Synergizing Survival: Uniting Acute Gastrointestinal Injury Grade and Disease Severity Scores in Critical Care Prognostication.

How to cite this article: Patnaik RK, Karan N. Synergizing Survival: Uniting Acute Gastrointestinal Injury Grade and Disease Severity Scores in Critical Care Prognostication. Indian J Crit Care Med 2024;28(6):529-530.

A first-level customization study of SAPS II with Norwegian Intensive Care and Pandemic Registry (NIPaR) data.

BACKGROUND: Severity scores and mortality prediction models (MPMs) are important tools for benchmarking and stratification in the intensive care unit (ICU) and need to be regularly updated using data from a local and contextual cohort. Simplified acute physiology score II (SAPS II) is widely used in European ICUs. METHODS: A first-level customization was performed on the SAPS II model using data from the Norwegian Intensive Care and Pandemic Registry (NIPaR). Two previous SAPS II models (Model A: the original SAPS II model and Model B: a SAPS II model based on NIPaR data from 2008 to 2010) were compared to the new Model C. Model C was based on patients from 2018 to 2020 (corona virus disease 2019 patients omitted; n = 43,891), and its performances (calibration, discrimination, and uniformity of fit) compared to the previous models (Model A and Model B). RESULTS: Model C was better calibrated than Model A with a Brier score 0.132 (95% confidence interval 0.130-0.135) versus 0.143 (95% confidence interval 0.141-0.146). The Brier score for Model B was 0.133 (95% confidence interval 0.130-0.135). In the Cox's calibration regression α ≈ 0 and ß ≈ 1 for both Model C and Model B but not for Model A. Uniformity of fit was similar for Model B and for Model C, both better than for Model A, across age groups, sex, length of stay, type of admission, hospital category, and days on respirator. The area under the receiver operating characteristic curve was 0.79 (95% confidence interval 0.79-0.80), showing acceptable discrimination. CONCLUSIONS: The observed mortality and corresponding SAPS II scores have significantly changed during the last decades and an updated MPM is superior to the original SAPS II. However, proper external validation is required to confirm our findings. Prediction models need to be regularly customized using local datasets in order to optimize their performances.

Association between nutritional status scores and the 30-day mortality in patients with acute kidney injury: an analysis of MIMIC-III database.

BACKGROUND: Studies have proven that the risk of acute kidney injury (AKI) increased in patients with malnutrition. Prognostic nutritional index (PNI) and geriatric nutritional risk index (GNRI) were general tools to predict the risk of mortality, but the prognostic value of them for in-hospital mortality among patients with AKI have not been validated yet. Herein, this study aims to explore the association between PNI and GNRI and 30-day mortality in patients with AKI. METHODS: Demographic and clinical data of 863 adult patients with AKI were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database in 2001-2012 in this retrospective cohort study. Univariate and multivariate Cox proportional regression analyses were used to explore the association between PNI and GNRI and 30-day mortality. The evaluation indexes were hazard ratios (HRs) and 95% confidence intervals (CIs). Subgroup analyses of age, Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology (SAPS-II) score were also performed. RESULTS: Totally, 222 (26.71%) patients died within 30 days. After adjusting for covariates, PNI ≥ 28.5 [HR = 0.71, 95%CI: (0.51-0.98)] and GNRI ≥ 83.25 [HR = 0.63, 95%CI: (0.47-0.86)] were both associated with low risk of 30-day mortality. These relationships were also found in patients who aged ≥ 65 years old. Differently, high PNI level was associated with low risk of 30-day mortality among patients with SOFA score < 6 or SAPS-II score < 43, while high GNRI was associated with low risk of 30-day mortality among those who with SOFA score ≥ 6 or SAPS-II score ≥ 43 (all P < 0.05). CONCLUSION: PNI and GNRI may be potential predictors of 30-day mortality in patients with AKI. Whether the PNI is more recommended for patients with mild AKI, while GNRI for those with severe AKI is needed further exploration.

Prognostic factors in patients treated with extracorporeal membrane oxygenation and continuous renal replacement therapy.

INTRODUCTION: Extracorporeal membrane oxygenation (ECMO) and Continuous renal replacement therapy (CRRT) are treatments for critically ill patients with respiratory failure and acute kidney injury. However, no reliable factors have been identified to predict survival in patients treated with both ECMO and CRRT. The aim of this study was to identify prognostic factors for discharging intensive care unit (ICU) patients who required CRRT during ECMO. METHODS: We retrospectively analyzed data from patients who required CRRT in addition to the ECMO, between April 2015 and March 2018. The patients were divided into two groups: patients who survived and patients who died during ICU hospitalization. We determined their demographic and clinical characteristics, Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, Simplified Acute Physiology Score II (SAPS II) scores, and sequential organ failure assessment (SOFA) scores. Further, we assessed whether these characteristics differed between individuals who did or did not survive the ICU hospitalization. RESULTS: We found that the APACHE II and SAPS II scores differed significantly between both ECMO and CRRT treated patients who did or did not survive hospitalization. Further, intracranial hemorrhage during ECMO and CRRT therapy was associated with lower survival rate. CONCLUSIONS: Using APACHE II and SAPS II scores might be helpful in making treatment decisions for patients treated with ECMO and CRRT. Intracranial hemorrhage could be a poor prognostic factor. Our findings indicate the potential utility of APACHE II and SAPS II scores to predict mortality in patients treated with both ECMO and CRRT.

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.

Preoperative and ICU Scoring Models for Predicting the In-Hospital Mortality of Patients With Ruptured Abdominal Aortic Aneurysms.

OBJECTIVES: This study's objective was to compare several preoperative and intensive care unit (ICU) prognostic scoring systems for predicting the in-hospital mortality of ruptured abdominal aortic aneurysms (RAAAs). DESIGN: Retrospective cohort study. SETTING: Single tertiary university center. PARTICIPANTS: The study comprised 157 patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 157 patients (82% male) presented with RAAA at Charité University Hospital from January 2011 to December 2020. The mean age was 74 years (standard deviation ten years). In-hospital mortality was 29% (n = 45), of whom nine patients (6%) died en route to the operating room, 13 (8%) on the operating table, and 23 (15%) in the ICU. A total of 135 patients (86%) were admitted to the ICU. All six models demonstrated good discriminating performance between survivors and nonsurvivors. Overall, the area under the curve (AUC) for RAAA preoperative scores was greater than those for ICU scores. The largest AUC was achieved with the Vascular Study Group of New England (VSGNE) RAAA risk score (AUC = 0.87 for all patients, AUC = 0.84 for patients admitted to the ICU), followed by Hardman Index (AUC = 0.83 for all patients, AUC = 0.81 for patients admitted to the ICU), and Glasgow Aneurysm Score (AUC = 0.74 for all patients, AUC = 0.83 for patients admitted to the ICU). The largest AUC for ICU scores (only patients admitted to the ICU) was achieved with Simplified Acute Physiology Score II (0.75), followed by Sepsis-related Organ Failure Assessment (0.73), and Acute Physiology and Chronic Health Evaluation II (0.71). CONCLUSIONS: Preoperative and ICU scores can predict the mortality of patients presenting with RAAA. In addition, the discriminatory ability of preoperative scores between survivors and nonsurvivors was larger than that for ICU scores.

Prognostic scores and early management of septic patients in the emergency department of a secondary hospital: results of a retrospective study.

BACKGROUND: Sequential Organ Failure Assessment (SOFA) and other illness prognostic scores predict adverse outcomes in critical patients. Their validation as a decision-making tool in the emergency department (ED) of secondary hospitals is not well established. The aim of this study was to compare SOFA, NEWS2, APACHE II, and SAPS II scores as predictors of adverse outcomes and decision-making tool in ED. METHODS: Data of 121 patients (age 73 ± 10 years, 58% males, Charlson Comorbidity Index 5.7 ± 2.1) with a confirmed sepsis were included in a retrospective study between January 2017 and February 2020. Scores were computed within the first 24 h after admission. Primary outcome was the occurrence of either in-hospital death or mechanical ventilation within 7 days. Secondary outcome was 30-day all-cause mortality. RESULTS: Patients older than 64 years (elderly) represent 82% of sample. Primary and secondary outcomes occurred in 40 and 44%, respectively. Median 30-day survival time of dead patients was 4 days (interquartile range 1-11). The best predictive score based on the area under the receiver operating curve (AUROC) was SAPS II (0.823, 95% confidence interval, CI, 0.744-0.902), followed by APACHE II (0.762, 95% CI 0.673-0.850), NEWS2 (0.708, 95% CI 0.616-0.800), and SOFA (0.650, 95% CI 0.548-0.751). SAPS II cut-off of 49 showed the lowest false-positive rate (12, 95% CI 5-20) and the highest positive predictive value (80, 95% CI 68-92), whereas NEWS2 cut-off of 7 showed the lowest false-negative rate (10, 95% CI 2-19) and the highest negative predictive value (86, 95% CI 74-97). By combining NEWS2 and SAPS II cut-offs, we accurately classified 64% of patients. In survival analysis, SAPS II cut-off showed the highest difference in 30-day mortality (Hazards Ratio, HR, 5.24, 95% CI 2.99-9.21, P < 0.001). Best independent negative predictors of 30-day mortality were body temperature, mean arterial pressure, arterial oxygen saturation, and hematocrit levels. Positive predictors were male sex, heart rate and serum sodium concentration. CONCLUSIONS: SAPS II is a good prognostic tool for discriminating high-risk patient suitable for sub-intensive/intensive care units, whereas NEWS2 for discriminating low-risk patients for low-intensive units. Our results should be limited to cohorts with a high prevalence of elderly or comorbidities.

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.

BACKGROUND: Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression and scoring system. The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models. METHODS: Using the MIMIC-III v1.4, we identified patients with sepsis-3. The data was split into two groups based on death or survival within 30 days and variables, selected based on clinical significance and availability by stepwise analysis, were displayed and compared between groups. Three predictive models including conventional logistic regression model, SAPS-II score prediction model and XGBoost algorithm model were constructed by R software. Then, the performances of the three models were tested and compared by AUCs of the receiver operating characteristic curves and decision curve analysis. At last, nomogram and clinical impact curve were used to validate the model. RESULTS: A total of 4559 sepsis-3 patients are included in the study, in which, 889 patients were death and 3670 survival within 30 days, respectively. According to the results of AUCs (0.819 [95% CI 0.800-0.838], 0.797 [95% CI 0.781-0.813] and 0.857 [95% CI 0.839-0.876]) and decision curve analysis for the three models, the XGboost model performs best. The risk nomogram and clinical impact curve verify that the XGboost model possesses significant predictive value. CONCLUSIONS: Using machine learning technique by XGboost, more significant prediction model can be built. This XGboost model may prove clinically useful and assist clinicians in tailoring precise management and therapy for the patients with sepsis-3.

Performance of SAPS II according to ICU length of stay: Protocol for an observational study.

BACKGROUND: Severity scores, including the Simplified Acute Physiology Score (SAPS) II, are widely used in the intensive care unit (ICU) to predict mortality outcomes using data from ICU admission or shortly hereafter. For patients with longer ICU length of stay (LOS), the predictive performance of admission-based severity scores may deteriorate compared to patients with shorter ICU LOS. This protocol and statistical analysis plan outlines a study that will assess the influence of ICU LOS on the performance of SAPS II for predicting 90-day post-ICU mortality. METHODS: A Danish nationwide cohort study including adult (≥18 years) ICU patients admitted to a Danish ICU between 1 January 2012 and 30 June 2016. The study will be conducted using the Danish Intensive Care Database (DID), which contains data routinely, prospectively, and consecutively reported for all Danish ICU admissions. Discrimination of SAPS II for predicting 90-day post-ICU mortality will be assessed for the entire cohort and stratified according to ICU LOS. A first-level recalibration of SAPS II will be performed, and if adequate, standardised mortality ratios and calibration stratified according to ICU LOS will be reported. CONCLUSIONS: The outlined large, nationwide cohort study will provide important, contemporary information about the influence of ICU LOS on severity score performance relevant for ICU clinicians, researchers, and administrators. Publication of the protocol and statistical analysis plan prior to study conduct ensures transparency, and limits the risk of publication bias, post hoc changes in analyses, and challenges with multiple comparisons.

Performance of SAPS II according to ICU length of stay: A Danish nationwide cohort study.

BACKGROUND: Intensive care unit (ICU) severity scores use data available at admission or shortly thereafter. There are limited contemporary data on how the prognostic performance of these scores is affected by ICU length of stay (LOS). METHODS: We conducted a nationwide cohort study using routinely collected health data from the Danish Intensive Care Database. We included adults with ICU admissions ≥24 hours between 1 January 2012 and 30 June 2016, who survived to ICU discharge and had valid ICU LOS and vital status data registered. We assessed discrimination of the Simplified Acute Physiology Score (SAPS) II for predicting mortality 90 days after ICU discharge, followed by recalibration of the model and assessment of standardized mortality ratios (SMRs) and calibration. Performance was assessed in the entire cohort and stratified by ICU LOS quartiles. RESULTS: We included 44 523 patients. Increasing SAPS II was associated with increasing ICU LOS. Overall discrimination (area under the receiver-operating characteristics curve) of SAPS II was 0.70 (95% CI: 0.70-0.71), with decreasing discrimination from the first (0.75, 95% CI: 0.73-0.76) to the last (0.64, 95% CI: 0.63-0.65) ICU LOS quartile. SMRs were lower (less deaths) than expected in the first ICU LOS quartile and higher (more deaths) than expected in the last two ICU LOS quartiles. Calibration decreased with increasing ICU LOS. CONCLUSIONS: We observed that discrimination and calibration of SAPS II decreased with increasing ICU LOS, and that this affected SMRs. These findings should be acknowledged when using SAPS II for clinical, research and administrative purposes.

Predictors of Need for Critical Care Support, Adverse Events, and Outcome after Stroke Thrombolysis.

BACKGROUND: Results from trials and international registries exhibit heterogeneity regarding safety, efficacy, markers of prognosis, and markers of the need for critical care support after intravenous thrombolysis (IVT) for strokes. The purpose of our study was to indentify such markers after performance of comparisons among patients who received thrombolysis in our intensive care unit. MATERIALS AND METHODS: Our study included 124 patients who received IVT in accordance with international criteria. Outcome measures of univariate and regression analyses resulted from comparisons between groups of patients with or without the need for critical care support (advanced life support and neurocritical care interventions), groups of patients developing or not developing primary adverse events (symptomatic intracranial hemorrhage [SICH] and/or Death and/or Serious systemic bleeding and/or New stroke) and groups of patients with different main outcome variables (mortality, functional independence at 3 months). RESULTS: Our results suggested that higher severity scores (Simplified Acute Physiology Score II, National Institutes of Health Stroke Scale) correlated with the need for critical care support, primary adverse events, and main outcome variables, whereas older age was significantly associated with fewer adverse events. Hyperlipidemia, symptom-to-needle time, and vascular disease were associated with functional capacity at 3 months, whereas diabetes mellitus and vascular disease correlated with the need for critical care support. CONCLUSION: Patients' age, hyperlipidemia, presence of vascular disease, Simplified Acute Physiology Score II (a novel marker), and National Institutes of Health Stroke Scale at 2 hours and at 7 days are independent predictors of the need for critical care support, adverse events, and clinical outcomes after thrombolysis.

Determinants of the calibration of SAPS II and SAPS 3 mortality scores in intensive care: a European multicenter study.

BACKGROUND: The aim of the Simplified Acute Physiology Score (SAPS) II and SAPS 3 is to predict the mortality of patients admitted to intensive care units (ICUs). Previous studies have suggested that the calibration of these scores may vary across countries, centers, and/or characteristics of patients. In the present study, we aimed to assess determinants of the calibration of these scores. METHODS: We assessed the calibration of the SAPS II and SAPS 3 scores among 5266 patients admitted to ICUs during a 4-week period at 120 centers in 17 European countries. We obtained calibration curves, Brier scores, and standardized mortality ratios. Points attributed to SAPS items were reevaluated and compared with those of the original scores. Finally, we tested associations between the calibration and center characteristics. RESULTS: The mortality was overestimated by both scores: The standardized mortality ratios were 0.75 (95% CI 0.71-0.79) for the SAPS II score and 0.91 (95% CI 0.86-0.96) for the SAPS 3 score. This overestimation was partially explained by changes in associations between some items of the scores and mortality, especially the heart rate, Glasgow Coma Scale score, and diagnosis of AIDS for SAPS II. The calibration of both scores was better in countries with low health expenditures. The between-center variability in calibration curves was much greater than expected by chance. CONCLUSIONS: Both scores overestimate current mortality among European ICU patients. The magnitude of the miscalibration of SAPS II and SAPS 3 scores depends not only on patient characteristics but also on center characteristics. Furthermore, much between-center variability in calibration remains unexplained by these factors. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01422070 . Registered 19 August 2011.

The impact of frailty syndrome and risk scores on emergency cholecystectomy patients.

PURPOSE: Cholecystectomy, which is one of the most common surgical procedures, is also performed in the emergency setting. A number of risk scores have been introduced in recent studies; moreover, over the last few years literature has focused on surgical patients with frailty syndrome. The aim of the present study is to evaluate whether frailty syndrome and the risk scores are correlated with morbidity, post-operative hospital stay and the ICU admission rate following emergency cholecystectomy. METHODS: Eighty-five consecutive patients of >65 years of age who underwent cholecystectomy were selected from 2306 emergency procedures for inclusion in the present study. The patients were assessed for frailty syndrome and their scores were calculated on the basis of chart review. Univariate analyses were performed to compare severe frailty patients to intermediate frailty and robust patients. ROC and logistic regression analyses were performed with the end-points of morbidity, hospital stay and ICU admission. RESULTS: In addition to having worse ASA, inflammatory and risk values than robust patients, frailty syndrome patients also had higher rates of morbidity and ICU admission and longer hospitalization periods. A logistic regression analysis showed that the P-Possum was independently correlated with morbidity. Frailty and open surgery were independently correlated with longer hospitalization, whereas ICU admission was correlated with worse ASA and P-Possum values. CONCLUSIONS: Frailty syndrome significantly impacts the length of hospitalization in patients undergoing emergency cholecystectomy. Although the ORs were limited, the P-Possum value was independently associated with the outcome.

Better prognostic marker in ICU - APACHE II, SOFA or SAP II!

OBJECTIVES: This study was designed to determine the comparative efficacy of different scoring system in assessing the prognosis of critically ill patients. METHODS: This was a retrospective study conducted in medical intensive care unit (MICU) and high dependency unit (HDU) Medical Unit III, Civil Hospital, from April 2012 to August 2012. All patients over age 16 years old who have fulfilled the criteria for MICU admission were included. Predictive mortality of APACHE II, SAP II and SOFA were calculated. Calibration and discrimination were used for validity of each scoring model. RESULTS: A total of 96 patients with equal gender distribution were enrolled. The average APACHE II score in non-survivors (27.97+8.53) was higher than survivors (15.82+8.79) with statistically significant p value (<0.001). The average SOFA score in non-survivors (9.68+4.88) was higher than survivors (5.63+3.63) with statistically significant p value (<0.001). SAP II average score in non-survivors (53.71+19.05) was higher than survivors (30.18+16.24) with statistically significant p value (<0.001). CONCLUSION: All three tested scoring models (APACHE II, SAP II and SOFA) would be accurate enough for a general description of our ICU patients. APACHE II has showed better calibration and discrimination power than SAP II and SOFA.

The simplified acute physiology score II to predict hospital mortality in aneurysmal subarachnoid hemorrhage.

BACKGROUND: Early prediction of increased morbidity and mortality in aneurysmal subarachnoid hemorrhage (aSAH) remains crucial to improving patient management. Most prediction models lack external validation and focus on disease-specific items without considering physiological parameters and the past medical history. The aim was to assess the validity of the established Simplified Acute Physiology Score II (SAPS-II) in an aSAH cohort for the prediction of hospital mortality and to identify additional physiological and clinical predictors. METHODS: The predictive value of SAPS-II for hospital mortality was assessed in a retrospective analysis of 263 consecutive patients with aSAH. Additional physiological and clinical parameters including the past medical history were analyzed by forward selection multivariate analysis to identify independent predictors of hospital mortality and to improve the prediction model. RESULTS: The SAPS-II predicted hospital mortality with an area under the curve (AUC) of 0.834 with an odds ratio (OR) of 1.097 [95 % confidence interval 1.067-1.128) for each additional point. Forward selection multivariate analysis identified the Glasgow Coma Scale score (P < 0.001), history of chronic headache (P = 0.01) and medication with anticoagulants (P = 0.04) as independent predictors of hospital mortality. Adding these parameters to the SAPS-II, the AUC increased to 0.86. CONCLUSION: This study validates the predictive accuracy of SAPS-II for hospital mortality in aSAH patients. Additional parameters from the past medical history increase its predictive power. From a practical viewpoint, SAPS-II alone already represents a sufficient and powerful score to predict hospital mortality at an early time point and may help to improve patient management.

Outcome of patients with cirrhosis requiring mechanical ventilation in ICU.

INTRODUCTION: Mortality rate of patients with cirrhosis admitted to the intensive care unit (ICU) and requiring mechanical ventilation varies between 60 and 91%. The aim of our study is to assess the prognosis of these patients, their 1-year outcome and to analyze predictive factors of long-term mortality. METHODS: From May 2005 to May 2011, we studied 246 consecutive patients with cirrhosis requiring mechanical ventilation either at admission or during their ICU stay. RESULTS: Alcohol was the most common etiology of the cirrhosis (69%). Bleeding related to portal hypertension (30%) and severe sepsis (33%) were the most common reasons for admission. ICU and hospital mortality were respectively 65.9% and 70.3%. Prognostic severity scores, the need for other organ support therapy, infection, and total bilirubin value at ICU admission were significantly associated with ICU mortality. Eighty-four patients (34.1%) were discharged from the ICU. Among these patients, the one-year survival was only of 32%. Logistic regression analysis, using survival at one year as the endpoint, identified two independent risk factors: the length of ventilation (odds ratio [OR] = 1.1; 95% CI, 1.0-1.2; p = 0.02) and total bilirubin at ICU discharge (OR = 1.3; 95% CI, 1.1-1.5; p = 0.006). CONCLUSION: Patients with cirrhosis admitted to the liver ICU and who required mechanical ventilation have a poor prognosis with a 1-year mortality of 89%. At ICU discharge, a total bilirubin level higher than 64.5 µmol/L and length of ventilation higher than 9 days could help the hepatologists to identify patients at risk of death in the year following the ICU discharge.

The impact of rescue or maintenance therapy with EGFR TKIs for Stage IIIb-IV non-squamous non-small-cell lung cancer patients requiring mechanical ventilation.

BACKGROUND: The toxicity of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) is less than that of cytotoxic agents. The reports of dramatic response and improvement in performance status with the use of EGFR TKIs may influence a physician's decision-making for patients with non-squamous non-small cell lung cancer (NSCLC) and life-threatening respiratory distress. The aim of this study was to evaluate the outcome of rescue or maintenance therapy with EGFR TKI for stage IIIb-IV non-squamous NSCLC patients requiring mechanical ventilation. METHODS: Eighty-three Asian patients with stage IIIb-IV non-squamous NSCLC and who required mechanical ventilation between June 2005 and January 2010 were evaluated. RESULTS: Of the 83 patients, 16 (19%) were successfully weaned from the ventilator. The use of EGFR TKI as rescue or maintenance therapy during respiratory failure did not improve the rate of successful weaning (standard care 18% vs. with EGFR TKI, 22%; p = 0.81) in univariate and multivariate analyses. CONCLUSIONS: Rescue or maintenance therapy with EGFR TKI for stage IIIb-IV non-squamous NSCLC patients requiring mechanical ventilation was not associated with better outcome. An end-of-life discussion should be an important aspect in the care of this group of patients, since only 19% were successfully weaned from mechanical ventilation.

A retrospective analysis of obstetric patient's outcome in intensive care unit of a tertiary care center.

BACKGROUND AND AIMS: Admission to an intensive care unit (ICU) is considered as an objective marker of severe maternal morbidity. The aim was to assess the incidence and possible risk factors of obstetric patient admissions in the multidisciplinary ICU of a tertiary care center with emphasis on standardized mortality ratio (SMR). MATERIAL AND METHODS: A retrospective five year ICU record analysis was done for all pregnant women, who were admitted to multidisciplinary ICU of a tertiary care hospital during June 2007-12. RESULTS: During this 5-year period, 21,943 deliveries took place and 164 women required ICU admission. Out of these, the data of 151 patients were analyzed. Maternal mortality rate was 31.1% (47 deaths) for patients admitted to ICU. The simplified acute physiologic score (SAPS) II was 62 (55-68) in nonsurvivor versus 34.00 (28-46) in survivor group (P value < 0.001). The receiver operated characteristic curve was plotted using SAPS II scores and the area under the curve was 0.93 with 95% confidence interval (0.89-0.96). The calculated SMR was 0.97. CONCLUSIONS: Women admitted to ICU with diagnosis of puerperal sepsis and intrauterine death (IUD) with coexisting sepsis had higher mortality as compared to women with hypertensive disease of pregnancy and hemorrhage. The calculated SMR was less than one which is a predictor of good ICU care.

The value of five scoring systems in predicting the prognosis of patients with sepsis-associated acute respiratory failure.

Our study aimed to identify the optimal scoring system for predicting the prognosis of patients with sepsis-associated acute respiratory failure (SA-ARF). All data were taken from the fourth version of the Markets in Intensive Care Medicine (MIMIC-IV) database. Independent risk factors for death in hospitals were confirmed by regression analysis. The predictive value of the five scoring systems was evaluated by receiving operating characteristic (ROC) curves. Kaplan‒Meier curves showed the impact of acute physiology score III (APSIII) on survival and prognosis in patients with SA-ARF. Decision curve analysis (DCA) identified a scoring system with the highest net clinical benefit. ROC curve analysis showed that APS III (AUC: 0.755, 95% Cl 0.714-0.768) and Logical Organ Dysfunction System (LODS) (AUC: 0.731, 95% Cl 0.717-0.7745) were better than Simplified Acute Physiology Score II (SAPS II) (AUC: 0.727, 95% CI 0.713-0.741), Oxford Acute Severity of Illness Score (OASIS) (AUC: 0.706, 95% CI 0.691-0.720) and Sequential Organ Failure Assessment (SOFA) (AUC: 0.606, 95% CI 0.590-0.621) in assessing in-hospital mortality. Kaplan‒Meier survival analysis patients in the high-APS III score group had a considerably poorer median survival time. The DCA curve showed that APS III may provide better clinical benefits for patients. We demonstrated that the APS III score is an excellent predictor of in-hospital mortality.