The More, the Better? Modalities of Metastatic Status Extraction on Free Medical Reports Based on Natural Language Processing

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Impact of the Sars-Cov-2 outbreak on the initial clinical presentation of new solid cancer diagnoses: a systematic review and meta-analysis

International audienceBackground The COVID-19 pandemic might have delayed cancer diagnosis and management. The aim of this systematic review was to compare the initial tumor stage of new cancer diagnoses before and after the pandemic. Methods We systematically reviewed articles that compared the tumor stage of new solid cancer diagnoses before and after the initial pandemic waves. We conducted a random-effects meta-analysis to compare the rate of metastatic tumors and the distribution of stages at diagnosis. Subgroup analyses were performed by primary tumor site and by country. Results From 2,013 studies published between January 2020 and April 2022, we included 58 studies with 109,996 patients. The rate of metastatic tumors was higher after the COVID-19 outbreak than before (pooled OR: 1.29 (95% CI, 1.06-1.57), I 2 : 89% (95% CI, 86-91)). For specific cancers, common ORs reached statistical significance for breast (OR: 1.51 (95% CI 1.07-2.12)) and gynecologic (OR: 1.51 (95% CI 1.04-2.18)) cancers, but not for other cancer types. According to countries, common OR (95% CI) reached statistical significance only for Italy: 1.55 (1.01-2.39) and Spain: 1.14 (1.02-1.29). Rates were comparable for stage I-II versus III-IV in studies for which that information was available, and for stages I-II versus stage III in studies that did not include metastatic patients. Conclusions Despite inter-study heterogeneity, our meta-analysis showed a higher rate of metastatic tumors at diagnosis after the pandemic. The burden of social distancing policies might explain those results, as patients may have delayed seeking care

Développement d'un modèle de traitement automatique du langage pour calculer des indicateurs qualité du cancer du sein : une étude transversale multicentrique

International audienceObjectivesMedico-administrative data are promising to automate the calculation of Healthcare Quality and Safety Indicators. Nevertheless, not all relevant indicators can be calculated with this data alone. Our feasibility study objective is to analyze 1) the availability of data sources; 2) the availability of each indicator elementary variables, and 3) to apply natural language processing to automatically retrieve such information.MethodWe performed a multicenter cross-sectional observational feasibility study on the clinical data warehouse of Assistance Publique – Hôpitaux de Paris (AP-HP). We studied the management of breast cancer patients treated at AP-HP between January 2019 and June 2021, and the quality indicators published by the European Society of Breast Cancer Specialist, using claims data from the Programme de Médicalisation du Système d'Information (PMSI) and pathology reports. For each indicator, we calculated the number (%) of patients for whom all necessary data sources were available, and the number (%) of patients for whom all elementary variables were available in the sources, and for whom the related HQSI was computable. To extract useful data from the free text reports, we developed and validated dedicated rule-based algorithms, whose performance metrics were assessed with recall, precision, and f1-score.ResultsOut of 5785 female patients diagnosed with a breast cancer (60.9 years, IQR [50.0–71.9]), 5,147 (89.0%) had procedures related to breast cancer recorded in the PMSI, and 3732 (72.5%) had at least one surgery. Out of the 34 key indicators, 9 could be calculated with the PMSI alone, and 6 others became so using the data from pathology reports. Ten elementary variables were needed to calculate the 6 indicators combining the PMSI and pathology reports. The necessary sources were available for 58.8% to 94.6% of patients, depending on the indicators.The extraction algorithms developed had an average accuracy of 76.5% (min-max [32.7%–93.3%]), an average precision of 77.7% [10.0%–97.4%] and an average sensitivity of 71.6% [2.8% to 100.0%]. Once these algorithms applied, the variables needed to calculate the indicators were extracted for 2% to 88% of patients, depending on the indicators.DiscussionThe availability of medical reports in the electronic health records, of the elementary variables within the reports, and the performance of the extraction algorithms limit the population for which the indicators can be calculated.ConclusionsThe automated calculation of quality indicators from electronic health records is a prospect that comes up against many practical obstacles.ObjectifsLes données médico-administratives ne suffisent pas à automatiser le calcul des indicateurs de qualité et de sécurité des soins (IQSS). L'objectif de notre étude de faisabilité est d'analyser. 1) la disponibilité des sources de données ; 2) la disponibilité de chaque variable élémentaire par indicateur, et 3) d'appliquer des algorithmes de traitement du langage naturel pour extraire automatiquement ces informations.MéthodeNous avons réalisé une étude de faisabilité observationnelle transversale multicentrique sur l'entrepôt de données cliniques de l'Assistance Publique – Hôpitaux de Paris (AP-HP). Nous avons étudié la prise en charge des patients atteints de cancer du sein traités à l'AP-HP entre janvier 2016 et juin 2021, et les indicateurs publiés par l'European Society of Breast Cancer Specialist, à partir des données administratives du Programme de Médicalisation du Système d'Information (PMSI) et des comptes-rendus d'anatomopathologie. Pour chaque indicateur, nous avons calculé le nombre (%) de patients pour lesquels toutes les sources de données nécessaires étaient disponibles, et le nombre (%) de patients pour lesquels toutes les variables élémentaires étaient disponibles dans les sources, et pour lesquels l'IQSS associé était calculable. Pour extraire des données utiles des comptes rendus textuels, nous avons développé et validé des algorithmes dédiés basés sur des règles, dont les mesures de performance ont été évaluées par rappel, précision et score f1.RésultatsDes 5785 patientes diagnostiquées d'un cancer du sein (60,9 ans, IQR [50,0–71,9]), 5147 (89,0 %) avaient des actes liés au cancer enregistrés dans le PMSI, et 3 732 (72,5 %) avaient au moins une chirurgie. Des 34 indicateurs cibles, 9 étaient calculables avec le PMSI seul, et 6 autres le devenaient en utilisant les données présentes dans les comptes-rendus d'anatomopathologie. Dix variables élémentaires étaient nécessaires au calcul des 6 indicateurs combinant Programme de Médicalisation du Système d'Information et comptes-rendus d'anatomopathologie. Les comptes-rendus nécessaires étaient disponibles pour 58,8 % à 94,6 % des patients, suivant les indicateurs.Les algorithmes d'extraction textuelle avaient une exactitude moyenne de 76,5 % (min-max [32,7 %–93,3 %]), une précision moyenne de 77,7 % [10,0 %–97,4 %] et une sensibilité moyenne de 71,6 % [2,8 % à 100,0 %]. Une fois ces algorithmes appliqués, les variables nécessaires au calcul des indicateurs étaient possibles à extraire pour 2 % à 88 % des patients, suivant les indicateurs.DiscussionLa disponibilité des comptes-rendus dans l'entrepôt de données, celle des variables élémentaires au sein des comptes rendus, et la performance des algorithmes d'extraction limite la population pour laquelle les indicateurs sont calculables.ConclusionsLe calcul automatisé d'indicateurs qualité à partir des dossiers patients informatisés est une perspective qui se heurte à de nombreux freins pratiques

No changes in clinical presentation, treatment strategies and survival of pancreatic cancer cases during the SARS‐COV‐2 outbreak: A retrospective multicenter cohort study on real‐world data

International audienceThe SARS-COV-2 pandemic disrupted healthcare systems. We assessed its impact on the presentation, care trajectories and outcomes of new pancreatic cancers (PCs) in the Paris area. We performed a retrospective multicenter cohort study on the data warehouse of Greater Paris University Hospitals (AP-HP). We identified all patients newly referred with a PC between January 1, 2019, and June 30, 2021, and excluded endocrine tumors. Using claims data and health records, we analyzed the timeline of care trajectories, the initial tumor stage, the treatment categories: pancreatectomy, exclusive systemic therapy or exclusive best supportive care (BSC). We calculated patients' 1-year overall survival (OS) and compared indicators in 2019 and 2020 to 2021. We included 2335 patients. Referral fell by 29% during the first lockdown. The median time from biopsy and from first MDM to treatment were 25 days (16-50) and 21 days (11-40), respectively. Between 2019 and 2020 to 2021, the rate of metastatic tumors (36% vs 33%, P = .39), the pTNM distribution of the 464 cases with upfront tumor resection (P = .80), and the proportion of treatment categories did not vary: tumor resection (32% vs 33%), exclusive systemic therapy (49% vs 49%), exclusive BSC (19% vs 19%). The 1-year OS rates in 2019 vs 2020 to 2021 were 92% vs 89% (aHR = 1.42; 95% CI, 0.82-2.48), 52% vs 56% (aHR = 0.88; 95% CI, 0.73-1.08), 13% vs 10% (aHR = 1.00; 95% CI, 0.78-1.25), in the treatment categories, respectively. Despite an initial decrease in the number of new PCs, we did not observe any stage shift. OS did not vary significantly

Impact of the COVID-19 pandemic on clinical presentation, treatments, and outcomes of new breast cancer patients: A retrospective multicenter cohort study

International audienceBackgroundThe SARS CoV-2 pandemic disrupted healthcare systems. We compared the cancer stage for new breast cancers (BCs) before and during the pandemic.MethodsWe performed a retrospective multicenter cohort study on the data warehouse of Greater Paris University Hospitals (AP-HP). We identified all female patients newly referred with a BC in 2019 and 2020. We assessed the timeline of their care trajectories, initial tumor stage, and treatment received: BC resection, exclusive systemic therapy, exclusive radiation therapy, or exclusive best supportive care (BSC). We calculated patients' 1-year overall survival (OS) and compared indicators in 2019 and 2020.ResultsIn 2019 and 2020, 2055 and 1988, new BC patients underwent cancer treatment, and during the two lockdowns, the BC diagnoses varied by −18% and by +23% compared to 2019. De novo metastatic tumors (15% and 15%, p = 0.95), pTNM and ypTNM distributions of 1332 cases with upfront resection and of 296 cases with neoadjuvant therapy did not differ (p = 0.37, p = 0.3). The median times from first multidisciplinary meeting and from diagnosis to treatment of 19 days (interquartile 11–39 days) and 35 days (interquartile 22–65 days) did not differ. Access to plastic surgery (15% and 17%, p = 0.08) and to treatment categories did not vary: tumor resection (73% and 72%), exclusive systemic therapy (13% and 14%), exclusive radiation therapy (9% and 9%), exclusive BSC (5% and 5%) (p = 0.8). Among resected patients, the neoadjuvant therapy rate was lower in 2019 (16%) versus 2020 (20%) (p = 0.02). One-year OS rates were 99.3% versus 98.9% (HR = 0.96; 95% CI, 0.77–1.2), 72.6% versus 76.6% (HR = 1.28; 95% CI, 0.95–1.72), 96.6% versus 97.8% (HR = 1.09; 95% CI, 0.61–1.94), and 15.5% versus 15.1% (HR = 0.99; 95% CI, 0.72–1.37), in the treatment groups.ConclusionsDespite a decrease in the number of new BCs, there was no tumor stage shift, and OS did not vary