Pediatric surgical quality improvement in low- and middle-income countries: What data to collect?
Sarah J Ullrich1, Phyllis Kisa2, Arlene Muzira2
1Department of Surgery, Yale University School of Medicine, New Haven, CT.
Insights
Minimal data, including diagnosis, procedure, and district, can effectively predict surgical patient mortality in low-resource settings. This approach aids quality improvement in low- and middle-income countries (LMICs).
Area of Science:
- Global Surgery
- Health Informatics
- Surgical Quality Improvement
Background:
- Surgical access is increasing in low- and middle-income countries (LMICs), necessitating robust data for quality assessment.
- Existing data collection tools from high-income countries are often too complex for LMIC implementation.
- There is a need for a minimum dataset to accurately adjust risk and predict surgical outcomes in LMICs.
Purpose of the Study:
- To identify the minimum dataset required for effective risk adjustment of perioperative mortality in LMICs.
- To assess the impact of data granularity and variable reduction on predictive model accuracy.
- To inform the development of practical data collection tools for surgical quality improvement in resource-limited settings.
Main Methods:
- Retrospective analysis of 3,194 pediatric surgery patients at Mulago National Referral Hospital, Kampala, Uganda (2014-2018).
- Modeling was performed to evaluate the effects of reducing data granularity (e.g., age ranges vs. days) and the number of predictor variables.
- Area Under the Receiver Operating Curve (aUC) was used to assess model predictive performance.
Main Results:
- Models using minimal data (diagnosis, procedure, district) achieved high predictive accuracy for perioperative mortality (aUC = 0.915), comparable to models using all available variables (aUC = 0.932).
- Measuring age in ranges was found to be nearly as effective as using exact age in days for risk adjustment.
- Primary diagnosis was the most significant predictor of mortality in reduced models.
Conclusions:
- Effective risk adjustment for perioperative mortality in LMICs is feasible using a minimal set of objective variables.
- This simplified data approach can be readily implemented by healthcare providers and policymakers in LMICs to monitor and enhance patient outcomes.
- The findings support the development of practical, data-driven strategies for surgical quality improvement in resource-constrained environments.
Background:
As surgical access expands in low- and middle-income countries, risk-adjusted outcomes data are needed to measure and improve surgical quality. Existing data collection tools in high-income countries are complex and may be burdensome to implement in low and middle income countries. This study determined the minimum dataset needed for adequate risk adjustment to predict perioperative mortality using data collected in a low- and middle-income countries.
Methods:
All patients admitted to the pediatric surgery ward at Mulago National Referral Hospital in Kampala, Uganda, from January 1, 2014 through December 31, 2018 were included. Studies were performed modelling the effects of reducing data granularity and reducing number of variables on the area under the receiver operating curve.
Results:
Of the 3,194 patients included, 1,941(61%) were male, 957(30%) were neonates, 1,714 (54%) had an operation, and the overall mortality rate was 14%. Granularity reduction analyses found that measuring age in ranges was equivalent to recording age in days (area under the receiver operating curve = 0.776; 95% confidence interval, 0.754%-0.798%, vs 0.815, 95% confidence interval, 0.794%-0.837%). Variable reduction analyses found that models with 3 predictor variables (diagnosis, procedure, and district) reached a maximum area under the receiver operating curve of 0.915 (95% confidence interval, 0.903%-0.928%), which was equivalent to the model using all available predictor variables (area under the receiver operating curve = 0.932; 95% confidence interval, 0.922%-0.943%). For all 3-variable models, the primary diagnosis contributed most to predictive ability (P < .001).
Conclusion:
Effective risk adjustment for perioperative mortality can be performed in low and middle income countries using minimal, objective variables often already part of the patient's medical record. This approach can be used by clinicians, hospital administrators, and policymakers low- and middle-income countries looking to begin data collection to track and improve patient outcomes.
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