Browsing by Author "Chatrath, Hemant"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    De Novo Malignancy Post Liver Transplantation: A Single Center, Population Controlled Study
    (Wiley, 2013) Chatrath, Hemant; Berman, Kenneth; Vuppalanchi, Raj; Slaven, James; Kwo, Paul; Tector, A. Joseph; Chalasani, Naga; Ghabril, Marwan; Medicine, School of Medicine
    Background: With the growing numbers of liver transplant recipients, it is increasingly important to understand the risks of de novo malignancy after liver transplantation. Aim: To characterize the incidence of de novo malignancy after liver transplantation compared with a control non-transplant population. Methods: We studied 534 Indiana state residents undergoing liver transplantation at our center between 1997 and 2004, followed through August 2010. The incidence and predictors of malignancy were determined. The standardized incidence ratio (SIR) of cancer in our cohort was compared with age-, gender-, and period-matched state population using the Indiana State Cancer Registry. Results: After a mean follow-up of 5.7 ± 3.2 yr, 73 patients (13.7%) developed 80 cancers, with five- and 10-yr incidence rates of 11.7% and 24.8%, respectively. These included 24 (30%) skin, 16 (20%) hematologic, and 40 (50%) solid tumors. The most common solid cancers were aerodigestive. Compared with matched state population, liver transplant recipients had significantly higher incidence of all cancers (SIR: 3.1, 95% CI [Confidence interval]: 2.9-3.2), skin (melanoma) (SIR: 5.8, 95% CI: 4.7-7.0), hematologic (SIR: 7.1, 95% CI: 6.3-8.0), and solid (SIR: 2.7, 95% CI: 2.5-2.8) tumors. Conclusion: There is a significantly increased risk of de novo malignancies after liver transplantation, highlighting the need for surveillance strategies in this population.
  • Thumbnail Image
    Item
    Factors That Predict Short-term Intensive Care Unit Mortality in Patients With Cirrhosis
    (Elsevier, 2013) Bahirwani, Ranjeeta; Ghabril, Marwan; Forde, Kimberly A.; Chatrath, Hemant; Wolf, Karen M.; Uribe, Lindsay; Reddy, K. Rajender; Fuchs, Barry; Chalasani, Naga; Medicine, School of Medicine
    Background & aims: Despite advances in critical care medicine, the mortality rate is high among critically ill patients with cirrhosis. We aimed to identify factors that predict early (7 d) mortality among patients with cirrhosis admitted to the intensive care unit (ICU) and to develop a risk-stratification model. Methods: We collected data from patients with cirrhosis admitted to the ICU at Indiana University (IU-ICU) from December 1, 2006, through December 31, 2009 (n = 185), or at the University of Pennsylvania (Penn-ICU) from May 1, 2005, through December 31, 2010 (n = 206). Factors associated with mortality within 7 days of admission (7-d mortality) were determined by logistic regression analyses. A model was constructed based on the predictive parameters available on the first day of ICU admission in the IU-ICU cohort and then validated in the Penn-ICU cohort. Results: Median Model for End-stage Liver Disease (MELD) scores at ICU admission were 25 in the IU-ICU cohort (interquartile range, 23-34) and 32 in the Penn-ICU cohort (interquartile range, 26-41); corresponding 7-day mortalities were 28.3% and 53.6%, respectively. MELD score (odds ratio, 1.13; 95% confidence interval [CI], 1.07-1.2) and mechanical ventilation (odds ratio, 5.7; 95% CI, 2.3-14.1) were associated independently with 7-day mortality in the IU-ICU. A model based on these 2 variables separated IU-ICU patients into low-, medium-, and high-risk groups; these groups had 7-day mortalities of 9%, 27%, and 74%, respectively (concordance index, 0.80; 95% CI, 0.72-0.87; P < 10(-8)). The model was applied to the Penn-ICU cohort; the low-, medium-, and high-risk groups had 7-day mortalities of 33%, 56%, and 71%, respectively (concordance index, 0.67; 95% CI, 0.59-0.74; P < 10(-4)). Conclusions: A model based on MELD score and mechanical ventilation on day 1 can stratify risk of early mortality in patients with cirrhosis admitted to the ICU. More studies are needed to validate this model and to enhance its clinical utility.