Step towards a patient timeline in intensive care units

In Intensive Medicine, the presentation of medical information is done in many ways, depending on the type of data collected and stored. The way in which the information is presented can make it difficult for intensivists to quickly understand the patient's condition. When there is the need to cross between several types of clinical data sources the situation is even worse. This research seeks to explore a new way of presenting information about patients, based on the timeframe in which events occur. By developing an interactive Patient Timeline, intensivists will have access to a new environment in real-time where they can consult the patient clinical history and the data collected until the moment. The medical history will be available from the moment in which patients is admitted in the ICU until discharge, allowing intensivist to examine data regarding vital signs, medication, exams, among others. This timeline also intends to, through the use of information and models produced by the INTCare system, combine several clinical data in order to help diagnose the future patients’ conditions. This platform will help intensivists to make more accurate decision. This paper presents the first approach of the solution designed

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Pervasive patient timeline for intensive care units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

Retrospective Study of the Survival of Patients who Underwent Cardiopulmonary Resuscitation in an Intensive Care Unit

OBJECTIVE: To evaluate clinical and evolutive characteristics of patients admitted in an intensive care unit after cardiopulmonary resuscitation, identifying prognostic survival factors.METHODS: A retrospective study of 136 patients admitted between 1995 and 1999 to an intensive care unit, evaluating clinical conditions, mechanisms and causes of cardiopulmonary arrest, and their relation to hospital mortality.RESULTS: A 76% mortality rate independent of age and sex was observed. Asystole was the most frequent mechanism of death, and seen in isolation pulmonary arrest was the least frequent. Cardiac failure, need for mechanical ventilation, cirrhosis and previous stroke were clinically significant (p<0.01) death factors.CONCLUSION: Prognostic factors supplement the doctor's decision as to whether or not a patient will benefit from cardiopulmonary resuscitation.

Five-year evolution of drug prescribing in a university adult intensive care unit

Introduction: Drug prescription is difficult in ICUs as prescribers are many, drugs expensive and decisions complex. In our ICU, specialist clinicians (SC) are entitled to prescribe a list of specific drugs, negotiated with intensive care physicians (ICP). The objective of this investigation was to assess the 5-year evolution of quantity and costs of drug prescription in our adult ICU and identify the relative costs generated by ICP or SC. Methods: Quantities and costs of drugs delivered on a quarterly basis to the adult ICU of our hospital between 2004 and 2008 were extracted from the pharmacy database by ATC code, an international five-level classification system. Within each ATC first level, drugs with either high level of consumption, high costs or large variations in quantities and costs were singled out and split by type of prescriber, ICP or SC. Cost figures used were drug purchase prices by the hospital pharmacy. Results: Over the 5-year period, both quantities and costs of drugs increased, following a nonsteady, nonparallel pattern. Four ATC codes accounted for 80% of both quantities and costs, with ATC code B (blood and haematopoietic organs) amounting to 63% in quantities and 41% in costs, followed by ATC code J (systemic anti-infective, 20% of the costs), ATC code N (nervous system, 11% of the costs) and ATC code C (cardiovascular system, 8% of the costs). Prescription by SC amounted to 1% in drug quantities, but 19% in drug costs. The rate of increase in quantities and costs was seven times larger for ICP than for SC (Figure 1 overleaf ). Some peak values in costs and quantities were related to a very limited number of patients. Conclusions: A 5-year increase in quantities and costs of drug prescription in an ICU is a matter of concern. Rather unexpectedly, total costs and cost increases were generated mainly by ICP. A careful follow-up is necessary to try influencing this evolution through an institutional policy co-opted by all professional categories involved in the process.

Streamlining the medication process improves safety in the intensive care unit.

BACKGROUND: Multiple interventions were made to optimize the medication process in our intensive care unit (ICU). 1 Transcriptions from the medical order form to the administration plan were eliminated by merging both into a single document; 2 the new form was built in a logical sequence and was highly structured to promote completeness and standardization of information; 3 frequently used drug names, approved units, and fixed routes were pre-printed; 4 physicians and nurses were trained with regard to the correct use of the new form. This study was aimed at evaluating the impact of these interventions on clinically significant types of medication errors. METHODS: Eight types of medication errors were measured by a prospective chart review before and after the interventions in the ICU of a public tertiary care hospital. We used an interrupted time-series design to control the secular trends. RESULTS: Over 85 days, 9298 lines of drug prescription and/or administration to 294 patients, corresponding to 754 patient-days were collected and analysed for the three series before and three series following the intervention. Global error rate decreased from 4.95 to 2.14% (-56.8%, P < 0.001). CONCLUSIONS: The safety of the medication process in our ICU was improved by simple and inexpensive interventions. In addition to the optimization of the prescription writing process, the documentation of intravenous preparation, and the scheduling of administration, the elimination of the transcription in combination with the training of users contributed to reducing errors and carried an interesting potential to increase safety.

Impact of clinical practice guidelines on priorisation for intensive care beds allocation in high-risk acute coronary syndrome patients: does age play a role?

QUESTION UNDER STUDY: To assess which high-risk acute coronary syndrome (ACS) patient characteristics played a role in prioritising access to intensive care unit (ICU), and whether introducing clinical practice guidelines (CPG) explicitly stating ICU admission criteria altered this practice. PATIENTS AND METHODS: All consecutive patients with ACS admitted to our medical emergency centre over 3 months before and after CPG implementation were prospectively assessed. The impact of demographic and clinical characteristics (age, gender, cardiovascular risk factors, and clinical parameters upon admission) on ICU hospitalisation of high-risk patients (defined as retrosternal pain of prolonged duration with ECG changes and/or positive troponin blood level) was studied by logistic regression. RESULTS: Before and after CPG implementation, 328 and 364 patients, respectively, were assessed for suspicion of ACS. Before CPG implementation, 36 of the 81 high-risk patients (44.4%) were admitted to ICU. After CPG implementation, 35 of the 90 high-risk patients (38.9%) were admitted to ICU. Male patients were more frequently admitted to ICU before CPG implementation (OR=7.45, 95% CI 2.10-26.44), but not after (OR=0.73, 95% CI 0.20-2.66). Age played a significant role in both periods (OR=1.57, 95% CI 1.24-1.99), both young and advanced ages significantly reducing ICU admission, but to a lesser extent after CPG implementation. CONCLUSION: Prioritisation of access to ICU for high-risk ACS patients was age-dependent, but focused on the cardiovascular risk factor profile. CPG implementation explicitly stating ICU admission criteria decreased discrimination against women, but other factors are likely to play a role in bed allocation.

Pulse oximetry in pediatric intensive care: comparison with measured saturations and transcutaneous oxygen tension.

We evaluated a new pulse oximeter designed to monitor beat-to-beat arterial oxygen saturation (SaO2) and compared the monitored SaO2 with arterial samples measured by co-oximetry. In 40 critically ill children (112 data sets) with a mean age of 3.9 years (range 1 day to 19 years), SaO2 ranged from 57% to 100%, and PaO2 from 27 to 128 mm Hg, heart rates from 85 to 210 beats per minute, hematocrit from 20% to 67%, and fetal hemoglobin levels from 1.3% to 60%; peripheral temperatures varied between 26.5 degrees and 36.5 degrees C. Linear correlation analysis revealed a good agreement between simultaneous pulse oximeter values and both directly measured SaO2 (r = 0.95) and that calculated from measured arterial PaO2 (r = 0.95). The device detected several otherwise unrecognized drops in SaO2 but failed to function in four patients with poor peripheral perfusion secondary to low cardiac output. Simultaneous measurements with a tcPO2 electrode showed a similarly good correlation with PaO22 (r = 0.91), but the differences between the two measurements were much wider (mean 7.1 +/- 10.3 mm Hg, range -14 to +49 mm Hg) than the differences between pulse oximeter SaO2 and measured SaO2 (1.5% +/- 3.5%, range -7.5% to -9%) and were not predictable. We conclude that pulse oximetry is a reliable and accurate noninvasive device for measuring saturation, which because of its rapid response time may be an important advance in monitoring changes in oxygenation and guiding oxygen therapy.

A prospective randomised multi-centre controlled trial on tight glucose control by intensive insulin therapy in adult intensive care units: the Glucontrol study.

PURPOSE: An optimal target for glucose control in ICU patients remains unclear. This prospective randomized controlled trial compared the effects on ICU mortality of intensive insulin therapy (IIT) with an intermediate glucose control. METHODS: Adult patients admitted to the 21 participating medico-surgical ICUs were randomized to group 1 (target BG 7.8-10.0 mmol/L) or to group 2 (target BG 4.4-6.1 mmol/L). RESULTS: While the required sample size was 1,750 per group, the trial was stopped early due to a high rate of unintended protocol violations. From 1,101 admissions, the outcomes of 542 patients assigned to group 1 and 536 of group 2 were analysed. The groups were well balanced. BG levels averaged in group 1 8.0 mmol/L (IQR 7.1-9.0) (median of all values) and 7.7 mmol/L (IQR 6.7-8.8) (median of morning BG) versus 6.5 mmol/L (IQR 6.0-7.2) and 6.1 mmol/L (IQR 5.5-6.8) for group 2 (p < 0.0001 for both comparisons). The percentage of patients treated with insulin averaged 66.2 and 96.3%, respectively. Proportion of time spent in target BG was similar, averaging 39.5% and 45.1% (median (IQR) 34.3 (18.5-50.0) and 39.3 (26.2-53.6)%) in the groups 1 and 2, respectively. The rate of hypoglycaemia was higher in the group 2 (8.7%) than in group 1 (2.7%, p < 0.0001). ICU mortality was similar in the two groups (15.3 vs. 17.2%). CONCLUSIONS: In this prematurely stopped and therefore underpowered study, there was a lack of clinical benefit of intensive insulin therapy (target 4.4-6.1 mmol/L), associated with an increased incidence of hypoglycaemia, as compared to a 7.8-10.0 mmol/L target. (ClinicalTrials.gov # NCT00107601, EUDRA-CT Number: 200400391440).

Prospective evaluation of three point of care devices for glycemia measurement in a neonatal intensive care unit.

Hypoglycemia, if recurrent, may have severe consequences on cognitive and psychomotor development of neonates. Therefore, screening for hypoglycemia is a daily routine in every facility taking care of newborn infants. Point-of-care-testing (POCT) devices are interesting for neonatal use, as their handling is easy, measurements can be performed at bedside, demanded blood volume is small and results are readily available. However, such whole blood measurements are challenged by a wide variation of hematocrit in neonates and a spectrum of normal glucose concentration at the lower end of the test range. We conducted a prospective trial to check precision and accuracy of the best suitable POCT device for neonatal use from three leading companies in Europe. Of the three devices tested (Precision Xceed, Abbott; Elite XL, Bayer; Aviva Nano, Roche), Aviva Nano exhibited the best precision. None completely fulfilled the ISO-accuracy-criteria 15197: 2003 or 2011. Aviva Nano fulfilled these criteria in 92% of cases while the others were <87%. Precision Xceed reached the 95% limit of the 2003 ISO-criteria for values ≤4.2 mmol/L, but not for the higher range (71%). Although validated for adults, new POCT devices need to be specifically evaluated on newborn infants before adopting their routine use in neonatology.

Could Hyperglycaemia and Troponin I predict outcome in intensive care after congenital heart surgery in children?

Objective: To establish if hyperglycaemia and cardiac Troponin I (cTnI) after congenital heart surgery on cardiopulmonary bypass in children could predict outcome in intensive care unit. Methods: retrospective cohort study including 274 children (mean age 4.6 years; range 0 - 17 years-old). CTnI and glucose values were retrieved from our database. Integrated values (area under the curve (AUC)) were calculated for evaluation of sustained hyperglycaemia and then normalised per hour (48h-Gluc/h). Maximal cTnI, fi rst glucose value (Gluc1) and 48h-Gluc/h were then correlated with duration of mechanical ventilation, ICU stay and mortality using cut-off values. Results: The mean duration of mechanical ventilation was 5.1 ± 7.2 days and ICU stay was 11.0 ± 13.3 days, 11 patients (3.9%) died. Hyperglycaemia (>6.1 mmol/l) was present in 68% of children at admission and was sustained in 85% for 48 hours. The mean value of Gluc1 (7.3 ± 2.7 vs. 11.8 ± 6.4 mmol/l, p < 0.0001), 48h-Gluc/h (7.4 ± 1.4 vs. 9.9 ± 4.6 mmol/l/h, p < 0.0001) and cTnI max (16.7 ± 21.8 vs. 59.2 ± 41.4 mcg/l, p < 0.0001) were signifi cantly lower in survivors vs. non survivors. Cut-off values and odds ratio are summarised in Table 1. Analyses for duration of mechanical ventilation and for length of stay in ICU are depicted in Table 2. Conclusions: Hyperglycaemia is frequent after cardiopulmonary bypass and sustained in the fi rst 48 hours. Admission glycaemia and cTnI max are associated with a high risk of mortality, prolonged duration of mechanical ventilation and prolonged length of stay in ICU.