Early Prediction of Prognosis in Elderly Acute Stroke Patients

Abstract

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.

Developing a Clinical Prediction Score: Comparing Prediction Accuracy of Integer Scores to Statistical Regression Models

“…We prefer to avoid stepwise selection of variables as used by Canet et al 1 and Bautista et al 15 Choice of final predictors is not robust, such as when splitting data into training and test subsamples. Stepwise selection also considerably increases the risk of overfit.…”

“…We refer the reader to their publication for a complete explanation of methods. In brief, Bautista et al 15 collected data for 2 cohorts. The first sample consisted of baseline clinical data (defined as data available within 12 hours of hospital admission) for 584 elderly stroke patients (age ≥65 years) admitted to the intensive care unit (ICU) from the years 2005 to 2009.…”

“…We performed Wald tests to check whether linearity was an appropriate assumption for each continuous covariate. We compared the results of backward variable selection based on AIC to the results obtained on the training subsample in Bautista et al 15 We then fitted a logistic regression model with continuous covariates retained to the entire 2005-2009 sample, using only the covariates that were also available in the 2016-2017 sample. This was done to allow us to externally validate the models.…”

“…This was done to allow us to externally validate the models. We dichotomized continuous covariates using cut points that maximized the Youden 17 J-statistic in univariate ROC analyses as described previously, with the exception of white blood cell count (WBC), which was dichotomized at 11,000, the cut point used in Bautista et al 15 We fit another logistic regression model to the dichotomized inputs and scaled and rounded the regression coefficients to obtain an integer score, as discussed in section "Model Development." Finally, we internally validated the models by estimating bootstrap optimism-corrected AUC and IPA using 1000 bootstraps of the 2005-2009 sample, and externally validated www.anesthesia-analgesia.org…”

“…Application of Methods to Study of Early Prediction of Prognosis in Elderly Acute Stroke PatientsWhen performing backward selection on the basis of AIC using the development subset of the 2005-2009 sample used by Bautista et al,15 the final multivariable regression model included stroke type, age, smoking, mean arterial pressure <60 mm•Hg, Glasgow Coma Scale score, WBC, serum creatinine, history of congestive heart failure, and warfarin use. When performing backward selection on the basis of AIC using the entire 2005-2009 sample, the final multivariable regression model included the history of chronic obstructive pulmonary disease and serum glucose in addition to all of the predictors selected using the development subset.…”