
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39294206
72142
10.1038/s41598-024-72142-3
Article
The early prognostic value of the 1–4-day BCM/PA trend after admission in neurocritical patients
Peng Jingjing 1
Xiang Yanling 2
Liu Guangwei 3
Ling Shuya 4
Li Feng 204267@hospital.cqmu.edu.cn

3
1 https://ror.org/033vnzz93 grid.452206.7 0000 0004 1758 417X Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
2 https://ror.org/017z00e58 grid.203458.8 0000 0000 8653 0555 Department of Operation Anaesthesia, University-Town Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
3 https://ror.org/017z00e58 grid.203458.8 0000 0000 8653 0555 Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, People’s Republic of China
4 https://ror.org/033vnzz93 grid.452206.7 0000 0004 1758 417X Department of Internal Medicine-Cardiovascular Department, The First Branch of the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016 People’s Republic of China
18 9 2024
18 9 2024
2024
14 2180226 2 2023
4 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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 http://creativecommons.org/licenses/by-nc-nd/4.0/.
The purpose of this study was to investigate early stage dynamic changes in relevant indicators in neurocritical patients to identify biomarkers that can predict a poor prognosis at an early stage (1–4 days after admission). This study retrospectively collected clinical data, inflammatory indicators, and nutritional indicators from 77 patients at the neurology intensive care unit. The 3-month modified Rankin scale score was used as the outcome indicator. A linear mixed model was used to analyze changes in inflammatory indicators and nutritional indicators in neurocritical patients over time from 1–4 days after admission. Logistic regression was used to determine the independent risk factors for a poor prognosis in neurocritical patients and to construct a predictive model. The predictive efficacy of the model was verified using leave-one-out cross-validation and decision curve analysis methods. The analysis results showed that 1–4 days after admission, the inflammatory indicators of white blood cell and absolute monocyte counts and the nutritional indicators of body cell mass(BCM), fat-free mass, body cell mass/phase angle (BCM/PA), intracellular water, extracellular water, and skeletal muscle index increased overall, while the nutritional indicators of albumin and visceral fat area decreased overall. The logistic multivariate regression model showed that the Charlson comorbidity index (CCI) (odds ratio (OR) = 2.526, 95% CI [1.202, 5.308]), hemoglobin (Hb)(on admission)-Hb(min) (OR = 1.049, 95% CI [1.015, 1.083), BCM(on admission) (OR = 0.794, 95% CI [0.662, 0.952]), and the change in BCM/PA 1–4 days after admission (OR = 1.157, 95% CI [1.070, 1.252]) were independent risk factors for a poor prognosis in neurocritical patients. The predictive analysis showed that the predictive power of Model 1 with BCM/PA (area under the curve (AUC) = 0.95, 95% CI (0.90, 0.99)) was 93%, 65%, 141%, and 133% higher than that of Model 2 without BCM/PA, the CCI, the APACHE II score, and the NRS2002 score (all P < 0.05), respectively. The CCI, Hb(on admission)-Hb(min), BCM(on admission), and an increase in BCM/PA 1–4 days after admission were independently associated with a poor prognosis in neurocritical patients. Of these variables, BCM/PA may be a valid indicator for early stage prediction of a poor prognosis in neurocritical patients.

Keywords

Biomarkers
Bioelectrical impedance
Systemic inflammatory response syndrome
Modified Rankin Scale
Neurocritical patient
Subject terms

Biomarkers
Neurology
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

In recent years, the mortality and disability rates of neurocritical patients have increased yearly, which has resulted in substantial medical and social burdens1. A large number of studies have found that a persistent severe inflammatory response can lead to aggravation of the primary disease. It is an important cause of poor prognosis and even death in neurocritical patients2–6. Brain-body crosstalk is an important component of the pathophysiological process of systemic inflammatory response syndrome (SIRS) in neurocritical patients7,8. In related studies of patients with acute cerebral hemorrhage and status epilepticus, mortality and disability rates were higher in the patients with SIRS than in the non-SIRS group8,9. In a study of stroke patients, Vahidy10 found that after stroke, the spleen was activated and contracted, releasing a large number of immune cells into the bloodstream that migrate to the brain and infiltrate the brain parenchyma through the damaged blood–brain barrier. The number of immune cells in the brain reaches its peak within 1–4 days after stroke and further promotes the release of inflammatory factors in brain glial cells, causing an inflammatory cascade reaction that induces a large amount of neuronal cell necrosis, which affects the patient’s prognosis. Therefore, the severity of SIRS in neurocritical patients in the early stage (1–4 days after admission) may be the key to determining their prognosis, possibly related to neuronal cell necrosis due to inflammation. The pathogenesis is abnormal accumulation of misfolded/unfolded proteins within the endoplasmic reticulum of nerve cells11. Normally, DNA information transmission translates into polypeptide chains12–14, which are folded into functional proteins within the cellular endoplasmic reticulum, and protein folding is important for maintaining the balance of cellular homeostasis15–17. At the onset of neurocritical illness, abnormal accumulation of misfolded/unfolded proteins provokes endoplasmic reticulum stress in neuronal cells, which induces a cascade of cell death and inflammatory processes when endoplasmic reticulum stress is persistent and intense. The inflammatory cascade in turn aggravates endoplasmic reticulum stress, creating a vicious cycle that triggers neuronal cell death and further leading to the occurrence of a poor prognosis11.

Previous studies have suggested that the severity of SIRS is affected by nutritional status18.The main manifestation of malnutrition in neurocritical patients is protein-energy malnutrition (PEM)19. By damaging the immune system’s defense ability, PEM leads to a significant reduction in the body's resistance to infection20. Its pathophysiological mechanism manifests primarily as atrophy of thymus and lymph node immune tissues, impaired humoral and cellular immune functions, weakened leukocyte phagocytosis and reduced protein synthesis, which create favorable conditions for infection21. After infection, inflammatory cytokines act on the neuroendocrine system to stimulate the release of stress hormones (including cortisol and catecholamines) and increase catabolism, leading to further deterioration of immune system function in neurocritical patients, aggravating the degree of inflammatory response, accelerating death and leading to a poor prognosis22.f

Currently, the relevant indicators that can be used to predict adverse outcomes in neurocritical patients include neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, albumin, and hemoglobin. However, the specificity of these indicators is not high23–26, and most studies are based on a one-time cutoff point and lack observations of dynamic change trends; thus, the predictive performance of these indicators is controversial, and they are not suitable as early stage and effective prognostic indicators for neurocritical patients. In recent years, studies have confirmed that relevant indicators measured by bioelectrical impedance analysis (BIA), such as phase angle (PA), body cell mass(BCM), and hydration status, are associated with inflammatory responses and a poor prognosis in patients who are elderly, have cancer, are undergoing hemodialysis, or are critically ill27–31. They are expected to become novel indicators for predicting patient prognosis, but their predictive efficacy needs to be further verified.

Therefore, this study continuously and dynamically collected the clinical data (demographic characteristics + disease-related indicators), inflammatory indicators, and nutritional indicators (biochemical and BIA indicators) of 77 neurocritical patients who were admitted to the neurology intensive care unit (NICU) from January to July 2021 in the early stage (1–4 days after admission). A retrospective analysis of the changes in the data was conducted to identify objective occurrence and development patterns and to identify novel and sensitive early stage indicators for the objective prediction of poor prognosis in neurocritical patients.

Methods

Study design and participants

This was a case–control study. The subjects were patients who were admitted to the NICU of the First Affiliated Hospital of Chongqing Medical University from January 2021 to July 2021. Medical case data during hospitalization were collected. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Medical Ethics Committee of the First Affiliated Hospital of Chongqing Medical University. This study was registered with the China Clinical Trials Registry with registration number ChiCTR1800014324.

Inclusion criteria

(1) since the hospital is an adult hospital, the age of the included subjects was ≥ 18 years; (2) NICU admission ≥ 4 days; (3) patients or their guardians provided written informed consent; and (4) patients who can remain in a static supine position after sedation.

Exclusion criteria

(1) patients with moderate or severe disability and no self-care in life before admission; (2) patients with agitation, those who were unable to undergo BIA treatment due to the implantation of metal devices (such as pacemakers or artificial femoral heads), and those with incorrect BIA values; (3) patients whose conditions relapsed during hospitalization and those who required two or more transfers to the NICU; (4) patients with unstable hemodynamics; (5) patients who required surgical treatment; and (6) patients with incomplete data; (7) patients who received a red blood cell transfusion (Fig. 1).Fig. 1 Flowchart of patient recruitment.

Outcome indicator

The outcome indicator was the 3-month modified Rankin scale (mRS) score. According to the mRS score, the patients were divided into two groups: the poor prognosis group (mRS 3–6) and the good prognosis group (mRS 0–2).

Data collection

In this study, a retrospective data collection method was used to continuously and dynamically collect the clinical data, inflammatory indicators, and nutritional indicators (including biochemical and BIA indicators) of 77 NICU patients over 1–4 days of admission. All data were collected and compiled by two dedicated research assistants using the electronic medical record system and the BIA instrument recording system, and data entry was performed separately. After data entry was completed, the patient’s name and medical record number were deleted, and the patient was given a unique study number. All data were reviewed by a dedicated study coordinator to confirm their accuracy and to manually verify inconsistent or outlier values. Prior to the statistical analysis, the dataset was validated and cleaned to prevent any further changes and ensure the consistency and completeness of the data in the statistical report and analysis. All researchers who collected and compiled the data were unaware of the contents of the study.

Clinical data:

Demographic characteristics

Sex, age, and body mass index (BMI).

Disease-related indicators

Acute Physiology and Chronic Health Evaluation (APACHE) II score, CCI, Nutrition Risk Screening 2002 (NRS 2002) results, disease diagnosis, and history of previous neurological diseases.

Inflammation-related indicators

White blood cell (WBC) count, absolute neutrophil count (ANC), absolute lymphocyte count (ALC), absolute monocyte count (AMC), platelet count (PLT), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and high-sensitivity C-reactive protein (hsCRP).

Nutrition-related indicators

Nutritional biochemical indicators

Albumin (Alb), Hb, and random blood glucose (Glu).

BIA indicators

BCM, PA, BCM/PA, intracellular water (ICW), extracellular water (ECW), total body water (TBW), fat-free mass (FFM), skeletal muscle index (SMI), whole-body protein (WBP), and visceral fat area (VFA).

Definition of relevant indicators

CCI32: The patient’s medical history was obtained through the electronic medical record system, and CCI scores were calculated according to the patient’s comorbidities. APACHE II score33: The assessment value within 24 h after admission was used. If there were multiple assessment values, the highest assessment value was selected. NRS2002 score34: The assessment value within the first day of admission was used.

All laboratory indicators were evaluated at the Department of Laboratory Medicine, the First Affiliated Hospital of Chongqing Medical University, and uploaded to the electronic medical record system. The patients’ test values 1–4 days after admission were collected based on the time blood specimens being taken. In this context, Hb(on admission) is the value detected within 24 h after admission and Hb(min) is the minimum value of Hb detected within 1–4 days after admission. In this study, the normal reference ranges for the relevant laboratory indicators were WBC (3.50–9.50*109/L), ANC (1.80–6.30*109/L), ALC (1.10–3.20*109/L), AMC (0.10–0.60*109/L), PLT (85–303*109/L), hsCRP (0–10 mg/ml), Alb (35–50 g/L), Hb (130–175 g/L), and Glu (< 11.1 mmol/L). The BIA indicators were measured by physicians of the Department of Nutrition of the First Affiliated Hospital of Chongqing Medical University using BIA according to a uniform and standardized method on days 1–4 of admission, and the measurement values were collected. The BIA model was InBody S10 from Biospace Co., Ltd., South Korea35. In this study, the normal reference ranges for BIA indicators were PA (> 3 degrees), TBW (0.36–0.39%), TBW/FFM (72.7–74.3%), SMI (men > 7 kg/m2; women > 5.5 kg/m2), and VFA (< 100 cm2).

The guidelines of the European Working Group on sarcopenia suggest that the BIA indicator skeletal muscle mass is closely related to height, and the composite index SMI, which is the combination of skeletal muscle and height, can accurately reflect the nutritional status of the human body36. Therefore, this study included the composite indicator SMI, which was calculated using the following formula: SMI = skeletal muscle mass/height2. Furthermore, the BIA measurement BCM is the sum of the number of metabolically active and functionally intact somatic cells in human lean body mass. PA is the cotangent value of reactance (Xc) and impedance (Z) generated by a current flowing through the human body. Its formula is sin(PA) = Reactance(Xc)/Impedance(Z), which reflects the integrity of the cell membrane. Relevant studies have pointed out that a decrease in PA reflects poor structure and low function of the cell membrane as a result of a decrease in BCM, and there is a strong interaction between BCM and PA37. Therefore, this study innovatively combined BCM and PA detection into one BIA measurement as the second-level indicator BCM/PA and calculated the ratio to determine the prognostic value of this second-level indicator.

In recent years, a large amount of evidence has shown that there is a strong interaction among inflammatory cytokines and between inflammatory cytokines and platelets in the pathophysiological development of systemic inflammatory responses38. Therefore, this study included the analysis of relevant composite inflammatory indicators, including NLR and PLR. The NLR and PLR values were calculated using the same blood specimen. The calculation formulas are NLR = ANC/ALC and PLR = PLT/ALC.

Statistical description

Normally distributed measurement data are described as the mean ± standard deviation, and two independent sample t tests were used for comparisons between groups. Measurement data with a skewed distribution are described as the median and interquartile range, and the Mann–Whitney U test was used for comparisons between groups. Count data are described as the number of cases and rate, and comparisons between groups were performed using the chi-square test or Fisher’s exact probability test. This study was reported based on the TRIPOD guidelines for prediction model development/validation38. A mixed linear model was used to calculate the slopes of the changes in inflammatory and nutritional indicators over time on days 1–4 of admission, and the slope for each patient was calculated using the random effects model. Variables with P < 0.05 in the univariate analysis were included in the multivariate analysis, and the variables were screened using the stepwise method. Multivariate logistic regression was used to investigate the risk factors associated with the prognosis at discharge. The area under the receiver operator characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, integrated discrimination improvement index (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA) were used to compare the value of different indicators or models for predicting the prognosis at discharge. AUCs were compared using the Delong test. The leave-one-out cross-validation method was used for internal validation. The Delong test, IDI, NRI, and DCA were performed in the nsROC, PredictABEL, nricens, and rmda packages in R (version 4.0.0), respectively, and the other analyses were performed in SAS 9.4 (Copyright ©2016 SAS Institute, Inc., Cary, NC, USA).

Results

Baseline data analysis

A total of 77 neurocritical patients were included in this study. The analysis of their baseline data is shown in Table 1. There were 31 patients with a poor prognosis according to the mRS (3–6), of whom 16 died; 46 patients had a good prognosis according to the mRS (0–2). The analysis results showed that indicators within the first day of admission (age, APACHE II score, CCI, NRS2002, hsCPR, BCM/PA ratio, TBW) and Hb(on admission)-Hb(min) were higher in the poor prognosis group than in the good prognosis group, and the differences between the two groups were statistically significant (all P values < 0.05) (Table 1). Table 1 Comparison of baseline data between the poor prognosis group and the good prognosis group.

Indicator	Group	χ2/t/Z	P value	
Good (mRS 0–2) (n = 46)	Poor (mRS 3–6) (n = 31)	
Clinical data	
Sex					
Male	33(71.74)	16(51.61)	3.242	0.072	
Female	13(28.26)	15(48.39)			
Age, years	59.13 ± 16.23	71.45 ± 13.77	− 3.467	0.001	
BMI, kg/m2	24.43 ± 4.14	23.38 ± 3.85	1.087	0.281	
APACHE II	9.61 ± 4.96	15 ± 5.2	− 4.584	 < 0.001	
CCI	0.43 ± 0.75	1.48 ± 1.36	− 3.905	 < 0.001	
NRS2002	2.87 ± 2.01	5.13 ± 2	− 4.856	 < 0.001	
Diagnosis (classification)	
Cerebrovascular diseases	31(67.39)	21(67.74)	/	 > 0.999	
CNS infectious diseases	5(10.87)	3(9.68)			
Neuromyelitis optica	1(2.17)	1(3.23)			
Epilepsy	7(15.22)	4(12.90)			
Metabolic encephalopathy	1(2.17)	1(3.23)			
Degenerative diseases of the nervous system	1(2.17)	1(3.23)			
Previous history of neurological diseases					
No	41(89.13)	28(90.32)	/	 > 0.999	
Yes	5(10.87)	3(9.68)			
Inflammatory indicators within the first day of admission	
WBC, *109/L	9.63 ± 3.12	10.77 ± 4.72	− 1.181	0.244	
ANC, *109/L	7.78 ± 3.12	8.56 ± 3.87	− 0.98	0.33	
ALC, *109/L	1.22 ± 0.55	0.99 ± 0.51	1.806	0.075	
AMC, *109/L	0.55 ± 0.23	0.61 ± 0.44	− 0.766	0.448	
PLT, *109/L	208.96 ± 71.23	184.45 ± 60.1	1.574	0.12	
NLR	8.62 ± 7.44	10.57 ± 6.77	− 1.171	0.245	
PLR	196.58 ± 103.67	217.98 ± 106.03	− 0.876	0.384	
hsCPR, mg/L	5.22(2.46,10.87)	14.91(6.21,20)	− 3.317	0.001	
Nutritional biochemical indicators	
Alb(on admission), g/L	39.67 ± 5.02	37.32 ± 5.88	1.865	0.066	
Hb(on admission), g/L	140.57 ± 16.48	127.29 ± 24.69	2.625	0.012	
Hb(on admission)-Hb(min), g/L	9.5(5,20)	35(6,59)	3.166	0.002	
Glu(on admission), mmol/L	6.5(5.4,8.9)	7.9(6.5,8.7)	1.369	0.171	
BIA indicators within the first day of admission	
BCM, kg	30.54 ± 5.1	27.1 ± 5.8	2.659	0.01	
PA, degree	5.57 ± 0.86	4.06 ± 0.93	7.077	 < 0.001	
BCM/PA, kg/degree	5.53 ± 0.83	6.89 ± 1.64	− 4.04	 < 0.001	
ICW, L	21.33 ± 3.56	18.92 ± 4.06	2.663	0.01	
ECW, L	13.27 ± 2.07	12.53 ± 2.41	1.396	0.167	
TBW, %	0.38 ± 0.01	0.4 ± 0.01	− 6.733	 < 0.001	
FFM, kg	46.97 ± 7.57	42.57 ± 8.65	2.287	0.025	
TBW/FFM, %	73.64 ± 0.38	73.79 ± 0.37	− 1.722	0.089	
SMI, kg/m2	9.55 ± 1.32	8.53 ± 1.62	2.929	0.005	
WBP, kg	9.21 ± 1.54	8.18 ± 1.75	2.649	0.01	
VFA, cm2	93.9 ± 49.57	118.33 ± 63.64	− 1.834	0.071	
Outcome indicators	
Mortality rate within 3 months					
Survival	46(100.00)	15(48.39)	29.969	 < 0.001	
Death	0(0.00)	16(51.61)			
Mechanical ventilation time, days	0(0,0)	6(0,13)	4.597	 < 0.001	
NICU hospital stay, days	4(2,10)	13(7,23)	4.551	 < 0.001	
Total hospital stay, days	15.5(13,25)	25(13,37)	1.763	0.078	
Hospitalization expenses, thousand yuan	29.15(20.11,69.84)	112.97(56.2,192.59)	4.072	 < 0.001	
Cerebrovascular diseases which include ischemic stroke, intracerebral hemorrhage; CNS infectious diseases = Central nervous system infection diseases, CNS infectious diseases which include N-methyl-D-aspartate receptors, tuberculous meningitis, viral encephalitis, toxoplasma encephalitis, purulent meningitis; Degenerative diseases of the nervous system which include parkinson's disease, Alzheimer's disease.

Within the first day of admission, the Hb, BCM, PA, ICW, FFM, SMI, and WBP of the poor prognosis group were all lower than those of the good prognosis group, and the differences between the two groups were statistically significant (all P < 0.05). Additionally, among the outcome indicators, the differences in mortality, mechanical ventilation time, length of NICU stay, and hospitalization expenses between the two groups were statistically significant (all P values < 0.05) (Table 1).

Analysis of the change trends in inflammatory and nutritional indicators 1–4 days after admission

The inflammatory and nutritional indicators of the 77 neurocritical patients were repeatedly measured (1–4 days after admission) and were treated as time-dependent variables. The analysis of the change trends in the indicators over time is shown in Table 2. Inflammation indicators (WBC, AMC) showed an overall upward trend over time from day 1 to day 4 after admission. The nutritional index albumin (Alb) decreased gradually with time; BIA indicators (BCM, BCM/PA, ICW, ECW, FFM, SMI) showed an overall upward trend over time from day 1 to day 4 after admission. However, the trend of BIA indicator (VFA) changes with time is gradually declining (Table 2). Table 2 Trend analysis of the change in indicators in neurocritical patients 1–4 days after admission.

Variable	Day 1	Day 2	Day 3	Day 4	Coefficient	Standard error	t value	P value	
Inflammatory indicators	
WBC, *109/L	10.09 ± 3.85	10.98 ± 4.95	12.17 ± 5.47	11.50 ± 4.76	0.549	0.213	2.58	0.012	
ANC, *109/L	8.09 ± 3.44	8.47 ± 3.67	9.68 ± 4.93	9.37 ± 4.48	0.397	0.203	1.96	0.055	
ALC, *109/L	1.13 ± 0.54	1.20 ± 0.50	1.19 ± 0.54	1.18 ± 0.59	0.04	0.021	1.93	0.058	
AMC, *109/L	0.57 ± 0.33	0.72 ± 0.27	0.87 ± 0.39	0.84 ± 0.42	0.099	0.018	5.41	 < .0001	
PLT, *109/L	199.09 ± 67.65	183.24 ± 60.97	193.54 ± 74.38	189.85 ± 70.01	− 1.516	2.322	− 0.65	0.516	
NLR	9.40 ± 7.20	8.27 ± 4.67	10.30 ± 8.14	9.81 ± 5.99	0.061	0.363	0.17	0.868	
PLR	205.31 ± 104.47	177.82 ± 89.17	193.06 ± 111.96	187.92 ± 89.47	− 7.504	4.572	− 1.64	0.105	
Nutritional biochemical indicators	
Alb, g/L	38.71 ± 5.47	36.56 ± 5.51	36.25 ± 5.21	35.75 ± 5.64	− 0.984	0.227	− 4.34	 < .0001	
BIA indicators BCM, kg	29.22 ± 5.60	29.14 ± 5.76	29.53 ± 5.76	29.88 ± 5.49	0.119	0.059	2.02	0.047	
PA, degree	4.99 ± 1.15	4.92 ± 1.28	8.18 ± 18.43	4.96 ± 1.30	0.159	0.511	0.31	0.757	
BCM/PA, kg/degree	6.05 ± 1.37	6.19 ± 1.51	6.42 ± 2.17	6.31 ± 1.54	0.112	0.041	2.71	0.008	
ICW, L	20.41 ± 3.91	20.35 ± 4.01	20.62 ± 4.03	20.88 ± 3.83	0.086	0.04	2.14	0.035	
ECW, L	12.99 ± 2.22	13.02 ± 2.28	13.28 ± 2.22	13.35 ± 2.11	0.109	0.03	3.59	0.001	
TBW, %	0.39 ± 0.01	0.39 ± 0.01	0.39 ± 0.01	0.46 ± 0.47	0.015	0.021	0.7	0.486	
FFM, kg	45.28 ± 8.23	45.23 ± 8.41	45.90 ± 8.39	46.38 ± 7.95	0.244	0.095	2.58	0.012	
SMI, kg/m2	9.16 ± 1.52	9.15 ± 1.56	9.24 ± 1.52	9.41 ± 1.53	0.043	0.02	2.18	0.033	
WBP, kg	8.82 ± 1.69	8.79 ± 1.75	8.91 ± 1.73	9.00 ± 1.67	0.032	0.019	1.71	0.091	
VFA, cm2	103.27 ± 56.25	102.13 ± 52.49	102.40 ± 51.29	95.80 ± 54.02	− 1.879	0.711	− 2.64	0.01	

The results of the differential analysis based on time-dependent variables showed that the BCM/PA of the poor prognosis group had a higher change trend 1–4 days after admission than the good prognosis group (P < 0.05) (Table 3). Table 3 Differential analysis of the change trends in indicators between the two groups 1–4 days after admission.

Indicator	Group	t	P value	
Good (mRS 0–2) (n = 46)	Poor (mRS 3–6) (n = 31)	
Inflammatory indicators	
WBC slopes, *109/L/day	0.46 ± 0.79	0.68 ± 0.91	− 1.114	0.269	
AMC slopes, *109/L/day	0.09 ± 0.07	0.11 ± 0.09	− 0.936	0.352	
Nutritional biochemical indicators	
Alb slopes, g/L/day	− 0.88 ± 0.8	− 1.14 ± 1.07	1.24	0.219	
BIA indicators	
BCM slopes, kg/day	0.1 ± 0.17	0.14 ± 0.32	− 0.616	0.541	
BCM/PA slopes, kg/degree/day	0.04 ± 0.11	0.22 ± 0.21	− 4.448	 < 0.001	
ICW slopes, L/day	0.07 ± 0.11	0.11 ± 0.22	− 0.767	0.448	
ECW slopes, L/day	0.09 ± 0.1	0.14 ± 0.2	− 1.195	0.239	
FFM slopes, kg/day	0.2 ± 0.31	0.31 ± 0.57	− 0.939	0.353	
SMI slopes, kg/m2/day	0.04 ± 0.05	0.05 ± 0.1	− 0.829	0.412	
VFA slopes, cm2/day	− 1.62 ± 3.17	− 2.26 ± 3.81	0.793	0.43	

Correlation analysis for poor prognosis in neurocritical patients

The above variables with P < 0.05, which included age, APACHE II, CCI, NRS2002, indicators within the first day of admission (hsCPR, Hb, BCM, PA, BCM/PA, ICW, TBW, FFM, SMI, WBP), Hb(on admission)-Hb(min) and change in BCM/PA 1–4 days after admission (Table 1), were included in multivariate analysisin.

The results showed that CCI (odds ratio (OR) = 2.526, 95% confidence interval (CI) [1.202, 5.308]), Hb(on admission)-Hb(min) (OR = 1.049, 95% CI [1.015, 1.083]) and change in BCM/PA 1–4 days after admission (OR = 1.157, 95% CI [1.070, 1.252]) were independent risk factors for poor prognosis in neurocritical patients. In contrast, higher BCM (on admission) (30.54 ± 5.1) was a protective factor (OR = 0.794, 95% CI [0.662, 0.952]) (Table 4). Table 4 Results of the multivariate logistic regression model analysis for poor prognosis in neurocritical patients.

Variable	β	Standard error	χ2	P	OR(95%CI)	
CCI	0.926	0.379	5.978	0.014	2.526(1.202,5.308)	
Hb(on admission)-Hb(min), g/L	0.047	0.017	8.041	0.005	1.049(1.015,1.083)	
BCM (on admission), kg	− 0.231	0.093	6.179	0.013	0.794(0.662,0.952)	
BCM/PA slopes, *10−2, kg/degree/day	0.146	0.04	13.315	 < 0.001	1.157(1.070,1.252)	

Predictive analysis results

The variables that were significant in the multivariate analysis, including CCI, Hb(on admission)-Hb (min), and BCM(on admission), and the change in BCM/PA 1–4 days after admission were used to establish Model 1 and Model 2, respectively. The change in BCM/PA 1–4 days after admission was included in Model 1 but not in Model 2. The remaining predictors were the same in both Model 1 and Model 2. The results of the predictive analysis showed that the AUC of Model 1 (AUC = 0.95, 95% CI (0.90, 0.99)) was higher than that of Model 2 (AUC = 0.85, 95% CI (0.76, 0.95)) (P < 0.05). The accuracy, sensitivity, and specificity of Model 1 were all higher than those of Model 2, the CCI, the APACHE II score, and the NRS2002 score. The NRI indicator of IDI in Model 1 was higher than that in Model 2, CCI, APACHE II and NRS2002. That is, compared with Model 2, the CCI, the APACHE II score, and the NRS2002 score, the proportion of correct patient classifications by Model 1 increased by 20%, 43%, 40%, and 37%, respectively, and its predictive ability increased by 93%, 65%, 141% and 133%, respectively (all P < 0.05) (Table 5). Table 5 Comparison of the efficacy of different indicators for predicting the prognosis of neurocritical patients.

Predictors	AUC(95%CI)	P	Accuracy%	Sensitivity%	Specificity%	IDI(95%CI)	P	NRI(95%CI)	P	
Model 1	0.95(0.90,0.99)		85.71	93.55	80.43					
Model 2	0.85(0.76,0.95)	0.024a	81.82	80.65	82.61	0.93(0.57,0.93)a	 < 0.001a	0.20(0.09, 0.31)a	0.003a	
CCI	0.74(0.63,0.95)	 < 0.001a	70.13	70.97	69.57	0.65(0.39,0.65)a	 < 0.001a	0.43(0.31,0.55)a	 < 0.001a	
APACHEII	0.77(0.66,0.88)	0.002a	71.43	80.65	65.22	1.41(1.14,1.41)a	 < 0.001a	0.40(0.27,0.53)a	 < 0.001a	
NRS2002	0.79(0.68,0.89)	0.003a	72.73	80.65	67.39	1.33(1.01,1.33)a	 < 0.001a	0.37(0.23,0.51)a	 < 0.001a	
Model 1 cannot be calculated at admission since the indicator (BCM/PA) is to be obtained upon continuous measurement within the first 4 days after admission.

Model 1: Includes BCM/PA slopes, CCI, Hb(on admission)-Hb(min), and BCM(on admission).

Model 2: Includes CCI, Hb(on admission)-Hb(min), and BCM(on admission).

a: Compared with Model 1.

According to the results of the decision curve analysis, the standard net benefit values of Model 1 were all higher than those of Model 2, the CCI, the APACHE II score, and the NRS2002 (Fig. 2).Fig. 2 Decision curves of different indicators for predicting the prognosis of discharged patients.

Discussion

This was a case–control study that included 77 neurocritical patients. The study retrospectively analyzed the patients’ clinical data (demographic characteristics, disease-related indicators), inflammatory indicators, and nutritional indicators (nutritional biochemical indicators, BIA indicators). The results of the linear mixed model analysis showed that the inflammatory indicators (WBC and AMC) and BIA indicators (BCM, BCM/PA, ICW, ECW, FFM, and SMI) increased overall 1–4 days after admission, while the nutritional biochemical indicators (Alb) and BIA indicator (VFA) decreased overall. After adjusting for confounding factors using the logistic multivariate regression model, CCI, Hb(on admission)-Hb(min), BCM(on admission), and the change in BCM/PA 1–4 days after admission were independently correlated with a poor prognosis in neurocritical patients.

The CCI is an assessment scale that reflects patient comorbidity and can be used to predict patients’ mortality, disability rate, and risk of readmission39. Previous studies have shown that the CCI is independently associated with functional deterioration within 1 year and increased 30-day mortality in stroke patients32. This study also found that the CCI was independently associated with a 3-month poor prognosis in neurocritical patients. Furthermore, Arata26 et al. pointed out that decreased Hb during hospitalization is associated with a poor prognosis in neurocritical patients. Kellert40 et al. also found that the occurrence of anemia within 5 days after stroke was associated with an increase in the mortality rate of patients in the short term. Additionally, a decrease in Hb greater than 15 g/L during hospitalization in patients with acute stroke is an independent risk factor for poor prognosis at discharge26. This study found that the greater the decrease in Hb during hospitalization was, the worse the prognosis of neurocritical patients was; the decreasing values of Hb during hospitalization among the neurocritical patients was 35(6, 59) g/L, which was independently associated with the 3-month poor prognosis. Infection or inflammation may stimulate neutrophils to release the iron-binding protein lactoferrin. Lactoferrin is internalized by bacteria, sequestering iron and leading to iron deficiency anemia manifested by decreased hemoglobin. An increase in the hemoglobin D-value reflects worsening anemia in neurocritical patients and will accelerate the course of the disease and affect patient outcomes41.

BCM is an important indicator in the analysis of human body composition; it is defined as the total mass of metabolically active, viable, functional cells42. Increasing evidence shows that BCM is closely related to the inflammatory response in cancer patients, elderly patients, patients with chronic diseases, and hemodialysis patients, and it has an impact on patients’ clinical outcomes31,43–45. Toshimi46 et al. confirmed that low preoperative BCM (BCM ≤ 23 kg) was a risk factor for sepsis and death from infection after liver transplantation. This study also found that a low BCM (27.1 ± 5.8) kg at admission can affect the 3-month prognosis of neurocritical patients and can be used as an effective indicator for predicting poor prognosis and death.

At the same time, this study for the first time observed the relationship between the dynamic trend of BCM and other related indicators and the prognosis of neurocritical patients 1–4 days after admission. One to four days after admission is the peak period for the occurrence of SIRS in neurocritical patients, and the severity of the inflammatory response at this stage directly affects the patient’s prognosis. A study of the prognosis of neurocritical patients found that 43% of patients with status epilepticus developed SIRS at admission, which was independently related to their drug resistance and 30-day mortality9. SIRS occurred in 64% of patients with spontaneous intracerebral hemorrhage and in 78% of patients with spontaneous subarachnoid hemorrhage within 3 days of admission and was closely associated with increased cerebral hemorrhage, exacerbation of vasospasm, and delayed cerebral ischemia47–49. Fifty-six percent of patients with ischemic stroke will develop SIRS within 4 days of admission47. Moreover, the greater the severity of SIRS is, the higher the 3-month disability rate and mortality rate50. Severe brain injury can induce SIRS, and the occurrence of SIRS further aggravates the severity of brain injury and adversely affects patient prognosis. We further analyzed the trend and found that a gradual increase in BCM was an important factor in the poor prognosis of neurocritical patients. This relationship may be related to the pathological process of tissue cell degeneration and necrosis caused by acute inflammation in the SIRS state. Tissue and cell necrosis caused by acute inflammation specifically manifests as necroptosis, that is, cell swelling, cell membrane rupture, and the release of cell contents51–53. Therefore, cell swelling is an early stage manifestation of tissue cell programmed necrosis caused by acute inflammation, and the main reason for cell swelling is the increase in ICW content54. However, BIA estimates BCM by measuring ICW using the formula BCW = WBP + ICW55 . In this study, the WBP of neurocritical patients did not change significantly 1–4 days after admission, but both ICW and BCM gradually increased; that is, the more intracellular water there was, the more obvious the swelling was, which may reflect early stage changes in the acute inflammatory phase with reduced somatic cell function and programmed cell necrosis. However, in the multivariate analysis, we did not find that an increase in the BCM was independently associated with poor prognosis. The reason for this result may be related to inadequate consideration of the functional state of the cell membrane, i.e., its permeability56.

To improve the efficacy for predicting a poor prognosis, this study innovatively combined BCM and PA into the secondary index BCM/PA. PA is the cotangent value of the reactance (Xc) generated by the current flowing through the human cell membrane and the impedance (Z) generated by the water flowing inside and outside the cells. It can reflect the functional status of the cell membrane. The lower the PA value is, the more severe the damage to the cell membrane structure and the worse the cell’s functional status57. A number of previous studies have shown that a low PA value at a specific time point is an effective predictor of inflammatory status, adverse functional outcomes, and death in elderly, obese, cancer, and hemodialysis patients27–29,58,59. In this study, the PA of the patients in the poor prognosis group (4.06 ± 0.93) within the first day of admission was lower than that of the patients in the good prognosis group (5.57 ± 0.86), and the difference between the two groups was statistically significant (P < 0.05). Therefore, the gradual increase in BCM at low PA values may truly reflect the early stage changes in cell membrane damage, gradual swelling of cells, gradual deterioration of cell function, and programmed cell necrosis in the SIRS state. That is, the larger the BCM/PA value is, the more severe the degree of SIRS, and the worse the patient’s prognosis. Further analysis of the prediction model showed that the model that included the secondary index BCM/PA had significantly higher predictive value than the prediction model that did not include BCM/PA. Moreover, compared with the traditional prognostic scoring scales (CCI, APACHE II, and NRS2002), the sensitivity (93.55%) and specificity (80.43%) of the prediction model that included the secondary index BCM/PA were both higher, suggesting that the model has high predictive ability for the prognosis of neurocritical patients. Therefore, BCM/PA plays an important role in improving the predictive value of the model and can be used as an effective early stage predictive indicator of poor prognosis in neurocritical patients.

Limitations

1. This study is a single-center retrospective case–control study with a relatively small sample size. A larger sample size and prospective studies are needed in the future to verify the findings of this study. 2. Since the severity of SIRS in neurocritical patients in the early stage (1–4 days after admission) is the key to determining their prognosis, we focused on the correlation between the dynamic changes in relevant indicators during this time period and the 3-month prognosis of patients to identify effective indicators for the early stage prediction of patient prognosis. However, this study did not observe the impact of relevant indicators on the prognosis of neurocritical patients after 4 days of admission. Therefore, subsequent studies can explore the change in relevant indicators after 4 days of admission to more realistically depict the correlation between the pathological changes in SIRS and poor prognosis. 3. The prediction model that included BCM/PA was verified only internally, and the conclusion requires external verification. 4. BIA devices from different manufacturers are affected by age and geographical population, which may lead to differences in measurement results. Therefore, in clinical application, the use of BIA equipment that is appropriate for the research subjects should be carefully selected. The subjects in this study were all Asian individuals older than 18 years of age, which essentially ensured the homogeneity of the study samples and reduced the impact of measurement differences on the accuracy of human body composition indicators. 5. Indicators for the predictive model in this study can be directly measured by BIA tool. However, BCM/PA slope has to be calculated by using the statistical method in a specific program. In the future, collaborative efforts with BIA technology research team are needed to optimize software technology, so as to improve the accuracy and convenience of the predictive model in clinical application.

Conclusions

In summary, BCM/PA is a sensitive indicator of SIRS severity in the early stage and can effectively predict the 3-month prognosis of neurocritical patients. Its measurement is noninvasive and simple. Medical and nursing staff can complete the procedure and interpret the results within a few minutes after simple training. This method is promising and universally easy to use in the ICU, thus warranting further validation and promotion.

Abbreviations

Alb Albumin;

ALC Absolute lymphocyte count;

AMC Absolute monocyte count

ANC Absolute neutrophil count

APACHE II Acute Physiology and Chronic Health Evaluation II;

AUC Area under the receiver operator characteristic curve

BCM Body cell mass;

BIA Bioimpedance analysis;

BMI Body mass index;

CCI Charlson Comorbidity Index;

CI Confidence interval;

CNS Central nervous system;

DCA Decision curve analysis

ECW Extracellular water;

FFM Fat-free mass;

Glu Blood glucose

Hb Hemoglobin

hsCRP High-sensitivity C-reactive protein

ICW Intracellular water

IDI Integrated discrimination improvement index

NICU Neurology intensive care unit;

NLR Neutrophil-to-lymphocyte ratio

NRI Net reclassification improvement

NRS2002 Nutrition Risk Screening 2002

mRS Modified Rankin scale

OR Odds ratios;

PA Phase angle;

PEM Protein-energy malnutrition

PLR Platelet-to-lymphocyte ratio

PLT Platelet count

ROC Receiver operator characteristic

SIRS Systemic inflammatory response syndrome

SMI Skeletal muscle index

TBW Total body water.

WBC White blood cell

VFA Visceral fat area

Author contributions

F.L. J.P. and Y.X. contributed to the conception and study design. J.P. Y.X. G.L. and S.L. performed data acquisition; F.L. J.P. Y.X. G.L. and S.L. participated in the interpretation of data; J.P. and Y.X. performed statistical analysis and drafted the manuscript. F.L. and J.P. contributed to revisions of the manuscript. All authors read and approved the final manuscript, and all authors agreed to be accountable for all aspects of the work.

Data availability

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

Competing interests

The authors declare no competing interests.

Consent for publication

All the coauthors have approved this version of the manuscript and consent to publication.All the coauthors confirm that this manuscript has not been published elsewhere and is not under consideration by another journal.

Ethical approval/Informed consent

This retrospective study was approved by the institutions ethics committee.

Publisher's note

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

These authors contributed equally: Jingjing Peng and Yanling Xiang.
==== Refs
References

1. Global, regional, and national burden of epilepsy, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18(4), 357–375 (2019).
2. Sharma R Shultz SR Robinson MJ Infections after a traumatic brain injury: The complex interplay between the immune and neurological systems Brain Behav. Immun. 2019 79 63 74 10.1016/j.bbi.2019.04.034 31029794
Sharma, R. et al. Infections after a traumatic brain injury: The complex interplay between the immune and neurological systems. Brain Behav. Immun. 79, 63–74 (2019).31029794
3. Learoyd AE Woodhouse L Shaw L Infections Up to 76 Days After Stroke Increase Disability and Death[J] Transl Stroke Res 2017 8 6 541 548 10.1007/s12975-017-0553-3 28752410
Learoyd, A. E. et al. Infections Up to 76 Days After Stroke Increase Disability and Death[J]. Transl Stroke Res 8(6), 541–548 (2017).28752410
4. Badjatia N Monahan A Carpenter A Inflammation, negative nitrogen balance, and outcome after aneurysmal subarachnoid hemorrhage Neurology 2015 84 7 680 687 10.1212/WNL.0000000000001259 25596503
Badjatia, N. et al. Inflammation, negative nitrogen balance, and outcome after aneurysmal subarachnoid hemorrhage. Neurology 84(7), 680–687 (2015).25596503
5. Heikinheimo T Broman J Haapaniemi E Preceding and poststroke infections in young adults with first-ever ischemic stroke: Effect on short-term and long-term outcomes Stroke 2013 44 12 3331 3337 10.1161/STROKEAHA.113.002108 24135931
Heikinheimo, T. et al. Preceding and poststroke infections in young adults with first-ever ischemic stroke: Effect on short-term and long-term outcomes. Stroke 44(12), 3331–3337 (2013).24135931
6. Wastfelt M Cao Y Strom JO Predictors of post-stroke fever and infections: A systematic review and meta-analysis BMC Neurol. 2018 18 1 49 10.1186/s12883-018-1046-z 29685118
Wastfelt, M., Cao, Y. & Strom, J. O. Predictors of post-stroke fever and infections: A systematic review and meta-analysis. BMC Neurol. 18(1), 49 (2018).29685118
7. Huang Y Chen S Luo Y Crosstalk between Inflammation and the BBB in Stroke Curr. Neuropharmacol. 2020 18 12 1227 1236 10.2174/1570159X18666200620230321 32562523
Huang, Y. et al. Crosstalk between Inflammation and the BBB in Stroke. Curr. Neuropharmacol. 18(12), 1227–1236 (2020).32562523
8. Saand AR Yu F Chen J Systemic inflammation in hemorrhagic strokes-A novel neurological sign and therapeutic target? J. Cereb. Blood Flow Metab. 2019 39 6 959 988 10.1177/0271678X19841443 30961425
Saand, A. R. et al. Systemic inflammation in hemorrhagic strokes-A novel neurological sign and therapeutic target?. J. Cereb. Blood Flow Metab. 39(6), 959–988 (2019).30961425
9. Szklener S Korchut A Godek M Systemic inflammatory response syndrome in the course of status epilepticus: 7-year, two-center observational study Epilepsy Res. 2017 137 53 55 10.1016/j.eplepsyres.2017.09.003 28938136
Szklener, S. et al. Systemic inflammatory response syndrome in the course of status epilepticus: 7-year, two-center observational study. Epilepsy Res. 137, 53–55 (2017).28938136
10. Vahidy FS Parsha KN Rahbar MH Acute splenic responses in patients with ischemic stroke and intracerebral hemorrhage J. Cereb. Blood Flow Metab. 2016 36 6 1012 1021 10.1177/0271678X15607880 26661179
Vahidy, F. S. et al. Acute splenic responses in patients with ischemic stroke and intracerebral hemorrhage. J. Cereb. Blood Flow Metab. 36(6), 1012–1021 (2016).26661179
11. Shi M Chai Y Zhang J Endoplasmic reticulum stress-associated neuronal death and innate immune response in neurological diseases Front. Immunol. 2021 12 794580 10.3389/fimmu.2021.794580 35082783
Shi, M. et al. Endoplasmic reticulum stress-associated neuronal death and innate immune response in neurological diseases. Front. Immunol. 12, 794580 (2021).35082783
12. Marjani N Dareini M Asadzade-Lotfabad M Evaluation of the binding effect and cytotoxicity assay of 2-Ethyl-5-(4-methylphenyl) pyramido pyrazole ophthalazine trione on calf thymus DNA: Spectroscopic, calorimetric, and molecular dynamics approaches Luminescence 2022 37 2 310 322 10.1002/bio.4173 34862709
Marjani, N. et al. Evaluation of the binding effect and cytotoxicity assay of 2-Ethyl-5-(4-methylphenyl) pyramido pyrazole ophthalazine trione on calf thymus DNA: Spectroscopic, calorimetric, and molecular dynamics approaches. Luminescence 37(2), 310–322 (2022).34862709
13. Zare-Feizabadi N Amiri-Tehranizadeh Z Sharifi-Rad A Determining the interaction behavior of calf thymus DNA with anastrozole in the presence of histone H1: Spectroscopies and cell viability of MCF-7 cell line investigations DNA Cell Biol. 2021 40 8 1039 1051 10.1089/dna.2021.0052 34165362
Zare-Feizabadi, N. et al. Determining the interaction behavior of calf thymus DNA with anastrozole in the presence of histone H1: Spectroscopies and cell viability of MCF-7 cell line investigations. DNA Cell Biol. 40(8), 1039–1051 (2021).34165362
14. Dareini M Amiri TZ Marjani N A novel view of the separate and simultaneous binding effects of docetaxel and anastrozole with calf thymus DNA: Experimental and in silico approaches Spectrochim Acta A Mol. Biomol. Spectrosc. 2020 228 117528 10.1016/j.saa.2019.117528 31718965
Dareini, M. et al. A novel view of the separate and simultaneous binding effects of docetaxel and anastrozole with calf thymus DNA: Experimental and in silico approaches. Spectrochim Acta A Mol. Biomol. Spectrosc. 228, 117528 (2020).31718965
15. Chamani J Moosavi-Movahedi AA Rajabi O Cooperative alpha-helix formation of beta-lactoglobulin induced by sodium n-alkyl sulfates J. Colloid Interface Sci. 2006 293 1 52 60 10.1016/j.jcis.2005.06.015 15996676
Chamani, J. et al. Cooperative alpha-helix formation of beta-lactoglobulin induced by sodium n-alkyl sulfates. J. Colloid Interface Sci. 293(1), 52–60 (2006).15996676
16. Sadeghzadeh F Entezari AA Behzadian K Characterizing the binding of angiotensin converting enzyme I inhibitory peptide to human hemoglobin: Influence of electromagnetic fields Protein Pept. Lett. 2020 27 10 1007 1021 32334494
Sadeghzadeh, F. et al. Characterizing the binding of angiotensin converting enzyme I inhibitory peptide to human hemoglobin: Influence of electromagnetic fields. Protein Pept. Lett. 27(10), 1007–1021 (2020).32334494
17. Chamani J Moosavi-Movahedi AA Effect of n-alkyl trimethylammonium bromides on folding and stability of alkaline and acid-denatured cytochrome c: A spectroscopic approach J. Colloid Interface Sci. 2006 297 2 561 569 10.1016/j.jcis.2005.11.035 16338232
Chamani, J. & Moosavi-Movahedi, A. A. Effect of n-alkyl trimethylammonium bromides on folding and stability of alkaline and acid-denatured cytochrome c: A spectroscopic approach. J. Colloid Interface Sci. 297(2), 561–569 (2006).16338232
18. Anrather J Iadecola C Inflammation and stroke: An overview Neurotherapeutics 2016 13 4 661 670 10.1007/s13311-016-0483-x 27730544
Anrather, J. & Iadecola, C. Inflammation and stroke: An overview. Neurotherapeutics 13(4), 661–670 (2016).27730544
19. Smith SE Figley SA Schreyer DJ Protein-energy malnutrition developing after global brain ischemia induces an atypical acute-phase response and hinders expression of GAP-43 PLoS One 2014 9 9 e107570 10.1371/journal.pone.0107570 25259609
Smith, S. E. et al. Protein-energy malnutrition developing after global brain ischemia induces an atypical acute-phase response and hinders expression of GAP-43. PLoS One 9(9), e107570 (2014).25259609
20. Bresnahan KA Tanumihardjo SA Undernutrition, the acute phase response to infection, and its effects on micronutrient status indicators Adv. Nutr. 2014 5 6 702 711 10.3945/an.114.006361 25398733
Bresnahan, K. A. & Tanumihardjo, S. A. Undernutrition, the acute phase response to infection, and its effects on micronutrient status indicators. Adv. Nutr. 5(6), 702–711 (2014).25398733
21. Chandra RK Protein-energy malnutrition and immunological responses J. Nutr. 1992 122 3 Suppl 597 600 10.1093/jn/122.suppl_3.597 1542017
Chandra, R. K. Protein-energy malnutrition and immunological responses. J. Nutr. 122(3 Suppl), 597–600 (1992).1542017
22. Zhang J Shi K Li Z Organ- and cell-specific immune responses are associated with the outcomes of intracerebral hemorrhage FASEB J. 2018 32 1 220 229 10.1096/fj.201700324r 28877956
Zhang, J. et al. Organ- and cell-specific immune responses are associated with the outcomes of intracerebral hemorrhage. FASEB J. 32(1), 220–229 (2018).28877956
23. Zhu W Guo Z Yu S Higher neutrophil counts before thrombolysis for cerebral ischemia predict worse outcomes Neurology 2016 86 11 1077 10.1212/01.wnl.0000481976.41273.a1 26976518
Zhu, W., Guo, Z. & Yu, S. Higher neutrophil counts before thrombolysis for cerebral ischemia predict worse outcomes. Neurology 86(11), 1077 (2016).26976518
24. Guldolf K Vandervorst F Gens R Neutrophil-to-lymphocyte ratio predicts delirium after stroke Age Ageing 2021 50 5 1626 1632 10.1093/ageing/afab133 34218276
Guldolf, K. et al. Neutrophil-to-lymphocyte ratio predicts delirium after stroke. Age Ageing 50(5), 1626–1632 (2021).34218276
25. Soeters PB Wolfe RR Shenkin A Hypoalbuminemia: Pathogenesis and clinical significance JPEN J. Parenter. Enteral Nutr. 2019 43 2 181 193 10.1002/jpen.1451 30288759
Soeters, P. B., Wolfe, R. R. & Shenkin, A. Hypoalbuminemia: Pathogenesis and clinical significance. JPEN J. Parenter. Enteral Nutr. 43(2), 181–193 (2019).30288759
26. Abe A Sakamoto Y Nishiyama Y Decline in hemoglobin during hospitalization may be associated with poor outcome in acute stroke patients J. Stroke Cerebrovasc. Dis. 2018 27 6 1646 1652 10.1016/j.jstrokecerebrovasdis.2018.01.026 29478940
Abe, A. et al. Decline in hemoglobin during hospitalization may be associated with poor outcome in acute stroke patients. J. Stroke Cerebrovasc. Dis. 27(6), 1646–1652 (2018).29478940
27. Matthews L Bates A Wootton SA The use of bioelectrical impedance analysis to predict post-operative complications in adult patients having surgery for cancer: A systematic review Clin. Nutr. 2021 40 5 2914 2922 10.1016/j.clnu.2021.03.008 33962360
Matthews, L. et al. The use of bioelectrical impedance analysis to predict post-operative complications in adult patients having surgery for cancer: A systematic review. Clin. Nutr. 40(5), 2914–2922 (2021).33962360
28. Garlini LM Alves FD Ceretta LB Phase angle and mortality: A systematic review Eur. J. Clin. Nutr. 2019 73 4 495 508 10.1038/s41430-018-0159-1 29695763
Garlini, L. M. et al. Phase angle and mortality: A systematic review. Eur. J. Clin. Nutr. 73(4), 495–508 (2019).29695763
29. Tomeleri CM Cavaglieri CR de Souza MF Phase angle is related with inflammatory and oxidative stress biomarkers in older women Exp. Gerontol. 2018 102 12 18 10.1016/j.exger.2017.11.019 29197561
Tomeleri, C. M. et al. Phase angle is related with inflammatory and oxidative stress biomarkers in older women. Exp. Gerontol. 102, 12–18 (2018).29197561
30. Samoni S Vigo V Resendiz LI Impact of hyperhydration on the mortality risk in critically ill patients admitted in intensive care units: Comparison between bioelectrical impedance vector analysis and cumulative fluid balance recording Crit. Care 2016 20 95 10.1186/s13054-016-1269-6 27060079
Samoni, S. et al. Impact of hyperhydration on the mortality risk in critically ill patients admitted in intensive care units: Comparison between bioelectrical impedance vector analysis and cumulative fluid balance recording. Crit. Care 20, 95 (2016).27060079
31. Oliveira T Garagarza C Valente A Low body cell mass index in hemodialysis patients: Association with clinical parameters and survival Hemodial Int. 2020 24 2 228 236 10.1111/hdi.12822 32056356
Oliveira, T. et al. Low body cell mass index in hemodialysis patients: Association with clinical parameters and survival. Hemodial Int. 24(2), 228–236 (2020).32056356
32. Schmidt M Jacobsen JB Johnsen SP Eighteen-year trends in stroke mortality and the prognostic influence of comorbidity Neurology 2014 82 4 340 350 10.1212/WNL.0000000000000062 24363134
Schmidt, M. et al. Eighteen-year trends in stroke mortality and the prognostic influence of comorbidity. Neurology 82(4), 340–350 (2014).24363134
33. Su YY Li X Li SJ Predicting hospital mortality using APACHE II scores in neurocritically ill patients: A prospective study J. Neurol. 2009 256 9 1427 1433 10.1007/s00415-009-5129-z 19390767
Su, Y. Y. et al. Predicting hospital mortality using APACHE II scores in neurocritically ill patients: A prospective study. J. Neurol. 256(9), 1427–1433 (2009).19390767
34. Majari K Imani H Hosseini S Comparison of modified NUTRIC, NRS-2002, and MUST scores in Iranian critically Ill patients admitted to intensive care units: A prospective cohort study JPEN J. Parenter. Enteral Nutr. 2021 45 7 1504 1513 10.1002/jpen.2031 33073363
Majari, K. et al. Comparison of modified NUTRIC, NRS-2002, and MUST scores in Iranian critically Ill patients admitted to intensive care units: A prospective cohort study. JPEN J. Parenter. Enteral Nutr. 45(7), 1504–1513 (2021).33073363
35. Kyle UG Bosaeus I De Lorenzo AD Bioelectrical impedance analysis–part I: Review of principles and methods Clin. Nutr. 2004 23 5 1226 1243 10.1016/j.clnu.2004.06.004 15380917
Kyle, U. G. et al. Bioelectrical impedance analysis–part I: Review of principles and methods. Clin. Nutr. 23(5), 1226–1243 (2004).15380917
36. Cruz-Jentoft AJ Bahat G Bauer J Sarcopenia: Revised European consensus on definition and diagnosis Age Ageing 2019 48 1 16 31 10.1093/ageing/afy169 30312372
Cruz-Jentoft, A. J. et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 48(1), 16–31 (2019).30312372
37. Lukaski HC Kyle UG Kondrup J Assessment of adult malnutrition and prognosis with bioelectrical impedance analysis: Phase angle and impedance ratio Curr. Opin. Clin. Nutr. Metab. Care 2017 20 5 330 339 10.1097/MCO.0000000000000387 28548972
Lukaski, H. C., Kyle, U. G. & Kondrup, J. Assessment of adult malnutrition and prognosis with bioelectrical impedance analysis: Phase angle and impedance ratio. Curr. Opin. Clin. Nutr. Metab. Care 20(5), 330–339 (2017).28548972
38. Collins GS Reitsma JB Altman DG Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement BMJ 2015 350 g7594 10.1136/bmj.g7594 25569120
Collins, G. S. et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. BMJ 350, g7594 (2015).25569120
39. Roffman CE Buchanan J Allison GT Charlson comorbidities index J. Physiother. 2016 62 3 171 10.1016/j.jphys.2016.05.008 27298055
Roffman, C. E., Buchanan, J. & Allison, G. T. Charlson comorbidities index. J. Physiother. 62(3), 171 (2016).27298055
40. Kellert L Schrader F Ringleb P The impact of low hemoglobin levels and transfusion on critical care patients with severe ischemic stroke: STroke: RelevAnt Impact of HemoGlobin, Hematocrit and Transfusion (STRAIGHT)–an observational study J. Crit. Care 2014 29 2 236 240 10.1016/j.jcrc.2013.11.008 24332995
Kellert, L. et al. The impact of low hemoglobin levels and transfusion on critical care patients with severe ischemic stroke: STroke: RelevAnt Impact of HemoGlobin, Hematocrit and Transfusion (STRAIGHT)–an observational study. J. Crit. Care 29(2), 236–240 (2014).24332995
41. Fraenkel PG Anemia of inflammation: A review Med. Clin. North Am. 2017 101 2 285 296 10.1016/j.mcna.2016.09.005 28189171
Fraenkel, P. G. Anemia of inflammation: A review. Med. Clin. North Am. 101(2), 285–296 (2017).28189171
42. Moore FD Boyden CM Body cell mass and limits of hydration of the fat-free body: Their relation to estimated skeletal weight Ann. N. Y. Acad. Sci. 1963 110 62 71 10.1111/j.1749-6632.1963.tb17072.x 14062407
Moore, F. D. & Boyden, C. M. Body cell mass and limits of hydration of the fat-free body: Their relation to estimated skeletal weight. Ann. N. Y. Acad. Sci. 110, 62–71 (1963).14062407
43. McMillan DC Preston T Watson WS Relationship between weight loss, reduction of body cell mass and inflammatory response in patients with cancer Br. J. Surg. 1994 81 7 1011 1014 10.1002/bjs.1800810727 7922049
McMillan, D. C. et al. Relationship between weight loss, reduction of body cell mass and inflammatory response in patients with cancer. Br. J. Surg. 81(7), 1011–1014 (1994).7922049
44. McMillan DC Watson WS O'Gorman P Albumin concentrations are primarily determined by the body cell mass and the systemic inflammatory response in cancer patients with weight loss Nutr. Cancer 2001 39 2 210 213 10.1207/S15327914nc392_8 11759282
McMillan, D. C. et al. Albumin concentrations are primarily determined by the body cell mass and the systemic inflammatory response in cancer patients with weight loss. Nutr. Cancer 39(2), 210–213 (2001).11759282
45. Rondanelli M Talluri J Peroni G Beyond Body Mass Index. Is the Body Cell Mass Index (BCMI) a useful prognostic factor to describe nutritional, inflammation and muscle mass status in hospitalized elderly?: Body Cell Mass Index links in elderly Clin. Nutr. 2018 37 3 934 939 10.1016/j.clnu.2017.03.021 28408051
Rondanelli, M. et al. Beyond Body Mass Index. Is the Body Cell Mass Index (BCMI) a useful prognostic factor to describe nutritional, inflammation and muscle mass status in hospitalized elderly?: Body Cell Mass Index links in elderly. Clin. Nutr. 37(3), 934–939 (2018).28408051
46. Kaido T Mori A Ogura Y Pre- and perioperative factors affecting infection after living donor liver transplantation Nutrition 2012 28 11–12 1104 1108 10.1016/j.nut.2012.02.007 23044161
Kaido, T. et al. Pre- and perioperative factors affecting infection after living donor liver transplantation. Nutrition 28(11–12), 1104–1108 (2012).23044161
47. Kalita J Bastia J Bhoi SK Systemic inflammatory response syndrome predicts severity of stroke and outcome J. Stroke Cerebrovasc. Dis. 2015 24 7 1640 1648 10.1016/j.jstrokecerebrovasdis.2015.03.057 25959500
Kalita, J. et al. Systemic inflammatory response syndrome predicts severity of stroke and outcome. J. Stroke Cerebrovasc. Dis. 24(7), 1640–1648 (2015).25959500
48. Hagen M Sembill JA Sprugel MI Systemic inflammatory response syndrome and long-term outcome after intracerebral hemorrhage Neurol. Neuroimmunol. Neuroinflamm. 2019 6 5 e588 10.1212/NXI.0000000000000588 31355322
Hagen, M. et al. Systemic inflammatory response syndrome and long-term outcome after intracerebral hemorrhage. Neurol. Neuroimmunol. Neuroinflamm. 6(5), e588 (2019).31355322
49. Rass V Gaasch M Kofler M Systemic inflammatory response syndrome as predictor of poor outcome in nontraumatic subarachnoid hemorrhage patients Crit. Care Med. 2018 46 12 e1152 e1159 10.1097/CCM.0000000000003429 30252711
Rass, V. et al. Systemic inflammatory response syndrome as predictor of poor outcome in nontraumatic subarachnoid hemorrhage patients. Crit. Care Med. 46(12), e1152–e1159 (2018).30252711
50. Xu X Yuan L Wang W Systemic inflammatory response syndrome and outcomes in ischemic patients treated with endovascular treatment Clin. Interv. Aging 2020 15 2331 2340 10.2147/CIA.S281865 33324045
Xu, X. et al. Systemic inflammatory response syndrome and outcomes in ischemic patients treated with endovascular treatment. Clin. Interv. Aging 15, 2331–2340 (2020).33324045
51. Heckmann BL Tummers B Green DR Crashing the computer: Apoptosis vs. necroptosis in neuroinflammation Cell Death Differ. 2019 26 1 41 52 10.1038/s41418-018-0195-3 30341422
Heckmann, B. L., Tummers, B. & Green, D. R. Crashing the computer: Apoptosis vs. necroptosis in neuroinflammation. Cell Death Differ. 26(1), 41–52 (2019).30341422
52. Yuan J Amin P Ofengeim D Necroptosis and RIPK1-mediated neuroinflammation in CNS diseases Nat. Rev. Neurosci. 2019 20 1 19 33 10.1038/s41583-018-0093-1 30467385
Yuan, J., Amin, P. & Ofengeim, D. Necroptosis and RIPK1-mediated neuroinflammation in CNS diseases. Nat. Rev. Neurosci. 20(1), 19–33 (2019).30467385
53. Duprez L Takahashi N Van Hauwermeiren F RIP kinase-dependent necrosis drives lethal systemic inflammatory response syndrome Immunity 2011 35 6 908 918 10.1016/j.immuni.2011.09.020 22195746
Duprez, L. et al. RIP kinase-dependent necrosis drives lethal systemic inflammatory response syndrome. Immunity 35(6), 908–918 (2011).22195746
54. Lorenzo I Serra-Prat M Yebenes JC The role of water homeostasis in muscle function and frailty: A review Nutrients 2019 11 8 63 10.3390/nu11081857
Lorenzo, I., Serra-Prat, M. & Yebenes, J. C. The role of water homeostasis in muscle function and frailty: A review. Nutrients 11(8), 63 (2019).
55. Earthman C Traughber D Dobratz J Bioimpedance spectroscopy for clinical assessment of fluid distribution and body cell mass Nutr. Clin. Pract. 2007 22 4 389 405 10.1177/0115426507022004389 17644693
Earthman, C. et al. Bioimpedance spectroscopy for clinical assessment of fluid distribution and body cell mass. Nutr. Clin. Pract. 22(4), 389–405 (2007).17644693
56. Lang F Mechanisms and significance of cell volume regulation J. Am. Coll. Nutr. 2007 26 5 Suppl 613S 623S 10.1080/07315724.2007.10719667 17921474
Lang, F. Mechanisms and significance of cell volume regulation. J. Am. Coll. Nutr. 26(5 Suppl), 613S-623S (2007).17921474
57. Norman K Stobaus N Pirlich M Bioelectrical phase angle and impedance vector analysis–clinical relevance and applicability of impedance parameters Clin. Nutr. 2012 31 6 854 861 10.1016/j.clnu.2012.05.008 22698802
Norman, K. et al. Bioelectrical phase angle and impedance vector analysis–clinical relevance and applicability of impedance parameters. Clin. Nutr. 31(6), 854–861 (2012).22698802
58. Barrea L Muscogiuri G Pugliese G Phase angle as an easy diagnostic tool of meta-inflammation for the nutritionist Nutrients 2021 13 5 31 10.3390/nu13051446
Barrea, L. et al. Phase angle as an easy diagnostic tool of meta-inflammation for the nutritionist. Nutrients 13(5), 31 (2021).
59. Wirth R Volkert D Rosler A Bioelectric impedance phase angle is associated with hospital mortality of geriatric patients Arch. Gerontol. Geriatr. 2010 51 3 290 294 10.1016/j.archger.2009.12.002 20044156
Wirth, R. et al. Bioelectric impedance phase angle is associated with hospital mortality of geriatric patients. Arch. Gerontol. Geriatr. 51(3), 290–294 (2010).20044156
