Neurological Pupil Index and Delayed Cerebral Ischemia after Subarachnoid Hemorrhage: A Retrospective Multicentric Study

Bogossian, Elisa Gouvêa; Ortíz, Aaron Blandino; Esposito, V.; Caricato, Anselmo; Righy, Cássia; López, Berta; Giannì, Giuseppina; Macchini, Elisabetta; Sánchez, R.; Pisapia, Luca; Turon, R; Gonçalves, Bruno; Badenes, Rafael; Kurtz, Pedro; Taccone, Fabio Silvio

doi:10.1007/s12028-023-01744-y

Cited by 5 publications

(8 citation statements)

References 29 publications

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“…Instead, in the multivariate logistic regression model, the NPi OR reversed (i.e., OR < 1), suggesting that a high pupil reactivity is a “protective factor” for stroke diagnosis, although this did not reach statistical significance as an independent predictor of stroke. These results are in line with a recent study that reported that NPi failed to be an independent predictor of delayed cerebral ischemia in patients admitted to the N-ICU for subarachnoid hemorrhage [ 36 ]. These data could be explained considering that NPi is influenced almost exclusively by the brainstem function, making it a useful measure for the early detection of intracranial hypertension [ 18 , 37 ], while it is only marginally influenced by cortical activity, contrary to other AP variables [ 38 ].…”

Section: Discussionsupporting

confidence: 92%

Automated Pupillometry Is Able to Discriminate Patients with Acute Stroke from Healthy Subjects: An Observational, Cross-Sectional Study

Scala,

Miccoli,

Pafundi

et al. 2024

Brain Sciences

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Background: Automated pupillometry (AP) is a handheld, non-invasive tool that is able to assess pupillary light reflex dynamics and is useful for the detection of intracranial hypertension. Limited evidence is available on acute ischemic stroke (AIS) patients. The primary objective was to evaluate the ability of AP to discriminate AIS patients from healthy subjects (HS). Secondly, we aimed to compute a predictive score for AIS diagnosis based on clinical, demographic, and AP variables. Methods: We included 200 consecutive patients admitted to a comprehensive stroke center who underwent AP assessment through NPi-200 (NeurOptics®) within 72 h of stroke onset and 200 HS. The mean values of AP parameters and the absolute differences between the AP parameters of the two eyes were considered in the analyses. Predictors of stroke diagnosis were identified through univariate and multivariate logistic regressions; we then computed a nomogram based on each variable’s β coefficient. Finally, we developed a web app capable of displaying the probability of stroke diagnosis based on the predictive algorithm. Results: A high percentage of pupil constriction (CH, p < 0.001), a low constriction velocity (CV, p = 0.002), and high differences between these two parameters (p = 0.036 and p = 0.004, respectively) were independent predictors of AIS. The highest contribution in the predictive score was provided by CH, the Neurological Pupil Index, CV, and CV absolute difference, disclosing the important role of AP in the discrimination of stroke patients. Conclusions: The results of our study suggest that AP parameters, and in particular, those concerning pupillary constriction, may be useful for the early diagnosis of AIS.

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Section: Discussionsupporting

confidence: 92%

Automated Pupillometry Is Able to Discriminate Patients with Acute Stroke from Healthy Subjects: An Observational, Cross-Sectional Study

Scala,

Miccoli,

Pafundi

et al. 2024

Brain Sciences

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“…Both criteria had to be met to qualify as DCI. We further conducted a sensitivity analysis in which the evidence of focal infarction not related to aneurysm occlusion and not attributed to other causes was required additionally to the criteria described above for the definition of DCI (29, 33). Events were recorded prospectively as part of the Swiss Study On aSAH (Swiss SOS) registry (34) and were retrospectively verified by the investigators.…”

Section: Methodsmentioning

confidence: 99%

“…Both studies were however limited by the small number of events, with a total of seven identified DCIs per study. The relationship between NPi and DCI was further investigated in a recent larger study, which, although confirming an association between NPi and clinical outcome at 3 months as well as in-hospital mortality, could not demonstrate an association with DCI (29). This study had however several limitations, including the use of a single dichotomized pupillometry parameter, few measurements (every 8 hr), and the lack of integration of inter-eye differences (29, 30).…”

mentioning

confidence: 98%

“…The relationship between NPi and DCI was further investigated in a recent larger study, which, although confirming an association between NPi and clinical outcome at 3 months as well as in-hospital mortality, could not demonstrate an association with DCI (29). This study had however several limitations, including the use of a single dichotomized pupillometry parameter, few measurements (every 8 hr), and the lack of integration of inter-eye differences (29, 30). Furthermore, by using an absolute threshold, the dynamic changes of longitudinal pupil observations were not explored.…”

mentioning

confidence: 98%

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Dynamically Normalized Pupillometry for Detecting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

Klug,

Martins,

De Trizio

et al. 2024

Critical Care Explorations

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OBJECTIVES: Delayed cerebral ischemia (DCI) is a major driver of morbidity after aneurysmal subarachnoid hemorrhage (aSAH). Quantitative pupillometry has been shown to be of prognostic value after acute neurological injury. However, the evidence for the use of pupillometric features for the detection of DCI has been conflicting. The aim of this study was to investigate the prognostic value of frequent pupillometric monitoring for DCI detection. DESIGN: Observational cohort study from a prospective aSAH registry. SETTING: Tertiary referral center. PATIENTS: Adult patients with confirmed aSAH admitted to the ICU between March 2019 and December 2023. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: One hundred fourteen patients were included, of which 31 (27.2%) suffered from DCI. All patients underwent frequent pupillometry (every 3 hr). We determined the absolute value of the neurological pupil index (NPi) and constriction velocity (CV), and their value normalized to the maximal recorded value between the admission and the pupillometry measure to account for personalized baselines. The association between pupillometry values and the occurrence of DCI within 6–24 hours was investigated. Normalized CV had the best discriminative performance to identify DCI within 8 hours, with an area under the receiver operating characteristic curve of 0.82 (95% CI, 0.69–0.91). NPi, as well as non-normalized metrics, were not significantly associated with DCI. CONCLUSIONS: Normalized CV has a clinically and statistically significant association with the occurrence of DCI after aSAH. Frequent quantitative pupillometry could improve the multimodal monitoring of patients after aSAH with the goal of improving the identification of patients likely to benefit from therapeutic interventions.

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“…Studies of automated pupillometry have explored the detection of increased ICP [15,16,39], detection of delayed cerebral ischemia [40 ▪▪ ], and determination of treatment success in nonconvulsive status epilepticus [41]. Research has been most extensive in neurological prognostication [42 ▪▪ ,43–45].…”

Section: Automated Quantitative Pupillometrymentioning

confidence: 99%

Neuromonitoring in the ICU – what, how and why?

Mathur,

Meyfroidt,

Robba

et al. 2024

Current Opinion in Critical Care

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Purpose of review We selectively review emerging noninvasive neuromonitoring techniques and the evidence that supports their use in the ICU setting. The focus is on neuromonitoring research in patients with acute brain injury. Recent findings Noninvasive intracranial pressure evaluation with optic nerve sheath diameter measurements, transcranial Doppler waveform analysis, or skull mechanical extensometer waveform recordings have potential safety and resource-intensity advantages when compared to standard invasive monitors, however each of these techniques has limitations. Quantitative electroencephalography can be applied for detection of cerebral ischemia and states of covert consciousness. Near-infrared spectroscopy may be leveraged for cerebral oxygenation and autoregulation computation. Automated quantitative pupillometry and heart rate variability analysis have been shown to have diagnostic and/or prognostic significance in selected subtypes of acute brain injury. Finally, artificial intelligence is likely to transform interpretation and deployment of neuromonitoring paradigms individually and when integrated in multimodal paradigms. Summary The ability to detect brain dysfunction and injury in critically ill patients is being enriched thanks to remarkable advances in neuromonitoring data acquisition and analysis. Studies are needed to validate the accuracy and reliability of these new approaches, and their feasibility and implementation within existing intensive care workflows.

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Neurological Pupil Index and Delayed Cerebral Ischemia after Subarachnoid Hemorrhage: A Retrospective Multicentric Study

Cited by 5 publications

References 29 publications

Automated Pupillometry Is Able to Discriminate Patients with Acute Stroke from Healthy Subjects: An Observational, Cross-Sectional Study

Automated Pupillometry Is Able to Discriminate Patients with Acute Stroke from Healthy Subjects: An Observational, Cross-Sectional Study

Dynamically Normalized Pupillometry for Detecting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

Neuromonitoring in the ICU – what, how and why?

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