Impact of Missing Stroke Severity Data on the Accuracy of Hospital Ischemic Stroke Mortality Profiling

Abstract

Background: The Centers for Medicare and Medicaid Services have proposed 30-day ischemic stroke risk-standardized mortality rates that include adjustment for stroke severity using the National Institute of Health Stroke Scale (NIHSS), which is often undocumented. We used simulations to quantify the effect of missing NIHSS data on the accuracy of hospital-level ischemic stroke risk-standardized mortality rate profiling for 100 hypothetical hospitals with different case volumes. Methods and Results: We generated simulated data sets of patients with NIHSS scores and other predictors of 30-day mortality based on empirical analysis of data from 7654 patients with ischemic stroke in the Michigan Stroke Registry. We assigned and rank-ordered a true (known) 30-day mortality rate to each hospital in the simulated data sets of N=100 hospitals with either low (100 patients with stroke), medium (300), or high (500) case volumes. We then estimated and rank-ordered 30-day risk-standardized mortality rates for the N=100 hospitals in each simulated data set using hierarchical logistic regression models. In each data set, we systematically varied the rate of missing NIHSS data and whether missing NIHSS data was independent (missing completely at random) or dependent (missing not at random) on the NIHSS score. With no missing NIHSS data, the Spearman correlation between the true hospital performance rank order assigned during data set generation and the estimated 30-day risk-standardized mortality rate rank order was 0.72, 0.88, and 0.91 in low, medium, and high volume hospitals, respectively. However, this fell to as low as 0.50, 0.71, and 0.79 as missing NIHSS data reached 70%. Conclusions: Missing NIHSS data had substantial detrimental effects on the accuracy of profiling of ischemic stroke mortality, especially in lower volume hospitals. Even with complete NIHSS documentation, significant limitations in ischemic stroke mortality profiling remain.

The impact of disease severity adjustment on hospital standardised mortality ratios: Results from a service-wide analysis of ischaemic stroke admissions using linked pre-hospital, admissions and mortality data

“…Large amounts of missing data may have introduced bias particularly if the proportion of missing cases varied between hospitals, indicating differential bias in severity ascertainment. A recent publication has reported that missing severity data leads to inaccuracies in outlier classification, although residual bias remained, particularly for smaller hospitals [41]. Our severity measure is less prone to selective assessment with the advantage of maximising available cases for analysis (and therefore power), and minimizing differential ascertainment.…”

Association Between Plasma Trimethyllysine and Prognosis of Patients With Ischemic Stroke

“…The baseline data were collected prospectively using an electronic data capture system by face‐to‐face interviews, which included age, sex, ethnicity, region (North, Northeast, East, Central South, and West), hospital level (grade Ⅱ and grade Ⅲ), family income, education level, symptom to admission (<24 or ≥24 hours), intravenous thrombolysis and endovascular treatment, body mass index (calculated as weight in kilograms divided by height in meters squared, kg/m 2 ), current smoking, heavy drinking (≥2 standard alcohol consumption per day), medical history (stroke, coronary heart disease, hypertension, or diabetes), stroke type (TIA and IS), the causative subtypes of IS classified according to the TOAST (Trial of Org 10172 in Acute Stroke Treatment) criteria, 25 severity of stroke on admission (National Institutes of Health Stroke Scale score), 26 systolic blood pressure (SBP) at admission, high‐density lipoprotein cholesterol (HDL‐C), low‐density lipoprotein cholesterol (LDL‐C), triglyceride, fasting blood glucose (FBG) at admission, and discharge medications (antiplatelet, lipid‐lowering, 27 and antihypertensive drugs).…”

CO2combining power and outcomes in patients with acute ischaemic stroke or transient ischaemic attack

“…Open access weight in kilograms divided by height in metres squared, kg/m 2 ), medical history (hypertension, diabetes mellitus, dyslipidaemia, stroke or TIA, atrial fibrillation or flutter, peripheral vascular disease, heart failure, chronic obstructive pulmonary disease (COPD)), stroke type (ischemic stroke or TIA), aetiology classification of ischaemic stroke performed according to the TOAST (Trial of Org 10172 in Acute Stroke Treatment) criteria, 11 current smoker, in-hospital medication (cholesterol-lowering agents, antihypertensive agents, hypoglycaemic agents, antiplatelet agents and anticoagulant agents), severity of stroke on admission (National Institutes of Health Stroke Scale, NIHSS), 12 time from onset of symptoms to admission, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglyceride, fasting blood glucose, estimated glomerular filtration rate calculated using the creatinine-based Chronic Kidney Disease Epidemiological Collaboration equation 13 and highsensitivity C reactive protein (hs-CRP), and medication on discharge (cholesterol-lowering agents, antihypertensive agents, hypoglycaemic agents, antiplatelet agents and anticoagulant agents).…”