Excess length of stay and readmission following hospital-acquired bacteraemia: a population-based cohort study applying a multi-state model approach

How to avoid time-related types of bias in the analysis of clinical infectious diseases: demonstration and methods

BACKGROUND

Competing risk and immortal time bias can significantly affect the interpretation of analyses in infection disease research where patients may experience multiple outcomes. These biases can distort study results, leading to misestimation of event rates or therapy effects, and ultimately misinform clinical decisions.

OBJECTIVE

This article aims to explain the concepts of competing risk and immortal time bias while providing a clear understanding of how these biases arise and offering practical methods for addressing them in clinical studies. A study of patients in the intensive care unit (ICU) is used as an illustrative example.

SOURCES

References were compiled through searches of MEDLINE/PubMed and Google Scholar up to December 2024.

CONTENT

Competing risk bias occurs when a patient is at risk for more than one mutually exclusive event, such as death from different causes, which can skew survival analyses if not properly accounted for. Immortal time bias arises when periods during which the outcome cannot occur for patients with an intermediate exposure are improperly included in a survival analysis. This leads to altered effects and duration of stay estimation. In the ICU data example, ignoring competing risks results in impossible probability estimates and relative overestimation of incidence by as much as 52%. Ignoring time dependencies leads to the flawed conclusion that bloodstream infection is protective against patient death in the ICU, whereas a valid approach results in a significant harmful effect. Solutions such as multi-state models or extended Cox regression models are presented to mitigate these biases. Examples from published literature with proper handling of these biases are provided.

IMPLICATIONS

Properly accounting for competing risk and immortal time biases ensures more reliable and valid results, ultimately guiding better clinical decision-making and improving patient outcomes in the management of infectious diseases.

Same-day discharge following lumbar spine neural decompression

INTRODUCTION

The concept of same-day discharge following lumbar spine surgery for neural elements decompression, introduced in the 1980 s, represents a significant paradigm shift in postoperative care, offering potential benefits in terms of efficiency, convenience, and cost-effectiveness. However, concerns regarding patient safety and complications have persisted. This study aims to assess same-day discharge rates following lumbar spine decompression surgery, identify preoperative predictors, and compare outcomes between same-day discharge and hospitalization.

METHODS

A retrospective comparative analysis was conducted using data from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Participant Use File spanning 2016 to 2021. A total of 58,430 patients undergoing lumbar spine decompression procedures were included. Statistical analysis was performed using SPSS software, with significance determined at p < 0.05.

RESULTS

Significant differences were observed in preoperative factors, operative variables, and postoperative outcomes between patients discharged on the same day and those hospitalized. Preoperative comorbidities, operative time, length of stay, utilization of minimally invasive approaches, and 30-day mortality rates differed significantly between the two groups. Notably, same-day discharge demonstrated superior outcomes, with lower rates of unplanned intubation, readmission, and reoperation compared to hospitalization.

CONCLUSION

In conclusion, same-day discharge following lumbar spine decompression procedures holds promise as a safe and effective practice, offering advantages in terms of patient outcomes and healthcare resource utilization. Further research and implementation of standardized protocols are warranted to maximize the benefits of same-day discharge while ensuring patient safety and satisfaction.

Characteristics, costs, and outcomes associated with central-line-associated bloodstream infection and hospital-onset bacteremia and fungemia in US hospitals

OBJECTIVES

To compare characteristics and outcomes associated with central-line-associated bloodstream infections (CLABSIs) and electronic health record-determined hospital-onset bacteremia and fungemia (HOB) cases in hospitalized US adults.

METHODS

We conducted a retrospective observational study of patients in 41 acute-care hospitals. CLABSI cases were defined as those reported to the National Healthcare Safety Network (NHSN). HOB was defined as a positive blood culture with an eligible bloodstream organism collected during the hospital-onset period (ie, on or after day 4). We evaluated patient characteristics, other positive cultures (urine, respiratory, or skin and soft-tissue), and microorganisms in a cross-sectional analysis cohort. We explored adjusted patient outcomes [length of stay (LOS), hospital cost, and mortality] in a 1:5 case-matched cohort.

RESULTS

The cross-sectional analysis included 403 patients with NHSN-reportable CLABSIs and 1,574 with non-CLABSI HOB. A positive non-bloodstream culture with the same microorganism as in the bloodstream was reported in 9.2% of CLABSI patients and 32.0% of non-CLABSI HOB patients, most commonly urine or respiratory cultures. Coagulase-negative staphylococci and Enterobacteriaceae were the most common microorganisms in CLABSI and non-CLABSI HOB cases, respectively. In case-matched analyses, CLABSIs and non-CLABSI HOB, separately or combined, were associated with significantly longer LOS [difference, 12.1-17.4 days depending on intensive care unit (ICU) status], higher costs (by $25,207-$55,001 per admission), and a >3.5-fold increased risk of mortality in patients with an ICU encounter.

CONCLUSIONS

CLABSI and non-CLABSI HOB cases are associated with significant increases in morbidity, mortality, and cost. Our data may help inform prevention and management of bloodstream infections.