Sepsis, the Importance of Triage, and the Smart Triage program

Sepsis is a syndrome characterized by an inflammatory state resulting from a presumed or proven infection that results in organ dysfunction and/or death [1], with global disease-specific mortality of 20% and a case fatality ratio of 25% or higher in low-middle income countries (LMICs) [2,3]. Earlier treatment results in better outcomes; therefore, international guidelines for timely treatment were shortened from 3 hours to 1 hour in 2016 [4]. Treatment comprises a “sepsis bundle” of antimicrobials, fluid, and oxygen as clinically indicated [5].

While high-income countries have employed evidence-based patient triage strategies to expedite sepsis treatment [6], these strategies involve various laboratory investigations rarely available in LMIC settings, where delays in treatment leading to a high case fatality ratio are commonplace [7]. LMIC-specific guidelines such as the Emergency Triage Assessment and Treatment (ETAT) [8] and others [9,10], show good predictive power for mortality and improve clinical outcomes when successfully implemented [11–13]. However, the complexity of these guidelines introduces challenges in everyday usage and staff training in an environment where patient burden and new-staff turnover is high [14,15]. Infrastructural barriers to patient care organization and medication shortages further delay sepsis treatment even when a sick child has been identified through effective triage [15].

The Smart Triage program [16] tackles guideline complexity via a prediction model for admission to facilitate the identification of children at risk of deterioration. This model classifies children into three levels of risk categories, using clinical variables found in ETAT and other triage studies that are then used as inputs in an algorithm derived from machine learning techniques. Our methods for initial consideration of variables have been published [17], and the details of the final algorithm are under peer review. This model was developed using data from the trial’s pre-intervention phase, and has been integrated into a mobile application to reduce cognitive error and training time compared to a manual triage process. The triage results from the mobile application will be linked to a Clinician Dashboard local web page and a locally hosted Bluetooth treatment-tracking system to improve patient care organization. The Smart Triage program contains all of these components in a package, which will be implemented at the Jinja Regional Referral Hospital (JRRH) in Uganda, in an interventional trial for which the protocol is published [16].

Economic evaluation of LMIC triage programs

When considering new programs in low-resource settings, economic evaluations such as cost-effectiveness analyses can inform policy decisions and motivate further research. While cost-effectiveness analyses of quality improvement initiatives that deliver earlier sepsis care have demonstrated reduced cost and increased benefit over standard of care in high-income settings [18,19], such analyses are sparse for triage programs in LMICs. In the literature, studies may report partial costs without a formal economic evaluation [14], or perform cost-effectiveness analyses on intermediate outcomes without linkage to clinical endpoints [20]. One analysis used a robust decision-analytic model, but interventions evaluated spanned across the inpatient and post-discharge timeframes and were not isolated to the triage process [21]. Lastly, many analyses focused on the perspective of the healthcare system [13,20,22], but given the lack of universal health coverage in many LMICs, a societal perspective that also includes out-of-pocket (OOP) costs and time/productivity loses may be more appropriate. Therefore, in light of the literature gaps presented, we propose a cost-effectiveness analysis alongside the ongoing Smart Triage trial, using a decision analytic model, costing data from previous trials [23], linkage of intermediate outcomes to clinical endpoints [24], and a societal perspective. Our proposed economic evaluation serves to complement the previously published protocol of the Smart Triage trial [16], and will play a role in shaping policy and encouraging follow-up research surrounding the Smart Triage platform.

Study objectives

1. Calculate the incremental cost relative to years-of-life lost (YLL) averted from the Smart Triage program compared to the standard of care, using the pre-intervention phase at JRRH as the control.
2. Determine the probability of cost-effectiveness of the Smart Triage program under different willingness-to-pay thresholds, when implemented in a government regional referral hospital in Uganda, using a decision-analytic model from the societal perspective. The comparator will be an ETAT-based manual system widely used in Uganda, including our study site prior to implementation of Smart Triage.
3. Investigate the impact of OOP costs on cost-effectiveness by secondary analyses from the government and patient perspectives.

Description of parent clinical trial

The Smart Triage program and a corresponding clinical trial will be implemented in the pediatric outpatient department at JRRH in Uganda, which serves children up to age 18 presenting from the community at various degrees of disease severity. The outpatient department serves as a gateway to admission if necessary. All presenting children, except those with scheduled appointments, are eligible to participate. However, enrollment of all children is not possible due to a high patient load, and therefore a sampling framework as published in the parent trial’s protocol is used to obtain a representative sample [17]. The Smart Triage trial was designed as a difference in differences trial, with a Kenyan site for control. However, we will restrict this cost-effectiveness analysis to the Ugandan site at JRRH, to minimize the impact on our analysis due to variations in baseline ETAT implementation between countries. Therefore, we will compare the post-intervention data at JRRH with that of the pre-intervention phase, with the latter serving as control for our analysis.

The primary outcome of the Smart Triage trial is the difference in the proportion of children who receive a sepsis bundle of care within one hour before and after Smart Triage’s implementation. Enrolled children will be followed up to assess in-hospital mortality (if admitted), 7-day post-discharge mortality, and 7-day post-discharge healthcare utilization rates. Of note, Smart Triage does not aim to indiscriminately increase the number or proportion of children receiving sepsis bundle. Indication for sepsis treatment is determined by local clinicians both before and after Smart Triage implementation. Smart Triage simply facilitates the timelier delivery of sepsis treatment, if deemed indicated by local clinicians, through a data-driven quality improvement process.

Further details of the parent clinical trial such as program implementation, clinical trial protocol, and sample size calculations have been published [16] with appropriate clinical trial registration on clinicaltrials.gov (NCT04304235). Ethics approval has been obtained from Makerere University School of Public Health (MUSPH) Higher Degrees, Research and Ethics Committee. MUSPH provided approval for the parent study and this economic evaluation to be conducted at Jinja Regional Referral Hospital in Jinja, Uganda. Written informed consent for the parent clinical trial as well as follow-up analyses such as this economic evaluation will be obtained from all caregivers/participants prior to enrollment. Patient recruitment has commenced and program implementation is underway, with the earliest projected end-date of data collection being late 2021. While the parent trial’s protocol aims to establish clinical efficacy of the Smart Triage platform, this protocol aims to establish cost-effectiveness, and will be conducted alongside the parent clinical trial.

Decision analytic model

We will perform the economic evaluation of Smart Triage using a decision analytic model (Fig 1). The model structure is based on a similar model used to evaluate the cost-effectiveness of a post-discharge program in Uganda named Smart Discharges [25], and the model care pathways have been shown to have face validity by collaborators at JRRH. As patients in Uganda bear a significant portion of healthcare costs due to the lack of universal healthcare, we will take a societal perspective for our base case analysis [26]. Also, since Smart Triage is a program that facilitates immediate treatment for an acute illness, it would likely exert its strongest effects on short-term outcomes, and therefore we will use a 7-day horizon for our analysis. This 7-day horizon is also congruent with the recommended duration of antibiotic administration for a typical episode of sepsis [4]. Model structure and key assumptions are presented in Fig 1 and Table 1, respectively.

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Fig 1. Decision analytic model of post-triage and post-discharge care pathways, used for the cost-effectiveness analysis of Smart Triage.

https://doi.org/10.1371/journal.pone.0260044.g001

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Table 1. Key assumptions of the decision analytic model.

https://doi.org/10.1371/journal.pone.0260044.t001

Model inputs

Model and parameter uncertainty

Strengths & implications of study

The Smart Triage program is a pioneering triage infrastructure to expedite sepsis treatment using a mobile-based algorithm derived from existing guidelines combined with organizational assistance through an automated patient tracking system and an electronic clinician dashboard. The results of our proposed economic evaluation can further encourage follow-up trials and governmental scale-up efforts, especially when combined with subsequent budget impact analyses to model a district-level or national-level scale-up in Uganda [32]. To this end, we have designed Smart Triage to be of low technical maintenance after initial set up and have heavily involved local stakeholders during program implementation to ensure sustainability after study termination. This model of sustainability has been successfully used in similar projects by our group [33]. In addition, our implementation of Smart Triage as a package allows the program’s various components to work in tandem to tackle multiple coexisting barriers to care. Although this implementation strategy prevents the isolated cost-effectiveness analysis of each package component, we are conceptually unlikely to exert a clinically significant effect by tackling one barrier to care but not others. Finally, our unpublished study data thus far indicates a high follow-up rate of 98.3%, minimizing the impact of incomplete outcome data in our analysis. As we relied exclusively on telephone follow-up, we optimized follow-up through rigorous checks for phone number accuracy, repeat calls to unreached participants through a standardized process, and use of shared phone number, such as that of a neighbor, should a personal number not be available. Our final follow-up rate will be published upon completion of the trial.

To our knowledge, our analysis is the first proposed study to employ a decision-analytic model in evaluating the cost-effectiveness of a sepsis program focused on triage in an LMIC setting. This technique allows for a more comprehensive evaluation of cost-effectiveness compared to previous economic analyses of LMIC triage programs [13,20], and allows for comparison of cost-effectiveness to analyses of sepsis triage programs in high income settings [18,19]. Specifically, our model allows linkage of receiving sepsis bundle within one hour to mortality based on published data, thereby increasing the clinical interpretability of our results. Additionally, a decision-analytic model allows us to incorporate post-discharge follow-up data, such as healthcare utilization and post-discharge mortality, rather than artificially restricting our analyses to the inpatient timeframe. Lastly, the use of a probabilistic sensitivity analysis incorporates uncertainty into our analysis following international modelling guidelines [27].

To enhance external validity, JRRH was selected for our study site due to the similarity of its triage infrastructure and existing use of ETAT to many Ugandan centers of comparable catchment size [34]. Therefore, despite the single-center nature of our analysis, our estimates of clinical efficacy may be suggestive of that in a typical Ugandan regional referral center, a hypothesis awaiting confirmation through a multicenter follow-up trial. Additionally, our use of a costing dataset sampled from various geographically diverse regions across Uganda also increases external validity outside of JRRH [23]. Although this costing data arises from pneumonia cases, the similar treatment requirements to sepsis make this data the best option in the absence of data for all-cause sepsis. While healthcare utilization and associated costs of pneumonia may not be identical to sepsis, the two disease processes have significant overlap as pneumonia may progress to sepsis if sub-optimally treated, particularly in resource-constrained settings. Therefore, we chose to use secondary data for pneumonia care costs in their entirety, rather than an item-by-item selection of costs. To this end, our model could be updated with LMIC-specific all-cause sepsis costing data when available. Finally, to further optimize external validity, we used the national Ugandan average in daily wages, rather than the local average, to calculate the cost of lost wages from days of missed work [35]. Through these uses of multiregional and national linkage data to maximize external validity, our analysis is well-situated to inform further multicenter research from a clinical and economic standpoint.

As Smart Triage is being implemented during the COVID-19 pandemic, we have also taken steps to maximize our analysis’s external validity to the post-pandemic setting. To this end, we will perform a sensitivity analysis where time periods with decreases in estimated patient load greater than 50% will be excluded. These time periods may bias our results by artificially increasing proportion of children receiving sepsis bundle within one hour due to a lower triage burden not seen outside of a pandemic setting, or may conversely result in more extensive delays due to lower staffing ratios from staff redeployment. According to local staff, these decreases in patient load only occurred within the first two months of patient recruitment as of this protocol’s writing and may have resulted from caregivers’ hesitancy to seek care due to travel restrictions early in the pandemic. We were also informed by local administrative and clinical staff that while JRRH has put in place infectious disease precautions in response to the pandemic, the triage process remains unchanged from the pre-pandemic process. Therefore, changes observed in the clinical trial may be more easily attributable to Smart Triage rather than another change to the triage system made in response to the pandemic.

Limitations of study

Our linkage of receiving sepsis bundle within one hour to mortality provides a more clinically relevant endpoint to our analysis by calculating cost per YLL averted. However, we use data from high-income settings for this linkage, as LMIC-specific data is not yet available due to the recency of the one-hour recommendation for defining timely treatment [4]. Despite this apparent limitation, we postulate that this substitution is appropriate, as the relationship between timely sepsis bundle and mortality in a high-income setting is likely a conservative estimate of that in the LMIC setting for several reasons. Firstly, children are more likely to present later in their disease course due to pre-hospital barriers to care such as delayed caregiver recognition of illness or inefficient transportation [36]. Secondly, children in LMICs have a lower physiological reserve from a higher comorbidity burden and malnutrition rate. Lastly, these children may present in greater need of resuscitation than a high-income setting, due to the preponderance of dehydration from diarrheal diseases [3] aggregated by decreased access to clean drinking water. For reasons such as these, early sepsis bundle may conceptually be more crucial and exert an even greater mortality benefit on children in LMICs than high-income settings. Therefore, as a corollary, not only is our substitution with high-income setting linkage data appropriate, but a future repeated analysis with LMIC-specific data may reveal even greater cost-effectiveness than our proposed analysis. On the other hand, if we had foregone the linkage of a timely sepsis bundle to mortality due to the current unavailability of LMIC-specific linkage data, our analysis would generate only the cost per child receiving timely bundle, as opposed to cost per YLL-averted, with a significantly reduced clinical interpretability.

Another area where LMIC-specific evidence is lacking is long-term quality-of-life data and corresponding disability weights in sepsis survivors. Therefore, while cost per DALY averted is a widely used metric to calculate cost-effectiveness in the LMIC setting, we are unable to estimate years lived with disability for calculating DALYs. Extrapolating quality-of-life evidence from high-income settings into the LMIC setting would be inappropriate given vast differences in social support systems. We also lacked the resources to carry out our own LMIC-based valuation study where different health states are methodically assigned disability weights. When such evidence with appropriate valuation data becomes available in the LMIC setting, long-term outcomes may be incorporated into our economic evaluation through a Markov model. An estimate of the cost per DALY averted from such a model would enhance comparability with estimates of the cost-effectiveness of other common health systems interventions in LMICs.

Lastly, while our cost-effectiveness analysis will be an important first step in shaping local policy towards scaling up of Smart Triage should cost effectiveness be demonstrated, our analysis does not take into account the impact of scale up on issues such as reallocation of resources, impact on healthcare budget given sepsis’ burden of disease [32], and multicenter scale-up costs [37]. To this end, future directions of study may include a budget impact analysis factoring in scale-up costs, the number of centers for scale up, and the large burden of disease of sepsis in these centers.