ResearchPad - statistics-and-research-methods https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Adherence to the Standards for Reporting of Diagnostic Accuracy (STARD) 2015 Guidelines in Acute Point-of-Care Ultrasound Research]]> https://www.researchpad.co/article/Nee6a5caa-fab9-467b-8d9f-86f377e063b5 Incomplete reporting of diagnostic accuracy research impairs assessment of risk of bias and limits generalizability. Point-of-care ultrasound has become an important diagnostic tool for acute care physicians, but studies assessing its use are of varying methodological quality.ObjectiveTo assess adherence to the Standards for Reporting of Diagnostic Accuracy (STARD) 2015 guidelines in the literature on acute care point-of-care ultrasound.Evidence ReviewMEDLINE was searched to identify diagnostic accuracy studies assessing point-of-care ultrasound published in critical care, emergency medicine, or anesthesia journals from 2016 to 2019. Studies were evaluated for adherence to the STARD 2015 guidelines, with the following variables analyzed: journal, country, STARD citation, STARD-adopting journal, impact factor, patient population, use of supplemental material, and body region. Data analysis was performed in November 2019.FindingsSeventy-four studies were included in this systematic review for assessment. Overall adherence to STARD was moderate, with 66% (mean [SD], 19.7 [2.9] of 30 items) of STARD items reported. Items pertaining to imaging specifications, patient population, and readers of the index test were frequently reported (>66% of studies). Items pertaining to blinding of readers to clinical data and to the index or reference standard, analysis of heterogeneity, indeterminate and missing data, and time intervals between index and reference test were either moderately (33%-66%) or infrequently (<33%) reported. Studies in STARD-adopting journals (mean [SD], 20.5 [2.9] items in adopting journals vs 18.6 [2.3] items in nonadopting journals; P = .002) and studies citing STARD (mean [SD], 21.3 [0.9] items in citing studies vs 19.5 [2.9] items in nonciting studies; P = .01) reported more items. Variation by country and journal of publication were identified. No differences in STARD adherence were identified by body region imaged (mean [SD], abdominal, 20.0 [2.5] items; head and neck, 17.8 [1.6] items; musculoskeletal, 19.2 [3.1] items; thoracic, 20.2 [2.8] items; and other or procedural, 19.8 [2.7] items; P = .29), study design (mean [SD], prospective, 19.7 [2.9] items; retrospective, 19.7 [1.8] items; P > .99), patient population (mean [SD], pediatric, 20.0 [3.1] items; adult, 20.2 [2.7] items; mixed, 17.9 [1.9] items; P = .09), use of supplementary materials (mean [SD], yes, 19.2 [3.0] items; no, 19.7 [2.8] items; P = .91), or journal impact factor (mean [SD], higher impact factor, 20.3 [3.1] items; lower impact factor, 19.1 [2.4] items; P = .08).Conclusions and RelevanceOverall, the literature on acute care point-of-care ultrasound showed moderate adherence to the STARD 2015 guidelines, with more complete reporting found in studies citing STARD and those published in STARD-adopting journals. This study has established a current baseline for reporting; however, future studies are required to understand barriers to complete reporting and to develop strategies to mitigate them. ]]> <![CDATA[Assessment of Time-Series Machine Learning Methods for Forecasting Hospital Discharge Volume]]> https://www.researchpad.co/article/5c50f637d5eed0c48462c07e

Key Points

Question

What is the performance of a new time-series machine learning method for predicting hospital discharge volume?

Findings

In this cohort study of daily hospital discharge volumes at 2 academic medical centers (101 867 patient discharges), predictors of discharge volume were well calibrated. These findings were achieved even with shorter training sets and infrequent retraining.

Meaning

These results appear to demonstrate the feasibility of deploying simple time-series methods to more precisely estimate hospital discharge volumes based on historical data, and may facilitate better matching of resources with clinical volume.

]]>
<![CDATA[Assessment of Long-term Follow-up of Randomized Trial Participants by Linkage to Routinely Collected Data]]> https://www.researchpad.co/article/5c50f52ed5eed0c48462aa33

Key Points

Question

Does follow-up of clinical trial participants by linkage to routinely collected data sources provide important insights into the long-term benefits and harms of treatment?

Findings

This scoping review of the published literature found only 113 trials that had been extended by record linkage. Analysis showed that some benefits of treatment extend beyond the trial, and some harms of treatment only become apparent after the trial is complete.

Meaning

The fate of patients after participation in clinical trials is a neglected topic, and the authors recommend that researchers routinely request permission from trial participants to study long-term treatment effects using linkage to routinely collected data.

]]>