Preventable adverse drug events in critically ill HIV patients: Is the detection of potential drug-drug interactions a useful tool?

OBJECTIVES: The aim of this study was to develop a strategy to identify adverse drug events associated with drug-drug interactions by analyzing the prescriptions of critically ill patients. METHODS: This retrospective study included HIV/AIDS patients who were admitted to an intensive care unit between November 2006 and September 2008. Data were collected in two stages. In the first stage, three prescriptions administered throughout the entire duration of these patients’ hospitalization were reviewed, with the Micromedex database used to search for potential drug-drug interactions. In the second stage, a search for adverse drug events in all available medical, nursing and laboratory records was performed. The probability that a drug-drug interaction caused each adverse drug events was assessed using the Naranjo algorithm. RESULTS: A total of 186 drug prescriptions of 62 HIV/AIDS patients were analyzed. There were 331 potential drug-drug interactions, and 9% of these potential interactions resulted in adverse drug events in 16 patients; these adverse drug events included treatment failure (16.7%) and adverse reactions (83.3%). Most of the adverse drug reactions were classified as possible based on the Naranjo algorithm. CONCLUSIONS: The approach used in this study allowed for the detection of adverse drug events related to 9% of the potential drug-drug interactions that were identified; these adverse drug events affected 26% of the study population. With the monitoring of adverse drug events based on prescriptions, a combination of the evaluation of potential drug-drug interactions by clinical pharmacy services and the monitoring of critically ill patients is an effective strategy that can be used as a complementary tool for safety assessments and the prevention of adverse drug events

A secondary analysis of an international survey

Objective: This study aimed to evaluate Brazilian physicians' perceptions regarding the diagnosis, severity assessment, treatment and risk stratification of severe community-acquired pneumonia patients and to compare those perceptions to current guidelines. Methods: We conducted a cross-sectional international anonymous survey among a convenience sample of critical care, pulmonary, emergency and internal medicine physicians from Brazil between October and December 2008. The electronic survey evaluated physicians' attitudes towards the diagnosis, risk assessment and therapeutic interventions for patients with severe community-acquired pneumonia. Results: A total of 253 physicians responded to the survey, with 66% from Southeast Brazil. The majority (60%) of the responding physicians had > 10 years of medical experience. The risk assessment of severe community-acquired pneumonia was very heterogeneous, with clinical evaluation as the most frequent approach. Although blood cultures were recognized as exhibiting a poor diagnostic performance, these cultures were performed by 75% of respondents. In contrast, the presence of urinary pneumococcal and Legionella antigens was evaluated by less than 1/3 of physicians. The vast majority of physicians (95%) prescribe antibiotics according to a guideline, with the combination of a 3rd /4th generation cephalosporin plus a macrolide as the most frequent choice. Conclusion: This Brazilian survey identified an important gap between guidelines and clinical practice and recommends the institution of educational programs that implement evidence-based strategies for the management of severe community-acquired pneumonia.publishersversionpublishe

Delirium in postoperative nonventilated intensive care patients: risk factors and outcomes

BACKGROUND: Delirium features can vary greatly depending on the postoperative population studied; however, most studies focus only on high-risk patients. Describing the impact of delirium and risk factors in mixed populations can help in the development of preventive actions. METHODS: The occurrence of delirium was evaluated prospectively in 465 consecutive nonventilated postoperative patients admitted to a surgical intensive care unit (SICU) using the confusion assessment method (CAM). Patients with and without delirium were compared. A multiple logistic regression was performed to identify the main risk factors for delirium in the first 24 h of admission to the SICU and the main predictors of outcomes. RESULTS: Delirium was diagnosed in 43 (9.2%) individuals and was more frequent on the second and third days of admission. The presence of delirium resulted in longer lengths of SICU and hospital stays [6 days (3–13) vs. 2 days (1–3), p < 0.001 and 26 days (12–39) vs. 6 days (3–13), p <0.001, respectively], as well as higher hospital and SICU mortality rates [16.3% vs. 4.0%, p = 0.004 and 6.5% vs. 1.7%, p = 0.042, respectively]. The risk factors for delirium were age (odds ratio (OR), 1.04 [1.02-1.07]), Acute Physiologic Score (APS; OR, 1.11 [1.04-1.2]), emergency surgery (OR, 8.05 [3.58-18.06]), the use of benzodiazepines (OR, 2.28 [1.04-5.00]), and trauma (OR, 6.16 [4.1-6.5]). CONCLUSIONS: Delirium negatively impacts postoperative nonventilated patients. Risk factors can be used to detect high-risk patients in a mixed population of SICU patients

Evidence-based checklist to delay cardiac arrest in brain-dead potential organ donors: The DONORS cluster randomized clinical trial

Importance The effectiveness of goal-directed care to reduce loss of brain-dead potential donors to cardiac arrest is unclear. Objective To evaluate the effectiveness of an evidence-based, goal-directed checklist in the clinical management of brain-dead potential donors in the intensive care unit (ICU). Design, Setting, and Participants The Donation Network to Optimize Organ Recovery Study (DONORS) was an open-label, parallel-group cluster randomized clinical trial in Brazil. Enrollment and follow-up were conducted from June 20, 2017, to November 30, 2019. Hospital ICUs that reported 10 or more brain deaths in the previous 2 years were included. Consecutive brain-dead potential donors in the ICU aged 14 to 90 years with a condition consistent with brain death after the first clinical examination were enrolled. Participants were randomized to either the intervention group or the control group. The intention-to-treat data analysis was conducted from June 15 to August 30, 2020. Interventions Hospital staff in the intervention group were instructed to administer to brain-dead potential donors in the intervention group an evidence-based checklist with 13 clinical goals and 14 corresponding actions to guide care, every 6 hours, from study enrollment to organ retrieval. The control group provided or received usual care. Main Outcomes and Measures The primary outcome was loss of brain-dead potential donors to cardiac arrest at the individual level. A prespecified sensitivity analysis assessed the effect of adherence to the checklist in the intervention group. Results Among the 1771 brain-dead potential donors screened in 63 hospitals, 1535 were included. These patients included 673 males (59.2%) and had a median (IQR) age of 51 (36.3-62.0) years. The main cause of brain injury was stroke (877 [57.1%]), followed by trauma (485 [31.6%]). Of the 63 hospitals, 31 (49.2%) were assigned to the intervention group (743 [48.4%] brain-dead potential donors) and 32 (50.8%) to the control group (792 [51.6%] brain-dead potential donors). Seventy potential donors (9.4%) at intervention hospitals and 117 (14.8%) at control hospitals met the primary outcome (risk ratio [RR], 0.70; 95% CI, 0.46-1.08; P = .11). The primary outcome rate was lower in those with adherence higher than 79.0% than in the control group (5.3% vs 14.8%; RR, 0.41; 95% CI, 0.22-0.78; P = .006). Conclusions and Relevance This cluster randomized clinical trial was inconclusive in determining whether the overall use of an evidence-based, goal-directed checklist reduced brain-dead potential donor loss to cardiac arrest. The findings suggest that use of such a checklist has limited effectiveness without adherence to the actions recommended in this checklist

Diagnostic challenges of single plaque-like lesion paucibacillary leprosy

The diagnosis of single-lesion paucibacillary leprosy remains a challenge. Reviews by expert dermatopathologists and quantitative polymerase chain reaction (qPCR) results obtained from 66 single-plaque biopsy samples were compared. Histological findings were graded as high (HP), medium (MP) or low (LP) probability of leprosy or other dermatopathy (OD). Mycobacterium leprae-specific genes were detected using qPCR. The biopsies of 47 out of 57 clinically diagnosed patients who received multidrug therapy were classified as HP/MP, eight of which were qPCR negative. In the LP/OD (n = 19), two out of eight untreated patients showed positive qPCR results. In the absence of typical histopathological features, qPCR may be utilised to aid in final patient diagnosis, thus reducing overtreatment and delay in diagnosis

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

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Neurological manifestations of COVID-19 in adults and children

Different neurological manifestations of coronavirus disease 2019 (COVID-19) in adults and children and their impact have not been well characterized. We aimed to determine the prevalence of neurological manifestations and in-hospital complications among hospitalized COVID-19 patients and ascertain differences between adults and children. We conducted a prospective multicentre observational study using the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) cohort across 1507 sites worldwide from 30 January 2020 to 25 May 2021. Analyses of neurological manifestations and neurological complications considered unadjusted prevalence estimates for predefined patient subgroups, and adjusted estimates as a function of patient age and time of hospitalization using generalized linear models. Overall, 161 239 patients (158 267 adults; 2972 children) hospitalized with COVID-19 and assessed for neurological manifestations and complications were included. In adults and children, the most frequent neurological manifestations at admission were fatigue (adults: 37.4%; children: 20.4%), altered consciousness (20.9%; 6.8%), myalgia (16.9%; 7.6%), dysgeusia (7.4%; 1.9%), anosmia (6.0%; 2.2%) and seizure (1.1%; 5.2%). In adults, the most frequent in-hospital neurological complications were stroke (1.5%), seizure (1%) and CNS infection (0.2%). Each occurred more frequently in intensive care unit (ICU) than in non-ICU patients. In children, seizure was the only neurological complication to occur more frequently in ICU versus non-ICU (7.1% versus 2.3%, P &lt; 0.001). Stroke prevalence increased with increasing age, while CNS infection and seizure steadily decreased with age. There was a dramatic decrease in stroke over time during the pandemic. Hypertension, chronic neurological disease and the use of extracorporeal membrane oxygenation were associated with increased risk of stroke. Altered consciousness was associated with CNS infection, seizure and stroke. All in-hospital neurological complications were associated with increased odds of death. The likelihood of death rose with increasing age, especially after 25 years of age. In conclusion, adults and children have different neurological manifestations and in-hospital complications associated with COVID-19. Stroke risk increased with increasing age, while CNS infection and seizure risk decreased with age

[brás Cubas, Sepsis And The Evidence: Reflections On The Surviving Sepsis Campaign].

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Data-driven methodology to predict the ICU length of stay: A multicentre study of 99,492 admissions in 109 Brazilian units

Purpose: The length of stay (LoS) is one of the most used metrics for resource use in Intensive Care Units (ICU). We propose a structured data-driven methodology to predict the ICU length of stay and the risk of prolonged stay, and its application in a large multicentre Brazilian ICU database. Methods: Demographic data, comorbidities, complications, laboratory data, and primary and secondary diagnosis were prospectively collected and retrospectively analysed by a data-driven methodology, which includes eight different machine learning models and a stacking model. The study setting included 109 mixed-type ICUs from 38 Brazilian hospitals and the external validation was performed by 93 medical-surgical ICUs of 55 hospitals in Brazil. Results: A cohort of 99,492 adult ICU admissions were included from the 1st of January to the 31st of December 2019. The stacking model combining Random Forests and Linear Regression presented the best results to predict ICU length of stay (RMSE = 3.82; MAE = 2.52; R² = 0.36). The prediction model for the risk of long stay were accurate to early identify prolonged stay patients (Brier Score = 0.04, AUC = 0.87, PPV = 0.83, NPV = 0.95). Conclusion: The data-driven methodology to predict ICU length of stay and the risk of long-stay proved accurate in a large multicentre cohort of general ICU patients. The proposed models are helpful to predict the individual length of stay and to early identify patients with high risk of prolonged stay