Failure to rescue as a patient safety indicator for neurosurgical patients: are we there yet? A systematic review

Quality indicators in cranial neurosurgery: current insights and critical evaluation - a systematic review

In recent decades, there has been increasing interest in measuring the quality of care across all medical fields, including neurosurgery. This interest aims not only to optimize care but also to reduce healthcare costs. For this purpose, different quality indicators (QIs) have been developed. We performed a systematic review according to the PRISMA guidelines aiming at identifying studies that critically evaluate QIs applied in cranial neurosurgery. A total of 34 studies, suggesting 22 indicators, were identified. The most discussed indicator was the 30-day readmission rate, followed by the 30-day reoperation rate. The majority of QIs are influenced by baseline and underlying patient characteristics, reflecting the severity of the patient`s underlying disease, rather than adherence to best available evidence of treatment. Therefore, it is crucial to implement adequate risk adjustment strategies when applying QIs to compensate for differences in patient complexity and to ensure that departments that are treating high-risk patients do not have worse results. The review revealed several limitations of the currently used quality indicators. Most suggested indicators are attractive from a payer point of view, easy to measure and therefore convenient for reimbursement purposes. However, from a clinician's point of view, most indicators were considered poor performance markers as they do not correlate with meaningful outcome and do not reflect treatment quality. In addition, there is a lack of disease- and neurosurgery specific indicators. This highlights the need for clinicians to actively participate in developing more clinically relevant QIs tailored to neurosurgical practice.

A Predictive Model of Failure to Rescue After Thoracolumbar Fusion

OBJECTIVE

Although failure to rescue (FTR) has been utilized as a quality-improvement metric in several surgical specialties, its current utilization in spine surgery is limited. Our study aims to identify the patient characteristics that are independent predictors of FTR among thoracolumbar fusion (TLF) patients.

METHODS

Patients who underwent TLF were identified using relevant diagnostic and procedural codes from the National Surgical Quality Improvement Program (NSQIP) database from 2011-2020. Frailty was assessed using the risk analysis index (RAI). FTR was defined as death, within 30 days, following a major complication. Univariate and multivariable analyses were used to compare baseline characteristics and early postoperative sequelae across FTR and non-FTR cohorts. Receiver operating characteristic (ROC) curve analysis was used to assess the discriminatory accuracy of the frailty-driven predictive model for FTR.

RESULTS

The study cohort (N = 15,749) had a median age of 66 years (interquartile range, 15 years). Increasing frailty, as measured by the RAI, was associated with an increased likelihood of FTR: odds ratio (95% confidence interval [CI]) is RAI 21-25, 1.3 [0.8-2.2]; RAI 26-30, 4.0 [2.4-6.6]; RAI 31-35, 7.0 [3.8-12.7]; RAI 36-40, 10.0 [4.9-20.2]; RAI 41- 45, 21.5 [9.1-50.6]; RAI ≥ 46, 45.8 [14.8-141.5]. The frailty-driven predictive model for FTR demonstrated outstanding discriminatory accuracy (C-statistic = 0.92; CI, 0.89-0.95).

CONCLUSION

Baseline frailty, as stratified by type of postoperative complication, predicts FTR with outstanding discriminatory accuracy in TLF patients. This frailty-driven model may inform patients and clinicians of FTR risk following TLF and help guide postoperative care after a major complication.