"Real‐time" risk models of postoperative morbidity and mortality for liver transplants. Issue 1 (2nd November 2018)
- Record Type:
- Journal Article
- Title:
- "Real‐time" risk models of postoperative morbidity and mortality for liver transplants. Issue 1 (2nd November 2018)
- Main Title:
- "Real‐time" risk models of postoperative morbidity and mortality for liver transplants
- Authors:
- Marubashi, Shigeru
Ichihara, Naoaki
Kakeji, Yoshihiro
Miyata, Hiroaki
Taketomi, Akinobu
Egawa, Hiroto
Takada, Yasutsugu
Umeshita, Koji
Seto, Yasuyuki
Gotoh, Mitsukazu - Abstract:
- Abstract: Aim: A comprehensive description of morbidity and mortality risk factors for post liver transplant has not been available to date. In this study, we established real‐time risk models of postoperative morbidities and mortality in liver transplant recipients using two Japanese nationwide databases. Methods: Data from two Japanese nationwide databases were combined and used for this study. We developed real‐time prognostic models for morbidity and mortality from a derivation cohort (n = 1472) and validated the findings with an independent cohort (n = 395). Preoperative variables (C1), preoperative and intraoperative variables (C2), and all variables including postoperative morbidities within 30 days (C3) were analyzed to evaluate the independent risk factors for postoperative morbidity and mortality. Results: We established real‐time risk models for morbidity and mortality. Areas under the curve (AUC) of C1 and C2 risk models for mortality were 0.74 (0.63‐0.82) and 0.79 (0.69‐0.86), respectively. Multivariate logistic analysis using C3 showed that hemoglobin <10 g/dL, operative time (hours), and five postoperative morbidities (prolonged ventilation >48 hours, coma >24 hours, renal dysfunction, postoperative systemic sepsis, and serum total bilirubin ≥10 mg/dL) represented independent risk factors for mortality (AUC = 0.87, 95% confidence interval [CI]: 0.78‐0.93). Conclusions: Real‐time risk models of postoperative morbidities and mortality at various perioperativeAbstract: Aim: A comprehensive description of morbidity and mortality risk factors for post liver transplant has not been available to date. In this study, we established real‐time risk models of postoperative morbidities and mortality in liver transplant recipients using two Japanese nationwide databases. Methods: Data from two Japanese nationwide databases were combined and used for this study. We developed real‐time prognostic models for morbidity and mortality from a derivation cohort (n = 1472) and validated the findings with an independent cohort (n = 395). Preoperative variables (C1), preoperative and intraoperative variables (C2), and all variables including postoperative morbidities within 30 days (C3) were analyzed to evaluate the independent risk factors for postoperative morbidity and mortality. Results: We established real‐time risk models for morbidity and mortality. Areas under the curve (AUC) of C1 and C2 risk models for mortality were 0.74 (0.63‐0.82) and 0.79 (0.69‐0.86), respectively. Multivariate logistic analysis using C3 showed that hemoglobin <10 g/dL, operative time (hours), and five postoperative morbidities (prolonged ventilation >48 hours, coma >24 hours, renal dysfunction, postoperative systemic sepsis, and serum total bilirubin ≥10 mg/dL) represented independent risk factors for mortality (AUC = 0.87, 95% confidence interval [CI]: 0.78‐0.93). Conclusions: Real‐time risk models of postoperative morbidities and mortality at various perioperative time points in liver transplant recipients were established. These novel approaches may improve postoperative outcomes of liver transplant recipients. Furthermore, these real‐time risk models may be applicable to other surgical procedures. Abstract : Using two nationwide databases that cover 100% of all transplant surgeries in Japan, we evaluated and created novel risk models of morbidity and mortality. To this end, we used very simple variables at each time point of pre‐, intra‐, and postoperative periods. The results of these analyses could deliver timely feedback to surgeons, nurses, and other medical staff members for better understanding of the conditions of each patient. … (more)
- Is Part Of:
- Annals of gastroenterological surgery. Volume 3:Issue 1(2019)
- Journal:
- Annals of gastroenterological surgery
- Issue:
- Volume 3:Issue 1(2019)
- Issue Display:
- Volume 3, Issue 1 (2019)
- Year:
- 2019
- Volume:
- 3
- Issue:
- 1
- Issue Sort Value:
- 2019-0003-0001-0000
- Page Start:
- 75
- Page End:
- 95
- Publication Date:
- 2018-11-02
- Subjects:
- benchmarking -- feedback from database -- prediction -- risk calculator -- surgical quality
Digestive organs -- Surgery -- Periodicals
617.43 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2475-0328/issues ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/ags3.12217 ↗
- Languages:
- English
- ISSNs:
- 2475-0328
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9448.xml