Validation of an outcome prediction model for critically ill trauma patients without head injury

Thefeasibility, appropriateness, and applicability of trauma scoring systems in low and middle-income countries: a systematic review

Background

About 5.8 million people die each year as a result of injuries, and nearly 90% of these deaths occur in low and middle-income countries (LMIC). Trauma scoring is a cornerstone of trauma quality improvement (QI) efforts, and is key to organizing and evaluating trauma services. The objective of this review was to assess the appropriateness, feasibility, and QI applicability of traditional trauma scoring systems in LMIC settings.

Materials and methods

This systematic review searched PubMed, Scopus, CINAHL, and trauma-focused journals for articles describing the use of a standardized trauma scoring system to characterize holistic health status. Studies conducted in high-income countries (HIC) or describing scores for isolated anatomic locations were excluded. Data reporting a score’s capacity to discriminate mortality, feasibility of implementation, or use for QI were extracted and synthesized.

Results

Of the 896 articles screened, 336 were included. Over half of studies (56%) reported Glasgow Coma Scale, followed by Injury Severity Score (ISS; 51%), Abbreviated Injury Scale (AIS; 24%), Revised Trauma Score (RTS; 19%), Trauma and Injury Severity Score (TRISS; 14%), and Kampala Trauma Score (7%). While ISS was overwhelmingly predictive of mortality, 12 articles reported limited feasibility of ISS and/or AIS. RTS consistently underestimated injury severity. Over a third of articles (37%) reporting TRISS assessmentsobserved mortality that was greater than that predicted by TRISS. Several articles cited limited human resources as the key challenge to feasibility.

Conclusions

The findings of this review reveal that implementing systems designed for HICs may not be relevant to the burden and resources available in LMICs. Adaptations or alternative scoring systems may be more effective.

PROSPERO registration number

CRD42017064600.

Trauma intensive care unit survival: how good is an educated guess?

BACKGROUND

Resource utilization in medicine is becoming a more and more urgent issue with ongoing national discussions on healthcare coverage. In the management of a trauma system, large amounts of resources and money are expended on individual patients in hope of a "great save." In addition, those of us caring for these patients are required to estimate outcomes daily to the family in an effort to choose the best course of care for an individual patient. Hence, we undertook a study to analyze the accuracy of outcomes predictions of various members of the healthcare team.

METHODS

During a period of 38 months (July 2005 to August 2008), an observational study of patients admitted to a Level I Trauma Center Intensive Care Unit (ICU) was undertaken. Institutional Review Board permission was obtained before starting the study. Only patients older than 18 years were included. Patients who were moribund or expected discharge within 72 hours were excluded.Our traumatized ICU patients are cared for by a multidisciplinary team consisting of a trauma/ICU attending, all of whom have additional certification in surgical critical care and who rotate through the ICU on a weekly basis, a surgical ICU fellow, residents and medical students of several levels of training who rotate on a monthly basis, trauma advanced-level practitioners who rotate weekly, and bedside ICU nurses who work routine shifts. Respiratory therapists, nutritionists, ICU pharmacists, and other members of the rounding team were not included in the study because they do not provide global patient care. Regardless of admitting physician, the patients are managed by the team, and our practice of care is similar across the group, based on protocols and consensus.For each of the study patients, a survey tool was filled out by the ICU rounding team on hospital day 1 and hospital day 3. The tool was completed by members of the team providing global care to the patient and varied depending on the members of the group at each day's rounds. All current and admission data on injuries, study and laboratory results, and current patient status were available to all members of the team. Each member was expected to fill out the survey tool independently, and the results of the tool were not discussed during rounds.Concurrently, data were collected by the ICU fellow and research nurse. These data and the results of the survey tools were entered in a database for analysis after patient discharge. A retrospective analysis was undertaken to analyze the relative accuracy of the care, team members' assessment, and actual survival. Statistical analysis was done using by-chance accuracy comparisons.

RESULTS

Two hundred twenty-three patients had 326 observations performed. Day 3 accuracy improved for most groups. In all groups, accuracy was found to be statistically significantly better than by-chance accuracy. Given that the majority of patients in the trauma population are survivors, sensitivity and positive predictive value of the observer's ability to predict death were also evaluated.

CONCLUSIONS

Although significantly better than chance prediction, the ability of members of the ICU team to predict survival of trauma patients remains poor, particularly on initial evaluation. A period of clinical observation improves the accuracy. Unfortunately, experience of the observer does not seem to improve accuracy of survival prediction. This data indicate that care must be taken when describing likely outcomes to patient family members.

Re-evaluation of established risk scores by measurement of nucleated red blood cells in blood of surgical intensive care patients

BACKGROUND

In adults, the appearance of nucleated red blood cells (NRBC) in the peripheral blood is associated with several severe diseases. When NRBC are detected in the blood, the prognosis is poor. The purpose of this study was to identify the impact of NRBC on the clinical outcomes of surgical intensive care patients under consideration of established risk models.

METHODS

In a prospective study, the detection of NRBC in the peripheral blood of surgical intensive care patients was analyzed with regard to the in- hospital mortality. NRBC were daily measured with a Sysmex XE-2100. The prognostic significance of NRBC in blood was analyzed under consideration of established risk scores for intensive care patients, i.e., the Acute Physiology and Chronic Health Evaluation (APACHE II) and the Simplified Acute Physiology Score (SAPS II).

RESULTS

Two hundred seventy-one surgical intensive care patients were included in this study. The mean age was 61.3 years +/- 1.2 years (range, 18-98 years). The average APACHE II and SAPS II scores were 20.6 +/- 0.6 and 44.1 +/- 1.2, respectively. The in-hospital mortality of NRBC-positive patients was 51.7% (45 of 87). This was significantly higher (p < 0.001) than the mortality of NRBC-negative patients (12.0%, 22 of 184). The area under curve (C-statistic) was 0.77. Mortality increased with the NRBC concentration. On average, in NRBC-positive patients who died, NRBC were detected for the first time 13.3 days +/- 3.1 days (n = 45, median = 6 days) before death. Multiple logistic regression analysis under consideration of the APACHE II or the SAPS II revealed a significant association between NRBC and increased mortality, the mean odds ratio being 1.97 for each increase in the NRBC category (0/microL; 1-40/microL; 41-80/microL; 81-240/microL, >240/microL). In contrast, under consideration of the NRBC-data the mean odds ratios for the increase of one score point of the APACHE II and SAPS II were 1.10 and 1.05, respectively. Therefore, each step-up in the NRBC category is equivalent to approximately 7 APACHE II-score points and 14 SAPS II-score points, respectively.

CONCLUSIONS

The daily screening for NRBC in blood of surgical intensive care patients is of prognostic power with regard to the patients' in-hospital mortality. This prognostic significance of NRBC was independent of the scores APACHE II and SAPS II, respectively. Therefore, for prognostic purposes an adjustment of these established risk models by including the NRBC-results is feasible.

Critical care in Africa

This article identifies the key challenges facing critical care practitioners in Africa. As a continent with a large proportion of the poorest countries and a plethora of unique diseases that place large demands for critical resources, the provision of care is largely in the context of severely constrained human and material resources. Diverse cultures and social norms predicate sensitivity to community values in the provision of care. Such realities demand novel approaches to the provision of critical care.

Aplicación de las redes neuronales artificiales para la estratificación de riesgo de mortalidad hospitalaria

OBJECTIVE

To compare the ability of an artificial neural network (ANN) to predict hospital mortality with that of the Acute Physiology and Chronic Health Evaluation II (APACHE II) system and multiple logistic regression (LR). A secondary objective was to compare the allocation of individual probability among the models.

METHOD

The variables required for calculating the APACHE II were prospectively collected. A total of 1146 patients were divided (randomly 70% and 30%) into the Development (800) and the Validation (346) sets. With the same variables an LR model and an ANN were carried out (a 3-layer perceptron trained by algorithm backpropagation with bootstrap resampling and with 9 nodes in the hidden layer) in the Development set. The models developed were contrasted with the Validation set and their discrimination properties were evaluated using the area under the ROC curve (AUC [95% CI]) and calibration with the Hosmer-Lemeshow C (HLC) test. Differences between the probabilities were evaluated using the Bland-Altman test.

RESULTS

The Validation set showed an APACHE II with an AUC = 0.79 (0.75-0.84) and HLC = 11 (p = 0.329); LR model AUC = 0.81 (0.76-0.85) and HLC = 29 (p = 0.0001) and an ANN AUC = 0.82 (0.77-0.86) and HLC = 10 (p = 0.404). The patients with the most important differences in the allocation of probability between LR and ANN (8% of the total) were neurological. The worst results were found in trauma patients with an AUC of not greater than 0.75 in all the models. In respiratory patients, the ANN achieved the best AUC = 0.87 (0.78-0.91).

CONCLUSIONS

The ANN was able to stratify hospital mortality risk by using the APACHE II system variables. The ANN tended to achieve better results than LR, since, in order to work, it does not require lineal restrictions or independent variables. Allocation of individual probability differed in each model.

Outcome evaluation in a surgical intensive care unit in Barbados

The performance of the Acute Physiology and Chronic Health Evaluation II scoring system was prospectively assessed in the surgical intensive care unit at the Queen Elizabeth Hospital, Barbados. A total of 309 patients admitted consecutively during a 2-year period (1999-2001) were evaluated. Demographic data, diagnosis, Acute Physiology and Chronic Health Evaluation II score, duration of stay and hospital outcome were recorded. The predicted mortality for every patient and the costs incurred were also calculated. The overall observed mortality rate was 15.9% while the mean predicted mortality rate for our case-mix was 16.4%, which is comparable to results from developed countries. The cost incurred per patient was much lower at $13,636 (Barbados), compared to the patients' cost in North America ($60,000 Barbados).

Severity scoring and outcome assessment. Computerized predictive models and scoring systems

Severity of illness scoring systems and standardized death ratios are being used with increasing frequency as markers of quality of care and to compare and contrast the performance of ICUs. However, numerous factors unrelated to the quality of care delivered may impact the severity of illness score and standardized death ratios. This article reviews the commonly used severity scoring systems and factors that affect their predictive performance.