Bispectral Index in predicting in-hospital mortality in patients with ischemic stroke: A methodological study

Background: Ischemic stroke is a leading cause of death and functional disability worldwide. Several clinical scores or stroke scales, biological test or markers, clinical signs, and radiological imaging have been performed to predict both worse neurologic outcome and mortality for ischemic stroke. Objectives: The aim of our study was to investigate the association between early Bispectral Index scores and in-hospital mortality in patients with ischemic stroke. Methods: This is a comparative prospective methodological study, in which we evaluated the predictive accuracies of Bispectral Index, Glasgow Coma Scale, and Charlson Comorbidity Index for in-hospital mortality of patients with ischemic stroke. Receiver operating characteristic analysis was used for comparing the accuracy of the scoring systems, areas under receiver operating characteristic curves were calculated, and Youden J index was used for estimating associated cut-off values. Results: Among the 80 ischemic stroke patients, in-hospital mortality rate was 38.8% (n = 31). The areas under receiver operating characteristic curves were 0.984, 0.960, and 0.863 for Bispectral Index, Glasgow Coma Scale, and Charlson Comorbidity Index, respectively. The difference between areas under receiver operating characteristic curves for Bispectral Index and Glasgow Coma Scale was statistically similar. Besides, the difference between areas under receiver operating characteristic curves for Bispectral Index and Charlson Comorbidity Index, and the difference between areas under receiver operating characteristic curves for Glasgow Coma Scale and Charlson Comorbidity Index were statistically significant. The associated cut-off values were ⩽74, ⩽12, and >4 for Bispectral Index, Glasgow Coma Scale, and Charlson Comorbidity Index, respectively. For these cut-off points, sensitivity and specificity of Bispectral Index were 93.6% and 95.9%, sensitivity and specificity of Glasgow Coma Scale were 100.0% and 83.7%, and sensitivity and specificity of Charlson Comorbidity Index were 83.9% and 69.4%, respectively. However, accuracy of Bispectral Index was 95.0%, accuracy of Glasgow Coma Scale was 90.0%, and accuracy of Charlson Comorbidity Index was 75.0. Conclusion: Knowledge of the risk factors for mortality in patients with ischemic stroke can help to identify which patients have a higher risk of fatal outcome. The Bispectral Index score improved discrimination and classified patients with higher mortality better than both Glasgow Coma Scale and Charlson Comorbidity Index.

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Risikoabschätzung einer akuten Exazerbation bei COPD-Patienten im Rahmen einer pneumologischen Anschluss-Rehabilitation anhand der Prävalenz und Schwergradausprägung von Komorbiditäten

Pneumologie ◽

10.1055/a-1346-5504 ◽

2021 ◽

Author(s):

P. Luu ◽

S. Tulka ◽

S. Knippschild ◽

W. Windisch ◽

M. Spielmanns

Keyword(s):

Receiver Operating Characteristic ◽

Charlson Comorbidity Index ◽

Operating Characteristic ◽

Rating Scale ◽

Area Under The Curve ◽

Sensitivität Und Spezifität ◽

Comorbidity Index ◽

Receiver Operating

Zusammenfassung Einleitung Akute COPD-Exazerbationen (AECOPD) im Rahmen einer pneumologischen Rehabilitation (PR) sind häufige und gefährliche Komplikationen. Neben Einschränkungen der Lebensqualität führen sie zu einem Unterbrechung der PR und gefährden den PR-Erfolg. Eine Abhängigkeit zwischen dem Krankheitsstatus und einem erhöhten Risiko für eine AECOPD ist beschrieben. Dabei stellt sich die Frage, ob der Charlson Comorbidity Index (CCI) oder die Cumulative Illness Rating Scale (CIRS) dafür geeignet sind, besonders exazerbationsgefährdete COPD-Patienten in der PR im Vorfeld zu detektieren. Patienten und Methoden In einer retrospektiven Untersuchung wurden die Daten von COPD-Patienten, welche im Jahr 2018 eine PR erhielten, analysiert. Primärer Endpunkt der Untersuchung war die Punktzahl im CCI. Alle Daten wurden dem Klinikinformationssystem Phönix entnommen und COPD-Exazerbationen erfasst. Die laut Fallzahlplanung benötigten 44 Patienten wurden zufällig (mittels Zufallsliste für jede Gruppe) aus diesem Datenpool rekrutiert: 22 Patienten mit und 22 ohne Exazerbation während der PR. CCI und CIRS wurden für die eingeschlossenen Fälle für beide Gruppen bestimmt. Die Auswertung des primären Endpunktes (CCI) erfolgte durch den Gruppenvergleich der arithmetischen Mittel und der Signifikanzprüfung (Welch-Tests). Weitere statistische Lage- und Streuungsmaße wurden ergänzt (Median, Quartile, Standardabweichung).Zusätzlich wurde mittels Receiver Operating Characteristic (ROC)-Analyse sowohl für den CCI als auch für den CIRS ein optimaler Cutpoint zur Diskriminierung in AECOPD- und Nicht-AECOPD-Patienten gesucht. Ergebnisse 244 COPD-Patienten erhielten eine stationäre PR von durchschnittlich 21 Tagen, wovon 59 (24 %) während der PR eine behandlungspflichtige AECOPD erlitten. Die ausgewählten 22 Patienten mit einer AECOPD hatten einen mittleren CCI von 6,77 (SD: 1,97) und die 22 Patienten ohne AECOPD von 4,32 (SD: 1,17). Die Differenz von –2,45 war zu einem Signifikanzniveau von 5 % statistisch signifikant (p < 0,001; 95 %-KI: [–3,45 ; –1,46]). Die ROC-Analyse zeigte einen optimalen Cutpoint für den CCI bei 6 mit einer Sensitivität zur Feststellung einer AECOPD von 81,8 % und einer Spezifität von 86.,4 % mit einem Wert der AUC (area under the curve) von 0,87. Der optimale Cutpoint für den CIRS war 19 mit einer Sensitivität von 50 %, einer Spezifität von 77,2 % und einer AUC von 0,65. Schlussfolgerung COPD-Patienten mit einer akuten Exazerbation während der pneumologischen Rehabilitation haben einen höheren CCI. Mithilfe des CCI lässt sich mit einer hohen Sensitivität und Spezifität das Risiko einer AECOPD von COPD-Patienten im Rahmen eines stationären PR-Programms einschätzen.

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Association of Serum Fatty Acids at Admission with the Age of Onset of Acute Ischemic Stroke

Nutrients ◽

10.3390/nu12082411 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2411 ◽

Cited By ~ 1

Author(s):

Takahisa Mori ◽

Kazuhiro Yoshioka ◽

Yuhei Tanno ◽

Shigen Kasakura

Keyword(s):

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Age Of Onset ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Characteristic Curves ◽

Receiver Operating Characteristic Curves ◽

Receiver Operating

Dietary triglycerides influence fatty acid (FA) serum concentrations and weight percentages (wt %), which may be associated with the age of onset of acute ischemic stroke (AIS). We investigated the correlations between serum FA levels and proportions at admission and the age of onset of AIS. We evaluated patients with AIS admitted between 2016 and 2019 within 24 h of AIS onset and calculated the correlation coefficients between their ages, serum FA concentrations, and FA wt % values. Multiple linear regression analysis was performed to identify independent FAs indicating AIS age of onset. Furthermore, we estimated the threshold values of independent FAs for age of onset <60 years using receiver operating characteristic curves by logistic regression. A total of 525 patients (median age: 75 years) met the inclusion criteria. The concentration of dihomo-gamma-linolenic acid (DGLA) and wt % of docosahexaenoic acid (DHA) were significant independent variables for age of onset of AIS, and receiver operating characteristic curves for age of onset <60 years showed thresholds of ≥117.7 µmol/L for DGLA and ≤3.7% for DHA. An increased DGLA concentration and decreased DHA wt % were significantly associated with onset of AIS at a younger age.

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Natural Language Processing Enhances Prediction of Functional Outcome After Acute Ischemic Stroke

Journal of the American Heart Association ◽

10.1161/jaha.121.023486 ◽

2021 ◽

Author(s):

Sheng‐Feng Sung ◽

Chih‐Hao Chen ◽

Ru‐Chiou Pan ◽

Ya‐Han Hu ◽

Jiann‐Shing Jeng

Keyword(s):

Ischemic Stroke ◽

Natural Language Processing ◽

Natural Language ◽

Acute Ischemic Stroke ◽

Receiver Operating Characteristic ◽

Functional Outcome ◽

Language Processing ◽

Operating Characteristic ◽

Receiver Operating Characteristic Curves ◽

Receiver Operating

Background Conventional prognostic scores usually require predefined clinical variables to predict outcome. The advancement of natural language processing has made it feasible to derive meaning from unstructured data. We aimed to test whether using unstructured text in electronic health records can improve the prediction of functional outcome after acute ischemic stroke. Methods and Results Patients hospitalized for acute ischemic stroke were identified from 2 hospital stroke registries (3847 and 2668 patients, respectively). Prediction models developed using the first cohort were externally validated using the second cohort, and vice versa. Free text in the history of present illness and computed tomography reports was used to build machine learning models using natural language processing to predict poor functional outcome at 90 days poststroke. Four conventional prognostic models were used as baseline models. The area under the receiver operating characteristic curves of the model using history of present illness in the internal and external validation sets were 0.820 and 0.792, respectively, which were comparable to the National Institutes of Health Stroke Scale score (0.811 and 0.807). The model using computed tomography reports achieved area under the receiver operating characteristic curves of 0.758 and 0.658. Adding information from clinical text significantly improved the predictive performance of each baseline model in terms of area under the receiver operating characteristic curves, net reclassification improvement, and integrated discrimination improvement indices (all P <0.001). Swapping the study cohorts led to similar results. Conclusions By using natural language processing, unstructured text in electronic health records can provide an alternative tool for stroke prognostication, and even enhance the performance of existing prognostic scores.

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A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

Statistical Methods in Medical Research ◽

10.1177/0962280221995956 ◽

2021 ◽

pp. 096228022199595

Author(s):

Yalda Zarnegarnia ◽

Shari Messinger

Keyword(s):

Receiver Operating Characteristic Curve ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Characteristic Curve ◽

Case Control ◽

Paired Data ◽

Characteristic Curves ◽

Receiver Operating Characteristic Curves ◽

Operating Characteristic Curve ◽

Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

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