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Early variations of laboratory parameters predicting shunt-dependent hydrocephalus after subarachnoid hemorrhage.

ABSTRACT: Hydrocephalus is a frequent complication following subarachnoid hemorrhage. Few studies investigated the association between laboratory parameters and shunt-dependent hydrocephalus. This study aimed to investigate the variations of laboratory parameters after subarachnoid hemorrhage. We also attempted to identify predictive laboratory parameters for shunt-dependent hydrocephalus.Multiple imputation was performed to fill the missing laboratory data using Bayesian methods in SPSS. We used univariate and multivariate Cox regression analyses to calculate hazard ratios for shunt-dependent hydrocephalus based on clinical and laboratory factors. The area under the receiver operating characteristic curve was used to determine the laboratory risk values predicting shunt-dependent hydrocephalus.We included 181 participants with a mean age of 54.4 years. Higher sodium (hazard ratio, 1.53; 95% confidence interval, 1.13-2.07; p = 0.005), lower potassium, and higher glucose levels were associated with higher shunt-dependent hydrocephalus. The receiver operating characteristic curve analysis showed that the areas under the curve of sodium, potassium, and glucose were 0.649 (cutoff value, 142.75 mEq/L), 0.609 (cutoff value, 3.04 mmol/L), and 0.664 (cutoff value, 140.51 mg/dL), respectively.Despite the exploratory nature of this study, we found that higher sodium, lower potassium, and higher glucose levels were predictive values for shunt-dependent hydrocephalus from postoperative day (POD) 1 to POD 12-16 after subarachnoid hemorrhage. Strict correction of electrolyte imbalance seems necessary to reduce shunt-dependent hydrocephalus. Further large studies are warranted to confirm our findings.

SUBMITTER: Na MK

PROVIDER: S-EPMC5726740 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Early variations of laboratory parameters predicting shunt-dependent hydrocephalus after subarachnoid hemorrhage.

Na Min Kyun MK Won Yu Deok YD Kim Choong Hyun CH Kim Jae Min JM Cheong Jin Hwan JH Ryu Je Il JI Han Myung-Hoon MH

PloS one 20171212 12

<h4>Background and purpose</h4>Hydrocephalus is a frequent complication following subarachnoid hemorrhage. Few studies investigated the association between laboratory parameters and shunt-dependent hydrocephalus. This study aimed to investigate the variations of laboratory parameters after subarachnoid hemorrhage. We also attempted to identify predictive laboratory parameters for shunt-dependent hydrocephalus.<h4>Methods</h4>Multiple imputation was performed to fill the missing laboratory data u ...[more]

PMID: 29232410

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Project description:BackgroundShunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Machine learning (ML) defines modern data analysis techniques allowing accurate subject-based risk stratifications. We aimed at developing and testing different ML models to predict shunt-dependent hydrocephalus after aneurysmal SAH.MethodsWe consulted electronic records of patients with aneurysmal SAH treated at our institution between January 2013 and March 2019. We selected variables for the models according to the results of the previous works on this topic. We trained and tested four ML algorithms on three datasets: one containing binary variables, one considering variables associated with shunt-dependency after an explorative analysis, and one including all variables. For each model, we calculated AUROC, specificity, sensitivity, accuracy, PPV, and also, on the validation set, the NPV and the Matthews correlation coefficient (ϕ).ResultsThree hundred eighty-six patients were included. Fifty patients (12.9%) developed shunt-dependency after a mean follow-up of 19.7 (± 12.6) months. Complete information was retrieved for 32 variables, used to train the models. The best models were selected based on the performances on the validation set and were achieved with a distributed random forest model considering 21 variables, with a ϕ = 0.59, AUC = 0.88; sensitivity and specificity of 0.73 (C.I.: 0.39-0.94) and 0.92 (C.I.: 0.84-0.97), respectively; PPV = 0.59 (0.38-0.77); and NPV = 0.96 (0.90-0.98). Accuracy was 0.90 (0.82-0.95).ConclusionsMachine learning prognostic models allow accurate predictions with a large number of variables and a more subject-oriented prognosis. We identified a single best distributed random forest model, with an excellent prognostic capacity (ϕ = 0.58), which could be especially helpful in identifying low-risk patients for shunt-dependency.

Project description:Prior research has suggested that regional weather patterns impact the risk of rupture of cerebral aneurysms, but the findings in the literature have been inconsistent. Furthermore, no nationwide analysis to date has examined the association between meteorological factors and the post-procedural outcomes of patients after the treatment for ruptured cerebral aneurysms. The purpose of this study was to use a nationwide sample to analyze the association between specific meteorological parameters--temperature, precipitation, sunlight, and humidity--and hospital admission rate for and outcome after aneurysmal subarachnoid hemorrhage. Patients were identified using the Nationwide Inpatient Sample (2001-2010): Those with an ICD-9 diagnosis code for subarachnoid hemorrhage and a procedural code for aneurysm repair were included. Climate data were obtained from the State of the Climate Report 2010 released by the National Climatic Data Center. Multivariate regression models were constructed to analyze the association between average state monthly temperature, precipitation, and percent possible sunlight, as well as relative morning humidity and both monthly hospital admission rate, adjusted for annual state population in millions, and in-hospital mortality. 16,970 admissions were included from 723 hospitals across 41 states. Decreased daily sunlight and lower relative humidity were associated with an increased rate of admission for ruptured cerebral aneurysms (p<0.001), but had no association with differential inpatient mortality. No significant changes in these observed associations were seen when multivariate analyses were constructed. This is the first nationwide study to suggest that decreased sunlight and lower relative humidity are associated with admission for ruptured cerebral aneurysms. While it has been postulated that external atmospheric factors may cause hormonal and homeostatic changes that impact the risk of rupture of cerebral aneurysms, additional research is needed to confirm and further understand these relationships.

Project description:BACKGROUND:Proteomic analysis of cerebrospinal fluid (CSF) has shown great promise in identifying potential markers of injury in neurodegenerative diseases [1-13]. Here we compared CSF proteomes in healthy individuals, with patients diagnosed with traumatic brain injury (TBI) and subarachnoid hemorrhage (SAH) in order to characterize molecular biomarkers which might identify these different clinical states and describe different molecular mechanisms active in each disease state. METHODS:Patients presenting to the Neurosurgery service at the Louisiana State University Hospital-Shreveport with an admitting diagnosis of TBI or SAH were prospectively enrolled. Patients undergoing CSF sampling for diagnostic procedures were also enrolled as controls. CSF aliquots were subjected to 2-dimensional gel electrophoresis (2D GE) and spot percentage densities analyzed. Increased or decreased spot expression (compared to controls) was defined in terms of in spot percentages, with spots showing consistent expression change across TBI or SAH specimens being followed up by Matrix-Assisted Laser Desorption/Ionization mass spectrometry (MALDI-MS). Polypeptide masses generated were matched to known standards using a search of the NCBI and/or GenPept databases for protein matches. Eight hundred fifteen separately identifiable polypeptide migration spots were identified on 2D GE gels. MALDI-MS successfully identified 13 of 22 selected 2D GE spots as recognizable polypeptides. RESULTS:Statistically significant changes were noted in the expression of fibrinogen, carbonic anhydrase-I (CA-I), peroxiredoxin-2 (Prx-2), both ? and ? chains of hemoglobin, serotransferrin (Tf) and N-terminal haptoglobin (Hp) in TBI and SAH specimens, as compared to controls. The greatest mean fold change among all specimens was seen in CA-I and Hp at 30.7 and -25.7, respectively. TBI specimens trended toward greater mean increases in CA-I and Prx-2 and greater mean decreases in Hp and Tf. CONCLUSIONS:Consistent CSF elevation of CA-I and Prx-2 with concurrent depletion of Hp and Tf may represent a useful combination of biomarkers for the prediction of severity and prognosis following brain injury.

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Early variations of laboratory parameters predicting shunt-dependent hydrocephalus after subarachnoid hemorrhage.

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Early variations of laboratory parameters predicting shunt-dependent hydrocephalus after subarachnoid hemorrhage.

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