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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:Background and purposeCurrent guidelines do not support the routine use of corticosteroids in patients with aneurysmal subarachnoid hemorrhage (aSAH). However, corticosteroids use in aSAH has been practiced at some centers by convention. The aim of the study was to determine the incidence of hydrocephalus requiring ventriculoperitoneal shunt (VPS) placement as well as functional outcome on discharge and adverse events attributed to corticosteroids in patients with aSAH treated with different dexamethasone (DXM) treatment schemes.MethodsWe retrospectively analyzed 206 patients with aSAH stratified to three groups based on the DXM treatment scheme: no corticosteroids, short course of DXM (S-DXM; 4 mg every 6 h for 1 day followed by a daily total dose reduction by 25% and then by 50% on last day), and long course of DXM (L-DXM; 4 mg every 6 h for 5-7 days followed by reduction by 50% every other day). The primary outcome measure was the placement of a VPS, and the secondary outcome was a good functional outcome [modified Rankin Scale (mRS) 0-3] at hospital discharge. Safety measures were the incidence of infection (pneumonia, urinary tract infection, ventriculitis, meningitis), presence of delirium, and hyperglycemia.ResultsThere was no difference in the rate of external ventricular drain (EVD) (p = 0.164) and VPS placement (p = 0.792), nor in the rate of good outcome (p = 0.928) among three defined treatment regimens. Moreover, the median duration of treatment with EVD did not differ between subjects treated with no corticosteroids, S-DXM, and L-DXM (p = 0.905), and the probability of EVD removal was similar when stratified according to treatment regimens (log-rank; p = 0.256). Patients who received L-DXM had significantly more complications as compared to patients, who received no corticosteroids or S-DXM (78.4% vs. 58.6%; p = 0.005). After adjustment, L-DXM remained independently associated with increased risk of combined adverse events (OR = 2.72; 95%CI, 1.30-5.72; p = 0.008), infection (OR = 3.45; 95%CI, 1.63-7.30; p = 0.001) and hyperglycemia (OR = 2.05; 95%CI, 1.04-4.04; p = 0.039).ConclusionsDXM use among patients with aSAH did not relate to the rate of EVD and VPS placement, duration of EVD treatment, and functional disability at discharge but increased the risk of medical complications.

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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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