Abstract
Background
Multiple factors impact the prognosis of intracerebral hemorrhage (ICH). This study aimed to evaluate prognosis at 90 days after ICH in 561 patients using the numerical rating scale (NRS), the Braden scale, the Morse fall risk scale (MFS), and the enhanced modified early warning (MEW) scale.
Material/Methods
A retrospective study was performed among 561 patients with ICH diagnosed in our hospital. The primary outcome was the 90-day prognostic status, classified as poor or good. Disparities in NRS, Braden scale, MFS, and enhanced MEW scores were examined between the poor and good prognosis groups in ICH patients.
Results
Among the 561 patients successfully followed up, 258 cases (46.0%) were classified as having a poor prognosis. The Braden scale was significantly higher in the good prognosis group than the poor prognosis group at 90 days after onset (P<0.001). The MFS and the MEW were significantly lower in the good prognosis group than the poor prognosis group at 90 days after onset (P<0.001), while NRS showed no differences (P>0.05). Poor prognosis at the 90-day mark was found to be independently associated with the Braden scale (P=0.012, OR=0.849, 95% CI [0.748, 0.965]), the MFS scale (P=0.001, OR=1.148, 95% CI [1.070, 1.232]), and the enhanced modified early warning (MEW) score (P=0.001, OR=1.093, 95% CI [1.064, 1.123]).
Conclusions
The early admission Braden scale, MFS and the enhanced MEW score exhibited independent associations with poor prognoses at 90 days after ICH onset.
Keywords: Cerebral Hemorrhage, Neurology, Prognosis
Introduction
As the second leading cause of death globally and a significant contributor to disability, the incidence of stroke is expected to increase as life expectancy rises, particularly in low- and middle-income countries [1]. Intracerebral hemorrhage (ICH), accounting for 10–20% of all stroke cases, poses a significant public health concern [2–4]. Disparities in ICH incidence are evident, with 80% of cases occurring in low- and middle-income nations [2–4]. Despite prompt interventions, the prognosis for ICH remains bleak, with a mortality rate exceeding 40% within 1 month and limited recent advancements [5,6]. Furthermore, only a small percentage of survivors achieve independent living within 1 year, highlighting the prolonged recovery process and the significant burden on patients and healthcare systems [2–6]. These statistics underscore the high mortality rates and extended rehabilitation period associated with ICH, presenting substantial challenges for patients and healthcare providers [2–6].
ICH prognosis is influenced by various factors, posing a challenge to the development of comprehensive scoring systems and prediction models [1–6]. The digital numerical rating scale (NRS) is used to assess pain and fatigue after stroke by a 0–10-point scale [7]. The Braden scale for pressure injuries is used to predict complications after stroke [8]. The modified early warning score (MEWS), has shown to reflect the admission shock index and mortality in stroke patients, suggesting the potential for combining acute physiological parameters to predict adverse outcomes in stroke patients [9]. The Morse fall risk scale (MFS) is widely used in hospitals for assessing the risk of falls, using a simple scoring system comprising 6 items [10]. Machine learning shows the effectiveness of MFS in prediction the falls of stroke patients.
However, concerns have been raised regarding the accuracy of the NRS, Braden scale, MFS, and enhanced MEWS despite of their advantages. The predictive accuracy of these scales varies across studies, especially among surgical patients, underscoring the ongoing need for comprehensive assessment tools in clinical environments to accurately predict patient outcomes [7–11]. In addition, there is a lack of research on the correlation between the NRS, Braden scale, MFS, and enhanced MEWS and the prognosis of ICH. Therefore, this study aimed to evaluate prognosis at 90 days in 561 patients with ICH using the NRS, the Braden scale, the MFS, and the enhanced MEW scale.
Material and Methods
Ethics Statement
Our retrospective study was conducted at the Advanced Stroke Center of Chongqing University Three Gorges Hospital after obtaining approval from the Clinical Trial Ethics Committee of Chongqing University Three Gorges Hospital (No. 20220042). In compliance with the Declaration of Helsinki, oral informed consent was obtained from all participants, or their surrogates for deceased patients. The study was registered with a unique identifier, MR-50-23-001346, in the National Medical Research Registration and Archival Information System (https://www.medicalresearch.org.cn).
Participants
Data on patients meeting the diagnostic criteria for spontaneous ICH as per the 2019 Chinese Guidelines for the Diagnosis and Treatment of ICH were retrospectively collected from the Department of Neurology at Chongqing University Three Gorges Hospital from January 2021 to June 2022. The diagnostic criteria were: (1) Meeting the diagnostic criteria for spontaneous ICH with an acute onset; (2) Symptoms of functional impairment of the Vesta channel (a few are comprehensive neurological impairment), often accompanied by headache, vomiting, elevated blood pressure, and varying degrees of consciousness disorders; (3) Head CT or MRI showing bleeding focus; (4) Individuals aged 18 and above, non-pregnant, and admitted within 24 hours of symptom onset. Exclusion criteria were secondary hemorrhagic causes (eg, tumor, trauma, abnormal vascular structure, cerebral infarction hemorrhage transformation), craniotomy during hospitalization (the underlying situation and prognosis of these patients were poor, and these scores were often unevaluable for them), hospital deaths within 24 hours and not able to be scored, loss to follow-up, or refusal to participate. Patients with non-vascular cerebral causes were also excluded. A recruitment flow diagram is depicted in Figure 1.
Figure 1.
Flow diagram of eligibility of ICH cases in the current study. ICH – intracerebral hemorrhage; mRS – modified Rankin scale.
Clinical Data
The study collected a comprehensive dataset from patient medical records, including demographic information (such as sex and age), medical history (eg, hypertension, diabetes, smoking, alcohol use), admission details (eg, systolic blood pressure (SBP), diastolic blood pressure (DBP), random serum glucose levels, post-stroke modified Rankin scale (mRS), Glasgow coma scale (GCS), National Institute of Health stroke scale (NIHSS), and 4 nursing assessments (NRS scale, Braden scale, MFS, and MEWS). Each score was regularly assessed by at least 2 experienced nurses. Additionally, data on treatment modalities (medical and minimally invasive surgery [MIS] + medical), time intervals (in hours) from initial CT scan to symptom onset, and length of hospital stay (in days) were also recorded.
Imaging Evaluation
Hemorrhage location was classified into lobar, deep, cerebellar, brainstem [12], and mixed hematoma (comprising cases with 2 or more of these categories). Assessment also included hemorrhage into the ventricle [13] and subarachnoid space [14]. Hematoma volume was computed in milliliters using the ellipsoid formula (A×B×C/2). Unaware of the clinical details, 2 neuroradiological specialists analyzed all imaging studies. In instances of disagreement, a third expert was engaged to arbitrate and establish a final consensus on the classification.
Prognosis Data
Follow-up data were collected by telephone calls, questionnaires, and outpatient visits. The follow-up period started at discharge and ended at death or 90 days after onset. We assessed the mRS score for ICH cases 90 days after onset. A good prognosis was defined as an mRS score below 3, with all other outcomes classified as poor prognosis, including mortality (score of 6).
Statistical Analysis
Categorical variables are presented as frequencies and percentages and were compared using the likelihood-ratio chi-squared test. Continuity variables were presented as the mean±standard deviation or median and interquartile range (IQR). Group comparisons were performed using the t test or Mann-Whitney U test (shown in tables as T/U index), depending on the normality of the data distribution.
A good prognosis was characterized by an mRS score under 3 and survival 3 months after onset, whereas a poor prognosis included scores of 3 or higher, including mortality (score of 6). We first analyzed major differences in 4 nursing scores between the poor and good prognosis groups at 90 days after onset. We chose poor prognosis (recorded as 1)/good prognosis (recorded as 0) as the outcome variables of binary classification, and included the variables with P<0.1 in the univariate analysis into the multivariate analysis for binary logistics regression analysis. We also performed a correction analysis and calculated cutoff values for nursing scores associated with outcome measures. Statistical analyses were performed using R language version 4.0.4 and SPSS software version 22.
Results
Demographic Characteristics
A total of 687 patients diagnosed with ICH were analyzed, with 424 males (61.7%) and 263 females (38.3%), with a mean age of 64.94±11.35 years (median 66 [IQR range 57–73]). Of these patients, 395 (57.5%) were treated with medical therapy alone, while 292 (42.5%) underwent MIS. The median time from the initial CT scan to symptom onset was 7.35±6.57 hours, and the average hospital stay was 14.33±9.92 days. Common comorbidities included hypertension in 455 cases (66.2%), diabetes in 57 cases (8.3%), ischemic heart disease in 60 cases (8.7%), atrial fibrillation in 21 cases (3.1%), anticoagulant use in 28 cases (4.1%), antiplatelet use in 31 cases (4.5%), history of ischemic stroke in 42 cases (6.1%), smoking in 224 cases (32.6%), and alcohol consumption in 211 cases (30.7%).
The mean admission SBP was 168.47±26.13 mmHg, and the mean DBP was 94.67±17.92 mmHg. The mean random serum glucose level was 7.52±2.82 mmol/L. The causal classification conformed to the criteria of the structural lesion, medication, amyloid angiopathy, systemic/other disease, hypertension, undetermined classification system: structural vascular lesions in 27 cases (3.9%); medication-related causes in 7 cases (1.0%); amyloid angiopathy in 16 cases (2.3%); systemic diseases in 9 cases (1.3%); hypertension in 539 cases (78.5%); and undetermined causes in 89 cases (13.0%).
The mean hematoma volume was 22.04±22.42 ml. Cases were distributed as follows: lobar ICH (n=77, 11.2%), deep ICH (n=464, 67.5%), cerebellar ICH (n=39, 5.7%), brainstem ICH (n=32, 4.7%), and mixed hematoma (n=75, 10.9%). Additionally, ventricular hemorrhage was present in 215 cases (31.3%) and subarachnoid hemorrhage in 53 cases (7.7%).
The mRS score was 0 (IQR=0) before ICH and rose to 4 (IQR=1) after the event. Initial GCS and NIHSS scores at admission were 12 (IQR=7) and 13 (IQR=12), respectively. A median discharge mRS score of 4 (IQR=2) indicated an adverse prognosis in 64.8% of the 445 cases. After 90 days, the mRS score was 2 (IQR=3), with 46.0% of the 258 cases exhibiting a poor prognosis among the 561 cases successfully followed.
In the analysis on the effects of the scoring on the prognosis among the patients at 90 days after onset, we excluded patients who were unable to be scored after 90 days. There were 561 cases surviving at 3 months after onset, and the following analyses were performed among these 561 patients (Figure 1).
Differences in the 4 Nursing Scores Between the Poor Prognosis Group and the Good Prognosis Group 90 Days Post-Onset
There were no significant differences (P=0.884; T/U index=0.146) in the NRS scale between groups with poor (0.12±0.52) and good prognoses (0.16±0.66) (P>0.05). Significant differences were observed in the Braden scale (11.68±1.51 vs 12.98±1.76, P<0.001; T/U index=4.830), MFS (8.31±2.06 vs 7.28±2.18, P<0.001; T/U index=4.329), and MEWS (4.72±1.92 vs 2.58±2.03, P<0.001; T/U index=3.938) between groups with poor and good prognoses at 90 days after onset (Table 1). The cutoff values for the Braden scale, MFS, and MEWS were 14.5, 5.5, and 1.5, respectively. However, no significant differences were found in the NRS score at 90 days after onset (0.13±0.46 vs 0.15±0.57, P=0.884; T/U index=0.146) (Table 1).
Table 1.
Differences in the 4 nursing scores between the poor prognosis group and the good prognosis group 90 days after onset.
| Scale variables | 90 days after onset Poor prognosis (N=258) Mean±SD |
90 days after onset Good prognosis (N=303) Mean±SD |
P | T index/ U index | Cutoff value |
|---|---|---|---|---|---|
| NRS scale | 0.13±0.46 | 0.15±0.57 | 0.884 | 0.146 | NA |
| Braden scale | 11.68±1.51 | 12.98±1.76 | <0.001* | 4.830 | 14.5 |
| MFS | 8.31±2.06 | 7.28±2.18 | <0.001* | 4.329 | 5.5 |
| MEWS | 4.72±1.92 | 2.58±2.03 | <0.001* | 3.938 | 1.5 |
p<0.05.
SD – standard deviation; OR – odds ratio; NRS – Numerical Rating Scale; MEWS – Modified Early Warning Score; MFS – Morse Fall Risk Scale.
Univariate and Multivariate Analyses of Outcome Indicators
Significant differences were observed between groups with poor and good prognoses at 90 days after onset in terms of age (66.23±11.39 vs 62.79±10.91 years, P<0.001; T/U index=3.693), treatment modality (MIS+ medical) (128 cases [49.6%] vs 99 cases [32.7%], P<0.001; OR index=2.029), length of hospital stay (16.56±10.18 vs 10.59±14.12 days, P=0.004; T/U index=2.916), history of coronary heart disease (28 cases [10.9%] vs 18 cases [5.9%], P=0.035; OR index=1.928), pre-ICH mRS (0 [0%] vs 0 [0%], P=0.014; T/U index=2.447), post-ICH mRS (4 [1] vs 4 [1], P<0.001; T/U index=7.580), admission GCS (11 [7] vs 14 [4], P<0.001; T/U index=5.800), admission NIHSS (14 [9] vs 8 [11], P<0.001; T/U index=8.367), and hematoma volume (22.85±22.93 vs 16.92±17.13 ml, P<0.001; T/U index=4.159) (Table 2).
Table 2.
The demographic characteristics between poor and good prognosis groups at 90 days of onset.
| Characteristic data | Variables | Poor prognosis (N=258) N(%)/Mean±SD/Median(IQR) |
Good prognosis (N=303) N(%)/Mean±SD/Median(IQR) |
P | OR/ T index/ U index | |
|---|---|---|---|---|---|---|
| Demographic | Age, years | 66.23±11.39 | 62.79±10.91 | <0.001* | 3.693 | |
| Sex | Male | 152 (58.9) | 193 (63.7) | 0.246 | 1.224 | |
| Female | 106 (41.1) | 110 (36.3) | ||||
| Treatment modality | Only medical | 130 (50.4) | 204 (67.3) | <0.001* | 2.029 | |
| MIS+ medical | 128 (49.6) | 99 (32.7) | ||||
| Time from first CT to onset1, hours | 7.41±6.86 | 8.02±6.80 | 0.069 | 1.820 | ||
| Length of hospitalization, days | 16.56±10.18 | 10.59±14.12 | 0.004* | 2.916 | ||
| History | Hypertension | 0 | 80 (31.0) | 109 (36.0) | 0.215 | 1.250 |
| 1 | 178 (69.0) | 194 (64.0) | ||||
| Diabetes | 0 | 234 (90.7) | 279 (92.1) | 0.560 | 1.192 | |
| 1 | 24 (9.3) | 24 (7.9) | ||||
| Coronary heart disease | 0 | 230 (89.1) | 285 (94.1) | 0.035* | 1.928 | |
| 1 | 28 (10.9) | 18 (5.9) | ||||
| Atrial fibrillation | 0 | 251 (97.3) | 295 (97.4) | 0.957 | 1.028 | |
| 1 | 7 (2.7) | 8 (2.6) | ||||
| Hyperlipidemia | 0 | 249 (96.5) | 287 (94.7) | 0.305 | 0.648 | |
| 1 | 9 (3.5) | 16 (5.3) | ||||
| Anticoagulant drug use | 0 | 248 (96.1) | 291 (96.0) | 0.959 | 0.978 | |
| 1 | 10 (3.9) | 12 (4.0) | ||||
| Anti-plate drug use | 0 | 249 (96.5) | 287 (94.7) | 0.305 | 0.648 | |
| 1 | 9 (3.5) | 16 (5.3) | ||||
| Ischemic stroke | 0 | 242 (93.8) | 288 (95.0) | 0.518 | 1.269 | |
| 1 | 16 (6.2) | 15 (5.0) | ||||
| Smoking | 0 | 179 (69.4) | 193 (63.7) | 0.156 | 0.774 | |
| 1 | 79 (30.6) | 110 (36.3) | ||||
| Alcohol consumption | 0 | 185 (71.7) | 199 (65.7) | 0.126 | 0.755 | |
| 1 | 73 (28.3) | 104 (34.3) | ||||
| Admission data | Systolic blood pressure, mmHg | 168.52±24.35 | 166.70±26.33 | 0.331 | 0.971 | |
| Diastolic blood pressure, mmHg | 94.18±16.55 | 95.17±18.03 | 0.467 | 0.728 | ||
| Random serum glucose levels, mmol/L | 7.41±3.03 | 7.17±2.28 | 0.358 | 0.919 | ||
| SMASH-U Classification | Structural lessons | 8 (3.1) | 10 (3.3) | 0.553 | 0.795 | |
| Medication | 0 (0.0) | 1 (0.3) | ||||
| CAA | 6 (2.3) | 8 (2.6) | ||||
| Systemic disease | 1 (0.4) | 6 (2.0) | ||||
| Hypertension | 210 (81.4) | 237 (78.2) | ||||
| Undetermined | 33 (12.8) | 41 (13.5) | ||||
| Admission score | mRS before ICH | 0 (0) | 0 (0) | 0.014* | 2.447 | |
| mRS after ICH | 4 (1) | 4 (1) | <0.001* | 7.580 | ||
| GCS | 11 (7) | 14 (4) | <0.001* | 5.800 | ||
| NIHSS | 14 (9) | 8 (11) | <0.001* | 8.367 | ||
| CT imaging data | Hematoma location | Lobar | 23 (8.9) | 41 (13.5) | 0.194 | 1.522 |
| Deep | 185 (71.7) | 212 (70.0) | ||||
| Cerebellar | 10 (3.9) | 16 (5.3) | ||||
| Brainstem | 8 (3.1) | 10 (3.3) | ||||
| A mixed hematoma2 | 32 (12.4) | 24 (7.9) | ||||
| Hematoma volume, ml | 22.85±22.93 | 16.92±17.13 | <0.001* | 4.159 | ||
| Ventricular hemorrhage | 0 | 179 (69.4) | 229 (75.6) | 0.100 | 1.366 | |
| 1 | 79 (30.6) | 74 (24.4) | ||||
| Subarachnoid hemorrhage | 0 | 239 (92.6) | 289 (95.4) | 0.169 | 1.641 | |
| 1 | 19 (7.4) | 14 (4.6) | ||||
p<0.05
SD – standard deviation; IQR – inter quartile range; OR – odds ratio; MIS – minimally invasive surgery; CAA – cerebral amyloid angiopathy; mRS – modified Rankin scale; GCS – Glasgow coma scale; NIHSS – National Institute of Health Stroke Scale; CT – computed tomography; SMASH-U – structural lesion, medication, amyloid angiopathy, systemic/other disease, hypertension, undetermined classification system; ICH – intracerebral hemorrhage.
The time from the initial CT scan to symptom onset was determined as the duration from the onset or sudden exacerbation of symptoms to the commencement of the first CT examination in the emergency, outpatient, or inpatient setting.
A mixed hematoma was identified when a hematoma is situated in 2 or more areas: lobar, deep, cerebellar, and brainstem.
“0” means good prognosis, and “1” means poor prognosis.
The multivariate analysis identified several risk factors significantly associated with poor prognosis at 90 days after onset. These factors include the Braden scale (P=0.012, OR=0.849, 95% CI [0.748, 0.965]), the MFS scale (P=0.001, OR=1.148, 95% CI [1.070, 1.232]), and enhanced MEWS (P=0.001, OR=1.093, 95% CI [1.064, 1.123]), as illustrated in Table 3.
Table 3.
The multivariate analysis between poor and good prognosis groups at 90 days after onset.
| Risk factors | Prognostic indicators at 90 days after onset | ||||
|---|---|---|---|---|---|
| All cases (N=561) | Poor prognosis (N=258) N(%) or Mean±SD/Median(IQR) |
Good prognosis (N=303) N(%) or Mean±SD/Median(IQR) |
P | OR | 95% CI |
| Braden scale | 13.60±1.79 | 14.92±1.79 | 0.012* | 0.849 | 0.748, 0.965 |
| MFS | 7.79±2.73 | 8.01±2.49 | 0.001* | 1.148 | 1.070, 1.232 |
| MEW | 14 (9) | 8 (11) | 0.001* | 1.093 | 1.064, 1.123 |
p<0.05.
SD – standard deviation; IQR – interquartile range; OR – odds ratio; CI – confidence interval; MFS – Morse Fall Risk Scale; MEWS – Modified Early Warning Score.
Discussion
This study has identified the Braden scale, MFS scale, and MEWS score as independent predictors of poor prognosis 90 days after ICH onset.
Previous research has underscored the significance of hematoma volume and consciousness status in ICH outcomes, with early and timely MIS showing a better prognosis compared to conservative treatment alone [15–19]. The NRS is a widely used tool for pain assessment in clinical settings [20]. However, there is limited research on the relationship between NRS pain scores and ICH. A comparative study on various pain assessment scales, including visual analog scales, facial pain scales, and speech rating scales, indicated that the NRS has comparable effectiveness but higher sensitivity than other scales [21]. Another study assessing lower back pain severity and predictive outcomes, such as disability, found the NRS and visual analog scale were reliable in severity assessment, with the NRS showing better predictive value for adverse outcomes [22]. Recent studies have investigated the association between headaches and ICH prognosis. For instance, Bouchier et al used the NRS to evaluate headache intensity in patients with subarachnoid hemorrhage, revealing a significant association between high pain scores on the NRS and adverse outcomes such as re-hemorrhage, hydrocephalus, and death [23]. While the NRS shows promise in predicting poor prognosis in ICH patients [21–23], our study did not find a clear correlation between NRS scores and prognosis, suggesting caution in scale selection. We speculate that the prevalence of mild or absent pain may have limited discriminatory power in predicting outcomes. Also, this result, which differs from previous research [21–23] may have a relationship with the sample size of the current study and the 90-days follow-up time of evaluation.
The Braden scale is a widely recognized tool for evaluating pressure injuries due to its user-friendly design and comprehensive assessment of various risk factors compared to other scales [24,25]. Previous studies have indicated a possible link between the 6 Braden scale subscales and pneumonia development [26]. Pneumonia is a common complication following acute ischemic stroke and spontaneous ICH, leading to prolonged hospital stays, increased mortality, and morbidity, significantly impacting patient outcomes [27]. A recent study explored the feasibility of using the Braden scale to predict pneumonia occurrence after acute ischemic stroke [8], showing promising results validating its utility as a clinical assessment tool. Additionally, an investigation was conducted into the relationship between the Braden scale and pneumonia following spontaneous ICH. Among 629 ICH patients, 23.8% developed stroke-related pneumonia, with those in the pneumonia group having notably lower Braden scores (14.1±2.4) compared to the non-pneumonia group (16.5±2.6) [28]. The Braden scale exhibited a sensitivity of 74.3%, specificity of 64.7%, and accuracy of 72.0% in predicting pneumonia, with a lower Braden score identified as an independent risk factor (OR 0.696; 95% CI 0.631–0.768; P<0.001) for stroke-related pneumonia after ICH [28]. Our study results are consistent with these results [26–28], emphasizing the effectiveness of the Braden scale in anticipating pneumonia occurrence following spontaneous ICH and its potential role in predicting adverse outcomes in ICH patients. Overall, we found the Braden scale is a good independent predictor of unfavorable discharge outcomes, consistent with previous research [26–28] in this field.
The MFS scale is widely used globally to assess the likelihood of falls in adult patients, particularly during hospitalization and with a focus on stroke patients [29]. A recent study at 2 stroke centers recruited 400 patients within 72 hours of acute stroke onset, comprising 370 with cerebral infarction and 30 with ICH [30]. These patients underwent evaluations for stroke severity, disability, functional independence, fall risk, cognitive function, and ischemia and bleeding risk at admission, discharge, and 3- and 12-months after stroke [30]. The study revealed a significant link between fall risk and stroke severity, with high fall risk correlating with disability, recurrent stroke (both cerebral infarction and hemorrhage), mortality, and other adverse outcomes [30]. Notably, the current study results indicate that the fall rating scale serves as an independent predictor of unfavorable outcomes after discharge and at 90 days after stroke onset, which is consistent with previous studies [29,30]. Our present findings suggest that the MFS could offer valuable insights into the poor prognosis of patients with cerebral hemorrhage, and underscore the urgent need to improve interventions and care for these patients to mitigate disability and prevent falls.
The MEWS is a clinical tool used to identify deteriorating patients by evaluating various physiological parameters and assisting in decisions regarding escalated patient care [31]. The MEWS score has been refined into the Improved MEWS, incorporating additional factors like urine output and sensitivity to minor temperature changes [32]. Higher scores on the Improved MEWS have shown improved sensitivity and specificity in pinpointing patients needing transfer to high-dependency or intensive care units. Previous studies have shown a relationship between the admission shock index and mortality in stroke patients [9], suggesting the potential for combining acute physiological parameters to predict adverse outcomes in this group. A recent investigation by Knoery et al [33] found a significant association between high MEWS scores, increased hospital admission rates, and mortality at 7 days, 30 days, and 1 year among 2006 patients. The current study findings suggest that MEWS is associated with poor prognosis, which confirms the results of previous report [9,32,33]; this relationship between MEWS and poor prognosis weakens but is still significant after adjusting for multiple variables, which indicate a potential predictive function of this scoring system in identifying patients at risk for adverse outcomes.
The ICH score is a scoring system used to assess the prognosis of patients with ICH, which combines factors such as the patient’s age, degree of neurological impairment, amount of bleeding, and location of bleeding. However, ICH is often used to predict the prognosis within a short term of about 30 days. Previous studies showed that the ICH score may overestimate or underpredict the prognosis in an inaccurate way [34,35]. The current study used 4 scoring system to predict patient prognosis at 90 days after onset. Further research is needed to compare the ICH score and our scoring system.
This study has several limitations. Firstly, it was a single-center retrospective study, and the analysis on the prognosis at 90 days after onset excluded patients who underwent craniotomy or who died within 24 hours after onset, which may have introduced selection bias. Secondly, the nursing score, assessed by the on-duty nurse upon admission, may have introduced subjective bias despite professional training. Moreover, the prognosis of ICH is influenced by multiple factors that may be correlated with the nursing score, but adjusting for confounding variables could mitigate this correlation. To strengthen the findings, increasing the sample size and conducting prospective studies is recommended. Finally, the primary focus of this study was assessing the predictive capabilities of scoring systems to aid clinical diagnostic interventions, thus limiting in-depth mechanistic discussions.
Conclusions
Based on the results of existing studies, Braden scale, MFS, and enhanced MEWS have potential predictive value for adverse prognosis of ICH, and may be used as a set of useful risk prediction tools to guide clinicians in predicting the prognosis of patients with ICH. In clinical practice, according to the evaluation results of these early nursing scoring systems, timely implementation of corresponding nursing interventions is conducive to reducing or avoiding adverse events such as disability, recurrent stroke, and death in patients with ICH, and has high utility in the rehabilitation treatment of patients with ICH.
Acknowledgments
We are thankful to the imaging technicians for obtaining high-quality images, to the neurologists for their support, and to all participants for participating in the study.
Abbreviations
- ICH
intracerebral hemorrhage
- NRS
numerical rating scale
- MEWS
modified early warning score
- MFS
Morse fall risk scale
- SBP
systolic blood pressure
- DBP
diastolic blood pressure
- MIS
minimally invasive surgery
- mRS
modified Rankin scale
- IQR
interquartile range
- GCS
Glasgow coma scale
- GCS
Glasgow coma scale
- NIHSS
National Institute of Health Stroke Scale
Footnotes
Conflict of interest: None declared
Declaration of Figures’ Authenticity: All figures submitted have been created by the authors who confirm that the images are original with no duplication and have not been previously published in whole or in part.
Financial support: This work was sponsored by the Surface Project of Science and Health Joint of Chongqing (No. 2024MSXM051), Natural Science Foundation of Chongqing, China (No. 2022NSCQ-BHX5277), and Talents Special Project of Chongqing University Three Gorges Hospital (No. 2022YJKYXM-040)
Availability of Data and Materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
- 1.Akinyemi RO, Ovbiagele B, Adeniji OA, et al. Stroke in Africa: Profile, progress, prospects and priorities. Nat Rev Neurol. 2021;17:634–56. doi: 10.1038/s41582-021-00542-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Li Z, Khan S, Liu Y, et al. Therapeutic strategies for intracerebral hemorrhage. Front Neurol. 2022;13:1032343. doi: 10.3389/fneur.2022.1032343. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Montaño A, Hanley DF, Hemphill III JC. Hemorrhagic stroke. Handb Clin Neurol. 2021;176:229–48. doi: 10.1016/B978-0-444-64034-5.00019-5. [DOI] [PubMed] [Google Scholar]
- 4.Poomalai G, Prabhakar S, Sirala Jagadesh N. Functional ability and health problems of stroke survivors: An explorative study. Cureus. 2023;15:e33375. doi: 10.7759/cureus.33375. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Wang S, Zou X-L, Wu L-X, et al. Epidemiology of intracerebral hemorrhage: A systematic review and meta-analysis. Front Neurol. 2022;13:915813. doi: 10.3389/fneur.2022.915813. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Malhotra K, Zompola C, Theodorou A, et al. Prevalence, characteristics, and outcomes of undetermined intracerebral hemorrhage: A systematic review and meta-analysis. Stroke. 2021;51:3602–12. doi: 10.1161/STROKEAHA.120.031471. [DOI] [PubMed] [Google Scholar]
- 7.Chuang LL, Lin KC, Hsu AL, et al. Reliability and validity of a vertical numerical rating scale supplemented with a faces rating scale in measuring fatigue after stroke. Health Qual Life Outcomes. 2015;13:91. doi: 10.1186/s12955-015-0290-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Ding Y, Yan Y, Niu J, et al. Braden scale for assessing pneumonia after acute ischaemic stroke. BMC Geriatr. 2019;19(1):259. doi: 10.1186/s12877-019-1269-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.McCall SJ, Musgrave SD, Potter JF, et al. The shock index predicts acute mortality outcomes in stroke. Int J Cardiol. 2015;182:523–27. doi: 10.1016/j.ijcard.2014.12.175. [DOI] [PubMed] [Google Scholar]
- 10.Ji S, Jung H-W, Kim J, et al. Comparative study of the accuracy of at-point Clinical Frailty Scale and Morse Fall Scale in identifying high-risk fall patients among hospitalized adults. Ann Geriatr Med Res. 2023;27:99–105. doi: 10.4235/agmr.23.0057. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Kim YJ, Choi KO, Cho SH, Kim SJ. Validity of the Morse Fall Scale and the Johns Hopkins Fall Risk Assessment Tool for fall risk assessment in an acute care setting. J Clin Nurs. 2022;31:3584–94. doi: 10.1111/jocn.16185. [DOI] [PubMed] [Google Scholar]
- 12.Martini SR, Flaherty ML, Brown WM, et al. Risk factors for intracerebral hemorrhage differ according to hemorrhage location. Neurology. 2012;79:2275–82. doi: 10.1212/WNL.0b013e318276896f. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Robles LA, Volovici V. Hypertensive primary intraventricular hemorrhage: A systematic review. Neurosurg Rev. 2022;45:2013–26. doi: 10.1007/s10143-022-01758-8. [DOI] [PubMed] [Google Scholar]
- 14.Angermann M, Jablawi F, Angermann M, et al. Clinical outcome and prognostic factors of patients with perimesencephalic and nonperimesencephalic subarachnoid hemorrhage. World Neurosurg. 2022;165:e512–e19. doi: 10.1016/j.wneu.2022.06.086. [DOI] [PubMed] [Google Scholar]
- 15.Chen H-L, Shen W-Q, Liu P. A meta-analysis to evaluate the predictive validity of the braden scale for pressure ulcer risk assessment in long-term care. Ostomy Wound Manage. 2016;62:20–28. [PubMed] [Google Scholar]
- 16.Seong-Hi P, Lee HS. Assessing predictive validity of pressure ulcer risk scales – a systematic review and meta-analysis. Iran J Public Health. 2016;45:122. [PMC free article] [PubMed] [Google Scholar]
- 17.Shaaban A, Saqqur M, Saleh A, et al. Retrospective analysis of the surgical management of spontaneous supratentorial intracerebral hemorrhage: A single-center study. Qatar Med J. 2021;2021:53. doi: 10.5339/qmj.2021.53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Greenberg SM, Ziai WC, Cordonnier C, et al. 2022 Guideline for the management of patients with spontaneous intracerebral hemorrhage: A guideline from the American Heart Association/American Stroke Association. Stroke. 2022;53:e282–e361. doi: 10.1161/STR.0000000000000407. [DOI] [PubMed] [Google Scholar]
- 19.Shoamanesh A, Patrice Lindsay M, Castellucci LA, et al. Canadian stroke best practice recommendations: Management of spontaneous intracerebral hemorrhage, update 2020. Int J Stroke. 2021;16:321–41. doi: 10.1177/1747493020968424. [DOI] [PubMed] [Google Scholar]
- 20.Baamer RM, Iqbal A, Lobo DN, et al. Utility of unidimensional and functional pain assessment tools in adult postoperative patients: A systematic review. Br J Anaesth. 2022;128:874–88. doi: 10.1016/j.bja.2021.11.032. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Ferreira-Valente MA, Pais-Ribeiro JL, Jensen MP. Validity of four pain intensity rating scales. Pain. 2011;152:2399–404. doi: 10.1016/j.pain.2011.07.005. [DOI] [PubMed] [Google Scholar]
- 22.Shafshak TS, Elnemr R. The visual analogue scale versus numerical rating scale in measuring pain severity and predicting disability in low back pain. J Clin Rheumatol. 2021;27:282–85. doi: 10.1097/RHU.0000000000001320. [DOI] [PubMed] [Google Scholar]
- 23.Bouchier B, Demarquay G, Dailler F, et al. Course of headaches and predictive factors associated with analgesia failure following spontaneous subarachnoid hemorrhage: A prospective cohort study. J Neurosurg Anesthesiol. 2023;35:333–37. doi: 10.1097/ANA.0000000000000843. [DOI] [PubMed] [Google Scholar]
- 24.Kwong E, Pang S, Wong T, et al. Predicting pressure ulcer risk with the modified Braden, Braden, and Norton scales in acute care hospitals in Mainland China. Appl Nurs Res. 2005;18:122–28. doi: 10.1016/j.apnr.2005.01.001. [DOI] [PubMed] [Google Scholar]
- 25.Liang HCJ. Master’s thesis. Faculty of Health and Environmental Sciences, Auckland University of Technology; NZ: 2022. Exploring effective pressure injury prevention practices in the paediatric intensive care setting: An integrative review. [Google Scholar]
- 26.Griswold LH, Griffin RL, Swain T, Kerby JD. Validity of the Braden Scale in grading pressure ulcers in trauma and burn patients. J Surg Res. 2017;219:151–57. doi: 10.1016/j.jss.2017.05.095. [DOI] [PubMed] [Google Scholar]
- 27.Finlayson O, Kapral M, Hall R, et al. Risk factors, inpatient care, and outcomes of pneumonia after ischemic stroke. Neurology. 2011;77:1338–45. doi: 10.1212/WNL.0b013e31823152b1. [DOI] [PubMed] [Google Scholar]
- 28.Ding Y, Ji Z, Liu Y, Niu J. Braden scale for predicting pneumonia after spontaneous intracerebral hemorrhage. Rev Assoc Med Bras (1992) 2022;68:904–11. doi: 10.1590/1806-9282.20211339. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Alsubaie FT, Almutary HH, Sofar SM. Assessing the risk factors of fall among hospitalized adult patients: A scoping review. Evidence-Based Nursing Research. 2020;2(4):14. [Google Scholar]
- 30.Ong E, Meseguer E, Guidoux C, et al. Prevalence and outcome of potential candidates for left atrial appendage closure after stroke with atrial fibrillation: WATCH-AF Registry. Stroke. 2020;51:2355–63. doi: 10.1161/STROKEAHA.120.029267. [DOI] [PubMed] [Google Scholar]
- 31.Subbe CP, Kruger M, Rutherford P, Gemmel L. Validation of a modified Early Warning Score in medical admissions. QJM. 2001;94:521–26. doi: 10.1093/qjmed/94.10.521. [DOI] [PubMed] [Google Scholar]
- 32.Mahmoodpoor A, Sanaie S, Saghaleini SH, et al. Prognostic value of National Early Warning Score and Modified Early Warning Score on intensive care unit readmission and mortality: A prospective observational study. Front Med (Lausanne) 2022;9:938005. doi: 10.3389/fmed.2022.938005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Knoery C, Barlas RS, Vart P, et al. Modified early warning score and risk of mortality after acute stroke. Clin Neurol Neurosurg. 2021;202:106547. doi: 10.1016/j.clineuro.2021.106547. [DOI] [PubMed] [Google Scholar]
- 34.McCracken DJ, Lovasik BP, McCracken CE, et al. The intracerebral hemorrhage score: A self-fulfilling prophecy? Neurosurgery. 2019;84:741–48. doi: 10.1093/neuros/nyy193. [DOI] [PubMed] [Google Scholar]
- 35.Garton ALA, Gupta VP, Sudesh S, et al. The Intracerebral Hemorrhage Score: Changing perspectives on mortality and disability. World Neurosurg. 2020;135:e573–e79. doi: 10.1016/j.wneu.2019.12.074. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.