Secondary Logo

Journal Logo

Early Prediction of Prognosis in Elderly Acute Stroke Patients

Bautista, Alexander F., MD1; Lenhardt, Rainer, MD, MBA2,3; Yang, Dongsheng, MS4; Yu, Changhong, MS4; Heine, Michael F., MD2,3; Mascha, Edward J., PhD5; Heine, Cate6; Neyer, Thomas M., MD7; Remmel, Kerri, MD, PhD8,9; Akca, Ozan, MD, FCCM2,10

Critical Care Explorations: April 2019 - Volume 1 - Issue 4 - p e0007
doi: 10.1097/CCE.0000000000000007
Original Clinical Report
Free
SDC

Objectives: Acute stroke has a high morbidity and mortality in elderly population. Baseline confounding illnesses, initial clinical examination, and basic laboratory tests may impact prognostics. In this study, we aimed to establish a model for predicting in-hospital mortality based on clinical data available within 12 hours of hospital admission in elderly (≥ 65 age) patients who experienced stroke.

Design: Retrospective observational cohort study.

Setting: Academic comprehensive stroke center.

Patients: Elderly acute stroke patients—2005–2009 (n = 462), 2010–2012 (n = 122), and 2016–2017 (n = 123).

Interventions: None.

Measurements and Main Results: After institutional review board approval, we retrospectively queried elderly stroke patients’ data from 2005 to 2009 (training dataset) to build a model to predict mortality. We designed a multivariable logistic regression model as a function of baseline severity of illness and laboratory tests, developed a nomogram, and applied it to patients from 2010 to 2012. Due to updated guidelines in 2013, we revalidated our model (2016–2017). The final model included stroke type (intracerebral hemorrhage vs ischemic stroke: odds ratio [95% CI] of 0.92 [0.50–1.68] and subarachnoid hemorrhage vs ischemic stroke: 1.0 [0.40–2.49]), year (1.01 [0.66–1.53]), age (1.78 [1.20–2.65] per 10 yr), smoking (8.0 [2.4–26.7]), mean arterial pressure less than 60 mm Hg (3.08 [1.67–5.67]), Glasgow Coma Scale (0.73 [0.66–0.80] per 1 point increment), WBC less than 11 K (0.31 [0.16–0.60]), creatinine (1.76 [1.17–2.64] for 2 vs 1), congestive heart failure (2.49 [1.06–5.82]), and warfarin (2.29 [1.17–4.47]). In summary, age, smoking, congestive heart failure, warfarin use, Glasgow Coma Scale, mean arterial pressure less than 60 mm Hg, admission WBC, and creatinine levels were independently associated with mortality in our training cohort. The model had internal area under the curve of 0.83 (0.79–0.89) after adjustment for over-fitting, indicating excellent discrimination. When applied to the test data from 2010 to 2012, the nomogram accurately predicted mortality with area under the curve of 0.79 (0.71–0.87) and scaled Brier’s score of 0.17. Revalidation of the same model in the recent dataset from 2016 to 2017 confirmed accurate prediction with area under the curve of 0.83 (0.75–0.91) and scaled Brier’s score of 0.27.

Conclusions: Baseline medical problems, clinical severity, and basic laboratory tests available within the first 12 hours of admission provided strong independent predictors of in-hospital mortality in elderly acute stroke patients. Our nomogram may guide interventions to improve acute care of stroke.

1Department of Anesthesiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK.

2Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY.

3Neuroscience ICU, University of Louisville, Hospital, Louisville, KY.

4Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH.

5Departments of Quantitative Health Sciences and Outcomes Research, Cleveland Clinic, Cleveland, OH.

6Institute for Data Systems and Society, Centre College, Danville, KY.

7Department of Anesthesiology, University of Cincinnati, Cincinnati, OH.

8Department of Neurology, University of Louisville, Louisville, KY.

9Comprehensive Stroke Center, University of Louisville Hospital, Louisville, KY.

10Neuroscience ICU & Comprehensive Stroke Center, UofL Hospital, Louisville, KY.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals.lww.com/ccejournal).

Institutional Review Board Approval Status: This study was approved by the Human Studies Committee of University of Louisville (institutional review board number 13.0396). Due to the retrospective nature of the study and because there was no reason for reporting individual person’s data, informed consent was not required.

Address requests for reprints to: Ozan Akca, MD, FCCM, Comprehensive Stroke Center, 530 S. Jackson Street, University Hospital, Louisville, KY 40202. E-mail: ozan.akca@louisville.edu

Elderly population is defined as 65 years old and older. Recent data showed that the size of this age group has reached at 13.2% of U.S. population and expected to surpass 20% in the year 2030 (1). Impact of acute and critical care admissions remain a major concern in elderly patients (2–5). Several recent studies focused on older age, elderly’ baseline medical history, and admission primary diagnoses’ contribution to mortality in the acute care setting (6–8). Being informed about severity status of elderly in acute care setting may enable tailored decision-making and prevent mortality.

Stroke affects about 800,000 people per year in the United States, accounting for 1.7% of national health expenditures, and it is the fifth leading cause of death in the United States (9). Within the stroke types, about 87% are ischemic strokes (ISs), 10% intracerebral hemorrhages (ICHs), and 3% subarachnoid hemorrhages (SAHs) (10). These ratios are likely different in the elderly population, and severe stroke as well as hypertensive ICH are more common (11–15).

Assessment of real-time physiologic variables and the impact of baseline confounding medical problems on existing organ functionality were shown to be the most effective ways to measure acute severity status and form management plans (16). Several severity and prognostic assessment scales and models have been developed (17–23), but their complexity and lack of validation limit their clinical use. Therefore, we aimed to build and validate a real-time prognostic tool, which would allow us to predict the prognosis and mortality of our elderly acute stroke patients as early as in the first 12 hours of admission.

Back to Top | Article Outline

METHODS

Patients

After obtaining approval from the Human Studies Committee (institutional review board number 13.0396), we included patients aging 65 years old and older, who were diagnosed with stroke, and admitted between the years 2005–2009 (training dataset). In this retrospective observational cohort study, a recently extended definition of stroke was used, which included IS, ICH, and nontraumatic SAH (24). Study data were prospectively collected and stored in our clinical neuroscience database. The main dependent variables examined were patient disposition and mortality.

Our criteria for stroke patients’ ICU admission are as follows: 1) patients who received IV tissue plasminogen activator therapy, 2) patients with large hemispheric strokes, 3) strokes with posterior fossa involvement, 4) SAHs, 5) intracranial hemorrhages, 6) hemodynamically unstable patients, 7) Glasgow Coma Scale (GCS) of less than 9, 8) intubated patients, 9) patients with difficult to control seizures, 10) patients requiring beat-to-beat blood pressure monitoring (requiring arterial catheter management), and 11) patients with decompensated congestive heart failure (CHF) and chronic obstructive pulmonary disease (COPD) exacerbation.

In this study, we decided not to include patients who died within the first 48 hours of admission because of the following reasons: 1) Elderly patients who don’t survive for more than initial 48 hours generally suffer from a serious primary or secondary injury namely “not survivable” and 2) In our setting, these elderly patients typically are either extremely medically sick or in very severe coma state, which requires goals of care discussions to be activated to address patients’ will or a priori verbal guidance to their power of attorney. Additionally, in this analysis study, we did not include patients who eventually went although “withdrawal of life support.”

Back to Top | Article Outline

Protocol

We extracted a wide variety of patient data from the clinical database including demographic information, comorbidities, home medications, baseline hemodynamic variables, established severity-injury assessment scales, baseline laboratory values, and patients’ survival outcomes including disposition details. Clinical data from the years 2005 to 2009 were used as the training dataset to establish the prediction model. We first explored the univariable relationship between predictors and in-hospital mortality. Predictors were assessed with a backward variable selection for their independent contribution to in-hospital mortality. A selected best prediction model (converted to a nomogram) built from this initial dataset (training dataset) was used to predict mortality for validation purposes in a newer “test” dataset from the years 2010 to 2012. Because of the stroke guidelines recently were updated (2013) (9), we performed a second validation step by applying our prediction model to more recent patients from the years 2016 to 2017.

Back to Top | Article Outline

Measurements

We considered the following variables for predicting in-hospital mortality: age, gender, primary diagnoses (IS, ICH, SAH), concurrent cardiac diseases (coronary artery disease [CAD]), myocardial infarction, hypertension, and CHF, diabetes, COPD, smoking (current smokers), admission GCS, Acute Physiology and Chronic Health Evaluation (APACHE) III, Sequential Organ Failure Assessment (SOFA) scores, complete blood count, comprehensive metabolic panel, and the hemodynamic and oxygenation variables from the first 12 hours of admission. Within the range of clinically relevant cutoff thresholds, blood pressure and total WBC count data were converted to categorical variables in order to elevate their contributions to severity-mortality assessment. Hypotension is defined as mean arterial pressure (MAP) less than 60 mm Hg. Leukocytosis defined as WBC greater than or equal to 11,000/mm3. Home medications, specifically aspirin, warfarin, statins, and beta-blockers were considered in the analyses.

APACHE III (25) and SOFA scores (26) were used to assess the severity of illness in the first 12 hours of admission. The presedation (when/if sedation was required) GCS scores were used to evaluate the consciousness level of the patients.

Back to Top | Article Outline

Statistical Methods

Model Fitting.

Our training dataset contained baseline data (within the first 12 hours of ICU admission) on 462 patients from the years 2005 to 2009. We fit a multivariable logistic regression model predicting in-hospital mortality as a function of the following potential predictors: age, stroke type, CHF, COPD, smoking, WBC (≥ 11 K vs < 11 K), MAP less than 60 mm Hg, aspirin, statin, beta-blocker, warfarin, external ventricular drain, craniotomy requirements, GCS, temperature, creatinine, and glucose. Both linear and nonlinear forms of the continuous variables were considered. Backward variable selection was used, and the model with the best Bayesian information criterion (BIC) was chosen. The model with the lowest BIC was the best fit, regardless of whether variables were statistically significant (i.e., independent of p values). Due to their anatomic-pathologic and treatment approach differences as well as time-based changes in management, we forced stroke type and admission year into the final model regardless of statistical significance. We also tested the interaction between stroke type and MAP on mortality. Internal discrimination was assessed with an optimism-corrected (by 10-fold cross-validation) C-statistic (area under the curve [AUC]). Internal calibration was assessed with a plot of observed versus expected mortality. A nomogram was constructed to display the final model.

Back to Top | Article Outline

Model Validation.

Variable estimates from the training set model were applied to the test data from 2010 to 2012 (n = 122) to assess the ability of the model to predict mortality in new patients. We also validated the model on a newer dataset from 2016 to 2017 (n = 123). Discrimination was assessed with the C-statistic (AUC). Calibration was assessed with a plot of the observed versus the nomogram-predicted mortality probability and with the Hosmer-Lemeshow goodness of fit test of predicted versus observed event rate. Overall prediction was assessed with a Scaled Brier’s score. Brier’s score represents the square of the difference of the predictive ability found using the model compared with perfect predictability. Therefore, when the Brier’s score is “0,” this is the best case, and when it’s “1,” it represents the worst case.

With n equals to 462 patients and 108 events in the training dataset, we had sufficient data to allow appropriate fitting of a multivariable logistic regression model containing roughly 10 variables, based on the traditional rule of thumb of 10-events per variable for a logistic regression model.

Back to Top | Article Outline

RESULTS

A total of 462 elderly patients who were admitted to our neuroscience service for acute stroke diagnosis between 2005 and 2009 were included as the “training dataset.” The first “test dataset” included 122 patients from 2010 to 2012, and second “test data” included 123 patients from 2016 to 2017. Overall, length of stay was a median (interquartile range [IQR]) of 12 days (7–19 d) for patients who survived and 6 days (4–11 d) for patients who died.

Of 584 stroke patients from 2005 to 2012, the admission National Institute of Health stroke scale (NIHSS) data for our elderly IS population (mean ± SD: 14.7 ± 8.4). The three stroke types of ICH (n = 175), IS (n = 332), and SAH (n = 77) did not differ significantly on baseline variables except for the percent with MAP less than 60 and WBC (≥ 11 K vs < 11 K) (Appendix Table 1, Supplemental Digital Content 1, http://links.lww.com/CCX/A10). Of 332 patients with acute IS in the training dataset, the initial NIHSS mean (SD) was 13.4 (6.8) and baseline mRS was 4.7 (0.55). No difference was found among three types of strokes on initial NIHSS and baseline modified Rankin scale.

For 2016–2017 testing data, NIHSS data for our elderly IS population (mean ± SD: 14±8), the Hunt-Hess scale for the SAH patients (median ± IQR: 2 [2–4.5]), and the ICH score for the ICH patients (median ± IQR: 2 [1–3]).

First, we assessed the univariable association between mortality and demographics, diagnoses, baseline laboratory results, and severity of illness scores (Table 1). Age was 76 ± 7 and 79 ± 7 years for survivors and nonsurvivors, respectively (p = 0.012). The majority of patients had hypertension, CAD, and diabetes. A higher current smoker population was noted in the nonsurvivor (14%) compared with survivor group (7%) (p = 0.049). Baseline use of aspirin, statins, beta-blockers, and craniotomy requirements were not different between the survivor and nonsurvivors. The use of warfarin was higher in nonsurvivors (15% vs 29%; p = 0.0029) (Table 1).

TABLE 1

TABLE 1

Reasons of mortality were reported in the majority of the cases. Neurologic problems were the cause 63% of the time, and medical problems 29% of the time. Within the neurologic reasons, most common ones were the primary diagnosis (35%) and hemorrhagic transformation (18%). In the mean time, cardiac complications (10%) and sepsis (10%) were the most common medical reasons.(Appendix Table 3, Supplemental Digital Content 1, http://links.lww.com/CCX/A10)

Back to Top | Article Outline

Severity Assessment Scales

Compared with the nonsurvivors, survivors had higher means of GCS (12 ± 3 vs 9 ± 4; p < 0.001), lower APACHE III (41 ±13 vs 51 ± 17; p < 0.001), and SOFA score values (3.3 ± 2 vs 5.5 ± 2; p < 0.001).

Back to Top | Article Outline

Laboratory Variables

WBC was lower in survivors, 13/mm3 ± 5 in the survivor group versus 16 ± 14 in the nonsurvivors (p < 0.001). Higher maximum glucose levels were noted in the nonsurvivor group (168 ± 54 vs 147 ± 54; p < 0.001).

Back to Top | Article Outline

Training Data Versus Test Data

Compared with the “training data,” patients in the 2010–2012 “test data” were slightly younger (p = 0.03), less likely to have ICH and more SAH (p < 0.001), to have more CHF (p = 0.008), COPD (p = 0.02), smoking (p < 0.001), and less hypotension (p = 0.007). GCS scores were lower in the test dataset, which possibly contributed to higher mortality in the “test dataset” (p < 0.001). Compared with the training dataset, patients in the 2016–2017 had higher rates of ICH and smoking history (p < 0.001), but lower rates of IS (p < 0.001), higher rate of WBC greater than or equal to 11 K/uL (p < 0.001) (Table 2).

TABLE 2

TABLE 2

Back to Top | Article Outline

Model Development and Validation

Our final multivariable model for predicting mortality from the “training set” included stroke type (ICH vs IS: odds ratio [95% CI] of 0.92 [0.50–1.68] and SAH vs IS: 1.0 [0.40–2.49]), hospital admission year (1.01 [0.66–1.53]), age (1.78 [1.20–2.65] per 10 yr), smoking (8.0 [2.39–26.7]), MAP less than 60 mm Hg (3.08 [1.67–5.67]), GCS (0.73 [0.66–0.80] per 1 point increment), WBC less than 11 K (0.31 [0.16–0.60]), creatinine (1.76 [1.17–2.64] comparing 2 vs 1), CHF (2.49 [1.06–5.82]), and warfarin use (2.29 [1.17–4.47]) (Table 3 and Fig. 1; and Appendix Table 2, Supplemental Digital Content 1, http://links.lww.com/CCX/A10). The fitted model is acceptable with Hosmer-Lemeshow goodness of fit (p = 0.99).

TABLE 3

TABLE 3

Figure 1

Figure 1

Back to Top | Article Outline

Nomogram

Based on the final prediction model, we constructed a nomogram. Each variable corresponds to a particular point system. The total added points across variables correspond to a predicted probability of mortality. Internal AUC (95% CI) in the “training set” was 0.83(0.78–0.89) after adjustment for over-fitting, indicating excellent discrimination.

In a sensitivity analysis, we replaced the five factors comprising the APACHE III score with the APACHE III score itself. The AUC decreased from 0.83 to 0.73, a substantial loss of discrimination. As well, when we replaced the three SOFA components with the SOFA score itself, the AUC was reduced from 0.83 to 0.79. This justifies our consideration of the components of these scores instead of only the scores.

When the model was applied to the 2010–2012 “test data,” the AUC was 0.79 (0.71–0.87), still good discrimination, and the scaled Brier’s score was 0.17 (Fig. 2). However, calibration on the “test data” was poor (over-prediction), especially for predicted probabilities less than 0.40 with Hosmer-Lemeshow goodness of fit (p = 0.017), rejecting the null hypothesis of a “good fit.”

Figure 2

Figure 2

For the 2016–2017 “test data,” the results were consistent with that of the 2010–2012 “test data,” indicating the model was robust. The AUC was 0.83 (0.75–0.91) and scaled Brier’s score was 0.27 (Fig. 2). Calibration on the 2016–2017 “test data” appeared better than the earlier data, showing observed values closer to predicted values (45-degree line), but with Hosmer-Lemeshow goodness of fit (p = 0.015) still suggests lack of “good fit.”

Back to Top | Article Outline

DISCUSSION

The model built to assess in-hospital mortality of elderly acutely ill stroke patients provided a good to excellent discrimination in both the “training dataset” and the two “test validation datasets.” Because the predictions are obtained from individual patient characteristics assessed/measured within the first 12 hours of hospital admission, model’s prognostic importance and therapeutic potential for modifiable factors are noteworthy. Overall, the area under the receiver operating characteristic curve (0.79–0.83) showed acceptable to good discriminative ability, suggesting that the sensitivity and specificity of the model appear robust enough to be an aid in clinical judgment of acute stroke patients. The utility of this nomogram is to identify the sickest elderly stroke patients as early as within the few hours of admission. Implementing nomogram to the electronic medical record may provide additional severity trigger alerts to the stroke teams. Alerting stroke teams early to focus on potentially modifiable risk factors may help to prevent further progression of this high-risk patient population. Additionally, this prediction tool may further help providing patient-centered prognostics information to the surrogates.

Overall, the mortality rate of our patients was about 28%, which is comparable with other studies for the range and sickness levels (6). Mortality of IS is generally around ~10% (9), but in acutely ill IS patients, this rate may increase to 20–25% (27). For ICH, mortality is generally higher and ranges between 30% and 48% (28, 29). Mortality for SAH ranges between 27% and 44% (30). In a recent study, Rincon et al (31) reported expectedly high mortality rates for critically ill and ventilated IS (48%), ICH (59%), and SAH (44%) patients. In this cohort of elderly stroke patients, we did not find any statistically sound contribution of stroke type to the mortality. Possibly, major mortality reasons such as acute illness severity, coma, hypotension, decompensated CHF, and advanced age may have masked the specific contribution of different etiologies of stroke.

In our study, presence of hypotension, defined as MAP less than 60 mm Hg within the first 12-hour of admission, was associated with increased mortality. Such association is more evident in neurologically impaired elderly patients wherein the cerebral perfusion pressure altered by lowered MAP (32–35). Considering majority of IS patients are hypertensive at baseline, MAP less than 60 mm Hg is likely to compromise cerebral perfusion. Due to impaired cerebral autoregulation, penumbra tissue perfusion becomes directly pressure dependent, and hypotension may drastically compromise blood flow, which may result in larger strokes (32–34). Significant portion of ICH and SAH patients are also hypertensive at baseline, and relative decreases in blood pressure may compromise perfusion of other vital organs such as heart and kidneys. Poor physiologic adaptation during stress may further risk elderly patients’ chances to prevent secondary injuries (36). Cardiac issues and sepsis were the two most common medical mortality reasons of our patient population, and possibly hypotension may have contributed to both. However, it should be noted that our low-frequency blood pressure data sampling might have resulted an exaggerated contribution of hypotension to our prediction model.

Persistent leukocytosis correlates with poor functional outcomes especially for IS and SAH patients (37, 38). Although more prominent in SAH, stroke patient is prone to develop systemic inflammatory response due to progressing injury. WBC count is an important component of the severity assessment scores including APACHE and SAPS (25, 39). Although WBC alone can neither serve as the sole diagnostic step for infections nor trigger empiric antibiotic treatment, they do serve as a critical step in various infection diagnostic tools such as clinical pulmonary infection score and Centers for Disease Control and Prevention pneumonia criteria (40, 41).

Heart failure can predispose patients to cardiac thromboembolism. Additionally, low ejection fraction per se may result in chronic cerebral hypoperfusion (42, 43). American College of Cardiology/American Heart Association recommends evidence-based therapy for CHF to be individualized for elderly patients (43). Because elderly stroke patients are more vulnerable to CHF, immediate management according to the current guidelines may further decrease mortality (42).

Some factors in the nomogram, which contributed to patient mortality, were not modifiable upon admission such as GCS. Although each stroke type has its own established neurologic assessment score (e.g., NIHSS for IS) (44), GCS is universally one of the most commonly used neurologic assessment tools and takes an important part in severity assessment tools like APACHE and SOFA (25, 26). Although emphasizing GCS’ role in poor prognostics, it needs to be noted that interpretation of this scale is limited when patients are intubated and sedated, or intoxicated (23, 45).

Creatinine levels in elderly patients are associated with increased mortality (26, 46). Interesting finding of our study is creatinine levels’ association with mortality appears to start even within clinically established normal range. Therefore, renal functions need to be closely watched in elderly stroke population. One needs to avoid under hydration, hypotension, contrast requiring radiological assessments, and use of nonsteroidal anti-inflammatory agents (47).

Warfarin use at baseline was found to be associated with mortality. Such association was likely related to the bleeding risk due to warfarin. Similarly, active smoking’s contribution to mortality is also through many indirect mechanisms. Although the association found between active smoking and mortality in elderly acutely ill stroke patients is unsurprising, it is likely that this contribution depends on organ-system damage caused by years of exposure. Notably, neither warfarin nor smoking status is immediately modifiable to influence hard outcomes.

Although we did not find an association between stroke type and mortality, inclusion of all stroke types in the same pool of analysis, and disregarding their different pathology is a limitation of this study. Management of ICH and SAH have many differences compared with acute IS including details in blood pressure management (48–50). Management of acute hypertension is possibly the most important treatment of the ICH (51). Also, there are different blood pressure management recommendations within the acute IS patients depending on whether they are treated with fibrinolytic therapy or they have large-vessel occlusion (9, 52).

Overall, there are important limitations of this study as follows: 1) retrospective design, 2) being a single-center study, 3) having no a priori sample-size estimate, 4) using low-frequency data collection for some variables (e.g., MAP), and finally 5) using short-term outcomes (i.e., in-hospital mortality). Additionally, the number of modifiable risk factors may appear as a limitation.

Although the fitted model from the training dataset was acceptable (the Hosmer-Lemeshow test p = 0.99), the Hosmer-Lemeshow test for both testing datasets suggested poor fit. Possible reasons include small testing datasets, and the population changing over time compared with the training dataset. In spite of its shortcomings, our model maintained very good discrimination in repeated validation cohorts over time. The variables in the nomogram can be readily obtained, even as short as in the first hour of hospital admission. Therefore, the availability of such tool would help identification of high-risk population and enhance preventive strategies.

Our early prediction model for in-hospital mortality of elderly, acutely ill stroke patients resulted in a very good discrimination and calibration when applied to more recent data. Further validation of our prediction model in different stroke types at different medical centers and finding timely applicable acute care protocols to modify treatable medical conditions are our goals for future research.

Back to Top | Article Outline

ACKNOWLEDGMENTS

We are grateful to Kari Moore, APRN and Elizabeth Wise, APRN for their years of excellent care of our patients and continuous support to our clinical and research programs. Also, we would like to acknowledge our Neuroscience ICU & Comprehensive Stroke Center’s nursing and supporting staff.

Back to Top | Article Outline

REFERENCES

1. Ortman J, Velkoff V, Hogan H; An Aging Nation: The Older Population in the United States Population Estimates and ProjectionsReport Number P25–1140.2014Available at: https://www.census.gov/prod/2014pubs/p25-1140.pdf. Accessed March 20, 2019
2. Arabi Y, Venkatesh S, Haddad S, et al. A prospective study of prolonged stay in the intensive care unit: Predictors and impact on resource utilization.Int J Qual Health Care200214403–410
3. Chelluri L, Grenvik A, Silverman M. Intensive care for critically ill elderly: Mortality, costs, and quality of life. Review of the literature.Arch Intern Med19951551013–1022
4. Krumholz HM, Nuti SV, Downing NS, et al. Mortality, hospitalizations, and expenditures for the medicare population aged 65 years or older, 1999-2013.JAMA2015314355–365
5. Mayer SA, Copeland D, Bernardini GL, et al. Cost and outcome of mechanical ventilation for life-threatening stroke.Stroke2000312346–2353
6. Boumendil A, Somme D, Garrouste-Orgeas M, et al. Should elderly patients be admitted to the intensive care unit?Intensive Care Med2007331252
7. Garrouste-Orgeas M, Boumendil A, Pateron D, et al; ICE-CUB GroupSelection of intensive care unit admission criteria for patients aged 80 years and over and compliance of emergency and intensive care unit physicians with the selected criteria: An observational, multicenter, prospective study.Crit Care Med2009372919–2928
8. Vosylius S, Sipylaite J, Ivaskevicius J. Determinants of outcome in elderly patients admitted to the intensive care unit.Age Ageing200534157–162
9. Jauch EC, Saver JL, Adams HP Jr, et al; American Heart Association Stroke Council; Council on Cardiovascular Nursing; Council on Peripheral Vascular Disease; Council on Clinical CardiologyGuidelines for the early management of patients with acute ischemic stroke: A guideline for healthcare professionals from the American Heart Association/American Stroke Association.Stroke201344870–947
10. Mozaffarian D, Benjamin EJ, Go AS, et al; Writing Group Members; American Heart Association Statistics Committee; Stroke Statistics SubcommitteeHeart disease and stroke statistics-2016 update: A report from the American Heart Association.Circulation2016133e38–e360
11. Meyfroidt G, Bollaert PE, Marik PE. Acute ischemic stroke in the ICU: To admit or not to admit?Intensive Care Med201440749–751
12. Golestanian E, Liou JI, Smith MA. Long-term survival in older critically ill patients with acute ischemic stroke.Crit Care Med2009373107–3113
13. Kunitz SC, Gross CR, Heyman A, et al. The pilot Stroke Data Bank: Definition, design, and data.Stroke198415740–746
14. Qureshi AI, Suri MA, Safdar K, et al. Intracerebral hemorrhage in blacks. Risk factors, subtypes, and outcome.Stroke199728961–964
15. Sacco RL, Wolf PA, Bharucha NE, et al. Subarachnoid and intracerebral hemorrhage: Natural history, prognosis, and precursive factors in the Framingham study.Neurology198434847–854
16. Becker RB, Zimmerman JE. ICU scoring systems allow prediction of patient outcomes and comparison of ICU performance.Crit Care Clin199612503–514
17. Ferreira FL, Bota DP, Bross A, et al. Serial evaluation of the SOFA score to predict outcome in critically ill patients.JAMA20012861754–1758
18. Handschu R, Haslbeck M, Hartmann A, et al. Mortality prediction in critical care for acute stroke: Severity of illness-score or coma-scale?J Neurol20052521249–1254
19. Lemeshow S, Teres D, Avrunin JS, et al. Refining intensive care unit outcome prediction by using changing probabilities of mortality.Crit Care Med198816470–477
20. Minne L, Eslami S, de Keizer N, et al. Effect of changes over time in the performance of a customized SAPS-II model on the quality of care assessment.Intensive Care Med20123840–46
21. Sikka P, Jaafar WM, Bozkanat E, et al. A comparison of severity of illness scoring systems for elderly patients with severe pneumonia.Intensive Care Med2000261803–1810
22. Weingarten S, Bolus R, Riedinger MS, et al. The principle of parsimony: Glasgow Coma Scale score predicts mortality as well as the APACHE II score for stroke patients.Stroke1990211280–1282
23. Kasuya Y, Hargett JL, Lenhardt R, et al. Ventilator-associated pneumonia in critically ill stroke patients: Frequency, risk factors, and outcomes.J Crit Care201126273–279
24. Sacco RL, Kasner SE, Broderick JP, et al; American Heart Association Stroke Council, Council on Cardiovascular Surgery and Anesthesia; Council on Cardiovascular Radiology and Intervention; Council on Cardiovascular and Stroke Nursing; Council on Epidemiology and Prevention; Council on Peripheral Vascular Disease; Council on Nutrition, Physical Activity and MetabolismAn updated definition of stroke for the 21st century: A statement for healthcare professionals from the American Heart Association/American Stroke Association.Stroke2013442064–2089
25. Knaus WA, Wagner DP, Draper EA, et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults.Chest19911001619–1636
26. Vincent JL, Moreno R, Takala J, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine.Intensive Care Med199622707–710
27. Akca O, Ziegler C, Liu R, et al. Early mortality prediction in ischemic stroke.International Stroke Conference 2018January 23-26, 2018Los Angeles, California
28. Gaberel T, Magheru C, Parienti JJ, et al. Intraventricular fibrinolysis versus external ventricular drainage alone in intraventricular hemorrhage: A meta-analysis.Stroke2011422776–2781
29. Rincon F, Mayer SA. The epidemiology of intracerebral hemorrhage in the United States from 1979 to 2008.Neurocrit Care20131995–102
30. Nieuwkamp DJ, Setz LE, Algra A, et al. Changes in case fatality of aneurysmal subarachnoid haemorrhage over time, according to age, sex, and region: A meta-analysis.Lancet Neurol20098635–642
31. Rincon F, Kang J, Maltenfort M, et al. Association between hyperoxia and mortality after stroke: A multicenter cohort study.Crit Care Med201442387–396
32. Castillo J, Leira R, García MM, et al. Blood pressure decrease during the acute phase of ischemic stroke is associated with brain injury and poor stroke outcome.Stroke200435520–526
33. Leonardi-Bee J, Bath PM, Phillips SJ, et al; IST Collaborative GroupBlood pressure and clinical outcomes in the International Stroke Trial.Stroke2002331315–1320
34. Okumura K, Ohya Y, Maehara A, et al. Effects of blood pressure levels on case fatality after acute stroke.J Hypertens2005231217–1223
35. Hakala SM, Tilvis RS, Strandberg TE. Blood pressure and mortality in an older population. A 5-year follow-up of the Helsinki Ageing Study.Eur Heart J1997181019–1023
36. Akca O, Bautista AF, Lenhardt R. Is elderly ICU patient more prone to pneumonia?*.Crit Care Med201442742–744
37. Boehme AK, Kumar AD, Lyerly MJ, et al. Persistent leukocytosis-is this a persistent problem for patients with acute ischemic stroke?J Stroke Cerebrovasc Dis2014231939–1943
38. Dasenbrock HH, Rudy RF, Gormley WB, et al. 111 predictors of complications after clipping of unruptured intracranial aneurysms: A National Surgical Quality Improvement Program analysis.Neurosurgery201663Suppl 1147
39. Knaus WA, Draper EA, Wagner DP, et al. APACHE II: A severity of disease classification system.Crit Care Med198513818–829
40. Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting.Am J Infect Control200836309–332
41. Pugin J, Auckenthaler R, Mili N, et al. Diagnosis of ventilator-associated pneumonia by bacteriologic analysis of bronchoscopic and nonbronchoscopic “blind” bronchoalveolar lavage fluid.Am Rev Respir Dis19911431121–1129
42. Abdul-Rahim AH, Fulton RL, Frank B, et al; VISTA collaboratorsAssociations of chronic heart failure with outcome in acute ischaemic stroke patients who received systemic thrombolysis: Analysis from VISTA.Eur J Neurol201522163–169
43. Yancy CW, Jessup M, Bozkurt B, et al. 2013 ACCF/AHA guideline for the management of heart failure: Executive summary: A report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines.Circulation20131281810–1852
44. Brott T, Adams HP Jr, Olinger CP, et al. Measurements of acute cerebral infarction: A clinical examination scale.Stroke198920864–870
45. Zwingmann J, Lefering R, Bayer J, et al; TraumaRegister DGU(®)Outcome and risk factors in children after traumatic cardiac arrest and successful resuscitation.Resuscitation20159659–65
46. Coca SG. Acute kidney injury in elderly persons.Am J Kidney Dis201056122–131
47. Qureshi AI, Palesch YY, Barsan WG, et al; ATACH-2 Trial Investigators and the Neurological Emergency Treatment Trials NetworkIntensive blood-pressure lowering in patients with acute cerebral hemorrhage.N Engl J Med20163751033–1043
48. Hemphill JC 3rd, Greenberg SM, Anderson CS, et al; American Heart Association Stroke Council; Council on Cardiovascular and Stroke Nursing; Council on Clinical CardiologyGuidelines for the management of spontaneous intracerebral hemorrhage: A guideline for healthcare professionals from the American Heart Association/American Stroke Association.Stroke2015462032–2060
49. Powers WJ, Derdeyn CP, Biller J, et al; American Heart Association Stroke Council2015 American Heart Association/American Stroke Association focused update of the 2013 guidelines for the early management of patients with acute ischemic stroke regarding endovascular treatment: A guideline for healthcare professionals from the American Heart Association/American Stroke Association.Stroke2015463020–3035
50. Connolly ES Jr, Rabinstein AA, Carhuapoma JR, et al; American Heart Association Stroke Council; Council on Cardiovascular Radiology and Intervention; Council on Cardiovascular Nursing; Council on Cardiovascular Surgery and Anesthesia; Council on Clinical CardiologyGuidelines for the management of aneurysmal subarachnoid hemorrhage: A guideline for healthcare professionals from the American Heart Association/American Stroke Association.Stroke2012431711–1737
51. Majidi S, Suarez JI, Qureshi AI. Management of acute hypertensive response in intracerebral hemorrhage patients after ATACH-2 trial.Neurocrit Care201727249–258
52. Ahmed N, Wahlgren N, Brainin M, et al; SITS InvestigatorsRelationship of blood pressure, antihypertensive therapy, and outcome in ischemic stroke treated with intravenous thrombolysis: Retrospective analysis from Safe Implementation of Thrombolysis in Stroke-International Stroke Thrombolysis Register (SITS-ISTR).Stroke2009402442–2449
Keywords:

elderly; hypotension; intracerebral hemorrhage; ischemic stroke; mortality; subarachnoid hemorrhage

Supplemental Digital Content

Back to Top | Article Outline
Copyright © 2019 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine