Development and validation of diagnostic algorithms for the laboratory diagnosis of porphyrias

Porphyrias are rare metabolic disorders of the haem synthesis. They can present with acute neurovisceral attacks, cutaneous symptoms, or a combination of both. As they present with a wide variety of clinical symptoms, diagnosis is often delayed and correct interpretation of porphyria-related tests remains a challenge for many physicians. We developed and validated two algorithms for the laboratory diagnosis of porphyrias based on presenting symptoms. Based on a literature search and clinical/laboratory expertise, we developed algorithms for acute and cutaneous porphyrias. We validated these algorithms using all porphyria related laboratory test requests between January 1st 2000 and September 30th 2020 in UZ Leuven. In addition, we also evaluated our algorithm using samples from the European porphyria network (EPNET) external quality assessment scheme (2010-2021). Sensitivity of the algorithm for acute porphyria was 100.0% [74.9%-100.0%] (13 acute intermittent porphyria (AIP) and 1 variegate porphyria [VP]) with a specificity of 98.5% [91.0%-100.0%] (65 patients). Sensitivity of the algorithm for cutaneous porphyria was 100% [95.1%-100.0%] (7 VP, 59 porphyria cutanea tarda (PCT), 23 erythropoietic protoporphyria (EPP), 2 X-linked erythropoietic protoporphyria [XLEPP]) with a specificity of 93.9% [82.9%-98.5%]. There were no diagnostic samples of other types of porphyria. The algorithms correctly identified 18 of the 19 EPNET porphyria cases. One of the two hereditary coproporphyria cases was missed. The algorithms for acute and cutaneous porphyria showed high sensitivity and specificity and can be used to aid the clinician in correctly interpreting the laboratory findings of porphyria-related tests.

Automated summary

Porphyrias are rare metabolic disorders of the haem synthesis. A study developed and validated algorithms for the laboratory diagnosis of acute and cutaneous porphyrias based on presenting symptoms. The algorithms showed high sensitivity and specificity in correctly interpreting the laboratory findings of porphyria-related tests.