Comparison of Acute Physiology and Chronic Health Evaluation (APACHE) II score with organ failure scores to predict hospital mortality

Modified Cardiovascular Sequential Organ Failure Assessment Score in Sepsis: External Validation in Intensive Care Unit Patients

BACKGROUND

There is a need to update the cardiovascular (CV) Sequential Organ Failure Assessment (SOFA) score to reflect the current practice in sepsis. We previously proposed the modified CV SOFA score from data on blood pressure, norepinephrine equivalent dose, and lactate as gathered from emergency departments. In this study, we externally validated the modified CV SOFA score in multicenter intensive care unit (ICU) patients.

METHODS

A multicenter retrospective observational study was conducted on ICU patients at six hospitals in Korea. We included adult patients with sepsis who were admitted to ICUs. We compared the prognostic performance of the modified CV/total SOFA score and the original CV/total SOFA score in predicting 28-day mortality. Discrimination and calibration were evaluated using the area under the receiver operating characteristic curve (AUROC) and the calibration curve, respectively.

RESULTS

We analyzed 1,015 ICU patients with sepsis. In overall patients, the 28-day mortality rate was 31.2%. The predictive validity of the modified CV SOFA (AUROC, 0.712; 95% confidence interval [CI], 0.677-0.746; P < 0.001) was significantly higher than that of the original CV SOFA (AUROC, 0.644; 95% CI, 0.611-0.677). The predictive validity of modified total SOFA score for 28-day mortality was significantly higher than that of the original total SOFA (AUROC, 0.747 vs. 0.730; 95% CI, 0.715-0.779; P = 0.002). The calibration curve of the original CV SOFA for 28-day mortality showed poor calibration. In contrast, the calibration curve of the modified CV SOFA for 28-day mortality showed good calibration.

CONCLUSION

In patients with sepsis in the ICU, the modified SOFA score performed better than the original SOFA score in predicting 28-day mortality.

Using Comorbidity Statistical Modeling to Predict Inpatient Mortality: Insights Into the Burden on Hospitalized Patients

Background The expenditures of the United States for healthcare are the highest in the world. Assessment of inpatient disease classifications associated with death can provide useful information for risk stratification, outcome prediction, and comparative analyses to understand the most resource-intensive chronic illnesses. This project aims to adapt a comorbidity index model to the National Inpatient Sample (NIS) database of 2020 to predict one-year mortality for patients admitted with select International Classification of Diseases, 10th Edition (ICD-10) codes of diagnoses. Methodology A retrospective cohort study analyzed mortality with comorbidity using the Charlson comorbidity index model (CCI) in a sample population of an estimated 5,533,477 adult inpatients (individuals aged ≥18 years) obtained from the National Inpatient Database for 2020. A multivariate logistic regression model was constructed with in-hospital mortality as the outcome variable and identifying predictor variables as defined by the Clinical Classifications Software Refined Variables (CCSR) codes for selected ICD-10 diagnoses. Descriptive statistics and the base logistic regression analyses were conducted using SAS statistical software version 9.4 (SAS Institute, Cary, NC, USA). To avoid overpowering, a subsample (n = 100,000) was randomly selected from the original dataset. The initial CCI assigned weights to ICD-10 diagnoses based on the associated risk of death, and conditions with the greatest collective weights were included in a subsequent backward stepwise logistic regression model. Results The results of the base CCI regression analysis revealed 16 chronic conditions with P-values <0.20. Anemia (1,567,081, 28.32%), pulmonary disease (asthma, chronic obstructive pulmonary disease [COPD], pneumoconiosis; 1,210,892, 21.88%), and diabetes without complications (1,077,239, 19.47%) were the three most prevalent conditions associated with inpatient mortality. Results of the backward stepwise regression analysis revealed that severe liver disease/hepatic failure (adjusted odds ratio [aOR] 10.50; 95% confidence interval [CI] 10.40-10.59), acute myocardial infarction (aOR 2.85; 95% CI 2.83-2.87) and malnutrition (aOR 2.15, 95% CI 2.14-2.16) were three most important risk factors and had the highest impact on inpatient mortality (P-value <0.0001). The concordance statistic (c-statistic) or the area under the curve (AUC) for the final model was 0.752. Conclusions The CCI model proved to be a valuable approach in categorizing morbidity classifications associated with the greatest risk of death using a national sample of hospitalized patients in 2020. Study findings provide an objective approach to compare patient populations that bear important implications for healthcare system improvements, clinician treatment approaches, and ultimately decision decision-makers poised to influence advanced models of care and prevention strategies that limit disease progression and improve patient outcomes.

Investigating the Utility of the SOFA Score and Creating a Modified SOFA Score for Predicting Mortality in the Intensive Care Units in a Tertiary Hospital in Jordan

Background

The utility of the Sequential Organ Failure Assessment (SOFA) score in predicting mortality in the intensive care unit (ICU) has been demonstrated before, but serial testing in various settings is required to validate and improve the score. This study examined the utility of the SOFA score in predicting mortality in Jordanian ICU patients and aimed to find a modified score that required fewer laboratory tests.

Methods

A prospective observational study was conducted at Jordan University Hospital (JUH). All adult patients admitted to JUH ICUs between June and December 2020 were included in the study. SOFA scores were measured daily during the whole ICU stay. A modified SOFA score (mSOFA) was constructed from the available laboratory, clinical, and demographic data. The performance of the SOFA, mSOFA, qSOFA, and SIRS in predicting ICU mortality was assessed using the area under the receiver operating characteristic curve (AUROC).

Results

194 patients were followed up. SOFA score (mean ± SD) at admission was significantly higher in non-survivors (7.5 ± 3.9) compared to survivors (2.4 ± 2.2) and performed the best in predicting ICU mortality (AUROC = 0.8756, 95% CI: 0.8117-0.9395) compared to qSOFA (AUROC = 0.746, 95% CI: 0.655-0.836) and SIRS (AUROC = 0.533, 95% CI: 0.425-0.641). The constructed mSOFA included points for the hepatic and CNS SOFA scores, in addition to one point each for the presence of chronic kidney disease or the use of breathing support; it performed as well as the SOFA score in this cohort or better than the SOFA score in a subgroup of patients with heart disease.

Conclusion

SOFA score was a good predictor of mortality in a Jordanian ICU population and better than qSOFA, while SIRS could not predict mortality. Furthermore, the proposed mSOFA score which employed fewer laboratory tests could be used after validation from larger studies.

Impact of hospitalist care model on patient outcomes in acute medical unit: a retrospective cohort study

OBJECTIVE

To assess a newly introduced, hospitalist-run, acute medical unit (AMU) care model at a tertiary care hospital in the Republic of Korea.

DESIGN

Retrospective cohort study.

SETTING

Tertiary care hospital in the Republic of Korea.

PARTICIPANTS

We evaluated 6391 medical inpatients admitted through the emergency department (ED) from 1 June 2016 to 31 May 2017.

INTERVENTIONS

The study compared multiple outcomes among medical inpatients from the ED between the non-hospitalist group and the AMU hospitalist group.

OUTCOME MEASURES

In-hospital mortality (IHM), intensive care unit (ICU) admission rate, hospital length of stay (LOS), ED-LOS and unscheduled readmission rates were defined as patient outcomes and compared between the two groups.

RESULTS

Compared with the non-hospitalist group, the AMU hospitalist group had lower IHM (OR: 0.43, p<0.001), a lower ICU admission rate (OR: 0.72, p=0.013), a shorter LOS (coefficient: -0.984, SE: 0.318; p=0.002) and a shorter ED-LOS (coefficient: -3.021, SE: 0.256; p<0.001). There were no significant differences in the 10-day or 30-day readmission rates (p=0.974, p=0.965, respectively).

CONCLUSIONS

The AMU hospitalist care model was associated with reductions in IHM, ICU admission rate, LOS and ED-LOS. These findings suggest that the AMU hospitalist care model has the potential to be adopted into other healthcare systems to improve care for patients with acute medical needs.

Predictors of survival from Acinetobacter in Saudi Arabian intensive care units: A prospective, one-year, six-city, ten-center cohort study

BACKGROUND

Antibiotic-resistant Acinetobacter baumannii is a continuously-emerging worldwide health crisis, with mortality rates approaching 50% in intensive care unit (ICU) patients. The objective of this study was to evaluate regional, patient-related, and organism-related predictors of survival among critically-ill patients with confirmed Acinetobacter infection.

METHODS

This prospective cohort study was conducted within ten ICUs across six geographically- and climatologically-distinct cities across Saudi Arabia over 13 months.

RESULTS

Of 169 patients with confirmed Acinetobacter infection enrolled in the study, 80 (47.6%) died. Survivors were statistically younger, predominantly male, more likely to be admitted for trauma, less likely to have hypertension, diabetes, or have undergone hemodialysis, and more likely to have been treated with antibiotics prior to having a positive culture for Acinetobacter, but less likely to have received an aminoglycoside. Survivors also had lower baseline APACHE II and SOFA scores and were infected with stains of Acinetobacter that had less meropenem- or colistin-resistance. Multivariate analysis identified the following independent predictors of survival: younger age, lower ICU-day#1 APACHE-II and ICU-day#3 SOFA scores, being admitted for trauma, and having no history of hemodialysis.

CONCLUSIONS

Patient-related factors outweigh regional and hospital-related factors as predictors of survival among critically-ill patients with Acinetobacter infection.

Electrochemical enzyme-based blood ATP and lactate sensor for a rapid and straightforward evaluation of illness severity

This study aimed to develop an electrochemical system for measuring blood ATP and lactate levels in a single format. The ratio of lactate to ATP levels was previously reported to provide an alternative illness severity score. Although severity evaluation is crucial to treat patients with acute disease admitted to intensive care units, no sensors are currently available to simply and rapidly measure ATP and lactate levels using the same detection method. Therefore, we constructed an integrated sensing system for ATP and lactate using enzymatic reactions and two sets of electrodes integrated into a chip connected to a single potentiostat operated by a microcontroller. The enzymatic system involves adenylate kinase, pyruvate kinase, and pyruvate oxidase for ATP, and lactate oxidase for lactate, both of which produce hydrogen peroxide. Multiplex enzyme-based reactions were designed to minimize the corresponding operations significantly without enzyme immobilization onto the electrodes. The system was robust in the presence of potentially interfering blood components, such as ascorbate, pyruvate, ADP, urate, and potassium ions. The ATP and lactate levels in the blood were successfully measured using the new sensor with good recoveries. The analytical results of blood samples obtained using our sensor were in good agreement with those using conventional methods. Integrating electrode-based analysis and a microcontroller-based system saved further operations, enabling the straightforward measurement of ATP and lactate levels within 5 min. The proposed sensor may serve as a useful tool in the management of serious infectious diseases.

Association of the All-Patient Refined Diagnosis-Related Groups Severity of Illness and Risk of Mortality Classification with Outcomes

OBJECTIVES

Diagnosis-related groups (DRGs) is a patient classification system used to characterize the types of patients that the hospital manages and to compare the resources needed during hospitalization. The DRG classification is based on International Classification of Diseases diagnoses, procedures, demographics, discharge status, and complications or comorbidities and compares hospital resources and outcomes used to determine how much Medicare pays the hospital for each "product/medical condition." The All-Patient Refined DRG (APR-DRG) incorporated severity of illness (SOI) and risk of mortality (ROM) into the DRG system to adjust for patient complexity to compare resource utilization, complication rates, and lengths of stay.

METHODS

This study included 18,478 adult patients admitted to a tertiary care center in Lubbock, Texas during a 1-year period. We recorded the APR-DRG SOI and ROM and some clinical information on these patients, including age, sex, admission shock index, admission glucose and lactate levels, diagnoses based on International Classification of Diseases, Tenth Revision discharge coding, length of stay, and mortality. We compared the levels of SOI and ROM across this clinical information.

RESULTS

As the levels of SOI and ROM increase (which indicates increased disease severity and risk of mortality), age, glucose levels, lactate levels, shock index, length of stay, and mortality increased significantly (P < 0.001). Multiple logistic regression analysis demonstrated that each unit increase in ROM and SOI level was significantly associated with an 11.45 and a 10.37 times increase in the odds of in-hospital mortality, respectively. The C-statistics for the corresponding models are 0.947 and 0.929, respectively. When both ROM and SOI were included in the model, the magnitudes of increase in odds of in-hospital mortality were 5.61 and 1.17 times for ROM and SOI, respectively. The C-statistic is 0.949.

CONCLUSIONS

This study indicates that the APR-DRG SOI and ROM scores provide a classification system that is associated with mortality and correlates with other clinical variables, such as the shock index and lactate levels, which are available on admission.

Sleep and circadian rhythm disturbances in intensive care unit (ICU)-acquired delirium: a case-control study

OBJECTIVE

The relationships among sleep, circadian rhythm, and intensive care unit (ICU)-acquired delirium are complex and remain unclear. This study aimed to examine the pathophysiological mechanisms of sleep and circadian rhythm disturbances in patients with ICU-acquired delirium.

METHODS

This study included critical adult patients aged 18 to 75 years who were treated in the ICU. Twenty-four-hour polysomnography was performed and serum melatonin and cortisol levels were measured six times during polysomnography. Receiver operating characteristic curves and binomial logistic regression were used to evaluate the potential of sleep, melatonin, and cortisol as indicators of delirium in the ICU.

RESULTS

Patients with delirium (n = 24) showed less rapid eye movement (REM) sleep compared with patients without delirium (n = 24, controls). Melatonin levels were lower and cortisol levels were higher in the delirium group than in the control group. REM sleep, melatonin, and cortisol were significantly associated with delirium. The optimal cutoff values of REM sleep and mean melatonin and cortisol levels that predicted delirium were ≤1.05%, ≤422.09 pg/mL, and ≥212.14 ng/mL, respectively.

CONCLUSIONS

REM sleep, and melatonin and cortisol levels are significantly associated with the risk of ICU-acquired delirium. Improved sleep and readjustment of circadian rhythmicity may be therapeutic targets of ICU-acquired delirium.

COVID-19 Pandemic Healthcare Resource Allocation, Age and Frailty

The current coronavirus pandemic presents the greatest healthcare crisis in living memory. Hospitals across the world have faced unprecedented pressure. In the face of this tidal wave of demand for limited healthcare resources, how are clinicians to identify patients most likely to benefit? Should age or frailty be discriminators? This paper seeks to analyse the current evidence-base, seeking a nuanced approach to pandemic decision-making, such as admission to critical care.

Impact of Religiosity on Delirium Severity Among Critically Ill Shi'a Muslims: A Prospective Multi-Center Observational Study

This study assesses the impact of religiosity on delirium severity and patient outcomes among Shi'a Muslim intensive care unit (ICU) patients. We conducted a prospective observational cohort study in 21 ICUs from 6 Iranian academic medical centers. Delirium was assessed using the Confusion Assessment Method for the ICU (CAM-ICU) tool. Eligible patients were intubated, receiving mechanical ventilation (MV) for ≥ 48 h. Illness severity was assessed using Acute Physiology and Chronic Health Evaluation II (APACHE II) scores. A total of 4200 patients were enrolled. Patient religiosity was categorized as more (40.6%), moderate (42.3%), or less (17.1%) based on responses to patient and surrogate questionnaires. The findings suggest that lower pre-illness religiosity may be associated with greater delirium severity, MV duration, and ICU and hospital LOS. The lower mortality in the less religiosity group may be related in part to a greater proportion of female patients, but it remains unclear whether and to what extent greater religiosity impacted treatment decisions by patients and families. Further investigation is needed to validate and clarify the mechanism of the mortality findings.

Age, Frailty, Resuscitation and Intensive Care: With Reference to COVID-19

Discussion regarding cardiopulmonary resuscitation and admission to an intensive care unit is frequently fraught in the context of older age. It is complicated by the fact that the presence of multiple comorbidities and frailty adversely impact on prognosis. Cardiopulmonary resuscitation and mechanical ventilation are not appropriate for all. Who decides and how? This paper discusses the issues, biases, and potential harms involved in decision-making. The basis of decision making requires fairness in the distribution of resources/healthcare (distributive justice), yet much of the printed guidance has taken a utilitarian approach (getting the most from the resource provided). The challenge is to provide a balance between justice for the individual and population justice.

End-of-life care in patients with a highly transmissible respiratory virus: implications for COVID-19

Symptom management and end-of-life care are core skills for all physicians, although in ordinary times many anesthesiologists have fewer occasions to use these skills. The current coronavirus disease (COVID-19) pandemic has caused significant mortality over a short time and has necessitated an increase in provision of both critical care and palliative care. For anesthesiologists deployed to units caring for patients with COVID-19, this narrative review provides guidance on conducting goals of care discussions, withdrawing life-sustaining measures, and managing distressing symptoms.

Administrative and Claims Data Help Predict Patient Mortality in Intensive Care Units by Logistic Regression: A Nationwide Database Study

Background

Increasing attention has been paid to the predictive power of different prognostic scoring systems for decades. In this study, we compared the abilities of three commonly used scoring systems to predict short-term and long-term mortalities, with the intention of building a better prediction model for critically ill patients. We used the data from the National Health Insurance Research Database (NHIRD) in Taiwan, which included information on patient age, comorbidities, and presence of organ failure to build a new prediction model for short-term and long-term mortalities.

Methods

We retrospectively collected the medical records of patients in the intensive care unit of a regional hospital in 2012 and linked them to the claims data from the NHIRD. The Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Elixhauser Comorbidity Index (ECI), and Charlson Comorbidity Index (CCI) were compared for their predictive abilities. Multiple logistic regression tests were performed, and the results were presented as receiver operating characteristic curves and C-statistic.

Results

The APACHE II score has the best predictive power for inhospital mortality (0.79; C − statistic = 0.77 − 0.83) and 1-year mortality (0.77; C − statistic = 0.74 − 0.79). The ECI and CCI alone have poorer predictive power and need to be combined with other variables to be comparable to the APACHE II score, as predictive tools. Using CCI together with age, sex, and whether or not the patient required mechanical ventilation is estimated to have a C-statistic of 0.773 (95% CI 0.744-0.803) for inhospital mortality, 0.782 (95% CI 0.76-0.81) for 30-day mortality, and 0.78 (95% CI 0.75-0.80) for 1-year mortality.

Conclusions

We present a new prognostic model that combines CCI with age, sex, and mechanical ventilation status and can predict mortality, comparable to the APACHE II score.

The Delta Neutrophil Index Predicts the Development of In-hospital Hypotension in Initially Stable Patients with Pyogenic Liver Abscess

Prompt diagnosis and timely treatment are important for reducing morbidity and mortality from pyogenic liver abscess (PLA). The purpose of this study was to investigate the importance of the delta neutrophil index (DNI) reflecting the fraction of immature granulocytes as a predictor of the development of in-hospital hypotension in initially stable patients with PLA. We retrospectively identified 308 consecutive patients (>18 years) who were hemodynamically stable at presentation and diagnosed with PLA in the emergency department (ED) between January 2011 and September 2017. The outcome of interest was in-hospital hypotension 1–24 hours after admission to the ED. A high DNI at ED admission was an independent predictor of the development of in-hospital hypotension in initially stable patients with PLA (odds ratio [OR]: 1.44, 95.0% confidence interval [CI]: 1.06–1.95; P = 0.02). A DNI > 3.3% was associated with in-hospital hypotension at ED admission (OR: 5.37, 95.0% CI: 2.91–9.92; P < 0.001). The development of in-hospital hypotension was associated with an increased risk of 30-day mortality (HR: 8.55, 95.0% CI: 2.57–28.4; P < 0.001). A high DNI independently predicts the development of in-hospital hypotension in initially stable patients with PLA. In-hospital hypotension is associated with an increased risk of 30-day mortality.

Comparison of Various Severity Assessment Scoring Systems in Patients with Sepsis in a Tertiary Care Teaching Hospital

BACKGROUND

Sepsis is a complex condition defined by the systemic response to infection. Severity assessment scoring systems are used to aid the physician in deciding whether aggressive treatment is needed or not. In this study, various severity assessment scoring systems, namely Acute Physiology and Chronic Health Evaluation II (APACHE II), Rapid Emergency Medicine Score (REMS), Sequential Organ Failure Assessment (SOFA), Multiple Organ Dysfunction Score (MODS), Predisposition, Infection, Response, and Organ Dysfunction (PIRO), and Mortality in Emergency Department Sepsis (MEDS), were compared to assess their sensitivity and specificity.

MATERIALS AND METHODS

A prospective cohort study was conducted over 6 months. The study was conducted in the intensive care unit (ICU) of a tertiary care teaching hospital. All patients above 18 years of age with confirmed sepsis diagnosis and a well-defined outcome were included in the study.

RESULTS

A total of 193 patients were included in the study. The mean age was 57.2 ± 15.3 (mean ± standard deviation) years. Majority of the patients were male, 125 (64.76%). Overall mortality was 108 (55.9%). The calculated area under the receiver operating characteristic curve was 0.86 (95% confidence interval [CI]: 0.80-0.90) for APACHE II, 0.81 (95% CI: 0.75-0.87) for REMS, 0.80 (95% CI: 0.74-0.86) for SOFA, 0.74 (95% CI: 0.67-0.80) for MODS, 0.78 (95% CI: 0.71-0.84) for PIRO, and 0.77 (95% CI: 0.71-0.83) for MEDS. Sensitivity and specificity for APACHE II were 81.5 and 75.3, respectively.

CONCLUSIONS

In our study, APACHE II score was found to be the most sensitive and specific in predicting the severity of sepsis compared to other scores.

Platelet dysfunction in critically ill patients

: Thrombelastography Platelet Mapping (TEG-PM) allows for measurement of maximal potential clot strength (MA) and strength from stimulation of arachidonic acid (MA-AA) and adenosine disphosphate (MA-ADP) receptors. This study was conducted to assess degree of platelet dysfunction in critically ill adult patients. A retrospective study of critically ill, adult, nontrauma patients in a medical/surgical ICU was conducted from August 2013 to September 2014. All patients who underwent TEG-PM were enrolled. Patients with intracerebral hemorrhage, following cardiac surgery, or without an APACHE II score were excluded. Patients were divided into those with and without aspirin use. Demographics, APACHE II score, and laboratory results were abstracted. Student t test was used to test significance. A total of 79 patients were enrolled (61% male). Average age and APACHE II score were 61 ± 16 years and 18 ± 9, respectively. Factor-associated coagulation measures and MA were normal in all groups but MA-AA and MA-ADP were significantly reduced irrespective of anticoagulant use. Compared to the nonanticoagulated cohort, MA-AA was significantly reduced in those on aspirin. There was no difference in mortality or length of stay in any cohort. Inhibition of the AA and ADP pathways is common in critically ill patients. Clinical correlation with propensity for bleeding and need for transfusion requires further assessment.

The Association Between Sequential Organ Failure Assessment Scores and Mortality in Patients With Sepsis During the First Week: The JSEPTIC DIC Study

OBJECTIVE

Predicting prognosis is a complex process, particularly in patients with severe sepsis or septic shock. This study aimed to determine the relationship between the Sequential Organ Failure Assessment (SOFA) scores for individual organs during the first week of admission and the in-hospital mortality in patients with sepsis.

METHODS

This study was a post hoc evaluation of the Japan Septic Disseminated Intravascular Coagulation study and included patients admitted to 42 intensive care units in Japan for severe sepsis or septic shock, between January 2011 and December 2013. We assessed the relationship between the organ and total SOFA scores on days 1, 3, and 7 following admission and the in-hospital mortality using logistic regression analysis.

RESULTS

We evaluated 2732 patients and found the in-hospital mortality rate was 29.1%. The mean age of the patients (standard deviation) was 70.5 (14.1) years, and the major primary site of infection was the abdomen (33.6%). The central nervous system (CNS) SOFA score exhibited the strongest relationship with mortality on days 1 (adjusted odds ratio [aOR]: 1.49, 95% confidence interval [CI]: 1.40-1.59), 3 (aOR: 1.75, 95% CI: 1.62-1.89), and 7 (aOR: 1.93, 95% CI: 1.77-2.10). The coagulation SOFA scores showed a weak correlation with mortality on day 1, but a strong correlation with mortality on day 7 (aOR: 2.04, 95% CI: 1.87-2.24).

CONCLUSIONS

The CNS SOFA scores were associated with mortality in patients with severe sepsis on days 1, 3, and 7 following hospitalization. The coagulation SOFA score was associated with mortality on day 7. In clinical situations, the CNS SOFA scores during the acute phase and the CNS SOFA and coagulation SOFA scores during the subsequent phases should be evaluated in order to determine patient prognosis.

SOFA score in septic patients: incremental prognostic value over age, comorbidities, and parameters of sepsis severity

Several widely used scoring systems for septic patients have been validated in an ICU setting, and may not be appropriate for other settings like Emergency Departments (ED) or High-Dependency Units (HDU), where a relevant number of these patients are managed. The purpose of this study is to find reliable tools for prognostic assessment of septic patients managed in an ED-HDU. In 742 patients diagnosed with sepsis/severe sepsis/septic shock, not-intubated, admitted in ED between June 2008 and April 2016, SOFA, qSOFA, PIRO, MEWS, Charlson Comorbidity Index, MEDS, and APACHE II were calculated at ED admission (T0); SOFA and MEWS were also calculated after 24 h of ED-High-Dependency Unit stay (T1). Discrimination and incremental prognostic value of SOFA score over demographic data and parameters of sepsis severity were tested. Primary outcome is 28-day mortality. Twenty-eight day mortality rate is 31%. The different scores show a modest-to-moderate discrimination (T0 SOFA 0.695; T1 SOFA 0.741; qSOFA 0.625; T0 MEWS 0.662; T1 MEWS 0.729; PIRO: 0.646; APACHE II 0.756; Charlson Comorbidity Index 0.596; MEDS 0.674, all p < 0.001). At a multivariate stepwise Cox analysis, including age, Charlson Comorbidity Index, MEWS, and lactates, SOFA shows an incremental prognostic ability both at T0 (RR 1.165, IC 95% 1.009-1.224, p < 0.0001) and T1 (RR 1.168, IC 95% 1.104-1.234, p < 0.0001). SOFA score shows a moderate prognostic stratification ability, and demonstrates an incremental prognostic value over the previous medical conditions and clinical parameters in septic patients.

Sequential Organ Failure Assessment predicts outcomes of pulse indicator contour continuous cardiac output-directed goal therapy: A prospective study

According to the new sepsis definitions, septic shock is defined as a subset of sepsis in which the underlying circulatory and cellular/metabolic abnormalities are profound enough to substantially increase mortality. We evaluated the predictive efficacy of the Sequential Organ Failure Assessment (SOFA) score in critically ill patients with septic shock undergoing pulse indicator contour continuous cardiac output (PiCCO)-directed goal therapy (PDGT).We conducted a single-center, prospective, observational study of 52 patients with septic shock undergoing PDGT. The putative prognostic factors, including the severity scores (SOFA and Acute Physiology and Chronic Health Evaluation II [APACHE II] scores), were analyzed within 24 hours after diagnosis of septic shock. We assessed and compared the predictive efficacy of risk factors for 28-day mortality of patients with septic shock undergoing PDGT.Among the patients with septic shock undergoing PDGT, the SOFA scores of nonsurvivors were significantly higher than those of survivors (P < .001); the area under the receiver operating characteristics curve was higher for SOFA than for APACHE II (P = .005). The outcomes of the logistic regression analysis for 28-day mortality showed that the odds ratio, 95% confidence interval, and P-value of SOFA were 1.6, 1.2 to 2.1, and <.001, respectively.The predictive model of the SOFA score is able to accurately predict the outcomes of critically ill patients with septic shock undergoing PDGT.

Tools for outcome prediction in patients with community acquired pneumonia

INTRODUCTION

Community-acquired pneumonia (CAP) is one of the most common causes of mortality world-wide. The mortality rate of patients with CAP is influenced by the severity of the disease, treatment failure and the requirement for hospitalization and/or intensive care unit (ICU) management, all of which may be predicted by biomarkers and clinical scoring systems. Areas covered: We review the recent literature examining the efficacy of established and newly-developed clinical scores, biological and inflammatory markers such as C-Reactive protein (CRP), procalcitonin (PCT) and Interleukin-6 (IL-6), whether used alone or in conjunction with clinical severity scores to assess the severity of CAP, predict treatment failure, guide acute in-hospital or ICU admission and predict mortality. Expert commentary: The early prediction of treatment failure using clinical scores and biomarkers plays a developing role in improving survival of patients with CAP by identifying high-risk patients requiring hospitalization or ICU admission; and may enable more efficient allocation of resources. However, it is likely that combinations of scoring systems and biomarkers will be of greater use than individual markers. Further larger studies are needed to corroborate the additive value of these markers to clinical prediction scores to provide a safer and more effective assessment tool for clinicians.

Combining quick Sequential Organ Failure Assessment with plasma lactate concentration is comparable to standard Sequential Organ Failure Assessment score in predicting mortality of patients with and without suspected infection

PURPOSE

We sought to determine whether quick Sequential Organ Failure Assessment (qSOFA) score can be used to predict mortality of patients without suspected infection.

MATERIALS AND METHODS

Using prospectively collected data within the first hour of intensive care unit admission, the predictive ability of qSOFA was compared with the Simplified Acute Physiology Score III, Admission Mortality Prediction Model III, Acute Physiology and Chronic Health Evaluation II model, and standard (full-version) SOFA score using area under the receiver operating characteristic (AUROC) curve and Brier score.

RESULTS

Of the 2322 patients included, 279 (12.0%) died after intensive care unit admission. The qSOFA score had a modest ability to predict mortality of all critically ill patients (AUROC, 0.672; 95% confidence interval [CI], 0.638-0.707; Brier score 0.099) including the noninfected patients (AUROC, 0.685; 95% CI, 0.637-0.732; Brier score 0.081). The overall predictive ability and calibration of the qSOFA was comparable to other prognostic scores. Combining qSOFA score with lactate concentrations further enhanced its predictive ability (AUROC, 0.730; 95% CI, 0.694-0.765; Brier score 0.097), comparable to the standard SOFA score.

CONCLUSIONS

The qSOFA score had a modest ability to predict mortality of both septic and nonseptic patients; combining qSOFA with plasma lactate had a predictive ability comparable to the standard SOFA score.

Scoring Systems in Assessing Survival of Critically Ill ICU Patients

Background

The aim of this study was to determine which of the most commonly used scoring systems for evaluation of critically ill patients in the ICU is the best and simplest to use in our hospital.

Material/Methods

This prospective study included 60 critically ill patients. After admittance to the ICU, APACHE II, SAPS II, and MPM II₀ were calculated. During further treatment in the ICU, SOFA and MPM II were calculated at 24 h, 48 h, and 72 h and 7 days after admittance using laboratory and radiological measures.

Results

In comparison with survivors, non-survivors were older (p<0.01) and spent significantly more days on mechanical ventilation (p<0.01). ARDS was significantly more common in patients who survived compared to those who did not (chi-square=7.02, p<0.01), which is not the case with sepsis (chi-square=0.388, p=0.53). AUROC SAPS II was 0.690, and is only slightly higher than the other 2 AUROC incipient scoring systems, MPM II and APACHE II (0.654 and 0.623). The APACHE II has the highest specificity (81.8%) and MPM II the highest sensitivity (85.2%). MPM II_7day AUROC (1.0) shows the best discrimination between patients who survived and those who did not. MPM II₄₈ (0.836), SOFA₇₂ (0.821) and MPM II₇₂ (0.817) also had good discrimination scores.

Conclusions

APACHE II and SAPS II measured on admission to the ICU were significant predictors of complications. MPM II_7day has the best discriminatory power, followed by SOFA_7day and MPM II₄₈. MPM II_7day has the best calibration followed by SOFA_7day and APACHE II.

Comparison of risk prediction scoring systems for ward patients: a retrospective nested case-control study

INTRODUCTION

The rising prevalence of rapid response teams has led to a demand for risk-stratification tools that can estimate a ward patient's risk of clinical deterioration and subsequent need for intensive care unit (ICU) admission. Finding such a risk-stratification tool is crucial for maximizing the utility of rapid response teams. This study compares the ability of nine risk prediction scores in detecting clinical deterioration among non-ICU ward patients. We also measured each score serially to characterize how these scores changed with time.

METHODS

In a retrospective nested case-control study, we calculated nine well-validated prediction scores for 328 cases and 328 matched controls. Our cohort included non-ICU ward patients admitted to the hospital with a diagnosis of infection, and cases were patients in this cohort who experienced clinical deterioration, defined as requiring a critical care consult, ICU admission, or death. We then compared each prediction score's ability, over the course of 72 hours, to discriminate between cases and controls.

RESULTS

At 0 to 12 hours before clinical deterioration, seven of the nine scores performed with acceptable discrimination: Sequential Organ Failure Assessment (SOFA) score area under the curve of 0.78, Predisposition/Infection/Response/Organ Dysfunction Score of 0.76, VitalPac Early Warning Score of 0.75, Simple Clinical Score of 0.74, Mortality in Emergency Department Sepsis of 0.74, Modified Early Warning Score of 0.73, Simplified Acute Physiology Score II of 0.73, Acute Physiology and Chronic Health Evaluation II of 0.72, and Rapid Emergency Medicine Score of 0.67. By measuring scores over time, it was found that average SOFA scores of cases increased as early as 24 to 48 hours prior to deterioration (P = 0.01). Finally, a clinical prediction rule which also accounted for the change in SOFA score was constructed and found to perform with a sensitivity of 75% and a specificity of 72%, and this performance is better than that of any SOFA scoring model based on a single set of physiologic variables.

CONCLUSIONS

ICU- and emergency room-based prediction scores can also be used to prognosticate risk of clinical deterioration for non-ICU ward patients. In addition, scoring models that take advantage of a score's change over time may have increased prognostic value over models that use only a single set of physiologic measurements.

A risk prediction score for kidney failure or mortality in rhabdomyolysis

IMPORTANCE

Rhabdomyolysis ranges in severity from asymptomatic elevations in creatine phosphokinase levels to a life-threatening disorder characterized by severe acute kidney injury requiring hemodialysis or continuous renal replacement therapy (RRT).

OBJECTIVE

To develop a risk prediction tool to identify patients at greatest risk of RRT or in-hospital mortality.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective cohort study of 2371 patients admitted between January 1, 2000, and March 31, 2011, to 2 large teaching hospitals in Boston, Massachusetts, with creatine phosphokinase levels in excess of 5000 U/L within 3 days of admission. The derivation cohort consisted of 1397 patients from Massachusetts General Hospital, and the validation cohort comprised 974 patients from Brigham and Women's Hospital.

MAIN OUTCOMES AND MEASURES

The composite of RRT or in-hospital mortality.

RESULTS

The causes and outcomes of rhabdomyolysis were similar between the derivation and validation cohorts. In total, the composite outcome occurred in 19.0% of patients (8.0% required RRT and 14.1% died during hospitalization). The highest rates of the composite outcome were from compartment syndrome (41.2%), sepsis (39.3%), and following cardiac arrest (58.5%). The lowest rates were from myositis (1.7%), exercise (3.2%), and seizures (6.0%). The independent predictors of the composite outcome were age, female sex, cause of rhabdomyolysis, and values of initial creatinine, creatine phosphokinase, phosphate, calcium, and bicarbonate. We developed a risk-prediction score from these variables in the derivation cohort and subsequently applied it in the validation cohort. The C statistic for the prediction model was 0.82 (95% CI, 0.80-0.85) in the derivation cohort and 0.83 (0.80-0.86) in the validation cohort. The Hosmer-Lemeshow P values were .14 and .28, respectively. In the validation cohort, among the patients with the lowest risk score (<5), 2.3% died or needed RRT. Among the patients with the highest risk score (>10), 61.2% died or needed RRT.

CONCLUSIONS AND RELEVANCE

Outcomes from rhabdomyolysis vary widely depending on the clinical context. The risk of RRT or in-hospital mortality in patients with rhabdomyolysis can be estimated using commonly available demographic, clinical, and laboratory variables on admission.

Soluble receptor for advanced glycation end products predicts 28-day mortality in critically ill patients with sepsis

OBJECTIVE

Multiple biomarkers are used to assess sepsis severity and prognosis. Increased levels of the soluble receptor for advanced glycation end products (sRAGE) were previously observed in sepsis but also in end-organ injury without sepsis. We evaluated associations between sRAGE and (i) 28-day mortality, (ii) sepsis severity, and (iii) individual organ failure. Traditional biomarkers procalcitonin (PCT), C-reactive protein (CRP) and lactate served as controls.

METHODS

sRAGE, PCT, CRP, and lactate levels were observed on days 1 (D1) and 3 (D3) in 54 septic patients. We also assessed the correlation between the biomarkers and acute respiratory distress syndrome (ARDS), acute kidney injury (AKI) and acute heart failure.

RESULTS

There were 38 survivors and 16 non-survivors. On D1, non-survivors had higher sRAGE levels than survivors (p = 0.027). On D3, sRAGE further increased only in non-survivors (p < 0.0001) but remained unchanged in survivors. Unadjusted odds ratio (OR) for 28-day mortality was 8.2 (95% CI: 1.02-60.64) for sRAGE, p = 0.048. Receiver operating characteristic analysis determined strong correlation with outcome on D3 (AUC = 0.906, p < 0.001), superior to other studied biomarkers. sRAGE correlated with sepsis severity (p < 0.00001). sRAGE showed a significant positive correlation with PCT and CRP on D3. In patients without ARDS, sRAGE was significantly higher in non-survivors (p < 0.0001) on D3.

CONCLUSION

Increased sRAGE was associated with 28-day mortality in patients with sepsis, and was superior compared to PCT, CRP and lactate. sRAGE correlated with sepsis severity. sRAGE was increased in patients with individual organ failure. sRAGE could be used as an early biomarker in prognostication of outcome in septic patients.

Consideration of additional factors in Sequential Organ Failure Assessment score

PURPOSE

The Sequential Organ Failure Assessment (SOFA) score, originally developed to assess organ failure status, is widely used as a prognostic indicator in intensive care unit patients. Additional prognostic factors, such as age and comorbidities, may complement the predictive performance of the SOFA.

METHODS

In total, 1049 consecutive patients were enrolled prospectively. SOFA and other admission-based intensive care unit scores were recorded during the first 24 hours. A complemented SOFA (cSOFA) score model was constructed by adding age and comorbidity scores to the original SOFA score, based on logistic regression analysis. The predictive performance was evaluated with regard to hospital mortality by receiver operating characteristics analysis. The Hosmer-Lemeshow goodness-of-fit test was used to assess calibration of the model, and leave-one-out cross-validation was performed.

RESULTS

The cSOFA score (maximum 30 points) was calculated as the SOFA score (24 points) + age score (2 points) + comorbidity score (4 points). The cSOFA score model showed satisfactory calibration and cross-validation performance. The AUC (95% CI) of the cSOFA score (0.812 [0.787-0.835]) was higher than the SOFA score (0.743 [0.715-0.769], P < .0001).

CONCLUSION

The performance of the SOFA score to predict hospital mortality can be improved by considering age and comorbidity factors.

Diagnostic and prognostic value of presepsin in the management of sepsis in the emergency department: a multicenter prospective study

Introduction

Sepsis, severe sepsis and septic shock are common conditions with high mortality. Their early diagnosis in the Emergency Department (ED) is one of the keys to improving survival. Procalcitonin (PCT) has been used as a biomarker in septic patients but has limited specificity and can be elevated in other scenarios of systemic inflammatory response syndrome (SIRS). Soluble CD14 (sCD14) or presepsin is the free fragment of a glycoprotein expressed on monocytes and macrophages. Preliminary reports suggest that levels of presepsin are significantly higher in septic patients than in healthy individuals. The aim of this study is to investigate the diagnostic and prognostic value of presepsin compared to PCT in people presenting at the ED with SIRS and suspected sepsis or septic shock.

Methods

This study was conducted in two major hospitals in Turin, Italy. One hundred six patients presenting to the EDs with suspected sepsis or septic shock were included, and another eighty-three patients affected by SIRS, but with no clinical evidence of infection, were recruited as controls. Blood samples were collected at first medical evaluation and for some patients after 24 and 72 h. The samples were analyzed using the PATHFAST Presepsin assay for sCD14, and commercial kits were used for other determinations (for example, PCT). Definitive diagnosis and survival rates were obtained afterward by analysis of digital medical records.

Results

Elevated concentrations of presepsin at presentation were observed in septic patients compared to control patients. The same trend was observed for mean values of PCT. Higher values of presepsin were observed in septic patients at presentation (time 0). The diagnostic accuracy of PCT was generally higher, and areas under the curve (AUCs) were 0.875 for PCT and 0.701 for presepsin. Mean presepsin values were significantly higher in nonsurvivor septic patients (60-day mortality) than in survivors. No significant correlation was noted between PCT and survival.

Conclusions

In our experience, presepsin was useful in the early diagnosis of infection in a complex population of patients with SIRS, sepsis, severe sepsis and septic shock who presented to the ED. Presepsin showed a significant prognostic value, and initial values were significantly correlated with in-hospital mortality of patients affected by sepsis, severe sepsis or septic shock.

Simple translational equations to compare illness severity scores in intensive care trials

PURPOSE

Comparison of illness severity for intensive care unit populations assessed according to different scoring systems should increase our ability to compare and meta-analyze past and future trials but is currently not possible. Accordingly, we aimed to establish a methodology to translate illness severity scores obtained from one system into another.

MATERIALS AND METHODS

Using the Australian and New-Zealand intensive care adult patient database, we obtained simultaneous admission Acute Physiology and Chronic Health Evaluation (APACHE) II and APACHE III scores and Simplified Acute Physiology Score (SAPS) II in 634428 patients admitted to 153 units between 2001 and 2010. We applied linear regression analyses to create models enabling translation of one score into another. Sensitivity analyses were performed after removal of diagnostic categories excluded from the original APACHE database, after matching for similar risk of death, after splitting data according to country of origin (Australia or New Zealand) and after splitting admissions occurring before or after 2006.

RESULTS

The translational models were APACHE III=3.08×APACHE II+5.75; APACHE III=1.47×SAPS II+8.6; and APACHE II=0.36×SAPS II+4.4. The area under the receiver operating curve for mortality prediction was 0.853 (95% confidence interval, 0.851-0.855) for the "APACHE II derived APACHE III" score and 0.854 (0.852-0.855) for the "SAPS II derived APACHE III" vs 0.854 (0.852-0.855) for the original APACHE III score. Similarly, it was 0.841 (0.839-0.843) for the "SAPS II derived APACHE II score" vs 0.842 (0.840-0.843) for the original APACHE II score. Correlation coefficients as well as intercepts remained very similar in all subgroups analyses.

CONCLUSIONS

Simple and robust translational formulas can be developed to allow clinicians to compare illness severity between studies involving critically ill patients. Further studies in other countries and health care systems are needed to confirm the generalizability of these results.

Application of a modified sequential organ failure assessment score to critically ill patients

The purpose of the present study was to explore the usefulness of the Mexican sequential organ failure assessment (MEXSOFA) score for assessing the risk of mortality for critically ill patients in the ICU. A total of 232 consecutive patients admitted to an ICU were included in the study. The MEXSOFA was calculated using the original SOFA scoring system with two modifications: the PaO₂/FiO₂ ratio was replaced with the SpO₂/FiO₂ ratio, and the evaluation of neurologic dysfunction was excluded. The ICU mortality rate was 20.2%. Patients with an initial MEXSOFA score of 9 points or less calculated during the first 24 h after admission to the ICU had a mortality rate of 14.8%, while those with an initial MEXSOFA score of 10 points or more had a mortality rate of 40%. The MEXSOFA score at 48 h was also associated with mortality: patients with a score of 9 points or less had a mortality rate of 14.1%, while those with a score of 10 points or more had a mortality rate of 50%. In a multivariate analysis, only the MEXSOFA score at 48 h was an independent predictor for in-ICU death with an OR = 1.35 (95%CI = 1.14-1.59, P < 0.001). The SOFA and MEXSOFA scores calculated 24 h after admission to the ICU demonstrated a good level of discrimination for predicting the in-ICU mortality risk in critically ill patients. The MEXSOFA score at 48 h was an independent predictor of death; with each 1-point increase, the odds of death increased by 35%.

Global muscle strength but not grip strength predicts mortality and length of stay in a general population in a surgical intensive care unit

BACKGROUND

Paresis acquired in the intensive care unit (ICU) is common in patients who are critically ill and independently predicts mortality and morbidity. Manual muscle testing (MMT) and handgrip dynamometry assessments have been used to evaluate muscle weakness in patients in a medical ICU, but similar data for patients in a surgical ICU (SICU) are limited.

OBJECTIVE

The purpose of this study was to evaluate the predictive value of strength measured by MMT and handgrip dynamometry at ICU admission for in-hospital mortality, SICU length of stay (LOS), hospital LOS, and duration of mechanical ventilation.

DESIGN

This investigation was a prospective, observational study.

METHODS

One hundred ten patients were screened for eligibility for testing in the SICU of a large, academic medical center. The Acute Physiology and Chronic Health Evaluation (APACHE) II score, diagnoses, and laboratory data were collected. Measurements were obtained by MMT quantified with the sum (total) score on the Medical Research Council Scale and by handgrip dynamometry. Outcome data, including in-hospital mortality, SICU LOS, hospital LOS, and duration of mechanical ventilation, were collected for all participants.

RESULTS

One hundred seven participants were eligible for testing; 89% were tested successfully at a median of 3 days (25th-75th percentiles=3-6 days) after admission. Sedation was the most frequent barrier to testing (70.6%). Manual muscle testing was identified as an independent predictor of mortality, SICU LOS, hospital LOS, and duration of mechanical ventilation. Grip strength was not independently associated with these outcomes.

LIMITATIONS

This study did not address whether muscle weakness translates to functional outcome impairment.

CONCLUSIONS

In contrast to handgrip strength, MMT reliably predicted in-hospital mortality, duration of mechanical ventilation, SICU LOS, and hospital LOS.

Prognostic models in the clinical arena

Making a prognosis is to predict the course of a disease and estimate the probability (or risk) of the appearance of a given outcome in relationship to clinical or non-clinical characteristics. Prognostic assessment is usually modelled by multivariable mathematic equations (prognostic models). In this article we describe what a prognostic model is, how to build a good one, why and how it is important to evaluate its generalizability and accuracy by means of discrimination, calibration and reclassification.

Cross-validation of a Sequential Organ Failure Assessment score-based model to predict mortality in patients with cancer admitted to the intensive care unit

PURPOSE

This study aims to validate the performance of the Sequential Organ Failure Assessment (SOFA) score to predict death of critically ill patients with cancer.

MATERIAL AND METHODS

We conducted a retrospective observational study including adults admitted to the intensive care unit (ICU) between January 1, 2006, and December 31, 2008. We randomly selected training and validation samples in medical and surgical admissions to predict ICU and in-hospital mortality. By using logistic regression, we calculated the probabilities of death in the training samples and applied them to the validation samples to test the goodness-of-fit of the models, construct receiver operator characteristics curves, and calculate the areas under the curve (AUCs).

RESULTS

In predicting mortality at discharge from the unit, the AUC from the validation group of medical admissions was 0.7851 (95% confidence interval [CI], 0.7437-0.8264), and the AUC from the surgical admissions was 0.7847 (95% CI, 0.6319-0.937). The AUCs of the SOFA score to predict mortality in the hospital after ICU admission were 0.7789 (95% CI, 0.74-0.8177) and 0.7572 (95% CI, 0.6719-0.8424) for the medical and surgical validations groups, respectively.

CONCLUSIONS

The SOFA score had good discrimination to predict ICU and hospital mortality. However, the observed underestimation of ICU deaths and unsatisfactory goodness-of-fit test of the model in surgical patients to indicate calibration of the score to predict ICU mortality is advised in this group.

The utility of acute physiology and chronic health evaluation II scores for prediction of mortality among intensive care unit (ICU) and non-ICU patients with methicillin-resistant Staphylococcus aureus bacteremia

OBJECTIVE

Bloodstream infections due to methicillin-resistant Staphylococcus aureus (MRSA) have been associated with significant risk of in-hospital mortality. The acute physiology and chronic health evaluation (APACHE) II score was developed and validated for use among intensive care unit (ICU) patients, but its utility among non-ICU patients is unknown. The aim of this study was to determine the ability of APACHE II to predict death at multiple time points among ICU and non-ICU patients with MRSA bacteremia.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Secondary analysis of data from 200 patients with MRSA bacteremia at 2 hospitals.

METHODS

Logistic regression models were constructed to predict overall in-hospital mortality and mortality at 48 hours, 7 days, 14 days, and 30 days using APACHE II scores separately in ICU and non-ICU patients. The performance of APACHE II scores was compared with age adjustment alone among all patients. Discriminatory ability was assessed using the c-statistic and was compared at each time point using χ(2) tests. Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test.

RESULTS

APACHE II was a significant predictor of death at all time points in both ICU and non-ICU patients. Discrimination was high in all models, with c-statistics ranging from 0.72 to 0.84, and was similar between ICU and non-ICU patients at all time points. APACHE II scores significantly improved the prediction of overall and 48-hour mortality compared with age adjustment alone.

CONCLUSIONS

The APACHE II score may be a valid tool to control for confounding or for the prediction of death among ICU and non-ICU patients with MRSA bacteremia.

Changes in Case-Mix and Outcomes of Critically Ill Patients in an Australian Tertiary Intensive Care Unit

Critical care service is expensive and the demand for such service is increasing in many developed countries. This study aimed to assess the changes in characteristics of critically ill patients and their effect on long-term outcome. This cohort study utilised linked data between the intensive care unit database and state-wide morbidity and mortality databases. Logistic and Cox regression was used to examine hospital survival and five-year survival of 22,298 intensive care unit patients, respectively. There was a significant increase in age, severity of illness and Charlson Comorbidity Index of the patients over a 16-year study period. Although hospital mortality and median length of intensive care unit and hospital stay remained unchanged, one- and five-year survival had significantly improved with time, after adjusting for age, gender, severity of illness, organ failure, comorbidity, ‘new’ cancer and diagnostic group. Stratified analyses showed that the improvement in five-year survival was particularly strong among patients admitted after cardiac surgery (P=0.001). In conclusion, although critical care service is increasingly being provided to patients with a higher severity of acute and chronic illnesses, long-term survival outcome has improved with time suggesting that critical care service may still be cost-effectiveness despite the changes in case-mix.

Landmark survival as an end-point for trials in critically ill patients--comparison of alternative durations of follow-up: an exploratory analysis

Introduction

Interventional ICU trials have followed up patients for variable duration. However, the optimal duration of follow-up for the determination of mortality endpoint in such trials is uncertain. We aimed to determine the most logical and practical mortality end-point in clinical trials of critically ill patients.

Methods

We performed a retrospective analysis of prospectively collected data involving 369 patients with one of the three specific diagnoses (i) Sepsis (ii) Community acquired pneumonia (iii) Non operative trauma admitted to the Royal Perth Hospital ICU, a large teaching hospital in Western Australia (WA cohort). Their in-hospital and post discharge survival outcome was assessed by linkage to the WA Death Registry. A validation cohort involving 4609 patients admitted during same time period with identical diagnoses from 55 ICUs across Australia (CORE cohort) was used to compare the patient characteristics and in-hospital survival to look at the Australia-wide applicability of the long term survival data from the WA cohort.

Results

The long term outcome data of the WA cohort indicate that mortality reached a plateau at 90 days after ICU admission particularly for sepsis and pneumonia. Mortality after hospital discharge before 90 days was not uncommon in these two groups. Severity of acute illness as measured by the total number of organ failures or acute physiology score was the main predictor of 90-day mortality. The adjusted in-hospital survival for the WA cohort was not significantly different from that of the CORE cohort in all three diagnostic groups; sepsis (P = 0.19), community acquired pneumonia (P = 0.86), non-operative trauma (P = 0.47).

Conclusions

A minimum of 90 days follow-up is necessary to fully capture the mortality effect of sepsis and community acquired pneumonia. A shorter period of follow-up time may be sufficient for non-operative trauma.

Evaluation of SOFA-based models for predicting mortality in the ICU: A systematic review

Introduction

To systematically review studies evaluating the performance of Sequential Organ Failure Assessment (SOFA)-based models for predicting mortality in patients in the intensive care unit (ICU).

Methods

Medline, EMBASE and other databases were searched for English-language articles with the major objective of evaluating the prognostic performance of SOFA-based models in predicting mortality in surgical and/or medical ICU admissions. The quality of each study was assessed based on a quality framework for prognostic models.

Results

Eighteen articles met all inclusion criteria. The studies differed widely in the SOFA derivatives used and in their methods of evaluation. Ten studies reported about developing a probabilistic prognostic model, only five of which used an independent validation data set. The other studies used the SOFA-based score directly to discriminate between survivors and non-survivors without fitting a probabilistic model. In five of the six studies, admission-based models (Acute Physiology and Chronic Health Evaluation (APACHE) II/III) were reported to have a slightly better discrimination ability than SOFA-based models at admission (the receiver operating characteristic curve (AUC) of SOFA-based models ranged between 0.61 and 0.88), and in one study a SOFA model had higher AUC than the Simplified Acute Physiology Score (SAPS) II model. Four of these studies used the Hosmer-Lemeshow tests for calibration, none of which reported a lack of fit for the SOFA models. Models based on sequential SOFA scores were described in 11 studies including maximum SOFA scores and maximum sum of individual components of the SOFA score (AUC range: 0.69 to 0.92) and delta SOFA (AUC range: 0.51 to 0.83). Studies comparing SOFA with other organ failure scores did not consistently show superiority of one scoring system to another. Four studies combined SOFA-based derivatives with admission severity of illness scores, and they all reported on improved predictions for the combination. Quality of studies ranged from 11.5 to 19.5 points on a 20-point scale.

Conclusions

Models based on SOFA scores at admission had only slightly worse performance than APACHE II/III and were competitive with SAPS II models in predicting mortality in patients in the general medical and/or surgical ICU. Models with sequential SOFA scores seem to have a comparable performance with other organ failure scores. The combination of sequential SOFA derivatives with APACHE II/III and SAPS II models clearly improved prognostic performance of either model alone. Due to the heterogeneity of the studies, it is impossible to draw general conclusions on the optimal mathematical model and optimal derivatives of SOFA scores. Future studies should use a standard evaluation methodology with a standard set of outcome measures covering discrimination, calibration and accuracy.

Estimating long-term survival of critically ill patients: the PREDICT model

Background

Long-term survival outcome of critically ill patients is important in assessing effectiveness of new treatments and making treatment decisions. We developed a prognostic model for estimation of long-term survival of critically ill patients.

Methodology and Principal Findings

This was a retrospective linked data cohort study involving 11,930 critically ill patients who survived more than 5 days in a university teaching hospital in Western Australia. Older age, male gender, co-morbidities, severe acute illness as measured by Acute Physiology and Chronic Health Evaluation II predicted mortality, and more days of vasopressor or inotropic support, mechanical ventilation, and hemofiltration within the first 5 days of intensive care unit admission were associated with a worse long-term survival up to 15 years after the onset of critical illness. Among these seven pre-selected predictors, age (explained 50% of the variability of the model, hazard ratio [HR] between 80 and 60 years old = 1.95) and co-morbidity (explained 27% of the variability, HR between Charlson co-morbidity index 5 and 0 = 2.15) were the most important determinants. A nomogram based on the pre-selected predictors is provided to allow estimation of the median survival time and also the 1-year, 3-year, 5-year, 10-year, and 15-year survival probabilities for a patient. The discrimination (adjusted c-index = 0.757, 95% confidence interval 0.745–0.769) and calibration of this prognostic model were acceptable.

Significance

Age, gender, co-morbidities, severity of acute illness, and the intensity and duration of intensive care therapy can be used to estimate long-term survival of critically ill patients. Age and co-morbidity are the most important determinants of long-term prognosis of critically ill patients.

Determinants of long-term survival after intensive care

OBJECTIVE

To identify prognostic determinants of long-term survival for patients treated in intensive care units (ICUs) who survived to hospital discharge.

DESIGN

An ICU clinical cohort linked to state-wide hospital records and death registers.

SETTING AND PATIENTS

Adult patients admitted to a 22-bed ICU at a major teaching hospital in Perth, Western Australia, between 1987 and 2002 who survived to hospital discharge (n = 19,921) were followed-up until December 31, 2003.

MEASUREMENTS

The main outcome measures are crude and adjusted survival.

MAIN RESULTS

The risk of death in the first year after hospital discharge was high for patients who survived the ICU compared with the general population (standardized mortality rate [SMR] at 1 yr = 2.90, 95% confidence interval [CI] 2.73-3.08) and remained higher than the general population for every year during 15 yrs of follow up (SMR at 15 yrs = 2.01, 95% CI 1.64-2.46). Factors that were independently associated with survival during the first year were older age (hazard ratio [HR] = 4.09; 95% CI 3.20-5.23), severe comorbidity (HR = 5.23; 95% CI 4.25-6.43), ICU diagnostic group (HR range 2.20 to 8.95), new malignancy (HR = 4.60; 95% CI 3.68-5.76), high acute physiology score on admission (HR = 1.55; 95% CI 1.23-1.96), and peak number of organ failures (HR = 1.51; 95% CI 1.11-2.04). All of these factors were independently associated with subsequent survival for those patients who were alive 1 yr after discharge from the hospital with the addition of male gender (HR = 1.17; 95% CI 1.10-1.25) and prolonged length of stay in ICU (HR = 1.42; 95% CI 1.29-1.55).

CONCLUSIONS

Patients who survived an admission to the ICU have worse survival than the general population for at least 15 yrs. The factors that determine long-term survival include age, comorbidity, and primary diagnosis. Severity of illness was also associated with long-term survival and this suggests that an episode of critical illness, or its treatment, may shorten life-expectancy.