A Comparison of the Intracerebral Hemorrhage Score and the Acute Physiology and Chronic Health Evaluation II Score for 30-Day Mortality Prediction in Spontaneous Intracerebral Hemorrhage

Guidelines for Neuroprognostication in Critically Ill Adults with Intracerebral Hemorrhage

BACKGROUND

The objective of this document is to provide recommendations on the formal reliability of major clinical predictors often associated with intracerebral hemorrhage (ICH) neuroprognostication.

METHODS

A narrative systematic review was completed using the Grading of Recommendations Assessment, Development, and Evaluation methodology and the Population, Intervention, Comparator, Outcome, Timing, Setting questions. Predictors, which included both individual clinical variables and prediction models, were selected based on clinical relevance and attention in the literature. Following construction of the evidence profile and summary of findings, recommendations were based on Grading of Recommendations Assessment, Development, and Evaluation criteria. Good practice statements addressed essential principles of neuroprognostication that could not be framed in the Population, Intervention, Comparator, Outcome, Timing, Setting format.

RESULTS

Six candidate clinical variables and two clinical grading scales (the original ICH score and maximally treated ICH score) were selected for recommendation creation. A total of 347 articles out of 10,751 articles screened met our eligibility criteria. Consensus statements of good practice included deferring neuroprognostication-aside from the most clinically devastated patients-for at least the first 48-72 h of intensive care unit admission; understanding what outcomes would have been most valued by the patient; and counseling of patients and surrogates whose ultimate neurological recovery may occur over a variable period of time. Although many clinical variables and grading scales are associated with ICH poor outcome, no clinical variable alone or sole clinical grading scale was suggested by the panel as currently being reliable by itself for use in counseling patients with ICH and their surrogates, regarding functional outcome at 3 months and beyond or 30-day mortality.

CONCLUSIONS

These guidelines provide recommendations on the formal reliability of predictors of poor outcome in the context of counseling patients with ICH and surrogates and suggest broad principles of neuroprognostication. Clinicians formulating their judgments of prognosis for patients with ICH should avoid anchoring bias based solely on any one clinical variable or published clinical grading scale.

Scoping Review and Commentary on Prognostication for Patients with Intracerebral Hemorrhage with Advances in Surgical Techniques

INTRODUCTION

The intracerebral hemorrhage (ICH) score provides an estimate of 30-day mortality for patients with intracerebral hemorrhage in order to guide research protocols and clinical decision making. Several variations of such scoring systems have attempted to optimize its prognostic value. More recently, minimally invasive surgical techniques are increasingly being used with promising results. As more patients become candidates for surgical intervention, there is a need to re-discuss the best methods for predicting outcomes with or without surgical intervention.

METHODS

We systematically performed a scoping review with a comprehensive literature search by two independent reviewers using the PubMed and Cochrane databases for articles pertaining to the "intracerebral hemorrhage score." Relevant articles were selected for analysis and discussion of potential modifications to account for increasing surgical indications.

RESULTS

A total of 64 articles were reviewed in depth and identified 37 clinical grading scales for prognostication of spontaneous intracerebral hemorrhage. The original ICH score remains the most widely used and validated. Various authors proposed modifications for improved prognostic accuracy, though no single scale showed consistent superiority. Most recently, scales to account for advances in surgical techniques have been developed but lack external validation.

CONCLUSION

We provide the most comprehensive review to date of prognostic grading scales for patients with intracerebral hemorrhage. Current prognostic tools for patients with intracerebral hemorrhage remain limited and may overestimate risk of a poor outcome. As minimally invasive surgical techniques are developed, prognostic scales should account for surgical candidacy and outcomes.

Mortality Prediction in Cerebral Hemorrhage Patients Using Machine Learning Algorithms in Intensive Care Units

Objectives: This study aims to investigate whether the machine learning algorithms could provide an optimal early mortality prediction method compared with other scoring systems for patients with cerebral hemorrhage in intensive care units in clinical practice.

Methods: Between 2008 and 2012, from Intensive Care III (MIMIC-III) database, all cerebral hemorrhage patients monitored with the MetaVision system and admitted to intensive care units were enrolled in this study. The calibration, discrimination, and risk classification of predicted hospital mortality based on machine learning algorithms were assessed. The primary outcome was hospital mortality. Model performance was assessed with accuracy and receiver operating characteristic curve analysis.

Results: Of 760 cerebral hemorrhage patients enrolled from MIMIC database [mean age, 68.2 years (SD, ±15.5)], 383 (50.4%) patients died in hospital, and 377 (49.6%) patients survived. The area under the receiver operating characteristic curve (AUC) of six machine learning algorithms was 0.600 (nearest neighbors), 0.617 (decision tree), 0.655 (neural net), 0.671(AdaBoost), 0.819 (random forest), and 0.725 (gcForest). The AUC was 0.423 for Acute Physiology and Chronic Health Evaluation II score. The random forest had the highest specificity and accuracy, as well as the greatest AUC, showing the best ability to predict in-hospital mortality.

Conclusions: Compared with conventional scoring system and the other five machine learning algorithms in this study, random forest algorithm had better performance in predicting in-hospital mortality for cerebral hemorrhage patients in intensive care units, and thus further research should be conducted on random forest algorithm.

Gap Analysis Regarding Prognostication in Neurocritical Care: A Joint Statement from the German Neurocritical Care Society and the Neurocritical Care Society

Background/Objective

Prognostication is a routine part of the delivery of neurocritical care for most patients with acute neurocritical illnesses. Numerous prognostic models exist for many different conditions. However, there are concerns about significant gaps in knowledge regarding optimal methods of prognostication.

Methods

As part of the Arbeitstagung NeuroIntensivMedizin meeting in February 2018 in Würzburg, Germany, a joint session on prognostication was held between the German NeuroIntensive Care Society and the Neurocritical Care Society. The purpose of this session was to provide presentations and open discussion regarding existing prognostic models for eight common neurocritical care conditions (aneurysmal subarachnoid hemorrhage, intracerebral hemorrhage, acute ischemic stroke, traumatic brain injury, traumatic spinal cord injury, status epilepticus, Guillain–Barré Syndrome, and global cerebral ischemia from cardiac arrest). The goal was to develop a qualitative gap analysis regarding prognostication that could help inform a future framework for clinical studies and guidelines.

Results

Prognostic models exist for all of the conditions presented. However, there are significant gaps in prognostication in each condition. Furthermore, several themes emerged that crossed across several or all diseases presented. Specifically, the self-fulfilling prophecy, lack of accounting for medical comorbidities, and absence of integration of in-hospital care parameters were identified as major gaps in most prognostic models.

Conclusions

Prognostication in neurocritical care is important, and current prognostic models are limited. This gap analysis provides a summary assessment of issues that could be addressed in future studies and evidence-based guidelines in order to improve the process of prognostication.

Comparison of Conventional Intensive Care Scoring Systems and Prognostic Scores Specific for Intracerebral Hemorrhage in Predicting One-Year Mortality

Background

Specific prognostic models for intracerebral hemorrhage (ICH) have short and simple features, whereas intensive care unit (ICU) severity scales include more complicated parameters. Even though newly developed ICU severity scales have disease-specific properties, they still lack radiologic parameters, which is crucial for ICH.

Aims

To compare the performance of the Simplified Acute Physiology Score (SAPS) III, Acute Physiology and Chronic Health Evaluation (APACHE) IV, Logistic Organ Dysfunction Score (LODS), ICH, max-ICH, ICH functional outcome score (ICH-FOS), and Essen-ICH for prediction of in-hospital and one-year mortality of patients with ICH.

Methods

A single-center analysis of 137 patients with ICH was conducted over 5 years. The performance of scoring systems was evaluated with receiver operating characteristic analysis. The independent predictors of one-year mortality were investigated with a multivariate logistic regression analysis. The SAPS-III score was calculated both in the emergency department (ED) and ICU.

Results

Among the independent variables, the need for mechanical ventilation, hematoma volume, the presence of intraventricular hemorrhage, and hematoma originating from both lobar and nonlobar regions were found as the strongest predictor of one-year mortality. For in-hospital mortality, the discriminative power of SAPS-II, APACHE-IV, and LODS was excellent, and for SAPS-III-ICU and SAPS-III-ED, it was good. For one-year mortality, the discriminative power of SAPS-II, APACHE-IV, LODS, and SAPS-III-ICU was good, and for SAPS-III-ED, Essen-ICH, ICH, max-ICH, and ICH-FOS, it was fair.

Conclusions

Although all three ICH-specific prognostic scales performed satisfactory results for predicting one-year mortality, the common intensive care severity scoring showed better performance. SAPS-III scores may be recommended for use in EDs after proper customization.

Electronic supplementary material

The online version of this article (10.1007/s12028-020-00987-3) contains supplementary material, which is available to authorized users.

Determining and Comparing Predictive and Intensity Value of Severity Scores - "Sequential Organ Failure Assessment Score," "Acute Physiology and Chronic Health Evaluation 4," and "Poisoning Severity Score" - in Short-Term Clinical Outcome of Patients with Poisoning in an ICU

Introduction:

Today, poisoning is one of the problems of society and it is always one of the ten leading causes of death among youth. This study aimed to determine and compare the predictive and intensity value of three standard criteria of “Sequential Organ Failure Assessment (SOFA) score,” “Acute Physiology and Chronic Health Evaluation (APACHE) 4,” and “Poisoning Severity Score (PSS)” in short-term clinical outcome of poisoned patients.

Methods:

The prospective study conducted on 120 patients of critical care units. Data were collected using a demographic form and three criteria forms. The researcher was visiting the critical care unit daily and was filling out the demographic form of each patient in the first 24 h of hospital admission. The data were analyzed using SPSS version 16.

Results:

The results showed the mean age of patients was 35.73 ± 18.46 years with the most frequency among male patients (66.7%). The mean of criteria scores of “SOFA score,” “APACHE 4,” and “PSS” was 7.3 ± 2.97, P = 0.009; 62.43 ± 12.48, P = 0.58; and 2.4 ± 0.5, P = 0.001, respectively. The accuracy, sensitivity, specificity, positive and negative predictive values, and area under the curve of “SOFA score,” “APACHE 4,” and “PSS” were 86.2, 70.6, 94.4, 98.6, 36.2, 0.897; 83.5, 90.2, 44.4, 90.2, 44.4, 0.808; and 16.7, 100, 2, 100, 15.3, 0.786, respectively. Predicted mortality rate in “SOFA score” and “APACHE 4” was 18.7% ±20.2% and 2.63% ±2.6%, respectively. Real mortality rate, predictive duration of hospitalization by APACHE 4 criteria, and real duration of hospitalization were 15%, 1.79 ± 1.35, and 4.04 ± 4.08, respectively.

Conclusion:

The study showed that “SOFA score” was more predictive in clinical outcomes due to poisoning and it is recommended to poisoning centers as effective criteria.