Pupil Abnormality Frequency in the First 72 Hours Improves IMPACT score in Traumatic Brain Injury

Pupil Abnormality Frequency in the First 72 Hours Improves IMPACT score in Traumatic Brain Injury | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Pupil Abnormality Frequency in the First 72 Hours Improves IMPACT score in Traumatic Brain Injury Divya Veerapaneni , View ORCID Profile Naveen Arunachalam Sakthiyendran , So Yeon Kim , Chuong Nguyen , View ORCID Profile Ali Daneshmand , Mohamad Abdalkader , Shariq Mohammed , Josée Dupuis , Kevin N. Sheth , Emily J. Gilmore , David Greer , View ORCID Profile Charlene J. Ong doi: https://doi.org/10.1101/2024.11.23.24317826 Divya Veerapaneni 1 Department of Neurology, The University of Texas Southwestern Medical Center , 5323 Harry Hines Blvd, Dallas, TX 75390 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Naveen Arunachalam Sakthiyendran 2 Boston University Chobanian and Avedisian School of Medicine , 85 East Concord St, Boston, MA 02118 BS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Naveen Arunachalam Sakthiyendran So Yeon Kim 2 Boston University Chobanian and Avedisian School of Medicine , 85 East Concord St, Boston, MA 02118 MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chuong Nguyen 3 Department of Neurology, Boston Medical Center, 1 Boston Medical Center PI , Boston, MA 02118 BS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ali Daneshmand 2 Boston University Chobanian and Avedisian School of Medicine , 85 East Concord St, Boston, MA 02118 3 Department of Neurology, Boston Medical Center, 1 Boston Medical Center PI , Boston, MA 02118 MD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ali Daneshmand Mohamad Abdalkader 4 Department of Radiology, Boston Medical Center, 1 Boston Medical Center PI , Boston, MA 02118 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shariq Mohammed 5 Department of Biostatistics, Boston University School of Public Health , 801 Massachusetts Avenue, Crosstown Center, Boston, MA 02118 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Josée Dupuis 5 Department of Biostatistics, Boston University School of Public Health , 801 Massachusetts Avenue, Crosstown Center, Boston, MA 02118 6 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 2001 McGill College Avenue , Montreal, Quebec, Canada H3A 1G1 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kevin N. Sheth 7 Center for Brain and Mind Health, Yale School of Medicine , 15 York Street LCI, 1003C, New Haven, CT 06510 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emily J. Gilmore 8 Department of Neurology, Yale School of Medicine , 333 Cedar Street, New Haven, CT 06510 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site David Greer 2 Boston University Chobanian and Avedisian School of Medicine , 85 East Concord St, Boston, MA 02118 3 Department of Neurology, Boston Medical Center, 1 Boston Medical Center PI , Boston, MA 02118 MD, MA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Charlene J. Ong 2 Boston University Chobanian and Avedisian School of Medicine , 85 East Concord St, Boston, MA 02118 3 Department of Neurology, Boston Medical Center, 1 Boston Medical Center PI , Boston, MA 02118 MD, MPHS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Charlene J. Ong For correspondence: cjong{at}bu.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Importance In patients with traumatic brain injury (TBI), baseline pupillary assessment is common. However, the incidence and frequency of pupil abnormalities within the first several days remain poorly characterized. Objectives Our aim was to test the association between pupil abnormality frequency over the first 72 hours of admission and clinical outcomes. Design We conducted a retrospective observational study of patients with a primary diagnosis of TBI with at least three quantitative pupillometry measurements within 72 hours at a single-center ICU from 2018 to 2022. Outcomes and Measures Neurological Pupil index (NPi), a quantitative composite metric for pupil reactivity, was obtained at each clinical neurologic assessment over 72 hours. Pupil measurements were defined as abnormal if they had a NPi of <3 in either eye, NPi asymmetry ≥0.7, or pupil size asymmetry ≥1mm. We tested the association of increased frequency of pupil abnormalities over 72 hours and unfavorable discharge disposition (death, hospice, or long-term care) using multivariable logistic regression, adjusting for confounders. We then compared whether the IMPACT model was improved by the frequency of pupil abnormalities using goodness-of-fit. Results Of 131 patients, median age was 59 years, and 30% were women. Thirty-five percent had unfavorable discharge disposition. Pupil abnormalities occurred in 62%, 61%, and 88% of mild, moderate, and severe TBI patients, respectively. Odds ratio of unfavorable discharge for every 1% increase in pupil abnormality frequency was 1.03 (95% Cl, 1.01-1.05), equivalent to one additional abnormal pupil measurement within a 72-hour period. The adjusted IMPACT TBI model’s goodness-of-fit improved with pupil abnormality frequency ( X 2 =5.67, p =0.02). Conclusions and Relevance Pupil abnormalities occur commonly in TBI and have the highest frequency in severe TBI. Increased pupil abnormality frequency is associated with unfavorable discharge disposition and improves performance of prognostic TBI models. Key Points The goal of this study was to test the association between pupil abnormality frequency over the first 72 hours of admission and unfavorable discharge disposition and whether it improved the IMPACT model. In this retrospective observational study of 131 TBI patients at a single-center, we found that an increased frequency of pupil abnormalities across 72-hours significantly correlated with unfavorable discharge in patients and improved the IMPACT model’s goodness-of-fit. These findings highlight the potential of long-term pupillary metrics and their role as not only a prognostic indicator in patients but also a tool that improves the performance of prognostic TBI models. Background Traumatic brain injury (TBI) is a leading cause of mortality and morbidity worldwide. 1 Absent unilateral or bilateral pupil reactivity on admission is associated with worse outcomes in TBI patients 2 , 3 and is used in the IMPACT TBI model, 4 , 5 a well-established model for predicting clinical outcomes in patients with moderate and severe TBI. Moreover, close neurologic monitoring including subjective pupil size and reactivity assessment is used to inform clinical decision-making for TBI patients during admission. 6 , 7 However, quantification of pupil abnormalities over time and between TBI severity groups is not well characterized despite the increasing availability of quantitative pupillometers in intensive care settings. 9 Moreover, while prior studies have demonstrated an association between reduced pupil reactivity and long term outcome in heterogenous acute brain injury patients including TBI, 10 it is unclear whether inclusion of longitudinal pupillometry can improve on existing prediction scales. Understanding the expected frequency of pupil abnormalities and their association with clinical outcomes in the first 72 hours post-TBI may improve detection of early neurologic deterioration and outcome prognostication. Our aim was to determine the prognostic value of pupil abnormality frequency by 1) characterizing the prevalence of pupil abnormalities within different TBI severities 2) testing its association with clinical outcomes and 3) investigating whether pupil abnormality frequency improves existing TBI prognostic models. We hypothesized that increased pupil abnormality frequency is associated with worse outcomes and that the inclusion of pupil abnormality frequency over the first 72 hours of admission improves TBI prognostication. Methods Study Population We conducted a single-center retrospective study of TBI patients admitted to the Boston Medical Center surgical and neurological intensive care units between 2018 and 2022 and who had at least three quantitative pupil measurements collected within the first 72 hours of hospitalization. Exclusion criteria included pre-existing eye conditions that may impact pupil reactivity such as diabetic retinopathy and glaucoma ( eFigure 1 ). Download figure Open in new tab Figure 1: Percent Incidence and Median Frequency of Pupil Abnormalities between Traumatic Brain Injury Severity Groups ( n = 131) Percent incidence and median frequency of pupil abnormalities. Incidence and median frequency of unilateral pupil abnormalities were higher compared to bilateral pupil abnormalities across most TBI severity groups. Frequency was reported as percentage and includes only patients with nonzero frequencies. Abbreviations: TBI = Traumatic Brain Injury, % = Percent Pupil Abnormalities: Neurological Pupil index (NPi) 0.7, or Size difference between both eyes (Diff Size) > 1mm Unilateral Pupil Abnormalities: Unilateral NPi 0.7, or Diff Size > 1mm Bilateral Pupil Abnormalities: Bilateral NPi < 3 Data Collection We collected demographic and clinical information from the electronic health record through the Clinical Data Warehouse ( eMethods ). Using the admission Glasgow Coma Scale (GCS) in the intensive care unit post-resuscitation, patients were categorized as mild (GCS 13-15), moderate (GCS 9-12), or severe (GCS < 9) TBI. 11 For each patient, we measured radiographic features including midline shift and effacement of basal cisterns from the most severe brain CT in the first 72 hours to compute the Marshall CT score, 12 a radiographic classification system for TBI, after establishing interrater reliability of at least 0.70 (95% CI, 0.42-0.99). The most severe brain CT in the first 72 hours was used for the analysis to reflect the overall severity of the patient’s condition after potential deterioration rather than the patient’s baseline at admission. Trained nursing staff collected pupillometry data using the NeurOptics NPI-300 TM pupillometer (NeurOptics Inc., Irvine CA, USA) during standard neurological checks (every one, two, or four hours). The pupillometer reports the Neurological Pupil index (NPi), a composite score of pupil reactivity based on resting and constricted pupil size, percent change, constriction velocity, dilation velocity, and latency. Scores range from 0 to 5, with values < 3 considered abnormal by the manufacturer. This report was prepared according to Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines. 13 Outcomes The primary outcome was unfavorable discharge disposition, defined as death, hospice, or long-term facility placement. Other outcomes included death at discharge, and documented evidence of eye tracking at discharge ( eMethods ). Outcomes were determined from discharge summaries and ascertained by two reviewers (C.J.O. and D.V). Exposures The primary exposure was pupil abnormality frequency calculated per patient and defined as the percentage of abnormal pupil measurements over 72 hours. Pupils were considered “abnormal” if NPi 0.7, or resting pupil size asymmetry between eyes (Diff Size) > 1mm. Definitions were based on manufacturer recommendations (NPi 0.7) and prior work on anisocoria in the neuro-ICU (size > 1mm). 14 Other metrics including constriction velocity, dilation velocity, and latency are encompassed in the NPi variable. Secondary exposures were frequencies of unilateral and bilateral pupil abnormalities. Unilateral pupil abnormalities were defined as unilateral NPi < 3, Diff NPi ≥ 0.7, or Diff Size ≥ 1mm, while bilateral pupil abnormalities were defined as bilateral NPi < 3. Diff Size and Diff NPi were considered unilateral pupil abnormalities since one abnormal pupil would be sufficient to make pupils asymmetric between eyes. We also explored incidence, defined dichotomously as present or absent if a pupil abnormality ever occurred during the first 72 hours in a patient. Sensitivity analyses including only NPi 0.7 or size > 1mm were also performed ( eMethods ). Analysis Baseline cohort characteristics were summarized for the full cohort and stratified by TBI severity group with categorical variables reported as frequency (proportion) and continuous variables reported as median (25 th – 75 th percentile) as shown in Table 1 . When reporting pupil abnormality frequencies, we only included frequencies for patients with at least one pupil abnormality ( Table 2 ). To compare the incidence and frequency of pupil abnormalities between different TBI severity groups, we used the chi-square test and Fisher’s exact test for categorical variables. For continuous variables, we employed ANOVA and the Kruskal-Wallis test ( eMethods ). View this table: View inline View popup Download powerpoint Table 1. Baseline Characteristics of Traumatic Brain Injury Patients ( n = 131) View this table: View inline View popup Download powerpoint Table 2. Incidence and Frequency of Pupil Abnormalities over first 72 hours in Traumatic Brain Injury Patients ( n = 131) To test our primary hypothesis, we performed multivariable logistic regression of pupil abnormality frequency and unfavorable discharge disposition after adjusting for hypothesized confounders including age, race, mechanism of injury, TBI severity, and Marshall CT score. 15 , 16 We performed similar analyses using secondary exposures and outcomes and performed subgroup analysis of patients with severe TBI. Given that the data was not normally distributed, reporting between the median or mean was chosen based on sample size and distribution. To determine the prognostic value of pupil abnormality frequency, we modeled unfavorable discharge disposition and death at discharge in moderate and severe TBI using the core IMPACT TBI model and IMPACT plus pupil abnormality frequency over the first 72 hours. Features of the base IMPACT model included age, the motor component of GCS on admission, and pupil reactivity on admission. Pupil reactivity was defined subjectively and dichotomously as either bilateral or unilateral pupil reactivity, with bilateral pupil reactivity as the reference. We compared model goodness-of-fit using the likelihood ratio test and evaluated model performance using receiver operating characteristic (ROC) curve ( eMethods ). 16 Significance threshold of α=0.05 was used our primary hypothesis. All other tests were hypothesis generating only. Preprocessing and analysis of data were done using code publicly available at https://github.com/fordivyav/TBI_Pupillometry ( eMethods ). The study was approved by the Boston Medical Center Institutional Review Board. Informed consent was waived due to the anonymous nature of the study. Anonymized data for this study are available from the senior author (C.J.O.) upon reasonable request. Results Cohort Characteristics Our final cohort consisted of 131 patients with 3877 total pupil measurements. Thirty percent of the cohort was female. Median age was 59 years [38, 72] (25 th , 75 th percentile). Thirty-seven percent (n=48) of patients had mild TBI, 14% (n=18) had moderate TBI, and 50% (n=65) had severe TBI. Seven percent (n=9) of the cohort had penetrating injuries. Median Marshall CT score was 2 [2, 5]. Unfavorable discharge disposition occurred in 35% (n=46) of patients, with 22% (n=29) of patients deceased upon discharge, with 4.6% of the total cohort who died prior to 72 hours ( Table 1 ). Patients underwent a median of 23 [13, 44] pupil measurements. Median time to first pupil measurement was 3 hours [2, 6]. Median NPi over 72 hours was 4.4 [4.1, 4.6]. Seventy-five percent (n=98) of TBI patients experienced at least one pupil abnormality. Among those patients with pupil abnormality, median frequency was 18% [10, 40]. Of patients with at least one abnormal pupil measurement, 97% (n=95) of patients experienced unilateral pupil abnormalities, and 24% (n=24) experienced bilateral pupil abnormalities. Eighteen percent (n=18) had occurences of both unilateral only and bilateral pupil abnormalities. Median frequency of unilateral and bilateral pupil abnormalities was 17% [8-29], and 15% [7-92%] respectively ( Table 2 , Figure 1 , eTable 1 ). Patients with unfavorable discharge disposition had increased median frequency of pupil abnormalities compared to patients with favorable discharge disposition (26 [8, 70] vs. 8% [0, 20]) ( eFigure 2 ). Download figure Open in new tab Figure 2: Violin Plots of Pupil Abnormality Frequency by TBI Severity Pupil abnormalities including unilateral and bilateral pupil abnormalities differed significantly across all TBI severity groups. Frequencies were reported as percentages and only include patients with nonzero frequencies. Pupil Abnormalities and unilateral pupil abnormalities occurred in all TBI severity groups, with increased median frequency associated with increased TBI severity. In contrast, bilateral pupil abnormalities were rare in the full cohort with most bilateral Pupil Abnormalities occurring in severe TBI. Abbreviations: % = Percent, TBI = Traumatic Brain Injury, * = statistically significant difference across TBI severity groups ( p < 0.05) Pupil Abnormalities across TBI Severity Groups There were significant differences observed in age, intracranial pressure monitor use, and discharge outcomes among TBI severity groups ( Table 1 ). Patients with severe TBI were more likely to experience at least one pupil abnormality (88% severe vs. 62% mild vs. 61% moderate) ( p =0.002) ( eFigure 4 ). Bilateral pupil abnormality incidence was much higher in severe TBI (31%) compared to mild and moderate TBI (6% each) ( p =0.001) ( Figures 1 - 2 ). In patients who experienced pupil abnormalities (n=98), the frequency differed across TBI severity groups as follows: 20% [12, 55] in severe, 30% [12, 43] in moderate, and 18% [10, 40] in mild ( p <0.001) ( Table 2 , eFigures 3 - 4 ). Download figure Open in new tab Figure 3: Schematic for IMPACT Model with and without Abnormal Pupil Frequency Created with BioRender.com. The IMPACT model consisted of pupil reactivity on admission (both, one, or neither reactive), Glasgow Coma Scale motor component on admission, and age trained on our study cohort. The IMPACT model with pupil abnormality frequency over the first 72 hours of admission had improved goodness-of-fit ( p = 0.02) compared to the IMPACT model alone. Abbreviations: IMPACT Model = IMPACT TBI Model, TBI = Traumatic Brain Injury, OR = Odds Ratio, GCS = Glasgow Coma Scale, Abnormal Pupil Frequency = Pupil Abnormality Frequency, Hrs = hours a Abnormal Pupil Frequency : Pupil abnormality frequency over the first 72 hours of admission (Neurological Pupil index [NPi] 0.7, or Size difference between both eyes > 1mm) To gain insight into the relation between pupil abnormalities in patients without severe TBI, we describe the hospital courses for mild and moderate TBI patients with NPi < 3 ( eTable 2 ). Pupil Abnormality Frequency and Discharge Dispostion In our adjusted multivariable model, we found that a 1% increase in pupil abnormality frequency was associated with unfavorable discharge disposition (OR: 1.03; 95% Cl, 1.01-1.05). We observed that the OR increased in our stratified analysis of bilateral pupil abnormality frequency (OR: 1.22; 95% Cl, 1.07-1.45), but not in unilateral pupil abnormality frequency (OR: 1.02; 95% Cl, 1.00-1.04) ( Table 3 ). Both bilateral and unilateral pupil abnormality frequency were associated with mortality ( Table 3 ). Comprehensive results including subanalysis of our severe TBI subgroup and single pupil abnormality features are included in eTables 3 - 6 . View this table: View inline View popup Download powerpoint Table 3. Multivariable Models of Pupil Abnormalities Frequency on Discharge Outcomes ( n = 131) IMPACT Score The likelihood ratio test revealed that the IMPACT model plus pupil abnormality frequency had an improved goodness-of-fit compared to the core IMPACT model alone when predicting unfavorable discharge disposition ( X 2 =5.67, p =0.02) ( Figure 3 , eTable 7 ). The IMPACT model for death at discharge also showed improved goodness-of-fit with the addition of pupil abnormality frequency ( p <0.01) ( eTable 7 ). Absent bilateral pupil reactivity at baseline (/3=13.6) and pupil abnormality frequency (/3=2.06 per 1% increase) were the most important coefficients for predicting unfavorable discharge disposition and death at discharge ( eTable 7 , eFigure 5 ). While the ROC curve showed improved performance for the IMPACT model with pupil abnormality frequency (Area under Curve [AUC] 0.78) compared to the IMPACT model alone (AUC 0.75), the differences in AUC were not statistically significant for our primary outcome ( p =0.2) ( eFigure 6 ). Similar results were seen when predicting death at discharge using the IMPACT model with (AUC 0.89) and without pupil abnormality frequency (AUC 0.81) ( p =0.056) ( eFigure 6 ). Discussion In this study, we found that increased frequency of abnormal pupil measurements within the first 72 hours of admission is associated with unfavorable discharge disposition and improves the IMPACT TBI model, which uses subjective pupil reactivity assessment at baseline. These findings are significant because longitudinal pupillometric information is not standardly used for prognostication or treatment decision-making but has the potential to improve care. Our study reveals that abnormal pupil measurements over the first 72 hours are common among TBI patients admitted to the ICU (75%), especially in severe TBI (88%). While patients with severe TBI are more likely to experience at least one pupil abnormality, they remain common among moderate and mild TBI patients as well. Our observation that at least one NPi < 3 occurred in 42% of patients is consistent with results from a recent prospective study investigating the association of abnormal pupil reactivity in patients with heterogenous acute brain injury (40%). 18 The difference in prevalence of unilateral (73%) and bilateral (18%) pupil abnormalities in patients who had at least one abnormal pupil measurement is most likely due to the more inclusive definition we used for unilateral pupil abnormalities including size difference and NPi difference. In patients who experienced pupil abnormalities, the median frequency over the first 72 hours was higher in moderate and severe TBI patients compared to those with mild TBI. While moderate TBI patients had a non-statistically significant higher frequency of pupil abnormalities than severe TBI patients, we posit this may be because they experienced neurologic worsening events within the first 72 hours of admission ( eTable 2 ), evidenced by the significant association between increased pupil abnormality frequency and discharge disposition. Our review of mild and moderate TBI patients with NPi < 3 revealed that patients with mild TBI and pupil abnormalities were frequently complicated by intraparenchymal or subdural hemorrhages, some of which underwent surgical treatment ( eResults ). We infer that these cases support the hypothesis that pupil abnormality incidence and frequency signify increased severity that may not be reflected on admission. This finding is likely reflective that patients with milder TBI phenotypes at admission can deteriorate during the hospital course. Following quantitative pupillometry in moderate or even mild TBI patients may be important to identify neurologic worsening, which can occur in 35% of TBI patients within the first 24 hours of admission. 19 Our findings regarding the association of pupil abnormality frequency and outcome are consistent with prior studies examining quantitative poor pupil reactivity over admission in heterogeneous causes of acute brain injury or in small case series of TBI .16,20,21 Our observation that the relation between pupil abnormality frequency and outcome remains significant for bilateral but not for unilateral pupil abnormalities may be due to the fact that the bilateral abnormalities can signify irreversible intrinsic damage to pathways for recovery in the midbrain and brainstem, whereas unilateral abnormalities may be more context dependent and reversible. Our study demonstrates that the inclusion of pupil abnormality frequency over the first 72 hours of admission improves goodness-of-fit for current prognostic models. While the ROC curves for the IMPACT TBI models improved with the addition of pupil abnormality frequency for both outcomes, results were not statistically significant, most likely due to the limited number of moderate and severe TBI patients. There are several study limitations. As a retrospective, observational study, we cannot elucidate any cause-effect relationship or exclude all potential confounding factors, but this serves as a preliminary analysis into the role of pupil abnormalities in TBI prognostication. We did not control for pain, ambient light, or cognitive load, which have been shown to affect pupil reactivity. 22 - 24 While our study was single-center with a relatively small sample size, a post hoc analysis revealed we had a power of 98% with a sample size of 131 when detecting the observed difference in abnormal pupil frequency between patients with favorable and unfavorable outcomes. Additional tests for other exposures were not powered sufficiently and thus are hypothesis generating only. We had varying number of measurements per patient, which can lead to bias. Moreover, we acknowledge that early mortality or withdrawal of life-sustaining therapy prior to 72 hours may have confounded results, though this was a minority of our patients (4.6%). While we conducted multiple tests of association, we selected only one primary hypothesis to minimize this risk of false-positive associations. We acknowledge that the IMPACT model was designed to detect unfavorable outcome and six-month mortality, and that we did not have access to long-term follow up to compare outcomes as originally designed. Despite these limitations, our study had several strengths. We had access to over 3,000 unique pupil measurements obtained from a heterogeneous and diverse patient cohort. We performed subgroup analyses and explored clinically meaningful outcomes to better understand pupil abnormalities in TBI. Conclusions Pupil abnormalities are common in TBI patients over the first 72 hours of injury, especially in those with severe TBI. Increased pupil abnormality frequency may be associated with higher risk of unfavorable discharge disposition, and pupil abnormality inclusion improves the IMPACT TBI model for prognostication. Future studies should be conducted to further evaluate and externally validate whether the inclusion of pupil abnormality frequency in current models improves prognostication and treatment decisions. Author contributions Dr. Ong and Dr. Veerapeneni designed the study. So Yeon Kim was involved in data acquisition. Dr. Veerapeneni was involved in data analsyis and interpretation. Dr. Veerapeneni, Naveen Arunachalam Sakthiyendran, Dr. Mohammed, Dr. Dupuis, and Dr. Ong were involved in drafting the original manuscript. Dr. Veerapeneni, Naveen Arunachalam Sakthiyendran, Dr. Daneshmand, Dr. Sheth, Dr. Abdalkader, Dr. Greer, Dr. Mohammed, Dr. Gilmore, Dr. Dupuis, and Dr. Ong were involved in critical revisions. Dr. Daneshmand and Dr. Abdlakdaer were involved in validation. Dr. Veerapaneni and Chuong Nguyen helped with data analysis software. So Yeon Kim helped with project administrative tasks and resources. Dr. Ong helped supervise and aquire funding for the project. Data Availability All data produced in the present study are available upon reasonable request to the authors Statement of Human Rights This study is adherent to ethical guidelines and approved by the Boston Medical Center Institutional Review Board. For this type of study, formal consent is not required. Supplementary Appendix for eMethods: Data Collection and Preprocessing of Pupillometry Data Data Collection All extracted demographic and imaging data were maintained in REDCap (Research Electronic Data Capture) 1 , a secure, web-based software platform designed to support data capture for research studies. Race, admission Glasgow Coma Scale, mechanism of trauma (blunt vs. penetrating), emergent cranial surgery, use of intracranial pressure monitor, external ventricular drain placement, Marshall computed tomography (CT) score, presence of exclusion criteria, death at discharge, and eye tracking on discharge were obtained through chart review. Details for criteria used for classification are below: View this table: View inline View popup Download powerpoint To determine reliability of the Marshall CT scores arbitrated by D.V., we randomly selected 10% of patients and gave their corresponding brain images to a second trained researcher (A.D.) for re-evaluation of the Marshall score. Inter-rater reliability was computed by comparing CT scores from D.V. and A.D. and found to be 70%. Discrepant features were reviewed by D.V. and A.D. to establish consensus. eMethods: Exposures We characterized pupil abnormality as the following for further exploratory analysis: Isolated Features of Pupil Abnormalities NPi 0.7 Diff Size > 1mm All pupil abnormalities are inclusive. In addition to frequency, incidence, and continuous variables of NPi, Diff NPi, and Diff Size were also examined as part of exploratory analysis. eMethods: Outcomes Our exploratory outcome was eye tracking on discharge. This was defined as “awake or alert” or able to track objects on discharge exam. We chose this as an exploratory outcome because conscientiousness is an important clinical factor to patients and their families, especially relating to goals of care discussions. eMethods: IMPACT Models To determine whether pupillometry over 72 hours improves current prognostic TBI models, we used the three parameters from the core IMPACT model: age, motor component of admission Glasgow Coma Scale, and pupil reactivity on admission (none, unilateral, or bilateral reactivity) to predict both unfavorable discharge disposition and death at discharge. All 3 input factors were obtained through chart review. All input factors had single values for each patient. Pupil reactivity was at baseline. Input parameters were then normalized including pupil abnormality frequency. Pupil abnormality frequency was a single measurement for each patient. The IMPACT model was created by training a logistic regression model to predict unfavorable discharge disposition on our whole cohort using the normalized input parameters. Then the combined IMPACT and pupil frequency model was created using the same method except with the addition of the normalized pupil abnormality frequency input parameter. We used the likelihood ratio test to evaluate goodness-of-fit between the two models for both outcomes (unfavorable discharge disposition and death at discharge). In addition, we also created a receiver operating characteristic (ROC) curve to compare the IMPACT model with and without abnormal pupil frequency for both outcomes. Our R pseudocode for the likelihood ratio test was the following: - IMPACT model = glm(Outcome ∼ Age + Motor Component of Glasgow Coma Scale + Pupil Reactivity on admission, data = study cohort) - IMPACT + Abnormal Pupil Frequency model = glm(Outcome ∼ Age + Motor Component of Glasgow Coma Scale + Pupil Reactivity on admission + Pupil Abnormalities Frequency over 72 hours, data = study cohort). - lrtest(IMPACT model, IMPACT + Abnormal Pupil Frequency model) eMethods: Preprocessing of Data and Statistical Analyses Computer Programming Languages Preprocessing of data and statistical analyses was done using Python Version 3.9.13 (Van Rossum, G., & Drake, F. L. (2009). Python 3 Reference Manual . Scotts Valley, CA: CreateSpace) and R Version 4.2.0 (R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria). Preprocessing of Pupillometry Data Pupillometry data was preprocessed using both Python Version 3.9.13 (Van Rossum, G., & Drake, F. L. (2009). Python 3 Reference Manual . Scotts Valley, CA: CreateSpace) and R Version 4.2.0 (R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria) and is publicly available at https://github.com/fordivyav/TBI_Pupillometry . Pupil measurements were preprocessed as follows: For all pupil observations during a hospital admission, we calculated the minimum NPI, NPI difference, and size difference between both the left and right eyes for the patient. We defined the “start of hospitalization” for each patient as either the admission time (if precedes the time of first pupil measurement) or else the presentation time. We then filtered for pupil data up to 72 hours after the “start of hospitalization.” Patients with less than three pupil observations within the first 72 hours were excluded from our cohort. To create the final dataset, we took the 72-hour dataset for all patients and computed the median NPi, minimum NPi, NPi difference, and size difference for each patient. We computed pupil abnormality frequencies by calculated the number of pupil measurements in which the pupil abnormality was present divided by the total number of pupil measurements in the 72-hour dataset for the patient. Incidence for each pupil abnormality was then computed at the patient level. Thus, incidence and frequency of pupil abnormality was consolidated as a single value per patient using data from pupil measurements up to 72 hours after admission. We used the python package “tableone” 2 to perform the Chi square or ANOVA test on demographic information, clinical data, and pupil data across the TBI severity groups. For categorical variables in which the expected value for any cell was less than five for the Chi square test, we used the Fisher’s exact test instead. Similarly, for frequency of pupil abnormalities we used the Kruskal-Wallis test to account for the zero-inflated distributions. Because frequency had a zero-inflated distribution, we also reported frequency (%) as median [Q1, Q3] of only non-zero frequency values. For post-hoc power analyses, we performed the Wilcoxon-Mann-Whitney test in R for both primary and secondary exposures and our primary outcome. eResults: Descriptive Analysis of Hospital Course We conducted descriptive analysis of hospitalization course for patients with mild or moderate TBI who have NPi < 3. Of eleven mild TBI patients with NPi < 3, the only patient who died had an intraparenchymal hemorrhage with bilateral NPi 0.7, and diff Size > 1. Of mild TBI patients who had bilateral NPi < 3, one had intraparenchymal hemorrhage, one had subdural hematoma vs. epidural hematoma, and one had subdural hematoma, intraparenchymal hemorrhage, and frontotemporal hematoma, of which one expired, one underwent a craniotomy, and the other improved and was discharged home after 4 days, respectively. Only one patient with a subdural hematoma, intraparenchymal hemorrhage, and subarachnoid hemorrhage after a motor vehicle accident who underwent emergent craniotomy was discharged to long term care. Out of the three moderate TBI patients with NPi < 3, none had unfavorable discharge disposition. Only one had bilateral NPi < 3 due to a gunshot wound, underwent craniectemy, and was discharged to rehab after 71 days. The rest were discharged to rehab. View this table: View inline View popup Download powerpoint eTable 1: Incidence and Frequency of Pupil Abnormalities over first 72 hours in Traumatic Brain Injury Patients View this table: View inline View popup eTable 2: Hospital Course for Mild and Moderate Traumatic Brain Injury Patients with NPi < 3 View this table: View inline View popup Download powerpoint eTable 3: Subgroup Analysis of Patients with Severe Traumatic Brain Injury in first 72 hours View this table: View inline View popup Download powerpoint eTable 4: Multivariable Models of Median Continuous Pupil Values over first 72 hours and Discharge Outcomes View this table: View inline View popup Download powerpoint eTable 5: Multivariable Models of Pupil Abnormality Incidence within first 72 hours and Discharge Outcomes View this table: View inline View popup Download powerpoint eTable 6: Multivariable Models of Pupil Abnormality Frequency within first 72 Hours and Discharge Outcomes View this table: View inline View popup Download powerpoint eTable 7: Likelihood Ratio Tests of IMPACT Model with and without Abnormal Pupil Frequency View this table: View inline View popup Download powerpoint eTable 8: Parameters of IMPACT Models with and without Abnormal Pupil Frequency Download figure Open in new tab eFigure 1: Inclusion and Exclusion Criteria Inclusion and Exclusion Criteria for our study cohort of 131 patients. Abbreviations: ICU = Intensive Care Unit, TBI = Traumatic Brain Injury, GCS = Glasgow Coma Scale on Admission, E = Eye, V = Verbal, M = Movement Unfavorable Discharge Disposition: discharge to long-term care, hospice, or death Download figure Open in new tab eFigure 2: Violin Plots of Pupil Abnormality Frequency by Discharge Disposition Distributions of pupil abnormalities for patients with at least one pupil abnormality. Pupil abnormality and bilateral pupil abnormality frequencies had higher medians and interquartile percentiles in unfavorable vs. favorable discharge disposition. For unilateral pupil abnormalities, patients with unfavorable discharge disposition had higher 75 th percentiles and more variable distribution compared to patients with favorable discharge disposition. Abbreviations: % = Percent Download figure Open in new tab eFigure 3: Minimum NPi, NPi Difference, and Size Difference by TBI Severity Trajectories of minimum NPi, NPi difference, and size difference over the first 72 hours of hospitalization for each patient by traumatic brain injury (TBI) severity group. Each colored line represents a different patient. Thresholds for minimum NPi, NPi difference, and size difference are depicted in dashed black lines as 3, 0.7, and 1, respectively. There is an increased number of patients with abnormal minimum NPi, NPi difference, and size difference in patients with severe TBI compared to patients with mild or moderate TBI. Abbreviations: TBI = Traumatic Brain Injury, NPi = Neurological Pupil index, Min NPI = Minimum NPI between both eyes, Diff NPi = NPi difference between both eyes, Diff Size = Difference in resting pupil size between both eyes Download figure Open in new tab eFigure 4: Minimum NPi, NPi Difference, and Size Difference by Discharge Outcomes Trajectories of minimum NPi, NPi difference, and size difference for each patient over the first 72 hours of hospital stay by unfavorable vs. favorable discharge outcome, with thresholds for minimum NPi, NPi difference, and size difference depicted in dashed black lines as 3, 0.7, and 1, respectively. Unfavorable Discharge Disposition was defined as discharge disposition of deceased, hospice, or long-term care, while favorable discharge disposition was defined as discharge disposition of left against medical advice, home, skilled nursing facility, or rehab facility. Abbreviations: NPi = Neurological Pupil index, Min NPi = Minimum NPi between both eyes, Diff NPi = Difference in NPi between both eyes, Diff Size = Difference in resting pupil size between both eyes Download figure Open in new tab eFigure 5: Odds Ratios for Parameters in IMPACT and Abnormal Pupil Frequency Models Coefficients for features of the core IMPACT model trained using logistic regression analysis on our dataset. For both unfavorable discharge disposition and death at discharge, zero reactive pupils on admission and pupil abnormality frequency over the first 72 hours were the most important coefficients. Abbreviations: IMPACT = IMPACT TBI, Abnormal Pupil Frequency = Pupil Abnormalities Frequency, GCS = Glasgow Coma Scale Download figure Open in new tab eFigure 6: ROC Curves for IMPACT Model with and without Abnormal Pupil Frequency ROC curves comparing IMPACT model with and without abnormal pupil frequency for both primary and secondary outcomes. For unfavorable discharge disposition, the IMPACT and abnormal pupil frequency model had improved area under the curve (AUC) (AUC = 0.78) compared to the IMPACT model alone (AUC = 0.75) although not statistically significant (DeLong’s test p = 0.2). For death at discharge, the IMPACT and abnormal pupil frequency model had improved area under the curve (AUC) (AUC = 0.89) compared to the IMPACT model alone (AUC = 0.81) although also not statistically significant (DeLong’s test p = 0.056). Abbreviations: ROC = receiver operating characteristic, IMPACT = IMPACT TBI, Frequency = Pupil Abnormalities Frequency, GCS = Glasgow Coma Scale Acknowledgements We would like to thank the neurocritical care nurses at Boston Medical Center for their valuable contribution of pupillometric data to this study. Additionally, we thank Linda Rosen from the Boston University Clinical Data Warehouse for helping to procure data from electronic health records. Footnotes Financial Support Statement The corresponding author C.J.O. receives support from National Institutes of Health (NIH)/National Institute of Neurologic Disorders and Stroke K23NS116033. Conflict of interest disclosure: The authors declare no conflicts of interest. This report was prepared according to Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines. C.J.O. receives support from National Institutes of Health (NIH)/National Institute of Neurologic Disorders and Stroke K23NS116033 for this study. This manuscript has not been published and is not under consideration elsewhere. This study is adherent to ethical guidelines and approved by the Boston Medical Center Institutional Review Board. Informed consent was waived due to the anonymous nature of the study. References 1. ↵ Brazinova A , Rehorcikova V , Taylor MS , et al. Epidemiology of Traumatic Brain Injury in Europe: A Living Systematic Review . J Neurotrauma 2021 ; 38 ( 10 ): 1411 – 1440 , doi: 10.1089/neu.2015.4126 OpenUrl CrossRef PubMed 2. ↵ El Ahmadieh TY , Bedros N , Stutzman SE , et al. Automated Pupillometry as a Triage and Assessment Tool in Patients with Traumatic Brain Injury . World Neurosurg 2021 ; 145 (e163-e169 , doi: 10.1016/j.wneu.2020.09.152 OpenUrl CrossRef PubMed 3. ↵ Hoffmann M , Lefering R , Rueger JM , et al. Pupil evaluation in addition to Glasgow Coma Scale components in prediction of traumatic brain injury and mortality . Br J Surg 2012 ; 99 Suppl 1(122-30 , doi: 10.1002/bjs.7707 OpenUrl CrossRef PubMed 4. ↵ Marmarou A , Lu J , Butcher I , et al. Prognostic value of the Glasgow Coma Scale and pupil reactivity in traumatic brain injury assessed pre-hospital and on enrollment: an IMPACT analysis . J Neurotrauma 2007 ; 24 ( 2 ): 270 – 80 , doi: 10.1089/neu.2006.0029 OpenUrl CrossRef PubMed Web of Science 5. ↵ Han J , King NK , Neilson SJ , et al. External validation of the CRASH and IMPACT prognostic models in severe traumatic brain injury . J Neurotrauma 2014 ; 31 ( 13 ): 1146 – 52 , doi: 10.1089/neu.2013.3003 OpenUrl CrossRef PubMed 6. ↵ Anderson M , Elmer J , Shutter L , et al. Integrating Quantitative Pupillometry Into Regular Care in a Neurotrauma Intensive Care Unit . J Neurosci Nurs 2018 ; 50 ( 1 ): 30 – 36 , doi: 10.1097/JNN.0000000000000333 OpenUrl CrossRef PubMed 7. ↵ Alali AS , Temkin N , Barber J , et al. A clinical decision rule to predict intracranial hypertension in severe traumatic brain injury . J Neurosurg 2018 ; 131 ( 2 ): 612 – 619 , doi: 10.3171/2018.4.JNS173166 OpenUrl CrossRef PubMed 8. Monai E , Favaretto C , Salvalaggio A , et al. Pupillary dynamics predict long-term outcome in a cohort of acute traumatic brain injury coma patients . Ann Clin Transl Neurol 2023 ; 10 ( 10 ): 1854 – 1862 , doi: 10.1002/acn3.51879 OpenUrl CrossRef PubMed 9. ↵ Couret D , Boumaza D , Grisotto C , et al. Reliability of standard pupillometry practice in neurocritical care: an observational, double-blinded study . Crit Care 2016 ; 20 ( 99 , doi: 10.1186/s13054-016-1239-z OpenUrl CrossRef PubMed 10. ↵ Oddo , M. , Taccone , F. S. , Petrosino , M. , Badenes , R. , Blandino-Ortiz , A. , Bouzat , P. , Caricato , A. , Chesnut , R. M. , Feyling , A. C. , Ben-Hamouda , N. , Hemphill , J. C. , Koehn , J. , Rasulo , F. , Suarez , J. I. , Elli , F. , Vargiolu , A. , Rebora , P. , Galimberti , S. , Citerio , G. , & ORANGE study investigators ( 2023 ). The Neurological Pupil index for outcome prognostication in people with acute brain injury (ORANGE): a prospective, observational, multicentre cohort study . The Lancet. Neurology , 22 ( 10 ), 925 – 933 . doi: 10.1016/S1474-4422(23)00271-5 OpenUrl CrossRef PubMed 11. ↵ Carney N , Totten AM , O’Reilly C , et al. Guidelines for the Management of Severe Traumatic Brain Injury, Fourth Edition . Neurosurgery 2017 ; 80 ( 1 ): 6 – 15 , doi: 10.1227/NEU.0000000000001432 OpenUrl CrossRef PubMed 12. ↵ Marshall LF , Marshall SB , Klauber MR , et al. A new classification of head injury based on computerized tomography . Journal of Neurosurgery 1991 ; 75 ( Supplement ): S14 – S20 , doi: 10.3171/sup.1991.75.1s.0s14 OpenUrl CrossRef Web of Science 13. ↵ von Elm E , Altman DG , Egger M , et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies . Lancet 2007 ; 370 ( 9596 ):1453-7 , doi: 10.1016/S0140-6736(07)61602-X OpenUrl CrossRef PubMed Web of Science 14. ↵ Prescott BR , Saglam H , Duskin JA , et al. Anisocoria and Poor Pupil Reactivity by Quantitative Pupillometry in Patients With Intracranial Pathology . Crit Care Med 2022 ; 50 ( 2 ): e143 – e153 , doi: 10.1097/CCM.0000000000005272 OpenUrl CrossRef PubMed 15. ↵ Amorim RL , Oliveira LM , Malbouisson LM , et al. Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population . Front Neurol 2019 ; 10 ( 1366 , doi: 10.3389/fneur.2019.01366 OpenUrl CrossRef 16. ↵ Luz Teixeira T , Peluso L , Banco P , et al. Early Pupillometry Assessment in Traumatic Brain Injury Patients: A Retrospective Study . Brain Sci 2021 ; 11 ( 12 ) , doi: 10.3390/brainsci11121657 OpenUrl CrossRef 17. Turnbull BW , Weiss L . A likelihood ratio statistic for testing goodness of fit with randomly censored data . Biometrics 1978 ; 34 ( 3 ): 367 – 75 OpenUrl CrossRef PubMed Web of Science 18. ↵ Oddo M , Taccone FS , Petrosino M , et al. The Neurological Pupil index for outcome prognostication in people with acute brain injury (ORANGE): a prospective, observational, multicentre cohort study . Lancet Neurol 2023 ; 22 ( 10 ): 925 – 933 , doi: 10.1016/S1474-4422(23)00271-5 OpenUrl CrossRef PubMed 19. ↵ Trent T , Vashisht A , Novakovic S , et al. Pupillary light reflex measured with quantitative pupillometry has low sensitivity and high specificity for predicting neuroworsening after traumatic brain injury . J Am Assoc Nurse Pract 2023 ; 35 ( 2 ): 130 – 134 , doi: 10.1097/JXX.0000000000000822 OpenUrl CrossRef PubMed 20. Jahns FP , Miroz JP , Messerer M , et al. Quantitative pupillometry for the monitoring of intracranial hypertension in patients with severe traumatic brain injury . Crit Care 2019 ; 23 ( 1 ): 155 , doi: 10.1186/s13054-019-2436-3 OpenUrl CrossRef PubMed 21. Romagnosi F , Bernini A , Bongiovanni F , et al. Neurological Pupil Index for the Early Prediction of Outcome in Severe Acute Brain Injury Patients . Brain Sci 2022 ; 12 ( 5 ) , doi: 10.3390/brainsci12050609 OpenUrl CrossRef 22. ↵ Larson MD , Behrends M . Portable infrared pupillometry: a review . Anesth Analg 2015 ; 120 ( 6 ): 1242 – 53 , doi: 10.1213/ANE.0000000000000314 OpenUrl CrossRef PubMed 23. Diamond JP . The Pupil. Anatomy, Physiology and Clinical Applications . Br J Ophthalmol 2001 ; 85 ( 1 ): 121E , doi: 10.1136/bjo.85.1.121e OpenUrl CrossRef PubMed 24. ↵ Steinhauer SR , Hakerem G . The pupillary response in cognitive psychophysiology and schizophrenia . Ann N Y Acad Sci 1992 ; 658 (182-204 , doi: 10.1111/j.1749-6632.1992.tb22845.x OpenUrl CrossRef PubMed Web of Science References 1. Harris PA , Taylor R , Thielke R , et al. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support . J Biomed Inform 2009 ; 42 ( 2 ): 377 – 81 , doi: 10.1016/j.jbi.2008.08.010 OpenUrl CrossRef PubMed Web of Science 2. Pollard TJ , Johnson AEW , Raffa JD , Mark RG. tableone: An open source Python package for producing summary statistics for research papers . JAMIA Open 2018 ; 1 ( 1 ): 26 – 31 , doi: 10.1093/jamiaopen/ooy012 OpenUrl CrossRef PubMed View the discussion thread. 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Share Pupil Abnormality Frequency in the First 72 Hours Improves IMPACT score in Traumatic Brain Injury Divya Veerapaneni , Naveen Arunachalam Sakthiyendran , So Yeon Kim , Chuong Nguyen , Ali Daneshmand , Mohamad Abdalkader , Shariq Mohammed , Josée Dupuis , Kevin N. Sheth , Emily J. Gilmore , David Greer , Charlene J. Ong medRxiv 2024.11.23.24317826; doi: https://doi.org/10.1101/2024.11.23.24317826 Share This Article: Copy Citation Tools Pupil Abnormality Frequency in the First 72 Hours Improves IMPACT score in Traumatic Brain Injury Divya Veerapaneni , Naveen Arunachalam Sakthiyendran , So Yeon Kim , Chuong Nguyen , Ali Daneshmand , Mohamad Abdalkader , Shariq Mohammed , Josée Dupuis , Kevin N. Sheth , Emily J. Gilmore , David Greer , Charlene J. 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