Wednesday, May 31, 2023

Predictive validity of the prognosis on admission aneurysmal subarachnoid haemorrhage scale for the outcome of patients with aneurysmal subarachnoid haemorrhage – Scientific Reports

Source of data

This multicentre prospective observational study is the major update of our published previous study31, which collected data on patients with aneurysmal SAH consecutively admitted to the three national tertiary hospitals (Vietnam-Germany Friendship, Bach Mai, and Hanoi Medical University Hospital) in Hanoi, Vietnam, between August 2019 and June 2021. These hospitals are designated central hospitals in northern Vietnam by the Ministry of Health of Vietnam, of which the first is a surgical hospital with 1500 beds, the second is a large general hospital with 3200 beds, and the last is a small general hospital with 580 beds. Each participating hospital had at least two representatives (i.e., fully trained clinicians or surgeons) who were a part of the study team. Participation was voluntary and unfunded. All patients received a follow-up till discharge from the hospital or death in the hospital and had clinic visits or phone contacts on days 30th and 90th after ictus for the modified Rankin Scale (mRS) assessments, mRS ranges from 0 (no disability) to 6 (death)32, and evaluation of chronic hydrocephalus.


This study included all patients (aged 18 years or older) presenting with aneurysmal SAH to the three central hospitals within 4 days of ictus. We defined a case of aneurysmal SAH as a person who had the presence of blood visible on a head computed tomography (CT) scan (or in case the CT scan was negative, the presence of xanthochromia in the cerebral spinal fluid) in combination with an aneurysm confirmed on CT or digital subtraction angiography (DSA)16. We excluded patients for whom the GCS on admission was unable to be scored (e.g., patients intubated and under sedation before arrival at the central hospital) or patients who became lost at 90 days of follow-up during the study period. In the case of aphasia, patients were classified according to the clinically possible GCS scores derived from their eye and motor scores33,34. When different possible verbal scores placed patients in different categories, these patients were excluded.

All patients were managed following the American Heart Association (AHA)/American Stroke Association (ASA) guidelines for the management of aneurysmal SAH16. Aneurysm repair with endovascular coiling or surgical clipping was performed as early as possible and immediately if rebleeding occurred. The decision to treat the cerebral aneurysms was made based on the discretion of the physician in charge of the patients and the availability of endovascular coiling or neurosurgical clipping, which depended on the participating hospital and the financial situation (either insurance or patient self-pay).

Data collection

The data for each study patient were recorded from the same unified samples (case record form). A case record form (CRF) was adopted across the study sites to collect the common variables. Data were entered by a researcher or investigator into the study database via EpiData Entry software (EpiData Association, Denmark, Europe), which was used for simple or programmed data entry and data documentation that could prevent data entry errors or mistakes. We also checked the data for implausible outliers and missing fields and contacted hospital representatives for clarification. Patient identifiers were not entered into the database to protect the patients’ confidentiality.

Outcome measures

The primary outcome of this study was poor neurological function (poor outcome) on day 90th after ictus, which was defined as mRS scores of 4 (moderately severe disability) to 6 (death)35,36. We also examined the following secondary outcomes: poor outcome on day 30th after ictus, 30- and 90-day mortality rates, and incidence rate of complications.

Predictor measures

We defined exposure variables as SAH grading scales (i.e., the PAASH, WFNS, and H&H grading scales) at the time of admission to the hospital. Based on the admission GCS, we divided patients into the five categories of the PAASH grading scale, including grade I (GCS of 15), II (GCS of 11–14), III (GCS of 8–10), IV (GCS of 4–7), and V (GCS of 3)29, and into the five categories of the WFNS grading scale ranging from grade I (GCS score of 15) to V (GCS scores of 3–6), of which focal deficits make up 1 additional grade for patients with a GCS score of 14 or 1321. Based on the clinical condition on admission, we also classified patients into the five severity groups according to the H&H grading scale, which consists of five grades ranging from minimally symptomatic to coma20. All data elements required for calculating the GCS score and for classifying patients according to the PAASH, WFNS, or H&H grading scale at the time of admission to the hospital were prospectively assessed and collected on the same unified CRF by a fully trained clinician or surgeon of the participating hospitals and then were entered by a researcher or investigator into a study database via the EpiData Entry software for later analysis.

We determined confounding factors as variables collected on the same unified CRF by a fully trained clinician or surgeon. The CRF included variables based on the unruptured intracranial aneurysm (UIA) and SAH work group (WG) recommendations37, such as information on:

  1. i.

    Medical histories (e.g., stroke, UIA, etc.) and clinical presentation (e.g., GCS and focal neurological signs).

  2. ii.

    Admission head CT scan (e.g., presence of SAH, intraventricular haemorrhage (IVH) or intracerebral haemorrhage (ICH), and Fisher scale) and follow-up head CT scan during hospitalization (e.g., presence of SAH, IVH or ICH) or on days 30th and 90th after ictus (e.g., the presence of chronic hydrocephalus). We also collected data on the aneurysm site and aneurysm size from DSA or multi-slice CT (MSCT) angiography scan.

  3. iii.

    Surgical and endovascular interventions (i.e., surgical clipping or endovascular coiling), rescue therapies (e.g., surgical haematoma evacuation, defined as any surgical procedure evacuating epidural, subdural, intraventricular, or intraparenchymal haematoma, such as decompressive craniectomy, open craniotomy, or minimally invasive surgery; external ventricular drain (EVD) placement; ventriculoperitoneal (VP) shunt), and intensive care unit (ICU) therapies (e.g., mechanical ventilation).

  4. iv.

    Neurological complications (e.g., rebleeding, which included bleeding into the subarachnoid space, intracerebral, intraventricular, or subdural compartments; delayed cerebral ischaemia (DCI), hydrocephalus). Rebleeding from a ruptured aneurysm was classified into two subtypes: early or late rebleeding. We defined early or late rebleeding as rebleeding occurring in the hospital before or after an aneurysm repair, respectively.

  5. v.

    Clinical time course (e.g., time from ictus to hospital arrival, length of hospitalization)

  6. vi.

    We also collected data on demographics (i.e., sex, age) and system variables, which are available as the online supplement to a previously published paper31.

Sample size

In the present study, poor neurological function on day 90th after the ictus served as the primary outcome. Therefore, based on the rate of poor neurological function on day 90th after the ictus (39.1%) reported in a previously published study30, we used the formula to find the minimum sample size for estimating a population proportion, with a confidence level of 95% and a confidence interval (margin of error) of ± 4.7%, and an assumed population proportion of 39.1%. As a result, our sample size should be at least 415 patients. Therefore, our sample size was sufficient and reflected a normal distribution.

$$n=\frac{{z}^{2}x \widehat{p}\left(1-\widehat{p}\right)}{{\varepsilon }^{2}}$$

where z is the z score (z score for a 95% confidence level is 1.96); ε is the margin of error (ε for a confidence interval of ± 4.7% is 0.047); \(\hat{p}\) is the population proportion (\(\hat{p}\) for a population proportion of 39.1% is 0.391); n is the sample size

Statistical analyses

We used IBM® SPSS® Statistics 22.0 (IBM Corp., Armonk, United States of America) and Analyse-it statistical software (Analyse-it Software, Ltd., Leeds, United Kingdom) for data analysis. We report the data as numbers (no.) and percentages (%) for categorical variables and medians and interquartile ranges (IQRs) or means and standard deviations (SDs) for continuous variables. Furthermore, comparisons were made between poor and good outcomes at 30 and 90 days of ictus for each variable using the Chi-squared test or Fisher’s exact test for categorical variables and the Mann–Whitney U test, Kruskal–Wallis test, or one-way analysis of variance for continuous variables.

Odds ratios (ORs) for a poor outcome on days 30th and 90th after ictus with 95% confidence intervals (CIs) were calculated for each grade of the 5-category SAH grading scales (i.e., the PAASH, WFNS, and H&H scales) with a univariable logistic regression model, with grade I taken as the reference. In all of the SAH grading scales (i.e., the PAASH, WFNS, and H&H scales), significant intergrade differences in the outcome (mean mRS scores) on days 30th and 90th after ictus that were determined using the Kruskal–Wallis H test with the Dunn-Bonferroni principle as a post hoc analysis.

We converted from descriptive SAH grading scales (i.e., the PAASH, WFNS, and H&H scales) to numerical SAH grading scales in ascending order (Table S1 as shown in Additional file 1). Receiver operator characteristic (ROC) curves were plotted, and the areas under the ROC curve (AUROC) were calculated to determine the discriminatory ability of all SAH grading scales for the prognosis of the patients at the time of evaluation. The cut-off value of each SAH grading scale was determined by ROC curve analysis and defined as the cut-off point with the maximum value of Youden’s index (i.e., sensitivity + specificity − 1). Based on the cut-off value of the scales, we assigned the patients to two severity groups: either the grade that was less than the cut-off value or another that was greater than or equal to the cut-off value. We also performed a pairwise comparison among the AUROCs of the PAASH, WFNS, and H&H scales for predicting the poor outcome on days 30th and 90th after ictus by using the Z-statistics.

We assessed the factors associated with 90-day poor outcomes using logistic regression analysis. To reduce the number of predictors and the multicollinearity issue and resolve the overfitting, we used different methods to select variables as follows: (a) we put all variables (including exposure and confounding factors) of demographics, baseline characteristics, clinical and laboratory characteristics, neuroimaging findings, clinical time course, treatments, and complications into the univariable logistic regression analyses; (b) we selected variables if the p value was < 0.05 in the univariable logistic regression analyses between the good and poor outcomes on day 90th after ictus, as well as those that are clinically crucial to put in the multivariable logistic regression model. These variables included demographics (i.e., age), risk factors for aneurysmal SAH (i.e., hypertension), comorbidities (i.e., diabetes mellitus), initial neuroimaging findings (i.e., location of blood within the subarachnoid space, the occurrence of IVH and ICH, and aneurysm location), the severity of the aneurysmal SAH on admission (i.e., the grades of PAASH, WFNS, and H&H grading scales that was either greater than or equal to the cut-off value), treatments (i.e., aneurysm repairs, nimodipine), and complications (i.e., rebleeding, DCI, acute hydrocephalus, and pneumonia). Using a stepwise backward elimination method, we started with the full multivariable logistic regression model that included the selected variables. This method then deleted the variables stepwise from the full model until all remaining variables were independently associated with the risk of 90-day poor outcomes in the final model. Similarly, we used these methods of variable selection and analysis for assessing factors associated with 30-day poor outcomes. For examining the effect size of each grade of the SAH grading scales, in combination with confounding factors, for predicting the 30- and 90-day poor outcomes, we replaced the severity group variables (i.e., the grades of PAASH, WFNS, and H&H grading scales that was either greater than or equal to the cut-off value) with each SAH grading scale (i.e., the PAASH, WFNS, or H&H scale), with grade I taken as the reference, in this multivariable logistic regression model, with the same set of confounding variables. We presented the odds ratios (ORs) and 95% confidence intervals (CIs) in the univariable logistic regression analyses and the adjusted odds ratios (AORs) and 95% CIs in the multivariable logistic regression models.

For all analyses, the significance levels were two-tailed, and we considered p < 0.05 to be statistically significant.

Ethical issues

The Hanoi Medical University (Approval number: 3335/QĐ-ĐHYHN), Vietnam-Germany Friendship Hospital (Approval number: 818/QĐ-VĐ; Research code: KH04.2020), and Bach Mai Hospital (Approval number: 3288/QĐ-BM; Research code: BM_2020_1247) Scientific and Ethics Committees approved this study. This study was conducted according to the principles of the Declaration of Helsinki. The Vietnam-Germany Friendship Hospital Scientific and Ethics Committees waived written informed consent for this non interventional study, and public notification of the study was made by public posting, according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement—the TRIPOD checklist—for reporting a study developing or validating a multivariable prediction model for diagnosis or prognosis38. The authors who performed the data analysis kept the datasets in password-protected systems, and we only present anonymized data.

Source link

Related Articles

Leave a Reply

Stay Connected

- Advertisement -spot_img

Latest Articles

%d bloggers like this: