Creation and Validation of a Stroke Scale to Increase Utility of National Inpatient Sample Administrative Data for Clinical Stroke Research

The Neiman Imaging Comorbidity Index: Development and Validation in a National Commercial Claims Database

OBJECTIVE

To build the Neiman Imaging Comorbidity Index (NICI), based on variables available in claims datasets, which provides good discrimination of an individual's chance of receiving advanced imaging (CT, MR, PET), and thus, utility as a control variable in research.

METHODS

This retrospective study used national commercial claims data from Optum's deidentified Clinformatics Data Mart database from the period January 1, 2018 to December 31, 2019. Individuals with continuous enrollment during this 2-year study period were included. Lasso (least absolute shrinkage and selection operator) regression was used to predict the chance of receiving advanced imaging in 2019 based on the presence of comorbidities in 2018. A numerical index was created in a development cohort (70% of the total dataset) using weights assigned to each comorbidity, based on regression β coefficients. Internal validation of assigned scores was performed in the remaining 30% of claims, with comparison to the commonly used Charlson Comorbidity Index.

RESULTS

The final sample (development and validation cohorts) included 10,532,734 beneficiaries, of whom 2,116,348 (20.1%) received advanced imaging. After model development, the NICI included nine comorbidities. In the internal validation set, the NICI achieved good discrimination of receipt of advanced imaging with a C statistic of 0.709 (95% confidence interval [CI] 0.708-0.709), which predicted advanced imaging better than the CCI (C 0.692, 95% CI 0.691-0.692). Controlling for age and sex yielded better discrimination (C 0.748, 95% CI 0.748-0.749).

DISCUSSION

The NICI is an easily calculated measure of comorbidity burden that can be used to adjust for patients' chances of receiving advanced imaging. Future work should explore external validation of the NICI.

Coronavirus Disease 2019 Infection in Cervical Artery Dissections

OBJECTIVE

Most cervical artery dissection (CeAD) cases are spontaneous or due to minor traumas, and preceding viral infections have been suggested to be a triggering event for CeAD in some. Herein, we analyze the prevalence of coronavirus disease 2019 (COVID-19) in hospitalized patients with CeAD using a national database.

METHODS

The National Inpatient Sample was queried from April 2020 to December 2020 for patients with a diagnosis of CeAD using International Classification of Diseases, 10th edition-Clinical Modification codes. Among these, patients with COVID-19 were identified. Multivariable logistic regression was conducted to assess the patient profile of those with COVID-19, in-patient mortality, and home discharge among patients with CeAD.

RESULTS

There were 360 (2.32%) hospitalizations involving COVID-19 among 15,500 with CeAD. Concomitant acute ischemic stroke constituted 43.06% of those with a COVID-19 diagnosis, whereas it was 43.73% among those without a COVID-19 diagnosis ( P = 0.902). Home discharges were less common in patients with COVID-19 and CeAD compared to CeAD alone (34.85% vs. 48.63%; P = 0.03), but this was likely due to other factors as multivariate regression analysis did not show an association between COVID-19 and home discharges (odds ratio: 0.69; 95% CI: 0.39 to 1.25; P = 0.22). COVID-19 diagnosis had similar odds of inpatient mortality (odds ratio: 1.11; 95% CI: 0.43 to 2.84; P = 0.84).

CONCLUSION

The prevalence of COVID-19 among hospitalized patients with CeAD is low with 2.32% of all CeAD cases. Concomitant COVID infection did not lead to an increased risk of stroke in CeAD. However, potentially worse functional outcomes (fewer home discharges) without an increase in mortality were seen in patients with COVID and CeAD.

Endovascular thrombectomy for posterior cerebral artery strokes in the national inpatient sample (EaT PeCANpIeS) study

BACKGROUND

Posterior cerebral arteries with acute ischemic strokes (PCA-AISs) comprise around 2% of all acute ischemic strokes and may result in significant long-term deficits. Current guidance regarding endovascular thrombectomy (EVT) for PCA-AIS is insufficient as no published randomized trials exist.

METHODS

An analysis of the National Inpatient Sample database compared medical management versus EVT for PCA-AIS. Propensity score matching was applied to adjust for nonrandomization.

RESULTS

The study included 19,655 patients. Before matching, the EVT cohort had significantly higher National Institutes of Health Stroke Scale (NIHSS) (10.21 vs. 4.67, p < 0.001), had lower rates of favorable functional outcomes, functional independence, and higher rates of intracranial hemorrhage (ICH) and inpatient mortality. After matching, no differences in functional outcomes were identified, but revealed a higher proportion of ICH in the EVT group (17.45% vs. 8.98%, p < 0.001). However, NIHSS subgroup analysis identified improved functional outcomes associated with the EVT group who presented with an NIHSS between 10 and 19 both in terms of rates of favorable functional outcomes (35.56% vs. 12.09%, p < 0.001) and rates of functional independence (26.67% vs. 9.34%, p < 0.01). On further investigation, the clinical benefit, in the NIHSS 10-19 subgroup, was driven by patients receiving EVT in combination with intravenous thrombolysis (IVT).

CONCLUSIONS

This analysis shows that current national practices utilize EVT for more severe PCA strokes. Clinical benefit was only detected in patients with moderate stroke severity (NIHSS 10-19) who were treated with combined EVT and IVT. Further work is needed to investigate the features of PCA-AIS that might benefit from EVT the most.

Machine learning prediction of hospital discharge disposition for inpatients with acute ischemic stroke following mechanical thrombectomy in the United States

BACKGROUND AND PURPOSE

Predicting patient recovery and discharge disposition following mechanical thrombectomy remains a challenge in patients with ischemic stroke. Machine learning offers a promising prognostication approach assisting in personalized post-thrombectomy care plans and resource allocation. As a large national database, National Inpatient Sample (NIS), contain valuable insights amenable to data-mining. The study aimed to develop and evaluate ML models predicting hospital discharge disposition with a focus on demographic, socioeconomic and hospital characteristics.

MATERIALS AND METHODS

The NIS dataset (2006-2019) was used, including 4956 patients diagnosed with ischemic stroke who underwent thrombectomy. Demographics, hospital characteristics, and Elixhauser comorbidity indices were recorded. Feature extraction, processing, and selection were performed using Python, with Maximum Relevance - Minimum Redundancy (MRMR) applied for dimensionality reduction. ML models were developed and benchmarked prior to interpretation of the best model using Shapley Additive exPlanations (SHAP).

RESULTS

The multilayer perceptron model outperformed others and achieved an AUROC of 0.81, accuracy of 77 %, F1-score of 0.48, precision of 0.64, and recall of 0.54. SHAP analysis identified the most important features for predicting discharge disposition as dysphagia and dysarthria, NIHSS, age, primary payer (Medicare), cerebral edema, fluid and electrolyte disorders, complicated hypertension, primary payer (private insurance), intracranial hemorrhage, and thrombectomy alone.

CONCLUSION

Machine learning modeling of NIS database shows potential in predicting hospital discharge disposition for inpatients with acute ischemic stroke following mechanical thrombectomy in the NIS database. Insights gained from SHAP interpretation can inform targeted interventions and care plans, ultimately enhancing patient outcomes and resource allocation.

External Validation and Recalibration of a Mortality Prediction Model for Patients with Ischaemic Stroke

BACKGROUND

Stroke is a highly prevalent disease that can provoke severe disability. We evaluate a predictive model based on the Minimum Basic Data Set (MBDS) compiled by the Spain Health Ministry, obtained for the period 2008-2012 for patients with ischaemic stroke in Spain, to establish the model's validity and to optimise its calibration. The MBDS is the main clinical-administrative database for hospitalisations recorded in Spain, and to our knowledge, no predictive models for stroke mortality have previously been developed using this resource. The main study aim is to perform an external validation and recalibration of the coefficients of this predictive model with respect to a chronologically later cohort.

MATERIAL AND METHODS

External validation (testing the model on a different cohort to assess its performance) and recalibration (validation with optimisation of model coefficients) were performed using the MBDS for patients admitted for ischaemic stroke in the period 2016-2018. A cohort study was designed, in which a recalibrated model was obtained by applying the variables of the original model without their coefficients. The variables from the original model were then applied to the subsequent cohort, together with the coefficients from the initial model. The areas under the curve (AUC) of the recalibration and the external validation procedure were compared.

RESULTS

The recalibrated model produced an AUC of 0.743 and was composed of the following variables: age (odds ratio, OR:1.073), female sex (OR:1.143), ischaemic heart disease (OR:1.192), hypertension (OR:0.719), atrial fibrillation (OR:1.414), hyperlipidaemia (OR:0.652), heart failure (OR:2.133) and posterior circulation stroke (OR: 0.755). External validation produced an AUC of 0.726.

CONCLUSIONS

The recalibrated clinical model thus obtained presented moderate-high discriminant ability and was generalisable to predict death for patients with ischaemic stroke. Rigorous external validation slightly decreased the AUC but confirmed the validity of the baseline model for the chronologically later cohort.

Acute ischemic strokes in patients with developmental disabilities: A cross-sectional analysis

OBJECTIVE

Patients with developmental disabilities (DD) are frequently excluded from acute ischemic stroke (AIS) randomized control trials. We sought to evaluate the impact of having DD on this patient cohort.

METHODS

The National Inpatient Sample was analyzed to explore the impact of AIS and treatment on discharge dispositions in patients with DD. Clinical characteristics, treatments, and outcomes were compared to fully-abled patients with AIS.

RESULTS

1,605,723 patients with AIS were identified from 2010-2019, of whom 4094 (0.30%) had a DD. AIS patients with DD were younger (60.31 vs 70.93 years, p < 0.01), less likely to be Caucasian (66.37%vs 68.09%, p = 0.01), and had higher AIS severity (0.63 vs 0.58, p < 0.01). Tissue plasminogen activator (tPA) was administered in 99,739 (6.2%) fully-abled patients and 196 (4.79%) of patients with DD (p < 0.01). Endovascular thrombectomy (EVT) was performed in 21,066 (1.31%) of fully-abled patients and 35 (0.85%) of patients with DD (p < 0.01). The presence of developmental disabilities were predictive of lower rates of tPA (OR:0.71,CI:0.56-0.87,p < 0.01) and EVT (OR:0.24,CI:0.16-0.36,p < 0.01). In a propensity score-matched cohort of all AIS patients who underwent EVT, there was no difference in functional outcome (p = 0.41), in-hospital mortality (0.10), and LOS (p = 0.79).

CONCLUSION

AIS patients with DD were less likely to receive tPA and EVT compared to fully-abled patients. Individuals with DD had higher mortality and worse discharge disposition. There was no significant difference in post-EVT outcomes between fully-abled patients and patients with developmental disabilities. In the absence of prospective clinical trials, population based cross-sectional analyses such as the present study provide valuable clinical insight.

A double-hit: End-stage renal disease patients suffer worse outcomes in intracerebral hemorrhage

BACKGROUND

Intracerebral hemorrhage (ICH) carries significant morbidity and mortality. Previous single-center retrospective analysis suggests that end-stage renal disease (ESRD) is a risk factor for severe ICH and worse outcomes. This investigation aims to examine the impact of ESRD on ICH severity, complications, and outcomes using a multicenter national database.

METHODS

The International Classification of Disease, Ninth and Tenth Revision Clinical Modification codes were used to query the National Inpatient Sample for patients with ICH and ESRD between 2010 and 2019. Primary endpoints were the functional outcome, length of stay (LOS), and in-hospital mortality. Multivariate variable regression models and a propensity-score matched analysis were established to analyze patient outcomes associated with baseline patient characteristics.

RESULTS

We identified 211,266 patients with ICH, and among them, 7,864 (3.77%) patients had a concurrent diagnosis of ESRD. Patients with ESRD were younger (60.85 vs. 67.64, P < 0.01) and demonstrated increased ICH severity (0.78 vs. 0.77, P < 0.01). ESRD patients experienced higher rates of sepsis (15.9% vs. 6.15%, P < 0.01), acute myocardial infarction (8.05% vs. 3.65%, P < 0.01), and cardiac arrest (5.94% vs. 2.4%, P < 0.01). In addition, ESRD predicted poor discharge disposition (odds ratio [OR]: 2.385, 95% confidence interval [CI]: 2.227-2.555, P < 0.01), longer hospital LOS (OR: 1.629, 95% CI: 1.553-1.709, P < 0.01), and in-hospital mortality (OR: 2.786, 95% CI: 2.647-2.932, P < 0.01).

CONCLUSIONS

This study utilizes a multicenter database to analyze the effect of ESRD on ICH outcomes. ESRD is a significant predictor of poor functional outcomes, in-hospital mortality, and prolonged stay in the ICH population.

Drivers of Ischemic Stroke Hospital Cost Trends Among Older Adults in the United States

PURPOSE

The increased use of neuroimaging and innovations in ischemic stroke (IS) treatment have improved outcomes, but the impact on median hospital costs is not well understood.

METHODS

A retrospective study was conducted using Medicare 5% claims data for 75,525 consecutive index IS hospitalizations for patients aged ≥65 years from 2012 to 2019 (values in 2019 dollars). IS episode cost was calculated in each year for trend analysis and stratified by cost components, including neuroimaging (CT angiography [CTA], CT perfusion [CTP], MRI, and MR angiography [MRA]), treatment (endovascular thrombectomy [EVT] and/or intravenous thrombolysis), and patient sociodemographic factors. Logistic regression was performed to analyze the drivers of high-cost episodes and median regression to assess drivers of median costs.

RESULTS

The median IS episode cost increased by 4.9% from $9,509 in 2012 to $9,973 in 2019 (P = .0021). Treatment with EVT resulted in the greatest odds of having a high-cost (>$20,000) hospitalization (odds ratio [OR], 71.86; 95% confidence interval [CI], 54.62-94.55), as did intravenous thrombolysis treatment (OR, 3.19; 95% CI, 2.90-3.52). Controlling for other factors, neuroimaging with CTA (OR, 1.72; 95% CI, 1.58-1.87), CTP (OR, 1.32; 95% CI, 1.14-1.52), and/or MRA (OR, 1.26; 95% CI, 1.15-1.38) had greater odds of having high-cost episodes than those without CTA, CTP, and MRA. Length of stay > 4 days (OR, 4.34; 95% CI, 3.99-4.72) and in-hospital mortality (OR, 1.85; 95% CI, 1.63-2.10) were also associated with high-cost episodes.

CONCLUSIONS

From 2012 to 2019, the median IS episode cost increased by 4.9%, with EVT as the main cost driver. However, the increasing treatment cost trends have been partially offset by decreases in median length of stay and in-hospital mortality.

Trends in admissions for intracranial dissections in the United States

INTRODUCTION

Intracranial artery dissection (IAD) is rarer than cervical artery dissections (CeAD), and information is based on limited series with small cohorts. There are only several small-scale studies attempting to characterize the natural history of the disease. Herein, we analyze the prevalence of IADs in hospitalized patients using a national database.

METHODS

The National Inpatient Sample was queried from 2016-2019 for patients with a diagnosis of unruptured intracranial dissection (uIAD) using ICD-10-CM codes (I67.0). Moreover, patients with acute ischemic stroke (AIS) and CeAD were extracted to compare its prevalence among patients with concomitant AIS (+/-dissections). The Cochrane-Armitage test was conducted to assess trends in the prevalence of uIADs among those with concomitant AIS or among all craniocervical dissections.

RESULTS

There were 725 hospitalizations involving uIAD, while there were 62,220 involving CeADs. uIADs represented 5.1 per million hospitalizations across 2016-2019. The average age of presentation was 56.9 years (SE: 1.62), while it was 54.4 (SE: 0.17) for CeADs (p = 0.13). Females were represented among 44.8% (n = 325) of uIADs, a similar proportion compared to CeADs (44.3%%, n = 27,530; p = 0.89). Compared to CeADs, AIS and motor deficits were more common in uIAD (71.72% vs. 47.0%; p < 0.001). There were 18.6 uIAD with concomitant AIS per 100,000 with AIS. uIADs represented 1.75% of all dissections with concomitant AIS (n = 520/29,750). There was no trend in the average age of presentation for uIADs. Proportion of females among those with uIADs increased from 36.8% in 2016 to 59.5% in 2019 (trend: +9.46% per year; 95% CI: 3.13 to 15.8; p = 0.004). There was no trend in the proportion of races among those with uIADs.

CONCLUSION

The prevalence of uIADs among hospitalized patients is very low, and only 1.75% of craniocervical dissection-related AIS is due to uIAD. Compared to CeADs, patients were more likely to be male, and uIAD more commonly led to acute ischemic stroke and motor deficits. The trend in age remained stable across the four years analyzed, while the proportion of females increased. There was no trend in the proportion of races among uIADs, however.

Updated Trends, Disparities, and Clinical Impact of Neuroimaging Utilization in Ischemic Stroke in the Medicare Population: 2012 to 2019

OBJECTIVE

The purpose of this study was to update trends, investigate sociodemographic disparities, and evaluate the impact on mortality of stroke neuroimaging across the United States from 2012 to 2019.

METHODS

Retrospective cohort study using CMS Medicare 5% Research Identifiable Files, representing consecutive ischemic stroke emergency department or hospitalized patients aged ≥65 years. A total of 85,547 stroke episodes with demographic and clinical information were analyzed using Cochran-Mantel-Haenszel tests and logistic regression. Outcome measures were neuroimaging (CT angiography [CTA], CT perfusion [CTP], MRI, MR angiography [MRA]) utilization, acute treatment (endovascular thrombectomy [EVT] and intravenous thrombolysis [IVT]), and mortality while in the hospital and at 30 days and 1 year post discharge.

RESULTS

Significantly increasing utilization trends for CTA (250%), CTP (428%) and MRI (18%), and a decreasing trend for MRA (-33%) were observed from 2012 to 2019 (P < .0001). Controlling for covariates in the logistic regression models, CTA and CTP were significantly associated with higher EVT and IVT utilization. Although CTA, MRI, and MRA were associated with lower mortality, CTP was associated with higher mortality post discharge. Less neuroimaging was performed in rural patients; older patients (≥80 years) had lower utilization of CTA, MRI, and MRA; female patients had lower rates of CTA; and Black patients had lower utilization of CTA and CTP.

CONCLUSIONS

CTA and CTP utilization increased in the Medicare ischemic stroke population from 2012 to 2019 and both were associated with greater EVT and IVT use. However, disparities exist in neuroimaging utilization across all demographic groups, and further understanding of the root causes of these disparities will be crucial to achieving equity in stroke care.

The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature

PURPOSE OF REVIEW

To critically appraise literature on recent advances and methods using "big data" to evaluate stroke outcomes and associated factors.

RECENT FINDINGS

Recent big data studies provided new evidence on the incidence of stroke outcomes, and important emerging predictors of these outcomes. Main highlights included the identification of COVID-19 infection and exposure to a low-dose particulate matter as emerging predictors of mortality post-stroke. Demographic (age, sex) and geographical (rural vs. urban) disparities in outcomes were also identified. There was a surge in methodological (e.g., machine learning and validation) studies aimed at maximizing the efficiency of big data for improving the prediction of stroke outcomes. However, considerable delays remain between data generation and publication. Big data are driving rapid innovations in research of stroke outcomes, generating novel evidence for bridging practice gaps. Opportunity exists to harness big data to drive real-time improvements in stroke outcomes.

Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment

Background: Stroke is the second cause of mortality worldwide and the first in women. The aim of this study is to develop a predictive model to estimate the risk of mortality in the admission of patients who have not received reperfusion treatment. Methods: A retrospective cohort study was conducted of a clinical–administrative database, reflecting all cases of non-reperfused ischaemic stroke admitted to Spanish hospitals during the period 2008–2012. A predictive model based on logistic regression was developed on a training cohort and later validated by the “hold-out” method. Complementary machine learning techniques were also explored. Results: The resulting model had the following nine variables, all readily obtainable during initial care. Age (OR 1.069), female sex (OR 1.202), readmission (OR 2.008), hypertension (OR 0.726), diabetes (OR 1.105), atrial fibrillation (OR 1.537), dyslipidaemia (0.638), heart failure (OR 1.518) and neurological symptoms suggestive of posterior fossa involvement (OR 2.639). The predictability was moderate (AUC 0.742, 95% CI: 0.737–0.747), with good visual calibration; Pearson’s chi-square test revealed non-significant calibration. An easily consulted risk score was prepared. Conclusions: It is possible to create a predictive model of mortality for patients with ischaemic stroke from which important advances can be made towards optimising the quality and efficiency of care. The model results are available within a few minutes of admission and would provide a valuable complementary resource for the neurologist.

Disparities in the Use of Mechanical Thrombectomy Alone Compared with Adjunctive Intravenous Thrombolysis in Acute Ischemic Stroke in the United States

BACKGROUND AND PURPOSE

For patients with large-vessel occlusion, mechanical thrombectomy (MT) without IV-tPA is a proved strategy. The relative benefit of direct MT versus MT+IV-tPA for patients with indications for IV-tPA is being actively investigated. We used a national inpatient database to assess trends in use and patient profiles after MT+IV-tPA versus mechanical thrombectomy alone.

MATERIALS AND METHODS

The National Inpatient Sample was queried between 2013 and 2018 for patients undergoing mechanical thrombectomy for acute ischemic stroke. Patients who received mechanical thrombectomy alone were compared with those who underwent MT+IV-tPA. The Cochran-Armitage test was conducted to assess the linear trend of use of mechanical thrombectomy alone among the entire cohort and between admissions involving non-White and White patients. All estimates were nationalized using discharge weights.

RESULTS

A total of 89,645 weighted admissions were identified pertaining to mechanical thrombectomy for acute ischemic stroke from 2013 to 2018. Of these, 59,935 (66.9%) admissions involved mechanical thrombectomy alone. There was an increase in the trend toward the use of mechanical thrombectomy alone (trend: 3.26%; P < .001) per year. Multivariable regression analysis regarding patient profiles indicated that patients who identified as Black (OR = 0.83, P = .001) or Hispanic (OR = 0.79; P < .001) were more likely to undergo mechanical thrombectomy alone compared with those who identified as White. There was no statistically significant difference in the slope between non-White and White populations receiving mechanical thrombectomy alone (trend: +0.93% in favor of non-White; P = .096).

CONCLUSIONS

Our results indicated that mechanical thrombectomy alone was used more frequently than MT+IV-tPA among patients with acute ischemic stroke. The disparity between those who identify as White and non-White persisted across the years, though it is closing.