Which Emergent Medication Should I Give Next? Repeated Use of Emergent Medications to Treat Acute Agitation

Agitation is a common symptom encountered among patients treated in psychiatric emergency settings. While there are many guidelines available for initial management of the acutely agitated patient, there is a notable dearth of guidelines that delineate recommended approaches to the acutely agitated patient in whom an initial medication intervention has failed. This manuscript aims to fill this gap by examining evidence available in the literature and providing clinical algorithms suggested by the authors for sequential medication administration in patients with persistent acute agitation in psychiatric emergency settings. We discuss risk factors for medication-related adverse events and provide options for patients who are able to take oral medications and for patients who require parenteral intervention. We conclude with a discussion of the current need for well-designed studies that examine sequential medication options in patients with persistent acute agitation.

Familial hypercholesterolaemia with cardiovascular disease：a case report

A 56-year-old female with definite FH was reported based on clinical algorithms. Whole exome sequencing identified a heterozygous LDLR mutation (c.1599G>A), which is pathogenic according to ACMG guidelines. Sanger sequencing was performed in family members, and the mutation site was co-segregated with the disease in the family.

Creation of clinical algorithms for decision-making in oncology: an example with dose prescription in radiation oncology

In oncology, decision-making in individual situations is often very complex. To deal with such complexity, people tend to reduce it by relying on their initial intuition. The downside of this intuitive, subjective way of decision-making is that it is prone to cognitive and emotional biases such as overestimating the quality of its judgements or being influenced by one’s current mood. Hence, clinical predictions based on intuition often turn out to be wrong and to be outperformed by statistical predictions. Structuring and objectivizing oncological decision-making may thus overcome some of these issues and have advantages such as avoidance of unwarranted clinical practice variance or error-prevention. Even for uncertain situations with limited medical evidence available or controversies about the best treatment option, structured decision-making approaches like clinical algorithms could outperform intuitive decision-making. However, the idea of such algorithms is not to prescribe the clinician which decision to make nor to abolish medical judgement, but to support physicians in making decisions in a systematic and structured manner. An example for a use-case scenario where such an approach may be feasible is the selection of treatment dose in radiation oncology. In this paper, we will describe how a clinical algorithm for selection of a fractionation scheme for palliative irradiation of bone metastases can be created. We explain which steps in the creation process of a clinical algorithm for supporting decision-making need to be performed and which challenges and limitations have to be considered.

Interdisciplinary Approach to Thrombosis Management in COVID-19 at a Large Academic Center

There is an increasing recognition of association of COVID-19 with a distinct coagulopathy and increased risk of thrombosis. Unfortunately, effective strategies to prevent and treat thrombosis in this patient population remain uncertain. In the setting of a worsening pandemic, there is an urgent need to provide practical guidance to the clinicians on management of the coagulopathy, while waiting for the results from large systematic trials to establish best practices. At our institution, we convened an interdisciplinary group of 25 experts in the field of thrombosis from different medical specialties to review available literature and brainstorm management strategies. The group provided a 3-tiered anticoagulation algorithm for patients with COVID-19 along with a pathway for multidisciplinary review of difficult or refractory cases, which are described in this manuscript. In these unprecedented times where medical decision making is made difficult by both the novelty of the disease and paucity of robust data, clinical algorithms such as the one presented here may prove to be helpful for frontline providers caring for individual patients.

Refining clinical algorithms for a neonatal digital platform for low-income countries: a modified Delphi technique

ObjectivesTo determine whether a panel of neonatal experts could address evidence gaps in local and international neonatal guidelines by reaching a consensus on four clinical decision algorithms for a neonatal digital platform (NeoTree).DesignTwo-round, modified Delphi technique.Setting and participantsParticipants were neonatal experts from high-income and low-income countries (LICs).MethodsThis was a consensus-generating study. In round 1, experts rated items for four clinical algorithms (neonatal sepsis, hypoxic ischaemic encephalopathy, respiratory distress of the newborn, hypothermia) and justified their responses. Items meeting consensus for inclusion (≥80% agreement) were incorporated into the algorithms. Items not meeting consensus were either excluded, included following revisions or included if they contained core elements of evidence-based guidelines. In round 2, experts rated items from round 1 that did not reach consensus.ResultsFourteen experts participated in round 1, 10 in round 2. Nine were from high-income countries, five from LICs. Experts included physicians and nurse practitioners with an average neonatal experience of 20 years, 12 in LICs. After two rounds, a consensus was reached on 43 of 84 items (52%). Per experts’ recommendations, items in line with local and WHO guidelines yet not meeting consensus were still included to encourage consistency for front-line healthcare workers. As a result, the final algorithms included 53 items (62%).ConclusionFour algorithms in a neonatal digital platform were reviewed and refined by consensus expert opinion. Revisions to NeoTree will be made in response to these findings. Next steps include clinical validation of the algorithms.

Model for estimating the heterogeneity of the distribution of globule characteristics in images of skin neoplasms

The problem of skin melanoma diagnostics from digital images of the tumor is considered. Clinical algorithms for detecting skin melanoma are briefly described. An overview of the works devoted to the automated assessment of the asymmetry of the distribution of shape, color, area of globules – important signs of melanoma – is given. A model for estimating the heterogeneity of the distribution of the characteristics of globules on digital images in the skin neoplasms diagnosis is developed and models of signs of heterogeneity of this distribution are proposed. The comparative evaluation of the proposed models was carried out experimentally using a software system developed in C++. The most informative features are identified. The greatest accuracy 93 % in estimating the heterogeneity of the distribution of the characteristics of globules was shown by the sign “the reduced inverse of the greatest frequency of occurrence of the measured areas of globules”. The results obtained can be applied in the development of systems to support medical decision-making in the diagnosis of melanoma.