Benchmarking short-term postoperative mortality across neurosurgery units: is hospital administrative data good enough for risk-adjustment?

BACKGROUND: Surgical mortality indicators should be risk-adjusted when evaluating the performance of organisations. This study evaluated the performance of risk-adjustment models that used English hospital administrative data for 30-day mortality after neurosurgery. METHODS: This retrospective cohort study used Hospital Episode Statistics (HES) data from 1 April 2013 to 31 March 2018. Organisational-level 30-day mortality was calculated for selected subspecialties (neuro-oncology, neurovascular and trauma neurosurgery) and the overall cohort. Risk adjustment models were developed using multivariable logistic regression and incorporated various patient variables: age, sex, admission method, social deprivation, comorbidity and frailty indices. Performance was assessed in terms of discrimination and calibration. RESULTS: The cohort included 49,044 patients. Overall, 30-day mortality rate was 4.9%, with unadjusted organisational rates ranging from 3.2 to 9.3%. The variables in the best performing models varied for the subspecialties; for trauma neurosurgery, a model that included deprivation and frailty had the best calibration, while for neuro-oncology a model with these variables plus comorbidity performed best. For neurovascular surgery, a simple model of age, sex and admission method performed best. Levels of discrimination varied for the subspecialties (range: 0.583 for trauma and 0.740 for neurovascular). The models were generally well calibrated. Application of the models to the organisation figures produced an average (median) absolute change in mortality of 0.33% (interquartile range (IQR) 0.15-0.72) for the overall cohort model. Median changes for the subspecialty models were 0.29% (neuro-oncology, IQR 0.15-0.42), 0.40% (neurovascular, IQR 0.24-0.78) and 0.49% (trauma neurosurgery, IQR 0.23-1.68). CONCLUSIONS: Reasonable risk-adjustment models for 30-day mortality after neurosurgery procedures were possible using variables from HES, although the models for trauma neurosurgery performed less well. Including a measure of frailty often improved model performance.

Assessing national patterns and outcomes of pituitary surgery: is hospital administrative data good enough?

PURPOSE: Patterns of surgical care, outcomes, and quality of care can be assessed using hospital administrative databases but this requires accurate and complete data. The aim of this study was to explore whether the quality of hospital administrative data was sufficient to assess pituitary surgery practice in England. METHODS: The study analysed Hospital Episode Statistics (HES) data from April 2013 to March 2018 on all adult patients undergoing pituitary surgery in England. A series of data quality indicators examined the attribution of cases to consultants, the coding of sellar and parasellar lesions, associated endocrine and visual disorders, and surgical procedures. Differences in data quality over time and between neurosurgical units were examined. RESULTS: A total of 5613 records describing pituitary procedures were identified. Overall, 97.3% had a diagnostic code for the tumour or lesion treated, with 29.7% (n = 1669) and 17.8% (n = 1000) describing endocrine and visual disorders, respectively. There was a significant reduction from the first to the fifth year in records that only contained a pituitary tumour code (63.7%-47.0%, p < .001). The use of procedure codes that attracted the highest tariff increased over time (66.4%-82.4%, p < .001). Patterns of coding varied widely between the 24 neurosurgical units. CONCLUSION: The quality of HES data on pituitary surgery has improved over time but there is wide variation in the quality of data between neurosurgical units. Research studies and quality improvement programmes using these data need to check it is of sufficient quality to not invalidate their results.

Patterns and outcomes of neurosurgery in England over a five-year period: A national retrospective cohort study.

BACKGROUND: Neurosurgical practice has seen major changes over several decades. There are no recent evaluations of national neurosurgical practice. The aim of this observational study was to describe neurosurgical practice in England and to use outcomes to assess and benchmark the quality of care in neurosurgery. MATERIAL AND METHODS: This national retrospective cohort study analysed Hospital Episode Statistics (HES) data from April 2013 to March 2018 for all adult admissions with a specialty code for neurosurgery. The epidemiology of patients and RCS Charlson comorbidities were derived and procedure incidence rates per 100,000 person-years calculated. Post-operative outcomes for elective and non-elective patients included: median length of stay, the proportion of patients requiring additional inpatient neurosurgical procedures, the proportion of patients discharged to their usual address, and in-hospital mortality rates. RESULTS: During the 5-year study period, there were 371,418 admissions to neurosurgery. The proportion of admissions involving a neurosurgical procedure was 77.3% (n = 287,077). Of these, 45% were for cranial surgery and 37% for spinal. Overall, 68.3% were elective procedures. The incidence rates of most procedures were low (<20 per 100,000 person-years). Following elective neurosurgical procedures, in-hospital mortality rates for cranial and spinal surgery were 0.5% (95% CI, 0.5-0.6) and 0.1% (95% CI, 0.04-0.1), respectively. After non-elective neurosurgery, mortality rates were 7.4% (95% CI, 7.2-7.6) and 1.3% (95% CI, 1.2-1.5) for cranial and spinal surgery, respectively. Approximately 1 in 4 patients had additional procedures following non-elective cranial surgery (24%; 95% CI, 23.6-24.3). Outcomes were highly variable across different subspecialty areas. CONCLUSIONS: The incidence rates of neurosurgical procedures are low within England, and neurosurgical units have a high volume of non-surgical admissions. In-hospital mortality rates after elective neurosurgery are low but there may be opportunities for quality improvement programmes to improve outcomes for non-elective surgery as well as ensuring equitable access to treatment.

Mortality as an indicator of quality of neurosurgical care in England: a retrospective cohort study.

OBJECTIVES: Postoperative mortality is a widely used quality indicator, but it may be unreliable when procedure numbers and/or mortality rates are low, due to insufficient statistical power. The objective was to investigate the statistical validity of postoperative 30-day mortality as a quality metric for neurosurgical practice across healthcare providers. DESIGN: Retrospective cohort study. SETTING: Hospital Episode Statistics data from all neurosurgical units in England. PARTICIPANTS: Patients who underwent neurosurgical procedures between April 2013 and March 2018. Procedures were grouped using the National Neurosurgical Audit Programme classification. OUTCOMES MEASURED: National 30-day postoperative mortality rates were calculated for elective and non-elective neurosurgical procedural groups. The study estimated the proportion of neurosurgeons and NHS trusts in England that performed sufficient procedures in 3-year and 5-year periods to detect unusual performance (defined as double the national rate of mortality). The actual difference in mortality rates that could be reliably detected based on procedure volumes of neurosurgeons and units over a 5-year period was modelled. RESULTS: The 30-day mortality rates for all elective and non-elective procedures were 0.4% and 6.1%, respectively. Only one neurosurgeon in England achieved the minimum sample size (n=2402) of elective cases in 5 years needed to detect if their mortality rate was double the national average. All neurosurgical units achieved the minimum sample sizes for both elective (n=2402) and non-elective (n=149) procedures. In several neurosurgical subspecialties, approximately 80% of units (or more) achieved the minimum sample sizes needed to detect if their mortality rate was double the national rate, including elective neuro-oncology (baseline mortality rate=2.3%), non-elective neuro-oncology (rate=5.7%), neurovascular (rate=6.7%) and trauma (rate=11%). CONCLUSION: Postoperative mortality lacks statistical power as a measure of individual neurosurgeon performance. Neurosurgical units in England performed sufficient procedure numbers overall and in several subspecialty areas to support the use of mortality as a quality indicator.