Association between hospital volume and mortality in status epilepticus: a national cohort study Robert Goulden MBBS 1 , 2 ; Tony Whitehouse MD 1 ; Nick Murphy MBBS 1 ,3 ; Tom Hayton PhD 1, 3 ; Zahid Khan FRCA 1 ; Catherine Snelson MRCP 1 ; Julian Bion MD 1 ,3 ; Tonny Veenith FRCA 1, 3 1. Department of Critical Care medicine, University Hospital of Birmingham NHS trust, Birmingham, UK. 2. Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada. 3. Perioperative and Critical Care Research Group, University of Birmingham, Birmingham, UK. 4. Department of Neurology, University Hospital of Birmingham NHS trust, Birmingham, UK. Corresponding author: Robert Goulden, Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 1020 Pine Ave West, Montreal QC, H3A 1A2, Canada Email:
[email protected] Tel: +14383917982 Institution where worked performed: Queen Elizabeth Hospital Birmingham, University Hospital of Birmingham NHS trust Financial support: TV received a Queen Elizabeth Hospital Birmingham Charity Award for time spent on this study. TV is also supported by Epilepsy Research UK unconditionally for this study. No further specific funding was received. Declaration of interests: the authors declare no relevant conflicts of interest. Acknowledgments: the authors would like to thank David Harrison for comments on an earlier draft of this manuscript. Keywords: Status Epilepticus; Epilepsy; Seizure; High-Volume Hospitals Word count: 2965
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Abstract Objective In various medical and surgical conditions, research has found that centres with higher patient volumes have better outcomes. This relationship has not previously been explored for status epilepticus (SE). This study sought to examine whether centres that see higher volumes of patients with SE have lower in-hospital mortality than low volume centres.
Design Cohort study, using 2010 to 2015 data from the nationwide Case Mix Programme (CMP) database of the UK’s Intensive Care National Audit and Research Centre (ICNARC).
Setting >90% of intensive care units in England, Wales, and Northern Ireland.
Patients 20,922 adult critical care admissions with a primary or secondary diagnosis of status epilepticus or prolonged seizure.
Interventions Annual hospital SE admission volume.
Measurements and main results We used multiple logistic regression to evaluate the association between hospital annual SE admission volume and in-hospital mortality. Hospital volume was modelled as a non-linear variable using restricted cubic splines, and generalised estimating equations with robust standard errors were used to account for clustering by institution. There were 2,462 in-hospital deaths (11.8%). There was no significant association between treatment volume and in-hospital mortality for SE (p=0.62). This conclusion was unchanged across a number of subgroup and sensitivity analyses, although we lacked data on seizure duration and medication use. Secondary analyses suggest that many high-risk patients were already transferred from low to high volume centres.
Conclusions and relevance We find no evidence that higher volume centres are associated with lower mortality in SE overall. It is likely that national guidelines and local pathways in the UK allow efficient patient transfer from smaller centres like district general hospitals to provide satisfactory patient care in SE. Future research using more granular data should explore this association for the sub-group of patients with refractory and super-refractory SE.
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Introduction Status epilepticus (SE) is defined by the International League Against Epilepsy as a condition resulting from the failure of mechanisms of seizure termination or abnormal initiation leading to prolonged seizures(1). Convulsive status epilepticus is a life-threatening medical emergency with a mortality rate between 10-33%(2). Nonconvulsive status epilepticus is defined as ongoing seizure activity without convulsions, and lack of good quality evidence on its management has led to recommendations taken from the existing literature on convulsive status epilepticus(3, 4). Seizure duration in SE is a potentially modifiable factor associated with mortality and morbidity(5). The annual incidence of SE is reported to be 10-16 per 100,000 persons in Europe and 18-41 per 100,000 persons in the United States(2, 6). Prolonged SE can be challenging for clinicians because of its diagnostic, therapeutic, and monitoring complexities. Successful management requires early identification, aggressive treatment of triggering factors, and measures to reduce the impact of secondary damage induced by seizures. This relies on a multi-disciplinary team involving critical care physicians, neurologists and neurophysiologists in critical care. Meeting these challenges can be particularly difficult for smaller critical care units seeing low volumes of SE patients, which may result in poorer patient outcomes. A volume outcome relationship has been demonstrated for a number of surgical and medical conditions(7–10). A positive effect on clinical outcomes following centralisation of care into high volume units has been reported in trauma(11), cardiac surgery(12), neonatal intensive care(13), acute stroke care(14), and traumatic brain injury(15). Neuro-critical care centres with high institutional caseloads of subarachnoid haemorrhage have been shown to have lower mortality(16– 18), and the introduction of a specialist neuro-critical team in a tertiary care centre has been associated with a reduction in mortality(19). Nevertheless, a number of other studies have found no effect of volume on clinical outcomes, including several in critical care(20–23). A recent systematic review found a positive volume-outcome association in critical care in 63% of studies(24). There are currently no studies of the volume-outcome relationship in SE. We evaluated the association between hospital treatment volume and in-hospital mortality in SE, using a large nationwide cohort of critical care unit admissions in the United Kingdom over a 6-year period.
Materials and methods Study population We used the Case Mix Programme (CMP) database of adult critical care admissions maintained by the Intensive Care National Audit and Research Centre (ICNARC). This contains data on all patients admitted to 90% of general critical care units (intensive care units (ICUs) and high dependency units (HDUs)) in England, Wales, and Northern Ireland, as well as to specialist neuro-critical care units. Data for CMP is extracted from clinical records by trained data collectors and undergoes extensive validation checks before being pooled(25). Our study population included all patients aged 16 years or older in the CMP database with ‘status epilepticus or prolonged seizure’ listed as a primary or secondary cause for admission in the patient’s medical records within 24 hours of ICU entry. If patients were re-admitted to ICU during 3
the same hospital stay, the second admission was excluded from analysis. We also excluded patients who were transferred in from another hospital or transferred out to another ICU (same or different hospital). We included all admissions from 1 January 2010 to 31 December 2015, the maximal period for which ICNARC’s updated mortality risk prediction model was available(25).
Outcome and exposure variables The primary outcome was in-hospital mortality. The primary exposure for each patient was the number of patients with SE admitted to critical care units in the same hospital in the same calendar year. This meant that the volume classification of each hospital could change from year to year. An alternative approach would be to classify exposure by the mean annual volume seen in each unit over a 6-year period. However, using an updated annual volume was more consistent with a hypothesised mechanism that sustained regular exposure improves performance. We chose to analyse by hospital, rather than ICU, as we felt that in the small number of hospitals with multiple ICUs, the institutional expertise would be shared across units. The primary exposure variable and the associated volume quartiles were calculated before exclusion of patients who were transferred between hospitals. We adjusted for the following potential confounders: age, sex, calendar year, ICU type, admission source, acute illness severity, secondary admission reason, severe co-morbidities, and functional status. There is a high risk of confounding by patient’s illness severity in volume-outcome relationships, creating a confounding effect by indication. Patients with SE and complex comorbidities or with seizures secondary to an acute neurological insult (e.g. intracranial haemorrhage, traumatic brain injury) may be more likely to be admitted to specialist, large-volume centres. Consequently, to better isolate the effect of treatment volume, it is vital to adjust for hospital and ICU type, as well as patient illness severity. Hospital type was classified as university vs. nonuniversity, and ICU type classified as neuro-critical care unit vs. general ICU/HDU. Acute illness severity was based on the updated ICNARC predicted mortality risk model, which takes into account an extensive range of clinical and laboratory parameters(25). Admission source was classified as ward, emergency department, theatre, or other critical care area (e.g. coronary care, HDU). In the CMP database, admissions receive a primary and (optional) secondary reason for admission. We classified patients as having SE listed as a sole, primary of two, or secondary admission reason, as well as any other relevant diagnosis listed (grouped into 10 categories). Severe co-morbidities were classified as for the APACHE scoring system, including cirrhotic liver disease, dialysis-dependant kidney disease, NYHA functional class 4 heart disease, severe respiratory disease, metastatic cancer, haematological malignancy, or immunological dysfunction. Functional status was classified in three levels: independent for activities of daily living (ADLs), partly dependent for ADLs, or totally dependent for ADLs.
Statistical analysis All analyses were performed using Stata 15.1 (StataCorp, College Station, Texas). Two-sided alpha for the primary analysis was set at 0.05. Due to multiple testing, p-values for secondary analyses should be interpreted as indicators of precision and not as binary null hypothesis significance tests. The association between hospital volume and in-hospital mortality was evaluated using multiple logistic regression. To allow for non-linearity, hospital volume was modelled using restricted cubic splines with 4 knots (at the 5th, 35th, 65th, and 95th centile). Age and ICNARC predicted mortality were 4
also modelled using restricted cubic splines (with the same knots centiles as the primary exposure), while all other variables were categorical. To account for clustering by hospital, generalised estimating equations with an exchangeable working correlation structure and robust standard errors were used. Failure to account for clustering may lead to over-estimation of the statistical significance of volume-outcome associations(26). The Wald test was used to evaluate the statistical significance of the volume-outcome association. To aid interpretation of the results, odds ratios for all observed hospital volumes were calculated from the model and plotted. In a secondary analysis, we also modelled hospital volume in quartiles. In a sensitivity analysis, we included the use of mechanical ventilation, sedation, or paralysis in the first 24 hours of ICU admission, as well as the lowest recorded Glasgow Coma Scale (GCS) in the same period, as additional potential confounders. These were not included in the primary analysis as they may be consequences of initial treatment, creating a risk of overadjustment bias(27). The benefits of care in a high-volume centre may be limited to patients with more severe and complex seizures, such as those with refractory SE. As our database lacked information on precise seizure duration and treatment, we used several potential proxy measures of seizure severity to define our subgroup analyses(28, 29). These were: use of mechanical ventilation during the first 24 hours of ICU admission, use of sedation or paralysis during the first 24 hours of ICU admission, lowest level of consciousness during first 24 hours of ICU admission (defined using GCS and dichotomized at the median value), age (dichotomized at the median value), presence of an additional diagnosis as the reason for admission, hospital type, and ICU type. To aid interpretation and statistical testing of these subgroup analyses, patient volume was modelled in quartiles. As a secondary outcome, we looked at hospital length of stay among survivors, using multiple linear regression along with the same allowances for non-linearity, clustering, and potential confounding as the primary analysis. In a further secondary analysis, we explored the characteristics of patients who were excluded from our main analysis because they were transferred from other hospitals. This was to evaluate if high risk patients were already being selectively transferred to high volume centres. Chi-squared tests and the Wilcoxon rank-sum test were used to evaluate the significance of any differences in the characteristics of these patients from those admitted from within the same hospital (our main study population).
Role of the funding source No funder had a role in the design, conduct, or interpretation of this study.
Ethics ICNARC’s access to patient data for the CMP is approved under section 251 of the NHS Act 2006 (approval PIAG 2-10(f)/2005). The research protocol for this study was approved by ICNARC’s Independent Data Access Advisory Group.
Results There were 24,716 admissions with SE during the study period, distributed among 277 critical care units within 237 hospitals. After exclusion of admissions under the age of 16 (957 [4%]), readmissions to ICU within the same hospital stay (471 [2%]), transfers from other acute hospitals 5
(1433 [6%]), transfers to other ICUs (806 [3%]), and those with missing transfer information (58 [0.2%]), 21,086 admissions met our inclusion criteria. We performed a complete-case analysis. Complete data for all variables in our main analysis was available for 20,922 admissions (99.2% of eligible admissions), which formed our primary study population.
In-hospital mortality in SE There were 2,462 in-hospital deaths (11.8%).
Patient characteristics Patient characteristics by quartile of annual hospital admission volume are listed in Table 1. The median annual admission volume was 24 (range 1-120). The mean age was 51, with a slight preponderance of males (56%). In general, high volume centres were more likely to be universityaffiliated and to be specialist neuro-critical care units. Relative to lower volume quartiles, admissions in higher volume quartiles were more likely to come directly from surgery or other critical care areas. Mortality and most illness severity markers were similar across volume quartiles. Table 1. Characteristics of ICU admissions with status epilepticus between 2010 and 2015, by volume quartile. Values are numbers (%) unless otherwise noted. Characteristic Total Quartile 1 Quartile 2 Quartile 3 Quartile 4 n 20922 5305 5607 4957 5053 Annual admissions, median 24 (1-120) 12 (1-15) 20 (16-24) 30 (25-38) 60 (39-120) (range) Age, mean (SD) 51 (18.9) 51.1 (18.5) 51 (19.1) 51 (18.8) 50.8 (19.1) Male sex 11563 (55%) 2860 (54%) 3073 (55%) 2779 (56%) 2851 (56%) ICNARC predicted mortality risk, mean 11.7% 12.1% 12.2% 11.7% 11.9% a ≥1 severe comorbidity 2111 (10%) 561 (11%) 496 (9%) 519 (10%) 535 (11%) Dependent for ADLs Partially 5703 (27%) 1528 (29%) 1576 (28%) 1348 (27%) 1251 (25%) Completely 806 (4%) 221 (4%) 240 (4%) 191 (4%) 154 (3%) University hospital 11568 (55%) 1641 (31%) 2473 (44%) 3080 (62%) 4374 (87%) Neurointensive care unit 3723 (18%) 104 (2%) 165 (3%) 841 (17%) 2613 (52%) Admission source Ward 5857 (28%) 1624 (31%) 1501 (27%) 1352 (27%) 1380 (27%) Emergency department 12852 (61%) 3251 (61%) 3727 (66%) 3168 (64%) 2706 (54%) Other critical care area 1176 (6%) 268 (5%) 259 (5%) 231 (5%) 418 (8%) Surgery 1037 (5%) 162 (3%) 120 (2%) 206 (4%) 549 (11%) Second diagnosis recorded 9953 (48%) 2239 (42%) 2523 (45%) 2475 (50%) 2716 (54%) In-hospital mortality 2462 (12%) 627 (12%) 654 (12%) 582 (12%) 599 (12%) a. Severe co-morbidities were classified as for the Apache scoring system, including cirrhotic liver disease, dialysis-dependant kidney disease, NYHA functional class 4 heart disease, severe respiratory disease, metastatic cancer, haematological malignancy, or immunological dysfunction.
Volume-outcome association In the multiple logistic regression analysis, increased hospital volume was not associated with reduced mortality (p=062). Figure 1 shows the odds ratio (95% CI) for in-hospital mortality across 6
treatment volumes, with a null association throughout. In the same model, the OR (95% CI) for university hospitals (vs. non-university hospitals) was 0.96 (0.84-1.10) and for neuro-critical care units (vs. general ICUs) was 1.17 (0.97-1.41). When modelled as quartiles, the adjusted OR (95% CI) for in-hospital mortality relative to the lowest quartile was 0.96 (0.82-1.11) for quartile 2, 0.92 (0.781.08) for quartile 3, and 0.89 (0.74-1.06) for quartile 4. In a sensitivity analysis which also included use of mechanical ventilation, sedation, paralysis, and lowest GCS in the first 24 hours as potential confounders, there was similarly no association between hospital volume and mortality (Appendix). Figure 1. Adjusted odds ratio (95% CI) for association between annual admission volume and all-cause mortality in status epilepticus, relative to median admission volume of 24.
Association modelled using restricted cubic splines with 4 knots. Model adjusted for age, sex, calendar year, ICU type, admission source, ICNARC mortality risk score, secondary admission reason, severe co-morbidities, and functional status.
Subgroup analyses are reported in Table 2. We found no consistent evidence that the effect of hospital volume varied by potential proxy measures of seizure severity. Of the seven variables considered, only age (p=0.01) and ICU type (p=0.02) had statistically significant interaction terms.
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Table 2 Adjusted odds ratio (95% CI) for association between annual admission volume and all-cause mortality in status epilepticus by subgroups. Subgroup
1 (1-15) 1.00
Quartile (annual patient volume) 2 (16-24) 3 (25-38) 4 (39-120) 0.96 (0.82-1.11) 0.92 (0.78-1.08) 0.89 (0.74-1.06)
Interaction p-valueb
Total cohort Mechanical ventilationc 0.96 No 1.00 0.98 (0.74-1.31) 0.95 (0.71-1.28) 0.83 (0.60-1.16) Yes 1.00 0.96 (0.81-1.14) 0.94 (0.79-1.13) 0.94 (0.76-1.16) Sedation and/or paralysisc No 1.00 1.01 (0.76-1.33) 0.88 (0.65-1.20) 0.82 (0.60-1.13) 0.97 Some of the time 1.00 0.96 (0.77-1.20) 0.86 (0.67-1.12) 0.80 (0.60-1.08) Whole time 1.00 0.94 (0.75-1.18) 1.05 (0.82-1.18) 1.06 (0.80-1.42) c Lowest GCS ≥12 1.00 1.07 (0.86-1.33) 0.89 (0.69-1.15) 0.85 (0.63-1.15) 0.52 50 1.00 0.97 (0.82-1.14) 0.92 (0.76-1.10) 0.82 (0.67-1.01) Additional diagnosis SE sole diagnosis 1.00 1.03 (0.83-1.29) 0.86 (0.65-1.12) 0.78 (0.57-1.06) 0.53 SE primary of two 1.00 0.81 (0.64-1.02) 0.95 (0.74-1.22) 0.89 (0.66-1.19) SE secondary of two 1.00 1.05 (0.79-1.42) 0.97 (0.71-1.30) 1.06 (0.77-1.47) Hospital type Non-university 1.00 1.06 (0.87-1.30) 0.97 (0.77-1.22) 0.79 (0.58-1.08) 0.35 University 1.00 0.86 (0.68-1.08) 0.86 (0.67-1.12) 0.88 (0.69-1.14) ICU type General 1.00 0.98 (0.85-1.15) 0.93 (0.79-1.10) 0.88 (0.73-1.07) 0.02 Neuro-critical care 1.00 0.48 (0.26-0.87) 0.70 (0.42-1.19) 0.75 (0.51-1.11) a. Model adjusted for age, sex, calendar year, ICU type, admission source, ICNARC mortality risk score, secondary admission reason, severe co-morbidities, and functional status. b. P-value derived from joint Wald test of all volume*subgroup categorical interaction coefficients. c. Within first 24 hours of ICU admission.
The association between hospital volume and hospital length of stay among survivors was initially modelled using restricted cubic splines (Figure e1, Appendix). However, as this suggested a linear association, the analysis was repeated with volume as a linear variable. Each 10-patient increase in annual admission volume was associated with a 0.47 day (95% CI: 0.02-0.92 days) reduction in hospital length of stay in the fully-adjusted model. However, in this same analysis, the length of stay was 2.14 days (95% CI: 0.58-3.70 days) longer in university hospitals (vs. non-university), and 9.40 days (95% CI: 5.94-12.86 days) longer in neuro-critical care units (vs. general ICUs). Table 3 compares the characteristics of admissions transferred from other hospitals (excluded from our main study population), with admissions from within the same hospital (included in our main study population). Patients were more likely to be transferred into higher volume centres than lower 8
volume centres. Transferred patients had higher predicted mortality and correspondingly higher observed mortality rates. They were also more likely to have a second diagnosis identified at admission. Table 3 Characteristics of ICU admissions transferred from other hospitals vs. those admitted from within the same hospital. Values are numbers (%) unless otherwise noted. Characteristic
Transferred from other hospital 1417a
Admitted from same hospital 20922
Test of differencea
N Hospital volume quartile Quartile 1 363 (26%) 5305 (25%) Quartile 2 292 (21%) 5607 (27%) p
