ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials.

BACKGROUND: Screening for eligible patients continues to pose a great challenge for many clinical trials. This has led to a rapidly growing interest in standardizing computable representations of eligibility criteria (EC) in order to develop tools that leverage data from electronic health record (EHR) systems. Although laboratory procedures (LP) represent a common entity of EC that is readily available and retrievable from EHR systems, there is a lack of interoperable data models for this entity of EC. A public, specialized data model that utilizes international, widely-adopted terminology for LP, e.g. Logical Observation Identifiers Names and Codes (LOINC®), is much needed to support automated screening tools. OBJECTIVE: The aim of this study is to establish a core dataset for LP most frequently requested to recruit patients for clinical trials using LOINC terminology. Employing such a core dataset could enhance the interface between study feasibility platforms and EHR systems and significantly improve automatic patient recruitment. METHODS: We used a semi-automated approach to analyze 10,516 screening forms from the Medical Data Models (MDM) portal's data repository that are pre-annotated with Unified Medical Language System (UMLS). An automated semantic analysis based on concept frequency is followed by an extensive manual expert review performed by physicians to analyze complex recruitment-relevant concepts not amenable to automatic approach. RESULTS: Based on analysis of 138,225 EC from 10,516 screening forms, 55 laboratory procedures represented 77.87% of all UMLS laboratory concept occurrences identified in the selected EC forms. We identified 26,413 unique UMLS concepts from 118 UMLS semantic types and covered the vast majority of Medical Subject Headings (MeSH) disease domains. CONCLUSIONS: Only a small set of common LP covers the majority of laboratory concepts in screening EC forms which supports the feasibility of establishing a focused core dataset for LP. We present ELaPro, a novel, LOINC-mapped, core dataset for the most frequent 55 LP requested in screening for clinical trials. ELaPro is available in multiple machine-readable data formats like CSV, ODM and HL7 FHIR. The extensive manual curation of this large number of free-text EC as well as the combining of UMLS and LOINC terminologies distinguishes this specialized dataset from previous relevant datasets in the literature.

Efficacy of octreotide in the prevention of complications after pancreaticoduodenectomy in patients with soft pancreas and non-dilated pancreatic duct: A prospective randomized trial.

BACKGROUND: The efficacy of octreotide to prevent postoperative pancreatic fistula (POPF) of pancreaticoduodenectomy (PD) is still controversial. This study aimed to evaluate the effect of postoperative use of octreotide on the outcomes after PD. METHODS: This is a prospective randomized controlled trial for postoperative use of octreotide in patients undergoing PD. Patients with soft pancreas and pancreatic duct < 3 mm were randomized to 2 groups. Group I did not receive postoperative octreotide. Group II received postoperative octreotide. The primary end of the study is to compare the rate of POPF. RESULTS: A total of 104 patients were included in the study and were divided into two randomized groups. There were no significant difference in overall complications and its severity. POPF occurred in 11 patients (21.2%) in group I and 10 (19.2%) in group II, without statistical significance (P = 0.807). Also, there was no significant differences between both groups regarding the incidence of biliary leakage (P = 0.083), delayed gastric emptying (P = 0.472), and early postoperative mortality (P = 0.727). CONCLUSIONS: Octreotide did not reduce postoperative morbidities, reoperation and mortality rate. Also, it did not affect the incidence of POPF and its clinically relevant variants.

Portal of Medical Data Models: Stakeholder Feedback and Requirements.

The Portal of Medical Data Models has been developed since 2011 by the University of Münster. Its main goals are transparency, standardization and secondary use of medical metadata. Via two online surveys feedback from stakeholders of German health research was collected regarding the portal's contents. The surveys confirmed great interest in secondary use of medical forms.