Introduction

Metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction with alcohol-related steatotic liver disease (MetALD) are two separate entities within the spectrum of steatotic liver disease (SLD).

Aims & Methods

This meta-analysis aims to compare the risks of hepatic and extrahepatic outcomes between MASLD and MetALD. We systematically searched for observational cohort studies published up to March 1, 2025, comparing clinical outcomes between individuals with MASLD and MetALD. The primary outcome was liver-related events, while secondary outcomes included hepatocellular carcinoma (HCC), liver-related mortality, cardiovascular events, extrahepatic cancers, and all-cause mortality. We used random-effect models to calculate pooled hazard ratios (HRs) with 95% confidence intervals (95% CI).

Results

Twenty-four studies involving 11,396,962 individuals were analyzed (9,622,716 with MASLD and 1,774,246 with MetALD). Individuals with MetALD had significantly higher risks of liver-related events (HR 1.62, 95% CI 1.16-2.25, p=0.02) and HCC (HR 1.33, 95%CI 1.00-1.77, p=0.04) than those with MASLD. The risk of extrahepatic cancers was also increased in MetALD (HR 1.03, 95% CI 1.01-1.06, p<0.001), while the rates of cardiovascular events (HR 0.96, 95% CI 0.85-1.09, p=0.48), extrahepatic cancer-related mortality (HR 1.44, 95% CI 0.97-2.15, p=0.07), and all-cause mortality (HR 1.08, 95% CI 0.97-1.19, p=0.14) did not differ between these two conditions.

Conclusion

MetALD confers a higher risk of liver-related outcomes, HCC, and extrahepatic cancers than MASLD, while all-cause mortality and cardiovascular risk are similar between the two conditions. These findings emphasize the need for distinct clinical strategies for these related yet different entities within the SLD spectrum and highlight the importance of conducting pharmacological clinical trials in this context.

Disclosure

Nothing to disclose

Introduction

Aims & Methods

To establish a methodology for accurately estimating the required length of PICC lines.
Between November 2023 and April 2025, various body measurements were collected during PICC line placements. The depth of the selected vein was measured sonographically. Patients’ height, weight, and sex were documented. The PICC line length was determined based on clinical expertise. A post-insertion chest X-ray was performed to confirm the central position of the catheter tip. The distance from the catheter tip to the carina was measured to objectively define central placement. Multiple formulas in clinical use were compared for their accuracy. A regression analysis was conducted to develop a formula that is both accurate and simple to apply.

Results

​A total of 60 PICC lines were successfully inserted without complications. The median patient height was 168 cm (range: 164.3–175.8 cm), weight 59 kg (range: 50.0–83.8 kg), and age 56.5 years (range: 46–65.8). Thirty-five insertions were performed in female patients. The ideal PICC line length was 42 cm (range: 38–44.5 cm). Of the five tested formulas, the best correlations were found with Formula 5 (puncture site to jugulum + jugulum to 3rd intercostal space) with r = 0.68 (p = 0.0005), and Formula 3 (puncture site to acromion + clavicle length + jugulum to 4th intercostal space) with r = 0.67 (p = 0.0005). In simple linear regression, the distances from puncture site to acromion and to jugulum were significantly correlated (p = 0.0007 and p = 0.0019, respectively). The best-fit model in multiple regression included age, height, weight, arm side, puncture site to jugulum, and puncture site to 4th intercostal space (p = 0.0005).

Conclusion

An optimal formula for calculating the correct PICC line length has been identified.

References

Table 1: comparison between the various existing formulas and the newly developed one.