Data entry errors and design for model-based tight glycemic control in critical care

Improving data quality in observational research studies: Report of the Cure Glomerulonephropathy (CureGN) network

Background

High data quality is of crucial importance to the integrity of research projects. In the conduct of multi-center observational cohort studies with increasing types and quantities of data, maintaining data quality is challenging, with few published guidelines.

Methods

The Cure Glomerulonephropathy (CureGN) Network has established numerous quality control procedures to manage the 70 participating sites in the United States, Canada, and Europe. This effort is supported and guided by the activities of several committees, including Data Quality, Recruitment and Retention, and Central Review, that work in tandem with the Data Coordinating Center to monitor the study. We have implemented coordinator training and feedback channels, data queries of questionable or missing data, and developed performance metrics for recruitment, retention, visit completion, data entry, recording of patient-reported outcomes, collection, shipping and accessing of biological samples and pathology materials, and processing, cataloging and accessing genetic data and materials.

Results

We describe the development of data queries and site Report Cards, and their use in monitoring and encouraging excellence in site performance. We demonstrate improvements in data quality and completeness over 4 years after implementing these activities. We describe quality initiatives addressing specific challenges in collecting and cataloging whole slide images and other kidney pathology data, and novel methods of data quality assessment.

Conclusions

This paper reports the CureGN experience in optimizing data quality and underscores the importance of general and study-specific data quality initiatives to maintain excellence in the research measures of a multi-center observational study.

Assessment of Glycemic Control Protocol (STAR) Through Compliance Analysis Amongst Malaysian ICU Patients

Purpose

This paper presents an assessment of an automated and personalized stochastic targeted (STAR) glycemic control protocol compliance in Malaysian intensive care unit (ICU) patients to ensure an optimized usage.

Patients and Methods

STAR proposes 1–3 hours treatment based on individual insulin sensitivity variation and history of blood glucose, insulin, and nutrition. A total of 136 patients recorded data from STAR pilot trial in Malaysia (2017–quarter of 2019*) were used in the study to identify the gap between chosen administered insulin and nutrition intervention as recommended by STAR, and the real intervention performed.

Results

The results show the percentage of insulin compliance increased from 2017 to first quarter of 2019* and fluctuated in feed administrations. Overall compliance amounted to 98.8% and 97.7% for administered insulin and feed, respectively. There was higher average of 17 blood glucose measurements per day than in other centres that have been using STAR, but longer intervals were selected when recommended. Control safety and performance were similar for all periods showing no obvious correlation to compliance.

Conclusion

The results indicate that STAR, an automated model-based protocol is positively accepted among the Malaysian ICU clinicians to automate glycemic control and the usage can be extended to other hospitals already. Performance could be improved with several propositions.

Stochastic targeted (STAR) glycemic control: design, safety, and performance

INTRODUCTION

Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach that directly accounts for intra- and interpatient variability with a stochastically derived maximum 5% risk of blood glucose (BG) below 72 mg/dl. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in virtual and clinical pilot trials.

METHODS

Clinically validated virtual trials using data from 370 patients in the SPRINT (Specialized Relative Insulin and Nutrition Titration) study were used to design the STAR protocol and test its safety, performance, and required clinical effort prior to clinical pilot trials. Insulin and nutrition interventions were given every 1-3 h as chosen by the nurse to allow them to manage workload. Interventions were designed to maximize the overlap of the model-predicted (5-95(th) percentile) range of BG outcomes with the 72-117 mg/dl band and thus provide a maximum 5% risk of BG <72 mg/dl. Interventions were calculated using clinically validated computer models of human metabolism and its variability in critical illness. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of the American College of Chest Physicians/Society of Critical Care Medicine (ACCP/SCCM) goal (25 kg/kcal/h). Insulin doses were limited (8 U/h maximum), with limited increases based on current rate (0.5-2.0 U/h). Initial clinical pilot trials involved 3 patients covering ~450 h. Approval was granted by the Upper South A Regional Ethics Committee.

RESULTS

Virtual trials indicate that STAR provides similar glycemic control performance to SPRINT with 2-3 h (maximum) measurement intervals. Time in the 72-126 mg/dl and 72-145 mg/dl bands was equivalent for all controllers, indicating that glycemic outcome differences between protocols were only shifted in this range. Safety from hypoglycemia was improved. Importantly, STAR using 2-3 h (maximum) intervention intervals reduced clinical burden up to 30%, which is clinically very significant. Initial clinical trials showed glycemic performance, safety, and management of inter- and intrapatient variability that matched or exceeded the virtual trial results.

CONCLUSIONS

In virtual trials, STAR TGC provided tight control that maximized the likelihood of BG in a clinically specified glycemic band and reduced hypoglycemia with a maximum 5% (or lower) expected risk of light hypoglycemia (BG <72 mg/dl) via model-based management of intra- and interpatient variability. Clinical workload was self-managed and reduced up to 30% compared with SPRINT. Initial pilot clinical trials matched or exceeded these virtual results.