Predictors of Mortality, Withdrawal of Life-Sustaining Measures, and Discharge Disposition in Octogenarians with Subdural Hematomas

Kashkoush, Ahmed; Petitt, Jordan C.; Ladhani, Husayn A; Ho, Vanessa P.; Kelly, Michael L.; Ghneim, Mira; Albrecht, Jennifer S.; Brasel, Karen J.; Livaris, Anna; Watras, Jill; Michetti, Christopher P; Haan, James M.; Lightwine, Kelly; Winfield, Robert D.; Adams, Sasha D.; Podbielski, Jeanette; Armen, Scott B.; Zacko, J. Christopher; Nasrallah, Fady; Schaffer, Kathryn B.; Dunn, Julie; Widdel, Lars; Schroeppel, Thomas J.; Stillman, Zachery; Cooper, Zara; Stein, Deborah M.; Adams, Charles A.; Lueckel, Stephanie N.; Murry, Jason; Patel, Nikita; Hsu, Cindy H.; Bhatti, Umer F.; Lissauer, Matthew; LaFonte, Marc; Najafi, Kaveh; Lewandowski, Karen; Mukherjee, Kaushik; Imperio-Lagabon, Kristelle; Martin, Niels; Hirsch, Kathleen; Berry, Cherisse; Freitas, Derek; Cullinane, Daniel C.; Ramawi, Roshini; Truitt, Michael S.; Pearcy, Chris; Hashimi, Habiba; Kaups, Krista L.; Claridge, Jeffrey A.; Ballou, Jessica; Croce, Martin A.; Markle, Stephanie; Osserwaarde, Sally; Posluszny, Joseph A.; Stocker, Benjamin; Hranjec, Tjasa; Solomon, Rachele; Martinek, Lucy; Gupta, Alok; Grabo, Daniel; Khan, Uzer; Tatum, Danielle; Jacome, Tomas; Gates, Jonathan D.; Jawani, Alisha; Berndtson, Allison E.; Curry, Terry; Bala, Miklosh; Dultz, Linda A.; Houshmand, Natasha; Pieri, Paola G.; Zielinski, Martin D.; Hughes, James L.; Hartwell, Jennifer L.; Malhotra, Ajai K.; Lee, Tim; Petrone, Patrizio; Joseph, D’Andrea; Marshall, Gary T.; Carrick, Matthew M.; Pathak, Abhijit; Zandt, Andrea Van; Glass, Nina E.; Livingston, David H.; Gregg, Shea C.; Webb, Travis P.; Drumheller, Byron C.; Kozar, Rosemary A.; Barraco, Robert D.; Joseph, Bellal

doi:10.1016/j.wneu.2021.09.121

Cited by 8 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The relapse was the dependent variable, and multiple regression analysis was gradually made; thus, the regression equation was established. [ 14 , 15 ] Relapse relative risk value odds ratio and 95% confidence interval (CI) were determined. Stepwise multivariate logistic regression analysis on relapse risk factors (α = 0.05, Backwards).…”

Section: Methodsmentioning

confidence: 99%

Establishment of relapse risk model and multivariate logistic regression analysis on risk factors of relapse in children with primary nephrotic syndrome

et al. 2022

View full text Add to dashboard Cite

This study aimed to investigate relapse risk factors in children with primary nephrotic syndrome (PNS) for prevention and early intervention via logistic regression. One hundred thirty-seven children with PNS were enrolled in this study. Clinical variables were analyzed by single-factor and multiple regression analysis to establish the regression equation. The predictive ability of the regression equation was investigated by the receiver operating characteristic curve (ROC). Files of 17 patients were lost, and 120 patients were enrolled finally in the study, among whom 55 cases (45.8%) had frequently relapsed. Single-factor analysis and multiple regression analysis revealed that concurrent infection on first onset, irregular glucocorticoid therapy, severe hypoalbuminemia, and persistent severe hyperlipidemia were the significant risk factors for frequent relapse on PNS (P < .05), among which infection remained to be the main inductive factor. Among the 4 indicators, serum albumin had the best diagnostic efficacy based on the area under the ROC curve (0.933), sensitivity (89.09%), and specificity (81.54%). The area under curve, sensitivity, and specificity for the combined diagnostic model of the 4 indices were 97.8%, 98.18%, and 90.77%, respectively, which had good predictive power for the relapse of patients. Concurrent infection, irregular glucocorticoid therapy, severe hypoalbuminemia, and persistent severe hyperlipemia were all the risk factors for PNS relapse. The established logistic regression model based on these factors above is reliable for predicting frequent PNS relapse. Much attention should be paid to these critical factors, and early intervention should be taken to reduce the incidence of relapse.

show abstract

Section: Methodsmentioning

confidence: 99%