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Increasing Frailty Predicts Worse Outcomes and Increased Complications After Angiogram-Negative Subarachnoid Hemorrhages
Journal Pre-proof Increasing Frailty Predicts Worse Outcomes and Increased Complications Following Angiogram-negative Subarachnoid Hemorrhages Matthew McIntyre, BA, Chirag Gandhi, MD, James Dragonette, BS, Meic Schmidt, MD MBA, Chad Cole, MD MSc, Justin Santarelli, MD, Rachel Lehrer, RPA-C, Fawaz Al-Mufti, MD, Christian Bowers, MD PII:
S1878-8750(19)32618-X
DOI:
https://doi.org/10.1016/j.wneu.2019.10.003
Reference:
WNEU 13474
To appear in:
World Neurosurgery
Received Date: 7 July 2019 Revised Date:
30 September 2019
Accepted Date: 1 October 2019
Please cite this article as: McIntyre M, Gandhi C, Dragonette J, Schmidt M, Cole C, Santarelli J, Lehrer R, Al-Mufti F, Bowers C, Increasing Frailty Predicts Worse Outcomes and Increased Complications Following Angiogram-negative Subarachnoid Hemorrhages, World Neurosurgery (2019), doi: https:// doi.org/10.1016/j.wneu.2019.10.003. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Elsevier Inc. All rights reserved.
Increasing Frailty Predicts Worse Outcomes and Increased Complications Following Angiogram-negative Subarachnoid Hemorrhages Authors: Matthew McIntyre BA1, Chirag Gandhi MD2, James Dragonette BS1, Meic Schmidt MD MBA2, Chad Cole MD MSc2, Justin Santarelli MD2, Rachel Lehrer RPA-C2, Fawaz AlMufti MD2, Christian Bowers MD2 Affiliations: 1School of Medicine, New York Medical College, Valhalla, New York. Department of Neurological Surgery Westchester Medical Center, Valhalla, New York
2
Correspondence to: Christian Bowers MD Department of Neurosurgery, Westchester Medical Center, New York Medical College 110 Woods Road, Valhalla, NY, 10595, USA Phone: (914) 493-7000; Fax: (914) 493-2505 Email: [email protected] Short Title: Frailty Predicts Outcomes after ANSAH Keywords: Subarachnoid hemorrhage, angiogram-negative, frailty, modified frailty index, mortality, complications
ABSTRACT Background The effect of frailty on outcomes following Angiogram-negative subarachnoid hemorrhages (ANSAH) is currently unknown. We investigated frailty’s effects on ANSAH outcomes, including mortality and in-hospital complications. Methods Patients with non-traumatic SAH and cerebral angiograms with an unidentifiable hemorrhage source were retrospectively reviewed from 2014 to 2018. The cohort was divided into non-frail (modified frailty index [mFI] of 0) and frail (mFI≥1) groups based on pre-hemorrhage characteristics. Primary outcomes were mortality rate and discharge location. Multivariate logistic regression analyses determined predictors of ANSAH severity and primary endpoints. Receiver operating characteristic (ROC) curves used to discriminate risks for primary endpoints comparing mFI, Hunt&Hess and Fisher scores, and age. Results We included 75 patients with a mean age of 55.4±1.5 years, comprised of 42 (56%) females, and 41 (54.7%) with perimesencephalic bleeds. A total of 32/75 (42.7%) patients were classified as frail. Frail individuals were 6.2 times less likely to be discharged home (OR=0.16; 95%CI: 0.050.5; p=0.0009) and all mortalities occurred in frail patients (12.5% [n=4/32]; p=0.0296). The only independent predictor of mortality was higher mFI (OR=5.4;95%CI: 1.5-19.1; p=0.009), while lower mFI best predicted discharge home (OR=0.39; 95%CI: 0.17-0.88; p=0.023). ROC analysis showed that mFI best predicted both mortality (AUC=0.9718; p=0.0016) and discharge home (AUC=0.7998; p<0.0001). Conclusions
Frail ANSAH patients have poorer outcomes and increased mortality compared to non-frail patients. While prospective study is needed, this information significantly impacts our understanding of ANSAH outcomes and frailty should be used for prognostication as it was a better predictor than Hunt/Hess or Fisher Scores.
INTRODUCTION Subarachnoid hemorrhage (SAH) is associated with a mortality rate of 18% and high morbidity rates for those who survive the initial hemorrhage.1–3 SAH is often a consequence of trauma or aneurysm rupture; however, 11-19% of spontaneous SAH are ultimately found to be idiopathic, despite multiple diagnostic angiograms.4–7 Angiogram-negative subarachnoid hemorrhages (ANSAH) are traditionally thought to have a more benign course than aneurysmal SAH, given their reduced incidence of rebleeding and higher rate of good outcomes.8 While SAH carries the risks of symptomatic vasospasm, rebleeding, cardiac arrhythmias, and pulmonary edema, ANSAH also have significant rates of ischemic complications, vasospasm, chronic headaches, developing hydrocephalus, and permanent impaired cognitive function.9–11
Frailty is a state of reduced physiologic reserve. A common validated measure of frailty is the modified frailty index (mFI), which is associated with worse outcomes and higher mortality following various types of surgery.12–14 In terms of neurosurgical procedures, each unit increase in mFI is associated with a 3-5 increased odds of mortality and a 1.5-1.6 increased odds of morbidity, independent of age.15,16 To date, no studies have been performed examining the underlying effect of frailty on outcome for ANSAH patients. This study’s primary objective was to examine frailty’s effect on ANSAH patients’ mortality, complication rates, and outcomes.
MATERIALS AND METHODS Study Design and Setting This retrospective cohort study was conducted between November 2018 and February 2019 at a quaternary academic medical center with high patient volume (Westchester Medical Center in
Valhalla, New York), between June 2014-July 2018 after Institutional Review Board Approval of New York Medical College & Westchester Medical Center. Data was collected for the a priori selected variables.
Participant Selection Patients were identified after reviewing the digital subtraction angiogram (DSA) institutional database for those with a cryptogenic SAH after DSA. All individuals who were found to have at least one DSA-negative subarachnoid hemorrhage (ANSAH) were included. ANSAH was defined by negative DSA for vascular hemorrhage source regardless of individual CT or MRI findings. Exclusion criteria were a history of trauma or neurosurgical procedure, an absence of radiographic evidence of a SAH including cerebrospinal fluid-positive CT-negative SAH, or a subsequent positive DSA for vascular source of hemorrhage on follow-up imaging. Perimesencephalic ANSAH (PMH-SAH) was defined as the presence of SAH isolated to the perimesencephalic cisterns with extension into the ambient or pre-pontine cisterns or basal parts of the Sylvian fissures. PMH-SAH did not include blood extending into the lateral Sylvian fissure, anterior interhemispheric fissure, or lateral ventricles. Non-perimesencephalic ANSAH (NPAN-SAH) was characterized by the presence of blood primarily in the suprasellar, sylvian (basal and lateral), and interhemispheric cisterns.7,17
Measures and outcomes For each patient, the demographics, ANSAH type, anticoagulation/antiplatelet (AC/AP) medication use, body mass index (BMI), smoking history, hyperlipidemia, admission Glasgow coma score (GCS) and peri-partum status (of women <50y) were collected. A modified frailty
index (mFI) score was calculated as previously described based on pre-hemorrhage co-morbid conditions12, and variables are shown in Table 1. These variables were hypertension requiring medication, congestive heart failure, myocardial infarction, previous percutaneous coronary intervention or angina, transient ischemic attack or cerebrovascular accident (CVA) without neurological deficit, CVA with neurological deficit, peripheral vascular disease or ischemic rest pain, chronic obstructive pulmonary disease or current pneumonia, diabetes mellitus, nonindependent functional status, and impaired sensorium. Non-independent functional status was defined as requiring assistance from another person for activities of daily living.18–21 Each variable was assigned one point and the mFI was calculated on a scale of 0 (i.e. no variables present) to 11 (i.e. all variables present). Patients were grouped as mFI = 0 (non-frail) or mFI ≥1 (frail). The cutoff of mFI ≥1 was determined based the receiver operating characteristic (ROC) analysis described below. Primary outcomes were discharge home and in-hospital mortality. Secondary measures were Hunt & Hess (HH) scores, Fisher scores, external ventricular drain (EVD) requirement, discharge GCS score, hospital length of stay (LOS), intensive care unit (ICU) LOS, need for tracheostomy, gastrectomy tube, radiographic evidence of vasospasm, deep vein thrombosis (DVT), pulmonary embolism (PE), pneumonia, intubation, and vasopressor use.
Statistical Analysis Normal distributions were determined using an Anderson-Darling normality test. A T-test or ANOVA with Tukey’s post-hoc analysis was used for continuous variables, and data are shown using mean ± standard error of the mean (SEM). A Fisher’s exact test was used for binary variables, and odds ratios (OR) are shown with 95% confidence intervals (95%CI). Receiver operator curves (ROC) of mFI, age, HH, and Fisher scores were plotted against mortality and
discharge home. Area under the curve (AUC) was used to compare discriminatory power with an AUC of 1.0 considered perfect discrimination and 0.5 equal to chance. ROC significance was determined using the Wilson and Brown method. AUC and Standard error are shown. Given the lack of consensus of an appropriate cutoff for frailty18, we utilized the ROC analysis to determine this threshold that optimally predicts outcomes for ANSAH patients specifically. ROC curve for discharge home was used to determine mFI cutoff of 1 as the appropriate whole number that maximizes sensitivity and specificity for that outcome.22 Discharge home ROC was used for cutoff determination due to significance and higher rate of positive findings (n= 53 discharge home) compared to mortality ROC (n=4 deaths). To establish the predictors of HH and Fisher scores, multivariate linear regressions were performed using least squares method using age, sex, race, ANSAH type, BMI, AC/AP use and hyperlipidemia status. To establish the predictors of mortality and discharge home, the same variables as above were used, with the addition of HH and Fisher scores, using multivariate logistic regressions utilizing the forward conditional method. Only variables significant in univariate logistic regression were used in the multivariate analysis. No collinearity was detected in any multivariate analysis, as defined as a variance inflation factor of <1 or >10, and therefore both HH and Fisher scores were included in the same multivariate analyses. Statistical analysis was performed using Prism 8.0.1 (GraphPad Software, Inc. La Jolla, CA) and SPSS version 25 (International Business Machines Corp. Armonk, NY). We defined significance at p<0.05.
RESULTS Baseline Features and Demographics
Between June, 2014 and July, 2018, 75 patients met our inclusion criteria with all patients having at least one negative DSA (Figure 1). The majority were white (52/75, 69.3%), female (42/75 56%), and had an average age of 55.4 ± 1.5 years (range 21-86). Age was normally distributed across the cohort (A2=0.4834; p=0.2229). Patients generally presented with good clinical and radiographic SAH grades; however, 9.3% (7/75) of patients had a HH score of 4 or 5, while 33.3% (25/75) had a Fisher score of 4. The majority (41/75, 54.7%) were PMH-SAH, while the remainder (34/75, 45.3%) were NPAN-SAH.
A total of 43/75 (57.3%) patients were classified as non-frail and 32/75 (42.7%) were classified as frail. The variables used to define mFI are presented in Table 1. The most prevalent mFI variable observed was a history of hypertension on medications (22/32, 68.8%), followed by diabetes mellitus (9/32, 28.1%). Patient baseline characteristics stratified by group are presented in Table 2. The frail group were significantly older (62.1 vs. 50.4 years; p<0.0001), had more prevalent usage of AC/AP medications (37.5% vs. 11.6%; p=0.0118), a higher BMI (27.7 vs. 24.9 kg/m2; p=0.027), and increased hyperlipidemia prevalence (50% vs. 16.3%; p=0.0024) indicating a poorer overall health status. Sex, race, smoking history, history of hypothyroidism, and percentage of peri-partum women <50 years were not different between groups.
In-Hospital Outcomes When compared to non-frail patients, those with an mFI ≥1 had more severe ANSAHs as measured by higher HH (2.4 vs. 1.5; p=0.0002) and Fisher scores (3.3 vs. 2.6; p=0.0035) (Table 3). Frail patients had lower GCS scores compared to non-frail patients on both admission (12.7 ± 0.7 vs. 14.9 ± 0.1; p=0.0039) and discharge (13.4 ± 0.7 vs. 15 ± 0.0; p=0.0168). An impairment-
dependent relationship between frailty and HH (p<0.0001) and Fisher (p=0.0036) scores, along with decreased discharge (p<0.0001) GCS scores were observed (Figure 2). Frail patients were more likely to require an EVD (OR=5.2; 95%CI: 1.7-14.8; p=0.0057), have a longer hospital LOS (17.0 days vs. 9.3 days; p=0.0227) and a longer ICU LOS (12.8 days vs. 6.3 days; p=0.0412). Interestingly, men were more likely to receive an EVD versus women (OR=7.0; 95%CI: 2.1-21.1; p=0.0021) and no patients required a permanent cerebrospinal fluid diverting shunt. Frail patients had no increase in vasospasm incidence (OR = 0.7; 95%CI: 0.3-1.8; p=0.4788) and all mortalities occurred in the frail group (12.5% [n=4/32]; p=0.0296).
In-hospital Complications Frail ANSAH patients experienced more frequent in-hospital complications (Table 3). Specifically, frail patients were more likely to develop a pneumonia (OR = 11.8; 95%CI: 1.8135.0; p= 0.0092), and be intubated during admission (OR=7; 95%CI: 1.7-24.8; p=0.0055), and trended to have more DVT compared to non-frail patients (OR=4.7; 95%CI: 1.05-23.9; p=0.0663). All patients who received a tracheostomy (n=4, p=0.0296) or gastrostomy tube (n=6, p=0.0045) were in the frail group. Frail individuals were also 6.2 times less likely to be discharged home compared to their non-frail counterparts (OR=0.16; 95%CI: 0.05-0.5; p=0.0009).
Subgroup Analysis of ANSAH type Given the well-known effect of ANSAH type on functional outcomes, a subgroup analysis was performed (Table 4). Those with NPAN-SAH were found to have lower discharge GCS (p=0.0341), but no differences in age, sex, admission GCS, hospital LOS, ICU LOS, vasospasm,
EVD, tracheostomy, gastrotomy tube, DVT, PE, pneumonia, intubation, or discharge home (P>0.05). All 4 mortalities occurred in the NPAN-SAH group (p=0.0382). Frail individuals were also more likely to have NPAN-SAH compared to non-frail patients (OR = 3.45; 95%CI: 1.388.36; p=0.0184).
Predictors of ANSAH severity To examine the potentially confounding effect of age, sex, race, ANSAH type, BMI, AC/AP use, and hyperlipidemia on the ability of mFI to predict ANSAH severity, as measured by HH/Fisher scores, mortality, and discharge home, multivariate regressions were performed. In multivariate linear regression, mFI was the only independent risk factor for predicting HH (t=2.634; p=0.0106) and Fisher (t=3.141; p=0.0026) scores. Interestingly, in multivariate logistic regression, increased mFI was the only independent risk factor for increased mortality (OR=5.39; 95%CI: 1.53-19.05; p=0.009) (Table 5), while discharge home was independently predicted only by lower mFI (OR=0.391; 95%CI: 0.17-0.89; p=0.022), female sex (OR=5.25; 95%CI: 1.1623.75; p=0.038), and lower HH score (OR=0.390; 95%CI: 0.17-0.89; p=0.027) (Table 5). In multivariate analysis, age, AC/AP use and ANSAH type were not independently predictive of HH score, Fisher score, discharge home, or mortality (P>0.05).
Predictors of Clinical Outcomes To determine the clinical utility of using mFI to determine risk and discriminate between those who were likely to expire during the stay or be discharged home, receiver operating characteristic (ROC) curves were generated and areas under the curve (AUC) were calculated for age, mFI, HH and Fisher Scores (Figure 3). The best determinate of mortality was mFI
(AUC=0.972±0.027; p<0.0016) followed HH (AUC=0.859±0.070; p=0.0164), Fisher score (AUC=0.848 ± 0.057; p=0.02) and age (AUC=0.838±0.078; p=0.0236). The best determinate of discharge home was mFI (AUC=0.800±0.065; p<0.0001) followed by age (AUC=0.710 ± 0.068; p=0.005), HH score (AUC=0.709±0.078; p=0.0054), and Fisher score (AUC=0.689±0.068; p=0.0119). Using the ROC curves, mFI≥1 was confirmed to be the ideal cutoff for predicting mortality and discharge location as this value has the highest associated sensitivity and specificity for these endpoints.
DISCUSSION In this study we show that the modified frailty index (mFI) is predictive of poorer outcomes, more complications, and increased mortality following an ANSAH, independent of age, ANSAH type, AC/AP use, HH, and Fisher scores. Following an ANSAH, frailty (mFI ≥1) was associated with worse clinical and radiographic hemorrhages, longer hospital and ICU LOS, lower discharge GCS, and more complications. These results highlight the importance of considering frailty in the decision-making algorithm and treatment planning following an ANSAH.
Frailty is a measure of reduced physiological reserve that is a known risk factor for adverse events, increased morbidity, and mortality following a surgical intervention as demonstrated across multiple surgical subspecialties.19,23,24 Seib et al demonstrated that in a retrospective cohort study of 1,480,828 patients over age 40 who underwent common ambulatory surgery, patients with a high mFI had significantly higher odds of serious complications.12 In addition, a national database of 971,434 inpatients who underwent surgery, Velanovich et al found an increased risk of mortality and morbidity for each unit increase in mFI.19 These results, together
with the present study, highlight frailty as an important indicator of outcomes that should be carefully considered when evaluating surgical patients.
Prior work has shown that patients who develop ANSAHs have more co-morbidities and AC/AP use than those with aneurysmal SAH, however little is known regarding the effect these underlying comorbidities have on ANSAH outcomes.25,26 Dalbjerg et al showed that among patients with an ANSAHs, those receiving anticoagulant therapy, who smoke, or have excessive alcohol use, but not increased age, have an increased odds of poor outcomes at discharge.27 Similarly, Hui et al showed that ANSAH patients with anti-thrombotic states, whether iatrogenic or endogenous, have higher HH and Fisher scores, more vasospasm, increased hydrocephalus rates, and worse outcomes.28 Interestingly, in multivariate analysis we failed to show any association with AC/AP use with HH score, Fisher score, discharge home or mortality. Of note, we included all medications with AC/AP potential (aspirin, clopidogrel, warfarin etc.) in the same group, which may have confounded this analysis. Nevertheless, the mFI remained a better predictor of outcomes thus reflecting that the underlying conditions, as measured by frailty, may more strongly influence outcomes than AC/AP medications.
Other confounders that were addressed in the analysis were age and ANSAH grade. We found mFI to be a better predictor of outcomes compared to age. We also reaffirmed that the HH and Fisher scores are valid measures of hemorrhage severity in the ANSAH population; however, for discharge home and mortality rates, the mFI was a superior predictor of these outcomes in both ROC and multivariate analysis. These data indicate that evaluating mFI may have particular use in conditions that have a relatively low mortality rate. Imaoka et al showed that among patients
with surgically treated spontaneous intraparenchymal hemorrhage (a disease with a high mortality rate), the mFI had an AUC of 0.65 for poor outcomes and only a AUC of 0.8 for mortality, thus showing that the mFI may be more useful in diseases with low mortality such as ANSAHs.22 Moreover, in the present study in-hospital complications, especially pneumonia, intubation, and tracheostomy and gastrostomy tubes, were also highly associated with frailty.
To date, the preponderance of research on ANSAH outcomes has focused on hemorrhage location; i.e., whether the hemorrhage is perimesencephalic (PMH-SAH) or nonperimesencephalic (NPAN-SAH). PMH-SAH represents approximately half of ANSAH.7,29 Many studies have shown that PMH-SAH have more benign clinical courses than NPAN-SAH; however, most studies, including our own, are limited by a small sample size.8,30–32 Hoping to overcome this obstacle, meta-analyses have shown that while PMH-SAH have better outcomes than NPAN-SAH, they are also associated with higher rates of re-hemorrhage.9 Interestingly, two recent meta-analyses from the same group show that PMH-SAH are associated with lower rates of hydrocephalus, delayed cerebral ischemia, and vasospasm compared to NPAN-SAH, however, they also found a significant publication bias on this subject.33–35 Likewise, while we did show that NPAH-SAH was associated with higher mortality and lower discharge GCS, there was no significant association with LOS, vasospasm, EVD use, tracheostomy, gastrostomy tube, discharge home, or other in-hospital complications. In fact, while we found that frailty was associated with NPAN-SAH, we failed to show any influence of ANSAH type on discharge home and mortality in multivariate analysis. Zhong et al showed that NPAN-SAH was associated with higher rates of hypertension, hyperlipidemia, and smoking compared to PMH-SAH, which supports the hypothesis that underlying comorbid conditions influence both ANSAH type and
ANSAH outcomes.36 Together, this suggests that clinical differences in ANSAH type may be related to frailty.
Finally, for patients with an ANSAH, there remains an ongoing debate regarding when and how often to repeat DSA studies in the search for an identifiable cause of the bleed.37–39 Some have suggested that the mFI may have utility in determining who should receive endovascular treatment for aneurysmal SAH.40 This study confirms that the mFI is an independent risk factor for predicting poor outcomes, but future prospective study is needed to verify and refine its predictive ability. The comorbidities that make up the mFI are all chronic health conditions such as congestive heart failure, diabetes, hypertension, etc, such that the mFI is not modifiable in a way where treatment at the time of ANSAH could change outcomes. However, perhaps ANSAH patients with increased frailty could be labelled as high-risk for complications and increased mortality such that there could be an increased awareness when these patients are admitted. If proven to have predictive value, the mFI could be used in family discussions on the decision for end of life care, providing an objective measure to help families and clinicians decision making.
Limitations The main limitation of this study was its retrospective design. While we provide evidence that frailty may be of clinical value in determining ANSAH prognosis, a prospective study is needed to ensure unbiased data acquisition, especially in the case of the mFI variables and other potential confounding factors, and to examine the long-term implications of frailty among ANSAH patients. Second, given the relatively rare occurrence of ANSAHs, we were limited by sample size. Despite this, we were able to show a significant influence of frailty on ANSAH
outcomes. However, larger study is needed to parse the combined influences of HH score, Fisher score, frailty, and hemorrhage location on ANSAH outcomes. We are currently initiating a prospective multi-center study to verify these results. Third, given limitations with our electronic medical record and loss to follow-up, we were not able to assess long-term mortality or modified Rankin scores for our population. Moreover, while mFI is perhaps the most common measure of frailty, there are many other validated measures of decreased physiologic reserve.15 Future analysis is also needed to compare the mFI to other frailty scores in the setting of ANSAH.
CONCLUSIONS Frailty (mFI≥1), as a binary variable, was associated with worse clinical and radiographic hemorrhages, more in-hospital complications, poorer outcomes, and higher mortality following an ANSAH. The mFI, as a continuous measure, was found to be the best independent predictor of mortality and discharge home in multivariate analysis independent of age, AC/AP use, and ANSAH type. We also showed, using the ideal cutoff of mFI≥1, that frailty was associated with higher HH and Fisher scores, lower discharge GCS, longer length of stay, and more complications. These results confirm mFI as a measure that correlates with poor outcomes, and that frailty should be carefully considered in the planning and decision-making process for ANSAH patients.
ACKNOWLEDGMENTS The authors acknowledge Superior Medical Experts for editing assistance. Funding N/a
DISCLOSURES The authors have no disclosures to report.
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FIGURE LEGENDS Figure 1: Patient Selection Figure 2: Hemorrhage severity, admission and discharge GCS were dose-dependently influenced by frailty. GCS= Glasgow coma scale. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001 Figure 3: Receiver operating characteristic (ROC) analysis of discriminatory power of mFI, HH, Fisher scores, and age for predicting A) EVD requirement [Area under the curve (AUC) = 0.76, 0.824, 0.820, and 0.64, respectively], B) mortality (AUC = 0.97, 0.86, 0.85, and 0.84, respectively), and C) discharge home (AUC = 0.80, 0.71, 0.69, and 0.71, respectively). Mortality and discharge home, but not EVD requirement, was best predicted by mFI. Ideal cutoff value of mFI that could be used clinically was determined using discharge home ROC and was found to be mFI=1. Table 1: Distribution of the 11 modified frailty index (mFI) characteristics Table 2: Patient Characteristics by Frailty Group. Continuous variables are shown with the mean ± standard error of the mean. Binary variables are shown with the absolute count and percentage of sample. AC/AP = anticoagulant/antiplatelet medications; GCS=Glasgow coma score Table 3: Frailty and In-Hospital Outcomes and Complications. Odds ratio with 95% Confidence intervals (CI) are shown for categorical variables while mean ± SEM are shown for continuous variables. ANSAH= Angiogram-negative subarachnoid hemorrhage, EVD=External ventricular drain, GCS=Glasgow coma scale, ICU = intensive care unit, LOS=length of stay.
Table 4: The Effect of ANSAH type on outcomes and complications. Odds ratio with 95% Confidence intervals (CI) are shown for categorical variables while mean ± SEM are shown for continuous variables. EVD=External ventricular drain, GCS=Glasgow coma scale, ICU = intensive care unit, LOS=length of stay, NPAN=non-perimesencephalic subarachnoid hemorrhage, PMH=perimesencephalic subarachnoid hemorrhage Table 5. Univariate and Multivariate logistic regression for mortality and discharge home. The only independent predictor of mortality in multivariate regression was the modified frailty index. The best predictor of discharge location in multivariate regression was the modified frailty index. AC/AP = anticoagulant/antiplatelet medications, ANSAH=angiogram-negative subarachnoid hemorrhage, n.s. = not significant, OR=odds ratio
Table 1 History of: Hypertension on medication Congestive Heart Failure Diabetes mellitus Transient ischemic attack or cerebrovascular accident without neurological deficit Non-independent functional status Myocardial Infarction Peripheral Vascular Disease or ischemic rest pain Cerebral vascular accident with deficit Chronic Obstructive Pulmonary Disease or current pneumonia Previous coronary intervention or angina Impaired Sensorium
mFI = 1 (n=16) 10 (62.5%) 0 (0%) 1 (6.3%)
mFI = 2 (n=10) 6 (60%) 0 (0%) 5 (50%)
mFI ≥ 3 (n=6) 6 (100%) 3 (50%) 3 (50%)
2 (12.5%)
1 (10%)
4 (67%)
1 (6.3%) 0 (0%) 0 (0%) 0 (0%) 1 (6.3%) 1 (6.3%) 0 (0%)
1 (10%) 0 (0%) 3 (30%) 0 (0%) 1 (10%) 2 (20%) 1 (10%)
3 (50%) 2 (33%) 4 (67%) 0 (0%) 2 (33%) 1 (17%) 0 (0%)
Table 2 Characteristic Age (y) Female, n (%) White, n (%) AC/AP use, n (%) Body Mass Index (kg/m2) Ever smoker, n (%) Hyperlipidemia, n (%) Peri-Partum, n (% of women<50) Admission GCS (median) Hypothyroidism, n (%)
Overall (n=75) 55.4 ± 1.5 42 (56%) 52 (69.3%) 17 (22.7%) 26.1 ± 0.7 29 (38.7%) 23 (30.7%)
mFI = 0 (n=43) 50.4 ± 1.6 26 (60.5%) 28 (65.1%) 5 (11.6%) 24.9 ± 0.6 14 (32.6%) 7 (16.3%)
mFI ≥1 (n=32) 62.1 ± 2.5 16 (50%) 24 (75%) 12 (37.5%) 27.7 ± 1.4 15 (46.9%) 16 (50.0%)
<0.0001 0.4811 0.4506 0.0118 0.0422 0.2376 0.0024
3 (4.0%)
2 (4.7%)
1 (3.1%)
>0.9999
15 ± 0.4 (15) 10 (13.3%)
14.9 ± 0.1 (15) 3 (7.0%)
12.7 ± 0.7 (15) 7 (21.9%)
0.0039
P-value
0.0872
Table 3.
Characteristic Hunt & Hess Score (median) Fisher Score (median) Perimesencephalic ANSAH Discharge GCS Required EVD, n (%) Mortality, n (%) Hospital LOS (days) ICU LOS (days) Vasospasm, n (%) Tracheostomy, n (%) Gastrostomy Tube, n (%) Deep Vein Thrombosis, n (%) Pulmonary Embolism, n (%) Pneumonia, n (%) Intubation, n (%) Pressor requirement, n (%) Discharge home, n (%)
mFI = 0 (n=43) 1.5 ± 0.09 (1) 2.6 ± 0.1 (3) 29 (67.4%) 15 ± 0.0 (15) 5 (11.6%) 0 (0.0%) 9.3 ± 0.8 6.3 ± 0.7 17 (39.5%) 0 (0.0%) 0 (0.0%) 2 (4.7%) 0 (0.0%) 1 (2.3%) 3 (7.0%) 1 (2.3%) 37 (86%)
mFI ≥1 (n=32) 2.4 ± 0.2 (2) 3.3 ± 0.2 (4) 12 (37.5%) 13.4 ± 0.7 (15) 13 (40.6%) 4 (12.5%) 17.0 ± 3.7 12.8 ± 3.5 10 (31.3%) 4 (12.5%) 6 (18.8%) 6 (18.8%) 1 (3.1%) 7 (21.9%) 11 (34.4%) 3 (9.4%) 16 (50%)
Odds Ratio (95%CI)
P-value
0.29 (0.1-0.7) 5.2 (1.7-14.8) ∞ (1.4-∞) 0.7 (0.3-1.8) ∞ (1.36-∞) ∞ (2.02-∞) 4.7 (1.05-23.9) ∞ (0.15-∞) 11.8 (1.8-135.0) 7.0 (1.7-24.8) 4.3 (0.6-57.6) 0.16 (0.05-0.5)
0.0002 0.0035 0.0184 0.0168 0.0057 0.0296 0.0227 0.0412 0.4788 0.0296 0.0045 0.0663 0.4267 0.0092 0.0055 0.3066 0.0009
Table 4: Characteristic Discharge GCS (median) Required EVD, n (%) Mortality, n (%) Hospital LOS (days) ICU LOS (days) Vasospasm, n (%) Tracheostomy, n (%) Gastrostomy Tube, n (%) Deep Vein Thrombosis, n (%) Pulmonary Embolism, n (%) Pneumonia, n (%) Intubation, n (%) Pressor requirement, n (%) Discharge home, n (%)
PMH-SAH (n=41) 14.9 ± 0.1 (15) 8 (19.5%) 0 (0.0%) 11.3 ± 0.7 7.8 ± 0.7 15 (36.6%) 1 (2.4%) 3 (7.3%) 4 (9.8%) 1 (2.4%) 3 (7.3%) 5 (12.2%) 0 (0.0%) 33 (80.5%)
NPAN-SAH (n=34) 13.6 ± 0.7 (15) 10 (29.4%) 4 (11.8%) 14.1 ± 3.6 10.6 ± 3.3 12 (35.3%) 3 (8.8%) 3 (8.8%) 4 (11.8%) 0 (0.0%) 5 (14.7%) 9 (26.5%) 4 (11.8%) 20 (58.9%)
Odds Ratio (95%CI) 0.6 (0.2-1.60 0.0 (0-0.8) 1.1 (0.4-2.8) 0.3 (0.02-1.8) 0.8 (0.2-3.7) 0.8 (0.2-3.0) ∞ (0.09-∞) 0.5 (0.1-2.0) 0.4 (0.1-1.4) 0 (0-0.8) 2.7 (0.9-8.1)
P-value 0.0341 0.4172 0.0382 0.4086 0.3882 >0.9999 0.3233 >0.9999 >0.9999 >0.9999 0.4558 0.1429 0.0382 0.0731
P-value
Mortality
Modified Frailty Index Hunt & Hess Score Fisher Score ANSAH Type Age Sex Race Body Mass Index AC/AP Use Hyperlipidemia
Univariate OR (95%CI) 5.41 (1.54-19.06) 2.47 (1.14-5.35) n.s. n.s. 1.11 (1.01-1.21) n.s. n.s. n.s. 12.21 (1.18-126.48) n.s.
Discharge Home
Table 5. Characteristic
P-value
0.009 0.022 n.s. n.s. 0.026 n.s. n.s. n.s. 0.036 n.s.
Multivariate OR (95%CI) 5.39 (1.53-19.05) n.s. n.s. n.s. -
Modified frailty index Hunt & Hess Score Fisher Score ANSAH Type Age Sex Race Body mass index AC/AP Use Hyperlipidemia
0.27 (0.13-0.53) 0.34 (0.19-0.63) 0.44 (0.23-0.84) n.s. 0.95 (0.90-0.99) 3.58 (1.23-10.40) n.s. n.s. 0.24 (0.08-0.75) 0.24 (0.08-0.70)
0.00017 0.001 0.013 n.s. 0.013 0.019 n.s. n.s. 0.014 0.009
0.39 (0.17-0.88) 0.39 (0.17-0.89) n.s. n.s. 5.25 (1.16-23.75) n.s. n.s.
0.023 0.026 n.s. n.s. 0.031 n.s. n.s.
0.009 n.s. n.s. n.s. -
AC/AP: anticoagulation/antiplatelet medications ANSAH: Angiogram-Negative Subarachnoid Hemorrhage AUC: Area Under the Curve BMI: Body Mass Index CI: Confidence Interval CT: Computed Tomography DSA: Digital Subtraction Angiogram DVT: Deep Vein Thrombosis EVD: External Ventricular Drain GCS: Glasgow Coma Score HH: Hunt & Hess ICU: Intensive Care Unit LOS: Length of Stay mFI: modified frailty index MRI: Magnetic Resonance Imaging NPAN-SAH: Non-perimesencephalic ANSAH OR: Odds ratio PE: Pulmonary Embolism PMH-SAH: Perimesencephalic ANSAH ROC: Receiver Operating Characteristic SAH: Subarachnoid Hemorrhage
S1878-8750(19)32618-X
DOI:
https://doi.org/10.1016/j.wneu.2019.10.003
Reference:
WNEU 13474
To appear in:
World Neurosurgery
Received Date: 7 July 2019 Revised Date:
30 September 2019
Accepted Date: 1 October 2019
Please cite this article as: McIntyre M, Gandhi C, Dragonette J, Schmidt M, Cole C, Santarelli J, Lehrer R, Al-Mufti F, Bowers C, Increasing Frailty Predicts Worse Outcomes and Increased Complications Following Angiogram-negative Subarachnoid Hemorrhages, World Neurosurgery (2019), doi: https:// doi.org/10.1016/j.wneu.2019.10.003. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Elsevier Inc. All rights reserved.
Increasing Frailty Predicts Worse Outcomes and Increased Complications Following Angiogram-negative Subarachnoid Hemorrhages Authors: Matthew McIntyre BA1, Chirag Gandhi MD2, James Dragonette BS1, Meic Schmidt MD MBA2, Chad Cole MD MSc2, Justin Santarelli MD2, Rachel Lehrer RPA-C2, Fawaz AlMufti MD2, Christian Bowers MD2 Affiliations: 1School of Medicine, New York Medical College, Valhalla, New York. Department of Neurological Surgery Westchester Medical Center, Valhalla, New York
2
Correspondence to: Christian Bowers MD Department of Neurosurgery, Westchester Medical Center, New York Medical College 110 Woods Road, Valhalla, NY, 10595, USA Phone: (914) 493-7000; Fax: (914) 493-2505 Email: [email protected] Short Title: Frailty Predicts Outcomes after ANSAH Keywords: Subarachnoid hemorrhage, angiogram-negative, frailty, modified frailty index, mortality, complications
ABSTRACT Background The effect of frailty on outcomes following Angiogram-negative subarachnoid hemorrhages (ANSAH) is currently unknown. We investigated frailty’s effects on ANSAH outcomes, including mortality and in-hospital complications. Methods Patients with non-traumatic SAH and cerebral angiograms with an unidentifiable hemorrhage source were retrospectively reviewed from 2014 to 2018. The cohort was divided into non-frail (modified frailty index [mFI] of 0) and frail (mFI≥1) groups based on pre-hemorrhage characteristics. Primary outcomes were mortality rate and discharge location. Multivariate logistic regression analyses determined predictors of ANSAH severity and primary endpoints. Receiver operating characteristic (ROC) curves used to discriminate risks for primary endpoints comparing mFI, Hunt&Hess and Fisher scores, and age. Results We included 75 patients with a mean age of 55.4±1.5 years, comprised of 42 (56%) females, and 41 (54.7%) with perimesencephalic bleeds. A total of 32/75 (42.7%) patients were classified as frail. Frail individuals were 6.2 times less likely to be discharged home (OR=0.16; 95%CI: 0.050.5; p=0.0009) and all mortalities occurred in frail patients (12.5% [n=4/32]; p=0.0296). The only independent predictor of mortality was higher mFI (OR=5.4;95%CI: 1.5-19.1; p=0.009), while lower mFI best predicted discharge home (OR=0.39; 95%CI: 0.17-0.88; p=0.023). ROC analysis showed that mFI best predicted both mortality (AUC=0.9718; p=0.0016) and discharge home (AUC=0.7998; p<0.0001). Conclusions
Frail ANSAH patients have poorer outcomes and increased mortality compared to non-frail patients. While prospective study is needed, this information significantly impacts our understanding of ANSAH outcomes and frailty should be used for prognostication as it was a better predictor than Hunt/Hess or Fisher Scores.
INTRODUCTION Subarachnoid hemorrhage (SAH) is associated with a mortality rate of 18% and high morbidity rates for those who survive the initial hemorrhage.1–3 SAH is often a consequence of trauma or aneurysm rupture; however, 11-19% of spontaneous SAH are ultimately found to be idiopathic, despite multiple diagnostic angiograms.4–7 Angiogram-negative subarachnoid hemorrhages (ANSAH) are traditionally thought to have a more benign course than aneurysmal SAH, given their reduced incidence of rebleeding and higher rate of good outcomes.8 While SAH carries the risks of symptomatic vasospasm, rebleeding, cardiac arrhythmias, and pulmonary edema, ANSAH also have significant rates of ischemic complications, vasospasm, chronic headaches, developing hydrocephalus, and permanent impaired cognitive function.9–11
Frailty is a state of reduced physiologic reserve. A common validated measure of frailty is the modified frailty index (mFI), which is associated with worse outcomes and higher mortality following various types of surgery.12–14 In terms of neurosurgical procedures, each unit increase in mFI is associated with a 3-5 increased odds of mortality and a 1.5-1.6 increased odds of morbidity, independent of age.15,16 To date, no studies have been performed examining the underlying effect of frailty on outcome for ANSAH patients. This study’s primary objective was to examine frailty’s effect on ANSAH patients’ mortality, complication rates, and outcomes.
MATERIALS AND METHODS Study Design and Setting This retrospective cohort study was conducted between November 2018 and February 2019 at a quaternary academic medical center with high patient volume (Westchester Medical Center in
Valhalla, New York), between June 2014-July 2018 after Institutional Review Board Approval of New York Medical College & Westchester Medical Center. Data was collected for the a priori selected variables.
Participant Selection Patients were identified after reviewing the digital subtraction angiogram (DSA) institutional database for those with a cryptogenic SAH after DSA. All individuals who were found to have at least one DSA-negative subarachnoid hemorrhage (ANSAH) were included. ANSAH was defined by negative DSA for vascular hemorrhage source regardless of individual CT or MRI findings. Exclusion criteria were a history of trauma or neurosurgical procedure, an absence of radiographic evidence of a SAH including cerebrospinal fluid-positive CT-negative SAH, or a subsequent positive DSA for vascular source of hemorrhage on follow-up imaging. Perimesencephalic ANSAH (PMH-SAH) was defined as the presence of SAH isolated to the perimesencephalic cisterns with extension into the ambient or pre-pontine cisterns or basal parts of the Sylvian fissures. PMH-SAH did not include blood extending into the lateral Sylvian fissure, anterior interhemispheric fissure, or lateral ventricles. Non-perimesencephalic ANSAH (NPAN-SAH) was characterized by the presence of blood primarily in the suprasellar, sylvian (basal and lateral), and interhemispheric cisterns.7,17
Measures and outcomes For each patient, the demographics, ANSAH type, anticoagulation/antiplatelet (AC/AP) medication use, body mass index (BMI), smoking history, hyperlipidemia, admission Glasgow coma score (GCS) and peri-partum status (of women <50y) were collected. A modified frailty
index (mFI) score was calculated as previously described based on pre-hemorrhage co-morbid conditions12, and variables are shown in Table 1. These variables were hypertension requiring medication, congestive heart failure, myocardial infarction, previous percutaneous coronary intervention or angina, transient ischemic attack or cerebrovascular accident (CVA) without neurological deficit, CVA with neurological deficit, peripheral vascular disease or ischemic rest pain, chronic obstructive pulmonary disease or current pneumonia, diabetes mellitus, nonindependent functional status, and impaired sensorium. Non-independent functional status was defined as requiring assistance from another person for activities of daily living.18–21 Each variable was assigned one point and the mFI was calculated on a scale of 0 (i.e. no variables present) to 11 (i.e. all variables present). Patients were grouped as mFI = 0 (non-frail) or mFI ≥1 (frail). The cutoff of mFI ≥1 was determined based the receiver operating characteristic (ROC) analysis described below. Primary outcomes were discharge home and in-hospital mortality. Secondary measures were Hunt & Hess (HH) scores, Fisher scores, external ventricular drain (EVD) requirement, discharge GCS score, hospital length of stay (LOS), intensive care unit (ICU) LOS, need for tracheostomy, gastrectomy tube, radiographic evidence of vasospasm, deep vein thrombosis (DVT), pulmonary embolism (PE), pneumonia, intubation, and vasopressor use.
Statistical Analysis Normal distributions were determined using an Anderson-Darling normality test. A T-test or ANOVA with Tukey’s post-hoc analysis was used for continuous variables, and data are shown using mean ± standard error of the mean (SEM). A Fisher’s exact test was used for binary variables, and odds ratios (OR) are shown with 95% confidence intervals (95%CI). Receiver operator curves (ROC) of mFI, age, HH, and Fisher scores were plotted against mortality and
discharge home. Area under the curve (AUC) was used to compare discriminatory power with an AUC of 1.0 considered perfect discrimination and 0.5 equal to chance. ROC significance was determined using the Wilson and Brown method. AUC and Standard error are shown. Given the lack of consensus of an appropriate cutoff for frailty18, we utilized the ROC analysis to determine this threshold that optimally predicts outcomes for ANSAH patients specifically. ROC curve for discharge home was used to determine mFI cutoff of 1 as the appropriate whole number that maximizes sensitivity and specificity for that outcome.22 Discharge home ROC was used for cutoff determination due to significance and higher rate of positive findings (n= 53 discharge home) compared to mortality ROC (n=4 deaths). To establish the predictors of HH and Fisher scores, multivariate linear regressions were performed using least squares method using age, sex, race, ANSAH type, BMI, AC/AP use and hyperlipidemia status. To establish the predictors of mortality and discharge home, the same variables as above were used, with the addition of HH and Fisher scores, using multivariate logistic regressions utilizing the forward conditional method. Only variables significant in univariate logistic regression were used in the multivariate analysis. No collinearity was detected in any multivariate analysis, as defined as a variance inflation factor of <1 or >10, and therefore both HH and Fisher scores were included in the same multivariate analyses. Statistical analysis was performed using Prism 8.0.1 (GraphPad Software, Inc. La Jolla, CA) and SPSS version 25 (International Business Machines Corp. Armonk, NY). We defined significance at p<0.05.
RESULTS Baseline Features and Demographics
Between June, 2014 and July, 2018, 75 patients met our inclusion criteria with all patients having at least one negative DSA (Figure 1). The majority were white (52/75, 69.3%), female (42/75 56%), and had an average age of 55.4 ± 1.5 years (range 21-86). Age was normally distributed across the cohort (A2=0.4834; p=0.2229). Patients generally presented with good clinical and radiographic SAH grades; however, 9.3% (7/75) of patients had a HH score of 4 or 5, while 33.3% (25/75) had a Fisher score of 4. The majority (41/75, 54.7%) were PMH-SAH, while the remainder (34/75, 45.3%) were NPAN-SAH.
A total of 43/75 (57.3%) patients were classified as non-frail and 32/75 (42.7%) were classified as frail. The variables used to define mFI are presented in Table 1. The most prevalent mFI variable observed was a history of hypertension on medications (22/32, 68.8%), followed by diabetes mellitus (9/32, 28.1%). Patient baseline characteristics stratified by group are presented in Table 2. The frail group were significantly older (62.1 vs. 50.4 years; p<0.0001), had more prevalent usage of AC/AP medications (37.5% vs. 11.6%; p=0.0118), a higher BMI (27.7 vs. 24.9 kg/m2; p=0.027), and increased hyperlipidemia prevalence (50% vs. 16.3%; p=0.0024) indicating a poorer overall health status. Sex, race, smoking history, history of hypothyroidism, and percentage of peri-partum women <50 years were not different between groups.
In-Hospital Outcomes When compared to non-frail patients, those with an mFI ≥1 had more severe ANSAHs as measured by higher HH (2.4 vs. 1.5; p=0.0002) and Fisher scores (3.3 vs. 2.6; p=0.0035) (Table 3). Frail patients had lower GCS scores compared to non-frail patients on both admission (12.7 ± 0.7 vs. 14.9 ± 0.1; p=0.0039) and discharge (13.4 ± 0.7 vs. 15 ± 0.0; p=0.0168). An impairment-
dependent relationship between frailty and HH (p<0.0001) and Fisher (p=0.0036) scores, along with decreased discharge (p<0.0001) GCS scores were observed (Figure 2). Frail patients were more likely to require an EVD (OR=5.2; 95%CI: 1.7-14.8; p=0.0057), have a longer hospital LOS (17.0 days vs. 9.3 days; p=0.0227) and a longer ICU LOS (12.8 days vs. 6.3 days; p=0.0412). Interestingly, men were more likely to receive an EVD versus women (OR=7.0; 95%CI: 2.1-21.1; p=0.0021) and no patients required a permanent cerebrospinal fluid diverting shunt. Frail patients had no increase in vasospasm incidence (OR = 0.7; 95%CI: 0.3-1.8; p=0.4788) and all mortalities occurred in the frail group (12.5% [n=4/32]; p=0.0296).
In-hospital Complications Frail ANSAH patients experienced more frequent in-hospital complications (Table 3). Specifically, frail patients were more likely to develop a pneumonia (OR = 11.8; 95%CI: 1.8135.0; p= 0.0092), and be intubated during admission (OR=7; 95%CI: 1.7-24.8; p=0.0055), and trended to have more DVT compared to non-frail patients (OR=4.7; 95%CI: 1.05-23.9; p=0.0663). All patients who received a tracheostomy (n=4, p=0.0296) or gastrostomy tube (n=6, p=0.0045) were in the frail group. Frail individuals were also 6.2 times less likely to be discharged home compared to their non-frail counterparts (OR=0.16; 95%CI: 0.05-0.5; p=0.0009).
Subgroup Analysis of ANSAH type Given the well-known effect of ANSAH type on functional outcomes, a subgroup analysis was performed (Table 4). Those with NPAN-SAH were found to have lower discharge GCS (p=0.0341), but no differences in age, sex, admission GCS, hospital LOS, ICU LOS, vasospasm,
EVD, tracheostomy, gastrotomy tube, DVT, PE, pneumonia, intubation, or discharge home (P>0.05). All 4 mortalities occurred in the NPAN-SAH group (p=0.0382). Frail individuals were also more likely to have NPAN-SAH compared to non-frail patients (OR = 3.45; 95%CI: 1.388.36; p=0.0184).
Predictors of ANSAH severity To examine the potentially confounding effect of age, sex, race, ANSAH type, BMI, AC/AP use, and hyperlipidemia on the ability of mFI to predict ANSAH severity, as measured by HH/Fisher scores, mortality, and discharge home, multivariate regressions were performed. In multivariate linear regression, mFI was the only independent risk factor for predicting HH (t=2.634; p=0.0106) and Fisher (t=3.141; p=0.0026) scores. Interestingly, in multivariate logistic regression, increased mFI was the only independent risk factor for increased mortality (OR=5.39; 95%CI: 1.53-19.05; p=0.009) (Table 5), while discharge home was independently predicted only by lower mFI (OR=0.391; 95%CI: 0.17-0.89; p=0.022), female sex (OR=5.25; 95%CI: 1.1623.75; p=0.038), and lower HH score (OR=0.390; 95%CI: 0.17-0.89; p=0.027) (Table 5). In multivariate analysis, age, AC/AP use and ANSAH type were not independently predictive of HH score, Fisher score, discharge home, or mortality (P>0.05).
Predictors of Clinical Outcomes To determine the clinical utility of using mFI to determine risk and discriminate between those who were likely to expire during the stay or be discharged home, receiver operating characteristic (ROC) curves were generated and areas under the curve (AUC) were calculated for age, mFI, HH and Fisher Scores (Figure 3). The best determinate of mortality was mFI
(AUC=0.972±0.027; p<0.0016) followed HH (AUC=0.859±0.070; p=0.0164), Fisher score (AUC=0.848 ± 0.057; p=0.02) and age (AUC=0.838±0.078; p=0.0236). The best determinate of discharge home was mFI (AUC=0.800±0.065; p<0.0001) followed by age (AUC=0.710 ± 0.068; p=0.005), HH score (AUC=0.709±0.078; p=0.0054), and Fisher score (AUC=0.689±0.068; p=0.0119). Using the ROC curves, mFI≥1 was confirmed to be the ideal cutoff for predicting mortality and discharge location as this value has the highest associated sensitivity and specificity for these endpoints.
DISCUSSION In this study we show that the modified frailty index (mFI) is predictive of poorer outcomes, more complications, and increased mortality following an ANSAH, independent of age, ANSAH type, AC/AP use, HH, and Fisher scores. Following an ANSAH, frailty (mFI ≥1) was associated with worse clinical and radiographic hemorrhages, longer hospital and ICU LOS, lower discharge GCS, and more complications. These results highlight the importance of considering frailty in the decision-making algorithm and treatment planning following an ANSAH.
Frailty is a measure of reduced physiological reserve that is a known risk factor for adverse events, increased morbidity, and mortality following a surgical intervention as demonstrated across multiple surgical subspecialties.19,23,24 Seib et al demonstrated that in a retrospective cohort study of 1,480,828 patients over age 40 who underwent common ambulatory surgery, patients with a high mFI had significantly higher odds of serious complications.12 In addition, a national database of 971,434 inpatients who underwent surgery, Velanovich et al found an increased risk of mortality and morbidity for each unit increase in mFI.19 These results, together
with the present study, highlight frailty as an important indicator of outcomes that should be carefully considered when evaluating surgical patients.
Prior work has shown that patients who develop ANSAHs have more co-morbidities and AC/AP use than those with aneurysmal SAH, however little is known regarding the effect these underlying comorbidities have on ANSAH outcomes.25,26 Dalbjerg et al showed that among patients with an ANSAHs, those receiving anticoagulant therapy, who smoke, or have excessive alcohol use, but not increased age, have an increased odds of poor outcomes at discharge.27 Similarly, Hui et al showed that ANSAH patients with anti-thrombotic states, whether iatrogenic or endogenous, have higher HH and Fisher scores, more vasospasm, increased hydrocephalus rates, and worse outcomes.28 Interestingly, in multivariate analysis we failed to show any association with AC/AP use with HH score, Fisher score, discharge home or mortality. Of note, we included all medications with AC/AP potential (aspirin, clopidogrel, warfarin etc.) in the same group, which may have confounded this analysis. Nevertheless, the mFI remained a better predictor of outcomes thus reflecting that the underlying conditions, as measured by frailty, may more strongly influence outcomes than AC/AP medications.
Other confounders that were addressed in the analysis were age and ANSAH grade. We found mFI to be a better predictor of outcomes compared to age. We also reaffirmed that the HH and Fisher scores are valid measures of hemorrhage severity in the ANSAH population; however, for discharge home and mortality rates, the mFI was a superior predictor of these outcomes in both ROC and multivariate analysis. These data indicate that evaluating mFI may have particular use in conditions that have a relatively low mortality rate. Imaoka et al showed that among patients
with surgically treated spontaneous intraparenchymal hemorrhage (a disease with a high mortality rate), the mFI had an AUC of 0.65 for poor outcomes and only a AUC of 0.8 for mortality, thus showing that the mFI may be more useful in diseases with low mortality such as ANSAHs.22 Moreover, in the present study in-hospital complications, especially pneumonia, intubation, and tracheostomy and gastrostomy tubes, were also highly associated with frailty.
To date, the preponderance of research on ANSAH outcomes has focused on hemorrhage location; i.e., whether the hemorrhage is perimesencephalic (PMH-SAH) or nonperimesencephalic (NPAN-SAH). PMH-SAH represents approximately half of ANSAH.7,29 Many studies have shown that PMH-SAH have more benign clinical courses than NPAN-SAH; however, most studies, including our own, are limited by a small sample size.8,30–32 Hoping to overcome this obstacle, meta-analyses have shown that while PMH-SAH have better outcomes than NPAN-SAH, they are also associated with higher rates of re-hemorrhage.9 Interestingly, two recent meta-analyses from the same group show that PMH-SAH are associated with lower rates of hydrocephalus, delayed cerebral ischemia, and vasospasm compared to NPAN-SAH, however, they also found a significant publication bias on this subject.33–35 Likewise, while we did show that NPAH-SAH was associated with higher mortality and lower discharge GCS, there was no significant association with LOS, vasospasm, EVD use, tracheostomy, gastrostomy tube, discharge home, or other in-hospital complications. In fact, while we found that frailty was associated with NPAN-SAH, we failed to show any influence of ANSAH type on discharge home and mortality in multivariate analysis. Zhong et al showed that NPAN-SAH was associated with higher rates of hypertension, hyperlipidemia, and smoking compared to PMH-SAH, which supports the hypothesis that underlying comorbid conditions influence both ANSAH type and
ANSAH outcomes.36 Together, this suggests that clinical differences in ANSAH type may be related to frailty.
Finally, for patients with an ANSAH, there remains an ongoing debate regarding when and how often to repeat DSA studies in the search for an identifiable cause of the bleed.37–39 Some have suggested that the mFI may have utility in determining who should receive endovascular treatment for aneurysmal SAH.40 This study confirms that the mFI is an independent risk factor for predicting poor outcomes, but future prospective study is needed to verify and refine its predictive ability. The comorbidities that make up the mFI are all chronic health conditions such as congestive heart failure, diabetes, hypertension, etc, such that the mFI is not modifiable in a way where treatment at the time of ANSAH could change outcomes. However, perhaps ANSAH patients with increased frailty could be labelled as high-risk for complications and increased mortality such that there could be an increased awareness when these patients are admitted. If proven to have predictive value, the mFI could be used in family discussions on the decision for end of life care, providing an objective measure to help families and clinicians decision making.
Limitations The main limitation of this study was its retrospective design. While we provide evidence that frailty may be of clinical value in determining ANSAH prognosis, a prospective study is needed to ensure unbiased data acquisition, especially in the case of the mFI variables and other potential confounding factors, and to examine the long-term implications of frailty among ANSAH patients. Second, given the relatively rare occurrence of ANSAHs, we were limited by sample size. Despite this, we were able to show a significant influence of frailty on ANSAH
outcomes. However, larger study is needed to parse the combined influences of HH score, Fisher score, frailty, and hemorrhage location on ANSAH outcomes. We are currently initiating a prospective multi-center study to verify these results. Third, given limitations with our electronic medical record and loss to follow-up, we were not able to assess long-term mortality or modified Rankin scores for our population. Moreover, while mFI is perhaps the most common measure of frailty, there are many other validated measures of decreased physiologic reserve.15 Future analysis is also needed to compare the mFI to other frailty scores in the setting of ANSAH.
CONCLUSIONS Frailty (mFI≥1), as a binary variable, was associated with worse clinical and radiographic hemorrhages, more in-hospital complications, poorer outcomes, and higher mortality following an ANSAH. The mFI, as a continuous measure, was found to be the best independent predictor of mortality and discharge home in multivariate analysis independent of age, AC/AP use, and ANSAH type. We also showed, using the ideal cutoff of mFI≥1, that frailty was associated with higher HH and Fisher scores, lower discharge GCS, longer length of stay, and more complications. These results confirm mFI as a measure that correlates with poor outcomes, and that frailty should be carefully considered in the planning and decision-making process for ANSAH patients.
ACKNOWLEDGMENTS The authors acknowledge Superior Medical Experts for editing assistance. Funding N/a
DISCLOSURES The authors have no disclosures to report.
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FIGURE LEGENDS Figure 1: Patient Selection Figure 2: Hemorrhage severity, admission and discharge GCS were dose-dependently influenced by frailty. GCS= Glasgow coma scale. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001 Figure 3: Receiver operating characteristic (ROC) analysis of discriminatory power of mFI, HH, Fisher scores, and age for predicting A) EVD requirement [Area under the curve (AUC) = 0.76, 0.824, 0.820, and 0.64, respectively], B) mortality (AUC = 0.97, 0.86, 0.85, and 0.84, respectively), and C) discharge home (AUC = 0.80, 0.71, 0.69, and 0.71, respectively). Mortality and discharge home, but not EVD requirement, was best predicted by mFI. Ideal cutoff value of mFI that could be used clinically was determined using discharge home ROC and was found to be mFI=1. Table 1: Distribution of the 11 modified frailty index (mFI) characteristics Table 2: Patient Characteristics by Frailty Group. Continuous variables are shown with the mean ± standard error of the mean. Binary variables are shown with the absolute count and percentage of sample. AC/AP = anticoagulant/antiplatelet medications; GCS=Glasgow coma score Table 3: Frailty and In-Hospital Outcomes and Complications. Odds ratio with 95% Confidence intervals (CI) are shown for categorical variables while mean ± SEM are shown for continuous variables. ANSAH= Angiogram-negative subarachnoid hemorrhage, EVD=External ventricular drain, GCS=Glasgow coma scale, ICU = intensive care unit, LOS=length of stay.
Table 4: The Effect of ANSAH type on outcomes and complications. Odds ratio with 95% Confidence intervals (CI) are shown for categorical variables while mean ± SEM are shown for continuous variables. EVD=External ventricular drain, GCS=Glasgow coma scale, ICU = intensive care unit, LOS=length of stay, NPAN=non-perimesencephalic subarachnoid hemorrhage, PMH=perimesencephalic subarachnoid hemorrhage Table 5. Univariate and Multivariate logistic regression for mortality and discharge home. The only independent predictor of mortality in multivariate regression was the modified frailty index. The best predictor of discharge location in multivariate regression was the modified frailty index. AC/AP = anticoagulant/antiplatelet medications, ANSAH=angiogram-negative subarachnoid hemorrhage, n.s. = not significant, OR=odds ratio
Table 1 History of: Hypertension on medication Congestive Heart Failure Diabetes mellitus Transient ischemic attack or cerebrovascular accident without neurological deficit Non-independent functional status Myocardial Infarction Peripheral Vascular Disease or ischemic rest pain Cerebral vascular accident with deficit Chronic Obstructive Pulmonary Disease or current pneumonia Previous coronary intervention or angina Impaired Sensorium
mFI = 1 (n=16) 10 (62.5%) 0 (0%) 1 (6.3%)
mFI = 2 (n=10) 6 (60%) 0 (0%) 5 (50%)
mFI ≥ 3 (n=6) 6 (100%) 3 (50%) 3 (50%)
2 (12.5%)
1 (10%)
4 (67%)
1 (6.3%) 0 (0%) 0 (0%) 0 (0%) 1 (6.3%) 1 (6.3%) 0 (0%)
1 (10%) 0 (0%) 3 (30%) 0 (0%) 1 (10%) 2 (20%) 1 (10%)
3 (50%) 2 (33%) 4 (67%) 0 (0%) 2 (33%) 1 (17%) 0 (0%)
Table 2 Characteristic Age (y) Female, n (%) White, n (%) AC/AP use, n (%) Body Mass Index (kg/m2) Ever smoker, n (%) Hyperlipidemia, n (%) Peri-Partum, n (% of women<50) Admission GCS (median) Hypothyroidism, n (%)
Overall (n=75) 55.4 ± 1.5 42 (56%) 52 (69.3%) 17 (22.7%) 26.1 ± 0.7 29 (38.7%) 23 (30.7%)
mFI = 0 (n=43) 50.4 ± 1.6 26 (60.5%) 28 (65.1%) 5 (11.6%) 24.9 ± 0.6 14 (32.6%) 7 (16.3%)
mFI ≥1 (n=32) 62.1 ± 2.5 16 (50%) 24 (75%) 12 (37.5%) 27.7 ± 1.4 15 (46.9%) 16 (50.0%)
<0.0001 0.4811 0.4506 0.0118 0.0422 0.2376 0.0024
3 (4.0%)
2 (4.7%)
1 (3.1%)
>0.9999
15 ± 0.4 (15) 10 (13.3%)
14.9 ± 0.1 (15) 3 (7.0%)
12.7 ± 0.7 (15) 7 (21.9%)
0.0039
P-value
0.0872
Table 3.
Characteristic Hunt & Hess Score (median) Fisher Score (median) Perimesencephalic ANSAH Discharge GCS Required EVD, n (%) Mortality, n (%) Hospital LOS (days) ICU LOS (days) Vasospasm, n (%) Tracheostomy, n (%) Gastrostomy Tube, n (%) Deep Vein Thrombosis, n (%) Pulmonary Embolism, n (%) Pneumonia, n (%) Intubation, n (%) Pressor requirement, n (%) Discharge home, n (%)
mFI = 0 (n=43) 1.5 ± 0.09 (1) 2.6 ± 0.1 (3) 29 (67.4%) 15 ± 0.0 (15) 5 (11.6%) 0 (0.0%) 9.3 ± 0.8 6.3 ± 0.7 17 (39.5%) 0 (0.0%) 0 (0.0%) 2 (4.7%) 0 (0.0%) 1 (2.3%) 3 (7.0%) 1 (2.3%) 37 (86%)
mFI ≥1 (n=32) 2.4 ± 0.2 (2) 3.3 ± 0.2 (4) 12 (37.5%) 13.4 ± 0.7 (15) 13 (40.6%) 4 (12.5%) 17.0 ± 3.7 12.8 ± 3.5 10 (31.3%) 4 (12.5%) 6 (18.8%) 6 (18.8%) 1 (3.1%) 7 (21.9%) 11 (34.4%) 3 (9.4%) 16 (50%)
Odds Ratio (95%CI)
P-value
0.29 (0.1-0.7) 5.2 (1.7-14.8) ∞ (1.4-∞) 0.7 (0.3-1.8) ∞ (1.36-∞) ∞ (2.02-∞) 4.7 (1.05-23.9) ∞ (0.15-∞) 11.8 (1.8-135.0) 7.0 (1.7-24.8) 4.3 (0.6-57.6) 0.16 (0.05-0.5)
0.0002 0.0035 0.0184 0.0168 0.0057 0.0296 0.0227 0.0412 0.4788 0.0296 0.0045 0.0663 0.4267 0.0092 0.0055 0.3066 0.0009
Table 4: Characteristic Discharge GCS (median) Required EVD, n (%) Mortality, n (%) Hospital LOS (days) ICU LOS (days) Vasospasm, n (%) Tracheostomy, n (%) Gastrostomy Tube, n (%) Deep Vein Thrombosis, n (%) Pulmonary Embolism, n (%) Pneumonia, n (%) Intubation, n (%) Pressor requirement, n (%) Discharge home, n (%)
PMH-SAH (n=41) 14.9 ± 0.1 (15) 8 (19.5%) 0 (0.0%) 11.3 ± 0.7 7.8 ± 0.7 15 (36.6%) 1 (2.4%) 3 (7.3%) 4 (9.8%) 1 (2.4%) 3 (7.3%) 5 (12.2%) 0 (0.0%) 33 (80.5%)
NPAN-SAH (n=34) 13.6 ± 0.7 (15) 10 (29.4%) 4 (11.8%) 14.1 ± 3.6 10.6 ± 3.3 12 (35.3%) 3 (8.8%) 3 (8.8%) 4 (11.8%) 0 (0.0%) 5 (14.7%) 9 (26.5%) 4 (11.8%) 20 (58.9%)
Odds Ratio (95%CI) 0.6 (0.2-1.60 0.0 (0-0.8) 1.1 (0.4-2.8) 0.3 (0.02-1.8) 0.8 (0.2-3.7) 0.8 (0.2-3.0) ∞ (0.09-∞) 0.5 (0.1-2.0) 0.4 (0.1-1.4) 0 (0-0.8) 2.7 (0.9-8.1)
P-value 0.0341 0.4172 0.0382 0.4086 0.3882 >0.9999 0.3233 >0.9999 >0.9999 >0.9999 0.4558 0.1429 0.0382 0.0731
P-value
Mortality
Modified Frailty Index Hunt & Hess Score Fisher Score ANSAH Type Age Sex Race Body Mass Index AC/AP Use Hyperlipidemia
Univariate OR (95%CI) 5.41 (1.54-19.06) 2.47 (1.14-5.35) n.s. n.s. 1.11 (1.01-1.21) n.s. n.s. n.s. 12.21 (1.18-126.48) n.s.
Discharge Home
Table 5. Characteristic
P-value
0.009 0.022 n.s. n.s. 0.026 n.s. n.s. n.s. 0.036 n.s.
Multivariate OR (95%CI) 5.39 (1.53-19.05) n.s. n.s. n.s. -
Modified frailty index Hunt & Hess Score Fisher Score ANSAH Type Age Sex Race Body mass index AC/AP Use Hyperlipidemia
0.27 (0.13-0.53) 0.34 (0.19-0.63) 0.44 (0.23-0.84) n.s. 0.95 (0.90-0.99) 3.58 (1.23-10.40) n.s. n.s. 0.24 (0.08-0.75) 0.24 (0.08-0.70)
0.00017 0.001 0.013 n.s. 0.013 0.019 n.s. n.s. 0.014 0.009
0.39 (0.17-0.88) 0.39 (0.17-0.89) n.s. n.s. 5.25 (1.16-23.75) n.s. n.s.
0.023 0.026 n.s. n.s. 0.031 n.s. n.s.
0.009 n.s. n.s. n.s. -
AC/AP: anticoagulation/antiplatelet medications ANSAH: Angiogram-Negative Subarachnoid Hemorrhage AUC: Area Under the Curve BMI: Body Mass Index CI: Confidence Interval CT: Computed Tomography DSA: Digital Subtraction Angiogram DVT: Deep Vein Thrombosis EVD: External Ventricular Drain GCS: Glasgow Coma Score HH: Hunt & Hess ICU: Intensive Care Unit LOS: Length of Stay mFI: modified frailty index MRI: Magnetic Resonance Imaging NPAN-SAH: Non-perimesencephalic ANSAH OR: Odds ratio PE: Pulmonary Embolism PMH-SAH: Perimesencephalic ANSAH ROC: Receiver Operating Characteristic SAH: Subarachnoid Hemorrhage