Next Article in Journal
Comparison of Magnetic Resonance Imaging Scales for Assessment of Interval Changes of Arthropathy in Boys with Severe Hemophilia
Previous Article in Journal
Narrative and Pictorial Review on State-of-the-Art Endovascular Treatment for Focal Non-Infected Lesions of the Abdominal Aorta: Anatomical Challenges, Technical Solutions, and Clinical Outcomes
Previous Article in Special Issue
In Cervical Myelopathy: Clinical Effect of the Posterior Myelon Shifting After Dorsal Decompression and Instrumentation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 14
Issue 13
10.3390/jcm14134799
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Clinical Predictors of Inpatient Mortality and Poor Postoperative Course After aSAH Microsurgical Clipping: A 10-Year Experience from a Peruvian Tertiary Care Center

by
Fernando Terry
1,
Alejandro Enríquez-Marulanda
2,
Nathaly Chinchihualpa-Paredes
3,
Meiling Carbajal-Galarza
4,
Claudia L Vidal-Cuellar
4,
Guiliana Mas-Ubillus
4,
Bruno Diaz-Llanes
5,
Carlos Quispe-Vicuña
6,
Niels Pacheco-Barrios
2,7,
Rommel Arbulu-Zuazo
5,8,
Ziev B. Moses
2,
Joel Sequeiros
9,
Evan Luther
10,
Robert M. Starke
11,
Philipp Taussky
2 and
Jaime Lopez-Calle
5,*
1
Facultad de Medicina Humana, Universidad de San Martín de Porres, Lima 15101, Peru
2
Department of Neurosurgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
3
Department of Neurology, University of Cincinnati, Cincinnati, OH 45219, USA
4
School of Medicine, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
5
Department of Neurosurgery, Clínica Internacional, Lima 15001, Peru
6
Grupo de Investigación Neurociencias, Metabolismo, Efectividad Clínica y Sanitaria (NEMECS), Universidad Científica del Sur, Lima 15142, Peru
7
Carrera de Medicina Humana, Universidad Científica del Sur, Lima 15142, Peru
8
Department of Neurosurgery, Hospital Nacional Arzobispo Loayza, Lima 15013, Peru
9
Department of Neurological Surgery, Division of CNS Endovascular Surgery, University of Louisville, Louisville, KY 40202, USA
10
Department of Neurosurgery, Allegheny General Hospital, Pittsburgh, PA 15212, USA
11
Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2025, 14(13), 4799; https://doi.org/10.3390/jcm14134799
Submission received: 8 March 2025 / Revised: 21 April 2025 / Accepted: 28 April 2025 / Published: 7 July 2025
(This article belongs to the Special Issue Acute Care for Traumatic Injuries and Surgical Outcomes)
Browse Figures
Versions Notes

Abstract

Background/Objectives: Aneurysmal subarachnoid hemorrhage (aSAH) is a medical emergency with a high mortality rate requiring urgent treatment. This study aimed to identify clinical predictors of inpatient mortality and poor postoperative course after aSAH surgical clipping. Methods: We performed a retrospective review of medical records for 210 patients with aSAH treated via surgical clipping at our institution between 2010 and 2019. Baseline demographic data and clinical characteristics related to aSAH were collected. To identify factors associated with inpatient mortality and a poor postoperative course after aSAH microsurgical clipping, we conducted a univariate and bivariate analysis, as well as a multivariate analysis via the Poisson regression model. Results: The overall cumulative mortality over the 10-year study period was 11.43%. A severe WFNS scale score (aRR: 2.86; 95% CI: 1.28–6.39; p = 0.011) and having 1 (aRR: 5.76; 95% CI: 2.02–16.39, p = 0.001) or ≥2 (aRR: 18.86; 95% CI: 5.16–68.90, p < 0.001) postoperative neurosurgical complications were associated with an increased risk of inpatient mortality. A moderate (aRR: 3.71; 95% CI: 1.45–9.50; p = 0.006) or severe (aRR: 4.18; 95% CI: 1.12–15.60; p = 0.034) Glasgow scale score on admission, and presenting 1 (aRR: 2.31; 95% CI: 1.27–4.19; p = 0.006) or ≥2 postoperative clinical complications (aRR: 3.34; 95% CI: 1.83–6.10; p < 0.001) were associated with an increased risk of a poor postoperative course. Conclusions: While promising and widely supported by the published literature, these findings require further validation in a larger prospective and multi-centered study to adequately propose health policies on neurointensive care for the Peruvian population. Ultimately, developing socioeconomic setting-focused intervention algorithms and clinical practice guidelines could enhance the survival and postoperative course of patients presenting with aSAH.

1. Introduction

Aneurysmatic subarachnoid hemorrhage (aSAH) is a medical emergency accounting for as much as 5% of cerebrovascular events, with a total mortality of 20–35% [1,2]. In addition, it has been associated with negative postoperative clinical outcomes, such as physical disability, functional dependence, and so on, in a third of surviving patients [3,4]. Moreover, given that it mainly affects middle-aged adults, a substantial decrease in work productivity is foreseen [5]. Its worldwide incidence is reported as 7.9 cases per 100,000 people per year [6], varying according to gender, age, and geographical location [7]. From a regional perspective, Latin America shows a lower incidence of aSAH (4.8 cases per 100,000 people per year) compared with other countries, such as Japan and Finland [6]. Nevertheless, further studies are required to determine the current epidemiological trends in low-income countries such as Peru.
The mortality rates for this disease have decreased in recent decades due to the advances made in the understanding of its pathophysiology [8] and the recommendations on its acute treatment [9,10,11]. It is necessary to identify risk factors for poor postoperative outcomes, disability, and mortality to ensure optimal survival outcomes after aSAH. Multiple reviews have reported heavy smoking, arterial hypertension, alcohol consumption, and non-white race as risk factors [12,13]. Nevertheless, most studies have been conducted in high-income countries, with a notable lack of research in middle- and low-income settings [8]. Surgical treatment of SAH is based on patient stabilization, the prevention of rebleeding, and the management of neurological complications. One of the mainstays is the exclusion of the ruptured aneurysm via surgical clipping or endovascular embolization, with the aim of preventing rebleeding. Additionally, it requires the control of intracranial pressure, the management of cerebral vasospasm, and the maintenance of adequate cerebral perfusion. The choice of surgical approach depends on factors such as the patient’s clinical condition, the aneurysm’s location and morphology, and the treating team’s experience [14].
Low- and middle-income countries face significant challenges in accessing and delivering adequate surgical care, which results in higher mortality and disability rates [15]. Peru, a country with vast socioeconomic and geographic diversity, exemplifies these challenges [16]. The treatment of aSAH requires early and effective intervention to improve the patient’s prognosis. However, achieving an early surgical intervention depends on a rapid medical response, access to diagnostic tools such as tomography and angiography, and the availability of skilled surgical teams [17]. Unfortunately, many healthcare facilities in Peru lack the necessary infrastructure, which leads to delays in diagnosis, referral, and treatment [18]. Given the stark differences between the healthcare realities in high-income countries, where most studies have been conducted, it is vital to identify the unique risk factors for poor outcomes [16].
Ruptured aneurysms can be managed via endovascular embolization or surgical clipping [19]. In most cases, the aneurysm’s shape, parent vessel anatomy, and location determine the preferred treatment approach. At present, microsurgical clipping is the preferred treatment for ruptured middle cerebral artery (MCA) aneurysms [20,21,22]. The literature reports the time of surgery, history of hypertension, smoking, advanced age, and a high World Federation of Neurosurgical Societies (WFNS) score, among others [23,24,25,26], as predictors of the postoperative course; nonetheless, as study designs and eligibility criteria tend to be highly variable, it is important to interpret them according to the clinical scenario to ensure their rapid and appropriate management. An aSAH mortality rate of 27.5% has been reported in the Peruvian context [27]; however, there is a marked limitation due to the small number of studies published, impairing the proper identification of predictors for a poor postoperative course.
Given this, our study aimed to identify clinical predictors of inpatient mortality and a poor postoperative course after surgical clipping for aSAH in a Peruvian tertiary care center between 2010 and 2019.

2. Materials and Methods

2.1. Study Cohort and Patient Selection

This retrospective analysis utilized a prospectively collected database from a single tertiary care center in Peru (“Hospital Nacional Arzobispo Loayza (HNAL)”), including all the adult patients with an angiographic or angio-tomographic diagnosis of aSAH and treated via microsurgical clipping between 2010 and 2019. Patients were identified from institutional medical records using the ICD-10 code 60.0. The exclusion criteria included sentinel bleeding from unruptured aneurysms, death before hospital admission, and a lack of follow-up data. This study was approved by the local Institutional Review Board (IRB) with protocol #02687-2022, and informed consent was waived due to the observational nature of the study. This study adhered to the Strengthening Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines (Supplementary Table S1) [28].

2.2. Demographic and Clinical Characteristics

Data were collected using a standardized data sheet, including the following characteristics: patient demographics [gender (male or female), age (in years), comorbidities (none; ≥1), and family history of aneurysms]; clinical characteristics [thunderclap headache, time (in days) from aSAH onset to admission, Glasgow scale score on admission (mild [13–15 points], moderate [9–12 points], or severe [<9 points]), WFNS scale score on admission (mild [1–3 points] or severe [4–5 points]), diagnostic test (computed tomography angiography [CTA], angiography, or both), Fisher computerized tomography (CT) scale score on admission (class I, II, III, or IV), and aneurysms per patient]; aneurysm characteristics [location, maximum diameter (small/medium [7–14 mm], large [15–25 mm], or giant [>25 mm])]; and intervention characteristics [craniotomy approach (mini-pterional, lateral supraorbital, frontal, or decompressive hemicraniectomy), number of clipped aneurysms, need for proximal control with transient parent artery clipping, and transient clipping time].

2.3. Postoperative Outcomes

The primary outcome was the overall inpatient mortality, defined as death from any cause occurring after discharge from the ICU and during hospitalization in the neurosurgery service. The secondary outcome was a poor postoperative course, measured with the modified Rankin scale (mRS) score at discharge (no or mild disability [0–2 points] or disability [3–6 points]).

2.4. Statistical Methods

Categorical variables are presented as frequencies and percentages. Continuous variables are presented as medians (interquartile ranges [IQRs] or means ± standard deviations (SDs), depending on the normality of the data, assessed using the Shapiro–Wilk test. The bivariate analysis included the chi-square test or Fisher’s exact test to analyze the categorical variables, and an unpaired Student’s t-test or the Mann–Whitney U test, depending on the normality of continuous data, in order to compare the characteristics between the cohorts according to the outcome distribution.
For the multivariate analysis, clinically significant variables identified in the univariate and bivariate analyses were included in a Poisson regression with a robust variance estimator [29]. Both unadjusted and adjusted Poisson regression models were built to identify significant clinical predictors of inpatient mortality and a poor postoperative course (the mRS score at discharge). The crude (cRR) and adjusted (aRR) relative risks, 95% confidence intervals (CIs), and p-values are reported. Statistical significance was set at p < 0.05. All statistical analyses were performed using STATA 18.0/BE.

3. Results

3.1. Baseline Characteristics

Of the 213 patients initially identified via the retrospective screening of medical records, only 3 were excluded (Figure 1). A total of 210 patients were included in the final analysis. The male population was predominant (144/210 (68.6%)), with a median age of 52.5 years. The highest proportion of participants were ≤ 65 years old (178/210 (84.8%)), with at least one comorbidity (87/210 (52.7%)) and no family history of aneurysms (162/210 (98.8%)). Most cases presented as a thunderclap headache (207/210 (99.0%)), with a mild Glasgow scale score (181/210 (86.6%)) and a mild WFNS scale score (184/210 (87.6%)). The median time from aSAH presentation to admission was 2 days, and most patients received surgical intervention more than 10 days after aSAH presentation (92/210 (45.3%)). The most common diagnostic method was CTA (168/210 (80.4%)). The most frequent Fisher CT classification was class III (121/210 (57.9%)), and there was a median of one aneurysm per patient (Table 1). A total of 15 patients presented with rebleeding during the waiting time. Other clinical characteristics and perioperative complications are described in Tables S2 and S3.
Regarding the aneurysm characteristics according to location, a total of 259 aneurysms were identified among the 210 included patients. A single aneurysm was identified in 171 patients, 2 in 32 patients, 3 in 4 patients, and 4 in 3 patients. Most aneurysms occurred in the anterior circulation (258/259 (99.6%)), with the highest frequency emerging from the posterior communicating artery (PComm) (110/259 (42.5%)) (Figure 2). Most of the aneurysms were classified as having a small/medium maximum diameter (253/259 (97.7%)), and only 6 aneurysms were deemed as large (Table 2).
Regarding the intervention characteristics, most had a mini-pterional approach (184/210 (87.2%)), single-aneurysm clipping (171/210 (81.4%)), and no need for transient clipping in the parent artery (155/259 (59.8%)). For both the first and second aneurysms, the time of transient clipping mostly lasted for 5–10 min (45/102 (44.12%) and 10/22 (45.45%), respectively) (Table 3). External ventricular drainage (EVD) was performed before surgery in only four cases of acute symptomatic hydrocephalus. Meanwhile, microsurgical fenestration of the lamina terminalis was performed before clipping in those cases with asymptomatic hydrocephalus or persistent cerebral edema despite cisternal opening (7.14%). The patients’ pre-, intra-, and postoperative characteristics are described in Table S4.

3.2. Patient Characteristics According to Mortality and Postoperative Outcomes

A total of 24 (11.4%) patients were deceased after microsurgical clipping, with a higher proportion among those with a severe Glasgow scale score on admission (7/10 (70.0%); p < 0.001), a severe WFNS scale score on admission (9/26 (34.6%); p < 0.001), at least one neurosurgical intraoperative complication (10/87 (18.3%); p = 0.022), at least two neurosurgical postoperative complications (7/13 (53.8%); p < 0.001), other neurosurgical postoperative complications (5/10 (50.0%); p < 0.001), and a clinical postoperative complication (9/45 (20.0%); p = 0.026) (Table 4 and Table S5).
Less than half (56/210 (26.7%)) of the population had moderate/severe disability at discharge in activities of daily living after the aneurysm microsurgical clipping, with a higher proportion among those older than 65 years on admission (14/32 (43.7%); p = 0.018), a severe Glasgow scale score on admission (10/10 (100.0%); p = 0.009), a severe WFNS clinical scale score on admission (17/26 (65.4%); p < 0.001), a need for transient clipping of the parent artery (34/103 (33.0%); p = 0.041), ≥1 intraoperative neurosurgical complication (23/65 (35.4%); p = 0.049), ≥2 postoperative neurosurgical complications (10/13 (76.9%); p < 0.001), ≥1 cerebral infarction and/or vasospasm (22/45 (48.9%); p < 0.001), and ≥2 postoperative clinical complications (18/31 (58.1%); p < 0.001) (Table 4).
At 6 months, only 25 patients reported mRS data, with the majority (12/25 (48%)) reporting mild disability (score: 1). At the 1-year follow-up, only 19 patients reported mRS data, with the majority (12/19 (63.2%)) also reporting mild disability (Table S4).
Around 14.97% (28/187) of the population presented a moderate/severe disability at discharge, with a higher proportion among those older than 65 years (8/27 (29.6%); p = 0.021), with a severe Glasgow scale score on admission (100.0%; p < 0.001), with a severe WFNS clinical scale score on admission (7/17 (41.2%); p = 0.001), with ≥2 postoperative neurosurgical complications (2/6 (33.3%); p = 0.033), with other postoperative neurosurgical complications (3/6 (50.0%); p = 0.018), and ≥2 postoperative clinical complications (9/25 (36.0%); p = 0.001) (Table S5).

3.3. Clinical Predictors of Inpatient Mortality

A first Poisson regression model was constructed to identify clinical predictors for inpatient mortality. The unadjusted model revealed the following predictors of mortality: a severe Glasgow score on admission (RR: 8.45; 95% CI: 4.48–15.92; p < 0.001), a severe WFNS scale score on admission (RR: 4.25; 95% CI: 2.07–8.71; p < 0.001), ≥1 intraoperative neurosurgical complications (RR: 2.4; 95% CI: 1.12–5.16; p = 0.025), ≥2 postoperative neurosurgical complications (RR: 11.77; 95% CI: 4.86–28.48; p < 0.001), ≥1 cerebral infarction and/or vasospasm (RR: 5.83; 95% CI: 2.43–13.95; p < 0.001), and ≥2 postoperative clinical complications (RR: 2.73; 95% CI: 1.05–7.12; p = 0.040).
After adjusting for the baseline characteristics, one postoperative neurosurgical complication (aRR: 5.76; 95% CI: 2.02–16.39; p = 0.001), ≥2 complications (aRR: 18.86; 95% CI: 5.16–68.90; p < 0.001), and a severe WFNS scale score on admission (aRR: 2.86; 95% CI: 1.28–6.39; p = 0.011) remained as strong predictors of inpatient mortality (Table 5).

3.4. Clinical Predictors of Poor Postoperative Course

A second Poisson regression model was constructed to identify clinical predictors of a poor postoperative course (with disability defined as an mRS score of 3–6). The unadjusted model revealed the significance of all the variables assessed. The adjusted model identified that a moderate (aRR: 3.71; 95% CI: 1.45–9.50; p = 0.006) or severe (aRR: 4.18; 95% CI: 1.12–15.60; p = 0.034) Glasgow scale score on admission, presenting cerebral infarction and/or vasospasm (aRR: 2.25; 95% CI: 1.39–3.66; p = 0.001) or other postoperative neurosurgical complications (aRR: 3.11; 95% CI: 1.40–6.91; p = 0.005), and presenting one (aRR: 2.31; 95% CI: 1.27–4.19; p = 0.006) or ≥2 postoperative clinical complications (aRR: 3.34; 95% CI: 1.83–6.10; p < 0.001) were associated with an increased risk of having functional disability in daily activities at discharge (Table 6).
Another Poisson regression model identified a moderate (aRR: 14.64; 95% CI: 4.28–50.09; p < 0.001) or severe (aRR: 60.07; 95% CI: 11.96–301.62; p < 0.001) Glasgow scale score on admission, other neurosurgical complications (aRR: 8.29; 95% CI: 2.60–26.44; p < 0.001), and ≥2 postoperative clinical complications (aRR: 4.04; 95% CI: 1.67–9.79; p = 0.002) as significant predictors of a moderate/severe Glasgow scale score at discharge. A severe WFNS scale on admission (aRR: 0.15; 95% CI: 0.04–0.54; p = 0.004) was associated with a lower risk of a moderate/severe Glasgow scale score at discharge (Table S6).
A summary of the clinical predictors is presented in Figure 3.

3.5. Sensitivity Analysis

Considering that the occurrence of aSAH is age-dependent, we analyzed the clinical predictors according to age. For patients aged up to 65 years, only those with cerebral infarction and/or vasospasm and those with other postoperative neurosurgical complications had significantly higher mortality. Furthermore, this same pattern was repeated only for moderate/severe disability. We also added the variable of ≥2 postoperative clinical complications as a significant predictor. The variable of intraoperative neurosurgical complications was excluded to emphasize the impact of both clinical and neurosurgical postoperative complications. We found that a severe WFNS score on admission or presenting any postoperative clinical or neurosurgical complication significantly increased the risk of moderate/severe disability (Tables S7 and S8).

4. Discussion

This decade-long retrospective study provides valuable information on the clinical predictors of in-hospital mortality and a poor postoperative course in patients with aneurysmal SAH after microsurgical clipping at a Latin American high-volume center. Our findings reveal that a severe WFNS score and a moderate/severe Glasgow scale score on admission, as well as presenting postoperative neurosurgical or clinical complications, were associated with an increased risk of mortality and poor postoperative course. These results align with those observed in high-income settings, despite the unique challenges faced within the Peruvian healthcare system.
An overall mortality of 11.43% was observed among patients undergoing surgical clipping, which is consistent with the results of other observational studies. Deutsch et al. (2018) [30] and Ikawa et al. (2020) [31] reported in-hospital mortality rates after aneurysm surgical clipping of 11.4% and 7.1%, respectively. Over the past 40 years, the SAH case fatality rate decreased at a worldwide level by an average of −1.5%/year [32]. In addition, presenting a severe WFNS clinical scale score and postoperative neurosurgical complications significantly increased the risk of mortality, similar to those findings from high-income countries. For instance, Odensass et al. (2024) [33] found that the risk was almost doubled (HR: 1.53; 95% CI: 1.06–2.22; p = 0.025), and Said et al. (2024) [34] found that a WFNS score of >3 (OR: 3.87; 95% CI: 2.83–5.28; p < 0.001) was strongly associated with an increased risk of mortality. This is because poor clinical conditions (WFNS > 3) and severe SAH (Fisher classes III-IV) on admission were established as risk factors for death according to the published literature [23], while a high WFNS score (4–5) was more strongly associated with an increased risk of a poor postoperative course at 90 days (aOR: 6.38; 95% CI: 2.66–15.31; p < 0.001) in patients with aSAH [35].
A moderate/severe Glasgow scale score on admission and postoperative complications were significantly associated with an increased risk of postoperative disability. Turek et al. (2016) [36] evaluated 190 Polish patients and found that postoperative complications were associated with an increased risk of a poor postoperative course (OR: 15.4; 95% CI: 3.3–73.0; p < 0.001). Bae et al. (2021) [37] evaluated the predictive ability of a modified scale in patients with aSAH by combining the GCS with the modified Fisher scale (GCS-F). The study found that lower scores for the GCS-F at admission were associated with less-favorable outcomes (OR: 0.53; 95% CI: 0.33–0.85; p = 0.0094) [37].
A notable finding of our study was that a severe WFNS score on admission and the occurrence of cerebral infarcts were associated with a lower risk of moderate/severe Glasgow scale scores at discharge. This could be due to a paradoxical effect where patients with these factors—thus classified as more-severe cases—receive more intensive and prioritized care during the postoperative period; a scenario that has also been reported in international cohorts of SAH patients [38], although they underwent endovascular treatment. However, this result might be biased by our small sample size, so this finding requires further investigation to fully understand the underlying mechanisms, especially in low–middle-income settings such as Peru, where disparities in healthcare may influence clinical outcomes [39].
Peru faces one of the highest levels of inequality in the world, with significant socioeconomic and geographical disparities that affect healthcare delivery [16,40]. In 2019, Peru allocated just USD 370 per person to healthcare, compared with USD 10,866 per person in the United States and between USD 4500 and USD 6500 in countries such as the United Kingdom, Australia, and Germany. These limited financial resources contribute to a healthcare environment that is both financially constrained and systemically inequitable. Notably, this is reflected in limitations in healthcare delivery (especially to rural regions) and a severe shortage of healthcare professionals (with only 0.82 physicians per 1000 people, most of whom are concentrated in the capital city, Lima) and trained personnel in general [16]. Moreover, the lack of adequate infrastructure, essential medicines, and logistical resources further exacerbates inequalities within the Ministry of Health.
Although tertiary-level hospitals in Peru provide more specialized care [41], most healthcare facilities lack the capacity for urgent care, and those with neuroimaging capabilities are typically centralized or only available in the private sector [18]. These systemic barriers worsen patient severity upon admission and significantly increase the likelihood of postoperative complications. In many cases, the absence of specialized diagnostic tools and the unavailability of skilled medical professionals delay timely intervention, contributing to adverse outcomes.
We found that most patients received surgical treatment more than 10 days after the presentation of aSAH. This is alarming, considering delays in treatment beyond 12 hours worsen patients’ overall prognosis [38], explaining the mortality and poor postoperative course reported in our study. This delay may be due to several causes, such as diagnostic errors, which may occur in up to 14% of cases of acute SAH [42], and up to 70% of delays may stem from the same cause [43]. Similar rates have been reported in Lima, where one study reported an average time from symptom onset to surgical intervention of 7.1 days [17]. Another study reported that more than half of the patients were operated on within the first 96 h after symptom onset; however, there were still cases with significant delays [44]. Another cause of these delays can be attributed to limitations in hospital infrastructure, the availability of specialized resources, a weak referral system between health centers within the country, and delayed clinical diagnosis due to a lack of professional expertise [18].
Our results reinforce the existing evidence on determinants of aSAH patient prognosis. This highlights the need for a thorough initial evaluation and perioperative strategies encompassed in health policies aimed at minimizing complications, aiming to improve functional outcomes and reduce postoperative disability.
We must outline the continued preference for microsurgical clipping over endovascular techniques in many developing countries, such as Peru. Other studies conducted in Peru have also reported this preference. For instance, a study carried out in a public referral hospital revealed that all patients were treated with surgical clipping [17], while in a private hospital, 65.2% of patients received clipping treatment [45]. While endovascular coiling has become the standard approach in high-income countries due to its less invasive nature and shorter recovery times, it remains less commonly used in many low–middle-income settings [46,47]. Additionally, clipping offers the advantage of achieving complete occlusion of the aneurysm, preventing recurrence, and not requiring prolonged anticoagulation therapy [48]. Furthermore, the lack of infrastructure and materials for embolization, especially in the public sector, limits access to the endovascular approach [45]. A cost-effectiveness study conducted in Colombia, a middle-income country, found that clipping is the most cost-effective option for treating anterior circulation aneurysms [49]. This makes surgical clipping a more viable option in settings where healthcare resources are limited, particularly in the public healthcare system.
Another aspect to be addressed is the presence of hydrocephalus and vasospasm, which are two relevant complications related to aSAH. Our study reported that 4 patients (1.9%) presented with hydrocephalus, 28 (13.3%) presented with vasospasm, and only 1 presented with both complications. These rates are lower than those reported in the published literature, where hydrocephalus and vasospasm have shown a prevalence of 15% [50] and 38.4% [51], respectively. These lower rates could stem from the small sample size addressed, supporting the need to evaluate these complications in future studies.
This study had several limitations. First, the retrospective design introduced the potential for recall bias and may have excluded patients with incomplete data or those lost to follow-up. Moreover, it restricted the integrity and reliability of the data by favoring the presence of referral and selection bias. Second, the prioritized outcomes were only collected at discharge, and no long-term follow-up was performed, limiting the further identification of risk factors and underestimating the significance of those already identified. Third, the single-center design limited the generalizability of our findings, as the population was largely homogeneous, representing a low-to-middle socioeconomic status with preferred treatment in a public tertiary care center. Fourth, the relatively small sample size (n = 210) limited the statistical power of our analysis. Lastly, there is the risk of not having considered other confounding variables that influence the likelihood of inpatient mortality or poor postoperative course. Despite these limitations, our study holds significant value for understanding the Peruvian context. The “Hospital Nacional Arzobispo Loayza (HNAL),” being a high-complexity tertiary center in Lima, provides a representative overview of patients with SAH treated at advanced healthcare facilities within the country. Our findings reflect the challenges and constraints within the Peruvian healthcare system, including the limited availability of specialized resources, the centralization of neuroimaging services, and causes for delay in treatment. In addition, various statistical models were used to ensure adequate data analysis and control for potential confounders.

5. Conclusions

We observed an overall mortality and postoperative functional disability of 11.43% and 26.6%, respectively. These outcomes were conditioned by predictors such as a severe WFNS score on admission, a moderate/severe Glasgow scale score on admission, and presenting postoperative neurosurgical complications. While promising and widely supported by the published literature, these findings require further validation in a larger prospective and multi-centered study to adequately propose health policies on neurointensive care for the Peruvian population. Ultimately, developing socioeconomic setting-focused intervention algorithms and clinical practice guidelines could enhance the survival and postoperative course of patients presenting with aSAH.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jcm14134799/s1. Table S1. STROBE statement: checklist of items that should be included in reports of cohort studies. Table S2. Clinical characteristics of patients operated on for aneurysmal subarachnoid hemorrhage seen in the neurosurgery service of the Hospital Nacional Arzobispo Loayza during 2010–2019. Table S3. Perioperative complications of patients operated on for aneurysmal subarachnoid hemorrhage at the neurosurgery service of the Hospital Nacional Arzobispo Loayza during 2010–2019. Table S4. Discharge and follow-up status of patients operated on for aneurysmal subarachnoid hemorrhage at the neurosurgery service of the Hospital Nacional Arzobispo Loayza during 2010–2019. Table S5. Characteristics of patients operated on for aneurysmal subarachnoid hemorrhage in the neurosurgery service of the Hospital Nacional Arzobispo Loayza during 2010–2019, according to Glasgow Scale at discharge. Table S6. Poisson regression analysis predicting moderate/severe Glasgow Scale scores at discharge. Table S7. Sensitivity analysis for mortality risk in patients operated on for aneurysmal subarachnoid hemorrhage at the neurosurgery service of the Hospital Nacional Arzobispo Loayza during 2010–2019. Table S8. Sensitivity analysis for mRS (disability) risk in patients operated on for aneurysmal subarachnoid hemorrhage at the neurosurgery service of the Hospital Nacional Arzobispo Loayza during 2010–2019.

Author Contributions

Conceptualization, J.L.-C., R.A.-Z. and G.M.-U.; methodology, J.L.-C., R.A.-Z., N.C.-P., G.M.-U. and F.T.; software, J.L.-C., R.A.-Z., N.C.-P., M.C.-G. and G.M.-U.; validation, J.L.-C., R.A.-Z. and G.M.-U.; formal analysis, J.L.-C., M.C.-G., R.A.-Z., G.M.-U., A.E.-M., F.T. and C.Q.-V.; investigation, J.L.-C., R.A.-Z., C.L.V.-C., A.E.-M., G.M.-U. and F.T.; resources, J.L.-C., R.A.-Z., G.M.-U. and F.T.; data curation, J.L.-C., R.A.-Z., A.E.-M. and G.M.-U.; writing—original draft preparation, J.S. and F.T.; writing—review and editing, J.L.-C., E.L., R.M.S., B.D.-L., N.P.-B., R.A.-Z., G.M.-U., C.Q.-V. and F.T.; visualization, J.L.-C., P.T., R.A.-Z., G.M.-U. and F.T.; supervision, J.L.-C., Z.B.M., R.A.-Z., G.M.-U., J.S. and F.T.; project administration, J.L.-C., R.A.-Z., G.M.-U., J.S. and F.T.; funding acquisition, none. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted per the Declaration of Helsinki and approved by the IRB of the Arzobispo Loayza National Hospital (IRB#:02687-2022; approval date: 29 March 2022).

Informed Consent Statement

Patient consent was waived because the information was obtained from medical records before approval by the hospital’s Ethics Committee (IRB).

Data Availability Statement

Additional information about the statistical analysis and propensity score matching can be found in the Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations were used in this manuscript:
aSAHAneurysmatic subarachnoid hemorrhage
IQRInterquartile range
WFNSWorld Federation of Neurological Surgeons
CIConfidence interval

References

  1. van Donkelaar, C.E.; Bakker, N.A.; Birks, J.; Veeger, N.J.G.M.; Metzemaekers, J.D.M.; Molyneux, A.J.; Groen, R.J.M.; van Dijk, J.M.C. Prediction of Outcome After Aneurysmal Subarachnoid Hemorrhage. Stroke 2019, 50, 837–844. [Google Scholar] [CrossRef] [PubMed]
  2. Stienen, M.N.; Germans, M.; Burkhardt, J.-K.; Neidert, M.C.; Fung, C.; Bervini, D.; Zumofen, D.; Röthlisberger, M.; Marbacher, S.; Maduri, R.; et al. Predictors of In-Hospital Death After Aneurysmal Subarachnoid Hemorrhage: Analysis of a Nationwide Database (Swiss SOS [Swiss Study on Aneurysmal Subarachnoid Hemorrhage]). Stroke 2018, 49, 333–340. [Google Scholar] [CrossRef] [PubMed]
  3. Rinkel, G.J.E.; Algra, A. Long-term outcomes of patients with aneurysmal subarachnoid haemorrhage. Lancet Neurol. 2011, 10, 349–356. [Google Scholar] [CrossRef]
  4. Roquer, J.; Cuadrado-Godia, E.; Guimaraens, L.; Conesa, G.; Rodríguez-Campello, A.; Capellades, J.; García-Arnillas, M.P.; Fernández-Candil, J.L.; Avellaneda-Gómez, C.; Giralt-Steinhauer, E.; et al. Short- and long-term outcome of patients with aneurysmal subarachnoid hemorrhage. Neurology 2020, 95, e1819–e1829. [Google Scholar] [CrossRef]
  5. Al-Khindi, T.; Macdonald, R.L.; Schweizer, T.A. Cognitive and functional outcome after aneurysmal subarachnoid hemorrhage. Stroke 2010, 41, e519–e536. [Google Scholar] [CrossRef]
  6. Etminan, N.; Chang, H.-S.; Hackenberg, K.; de Rooij, N.K.; Vergouwen, M.D.I.; Rinkel, G.J.E.; Algra, A. Worldwide Incidence of Aneurysmal Subarachnoid Hemorrhage According to Region, Time Period, Blood Pressure, and Smoking Prevalence in the Population. JAMA Neurol. 2019, 76, 588–597. [Google Scholar] [CrossRef]
  7. de Rooij, N.K.; Linn, F.H.H.; van der Plas, J.A.; Algra, A.; Rinkel, G.J.E. Incidence of subarachnoid haemorrhage: A systematic review with emphasis on region, age, gender and time trends. J. Neurol. Neurosurg. Psychiatry 2007, 78, 1365–1372. [Google Scholar] [CrossRef]
  8. Caranci, F.; Briganti, F.; Cirillo, L.; Leonardi, M.; Muto, M. Epidemiology and genetics of intracranial aneurysms. Eur. J. Radiol. 2013, 82, 1598–1605. [Google Scholar] [CrossRef]
  9. Mukhtar, T.K.; Molyneux, A.J.; Hall, N.; Yeates, D.R.G.; Goldacre, R.; Sneade, M.; Clarke, A.; Goldacre, M.J. The falling rates of hospital admission, case fatality, and population-based mortality for subarachnoid hemorrhage in England, 1999–2010. J. Neurosurg. 2016, 125, 698–704. [Google Scholar] [CrossRef]
  10. Vergouwen, M.D.I.; Jong-Tjien-Fa, A.V.; Algra, A.; Rinkel, G.J.E. Time trends in causes of death after aneurysmal subarachnoid hemorrhage: A hospital-based study. Neurology 2016, 86, 59–63. [Google Scholar] [CrossRef]
  11. Nieuwkamp, D.J.; Setz, L.E.; Algra, A.; Linn, F.H.H.; de Rooij, N.K.; Rinkel, G.J.E. Changes in case fatality of aneurysmal subarachnoid haemorrhage over time, according to age, sex, and region: A meta-analysis. Lancet Neurol. 2009, 8, 635–642. [Google Scholar] [CrossRef]
  12. Feigin, V.L.; Rinkel, G.J.E.; Lawes, C.M.M.; Algra, A.; Bennett, D.A.; van Gijn, J.; Anderson, C.S. Risk factors for subarachnoid hemorrhage: An updated systematic review of epidemiological studies. Stroke 2005, 36, 2773–2780. [Google Scholar] [CrossRef]
  13. Clarke, M. Systematic review of reviews of risk factors for intracranial aneurysms. Neuroradiology 2008, 50, 653–664. [Google Scholar] [CrossRef] [PubMed]
  14. Osgood, M.L. Aneurysmal Subarachnoid Hemorrhage: Review of the Pathophysiology and Management Strategies. Curr. Neurol. Neurosci. Rep. 2021, 21, 50. [Google Scholar] [CrossRef] [PubMed]
  15. Garcia, R.M.; Shiraishi-Zapata, C.J.; Zúñiga Vallejos, R.C.; Gil Chiroque, D.P.; Oyanguren Maldonado, M.A.; Paico Palacios, J.C.; Romero, E.E.; Villarreal Álamo, A.H.; Castillo Tovar, J.S.; Aguirre Uribe, S.J.; et al. Surgical care and trauma patients capacity in Perú—Cross-sectional study. Colomb. J. Anesthesiol. 2022, 51, e1058. [Google Scholar] [CrossRef] [PubMed]
  16. Almubaslat, F.; Sanchez-Boluarte, S.S.; Diaz, M.M. A review of neurological health disparities in Peru. Front. Public Health 2023, 11, 1210238. [Google Scholar] [CrossRef]
  17. Loconi-Vallejos, A.; Jorge-Dejo, C.; Azurín-Peña, M.; Garcia-Solorzano, F.O. Timing of aneurysm treatment in subarachnoid hemorrhage and grade of functional capacity at discharge: A retrospective cohort study. Egypt. J. Neurol. Psychiatry Neurosurg. 2023, 59, 89. [Google Scholar] [CrossRef]
  18. Davalos, L.F.; Málaga, G. El accidente cerebrovascular en el Perú: Una enfermedad prevalente olvidada y desatendida. Rev. Peru. Med. Exp. Salud Publica 2014, 31, 400–401. [Google Scholar] [CrossRef]
  19. Orakdogen, M.; Emon, S.T.; Somay, H.; Engin, T.; Ates, O.; Berkman, M.Z. Prognostic Factors in Patients who Underwent Aneurysmal Clipping due to Spontaneous Subarachnoid Hemorrhage. Turk. Neurosurg. 2016, 26, 840–848. [Google Scholar] [CrossRef]
  20. Connolly, E.S.; Rabinstein, A.A.; Carhuapoma, J.R.; Derdeyn, C.P.; Dion, J.; Higashida, R.T.; Hoh, B.L.; Kirkness, C.J.; Naidech, A.M.; Ogilvy, C.S.; et al. Guidelines for the management of aneurysmal subarachnoid hemorrhage: A guideline for healthcare professionals from the American Heart Association/american Stroke Association. Stroke 2012, 43, 1711–1737. [Google Scholar] [CrossRef]
  21. Spetzler, R.F.; McDougall, C.G.; Zabramski, J.M.; Albuquerque, F.C.; Hills, N.K.; Russin, J.J.; Partovi, S.; Nakaji, P.; Wallace, R.C. The Barrow Ruptured Aneurysm Trial: 6-year results. J. Neurosurg. 2015, 123, 609–617. [Google Scholar] [CrossRef] [PubMed]
  22. Zijlstra, I.A.; van der Steen, W.E.; Verbaan, D.; Majoie, C.B.; Marquering, H.A.; Coert, B.A.; Vandertop, W.P.; van den Berg, R. Ruptured middle cerebral artery aneurysms with a concomitant intraparenchymal hematoma: The role of hematoma volume. Neuroradiology 2018, 60, 335–342. [Google Scholar] [CrossRef] [PubMed]
  23. Abulhasan, Y.B.; Alabdulraheem, N.; Simoneau, G.; Angle, M.R.; Teitelbaum, J. Mortality after Spontaneous Subarachnoid Hemorrhage: Causality and Validation of a Prediction Model. World Neurosurg. 2018, 112, e799–e811. [Google Scholar] [CrossRef] [PubMed]
  24. Dijkland, S.A.; Roozenbeek, B.; Brouwer, P.A.; Lingsma, H.F.; Dippel, D.W.; Vergouw, L.J.; Vergouwen, M.D.; van der Jagt, M. Prediction of 60-Day Case Fatality After Aneurysmal Subarachnoid Hemorrhage: External Validation of a Prediction Model. Crit. Care Med. 2016, 44, 1523–1529. [Google Scholar] [CrossRef]
  25. Lantigua, H.; Ortega-Gutierrez, S.; Schmidt, J.M.; Lee, K.; Badjatia, N.; Agarwal, S.; Claassen, J.; Connolly, E.S.; Mayer, S.A. Subarachnoid hemorrhage: Who dies, and why? Crit. Care 2015, 19, 309. [Google Scholar] [CrossRef]
  26. Shen, J.; Yu, J.; Huang, S.; Mungur, R.; Huang, K.; Pan, X.; Yu, G.; Xie, Z.; Zhou, L.; Liu, Z.; et al. Scoring Model to Predict Functional Outcome in Poor-Grade Aneurysmal Subarachnoid Hemorrhage. Front. Neurol. 2021, 12, 601996. [Google Scholar] [CrossRef]
  27. Rojas-Panta, G.; Reyes-Narro, G.F.; Toro-Huamanchumo, C.; Choque-Velasquez, J.; Saal-Zapata, G. Prognostic value of scales for aneurysmal subarachnoid hemorrhage: Report of a reference center in Peru. Neurocirugia 2024, 35, 1–5. [Google Scholar] [CrossRef]
  28. Sharp, S.J.; Poulaliou, M.; Thompson, S.G.; White, I.R.; Wood, A.M. A review of published analyses of case-cohort studies and recommendations for future reporting. PLoS ONE 2014, 9, e101176. [Google Scholar] [CrossRef]
  29. Hoh, B.L.; Ko, N.U.; Amin-Hanjani, S.; Chou, S.H.-Y.; Cruz-Flores, S.; Dangayach, N.S.; Derdeyn, C.P.; Du, R.; Hänggi, D.; Hetts, S.W.; et al. 2023 Guideline for the Management of Patients with Aneurysmal Subarachnoid Hemorrhage: A Guideline from the American Heart Association/American Stroke Association. Stroke 2023, 54, e314–e370. [Google Scholar] [CrossRef]
  30. Deutsch, B.C.; Neifert, S.N.; Caridi, J.M. No Disparity in outcomes between surgical clipping and endovascular coiling after aneurysmal subarachnoid hemorrhage. World Neurosurg. 2018, 120, e318–e325. [Google Scholar] [CrossRef]
  31. Ikawa, F.; Michihata, N.; Matsushige, T.; Abiko, M.; Ishii, D.; Oshita, J.; Okazaki, T.; Sakamoto, S.; Kurogi, R.; Iihara, K.; et al. In-hospital mortality and poor outcome after surgical clipping and endovascular coiling for aneurysmal subarachnoid hemorrhage using nationwide databases: A systematic review and meta-analysis. Neurosurg. Rev. 2020, 43, 655–667. [Google Scholar] [CrossRef] [PubMed]
  32. Mahlamäki, K.; Rautalin, I.; Korja, M. Case Fatality Rates of Subarachnoid Hemorrhage Are Decreasing with Substantial between-Country Variation: A Systematic Review of Population-Based Studies between 1980 and 2020. Neuroepidemiology 2022, 56, 402–412. [Google Scholar] [CrossRef] [PubMed]
  33. Odensass, S.; Gümüs, M.; Said, M.; Rodemerk, J.; Darkwah Oppong, M.; Li, Y.; Ahmadipour, Y.; Dammann, P.; Wrede, K.H.; Sure, U.; et al. Predictors of survival after aneurysmal subarachnoid hemorrhage: The long-term observational cohort study. Clin. Neurol. Neurosurg. 2024, 247, 108605. [Google Scholar] [CrossRef]
  34. Said, M.; Gümüs, M.; Darkwah Oppong, M.; Dömer, P.; Helgers, S.O.A.; Dammann, P.; Wrede, K.H.; Woitzik, J.; Sure, U.; Jabbarli, R. Risk Score for Early Prediction of In-Hospital Mortality After Aneurysmal Subarachnoid Hemorrhage: Pooled Analysis with Score Construction and Validation. World Neurosurg. 2024, 194, 123426. [Google Scholar] [CrossRef] [PubMed]
  35. Nguyen, T.A.; Mai, T.D.; Vu, L.D.; Dao, C.X.; Ngo, H.M.; Hoang, H.B.; Tran, T.A.; Pham, T.Q.; Pham, D.T.; Nguyen, M.H.; et al. Validation of the accuracy of the modified World Federation of Neurosurgical Societies subarachnoid hemorrhage grading scale for predicting the outcomes of patients with aneurysmal subarachnoid hemorrhage. PLoS ONE 2023, 18, e0289267. [Google Scholar] [CrossRef]
  36. Turek, G.; Lewszuk, A.; Kochanowicz, J.; Lyson, T.; Zielinska-Turek, J.; Gorbacz, K.; Mariak, Z. Early outcomes and perioperative complications of endovascular embolization in patients with aneurysmal SAH. Neurol. Neurochir. Pol. 2016, 50, 342–348. [Google Scholar] [CrossRef]
  37. Bae, I.-S.; Chun, H.-J.; Choi, K.-S.; Yi, H.-J. Modified Glasgow coma scale for predicting outcome after subarachnoid hemorrhage surgery. Medicine 2021, 100, e25815. [Google Scholar] [CrossRef]
  38. Brawanski, N.; Dubinski, D.; Bruder, M.; Berkefeld, J.; Hattingen, E.; Senft, C.; Seifert, V.; Konczalla, J. Poor grade subarachnoid hemorrhage: Treatment decisions and timing influence outcome. Should we, and when should we treat these patients? Brain Hemorrhages 2021, 2, 29–33. [Google Scholar] [CrossRef]
  39. Díaz-Ruiz, R.; Vargas-Fernández, R.; Rojas-Roque, C.; Hernández-Vásquez, A. Socioeconomic inequalities in the use of medical consultation services in Peru, 2019. Int. J. Equity Health 2024, 23, 10. [Google Scholar] [CrossRef]
  40. Casamassa, S. The Poor Majority: Inequality in Mexico and Peru. Glob. Major. E-J. 2021, 12, 4–22. [Google Scholar]
  41. Resolución Ministerial, N. 546-2011-Ministerio de Salud del Perú (MINSA). Available online: https://www.gob.pe/institucion/minsa/normas-legales/243402-546-2011-minsa (accessed on 14 February 2025).
  42. Doukas, A.; Barth, H.; Petridis, K.A.; Mehdorn, M.; von der Brelie, C. Misdiagnosis of acute subarachnoid hemorrhage in the era of multimodal diagnostic options. Am. J. Emerg. Med. 2019, 37, 2079–2083. [Google Scholar] [CrossRef] [PubMed]
  43. Bentancourt, V.; Jaume, A.; Aboal, C. Errores diagnósticos en la hemorragia subaracnoidea aneurismática. Rev. Urug. Med. Int. 2016, 1, 62–68. [Google Scholar]
  44. Gonzalo Rojas, D.; Juan Garay, H.; Wesley Alaba, G.; César Rodriguez, C.; Rolando Lovaton, E.; Relix Huaman, H. Definitive treatment of cerebral aneurysms at the Cayetano Heredia National Hospital in Lima Peru: A case series results. Peruv. J. Neurosurg. 2021, 3. [Google Scholar] [CrossRef]
  45. Ayala-García, R.; Álvarez-Carranza, J. Hemorragia subaracnoidea aneurismática en un centro privado de Lima, Perú. Interciencia Méd. 2023, 13, 7–13. [Google Scholar] [CrossRef]
  46. Belavadi, R.; Gudigopuram, S.V.R.; Raguthu, C.C.; Gajjela, H.; Kela, I.; Kakarala, C.L.; Hassan, M.; Sange, I. Surgical Clipping Versus Endovascular Coiling in the Management of Intracranial Aneurysms. Cureus 2021, 13, e20478. [Google Scholar] [CrossRef] [PubMed]
  47. Brinjikji, W.; Rabinstein, A.A.; Lanzino, G.; Kallmes, D.F.; Cloft, H.J. Effect of age on outcomes of treatment of unruptured cerebral aneurysms: A study of the National Inpatient Sample 2001–2008. Stroke 2011, 42, 1320–1324. [Google Scholar] [CrossRef]
  48. Ahmed, S.I.; Javed, G.; Bareeqa, S.B.; Samar, S.S.; Shah, A.; Giani, A.; Aziz, Z.; Tasleem, A.; Humayun, S.H. Endovascular Coiling Versus Neurosurgical Clipping for Aneurysmal Subarachnoid Hemorrhage: A Systematic Review and Meta-analysis. Cureus 2019, 11, e4320. [Google Scholar] [CrossRef] [PubMed]
  49. Harker, P.; Quitián-Reyes, H.; Feo-Lee, O.H.; Iragorri, N.; Puentes, J.C.; Harker, P.; Quitián-Reyes, H.; Feo-Lee, O.H.; Iragorri, N.; Puentes, J.C. Cost-Effectiveness Analysis Applied to the Treatment of Unruptured Anterior Circulation Aneurysms in a Middle-Income Country. Univ. Medica 2020, 61, 3–10. [Google Scholar] [CrossRef]
  50. Won, Y.D.; Cheong, J.W.; Park, Y.K.; Yoon, B.-H.; Kim, J.H.; Kang, H.I. Shunt-dependent hydrocephalus and shunt failure rate in patients with aneurysmal subarachnoid hemorrhage in Korea. J. Neurosurg. 2024, 141, 1254–1261. [Google Scholar] [CrossRef]
  51. Khanafer, A.; Bhogal, P.; Hellstern, V.; Harmening, C.; Bäzner, H.; Ganslandt, O.; Henkes, H. Vasospasm-Related Death after Aneurysmal Subarachnoid Hemorrhage: A Retrospective Case-Control Study. J. Clin. Med. 2022, 11, 4642. [Google Scholar] [CrossRef]
Jcm 14 04799 g001
Figure 1. STROBE patient selection flowchart.
Figure 1. STROBE patient selection flowchart.
Jcm 14 04799 g001
Jcm 14 04799 g002
Figure 2. Maximum aneurysm diameters according to location (n = 259).
Figure 2. Maximum aneurysm diameters according to location (n = 259).
Jcm 14 04799 g002
Jcm 14 04799 g003
Figure 3. Predictors of Inpatient Mortality and Disability at Discharge.
Figure 3. Predictors of Inpatient Mortality and Disability at Discharge.
Jcm 14 04799 g003
Table 1. Baseline characteristics (n = 210).
Table 1. Baseline characteristics (n = 210).
Baseline Characteristics
Gender, n (%)Female66 (31.4)
Male144 (68.6)
Age median (IQR), years52.5 (22)
Age group, n (%)≤65 yo178 (84.8)
>65 yo32 (15.2)
Comorbidities, n (%)None78 (47.3)
≥187 (52.7)
Familiar aneurysm history, n (%)No162 (98.8)
Yes2 (1.2)
Thunderclap headacheNo2 (1.0)
Yes207 (99.0)
aSAH-to-admission median (IQR), days2 (6.42)
Glasgow scale on admissionMild (13–15)181 (86.6)
Moderate (9–12)18 (8.6)
Severe (<9)10 (4.8)
WFNS SAH scale on admissionMild (1–3)184 (87.6)
Severe (4–5)26 (12.4)
aSAH-to-surgery, median (IQR), days10 (13)
aSAH-to-surgery, median (IQR), days≤3 days43 (21.2)
4–10 days68 (33.5)
>10 days92 (45.3)
Diagnostic test, n (%)CT angiography168 (80.4)
Angiography18 (8.6)
Both23 (11.0)
Fisher CT scale on admissionClass I9 (4.3)
Class II20 (9.6)
Class III121 (57.9)
Class IV59 (28.2)
Aneurysms per patient, median (IQR)1 (0)
Categorical variables are described using frequencies and proportions. Continuous variables are described using medians and interquartile ranges. Abbreviations: WFNS: World Federation of Neurological Surgeons; aSAH: aneurysmatic subarachnoid hemorrhage; IQR: interquartile range; CT: computed tomography.
Table 2. Aneurysm characteristics according to location (n = 259).
Table 2. Aneurysm characteristics according to location (n = 259).
CharacteristicsAneurysm Location (n = 259)
Anterior Circulation
(n = 258)		Posterior Circulation
(n = 1)
AComm
(n = 39)		ACA
(n = 12)		MCA
(n = 65)		AchA
(n = 6)		Carotid Bifurcation
(n = 14)		ICA
(n = 12)		PComm
(n = 110)		Basilar Artery
(n = 1)
Maximum
diameter		Small/medium (<15 mm)39 (100)
6 [5,6,7,8] *		12 (100)
3.5 [3–5.25] *		63 (96.9)
6 [4,5,6,7,8] *		6 (100)
5 [4.25–5.75] *		13 (92.9)
6 [5,6,7,8] *		11 (91.7)
6 [3.5–8.5] *		108 (98.2)
7 [5,6,7,8,9] *		1 (100)
5 [5] *
Large (15–25 mm)--2 (3.1)
18 [16,17,18,19,20] *		-1 (7.1)
20 [20] *		1 (8.3)
16 [16] *		2 (1.8)
17.5 [15,16,17,18,19,20] *		-
Giant (>25 mm)--------
Microsurgical clipping38 (16.4)12 (5.2)55 (23.7)6 (2.6)12 (5.2)8 (3.5)101 (43.5)-
* Median [IQR] in mm. Abbreviations: AComm: anterior communicating artery; PComm: posterior communicating artery; AchA: anterior choroidal artery; ACA: anterior cerebral artery; MCA: middle cerebral artery; ICA: internal carotid artery.
Table 3. Intervention characteristics.
Table 3. Intervention characteristics.
Clipping Characteristics
Craniotomy approach, n (%)
Mini-pterional184 (87.2)
Lateral supraorbital10 (4.7)
Frontal10 (4.7)
Decompressive hemicraniectomy7 (3.3)
Transitory clipping in parenteral artery 104 (40.2)
Transitory clipping time in 1st aneurysm
<5 min30 (29.41)
5–10 min45 (44.12)
>10 min (prolonged)27 (26.47)
Transitory clipping time in 2nd aneurysm
<5 min8 (36.36)
5–10 min10 (45.45)
>10 min (prolonged)4 (18.18)
Transitory clipping time in 3rd aneurysm
<5 min4 (100)
5–10 min0
>10 min (prolonged)0
Transitory clipping time in 4th aneurysm
<5 min1 (100)
5–10 min0
>10 min (prolonged)0
Categorical variables are described using frequencies and proportions. Continuous variables are described using medians and interquartile ranges.
Table 4. Patient characteristics according to mortality and mRS at discharge.
Table 4. Patient characteristics according to mortality and mRS at discharge.
CharacteristicsMortalitypmRSp
No (n = 186), n (%)Yes (n = 24), n (%)No/Mild Disability (n = 154), n (%)Moderate/Severe Disability (n = 56), n (%)
Gender 0.800 a 0.638 a
Female59 (89.4)7 (10.6) 47 (71.2)19 (28.8)
Male127 (88.2)17 (11.8) 107 (74.3)37 (25.7)
Age 0.418 a 0.018 a
≤65 years old159 (89.3)19 (10.7) 136 (76.4)42 (23.6)
>65 years old27 (84.4)5 (15.6) 18 (56.3)14 (43.7)
History of any chronic disease 0.993 a 0.426 a
None69 (88.5)9 (11.5) 59 (75.6)19 (24.4)
≥177 (88.5)10 (11.5) 61 (70.1)26 (29.9)
Time from stroke to admission (in days), median (IQR)2 (6.5)5.5 (7.0)0.060 b2 (6.5)2.5 (6)0.743 b
Glasgow scale score on admission, categorized <0.001 a 0.009 a
Mild (15–13)166 (91.7)15 (8.3) 144 (79.6)37 (20.4)
Moderate (12–9)17 (94.4)1 (5.6) 10 (55.6)8 (44.4)
Severe (<9)3 (30.0)7 (70.0) 0 (0.0)10 (100.0)
WFNS clinical scale score on admission <0.001 a <0.001 a
Mild (1 to 3)169 (91.8)15 (8.1) 145 (78.8)39 (21.2)
Severe (4 to 5)17 (65.4)9 (34.6) 9 (34.6)17 (65.4)
Fisher tomographic scale score on admission 0.516 a 0.260 a
I—no evidence of bleeding in cisterns or ventricles.9 (100.0)0 (0.0) 9 (100.0)0 (0.0)
II—thin diffuse blood, with a layer of < 1 mm in cisterns measured vertically19 (95.0)1 (5.0) 16 (80.0)4 (20.0)
III—thick cisternal clot of >1 mm in cisterns, measured vertically107 (88.4)14 (11.6) 87 (71.9)34 (28.1)
IV—intraparenchymal hematoma, intraventricular hemorrhage, and +/− diffuse bleeding51 (86.4)8 (13.6) 42 (71.2)17 (28.8)
Time from stroke to operation (in days), median (IQR)9 (12.0)10 (23.0)0.480 b9 (12.0)10 (12.0)0.708 b
Need for transient clipping in parenteral artery 0.161 a 0.041 a
No98 (91.6)9 (8.4) 85 (79.4)22 (20.6)
Yes88 (85.4)15 (14.6) 69 (77.0)34 (33.0)
Intraoperative neurosurgical complications 0.022 a 0.049 a
None132 (92.3)11 (7.7) 111 (77.6)32 (22.4)
≥149 (81.7)11 (18.3) 42 (64.6)23 (35.4)
Postoperative neurosurgical complications <0.001 a <0.001 a
None146 (95.4)7 (4.6) 128 (83.7)25 (16.3)
132 (76.2)10 (23.8) 22 (52.4)20 (47.6)
≥26 (46.1)7 (53.8) 3 (23.1)10 (76.9)
Specific postoperative neurosurgical complications <0.001 a <0.001 a
No complications146 (95.4)7 (4.6) 128 (52.0)25 (47.9)
≥1 cerebral infarction and/or vasospasm33 (73.3)12 (26.7) 23 (51.1)22 (48.9)
Other complications5 (50.0)5 (50.0) 2 (20.0)8 (80.0)
Postoperative clinical complications 0.026 a <0.001 a
None118 (92.9)9 (7.1) 111 (87.4)16 (12.6)
136 (80.0)9 (20.0) 25 (55.6)20 (44.4)
≥225 (80.6)6 (19.3) 13 (41.9)18 (58.1)
Categorical variables are described using frequencies and proportions. Continuous variables are described using medians and interquartile ranges. Statistical tests: a Pearson’s chi-squared test; b Mann–Whitney U test. p-values < 0.05 are in bold. Abbreviations: mRS: modified Rankin functional scale; WFNS: World Federation of Neurosurgical Societies.
Table 5. Poisson regression analysis predicting inpatient mortality.
Table 5. Poisson regression analysis predicting inpatient mortality.
VariableCrude ModelAdjusted Model *
RR95% CIpRR95% CIp
Age
≤65 years oldRef. Ref.
>65 years old1.460.59–3.650.4130.680.27–1.710.410
Glasgow scale score on admission, categorized
Mild (15–13)Ref. Ref.
Moderate (12–9)0.670.09–4.810.691---
Severe (<9)8.454.48–15.92<0.001---
WFNS clinical scale score on admission
Mild (1 to 3)Ref. Ref.
Severe (4 to 5)4.252.07–8.71<0.0012.861.28–6.390.011
Intraoperative neurosurgical complications
NoneRef. Ref.
≥12.41.12–5.160.0251.400.63–3.400.404
Postoperative neurosurgical complications
NoneRef. Ref.
15.22.1–12.87<0.0015.762.02–16.390.001
≥211.774.86–28.48<0.00118.865.16–68.90<0.001
Specific postoperative neurosurgical complications
No complicationsRef. Ref.
At least cerebral infarction and/or vasospasm5.832.43–13.95<0.0010.470.20–1.110.086
Other complications10.934.20–28.40<0.001---
Reported postoperative clinical complications
NoneRef. Ref.
12.821.19–6.680.0181.900.80–4.530.146
≥22.731.05–7.120.0402.230.79–6.310.131
Unadjusted and adjusted Poisson regression model results are reported as RRs (95% CI and p-value). Abbreviations: RR: risk ratio; CI: confidence interval; WFNS: World Federation of Neurosurgical Societies. Statistical test: Generalized linear model [family(Poisson) link(log)] (robust). p-values < 0.05 are in bold. * Adjusted for all variables from the table except Glasgow score on admission, categorized.
Table 6. Poisson regression analysis predicting mRS at discharge.
Table 6. Poisson regression analysis predicting mRS at discharge.
VariablemRS (Disability)
Crude ModelAdjusted Model **
RR95% CI pRR95% CIp
Age
≤65 years oldRef. Ref.
>65 years old1.851.15–2.980.0111.260.72–2.200.423
Glasgow scale score on admission, categorized
Mild (15–13)Ref. Ref.
Moderate (12–9)2.171.20–3.930.0103.711.45–9.500.006
Severe (<9)4.893.67–6.53<0.0014.181.12–15.600.034
WFNS clinical scale score on admission
Mild (1 to 3)Ref. Ref.
Severe (4 to 5)3.082.08–4.58<0.0010.390.11–1.350.137
Need for transient clipping in parenteral artery
NoRef.
Yes1.611.01–2.550.0461.440.85–2.410.173
Intraoperative neurosurgical complications
NoneRef. Ref.
≥11.581.01–2.480.046---
Postoperative neurosurgical complications
NoneRef. Ref.
11.490.93–2.390.0981.060.61–1.820.844
≥22.681.23–5.850.0131.500.58–3.900.405
Specific postoperative neurosurgical complications
No complicationsRef. Ref.
Cerebral infarction and/or vasospasm2.991.87–4.78<0.0012.251.39–3.660.001
Other complications4.903.04–7.87<0.0013.111.40–6.910.005
Postoperative clinical complications
None Ref. Ref.
13.532.01–6.20<0.0012.311.27–4.190.006
≥24.612.66–7.98<0.0013.341.83–6.10<0.001
Unadjusted and adjusted Poisson regression models are reported as RRs (95% CI and p-value). Abbreviations: RR: risk ratio; CI: confidence interval; WFNS: World Federation of Neurosurgical Societies. Statistical test: Generalized linear model [family(Poisson) link(log)] (robust). p-values < 0.05 are in bold. ** Adjusted for all variables from the table.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Terry, F.; Enríquez-Marulanda, A.; Chinchihualpa-Paredes, N.; Carbajal-Galarza, M.; Vidal-Cuellar, C.L.; Mas-Ubillus, G.; Diaz-Llanes, B.; Quispe-Vicuña, C.; Pacheco-Barrios, N.; Arbulu-Zuazo, R.; et al. Clinical Predictors of Inpatient Mortality and Poor Postoperative Course After aSAH Microsurgical Clipping: A 10-Year Experience from a Peruvian Tertiary Care Center. J. Clin. Med. 2025, 14, 4799. https://doi.org/10.3390/jcm14134799

AMA Style

Terry F, Enríquez-Marulanda A, Chinchihualpa-Paredes N, Carbajal-Galarza M, Vidal-Cuellar CL, Mas-Ubillus G, Diaz-Llanes B, Quispe-Vicuña C, Pacheco-Barrios N, Arbulu-Zuazo R, et al. Clinical Predictors of Inpatient Mortality and Poor Postoperative Course After aSAH Microsurgical Clipping: A 10-Year Experience from a Peruvian Tertiary Care Center. Journal of Clinical Medicine. 2025; 14(13):4799. https://doi.org/10.3390/jcm14134799

Chicago/Turabian Style

Terry, Fernando, Alejandro Enríquez-Marulanda, Nathaly Chinchihualpa-Paredes, Meiling Carbajal-Galarza, Claudia L Vidal-Cuellar, Guiliana Mas-Ubillus, Bruno Diaz-Llanes, Carlos Quispe-Vicuña, Niels Pacheco-Barrios, Rommel Arbulu-Zuazo, and et al. 2025. "Clinical Predictors of Inpatient Mortality and Poor Postoperative Course After aSAH Microsurgical Clipping: A 10-Year Experience from a Peruvian Tertiary Care Center" Journal of Clinical Medicine 14, no. 13: 4799. https://doi.org/10.3390/jcm14134799

APA Style

Terry, F., Enríquez-Marulanda, A., Chinchihualpa-Paredes, N., Carbajal-Galarza, M., Vidal-Cuellar, C. L., Mas-Ubillus, G., Diaz-Llanes, B., Quispe-Vicuña, C., Pacheco-Barrios, N., Arbulu-Zuazo, R., Moses, Z. B., Sequeiros, J., Luther, E., Starke, R. M., Taussky, P., & Lopez-Calle, J. (2025). Clinical Predictors of Inpatient Mortality and Poor Postoperative Course After aSAH Microsurgical Clipping: A 10-Year Experience from a Peruvian Tertiary Care Center. Journal of Clinical Medicine, 14(13), 4799. https://doi.org/10.3390/jcm14134799

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop