Dataset Information

Critical Steps in Data Management During a Crisis.

ABSTRACT: Data management is essential in a flow cytometry (FCM) shared resource laboratory (SRL) for the integrity of collected data and its long-term preservation, as described in the Cytometry publication from 2016, ISAC Flow Cytometry Shared Resource Laboratory (SRL) Best Practices (Barsky et al.: Cytometry Part A 89A(2016): 1017-1030). The SARS-CoV-2 pandemic introduced an array of challenges in the operation of SRLs. The subsequent laboratory shutdowns and access restrictions brought to the forefront well-established practices that withstood the impact of a sudden change in operations and illuminated areas that need improvement. The most significant challenges from a data management perspective were data access for remote analysis and workstation management. Notably, lessons learned from this challenge emphasize the importance of safeguarding collected data from loss in various emergencies such as fire or natural disasters where the physical hardware storing data could be directly affected. Here, we describe two data management systems that have been successful during the current emergency created by the pandemic, specifically remote access and automated data transfer. We will discuss other situations that could arise and lead to data loss or challenges in interpreting data. © 2020 International Society for Advancement of Cytometry.

SUBMITTER: Black M

PROVIDER: S-EPMC7753671 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Critical Steps in Data Management During a Crisis.

Black Michele M Moncada Karla K Herstad Kyle K

Cytometry. Part A : the journal of the International Society for Analytical Cytology 20201124 1

Data management is essential in a flow cytometry (FCM) shared resource laboratory (SRL) for the integrity of collected data and its long-term preservation, as described in the Cytometry publication from 2016, ISAC Flow Cytometry Shared Resource Laboratory (SRL) Best Practices (Barsky et al.: Cytometry Part A 89A(2016): 1017-1030). The SARS-CoV-2 pandemic introduced an array of challenges in the operation of SRLs. The subsequent laboratory shutdowns and access restrictions brought to the forefron ...[more]

PMID: 33197114

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Project description:BackgroundMalaria still causes high morbidity and mortality around the world, mainly in sub-Saharan Africa. Community case management of malaria (CCMm) by community health workers (CHWs) is one of the strategies to combat the disease by increasing access to malaria treatment. Currently, the World Health Organization recommends to treat only confirmed malaria cases, rather than to give presumptive treatment.ObjectivesThis systematic review aims to provide a comprehensive overview of the success or failure of critical steps in CCMm with rapid diagnostic tests (RDTs).MethodsThe databases of Medline, Embase, the Cochrane Library, the library of the 'Malaria in Pregnancy' consortium, and Web of Science were used to find studies on CCMm with RDTs in SSA. Studies were selected according to inclusion and exclusion criteria, subsequently risk of bias was assessed and data extracted.Results27 articles were included. CHWs were able to correctly perform RDTs, although specificity levels were variable. CHWs showed high adherence to test results, but in some studies a substantial group of RDT negatives received treatment. High risk of bias was found for morbidity and mortality studies, therefore, effects on morbidity and mortality could not be estimated. Uptake and acceptance by the community was high, however negative-tested patients did not always follow up referral advice. Drug or RDT stock-outs and limited information on CHW motivation are bottlenecks for sustainable implementation. RDT-based CCMm was found to be cost effective for the correct treatment of malaria in areas with low to medium malaria prevalence, but study designs were not optimal.DiscussionTrained CHWs can deliver high quality care for malaria using RDTs. However, lower RDT specificity could lead to missed diagnoses of non-malarial causes of fever. Other threats for CCMm are non-adherence to negative test results and low referral completion. Integrated CCM may solve some of these issues. Unfortunately, morbidity and mortality are not adequately investigated. More information is needed about influencing sociocultural aspects, CHW motivation and stock supply.ConclusionCCMm is generally well executed by CHWs, but there are several barriers for its success. Integrated CCM may overcome some of these barriers.

Project description:BackgroundTo understand delirium heterogeneity, prior work relied on psychomotor symptoms or risk factors to identify subtypes. Data-driven approaches have used machine learning to identify biologically plausible, treatment-responsive subtypes of other acute illnesses but have not been used to examine delirium.MethodsWe conducted a secondary analysis of a large, multicenter prospective cohort study involving adults in medical or surgical ICUs with respiratory failure or shock who experienced delirium per the Confusion Assessment Method for the ICU. We used data collected before delirium diagnosis in an unsupervised latent class model to identify delirium subtypes and then compared demographics, clinical characteristics, and outcomes between subtypes in the final model.FindingsThe 731 patients who developed delirium during critical illness had a median age of 63 [IQR, 54-72] years, a median Sequential Organ Failure Assessment score of 8.0 [6.0-11.0] and 613 [83.4%] were mechanically ventilated at delirium identification. A four-class model best fit the data with 50% of patients in subtype (ST) 1, 18% in subtype 2, 17% in subtype 3, and 14% in subtype 4. Subtype 2-which had more shock and kidney impairment-had the highest mortality (33% [ST2] vs. 17% [ST1], 25% [ST3], and 17% [ST4], p = 0.003). Subtype 4-which received more benzodiazepines and opioids-had the longest duration of delirium (6 days [ST4] vs. 3 [ST1], 4 [ST2], and 3 days [ST3], p < 0.001) and coma (4 days [ST4] vs. 2 [ST1], 1 [ST2], and 2 days [ST3], p < 0.001). Each of the four data-derived delirium subtypes was observed within previously identified psychomotor and risk factor-based delirium subtypes. Clinically significant cognitive impairment affected all subtypes at follow-up, but its severity did not differ by subtype (3-month, p = 0.26; 12-month, p = 0.80).InterpretationThe four data-derived delirium subtypes identified in this study should now be validated in independent cohorts, examined for differential treatment effects in trials, and inform mechanistic work evaluating treatment targets.FundingNational Institutes of Health (T32HL007820, R01AG027472).

Dataset Information

Critical Steps in Data Management During a Crisis.

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Critical Steps in Data Management During a Crisis.

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