Comparison of APACHE IV with APACHE II, SAPS 3, MELD, MELD-Na, and CTP scores in predicting mortality after liver transplantation

Predictive Factors for the Prognosis of Alcoholic Liver Cirrhosis

Alcoholic liver cirrhosis (ALC) is a disease with multiple complications and is associated with poor prognosis and significant mortality. Identifying risk factors associated with a poor outcome is important to ensure effective treatment and increase life expectancy. We aimed to evaluate the predictive values of complications regarding mortality in ALC. We retrospectively analyzed 1429 patients with ALC hospitalized between January 2019 and April 2022 at the Institute of Gastroenterology and Hepatology Iasi. The electronic medical records were interrogated to obtain information about demographic data, complications, comorbidities, and prognostic scores: MELD-Na (model for end-stage liver disease-sodium) and CTP (Child−Turcotte−Pugh). Based on uni- and multivariate analysis, independent predictors of mortality were identified. The mean age at diagnosis was 56.15 ± 11.49 years with a ratio of 2:1 in favor of males. There were 296 deaths (20.8%), most of them during the first hospitalization (208/14.6%). It was observed during the univariate analysis that complications of the disease negatively affected the survival rate, significant values being related to infections (sepsis; OR = 21.98; p < 0.001; spontaneous bacterial peritonitis (SBP) (OR = 11.94; p < 0.001) and hepatorenal syndrome (HRS) (OR = 9.35; p < 0.001). The independent predictors, confirmed by multivariate analysis, were the association of variceal bleeding, infections, and hepatic encephalopathy or ascites, each combination being responsible for two out of 10 of the deaths during the first admission. The prognosis of the disease was negatively influenced by the worsening of liver dysfunction and the appearance of complications. The main predictors of mortality were infections, hepatic encephalopathy, variceal bleeding, and hepatorenal syndrome. Improving compliance and strict application of specific follow-up and treatment strategies could contribute to a better prognosis of patients with alcoholic liver cirrhosis.

The psoas muscle depletion index is related to the degree of cirrhosis and skeletal muscle loss in patients with end-stage liver disease

Objective: To establish a new psoas muscle depletion index (PDI) from healthy young donors and to explore the correlation between the PDI and the severity of cirrhosis in patients with endstage liver disease (ESLD). Methods: Clinical data of 461 healthy donors were collected during the period 2014-2019, and clinical data of 331 patients with ESLD were collected during the period 2014-2018. The patients were divided into four groups by PDI severity: PDI ≥ 0.90, PDI = 0.75-0.90, PDI = 0.50-0.75 and PDI ≤ 0.50 (Gsev). Differences in international normalised ratio (INR), total bilirubin and serum creatinine levels, and Child-Pugh (CP) and model for end-stage liver disease (MELD) scores were compared. The sarcopenia incidence according to the PDI and the psoas muscle index (PMI) in different weight groups were also compared. Results: Gsev had the highest CP (10.2 ± 2.1) and MELD (20.1 ± 7.4) scores and total bilirubin (166.3 ± 192.0 umol/L) and blood creatinine (92.9 ± 90.2 umol/L) levels and the lowest haemoglobin (93.8 ± 21.7 g/L) and blood albumin (30.9 ± 5.8 g/L) levels. Gsev showed significant changes in INR (1.74 ± 0.65) and blood sodium (135.3 ± 5.65 mmol/L). If PDI <0.75 was used as the diagnostic criterion for sarcopenia, the incidence was 53.3% in patients weighing >90 kg and 53.6% in those weighing <60 kg. This differed from the PMI, with an incidence of 3.3% in patients weighing >90 kg. Conclusions: The PDI had no significant correlation with body height, body weight or body mass index (BMI) in healthy individuals and patients with ESLD. The PDI was significantly correlated with the severity of cirrhosis and loss of skeletal muscle.

Modelling kidney outcomes based on MELD eras - impact of MELD score in renal endpoints after liver transplantation

Background

Acute kidney injury is a common complication in solid organ transplants, notably liver transplantation. The MELD is a score validated to predict mortality of cirrhotic patients, which is also used for organ allocation, however the influence of this allocation criteria on AKI incidence and mortality after liver transplantation is still uncertain.

Methods

This is a retrospective single center study of a cohort of patients submitted to liver transplant in a tertiary Brazilian hospital: Jan/2002 to Dec/2013, divided in two groups, before and after MELD implementation (pre-MELD and post MELD). We evaluate the differences in AKI based on KDIGO stages and mortality rates between the two groups.

Results

Eight hundred seventy-four patients were included, 408 in pre-MELD and 466 in the post MELD era. The proportion of patients that developed AKI was lower in the post MELD era (p 0.04), although renal replacement therapy requirement was more frequent in this group (p < 0.01). Overall mortality rate at 28, 90 and 365 days was respectively 7%, 11% and 15%. The 1-year mortality rate was lower in the post MELD era (20% vs. 11%, p < 0.01). AKI incidence was 50% lower in the post MELD era even when adjusted for clinically relevant covariates (p < 0.01).

Conclusion

Liver transplants performed in the post MELD era had a lower incidence of AKI, although there were more cases requiring dialysis. 1-year mortality was lower in the post MELD era, suggesting that patient care was improved during this period.

Association between the Predicted Value of APACHE IV Scores and Intensive Care Unit Mortality: A Secondary Analysis Based on EICU Dataset

Objective

The evidence regarding the relationship between Acute Physiological and Chronic Health Assessment (APACHE) IV scores and emergency intensive care unit (EICU) mortality in patients following organ transplantation remains controversial. The purpose of this study was to investigate the relationship between APACHE IV score and EICU mortality.

Methods

Data from 391 American men and women admitted to the EICU after undergoing organ transplants including heart, bone marrow, liver, kidney, lung, and pancreas in the United States. We used this data to analyze the relationship between APACHE IV scores and in-hospital mortality in the postoperative EICU. The primary endpoint was ICU hospitalization mortality after organ transplantation. The entire study data was extracted from the EICU database and uploaded to the DataDryad website.

Results

Interaction tests indicate age, respiratory failure, and hormone use can modify the association between APACHE IV and EICU mortality. A stronger association of APACHE and mortality can be observed at <60 years old, no respiratory failure, and no use of hormones. In contrast, there was no association between respiratory failure, hormone use, APACHE, and ICU mortality in patients over 60 years of age.

Conclusion

When using the APACHE score for risk stratification of critically ill patients after transplantation, the patient's age, respiratory failure, and use of hormones should be taken into account.

Noninvasive Real-Time Mortality Prediction in Intensive Care Units Based on Gradient Boosting Method: Model Development and Validation Study

BACKGROUND

Monitoring critically ill patients in intensive care units (ICUs) in real time is vitally important. Although scoring systems are most often used in risk prediction of mortality, they are usually not highly precise, and the clinical data are often simply weighted. This method is inefficient and time-consuming in the clinical setting.

OBJECTIVE

The objective of this study was to integrate all medical data and noninvasively predict the real-time mortality of ICU patients using a gradient boosting method. Specifically, our goal was to predict mortality using a noninvasive method to minimize the discomfort to patients.

METHODS

In this study, we established five models to predict mortality in real time based on different features. According to the monitoring, laboratory, and scoring data, we constructed the feature engineering. The five real-time mortality prediction models were RMM (based on monitoring features), RMA (based on monitoring features and the Acute Physiology and Chronic Health Evaluation [APACHE]), RMS (based on monitoring features and Sequential Organ Failure Assessment [SOFA]), RMML (based on monitoring and laboratory features), and RM (based on all monitoring, laboratory, and scoring features). All models were built using LightGBM and tested with XGBoost. We then compared the performance of all models, with particular focus on the noninvasive method, the RMM model.

RESULTS

After extensive experiments, the area under the curve of the RMM model was 0.8264, which was superior to that of the RMA and RMS models. Therefore, predicting mortality using the noninvasive method was both efficient and practical, as it eliminated the need for extra physical interventions on patients, such as the drawing of blood. In addition, we explored the top nine features relevant to real-time mortality prediction: invasive mean blood pressure, heart rate, invasive systolic blood pressure, oxygen concentration, oxygen saturation, balance of input and output, total input, invasive diastolic blood pressure, and noninvasive mean blood pressure. These nine features should be given more focus in routine clinical practice.

CONCLUSIONS

The results of this study may be helpful in real-time mortality prediction in patients in the ICU, especially the noninvasive method. It is efficient and favorable to patients, which offers a strong practical significance.

Characteristics and clinical subtypes of cancer patients in the intensive care unit: a retrospective observational study for two large databases

Background

Previous studies have reported very different mortality rates among cancer patients in the intensive care unit (ICU), implying different clinical subtypes. We aimed to reveal the clinical subtypes and demonstrate the importance of segregating the patients in clinical research, and to report the ICD-level mortality of cancer patients in the ICU.

Methods

Two ICU databases (MIMIC-III and eICU) were utilized to identify cancer patients. Mortality based on ICD-level diagnoses were calculated, and K-means clustering was used to identify different clinical subtypes in the MIMIC database. Clinical characteristics and outcomes were compared among subtypes, and the calibration of SAPS II and APACHE IV among different subtypes was evaluated.

Results

In total, 6,505 (13.8%) cancer patients of the MIMIC database and 7,351 (4.9%) ones in eICU database, were enrolled in the study. Metastasis involving pleura, metastasis involving the liver, and acute myeloid leukemia were in the top 5 diagnoses with the highest mortality in both databases. Clinical subtypes identified by K-means clustering were closely associated with admission type (elective or emergency) and clinical service provider (surgical or medical). In a four-cluster pattern, nearly all patients in the first cluster were elective admissions (99.1%), whereas in the rest of the clusters, most were emergency admissions (93.7%). Most surgical patients were in the 1+2 clusters (92.0%) and most medical patients were in the 3+4 clusters (93.5%). Most characteristics and outcomes as well as the calibration of SAPS II and APACHE IV scoring systems were significantly different among clinical subtypes.

Conclusions

Different clinical subtypes can be well identified by admission type and clinical service provider among ICU patients with cancer. Caution should be exercised when considering these patients as a whole population both in clinical practice and research. Moreover, APACHE IV has better calibration than SAPS II for cancer patients at low risk of mortality in the ICU.

Analysis of mortality prognostic factors using model for end-stage liver disease with incorporation of serum-sodium classification for liver cirrhosis complications: A retrospective cohort study

Since the progression of cirrhosis is accelerated each time a complication recurs, the management and treatment of the complication is critical in enhancement of the quality of life and expectation of life in patients. The use of model for end-stage liver disease with incorporation of serum-sodium (MELD-Na) with physiological indicators can be used to assess severity and differentiate therapeutic interventions.This study is aimed to determine the mean survival period and cumulative survival rate by classifying patients into high-risk and low-risk groups based on MELD-Na, a predictor of mortality in liver disease, and to investigate the mortality prognostic factors.A retrospective cohort study, which follows the STROBE checklist, was performed. 263 patients who were diagnosed with liver cirrhosis complications for the first time and hospitalized were selected as the subjects of this study. The collected data were analyzed based on the survival package provided by the statistical program R version 3.4.2.Subjects were classified into high-risk and low-risk groups using MELD-Na 14 points where sensitivity and specificity crossed the cut-off point. Gender, age, and primary caregiver were significant variables in the mortality high-risk group, and AST, albumin, and primary caregiver were significant variables in the mortality low-risk group. Based on these mortality prognostic factors, it is possible to present the factors affecting mortality in patients who were diagnosed with liver cirrhosis complications for the first time. The classification of patients by risk level could be the foundation to provide accurate guidelines for management and it is necessary to modify prognostic factors and apply nursing interventions to manage complications.

Is Muscle MELD a More Promising Predictor for Mortality After Living Donor Liver Transplantation?

BACKGROUND

To improve the outcome of living donor liver transplantation (LDLT), a scoring system that could predict accurately the patient and graft survival posttransplant is necessary. The aim of this study is to evaluate our previously proposed Muscle-model for end-stage liver disease (M-MELD) score and to compare it with the other available scores to find the best system that correlates with postoperative outcome after liver transplant.

METHODS

We retrospectively reviewed the data of 199 patients who underwent LDLT from January 2010 to July 2016 and calculated the preoperative MELD, MELD Na, the product of donor age and MELD (D-MELD), M-MELD, integrated MELD, and the balance of risk (BAR) score in all patients. The area under the receiver operating characteristics curves (AUCs) of each score was computed and compared at 3-, 6-months, and 1-year after LDLT.

RESULTS

The M-MELD, D-MELD, and integrated MELD had a good discriminative performance in predicting 3-month mortality after LDLT with AUCs > 0.7, while the M-MELD was the only score that showed a good discriminative performance in predicting 6-month and 1-year mortality after LDLT with AUCs > 0.7.

CONCLUSION

Muscle-MELD score is a simple and useful predictor of patient survival after LDLT which showed a better predictive performance than other available scores.