Scoring systems in the intensive care unit: A compendium

External validation of INCREMENT-CPE score in a retrospective cohort of carbapenemase-producing Klebsiella pneumoniae bloodstream infections in critically ill patients

OBJECTIVES

Our aim was to validate the INCREMENT-CPE score (ICS) in patients hospitalized in the intensive care unit (ICU) with bacteraemia due to carbapenemase-producing Klebsiella pneumoniae (CP-Kp).

METHODS

The study was conducted in the ICU of the University General Hospital of Patras, Greece, during a 10-year period (2010-2019). Patients with monomicrobial bacteraemia due to CP-Kp were included. Primary outcome was 14-day mortality. MICs of meropenem, tigecycline, fosfomycin and ceftazidime/avibactam were determined by Etest, whereas for colistin the broth microdilution method was applied. PCR for bla_KPC, bla_VIM, bla_NDM and bla_OXA genes was used.

RESULTS

Among 384 CP-Kp bacteraemias, most were primary (166, 43.2%) followed by catheter-related (143, 37.2%). Most isolates carried bla_KPC (318, 82.8%). Fourteen-day mortality was 26.3% (101 patients). ICS score was 11.1 ± 4.2. An ICS ≥10 showed a sensitivity of 98.0% and a negative predictive value of 98.7%. The area under the curve of ICS (0.800) was comparable to those of the Pitt bacteraemia score (0.799), the Simplified Acute Physiology Score II (SAPS II) (0.797) and the Sequential Organ Failure Assessment score (SOFA) (0.815).

CONCLUSIONS

ICS showed predictive efficacy similar to that of the SAPS II, SOFA and Pitt bacteraemia scores.

Incidence and Risk Factors of Delirium in the Intensive Care Unit: A Prospective Cohort

Purpose

The purpose of this study was to determine the incidence, risk factors, and impact of delirium on outcomes in ICU patients. In addition, the scoring systems were measured consecutively to characterize how these scores changed with time in patients with and without delirium. Material and Methods. A prospective cohort study enrolling 400 consecutive patients admitted to the ICU between 2018 and 2019 due to trauma or surgery. Patients were followed up for the development of delirium over ICU days using the Confusion Assessment Method (CAM) for the ICU and Intensive Care Delirium Screening Checklist (ICDSC). Cox model logistic regression analysis was used to explore delirium risk factors.

Results

Delirium occurred in 108 (27%) patients during their ICU stay, and the median onset of delirium was 4 (IQR 3–4) days after admission. According to multivariate cox regression, the expected hazard for delirium was 1.523 times higher in patients who used mechanical ventilator as compared to those who did not (HR: 1.523, 95% CI: 1.197-2.388, P < 0.001).

Conclusion

Our findings suggest that an important opportunity for improving the care of critically ill patients may be the determination of modifiable risk factors for delirium in the ICU. In addition, the scoring systems (APACHE IV, SOFA, and RASS) are useful for the prediction of delirium in critically ill patients.

Pediatric Simplified Acute Physiology Score II: Establishment of a New, Repeatable Pediatric Mortality Risk Assessment Score

Objectives: In critical care it is crucial to appropriately assess the risk of mortality for each patient. This is especially relevant in pediatrics, with its need for accurate and repeatable scoring. Aim of this study was to evaluate an age-adapted version of the expanded Simplified Acute Physiology Score II; (p-SAPS II), a repeatable, newly-designed scoring system compared to established scores (Pediatric Sequential Organ Failure Assessment Score/pSOFA, Pediatric Logistic Organ Dysfunction Score-2/PELOD-2 and Pediatric Index of Mortality 3/PIM3). Design: This retrospective cohort pilot study included data collected from patients admitted to the Pediatric Intensive Care Unit (PICU) at the Medical University of Vienna between July 2017 through December 2018. Patients: 231 admissions were included, comprising neonates (gestational age of ≥ 37 weeks) and patients up to 18 years of age with a PICU stay longer than 48 h. Main Outcomes: Mortality risk prediction and discrimination between survivors and non-survivors were the main outcomes of this study. The primary statistical methods for evaluating the performance of each score were the area under the receiver operating characteristic curve (AUROC) and goodness-of-fit test. Results: Highest AUROC curve was calculated for p-SAPS II (AUC = 0.86; 95% CI: 0.77-0.96; p < 0.001). This was significantly higher than the AUROCs of PELOD-2/pSOFA but not of PIM3. However, in a logistic regression model including p-SAPS II and PIM3 as covariates, p-SAPS II had a significant effect on the accuracy of prediction (p = 0.003). Nevertheless, according to the goodness-of-fit test for p-SAPS II and PIM3, p-SAPS II overestimated the number of deaths, whereas PIM3 showed acceptable estimations. Repeatability testing showed increasing AUROC values for p-SAPS II throughout the clinical stay (0.96 at day 28) but still no significant difference to PIM 3. The prediction accuracy, although improved over the days and even exceeded PIM 3. Conclusions: The newly-created p-SAPS II performed better than the established PIM3 in terms of discriminating between survivors and non-survivors. Furthermore, p-SAPS II can be assessed repeatably throughout a patient's PICU stay what improves mortality prediction. However, there is still a need to optimize calibration of the score to accurately predict mortality sooner throughout the clinical stay.

Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation

Extensive monitoring in intensive care units (ICUs) generates large quantities of data which contain numerous trends that are difficult for clinicians to systematically evaluate. Current approaches to such heterogeneity in electronic health records (EHRs) discard pertinent information. We present a deep learning pipeline that uses all uncurated chart, lab, and output events for prediction of in-hospital mortality without variable selection. Over 21,000 ICU patients and tens of thousands of variables derived from the MIMIC-III database were used to train and validate our model. Recordings in the first few hours of a patient's stay were found to be strongly predictive of mortality, outperforming models using SAPS II and OASIS scores, AUROC 0.72 and 0.76 at 24 h respectively, within just 12 h of ICU admission. Our model achieves a very strong predictive performance of AUROC 0.85 (95% CI 0.83-0.86) after 48 h. Predictive performance increases over the first 48 h, but suffers from diminishing returns, providing rationale for time-limited trials of critical care and suggesting that the timing of decision making can be optimised and individualised.

Utilizing heart rate variability to predict ICU patient outcome in traumatic brain injury

Background

Prediction of patient outcome in medical intensive care units (ICU) may help for development and investigation of early interventional strategies. Several ICU scoring systems have been developed and are used to predict clinical outcome of ICU patients. These scores are calculated from clinical physiological and biochemical characteristics of patients. Heart rate variability (HRV) is a correlate of cardiac autonomic regulation and has been evident as a marker of poor clinical prognosis. HRV can be measured from the electrocardiogram non-invasively and monitored in real time. HRV has been identified as a promising ‘electronic biomarker’ of disease severity. Traumatic brain injury (TBI) is a subset of critically ill patients admitted to ICU, with significant morbidity and mortality, and often difficult to predict outcomes. Changes of HRV for brain injured patients have been reported in several studies. This study aimed to utilize the continuous HRV collection from admission across the first 24 h in the ICU in severe TBI patients to develop a patient outcome prediction system.

Results

A feature extraction strategy was applied to measure the HRV fluctuation during time. A prediction model was developed based on HRV measures with a genetic algorithm for feature selection. The result (AUC: 0.77) was compared with earlier reported scoring systems (highest AUC: 0.76), encouraging further development and practical application.

Conclusions

The prediction models built with different feature sets indicated that HRV based parameters may help predict brain injury patient outcome better than the previously adopted illness severity scores.

Predictive value of the negative inspiratory force index as a predictor of weaning success: a crosssectional study

BACKGROUND

Identifying when intubated patients are ready to be extubated remains challenging. The negative inspiratory force (NIF) is a recommended predictor of weaning success. However, little is known about the role of NIF in the weaning process for the Asian surgical intensive population, especially for the Vietnamese population. Here, we aimed to investigate the cutoff threshold and predictive value of the NIF index for predicting the success of ventilator weaning in Vietnamese surgical intensive care patients.

METHODS

A cross-sectional study was conducted at the Surgical Intensive Care Unit of Viet Duc Hospital from October 2016 to August 2017. A total of 64 patients aged 16-70 years undergoing ventilatory support through an orotracheal tube satisfied the criteria for readiness to begin weaning. The correlation between the NIF index with outcomes of the weaning process was analyzed. Specificity (Sp), sensitivity (Se), positive predictive value (PPV), negative predictive value (NPV), receiver operating characteristic (ROC) curve, and area under the curve (AUC) were calculated.

RESULTS

The success rate of the entire weaning process was 67.2% (43/64). The median NIF values were -26.0 cm H2O (interquartile range [IQR], -28.0 to -25.0) in the successful weaning group and -24.0 cm H2O (IQR, -25.0 to -23.0) in the weaning failure group (P<0.001). According to ROC analysis, an NIF value ≤-25 cm H2O predicted weaning success (AUC, 0.836) with 91% Se, 62% Sp, 83% PPV, and 77% NPV.

CONCLUSIONS

An NIF cutoff threshold ≤-25 cm H2O can be used as predictor of weaning success in Vietnamese surgical intensive care patients.

Analysis of Consciousness Level Using Galvanic Skin Response during Therapeutic Effect

The neurological status of patients in the Intensive Care Units (ICU) is determined by the Glasgow Coma Scale (GCS). Patients in coma are thought to be unaware of what is happening around them. However, many studies show that the family plays an important role in the recovery of the patient and is a great emotional resource. In this study, Galvanic Skin Response (GSR) signals were analyzed from 31 patients with low consciousness levels between GCS 3 and 8 to determine relationship between consciousness level and GSR signals as a new approach. The effect of family and nurse on unconscious patients was investigated by GSR signals recorded with a new proposed protocol. The signals were recorded during conversation and touching of the patient by the nurse and their families. According to numerical results, the level of consciousness can be separated using GSR signals. Also, it was found that family and nurse had statistically significant effects on the patient. Patients with GCS 3,4, and 5 were considered to have low level of consciousness, while patients with GCS 6,7, and 8 were considered to have high level of consciousness. According to our results, it is obtained lower GSR amplitude in low GCS (3, 4, 5) compared to high GCS (7, 8). It was concluded that these patients were aware of therapeutic affect although they were unconscious. During the classification stage of this study, the class imbalance problem, which is common in medical diagnosis, was solved using Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN) and random oversampling methods. In addition, level of consciousness was classified with 92.7% success using various decision tree algorithms. Random Forest was the method which provides higher accuracy compared to all other methods. The obtained results showed that GSR signal analysis recorded in different stages gives very successful GCS score classification performance according to literature studies.

A simple, rapid, interpretable, actionable and implementable digital PCR based mortality index

Mortality assessments are conducted for both civil and commercial purposes. Recent advances in epigenetics have resulted in DNA methylation tools to assess risk and aid in this task. However, widely available array-based algorithms are not readily translatable into clinical tools and do not provide a good foundation for clinical recommendations. Further, recent work shows evidence of heritability and possible racial bias in these indices. Using a publicly available array data set, the Framingham Heart Study (FHS), we develop and test a five-locus mortality-risk algorithm using only previously validated methylation biomarkers that have been shown to be free of racial bias, and that provide specific assessments of smoking, alcohol consumption, diabetes and heart disease. We show that a model using age, sex and methylation measurements at these five loci outperforms the 513 probe Levine index and approximates the predictive power of the 1030 probe GrimAge index. We then show each of the five loci in our algorithm can be assessed using a more powerful, reference-free digital PCR approach, further demonstrating that it is readily clinically translatable. Finally, we show the loci do not reflect ethnically specific variation. We conclude that this algorithm is a simple, yet powerful tool for assessing mortality risk. We further suggest that the output from this or similarly derived algorithms using either array or digital PCR can be used to provide powerful feedback to patients, guide recommendations for additional medical assessments, and help monitor the effect of public health prevention interventions.

Performance of Severity Indices to Estimate Postoperative Complications of Myocardial Revascularization

Fundamento

Os pacientes em pós-operatório (PO) de cirurgia de revascularização miocárdica (CRM) internados em unidade de terapia intensiva (UTI) apresentam risco de complicações que aumentam o tempo de permanência e a morbimortalidade. Portanto, é fundamental o reconhecimento precoce desses riscos para otimizar estratégias de prevenção e desfecho clínico satisfatório.

Objetivo

Analisar o desempenho de índices de gravidade na predição de complicações em pacientes no PO de CRM durante a permanência na UTI.

Métodos

Estudo transversal, com análise retrospectiva de prontuários eletrônicos de pacientes com idade ≥ 18 anos submetidos à CRM isolada e admitidos na UTI de um hospital cardiológico, em São Paulo, Brasil. As áreas sob as curvas receiver operating characteristic (AUC) com intervalo de confiança de 95% foram analisadas para verificar a acurácia dos índices European System for Cardiac Operative Risk Evaluation (EuroScore), Acute Physiology and Chronic Health Evaluation (APACHE II), Simplified Acute Physiology Score (SAPS II) e Sequential Organ Failure Assessment (SOFA) na predição de complicações.

Resultados

A casuística foi composta por 366 pacientes (64,58±9,42 anos; 75,96% sexo masculino). As complicações identificadas foram respiratórias (24,32%), cardiológicas (19,95%), neurológicas (10,38%), hematológicas (10,38%), infecciosas (6,56%) e renais (3,55%). O APACHE II apresentou satisfatório desempenho para a predição de complicações neurológicas (AUC 0,72) e renais (AUC 0,78).

Conclusão

O APACHE II se destacou na previsão das complicações neurológicas e renais. Nenhum dos índices teve bom desempenho na predição das outras complicações analisadas. Portanto, os índices de gravidade não devem ser utilizados indiscriminadamente com o objetivo de predizer todas as complicações frequentemente apresentadas por pacientes após CRM. (Arq Bras Cardiol. 2020; 115(3):452-459)

Performance of Prognostic Scoring Systems in Trauma Patients in the Intensive Care Unit of a Trauma Center

BACKGROUND

Prediction of mortality outcomes in trauma patients in the intensive care unit (ICU) is important for patient care and quality improvement. We aimed to measure the performance of 11 prognostic scoring systems for predicting mortality outcomes in trauma patients in the ICU.

METHODS

Prospectively registered data in the Trauma Registry System from 1 January 2016 to 31 December 2018 were used to extract scores from prognostic scoring systems for 1554 trauma patients in the ICU. The following systems were used: the Trauma and Injury Severity Score (TRISS); the Acute Physiology and Chronic Health Evaluation (APACHE II); the Simplified Acute Physiology Score (SAPS II); mortality prediction models (MPM II) at admission, 24, 48, and 72 h; the Multiple Organ Dysfunction Score (MODS); the Sequential Organ Failure Assessment (SOFA); the Logistic Organ Dysfunction Score (LODS); and the Three Days Recalibrated ICU Outcome Score (TRIOS). Predictive performance was determined according to the area under the receiver operator characteristic curve (AUC).

RESULTS

MPM II at 24 h had the highest AUC (0.9213), followed by MPM II at 48 h (AUC: 0.9105). MPM II at 24, 48, and 72 h (0.8956) had a significantly higher AUC than the TRISS (AUC: 0.8814), APACHE II (AUC: 0.8923), SAPS II (AUC: 0.9044), MPM II at admission (AUC: 0.9063), MODS (AUC: 0.8179), SOFA (AUC: 0.7073), LODS (AUC: 0.9013), and TRIOS (AUC: 0.8701). There was no significant difference in the predictive performance of MPM II at 24 and 48 h (p = 0.37) or at 72 h (p = 0.10).

CONCLUSIONS

We compared 11 prognostic scoring systems and demonstrated that MPM II at 24 h had the best predictive performance for 1554 trauma patients in the ICU.

Relationship between nutritional status on admission to the intensive care unit and clinical outcomes

AIM

To determine the prevalence of malnutrition on admission to the intensive care unit (ICU) and the relationship between nutritional status on admission and clinical outcomes in adult critically ill patients.

METHODS

This was a prospective study in an adult ICU. Patients with expected length of stay (LOS) >48 hours in ICU were assessed for nutritional status using the patient generated-subjective global assessment (PG-SGA) within 48 hours of admission to ICU.

RESULTS

Primary outcomes were ICU and hospital mortality, ICU and hospital LOS and length of mechanical ventilation. A total of 166 patients were enrolled in this study. Patients were aged 59 ± 17 years on average with a mean BMI of 29 ± 7 kg/m² and a mean Acute Physiology and Chronic Health Evaluation II score of 19 ± 7. The prevalence of malnutrition in critically ill patients was 36% (n = 60). Mortality rate of malnourished patients was 9% (n = 15) compared to 7.8% (n = 13) in well-nourished patients (adjusted odds ratio, 2.17; 95% confidence interval, 0.9-5.03, P = .069). There was no difference in hospital mortality between malnourished patients and well-nourished patients (10.2% vs 10.2% adjusted odds ratio, 1.93; 95% confidence interval, 0.89-4.19, P = .096). There was no relationship between nutritional status and length of mechanical ventilation (3.0 vs 1.0 days, P = .382)or ICU LOS (4.7 vs 4.8 days, P = .59). Malnourished patients had a longer LOS in hospital than well-nourished patients (24 vs 17 days, P = .03).

CONCLUSION

Malnutrition is an independent risk factor for increased hospital LOS.

Multiple organ dysfunction syndrome: Contemporary insights on the clinicopathological spectrum

Multiorgan dysfunction syndrome (MODS) remains a major complication and challenge to treat patients with critical illness in different intensive care unit settings. The exact mechanism and pathophysiology of MODS is complex and remains unexplored. We reviewed the literature from January 2011 to August 2019 to analyze the underlying mechanisms, prognostic factors, MODS scoring systems, organ systems dysfunctions, and the management of MODS. We used the search engines PubMed, MEDLINE, Scopus, and Google Scholar with the keywords "multiple organ dysfunction syndrome," "intensive care units," "multiorgan failure," "MODS scoring system," and "MODS management." The initial search yielded 3550 abstracts, of which 91 articles were relevant to the scope of the present article. A better understanding of a disease course will help differentiate the signs of an intense inflammatory response from the early onset of sepsis and minimize the inappropriate use of medications. This, in turn, will promote organtargeted therapy and prevent occurrence and progression of MODS.

Intensive care unit-acquired hyponatremia in critically ill medical patients

Background

Previous research has focused on intensive care unit (ICU)-acquired hypernatremia; however, ICU-acquired hyponatremia has frequently been overlooked and has rarely been studied in surgical or mixed ICUs. The aim of this study is to investigate the incidence of ICU-acquired hyponatremia, the risk factors associated with its development, and its impact on outcomes in critically ill medical patients.

Methods

We conducted a retrospective cohort study based on the prospective registry of all critically ill patients admitted to the medical ICU from January 2015 to December 2018. Baseline characteristics and management variables were compared between ICU-acquired hyponatremia and normonatremia patients.

Results

Of 1342 patients with initial normonatremia, ICU-acquired hyponatremia developed in 217 (16.2%) patients and ICU-acquired hypernatremia developed in 117 (8.7%) patients. The Sequential Organ Failure Assessment (8.0 vs 7.0, P = 0.009) and Simplified Acute Physiology Score 3 scores (55.0 vs 51.0, P = 0.005) were higher in ICU-acquired hyponatremia patients compared with normonatremia patients. Baseline sodium (137.0 mmol/L vs 139.0 mmol/L, P < 0.001), potassium (4.2 mmol/L vs 4.0 mmol/L, P = 0.001), and creatinine (0.98 mg/dL vs 0.88 mg/dL, P = 0.034) levels were different between the two groups. Net volume balance over first 3 days was higher in ICU-acquired hyponatremia patients (19.4 mL/kg vs 11.5 mL/kg, P = 0.004) and was associated with the development of ICU-acquired hyponatremia (adjusted odds ratio, 1.004; 95% confidence interval, 1.002–1.007; P = 0.001). ICU mortality was similar in both groups (15.2% vs. 14.4%, P = 0.751), but renal replacement therapy was more commonly required in ICU-acquired hyponatremia patients (13.4% vs 7.4%, P = 0.007).

Conclusions

ICU-acquired hyponatremia is not uncommon in critically ill medical patients. Increased volume balance is associated with its development. ICU-acquired hyponatremia is related to increased use of renal replacement therapy but not to mortality.

Mortality prediction models in the adult critically ill: A scoping review

BACKGROUND

Mortality prediction models are applied in the intensive care unit (ICU) to stratify patients into different risk categories and to facilitate benchmarking. To ensure that the correct prediction models are applied for these purposes, the best performing models must be identified. As a first step, we aimed to establish a systematic review of mortality prediction models in critically ill patients.

METHODS

Mortality prediction models were searched in four databases using the following criteria: developed for use in adult ICU patients in high-income countries, with mortality as primary or secondary outcome. Characteristics and performance measures of the models were summarized. Performance was presented in terms of discrimination, calibration and overall performance measures presented in the original publication.

RESULTS

In total, 43 mortality prediction models were included in the final analysis. In all, 15 models were only internally validated (35%), 13 externally (30%) and 10 (23%) were both internally and externally validated by the original researchers. Discrimination was assessed in 42 models (98%). Commonly used calibration measures were the Hosmer-Lemeshow test (60%) and the calibration plot (28%). Calibration was not assessed in 11 models (26%). Overall performance was assessed in the Brier score (19%) and the Nagelkerke's R² (4.7%).

CONCLUSIONS

Mortality prediction models have varying methodology, and validation and performance of individual models differ. External validation by the original researchers is often lacking and head-to-head comparisons are urgently needed to identify the best performing mortality prediction models for guiding clinical care and research in different settings and populations.

Evaluating the influence of data collector training for predictive risk of death models: an observational study

BACKGROUND

Severity-of-illness scoring systems are widely used for quality assurance and research. Although validated by trained data collectors, there is little data on the accuracy of real-world data collection practices.

OBJECTIVE

To evaluate the influence of formal data collection training on the accuracy of scoring system data in intensive care units (ICUs).

STUDY DESIGN AND METHODS

Quality assurance audit conducted using survey methodology principles. Between June and December 2018, an electronic document with details of three fictitious ICU patients was emailed to staff from 19 Australian ICUs who voluntarily submitted data on a web-based data entry form. Their entries were used to generate severity-of-illness scores and risks of death (RoDs) for four scoring systems. The primary outcome was the variation of severity-of-illness scores and RoDs from a reference standard.

RESULTS

50/83 staff (60.3%) submitted data. Using Bayesian multilevel analysis, severity-of-illness scores and RoDs were found to be significantly higher for untrained staff. The mean (95% high-density interval) overestimation in RoD due to training effect for patients 1, 2 and 3, respectively, were 0.24 (0.16, 0.31), 0.19 (0.09, 0.29) and 0.24 (0.1, 0.38) respectively (Bayesian factor >300, decisive evidence). Both groups (trained and untrained) had wide coefficients of variation up to 38.1%, indicating wide variability. Untrained staff made more errors in interpreting scoring system definitions.

INTERPRETATION

In a fictitious patient dataset, data collection staff without formal training significantly overestimated the severity-of-illness scores and RoDs compared with trained staff. Both groups exhibited wide variability. Strategies to improve practice may include providing adequate training for all data collection staff, refresher training for previously trained staff and auditing the raw data submitted by individual ICUs. The results of this simulated study need revalidation on real patients.

The Major Decrease in Resource Utilization in Recent Decades Seems Guided by Demographic Changes: Fast Tracking-Real Concept or Demographics

OBJECTIVE

To identify dynamics of associations and potential areas for optimization of patient turnover between various patient profile and comorbidity indicators and selected system performance indicators such as ventilation time, length of stay in the intensive care unit, and in-hospital stay.

DESIGN

Retrospective study of prospectively registered data (2000-2017).

SETTING

Three university hospitals.

PARTICIPANTS

The study comprised 38,100 adult cardiac surgical patients registered in the Western Denmark Heart Registry.

INTERVENTIONS

Analysis of dynamics in patient indicators and system performance indicators, including effect on the selected performance parameters.

MEASUREMENTS AND MAIN RESULTS

Comorbidity, calculated from EuroSCORE, decreased from 2.5 ± 2.2 to 1.5 ± 2.0 (p < 0.001), whereas the average age of patients increased from 65.1 ± 9.9 years to 67.6 ± 10.8 years (p < 0.001). Median ventilation time decreased from 380 to 275 minutes (p < 0.0001). The mean length of stay in the intensive care unit demonstrated a statistically significant decrease from 35.1 hours between 2000 to 2002 to 31.8 hours between 2015 to 2017 (p = 0.004), and the median time was unchanged at 22.0 hours throughout the observation period. The median in-hospital stay decreased from 6.5 to 5.1 days (p < 0.001) and the mean in-hospital stay from 8.7 days (2003-2005) to 7.0 days (2015-2017; p < 0.001). Logistic regression analysis of performance factors showed a statistically significant negative independent effect on most comorbidity and surgical factors.

CONCLUSION

The increase in performance parameters appears to be highly associated with decreased comorbidities and fast-tracking protocols and may only offer limited effect in additional patient turnover.

The Pros and Cons of the Prediction Game: The Never-ending Debate of Mortality in the Intensive Care Unit

Background: The Simplified Acute Physiology Score (SAPS) II, Acute Physiology and Chronic Health Evaluation (APACHE) II, and Sequential Organ Failure Assessment (SOFA) scales are scoring systems used in intensive care units (ICUs) worldwide. We aimed to investigate their usefulness in predicting short- and long-term prognosis in the local ICU. Methods: This single-center observational study covered 905 patients admitted from 1 January 2015 to 31 December 2017 to a tertiary mixed ICU. SAPS II, APACHE II, and SOFA scores were calculated based on the worst values from the first 24 h post-admission. Patients were divided into surgical (SP) and nonsurgical (NSP) subjects. Unadjusted ICU and post-ICU discharge mortality rates were considered the outcomes. Results: Baseline SAPS II, APACHE II, and SOFA scores were 41.1 ± 20.34, 14.07 ± 8.73, and 6.33 ± 4.12 points, respectively. All scores were significantly lower among SP compared to NSP (p < 0.05). ICU mortality reached 35.4% and was significantly lower for SP (25.3%) than NSP (57.9%) (p < 0.001). The areas under the receiver-operating characteristic (ROC) curves were 0.826, 0.836, and 0.788 for SAPS II, APACHE II, and SOFA scales, respectively, for predicting ICU prognosis, and 0.708, 0.709, and 0.661 for SAPS II, APACHE II, and SOFA, respectively, for post-ICU prognosis. Conclusions: Although APACHE II and SAPS II are good predictors of ICU mortality, they failed to predict survival after discharge. Surgical patients had a better prognosis than medical ICU patients.

The predictive power of SAPS-3 and SOFA scores and their relations with patient outcomes in the Surgical Intensive Care Unit

Objectives

Individual risk of surgical patients is more often underestimated and there is not an absolute criterion demonstrating which patient deserves intensive care. Since a nominative assessment of these patients to quantify the intensity of critical illness is not appropriate, prognostic scores are used to assess the mortality rate and prognosis for critical patients including surgical ones. This study aimed to test the calibration power of SAPS-3 score and SOFA score of surgical patients undergoing gastrointestinal surgery, and identify any relation with patient outcomes in the department of surgical ICU.

Material and Methods

This retrospective observational study was conducted during the period between August 2017 and December 2017. It was performed at a Gastroenterological Surgical ICU, a tertiary care hospital in Ankara, Türkiye. To calculate SAPS-3 and SOFA score, physiological data and laboratory analysis on the day of ICU admission were used. Records were reviewed from hospitalization to medical discharge or hospital mortality. Statistical analysis included Mann Whitney U-test and ROC-curves to predict 30-day mortality.

Results

A total of 233 patients admitted to the Gastroenterological Surgical ICU were included into the study and the main reason for ICU admission was surgical problems. Mortality rate was 2.6 % (6 patients). Average SAPS -3 score was 32.5 and SOFA score was 30.1. A significant correlation was observed with the SAPS-3 score, but not with the SOFA score statistically in mortality as a dependent factor. The discriminative power, assessed using the AUC and the probability of death estimation, was satisfactory with the SAPS-3 scores (AUC 0.754) while it was lower with the SOFA score (AUC 0.631).

Conclusion

We found that SAPS-3 score was significantly correlated not only with mortality rate, but also with LOS in the ICU. Nonetheless, SOFA score was not related to mortality, but related to LOS in the ICU. Prognostic score systems are used to estimate mortality but they may be used to identify LOS in the ICU and postoperative complications. It can be concluded that SAPS-3 and SOFA scores may be used to prognosticate postoperative ICU requirement.

Prognosis predictive value of the Oxford Acute Severity of Illness Score for sepsis: a retrospective cohort study

Background

The Oxford Acute Severity of Illness Score (OASIS) has shown fair prognosis predictive value in critically ill patients, but its predictive value has not been assessed in septic patients.

Objective

The aim of this study was to evaluate the performance of the OASIS for the assessment of mortality in septic patients, especially when compared with the Sepsis-related Organ Failure Assessment (SOFA) score.

Methods

A retrospective cohort study was conducted using data from a public database and septic patients were identified using the Sepsis-3 criteria. The primary outcome was hospital mortality. Data were mainly analyzed using multivariable logistic regression and receiver operating characteristic (ROC) curves. Sensitive analyses were performed in patients with an ICD-9-CM code for sepsis and ROC curves analyses were also conducted in septic patients stratified by the Simplified Acute Physiology Score (SAPS) II as subgroup analyses.

Results

A total of 10,305 septic patients were included. The OASIS was found to be significantly associated with hospital mortality (odds ratio 1.07 per one-point increase, 95% confidence interval [1.06–1.08]), while ROC curves analyses showed the discriminatory power of the OASIS for hospital mortality was statistically significantly lower than that of the SOFA score (area under the ROC curve: 0.652 vs 0.682, p < 0.001). Results of sensitive analyses were consistent, but the significant difference existed only when the SAPS II was higher than 50 according to results of the subgroup analyses.

Conclusions

The OASIS might serve as an initial predictor of clinical outcomes for septic patients, but one should be circumspect when it is applied to severer patients.

Monocyte Chemoattractant Protein-1, a Possible Biomarker of Multiorgan Failure and Mortality in Ventilator-Associated Pneumonia

Ventilator-associated pneumonia (VAP) leads to increased patients’ mortality and medical expenditure. Monocyte chemoattractant protein-1 (MCP-1) plays a role in the pathogenesis of lung inflammation and infection. Therefore, the plasma concentration of MCP-1 was assessed and correlated with the clinical course in VAP patients. This retrospective observational study recruited 45 healthy volunteers, 12 non-VAP subjects, and 30 VAP patients. The diagnostic criteria for VAP were based on the American Thoracic Society guidelines, and the level of plasma MCP-1 was determined by ELISA. Plasma MCP-1 concentration was significantly elevated in the acute stage in VAP patients when compared with the control (p < 0.0001) and non-VAP patient groups (p = 0.0006). Subsequently, it was remarkably decreased following antibiotic treatment. Moreover, plasma MCP-1 concentration was positively correlated with indices of pulmonary dysfunction, including the lung injury score (p = 0.02) and the oxygenation index (p = 0.02). When patients with VAP developed adult respiratory distress syndrome (ARDS), their plasma MCP-1 concentrations were significantly higher than those of patients who did not develop ARDS (p = 0.04). Moreover, plasma MCP-1 concentration was highly correlated with organ failure scores, including simplified acute physiology score II (SAPS II, p < 0.0001), sequential organ failure assessment score (SOFA, p < 0.0001), organ dysfunctions and/or infection (ODIN, p < 0.0001), predisposition, insult response and organ dysfunction (PIRO, p = 0.005), and immunodeficiency, blood pressure, multilobular infiltrates on chest radiograph, platelets and hospitalization 10 days before onset of VAP (IBMP-10, p = 0.004). Our results demonstrate that plasma MCP-1 is an excellent marker for recognizing VAP when the cut-off level is set to 347.18 ng/mL (area under the curve (AUC) = 0.936, 95% CI = 0.863–0.977). In conclusion, MCP-1 not only could be a biological marker related to pulmonary dysfunction, organ failure, and mortality in patients with VAP, but also could be used for early recognition of VAP.

Hepatic and Renal Biochemical Markers as Predictors of Mortality Among Critically Ill Systemic Inflammatory Response Syndrome Patients

Systemic inflammatory response syndrome (SIRS) is a frequently encountered complication seen in intensive care unit patients and remains a common cause of mortality. Assessing prognosis of those becomes a priority and indeed we have various efficient scoring systems for the same. However they use enormous data and involve complex calculations for scoring. We intended to find a simple, inexpensive, accurate diagnostic tool of certain markers to predict mortality outcome among critically ill SIRS patients and to evaluate their efficiency in comparison to Acute Physiology and Chronic Health Evaluation II (APACHE II) scoring system. Eighty-seven patients were selected and general hepatic, renal and urinary investigations were done for them at 24 h of admission and were followed up for a period of 4 weeks from admission date to classify them as survivors and non-survivors. Twenty-one percent patients had succumbed to death during study period. Urine albumin-creatinine ratio, alanineaminotransferase, aspartate aminotransferase and prothrombin time/International Normalized Ratio were found to be correlating with APACHE II scores and mortality significantly. Specific individual cut-offs were found for these parameters and were combined to form combined predictors which showed good discrimination (AUC = 0.715) and good calibration (p = 0.811) with specificity of 98.6% in predicting mortality. SIRS patients falling above combined predictor's cutoff are 54 times more likely to have an unfavorable outcome compared to the ones below. Overall predictive accuracy of first day combined predictors was such that within 24 h of ICU admission 87% of ICU SIRS admissions could be given a risk estimate for hospital death.

Acute Physiology and Chronic Health Evaluation II score for the assessment of mortality prediction in the intensive care unit: a single-centre study from Iran

BACKGROUND

The Acute Physiology and Chronic Health Evaluation (APACHE) II is still commonly used as an index of illness severity in patients admitted to intensive care unit (ICU) and has been validated for many research and clinical audit purposes.

AIMS AND OBJECTIVES

To investigate the diagnostic value of the APACHE II score for predicting mortality rate of critically ill patients.

DESIGN

This was a single-centre, retrospective study of 200 Iranian patients admitted in the medical-surgical adult ICU from June 2012 to May 2013.

METHODS

Demographic data, pre-existing comorbidities and variables required for calculating the APACHE II score were recorded. Receiver operating characteristic (ROC) curves were constructed, and the area under the ROC curves was calculated to assess the predictive value of the APACHE II score.

RESULTS

Of the 200 patients with a mean age of 55·27 ± 21·59 years enrolled in the study, 112 (54%) were admitted in the medical ICU and 88 (46%) in the surgical ICU. Finally, 116 patients (58%) died, and 84 patients (42%) survived. The overall actual and predicted ICU mortality were 58% and 25·16%, respectively. The mean APACHE II score was 16·31 in total patients, 17·78 in medical ICU and 14·45 in surgical ICU patients (P = 0·003). Overall, the APACHE II score had the highest prognostic value for predicting the mortality rate of critically ill patients with an area under the cure of 0·88, and with a cut-off value of 15, the APACHE II score predicted mortality of patients with a sensitivity of 85·3%, a specificity of 77·4%, a positive predictive value of 83·9% and a negative predictive value of 73·9%.

CONCLUSION

This study shows that an APACHE II score of 15 provides the best diagnostic accuracy to predict mortality of critically ill patients. Our observed mortality rate was greater than the predicted death rate, in comparison to the other prestigious centres in the world. Therefore, it appears that we must improve our intensive care to reduce mortality.

RELEVANCE TO CLINICAL PRACTICE

There is a need to create a suitable scoring system to predict the mortality rate of critically ill patients in accordance with the advanced technological equipment and experienced physicians and nurses in that ICU.

The Effectiveness of Scoring Systems in the Prediction of Diagnosis-Based Mortality

Scoring systems are used for mortality and morbidity rating in intensive care conditions, prognosis prediction, standardization of scientific data and the monitoring of clinical quality. The aim of this study was to retrospectively analyze the efficacy of APACHE II (Acute Physiology and Chronic Health Evaluation), APACHE IV and SAPS (Simplified Acute Physiology Score) III prognostic scorings in the prediction of mortality and disease severity of patients admitted to the Anesthesia and Reanimation Clinic Intensive Care Unit (ICU) in Bakırköy Dr. Sadi Konuk Training and Research Hospital according to general and specific diagnoses. A total of 1896 patient files were included in the study. With the exception of single system or head trauma patient groups, a statistically significant difference was found in the mortality prediction rates in all other diagnosis groups (P < 0.05). The discrimination calculated with AUROC fields was sufficient in all groups, and calibration was evaluated as good except for the neurological and neurosurgical patient group. In respect of standard mortality prediction, APACHE II and IV were good in cases of sepsis, and SAPS III made almost exact predictions for cardiovascular diseases, APACHE II for neurological diseases, and APACHE IV for gastrointestinal system diseases. From the results of this study, it was seen that different scoring systems vary in predictions according to the diagnoses, therefore, it can be recommended that the diagnosis should be taken into account more when applying scoring systems.

Development of a risk prediction model (Hangang) and comparison with clinical severity scores in burn patients

PURPOSE

The purpose of this study was to develop a new prediction model to reflect the risk of mortality and severity of disease and to evaluate the ability of the developed model to predict mortality among adult burn patients.

METHODS

This study included 2009 patients aged more than 18 years who were admitted to the intensive care unit (ICU) within 24 hours after a burn. We divided the patients into two groups; those admitted from January 2007 to December 2013 were included in the derivation group and those admitted from January 2014 to September 2017 were included in the validation group. Shrinkage methods with 10-folds cross-validation were performed to identify variables and limit overfitting of the model. The discrimination was analyzed using the area under the curve (AUC) of the receiver operating characteristic curve. The Brier score, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were also calculated. The calibration was analyzed using the Hosmer-Lemeshow goodness-of-fit test (HL test). The clinical usefulness was evaluated using a decision-curve analysis.

RESULTS

The Hangang model showed good calibration with the HL test (χ2 = 8.785, p = 0.361); the highest AUC and the lowest Brier score were 0.943 and 0.068, respectively. The NRI and IDI were 0.124 (p-value = 0.003) and 0.079 (p-value <0.001) when compared with FLAMES, respectively.

CONCLUSIONS

This model reflects the current risk factors of mortality among adult burn patients. Furthermore, it was a highly discriminatory and well-calibrated model for the prediction of mortality in this cohort.

Thrombocytopenia After Transcatheter Valve-in-Valve Implantation: Prognostic Marker or Mere Finding?

Objective

To analyze the behavior of platelets after transcatheter valve-in-valve implantation for the treatment of degenerated bioprosthesis and how they correlate with adverse events upon follow-up.

Methods

Retrospective analysis of 28 patients who received a valve-in-valve implant, 5 in aortic, 18 in mitral and 5 in tricuspid positions. Data were compared with 74 patients submitted to conventional redo valvular replacements during the same period, and both groups' platelet curves were analyzed. Statistical analysis was conducted using the IBM SPSS Statistics(r) 20 for Windows.

Results

All patients in the valve-in-valve group developed thrombocytopenia, 25% presenting mild (<150.000/µL), 54% moderate (<100.000/µL) and 21% severe (<50.000/µL) thrombocytopenia. The platelet nadir was on the 4^th postoperative day for aortic ViV, 2^nd for mitral and 3^rd for tricuspid patients, with the majority of patients recovering regular platelet count. However, the aortic subgroup comparison between valve-in-valve and conventional surgery showed a statistically significant difference from the 7^th day onwards, where valve-in-valve patients had more severe and longer lasting thrombocytopenia. This, however, did not translate into a higher postoperative risk. In our study population, postoperative thrombocytopenia did not correlate with greater occurrence of adverse outcomes and only normal preoperative platelet count could significantly predict a postoperative drop >50%.

Conclusion

Although thrombocytopenia is an extremely common finding after valve-in-valve procedures, the degree of platelet count drop did not correlate with greater incidence of postoperative adverse outcomes in our study population.

A Risk Prediction Model for Invasive Fungal Disease in Critically Ill Patients in the Intensive Care Unit

PURPOSE

Developing a risk prediction model for invasive fungal disease based on an analysis of the disease-related risk factors in critically ill patients in the intensive care unit (ICU) to diagnose the invasive fungal disease in the early stages and determine the time of initiating early antifungal treatment.

METHODS

Data were collected retrospectively from 141 critically ill adult patients with at least 4 days of general ICU stay at Sun Yat-sen Memorial Hospital, Sun Yat-sen University during the period from February 2015 to February 2016. Logistic regression was used to develop the risk prediction model. Discriminative power was evaluated by the area under the receiver operating characteristics (ROC) curve (AUC).

RESULTS

Sequential organ failure assessment (SOFA) score, antibiotic treatment period, and positive culture of Candida albicans other than normally sterile sites are the three predictors of invasive fungal disease in critically ill patients in the ICU. The model performs well with an ROC-AUC of .73.

CONCLUSION

The risk prediction model performs well to discriminate between critically ill patients with or without invasive fungal disease. Physicians could use this prediction model for early diagnosis of invasive fungal disease and determination of the time to start early antifungal treatment of critically ill patients in the ICU.

Use of photoplethysmography to predict mortality in intensive care units

PURPOSE

The aim of this study was to evaluate and compare the capacity to predict hemodynamic variables obtained with photoplethysmography (PPG) and Acute Physiology and Chronic Health Evaluation (APACHE II) in patients hospitalized in the intensive care unit (ICU).

MATERIALS AND METHODS

A prospective cohort study was conducted in the adult ICU of Hospital Nossa Senhora da Conceição, located in Tubarão, Santa Catarina, Brazil. The data collected included the diagnosis for hospitalization, age, gender, clinical or surgical profile, PPG pulse curve signal, and APACHE II score in the first 24 hours. A bivariate and a multivariate logistic regressions were performed, with death as an outcome. A mortality model using artificial neural networks (ANNs) was proposed.

RESULTS

A total of 190 individuals were evaluated. Most of them were males (6:5), with a median age of 67 (54-75) years, and the main reasons for hospitalization were cardiovascular and neurological causes; half of them were surgical cases. APACHE II median score was 14 (8-19), with a median length of stay of 6 (3-15) days, and 28.4% of the patients died. The following factors were associated with mortality: age (OR=1.023; 95% CI 1.001-1.044; P=0.039), clinical profile (OR=5.481; 95% CI 2.646-11.354; P<0.001), APACHE II (OR=1.168; 95% CI 1.106-1.234; P<0.001), heart rate in the first 24 hours (OR=1.020; 95% CI 1.001-1.039; P=0.036), and time between the systolic and diastolic peak (∆T) intervals obtained with PPG (OR=0.989; 95% CI 0.979-0.998; P=0.015). Compared with the accuracy (area under the receiver-operating characteristic curve) 0.780 of APACHE II (95% CI 0.711-0.849; P<0.001), the multivariate logistic model showed a larger area of 0.858 (95% CI 0.803-0.914; P<0.001). In the model using ANNs, the accuracy was 0.895 (95% CI 0.851-0.940; P<0.001).

CONCLUSION

The mortality models using variables obtained with PPG, with the inclusion of epidemiological parameters, are very accurate and, if associated to APACHE II, improve prognostic accuracy. The use of ANN was even more accurate, indicating that this tool is important to help in the clinical judgment of the intensivist.

Prediction of ICU mortality in critically ill children : Comparison of SOFA, GCS, and FOUR score

INTRODUCTION

The SOFA (Sequential Organ Failure Assessment), GCS (Glasgow Coma Scale), and FOUR (Full Outline of UnResponsiveness) scores are the most commonly used scoring systems to predict the risk of mortality and morbidity in intensive care units (ICUs). The aim of the current study was to compare the predictive ability of these three models for predicting medical/surgical ICU mortality in critically ill children.

METHODS

In the current observational and prospective study, a total of 90 consecutive patients, age ≤18 years, admitted to medical and surgical ICUs, were enrolled. The SOFA, GCS, FOUR score and demographic characteristics of all children were recorded on the first day of admission. For statistical analyses, a receiver operator characteristic (ROC) curve, the Hosmer-Lemeshow goodness of fit test, and logistic regression were used (95% confidence interval).

RESULTS

The SOFA, GCS, and FOUR scores between survivors and nonsurvivors were statistically different (p = 0.002, p < 0.001, p = 0.004, respectively). The discrimination power for SOFA, GCS, and FOUR score was moderate (area under ROC [AUC] curve: 75.1%; standard error [SE]: 6.0%, 72.9% [SE: 7.2%], 78.7% [SE: 6.6%], respectively). The only well-calibrated model was GCS (x² = 2.76, p = 0.59).

CONCLUSIONS

The performance of the three predictive models SOFA, GCS, and FOUR score for predicting outcomes in children admitted to medical and surgical ICUs was good. The discrimination was moderate for all three models, and calibration was good just for GCS. GCS was superior in predicting outcome in critically ill children; however, further studies are needed to validate these scores in the pediatric population.

A machine learning-based model for 1-year mortality prediction in patients admitted to an Intensive Care Unit with a diagnosis of sepsis

INTRODUCTION

Sepsis is associated to a high mortality rate, and its severity must be evaluated quickly. The severity of illness scores used are intended to be applicable to all patient populations, and generally evaluate in-hospital mortality. However, patients with sepsis continue to be at risk of death after hospital discharge.

OBJECTIVE

To develop a model for predicting 1-year mortality in critical patients diagnosed with sepsis.

PATIENTS

The data corresponding to 5650 admissions of patients with sepsis from the Medical Information Mart for Intensive Care (MIMIC-III) database were evaluated, randomly divided as follows: 70% for training and 30% for validation.

DESIGN

A retrospective register-based cohort study was carried out. The clinical information of the first 24h after admission was used to develop a 1-year mortality prediction model based on Stochastic Gradient Boosting (SGB) methodology. Variable selection was addressed using Least Absolute Shrinkage and Selection Operator (LASSO) and SGB variable importance methodologies. The predictive power was evaluated using the area under the ROC curve (AUROC).

RESULTS

An AUROC of 0.8039 (95% confidence interval (CI): [0.8033 0.8045]) was obtained in the validation subset. The model exceeded the predictive performances obtained with traditional severity of disease scores in the same subset.

CONCLUSION

The use of assembly algorithms, such as SGB, for the generation of a customized model for sepsis yields more accurate 1-year mortality prediction than the traditional scoring systems such as SAPS II, SOFA or OASIS.

Prognostic Factors in Acute Aluminium Phosphide Poisoning: A Risk-Prediction Nomogram Approach

Aluminium phosphide (AlP) is a toxic agent associated with a high mortality rate following acute exposure from various routes. The aim of this study was to determine the clinical and laboratory findings useful for predicting the medical outcome of AlP-poisoned patients using established scoring systems. This is a prospective study of AlP-poisoned patients from 2010 to 2015 in Ardabil, Iran. All patients that presented with a confirmed diagnosis of acute AlP poisoning in the study interval were included in the study. Clinical and laboratory data, using Acute Physiology and Chronic Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA) and Simplified Acute Physiology Score II (SAPS II) scoring systems, were compared for their predictive value in determining differences between survived and non-survived patients. Univariate analysis (Mann-Whitney or t-test), multiple logistic regression analysis, receiver operating characteristic (ROC) curve analysis and the Pearson correlation test were performed using STATA/SE 13.0 and the Nomolog Software. A total of 68 AlP-poisoned patients with confirmed acute AlP poisoning were included for evaluation. Of these, 36 were non-survived. Multiple logistic regression analysis was performed using parameters and values derived from patient clinical and laboratory data, and revealed that four factors were significant for predicting mortality: Glasgow coma score (GCS); systolic blood pressure (SBP); urinary output (UOP); and serum HCO₃ . A four-variable, risk-prediction nomogram was developed for identifying high-risk patients and predicting the risk of mortality. Study results showed that SBP of <92.5 mmHg (p = 0.006); HCO₃^- < 12.9 mEq/L (p = 0.01), UOP < 1725 mL/day (p = 0.04); and GCS < 14.5 (p = 0.003) were significant predictors of AlP mortality. Scoring systems analysis showed SAPS II score >24.5, APACHE II score >8.5 and SOFA score >7.5 were predictive of non-survival patients. The results of our study showed that SBP, GCS, UOP and serum HCO₃ levels are the best prognostic factors for predicting mortality in AlP-poisoned patients. According to the area under the ROC curve of the APACHE II score, when compared with SOFA and SAPS II scores, the APACHE II score can more effectively discriminate between non-survivors and survived patients.

Dengue score as a diagnostic predictor for pleural effusion and/or ascites: external validation and clinical application

BACKGROUND

The Dengue Score is a model for predicting pleural effusion and/or ascites and uses the hematocrit (Hct), albumin concentration, platelet count and aspartate aminotransferase (AST) ratio as independent variables. As this metric has not been validated, we conducted a study to validate the Dengue Score and assess its clinical application.

METHODS

A retrospective study was performed at a private hospital in Jakarta, Indonesia. Patients with dengue infection hospitalized from January 2011 through March 2016 were included. The Dengue Score was calculated using four parameters: Hct increase≥15.1%, serum albumin≤3.49 mg/dL, platelet count≤49,500/μL and AST ratio ≥ 2.51. Each parameter was scored as 1 if present and 0 if absent. To validate the Dengue Score, goodness-of-fit was used to assess calibration, and the area under the receiver operating characteristic curve (AROC) was used to assess discrimination. Associations between clinical parameters and Dengue Score groups were determined by bivariate analysis.

RESULTS

A total of 207 patients were included in this study. The calibration of the Dengue Score was acceptable (Hosmer-Lemeshow test, p = 0.11), and the score's discriminative ability was good (AROC = 0.88 (95% CI: 0.83-0.92)). At a cutoff of ≥2, the Dengue Score had a positive predictive value (PPV) of 79.03% and a negative predictive value (NPV) of 90.36% for the diagnostic prediction of pleural effusion and/or ascites. Compared with the Dengue Score ≤ 1 group, the Dengue Score = 2 group was significantly associated with hemoconcentration> 20% (p = 0.029), severe thrombocytopenia (p = 0.029), and increased length of hospital stay (p = 0.003). Compared with the Dengue Score = 2 group, the Dengue Score ≥ 3 group was significantly associated with hemoconcentration> 20% (p = 0.001), severe thrombocytopenia (p = 0.024), severe dengue (p = 0.039), and increased length of hospital stay (p = 0.011).

CONCLUSION

The Dengue Score performed well and can be used in daily practice to help clinicians identify patients who have plasma leakage associated with severe dengue.

The Reliability of Surgical Apgar Score in Predicting Immediate and Late Postoperative Morbidity and Mortality: A Narrative Review

Surgical Apgar Score is a simple, 10-point scoring system in which a low score reliably identifies those patients at risk for adverse perioperative outcomes. Surgical techniques and anesthesia management should be directed in such a way that the Surgical Apgar Score remains higher to avoid postoperative morbidity and mortality.

Performance of critical care prognostic scoring systems in low and middle-income countries: a systematic review

Background

Prognostic models—used in critical care medicine for mortality predictions, for benchmarking and for illness stratification in clinical trials—have been validated predominantly in high-income countries. These results may not be reproducible in low or middle-income countries (LMICs), not only because of different case-mix characteristics but also because of missing predictor variables. The study objective was to systematically review literature on the use of critical care prognostic models in LMICs and assess their ability to discriminate between survivors and non-survivors at hospital discharge of those admitted to intensive care units (ICUs), their calibration, their accuracy, and the manner in which missing values were handled.

Methods

The PubMed database was searched in March 2017 to identify research articles reporting the use and performance of prognostic models in the evaluation of mortality in ICUs in LMICs. Studies carried out in ICUs in high-income countries or paediatric ICUs and studies that evaluated disease-specific scoring systems, were limited to a specific disease or single prognostic factor, were published only as abstracts, editorials, letters and systematic and narrative reviews or were not in English were excluded.

Results

Of the 2233 studies retrieved, 473 were searched and 50 articles reporting 119 models were included. Five articles described the development and evaluation of new models, whereas 114 articles externally validated Acute Physiology and Chronic Health Evaluation, the Simplified Acute Physiology Score and Mortality Probability Models or versions thereof. Missing values were only described in 34% of studies; exclusion and or imputation by normal values were used. Discrimination, calibration and accuracy were reported in 94.0%, 72.4% and 25% respectively. Good discrimination and calibration were reported in 88.9% and 58.3% respectively. However, only 10 evaluations that reported excellent discrimination also reported good calibration. Generalisability of the findings was limited by variability of inclusion and exclusion criteria, unavailability of post-ICU outcomes and missing value handling.

Conclusions

Robust interpretations regarding the applicability of prognostic models are currently hampered by poor adherence to reporting guidelines, especially when reporting missing value handling. Performance of mortality risk prediction models in LMIC ICUs is at best moderate, especially with limitations in calibration. This necessitates continued efforts to develop and validate LMIC models with readily available prognostic variables, perhaps aided by medical registries.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-017-1930-8) contains supplementary material, which is available to authorized users.

Development a Reverse Triage System Based on Modified Sequential Organ Failure Assessment for Increasing the Critical Care Surge Capacity

Context

The capacity completeness are one of the serious problems in the bed's managements of the critical care units in a crisis and disaster situation. Reverse triage can help to hospital surge capacity in this situations.

Aims

The aim of this study was to develop a reverse triage system based on Modified Sequential Organ Failure Assessment (MSOFA) for increasing critical care surge capacity.

Settings and Design

This study was a prospective design that performed on the medical patients in critical care unit.

Subjects and Methods

The MSOFA score for each patient was calculated in admission time and be continued until discharging time from critical care unit.

Statistical Analysis Used

The Cox regression method was used to determine the relative risk values. At last, the patients were divided into three levels for reverse triage.

Results

Four hundred and twenty patients were participated in this study. The mean of patients' MSOFA scores in the 1^st day of admission in Critical Care was 5.40 ± 3.8. The relative risk of internal patients discharge from critical care was (8.2%). Death relative risks were <25%, higher than 70% and between 25.1% and 69.9% for three color level of green, black, and red, respectively.

Conclusion

The MSOFA scores can contribute to the design a leveling system for discharging patients from critical care unit. Based on this system, the members of the caring team can predict the final health status of the patient.

Simple Mortality Predictive Models for Improving Critical Care in Resource-Limited Settings: An Insight on the Modified Early Warning Score and Rapid Emergency Medical Score

Mortality rate among critically ill patients admitted to the Intensive Care Unit is high, particularly in low-income countries (LIC). Many scores have been developed to predict these fatal outcomes. In LIC, the applicability of scoring systems is precluded by the unavailability of resources to compile all the parameters of these scores. Herein, we highlight the advantages of two models: the Modified Early Warning Score (MEWS) and the Rapid Emergency Medical Score (REMS). The REMS and the MEWS have the advantage of being accurate, simple, inexpensive, and practical for LIC.

Mortality Prediction Using Acute Physiology and Chronic Health Evaluation II and Acute Physiology and Chronic Health Evaluation IV Scoring Systems: Is There a Difference?

Background:

Mortality prediction in the Intensive Care Unit (ICU) setting is complex, and there are several scoring systems utilized for this process. The Acute Physiology and Chronic Health Evaluation (APACHE) II has been the most widely used scoring system; although, the more recent APACHE IV is considered an updated and advanced prediction model. However, these two systems may not give similar mortality predictions.

Objectives:

The aim of this study is to compare the mortality prediction ability of APACHE II and APACHE IV scoring systems among patients admitted to a tertiary care ICU.

Methods:

In this prospective longitudinal observational study, APACHE II and APACHE IV scores of ICU patients were computed using an online calculator. The outcome of the ICU admissions for all the patients was collected as discharged or deceased. The data were analyzed to compare the discrimination and calibration of the mortality prediction ability of the two scores.

Results:

Out of the 1670 patients' data analyzed, the area under the receiver operating characteristic of APACHE II score was 0.906 (95% confidence interval [CI] – 0.890–0.992), and APACHE IV score was 0.881 (95% CI – 0.862–0.890). The mean predicted mortality rate of the study population as given by the APACHE II scoring system was 44.8 ± 26.7 and as given by APACHE IV scoring system was 29.1 ± 28.5. The observed mortality rate was 22.4%.

Conclusions:

The APACHE II and IV scoring systems have comparable discrimination ability, but the calibration of APACHE IV seems to be better than that of APACHE II. There is a need to recalibrate the scales with weights derived from the Indian population.

Simplified Mortality Score for the Intensive Care Unit (SMS-ICU): protocol for the development and validation of a bedside clinical prediction rule

INTRODUCTION

Mortality prediction scores are widely used in intensive care units (ICUs) and in research, but their predictive value deteriorates as scores age. Existing mortality prediction scores are imprecise and complex, which increases the risk of missing data and decreases the applicability bedside in daily clinical practice. We propose the development and validation of a new, simple and updated clinical prediction rule: the Simplified Mortality Score for use in the Intensive Care Unit (SMS-ICU).

METHODS AND ANALYSIS

During the first phase of the study, we will develop and internally validate a clinical prediction rule that predicts 90-day mortality on ICU admission. The development sample will comprise 4247 adult critically ill patients acutely admitted to the ICU, enrolled in 5 contemporary high-quality ICU studies/trials. The score will be developed using binary logistic regression analysis with backward stepwise elimination of candidate variables, and subsequently be converted into a point-based clinical prediction rule. The general performance, discrimination and calibration of the score will be evaluated, and the score will be internally validated using bootstrapping. During the second phase of the study, the score will be externally validated in a fully independent sample consisting of 3350 patients included in the ongoing Stress Ulcer Prophylaxis in the Intensive Care Unit trial. We will compare the performance of the SMS-ICU to that of existing scores.

ETHICS AND DISSEMINATION

We will use data from patients enrolled in studies/trials already approved by the relevant ethical committees and this study requires no further permissions. The results will be reported in accordance with the Transparent Reporting of multivariate prediction models for Individual Prognosis Or Diagnosis (TRIPOD) statement, and submitted to a peer-reviewed journal.

Clinical factors associated with weaning failure in patients requiring prolonged mechanical ventilation

BACKGROUND

For patients requiring prolonged mechanical ventilation (PMV), weaning is difficult and mortality is very high. PMV has been defined recently, by consensus, as constituting ≥21 consecutive days of mechanical ventilation (MV) for ≥6 hours per day. This study aimed to evaluate the clinical factors predicting weaning failure in patients undergoing PMV in medical intensive care unit (ICU).

METHODS

We retrospectively reviewed the clinical and laboratory characteristics of 127 patients who received MV for more than 21 days in the medical ICU at Chonnam National University Hospital in South Korea between January 2005 and December 2014. Patients who underwent surgery or experienced trauma were excluded from this study.

RESULTS

Among the 127 patients requiring PMV, 41 (32.3%) were successfully weaned from MV. The median age of the weaning failure group was higher than that of the weaning success group (74.0 vs. 70.0 years; P=0.003). The proportion of male patients was 58.5% in the weaning success group and 72.1% in the weaning failure group, respectively. The most common reasons for ICU admission were respiratory causes (66.1%) followed by cardiovascular causes (16.5%) in both groups. ICU mortality and in-hospital mortality rates were 55.1% and 55.9%, respectively. In the multivariate analysis, respiratory causes of ICU admission [odds ratio (OR), 3.98; 95% confidence interval (CI), 1.29-12.30; P=0.016] and a high sequential organ failure assessment (SOFA) score on day 21 of MV (OR, 1.47; 95% CI, 1.17-1.85; P=0.001) were significantly associated with weaning failure in patients requiring PMV. The area under the receiver operating characteristic (ROC) curve of the SOFA score on day 21 of MV for predicting weaning failure was 0.77 (95% CI, 0.67-0.87; P=0.000).

CONCLUSIONS

Respiratory causes of ICU admission and a high SOFA score on day 21 of MV could be predictive of weaning failure in patients requiring PMV.

Predictive Performance of the Simplified Acute Physiology Score (SAPS) II and the Initial Sequential Organ Failure Assessment (SOFA) Score in Acutely Ill Intensive Care Patients: Post-Hoc Analyses of the SUP-ICU Inception Cohort Study

Purpose

Severity scores including the Simplified Acute Physiology Score (SAPS) II and the Sequential Organ Failure Assessment (SOFA) score are used in intensive care units (ICUs) to assess disease severity, predict mortality and in research. We aimed to assess the predictive performance of SAPS II and the initial SOFA score for in-hospital and 90-day mortality in a contemporary international cohort.

Methods

This was a post-hoc study of the Stress Ulcer Prophylaxis in the Intensive Care Unit (SUP-ICU) inception cohort study, which included acutely ill adults from ICUs across 11 countries (n = 1034). We compared the discrimination of SAPS II and initial SOFA scores, compared the discrimination of SAPS II in our cohort with the original cohort, assessed the calibration of SAPS II customised to our cohort, and compared the discrimination for 90-day mortality vs. in-hospital mortality for both scores. Discrimination was evaluated using areas under the receiver operating characteristics curves (AUROC). Calibration was evaluated using Hosmer-Lemeshow’s goodness-of-fit Ĉ-statistic.

Results

AUROC for in-hospital mortality was 0.80 (95% confidence interval (CI) 0.77–0.83) for SAPS II and 0.73 (95% CI 0.69–0.76) for initial SOFA score (P<0.001 for the comparison). Calibration of the customised SAPS II for predicting in-hospital mortality was adequate (P = 0.60). Discrimination of SAPS II was reduced compared with the original SAPS II validation sample (AUROC 0.80 vs. 0.86; P = 0.001). AUROC for 90-day mortality was 0.79 (95% CI 0.76–0.82; P = 0.74 for comparison with in-hospital mortality) for SAPS II and 0.71 (95% CI 0.68–0.75; P = 0.66 for comparison with in-hospital mortality) for the initial SOFA score.

Conclusions

The predictive performance of SAPS II was similar for in-hospital and 90-day mortality and superior to that of the initial SOFA score, but SAPS II’s performance has decreased over time. Use of a contemporary severity score with improved predictive performance may be of value.

Using electronic health record collected clinical variables to predict medical intensive care unit mortality

BACKGROUND

Clinical decision support systems are used to help predict patient stability and mortality in the Intensive Care Unit (ICU). Accurate patient information can assist clinicians with patient management and in allocating finite resources. However, systems currently in common use have limited predictive value in the clinical setting. The increasing availability of Electronic Health Records (EHR) provides an opportunity to use medical information for more accurate patient stability and mortality prediction in the ICU.

OBJECTIVE

Develop and evaluate an algorithm which more accurately predicts patient mortality in the ICU, using the correlations between widely available clinical variables from the EHR.

METHODS

We have developed an algorithm, AutoTriage, which uses eight common clinical variables from the EHR to assign patient mortality risk scores. Each clinical variable produces a subscore, and combinations of two or three discretized clinical variables also produce subscores. A combination of weighted subscores produces the overall score. We validated the performance of this algorithm in a retrospective study on the MIMIC III medical ICU dataset.

RESULTS

AutoTriage 12 h mortality prediction yields an Area Under Receiver Operating Characteristic value of 0.88 (95% confidence interval 0.86 to 0.88). At a sensitivity of 80%, AutoTriage maintains a specificity of 81% with a diagnostic odds ratio of 16.26.

CONCLUSIONS

Through the multidimensional analysis of the correlations between eight common clinical variables, AutoTriage provides an improvement in the specificity and sensitivity of patient mortality prediction over existing prediction methods.

Nutrition in critical illness: a current conundrum

Critically ill people are unable to eat. What’s the best way to feed them? Nutrition authorities have long recommended providing generous amounts of protein and calories to critically ill patients, either intravenously or through feeding tubes, in order to counteract the catabolic state associated with this condition. In practice, however, patients in modern intensive care units are substantially underfed. Several large randomized clinical trials were recently carried out to determine the clinical implications of this situation. Contradicting decades of physiological, clinical, and observational data, the results of these trials have been claimed to justify the current practice of systematic underfeeding in the intensive care unit. This article explains and suggests how to resolve this conundrum.

de Morton Mobility Index Is Feasible, Reliable, and Valid in Patients With Critical Illness

BACKGROUND

Intensive care unit (ICU) stays often lead to reduced physical functioning. Change in physical functioning in patients in the ICU is inadequately assessed through available instruments. The de Morton Mobility Index (DEMMI), developed to assess mobility in elderly hospitalized patients, is promising for use in patients who are critically ill.

OBJECTIVE

The aim of this study was to evaluate the clinimetric properties of the DEMMI for patients in the ICU.

DESIGN

A prospective, observational reliability and validity study was conducted.

METHODS

To evaluate interrater and intrarater reliability (intraclass correlation coefficients), patients admitted to the ICU were assessed with the DEMMI during and after ICU stay. Validity was evaluated by correlating the DEMMI with the Barthel Index (BI), the Katz Index of Independence in Activities of Daily Living (Katz ADL), and manual muscle testing (MMT). Feasibility was evaluated based on the percentage of participants in which the DEMMI could be assessed, the floor and ceiling effects, and the number of adverse events.

RESULTS

One hundred fifteen participants were included (Acute Physiology and Chronic Health Evaluation II [APACHE II] mean score=15.2 and Sepsis-related Organ Failure Assessment [SOFA] mean score=7). Interrater reliability was .93 in the ICU and .97 on the wards, whereas intrarater reliability during the ICU stay was .68. Validity (Spearman rho coefficient) during the ICU stay was .56, -.45, and .57 for the BI, Katz ADL, and MMT, respectively. The DEMMI showed low floor and ceiling effects (2.6%) during and after ICU discharge. There were no major adverse events.

LIMITATIONS

Rapid changes in participants' health status may have led to underestimation of intrarater reliability.

CONCLUSION

The DEMMI was found to be clinically feasible, reliable, and valid for measuring mobility in an ICU population. Therefore, the DEMMI should be considered a preferred instrument for measuring mobility in patients during and after their ICU stay.