Predicting posttraumatic hydrocephalus: derivation and validation of a risk scoring system based on clinical characteristics

2017 ◽  
Vol 32 (5) ◽  
pp. 1427-1435 ◽  
Author(s):  
Hao Chen ◽  
Fang Yuan ◽  
Shi-Wen Chen ◽  
Yan Guo ◽  
Gan Wang ◽  
...  
Neurosurgery ◽  
2017 ◽  
Vol 64 (CN_suppl_1) ◽  
pp. 250-250
Author(s):  
Hao Chen

Abstract INTRODUCTION Posttraumatic hydrocephalus (PTH) is a common complication of traumatic brain injury (TBI) and often has a high risk of clinical deterioration and worse outcomes. The incidence and risk factors for the development of PTH after decompressive craniectomy (DC) has been assessed in previous studies, but rare studies identify patients with higher risk for PTH among all TBI patients. This study aimed to develop and validate a risk scoring system to predict PTH after TBI. METHODS Demographics, injury severity, duration of coma, radiologic findings, and DC were evaluated to determine the independent predictors of PTH during hospitalization until 6 months following TBI through logistic regression analysis. A risk stratification system was created by assigning a number of points for each predictor and validated both internally and externally. The model accuracy was assessed by the area under the receiver operating characteristic curve (AUC). RESULTS >Of 526 patients in the derivation cohort, 57 (10.84%) developed PTH during 6 months follow up. Age >50 (Odd ratio [OR] = 1.91, 95% confidence interval [CI] 1.09 3.75, 4 points), duration of coma = 1 w (OR = 5.68, 95% CI 2.57 13.47, 9 points), Fisher grade III (OR = 2.19, 95% CI 1.24 4.36, 5 points) or IV (OR = 3.87, 95% CI 1.93 8.43, 7 points), bilateral DC (OR = 6.13, 95% CI 2.82 18.14, 9 points), and extra herniation after DC (OR = 2.36, 95% CI 1.46 4.92, 5 points) were independently associated with PTH. Rates of PTH for the low- (0-12 points), intermediate- (13-22 points) and high-risk (23-34 points) groups were 1.16%, 35.19% and 78.57% (P < 0.0001). The corresponding rates in the validation cohort, where 17/175 (9.71%) developed PTH, were 1.35%, 37.50% and 81.82% (P < 0.0001). The risk score model exhibited good-excellent discrimination in both cohorts, with AUC of 0.839 versus 0.894 (derivation versus validation) and good calibration (Hosmer-Lemshow P = 0.56 versus 0.68). CONCLUSION A risk scoring system based on clinical characteristics accurately predicted PTH. This model will be useful to identify patients at high risk for PTH who may be candidates for preventive interventions, and to improve their outcomes.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4748-4748
Author(s):  
Yachun Jia ◽  
Guangyao Kong ◽  
Aili He

Abstract A Risk Scoring System for Prognosis of Multiple Myeloma Background: Multiple myeloma (MM) is the second most frequently-occurring hematologic malignancy characterized by anemia, renal damage, osteolytic lesions and hypercalcemia (Kumar, et al. 2017), without specific prognostic indicators. Protein arginine methyltransferases 3 (PRMT3) is an enzyme which participates in the progression of some malignant diseases (Xiao, et al. 2017). However, the prognostic value of PRMT3 in MM remains unclear. In this study, we developed a risk scoring system based on the expression of PRMT3 to distinguish MM cohorts with different clinical characteristics. Methods: we integrated 4 datasets from The Cancer Genome Atlas (TCGA) or gene expression omnibus (GEO) and analyzed the correlation between PRMT3 expression and R-ISS stage, diseases progression, clinical characteristics or prognosis. Furthermore, we collected a cohort of newly diagnosed MM and healthy donor samples and then performed qRT-PCR to verify the expression of PRMT3. A risk scoring system was established to point out the prognostic indicator for clinical outcome of MM. The predictive power was evaluated by using Receiver Operating Characteristic (ROC) and Kaplan-Meier survival curve. Results: By extensive data analysis, we found the expression of PRMT3 was upregulated during the progression of myeloma (Figure 1 A p=0.573, 0.028, 0.02, respectively). The expression level of PRMT3 in relapsed MM patients was higher than that in newly diagnosed MM patients (Figure 1 B p=0.02, 0.016, 0.002, respectively). Meanwhile, the expression of PRMT3 was also increased in MM patients with advanced R-ISS stage (Figure 1 C p=0.001, 0.042, respectively). Moreover, the validation in a new cohort of MM samples showed the expression of PRMT3 was higher in MM patients compared to normal controls (Figure 1 D p=0.012). MM patients with high expression of PRMT3 showed prolonged Event Free Survival (EFS) and Overall Survival (OS) (Figure 2 A&B EFS: p=0.008, OS: p=0.001). Furthermore, we found the expression of PRMT3 had a positive correlation with B2M(p=0.018), HGB (p=0.001), aspirate plasma cells (p=0.002) and bone marrow biopsy plasma cells (p=0.001 Table not shown). Meanwhile, univariate and multivariate analysis showed that B2M, LDH, ALB, MRI and PRMT3 were independent adverse prognostic factors for OS in MM patients (p&lt;0.001, p&lt;0.001, p=0.0044, 0.0403, 0.0312, Table not shown). Finally, we established a risk scoring system which performed remarkable predicting effectiveness among MM patients. The ROC curve showed that the risk model performed well in 3-year OS (Figure 2 C AUC=0.749). A threshold score 1.26897 was recommended to distinguish the high and low risk score groups. Patients with higher risk score had a shorter OS than those with lower risk score (median 27.45 months vs. 50.13 months, p&lt;0.001 Figure 2 D). Conclusion: Our study identified that PRMT3 was upregulated in MM patients and that increased PRMT3 was an independent adverse prognostic factor for OS in MM. The risk scoring system based on the expression of PRMT3 provided distinct insights into the prognosis of MM patients. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.


2020 ◽  
Author(s):  
Haibei Xin ◽  
Guanxiong Zhang ◽  
Wei Zhou ◽  
Shanshan Li ◽  
Minfeng Zhang ◽  
...  

2020 ◽  
Vol 26 (10) ◽  
pp. S136-S137
Author(s):  
Syed Adeel Ahsan ◽  
Jasjit Bhinder ◽  
Syed Zaid ◽  
Parija Sharedalal ◽  
Chhaya Aggarwal-Gupta ◽  
...  

Author(s):  
Dylan J. Martini ◽  
Meredith R. Kline ◽  
Yuan Liu ◽  
Julie M. Shabto ◽  
Bradley C. Carthon ◽  
...  

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 853
Author(s):  
Jee-Yun Kim ◽  
Jeong Yee ◽  
Tae-Im Park ◽  
So-Youn Shin ◽  
Man-Ho Ha ◽  
...  

Predicting the clinical progression of intensive care unit (ICU) patients is crucial for survival and prognosis. Therefore, this retrospective study aimed to develop the risk scoring system of mortality and the prediction model of ICU length of stay (LOS) among patients admitted to the ICU. Data from ICU patients aged at least 18 years who received parenteral nutrition support for ≥50% of the daily calorie requirement from February 2014 to January 2018 were collected. In-hospital mortality and log-transformed LOS were analyzed by logistic regression and linear regression, respectively. For calculating risk scores, each coefficient was obtained based on regression model. Of 445 patients, 97 patients died in the ICU; the observed mortality rate was 21.8%. Using logistic regression analysis, APACHE II score (15–29: 1 point, 30 or higher: 2 points), qSOFA score ≥ 2 (2 points), serum albumin level < 3.4 g/dL (1 point), and infectious or respiratory disease (1 point) were incorporated into risk scoring system for mortality; patients with 0, 1, 2–4, and 5–6 points had approximately 10%, 20%, 40%, and 65% risk of death. For LOS, linear regression analysis showed the following prediction equation: log(LOS) = 0.01 × (APACHE II) + 0.04 × (total bilirubin) − 0.09 × (admission diagnosis of gastrointestinal disease or injury, poisoning, or other external cause) + 0.970. Our study provides the mortality risk score and LOS prediction equation. It could help clinicians to identify those at risk and optimize ICU management.


Author(s):  
ShuJie Liao ◽  
Lei Jin ◽  
Wan‐Qiang Dai ◽  
Ge Huang ◽  
Wulin Pan ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document