Analyzing counterintuitive data

TL;DR

This study investigates unexpected findings where higher postoperative pain scores in CABG patients are linked to lower mortality and shorter hospital stays, highlighting the need for further research into counterintuitive data in healthcare analytics.

Contribution

It presents the first analysis of counterintuitive pain-outcome associations in post-CABG patients, emphasizing the importance of scrutinizing unexpected data patterns in electronic health records.

Findings

Higher pain scores correlated with lower mortality.

Increased pain associated with shorter hospital stays.

Counterintuitive results highlight need for further investigation.

Abstract

Purpose: To explore the issue of counterintuitive data via analysis of a representative case and further discussion of those situations in which the data appear to be inconsistent with current knowledge. Case: 844 postoperative CABG patients, who were extubated within 24 hours of surgery were identified in a critical care database (MIMIC-III). Nurse elicited pain scores were documented throughout their hospital stay on a scale of 0 to 10. Levels were tracked as mean, median, and maximum values, and categorized as no (0/10), mild (1-3), moderate (4-6) and severe pain (7-10). Regression analysis was employed to analyze the relationship between pain scores and outcomes of interest (mortality and hospital LOS). After covariate adjustment, increased levels of pain were found to be associated with lower mortality rates and reduced hospital LOS. Conclusion: These counterintuitive results for post-CABG pain related outcomes have not been previously reported. While not representing strong enough evidence to alter clinical practice, confirmed and reliable results such as these should serve as a research trigger and prompt further studies into unexpected associations between pain and patient outcomes. With the advent of frequent secondary analysis of electronic health records, such counterintuitive data results are likely to become more frequent. We discuss the issue of counterintuitive data in extended fashion, including possible reasons for, and approaches to, this phenomenon.