Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2026
Standards for reporting diagnostic accuracy studies using intraoperative neurophysiological monitoring.
СтатьяНазвание и аннотация: Перевод в очереди
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Thirumala Parthasarathy D, Balzer Jeffrey R, Szelenyi Andrea, Seidel Kathleen, Husain Aatif M, Bossuyt Patrick M, Drost Gea, STARD-IONM Working Group
Аннотация
OBJECTIVE: Intraoperative neurophysiological monitoring (IONM) plays a critical role in preserving neural function during surgery. Comparing IONM research studies is challenging due to methodological heterogeneity and inconsistent reporting. The Standards for Reporting Diagnostic Accuracy Studies Using Intraoperative Neurophysiological Monitoring (STARD-IONM) checklist was developed to improve transparency, reproducibility and comparability.
METHODS: We used a four-phase consensus process. Phase 1 convened an expert steering group to define the rationale, plan the protocol, and establish the working group. Phase 2 adapted the STARD 2015 checklist to IONM, with feedback from studies. Phase 3 invited input from professional societies, and the broader community. Phase 4 focuses on dissemination and implementation.
RESULTS: A review of 12 IONM studies using the 30-item STARD-2015 checklist revealed frequent underreporting of methods, missing data handling, adverse events, and blinding of index-test interpretation and outcome assessment. Based on these findings and expert consensus, we developed the STARD-IONM checklist. The checklist retains the STARD-2015 framework while adding IONM-specific reporting elements, including alert thresholds; signal-event classification as no deterioration, partial recovery, reversible deterioration, or irreversible deterioration; anesthesia and physiology reporting; and an evaluation framework to support standardized reporting. Dissemination and implementation are currently underway.
CONCLUSIONS: STARD-IONM extends STARD-2015 by providing specific reporting recommendations that address the methodological challenges in IONM diagnostic-accuracy research.
SIGNIFICANCE: Adoption of STARD-IONM can enhance reporting quality, transparency, reproducibility, thereby facilitating more rigorous evaluation and comparison of IONM studies across surgical domains.
METHODS: We used a four-phase consensus process. Phase 1 convened an expert steering group to define the rationale, plan the protocol, and establish the working group. Phase 2 adapted the STARD 2015 checklist to IONM, with feedback from studies. Phase 3 invited input from professional societies, and the broader community. Phase 4 focuses on dissemination and implementation.
RESULTS: A review of 12 IONM studies using the 30-item STARD-2015 checklist revealed frequent underreporting of methods, missing data handling, adverse events, and blinding of index-test interpretation and outcome assessment. Based on these findings and expert consensus, we developed the STARD-IONM checklist. The checklist retains the STARD-2015 framework while adding IONM-specific reporting elements, including alert thresholds; signal-event classification as no deterioration, partial recovery, reversible deterioration, or irreversible deterioration; anesthesia and physiology reporting; and an evaluation framework to support standardized reporting. Dissemination and implementation are currently underway.
CONCLUSIONS: STARD-IONM extends STARD-2015 by providing specific reporting recommendations that address the methodological challenges in IONM diagnostic-accuracy research.
SIGNIFICANCE: Adoption of STARD-IONM can enhance reporting quality, transparency, reproducibility, thereby facilitating more rigorous evaluation and comparison of IONM studies across surgical domains.