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

Review of the Predictive Value of Biomarkers in Sepsis Mortality

Sepsis is a leading cause of mortality among severely ill individuals, primarily due to its potential to induce fatal organ dysfunction. For clinicians, it is vital to have appropriate indicators, including the physiological status and personal experiences of patients with sepsis, to monitor the condition and assess prognosis. This approach aids in preventing the worsening of the illness and reduces mortality. Recent guidelines for sepsis focus on improving patient outcomes through early detection and timely treatment. Nonetheless, identifying severe cases and predicting their prognoses remain challenging. In recent years, there has been considerable interest in utilising the C-reactive protein (CRP)/albumin ratio (CAR) to evaluate the condition and forecast the prognosis of patients with sepsis. This research concentrates on the significance of CAR in the pathological process of sepsis, its association with prognosis, and the latest developments in employing procalcitonin, lactic acid, CRP, and other potential biomarkers. The CAR, with its predictive value for sepsis prognosis and mortality, is increasingly used as a clinical biochemical marker in diagnosing and monitoring patients with sepsis.

Is it Time to Develop an Indian Sepsis-related Mortality Prediction Score?

How to cite this article: Dedeepiya VD. Is it Time to Develop an Indian Sepsis-related Mortality Prediction Score? Indian J Crit Care Med 2024;28(4):320-322.

Prognostic evaluation of quick sequential organ failure assessment score in ICU patients with sepsis across different income settings

BACKGROUND

There is conflicting evidence on association between quick sequential organ failure assessment (qSOFA) and sepsis mortality in ICU patients. The primary aim of this study was to determine the association between qSOFA and 28-day mortality in ICU patients admitted for sepsis. Association of qSOFA with early (3-day), medium (28-day), late (90-day) mortality was assessed in low and lower middle income (LLMIC), upper middle income (UMIC) and high income (HIC) countries/regions.

METHODS

This was a secondary analysis of the MOSAICS II study, an international prospective observational study on sepsis epidemiology in Asian ICUs. Associations between qSOFA at ICU admission and mortality were separately assessed in LLMIC, UMIC and HIC countries/regions. Modified Poisson regression was used to determine the adjusted relative risk (RR) of qSOFA score on mortality at 28 days with adjustments for confounders identified in the MOSAICS II study.

RESULTS

Among the MOSAICS II study cohort of 4980 patients, 4826 patients from 343 ICUs and 22 countries were included in this secondary analysis. Higher qSOFA was associated with increasing 28-day mortality, but this was only observed in LLMIC (p < 0.001) and UMIC (p < 0.001) and not HIC (p = 0.220) countries/regions. Similarly, higher 90-day mortality was associated with increased qSOFA in LLMIC (p < 0.001) and UMIC (p < 0.001) only. In contrast, higher 3-day mortality with increasing qSOFA score was observed across all income countries/regions (p < 0.001). Multivariate analysis showed that qSOFA remained associated with 28-day mortality (adjusted RR 1.09 (1.00-1.18), p = 0.038) even after adjustments for covariates including APACHE II, SOFA, income country/region and administration of antibiotics within 3 h.

CONCLUSIONS

qSOFA was independently associated with 28-day mortality in ICU patients admitted for sepsis. In LLMIC and UMIC countries/regions, qSOFA was associated with early to late mortality but only early mortality in HIC countries/regions.

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 &lt; 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.

An improved prognostic model for predicting the mortality of critically ill patients: a retrospective cohort study

A simple prognostic model is needed for ICU patients. This study aimed to construct a modified prognostic model using easy-to-use indexes for prediction of the 28-day mortality of critically ill patients. Clinical information of ICU patients included in the Medical Information Mart for Intensive Care III (MIMIC-III) database were collected. After identifying independent risk factors for 28-day mortality, an improved mortality prediction model (mionl-MEWS) was constructed with multivariate logistic regression. We evaluated the predictive performance of mionl-MEWS using area under the receiver operating characteristic curve (AUROC), internal validation and fivefold cross validation. A nomogram was used for rapid calculation of predicted risks. A total of 51,121 patients were included with 34,081 patients in the development cohort and 17,040 patients in the validation cohort (17,040 patients). Six predictors, including Modified Early Warning Score, neutrophil-to-lymphocyte ratio, lactate, international normalized ratio, osmolarity level and metastatic cancer were integrated to construct the mionl-MEWS model with AUROC of 0.717 and 0.908 for the development and validation cohorts respectively. The mionl-MEWS model showed good validation capacities with clinical utility. The developed mionl-MEWS model yielded good predictive value for prediction of 28-day mortality in critically ill patients for assisting decision-making in ICU patients.

Characteristics and outcomes of patients triaged as critically ill in the emergency department of a tertiary care hospital in Bhutan

Background

In Bhutan, where the Emergency Medical System is forming and evolving, the number of acutely ill patients requiring critical care, both in the emergency departments and intensive care units, is steadily increasing. Given the lack of baseline data and the ever-increasing number of critical care patients, this study was aimed at describing the characteristics and outcomes of patients triaged as critically ill in the emergency department.

Methods

An observational study was conducted over a yearlong period in the emergency department where all patients triaged as critically ill were approached for inclusion in the study. A case record form was used for the purpose of data collection. Epidata analysis was used for descriptive analysis and SPSS was used for binary logistic regression.

Results

A total of 657 critically ill patients of all age groups visited the emergency department over the 1-year study period, with adults constituting the majority (81%). The majority (67%) of these patients had a favorable outcome of surviving to discharge. The most common diagnosis among critically ill neonates was neonatal sepsis. Among the critically ill pediatrics and adults, sepsis, respiratory illnesses, and trauma were the most common diagnoses. Intubation followed by mechanical ventilation and blood product transfusion were the most common lifesaving interventions performed on critically ill patients.

Conclusion

The findings from this study constitute the first ever local database, at the national referral hospital in Bhutan, of critically ill patients treated in the emergency department. It highlights the central role the emergency department plays in their management and provides information for strengthening critical care services. It also highlights the areas of improvement and identifies high yield areas of training for the emergency department.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12245-022-00468-8.

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.

Performance evaluation of a multinational data platform for critical care in Asia

Background: The value of medical registries strongly depends on the quality of the data collected. This must be objectively measured before large clinical databases can be promoted for observational research, quality improvement, and clinical trials. We aimed to evaluate the quality of a multinational intensive care unit (ICU) network of registries of critically ill patients established in seven Asian low- and middle-income countries (LMICs). Methods: The Critical Care Asia federated registry platform enables ICUs to collect clinical, outcome and process data for aggregate and unit-level analysis. The evaluation used the standardised criteria of the Directory of Clinical Databases (DoCDat) and a framework for data quality assurance in medical registries. Six reviewers assessed structure, coverage, reliability and validity of the ICU registry data. Case mix and process measures on patient episodes from June to December 2020 were analysed. Results: Data on 20,507 consecutive patient episodes from 97 ICUs in Afghanistan, Bangladesh, India, Malaysia, Nepal, Pakistan and Vietnam were included. The quality level achieved according to the ten prespecified DoCDat criteria was high (average score 3.4 out of 4) as was the structural and organizational performance -- comparable to ICU registries in high-income countries. Identified strengths were types of variables included, reliability of coding, data completeness and validation. Potential improvements included extension of national coverage, optimization of recruitment completeness validation in all centers and the use of interobserver reliability checks. Conclusions: The Critical Care Asia platform evaluates well using standardised frameworks for data quality and equally to registries in resource-rich settings.

Performance evaluation of a multinational data platform for critical care in Asia

Background: The value of medical registries strongly depends on the quality of the data collected. This must be objectively measured before large clinical databases can be promoted for observational research, quality improvement, and clinical trials. We aimed to evaluate the quality of a multinational intensive care unit (ICU) network of registries of critically ill patients established in seven Asian low- and middle-income countries (LMICs). Methods: The Critical Care Asia federated registry platform enables ICUs to collect clinical, outcome and process data for aggregate and unit-level analysis. The evaluation used the standardised criteria of the Directory of Clinical Databases (DoCDat) and a framework for data quality assurance in medical registries. Six reviewers assessed structure, coverage, reliability and validity of the ICU registry data. Case mix and process measures on patient episodes from June to December 2020 were analysed. Results: Data on 20,507 consecutive patient episodes from 97 ICUs in Afghanistan, Bangladesh, India, Malaysia, Nepal, Pakistan and Vietnam were included. The quality level achieved according to the ten prespecified DoCDat criteria was high (average score 3.4 out of 4) as was the structural and organizational performance -- comparable to ICU registries in high-income countries. Identified strengths were types of variables included, reliability of coding, data completeness and validation. Potential improvements included extension of national coverage, optimization of recruitment completeness validation in all centers and the use of interobserver reliability checks. Conclusions: The Critical Care Asia platform evaluates well using standardised frameworks for data quality and equally to registries in resource-rich settings.

Carbapenem-Resistant Gram-Negative Bacteria-Related Healthcare-Associated Ventriculitis and Meningitis: Antimicrobial Resistance of the Pathogens, Treatment, and Outcome

Carbapenem-resistant Gram-negative bacteria (CRGNB)-related health care-associated ventriculitis and meningitis (HCAVM) is dangerous. We aimed to report the antimicrobial resistance of the pathogens, treatment, and outcome. All cases with CRGNB-related HCAVM in2012-2020 were recruited. Antimicrobial agents were classified as active, untested, or inactive using antimicrobial susceptibility tests. The treatment stage was classified as empirical or targeted according to the report of pathogens. The treatment effect was classified as ineffective or effective according to HCAVM-related parameters. Overall, 92 cases were recruited. For most antimicrobial agents, the resistance rate was higher than 70.0%. The polymyxin resistance rate was the lowest at 11.6%. The chloramphenicol, trimethoprim-sulfamethoxazole, amikacin, levofloxacin, and tetracycline resistance rates were relatively low, ranging from 21.1% to 64.1%. The meropenem resistance rate was 81.9%. There was no significant trend for any antimicrobial agent tested. Meropenem was the most common antimicrobial agent used in empirical treatment; trimethoprim-sulfamethoxazole and polymyxin were the most used active antimicrobial agents, and meropenem/sulbactam and polymyxin were the most used untested antimicrobial agents in targeted treatment. In total, 42 (45.7%) cases received ineffective treatments. The ineffective treatment rate of cases that received active antimicrobial agents was lower than that of cases that received untested antimicrobial agents and cases that received inactive antimicrobial agents (29.3% [12/41] versus 46.2% [18/39] versus 100.0% [12/12], P < 0.001). Antimicrobial resistance was prevalent but without increasing trends. Active antimicrobial agents are necessary. Additionally, untested antimicrobial agents, including meropenem/sulbactam and polymyxin, might be optional. Inactive antimicrobial agents must be replaced. IMPORTANCE Carbapenem-resistant Gram-negative bacteria-related health care-associated ventriculitis and meningitis is a clinical threat because of the poor outcome and challenges in treatment. We reached several conclusions: (i) the antimicrobial resistance of pathogens is severe, and some antimicrobial agents represented by polymyxin are optional according to the antimicrobial susceptibility tests; (ii) in the background that the portion of carbapenems resistance in Gram-negative bacteria is increasing, there is no increasing trend for the antimicrobial resistance of carbapenem-resistant Gram-negative bacteria in the 9-year study; (iii) meropenem is the main antimicrobial agent in treatment, and trimethoprim-sulfamethoxazole, tigecycline, polymyxin, and meropenem/sulbactam are commonly used in the targeted treatment; (iv) the treatment effect was poor and affected by the treatment: timely active antimicrobial agents should be given. And untested antimicrobial agents represented by polymyxin and meropenem/sulbactam might be optional. Inactive antimicrobial agents must be replaced.

Sepsis Management in Southeast Asia: A Review and Clinical Experience

Sepsis is a life-threatening condition that causes a global health burden associated with high mortality and morbidity. Often life-threatening, sepsis can be caused by bacteria, viruses, parasites or fungi. Sepsis management primarily focuses on source control and early broad-spectrum antibiotics, plus organ function support. Comprehensive changes in the way we manage sepsis patients include early identification, infective focus identification and immediate treatment with antimicrobial therapy, appropriate supportive care and hemodynamic optimization. Despite all efforts of clinical and experimental research over thirty years, the capacity to positively influence the outcome of the disease remains limited. This can be due to limited studies available on sepsis in developing countries, especially in Southeast Asia. This review summarizes the progress made in the diagnosis and time associated with sepsis, colistin resistance and chloramphenicol boon, antibiotic abuse, resource constraints and association of sepsis with COVID-19 in Southeast Asia. A personalized approach and innovative therapeutic alternatives such as CytoSorb® are highlighted as potential options for the treatment of patients with sepsis in Southeast Asia.

Alternatives of Risk Prediction Models for Preeclampsia in a Low Middle-Income Setting

Abstract Objectives:  To  develop prediction models for the first-trimester prediction of PE (PE) using the established biomarkers including maternal characteristics and history, mean arterial pressure (MAP), uterine artery Doppler pulsatility index (UtA-PI ), and Placental Growth Factor (PlGF)) in combination with Ophthalmic artery Doppler peak ratio (PR). Methods:  This was a  prospective observational study in women attending a first-trimester screening at 11-14 weeks’  gestation. Maternal characteristics and history, measurement of MAP, ultrasound examination for UtA-PI measurement, maternal ophthalmic PR Doppler measurement, and serum  PlGF collection were performed during the visit. Logistic regression  analysis was used to determine if the maternal factor had a significant contribution in predicting PE. The Receiving Operator Curve (ROC) analysis was used to determine the area under the curve  (AUC), positive predictive value  (PPV), negative prefictive value (NPV) and positive screening cut-off in predicting the occurrence of PE at any gestational age.   Results: Of the 946 eligible participants, 71 (7,49%) subjects were affected by PE. Based on the ROC curves, optimal high-risk cutoff value for prediction of preeclampsia at any gestational age for model 2 (primary care model) in this Indonesia study population were 63% with the sensitivity and specificity of 71.8% and 71.2%, respectively. Both sensitivity and specificity for model 3 (complete model) were 70.4% and 74.9%, respectively for the cutoff value 58%. The area under the curve of model 2, model 3 was 0.7651 (95% CI: 0.7023-0.8279)) and 0.7911 (95% CI: 0.7312-0.8511), respectively, for predicting PE. In addition, PPV and NPV for model 2 were 16.8% and 96.9%, respectively. PPV and NPV for model 3 were 18.55 and 96.9%, respectively.   Conclusion: The prediction models of preeclampsia vary depending upon healthcare resource. Complete model is clinically superior to primary care model but it is not statistically significant.  Prognostic models should be easy to use, informative and low cost with great potential to improve maternal and neonatal health in Low Middle Income Country settings. 

Predictors of Mortality and Outcomes of Ventilated Patients Managed in a Resource-Limited Acute Surgical Ward

BACKGROUND

Acute care surgery is an important component of health care in the developed nations. However, in Malaysia, acute care surgery is yet to be recognized as a specific subspecialty service. Due to high demands of limited ICU beds, some patients have to be ventilated in the wards. This study aims to describe the outcomes of acute surgical patients that required mechanical ventilation.

METHODS

This is a retrospective review of all mechanically ventilated surgical patients in the wards, in a tertiary hospital, in 2020. Sixty-two patients out of 116 patients ventilated in surgical wards fulfilled the inclusion criteria. Demography, surgical diagnosis and procedures and physiologic, biochemical and survival data were analyzed to explore the outcomes and predictors of mortality.

RESULTS

Twenty-two out of 62 patients eventually gained ICU admission. Mean time from intubation to ICU entry and mean length of ICU stay were 48 h (0 to 312) and 10 days (1 to 33), respectively. Survival for patients admitted to ICU compared to ventilation in the acute surgery wards was 54.5% (12/22) vs 17.5% (7/40). Thirty-four patients underwent surgery, and the majority were bowel-related emergency operations. SAPS2 score validation revealed AUC of 0.701. More than half of patients with mortality risk < 50% eventually were not admitted to ICU.

CONCLUSIONS

ICU care for critically ill surgical patients provides better survival. There is a need to improve triaging for intensive care, especially for low-mortality-risk patients using risk scores which are locally validated.

Non-linear relationship between albumin-corrected calcium and 30-day in-hospital mortality in ICU patients: A multicenter retrospective cohort study

OBJECTIVE

Albumin-corrected calcium is usually calculated to reflect the real serum calcium level of the whole body by physicians. However, studies on the association between albumin-corrected calcium and 30-day in-hospital mortality in Intensive Care Unit (ICU) patients are rare. The purpose of our study was to explore the association between baseline albumin-corrected calcium and 30-day in-hospital mortality in the American ICU population.

METHODS

A multicenter retrospective cohort study of 102,245 ICU patients in the eICU-CRD v2.0 from the USA during 2014-2015 was performed. The average age was 63.7 ± 16.9 years, of which 55,313 (53.7%) were men and 47,758 (46.3%) were women. The association between albumin-corrected calcium and 30-day in-hospital mortality was analyzed by Cox proportional-hazards regression, smooth curve fitting, piecewise linear regression, subgroup analyses, and a series of sensitivity analyses.

RESULTS

We found that among ICU patients with calcium abnormalities, more than 95% were mild hypocalcemia or mild hypercalcemia. The risk of 30-day in-hospital mortality will increase by 10% in the ≥7.5-< 8.5 mg/dl subgroup (OR=1.1, 95% CI 1.0-1.3) or 20% in the ≥10.3-<12 mg/dl subgroup (OR=1.2, 95% CI 1.1-1.3) when the albumin-corrected calcium level increases by 1 mg/dl. Additionally, the relationship between albumin-corrected calcium and 30-day in-hospital mortality was U shaped; the inflection point was 8.9 mg/dl (log likelihood ratio test P = 0.005). Finally, after a series of sensitivity analyses, we found that the relationship between albumin-corrected calcium and 30-day in-hospital mortality remained significant.

CONCLUSION

In a large nationally representative cohort of ICU patients, abnormalities in albumin-corrected calcium, particularly slight hypocalcemia or slight hypercalcemia, were associated with an increased 30-day in-hospital mortality risk, and yet the findings in this study need to be further confirmed by prospective studies.

Risk Prediction Models for Inadvertent Intraoperative Hypothermia: A Systematic Review

PURPOSES

Inadvertent intraoperative hypothermia (core temperature <36°C) is a common surgical complication with several adverse events. Hypothermia prediction models can be a tool for providing the healthcare staff with information on the risk of inadvertent hypothermia. Our systematic review aimed to identify, demonstrate, and evaluate the available intraoperative hypothermia risk prediction models in surgical populations.

DESIGN

This study is a systematic review of literature.

METHODS

We systematically searched multiple databases (Ovid MEDLINE, Web of Science, Embase, and Cochrane Center Register of Controlled Trials). Two reviewers independently examined abstracts and the full text for eligibility. Data collection was guided by the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS checklist), and methodological quality and applicability were assessed by the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

FINDINGS

A total of 3,672 references were screened, of which eight articles were included in this study. All the models had a high risk of bias since most of them lacked model validation. Also, they failed to report the model performance and final model presentations, which restricted their clinical application.

CONCLUSIONS

The researchers should present models in a more standard way and improve the existing models to increase their predictive values for clinical application.

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.

A catalogue of tools and variables from crisis and routine care to support decision-making about allocation of intensive care beds and ventilator treatment during pandemics: Scoping review

PURPOSE

This scoping review sought to identify objective factors to assist clinicians and policy-makers in making consistent, objective and ethically sound decisions about resource allocation when healthcare rationing is inevitable.

MATERIALS AND METHODS

Review of guidelines and tools used in ICUs, hospital wards and emergency departments on how to best allocate intensive care beds and ventilators either during routine care or developed during previous epidemics, and association with patient outcomes during and after hospitalisation.

RESULTS

Eighty publications from 20 countries reporting accuracy or validity of prognostic tools/algorithms, or significant correlation between prognostic variables and clinical outcomes met our eligibility criteria: twelve pandemic guidelines/triage protocols/consensus statements, twenty-two pandemic algorithms, and 46 prognostic tools/variables from non-crisis situations. Prognostic indicators presented here can be combined to create locally-relevant triage algorithms for clinicians and policy makers deciding about allocation of ICU beds and ventilators during a pandemic. No consensus was found on the ethical issues to incorporate in the decision to admit or triage out of intensive care.

CONCLUSIONS

This review provides a unique reference intended as a discussion starter for clinicians and policy makers to consider formalising an objective a locally-relevant triage consensus document that enhances confidence in decision-making during healthcare rationing of critical care and ventilator resources.

Prediction models for acute kidney injury in critically ill patients: a protocol for systematic review and critical appraisal

INTRODUCTION

Acute kidney injury (AKI) has high morbidity and mortality in intensive care units, which can lead to chronic kidney disease, more costs and longer hospital stay. Early identification of AKI is crucial for clinical intervention. Although various risk prediction models have been developed to identify AKI, the overall predictive performance varies widely across studies. Owing to the different disease scenarios and the small number of externally validated cohorts in different prediction models, the stability and applicability of these models for AKI in critically ill patients are controversial. Moreover, there are no current risk-classification tools that are standardised for prediction of AKI in critically ill patients. The purpose of this systematic review is to map and assess prediction models for AKI in critically ill patients based on a comprehensive literature review.

METHODS AND ANALYSIS

A systematic review with meta-analysis is designed and will be conducted according to the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Three databases including PubMed, Cochrane Library and EMBASE from inception through October 2020 will be searched to identify all studies describing development and/or external validation of original multivariable models for predicting AKI in critically ill patients. Random-effects meta-analyses for external validation studies will be performed to estimate the performance of each model. The restricted maximum likelihood estimation and the Hartung-Knapp-Sidik-Jonkman method under a random-effects model will be applied to estimate the summary C statistic and 95% CI. 95% prediction interval integrating the heterogeneity will also be calculated to pool C-statistics to predict a possible range of C-statistics of future validation studies. Two investigators will extract data independently using the CHARMS checklist. Study quality or risk of bias will be assessed using the Prediction Model Risk of Bias Assessment Tool.

ETHICS AND DISSEMINATION

Ethical approval and patient informed consent are not required because all information will be abstracted from published literatures. We plan to share our results with clinicians and publish them in a general or critical care medicine peer-reviewed journal. We also plan to present our results at critical care international conferences.

OSF REGISTRATION NUMBER

10.17605/OSF.IO/X25AT.

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.

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.

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.

sTREM-1 predicts mortality in hospitalized patients with infection in a tropical, middle-income country

BACKGROUND

Few studies of biomarkers as predictors of outcome in infection have been performed in tropical, low- and middle-income countries where the burden of sepsis is highest. We evaluated whether selected biomarkers could predict 28-day mortality in infected patients in rural Thailand.

METHODS

Four thousand nine hundred eighty-nine adult patients admitted with suspected infection to a referral hospital in northeast Thailand were prospectively enrolled within 24 h of admission. In a secondary analysis of 760 patients, interleukin-8 (IL-8), soluble tumor necrosis factor receptor 1 (sTNFR-1), angiopoietin-1 (Ang-1), angiopoietin-2 (Ang-2), and soluble triggering receptor expressed by myeloid cells 1 (sTREM-1) were measured in the plasma. Association with 28-day mortality was evaluated using regression; a parsimonious biomarker model was selected using the least absolute shrinkage and selection operator (LASSO) method. Discrimination of mortality was assessed by receiver operating characteristic curve analysis and verified by multiple methods.

RESULTS

IL-8, sTNFR-1, Ang-2, and sTREM-1 concentrations were strongly associated with death. LASSO identified a three-biomarker model of sTREM-1, Ang-2, and IL-8, but sTREM-1 alone provided comparable mortality discrimination (p = 0.07). sTREM-1 alone was comparable to a model of clinical variables (area under receiver operating characteristic curve [AUC] 0.81, 95% confidence interval [CI] 0.77-0.85 vs AUC 0.79, 95% CI 0.74-0.84; p = 0.43). The combination of sTREM-1 and clinical variables yielded greater mortality discrimination than clinical variables alone (AUC 0.83, 95% CI 0.79-0.87; p = 0.004).

CONCLUSIONS

sTREM-1 predicts mortality from infection in a tropical, middle-income country comparably to a model derived from clinical variables and, when combined with clinical variables, can further augment mortality prediction.

TRIAL REGISTRATION

The Ubon-sepsis study was registered on ClinicalTrials.gov ( NCT02217592 ), 2014.

Comparing the performance of SOFA, TPA combined with SOFA and APACHE-II for predicting ICU mortality in critically ill surgical patients: A secondary analysis

INTRODUCTION

Total psoas muscle area (TPA) can indicate the status of the entire human body's skeletal muscle mass. It has been reported that lower TPA can increase the risk of mortality in critically ill patients. The aim of our study was to evaluate the relationship between TPA and ICU mortality and to compare the performance of Sequential Organ Failure Assessment (SOFA), TPA combined with SOFA and Acute Physiology, Chronic Health Evaluation (APACHE-II) for predicting ICU mortality in critically ill surgical patients.

METHODS

This study was a retrospective observational cohort study with a total of 96 critically ill surgical patients, ages 21-96 years old. Main outcome measures included difficult-to-wean (DTW), operation methods, ICU mortality, ICU stay, APACHE II, sepsis and SOFA. CT-scan assessed the TPA. It is acknowledged that the entire study was completed by Hao-Wei Kou et al. and the data were uploaded from plosone.com. The authors used this data only for secondary analysis.

RESULTS

The results showed that TPA is a protective factor for ICU mortality (OR: 0.99 95% [0.99, 1.00], P = 0.0269). In addition, when we defined sarcopenia-based TPA, our study showed that sarcopenia increased the risk of ICU mortality (OR:3.73 (1.27, 10.98) P = 0.0167. Furthermore, discrimination of ICU mortality was significantly higher using SOFA (AUROC, 0.7810 [99% CI, 0.6658-0.8962]) than either TPA (AUROC, 0.7023 [99% CI, 0.5552-0.8494]) or APACHE II score (AUROC, 0.7447 [99% CI, 0.6289-0.8604]). Additionally, when we combined TPA with SOFA score, the ROC of TPA + SOFA (AUROC, 0.8647 [99% CI, 0.7881-0.9412]) was the highest when compared to the other three models.

CONCLUSION

The relationship between TPA and ICU mortality is negative in critically ill surgical patients. In addition, the combination of TPA and SOFA was the best tool among the three scoring systems in providing significant discriminative ability when predicting ICU mortality in critically ill surgical patients.

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.

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.

Prognostic models for adverse pregnancy outcomes in low-income and middle-income countries: a systematic review

INTRODUCTION

Ninety-nine per cent of all maternal and neonatal deaths occur in low-income and middle-income countries (LMIC). Prognostic models can provide standardised risk assessment to guide clinical management and can be vital to reduce and prevent maternal and perinatal mortality and morbidity. This review provides a comprehensive summary of prognostic models for adverse maternal and perinatal outcomes developed and/or validated in LMIC.

METHODS

A systematic search in four databases (PubMed/Medline, EMBASE, Global Health Library and The Cochrane Library) was conducted from inception (1970) up to 2 May 2018. Risk of bias was assessed with the PROBAST tool and narratively summarised.

RESULTS

1741 articles were screened and 21 prognostic models identified. Seventeen models focused on maternal outcomes and four on perinatal outcomes, of which hypertensive disorders of pregnancy (n=9) and perinatal death including stillbirth (n=4) was most reported. Only one model was externally validated. Thirty different predictors were used to develop the models. Risk of bias varied across studies, with the item 'quality of analysis' performing the least.

CONCLUSION

Prognostic models can be easy to use, informative and low cost with great potential to improve maternal and neonatal health in LMIC settings. However, the number of prognostic models developed or validated in LMIC settings is low and mirrors the 10/90 gap in which only 10% of resources are dedicated to 90% of the global disease burden. External validation of existing models developed in both LMIC and high-income countries instead of developing new models should be encouraged.

PROSPERO REGISTRATION NUMBER

CRD42017058044.

External validation of the Simplified Mortality Score for the Intensive Care Unit (SMS-ICU)

BACKGROUND

The Simplified Mortality Score for the Intensive Care Unit (SMS-ICU) is a clinical prediction model, which estimates the risk of 90-day mortality in acutely ill adult ICU patients using 7 readily available variables. We aimed to externally validate the SMS-ICU and compare its discrimination with existing prediction models used with 90-day mortality as the outcome.

METHODS

We externally validated the SMS-ICU using data from 3282 patients included in the Stress Ulcer Prophylaxis in the Intensive Care Unit trial, which randomised acutely ill adult ICU patients with risk factors for gastrointestinal bleeding to prophylactic pantoprazole or placebo in 33 ICUs in Europe. We assessed discrimination, calibration and overall performance of the SMS-ICU and compared discrimination with the commonly used and more complex SAPS II and SOFA scores.

RESULTS

Mortality at day 90 was 30.7%. The discrimination (area under the receiver operating characteristic curve) for the SMS-ICU was 0.67 (95% CI: 0.65-0.69), as compared with 0.68 (95% CI: 0.66-0.70, P = 0.35) for SAPS II and 0.63 (95% CI: 0.61-0.65, P < 0.001) for the SOFA score. Calibration (intercept and slope) was 0.001 and 0.786, respectively, and Nagelkerke's R² (overall performance) was 0.06. The proportions of missing data for the SMS-ICU, SAPS II and SOFA scores were 0.2%, 8.5% and 6.8%, respectively.

CONCLUSIONS

Discrimination for 90-day mortality of the SMS-ICU in this cohort was poor, but similar to SAPS II and better than that of the SOFA score with markedly less missing data.

Performance of SAPS II according to ICU length of stay: A Danish nationwide cohort study

BACKGROUND

Intensive care unit (ICU) severity scores use data available at admission or shortly thereafter. There are limited contemporary data on how the prognostic performance of these scores is affected by ICU length of stay (LOS).

METHODS

We conducted a nationwide cohort study using routinely collected health data from the Danish Intensive Care Database. We included adults with ICU admissions ≥24 hours between 1 January 2012 and 30 June 2016, who survived to ICU discharge and had valid ICU LOS and vital status data registered. We assessed discrimination of the Simplified Acute Physiology Score (SAPS) II for predicting mortality 90 days after ICU discharge, followed by recalibration of the model and assessment of standardized mortality ratios (SMRs) and calibration. Performance was assessed in the entire cohort and stratified by ICU LOS quartiles.

RESULTS

We included 44 523 patients. Increasing SAPS II was associated with increasing ICU LOS. Overall discrimination (area under the receiver-operating characteristics curve) of SAPS II was 0.70 (95% CI: 0.70-0.71), with decreasing discrimination from the first (0.75, 95% CI: 0.73-0.76) to the last (0.64, 95% CI: 0.63-0.65) ICU LOS quartile. SMRs were lower (less deaths) than expected in the first ICU LOS quartile and higher (more deaths) than expected in the last two ICU LOS quartiles. Calibration decreased with increasing ICU LOS.

CONCLUSIONS

We observed that discrimination and calibration of SAPS II decreased with increasing ICU LOS, and that this affected SMRs. These findings should be acknowledged when using SAPS II for clinical, research and administrative purposes.

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.

Addressing the information deficit in global health: lessons from a digital acute care platform in Sri Lanka

Lack of investment in low-income and middle-income countries (LMICs) in systems capturing continuous information regarding care of the acutely unwell patient is hindering global efforts to address inequalities, both at facility and national level. Furthermore, this of lack of data is disempowering frontline staff and those seeking to support them, from progressing setting-relevant research and quality improvement. In contrast to high-income country (HIC) settings, where electronic surveillance has boosted the capability of governments, clinicians and researchers to engage in service-wide healthcare evaluation, healthcare information in resource-limited settings remains almost exclusively paper based. In this practice paper, we describe the efforts of a collaboration of clinicians, administrators, researchers and healthcare informaticians working in South Asia, in addressing the inequality in access to patient information in acute care. Harnessing a clinician-led collaborative approach to design and evaluation, we have implemented a national acute care information platform in Sri Lanka that is tailored to priorities of frontline staff. Iterative adaptation has ensured the platform has the flexibility to integrate with legacy paper systems, support junior team members in advocating for acutely unwell patients and has made information captured accessible to diverse stakeholders to improve service delivery. The same platform is now empowering clinicians to participate in international research and drive forwards improvements in care. During this journey, we have also gained insights on how to overcome well-described barriers to implementation of digital information tools in LMIC. We anticipate that this north-south collaborative approach to addressing the challenges of health system implementation in acute care may provide learning and inspiration to other partnerships seeking to engage in similar work.

A learning health systems approach to improving the quality of care for patients in South Asia

Poor quality of care is a leading cause of excess morbidity and mortality in low- and middle- income countries (LMICs). Improving the quality of healthcare is complex, and requires an interdisciplinary team equipped with the skills to design, implement and analyse setting-relevant improvement interventions. Such capacity is limited in many LMICs. However, training for healthcare workers in quality improvement (QI) methodology without buy-in from multidisciplinary stakeholders and without identifying setting-specific priorities is unlikely to be successful. The Care Quality Improvement Network (CQIN) was established between Network for Improving Critical care Systems and Training (NICST) and University College London Centre for Perioperative Medicine, with the aim of building capacity for research and QI. A two-day international workshop, in collaboration with the College of Surgeons of Sri Lanka, was conducted to address the above deficits. Innovatively, the CQIN adopts a learning health systems (LHS) approach to improving care by leveraging information captured through the NICST electronic multi-centre acute and critical care surveillance platform. Fifty-two delegates from across the CQIN representing clinical, civic and academic healthcare stakeholders from six countries attended the workshop. Mapping of care processes enabled identification of barriers and drivers to the delivery of care and facilitated the selection of feasible QI methods and matrices. Six projects, reflecting key priorities for improving the delivery of acute care in Asia, were collaboratively developed: improving assessment of postoperative pain; optimising sedation in critical care; refining referral of deteriorating patients; reducing surgical site infection after caesarean section; reducing surgical site infection after elective general surgery; and improving provision of timely electrocardiogram recording for patients presenting with signs of acute myocardial infarction. Future project implementation and evaluation will be supported with resources and expertise from the CQIN partners. This LHS approach to building capacity for QI may be of interest to others seeing to improve care in LMICs.

Pakistan Registry of Intensive CarE (PRICE): Expanding a lower middle-income, clinician-designed critical care registry in South Asia

Introduction

In resource-limited settings - with inequalities in access to and outcomes for trauma, surgical and critical care - intensive care registries are uncommon.

Aim

The Pakistan Society of Critical Care Medicine, Intensive Care Society (UK) and the Network for Improving Critical Care Systems and Training (NICST) aim to implement a clinician-led real-time national intensive care registry in Pakistan: the Pakistan Registry of Intensive CarE (PRICE).

Method

This was adapted from a successful clinician co-designed national registry in Sri Lanka; ICU information has been linked to real-time dashboards, providing clinicians and administrators individual patient and service delivery activity respectively.

Output

Commenced in August 2017, five ICU's (three administrative regions - 104 beds) were recruited and have reported over 1100 critical care admissions to PRICE.

Impact and future

PRICE is being rolled out nationally in Pakistan and will provide continuous granular healthcare information necessary to empower clinicians to drive setting-specific priorities for service improvement and research.

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.