Simplified Acute Physiology Score II as Predictor of Mortality in Intensive Care Units: A Decision Curve Analysis

Decision Curve Analysis of In-Hospital Mortality Prediction Models: The Relative Value of Pre- and Intraoperative Data For Decision-Making

BACKGROUND

Clinical prediction modeling plays a pivotal part in modern clinical care, particularly in predicting the risk of in-hospital mortality. Recent modeling efforts have focused on leveraging intraoperative data sources to improve model performance. However, the individual and collective benefit of pre- and intraoperative data for clinical decision-making remains unknown. We hypothesized that pre- and intraoperative predictors contribute equally to the net benefit in a decision curve analysis (DCA) of in-hospital mortality prediction models that include pre- and intraoperative predictors.

METHODS

Data from the VitalDB database featuring a subcohort of 6043 patients were used. A total of 141 predictors for in-hospital mortality were grouped into preoperative (demographics, intervention characteristics, and laboratory measurements) and intraoperative (laboratory and monitor data, drugs, and fluids) data. Prediction models using either preoperative, intraoperative, or all data were developed with multiple methods (logistic regression, neural network, random forest, gradient boosting machine, and a stacked learner). Predictive performance was evaluated by the area under the receiver-operating characteristic curve (AUROC) and under the precision-recall curve (AUPRC). Clinical utility was examined with a DCA in the predefined risk preference range (denoted by so-called treatment threshold probabilities) between 0% and 20%.

RESULTS

AUROC performance of the prediction models ranged from 0.53 to 0.78. AUPRC values ranged from 0.02 to 0.25 (compared to the incidence of 0.09 in our dataset) and high AUPRC values resulted from prediction models based on preoperative laboratory values. A DCA of pre- and intraoperative prediction models highlighted that preoperative data provide the largest overall benefit for decision-making, whereas intraoperative values provide only limited benefit for decision-making compared to preoperative data. While preoperative demographics, comorbidities, and surgery-related data provide the largest benefit for low treatment thresholds up to 5% to 10%, preoperative laboratory measurements become the dominant source for decision support for higher thresholds.

CONCLUSIONS

When it comes to predicting in-hospital mortality and subsequent decision-making, preoperative demographics, comorbidities, and surgery-related data provide the largest benefit for clinicians with risk-averse preferences, whereas preoperative laboratory values provide the largest benefit for decision-makers with more moderate risk preferences. Our decision-analytic investigation of different predictor categories moves beyond the question of whether certain predictors provide a benefit in traditional performance metrics (eg, AUROC). It offers a nuanced perspective on for whom these predictors might be beneficial in clinical decision-making. Follow-up studies requiring larger datasets and dedicated deep-learning models to handle continuous intraoperative data are essential to examine the robustness of our results.

Comparison of Four Severity Assessment Scoring Systems in Critically Ill Patients for Predicting Patient Outcomes: A Prospective Observational Study From a Single Tertiary Center in Central India

Background and aim A variety of scoring systems are employed in intensive care units (ICUs) with the objective of predicting patient morbidity and mortality. The present study aimed to compare four different severity assessment scoring systems, namely, Acute Physiology and Chronic Health Evaluation II (APACHE II), Rapid Emergency Medicine Score (REMS), Sequential Organ Failure Assessment (SOFA), and Simplified Acute Physiologic Score II (SAPS II) to predict prognosis of all patients admitted to a mixed medical ICU of a tertiary care teaching hospital in central India. Methods The prospective observational study included 1136 patients aged 18 years or more, admitted to the mixed medical ICU. All patients underwent severity assessment using the four scoring systems, namely APACHE II, SOFA, REMS, and SAPS II, after admission. Predicted mortality was calculated from each of the scores and actual patient outcomes were noted. Receiver operating curve analysis was undertaken to identify the cut-off value of individual scoring systems for predicting mortality with optimum sensitivity and specificity. Calibration and discrimination were employed to ascertain the validity of each scoring model. Bivariate and multivariable logistic regression analyses among the study participants were conducted to identify the best scoring system, after adjusting for potential confounders. Results Final analysis was done on 957 study participants (mean (±SD) age-58.4 (±12.9) years; males-62.2%). The mortality rate was 14.7%. APACHE II, SOFA, SAPS II, and REMS scores were significantly higher among the non-survivors as compared to the survivors (p<0.05). SAPS II was found to have the highest AUC of 0.981 (p<0.001). SAPS II score >58 had 93.6% sensitivity, 94.1% specificity, 73.3% PPV, 98.8% NPV, and 94.0% diagnostic accuracy in predicting mortality. This scoring system also had the best calibration. Binary logistic regression showed that all four scoring systems were significantly associated with ICU mortality. After adjusting for each other, only SAPS II remained significantly associated with ICU mortality. Conclusion Both SAPS II and APACHE II were observed to have good calibration and discriminatory power; however, SAPS II had the best prediction power suggesting that it may be a useful tool for clinicians and researchers in assessing the severity of illness and mortality risk in critically ill patients.

Is 1 Day of Focused Training in Echocardiographic Assessment Using Subxiphoid-Only (EASy) Examination Enough? A Tertiary Hospital Response to the COVID-19 Crisis and the Use of the EASy Examination to Support Unit-Wide Image Acquisition

OBJECTIVES

We assessed the efficacy of 1-day training in echocardiography assessment using subxiphoid-only (EASy) followed by supervised image interpretation and decision-making during patient rounds as a novel approach to scaling up the use of point-of-care ultrasound (POCUS) in critically ill patients.

DESIGN

Retrospective analysis of medical records and EASy examination images.

SETTING

Tertiary care academic hospital.

PATIENTS

A total of 14 adults (> 18 yr old) with COVID-19-associated respiratory failure under the care of Albany Medical Center's surge response team from April 6-17, 2020 who received at least one EASy examination.

INTERVENTIONS

Residents (previously novice sonographers) were trained in EASy examination using 1 day of didactic and hands-on training, followed by independent image acquisition and supervised image interpretation, identification of hemodynamic patterns, and clinical decision-making facilitated by an echocardiography-certified physician during daily rounds.

MEASUREMENTS AND MAIN RESULTS

We recorded the quality of resident-obtained EASy images, scanning time, and frequency with which the supervising physician had to repeat the examination or obtain additional images. A total of 63 EASy examinations were performed; average scanning time was 4.3 minutes. Resident-obtained images were sufficient for clinical decision-making on 55 occasions (87%), in the remaining 8 (13%) the supervising physician obtained further images.

CONCLUSIONS

EASy examination is an efficient, valuable tool under conditions of scarce resources. The educational model of 1-day training followed by supervised image interpretation and decision-making allows rapid expansion of the pool of sonographers and implementation of bedside echocardiography into routine ICU patient management.

Association between white blood cell-to-haemoglobin ratio and 30 day mortality in heart failure in intensive care unit

AIMS

The short-term mortality of heart failure (HF) patients admitted to the intensive care unit (ICU) is reported to be high. This study aims to explore the association between white blood cell-to-haemoglobin ratio (WHR) and 30 day mortality from the admission to the ICU.

METHODS AND RESULTS

This retrospective cohort study was performed based on the Medical Information Mart for Intensive Care III (MIMIC-III) database (2001-12) and MIMIC-IV database (2008-19). Covariables were selected using the least absolute shrinkage and selection operator regression. Based on the optimal cutoff point selected using the survminer package, WHR was divided into high-ratio group (≥1.6) and low-ratio group (<1.6). The association between WHR and the risk of 30 day mortality was explored using univariate and multivariable Cox regression models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the prediction performance of WHR. A total of 13 702 patients were included. After adjusting the potential covariates, high WHR was associated with a greater risk of 30 day mortality compared with low WHR [hazard ratio = 1.16, 95% confidence interval (CI): 1.07-1.27, P < 0.001]. WHR also showed a good performance for the prediction of risk of 30 day mortality (AUC = 0.751, 95% CI: 0.746-0.756).

CONCLUSIONS

WHR was positively associated with and performed well to predict 30 day mortality, indicating that WHR may be a reliable index to assess the prognosis of HF patients admitted to the ICU.

Interleukin 33, soluble suppression of tumorigenicity 2, interleukin 27, and galectin 3 as predictors for outcome in patients admitted to intensive care units

Intensive care units (ICUs) are expert hospital areas that provide treatment and 24 h care for people who are very sick. Sepsis represents a serious, severe condition and it can lead to septic shock and multiple organ dysfunction syndromes and is one of the most common reasons for patients' hospitalization in ICUs. We wanted to explore the prognostic values of interleukin (IL) 33, soluble suppression of tumorigenicity 2 (sST2), IL 27, and galectin 3 in critically-ill patients. We assumed that these parameters in combination or alone could predict mortality in ICU patients. This research represents a clinical non-randomized prospective study, performed at the Medical Military Academy, a tertiary care hospital in Belgrade, Serbia. The patients were divided in four groups: patients with sepsis (peritonitis, pancreatitis, trauma) and patients without sepsis (trauma). Total number of patients enrolled in the study was 151 and average years of patients were 56.48. The values greater than the cut-off were the predictors of mortality. The IL-33, IL-27 as well as galectin-3 can successfully predict the outcome of critically-ill patients in ICUs. The sST2, cannot predict death in critically-ill patients as a single prognostic factor. However, the combination of at least two biomarkers: IL-33, sST2, IL-27, and galectin-3, gives very significant results in predicting the outcome in patients admitted to ICUs.

Chest X-ray at Emergency Admission and Potential Association with Barotrauma in Mechanically Ventilated Patients: Experience from the Italian Core of the First Pandemic Peak

Barotrauma occurs in a significant number of patients with COVID-19 interstitial pneumonia undergoing mechanical ventilation. The aim of the current study was to investigate whether the Brixia score (BS) calculated on chest-X-rays acquired at the Emergency Room was associated with barotrauma. We retrospectively evaluated 117 SARS-CoV-2 patients presented to the Emergency Department (ED) and then admitted to the intensive care unit (ICU) for mechanical ventilation between February and April 2020. Subjects were divided into two groups according to the occurrence of barotrauma during their hospitalization. CXRs performed at ED admittance were assessed using the Brixia score. Distribution of barotrauma (pneumomediastinum, pneumothorax, subcutaneous emphysema) was identified in chest CT scans. Thirty-eight subjects (32.5%) developed barotrauma (25 pneumomediastinum, 24 pneumothorax, 24 subcutaneous emphysema). In the barotrauma group we observed higher Brixia score values compared to the non-barotrauma group (mean value 12.18 vs. 9.28), and logistic regression analysis confirmed that Brixia score is associated with the risk of barotrauma. In this work, we also evaluated the relationship between barotrauma and clinical and ventilatory parameters: SOFA score calculated at ICU admittance and number of days of non-invasive ventilation (NIV) prior to intubation emerged as other potential predictors of barotrauma.

Cerebrospinal fluid analysis of metabolites is not correlated to microdialysis measurements in acute brain injured patients

BACKGROUND

Cerebral microdialysis (CMD) has become an established bedside monitoring modality but its implementation remains complex and costly and is therefore performed only in a few well-trained academic centers. This study investigated the relationship between cerebrospinal fluid (CSF) and CMD glucose and lactate concentrations.

METHODS

Two centers retrospective study of prospectively collected data. Consecutive adult (>18 years) acutely brain injured patients admitted to the Intensive Care Unit between 2010 and 2021 were eligible if CSF and CMD glucose and lactate concentrations were concomitantly measured at least once.

RESULTS

Of 113 patients being monitored with an external ventricular drainage and CMD, 49 patients (25 from Innsbruck and 24 from Brussels) were eligible for the final analysis, including a total of 96 measurements. Median CMD glucose and lactate concentrations were 1.15 (0.51-1.57) mmol/L and 3.44 (2.24-5.37) mmol/L, respectively; median CSF glucose and lactate concentrations were 4.67 (4.03-5.34) mmol/L and 3.40 (2.85-4.10) mmol/L, respectively. For the first measurements, no correlation between CSF and CMD glucose concentrations (R2 <0.01; p = 0.95) and CSF and CMD lactate concentrations (R2 =0.16; p = 0.09) was found. Considering all measurements, the repeated measure correlation analysis also showed no correlation for glucose (rrm = -0.01; 95% Confidence Intervals -0.306 to 0.281; p = 0.93) and lactate (rrm = -0.11; 95% Confidence Intervals -0.424 to 0.236; p = 0.55).

CONCLUSIONS

In this study including acute brain injured patients, no correlation between CSF and brain tissue measurements of glucose and lactate was observed. As such, CSF measurements of such metabolites cannot replace CMD findings.

Association and prediction of red blood cell distribution width to albumin ratio in all-cause mortality of acute kidney injury in critically ill patients

Aim

The progression of acute kidney injury (AKI) might be associated with systemic inflammation. Our study aims to explore the association and predictive value of the red blood cell distribution width (RDW) to human serum albumin (ALB) ratio (RDW/ALB ratio), an inflammation-related indicator, in the risk of all-cause mortality and renal replacement therapy (RRT) in AKI patients admitted in intensive care units (ICU).

Methods

A retrospective cohort study was designed, and data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III). The primary outcome was the risk of all-cause mortality (1-month, 3-month, and 12-month), and the secondary outcome was the risk of RRT. The association between the RDW/ALB ratio and the risk of all-cause mortality and RRT was assessed using the Cox regression analysis, with results shown as hazard ratio (HR) and 95% confidence intervals (CIs). The relationship between the RDW/ALB ratio and crude probability of all-cause mortality or RRT was assessed using restricted cubic splines (RCS). The concordance index (C-index) was used to assess the discrimination of the prediction model.

Results

A total of 13,856 patients were included in our study. In the fully adjusted Cox regression model, we found that a high RDW/ALB ratio was associated with an increased risk of 1-month, 3-month, and 12-month all-cause mortality and RRT (all p &lt; 0.05). Moreover, RCS curves showed the linear relationship between the RDW/ALB ratio and the probability of all-cause mortality and RRT, and the probability was elevated with the increase of the ratio. In addition, the RDW/ALB ratio showed a good predictive performance in the risk of 1-month all-cause mortality, 3-month all-cause mortality, 12-month all-cause mortality, and RRT, with a C-index of 0.728 (95%CI: 0.719–0.737), 0.728 (95%CI: 0.721–0.735), 0.719 (95%CI: 0.713–0.725), and 0.883 (95%CI: 0.876–0.890), respectively.

Conclusion

The RDW/ALB ratio performed well to predict the risk of all-cause mortality and RRT in critically ill patients with AKI, indicating that this combined inflammatory indicator might be effective in clinical practice.

Postoperative high-sensitivity troponin T predicts 1-year mortality and days alive and out of hospital after orthotopic heart transplantation

BACKGROUND

Orthotopic heart transplantation (HTX) is the gold standard to treat end-stage heart failure. Numerous risk stratification tools have been developed in the past years. However, their clinical utility is limited by their poor discriminative ability. High sensitivity troponin T (hsTnT) is the most specific biomarker to detect myocardial cell injury. However, its prognostic relevance after HTX is not fully elucidated. Thus, this study evaluated the predictive value of postoperative hsTnT for 1-year survival and days alive and out of hospital (DAOH) after HTX.

METHODS

This retrospective cohort study included patients who underwent HTX at the University Hospital Duesseldorf, Germany between 2011 and 2021. The main exposure was hsTnT concentration at 48 h after HTX. The primary endpoints were mortality and DAOH within 1 year after surgery. Receiver operating characteristic (ROC) curve analysis, logistic regression model and linear regression with adjustment for risk index for mortality prediction after cardiac transplantation (IMPACT) were performed.

RESULTS

Out of 231 patients screened, 212 were included into analysis (mean age 55 ± 11 years, 73% male). One-year mortality was 19.7% (40 patients) and median DAOH was 298 days (229-322). ROC analysis revealed strongest discrimination for mortality by hsTnT at 48 h after HTX [AUC = 0.79 95% CI 0.71-0.87]. According to Youden Index, the cutoff for hsTnT at 48 h and mortality was 1640 ng/l. After adjustment for IMPACT score multivariate logistic and linear regression showed independent associations between hsTnT and mortality/DAOH with odds ratio of 8.10 [95%CI 2.99-21.89] and unstandardized regression coefficient of -1.54 [95%CI -2.02 to -1.06], respectively.

CONCLUSION

Postoperative hsTnT might be suitable as an early prognostic marker after HTX and is independently associated with 1-year mortality and poor DAOH.

Percutaneous Large-Bore Pulmonary Thrombectomy with the FlowTriever Device: Initial Experience in Intermediate-High and High-Risk Patients

OBJECTIVES

This retrospective cohort study investigates outcomes of patients with intermediate-high and high-risk pulmonary embolism (PE) who were treated with transfemoral mechanical thrombectomy (MT) using the large-bore Inari FlowTriever aspiration catheter system.

MATERIAL AND METHODS

Twenty-seven patients (mean age 56.1 ± 15.3 years) treated with MT for PE between 04/2021 and 11/2021 were reviewed. Risk stratification was performed according to European Society of Cardiology (ESC) guidelines. Clinical and hemodynamic characteristics before and after the procedure were compared with the paired Student's t test, and duration of hospital stay was analyzed with the Kaplan-Meier estimator. Procedure-related adverse advents were assessed.

RESULTS

Of 27 patients treated, 18 were classified as high risk. Mean right-to-left ventricular ratio on baseline CT was 1.7 ± 0.6. After MT, a statistically significant reduction in mean pulmonary artery pressures from 35.9 ± 9.6 to 26.1 ± 9.0 mmHg (p = 0.002) and heart rates from 109.4 ± 22.5 to 82.8 ± 13.8 beats per minute (p < 0.001) was achieved. Two patients died of prolonged cardiogenic shock. Three patients died of post-interventional complications of which a paradoxical embolism can be considered related to MT. One patient needed short cardiopulmonary resuscitation during the procedure due to clot displacement. Patients with PE as primary driver of clinical instability had a median intensive care unit (ICU) stay of 2 days (0.5-3.5 days). Patients who developed PE as a complication of an underlying medical condition spent 11 days (9.5-12.5 days) in the ICU.

CONCLUSION

In this small study population of predominantly high-risk PE patients, large-bore MT without adjunctive thrombolysis was feasible with an acceptable procedure-related complication rate.

The value of comorbidities and illness severity scores as prognostic tools for early outcome estimation in patients with aneurysmal subarachnoid hemorrhage

Aneurysmal subarachnoid hemorrhage (aSAH) is a severe cerebrovascular disease not only causing brain injury but also frequently inducing a significant systemic reaction affecting multiple organ systems. In addition to hemorrhage severity, comorbidities and acute extracerebral organ dysfunction may impact the prognosis after aSAH as well. The study objective was to assess the value of illness severity scores for early outcome estimation after aSAH. A retrospective analysis of consecutive aSAH patients treated from 2012 to 2020 was performed. Comorbidities were evaluated applying the Charlson comorbidity index (CCI) and the American Society of Anesthesiologists (ASA) classification. Organ dysfunction was assessed by calculating the simplified acute physiology score (SAPS II) 24 h after admission. Modified Rankin scale (mRS) at 3 months was documented. The outcome discrimination power was evaluated. A total of 315 patients were analyzed. Significant comorbidities (CCI > 3) and physical performance impairment (ASA > 3) were found in 15% and 12% of all patients, respectively. The best outcome discrimination power showed SAPS II (AUC 0.76), whereas ASA (AUC 0.65) and CCI (AUC 0.64) exhibited lower discrimination power. A SAPS II cutoff of 40 could reliably discriminate patients with good (mRS ≤ 3) from those with poor outcome (p < 0.0001). Calculation of SAPS II allowed a comprehensive depiction of acute organ dysfunctions and facilitated a reliable early prognosis estimation in our study. In direct comparison to CCI and ASA, SAPS II demonstrated the highest discrimination power and deserves a consideration as a prognostic tool after aSAH.

The Relationship Between Prognostic Nutritional Index and All-Cause Mortality in Critically Ill Patients: A Retrospective Study

Purpose

The effectiveness and prognostic value of the prognostic nutritional index (PNI) in critically ill patients are unknown. Hence, this study aimed to analyze the relationship between the PNI and all-cause mortality in critically ill patients.

Patients and Methods

Patient data were obtained from the Multiparameter Intelligent Monitoring in Intensive Care III database. The relationship between the PNI and in-hospital mortality was analyzed using receiver operating characteristic curve analysis and a logistic regression model. Propensity score matching (PSM) was used to eliminate the bias caused by confounding factors. The Kaplan-Meier curve and Cox regression model were used to test the effect of the PNI on 30-, 90-, 180-, and 365-day mortality.

Results

A low PNI score is an independent risk factor for in-hospital mortality in critically ill patients. A total of 3644 cases were successfully matched using PSM. The PSM group with balanced covariates obtained similar results in the three models, which were statistically significant. The Kaplan-Meier curve and Cox regression model showed that the PNI was negatively correlated with 30-, 90-, 180-, and 365-day all-cause mortality.

Conclusion

The PNI score is an independent risk factor for all-cause mortality in critically ill patients, where a low PNI score is associated with increased mortality.

Outcome in patients with open abdomen treatment for peritonitis: a multidomain approach outperforms single domain predictions

Numerous patient-related clinical parameters and treatment-specific variables have been identified as causing or contributing to the severity of peritonitis. We postulated that a combination of clinical and surgical markers and scoring systems would outperform each of these predictors in isolation. To investigate this hypothesis, we developed a multivariable model to examine whether survival outcome can reliably be predicted in peritonitis patients treated with open abdomen. This single-center retrospective analysis used univariable and multivariable logistic regression modeling in combination with repeated random sub-sampling validation to examine the predictive capabilities of domain-specific predictors (i.e., demography, physiology, surgery). We analyzed data of 1,351 consecutive adult patients (55.7% male) who underwent open abdominal surgery in the study period (January 1998 to December 2018). Core variables included demographics, clinical scores, surgical indices and indicators of organ dysfunction, peritonitis index, incision type, fascia closure, wound healing, and fascial dehiscence. Postoperative complications were also added when available. A multidomain peritonitis prediction model (MPPM) was constructed to bridge the mortality predictions from individual domains (demographic, physiological and surgical). The MPPM is based on data of n = 597 patients, features high predictive capabilities (area under the receiver operating curve: 0.87 (0.85 to 0.90, 95% CI)) and is well calibrated. The surgical predictor “skin closure” was found to be the most important predictor of survival in our cohort, closely followed by the two physiological predictors SAPS-II and MPI. Marginal effects plots highlight the effect of individual outcomes on the prediction of survival outcome in patients undergoing staged laparotomies for treatment of peritonitis. Although most single indices exhibited moderate performance, we observed that the predictive performance was markedly increased when an integrative prediction model was applied. Our proposed MPPM integrative prediction model may outperform the predictive power of current models.

Tertiary peritonitis: A disease that should not be ignored

Intra-abdominal infections can be classified into uncomplicated or complicated (peritonitis). Peritonitis is divided into primary, secondary, and tertiary. Tertiary peritonitis is the less common but the most severe among peritonitis stratifications, being defined as a recurrent intra-abdominal infection that occurs 48 h after a well-succeeded control of a secondary peritonitis. This disease has a complex pathogenesis that is closely related to the capacity of the peritoneal cavity to activate immunological processes. Patients who progress to persistent peritonitis are at an increased risk of developing several infectious complications such as sepsis and multiple organ failure syndrome. Moreover, tertiary peritonitis remains an important cause of hospital death mainly among patients with associated risk factors. The microbiological profile of organisms causing tertiary peritonitis is often different from that observed in other types of peritonitis. In addition, there is a high prevalence of multidrug-resistant pathogens causing this condition, and an appropriate and successful clinical management depends on an early diagnosis, which can be made easier with the use of clinical scores presenting a good prediction value during the intensive care unit admission. Complementarily, immediate therapy should be performed to control the infectious focus and to prevent new recurrences. In this sense, the treatment is based on initial antimicrobial therapy and well-performed peritoneal drainage.

A machine learning approach to predict healthcare-associated infections at intensive care unit admission: findings from the SPIN-UTI project

BACKGROUND

Identifying patients at higher risk of healthcare-associated infections (HAIs) in intensive care units (ICUs) represents a major challenge for public health. Machine learning could improve patient risk stratification and lead to targeted infection prevention and control interventions.

AIM

To evaluate the performance of the Simplified Acute Physiology Score (SAPS) II for HAI risk prediction in ICUs, using both traditional statistical and machine learning approaches.

METHODS

Data for 7827 patients from the 'Italian Nosocomial Infections Surveillance in Intensive Care Units' project were used in this study. The Support Vector Machines (SVM) algorithm was applied to classify patients according to sex, patient origin, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II at admission, presence of invasive devices, trauma, impaired immunity, and antibiotic therapy in 48 h preceding ICU admission.

FINDINGS

The performance of SAPS II for predicting HAI risk provides a receiver operating characteristic curve with an area under the curve of 0.612 (P<0.001) and accuracy of 56%. Considering SAPS II along with other characteristics at ICU admission, the SVM classifier was found to have accuracy of 88% and an AUC of 0.90 (P<0.001) for the test set. The predictive ability was lower when considering the same SVM model but with the SAPS II variable removed (accuracy 78%, AUC 0.66).

CONCLUSIONS

This study suggested that the SVM model is a useful tool for early prediction of patients at higher risk of HAIs at ICU admission.

Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients' characteristics at ICU admission. We used data from the "Italian Nosocomial Infections Surveillance in Intensive Care Units" network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient's origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

Score performance of SAPS 2 and SAPS 3 in combination with biomarkers IL-6, PCT or CRP

OBJECTIVE

We aimed to evaluate the effects of combining the Simplified-Acute-Physiology-Score (SAPS) 2 or the SAPS 3 with Interleukin-6 (IL-6) or Procalcitonin (PCT) or C-Reactive Protein (CRP) concentrations for predicting in-hospital mortality.

MATERIAL AND METHODS

This retrospective study was conducted in an interdisciplinary 22-bed intensive care unit (ICU) at a German university hospital. Within an 18-month period, SAPS 2 and SAPS 3 were calculated for 514 critically ill patients that were admitted to the internal medicine department. To evaluate discrimination performance, the area under the receiver operating characteristic curves (AUROCs) and the 95% confidence intervals (95% CIs) were calculated for each score, exclusively or in combination with IL-6 or PCT or CRP. DeLong test was used to compare different AUROCs.

RESULTS

The SAPS 2 exhibited a better discrimination performance than SAPS 3 with AUROCs of 0.81 (95% CI, 0.76-0.86) and 0.72 (95% CI, 0.66-0.78), respectively. Overall, combination of the SAPS 2 with IL-6 showed the best discrimination performance (AUROC 0.82; 95% CI, 0.77-0.87), albeit not significantly different from SAPS2. IL-6 performed better than PCT and CRP with AUROCs of 0.75 (95% CI, 0.69-0.81), 0.72 (95% CI, 0.66-0.77) and 0.65 (95% CI, 0.59-0.72), respectively. Performance of the SAPS 3 improved significantly when combined with IL-6 (AUROC 0.76; 95% CI, 0.69-0.81) or PCT (AUROC 0.73; 95% CI, 0.67-0.78).

CONCLUSIONS

Our analysis provided evidence that the risk stratification performance of the SAPS 3 and, to a lesser degree, also of the SAPS 2 can increase when combined with IL-6. A more accurate detection of aberrant or dysregulated systemic immunological responses (by IL-6) may explain the higher performance achieved by SAPS 3 + IL-6 vs. SAPS 3. Thus, implementation of IL-6 in critical care scores can improve prediction outcomes, especially in patients experiencing acute inflammatory conditions; however, statistical results may vary across hospital types and/or patient populations with different case mix.

Atrophy of Diaphragm and Pectoral Muscles in Critically Ill Patients

WHAT WE ALREADY KNOW ABOUT THIS TOPIC

Muscle atrophy is common in the critically ill, and diaphragm atrophy occurs during mechanical ventilation. It is not known whether wasting of diaphragm and nondiaphragm muscle is related.

WHAT THIS ARTICLE TELLS US THAT IS NEW

Ultrasound was used for serial assessment of diaphragm and pectoral muscle in 97 critically ill patients. Diaphragm and pectoral atrophy occurred in 48% and 29%, respectively, and was associated with septic shock (diaphragm) and steroid use (pectoral); atrophy of the two muscle types appears unrelated.

BACKGROUND

Muscle atrophy occurs early during critical illnesses. Although diffuse, this atrophy may specifically affect the diaphragm under artificial inactivity accompanying invasive mechanical ventilation. The primary objective of this study was to highlight diaphragm atrophy during the first 5 days of critical illness. Monitoring of pectoral thickness (a nonpostural muscle with mainly phasic function) served as a control.

METHODS

Diaphragm and pectoral thicknesses were measured by ultrasound within the first 24 h of admission in 97 critically ill patients, including 62 on mechanical ventilation. Thirty-five patients were reexamined at day 5.

RESULTS

Baseline median (interquartile) values of diaphragm and pectoral thicknesses at day 1 were 2.4 (2.0, 2.9) and 5.9 (4.7, 7.2) mm, respectively (n = 97). Higher values of diaphragm thickness at baseline were positively associated with male sex, chronic obstructive pulmonary disease, and diabetes. Diaphragm and pectoral atrophies (defined as a decrease of 10% or more between day 1 and day 5) were detected in 48% (17 of 35) and 29% (10 of 34) respectively, and were uncorrelated with each other. Diaphragm atrophy was significantly more frequent in patients with septic shock and in those with mechanical ventilation, as compared with their respective counterparts (71% [10 of 14] vs. 33% [7 of 21], P = 0.027 and 71% [17 of 28] vs. 0% [0 of 7], P = 0.004, respectively), whereas pectoral atrophy was more common in patients treated with steroids as compared with their counterparts (58% [7 of 12] vs. 14% [3 of 22], P = 0.006). A statistically significant association between diaphragm atrophy and outcome was not found. Pectoral atrophy seemed associated with less successful weaning from mechanical ventilation at day 14 (12% [1 of 8] vs. 58% [11 of 19], P = 0.043).

CONCLUSIONS

Ultrasound enables identification of specific early diaphragm atrophy that affects the majority of mechanically ventilated patients and septic shock patients. Diaphragm atrophy and pectoral muscle atrophy seem to be two unrelated processes.

Outcomes of kidney transplant recipients admitted to the intensive care unit: a retrospective study of 200 patients

Background

Risk of over-immunosuppression or immunization may mitigate the overall and long-term renal outcomes of kidney transplant recipients (KTR) admitted to the ICU in the modern era but remain poorly described. Thus, there is an unmet need to better characterize the survival of KTR admitted to the ICU, but also the renal and immunological outcomes of survivors.

Methods

Retrospective observational study that included 200 KTR admitted between 2010 and 2016 to the ICU of a teaching hospital (median age 61 years [IQR 50.7–68]; time from transplantation 41 months [IQR 5–119]). Survival curves were compared using the Log-rank test.

Results

Mortality rates following admission to the ICU was low (26.5% at month-6), mainly related to early mortality (20% in-hospital), and predicted by the severity of the acute condition (SAPS2 score) but also by Epstein Barr Virus proliferation in the weeks preceding the admission to the ICU. Acute kidney injury (AKI) was highly prevalent (85.1%). Progression toward chronic kidney disease (CKD) was observed in 45.1% of survivors. 15.1% of survivors developed new anti-HLA antibodies (donor-specific antibodies 9.2% of cases) that may impact the long-term renal transplantation function.

Conclusions

Notwithstanding the potential biases related to the retrospective and monocentric nature of this study, our findings obtained in a large cohort of KTR suggest that survival of KTR admitted in ICU is good but in-ICU management of these patients may alter both survival and AKI to CKD transition, as well as HLA immunization. Further interventional studies, including systematic characterization of the Epstein Barr virus proliferation at the admission (i.e., a potential surrogate marker of an underlying immune paralysis and frailty) will need to address the optimal management of immunosuppressive regimen in ICU to improve survival but also renal and immunological outcomes.

Assessment of the risk of QT-interval prolongation associated with potential drug-drug interactions in patients admitted to Intensive Care Units

Objectives

To evaluate the relationship between drug interactions and QT-interval prolongation in patients admitted to a general intensive care unit (ICU).

Methods

This study was approved by the Institutional Review Board and written informed consent was obtained from all patients. From May 2015 to July 2016, all patients over 18 years-old admitted to the ICU for more than 24 h and in whom the QT-interval on the ECG could be read were prospectively included in this observational, cross-sectional study. All medications administered in the 24 h prior to admission were recorded and the QT-interval was measured upon ICU admission and corrected with Bazzet’s formula (QTc). Drug-drug interactions involving drugs potentially associated with QTc prolongation (DDIQT) were searched and QTc increase associated with pharmacokinetic (PK-DDIQT) and pharmacodynamic (PD-DDIQT) interactions was assessed with multiple regression adjusted by patient varibles.

Results

The study population consisted of 283 patients, 54.4% males, mean age 57.6 ± 16.7 years-old. Forty five (15.9%) patients presented 65 DDIQT with predominance of pharmacodynamic (66.1%). The risk of DDIQT prescription increased with lower systolic blood pressure, in hypokalemia, in non-diabetics and with the number of medications. PK-DDIQT alone did not affect the QTc interval (7.75 ms, 95%CI: –22.4 to 37.9 ms, p = 0.61), but PD-DDIQT increased QTc by 28.4 ms (95%CI: 9.67 to 47.4 ms, p = 0.003). Most PD-DDIQT involved metoclopramide with ondansetron or amiodarone, and ondansetron with ciprofloxacin.

Conclusions

In patients exposed to drugs associated with prolonged QTc in the 24 h prior to ICU admission, pharmacodynamic DDIQT are associated with increased risk of QTc prolongation.

The prognostic accuracy evaluation of SAPS 3, SOFA and APACHE II scores for mortality prediction in the surgical ICU: an external validation study and decision-making analysis

BACKGROUND

The early postoperative period is critical for surgical patients. SOFA, SAPS 3 and APACHE II are prognostic scores widely used to predict mortality in ICU patients. This study aimed to evaluate these index tests for their prognostic accuracy for intra-ICU and in-hospital mortalities as target conditions in patients admitted to ICU after urgent or elective surgeries and to test whether they aid in decision-making. The process comprised the assessment of discrimination through analysis of the areas under the receiver operating characteristic curves and calibration of the prognostic models for the target conditions. After, the clinical relevance of applying them was evaluated through the measurement of the net benefit of their use in the clinical decision.

RESULTS

Index tests were found to discriminate regular for both target conditions with a poor calibration (C statistics-intra-ICU mortality AUROCs: APACHE II 0.808, SAPS 3 0.821 and SOFA 0.797/in-hospital mortality AUROCs: APACHE II 0.772, SAPS 3 0.790 and SOFA 0.742). Calibration assessment revealed a weak correlation between the observed and expected number of cases in several thresholds of risk, calculated by each model, for both tested outcomes. The net benefit analysis showed that all score's aggregate value in the clinical decision when the calculated probabilities of death ranged between 10 and 40%.

CONCLUSIONS

In this study, we observed that the tested ICU prognostic scores are fair tools for intra-ICU and in-hospital mortality prediction in a cohort of postoperative surgical patients. Also, they may have some potential to be used as ancillary data to support decision-making by physicians and families regarding the level of therapeutic investment and palliative care.

QTc interval prolongation in critically ill patients: Prevalence, risk factors and associated medications

PURPOSE

To investigate the prevalence and risk factors of acquired long QT syndrome (LQTS) on admission to a general Intensive Care Unit (ICU), and to assess the risk of LQTS associated with prescribed medications.

METHODS

Prospective observational, cross-sectional study approved by the Institutional Review Board. Between May 2014 and July 2016, 412 patients >18 years-old consecutively admitted to the ICU of a university hospital were included. LQTS was defined as a QT interval on the admission electrocardiogram corrected using Bazett's formula (QTc) >460 ms for men and >470 ms for women. All medications administered within 24 hours before admission were recorded. Logistic regression was used.

RESULTS

LQTS prevalence was 27.9%. In LQTS patients, 70.4% had ≥ 1 LQTS-inducing drug prescribed in the 24 hours prior to ICU admission versus 70.4% in non-LQTS patients (p = 0.99). Bradycardia and Charlson morbidity index score are independent risk factors for LQTS. Haloperidol (OR 4.416), amiodarone (OR 2.509) and furosemide (OR 1.895) were associated with LQTS, as well as another drug not yet described, namely clopidogrel (OR 2.241).

CONCLUSIONS

The LQTS is highly prevalent in critically ill patients, ICU patients are often admitted with LQTS-inducing medications, and patients with slow heart rate or with high Charlson comorbidity index should be evaluated for LQTS.

Could the use of bedside lung ultrasound reduce the number of chest x-rays in the intensive care unit?

Background

Lung ultrasound can be used as an alternative to chest radiography (CXR) for the diagnosis and follow-up of various lung diseases in the intensive care unit (ICU). Our aim was to evaluate the influence that introducing a routine daily use of lung ultrasound in critically ill patients may have on the number of CXRs and as a consequence, on medical costs and radiation exposure.

Methods

Data were collected by conducting a retrospective evaluation of the medical records of adult patients who needed thoracic imaging and were admitted to our academic polyvalent ICU. We compared the number of CXRs and relative costs before and after the introduction of lung ultrasound in our ICU.

Results

A total of 4134 medical records were collected from January 2010 to December 2014. We divided our population into two groups, before (Group A, 1869 patients) and after (Group B, 2265 patients) the introduction of a routine use of LUS in July 2012. Group A performed a higher number of CXRs compared to Group B (1810 vs 961, P = 0.012), at an average of 0.97 vs 0.42 exams per patient. The estimated reduction of costs between Groups A and B obtained after the introduction of LUS, was 57%. No statistically significant difference between the outcome parameters of the two groups was observed.

Conclusions

Lung ultrasound was effective in reducing the number of CXRs and relative medical costs and radiation exposure in ICU, without affecting patient outcome.