Evaluating the Concordance between ICD-10 and Stroke Severity as Measured by the NIHSS
Taha, M.; Habib, M.; Lomachinsky, V.; Hadar, P.; Newhouse, J. P.; Schwamm, L. H.; Blacker, D.; Moura, L. M. V. R.
Show abstract
BackgroundThe National Institutes of Health Stroke Scale (NIHSS) scores have been used to evaluate Acute Ischemic Stroke (AIS) severity in clinical settings. Through the International Classification of Diseases, Tenth Revision Code (ICD-10), documentation of NIHSS scores has been made possible for administrative purposes and has since been increasingly adopted in insurance claims. Per CMS guidelines, the stroke ICD-10 diagnosis code must be documented by the treating physician, but ICD-10 NIHSS scores can be documented by any healthcare provider involved in the patients care. Accuracy of the administratively collected NIHSS compared to expert clinical evaluation as documented in the Paul Coverdell registry is however still uncertain. MethodsLeveraging a linked dataset comprised of the Paul Coverdell National Acute Stroke Program (PCNASP) clinical registry and probabilistically matched individuals on Medicare Claims data, we sampled patients aged 65 and above admitted for AIS across nine states, from 2016 to 2019. We excluded those lacking documentation for either clinical or ICD-10 based NIHSS scores. We then examined score concordance from both databases and measured discordance as the absolute difference between the PCNASP and ICD-10-based NIHSS scores. ResultsAmong 66,837 matched patients, mean NIHSS scores for PCNASP and Medicare ICD-10 were 7.26 (95% CI: 7.20 - 7.32) and 7.40 (95% CI: 7.34 - 7.46), respectively. Concordance between the two scores was high as indicated by an intraclass correlation coefficient of 0.93. ConclusionThe high concordance between clinical and ICD-10 NIHSS scores highlights the latters potential as measure of stroke severity derived from structured claims data.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A Claims-Based Machine Learning Classifier of Modified Rankin Scale in Acute Ischemic Stroke 96%
- Persistent Inequities in Intravenous Thrombolysis for Acute Ischemic Stroke in the U.S.: Results from the NIS 95%
- Disparities in Access to Vascular Stroke Imaging and Carotid Revascularization: A Population Study 95%
Similar papers in this journal
- Leveraging Machine Learning for Enhanced and Interpretable Risk Prediction of Venous Thromboembolism in Acute Ischemic Stroke Care 94%
- Association of Mortality and Aspirin Prescription for COVID-19 Patients at the Veterans Health Administration 93%
- Confounding adjustment performance of ordinal analysis methods in stroke studies 92%
Similar papers in this journal
- Deep Learning-based Prediction of Early Cerebrovascular Events after Transcatheter Aortic Valve Replacement 92%
- Rapid Clinical Screening and Staging for COVID-19 Severe Outcome A Hospitalization Study in New York City 92%
- AKI Risk Score (AKI-RiSc): Developing an Interpretable Clinical Score for Early Identification of Acute Kidney Injury for Patients Presenting to the Emergency Department 91%
Similar papers in this journal
- Modified Rankin Scale Disability Status at Day 4 Poststroke is an Informative Predictor of Long-Term Day 90 Outcome 96%
- Vascular Risk Factor Prevalence and Trends in Native Americans With Ischemic Stroke - A National Inpatient Sample Analysis 94%
- Documented Goals of Care Conversations with Hospitalized Patients after Severe Stroke 94%
Similar papers in this journal
- Effectiveness and Cost-Effectiveness of TeleStroke Consultations to Support the Care of Stroke Patients Presenting to Regional Emergency Departments in Western Australia: An Economic Evaluation Case Study Protocol 93%
- Measurement of quality of stroke care with national electronic health records: a cohort during and after the COVID-19 pandemic 93%
- Outcome Disparities by Insurance Plan and Educational Attainment in Patients with Atrial Fibrillation 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.