Saharan Africa. Model discrimination was evaluated using the area under the curve with 95% confidence intervals (CI).

RESULTS

During the study, 499 patients were admitted to the study ICU, and after exclusions, there were 319 patients. The cohort was 62% female, with the mean age 31 years (IQR: 23-41), and 74% had surgery preceding ICU admission. Discrimination for hospital mortality ranged from 0.54 (95% CI 0.48, 0.60) for the Universal Vital Assessment (UVA) to 0.72 (95% CI 0.66, 0.78) for the Malawi Intensive care Mortality Evaluation (MIME). After tenfold cross-validation, these results were unchanged.

CONCLUSIONS

The MIME outperformed other models in this prospective study. Most ICU models developed for LICs had poor to modest discrimination for hospital mortality. Future research may contribute to a better risk stratification model for LICs by refining and enhancing the MIME.

SEVERITAS: An externally validated mortality prediction for critically ill patients in low and middle-income countries

OBJECTIVE

Severity of illness scores used in critical care for benchmarking, quality assurance and risk stratification have been mainly created in high-income countries. In low and middle-income countries (LMICs), they cannot be widely utilized due to the demand for large amounts of data that may not be available (e.g. laboratory results). We attempt to create a new severity prognostication model using fewer variables that are easier to collect in an LMIC.

SETTING

Two intensive care units, one private and one public, from São Paulo, Brazil PATIENTS: An ICU for the first time.

INTERVENTIONS

None.

MEASUREMENTS AND MAINS RESULTS

The dataset from the private ICU was used as a training set for model development to predict in-hospital mortality. Three different machine learning models were applied to five different blocks of candidate variables. The resulting 15 models were then validated on a separate dataset from the public ICU, and discrimination and calibration compared to identify the best model. The best performing model used logistic regression on a small set of 10 variables: highest respiratory rate, lowest systolic blood pressure, highest body temperature and Glasgow Coma Scale during the first hour of ICU admission; age; prior functional capacity; type of ICU admission; source of ICU admission; and length of hospital stay prior to ICU admission. On the validation dataset, our new score, named SEVERITAS, had an area under the receiver operating curve of 0.84 (0.82 - 0.86) and standardized mortality ratio of 1.00 (0.91-1.08). Moreover, SEVERITAS had similar discrimination compared to SAPS-3 and better discrimination than the simplified TropICS and R-MPM.

CONCLUSIONS

Our study proposes a new ICU mortality prediction model using simple logistic regression on a small set of easily collected variables may be better suited than currently available models for use in low and middle-income countries.

Critical care outcomes in resource-limited settings

PURPOSE OF REVIEW

The burden of critical illness in low-income and middle-income countries (LMICs) is substantial. A better understanding of critical care outcomes is essential for improving critical care delivery in resource-limited settings. In this review, we provide an overview of recent literature reporting on critical care outcomes in LMICs. We discuss several barriers and potential solutions for a better understanding of critical care outcomes in LMICs.

RECENT FINDINGS

Epidemiologic studies show higher in-hospital mortality rates for critically ill patients in LMICs as compared with patients in high-income countries (HICs). Recent findings suggest that critical care interventions that are effective in HICs may not be effective and may even be harmful in LMICs. Little data on long-term and morbidity outcomes exist. Better outcomes measurement is beginning to emerge in LMICs through decision support tools that report process outcome measures, studies employing mobile health technologies with community health workers and the development of context-specific severity of illness scores.

SUMMARY

Outcomes from HICs cannot be reliably extrapolated to LMICs, so it is important to study outcomes for critically ill patients in LMICs. Specific challenges to achieving meaningful outcomes studies in LMICs include defining the critically ill population when few ICU beds exist, the resource-intensiveness of long-term follow-up, and the need for reliable severity of illness scores to interpret outcomes. Although much work remains to be done, examples of studies overcoming these challenges are beginning to emerge.

Global Critical Care: Moving Forward in Resource-Limited Settings

Caring for critically ill patients is challenging in resource-limited settings, where the burden of disease and mortality from potentially treatable illnesses is higher than in resource-rich areas. Barriers to delivering quality critical care in these settings include lack of epidemiologic data and context-specific evidence for medical decision-making, deficiencies in health systems organization and resources, and institutional obstacles to implementation of life-saving interventions. Potential solutions include the development of common definitions for intensive care unit (ICU), intensivist, and intensive care to create a universal ICU organization framework; development of educational programs for capacity building of health care professionals working in resource-limited settings; global prioritization of epidemiologic and clinical research in resource-limited settings to conduct timely and ethical studies in response to emerging threats; adaptation of international guidelines to promote implementation of evidence-based care; and strengthening of health systems that integrates these interventions. This manuscript reviews the field of global critical care, barriers to safe high-quality care, and potential solutions to existing challenges. We also suggest a roadmap for improving the treatment of critically ill patients in resource-limited settings.

Intensive Care Unit Capacity Strain and Outcomes of Critical Illness in a Resource-Limited Setting: A 2-Hospital Study in South Africa

OBJECTIVE

To measure the association of intensive care unit (ICU) capacity strain with processes of care and outcomes of critical illness in a resource-limited setting.

METHODS

We performed a retrospective cohort study of 5332 patients referred to the ICUs at 2 public hospitals in South Africa using the country's first published multicenter electronic critical care database. We assessed the association between multiple ICU capacity strain metrics (ICU occupancy, turnover, census acuity, and referral burden) at different exposure time points (ICU referral, admission, and/or discharge) with clinical and process of care outcomes. The association of ICU capacity strain at the time of ICU admission with ICU length of stay (LOS), the primary outcome, was analyzed with a multivariable Cox proportional hazard model. Secondary outcomes of ICU triage decision (with strain at ICU referral), ICU mortality (with strain at ICU admission), and ICU LOS (with strain at ICU discharge), were analyzed with linear and logistic multivariable regression.

RESULTS

No measure of ICU capacity strain at the time of ICU admission was associated with ICU LOS, the primary outcome. The ICU occupancy at the time of ICU admission was associated with increased odds of ICU mortality (odds ratio = 1.07, 95% confidence interval: 1.02-1.11; P = .004), a secondary outcome, such that a 10% increase in ICU occupancy would be associated with a 7% increase in the odds of ICU mortality.

CONCLUSIONS

In a resource-limited setting in South Africa, ICU capacity strain at the time of ICU admission was not associated with ICU LOS. In secondary analyses, higher ICU occupancy at the time of ICU admission, but not other measures of capacity strain, was associated with increased odds of ICU mortality.

Critical Care Management of Peritonitis in a Low-Resource Setting

BACKGROUND

Management of critically ill patients is challenging in a low-resource setting. In Rwanda, peritonitis is a common surgical condition where patients often present late, with advanced disease. We aim to describe critical care management of patients with peritonitis in Rwanda.

METHODS

Data were collected at a tertiary referral hospital in Rwanda on patients undergoing operation for peritonitis over a 6-month period. Data included epidemiology, hospital course and outcomes. Patients requiring admission to the intensive care unit (ICU) were compared with those not requiring ICU admission using Chi-square and Wilcoxon rank-sum test.

RESULTS

Over a 6-month period, 280 patients were operated for peritonitis. Of these, 46 (16.4%) were admitted to the ICU. The most common diagnoses were intestinal obstruction (N = 17, 37.0%) and typhoid intestinal perforation (N = 6, 13.0%). Thirty-nine (89%) patients had sepsis. The median American Society of Anesthesiologist score was 3 (range 2-4), and the median Surgical Apgar Score was 4 (range 0-6). Twenty-four (52.2%) patients required vasopressors, with dopamine and adrenaline being the only vasopressors available. Patients admitted to the ICU, compared with non-critically ill patients, were more likely to have major complications (80.4 vs. 14%, p < 0.001), unplanned reoperation (28 vs. 10%, p < 0.001) and death (72 vs. 8%, p < 0.001).

CONCLUSION

Patients with peritonitis admitted to the ICU commonly presented with features of sepsis. Due to limited resources in this setting, interventions are primarily supportive with intravenous fluids, intravenous antibiotics, ventilator support and vasopressors. Morbidity and mortality remain high in this patient population.

Development of a Malawi Intensive care Mortality risk Evaluation (MIME) model, a prospective cohort study

INTRODUCTION

Intensive care medicine can contribute to population health in low-income countries by reducing premature mortality related to surgery, trauma, obstetrical and other medical emergencies. Quality improvement is guided by risk stratification models, which are developed primarily within high-income settings. Models validated for use in low-income countries are needed.

METHODS

This prospective cohort study consisted of 261 patients admitted to the intensive care unit (ICU) of Kamuzu Central Hospital in Malawi, from September 2016 to March 2018. The primary outcome was in-hospital mortality. We performed univariable analyses on putative predictors and included those with a significance of 0.15 in the Malawi Intensive care Mortality risk Evaluation model (MIME). Model discrimination was evaluated using the area under the curve.

RESULTS

Males made up 37.9% of the study sample and the mean age was 34.4 years. A majority (73.9%) were admitted to the ICU after a recent surgical procedure, and 59% came directly from the operating theater. In-hospital mortality was 60.5%. The MIME based on age, sex, admitting service, systolic pressure, altered mental status, and fever during the ICU course had a fairly good discrimination, with an AUC of 0.70 (95% CI 0.63-0.76).

CONCLUSIONS

The MIME has modest ability to predict in-hospital mortality in a Malawian ICU. Multicenter research is needed to validate the MIME and assess its clinical utility.

Health Professional Training and Capacity Strengthening Through International Academic Partnerships: The First Five Years of the Human Resources for Health Program in Rwanda

BACKGROUND

The Rwanda Human Resources for Health Program (HRH Program) is a 7-year (2012-2019) health professional training initiative led by the Government of Rwanda with the goals of training a large, diverse, and competent health workforce and strengthening the capacity of academic institutions in Rwanda.

METHODS

The data for this organizational case study was collected through official reports from the Rwanda Ministry of Health (MoH) and 22 participating US academic institutions, databases from the MoH and the College of Medicine and Health Sciences (CMHS) in Rwanda, and surveys completed by the co-authors.

RESULTS

In the first 5 years of the HRH Program, a consortium of US academic institutions has deployed an average of 99 visiting faculty per year to support 22 training programs, which are on track to graduate almost 4600 students by 2019. The HRH Program has also built capacity within the CMHS by promoting the recruitment of Rwandan faculty and the establishment of additional partnerships and collaborations with the US academic institutions.

CONCLUSION

The milestones achieved by the HRH Program have been substantial although some challenges persist. These challenges include adequately supporting the visiting faculty; pairing them with Rwandan faculty (twinning); ensuring strong communication and coordination among stakeholders; addressing mismatches in priorities between donors and implementers; the execution of a sustainability strategy; and the decision by one of the donors not to renew funding beyond March 2017. Over the next 2 academic years, it is critical for the sustainability of the 22 training programs supported by the HRH Program that the health-related Schools at the CMHS significantly scale up recruitment of new Rwandan faculty. The HRH Program can serve as a model for other training initiatives implemented in countries affected by a severe shortage of health professionals.

Performance of critical care prognostic scoring systems in low and middle-income countries: a systematic review

Background

Prognostic models—used in critical care medicine for mortality predictions, for benchmarking and for illness stratification in clinical trials—have been validated predominantly in high-income countries. These results may not be reproducible in low or middle-income countries (LMICs), not only because of different case-mix characteristics but also because of missing predictor variables. The study objective was to systematically review literature on the use of critical care prognostic models in LMICs and assess their ability to discriminate between survivors and non-survivors at hospital discharge of those admitted to intensive care units (ICUs), their calibration, their accuracy, and the manner in which missing values were handled.

Methods

The PubMed database was searched in March 2017 to identify research articles reporting the use and performance of prognostic models in the evaluation of mortality in ICUs in LMICs. Studies carried out in ICUs in high-income countries or paediatric ICUs and studies that evaluated disease-specific scoring systems, were limited to a specific disease or single prognostic factor, were published only as abstracts, editorials, letters and systematic and narrative reviews or were not in English were excluded.

Results

Of the 2233 studies retrieved, 473 were searched and 50 articles reporting 119 models were included. Five articles described the development and evaluation of new models, whereas 114 articles externally validated Acute Physiology and Chronic Health Evaluation, the Simplified Acute Physiology Score and Mortality Probability Models or versions thereof. Missing values were only described in 34% of studies; exclusion and or imputation by normal values were used. Discrimination, calibration and accuracy were reported in 94.0%, 72.4% and 25% respectively. Good discrimination and calibration were reported in 88.9% and 58.3% respectively. However, only 10 evaluations that reported excellent discrimination also reported good calibration. Generalisability of the findings was limited by variability of inclusion and exclusion criteria, unavailability of post-ICU outcomes and missing value handling.

Conclusions

Robust interpretations regarding the applicability of prognostic models are currently hampered by poor adherence to reporting guidelines, especially when reporting missing value handling. Performance of mortality risk prediction models in LMIC ICUs is at best moderate, especially with limitations in calibration. This necessitates continued efforts to develop and validate LMIC models with readily available prognostic variables, perhaps aided by medical registries.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-017-1930-8) contains supplementary material, which is available to authorized users.

Simplified prognostic model for critically ill patients in resource limited settings in South Asia

BACKGROUND

Current critical care prognostic models are predominantly developed in high-income countries (HICs) and may not be feasible in intensive care units (ICUs) in lower- and middle-income countries (LMICs). Existing prognostic models cannot be applied without validation in LMICs as the different disease profiles, resource availability, and heterogeneity of the population may limit the transferability of such scores. A major shortcoming in using such models in LMICs is the unavailability of required measurements. This study proposes a simplified critical care prognostic model for use at the time of ICU admission.

METHODS

This was a prospective study of 3855 patients admitted to 21 ICUs from Bangladesh, India, Nepal, and Sri Lanka who were aged 16 years and over and followed to ICU discharge. Variables captured included patient age, admission characteristics, clinical assessments, laboratory investigations, and treatment measures. Multivariate logistic regression was used to develop three models for ICU mortality prediction: model 1 with clinical, laboratory, and treatment variables; model 2 with clinical and laboratory variables; and model 3, a purely clinical model. Internal validation based on bootstrapping (1000 samples) was used to calculate discrimination (area under the receiver operating characteristic curve (AUC)) and calibration (Hosmer-Lemeshow C-Statistic; higher values indicate poorer calibration). Comparison was made with the Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II models.

RESULTS

Model 1 recorded the respiratory rate, systolic blood pressure, Glasgow Coma Scale (GCS), blood urea, haemoglobin, mechanical ventilation, and vasopressor use on ICU admission. Model 2, named TropICS (Tropical Intensive Care Score), included emergency surgery, respiratory rate, systolic blood pressure, GCS, blood urea, and haemoglobin. Model 3 included respiratory rate, emergency surgery, and GCS. AUC was 0.818 (95% confidence interval (CI) 0.800-0.835) for model 1, 0.767 (0.741-0.792) for TropICS, and 0.725 (0.688-0.762) for model 3. The Hosmer-Lemeshow C-Statistic p values were less than 0.05 for models 1 and 3 and 0.18 for TropICS. In comparison, when APACHE II and SAPS II were applied to the same dataset, AUC was 0.707 (0.688-0.726) and 0.714 (0.695-0.732) and the C-Statistic was 124.84 (p < 0.001) and 1692.14 (p < 0.001), respectively.

CONCLUSION

This paper proposes TropICS as the first multinational critical care prognostic model developed in a non-HIC setting.

Current challenges in the management of sepsis in ICUs in resource-poor settings and suggestions for the future

BACKGROUND

Sepsis is a major reason for intensive care unit (ICU) admission, also in resource-poor settings. ICUs in low- and middle-income countries (LMICs) face many challenges that could affect patient outcome.

AIM

To describe differences between resource-poor and resource-rich settings regarding the epidemiology, pathophysiology, economics and research aspects of sepsis. We restricted this manuscript to the ICU setting even knowing that many sepsis patients in LMICs are treated outside an ICU.

FINDINGS

Although many bacterial pathogens causing sepsis in LMICs are similar to those in high-income countries, resistance patterns to antimicrobial drugs can be very different; in addition, causes of sepsis in LMICs often include tropical diseases in which direct damaging effects of pathogens and their products can sometimes be more important than the response of the host. There are substantial and persisting differences in ICU capacities around the world; not surprisingly the lowest capacities are found in LMICs, but with important heterogeneity within individual LMICs. Although many aspects of sepsis management developed in rich countries are applicable in LMICs, implementation requires strong consideration of cost implications and the important differences in resources.

CONCLUSIONS

Addressing both disease-specific and setting-specific factors is important to improve performance of ICUs in LMICs. Although critical care for severe sepsis is likely cost-effective in LMIC setting, more detailed evaluation at both at a macro- and micro-economy level is necessary. Sepsis management in resource-limited settings is a largely unexplored frontier with important opportunities for research, training, and other initiatives for improvement.