Lauesenhayden0704

Decompensation is a major prehospital threat to survival from trauma/hemorrhage shock (T/HS) after controlling bleeding. We recently showed higher than expected mortality from a combat-relevant rat model of T/HS (27 mL/kg hemorrhage) with tourniquet (TQ) and permissive hypotensive resuscitation (PHR) with Plasmalyte. Mortality and fluid requirements were reduced by resuscitation with 25% albumin presaturated with oleic acid (OA-sat) compared with fatty-acid -free albumin or Plasmalyte. The objective of this follow-up analysis was to determine the role of decompensation and individual compensatory mechanisms in those outcomes. We observed two forms of decompensation slow (accelerating fluid volumes needed to maintain blood pressure) and acute (continuous fluid administration unable to prevent pressure drop). Combined incidence of decompensation was 71%. Itacnosertib concentration Nearly all deaths (21 of 22) were caused by acute decompensations that began as slow decompensations. The best hemodynamic measure for predicting acute decompensation was diastolic arterial pressure. Decompensation was due to vascular decompensation rather than loss of cardiac performance. Albumin concentration was lower in decompensating groups, suggesting decreased stressed volume, which may explain the association of low albumin on admission with poor outcomes after trauma. Our findings suggest that acute decompensation may be common after trauma and severe hemorrhage treated with TQ and PHR and OA-sat albumin may benefit early survival and reduce transfusion volume by improving venous constriction and preventing decompensation.

Significant progress has been made in the practice of conducting causal analysis using network models. Despite this progress, there is limited evidence that hospital risk managers are using these analytical models.

This article introduces the causal network, its related concepts, and methods of analysis. The article demonstrates how hospital risk managers can use existing regression software to construct a causal network and identify root causes of an adverse event.

Causal networks depict cause and effect in a set of variables. In this context, causes are strong correlations that meet 3 additional criteria (1) causes occur prior to effects, (2) there is an articulated mechanism for how causes lead to effects, and (3) the association between cause and effect is not spurious, meaning the association persists even after other variables are statistically controlled for (a method of analysis called counterfactual). A causal network can be constructed through repeated use of least absolute shrinkage and selecks. The recovered network allowed the identification of root and direct causes. It showed that hospital occupancy rate, and not emergency department efficiency, was root cause of excessive emergency department boarding.

Causal networks can provide insights into root, and direct, causes of an adverse event. These models provide empirical tests of causes of adverse events. We encourage the use of these methods by hospital risk managers.

Causal networks can provide insights into root, and direct, causes of an adverse event. These models provide empirical tests of causes of adverse events. We encourage the use of these methods by hospital risk managers.

Root cause analysis involves evaluation of causal relationships between exposures (or interventions) and adverse outcomes, such as identification of direct (eg, medication orders missed) and root causes (eg, clinician's fatigue and workload) of adverse rare events. To assess causality requires either randomization or sophisticated methods applied to carefully designed observational studies. In most cases, randomized trials are not feasible in the context of root cause analysis. Using observational data for causal inference, however, presents many challenges in both the design and analysis stages. Methods for observational causal inference often fall outside the toolbox of even well-trained statisticians, thus necessitating workforce training.

This article synthesizes the key concepts and statistical perspectives for causal inference, and describes available educational resources, with a focus on observational clinical data. The target audience for this review is clinical researchers with training in fundar functioning effectively within a multidisciplinary team.

A familiarity with causal inference methods can help risk managers empirically verify, from observed events, the true causes of adverse sentinel events.

A familiarity with causal inference methods can help risk managers empirically verify, from observed events, the true causes of adverse sentinel events.

High reliability organizations in health care must identify defects and systematically approach causal factors with subsequent process redesign to achieve goals important to patients, families, and staff. Root cause analysis (RCA) is a commonly leveraged strategy for reviewing adverse events and can yield immense benefits toward patient safety when applied alongside complementary change management strategies such as Lean and Six Sigma. We performed an RCA in response to a hospital-acquired venous thromboembolism (VTE) event in a postoperative patient for which pharmacologic VTE prophylaxis was not appropriately resumed following removal of an epidural catheter.

A multidisciplinary stakeholder team was assembled to further understand the details of the event. A current process map was created and non-value-added steps were identified. Causal analysis revealed that frequent staff turnover, variable methods of communication between stakeholders, inconsistent responsibilities with respect to ordering and admit, and failure modes and effects analysis. These strategies allowed us to design effective error-reducing strategies to achieve a more reliable process, which yielded reduced VTE prophylaxis administration defects that in turn has prevented recurrence of hospital-acquired VTE in patients with epidural catheters.

Blood administration failures and errors have been a crucial issue in health care settings. Failure mode and effects analysis is an effective tool for the analysis of failures and errors in such lifesaving procedures. These failures or errors would lead to adverse outcomes for patients during blood administration.

The study aimed to use health care failure mode and effect analysis (HFMEA) for assessing potential failure modes associated with blood administration processes among nurses; develop a categorization of blood administration errors; and identify underlying reasons, proactive measures for identified failure modes, and corrective actions for identified high-risk failures.

A cross-sectional descriptive study was conducted in surgical care units by using observation, HFMEA, and brainstorming techniques. Prioritization of detected potential failures was performed by Pareto analysis.

Eleven practical steps and 38 potential failure modes associated with 11 categories of errors were detected in this process.