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Prediction of adult post-hemorrhagic hydrocephalus: a risk score based on clinical data.

ABSTRACT: There is lacking research on risk factors and prediction models associated with Post-hemorrhagic hydrocephalus (PHH). Thus, this present study aimed to analyze the risk factors of PHH and establish a risk-scoring system through a large-scale study. A retrospective study of 382 patients with intracranial hemorrhage assessed age, history and diagnosis, Glasgow coma score (GCS), and fever time. After univariate and logistic regression analysis, a risk scoring system was established according to independent risk factors and evaluated using the area under the curve (AUC). Of the 382 patients, 133 (34.8%) had PHH, 43 (11.3%) received surgical treatment. Factor classification showed that age > 60 years old [odds ratio (OR): 0.347, II = 5 points], GCS < 5 (OR: 0.09, IV = 10 points), GCS 6‒8 (OR = 0.232, III = 6 points), fever time > 9 (OR: 0.202, III = 7 points), fever time 5-9 (OR: 0.341, II = 5 points), CSF-TP x time > 14,4000 group (OR: 0.267, IV = 6 points), and CSF-TP x time 9,601‒14,400 group (OR: 0.502, III = 3 points) were independent risk factors. The result of the receiver operating characteristic (ROC) prediction showed that AUC = 0.790 (0.744‒0.836). Low-risk (IV-VII), moderate (VIII-X), and high-risk group (XI-XIII) incidence of PHH were 11.76%, 50.55%, and 70.00% (p < 0.001), respectively. The coincidence rates in the validation cohort were 26.00%, 74.07%, and 100.0% (p < 0.001), respectively. AUC value was 0.860 (0.780‒0.941). The predictive model was conducive to determining the occurrence of PHH and facilitating early intervention.

SUBMITTER: Wang Z

PROVIDER: S-EPMC9288433 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Prediction of adult post-hemorrhagic hydrocephalus: a risk score based on clinical data.

Wang Zhiwen Z Xi Bin B Yu Bingxiao B Zhou Junhui J Wang Min M Wang Changfeng C Liu Ruen R

Scientific reports 20220716 1

There is lacking research on risk factors and prediction models associated with Post-hemorrhagic hydrocephalus (PHH). Thus, this present study aimed to analyze the risk factors of PHH and establish a risk-scoring system through a large-scale study. A retrospective study of 382 patients with intracranial hemorrhage assessed age, history and diagnosis, Glasgow coma score (GCS), and fever time. After univariate and logistic regression analysis, a risk scoring system was established according to ind ...[more]

PMID: 35842469

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Project description:BACKGROUND:Presently, for patients presenting with suspected Normal Pressure Hydrocephalus (NPH) who undergo temporary drainage of cerebrospinal fluid (CSF) there is no defined model to differentiate chance improvement form clinical significance change at the individual patient level. To address this lack of information we computed standard regression based clinical change models for the 10 Meter Walk Test, Timed Up & Go, Dual Timed Up & Go, 6-Minute Walk Test, Mini-Balance Evaluation Systems Test, Montreal Cognitive Assessment, and Symbol Digit Modalities using data from patients with suspected NPH that underwent temporary drainage of CSF. These clinically significant change modes can classify clinically significant improvement following temporary drainage of CSF at the individual patient level. This allows for physicians to differentiate a clinically significant improvement in symptoms from chance improvement. METHODS:Data was collected from 323 patients, over the age of 60, with suspected NPH that underwent temporary drainage of CSF with corresponding gait and cognitive testing. McSweeney Standardized Regression Based Clinical Change Models were computed for standard gait and cognitive measures: Timed Up & Go, Dual Timed Up & Go, 10 Meter Walk Test, MiniBESTest, 6-Minute Walk Test, Montreal Cognitive Assessment, and Symbol Digit Modalities Test. To assess the discriminate validity of the measures we used correlations, Chi2, and regression analyses. RESULTS:The clinical change models explained 69-91.8% of the variability in post-drain performance (p?<? 0.001). As patient scores became more impaired, the percent change required for improvement to be clinically significant increased for all measures. We found that the measures were not discriminate, the Timed Up & Go was highly related to the 10 Meter Walk Test (r?=?0.85, R2?=?0.769-0.738, p?<? 0.001), MiniBESTest (r?=?-?0.67, R2?=?0.589-0.734, p?<? 0.001), and 6?Minute Walk Test (r?=?-?0.77, R2?=?0.71-0.734, p?<? 0.001). CONCLUSION:Standardized Regression Based Clinically Significant Change Models allow for physicians to use an evidence-based approach to differentiate clinically significant change from chance improvement at the individual patient level. The Timed Up & Go was shown to be predictive of detailed measures of gait velocity, balance, and endurance.

Project description:We aimed to develop and validate a risk score to aid in the early identification of laboratory-confirmed dengue patients at high risk of severe dengue (SD) (i.e. severe plasma leakage with shock or respiratory distress, or severe bleeding or organ impairment). We retrospectively analyzed data of 1184 non-SD patients at hospital presentation and 69 SD patients before SD onset. We fit a logistic regression model using 85% of the population and converted the model coefficients to a numeric risk score. Subsequently, we validated the score using the remaining 15% of patients. Using the derivation cohort, two scoring algorithms for predicting SD were developed: models 1 (dengue illness ≤4 days) and 2 (dengue illness >4 days). In model 1, we identified four variables: age ≥65 years, minor gastrointestinal bleeding, leukocytosis, and platelet count ≥100×10(9) cells/L. Model 1 (ranging from -2 to +6 points) showed good discrimination between SD and non-SD, with an area under the receiver operating characteristic curve (AUC) of 0.848 (95% confidence interval [CI], 0.771-0.924). The optimal cutoff value for model 1 was 1 point, with a sensitivity and specificity for predicting SD of 70.3% and 90.6%, respectively. In model 2 (ranging from 0 to +3 points), significant predictors were age ≥65 years and leukocytosis. Model 2 showed an AUC of 0.859 (95% CI, 0.756-0.963), with an optimal cutoff value of 1 point (sensitivity, 80.3%; specificity, 85.8%). The median interval from hospital presentation to SD was 1 day. This finding underscores the importance of close monitoring, timely resuscitation of shock including intravenous fluid adjustment and early correction of dengue-related complications to prevent the progressive dengue severity. In the validation data, AUCs of 0.904 (95% CI, 0.825-0.983) and 0.917 (95% CI, 0.833-1.0) in models 1 and 2, respectively, were achieved. The observed SD rates (in both cohorts) were <3% for patients with a score <1 point, but >50% for those with a score of ≥2 points, irrespective of the day of illness onset, suggesting that our simple risk score can be easily implemented in resource-limited countries for early prediction of dengue patients at risk of SD provided that they have rapid dengue confirmed tests. For patients with other acute febrile illnesses or bacterial infections usually have SD risk score of >1. Thus, these scoring algorithms cannot totally replace good clinical judgement of the physician, and most importantly, early differentiating dengue from other febrile illnesses is critical for appropriate monitoring and management.

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Dataset Information

Prediction of adult post-hemorrhagic hydrocephalus: a risk score based on clinical data.

Publications

Prediction of adult post-hemorrhagic hydrocephalus: a risk score based on clinical data.

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