Development of the Subdural Hematoma in the Elderly (SHE) score to predict mortality

Elizabeth N. Alford Department of Neurosurgery;

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Lauren E. Rotman Department of Neurosurgery;

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Matthew S. Erwood Department of Neurosurgery;

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Robert A. Oster Department of Medicine, Division of Preventive Medicine; and

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Matthew C. Davis Department of Neurosurgery;

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H. Bruce C. Pittman Department of Neurosurgery;

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H. Evan Zeiger Department of Neurology, University of Alabama at Birmingham, Alabama

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Winfield S. Fisher III Department of Neurosurgery;

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OBJECTIVE

The purpose of this study was to describe the development of a novel prognostic score, the Subdural Hematoma in the Elderly (SHE) score. The SHE score is intended to predict 30-day mortality in elderly patients (those > 65 years of age) with an acute, chronic, or mixed-density subdural hematoma (SDH) after minor, or no, prior trauma.

METHODS

The authors used the Prognosis Research Strategy group methods to develop the clinical prediction model. The training data set included patients with acute, chronic, and mixed-density SDH. Based on multivariate analyses from a large data set, in addition to review of the extant literature, 3 components to the score were selected: age, admission Glasgow Coma Scale (GCS) score, and SDH volume. Patients are given 1 point if they are over 80 years old, 1 point for an admission GCS score of 5–12, 2 points for an admission GCS score of 3–4, and 1 point for SDH volume > 50 ml. The sum of points across all categories determines the SHE score.

RESULTS

The 30-day mortality rate steadily increased as the SHE score increased for all SDH acuities. For patients with an acute SDH, the 30-day mortality rate was 3.2% for SHE score of 0, and the rate increased to 13.1%, 32.7%, 95.7%, and 100% for SHE scores of 1, 2, 3, and 4, respectively. The model was most accurate for acute SDH (area under the curve [AUC] = 0.94), although it still performed well for chronic (AUC = 0.80) and mixed-density (AUC = 0.87) SDH.

CONCLUSIONS

The SHE score is a simple clinical grading scale that accurately stratifies patients’ risk of mortality based on age, admission GCS score, and SDH volume. Use of the SHE score could improve counseling of patients and their families, allow for standardization of clinical treatment protocols, and facilitate clinical research studies in SDH.

ABBREVIATIONS

AUC = area under the curve; GCS = Glasgow Coma Scale; GOS = Glasgow Outcome Scale; NN = neural network; PROGRESS = Prognosis Research Strategy; SDH = subdural hematoma; SHE = Subdural Hematoma in the Elderly.

OBJECTIVE

The purpose of this study was to describe the development of a novel prognostic score, the Subdural Hematoma in the Elderly (SHE) score. The SHE score is intended to predict 30-day mortality in elderly patients (those > 65 years of age) with an acute, chronic, or mixed-density subdural hematoma (SDH) after minor, or no, prior trauma.

METHODS

The authors used the Prognosis Research Strategy group methods to develop the clinical prediction model. The training data set included patients with acute, chronic, and mixed-density SDH. Based on multivariate analyses from a large data set, in addition to review of the extant literature, 3 components to the score were selected: age, admission Glasgow Coma Scale (GCS) score, and SDH volume. Patients are given 1 point if they are over 80 years old, 1 point for an admission GCS score of 5–12, 2 points for an admission GCS score of 3–4, and 1 point for SDH volume > 50 ml. The sum of points across all categories determines the SHE score.

RESULTS

The 30-day mortality rate steadily increased as the SHE score increased for all SDH acuities. For patients with an acute SDH, the 30-day mortality rate was 3.2% for SHE score of 0, and the rate increased to 13.1%, 32.7%, 95.7%, and 100% for SHE scores of 1, 2, 3, and 4, respectively. The model was most accurate for acute SDH (area under the curve [AUC] = 0.94), although it still performed well for chronic (AUC = 0.80) and mixed-density (AUC = 0.87) SDH.

CONCLUSIONS

The SHE score is a simple clinical grading scale that accurately stratifies patients’ risk of mortality based on age, admission GCS score, and SDH volume. Use of the SHE score could improve counseling of patients and their families, allow for standardization of clinical treatment protocols, and facilitate clinical research studies in SDH.

In Brief

The authors describe the development of a scoring system to predict mortality due to subdural hematoma in elderly patients with a history of minor or no prior trauma. The Subdural Hematoma in the Elderly (SHE) score considers patient age, admission neurological status, and subdural hematoma size, and shows significant predictive ability.

Subdural hematoma (SDH) is a common cause of intracranial hemorrhage in elderly patients (> 65 years old), with high associated mortality rates.5,8,11,22,31,36,43 In 2015, the incidence of SDH in patients 20–64 years of age was 3.1–8.1 per 100,000 person-years, compared with 24.3–57.3 and 73–135.5 per 100,000 person-years for patients aged 65–74 and 75–89 years, respectively.8 Risk factors for SDH in elderly patients include higher fall rates, increased use of anticoagulant and antiplatelet medications, increased cerebral atrophy causing cortical vein stress that leads to higher hematoma risk, and comorbidities such as liver disease and hematological disease with greater risk of coagulopathy and subsequent hematoma development and expansion.5,8,15,18,26,31

SDH is becoming increasingly common in elderly patients and is expected to become the most common neurosurgical condition by 2030.2,8,26 Gaist et al. reported an overall increase in SDH rates from 2000 to 2015, with the highest increase seen in patients > 75 years of age, from 55.1 per 100,000 person-years to 99.7 per 100,000 person-years.8 The cause of the increase in SDH incidence in elderly patients is multifactorial. The world’s population is aging, and since the early 2000s, the number of people older than 60 years of age has increased by nearly 200 million.41 As the number of elderly patients increases, so, too, do the number of individuals who are at increased risk for developing SDH. Additionally, rates of antithrombotic and anticoagulant medication use are increasing, with elderly patients having higher overall rates of use of these medications.8,9 Studies have demonstrated that the use of antithrombotic medications, with our without simultaneous anticoagulant medication use, is associated with increased risk of SDH development and increased 1-year mortality.5,8,9,18

Clinical prediction models are critical in the process of translational medicine, as they can inform patients and their families about the probabilities of developing a certain disease state or outcome. Clinical prediction models, when built on solid data, can prove valuable in assisting patients, caregivers, and physicians in medical decision-making.

SDH among elderly patients often presents a decision-making dilemma to families and physicians. Therefore, the purpose of this paper is to describe the development of a clinical prediction model for elderly patients (> 65 years old) with SDH (the Subdural Hematoma in the Elderly [SHE] score). Our hypothesis is that a valuable clinical tool could be created that strikes a balance between accuracy in prediction and simplicity in use.

Methods

Though there are no standardized guidelines for the ideal development of a clinical prediction model, there are several reports detailing suggested methods. There are numerous different techniques for developing a prediction model, including logistic regression, C5.0, neural networks (NNs), m5ʹ, and classification and regression tree (CART) algorithms. In head-to-head comparisons utilizing the same data sets, there is no clearly superior technique.40 C5.0, NNs, and other machine-based algorithms are intended for use with complex, high-dimensional data (dozens to thousands of independent variables). These techniques have a tendency to overfit the data and perform best when training, tuning, and test data sets are available.6 Our data set is not high-dimensional and is not sufficiently large to be split into multiple data sets. The Prognosis Research Strategy (PROGRESS) group methods for prediction-model development, which are based on logistic regression, are specifically designed for use in health outcomes research.35 Furthermore, the PROGRESS group methods can be used for both categorical and continuous outcomes, unlike C5.0 and NN. For these reasons, we chose to utilize the PROGRESS methods. Additionally, we used the checklist of recommendations for reporting on prediction models developed by investigators on the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD). As defined in the PROGRESS series, a prognostic model “is a formal combination of multiple predictors from which risks of a specific endpoint can be calculated for individual patients.”35

The primary outcome of interest for this prediction model (SHE score) is mortality. The secondary outcome of interest is functional status as measured by the Glasgow Outcome Scale (GOS). The target population is elderly patients (> 65 years of age) who have a diagnosis of SDH and have a history of no or only minor trauma. Patients may have acute, chronic, or mixed-density SDH. The SHE score is not intended to be used for patients who have sustained a high-velocity trauma, defined as any motor vehicle collision, pedestrian struck, or fall from a height of 10 feet or greater. The SHE score will be used by physicians or other healthcare providers, though it may also prove valuable in clinical research.

For the development of the SHE score, we used a large and contemporary data set collected at our institution, the results of which have been published previously.15 This data set included 469 patients, 277 with acute SDH, 89 with chronic SDH, and 103 with mixed acuity SDH. Of note, SDH and contusion volumes are calculated using the A × B × C/2 method.37 As presented by Kuhn et al.,15 univariate analyses found that increasing age, admission Glasgow Coma Scale (GCS) score, aspirin use, admission INR (International Normalized Ratio), presence of a skull fracture, contusion volume > 10 ml, and SDH volume > 50 ml were significantly associated with mortality, length of stay, and/or poorer GOS score at discharge. On multivariate analyses, age > 80 years, admission GCS score, and contusion volume ≥ 10 ml remained significant predictors of mortality, while age > 80 years, admission GCS score, contusion volume ≥ 10 ml, SDH volume ≥ 50 ml, and antiplatelet use significantly affected functional outcome at discharge. A recent systematic review of studies examining predictors of outcome in elderly patients with acute SDH found that a low GCS score was the most consistently reported negative prognostic factor.7 Studies have also reported advancing age7,20,30 and SDH volume or thickness3,10,24 as negative prognostic factors.

For development of the SHE score, we initially considered all variables that were significantly predictive of mortality or functional outcome on multivariate analyses: age (HR 1.7–2.9), admission GCS (HR 10.9–333.3), contusion volume (HR 2.2–5.6), SDH volume (HR 1.2–2.6), and antiplatelet use (HR 0.6–0.8). Based on the handling of these variables in the training data set and research conventions,12,39 we chose to code these variables as age < 80 versus ≥ 80 years, GCS scores 3–4 versus 5–12 versus 13–15, and SDH volume < 50 versus ≥ 50 ml. While dichotomizing age and SDH volume may result in loss of some valuable predictive information, we felt that the benefit in terms of ease of use outweighed this risk. Variables were added one at a time in order of decreasing hazard ratios. There was one exception; SDH volume was considered before contusion volume. Only 14% of patients in the training data set had a contusion present, and we felt that SDH volume was clinically more relevant than contusion volume. The 3-component score (admission GCS score, age, and SDH volume) had very good to excellent discriminative ability across all 3 acuity groups; therefore, the 3-component score was selected for detailed analysis.

Results

The SHE scoring system is shown in Table 1. As expected based on the results of multivariate analysis of the training data set, admission GCS score had the greatest influence on outcome. Based on the results of our prior study, this model was tested separately based on the acuity of SDH. The distribution of SHE scores, along with respective 30-day mortality rates, is shown in Table 2.

TABLE 1.

Summary of SHE scoring

CriterionPoints
Age (years)
 <800
 ≥801
Admission GCS score
 3–42
 5–121
 13–150
SDH volume (ml)
 <500
 ≥501
TABLE 2.

SDH mortality by SHE score

SHE Score
Variable01234
Acute SDH (n = 277)
 No. of patients9499492312
 30-day mortality, no. (%)3 (3.2)13 (13.1)16 (32.7)22 (95.7)12 (100)
Chronic SDH (n = 89)
 No. of patients12492530
 30-day mortality, no. (%)1 (8.3)6 (12.2)5 (20)1 (33.3)
Mixed-acuity SDH (n = 103)
 No. of patients13572850
 30-day mortality, no. (%)5 (38.5)4 (7.0)18 (64.3)3 (60)

For patients with acute SDH, the 30-day mortality rate was 3.2% for those with a SHE score of 0, and the rate increased to 13.1%, 32.7%, 95.7%, and 100%, for SHE scores of 1, 2, 3, and 4, respectively. The receiver operative characteristic (ROC) curve for acute SDH is shown in Fig. 1. The area under the curve (AUC) was 0.94, demonstrating that the SHE score is an excellent test for mortality. The SHE score also effectively discriminates functional outcome (as measured by GOS score) in acute SDH (Fig. 2). GOS scores of 4 and 5 were considered a good functional outcome. The rate of good functional outcome was 77.7% for a SHE score of 0, 54.5% for a SHE score of 1, 32.7% for a SHE score of 2, 4.3% for a SHE score of 3, and 0% for a SHE score of 4 (chi-square test = 69.6, df = 4, p < 0.00001). Kaplan-Meier survival curves (Fig. 3) were significantly different based on SHE score in acute SDH using the log-rank test (chi-square = 199.4, df = 4, p < 0.00001). For a SHE score of 0, estimated survival rates were 97.8%, 93.5%, and 89.9% at 1, 3, and 6 months, respectively. For a SHE score of 1, estimated survival rates were 86.6%, 82.4%, and 77.9% at 1, 3, and 6 months, respectively. For a SHE score of 2, estimated survival rates were 67.3%, 58.8%, and 53.9% at 1, 3, and 6 months, respectively. For of a SHE score of 3, estimated survival rates were 13.0%, 13.0%, and 8.7% at 1, 3, and 6 months, respectively. For a SHE score of 4, no patients survived 30 days or more.

FIG. 1.
FIG. 1.

ROC curve for the SHE score demonstrating excellent predictive capacity in patients with acute SDH, with an AUC of 0.941. Figure is available in color online only.

FIG. 2.
FIG. 2.

Functional outcomes of acute SDH significantly worsen with increasing SHE score (p < 0.00001). The rate of good outcomes decreases from 77.7% for SHE score of 0 to 54.5%, 32.7%, 4.3%, and 0% for SHE scores of 1, 2, 3, and 4, respectively. Figure is available in color online only.

FIG. 3.
FIG. 3.

Using the Kaplan-Meier method, survival of patients with acute SDH varies significantly by SHE score (p < 0.00001). Figure is available in color online only.

For patients with chronic SDH, the 30-day mortality rate was 8.3% for a SHE score of 0, and the rates increased to 12.2%, 20%, and 33.3%, for SHE scores of 1, 2, and 3, respectively. No patient with a chronic SDH had a SHE score of 4. As shown in Fig. 4, the ROC curve had an AUC of 0.80.

FIG. 4.
FIG. 4.

ROC curve for the SHE score demonstrating significant predictive capacity in patients with chronic SDH, with an AUC of 0.80. Figure is available in color online only.

For patients with mixed-density SDH, the 30-day mortality rate was 38.5% for a SHE score of 0, and the rates varied to 7.0%, 64.3%, and 60%, for SHE scores of 1, 2, and 3, respectively. No patient with a mixed-density SDH had a SHE score of 4. The ROC curve for mixed-density SDH (Fig. 5) had an AUC of 0.87.

FIG. 5.
FIG. 5.

ROC curve for the SHE score demonstrating significant predictive capacity in mixed-density patients with SDH, with an AUC of 0.874. Figure is available in color online only.

Additional Models

Additional models were constructed to evaluate whether the addition of more variables could improve the predictive ability of the SHE score. Model 4 included contusion volume, while Model 5 included both contusion volume and antiplatelet use. For both models, 1 point was assigned for contusion volume ≥ 10 ml. Antiplatelet use had a mild protective effect on functional outcome, so for Model 5, 1 point was assigned if a patient was not using antiplatelet medications. The ability of these models to predict mortality was tested separately based on SDH acuity.

For acute SDH, the ROC curve had an AUC of 0.94 for Model 4 and 0.93 for Model 5. For chronic SDH, the ROC curve had an AUC of 0.77 for Model 4 and 0.78 for Model 5. For mixed-density SDH, the ROC curve had an AUC of 0.69 for Model 4 and 0.67 for Model 5. The addition of contusion volume and/or antiplatelet use did not improve the discriminative ability of the SHE score.

Discussion

Prognostication is a critical aspect of patient-centered care and can aid physicians, patients, and caregivers in medical decision-making. It also standardizes communication among clinicians and documentation for research purposes. While prognostic grading schemes exist for intracerebral hemorrhage,12 ischemic stroke,25,34 traumatic brain injury,21,32 and subarachnoid hemorrhage,13,28,33 there is no analogous model in SDH. SDHs are becoming increasingly more common, especially among elderly patients, and have been suggested to be a so-called “sentinel event,” presaging a rapid decline in health. However, because SDH is a heterogeneous disease state, a clinical prediction model that could accurately identify those patients at highest risk of poor outcome would be clinically useful.

Abouzari et al. used artificial neural networks to predict outcome after surgery for chronic SDH.1 In their training data set, low admission GCS score and cerebral atrophy were significantly associated with an unfavorable outcome. Using a 4-layer artificial neural network, acceptable predictive capability (AUC = 0.767) was achieved. Recently, Kwon et al. developed a prognostic scoring system for chronic SDH that considers age, amount of midline shift, hematoma thickness, and neurological status on admission.16 There are six separate score components with a composite score ranging from 3 to 13; the score showed excellent outcome discrimination (AUC = 0.95). At present, there is no widely accepted prognostic scoring system for SDH. Currently available scoring systems rely on complex mathematical processes and/or have numerous components, thus making them difficult or impractical to use. We therefore sought to develop an easy-to-use, accurate clinical prediction tool for mortality among elderly patients (> 65 years) with SDH and a history of only minor (or no) trauma. The purpose of such a grading score is to provide a standardized metric that can be easily and simply calculated using patient characteristics (age), a baseline neuroimaging feature (SDH volume), and neurological status (admission GCS score). Ideally, the tool can be utilized both by specialists and nonspecialists alike.

The components of the SHE score warrant evaluation. Poor admission GCS score is the most consistent predictor of poor outcome in cases of SDH and has been shown to be so almost universally in studies on the topic.1,7,14,17,38,42 Increasing age has been repeatedly demonstrated to be associated with unfavorable functional outcome and increased mortality in SDH.4,16,23,42 However, it is not universally seen, as an analysis of patients with acute SDH found no effect of age on outcome.17 Other studies focus on frailty as opposed to simple chronological age, though those data are limited.7 SDH size has been studied in a number of ways, from thickness as measured on axial or coronal imaging, degree of midline shift, calculated volume, or measured volume using software tools.16,29,44–46 Several studies reported a significant associate between preoperative SDH size and poor outcome.16,27,46 We use the A × B × C/2 method to calculate SDH volume, which has been previously validated for this application.37,44 However, the A × B × C/2 method has been criticized as imprecise due to the assumption that SDHs are ellipsoid in shape.19

Due to disease heterogeneity, we evaluated the SHE score in separate groups based on SDH acuity. With our data set, the SHE score seems to have the best discriminative ability in acute SDH, though it also demonstrated acceptable levels of discrimination for chronic and mixed-density SDH. Significantly fewer patients in the training data set had chronic or mixed-density SDHs, which may have influenced the performance of the SHE score in these subsets. Independent validation based on SDH acuity—acute versus chronic versus mixed density—will be especially important to determine in which population(s) the SHE score is most useful.

Limitations

We used a large and contemporary data set in the development of the SHE score. The data set was retrospectively collected, with death dates confirmed by Social Security records. The data set is from a single US institution; therefore, the validity of the SHE score may not be generalizable to sufficiently different populations. Independent data from multiple centers offers a natural opportunity to validate the SHE score in a large population. As with any clinical tool, reliability among users and variation in model accuracy warrant exploration. Additionally, the training data set includes only SDHs that resulted from low-velocity trauma or in the absence of trauma. The SHE score does not predict mortality from SDH after a high-velocity traumatic injury, and care should be taken to use it in only appropriate patients. The dichotomization of continuous variables and subgrouping of admission GCS scores may be suboptimal. These chosen cut-points should be evaluated, and potentially fine-tuned, during external validation. Additionally, dichotomization of continuous variables results in loss of statistical power and allows for persistence of confounding; furthermore, the creation of cut-points introduces bias. The ideal statistical handling of continuous variables is countered by the pragmatic advantage of categorization in clinical and therapeutic settings.

Conclusions

The SHE score is simple and predicts mortality for patients with SDH. The SHE score has the best discriminative ability for acute SDH, although it also performed well in cases of chronic and mixed-density SDH. Following requisite external validation and evaluation of reliability, the SHE score may be a valuable tool not only as a framework for medical decision-making but also for clinical research in SDH.

Acknowledgments

Dr. Alford completed this work as a UAB Department of Neurosurgery Women’s Leadership Council Clinical Research Scholar.

Disclosures

The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Author Contributions

Conception and design: Alford. Acquisition of data: Alford, Erwood, Davis, Pittman, Zeiger. Analysis and interpretation of data: Alford. Drafting the article: Alford, Rotman. Critically revising the article: Alford, Rotman, Erwood, Oster, Davis. Reviewed submitted version of manuscript: all authors. Approved the final version of the manuscript on behalf of all authors: Alford. Statistical analysis: Oster. Study supervision: Fisher.

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    Oshiro EM, Walter KA, Piantadosi S, Witham TF, Tamargo RJ: A new subarachnoid hemorrhage grading system based on the Glasgow Coma Scale: a comparison with the Hunt and Hess and World Federation of Neurological Surgeons Scales in a clinical series. Neurosurgery 41:140148, 1997

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29

    Powers AY, Pinto MB, Aldridge AM, Tang OY, Chen JS, Berube RL, et al.: Factors associated with the progression of conservatively managed acute traumatic subdural hemorrhage. J Crit Care 48:243250, 2018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30

    Prasad GL, Anmol N, Menon GR: Outcome of traumatic brain injury in the elderly population: a tertiary center experience in a developing country. World Neurosurg 111:e228e234, 2018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31

    Raj R, Mikkonen ED, Kivisaari R, Skrifvars MB, Korja M, Siironen J: Mortality in elderly patients operated for an acute subdural hematoma: a surgical case series. World Neurosurg 88:592597, 2016

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32

    Sadaka F, Patel D, Lakshmanan R: The FOUR score predicts outcome in patients after traumatic brain injury. Neurocrit Care 16:95101, 2012

  • 33

    Sano H, Satoh A, Murayama Y, Kato Y, Origasa H, Inamasu J, et al.: Modified World Federation of Neurosurgical Societies subarachnoid hemorrhage grading system. World Neurosurg 83:801807, 2015

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34

    Saposnik G, Kapral MK, Liu Y, Hall R, O’Donnell M, Raptis S, et al.: Iscore: a risk score to predict death early after hospitalization for an acute ischemic stroke. Circulation 123:739749, 2011

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35

    Steyerberg EW, Moons KGM, van der Windt DA, Hayden JA, Perel P, Schroter S, et al.: Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 10:e1001381, 2013

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36

    Stippler M, Ramirez P, Berti A, Macindoe C, Villalobos N, Murray-Krezan C: Chronic subdural hematoma patients aged 90 years and older. Neurol Res 35:243246, 2013

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37

    Sucu HK, Gokmen M, Gelal F: The value of XYZ/2 technique compared with computer-assisted volumetric analysis to estimate the volume of chronic subdural hematoma. Stroke 36:9981000, 2005

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38

    Szczygielski J, Gund SM, Schwerdtfeger K, Steudel WI, Oertel J: Factors affecting outcome in treatment of chronic subdural hematoma in ICU patients: impact of anticoagulation. World Neurosurg 92:426433, 2016

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39

    Teasdale G, Maas A, Lecky F, Manley G, Stocchetti N, Murray G: The Glasgow Coma Scale at 40 years: standing the test of time. Lancet Neurol 13:844854, 2014

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40

    Thombre A: Comparing logistic regression, neural networks, c5.0 and M5ʹ classification techniques, in Perner P (ed): Proceedings of the 8th International Conference on Machine Learning and Data Mining in Pattern Recognition. Berlin: Springer, 2012, Vol 7376, pp 132140

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41

    United Nations Population Fund: Ageing in the Twenty-First Century: A Celebration and a Challenge. New York: UNFPA, 2012 (https://www.unfpa.org/publications/ageing-twenty-first-century) [Accessed January 31, 2019]

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42

    Weimer JM, Gordon E, Frontera JA: Predictors of functional outcome after subdural hematoma: a prospective study. Neurocrit Care 26:7079, 2017

  • 43

    Whitehouse KJ, Jeyaretna DS, Enki DG, Whitfield PC: Head injury in the elderly: what are the outcomes of neurosurgical care? World Neurosurg 94:493500, 2016

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44

    Won SY, Zagorcic A, Dubinski D, Quick-Weller J, Herrmann E, Seifert V, et al.: Excellent accuracy of ABC/2 volume formula compared to computer-assisted volumetric analysis of subdural hematomas. PLoS One 13:e0199809, 2018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45

    Won YD, Na MK, Ryu JI, Cheong JH, Kim JM, Kim CH, et al.: Radiologic factors predicting deterioration of mental status in patients with acute traumatic subdural hematoma. World Neurosurg 111:e120e134, 2018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46

    Yan C, Yang MF, Huang YW: A reliable nomogram model to predict the recurrence of chronic subdural hematoma after burr hole surgery. World Neurosurg 118:e356e366, 2018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand

Illustration from Ivan et al. (pp 1517–1528). Copyright Kenneth Probst. Published with permission.

  • FIG. 1.

    ROC curve for the SHE score demonstrating excellent predictive capacity in patients with acute SDH, with an AUC of 0.941. Figure is available in color online only.

  • FIG. 2.

    Functional outcomes of acute SDH significantly worsen with increasing SHE score (p < 0.00001). The rate of good outcomes decreases from 77.7% for SHE score of 0 to 54.5%, 32.7%, 4.3%, and 0% for SHE scores of 1, 2, 3, and 4, respectively. Figure is available in color online only.

  • FIG. 3.

    Using the Kaplan-Meier method, survival of patients with acute SDH varies significantly by SHE score (p < 0.00001). Figure is available in color online only.

  • FIG. 4.

    ROC curve for the SHE score demonstrating significant predictive capacity in patients with chronic SDH, with an AUC of 0.80. Figure is available in color online only.

  • FIG. 5.

    ROC curve for the SHE score demonstrating significant predictive capacity in mixed-density patients with SDH, with an AUC of 0.874. Figure is available in color online only.

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    McNett M, Amato S, Gianakis A, Grimm D, Philippbar SA, Belle J, et al.: The FOUR score and GCS as predictors of outcome after traumatic brain injury. Neurocrit Care 21:5257, 2014

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    Moussa WMM, Khedr WM, Elwany AH: Prognostic significance of hematoma thickness to midline shift ratio in patients with acute intracranial subdural hematoma: a retrospective study. Neurosurg Rev 41:483488, 2018

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    Ntaios G, Faouzi M, Ferrari J, Lang W, Vemmos K, Michel P: An integer-based score to predict functional outcome in acute ischemic stroke: the ASTRAL score. Neurology 78:19161922, 2012

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    Okano A, Oya S, Fujisawa N, Tsuchiya T, Indo M, Nakamura T, et al.: Analysis of risk factors for chronic subdural haematoma recurrence after burr hole surgery: optimal management of patients on antiplatelet therapy. Br J Neurosurg 28:204208, 2014

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    Orlando A, Levy AS, Rubin BA, Tanner A, Carrick MM, Lieser M, et al.: Isolated subdural hematomas in mild traumatic brain injury. Part 1: the association between radiographic characteristics and neurosurgical intervention. J Neurosurg [epub ahead of print June 15, 2018; DOI: 10.3171/2018.1.JNS171884]

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  • 28

    Oshiro EM, Walter KA, Piantadosi S, Witham TF, Tamargo RJ: A new subarachnoid hemorrhage grading system based on the Glasgow Coma Scale: a comparison with the Hunt and Hess and World Federation of Neurological Surgeons Scales in a clinical series. Neurosurgery 41:140148, 1997

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29

    Powers AY, Pinto MB, Aldridge AM, Tang OY, Chen JS, Berube RL, et al.: Factors associated with the progression of conservatively managed acute traumatic subdural hemorrhage. J Crit Care 48:243250, 2018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30

    Prasad GL, Anmol N, Menon GR: Outcome of traumatic brain injury in the elderly population: a tertiary center experience in a developing country. World Neurosurg 111:e228e234, 2018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31

    Raj R, Mikkonen ED, Kivisaari R, Skrifvars MB, Korja M, Siironen J: Mortality in elderly patients operated for an acute subdural hematoma: a surgical case series. World Neurosurg 88:592597, 2016

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32

    Sadaka F, Patel D, Lakshmanan R: The FOUR score predicts outcome in patients after traumatic brain injury. Neurocrit Care 16:95101, 2012

  • 33

    Sano H, Satoh A, Murayama Y, Kato Y, Origasa H, Inamasu J, et al.: Modified World Federation of Neurosurgical Societies subarachnoid hemorrhage grading system. World Neurosurg 83:801807, 2015

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34

    Saposnik G, Kapral MK, Liu Y, Hall R, O’Donnell M, Raptis S, et al.: Iscore: a risk score to predict death early after hospitalization for an acute ischemic stroke. Circulation 123:739749, 2011

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35

    Steyerberg EW, Moons KGM, van der Windt DA, Hayden JA, Perel P, Schroter S, et al.: Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 10:e1001381, 2013

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36

    Stippler M, Ramirez P, Berti A, Macindoe C, Villalobos N, Murray-Krezan C: Chronic subdural hematoma patients aged 90 years and older. Neurol Res 35:243246, 2013

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37

    Sucu HK, Gokmen M, Gelal F: The value of XYZ/2 technique compared with computer-assisted volumetric analysis to estimate the volume of chronic subdural hematoma. Stroke 36:9981000, 2005

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38

    Szczygielski J, Gund SM, Schwerdtfeger K, Steudel WI, Oertel J: Factors affecting outcome in treatment of chronic subdural hematoma in ICU patients: impact of anticoagulation. World Neurosurg 92:426433, 2016

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39

    Teasdale G, Maas A, Lecky F, Manley G, Stocchetti N, Murray G: The Glasgow Coma Scale at 40 years: standing the test of time. Lancet Neurol 13:844854, 2014

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40

    Thombre A: Comparing logistic regression, neural networks, c5.0 and M5ʹ classification techniques, in Perner P (ed): Proceedings of the 8th International Conference on Machine Learning and Data Mining in Pattern Recognition. Berlin: Springer, 2012, Vol 7376, pp 132140

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41

    United Nations Population Fund: Ageing in the Twenty-First Century: A Celebration and a Challenge. New York: UNFPA, 2012 (https://www.unfpa.org/publications/ageing-twenty-first-century) [Accessed January 31, 2019]

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42

    Weimer JM, Gordon E, Frontera JA: Predictors of functional outcome after subdural hematoma: a prospective study. Neurocrit Care 26:7079, 2017

  • 43

    Whitehouse KJ, Jeyaretna DS, Enki DG, Whitfield PC: Head injury in the elderly: what are the outcomes of neurosurgical care? World Neurosurg 94:493500, 2016

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44

    Won SY, Zagorcic A, Dubinski D, Quick-Weller J, Herrmann E, Seifert V, et al.: Excellent accuracy of ABC/2 volume formula compared to computer-assisted volumetric analysis of subdural hematomas. PLoS One 13:e0199809, 2018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45

    Won YD, Na MK, Ryu JI, Cheong JH, Kim JM, Kim CH, et al.: Radiologic factors predicting deterioration of mental status in patients with acute traumatic subdural hematoma. World Neurosurg 111:e120e134, 2018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46

    Yan C, Yang MF, Huang YW: A reliable nomogram model to predict the recurrence of chronic subdural hematoma after burr hole surgery. World Neurosurg 118:e356e366, 2018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation

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