Clinical and economic comparison of laparoscopic versus open hepatectomy for primary hepatolithiasis: a propensity score-matched cohort study.

BACKGROUND: It is unclear whether laparoscopic hepatectomy (LH) for hepatolithiasis confers better clinical benefit and lower hospital costs than open hepatectomy (OH). This study aim to evaluate the clinical and economic value of LH versus OH. METHODS: Patients undergoing OH or LH for primary hepatolithiasis at Yijishan Hospital of Wannan Medical College between 2015 and 2022 were divided into OH group and LH group. Propensity score matching (PSM) was used to balance the baseline data. Deviation-based cost modelling and weighted average median cost (WAMC) were used to assess and compare the economic value. RESULTS: A total of 853 patients were identified. After exclusions, 403 patients with primary hepatolithiasis underwent anatomical hepatectomy (OH n=143; LH n=260). PSM resulted in 2 groups of 100 patients each. Although LH required a longer median operation duration compared with OH (285.0 versus 240.0 min, respectively, P<0.001), LH patients had fewer wound infections, fewer pre-discharge overall complications (26 versus 43%, respectively, P=0.009), and shorter median postoperative hospital stays (8.0 versus 12.0 days, respectively, P<0.001). No differences were found in blood loss, major complications, stone clearance, and mortality between the two matched groups. However, the median overall hospital cost of LH was significantly higher than that of OH (CNY¥52,196.1 versus 45,349.5, respectively, P=0.007). Although LH patients had shorter median postoperative hospital stays and fewer complications than OH patients, the WAMC was still higher for the LH group than for the OH group with an increase of CNY¥9,755.2 per patient undergoing LH. CONCLUSION: The overall clinical benefit of LH for hepatolithiasis is comparable or even superior to that of OH, but with an economic disadvantage. There is a need to effectively reduce the hospital costs of LH and the gap between costs and diagnosis-related group reimbursement to promote its adoption.

Training Augmented Intelligent Capabilities for Pharmacovigilance: Applying Deep-learning Approaches to Individual Case Safety Report Processing.

INTRODUCTION: Regulations are increasing the scope of activities that fall under the remit of drug safety. Currently, individual case safety report (ICSR) collection and collation is done manually, requiring pharmacovigilance professionals to perform many transactional activities before data are available for assessment and aggregated analyses. For a biopharmaceutical company to meet its responsibilities to patients and regulatory bodies regarding the safe use and distribution of its products, improved business processes must be implemented to drive the industry forward in the best interest of patients globally. Augmented intelligent capabilities have already demonstrated success in capturing adverse events from diverse data sources. It has potential to provide a scalable solution for handling the ever-increasing ICSR volumes experienced within the industry by supporting pharmacovigilance professionals' decision-making. OBJECTIVE: The aim of this study was to train and evaluate a consortium of cognitive services to identify key characteristics of spontaneous ICSRs satisfying an acceptable level of accuracy determined by considering business requirements and effective use in a real-world setting. The results of this study will serve as supporting evidence for or against implementing augmented intelligence in case processing to increase operational efficiency and data quality consistency. METHODS: A consortium of ten cognitive services to augment aspects of ICSR processing were identified and trained through deep-learning approaches. The input data for model training were 20,000 ICSRs received by Celgene drug safety over a 2-year period. The data were manually made machine-readable through the process of transcription, which converts images into text. The machine-readable documents were manually annotated for pharmacovigilance data elements to facilitate the training and testing of the cognitive services. Once trained by cognitive developers, the cognitive services' output was reviewed by pharmacovigilance subject-matter experts against the accepted ground-truth for correctness and completeness. To be considered adequately trained and functional, each cognitive service was required to reach a threshold of F1 or accuracy score ≥ 75%. RESULTS: All ten cognitive services under development have reached an evaluative score ≥ 75% for spontaneous ICSRs. CONCLUSION: All cognitive services under development have achieved the minimum evaluative threshold to be considered adequately trained, demonstrating how machine-learning and natural language processing techniques together provide accurate outputs that may augment pharmacovigilance professionals' processing of spontaneous ICSRs quickly and accurately. The intention of augmented intelligence is not to replace the pharmacovigilance professional, but rather support them in their consistent decision-making so that they may better handle the overwhelming amount of data otherwise manually curated and monitored for ongoing drug surveillance requirements. Through this supported decision-making, pharmacovigilance professionals may have more time to apply their knowledge in assessing the case rather than spending it performing transactional tasks to simply capture the pertinent data within a safety database. By capturing data consistently and efficiently, we begin to build a corpus of data upon which analyses may be conducted and insights gleaned. Cognitive services may be key to an organization's transformation to more proactive decision-making needed to meet regulatory requirements and enhance patient safety.

Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2.

Scientific progress depends on formulating testable hypotheses informed by the literature. In many domains, however, this model is strained because the number of research papers exceeds human readability. Here, we developed computational assistance to analyze the biomedical literature by reading PubMed abstracts to suggest new hypotheses. The approach was tested experimentally on the tumor suppressor p53 by ranking its most likely kinases, based on all available abstracts. Many of the best-ranked kinases were found to bind and phosphorylate p53 (P value = 0.005), suggesting six likely p53 kinases so far. One of these, NEK2, was studied in detail. A known mitosis promoter, NEK2 was shown to phosphorylate p53 at Ser315 in vitro and in vivo and to functionally inhibit p53. These bona fide validations of text-based predictions of p53 phosphorylation, and the discovery of an inhibitory p53 kinase of pharmaceutical interest, suggest that automated reasoning using a large body of literature can generate valuable molecular hypotheses and has the potential to accelerate scientific discovery.