Comparision against MELD, MELD-Na, MELD-Plus7.
Abstract
Background: TODO
Methods: TODO
Results: TODO
Conclusion: TODO
1 Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Paul-List-Str. 13-15, D-04103 Leipzig, Germany.
2 Anesthesiology and Intensive Care Medicine, University Hospital Greifswald, Ferdinand-Sauerbruch-Straße, D-17475 Greifswald, Germany.
✉ Correspondence: Sebastian Gibb <mail@sebastiangibb.de>
Last updated: 2021-08-19
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Liver cirrhosis is the terminal result of fibrotic remodeling of liver tissue due to chronic damage. An organ failure is often irreversible and the only available therapy is liver transplantation. However, the shortage in grafts for transplantation from deceased donors requires risk stratification and precise allocation rules. The allocation of liver transplantation in most countries is based on disease severity determined by the model of end-stage liver disease (MELD) (Malinchoc et al. 2000; Wiesner et al. 2003; Organ Procurement and Transplantation Network 2021). The MELD score estimates the patients’ 3-month mortality risk based on laboratory results, namely bilirubin, creatinine and international normalized ratio (INR). In general the MELD score is extended by the sodium level (MELD-Na score) as this was found to be an important additional risk factor in liver cirrhosis (Kim et al. 2008; Organ Procurement and Transplantation Network 2021). The MELD was initially developed for predicting the survival of patients undergoing transjugular intrahepatic portosystemic shunts. Afterwards it was revalidated for predicting mortality risk in patients awaiting a liver transplantation. Although the MELD score should be an objective allocation score especially the creatinine and INR are highly dependent on the utilized laboratory methods (Trotter et al. 2004; Cholongitas et al. 2007). Patients with identical disease state could have very different MELD scores and thus get different priority on the liver transplantation waiting list. Furthermore often, e.g. for acute-on-chronic liver failure, the MELD score underestimates the mortality risk (Hernaez et al. 2020).
There have been some attempts to use the data extracted from more than 300.000 electronic medical records from two hospitals in the United States to improve the MELD score (Kartoun et al. 2017). The derived MELD-Plus7 and MELD-Plus9 risk score add albumin, white blood cell count, age and total cholesterol and length of stay to the MELD-Na variables. Despite its published prediction improvement the MELD-Plus scores are not used for transplant allocation yet.
As depicted by MELD-Plus risk scores better predictive scores often need more variables and are more complicated. To reduce the risk of overlooking or incorrectly calculating and interpreting the results, clinical decision support systems may be used and could improve patient safety. The research project on digital laboratory medicine (AMPEL) develops a clinical decision support system based on laboratory diagnostics that should support clinical practitioners in interpreting the laboratory results and taking the necessary medical interventions (Eckelt et al. 2020).
This study aims to find clinical and laboratory values that improve the risk stratification for liver transplantation over classical MELD, MELD-Na and MELD-Plus scores and could be implemented as part of the AMPEL clinical decision support system.
In a retrospective cohort study we followed 778 consecutive patients, who were recruited during the evaluation process for liver transplantation at the University Hospital of Leipzig from November 2012 to Juni 2015. For each patient we recorded 44 variables. Among them are age, sex, etiology of liver disease, complications as listed in Table 3.1 and 25 laboratory measurements.
Figure 2.1: Flowchart Inclusion/Exclusion.
We excluded 124 patients from our analysis (Figure 2.1). One was younger than 18 years. 24 had a liver transplantation before and in 99 cases the follow up data were missing or invalid.
The Ethics Committee at the Leipzig University Faculty of Medicine approved the retrospective usage of the data for our study (reference number: 039/14ff).
MELD and MELD-Na were calculated as described in Organ Procurement and Transplantation Network (2021) using the following formulas: \(MELD = 10 * (0.957 \ln(creatinine [mg/dl]) + 0.378 \ln(bilirubin [mg/dl]) + 1.120 * \ln(INR) + 0.643)\), Creatinine, bilirubin and INR values lower than 1.0 mg/dl were set to 1.0 mg/dl. The maximum allowed creatinine was 4.0 mg/dl. If patients received dialysis, creatinine was set to 4.0 mg/dl. For MELD-Na MELD was calculated as above and recalculated if greater than 11 using: \(MELD\text{-}Na = MELD_i + 1.32 * (137 - Na [mmol/l]) - (0.033 * MELD_i * (137 - Na [mmol/l]))\)
Serum sodium values lower than 125 mmol/l or higher than 137 mmol/l were set to 125 mmol/l and 137 mmol/l, respectively.
The MELD-Plus7 risk score was calculated as described in Kartoun et al. (2017): \[ \begin{aligned} L = 8.53499496 &+ \\ 2.59679650 &* \log_{10}(1 + creatinine [mg/dl]) + \\ 2.06503238 &* \log_{10}(1 + bilirubin [mg/dl]) + \\ 2.99724802 &* \log_{10}(1 + INR) - \\ 6.47834101 &* \log_{10}(1 + sodium [mmol/l]) - \\ 6.34990436 &* \log_{10}(1 + albumin [g/l]) + \\ 1.92811726 &* \log_{10}(1 + wbc [th/cumm]) + \\ 0.04070442 &* age [years] \end{aligned} \]
\[MELD\text{-}Plus = \frac{\exp(L)}{1 + \exp(L)}\]
The MELD-Plus9 score was ignored because the length of stay information was not available for our cohort and the model itself wasn’t superior to MELD-Plus7 in its original publication (Kartoun et al. 2017).
| Characteristic | Female, N = 240 | Male, N = 414 | Overall, N = 654 |
|---|---|---|---|
| General | |||
| Follow-up time [days] | 181 (17, 411) | 198 (54, 378) | 191 (38, 384) |
| Age | 56 (51, 64) | 58 (52, 63) | 58 (52, 64) |
| LTx | 20 (8.3%) | 40 (9.7%) | 60 (9.2%) |
| Laboratory measurements | |||
| Total bilirubin [µmol/l] | 21 (10, 49) | 26 (14, 51) | 24 (13, 50) |
| Cystatin C [mg/l] | 1.31 (1.04, 1.80) | 1.32 (1.08, 1.76) | 1.32 (1.07, 1.79) |
| (Missing) | 2 | 5 | 7 |
| Creatinine [µmol/l] | 73 (58, 91) | 85 (71, 110) | 80 (65, 104) |
| INR | 1.20 (1.06, 1.50) | 1.22 (1.10, 1.44) | 1.21 (1.09, 1.45) |
| (Missing) | 1 | 1 | 2 |
| Sodium [mmol/l] | 138.8 (135.4, 141.0) | 138.0 (135.0, 140.1) | 138.1 (135.2, 140.4) |
| (Missing) | 5 | 4 | 9 |
| WBC [exp 9/l] | 6.7 (4.7, 8.8) | 6.2 (4.6, 7.8) | 6.3 (4.7, 8.3) |
| (Missing) | 10 | 13 | 23 |
| IL-6 [pg/ml] | 10 (5, 36) | 14 (6, 47) | 13 (6, 42) |
| (Missing) | 27 | 29 | 56 |
| Albumin [g/l] | 41 (35, 46) | 38 (33, 43) | 39 (34, 45) |
| (Missing) | 2 | 4 | 6 |
| Total protein [g/l] | 71 (63, 76) | 71 (65, 76) | 71 (65, 76) |
| (Missing) | 2 | 4 | 6 |
| Cholesterine [mmol/l] | 4.64 (3.41, 5.56) | 4.28 (3.34, 5.15) | 4.40 (3.34, 5.31) |
| ALAT [µkat/l] | 0.29 (0.21, 0.42) | 0.36 (0.23, 0.56) | 0.33 (0.22, 0.48) |
| (Missing) | 2 | 8 | 10 |
| ASAT [µkat/l] | 0.68 (0.49, 1.05) | 0.81 (0.61, 1.23) | 0.77 (0.55, 1.16) |
| (Missing) | 3 | 5 | 8 |
| MELD | |||
| MELD score | 10 (7, 18) | 12 (9, 17) | 11 (8, 17) |
| (Missing) | 2 | 1 | 3 |
| MELD Category | |||
| [6,9] | 111 (47%) | 146 (35%) | 257 (39%) |
| [10,20) | 80 (34%) | 196 (47%) | 276 (42%) |
| [20,30) | 30 (13%) | 50 (12%) | 80 (12%) |
| [30,40) | 12 (5.0%) | 14 (3.4%) | 26 (4.0%) |
| [40,52) | 5 (2.1%) | 7 (1.7%) | 12 (1.8%) |
| (Missing) | 2 | 1 | 3 |
| MELD-Na score | 10 (7, 19) | 12 (9, 20) | 11 (8, 20) |
| (Missing) | 5 | 4 | 9 |
| MELD-Plus7 risk score | 0.04 (0.02, 0.09) | 0.05 (0.03, 0.11) | 0.04 (0.02, 0.11) |
| (Missing) | 17 | 18 | 35 |
| Entities | |||
| Cirrhosis | 203 (85%) | 391 (94%) | 594 (91%) |
| (Missing) | 1 | 0 | 1 |
| ALF | 6 (2.5%) | 2 (0.5%) | 8 (1.2%) |
| CLI | 30 (12%) | 21 (5.1%) | 51 (7.8%) |
| Etiology (more than 1 per patient possible) | |||
| Ethyltoxic | 114 (48%) | 297 (72%) | 411 (63%) |
| HBV | 4 (1.7%) | 16 (3.9%) | 20 (3.1%) |
| HCV | 17 (7.1%) | 28 (6.8%) | 45 (6.9%) |
| AIH | 22 (9.2%) | 9 (2.2%) | 31 (4.7%) |
| PBC | 17 (7.1%) | 0 (0%) | 17 (2.6%) |
| PSC | 5 (2.1%) | 11 (2.7%) | 16 (2.4%) |
| NASH | 17 (7.1%) | 31 (7.5%) | 48 (7.3%) |
| Cryptogenic | 36 (15%) | 32 (7.7%) | 68 (10%) |
| Complications (more than 1 per patient possible) | |||
| Dialysis | 14 (5.9%) | 19 (4.6%) | 33 (5.1%) |
| (Missing) | 1 | 1 | 2 |
| GIB | 58 (24%) | 104 (25%) | 162 (25%) |
| HCC | 18 (7.5%) | 103 (25%) | 121 (19%) |
| SBP | 31 (13%) | 60 (15%) | 91 (14%) |
| (Missing) | 0 | 1 | 1 |
| Mortality | |||
| Within 7 days | 16 (7.2) | 18 (4.5) | 34 (5.5) |
| Within 30 days | 25 (12) | 31 (8.1) | 56 (9.4) |
| Within 90 days | 32 (16) | 49 (13) | 81 (14) |
| Within 365 days | 39 (20) | 61 (18) | 100 (18) |
According to Wiesner et al. (2003) we divided our cohort into 5 MELD-score risk categories, with 257, 276, 80, 26, and 12 patients, respectively. As depicted in Figure ?? and Table 3.2 our observed 90 day mortality was 1.4 to 2.9 times higher than the predicted one. Just in the group with the lowest MELD scores the mortality was much lower.
| MELD category | Observed deaths (n) | Expected deaths (n) | Standardized mortality ratio (SMR) | Observed mortality (%) | Expected mortality (%) |
|---|---|---|---|---|---|
| [6,9] | 1 | 4.0 | 0.3 | 0.5 | 1.9 |
| [10,20) | 21 | 13.0 | 1.6 | 8.9 | 6.0 |
| [20,30) | 34 | 11.6 | 2.9 | 51.0 | 19.6 |
| [30,40) | 18 | 10.0 | 1.8 | 92.8 | 52.6 |
| [40,52) | 6 | 4.3 | 1.4 | 100.0 | 71.3 |
In this retrospective analysis of 654 consecutive patients, who were recruited during the evaluation process for liver transplantation at the University Hospital of Leipzig we compared 11 different statistical and machine learning algorithms.
…
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