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Caraceni P, Tufoni M, Zaccherini G, Riggio O, Angeli P, Alessandria C, Neri S, Foschi FG, Levantesi F, Airoldi A, Simone L, Svegliati-Baroni G, Fagiuoli S, Laffi G, Cozzolongo R, Di Marco V, Sangiovanni V, Morisco F, Toniutto P, Gasbarrini A, De Marco R, Piano S, Nardelli S, Elia C, Roncadori A, Baldassarre M, Bernardi M. On-treatment serum albumin level can guide long-term treatment in patients with cirrhosis and uncomplicated ascites. J Hepatol 2021; 74:340-349. [PMID: 32853747 DOI: 10.1016/j.jhep.2020.08.021] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 07/10/2020] [Accepted: 08/17/2020] [Indexed: 02/05/2023]
Abstract
BACKGROUND & AIMS The ANSWER study reported that long-term albumin administration in patients with cirrhosis and uncomplicated ascites improves survival. During treatment, serum albumin increased within a month and remained stable thereafter. In this post hoc analysis, we aimed to determine whether on-treatment serum albumin levels could guide therapy. METHODS Logistic regression was used to assess the association between baseline serum albumin and mortality, as well as to determine on-treatment factors associated with mortality and to predict the achievement of a given on-treatment serum albumin level. Survival was assessed by Kaplan-Meier estimates and second-order polynomial regression. Patients whose on-treatment serum albumin remained below normal were compared with a subset of patients from the control arm matched by principal score. RESULTS Baseline serum albumin was closely associated with 18-month mortality in untreated patients; albumin treatment almost effaced this relationship. On-treatment serum albumin and MELD-Na at month 1 were the sole independent variables associated with mortality. Second-order polynomial regression revealed that survival improved in parallel with increased 1-month on-treatment serum albumin. Kaplan-Meier estimations showed that any value of 1-month on-treatment serum albumin (0.1 g/dl intervals) in the range 2.5-4.5 g/dl discriminated patient survival. In the normal range of serum albumin, the best discriminant value was 4.0 g/dl. Compared to untreated patients, survival even improved in patients whose on-treatment serum albumin remained below normal. CONCLUSION Baseline serum albumin per se should not guide the decision to start albumin therapy. Conversely, 1-month on-treatment serum albumin levels are strongly associated with outcomes and could guide the use of albumin - 4.0 g/dl being the target threshold. However, even patients whose serum albumin remains below normal benefit from long-term albumin administration. LAY SUMMARY The ANSWER study has shown that long-term albumin administration improves survival and prevents the occurrence of major complications in patients with cirrhosis and ascites. This study shows that the achievement of these beneficial effects is related to a significant increase in serum albumin concentration. Even though the best results follow the achievement of a serum albumin concentration of 4 g/dl, a survival benefit is also achieved in patients who fail to normalise serum albumin.
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Affiliation(s)
- Paolo Caraceni
- Department of Medical and Surgical Sciences, University of Bologna, Italy; Bologna University Hospital Authority St. Orsola-Malpighi Polyclinic, Italy
| | - Manuel Tufoni
- Department of Medical and Surgical Sciences, University of Bologna, Italy; Bologna University Hospital Authority St. Orsola-Malpighi Polyclinic, Italy
| | - Giacomo Zaccherini
- Department of Medical and Surgical Sciences, University of Bologna, Italy; Bologna University Hospital Authority St. Orsola-Malpighi Polyclinic, Italy
| | - Oliviero Riggio
- Department of Clinical Medicine, "Sapienza" University of Rome, Italy
| | - Paolo Angeli
- Unit of Internal Medicine and Hepatology, Department of Medicine, University of Padua, Italy
| | - Carlo Alessandria
- Division of Gastroenterology and Hepatology, "Città della Salute e della Scienza" Hospital, University of Turin, Italy
| | - Sergio Neri
- Department of Clinical and Experimental Medicine, University of Catania, Italy
| | | | - Fabio Levantesi
- Internal Medicine, Hospital of Bentivoglio, A.U.S.L. of Bologna, Italy
| | - Aldo Airoldi
- Liver Unit, Department of Hepatology and Gastroenterology, Niguarda Hospital, Milan, Italy
| | | | | | - Stefano Fagiuoli
- Gastroenterology and Transplant Hepatology, Papa Giovanni XXIII Hospital, Bergamo, Italy
| | - Giacomo Laffi
- Careggi University Hospital, University of Florence, Italy
| | - Raffaele Cozzolongo
- Division of Gastroenterology, National Institute of Gastroenterology "S. De Bellis", Castellana Grotte (Bari), Italy
| | - Vito Di Marco
- Unit of Gastroenterology and Hepatology, Biomedical Department of Internal and Specialistic Medicine, University of Palermo, Italy
| | | | - Filomena Morisco
- Gastroenterology Unit, Department of Clinical Medicine and Surgery, "Federico II" University of Naples, Italy
| | - Pierluigi Toniutto
- Hepatology and Liver Transplantation Unit, Department of Medical Area, University of Udine, Italy
| | | | | | - Salvatore Piano
- Unit of Internal Medicine and Hepatology, Department of Medicine, University of Padua, Italy
| | - Silvia Nardelli
- Department of Clinical Medicine, "Sapienza" University of Rome, Italy
| | - Chiara Elia
- Division of Gastroenterology and Hepatology, "Città della Salute e della Scienza" Hospital, University of Turin, Italy
| | | | - Maurizio Baldassarre
- Department of Medical and Surgical Sciences, University of Bologna, Italy; Center for Applied Biomedical Research (CRBA), University of Bologna, Italy
| | - Mauro Bernardi
- Department of Medical and Surgical Sciences, University of Bologna, Italy.
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Porcher R, Jacot J, Wunder JS, Biau DJ. Identifying treatment responders using counterfactual modeling and potential outcomes. Stat Methods Med Res 2018; 28:3346-3362. [PMID: 30298794 DOI: 10.1177/0962280218804569] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Individualizing treatment according to patients' characteristics is central for personalized or precision medicine. There has been considerable recent research in developing statistical methods to determine optimal personalized treatment strategies by modeling the outcome of patients according to relevant covariates under each of the alternative treatments, and then relying on so-called predicted individual treatment effects. In this paper, we use potential outcomes and principal stratification frameworks and develop a multinomial model for left and right-censored data to estimate the probability that a patient is a responder given a set of baseline covariates. The model can apply to RCT or observational study data. This method is based on the monotonicity assumption, which implies that no patients would respond to the control treatment but not to the experimental one. We conduct a simulation study to evaluate the properties of the proposed estimation method. Results showed that the predictions of the probability of being a responder were well calibrated even if we observed variability and a small bias when many parameters were estimated. We finally applied the method to a cohort study on the selection of patients for additional radiotherapy after resection of a soft-tissue sarcoma.
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Affiliation(s)
- Raphaël Porcher
- Faculté de Médecine, Université Paris Decartes, Sorbonne Paris Cité, Paris, France.,Centre de Recherche Epidémiologie et Statistiques, INSERM U1153, Paris, France.,Centre d'Epidémiologie Clinique, Hôtel-dieu, Assistance Publique-Hôpitzaux de Paris, France
| | - Justine Jacot
- Centre de Recherche Epidémiologie et Statistiques, INSERM U1153, Paris, France.,Centre d'Epidémiologie Clinique, Hôtel-dieu, Assistance Publique-Hôpitzaux de Paris, France
| | - Jay S Wunder
- University Musculoskeletal Oncology Unit, Mount Sinai Hospital, Canada.,Division of Orthopaedic Surgery, Department of Surgery, University of Toronto, Canada
| | - David J Biau
- Faculté de Médecine, Université Paris Decartes, Sorbonne Paris Cité, Paris, France.,Centre de Recherche Epidémiologie et Statistiques, INSERM U1153, Paris, France.,Département de Chirurgie Orthopédique, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, France
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