High Neutrophil-to-Lymphocyte Ratio Predicts One-Year Risk of Pneumonia Post-Stroke Discharge.

INTRODUCTION: The neutrophil-to-lymphocyte ratio (NLR) may predict stroke-associated pneumonia, which is generally defined as pneumonia occurring in the first week after stroke. However, little is known whether the initial NLR is associated with pneumonia risk during the long-term follow-up in stroke survivors. We aimed to determine the relationship between admission NLR and the risk of post-stroke pneumonia within 1 year after discharge from acute stroke care. METHODS: Hospital databases were searched to identify adult patients hospitalized for acute stroke. Admission NLR was extracted using differential leukocyte counts. The outcome of interest was hospitalized pneumonia occurring within 1 year after discharge from hospitalization for stroke. Multivariable Cox proportional-hazards models were used to determine the independent effects of the NLR on the risk of pneumonia. RESULTS: In this study, 5,741 patients with acute stroke (mean age, 68 years; men, 62.1%) were analyzed. The median NLR was 2.72 (interquartile range, 1.78-4.49). Of the patients, 342 (6.0%) developed pneumonia within 1 year after discharge. In the multivariable models, the NLR was a significant predictor of pneumonia after discharge whether it was analyzed as a continuous or dichotomized variable. The corresponding adjusted hazard ratios were 1.037 (95% confidence interval [CI], 1.013-1.061) and 1.361 (95% CI, 1.087-1.704), respectively. CONCLUSION: The NLR could predict the risk of post-stroke pneumonia up to 1 year after discharge from acute stroke care. It may help identify high-risk stroke survivors, for whom appropriate interventions can be targeted.

Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing.

Background: Timely detection of atrial fibrillation (AF) after stroke is highly clinically relevant, aiding decisions on the optimal strategies for secondary prevention of stroke. In the context of limited medical resources, it is crucial to set the right priorities of extended heart rhythm monitoring by stratifying patients into different risk groups likely to have newly detected AF (NDAF). This study aimed to develop an electronic health record (EHR)-based machine learning model to assess the risk of NDAF in an early stage after stroke. Methods: Linked data between a hospital stroke registry and a deidentified research-based database including EHRs and administrative claims data was used. Demographic features, physiological measurements, routine laboratory results, and clinical free text were extracted from EHRs. The extreme gradient boosting algorithm was used to build the prediction model. The prediction performance was evaluated by the C-index and was compared to that of the AS5F and CHASE-LESS scores. Results: The study population consisted of a training set of 4,064 and a temporal test set of 1,492 patients. During a median follow-up of 10.2 months, the incidence rate of NDAF was 87.0 per 1,000 person-year in the test set. On the test set, the model based on both structured and unstructured data achieved a C-index of 0.840, which was significantly higher than those of the AS5F (0.779, p = 0.023) and CHASE-LESS (0.768, p = 0.005) scores. Conclusions: It is feasible to build a machine learning model to assess the risk of NDAF based on EHR data available at the time of hospital admission. Inclusion of information derived from clinical free text can significantly improve the model performance and may outperform risk scores developed using traditional statistical methods. Further studies are needed to assess the clinical usefulness of the prediction model.

Validation of Risk Scores for Predicting Atrial Fibrillation Detected After Stroke Based on an Electronic Medical Record Algorithm: A Registry-Claims-Electronic Medical Record Linked Data Study.

Background: Poststroke atrial fibrillation (AF) screening aids decisions regarding the optimal secondary prevention strategies in patients with acute ischemic stroke (AIS). We used an electronic medical record (EMR) algorithm to identify AF in a cohort of AIS patients, which were used to validate eight risk scores for predicting AF detected after stroke (AFDAS). Methods: We used linked data between a hospital stroke registry and a deidentified database including EMRs and administrative claims data. EMR algorithms were constructed to identify AF using diagnostic and medication codes as well as free clinical text. Based on the optimal EMR algorithm, the incidence rate of AFDAS was estimated. The predictive performance of 8 risk scores including AS5F, C2HEST, CHADS2, CHA2DS2-VASc, CHASE-LESS, HATCH, HAVOC, and Re-CHARGE-AF scores, were compared using the C-index, net reclassification improvement, integrated discrimination improvement, calibration curve, and decision curve analysis. Results: The algorithm that defines AF as any positive mention of AF-related keywords in electrocardiography or echocardiography reports, or presence of diagnostic codes of AF was used to identify AF. Among the 5,412 AIS patients without known AF at stroke admission, the incidence rate of AFDAS was 84.5 per 1,000 person-year. The CHASE-LESS and AS5F scores were well calibrated and showed comparable C-indices (0.741 versus 0.730, p = 0.223), which were significantly higher than the other risk scores. Conclusion: The CHASE-LESS and AS5F scores demonstrated adequate discrimination and calibration for predicting AFDAS. Both simple risk scores may help select patients for intensive AF monitoring.