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Article

Hepatic Impairment as a Risk Factor for Drug Safety: Suitability and Comparison of Four Liver Scores as Screening Tools

by
Kathrin Golla
1,2,*,
Andreas Benesic
3,
Hanna Mannell
1,4,
Tobias Dreischulte
5,
Eva Grill
6 and
Dorothea Strobach
1,2
1
Doctoral Program Clinical Pharmacy, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
2
Hospital Pharmacy, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
3
Department of Internal Medicine—Gastroenterology, Krankenhaus GmbH Weilheim-Schongau, Marie-Eberth Str. 6, 86956 Schongau, Germany
4
Department of Physiology, Institute for Theoretical Medicine, University of Augsburg, 86159 Augsburg, Germany
5
Institute of General Practice and Family Medicine, University Hospital, LMU Munich, Pettenkoferstr. 8a, 80336 Munich, Germany
6
Institute for Medical Information Processing, Biometrics and Epidemiology, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2023, 12(21), 6814; https://doi.org/10.3390/jcm12216814
Submission received: 11 September 2023 / Revised: 19 October 2023 / Accepted: 25 October 2023 / Published: 27 October 2023
(This article belongs to the Section Pharmacology)
Browse Figures
Versions Notes

Abstract

:
Hepatic impairment (HI) influences the pharmacokinetics and pharmacodynamics of drugs and represents an important risk factor for drug safety. A reliable screening tool for HI identification at hospital admission by pharmacists would be desirable but is currently lacking. Therefore, we tested four liver scores as potential screening instruments. We retrospectively recorded liver/bile diagnoses, symptoms and abnormalities (summarized as hepatic findings) of 200 surgical patients followed by an assessment of the relevance of these findings for drug therapy (rating). The agreement between the Model of Endstage Liver Disease (MELD), Non-alcoholic fatty liver disease fibrosis score (NFS), Fibrosis 4 index (FIB-4), and aspartate-aminotransferase to platelet ratio index (APRI) and the rating was quantified by Cohen’s Kappa. The performance of the scores in this setting was further evaluated by their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Of 200 patients, 18 (9%) had hepatic findings relevant for drug therapy. Fair agreement was found for FIB-4 and MELD and slight agreement for APRI and NFS compared to the rating. The highest values for sensitivity, specificity, PPV, and NPV were 41.2% (MELD), 99.3% (APRI), 66.7% (APRI), and 93.6% (MELD), respectively. Due to low performance, none of the scores can be recommended for clinical use as a single screening tool for HI at hospital admission.

1. Introduction

Hepatic impairment (HI) leads to significant alterations in the pharmacokinetics and pharmacodynamics of drugs. Depending on the type and severity of the underlying liver disease, metabolism, including the first pass effect, or elimination of drugs may be affected [1,2,3]. In addition, HI can cause a variety of pathophysiologic conditions such as renal dysfunction [4,5] or gastrointestinal bleeding [6]. Accordingly, HI has been identified as a risk factor for drug safety, leading to adverse drug reactions (ADR) [7,8,9]. To prevent ADR, drug therapy needs to be reviewed in terms of drug choice and dose adjustment. To reduce the risk of ADR, patients with HI and at risk for hepatic drug-related problems (DRP) first need to be identified. A pharmacist-led medication reconciliation (PhMR) at hospital admission, including a check for drug adjustments to a patient´s liver function, could help to achieve this aim. A DRP is defined as “an event or circumstance involving drug therapy that actually or potentially interferes with desired health outcomes” [10]. They concern treatment effectiveness or treatment safety and have a variety of potential causes, like incorrect drug selection, drug form, dose selection, or treatment duration [10]. Unfortunately, a suitable, preferably automatic, and reliable screening method to be used by pharmacists to identify patients at risk for hepatic DRP at hospital admission has neither been established nor validated.
To rapidly screen for patients at risk, pharmacists have to rely on documented liver and bile diagnoses or liver laboratory parameters (LLP). However, there are several issues with this approach. First, at hospital admission, patient documentation is often incomplete [11,12,13]. Second, chronic liver disease is judged to be underestimated [14] and about 50% of cirrhosis cases are first diagnosed upon emergency hospitalization [15]. Third, the assessment of liver function and identification of HI based on LLP is still challenging since a single parameter reflecting the complete physiology of the liver does not exist. LLP can be influenced by multiple extrahepatic factors and not all liver diseases result in LLP alterations [16]. Thus, their use as a screening tool for impaired liver function is limited.
Liver scores, combining different laboratory parameters, symptoms or comorbidities, display another option to assess liver function or liver remodeling. Originally developed by Child et al. [17] and modified by Pugh et al. [18], the Child–Pugh Score (CPS) can be used to assign patients into classes A, B, and C depending on the severity of liver cirrhosis [19]. Beside the laboratory parameters albumin, International Normalized Ratio (INR) or prothrombin time and bilirubin, CPS calculation also requires the assessment of two symptoms, ascites and encephalopathy, which may lead to varying results due to subjective assessment [19], and makes it impossible to calculate the CPS automatically or by pharmacists. Irrespective of this, the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) both recommend the CPS as a basis for the assessment of HI in pharmacokinetic studies from which dosage recommendations can be derived [20,21].
The Model of Endstage Liver Disease score (MELD), calculated from bilirubin, INR and serum creatinine, has been used by the Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing for the allocation of liver donations [22,23]. Since July 2023, the so-called MELD 3.0 has been used for this purpose [24,25]. In contrast to the CPS, the automatic calculation of the MELD is possible since it is based on laboratory parameters only. Additionally, Albarmawi et al. found a good correlation between the CPS and the MELD, thus, the MELD might also be converted to the CPS as a basis for drug adjustment to liver function [26].
The non-alcoholic fatty liver disease (NAFLD) fibrosis score (NFS) is calculated from the laboratory parameters aspartate-aminotransferase (AST), alanine-aminotransferase (ALT), platelet count and albumin, the Body Mass Index (BMI), age, and the impaired fasting glucose/diabetes status. It was originally developed and validated to classify patients with confirmed NAFLD for advanced/not advanced fibrosis [27]. The Fibrosis 4 index (FIB-4) is used for the prediction of fibrosis in patients with human immunodeficiency virus/hepatitis C virus (HCV) coinfection. The FIB-4 can be calculated by the routinely collected parameters AST, ALT, platelet count and age [28]. The AST to platelet ratio index (APRI) has been developed for the prediction of advanced fibrosis and cirrhosis in patients with chronic HCV infection. The actual AST, upper limit of normal of AST and platelet count are required for the calculation [29]. Beside the validated application areas, NFS, FIB-4, and APRI were tested as predictive models for the occurrence of cirrhosis in the general population [30], esophageal varices in hepatocellular carcinoma (HCC) patients [31], as well as mortality among COVID-19 patients [32] or inflammation in drug-induced liver injury [33].
The actual impact of a liver disease on the pharmacokinetics and pharmacodynamics of drugs depends on the kind and severity of hepatic changes. Several mechanisms may be involved. In particular, Cytochrome P450 (CYP) enzymes, which are the major contributors to phase-1 metabolism [34] and represent the predominant enzymes involved in the metabolism of drugs [35], may be influenced by the type and severity of the liver diseases [36,37,38,39,40]. Regarding phase-2 reactions, especially glucuronidation, it can be assumed that these are largely preserved in mild liver diseases [41]. Beside drug-metabolizing enzymes, hepatic transporters like organic anion transporters contribute to the clearance of drugs. Effects on hepatic transporter expression have been described for several liver diseases such as NASH or primary biliary cholangitis. When assessing pharmacokinetic changes in liver disease, this aspect must also be taken into account, since, among other things, altered systemic drug levels may occur [42]. In addition, renal or gastrointestinal transporters may also be affected by hepatic dysfunction [43]. Cirrhosis of the liver can also lead to an impaired blood flow through the liver, e.g., due to the formation of portosystemic shunts, which can cause changes in the bioavailability of drugs [44]. Some liver diseases may be clinically relevant, e.g., liver metastases or HCC, but they might have only a minor impact on the pharmacokinetics of drugs without concomitant cirrhosis [2]. Other liver irregularities, such as small liver cysts or hemangioma, may not have any clinical relevance at all [45]. Especially diseases like NAFLD, which may proceed to non-alcoholic steatohepatitis (NASH), possibly combined with fibrosis, or even cirrhosis [46], are of special interest from a pharmacokinetic point of view, as effects on drug-metabolizing enzymes seem to increase in parallel with the disease progression [36]. In addition, NAFLD is a widespread liver disease, affecting 30% of the worldwide population [47]. Moreover, the prevalence of NAFLD and its advanced stage NASH is expected to rise in the coming years [48].
Taken together, the appropriate identification of patients at risk for hepatic DRP by pharmacists is still challenging. Previously, we studied the MELD as a feasible screening tool for HI to be used by pharmacists at hospital admission. However, certain conditions such as interfering factors and the correct adjustment of MELD parameters to standard values must be taken into account. Moreover, the availability of bilirubin values was a limiting factor [49]. Other established liver scores might represent useful and feasible alternatives.
Thus, the aim of our study was to investigate and compare the suitability of the liver scores MELD, NFS, FIB-4, and APRI as general screening tools for HI by pharmacists in a retrospective surgical patient cohort. We performed interrater reliability analyses and calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the scores in terms of the identification of patients with liver or bile diagnoses, symptoms and abnormalities relevant for drug therapy. To our knowledge, this is the first study testing four different liver scores as potential screening tools for HI to be implemented in clinical routine.

2. Materials and Methods

We performed a retrospective chart review including the first 200 patients aged ≥18 years, who were admitted to the surgical department (including general, visceral, transplant, vascular surgery) of a large university hospital in Bavaria, Germany from March 2021 on and for whom PhMR at hospital admission was performed. Prior to this, a sample size calculation was conducted giving a minimum sample size of 186 patients under the assumption of a 45% dropout rate [50,51]. PhMR is routinely carried out from Monday to Friday and includes a detailed acquisition of prescribed, recently terminated and over-the-counter drugs. For each patient, laboratory parameters from the day of hospital admission, documented liver or bile diagnoses, symptoms or abnormalities and the diabetes status were recorded from the electronic patient information system (SAP-i.s.h.med, Cerner Corporation, North Kansas City, MO, USA). Documented laboratory parameters included ALT, AST, bilirubin, albumin, INR, platelet count, and serum creatinine. The study was performed in accordance with the Declaration of Helsinki. Ethics approval was obtained by the ethics committee of the University Hospital Munich (23-0186).
In an initial step, all confirmed liver and bile diagnoses, and all associated symptoms and structural or functional abnormalities of the liver, the gall bladder and bile ducts were recorded. These were summarized as “hepatic findings”. For this purpose, medical reports, discharge letters and findings of imaging techniques (computer tomography, sonography, magnet resonance imaging) were retrieved from the electronic patient documentation. Current and past hepatic findings were considered as well as those with an unknown status. For patients after liver resection or with evidence (current or past) of liver metastases, HCC, cholangiocellular carcinoma, hepatitides, cirrhosis, fibrosis, fatty liver or NASH, histological findings were searched and reviewed for fibrotic or cirrhotic changes. In patients with advanced fibrosis or cirrhosis, particular attention was paid to findings suggesting portal hypertension, such as ascites or collateral circulation. In a second step, the hepatic findings collected were assessed in terms of general relevance and relevance for drug therapy. The assessment was performed by an interprofessional team consisting of a hepatologist and two pharmacists and was based on medical and pharmacological considerations. The general clinical relevance of hepatic findings was assessed by an accomplished specialist in hepatology. Evaluation regarding the impact of hepatic findings on drug therapy was followed by a thorough consideration of the possible impact on pharmacokinetic and pharmacologic aspects. The aim of the assessment was to assign one of three possible categories to each previously identified hepatic finding in terms of its general relevance and relevance for drug therapy:
(1)
Relevant (hepatic finding has a general relevance or relevance for drug therapy without further assessment of the patient case);
(2)
Irrelevant (hepatic finding has no general relevance or relevance for drug therapy without further assessment of the patient case);
(3)
Patient individual judgement (to decide whether the hepatic finding has a general relevance or a relevance for drug therapy, a more in-depth consideration of the patient’s case must be made, taking into account the underlying disease, manifestation, current status, current symptoms, and severity of the hepatic finding).
The results of the interprofessional discussion were tabulated and served as a basis for the subsequent rating for the relevance of hepatic findings. Rating 1: assessment of all patients regarding general relevance of hepatic findings. Rating 2: assessment of all patients regarding relevance of hepatic findings for drug therapy. For this purpose, patients were assigned to the following categories: “Patients with relevant hepatic findings”, “Patients with hepatic findings relevant for drug therapy” and “Patients with hepatic findings not relevant”. The assignment into these categories was based on the current hepatic findings. The presence of hepatic findings with an unknown status and/or past hepatic findings only led to an assignment into the category “Patients with hepatic findings not relevant”. The only exceptions were conditions after liver transplantation and liver resection, which were recorded as past but considered and evaluated as current findings.
The MELD, NFS, APRI and FIB-4 were calculated for all patients with the parameters available at hospital admission. HI possibly relevant or relevant for drug therapy is expected for patients with CPS-B or C or an advanced fibrosis, thus, corresponding cutoff-values of the scores were used according to recommendations in the literature. The threshold for the MELD was set at 10 as MELD ≥ 10 has been correlated to CPS of B or C, indicating moderate to severe hepatic insufficiency [26]. MELD parameters were adjusted according to previous OPTN policies (effective date: 18 June 2020) prior to calculation [22]. The cutoff value for the NFS was accepted as >0.676 as this indicates an advanced fibrosis in NAFLD-patients according to Kleiner et al. [27,52]. In the original formula, the presence of abnormal fasting blood glucose levels or diabetes is considered [27]. Since it is not possible to determine if blood sampling was performed in fasting patients, only diabetes was included in the NFS calculation. For the FIB-4, the reported cutoff value of >3.25 for advanced fibrosis corresponding to Ishak 4–6 was used [28,53]. Wai et al. have determined a fibrosis cutoff value of >1.50 for the APRI, indicating a significant fibrosis according to Ishak 3–6, which was also accepted for this study [29,53]. Formulas and respective cutoff values are shown in Figure 1. Since the considered thresholds of the FIB-4, APRI, and NFS indicate significant fibrosis, the presence of low-grade or mild fibrosis was not considered an indicator for HI.
Continuous variables are presented with median and range, and categorical variables as frequency distribution. To assess the interrater reliability of the scores, the Kappa coefficient with related 95% confidence interval (95% CI) was calculated. Cohen´s Kappa was calculated for pairwise interrater reliability between the MELD, NFS, FIB-4 and APRI and each of the scores and Rating 1 and Rating 2. Fleiss´ Kappa was applied for the determination of interrater reliability between all scores. Statistical significance was accepted as p < 0.05. In order to correct for multiple testing, p-values were adjusted according to Holm–Bonferroni. The interpretation of Kappa followed the agreement classification of Landis and Koch [54]. Sensitivity, specificity, PPV, and NPV were calculated for each score regarding the identification of patients with hepatic findings relevant for drug therapy. Data were documented with Microsoft Excel® 2016 (Seattle, WA, USA) and statistical analyses were performed with IBM SPSS Statistics® version 29.0 (Armonk, NY, USA).

3. Results

Of the 200 patients, 102 (51%) were female and the median age was 62 years (20–86 years). For 115 (58%) patients, all four scores could be calculated. In detail, out of 200 patients, the MELD, APRI, FIB-4, and NFS were calculable for 180 (90%), 167 (84%), 166 (83%) and 115 (58%) patients, respectively. The most frequently missing parameters were bilirubin for the MELD (19, 10%), albumin for the NFS (80, 40%) and AST for the APRI and the FIB-4 (33, 17%). In comparison to the other scores, the MELD most frequently indicated a possible HI. Related to all 200 patients, the MELD indicated an HI for 12%, the APRI for 2% and the NFS and the FIB-4 for 5% of patients (Figure 2). Referring only to those patients, for whom a calculation of the respective score was possible, this corresponds to MELD, APRI, NFS, and FIB-4 indicating a possible HI in 13%, 2%, 8%, and 5% of patients, respectively (Table 1).

3.1. Categorisation of Hepatic Findings

A total of 548 hepatic findings were documented with at least one current, past or unknown status for 145 (73%) of the 200 screened patients. Of the 548 hepatic findings, 317 (58%) were categorized as current, 188 (34%) as past and 43 (8%) as unknown status. Overall, 76 different hepatic findings were identified and assessed by the interprofessional team (Table 2).
Of the 145 patients with a documented hepatic finding, 62 (43%) were classified as “Patients with a relevant hepatic finding”. For 18 (29%) of these patients, corresponding to 9% of all screened 200 patients, at least one hepatic finding was considered to probably have an impact on drug therapy. Hepatic findings were assessed as not relevant for 83 (57%) of the 145 patients (Table 1). At least one hepatic finding of unknown status was noted in 29 of 145 (20%) patients, including 26 patients with at least one additional current hepatic finding. Accordingly, 62 patients in Rating 1 had relevant hepatic findings and 138 patients irrelevant or no hepatic findings. In Rating 2, 18 patients had hepatic findings relevant for drug therapy, and 182 patients had hepatic findings not relevant for drug therapy or no findings.

3.2. Overall Interrater Reliability of Hepatic Scores

Testing for interrater reliability by Fleiss´ Kappa calculation between MELD, NFS, APRI and FIB-4 indicating a potential HI was possible for 115 patients. A Kappa value of 0.172 (95% CI 0.098–0.246, p < 0.001, p-value adjusted according to Holm–Bonferroni pB < 0.001) was calculated and interpreted as a slight agreement.

3.3. Pairwise Interrater Reliability of Hepatic Scores and Ratings

Next, we performed pairwise tests for interrater reliability between the four scores, and between the scores and the ratings. The results are presented in Table 3. Calculation between the MELD and the NFS was possible for 115 patients, the MELD and the FIB-4 for 166, the MELD and the APRI for 167, the NFS and the FIB-4 for 115, the NFS and the APRI for 115, and the FIB-4 and the APRI for 166 patients, respectively. The interrater reliability between the FIB-4 and the NFS reached moderate agreement. The pairwise agreements between the remaining scores were classified as slight for the MELD and the NFS, the MELD and the FIB-4, the MELD and the APRI, and the APRI and the FIB-4, and as poor for the NFS and the APRI.
Pairwise interrater reliability between the scores MELD, FIB-4, APRI, NFS and Ratings 1 and 2 was calculable for 180, 166, 167 and 115 patients, respectively. Fair agreement was found between Rating 2 and the MELD and FIB-4. Slight agreement was determined for the NFS and APRI and Rating 2 (Table 3). The Kappa values for interrater reliability indicate a slight agreement between each of the scores MELD, FIB-4 and APRI and Rating 1. For the NFS and Rating 1, poor agreement was found (Table 3).

3.4. Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value

Due to differences in the availability of the four scores tested, the calculations were performed in 180 cases for the MELD, 167 for the APRI, 166 for the FIB-4, and 115 for the NFS. The MELD had the highest values for sensitivity (41.2%) and the NPV (93.6%), and APRI had the highest values for specificity (99.3%) and PPV (66.7). The results for the scores used as screening tools for patients with hepatic findings relevant for drug therapy are presented in Table 4.

4. Discussion

To our knowledge, this is the first study testing the suitability of four different established liver scores as possible screening tools for hepatic impairment at hospital admission by pharmacists. In our retrospective surgical patient cohort, the number of patients identified to have possible HI differed widely from 13% by the MELD to 8% by the NFS, 5% by the FIB-4, and 2% by the APRI. Interrater reliability between the scores was only poor or slight, with the exception of a moderate agreement between the FIB-4 and the NFS. Likewise, only poor or slight agreement was found between the tested scores and the categorization of patients as having relevant hepatic findings or not. However, for the assessment of patients regarding relevance of hepatic findings for drug therapy, fair agreement was found for the FIB-4 and MELD, and slight agreement for the NFS and APRI. In addition, for all scores, values of 90.2% and higher were reached for specificity and the NPV, whereas the highest values for sensitivity and the PPV were only 41.2% (MELD) and 66.7% (APRI) regarding the identification of patients with hepatic findings relevant for drug therapy. Moreover, the study underlines the diversity of liver and bile diseases and symptoms, and questions how these can be represented by the considered liver scores. In sum, none of the scores tested in this study achieved excellent results and can thus be recommended to be used as an isolated screening tool to identify patients with HI at risk for hepatic DRP by pharmacists.
As described, only a slight overall agreement was found between the investigated scores regarding screening for HI. A possible explanation may be found in the different parameters of the scores, reflecting various etiologies of liver diseases. In fact, all scores have been developed for special hepatic patient groups [27,28,29,55,56], which might lead to the under- or overestimation of the clinical status of a patient when applying the scores for other etiologies. However, the use of these scores in different settings has been tested by others [30,31,32,33,57].
In addition, the results regarding the identification of patients with liver or bile diagnoses, symptoms or abnormalities relevant for drug therapy were unsatisfying. Compared to all scores, the greatest agreement with the assessment of patients regarding these hepatic findings was found for the MELD and FIB-4 (fair agreement). However, in the clinical settings, low agreement levels are considered unsatisfactory and not safe [58]. The sensitivity and specificity of the scores also highlight that none of the scores can be applied without restrictions. Although specificity values > 90% were calculated for each of the scores, the sensitivity calculations with a maximum value of 41.2% for the MELD show that only an insufficient number of patients with an HI relevant for drug therapy can be identified. Furthermore, none of the scores reflect the complex pathological changes affecting the pharmacokinetics of drugs.
The calculation of the scores was limited by the availability of parameters. In this study, the MELD, the APRI, and the FIB-4 could be calculated for the majority of patients, whereas the NFS was only available for 58% of patients. While the MELD, APRI and FIB-4 can be calculated automatically, NFS calculation requires knowledge of the diabetic status, which can only be obtained by screening the patient’s file. Alternatively, fasting blood glucose levels are needed, but the timing of standard blood sampling regarding meals is usually unknown and glucose levels in laboratory data of little benefit. Therefore, the NFS calculation is more time-consuming compared to the other scores. In addition, interfering factors of parameters of the MELD [49], the APRI, the FIB-4 and the NFS [59] might impact automatic interpretation.
Due to the broad variety of liver and bile diseases, as also seen in this study, a differentiated view on the potential effects on drug therapy is required, and the classification of liver and bile diseases relevant for drug therapy remains challenging. Therefore, the subdivision of hepatic findings made here can be questioned, especially since it strongly depends on the completeness of medical records and subjective clinical judgement. However, our ratings were based on an in-depth interprofessional discussion considering the possible effects of hepatic findings on pharmacokinetics, pharmacodynamics, and the choice of drugs. Nevertheless, there is still uncertainty regarding the effects of several hepatic findings. As an example, it remains challenging to assess the effects that HCC and fatty liver may have on CYP enzymes [36,40]. In addition, pathological changes following severe liver disease, like portal hypertension, ascites or hepatic encephalopathy, have to be considered for drug choice and dosage to prevent DRP and adverse effects in hepatic patients [60].
Moreover, alternative screening tools, such as screening for elevated LLP and ALT or the use of analytical CPS (considering laboratory parameters of CPS only) as tested by our group and others, did not perform well in the identification of patients at risk, and may under- or overestimate the number of patients with HI [49,61,62]. Considering this and the results of our study presented here, we conclude that a safe identification of patients suffering from HI at hospital admission can currently only be ensured by a trained pharmacist who is able to identify all patient-specific aspects related to a possible HI. Nevertheless, an initial automated screening using liver scores or other easily accessible parameters would still be desirable, and could facilitate the identification of patients at hepatic risk. It remains to be seen whether a combination of different scores or a solution involving artificial intelligence will lead to a more reliable assessment. Regardless of its insufficient performance in this study, the MELD should be emphasized, as it achieved the highest sensitivity, and along with the FIB-4 a fair agreement regarding the identification of patients with hepatic findings relevant for drug therapy. In addition, its parameters were available for most patients. Moreover, the MELD can be linked to the CPS [26], which is frequently used for drug adjustment to liver function following recommendations by the EMA and FDA [20,21]. An automatic MELD calculation for all inpatients by clinical decision support systems has indeed been suggested [57]. However, our study suggests that the use of the MELD as an indicator for HI cannot be recommended. Interestingly, a different version of the MELD has been recently incorporated into the OPTN guidelines for prioritization in liver allocation, including bilirubin, INR, serum creatinine, serum sodium, albumin and sex [24,25]. It remains to be seen whether this score is advantageous in the identification of patients with HI and in need of drug adjustment compared with the MELD considered in this study.
The lack of verification of the accuracy of the interprofessional assessment of the hepatic findings themselves, or rather the lack of verification of the accuracy of the rating of patients based on this assessment, is the key limitation of this study. Especially the rating of patients requiring an individual patient judgement depends on the experience level of the pharmacist or physician, and can be biased. However, as there is neither an established and validated guideline nor a tool to identify patients at hepatic risk to this date, our approach of assessing hepatic findings interprofessionally before rating the patients seemed to be the best currently available option, as it reflects the pharmaceutical and hepatological point of view. The study is further limited by its retrospective design, the availability of laboratory parameters and the incomplete documentation of liver or bile diagnoses, symptoms or abnormalities. Hepatic findings like “fatty liver” are often mentioned but poorly characterized, thus, their possible impact on drug therapy remains unclear. In addition, missing knowledge of patients´ liver disease is a known clinical problem [14,63]. A routinely performed screening for hepatic impairment by a trained pharmacist may help to improve this matter. Laboratory parameters of the scores can be influenced by multiple extrahepatic reasons, which could lead to false alerts suggesting hepatic impairment [49,59]. Moreover, the age might have an impact on the reliability of some scores. Thus, the use of the NFS and the FIB-4 in NAFLD-patients ≤ 35 years, for example, has been questioned [64]. In this study, the cutoff values of the original publications were applied, but the use of alternative thresholds can be discussed as well. For instance, optimized thresholds for the FIB-4 indicating or excluding significant fibrosis in NAFLD patients [65] and alternative cutoff values for the APRI in patients with HCV infection [66] have been published.

5. Conclusions

To summarize, the single use of the MELD, NFS, APRI or FIB-4 cannot be recommended as suitable screening tools for the safe identification of patients with hepatic impairment by pharmacists at hospital admission. Currently, reliable identifications of patients can only be facilitated by a trained pharmacist and an intensive review of the patient’s case, as performed in this study. It remains to be seen whether a combination of different scores will lead to a more reliable identification of patients at risk for hepatic drug-related problems. Further prospective studies testing alternative screening instruments for this purpose would therefore be desirable.

Author Contributions

Conceptualization, K.G., D.S. and A.B.; methodology, K.G., D.S., A.B., H.M., T.D. and E.G.; formal analysis, K.G.; investigation, K.G.; data curation, K.G.; writing—original draft preparation, K.G. and D.S.; writing—review and editing, H.M., A.B., T.D. and E.G.; visualization, K.G.; supervision, D.S., H.M., A.B., T.D. and E.G.; project administration, D.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the University Hospital Munich (protocol code 23-0186, 28 March 2023).

Informed Consent Statement

Patient consent was waived due to the retrospective design of the study.

Data Availability Statement

The datasets used during the current study are available from the corresponding author on reasonable request.

Acknowledgments

This work was supported by the interprofessional Doctoral Program Clinical Pharmacy, Ludwig Maximilians University Munich, Germany and contains parts of the doctoral thesis of Kathrin Golla.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Delco, F.; Tchambaz, L.; Schlienger, R.; Drewe, J.; Krahenbuhl, S. Dose adjustment in patients with liver disease. Drug Saf. 2005, 28, 529–545. [Google Scholar] [CrossRef]
  2. Verbeeck, R.K. Pharmacokinetics and dosage adjustment in patients with hepatic dysfunction. Eur. J. Clin. Pharmacol. 2008, 64, 1147–1161. [Google Scholar] [CrossRef]
  3. Cobbina, E.; Akhlaghi, F. Non-alcoholic fatty liver disease (NAFLD)—Pathogenesis, classification, and effect on drug metabolizing enzymes and transporters. Drug Metab. Rev. 2017, 49, 197–211. [Google Scholar] [CrossRef]
  4. Betrosian, A.P.; Agarwal, B.; Douzinas, E.E. Acute renal dysfunction in liver diseases. World J. Gastroenterol. 2007, 13, 5552–5559. [Google Scholar] [CrossRef]
  5. Garcia-Tsao, G.; Parikh, C.R.; Viola, A. Acute kidney injury in cirrhosis. Hepatology 2008, 48, 2064–2077. [Google Scholar] [CrossRef]
  6. Cremers, I.; Ribeiro, S. Management of variceal and nonvariceal upper gastrointestinal bleeding in patients with cirrhosis. Therap. Adv. Gastroenterol. 2014, 7, 206–216. [Google Scholar] [CrossRef]
  7. Franz, C.C.; Hildbrand, C.; Born, C.; Egger, S.; Ratz Bravo, A.E.; Krahenbuhl, S. Dose adjustment in patients with liver cirrhosis: Impact on adverse drug reactions and hospitalizations. Eur. J. Clin. Pharmacol. 2013, 69, 1565–1573. [Google Scholar] [CrossRef]
  8. Onder, G.; Petrovic, M.; Tangiisuran, B.; Meinardi, M.C.; Markito-Notenboom, W.P.; Somers, A.; Rajkumar, C.; Bernabei, R.; van der Cammen, T.J. Development and validation of a score to assess risk of adverse drug reactions among in-hospital patients 65 years or older: The GerontoNet ADR risk score. Arch. Intern. Med. 2010, 170, 1142–1148. [Google Scholar] [CrossRef]
  9. Naranjo, C.A.; Busto, U.; Mardones, R. Adverse drug reactions in liver cirrhosis. Eur. J. Clin. Pharmacol. 1978, 13, 429–434. [Google Scholar] [CrossRef]
  10. Pharmaceutical Care Network Europe Association. Classification for Drug Related Problems. Available online: https://www.pcne.org/upload/files/417_PCNE_classification_V9-1_final.pdf (accessed on 15 April 2023).
  11. Lorsbach, M.; Gillessen, A.; Revering, K.; Juhra, C. Information on medical history in the emergency department: Influence on therapy and diagnostic decisions. Med. Klin. Intensivmed. Notfallmedizin 2021, 116, 345–352. [Google Scholar] [CrossRef]
  12. Stiell, A.; Forster, A.J.; Stiell, I.G.; van Walraven, C. Prevalence of information gaps in the emergency department and the effect on patient outcomes. CMAJ 2003, 169, 1023–1028. [Google Scholar] [PubMed]
  13. Straub, C.; Teichert, D.; Blum, K.; Grandt, D. Loss of information in cross-sectoral treatment: Causes and solutions. Dtsch. Med. Wochenschr. 2022, 147, 269–272. [Google Scholar] [CrossRef] [PubMed]
  14. Marcellin, P.; Kutala, B.K. Liver diseases: A major, neglected global public health problem requiring urgent actions and large-scale screening. Liver Int. 2018, 38, 2–6. [Google Scholar] [CrossRef] [PubMed]
  15. Ratib, S.; Fleming, K.M.; Crooks, C.J.; Aithal, G.P.; West, J. 1 and 5 year survival estimates for people with cirrhosis of the liver in England, 1998–2009: A large population study. J. Hepatol. 2014, 60, 282–289. [Google Scholar] [CrossRef]
  16. Kwo, P.Y.; Cohen, S.M.; Lim, J.K. ACG Clinical Guideline: Evaluation of Abnormal Liver Chemistries. Am. J. Gastroenterol. 2017, 112, 18–35. [Google Scholar] [CrossRef]
  17. Child, C.G.; Turcotte, J.G. Surgery and portal hypertension. Major Probl. Clin. Surg. 1964, 1, 1–85. [Google Scholar]
  18. Pugh, R.N.; Murray-Lyon, I.M.; Dawson, J.L.; Pietroni, M.C.; Williams, R. Transection of the oesophagus for bleeding oesophageal varices. Br. J. Surg. 1973, 60, 646–649. [Google Scholar] [CrossRef]
  19. Tsoris, A.; Marlar, C.A. Use Of The Child Pugh Score In Liver Disease [Last Update 13 March 2023]. In StatPearls [Internet]; StatPearls Publishing: Treasure Island, FL, USA, 2023. Available online: https://www.ncbi.nlm.nih.gov/books/NBK542308/ (accessed on 8 September 2023).
  20. European Medicines Agency. Guideline on the Evaluation of the Pharmacokinetics of Medicinal Products in Patients with Impaired Hepatic Function. Available online: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-evaluation-pharmacokinetics-medicinal-products-patients-impaired-hepatic-function_en.pdf (accessed on 15 April 2023).
  21. Food and Drug Administration. Guidance for Industry Pharmacokinetics in Patients with Impaired Hepatic Function: Study Design, Data Analysis, and Impact on Dosing and Labeling. Available online: https://www.fda.gov/media/71311/download (accessed on 15 April 2023).
  22. Organ Procurement and Transplantation Network. Organ Procurement and Transplantation Network (OPTN) Policies (Effective Date: 18 June 2020). Available online: https://www.hhs.gov/guidance/sites/default/files/hhs-guidance-documents/optn_policies.pdf (accessed on 8 September 2023).
  23. Freeman, R.B., Jr.; Wiesner, R.H.; Harper, A.; McDiarmid, S.V.; Lake, J.; Edwards, E.; Merion, R.; Wolfe, R.; Turcotte, J.; Teperman, L.; et al. The new liver allocation system: Moving toward evidence-based transplantation policy. Liver Transpl. 2002, 8, 851–858. [Google Scholar] [CrossRef]
  24. United Network for Organ Sharing. Improvements to MELD, PELD, Status 1A and Status 1B to Take Effect July 13. Available online: https://unos.org/news/improvements-to-meld-and-peld-phase-1-data-entry-in-effect-june-15/ (accessed on 21 July 2023).
  25. Organ Procurement and Transplantation Network. Improving Liver Allocation: MELD, PELD, Status 1A, Status 1B. Available online: https://optn.transplant.hrsa.gov/media/3idbp5vq/policy-guid-change_impr-liv-alloc-meld-peld-sta-1a-sta-1b_liv.pdf (accessed on 21 July 2023).
  26. Albarmawi, A.; Czock, D.; Gauss, A.; Ehehalt, R.; Lorenzo Bermejo, J.; Burhenne, J.; Ganten, T.M.; Sauer, P.; Haefeli, W.E. CYP3A activity in severe liver cirrhosis correlates with Child-Pugh and model for end-stage liver disease (MELD) scores. Br. J. Clin. Pharmacol. 2014, 77, 160–169. [Google Scholar] [CrossRef]
  27. Angulo, P.; Hui, J.M.; Marchesini, G.; Bugianesi, E.; George, J.; Farrell, G.C.; Enders, F.; Saksena, S.; Burt, A.D.; Bida, J.P.; et al. The NAFLD fibrosis score: A noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology 2007, 45, 846–854. [Google Scholar] [CrossRef]
  28. Sterling, R.K.; Lissen, E.; Clumeck, N.; Sola, R.; Correa, M.C.; Montaner, J.; Sulkowski, M.S.; Torriani, F.J.; Dieterich, D.T.; Thomas, D.L.; et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 2006, 43, 1317–1325. [Google Scholar] [CrossRef] [PubMed]
  29. Wai, C.T.; Greenson, J.K.; Fontana, R.J.; Kalbfleisch, J.D.; Marrero, J.A.; Conjeevaram, H.S.; Lok, A.S. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology 2003, 38, 518–526. [Google Scholar] [CrossRef] [PubMed]
  30. Hagstrom, H.; Talback, M.; Andreasson, A.; Walldius, G.; Hammar, N. Ability of Noninvasive Scoring Systems to Identify Individuals in the Population at Risk for Severe Liver Disease. Gastroenterology 2020, 158, 200–214. [Google Scholar] [CrossRef] [PubMed]
  31. Huang, T.H.; Lin, M.T.; Wang, J.H.; Chang, K.C.; Yen, Y.H.; Kuo, F.Y.; Huang, C.C.; Hsiao, C.C.; Chiu, S.Y.; Lu, S.N.; et al. Clinical and novel application of FibroScan, FIB-4 and aspartate aminotransferase-to-platelet ratio index in liver fibrosis evaluation in patients with hepatocellular carcinoma and their roles in oesophageal variceal prediction. Int. J. Clin. Pract. 2021, 75, e13945. [Google Scholar] [CrossRef] [PubMed]
  32. Pranata, R.; Yonas, E.; Huang, I.; Lim, M.A.; Nasution, S.A.; Kuswardhani, R.A.T. Fibrosis-4 index and mortality in coronavirus disease 2019: A meta-analysis. Eur. J. Gastroenterol. Hepatol. 2021, 33, e368–e374. [Google Scholar] [CrossRef]
  33. Wan, M.; Xu, H.; Li, D.; Wang, L.; Li, X. Accuracy of gamma-glutamyl transpeptidase-to-platelet ratio (GPR), red cell distribution width (RDW), aspartate aminotransferase-to-platelet ratio index (APRI), and the fibrosis-4 index (FIB4) compared with liver biopsy in patients with drug-induced liver injury (DILI). Medicine 2021, 100, e24723. [Google Scholar] [CrossRef]
  34. Phang-Lyn, S.; Llerena, V.A. Biochemistry, Biotransformation [Last Update 14 August 2023]. In StatPearls [Internet]; StatPearls Publishing: Treasure Island, FL, USA, 2023. Available online: https://www.ncbi.nlm.nih.gov/books/NBK544353/ (accessed on 16 October 2023).
  35. Guengerich, F.P. Cytochrome p450 and chemical toxicology. Chem. Res. Toxicol. 2008, 21, 70–83. [Google Scholar] [CrossRef]
  36. Fisher, C.D.; Lickteig, A.J.; Augustine, L.M.; Ranger-Moore, J.; Jackson, J.P.; Ferguson, S.S.; Cherrington, N.J. Hepatic cytochrome P450 enzyme alterations in humans with progressive stages of nonalcoholic fatty liver disease. Drug Metab. Dispos. 2009, 37, 2087–2094. [Google Scholar] [CrossRef]
  37. Duthaler, U.; Bachmann, F.; Suenderhauf, C.; Grandinetti, T.; Pfefferkorn, F.; Haschke, M.; Hruz, P.; Bouitbir, J.; Krahenbuhl, S. Liver Cirrhosis Affects the Pharmacokinetics of the Six Substrates of the Basel Phenotyping Cocktail Differently. Clin. Pharmacokinet. 2022, 61, 1039–1055. [Google Scholar] [CrossRef]
  38. Frye, R.F.; Zgheib, N.K.; Matzke, G.R.; Chaves-Gnecco, D.; Rabinovitz, M.; Shaikh, O.S.; Branch, R.A. Liver disease selectively modulates cytochrome P450—Mediated metabolism. Clin. Pharmacol. Ther. 2006, 80, 235–245. [Google Scholar] [CrossRef]
  39. Jamwal, R.; Barlock, B.J. Nonalcoholic Fatty Liver Disease (NAFLD) and Hepatic Cytochrome P450 (CYP) Enzymes. Pharmaceuticals 2020, 13, 222. [Google Scholar] [CrossRef] [PubMed]
  40. Zhou, J.; Wen, Q.; Li, S.F.; Zhang, Y.F.; Gao, N.; Tian, X.; Fang, Y.; Gao, J.; Cui, M.Z.; He, X.P.; et al. Significant change of cytochrome P450s activities in patients with hepatocellular carcinoma. Oncotarget 2016, 7, 50612–50623. [Google Scholar] [CrossRef] [PubMed]
  41. Hoyumpa, A.M.; Schenker, S. Is glucuronidation truly preserved in patients with liver disease? Hepatology 1991, 13, 786–795. [Google Scholar] [CrossRef] [PubMed]
  42. Thakkar, N.; Slizgi, J.R.; Brouwer, K.L.R. Effect of Liver Disease on Hepatic Transporter Expression and Function. J. Pharm. Sci. 2017, 106, 2282–2294. [Google Scholar] [CrossRef] [PubMed]
  43. Drozdzik, M.; Oswald, S.; Drozdzik, A. Extrahepatic Drug Transporters in Liver Failure: Focus on Kidney and Gastrointestinal Tract. Int. J. Mol. Sci. 2020, 21, 5737. [Google Scholar] [CrossRef]
  44. Weersink, R.A.; Burger, D.M.; Hayward, K.L.; Taxis, K.; Drenth, J.P.H.; Borgsteede, S.D. Safe use of medication in patients with cirrhosis: Pharmacokinetic and pharmacodynamic considerations. Expert Opin. Drug Metab. Toxicol. 2020, 16, 45–57. [Google Scholar] [CrossRef]
  45. Marrero, J.A.; Ahn, J.; Rajender Reddy, K.; Americal College of Gastroenterology. ACG clinical guideline: The diagnosis and management of focal liver lesions. Am. J. Gastroenterol. 2014, 109, 1328–1347. [Google Scholar] [CrossRef]
  46. Tendler, D. Pathogenesis of nonalcoholic fatty liver disease. In UpToDate; Post, T.W., Ed.; UpToDate: Waltham, MA, USA, 2023; Available online: https://www.uptodate.com/contents/pathogenesis-of-nonalcoholic-fatty-liver-disease?search=angelman%20syndrome&topicRef=7706&source=related_link (accessed on 8 September 2023).
  47. Younossi, Z.M.; Golabi, P.; Paik, J.M.; Henry, A.; Van Dongen, C.; Henry, L. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): A systematic review. Hepatology 2023, 77, 1335–1347. [Google Scholar] [CrossRef]
  48. Estes, C.; Anstee, Q.M.; Arias-Loste, M.T.; Bantel, H.; Bellentani, S.; Caballeria, J.; Colombo, M.; Craxi, A.; Crespo, J.; Day, C.P.; et al. Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period 2016–2030. J. Hepatol. 2018, 69, 896–904. [Google Scholar] [CrossRef]
  49. Golla, K.; Mannell, H.; Benesic, A.; Dreischulte, T.; Grill, E.; Strobach, D. Feasibility of the MELD score as a screening tool for pharmacists to identify patients with impaired hepatic function at hospital admission. J. Clin. Pharm. Ther. 2022, 47, 676–684. [Google Scholar] [CrossRef]
  50. Arifin, W.N. Sample Size Calculator (Web). Available online: https://wnarifin.github.io/ssc/sskappa.html (accessed on 15 April 2023).
  51. Donner, A.; Eliasziw, M. A goodness-of-fit approach to inference procedures for the kappa statistic: Confidence interval construction, significance-testing and sample size estimation. Stat. Med. 1992, 11, 1511–1519. [Google Scholar] [CrossRef] [PubMed]
  52. Kleiner, D.E.; Brunt, E.M.; Van Natta, M.; Behling, C.; Contos, M.J.; Cummings, O.W.; Ferrell, L.D.; Liu, Y.C.; Torbenson, M.S.; Unalp-Arida, A.; et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 2005, 41, 1313–1321. [Google Scholar] [CrossRef] [PubMed]
  53. Ishak, K.; Baptista, A.; Bianchi, L.; Callea, F.; De Groote, J.; Gudat, F.; Denk, H.; Desmet, V.; Korb, G.; MacSween, R.N.; et al. Histological grading and staging of chronic hepatitis. J. Hepatol. 1995, 22, 696–699. [Google Scholar] [CrossRef] [PubMed]
  54. Landis, J.R.; Koch, G.G. The measurement of observer agreement for categorical data. Biometrics 1977, 33, 159–174. [Google Scholar] [CrossRef] [PubMed]
  55. Kamath, P.S.; Wiesner, R.H.; Malinchoc, M.; Kremers, W.; Therneau, T.M.; Kosberg, C.L.; D’Amico, G.; Dickson, E.R.; Kim, W.R. A model to predict survival in patients with end-stage liver disease. Hepatology 2001, 33, 464–470. [Google Scholar] [CrossRef] [PubMed]
  56. Malinchoc, M.; Kamath, P.S.; Gordon, F.D.; Peine, C.J.; Rank, J.; ter Borg, P.C. A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts. Hepatology 2000, 31, 864–871. [Google Scholar] [CrossRef]
  57. Roth, J.A.; Chrobak, C.; Schadelin, S.; Hug, B.L. MELD score as a predictor of mortality, length of hospital stay, and disease burden: A single-center retrospective study in 39,323 inpatients. Medicine 2017, 96, e7155. [Google Scholar] [CrossRef]
  58. McHugh, M.L. Interrater reliability: The kappa statistic. Biochem. Med. 2012, 22, 276–282. [Google Scholar] [CrossRef]
  59. Wai, J.W.; Fu, C.; Wong, V.W. Confounding factors of non-invasive tests for nonalcoholic fatty liver disease. J. Gastroenterol. 2020, 55, 731–741. [Google Scholar] [CrossRef]
  60. Hayward, K.L.; Weersink, R.A. Improving Medication-Related Outcomes in Chronic Liver Disease. Hepatol. Commun. 2020, 4, 1562–1577. [Google Scholar] [CrossRef]
  61. Strobach, D.; Poppele, A.; Mannell, H.; Andraschko, M.; Schiek, S.; Bertsche, T. Screening for impaired liver function as a risk factor for drug safety at hospital admission of surgical patients. Int. J. Clin. Pharm. 2020, 42, 124–131. [Google Scholar] [CrossRef] [PubMed]
  62. Párraga, L.P.; Bosch, P.V.; Martínez-López, I.; Delgado Sánchez, O.; Martínez Sotelo, J.; Calleja Hernández, M.Á. Evaluation of pharmaceutical intervention in patients with hepatic insufficiency in a hospital setting. Gastroenteroly Hepatol. 2018, 9, 241–247. [Google Scholar] [CrossRef]
  63. Scaglione, S.; Kliethermes, S.; Cao, G.; Shoham, D.; Durazo, R.; Luke, A.; Volk, M.L. The Epidemiology of Cirrhosis in the United States: A Population-based Study. J. Clin. Gastroenterol. 2015, 49, 690–696. [Google Scholar] [CrossRef] [PubMed]
  64. McPherson, S.; Hardy, T.; Dufour, J.F.; Petta, S.; Romero-Gomez, M.; Allison, M.; Oliveira, C.P.; Francque, S.; Van Gaal, L.; Schattenberg, J.M.; et al. Age as a Confounding Factor for the Accurate Non-Invasive Diagnosis of Advanced NAFLD Fibrosis. Am. J. Gastroenterol. 2017, 112, 740–751. [Google Scholar] [CrossRef]
  65. Shah, A.G.; Lydecker, A.; Murray, K.; Tetri, B.N.; Contos, M.J.; Sanyal, A.J.; Nash Clinical Research Network. Comparison of noninvasive markers of fibrosis in patients with nonalcoholic fatty liver disease. Clin. Gastroenterol. Hepatol. 2009, 7, 1104–1112. [Google Scholar] [CrossRef]
  66. Lin, Z.H.; Xin, Y.N.; Dong, Q.J.; Wang, Q.; Jiang, X.J.; Zhan, S.H.; Sun, Y.; Xuan, S.Y. Performance of the aspartate aminotransferase-to-platelet ratio index for the staging of hepatitis C-related fibrosis: An updated meta-analysis. Hepatology 2011, 53, 726–736. [Google Scholar] [CrossRef]
Jcm 12 06814 g001
Figure 1. Score formulas and cutoff-values [22,26,27,28,29,52,53].
Figure 1. Score formulas and cutoff-values [22,26,27,28,29,52,53].
Jcm 12 06814 g001
Jcm 12 06814 g002
Figure 2. Calculation of liver scores for 200 surgical patients at hospital admission and identification of patients with potential hepatic impairment (HI).
Figure 2. Calculation of liver scores for 200 surgical patients at hospital admission and identification of patients with potential hepatic impairment (HI).
Jcm 12 06814 g002
Table 1. Calculation of liver scores for predefined groups.
Table 1. Calculation of liver scores for predefined groups.
OverallPatients without Hepatic FindingsPatients with
Hepatic Findings
(Current, Past or Status Unknown)		Patients with
Hepatic Findings Not Relevant		Patients with
Relevant
Hepatic Findings		Patients with Hepatic Findings
Relevant for Drug Therapy
No. patients20055145836218
Female (n, %)102 (51%)27 (49%)75 (52%)51 (61%)24 (39%)7 (39%)
Age (years)62 (20–86)52 (20–86)64 (22–84)64 (23–84)64 (22–83)62 (38–79)
MELD
Available (n, %)180 (90%)44 (80%)136 (94%)77 (93%)59 (95%)17 (94%)
Median6.46.46.46.46.48.5
Range6.4–32.46.4–18.16.4–32.46.4–32.46.4–19.76.4–19.7
Indicating HI (n, %) a23 (13%)5 (11%)18 (13%)10 (13%)8 (14%)7 (41%)
NFS
Available (n, %)115 (58%)32 (58%)83 (57%)46 (55%)37 (60%)12 (67%)
Median−1.771−1.900−1.762−1.689−1.762−0.547
Range−9.176–1.635−6.889–1.635−9.176–1.592−9.176–1.566−8.631–1.592−4.430–1.592
Indicating HI (n, %) a9 (8%)2 (6%)7 (8%)5 (11%)2 (5%)2 (17%)
APRI
Available (n, %)167 (84%)39 (71%)128 (88%)69 (83%)59 (95%)16 (89%)
Median0.250.230.260.270.240.74
Range0.06–2.110.11–1.270.06–2.110.13–1.47 0.06–2.110.10–2.11
Indicating HI (n, %) a3 (2%)03 (2%)03 (5%)2 (13%)
FIB-4
Available (n, %)166 (83%)39 (71%)127 (88%)68 (82%)59 (95%)16 (89%)
Median1.341.011.401.471.381.56
Range0.18–6.480.18–2.860.32–6.480.44–5.460.32–6.480.48–6.48
Indicating HI (n, %) a9 (5%)09 (7%)4 (6%)5 (8%)5 (31%)
a In relation to the number of patients for whom the respective score was available.
Table 2. All documented hepatic findings (liver/bile diagnoses/symptoms/abnormalities) of all included patients (n = 200). Hepatic findings were subdivided into current, past and status unknown. The last two columns indicate the classification as relevant and relevant for drug therapy, assuming that the hepatic finding is currently present.
Table 2. All documented hepatic findings (liver/bile diagnoses/symptoms/abnormalities) of all included patients (n = 200). Hepatic findings were subdivided into current, past and status unknown. The last two columns indicate the classification as relevant and relevant for drug therapy, assuming that the hepatic finding is currently present.
Liver or Bile
Diagnoses/
Symptoms/
Abnormalities		OverallCurrentPastStatus
Unknown		Relevant Hepatic
Finding		Hepatic Finding
Relevant for Drug Therapy
Cholestasis-associated hepatic findings a6837247PIJPIJ
Dilatation of bile ducts261376PIJPIJ
Biliary stent151050PIJPIJ
Prominent bile ducts9801PIJPIJ
Stone passage6060YesPIJ
Choledochal stenosis4310YesPIJ
Percutaneous transhepatic
cholangiodrainage		3300PIJPIJ
Icterus3030YesYes
Cholestatic itching1010YesYes
Bile duct obstruction1010YesPIJ
Small, asymptomatic benign
neoplasms		666510NoNo
Cholecystitis4022162PIJNo
Post cholecystectomy39n.a.39n.a.NoNo
Fatty liver383305PIJPIJ
Cholecystolithiasis343040PIJNo
Liver metastases261970YesPIJ
Liver fibrosis/Fibrotic changes211551PIJPIJ
Cholestasis18765YesPIJ
Post liver resection b16n.a.16n.a.PIJPIJ
<1 month ago0n.a.0n.a.PIJPIJ
>1 month ago16n.a.16n.a.PIJPIJ
Focal fatty change111100NoNo
Gall bladder sludge11830NoNo
Ascites10352YesPIJ
Bilioma/Bile leakage9450YesNo
Cholangiocarcinoma8530YesPIJ
Hepatomegaly6510PIJPIJ
Portal vein stenosis6330YesPIJ
Portal hypertension6312YesYes
Gallbladder hydrops6222YesPIJ
Cholangitis6150YesPIJ
Hepatopathy5302YesPIJ
Choledocholithiasis5131YesPIJ
Post biliodigestive anastomosis
formation		5n.a.5n.a.NoNo
Prominent lymph nodes porta hepatis4400PIJNo
NASH4400YesYes
Abnormal lymph nodes liver hilus4310PIJNo
Hepatocellular carcinoma4310YesPIJ
Portal vein thrombosis
(+/− cavernous transformation)		4301YesPIJ
Periportal edema4112PIJPIJ
Esophageal varices4112YesYes
Hepatitis B4031YesPIJ
Biliary pancreatitis4040YesPIJ
Fluid retention post liver resection3210PIJNo
Congested liver3102YesPIJ
Liver cirrhosis2200YesYes
Symptomatic liver cyst2200YesPIJ
Cholesteatosis2200NoNo
Prominent liver lobe2200PIJNo
Portal-hypertensive gastropathy2200YesYes
Irregularities of liver surface/liver
capsule		2101PIJPIJ
Hepatitis A2020YesPIJ
Hepatitis without further definition2020YesPIJ
Liver abscess2020YesPIJ
Hepatitis C2020YesPIJ
Hepatosplenomegaly2020YesPIJ
Post liver transplantation2n.a.2n.a.PIJPIJ
Symptomatic liver adenoma1100YesPIJ
Gallbladder polyp1100PIJNo
Lipofucinosis of the liver1100NoNo
Superinfected necrosis1100YesPIJ
Superinfected hematoma1100YesPIJ
Liver parenchymal damages1100YesPIJ
VRE-detection in bile1100YesNo
Intrahepatic hemorrhage1010YesPIJ
Tumorous gall bladder1010YesPIJ
Tumor infiltration of the liver1010YesPIJ
Symptomatic polycystic
liver disease		1010YesPIJ
Symptomatic intrahepatic bile duct cyst1010YesPIJ
Gallbladder carcinoma1010YesPIJ
Biliary fistula1010YesNo
Bile duct injury1010YesNo
Hepatic encephalopathy1010YesYes
Post portal bifurcation resection1n.a.1n.a.NoNo
Liver vein thrombosis1001YesPIJ
Ischemic Type Biliary Lesions1001YesPIJ
Liver insufficiency1001YesYes
Collateral circulation1001YesYes
Intrahepatic fluid retention1001PIJPIJ
∑ c54831718843
n.a.: not applicable; PIJ: patient individual judgement depending on the underlying disease, manifestation, current status, current symptoms, and severity. a other causes possible; b 10 patients post hemihepatectomy, 6 of the 16 patients had both a hemihepatectomy and a partial liver resection; c the categories “Cholestasis-associated hepatic findings” and “Post liver resection” represent superordinate categories and are therefore not added to the sum.
Table 3. Pairwise interrater reliability between MELD, NFS, APRI and FIB-4, and between the scores and Ratings 1 and 2. p-values were adjusted according to Holm–Bonferroni (pB). Agreement interpretation followed Landis and Koch.
Table 3. Pairwise interrater reliability between MELD, NFS, APRI and FIB-4, and between the scores and Ratings 1 and 2. p-values were adjusted according to Holm–Bonferroni (pB). Agreement interpretation followed Landis and Koch.
MELDNFSFIB-4APRI
NFSκ = 0.183
(p = 0.043, pB = 0.345)
(95% CI −0.066–0.432)		---
FIB-4κ = 0.142
(p = 0.045, pB = 0.345)
(95% CI −0.060–0.344)		κ = 0.555
(p < 0.001, pB < 0.001)
(95% CI 0.261–0.849)		--
APRIκ = 0.053
(p = 0.277, pB = 1.000)
(95% CI −0.092–0.198)		κ = −0.016
(p = 0.770, pB = 1.000)
(95% CI −0.043–0.011)		κ = 0.143
(p = 0.031, pB = 0.281)
(95% CI −0.141–0.427)		-
Rating 1: Identification of patients with relevant hepatic findingsκ = 0.014
(p = 0.826, pB = 1.000)
(95% CI −0.111–0.139)		κ = −0.045
(p = 0.506, pB = 1.000)
(95% CI −0.165–0.075)		κ = 0.058
(p = 0.197, pB = 1.000)
(95% CI −0.040–0.156)		κ = 0.065
(p = 0.018, pB = 0.180)
(95% CI −0.006–0.136)
Rating 2: Identification of patients with hepatic findings relevant for drug therapyκ = 0.271
(p < 0.001, pB = 0.003)
(95% CI 0.067–0.475)		κ = 0.111
(p = 0.228, pB = 1.000)
(95% CI −0.128–0.350)		κ = 0.355
(p < 0.001, pB < 0.001)
(95% CI 0.104–0.606)		κ = 0.186
(p = 0.001, pB = 0.008)
(95% CI −0.049–0.421)
Agreement interpretation according to Landis and Koch [54]: Jcm 12 06814 i001 moderate agreement; Jcm 12 06814 i002 fair agreement; Jcm 12 06814 i003 slight agreement; Jcm 12 06814 i004 poor agreement.
Table 4. Sensitivity, specificity, positive predictive value, and negative predictive value of the tested scores regarding identification of patients with hepatic findings relevant for drug therapy. For the MELD 180 patients, for the NFS 115 patients, for the FIB-4 166 patients, and for the APRI 167 patients were considered. Prevalence indicates the occurrence of patients with hepatic findings relevant for drug therapy in the groups studied.
Table 4. Sensitivity, specificity, positive predictive value, and negative predictive value of the tested scores regarding identification of patients with hepatic findings relevant for drug therapy. For the MELD 180 patients, for the NFS 115 patients, for the FIB-4 166 patients, and for the APRI 167 patients were considered. Prevalence indicates the occurrence of patients with hepatic findings relevant for drug therapy in the groups studied.
MELDNFSFIB-4 APRI
Prevalence9.4%10.4%9.6%9.6%
Sensitivity41.2%16.7%31.3%12.5%
Specificity90.2%93.2%97.3%99.3%
Positive predictive value30.4%22.2%55.6%66.7%
Negative predictive value93.6%90.6%93.0%91.5%
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MDPI and ACS Style

Golla, K.; Benesic, A.; Mannell, H.; Dreischulte, T.; Grill, E.; Strobach, D. Hepatic Impairment as a Risk Factor for Drug Safety: Suitability and Comparison of Four Liver Scores as Screening Tools. J. Clin. Med. 2023, 12, 6814. https://doi.org/10.3390/jcm12216814

AMA Style

Golla K, Benesic A, Mannell H, Dreischulte T, Grill E, Strobach D. Hepatic Impairment as a Risk Factor for Drug Safety: Suitability and Comparison of Four Liver Scores as Screening Tools. Journal of Clinical Medicine. 2023; 12(21):6814. https://doi.org/10.3390/jcm12216814

Chicago/Turabian Style

Golla, Kathrin, Andreas Benesic, Hanna Mannell, Tobias Dreischulte, Eva Grill, and Dorothea Strobach. 2023. "Hepatic Impairment as a Risk Factor for Drug Safety: Suitability and Comparison of Four Liver Scores as Screening Tools" Journal of Clinical Medicine 12, no. 21: 6814. https://doi.org/10.3390/jcm12216814

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