Measuring Outcomes for Neurosurgical Procedures

Neurosurgery outcomes and complications in a monocentric 7-year patient registry

Introduction

Capturing adverse events reliably is paramount for clinical practice and research alike. In the era of “big data”, prospective registries form the basis of clinical research and quality improvement.

Research question

To present results of long-term implementation of a prospective patient registry, and evaluate the validity of the Clavien-Dindo grade (CDG) to classify complications in neurosurgery.

Materials and methods

A prospective registry for cranial and spinal neurosurgical procedures was implemented in 2013. The CDG – a complication grading focused on need for unplanned therapeutic intervention – was used to grade complications. We assess construct validity of the CDG.

Results

Data acquisition integrated into our hospital workflow permitted to include all eligible patients into the registry. We have registered 8226 patients that were treated in 11994 surgeries and 32494 consultations up until December 2020. Similarly, we have captured 1245 complications on 6308 patient discharge forms (20%) since full operational status of the registry. The majority of complications (819/6308 ​= ​13%) were treated without invasive treatment (CDG 1 or CDG 2). At discharge, there was a clear correlation of CDG and the Karnofsky Performance Status (KPS, rho ​= ​-0.29, slope -7 KPS percentage points per increment of CDG) and the length of stay (rho ​= ​0.43, slope 3.2 days per increment of CDG).

Discussion and conclusion

Patient registries with high completeness and objective capturing of complications are central to the process of quality improvement. The CDG demonstrates construct validity as a measure of complication classification in a neurosurgical patient population.

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A prospective registry for cranial and spinal neurosurgical procedures was implemented in 2013.

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We have registered 8226 patients that were treated in 11994 surgeries and 32494 consultations up until December 2020.

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There was a clear correlation of CDG with the Karnofsky Performance Status and with length of hospital stay.

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The Clavien-Dindo grading (CDG) demonstrates construct validity as a measure of complication severity in a neurosurgical patient population.

Machine learning-augmented objective functional testing in the degenerative spine: quantifying impairment using patient-specific five-repetition sit-to-stand assessment

OBJECTIVE

What is considered "abnormal" in clinical testing is typically defined by simple thresholds derived from normative data. For instance, when testing using the five-repetition sit-to-stand (5R-STS) test, the upper limit of normal (ULN) from a population of spine-healthy volunteers (10.5 seconds) is used to identify objective functional impairment (OFI), but this fails to consider different properties of individuals (e.g., taller and shorter, older and younger). Therefore, the authors developed a personalized testing strategy to quantify patient-specific OFI using machine learning.

METHODS

Patients with disc herniation, spinal stenosis, spondylolisthesis, or discogenic chronic low-back pain and a population of spine-healthy volunteers, from two prospective studies, were included. A machine learning model was trained on normative data to predict personalized "expected" test times and their confidence intervals and ULNs (99th percentiles) based on simple demographics. OFI was defined as a test time greater than the personalized ULN. OFI was categorized into types 1 to 3 based on a clustering algorithm. A web app was developed to deploy the model clinically.

RESULTS

Overall, 288 patients and 129 spine-healthy individuals were included. The model predicted "expected" test times with a mean absolute error of 1.18 (95% CI 1.13-1.21) seconds and R2 of 0.37 (95% CI 0.34-0.41). Based on the implemented personalized testing strategy, 191 patients (66.3%) exhibited OFI. Type 1, 2, and 3 impairments were seen in 64 (33.5%), 91 (47.6%), and 36 (18.8%) patients, respectively. Increasing detected levels of OFI were associated with statistically significant increases in subjective functional impairment, extreme anxiety and depression symptoms, being bedridden, extreme pain or discomfort, inability to carry out activities of daily living, and a limited ability to work.

CONCLUSIONS

In the era of "precision medicine," simple population-based thresholds may eventually not be adequate to monitor quality and safety in neurosurgery. Individualized assessment integrating machine learning techniques provides more detailed and objective clinical assessment. The personalized testing strategy demonstrated concurrent validity with quality-of-life measures, and the freely accessible web app (https://neurosurgery.shinyapps.io/5RSTS/) enabled clinical application.

Quality Programs in Neurosurgery: The Memorial Hermann/University of Texas Experience

The importance of outcome measures is steadily increasing due to the rise of "pay for performance" and the advent of population health. In 2007, a quality initiative was started due to poor performance on rankings such as the University Health Consortium (UHC) report card. Inherent to all such efforts are common challenges: how to engage the providers; how to gather and ensure the accuracy of the data; how to attribute results to individuals; how to ensure permanent improvements. After analysis, a strategy was developed that included an initial focus on 3 metrics (mortality, infection rates, and complications), leadership from practicing neurosurgeons, protocol development and adherence, and subspecialization. In addition, it was decided that the metrics would initially apply to attending physicians only, but that the entire team would need to be involved. Once the fundamental elements were established, the process could be extended to other measures and providers. To support this effort, special information system tools were developed and a support team formed. As the program matured, measured outcomes improved and more metrics were added (to a current total of 48). For example, UHC mortality ratios (observed over expected) decreased by 75%. Infection rates decreased 80%. The program now involves all trainee physicians, advanced practice providers, nurses, and other staff. This paper describes the design, implementation, and results of this effort, and provides a practical guide that may be useful to other groups undertaking similar initiatives.

Neurosurgery clinical registry data collection utilizing Informatics for Integrating Biology and the Bedside and electronic health records at the University of Rochester

In a population health-driven health care system, data collection through the use of clinical registries is becoming imperative to continue to drive effective and efficient patient care. Clinical registries rely on a department’s ability to collect high-quality and accurate data. Currently, however, data are collected manually with a high risk for error. The University of Rochester’s Department of Neurosurgery in conjunction with the university’s Clinical and Translational Science Institute has implemented the integrated use of the Informatics for Integrating Biology and the Bedside (i2b2) informatics framework with the Research Electronic Data Capture (REDCap) databases.

Length of hospital stay after craniotomy for tumor: a National Surgical Quality Improvement Program analysis

OBJECT

Although the length of hospital stay is often used as a measure of quality of care, data evaluating the predictors of extended hospital stay after craniotomy for tumor are limited. The goals of this study were to use multivariate regression to examine which preoperative characteristics and postoperative complications predict a prolonged hospital stay and to assess the impact of length of stay on unplanned hospital readmission.

METHODS

Data were extracted from the National Surgical Quality Improvement Program (NSQIP) database from 2007 to 2013. Patients who underwent craniotomy for resection of a brain tumor were included. Stratification was based on length of hospital stay, which was dichotomized by the upper quartile of the interquartile range (IQR) for the entire population. Covariates included patient age, sex, race, tumor histology, comorbidities, American Society of Anesthesiologists (ASA) class, functional status, preoperative laboratory values, preoperative neurological deficits, operative time, and postoperative complications. Multivariate logistic regression with forward prediction was used to evaluate independent predictors of extended hospitalization. Thereafter, hierarchical multivariate logistic regression assessed the impact of length of stay on unplanned readmission.

RESULTS

The study included 11,510 patients. The median hospital stay was 4 days (IQR 3-8 days), and 27.7% (n = 3185) had a hospital stay of at least 8 days. Independent predictors of extended hospital stay included age greater than 70 years (OR 1.53, 95% CI 1.28%-1.83%, p < 0.001); African American (OR 1.75, 95% CI 1.44%-2.14%, p < 0.001) and Hispanic (OR 1.68, 95% CI 1.36%-2.08%) race or ethnicity; ASA class 3 (OR 1.52, 95% CI 1.34%-1.73%) or 4-5 (OR 2.18, 95% CI 1.82%-2.62%) designation; partially (OR 1.94, 95% CI 1.61%-2.35%) or totally dependent (OR 3.30, 95% CI 1.95%-5.55%) functional status; insulin-dependent diabetes mellitus (OR 1.46, 95% CI 1.16%-1.84%); hematological comorbidities (OR 1.68, 95% CI 1.25%-2.24%); and preoperative hypoalbuminemia (OR 1.78, 95% CI 1.51%-2.09%, all p ≤ 0.009). Several postoperative complications were additional independent predictors of prolonged hospitalization including pulmonary emboli (OR 13.75, 95% CI 4.73%-39.99%), pneumonia (OR 5.40, 95% CI 2.89%-10.07%), and urinary tract infections (OR 11.87, 95% CI 7.09%-19.87%, all p < 0.001). The C-statistic of the model based on preoperative characteristics was 0.79, which increased to 0.83 after the addition of postoperative complications. A length of stay after craniotomy for tumor score was created based on preoperative factors significant in regression models, with a moderate correlation with length of stay (p = 0.43, p < 0.001). Extended hospital stay was not associated with differential odds of an unplanned hospital readmission (OR 0.97, 95% CI 0.89%-1.06%, p = 0.55).

CONCLUSIONS

In this NSQIP analysis that evaluated patients who underwent craniotomy for tumor, much of the variance in hospital stay was attributable to baseline patient characteristics, suggesting length of stay may be an imperfect proxy for quality. Additionally, longer hospitalizations were not found to be associated with differential rates of unplanned readmission.