Off-label drug use in an adult intensive care unit of a Brazilian hospital

Abstract The objective of this study was to determine the prevalence and describe the factors associated with off-label drug use in an adult intensive care unit (ICU) of a Brazilian hospital. An analytical, cross-sectional, prospective study was conducted in the adult ICU population from March 2018 to May 2018. Off-label use of medication was classified by indication, dosage, route of administration, type and volume of diluent, and duration of administration. Most patients were female (57.89%), non-elderly (56.14%), and had a mean age of 54.44 ± 17.15 years. The prevalence of off-label drug use was 70.31%, but was not associated with the clinical severity of the patients. A statistically significant association was observed between label use of drugs and prescribing potentially inappropriate medicines (PIM). The most common reasons for off-label drug use were therapeutic indication (19.58%) and volume of diluent (23.30%). Drug administration by enteral tubes accounted for the largest number of off-label uses due to route of administration (90.85%). There was a higher prevalence of off-label use of systemic antimicrobials (14.44%) and norepinephrine (9.28%). Our study provided a broad characterization of off-label drug use in an adult ICU and showed why it is important for health professionals to evaluate the specific risks and benefits of this practice

What is an intensive care unit? A report of the task force of the World Federation of Societies of Intensive and Critical Care Medicine.

Since their widespread introduction more than half a century ago, intensive care units (ICUs) have become an integral part of the health care system. Although most ICUs are found in high-income countries, they are increasingly a feature of health care systems in low- and middle-income countries. The World Federation of Societies of Intensive and Critical Care Medicine convened a task force whose objective was to answer the question "What is an ICU?" in an internationally meaningful manner and to develop a system for stratifying ICUs on the basis of the intensity of the care they provide. We undertook a scoping review of the peer-reviewed and gray literature to assemble existing models for ICU stratification. Based on these and on discussions among task force members by teleconference and 2 face-to-face meetings, we present a proposed definition and classification of ICUs. An ICU is an organized system for the provision of care to critically ill patients that provides intensive and specialized medical and nursing care, an enhanced capacity for monitoring, and multiple modalities of physiologic organ support to sustain life during a period of life-threatening organ system insufficiency. Although an ICU is based in a defined geographic area of a hospital, its activities often extend beyond the walls of the physical space to include the emergency department, hospital ward, and follow-up clinic. A level 1 ICU is capable of providing oxygen, noninvasive monitoring, and more intensive nursing care than on a ward, whereas a level 2 ICU can provide invasive monitoring and basic life support for a short period. A level 3 ICU provides a full spectrum of monitoring and life support technologies, serves as a regional resource for the care of critically ill patients, and may play an active role in developing the specialty of intensive care through research and education. A formal definition and descriptive framework for ICUs can inform health care decision-makers in planning and measuring capacity and provide clinicians and patients with a benchmark to evaluate the level of resources available for clinical care.

Perfil geográfico e clínico de pacientes admitidos na UTI através da Central de Regulação de Internações Hospitalares / Geographical and clinical profile of patients admitted to the ICUthrough the Center for Regulatory Hospitalizations

Traçar o perfil geográfico e clínico dos pacientes admitidos na Unidade de Terapia Intensiva através da Central de Regulação de Internações Hospitalares, identificando o índice prognóstico no momento da admissão.(AU)

To describe the geographical and clinical profile of patients admitted to the Intensive Care Unit through the Center for Regulatory Hospitalizations, identifying the prognostic index of patients at admission.(AU)

Perfil geográfico e clínico de pacientes admitidos na UTI através da Central de Regulação de Internações Hospitalares / Geographical and clinical profile of patients admitted to the ICUthrough the Center for Regulatory Hospitalizations

Traçar o perfil geográfico e clínico dos pacientes admitidos na Unidade de Terapia Intensiva através da Central de Regulação de Internações Hospitalares, identificando o índice prognóstico no momento da admissão.

To describe the geographical and clinical profile of patients admitted to the Intensive Care Unit through the Center for Regulatory Hospitalizations, identifying the prognostic index of patients at admission.

Visiting policies in Italian intensive care units: a nationwide survey.

OBJECTIVE: To investigate visiting policies in Italian intensive care units. DESIGN AND SETTING: Descriptive survey: a questionnaire was sent to all 303 units of the Italian Group for the Evaluation of Interventions in Intensive Care Medicine. RESULTS: Response rate was 85% (257/303). All ICUs except one had restricted visiting-hour policies. In five ICUs (2%) visits were not allowed. Median daily visiting time was 60 min (range 15 min-18 h); 55% of ICUs had one daily visiting slot. There were restrictions on number (92% of units) and type (17%) of visitors, and on child visits (69%). Policies were not modified for child patients in 9% of ICUs, nor for a dying patient in 21%. No waiting room was provided by 25% of ICUs. Gowning procedures were compulsory for visitors in 95% of units. In 35% of ICUs visitors were not required to wash their hands. A formal process for revision of visiting policies was underway in 33% of ICUs. In 66% of ICUs informative material on the unit was provided to the family on patient admission. Phone information on the patient was given relatively frequently (often/always, 34% of ICUs). Regional area and volume of admissions significantly influenced visiting hours. A visiting period longer than 60 min/day was significantly associated with more "open" attitudes towards visitors. CONCLUSIONS: Italian ICUs have very restrictive visiting policies, which are only partially liberalized when the patient is dying or is a child. However, one-third of ICUs are rethinking their policies. This survey may contribute towards the liberalization of visiting policies in Italy's ICUs.

Quality assessment in intensive care units: proposal for a scoring system in terms of structure and process.

OBJECTIVE: We present a score for assessing the quality of ICU care in terms of structure and process, based on bibliographic review, expert consultations, field test, analysis, and final consensus, and analyze its initial application in the field. DESIGN AND SETTING: This feasibility and observational study was conducted within the framework of a French regional clinical research project (NosoQual); 40 ICUs were visited and assessed between November 2002 and March 2003 according to standardized procedures. MEASUREMENTS AND RESULTS: The grid consisted of 95 variables. The overall score derived from seven independent quality dimensions: human resources, architecture, safety and environment, management of documentation, patient care management, risk management of infections and evaluation, and surveillance. The average level of achievement of the scores varied from 48% to 63% of theoretical maxima. Variability in the individual dimensional subscores was greater than that of the overall score (CV=15). CONCLUSIONS: Evaluation this scoring system encounters the limitation of the absence of a "gold standard." However, this is counterbalanced by the rigorous design methodology, the characteristic strengths of the quality dimensions. The survey also highlights also feasibility and the potential interest for specific tools for the assessment of ICUs.

National audit of critical care resources in South Africa - research methodology.

This article provides an in-depth description of the methodology that was followed and the quality control measures that were implemented during the audit of national critical care resources in South Africa.

Rating the quality of intensive care units: is it a function of the intensive care unit scoring system?

OBJECTIVE: Intensive care units (ICUs) use severity-adjusted mortality measures such as the standardized mortality ratio to benchmark their performance. Prognostic scoring systems such as Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score II, and Mortality Probability Model II0 permit performance-based comparisons of ICUs by adjusting for severity of disease and case mix. Whether different risk-adjustment methods agree on the identity of ICU quality outliers within a single database has not been previously investigated. The objective of this study was to determine whether the identity of ICU quality outliers depends on the ICU scoring system used to calculate the standardized mortality ratio. DESIGN, SETTING, PATIENTS: Retrospective cohort study of 16,604 patients from 32 hospitals based on the outcomes database (Project IMPACT) created by the Society of Critical Care Medicine. The ICUs were a mixture of medical, surgical, and mixed medical-surgical ICUs in urban and nonurban settings. Standardized mortality ratios for each ICU were calculated using APACHE II, Simplified Acute Physiology Score II, and Mortality Probability Model II. ICU quality outliers were defined as ICUs whose standardized mortality ratio was statistically different from 1. Kappa analysis was used to determine the extent of agreement between the scoring systems on the identity of hospital quality outliers. The intraclass correlation coefficient was calculated to estimate the reliability of standardized mortality ratios obtained using the three risk-adjustment methods. MEASUREMENTS AND MAIN RESULTS: Kappa analysis showed fair to moderate agreement among the three scoring systems in identifying ICU quality outliers; the intraclass correlation coefficient suggested moderate to substantial agreement between the scoring systems. The majority of ICUs were classified as high-performance ICUs by all three scoring systems. All three scoring systems exhibited good discrimination and poor calibration in this data set. CONCLUSION: APACHE II, Simplified Acute Physiology Score II, and Mortality Probability Model II0 exhibit fair to moderate agreement in identifying quality outliers. However, the finding that most ICUs in this database were judged to be high-performing units limits the usefulness of these models in their present form for benchmarking.

Identifying quality outliers in a large, multiple-institution database by using customized versions of the Simplified Acute Physiology Score II and the Mortality Probability Model II0.

OBJECTIVE: To assess whether customized versions of the Simplified Acute Physiology Score (SAPS) II and the Mortality Probability Model (MPM) II0 agree on the identity of intensive care unit quality outliers within a multiple-center database. DESIGN: Retrospective database analysis. SETTING AND PATIENTS: Patient subset of the Project IMPACT database consisting of 39,617 adult patients admitted to surgical, medical, and mixed surgical-medical intensive care units at 54 hospitals between 1995 and 1999 who met inclusion criteria for SAPS II and MPM II0. INTERVENTIONS: Customized versions of SAPS II and MPM II0 were obtained by fitting new logistic regressions to the data by using the risk score as the independent variable and outcome at hospital discharge as the dependent variable. The data set was divided randomly into a training set and a validation set. Each model was customized by using the training set; model performance was then assessed in the validation set by using the area under the receiver operating characteristic curve and the Hosmer-Lemeshow statistic. The final models were based on the entire data set. The level of agreement between the customized models on the identity of quality outliers was evaluated by using kappa analysis. MEASUREMENTS AND MAIN RESULTS: Both customized models exhibited good discrimination and good calibration in this database. The area under the receiver operating characteristic curve was 0.83 for MPM II0 and 0.872 for SAPS II following model customization. The Hosmer-Lemeshow statistic was 12.3 ( >.14) for MPM II0, and 8.17 (p >.42) for SAPS II, after customization. Kappa analysis showed only fair agreement between the two customized models with regard to the identity of the quality outliers: kappa = 0.44 (95% confidence interval, 0.24, 0.65). CONCLUSIONS: Customization of SAPS II and MPM II0 to the Project IMPACT database resulted in well-calibrated models. Despite this, the models exhibited only a moderate level of agreement in which hospitals were designated as quality outliers. Seventeen of the 54 hospitals were categorized differently depending on which of the two scoring systems was used. Therefore, the rating of quality of care appears, in part, to be a function of the prediction model used.

Relationship between TISS and ICU cost.

OBJECTIVE: To determine whether the therapeutic intervention scoring system (TISS) reliably reflects the cost of the overall intensive care unit (ICU) population, subgroups of that population and individual ICU patients. DESIGN: Prospective analysis of individual patient costs and comparison with TISS. SETTING: Adult, 12 bedded general medical and surgical ICU in a university teaching hospital. SUBJECTS: Two hundred fifty-seven consecutive patients including 52 coronary care (CCU), 99 cardiac surgery (CS) and 106 general ICU (GIC) cases admitted to the ICU during a 12-week period in 1994. A total of 916 TISS-scored patient days were analysed MAIN OUTCOME MEASURES: A variable cost (VC) that included consumables and service usage (nursing, physiotherapy, radiology and pathology staff costs) for individual patients was measured daily. Nursing costs were calculated in proportion to a daily nursing dependency score. A fixed cost (FC) was calculated for each patient to include medical, technical and clerical salary costs, capital equipment depreciation, equipment and central hospital costs. The correlation between cost and TISS was analysed using regression analysis. RESULTS: For the whole group (n = 257) the average daily FC was pound sterling 255 and daily VC was pound sterling 541 (SEM 10); range pound sterling 23-pound sterling 2,806. In the patient subgroups average daily cost (FC + VC) for CCU was pound sterling 476 (SEM 17.5), for CS pound sterling 766 (SEM 13.8) and for GIC pound sterling 873 (SEM 13.6). In the group as a whole, a strong correlation was demonstrated between VC and the TISS for each patient day (r = 0.87, p < 0.001) and this improved further when the total TISS score was compared with the total VC of the entire patient episode (r = 0.93, p < 0.001). This correlation was maintained in CCU, CS and GIC patient cohorts with only a small median difference between actual and predicted cost (2.2 % for GIC patients). However, in the individual patient, the range of error was up to +/- 65 % of the true variable cost. For the whole group the variable cost per TISS point was pound sterling 25. CONCLUSION: These results demonstrate that TISS reliably measures overall ICU population costs as well as those of the subgroups CCU, CS and GIC. However, the relationship between TISS and cost is less reliable for the individual patient.

Understanding articles comparing outcomes among intensive care units to rate quality of care. Evidence Based Medicine in Critical Care Group.

OBJECTIVES: Comparisons of risk-adjusted outcomes among intensive care units (ICUs) is a relatively new but rapidly expanding area of ICU health services research. By investigating those factors that lead ICUs to have patient outcomes that differ from the average, the overall quality of care across ICUs may be improved. Our goal is to teach clinicians how to evaluate these types of articles. CLINICAL EXAMPLE: An article describing the development and application of an index used to assess the clinical performance and cost-effectiveness of 25 ICUs. RECOMMENDATIONS: Valid comparisons of the outcomes among ICUs are made when: a) the outcome measures are accurate and comprehensive; b) the ICUs being compared serve similar patients; c) the sampling of patients is sufficient and unbiased; d) appropriate risk adjustment is undertaken by applying a valid model to reliably collected data; and e) the comparisons focus on care delivered in the ICU. To evaluate the results of the study, clinicians must evaluate how confident they are that the outcome differences being described are clinically important. Before changes in ICU policy are made based on these outcome differences, it is important to clarify which factors might have resulted in these extreme outcomes and whether these results are applicable in the ICU population that will see the impact of the changes. CONCLUSION: The potential for misinterpretation of outcome performance ratings may decrease if articles describing outcome differences are evaluated, using the criteria outlined in this article.

Effect of changing patient mix on the performance of an intensive care unit severity-of-illness model: how to distinguish a general from a specialty intensive care unit.

OBJECTIVE: To analyze the effects of patient mix diversity on performance of an intensive care unit (ICU) severity-of-illness model. DESIGN: Multiple patient populations were created using computer simulations. A customized version of the Mortality Probability Model (MPM) II admission model was used to ascertain probabilities of hospital mortality. Performance of the model was assessed using discrimination (area under the receiver operating characteristic curve) and calibration (goodness-of-fit testing). SETTING: Intensive care units. PATIENTS: Data were collected from 4,224 ICU patients from two Massachusetts hospitals (Baystate Medical Center, Springfield, MA; University of Massachusetts Medical Center, Worcester, MA) and two New York hospitals (Albany Medical Center, Albany, NY; Ellis Hospital, Schenectady, NY). INTERVENTIONS: Random samples were taken from a database. The percentage of patients with each model disease characteristic was varied by assigning weights (ranging from 0 to 10) to patients with a disease characteristic. Three simulations were run for each of 15 model variables at each of 16 weights, totaling 720 simulations. MEASUREMENTS AND MAIN RESULTS: The area under the receiver operating characteristic curve and model fit were assessed in each random sample. Removing patients with a given disease characteristic did not affect discrimination or calibration. Increasing frequency of patients with each disease characteristic above the original frequency caused discrimination and calibration to deteriorate. Model fit was more robust to increases in less frequently occurring patient conditions. From the goodness-of-fit test, a critical percentage for each admission model variable was determined for each disease characteristic, defined as the percentage at which the average p value for the test over the three replications decreased to < .10. CONCLUSIONS: The concept of critical percentages is potentially clinically important. It might provide an easy first step in checking applicability of a given severity-of-illness model and in defining a general medical-surgical ICU. If the critical percentages are exceeded, as might occur in a highly specialized ICU, the model would not be accurate. Alternative modeling approaches might be to customize the model coefficients to the population for more accurate probabilities or to develop specialized models. The MPM approach remained robust for a large variation in patient mix factors.

Description of various types of intensive and intermediate care units in France. French Multicentric Group of ICU Research.

The types of intensive care are multiple. The aim of this multicentric study was to describe activity of different ICUs using the same methods. 38 ICU were chosen by cooption, not randomization. Collected data concerned input (age, previous health status (HS), Simplified Acute Physiology Score or SAPS, Intensive Care Group (ICG), processes (TISS points), percentage of ventilated patients and pulmonary arterial lines and outcome (ICU death rate). The 3 ICG were: M = medical: all the none surgical patients; S = surgical patients operated in emergency setting during the week preceding or following ICU admission; E = surgical patients whose admission to ICU was scheduled at least 24 h before because of elective surgery. 3,687 patients were studied, classified as follows: M = 2175; S = 885; E = 627. The first part of the results concerned the differences between the three ICG: inputs, processes and outcome were very different in the three groups M, S, E, particularly in the E (elective) group, where therapeutic level was higher for low SAPS and mortality lower for high SAPS. The second part of the results concerns the differences between the ICUs. Intermediate units had older, less severe, and mainly medical patients. Surgical patients had better previous health status, were younger and scheduled for 40%. TISS points were higher, mainly by a higher rate of ventilated patients and patients with pulmonary artery lines on the first day. Specialized units characteristics depended mainly on the ICG.(ABSTRACT TRUNCATED AT 250 WORDS)