RESUMEN
BACKGROUND: International consensus on classifications of appendiceal mucinous neoplasms (AMNs) and associated pseudomyxoma peritonei (PMP) have been carefully made but clinicopathological associations supporting decision making remain scarce. OBJECTIVE: This study aimed to assess interdependence between AMNs and PMP and provide directions for clinical management. METHODS: This two-center retrospective cohort study reviewed patients with PMP treated with cytoreductive surgery and hyperthermic intraperitoneal chemotherapy between 2005 and 2021. The primary objective was to reassess histopathologic grade of AMNs and PMP according to the Peritoneal Surface Oncology Group International classification and to establish its interdependence. Secondary outcomes were recurrence rate, PMP grade progression, ovarian involvement, and overall survival (OS). RESULTS: Of 105 patients included, 78 (74.3%) had low-grade AMNs as the primary tumor, 8 (7.6%) had high-grade AMNs, 7 (6.7%) had mucinous adenocarcinoma (MAC), 1 (0.9%) had MAC with signet ring cells (SRC), and 11 (10.5%) had unidentified tumors. Overall, 11 patients (10.5%) had no PMP, 21 (20.0%) had acellular mucin, 56 (53.3%) had low-grade PMP, 12 (11.4%) had high-grade PMP, and 5 (4.8%) had PMP-SRC. In 11 cases (13.3%), AMNs and matching PMP grade differed. Over a 16-year follow-up, recurrence occurred in 31.8%, with three cases showing histopathologically changed PMP. Ovarian involvement was observed in 43/65 females (66.2%). Median OS was 13.8 years, and 5-year OS rates were 100%, 74.4%, 44.4%, and 20% for acellular mucin, low-grade PMP, high-grade PMP and PMP-SRC, respectively (p < 0.001). CONCLUSIONS: AMN histology does not always reflects its associated PMP grade, while PMP grade strongly influences survival. Ovarian involvement and recurrent PMP showing unchanged histopathological features are common.
RESUMEN
Background: A third of people with juvenile myoclonic epilepsy (JME) are drug-resistant. Three-quarters have a seizure relapse when attempting to withdraw anti-seizure medication (ASM) after achieving seizure-freedom. It is currently impossible to predict who is likely to become drug-resistant and safely withdraw treatment. We aimed to identify predictors of drug resistance and seizure recurrence to allow for individualised prediction of treatment outcomes in people with JME. Methods: We performed an individual participant data (IPD) meta-analysis based on a systematic search in EMBASE and PubMed - last updated on March 11, 2021 - including prospective and retrospective observational studies reporting on treatment outcomes of people diagnosed with JME and available seizure outcome data after a minimum one-year follow-up. We invited authors to share standardised IPD to identify predictors of drug resistance using multivariable logistic regression. We excluded pseudo-resistant individuals. A subset who attempted to withdraw ASM was included in a multivariable proportional hazards analysis on seizure recurrence after ASM withdrawal. The study was registered at the Open Science Framework (OSF; https://osf.io/b9zjc/). Findings: Our search yielded 1641 articles; 53 were eligible, of which the authors of 24 studies agreed to collaborate by sharing IPD. Using data from 2518 people with JME, we found nine independent predictors of drug resistance: three seizure types, psychiatric comorbidities, catamenial epilepsy, epileptiform focality, ethnicity, history of CAE, family history of epilepsy, status epilepticus, and febrile seizures. Internal-external cross-validation of our multivariable model showed an area under the receiver operating characteristic curve of 0·70 (95%CI 0·68-0·72). Recurrence of seizures after ASM withdrawal (n = 368) was predicted by an earlier age at the start of withdrawal, shorter seizure-free interval and more currently used ASMs, resulting in an average internal-external cross-validation concordance-statistic of 0·70 (95%CI 0·68-0·73). Interpretation: We were able to predict and validate clinically relevant personalised treatment outcomes for people with JME. Individualised predictions are accessible as nomograms and web-based tools. Funding: MING fonds.