Algorithms for early detection of silent liver fibrosis in the primary care setting – a scoping review

Figures & data

Figure 1. Fatty liver starts off as simple steatosis, which further can trigger inflammation and damage of the liver cells, potentially resulting in fibrosis of the liver. Further progression of the disease may lead to the end-stage of irreversible liver cirrhosis.

Figure 2. Flowchart showing the details of the eligibility process.

Table 1. Overview of the included papers.

Author	Year	Journal	Country	Algorithm	N
Grattagliano [15]	2013	Annals of Hepatology	Italy	Fibromax, FLI	282
Tavaglione [17]	2022	Clinical Gastroenterology and Hepatology	Italy,Finland UK	FNI	6949
Sripongpun [18]	2022	Hepatology	USA	SAFE score	13039
Cheuk-Fung Yip [19]	2017	Alimentary Pharmacology and Therapeutics	Hong Kong	NAFLD ridge score	922
Hydes [20]	2021	BMJ	England	CIRRUS	594 265
Blanes-Vidal [21]	2022	Nature portfolio	Denmark	LiverAID XXS (FIB-4, Forns index, APRI, LSM)	3352
McLernon [22]	2014	BMJ	Scotland	ALFI	107 630
Graupera [23]	2022	Clinical Gastroenterology and Hepatology	Spain Hong Kong Denmark England France	FIB-4, NAFLD Fibrosis Score	5129
Åberg [24]	2022	Journal of Hepatology	Finland, UK Denmark	CLivD score	33 867
Coste [25]	2022	Digestive and Liver Disease	Spain	FLI, NAFLD Fibrosis score, MS Critieria, Forns index, APRI, BARD, FIB-4, eLIFT, AST/ALT	3148
Younassi [26]	2021	Clinical and Translational Gastroenterology	USA	APRI, FIB-4, NAFLD Fibrosis score LSM	7555
Eslam [27]	2021	The American Journal of Gastroenterology	Hong Kong Germany	ADAPT, LSM	1032
Mansour [28]	2021	JHEP Reports	England	FIB-4, LSM	467
Harman [29]	2015	BM	England	AST/ALT ratio, BARD score LSM	504
Srivastava [30]	2019	Journal of Hepatology	England	FIB-4, LSM, ELF	3012
(Anonymized) [31]	2018	Gastroenterology	Denmark	ELF, FibroTest, LSM	289
Shaheen [32]	2020	CMAJ	Canada	2D Shear-wave elastography, FIB-4	2084
Bedogni [33]	2010	BMC GastroenterologyItaly	Italy	LAP	588
Zelber-Sagi [34]	2012	European Journal of Gastroenterology and Hepatology Israel	Israel	Adiponectin, HDL, Leptin	375

Table 2. Overview of algorithms for the diagnosis of fatty liver disease and their characteristics and performance.

Algorithms	Characteristics	Performance reporting
ADAPT [27]	Age, T2D, Plasma Pro-C3 (N-terminal type III collagen propeptide), platelets $ADAPT=exp\left({log}_{10}\left(\begin{array}{c}\frac{\left(age\times PROC3\right)}{\sqrt{Platelets}}\end{array}\right)\right)+Diabetes$	≥F3 AUROC .88 (95% CI .77–.95) ≥F2 AUROC 0.81 (95% CI 0.76–0.86)
ALFI model (The Algorithm for Liver Function Investigations) [22]	Bilirubin, albumin, ALP, GGT, either ALT or AST Model not available	Risk of having a liver disease diagnosis AUROC 0.78(95% Cl 0.72–0.84)
APRI (The AST to Platelet Ratio Index) [21,25,26]	AST, platelets APRI = (AST in IU/L)/(AST Upper Limit of Normal in IU/L)/(Platelets in 10^9/L)	≥F2 AUROC 0.71 (95% CI 0.64–0.85)
AST/ALT-ratio [25,29]	AST/ALT	≥F2 AUROC 0.51 (95% CI 0.50–0.74)
BARD score [25,29]	AST/ALT-ratio, BMI, T2D Model not available	≥F2 AUROC 0.71 (95% CI 0.58–0.85)
CIRRUS (The CIRRhosis Using Standard tests) [20]	Albumin, creatinine, bilirubin, MCV, Na, platelets, protein Predicted risk (PR)=exp (alb*−0.210+cr*−0.010+tb* 0.015+mcv *0.083+na*−0.067+plt*−0.018+tp* 0.145 + 0.886)/(1+exp (alb*−0.210+cr*−0.010+tb* 0.015+mcv *0.083+na*−0.067+plt*−0.018+tp* 0.145 + 0.886)) Albumin (alb), g/L; CIRRUS, CIRRhosis Using Standard tests; creatinine (cr), μmol/L; total bilirubin (tb), μmol/L; mean corpuscular volume (mcv), fL; sodium (na), mmol/L; platelet count (plt), 109/L; total protein, g/L.	Liver-related event within 5 years AUROC 0.84 (95% CI 0.82–0.86)
CLivD (The Chronic Liver Disease score) [24]	Age, gender, alcohol-use, waist-hip-ratio, diabetes, smoking, (GGT) Model not available	Advanced liver disease AUROC 0.78 (95% CI 0.68–0.87)
ELF (Enhanced Liver Fibrosis test) [30,31]	Hyaluronic acid, procollagen III, amino-terminal peptide, tissue inhibitor of matrix metalloproteinase I ELF score = 2.494 + 0.846*ln(HA) + 0.735*ln(PIIINP) + 0.391*ln(TIMP-1)	≥F3 AUROC 0.92 (95% Cl 0.89–0.96)
eLIFT [25]	Age, gender, GGT, AST, platelet count, prothrombin time Model not available	≥F2 AUROC 0.62 (95% CI 0.50–0.74)
FIB-4 (Fibrosis-4) [21,23,25,26,28,30,32]	Age, AST, platelets, ALT FIB-4 = (age x AST-level)/(Platelet count x √ALT)	≥F2 AUROC 0.65 (95% CI 0.53–0.78)
Fibromax* (*original content, updated to NASH FibroTest) [15]	*haptoglobin, bilirubin, gamma-GT, A1-apolipoprotein, alpha2-macroglobulin Model not available	≥F3 AUROC 0.90 (95% CI 0.86–0.94) (Fibrotest)
FLI (Fatty Liver Index) [15,25]	Waist circumference, BMI, TG, GGT	AUROC 0.89 (95%CI 0.84–0.95)
	$FLI=\left(\frac{e^{0.953\times loge\left(triglycerides\right)+0.139\times BMI+0.718\times loge\left(GGT\right)+0.053\times waistcircumference-15.745}}{1+e^{0.953\times loge\left(triglycerides\right)+0.139\times BMI+0.718\times loge\left(GGT\right)+0.053\times waistcircumference-15.745}}\right)\times 100$
FNI (The Fibrotic NASH Index) [17]	AST, HDL, HbA1c $FNI=\frac{e^{\left(\alpha +\beta +\gamma +\delta \right)}}{1+e^{\left(\alpha +\beta +\gamma +\delta \right)}};$ $\alpha$ = −10.33; $\beta$ = +2.54 × ln (AST$\left[U/L\right]$); $\gamma$ = +3.86 × ln ($HbA1c\left[\mathrm{\%}\right]$); $\delta$ = −1.66 × ln ($HDL\left[mg/dL\right]$);	AUROC 0.78 (95% CI 0.71–0.85)
Forns Index [21,25]	Age, platelets GGT, cholesterol. Forns index = (7.811 ‐ 3.131.ln(PLT count) + 0.781.ln(gamma glutamyl‐transferase (GGT)) + 3.467.ln(age) ‐ 0.014.(cholesterol))	≥F2 AUROC 0.78 (95% CI 0.67–0.88)
GGT to platelet ratio [31]	GGT/platelets
LAP (Lipid accumulation product) [33]	Waist circumference, fasting TG, sex LAP=[waist circumference (cm) − 58] × triglycerides (mmol/l)	Severe steatosis OR 4.28(95% CI 3.28–5.58)
Liver Aid XXS [21]	GGT, platelets, ALT, AST, cholesterol, albumin, ALP, INR, Na Model not available	LSM >8 kPa AUROC 0.86
NAFLD Fibrosis score [23,25,26]	Age, hyperglycemia, BMI, platelets, albumin, AST/ALT-ratio NAFLD fibrosis score = −1.675 + 0.037 × age (year) + 0.094 × BMI (kg/m^2) + 1.13 × IFG/diabetes (yes = 1, no = 0) + 0.99 × AST/ALT ratio − 0.013 × platelet count (×10^9/L) − 0.66 × albumin (g/dL)	≥F2 AUROC 0.82 (95%CI 0.74–0.90)
NAFLD Ridge score [19]	ALT, HDL, TG, HbA1c, WBC, hypertension NAFLD ridge score = −0.614 + 0.007 $\times$ ALT −0.214 $\times$ HDL – C + 0.053 $\times$ Triglyceride +0.144 $\times$ Hb$A_{1c}$+ 0.032 $\times$ WBC +0.132 $\times$ HT.	Intrahepatic fat content ±5%AUROC .88 (95% CI 0.84–0.91)
SAFE (The steatosis associated fibrosis estimator score) [18]	Age, BMI, T2D, platelets, ALT, AST, globulins SAFE = (2. 97 ×age) + (5. 99× BMI BMI > 40 set to 40]) + (62.85×diabetes |0 if absent, 1 if present) + (154. 85 × Ln(AST)) - (58.23 ×Ln(ALT)) + (195.48 × Ln(globulins, g/dL)) (141.61 × Ln(platelets, 10°/L)) − 75.	Distinguish ≥F2 from F0/1 AUROC ≥0.80

Table

Focus	Use of laboratory tests, biomarkers or algorithms for detection of patients with fatty liver disease
Population	Patients in general practice
Limitations	Articles written in English, German and Scandinavian languages

Table

The search strategy for Embase <1974 to 2022 May 26>
No.	Search strategy	Results
#1	exp fatty liver/	96,679
#2	(steatohepatitis or steatohepatitides or steatohepatopathy or steato-hepatosis or steatohepatosis or steatotic hepatopathy or hepatic lipidosis or hepatic steatosis or hepato-steatosis or hepatolipidosis or hepatosteatosis or ((fat or fatty or steatosis or steatotic or fibrosis or cirrhosis or lipidosis) adj3 liver*)).ti,ab,kf.	176,739
#3	1 or 2	199,084
#4	exp primary health care/	191,552
#5	(primary care or primary health-care or primary healthcare or first line care or primary medical care).ti,ab,kf.	214,643
#6	4 or 5	268,838
#7	biological marker/	383,158
#8	(biomarker* or surrogate end-point* or surrogate endpoint* or ((diagnostic or diagnosis or laboratory or lab) adj2 (test* or tool*)) or ((biologic* or laboratory or serum or clinical or viral or biochemical or immunologic or immune or surrogate) adj2 marker*)).ti,ab,kf.	888,129
#9	algorithm/	320,519
#10	(algorism* or algorithm* or algorhythm*).ti,ab,kf.	401,290
#11	exp artificial intelligence/ or exp machine learning/	329,132
#12	(((machine or artificial*) adj2 (intelligen* or learning*)) or (neural* adj2 net*) or natural language processing* or NLP or (data adj2 mining*) or (pattern* adj2 (recognition* or recogniz* or recognis*))).ti,ab,kf.	219,247
#13	7 or 8 or 9 or 10 or 11 or 12	1,740,654
#14	3 and 6 and 13	262
	Number of articles selected before removing duplicates	262

Table

The search strategy for Ovid MEDLINE(R) ALL <1946 to May 26, 2022>
No.	Search strategy	Results
#1	exp Fatty Liver/	41,440
#2	(steatohepatitis or steatohepatitides or steatohepatopathy or steato-hepatosis or steatohepatosis or steatotic hepatopathy or hepatic lipidosis or hepatic steatosis or hepato-steatosis or hepatolipidosis or hepatosteatosis or ((fat or fatty or steatosis or steatotic or fibrosis or cirrhosis or lipidosis) adj3 liver*)).ti,ab,kf.	120,853
#3	1 or 2	128,793
#4	exp Primary Health Care/	183,016
#5	(primary care or primary health-care or primary healthcare or first line care or primary medical care).ti,ab,kf.	163,121
#6	4 or 5	277,743
#7	Biomarkers/	330,207
#8	(biomarker* or surrogate end-point* or surrogate endpoint* or ((diagnostic or diagnosis or laboratory or lab) adj2 (test* or tool*)) or ((biologic* or laboratory or serum or clinical or viral or biochemical or immunologic or immune or surrogate) adj2 marker*)).ti,ab,kf.	591,536
#9	exp algorithms/	395,426
#10	(algorism* or algorithm* or algorhythm*).ti,ab,kf.	314,627
#11	exp Artificial Intelligence/	146,900
#12	(((machine or artificial*) adj2 (intelligen* or learning*)) or (neural* adj2 net*) or natural language processing* or NLP or (data adj2 mining*) or (pattern* adj2 (recognition* or recogniz* or recognis*))).ti,ab,kf.	180,774
#13	7 or 8 or 9 or 10 or 11 or 12	1,419,847
#14	3 and 6 and 13	162
	Number of articles selected before removing duplicates	162

Table

The search strategy for Cochrane Library
No.	Search strategy	Results
#1	MeSH descriptor: [Fatty Liver] explode all trees	1,602
#2	(steatohepatitis or steatohepatitides or steatohepatopathy or “steato-hepatosis” or steatohepatosis or “steatotic hepatopathy” or “hepatic lipidosis” or “hepatic steatosis” or “hepato-steatosis” or hepatolipidosis or hepatosteatosis or ((fat or fatty or steatosis or steatotic or fibrosis or cirrhosis or lipidosis) NEAR/3 liver*)):ti,ab,kw	12,895
#3	#1 OR #2	12,897
#4	MeSH descriptor: [Primary Health Care] explode all trees	8,315
#5	(“primary care” or “primary health-care” or “primary healthcare” or “first line care” or “primary medical care”):ti,ab,kw	23,268
#6	#4 OR #5	26,649
#7	MeSH descriptor: [Biomarkers] this term only	15,577
#8	(biomarker* or surrogate NEXT end NEXT point* or surrogate NEXT endpoint* or ((diagnostic or diagnosis or laboratory or lab) NEAR/2 (test* or tool*)) or ((biologic* or laboratory or serum or clinical or viral or biochemical or immunologic or immune or surrogate) NEAR/2 marker*)):ti,ab,kw	74,531
#9	MeSH descriptor: [Algorithms] explode all trees	4,683
#10	(algorism* or algorithm* or algorhythm*):ti,ab,kw	15,271
#11	MeSH descriptor: [Artificial Intelligence] explode all trees	1,369
#12	(((machine or artificial*) NEAR/2 (intelligen* or learning*)) or (neural* NEAR/2 net*) or natural language processing* or NLP or (data NEAR/2 mining*) or (pattern* NEAR/2 (recognition* or recogniz* or recognis*))):ti,ab,kw	5,724
#13	#7 OR #8 OR #9 OR #10 OR #11 OR #12	92,738
#14	#3 AND #6 AND #13	24
	Number of articles selected before removing duplicates	24

Table

The search strategy for Scopus
No.	Search strategy	Results
	(TITLE-ABS-KEY (steatohepatitis OR steatohepatitides OR steatohepatopathy OR “steato-hepatosis” OR steatohepatosis OR “steatotic hepatopathy” OR “hepatic lipidosis” OR “hepatic steatosis” OR “hepato-steatosis” OR hepatolipidosis OR hepatosteatosis OR ((fat OR fatty OR steatosis OR steatotic OR fibrosis OR cirrhosis OR lipidosis) W/2 liver*))) AND (TITLE-ABS-KEY (“primary care” OR “primary health-care” OR “primary healthcare” OR “first line care” OR “primary medical care”)) AND ((TITLE-ABS-KEY (biomarker* OR “surrogate end-point*” OR “surrogate endpoint*” OR ((diagnostic OR diagnosis OR laboratory OR lab) W/1 (test* OR tool*)) OR ((biologic* OR laboratory OR serum OR clinical OR viral OR biochemical OR immunologic OR immune OR surrogate) W/1 marker*))) OR (TITLE-ABS-KEY (algorism* OR algorithm* OR algorhythm*)) OR (TITLE-ABS-KEY (((machine OR artificial*) W/1 (intelligen* OR learning*)) OR (neural* W/1 net*) OR “natural language processing*” OR nlp OR (data W/1 mining*) OR (pattern* W/1 (recognition* OR recogniz* OR recognis*)))))
	Number of articles selected before removing duplicates	225

Table

The search strategy for Google Scholar
No.	Search strategy	Results*
#1	fatty liver primary health care	30
#2	fatty liver primary health care algorithm	30
#3	fatty liver primary health care artificial intelligence	30
#4	fatty liver primary health care biomarker	30
#5	fatty liver primary health care diagnostic tool	30
#6	fatty liver primary health care laboratory test	30
#7	fatty liver primary health care machine learning	30
#8	fatty liver primary health care neural networks	30
	Number of articles selected before removing duplicates	240

* Eight different searches was conducted, as Google Scholar does not work well with longer searches.

For each search, the first 30 articles were selected (articles with the highest Google Page Rank).

Grattagliano I, Ubaldi E, Fau - Napoli L, et al. Utility of noninvasive methods for the characterization of nonalcoholic liver steatosis in the family practice. The “VARES” Italian multicenter study. Ann Hepatol. 2013;12(1):70–77. (1665–2681 (Print)). doi: 10.1016/S1665-2681(19)31387-0

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