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Sepsis and Cognitive Assessment

Department of Clinical and Health Psychology, University of Florida, Gainesville, FL 32610, USA
Perioperative Cognitive Anesthesia Network, Department of Anesthesia University of Florida, Gainesville, FL 32610, USA
Department of Surgery, University of Florida, Gainesville, FL 32610, USA
Department of Anesthesiology, University of Florida, Gainesville, FL 32610, USA
Author to whom correspondence should be addressed.
J. Clin. Med. 2021, 10(18), 4269;
Submission received: 1 August 2021 / Revised: 10 September 2021 / Accepted: 13 September 2021 / Published: 20 September 2021
(This article belongs to the Special Issue Management of Chronic Critical Illness after Sepsis—Part II)


Sepsis disproportionally affects people over the age of 65, and with an exponentially increasing older population, sepsis poses additional risks for cognitive decline. This review summarizes published literature for (1) authorship qualification; (2) the type of cognitive domains most often assessed; (3) timelines for cognitive assessment; (4) the control group and analysis approach, and (5) sociodemographic reporting. Using key terms, a PubMed database review from January 2000 to January 2021 identified 3050 articles, and 234 qualified as full text reviews with 18 ultimately retained as summaries. More than half (61%) included an author with an expert in cognitive assessment. Seven (39%) relied on cognitive screening tools for assessment with the remaining using a combination of standard neuropsychological measures. Cognitive domains typically assessed were declarative memory, attention and working memory, processing speed, and executive function. Analytically, 35% reported on education, and 17% included baseline (pre-sepsis) data. Eight (44%) included a non-sepsis peer group. No study considered sex or race/diversity in the statistical model, and only five studies reported on race/ethnicity, with Caucasians making up the majority (74%). Of the articles with neuropsychological measures, researchers report acute with cognitive improvement over time for sepsis survivors. The findings suggest avenues for future study designs.

1. Introduction

Sepsis is one of the most common, expensive, and inadequately managed syndromes. A 2016 task force introduced an updated definition (Sepsis-3), explaining that sepsis is “a life-threatening organ dysfunction caused by a dysregulated host response to infection” [1,2,3,4]. Approximately 2 million adults are affected by sepsis within the United States annually with sepsis being responsible for one out of three hospital deaths [5,6]. Although the sepsis-associated hospitalization frequency has minimally changed for young individuals aged 18–49 years old, the hospitalization frequency has risen dramatically for those aged 65 and older, such that sepsis is currently considered a “disease of the aged” [2]. With increasing age, individuals with sepsis are more likely to endure critical illness with major organ damage resulting in chronic care conditions.
For the brain, sepsis might act as a major inflammatory stimulus and potentially increase the brain’s susceptibility to neurodegenerative disease [7,8]. Fitting with postulates of a brain reserve theory and threshold, sepsis may stimulate the deterioration of cognitive ability or enhance the risk for future progressive cognitive deterioration [9]. Brain health and cognition is also relevant to the risk of developing acute organ dysfunction [10]. By 2050, people aged 65 and older will reach 1.6 billion worldwide [11] and our healthcare system will face greater numbers of individuals with early to late-stage neurodegenerative disorders such as Alzheimer’s disease (AD) [12].
To assist sepsis cognitive research going forward, we conducted the current review to provide insight into the strengths and weaknesses of published research addressing cognition following sepsis. We summarize study designs, the inclusion of a cognitive expert on the team, if authors considered the impacts of education, sex, race/ethnicity, the timeline of testing and inclusion of a baseline, the time of testing, the type of cognitive measures, the statistical approach, and findings. We provide conclusions regarding current limitations and strengths of this literature.

2. Materials and Methods

The literature review process included a thorough PubMed search for publications up to 13 January 2021 (See Figure 1). Key terms included: “sepsis and cognition”, which yielded 850, “sepsis and cognitive” (850), “sepsis and memory” (699), “sepsis and thinking” (642), and “sepsis and attention” (1962). Across these search terms a total of 3050 unique articles were identified, with 2816 articles removed based on keywords missing from the article titles, and with 234 qualifying as full text reviews. Of these, 18 met all inclusion criteria. To be eligible for inclusion, articles needed to have a population with a mean age of ≥18 years, a quantitative assessment of a cognition post-septic episode, they needed to be peer-reviewed (i.e., no dissertations), and they needed to be written in English. No limits were placed on the date of publication or timeframe of the cognitive assessment.

3. Results

3.1. Author Inclusion of Neuropsychology Expert and Study Design

Of the 18 retained articles [13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30], 61% of them included a neuropsychologist as a co-author. 66.7% of studies used longitudinal designs, while 33.3% used cross-sectional designs.

3.2. Age, Education, Sex, Race and Ethnicity

The mean age ranged from 49 to 80.81 years across all studies, for a grand mean of 58.30 years. Analytically, 35% of studies considered premorbid intellectual abilities such as education, with a wide educational attainment range. Three studies (16.7%) reported a 7th grade education on average, four studies (22.2%) were comprised mainly of high school graduates (12 years), and two studies (11.1%) included mainly college graduates (16 years).
Fifteen studies (83.3%) reported on sex, which were generally evenly split across sexes with slightly more females (n = 1067) than males (n = 995).
Only five studies (27.8%) reported on race and/or ethnicity, with the majority of patients identifying as Caucasians (79.5%). Only three studies (16.7%) reported on the inclusion of patients identifying as Black/African-American, one study (5.6%) reported on individuals identifying as Hispanic, and one study (5.6%) reported on individuals identifying as Native American or other Pacific Islanders. See Table 1 for additional demographic information.

3.3. Baseline and Time of Testing

Few studies (17%) included baseline cognitive testing (pre-sepsis data), making it difficult to accurately identify cognitive change from premorbid abilities. Initial cognitive testing occurred within two months of hospital discharge for nine studies (50%), with seven of those assessments being within 48 h of discharge. Twelve articles (66.7%) had at least one follow-up assessment, and eleven of them conducted at least two follow-up testing sessions, providing data for the course of cognitive changes and recovery post sepsis. See Table 2 for more information on cognitive testing.

3.4. Type of Measures, Congitive Domains, and Reported Scores

Seven studies (39%) only administered cognitive screening tools, with the remaining using a combination of standard neuropsychological measures. Cognitive domains typically included declarative memory, attention and working memory, processing speed, and executive function. Eleven studies (61%) used raw scores or mean raw scores, two studies (11.1%) reported percentages of scores falling 1.5 or 2 standard deviations below the mean, one study (5.6%) reported the percentage of cognitive impairment, two studies (11.1%) included t-scores (age and/or education adjusted), and two studies (11.1%) included z-scores.

3.5. Statistical Analyses

Studies varied in terms of the statistical modeling employed: correlation/regression models (50%), structural equation modeling (5.6%), general estimating equations (11.1%), survival models (5.6%), parametric and nonparametric tests for group comparisons (33.3%), concordance rate models (5.6%), receiver operating characteristics (5.6%), weighted network analyses (5.6%), and linear mixed models (5.6%). Three studies (16.7%) statistically corrected for education, two studies (11.1%) reported age-adjusted t-scores, and one study (5.6%) reported education-adjusted t-scores. See Table 3 for further detail on the statistical analyses.

4. Discussion

Relevant research articles reviewed herein span 13 years (February 2008 to January 2021). The 18 publications addressing cognition varied with regards to the study design, demographic reporting, cognitive test type, and statistical approach. Despite 18 published articles, we continue to have a limited understanding of sepsis and cognition. Strengths include the number of studies considering cognitive domains in addition to general cognitive screeners, as well as the inclusion of an expert in cognitive assessment as part of the study team panel. Eleven studies report at least two follow-up testing sessions. Initial cognitive testing occurred within two months of hospital discharge for nine studies, with seven of those assessments being within 48 h of discharge. Across the publications, the themes suggest an acute decrease in cognitive function, with follow-up assessments showing cognitive improvement from acute levels. However, few studies considered premorbid or demographic factors within the statistical model, and less than half included a non-sepsis peer group to calculate practice effects. These design limitations challenge the accuracy of study findings, for there are associations between premorbid status and cognitive change [31,32,33,34], and there is a value to considering practice effects in post-trauma cognitive change models [35,36,37]. Our review findings suggest avenues for improvement as the field of sepsis and cognitive research moves forward.
Although more than one third of studies limited their investigation to global cognitive screening tools, the remaining studies used a combination of neuropsychological measures primarily assessing attention/working memory and declarative memory. These domains involve neurological systems typically assaulted by common comorbidities often associated with sepsis and small vessel vascular disease such as hypertension and diabetes [38,39,40]. Two studies additionally considered subdomains of measures of language (semantics) [21,23], and two looked at subdomains of higher cortical abstract reasoning [21,29], but they were limited in sample size. Hypothetically, within larger samples, comparing semantics and higher order functions to the more “vulnerable” cognitive domains would guide insight into cortical versus subcortical/white matter contributions, neuronal vulnerability, and potential mechanisms [41,42]. Longitudinal studies comparing cognitive domains in larger samples are needed.
Control groups are discussed in eight of the published investigations. Control groups used within a statistical design can provide information on “non-disease” practice effects as well as provide normative reference sources for calculating a standardized individual composite reliable change score [34,36,37,42]; the change is larger than reasonably expected due to the measurement error alone [43]. Control groups provide a reference for the expected performance. This information is particularly valuable when a researcher is concerned about the psychometric properties of cognitive measures (e.g., range of possible scores, normal distribution) and the test appropriateness for the population of interest (i.e., one would not administer a 16-word verbal list learning test to individuals with moderate cognitive impairment, for this would likely result in a poor performance and a floor effect). Patients who are acutely ill or cognitively compromised may perform at floor level; the test may be too difficult or not appropriate for identifying further deterioration. By contrast, individuals with a superior premorbid cognitive reserve who acquire sepsis may not be challenged appropriately with general cognitive screening tests. Control groups provide the research team with an external comparison to examine expected patterns of performance without complication from the disease state [37,42].
There was also a notable absence in the publications of the reporting of the years of education. Half of the publications reported this variable, and three considered education years in the statistical model. Given the wealth of research showing how education is a proxy for cognitive reserve, we consider the absence of educational reporting a concerning finding. The years of education have been used repeatedly in aging studies to help explain variability in cognition relative to pathology [44,45]. Cognitive reserve proxies, such as education, are shown to protect against impairment from traumatic brain injury [31,32,33] or operative/anesthesia exposure [34]. Considering education in analytical approaches may explain important variabilities in the outcome. The years of education are also a vital consideration if research teams are unable to acquire a premorbid/pre-sepsis cognitive estimation using formal test tools [46].
Sociodemographic considerations were limited, as was a consideration of medical comorbidities. Animal models [47] and some human studies [48,49] report sex as an important predictor of sepsis recovery. However, no study reported findings relative to participants’ sex. Although race or ethnicity were reported in five of the 18 studies, no study considered these variables relative to the cognitive trajectory. Today, we appreciate how race and ethnicity are surrogates for appreciating health disparities’ contribution to neurobiological responses [50]. Social determinants of health, including systemic racism experienced by many communities of color, are known to have negative ramifications on health outcomes and recovery [51,52,53]. It remains unknown if or how the cognitive sequela of sepsis differentiates across sex, race, and ethnicity. Sex, race, and ethnicity are proxies for health disparities and may modify neurobehavioral responses. This is an important area for future research. Further, individuals within different sociodemographics have a unique risk of sepsis (i.e., renal disease, congestive heart failure, myocardial infarction, chronic pulmonary disease, liver disease, peripheral vascular disease, peptic ulcer disease, and connective tissue disease) [38], and these are worthy of consideration relative to the pre-sepsis neuronal vulnerability status. We also identified a considerable patient heterogeneity in the current state of the literature with regards to the demographic characteristics and origin of sepsis. These are relevant design considerations.
Based on the current review, the authors consider sepsis cognitive research to be in an early developmental stage. There are noticeable methodological study design weaknesses that limit confidence in study findings. We encourage future researchers to consider study design suggestions from the fields of epilepsy (see [54] for a review of neuropsychology and epilepsy) and perioperative cognitive research—both of which have aggressively addressed challenges in longitudinal cognitive study design problems since the 1990s [42]. While sepsis poses a unique challenge due to the unexpected nature of the illness, researchers may wish to consider the benefits of including a control group, premorbid intellectual estimates, education, and other sociodemographic factors in future investigations. It should be noted that our review spanned a considerable timeframe (from February 2008 to January 2021), where the definition and management of sepsis rapidly evolved. We encourage a repeated literature review over the next 5 years.

Author Contributions

Conceptualization: L.C.J., C.C.P. and P.A.E.; Methodology: L.C.J., C.D. and C.C.P.; Data Curation: L.C.J. and C.D.; Writing—original draft preparation: L.C.J.; Writing—review and editing: C.D. and C.C.P.; Supervision: C.C.P. and P.A.E. All authors have read and agreed to the published version of the manuscript.


This research was supported by NIH K07 AG066813.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Publication Identification Process.
Figure 1. Publication Identification Process.
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Table 1. Demographic characteristics of the retained studies.
Table 1. Demographic characteristics of the retained studies.
1st AuthorYearSepsis Sample (n)Control Sample (n)Neuro ExpertMean AgeEducation ReportedSex
Regazzoni [13]2008137NoNo80.81NR67:70NR
Girard [14]201077NoYes61Median = 12 (IQR = 10–13) years40:37NR
Iwashyna [15]2010623NoNo76.9NR281:362Black: 128
Hispanic: 44
White: 451
Davydow [16]2012517NoNo76.1≤HS = 38.5%; Some College = 34.8; >College degree = 26.7%235:282White: 416
Black: 95
Other: 6
Merli [17]201331Yes (23)NoNRNRNRNR
Semmler [18]201325Yes (19)Yes55.64NR13:12NR
Götz [19]201536Yes (30)Yes59.8NR24:12NR
Götz [20]201636Yes (30)Yes59.8NR24:12NR
Needham [21]201683Yes (106)Yes52Mean = 13.9 ± 2.2 years39:44White: 68
Non-White: 15
Pierrakos [22]201728NoYes67.3NRNRNR
Brown [23]201840NoYesNR<HS = 13%; HS = 20%; Associate degree = 18%; Higher education = 10%19:21NR
Calasavara [24]201833NoYes49Mean = 7 (IQR = 4–8) years14:19NR
Kang [25]201836NoNo67.8Mean = 7.4 ± 5.8 years29:7NR
Orhun [26]2019MMSE < 24 = 7
MMSE 24–30 = 14
Yes (33)YesMMSE < 24: 57.3 ± 3.1
MMSE 24–30: 53.2 ± 3
MMSE < 24: Mean = 7.1 ± 1.1 years
MMSE 24–30: Mean = 8.7 ± 1.0 years
MMSE < 24 = 4:3
MMSE 24–30 = 8:6
Seidel [27]201920Yes (44)Yes53.8NR9:11NR
Mankowski [28]2020328NoNoYoung group = 35
Middle group = 58
Old group = 72
NR176:152White: 293
African American: 30
American Indian: 1
Other: 1
Unknown: 1
Brown [29]202130NoYes56<HS = 10%; HS = 23%; Some College = 33%; Associate degree = 7%; Bachelor degree = 17%; >Bachelor degree = 10%13:17Asian: 1
Native Hawaiian/other pacific islander: 1
White: 28
Wang [30]2021840Yes (20,893)No64.3<HS = 12.7%; HS = 26.8%; Some College = 29.3%; >College = 31.2%NR in the final sampleNR in the final sample
Abbreviations: HS = High School; IQR = Interquartile Range; MMSE = Mini-Mental State Exam; NR = Not Reported.
Table 2. Study designs and cognitive testing measures.
Table 2. Study designs and cognitive testing measures.
1st AuthorYearBaseline TestingTime of TestingTests (Estimated Length)Cognitive DomainsReported Score
Regazzoni [13]2008NoAdmitMMSE (10’)GlobalRaw score
Girard [14]2010No3 months, 12 months post sepsisMMSE, WAIS-III DS, TMT A&B, Coding, RAVLT, RCFT (Copy & Delay), VF (70’)Global, ATT, DM, PS,
T-scores (age and education adjusted based on specific population norms)
Iwashyna [15]2010Yes1998—death or 2006M-TICS (10’)Global% of cognitive impairment in sample
Davydow [16]2012YesMean = 7y post sepsisTICS or TICS-27 (10’)GlobalTICS raw score
Merli [17]2013NoAdmit, 3 months post dischargeMMSE, TMT A & B, Digit Symbol (18’)Global, ATT, PS, WMZ-Scores (test specific population norms)
Semmler [18]2013No6–24 months
post discharge
German Vocab. Test, NeuroCogFx, TMT A&B, AVLT, RCFT (70’)ATT, DM, FM, PI, STM, VFMean unweighted score, (zDiff) = ((Cognitive Test z-score) − (Multiple Choice Word Test-B z-score))) (test specific population norms)
Götz [19]2015No0–2 months, 5–8 months, 10–15 months post ICU dischargeDemTect & CDT (15’)GlobalDemTect raw score
Götz [20]2016No0–2 months, 5–8 months, 10–15 months post ICU dischargeDemTect & CDT (15’)GlobalDemTect raw score
Needham [21]2016No6 months and 12 months post ICU dischargeHayling Sentence Completion Test, VF, WAIS-III Similarities & DS, WMS-III LM (50’)ATT, DM, EF, VR, WMPercentage of scores 1.5 SD below the mean
Pierrakos [22]2017NoSepsis discharge (~8d post sepsis onset)MMSE, CDT (15’)GlobalMMSE raw score & MMSE recall sub-score
Brown [23]2018NoSepsis discharge, 3 months, 6 monthsMoCA, VF, WAIS-IV DS Similarities, WMS-IV LM, Hayling Sentence Completion Test (70’) Global, ATT, DM, EF, VF, VR, WMPercentage of scores 1.5 SD and 2.0 SD below the mean (test specific population norms)
Calasavara [24]2018No24 h post discharge, 1 year (median 393 days)MMSE, CERAD (Verbal Fluency, TMT A&B, BNT, List Learning, Praxis, List Recall & Recognition, Praxis Recall) (70’)Global, DM, EF, Language (naming, comprehension), PS, VFMMSE raw score, CERAD mean scores
Kang [25]2018No48 hours after ICU admitK-MoCA; K-MMSE; CoSAS-S (30’)GlobalRaw scores
Orhun [26]2019No0, 3 months, 12 months post sepsisMMSE & ACE-R (Orientation-attention, Memory, VF, Language, Visuospatial Function) (35’)GlobalMMSE raw scores; ACE-R Sub-scores means
Seidel [27]2019No2.6 ± 1.9 years post sepsisTAP, Go-No-Go paradigm, German version of AVLT (40’)ATT, DM, WMT-scores (age-adjusted based on test specific population norms)
Mankowski [28]2020No3, 6, 12 months post dischargeMMSE, HVLT-R (Total recall, Delayed recall, Retention), COWA (30’)Global, DM, VFMean raw scores
Brown [29]2021No6 months post dischargeHayling Sentence Completion Test (5’)EFMean raw scores
Wang [30]2021Yes6 months post sepsis; every 2 years (2006–2017)SIS, CERAD: WLL, WLD, AFT (20’)GlobalMean raw scores
Abbreviations: ACE-R = Addenbrooke’s Cognitive Examination Revised; AFT = Animal Fluency Test, ATT = Attention; AVLT = Auditory Verbal Learning Test; BNL = Below Normal Limits; BNT = Boston Naming Test; CERAD = Consortium to Establish a Registry for Alzheimer’s Disease, CoSAS-S = Computer Cognitive Senior Assessment System-Screen; COWA = Controlled Oral Word Association; DM = Declarative Memory; DS = Digit Span; EF = Executive Functioning; FM = Figural Memory; HS = High School; HVLT-R = Hopkins Verbal Learning Test - Revised; K-MMSE = Korean Mini Mental State Exam; K-MoCA = Korean-Montreal – Cognitive Assessment; MMSE = Mini Mental State Exam; MoCA = Montreal – Cognitive Assessment; NP = Neuropsychological; NR = Not Reported; PI = Premorbid Intelligence; RAVLT = Rey Auditory Verbal Learning Test; RCFT = Rey-Osterreith Complex Figure; SD = Standard Deviation; SIS = Six-Item Screener; STM = Short Term Memory; TAP = Test of Attentional Performance; TICS-27 = Telephone Interview for Cognitive Status-Modified; Six-Item Screener = TICS-M = Telephone Interview for Cognitive Status-Modified; TMT = Trail Making Test; VF = Verbal Fluency Test; VR = Verbal Reasoning; WM = Working Memory; WAIS-III = Weschler Adult Intelligence Scale Third Edition; WAIS-IV = Weschler Adult Intelligence Scale Fourth Edition; WLD = Word List Delayed Recall; WLL = Word List Learning; WM = Working Memory; WMS-III LM = Weschler Memory Scale Third Edition Logical Memory; WMS-IV LM = Weschler Memory Scale Fourth Edition Logical Memory.
Table 3. Statistical Analyses and Corrections, and Summary of Cognitive Findings.
Table 3. Statistical Analyses and Corrections, and Summary of Cognitive Findings.
1st AuthorYearStatistical MethodEducation CorrectionSex and/or Race CorrectionFindings
Regazzoni [13]2008Cox proportional hazard model and Kaplan-Meier testNoNoSepsis survivor MMSE mean = 20.14; scores predicted 1-year mortality.
Girard [14]2010Multiple nonlinear regression, propensity score matchingYesNoCognitive impairment at 3 months: 79% (62% severe); 12 months: 79% (36% severe). As duration of delirium increased, cognition decreased.
Iwashyna [15]2010Latent growth curve models, random effects models, and logistic regressions.NoNoRate of moderate or severe cognitive impairment among survivors (pre-sepsis) increased from 6.1% (95% CI: 4.2%, 8.0%) before severe sepsis to 16.7% (95% CI: 13.8%, 19.7%) at the 1st assessment after severe sepsis.
Davydow [16]2012Paired t-test, Pearson’s correlation, logistic regression.NoNoCognitive impairment in 18% of severe sepsis survivors. Pre-sepsis depression was the greatest predictor of post-sepsis cognitive impairment.
Merli [17]2013Logistic regression.YesNoNo sepsis survivors were cognitively impaired, but 42% of sepsis survivors showed a decline in performance.
Semmler [18]2013Student t-tests, Pearson’s correlation, ANOVA, and MANCOVA.No; Estimated premorbid verbal abilities instead.NoSepsis survivors impaired in 8 of 9 subtests (mainly learning and memory). Non-septic ICU survivors showed deficits in 6 subtests.
Götz [19]2015General estimating equations.NoNoSepsis survivors were impaired on periodic visual stimulation (familiar and unfamiliar pictures) and scored lower than HCs on the DemTect and CDT at all time points.
Götz [20]2016General estimating equations.NoNoSepsis survivors scored lower than HCs on the DemTect and CDT at all time points.
Needham [21]2016Joint survival models, linear regressions and logistic random intercept regression models.NoNo38% at 6m post-sepsis had cognitive impairment; 28% at 12m.
Pierrakos [22]2016Multivariable linear regression.If education >12 years, no MMSE was given; CDT only.No50% of sepsis survivors had cognitive decline, with greatest decline in information recall.
Brown [23]2018Concordance correlation coefficient and Fisher’s exact tests.NoNoAt discharge, 90% of survivors had MoCA< 25 (BNL). At 3 months, 70% BNL; 6 months, 57% BNL. Neuropsychology: 3 months, 43% impaired; 6 months, 57% impaired. WAIS-IV DS was the one test performance that did not improve from 3 to 6 months.
Calasavara [24]2018Student t-test, Mann-Whitney U, chi-square test, one sample t-test, marginal models, stepwise regression.NoNoAt discharge, survivors had lower MMSE and poor constructional praxis (p < 0.001). At 1 year, all performances normalized except for the BNT (p = 0.193) and constructional praxis (p < 0.001).
Kang [25]2018Receiver Operating Characteristics.NoNo53.1% of sepsis survivors were cognitively impaired on MMSE; 65.6% of sepsis survivors were cognitively impaired on MoCA
Orhun [26]2019Mann-Whitney U or Kruskal-Wallis tests, Dunn’s post-hoc test, Spearman correlation.NoNoInitial mean MMSE = 25.4 ± 3.9; 3 months: 27.8 ± 2.8; 12 months: 28.4 ± 1.4
Seidel [27]2019Two-tailed student t-test, Pearson correlation.NoNo55% of survivors had deficits in 1–2 domains and 20% in 3 or more domains.
Primary difficulties in learning, alertness, working memory, and memory decay rate.
Mankowski [28]2020Fisher exact test and Kruskal-Wallis test.NoNoYoung adults performed better than middle-aged and older adults. No group differences between the middle-aged and older adults.
Brown [29]2020Weighted network analysis.NoNo20% of survivors had impairment in executive domain according to the Hayling Sentence Completion Test
Wang [30]2021Multivariable linear mixed-effects models.YesNoSIS scores of sepsis survivors improved. AFT scores decreased, while WLD and WLL scores increased.
Abbreviations: AFT = Animal Fluency Test, ANOVA = Analysis of Variance; BNL = below normal limits; CDT = clock drawing test; CI = confidence interval; HC = Healthy Controls; ICU = Intensive Care Unit; MANCOVA = Multiple Analysis of Covariance; MoCA = Montreal Cognitive Assessment; MMSE = Mini Mental State Examination; SD = Standard Deviation; SIS = Six-Item Screener; WLD = Word List Delayed Recall; WLL = Word List Learning.
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Jones, L.C.; Dion, C.; Efron, P.A.; Price, C.C. Sepsis and Cognitive Assessment. J. Clin. Med. 2021, 10, 4269.

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Jones LC, Dion C, Efron PA, Price CC. Sepsis and Cognitive Assessment. Journal of Clinical Medicine. 2021; 10(18):4269.

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Jones, Laura C., Catherine Dion, Philip A. Efron, and Catherine C. Price. 2021. "Sepsis and Cognitive Assessment" Journal of Clinical Medicine 10, no. 18: 4269.

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