Reliability of the intracerebral hemorrhage score for predicting outcome in patients with intracerebral hemorrhage using oral anticoagulants

The National Institutes of Health Stroke Scale is comparable to the ICH score in predicting outcomes in spontaneous acute intracerebral hemorrhage

Background

Validating the National Institutes of Health NIH Stroke Scale (NIHSS) as a tool to assess deficit severity and prognosis in patients with acute intracerebral hemorrhage would harmonize the assessment of intracerebral hemorrhage (ICH) and acute ischemic stroke (AIS) patients, enable clinical use of a readily implementable and non-imaging dependent prognostic tool, and improve monitoring of ICH care quality in administrative datasets.

Methods

Among randomized trial ICH patients, the relation between NIHSS scores early after Emergency Department arrival and 3-month outcomes of dependency or death (modified Rankin Scale, mRS 3-6) and case fatality was examined. NIHSS predictive performance was compared to a current standard prognostic scale, the intracerebral hemorrhage score (ICH score).

Results

Among the 384 patients, the mean age was 65 (±13), with 66% being male. The median NIHSS score was 16 (interquartile range (IQR) 9-25), the mean initial hematoma volume was 29 mL (±38), and the ICH score median was 1 (IQR 0-2). At 3 months, the mRS had a median of 4 (IQR 2-6), with dependency or death occurring in 70% and case fatality in 26%. The NIHSS and ICH scores were strongly correlated (r = 0.73), and each was strongly correlated with the 90-day mRS (NIHSS, r = 0.61; ICH score, r = 0.62). The NIHSS performed comparably to the ICH score in predicting both dependency or death (c = 0.80 vs. 0.80, p = 0.83) and case fatality (c = 0.78 vs. 0.80, p = 0.29). At threshold values, the NIHSS predicted dependency or death with 74.1% accuracy (NIHSS 17.5) and case fatality with 75.0% accuracy (NIHSS 18.5).

Conclusion

The NIHSS forecasts 3-month functional and case fatality outcomes with accuracy comparable to the ICH Score. Widely documented in routine clinical care and administrative data, the NIHSS can serve as a valuable measure for clinical prognostication, therapy development, and case-mix risk adjustment in ICH patients.Clinical trial registrationClinicaltrials.gov, NCT00059332.

Predictors of 30-Day Mortality for Surgically Treated Patients with Spontaneous Supratentorial Intracerebral Hemorrhage and Validation of the Surgical Swedish Intracerebral Hemorrhage Score: A Retrospective Single-Center Analysis of 136 Cases

OBJECTIVE

We aimed to identify independent risk factors of 30-day mortality in patients with surgically treated spontaneous supratentorial intracerebral hemorrhage (ICH), validate the Surgical Swedish ICH (SwICH) score within Polish healthcare system, and compare the SwICH score to the ICH score.

METHODS

We carried out a single-center retrospective analysis of the medical data juxtaposed with computed tomography scans of 136 ICH patients treated surgically between 2008 and 2022. Statistical analysis was performed using the same characteristics as in the SwICH score and the ICH score. Backward stepwise logistic regression with both 5-fold crossvalidation and 1000× bootstrap procedure was used to create new scoring system. Finally predictive potential of these scales were compared.

RESULTS

The most important predictors of 30-day mortality were: ICH volume (P < 0.01), Glasgow Coma Scale at admission (P < 0.01), anticoagulant status (P = 0.03), and age (P < 0.01). The SwICH score appears to have a better predictive potential than the ICH score, although this did not reach statistical significance [area under the curve {AUC}: 0.789 (95% confidence interval {CI}: 0.715-0.863) vs. AUC: 0.757 (95% CI: 0.677-0.837)]. Moreover, based on the analyzed characteristics, we developed our score (encompassing: age, ICH volume, anticoagulants status, Glasgow Coma Scale at admission), [AUC of 0.872 (95% CI: 0.815-0.929)]. This score was significantly better than previous ones.

CONCLUSIONS

Differences in health care systems seem to affect the accuracy of prognostic scales for patients with ICH, including possible differences in indications for surgery and postoperative care. Thus, it is important to validate assessment tools before they can be applied in a new setting and develop population-specific scores. This may improve the effectiveness of risk stratification in patients with ICH.

Independent Factors Associated with 30-Day In-Hospital Mortality from Acute Spontaneous Intracerebral Hemorrhage

OBJECTIVE

This study aims to investigate independent factors associated with 30-day mortality in patients with acute spontaneous intracerebral hemorrhage (SICH) before treatment.

METHODS

A retrospective analysis was performed on medical records of patients hospitalized with acute SICH between 2019 and 2021. Data included personal history, hospital stay duration, symptom onset, chief complaint, underlying diseases, medication, and alcohol/smoking habits. Physical examination records comprised baseline blood pressure, Glasgow Coma Scale assessment, and pupil reaction evaluation. Diagnostic imaging, specifically computed tomography brain scans, was examined for hemorrhage details. Multivariable logistic analysis was utilized for data analysis.

RESULTS

Among 663 cases, 185 (27.9%) experienced mortality. Risk factors for mortality included chronic kidney disease, ischemic heart disease, loss of follow-up in hypertension clinic, and pontine hemorrhage. Conversely, motor response (m), reactive pupils, and basal cistern persistence significantly decreased the risk of mortality in multivariable analysis. Receiver operating characteristic analysis identified a m score of 5 as the cutoff for predicting survival.

CONCLUSIONS

Chronic kidney disease, ischemic heart disease, loss of hypertension follow-up, m, reactive pupils, pontine hemorrhage, and basal cistern persistence were independent variables associated with the 30-day mortality rate in SICH patients before treatment initiation. A m, pupil reaction, and basal cistern persistence serve as predictive tools for assessing mortality in SICH before treatment.

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

We hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional outcome at mRS 2, 3, and 4. Prediction of survival (mRS ≤ 5) was compared to results of the ICH Score. All models were tuned, validated, and tested in a nested 5-fold cross-validation approach. Receiver-operating-characteristic area under the curve (ROC AUC) of the machine learning classifier using image features only was 0.80 (95% CI [0.77; 0.82]) for predicting mRS ≤ 2, 0.80 (95% CI [0.78; 0.81]) for mRS ≤ 3, and 0.79 (95% CI [0.77; 0.80]) for mRS ≤ 4. Trained on survival prediction (mRS ≤ 5), the classifier reached an AUC of 0.80 (95% CI [0.78; 0.82]) which was equivalent to results of the ICH Score. If combined, the integrated model showed a significantly higher AUC of 0.84 (95% CI [0.83; 0.86], P value <0.05). Accordingly, sensitivities were significantly higher at Youden Index maximum cut-offs (77% vs. 74% sensitivity at 76% specificity, P value <0.05). Machine learning–based evaluation of quantitative high-end image features provided the same discriminatory power in predicting functional outcome as multidimensional clinical scoring systems. The integration of conventional scores and image features had synergistic effects with a statistically significant increase in AUC.