Skip to main content

The early clinical and laboratory predictors of GBS outcome: hospital-based study, Assiut University, Upper Egypt



This study was designed to identify factors that influence outcomes in a large group of well-defined Guillain–Barré syndrome (GBS) patients with a 3-month follow-up period. Sixty-two cases of GBS with a mean age of 37.15 ± 17.60 years (33 males and 29 females) were recruited in the first 2 weeks after onset. Clinical history, examination, and a variety of rating scales including Medial Research Council sum score (MRC), Erasmus Guillain-Barré respiratory insufficiency score (EGRIS), at admission and 10 days later were performed. Follow-up investigations at 3 months included the Hughes Disability Scale (HDS), and Overall Neuropathy Limitation Scale (ONLS).


64.5% of participants had cranial nerve deficits, 45% had neck muscle weakness, 30.6% had dysautonomia, and 8.1% were mechanically ventilated. C-reactive protein was elevated in 38.7%, and hyponatremia was recorded in 30.6% of patients. Older age, antecedent events particularly diarrhea, neck muscles weakness, low MRC sum score, impaired cough reflex, dysautonomia, and hyponatremia, were all significantly associated with poor outcomes at 3 months using HDS and ONLS. Regression analysis with dependent variables of HDS outcome showed that the presence of an antecedent event particularly diarrhea, neck muscle weakness, hyponatremia and the presence cytoalbuminous dissociation of CSF at onset, and low MRC sum score at 10th day after treatment, were predictors of poor outcome.


Clinical and laboratory predictors of poor outcome were older age, the presence of an antecedent event particularly diarrhea, low MRC sum score at the 10th day, elevated CRP, hyponatremia and the presence cytoalbuminous dissociation.


Guillain–Barré syndrome (GBS) is an acute polyradiculoneuropathy, characterized by a rapidly progressive, nearly symmetrical, flaccid weakness of the limbs [1, 2]. The clinical presentation, course, and the clinical recovery and outcome of GBS all are variable. In addition, GBS varies considerably between geographical regions [2,3,4]. In the acute phase, about 1/5 of GBS patients require mechanical ventilation and 3–4% of patients die of complications [1]. Despite therapy with intravenous immunoglobulin (IVIG) or plasma exchange, about 10–20% of patients remain severely disabled [2, 5]. The recovery from GBS takes a long time and is highly variable, therefore information on the nature, and early predictors of patient-perceived disability after GBS could be of help for patients, doctors, and caregivers both in the acute and chronic phase of disease.

Previous studies found a relationship between outcome and recent preceding infections (antecedent events), clinical presentation, electrophysiological subtype and laboratory findings [6,7,8,9]. Yao and colleagues studied the clinical presentation of GBS in four regions of China and found that a higher proportion of the axonal subtype in central and southwest China; progression in the latter region was more served at nadir and patients had the longest hospital stay [10]

Zhai and their colleagues [11] in their retrospective study of 294 patients with GBS found that the AMAN subtype was predominant in northern China (40.1%) and had shorter time to nadir, with prolonged hospitalization, and worse prognosis at discharge than AIDP. A higher GDS score on admission was a strong predictor for poor outcome at discharge and short-term follow-up, independent of treatment type or in-hospital management (Ruiz-Sandoval and their colleagues) [12]. Luijten and their colleagues found that clinical predictors of the need for mechanical ventilation (MV) were a shorter time from onset of weakness until admission, the presence of facial muscle weakness, bulbar palsy and neck muscle weakness [13].

However, most of the previous studies were retrospective, and the findings were derived from relatively small series or selected groups of patients with a limited set of suboptimal outcome measures [6,7,8,9].

The current study was designed to address these problems using a prospective study design to identify factors that influence outcomes in a large group of well-defined GBS patients with a 3-month follow-up period.


Sixty-two cases of GBS were recruited and diagnosed according to criteria of the National Institute of Neurological Disorders and Stroke (NINDS) (revised form 1990) [14] and the Brighton Collaboration in 2014 [15] during the period from October 20th 2020 to September 20th 2021.

Cases were recruited within the first 2 weeks of onset, with progressive bilateral weakness of upper and lower limbs, absent or decreased tendon reflexes in affected limbs (at some point in clinical course) as well as other features that support a diagnosis of GBS with the same inclusion and exclusion NINDS criteria.

At admission each patient underwent the following procedures.

History included age, sex, onset, time elapsed between onset of symptoms and admission, presence of other co-morbidites and preceding antecedent events including upper respiratory tract infection, flu-like symptoms or gastrointestinal tract infection (diarrhea or vomiting) and vaccination.

Clinical examination, vital signs including blood pressure, heart and respiratory rates, as well as arterial blood gases (ABG). Neurological assessment included cranial nerve examination and neck muscle weakness.

Autonomic dysfunction included variation in arterial blood pressure, arrhythmia, attacks of diarrhea, vomiting, and sweating. Hughes Disability Scale (HDS) [5, 16] as this scale has six levels ranges from 0 point (healthy), up to 6 points (dead) depending on the disability of lower limbs (capable of running, walking without assistant, walk with assistance, bedridden, need ventilation, and dead) [5, 16].

Medial Research Council sum score (MRC) [17,18,19] as this scale ranges from zero power up to 60 full powers and is scored as the sum of six muscle power in both upper and lower limbs in points. This scale was assessed at onset, at nadir and 10 days of treatment [17,18,19].

The Erasmus GBS Respiratory Insufficiency Score (EGRIS) is a model developed to estimate the risk of respiratory failure. At admission and according to EGRIS, three clinical factors are explored, the time from onset of weakness to admission, presence of facial and/or bulbar weakness, and severity of muscle weakness [7].

Overall Neuropathy Limitation Scale (ONLS), this scale is used to assess the limitations of patients with immune-mediated peripheral neuropathies; it is reliable, responsive and has construct validity in people with GBS, chronic inflammatory demyelinating polyneuropathy and paraprotein-associated demyelinating neuropathy. [20]

Laboratory investigations at admission, complete blood count (CBC), blood urea, creatinine, serum electrolytes (Na, k, Ca), total protein, albumin, and C-reactive protein (CRP). Arterial blood gases (ABG) included PaO2, PaCo2, pH, and SPo2 were also assessed.

Cerebrospinal fluid analysis (CSF) as 42 patients underwent analysis during the first 3 days of admission.

Treatment options as all patients received treatment immediately after diagnosis either by plasmapheresis (plasma Exchange 5 sessions one every other day) or intravenous immunoglobulin (IVIG) 0.4 g/kg/day for 5 consecutive days.

Patients were assessed on admission, at nadir and 10 days after the end of plasma exchange and IVIG, using the MRC sum score and EGRIS. HDS and ONLS GBS scales were assessed 3 months after the onset of illness. All patients received a conventional neurophysiological assessment (distal latency, nerve conduction study and F-wave of four limbs) within 1–3 days of admission in order to confirm the diagnosis.

Statistical analysis used SPSS (Statistical Package for the Social Science, version 17, IBM, and Armonk, New York). Because the data distributions did not differ statistically from normality (using Shapiro–Wilk test, P > 0.05), continuous variables are given as means ± SD; categorical variables are summarized as counts (percentage). Spearman's correlations were calculated between HDS, and ONLS scales outcome (at 3 months follow-up) and clinical or laboratory variables at baseline. Multiple ordinal and linear regression analysis was used to identify best predictors of outcome graded according to HDS score and ONLS score, respectively.


Sixty-two cases of GBS were recruited with a mean age of 37.15 ± 17.60 years (33/29 male/female ratio). 43 (69.4%) patients recorded an antecedent event before the onset of GBS symptoms, the commonest of which were high grade of fever 38 (61.3%), and upper respiratory tract infection while diarrhea with or without vomiting were observed in 13 cases (21%) (see Table 1). Clinical variants included sensory motor (47 patients 75.8%), pure motor (12 patients 19.4%), paraparetic (2 patients 3.2%), and Miller Fisher (1 case 1.6%). A number of specific clinical features were noted including 40 (64.5%) patients with involvement of cranial nerves, 28 (45%) patients with neck muscle weakness, 13 (21%) patients with papilledema, 19 (30.6%) patients with autonomic affection, and 5 (8.1%) mechanically ventilated patients and in ICU. C-reactive protein was positive in 24 (38.7%) patients, hypoalbuminemia and hyponatremia were observed in 20 (32.3%) and 19 (30.6%) patients, respectively, and cytoalbuminous dissociation in CSF was found in 24 (57.1%) patients. Details of outcome according to HDS are illustrated in Table 1. Table 2 shows correlations between clinical data and laboratory findings at baseline assessment and their relation to outcomes using both HDS and ONLS scales at 3 months.

Table 1 Demographic, clinical, laboratory, and outcome data among studied patients
Table 2 Spearman correlations of clinical data and baseline laboratory findings with 3-month outcome using the Hughes Disability Scale (HDS) and Overall Neuropathy Limitation Scale (ONLS)

Poor outcomes were associated with older age, comorbidities, antecedent events (especially diarrhea), neck muscle weakness, low MRC sum score at onset, nadir and at the 10th day of treatment, papilledema, impaired cough reflex and dysautonomia (Fig. 1).

Fig. 1
figure 1

Spearman correlations of MRC sum score at the 10th day after treatment and Erasmus Guillain–Barré Respiratory Insufficiency Score (EGRIS) at onset with 3-month outcome measured with the Hughes Disability Scale (HDS) and Overall Neuropathy Limitation Scale (ONLS). Outcomes in both scores were significantly correlated with MRC sum score at the 10th day of treatment and EGRIS at onset

Of the laboratory findings, only hyponatremia, hypoalbuminemia (Fig. 2), elevated CRP and cytoalbuminous dissociation in CSF were significantly correlated with poor outcomes (Table 2).

Fig. 2
figure 2

Spearman correlations between serum sodium and albumen and outcomes using both HDS and ONLS at end of 3 months. There are significant negative correlations between serum sodium and albumen at onset with both outcome scores

Multiple ordinal regression analysis (Table 3) was used to identify the best clinical and laboratory predictors of poor HDS outcome. These were the presence of an antecedent event particularly diarrhea, neck muscle weakness as well as elevated CRP, and low serum sodium at onset. Linear regression analysis was used to identify the best clinical and laboratory predictors of the ONLS Outcome Scale (Table 4) with cytoalbuminous dissociation and treatment plan as factors in addition to all those predictive of HDS outcome.

Table 3 Multiple ordinal regression analysis to identify the best clinical and laboratory predictors of HDS outcome
Table 4 Linear regression analysis to identify the best clinical and laboratory predictors of ONLS outcome

The best predictor of clinical rating scales of HDS outcome and ONLS (Table 5) using multivariate ordinal regression analysis and multivariate linear regression analysis, respectively, showed that low MRC sum score at the 10th day of treatment was the best single factor.

Table 5 Multiple ordinal and linear regression between baseline clinical rating scales and HDS and ONLS outcome after controlling for age and sex


Most previous studies have been retrospective, and the outcomes derived from relatively small series or selected variants of patients with relatively short follow-up and limited outcome measures [6,7,8,9]. The present study was a prospective longitudinal study involving a relatively large group of GBS recruited at an early stage of the disease.

The main findings were significant correlations between HDS and ONLS outcomes at 3 months and age at onset, the presence of antecedent events (especially diarrhea), low MRC sum score at onset, nadir, and the 10th day after treatment, neck muscle weakness, papilledema, impaired cough reflex, and dysautonomia. With regard to laboratory findings; hyponatremia, hypoalbuminemia and elevated CRP were correlated with poor outcomes.

Of these factors, multiple ordinal regressions showed that the best predictors for poor prognosis in both outcome measures were the presence of diarrhea, elevated CRP, and hypoalbuminemia. In addition, a low MRC sum score at the 10th day following admission was a predictor of poor HDS outcomes.

Regarding age, a total of 62 patients with GBS enrolled in our study with an average age of 37.15 ± 17.60 years (10–75) with more than half of them ≤ 40 years (54.8 ⁒). These distributions are consistent with Sudulagunta SR and colleagues, and Abdelkader Tunç [21, 22]. The age of the patients in years was significantly correlated with outcomes of HDS and ONLS scores meaning that older age was associated with poorer outcomes, as reported in several previous studies [8, 22,23,24,25,26,27,28]. There was no influence of sex on HDS or ONLS outcomes.

According to antecedent Events, an antecedent event was recorded in 43 (69.4%) patients of which 38 (61.3%) had fever, 30 (48.4%) had upper respiratory tract infection, and 13 (21%) had gastrointestinal infection including diarrhea with or without vomiting. Siddiqui and their colleagues [29] found a lower frequency of various antecedent events was recorded in 33 patients, including respiratory tract infection in 9 (14%) and diarrhea/vomiting in 13 (21%) patients. In contrast, Sudulagunta and colleagues, 2015 found that 80% of patients had a predisposing infection which was higher than our study; however, gastrointestinal infection was more common in that study (47.25%) than upper respiratory infection (34.73%) [21].

In the present study, the presence of a preceding antecedent event especially diarrhea was correlated with outcomes of HDS, and ONLS and was considered as one of the predictors for poor outcome. Hadden and colleagues, van Koningsveld and colleagues, and Rajabally and colleagues found similar results [24, 26, 27]. In contrast, Tunç and colleagues [22] found no correlations between outcome and the presence or absence of antecedent events although this may be related to the small sample size.

In the current study the duration of the antecedent event was 1.82 ± 1.52 days and the number of days between onset of symptoms and nadir was 3.32 ± 1.64 days. Twenty-seven patients (43.5%) presented with weakness within 3 days of onset; 23 additional patients were weak (37.1%) within 7 days or more. Similarly, Walgaard and colleagues, 2010 found that 35% of patients presented with weakness within 3 days from onset [7].

Comorbidities had little effect on outcome. Fourteen patients (22.6%) had diabetes and/or hypertension, but this was not related to outcome.

Low MRC sum scores at onset, at nadir and 10th day of treatment were associated with poor outcomes. Chiò A and colleagues, and McKhann GM and colleagues reported similar findings [23, 25]. Rajaballys and colleagues found that a low MRC sum score at admission or 7 days after admission predicted the need for mechanical ventilation [27]. Wen and colleagues found that MRC sum scores on admission and at nadir were significantly different between severe GBS and non-severe GBS groups [28], while Singh NK and colleagues found that severe weakness at nadir, and rapid onset of weakness were poor prognostic features [30]. Gonzalez-Suarez and colleagues reported that older age, severe deficits at onset, and cranial nerve involvement were poor prognostic factors [31].

More than three-quarters of the patients (47 patients with 75.8%) presented with the classic sensory–motor variant, while 12 patients (19.4%) presented with a pure motor variant, 2 patients (3.2%) had paraparesis and one patient (1.6%) had Miller Fisher syndrome. The pure motor type had significantly poorer outcome compared with classic sensory motor neuropathy consistent with other studies [15, 32,33,34].

Involvement of cranial nerves was observed in 40 patients (64.5%) with either unilateral or bilateral facial palsy, bulbar palsy or both but neither correlated with outcome. Other studies reported a lower incidence of cranial nerve involvement ranging from 25 to 34% [21, 22, 35, 36], while several previous reports found that involvement of cranial nerves was more common in severe forms of GBS and was associated with poor prognosis [27, 28, 31]. Verma, Chaudhari, Raut and colleagues found that cranial nerve involvement was associated with mechanical ventilation [36].

Neck muscle weakness was observed in 28 patients (45.2%) and was significantly correlated with HDS and ONLS outcomes. Malaga and their colleagues [37] showed that two clinical parameters (bulbar and neck weakness) early at onset are strongly associated with the risk of respiratory failure.

Malaga and their colleagues found that the frequency of respiratory failure in GBS was 14% [37]. In the present study, 32 patients (51.6%) had respiratory muscle involvement and an impaired cough reflex, but only 5 (8.1%) were intubated and mechanically ventilated (MV) which was lower than that reported by Sudulagunta and colleagues who found that 38.5% of patients were mechanically ventilated [21]. The impaired cough reflex and respiratory muscle involvement significantly correlated with poor outcomes using HDS and ONLS (p = 0.01, p = 0.035, respectively). However, neither was considered as predictor of poor prognosis. In contrast, EGRIS risk total score at onset for respiratory insufficiency significantly correlated with poor outcomes of HDS and ONLS at 3 months (p = 0.008, p = 0.000). The Erasmus GBS Outcome Scale also correlated with poor outcomes in HDS and ONLS. Respiratory problems may well arise because bulbar weakness leads to reduced protection of the airway and difficulty in clearing secretions. It can also cause upper airway collapse, which increases airway resistance and respiratory muscle load causing fatigue, and ultimately respiratory failure.

In the present study, an impaired cough reflex was correlated negatively with poor outcomes but was not considered as a predictor. Thirty-five (56.5%) patients were considered high-risk according to EGRIS which is consistent with Rajabally and colleagues. They found 65% patients had a high-risk score, 24% intermediate and 4% low-risk scores [27]. Shangab and their Colleagues found that 20 cases (24.4%) required MV at onset and axonal type was presented in 11 (55%) patients requiring intubation [38].Dysautonomia occurred in 19 patients (30.6%) and was significantly correlated with poorer outcomes in HDS and ONLS (P = 0.001, P = 0.001, respectively). Puyuan Wen and colleagues considered dysautonomia as a risk factor for severity with a significant difference in outcome in the severe GBS and non-severe GBS groups [28]. Islam and colleagues identified autonomic involvement as an important risk factor for mechanical ventilation and poor outcome [39]. Netto and colleagues found that older age, dysautonomia, and pulmonary complications were predictors of mortality in MV patients with GBS [40]. Zhai and their colleague [11] found that HDS (at admission), dysphagia, and dysautonomia were independent risk factors for GBS patients requiring MV.

Hyponatremia occurred in 19 (30.6%) patients and was significantly correlated with HDS and ONLS outcomes (p = 0.002, p = 0.001, respectively) and was considered as a predictor of poor prognosis, consistent with previous studies [41,42,43].

In GBS, inflammation, demyelination, and axonal damage produce reactive oxygen species associated with free radical toxicity [44, 45]. In the present study, 20 patients (32.3%) had hypoalbuminemia which was significantly correlated with poor outcomes of HDS and ONLS (p = 0.002, p = 0.017, respectively) but was not considered as one of the predictors for poor outcome. Tunç and colleagues found that serum albumin levels decreased consistently with the progression of the disease severity whils Su and colleagues demonstrated that high albumin was a protective factor for GBS patients [22, 46].

Elevated C-reactive protein occurred in 24 (38.7%) patients and was significantly correlated with poor outcomes only in HDS (p = 0.024). It was also predictive of poor outcome. Abdulkadir Tunç and colleagues demonstrated the negative impact of higher age and higher CRP levels measured at the end of the first month [22]. Although elevated CRP levels have been reported to be potential biomarkers for some inflammatory diseases [47], the association of GBS and CRP is still lacking [48].

24 (38.7%) of the 42 patients who underwent CSF analysis had cytoalbuminous dissociation and elevated protein in CSF protein, both of which were correlated with poor outcome although not considered as predictors. High CSF protein concentration is thought to indicate disruption of the blood–nerve barrier [49], but few previous studies have demonstrated that it is related to poor prognosis [49,50,51]. DiCapua DB and colleagues 2015 found that elevated levels of CSF protein occur in 50% of patients after the first week and in 80% of patients in the second week [49]. Sevki Sahin and colleagues reported a positive association between low baseline levels of CSF protein and good prognosis [52]. Vidrio-Becerra and colleagues, 2018 reported that CSF can be used as a prognostic indicator of severity, and that proteins greater than 100 translate into a torpid evolution with more complications [53]. Sahin and colleagues, 2017 found that CSF protein level was a prognostic indicator as it was negatively correlated with MRC outcome at 6 months follow-up, and it was an independent factor on regression analysis [52].

In the present study, 49 patients were treated with plasma exchange, while 13 were treated with IVIG according to the availability. There were no significant differences in outcomes of HDS between the two lines of treatment, however better outcome was recorded with IVIG than plasma exchange in relation to ONLS outcome (p = 0.016).


Clinical and laboratory predictors of poor outcome were older age, antecedent events (particularly diarrhea), low MRC sum score at the 10th day, elevated CRP, hyponatremia and cytoalbuminous dissociation.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author reasonable on request.



Complete blood count








C-reactive protein


Arterial blood gases


Intensive care unit


Mechanical ventilation


Compound muscle action potentials


Guillain–Barré syndrome


Intravenous immunoglobulin


Cerebrospinal fluid


National Institute of Neurological Disorders and Stroke


Nerve conduction study


Medical Research Council sum score


Erasmus GBS Outcome Score


Hughes GBS Disability Scale


Overall Neuropathy Limitation Scale


  1. van den Berg B, Walgaard C, Drenthen J, Fokke C, Jacobs BC, van Doorn PA. Guillain–Barré syndrome: pathogenesis, diagnosis, treatment and prognosis. Nat Rev Neurol. 2014;10(8):469–82.

    Article  PubMed  Google Scholar 

  2. Willison HJ, Jacobs BC, van Doorn PA. Guillain–Barré syndrome. Lancet. 2016;388(10045):717–27.

    Article  PubMed  Google Scholar 

  3. Mori M, Kuwabara S, Yuki N. Fisher syndrome: clinical features, immunopathogenesis and management. Expert Rev Neurother. 2012;12(1):39–51.

    Article  CAS  PubMed  Google Scholar 

  4. Kuwabara S, Yuki N. Axonal Guillain–Barré syndrome: concepts and controversies. Lancet Neurol. 2013;12(12):1180–8.

    Article  PubMed  Google Scholar 

  5. Hughes RA, Cornblath DRJTL. Guillain–Barre syndrome. 2005;366(9497):1653–66.

  6. Fokkink WR, Walgaard C, Kuitwaard K, Tio-Gillen AP, van Doorn PA, Jacobs BC. Association of albumin levels with outcome in intravenous immunoglobulin-treated Guillain–Barré Syndrome. JAMA Neurol. 2017;74(2):189–96.

    Article  PubMed  Google Scholar 

  7. Walgaard C, Lingsma HF, Ruts L, Drenthen J, van Koningsveld R, Garssen MJ, et al. Prediction of respiratory insufficiency in Guillain–Barré syndrome. Ann Neurol. 2010;67(6):781–7.

    PubMed  Google Scholar 

  8. Walgaard C, Lingsma HF, Ruts L, van Doorn PA, Steyerberg EW, Jacobs BC. Early recognition of poor prognosis in Guillain–Barre syndrome. Neurology. 2011;76(11):968–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Walgaard C, Lingsma HF, van Doorn PA, van der Jagt M, Steyerberg EW, Jacobs BC. Tracheostomy or not: prediction of prolonged mechanical ventilation in Guillain–Barré Syndrome. Neurocrit Care. 2017;26(1):6–13.

    Article  PubMed  Google Scholar 

  10. Yao J, Liu Y, Liu S, Lu Z. Regional differences of Guillain–Barré syndrome in China: from south to north. Front Aging Neurosci. 2022;14: 831890.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Zhai Q, Guo C, Xue F, Qiang J, Li C, Guo L. Guillain–Barré Syndrome in Northern China: a retrospective analysis of 294 patients from 2015 to 2020. J Clin Med. 2022;11(21):6323.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ruiz-Sandoval JL, Salvatella-Gutiérrez AP, López-Valencia G, Chiquete E, Ruiz-Herrera V, Pérez-Gómez HR, et al. Clinical characteristics and predictors of short-term outcome in Mexican adult patients with Guillain–Barré Syndrome. Neurol India. 2021;69(1):107–14.

    Article  PubMed  Google Scholar 

  13. Luijten LWG, Doets AY, Arends S, Dimachkie MM, Gorson KC, Islam B, et al. Modified erasmus GBS respiratory insufficiency score: a simplified clinical tool to predict the risk of mechanical ventilation in Guillain–Barré syndrome. J Neurol Neurosurg Psychiatry. 2022;94(4):300–8.

    Article  PubMed  Google Scholar 

  14. Leonhard SE, Mandarakas MR, Gondim FAA, Bateman K, Ferreira MLB, Cornblath DR, et al. Diagnosis and management of Guillain–Barré syndrome in ten steps. Nat Rev Neurol. 2019;15(11):671–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Fokke C, van den Berg B, Drenthen J, Walgaard C, van Doorn PA, Jacobs BCJB. Diagnosis of Guillain–Barré syndrome and validation of Brighton criteria. Brain. 2014;137(1):33–43.

    Article  PubMed  Google Scholar 

  16. Hughes RA, Rees JH. Clinical and epidemiologic features of Guillain–Barré syndrome. J Infect Dis. 1997;176(Suppl 2):S92–8.

    Article  PubMed  Google Scholar 

  17. John J. Grading of muscle power: comparison of MRC and analogue scales by physiotherapists. Medical Research Council. Int J Rehabil Res. 1984;7(2):173–81.

    Article  CAS  PubMed  Google Scholar 

  18. Hermans G, Clerckx B, Vanhullebusch T, Segers J, Vanpee G, Robbeets C, et al. Interobserver agreement of Medical Research Council sum-score and handgrip strength in the intensive care unit. Muscle Nerve. 2012;45(1):18–25.

    Article  PubMed  Google Scholar 

  19. Kleyweg RP, van der Meché FG, Schmitz PI. Interobserver agreement in the assessment of muscle strength and functional abilities in Guillain–Barré syndrome. Muscle Nerve. 1991;14(11):1103–9.

    Article  CAS  PubMed  Google Scholar 

  20. Graham RC, Hughes RA. A modified peripheral neuropathy scale: the overall neuropathy limitations scale. J Neurol Neurosurg Psychiatry. 2006;77(8):973–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sudulagunta SR, Sodalagunta MB, Sepehrar M, Khorram H, Bangalore Raja SK, Kothandapani S, et al. Guillain–Barré syndrome: clinical profile and management. Ger Med Sci. 2015;13:Doc16.

    PubMed  PubMed Central  Google Scholar 

  22. Tunç A. Early predictors of functional disability in Guillain–Barré Syndrome. Acta Neurol Belg. 2019;119(4):555–9.

    Article  PubMed  Google Scholar 

  23. Chiò A, Cocito D, Leone M, Giordana MT, Mora G, Mutani R. Guillain–Barré syndrome: a prospective, population-based incidence and outcome survey. Neurology. 2003;60(7):1146–50.

    Article  PubMed  Google Scholar 

  24. Hadden RD, Karch H, Hartung HP, Zielasek J, Weissbrich B, Schubert J, et al. Preceding infections, immune factors, and outcome in Guillain–Barré syndrome. Neurology. 2001;56(6):758–65.

    Article  CAS  PubMed  Google Scholar 

  25. McKhann GM, Griffin JW, Cornblath DR, Mellits ED, Fisher RS, Quaskey SA. Plasmapheresis and Guillain–Barré syndrome: analysis of prognostic factors and the effect of plasmapheresis. Ann Neurol. 1988;23(4):347–53.

    Article  CAS  PubMed  Google Scholar 

  26. van Koningsveld R, Steyerberg EW, Hughes RA, Swan AV, van Doorn PA, Jacobs BC. A clinical prognostic scoring system for Guillain–Barré syndrome. Lancet Neurol. 2007;6(7):589–94.

    Article  PubMed  Google Scholar 

  27. Rajabally YA, Uncini A. Outcome and its predictors in Guillain–Barre syndrome. J Neurol Neurosurg Psychiatry. 2012;83(7):711–8.

    Article  PubMed  Google Scholar 

  28. Wen P, Wang L, Liu H, Gong L, Ji H, Wu H, et al. Risk factors for the severity of Guillain–Barré syndrome and predictors of short-term prognosis of severe Guillain–Barré syndrome. Sci Rep. 2021;11(1):11578.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Siddiqui M, Majid S, Yusuf H, Mateen F. Electrophysiological pattern and predictors of functional outcome of patients with Guillain–Barre Syndrome at a Tertiary Care Hospital in Pakistan. J Coll Physicians Surg Pak. 2022;32(3):364–8.

    Article  PubMed  Google Scholar 

  30. Singh NK, Jaiswal AK, Misra S, Srivastava PK. Prognostic factors in Guillain–Barre’ syndrome. J Assoc Physicians India. 1994;42(10):777–9.

    CAS  PubMed  Google Scholar 

  31. González-Suárez I, Sanz-Gallego I, Rodríguez de Rivera FJ, Arpa J. Guillain–Barré syndrome: natural history and prognostic factors: a retrospective review of 106 cases. BMC Neurol. 2013;13:95.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Doets AY, Verboon C, van den Berg B, Harbo T, Cornblath DR, Willison HJ, et al. Regional variation of Guillain–Barré syndrome. Brain. 2018;141(10):2866–77.

    Article  PubMed  Google Scholar 

  33. Hiew FL, Ramlan R, Viswanathan S, Puvanarajah S. Guillain–Barré Syndrome, variants and forms fruste: reclassification with new criteria. Clin Neurol Neurosurg. 2017;158:114–8.

    Article  PubMed  Google Scholar 

  34. Wakerley BR, Kokubun N, Funakoshi K, Nagashima T, Hirata K, Yuki N. Clinical classification of 103 Japanese patients with Guillain–Barré syndrome. J Neurol Sci. 2016;369:43–7.

    Article  PubMed  Google Scholar 

  35. Ginanneschi F, Giannini F, Sicurelli F, Battisti C, Capoccitti G, Bartalini S, et al. Clinical features and outcome of the Guillain–Barre Syndrome: a single-center 11-year experience. Front Neurol. 2022;13: 856091.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Verma R, Chaudhari TS, Raut TP, Garg RK. Clinico-electrophysiological profile and predictors of functional outcome in Guillain–Barre syndrome (GBS). J Neurol Sci. 2013;335(1–2):105–11.

    Article  PubMed  Google Scholar 

  37. Malaga M, Rodriguez-Calienes A, Marquez-Nakamatsu A, Recuay K, Merzthal L, Bustamante-Paytan D, et al. Correction to: predicting mechanical ventilation using the EGRIS in Guillain–Barré Syndrome in a Latin American Country. Neurocrit Care. 2021;35(2):603.

    Article  PubMed  Google Scholar 

  38. Shangab M, Al KM. Clinical predictors for mechanical ventilation and prognosis in patients with Guillian–Barre syndrome: a 10-year experience. Neurol Sci. 2021;42(12):5305–9.

    Article  PubMed  Google Scholar 

  39. Islam Z, Papri N, Ara G, Ishaque T, Alam AU, Jahan I, et al. Risk factors for respiratory failure in Guillain–Barré syndrome in Bangladesh: a prospective study. Ann Clin Transl Neurol. 2019;6(2):324–32.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Netto AB, Taly AB, Kulkarni GB, Uma Maheshwara Rao GS, Rao S. Prognosis of patients with Guillain–Barré syndrome requiring mechanical ventilation. Neurol India. 2011;59(5):707–11.

    Article  PubMed  Google Scholar 

  41. Sipilä JO, Kauko T, Soilu-Hänninen M. Admission sodium level and prognosis in adult Guillain–Barré syndrome. Int J Neurosci. 2017;127(4):344–9.

    Article  PubMed  Google Scholar 

  42. Wang Y, Liu J. Hyponatremia is a predictor for poor outcome in Guillain–Barré syndrome. Neurol Res. 2015;37(4):347–51.

    Article  PubMed  Google Scholar 

  43. Hiew FL, Winer JB, Rajabally YA. Hyponatraemia in Guillain–Barré syndrome revisited. Acta Neurol Scand. 2016;133(4):295–301.

    Article  CAS  PubMed  Google Scholar 

  44. Ghabaee M, Jabedari B, Al EEN, Ghaffarpour M, Asadi F. Serum and cerebrospinal fluid antioxidant activity and lipid peroxidation in Guillain–Barre syndrome and multiple sclerosis patients. Int J Neurosci. 2010;120(4):301–4.

    Article  CAS  PubMed  Google Scholar 

  45. Gilgun-Sherki Y, Melamed E, Offen D. The role of oxidative stress in the pathogenesis of multiple sclerosis: the need for effective antioxidant therapy. J Neurol. 2004;251(3):261–8.

    Article  CAS  PubMed  Google Scholar 

  46. Su Z, Chen Z, Xiang Y, Wang B, Huang Y, Yang D, et al. Low serum levels of uric acid and albumin in patients with Guillain–Barre syndrome. Medicine (Baltimore). 2017;96(15): e6618.

    Article  CAS  PubMed  Google Scholar 

  47. Lobo SM. Sequential C-reactive protein measurements in patients with serious infections: does it help? Crit Care (London, England). 2012;16(3):130.

    Article  Google Scholar 

  48. Vaishnavi C, Kapoor P, Behura C, Singh SK, Prabhakar S. C-reactive protein in patients with Guillain–Barré syndrome. Indian J Pathol Microbiol. 2014;57(1):51–4.

    Article  PubMed  Google Scholar 

  49. DiCapua DB, Lakraj AA, Nowak RJ, Robeson K, Goldstein J, Patwa H. Relationship between cerebrospinal fluid protein levels and electrophysiologic abnormalities in Guillain–Barré Syndrome. J Clin Neuromuscul Dis. 2015;17(2):47–51.

    Article  PubMed  Google Scholar 

  50. Brettschneider J, Petzold A, Süssmuth S, Tumani H. Cerebrospinal fluid biomarkers in Guillain–Barré syndrome—where do we stand? J Neurol. 2009;256(1):3–12.

    Article  CAS  PubMed  Google Scholar 

  51. Kerasnoudis A, Pitarokoili K, Behrendt V, Gold R, Yoon MS. Increased cerebrospinal fluid protein and motor conduction studies as prognostic markers of outcome and nerve ultrasound changes in Guillain–Barré syndrome. J Neurol Sci. 2014;340(1–2):37–43.

    Article  CAS  PubMed  Google Scholar 

  52. Sahin S, Cinar N, Karsidag S. Are cerebrospinal fluid protein levels and plasma neutrophil/lymphocyte ratio associated with prognosis of Guillain–Barré Syndrome? Neurol Int. 2017;9(2):7032.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Vidrio-Becerra ME, Valle-Leal J, Loaiza-Sarabia ME, Alvarez-Bastidas L, Lachica-Valle JI, López-Morales CM. Value of protein concentration in cerebrospinal fluid in paediatric patients with Guillain–Barre syndrome. Med Clin (Barc). 2018;150(9):331–5.

    Article  PubMed  Google Scholar 

Download references


Not applicable.


Not applicable.

Author information

Authors and Affiliations



EMK, MM, MS contributed to study concept and design, acquisition of data, draft and revision of the report, statistical analyses, and interpretation of data. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Eman M. Khedr.

Ethics declarations

Ethics approval and consent to participate

An informed written consent was obtained from all the patients after approval by the institutional review board of Assiut University’s Faculty of Medicine. The Ethical approval of this study was done with number: 17101401 and registered on ID: NCT04927598 with posted date: 16/06/2021-(Retrospectively registered)- The confidentiality of patients’ information was maintained during all steps of the study. The research design adheres to the ethical principles outlined in the Helsinki Declaration of 1975.

Consent for publication

Not applicable.

Competing interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khedr, E.M., Mohamed, M.Z. & Shehab, M.M.M. The early clinical and laboratory predictors of GBS outcome: hospital-based study, Assiut University, Upper Egypt. Egypt J Neurol Psychiatry Neurosurg 59, 45 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: