Increasing SARS-CoV-2 testing capacity through specimen pooling: An acute care center experience

Global health crises often strain diagnostic testing resources. Traditional individual testing methods become unsustainable when demand exceeds supply. Prior research has shown that pooling specimens can help conserve reagents and equipment. However, the effectiveness of pooling in maintaining diagnostic accuracy remains a key question. No prior work had resolved how pooling impacts test performance during high-volume periods. This gap motivated exploration of automated pooling algorithms. The need to maintain turnaround time while reducing costs became critical. Validation of pooling methods in real-world settings was lacking. Understanding how pooling affects weakly positive specimens is essential for implementation.

This study aimed to evaluate specimen pooling as a strategy to increase SARS-CoV-2 testing capacity. The goal was to address resource limitations caused by high testing demand. A specific problem was the shortage of consumables during the pandemic. The motivation was to maintain diagnostic accuracy while reducing costs. The authors sought to validate a pooling algorithm in an acute care setting. They wanted to measure the impact on cost and performance. The study focused on a four-in-one pooling approach. The objective was to assess both cost savings and diagnostic reliability.

A four-in-one pooling algorithm was developed for SARS-CoV-2 testing. The algorithm was validated using a clinical microbiology laboratory setup. Correlation and agreement metrics were calculated to assess performance. A custom Excel tool was created to assist with result interpretation. Technologists used the tool for verification and data entry. Signals from pooled and individual tests were compared. Crossing point differences were measured to evaluate agreement. Post-implementation data tracked cost savings over eight months.

Validation showed a strong correlation between individual and pooled test signals. The average crossing point difference was 1.352 cycles. The 95% confidence interval ranged from -0.235 to 2.940. Overall agreement between individual and pooled tests was 96.8%. Agreement dropped below 60% for weakly positive specimens after a crossing point of 35. Post-implementation data revealed an 85.5% reduction in consumable costs. This reduction was sustained over eight months of operation. Pooling maintained diagnostic performance while increasing testing capacity.

The authors propose that pooling is an effective method for SARS-CoV-2 testing during resource shortages. They suggest that automated pooling algorithms can help conserve consumables. The study indicates that pooling maintains diagnostic accuracy for most specimens. The researchers propose that cost savings can be substantial with this approach. They suggest that pooling can increase testing capacity without compromising performance. The findings suggest that pooling is suitable for high-volume testing periods. The authors propose that this method can be used to extend resource availability. They suggest that pooling remains a viable option during ongoing pandemic surges.