APACHE III Outcome Prediction in Patients Admitted to the Intensive Care Unit with Sepsis Associated Acute Lung Injury

Bloodstream infection: Derivation and validation of a reliable and multidimensional prognostic score based on a machine learning model (BLISCO)

BACKGROUND

A bloodstream infection (BSI) prognostic score applicable at the time of blood culture collection is missing.

METHODS

In total, 4,327 patients with BSIs were included, divided into a derivation (80%) and a validation dataset (20%). Forty-two variables among host-related, demographic, epidemiological, clinical, and laboratory extracted from the electronic health records were analyzed. Logistic regression was chosen for predictive scoring.

RESULTS

The 14-day mortality model included age, body temperature, blood urea nitrogen, respiratory insufficiency, platelet count, high-sensitive C-reactive protein, and consciousness status: a score of ≥ 6 was correlated to a 14-day mortality rate of 15% with a sensitivity of 0.742, a specificity of 0.727, and an area under the curve of 0.783. The 30-day mortality model further included cardiovascular diseases: a score of ≥ 6 predicting 30-day mortality rate of 15% with a sensitivity of 0.691, a specificity of 0.699, and an area under the curve of 0.697.

CONCLUSIONS

A quick mortality score could represent a valid support for prognosis assessment and resources prioritizing for patients with BSIs not admitted in the intensive care unit.

Prognostic Performance of Sequential Organ Failure Assessment, Acute Physiology and Chronic Health Evaluation III, and Simplified Acute Physiology Score II Scores in Patients with Suspected Infection According to Intensive Care Unit Type

We investigated the prognostic performance of scoring systems by the intensive care unit (ICU) type. This was a retrospective observational study using data from the Marketplace for Medical Information in the Intensive Care IV database. The primary outcome was in-hospital mortality. We obtained Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation (APACHE) III, and Simplified Acute Physiology Score (SAPS) II scores in each ICU type. Prognostic performance was evaluated with the area under the receiver operating characteristic curve (AUROC) and was compared among ICU types. A total of 29,618 patients were analyzed, and the in-hospital mortality was 12.4%. The overall prognostic performance of APACHE III was significantly higher than those of SOFA and SAPS II (0.807, [95% confidence interval, 0.799-0.814], 0.785 [0.773-0.797], and 0.795 [0.787-0.811], respectively). The prognostic performance of SOFA, APACHE III, and SAPS II scores was significantly different between ICU types. The AUROC ranges of SOFA, APACHE III, and SAPS II were 0.723-0.826, 0.728-0.860, and 0.759-0.819, respectively. The neurosurgical and surgical ICUs had lower prognostic performance than other ICU types. The prognostic performance of scoring systems in patients with suspected infection is significantly different according to ICU type. APACHE III systems have the highest prediction performance. ICU type may be a significant factor in the prognostication.

Predictive models for short-term mortality and length of hospital stay among adults with community-onset bacteraemia before and during the COVID-19 pandemic: application of early data dynamics

BACKGROUND

The development of scoring systems to predict the short-term mortality and the length of hospital stay (LOS) in patients with bacteraemia is essential to improve the quality of care and reduce the occupancy variance in the hospital bed.

METHODS

Adults hospitalised with community-onset bacteraemia in the coronavirus disease 2019 (COVID-19) and pre-COVID-19 eras were captured as the validation and derivation cohorts in the multicentre study, respectively. Model I incorporated all variables available on day 0, Model II incorporated all variables available on day 3, and Models III, IV, and V incorporated the variables that changed from day 0 to day 3. This study adopted the statistical and machine learning (ML) methods to jointly determine the prediction performance of these models in two study cohorts.

RESULTS

A total of 3,639 (81.4%) and 834 (18.6%) patients were included in the derivation and validation cohorts, respectively. Model IV achieved the best performance in predicting 30-day mortality in both cohorts. The most frequently identified variables incorporated into Model IV were deteriorated consciousness from day 0 to day 3 and deteriorated respiration from day 0 to day 3. Model V achieved the best performance in predicting LOS in both cohorts. The most frequently identified variables in Model V were deteriorated consciousness from day 0 to day 3, a body temperature ≤ 36.0 °C or ≥ 39.0 °C on day 3, and a diagnosis of complicated bacteraemia.

CONCLUSIONS

For hospitalised adults with community-onset bacteraemia, clinical variables that dynamically changed from day 0 to day 3 were crucial in predicting the short-term mortality and LOS.

A Novel Composite Indicator of Predicting Mortality Risk for Heart Failure Patients With Diabetes Admitted to Intensive Care Unit Based on Machine Learning

BACKGROUND

Patients with heart failure (HF) with diabetes may face a poorer prognosis and higher mortality than patients with either disease alone, especially for those in intensive care unit. So far, there is no precise mortality risk prediction indicator for this kind of patient.

METHOD

Two high-quality critically ill databases, the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the Telehealth Intensive Care Unit (eICU) Collaborative Research Database (eICU-CRD) Collaborative Research Database, were used for study participants' screening as well as internal and external validation. Nine machine learning models were compared, and the best one was selected to define indicators associated with hospital mortality for patients with HF with diabetes. Existing attributes most related to hospital mortality were identified using a visualization method developed for machine learning, namely, Shapley Additive Explanations (SHAP) method. A new composite indicator ASL was established using logistics regression for patients with HF with diabetes based on major existing indicators. Then, the new index was compared with existing indicators to confirm its discrimination ability and clinical value using the receiver operating characteristic (ROC) curve, decision curve, and calibration curve.

RESULTS

The random forest model outperformed among nine models with the area under the ROC curve (AUC) = 0.92 after hyper-parameter optimization. By using this model, the top 20 attributes associated with hospital mortality in these patients were identified among all the attributes based on SHAP method. Acute Physiology Score (APS) III, Sepsis-related Organ Failure Assessment (SOFA), and Max lactate were selected as major attributes related to mortality risk, and a new composite indicator was developed by combining these three indicators, which was named as ASL. Both in the initial and external cohort, the new indicator, ASL, had greater risk discrimination ability with AUC higher than 0.80 in both low- and high-risk groups compared with existing attributes. The decision curve and calibration curve indicated that this indicator also had a respectable clinical value compared with APS III and SOFA. In addition, this indicator had a good risk stratification ability when the patients were divided into three risk levels.

CONCLUSION

A new composite indicator for predicting mortality risk in patients with HF with diabetes admitted to intensive care unit was developed on the basis of attributes identified by the random forest model. Compared with existing attributes such as APS III and SOFA, the new indicator had better discrimination ability and clinical value, which had potential value in reducing the mortality risk of these patients.

Analysis of the correlation between the longitudinal trajectory of SOFA scores and prognosis in patients with sepsis at 72 hour after admission based on group trajectory modeling

Background

To identify the distinct trajectories of the Sequential Organ Failure Assessment (SOFA) scores at 72 h for patients with sepsis in the Medical Information Mart for Intensive Care (MIMIC)-IV database and determine their effects on mortality and adverse clinical outcomes.

Methods

A retrospective cohort study was carried out involving patients with sepsis from the MIMIC-IV database. Group-based trajectory modeling (GBTM) was used to identify the distinct trajectory groups for the SOFA scores in patients with sepsis in the intensive care unit (ICU). The Cox proportional hazards regression model was used to investigate the relationship between the longitudinal change trajectory of the SOFA score and mortality and adverse clinical outcomes.

Results

A total of 16,743 patients with sepsis were included in the cohort. The median survival age was 66 years (interquartile range: 54-76 years). The 7-day and 28-day in-hospital mortality were 6.0% and 17.6%, respectively. Five different trajectories of SOFA scores according to the model fitting standard were determined: group 1 (32.8%), group 2 (30.0%), group 3 (17.6%), group 4 (14.0%) and group 5 (5.7%). Univariate and multivariate Cox regression analyses showed that, for different clinical outcomes, trajectory group 1 was used as the reference, while trajectory groups 2-5 were all risk factors associated with the outcome (P < 0.001). Subgroup analysis revealed an interaction between the two covariates of age and mechanical ventilation and the different trajectory groups of patients' SOFA scores (P < 0.05).

Conclusion

This approach may help identify various groups of patients with sepsis, who may be at different levels of risk for adverse health outcomes, and provide subgroups with clinical importance.

Mechanical power normalized to predicted body weight is associated with mortality in critically ill patients: a cohort study

Background

Mechanical power (MP), defined as the amount of energy produced by mechanical ventilation and released into the respiratory system, was reportedly a determining factor in the pathogenesis of ventilator-induced lung injury. However, previous studies suggest that the effects of MP were proportional to their involvement in the total lung function size. Therefore, MP normalized to the predicted body weight (norMP) should outperform the absolute MP value. The objective of this research is to determine the connection between norMP and mortality in critically ill patients who have been on invasive ventilation for at least 48 h.

Methods

This is a study of data stored in the databases of the MIMIC–III, which contains data of critically ill patients for over 50,000. The study involved critically ill patients who had been on invasive ventilation for at least 48 h. norMP was the relevant exposure. The major endpoint was ICU mortality, the secondary endpoints were 30-day, 90-day mortality; ICU length of stay, the number of ventilator-free days at day 28.

Result

The study involved a total of 1301 critically ill patients. This study revealed that norMP was correlated with ICU mortality [OR per quartile increase 1.33 (95% CI 1.16–1.52), p <  0.001]. Similarly, norMP was correlated with ventilator-free days at day 28, ICU length of stay. In the subgroup analysis, high norMP was associated with ICU mortality whether low or high Vt (OR 1.31, 95% CI 1.09–1.57, p = 0.004; OR 1.32, 95% CI 1.08–1.62, p = 0.008, respectively). But high norMP was associated with ICU mortality only in low PIP (OR 1.18, 95% CI 1.01–1.38, p = 0.034).

Conclusion

Our findings indicate that higher norMP is independently linked with elevated ICU mortality and various other clinical findings in critically ill patients with a minimum of 48 h of invasive ventilation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01497-1.

Sentiment Analysis for Necessary Preview of 30-Day Mortality in Sepsis Patients and the Control Strategies

This study was to preview the risk of 30-day mortality in sepsis patients using sentiment analysis. The clinical data of patients and nursing notes were collected from the Medical Information Mart for Intensive Care (MIMIC-III) database. The factors influencing 30-day mortality were analyzed using the Cox regression model. And, the prognostic index (PI) was estimated. The receiver operating characteristic (ROC) curve was used to determine the PI cut-off point and assess the prediction ability of the model. In total, 1844 of 3560 patients were eligible for the study, with a 30-day mortality of 37.58%. Multivariate Cox analysis showed that sentiment polarity scores, sentiment subjectivity scores, simplified acute physiology score (SAPS)-II, age, and intensive care unit (ICU) types were all associated with the risk of 30-day mortality (P < 0.05). In the preview of 30-day mortality, the area under the curve (AUC) of ROC was 0.78 (95%CI: 0.74–0.81,P < 0.001) when the cut-off point of PI was 0.467. The documented notes from nurses were described for the first time. Sentiment scores measured in nursing notes are associated with the risk of 30-day mortality in sepsis patients and may improve the preview of 30-day mortality.

Validation of APACHE II, APACHE III and SAPS II scores in in-hospital and one year mortality prediction in a mixed intensive care unit in Poland: a cohort study

BACKGROUND

There are several scores used for in-hospital mortality prediction in critical illness. Their application in a local scenario requires validation to ensure appropriate diagnostic accuracy. Moreover, their use in assessing post-discharge mortality in intensive care unit (ICU) survivors has not been extensively studied. We aimed to validate APACHE II, APACHE III and SAPS II scores in short- and long-term mortality prediction in a mixed adult ICU in Poland. APACHE II, APACHE III and SAPS II scores, with corresponding predicted mortality ratios, were calculated for 303 consecutive patients admitted to a 10-bed ICU in 2016. Short-term (in-hospital) and long-term (12-month post-discharge) mortality was assessed.

RESULTS

Median APACHE II, APACHE III and SAPS II scores were 19 (IQR 12-24), 67 (36.5-88) and 44 (27-56) points, with corresponding in-hospital mortality ratios of 25.8% (IQR 12.1-46.0), 18.5% (IQR 3.8-41.8) and 34.8% (IQR 7.9-59.8). Observed in-hospital mortality was 35.6%. Moreover, 12-month post-discharge mortality reached 17.4%. All the scores predicted in-hospital mortality (p < 0.05): APACHE II (AUC = 0.78; 95%CI 0.73-0.83), APACHE III (AUC = 0.79; 95%CI 0.74-0.84) and SAPS II (AUC = 0.79; 95%CI 0.74-0.84); as well as mortality after hospital discharge (p < 0.05): APACHE II (AUC = 0.71; 95%CI 0.64-0.78), APACHE III (AUC = 0.72; 95%CI 0.65-0.78) and SAPS II (AUC = 0.69; 95%CI 0.62-0.76), with no statistically significant difference between the scores (p > 0.05). The calibration of the scores was good.

CONCLUSIONS

All the scores are acceptable predictors of in-hospital mortality. In the case of post-discharge mortality, their diagnostic accuracy is lower and of borderline clinical relevance. Further studies are needed to create scores estimating the long-term prognosis of subjects successfully discharged from the ICU.

Supplementation with selenium and coenzyme Q10 in critically ill patients

Multiple organ dysfunction and resultant mortality in critically ill patients has been linked with impaired cellular energy supply and oxidative stress. Clinical studies supplementing selenium, on the basis of its role as a key cofactor of antioxidant enzymes, have reported variable outcomes in critically ill patients. However, the synergistic interaction between selenium and coenzyme Q10, which has essential roles in cellular energy supply and as an antioxidant, has not been considered in such studies. This article reviews the link between selenium and coenzyme Q10, and the potential role of their co-supplementation in critical illness.

The IL1β-HER2-CLDN18/CLDN4 axis mediates lung barrier damage in ARDS

Objective: The high mortality rate associated with acute respiratory distress syndrome (ARDS) is a major challenge for intensive care units. In the present study, we applied bioinformatics and animal models to identify core genes and potential corresponding pathways in ARDS.

Results: Using bioinformatics analysis, IL-1β was identified as the core gene of ARDS. Cell experiments showed that up-regulation of IL-1β downregulates claudin18 to promote lung barrier function damage by regulating the IL-1β-HER2/HER3 axis, further promoting the development of ARDS. This was validated in the animal models.

Conclusion: IL-1β promotes the development of ARDS by regulating the IL-1β-HER2/HER3 axis. These findings deepen the understanding of the pathological mechanisms of ARDS.

Methods: Transcription data sets related to ARDS were subjected to differential expression gene analysis, functional enrichment analysis, and receiver operating characteristic curve analysis and, so as to identify core genes in ARDS. Cell experiments were used to further explore the effects of core genes on lung barrier function damage. Animal models were applied to validate the effects of core gene in mediating biological signal pathways in ARDS.

Validation the performance of New York Sepsis Severity Score compared with Sepsis Severity Score in predicting hospital mortality among sepsis patients

PURPOSE

The aim of this study was to compare the performance of the New York Sepsis Severity Score (NYSSS) with the Sepsis Severity Score (SSS) and Acute Physiology and Chronic Health Evaluation and Simplified Acute Physiology Scores for predicting mortality in sepsis patients.

METHOD

A retrospective analysis was conducted in the intensive care unit. The primary outcome was in-hospital mortality.

RESULTS

Overall 1680 sepsis patients were enrolled. The hospital mortality rate was 44.4%. The NYSSS underestimated actual mortality with standard mortality ratio (SMR) of 1.28 (95%CI 1.19-1.38). However, the SSS slightly overestimated the actual mortality with an SMR of 0.94 (0.88-1.01). The NYSSS had moderate discrimination with an AUC of 0.772 (0.750-0.794), in contrast to the SSS which had good discrimination with an AUC of 0.889 (0.873-0.904). The AUC of the SSS was statistically higher than that of the NYSSS. The AUCs of both the NYSSS and SSS were significantly lower than other standard severity scores. The calibrations for all severity scores were poor. The SSS had better overall performance than the NYSSS (Brier score 0.149 and 0.201, respectively).

CONCLUSION

The SSS had better discrimination and overall performance than the NYSSS. However, both sepsis severity scores were poorly calibrated.

Validation of the Sepsis Severity Score Compared with Updated Severity Scores in Predicting Hospital Mortality in Sepsis Patients

INTRODUCTION

Recently, the Sepsis Severity Score (SSS) was constructed to predict mortality in sepsis patients. The aim of this study was to compare performance of the SSS with the Acute Physiology and Chronic Health Evaluation (APACHE) II-IV, Simplified Acute Physiology Score (SAPS) II, and SAPS 3 scores in predicting hospital outcome in sepsis patients.

METHODS

A retroprospective analysis was conducted in the medical intensive care unit of a tertiary university hospital.

RESULTS

A total of 913 patients were enrolled; 476 of these patients (52.1%) had septic shock. The median SSS was 80 (range 20-137). The SSS presented good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.892. However, the AUC of the SSS did not differ significantly from that of APACHE II (P = 0.07), SAPS II (P = 0.06), and SAPS 3 (P = 0.11). The APACHE IV score showed the best discrimination with an AUC of 0.948 and the overall performance by a Brier score of 0.096. The AUC of the APACHE IV score was statistically greater than the SSS, APACHE II, SAPS II, and SAPS 3 (P <0.0001 for all) and APACHE III (P = 0.0002). The calibration of all scores was poor with the Hosmer-Lemeshow goodness-of-fit H test <0.05.

CONCLUSIONS

The SSS provided as good discrimination as the APACHE II, SAPS II, and SAPS 3 scores. However, the APACHE IV score had the best discrimination and overall performance in our sepsis patients. The SSS needs to be adapted and modified with new parameters to improve its performance.

Assessing Intensity of Nursing Care Needs Using Electronically Available Data

Although previous research has confirmed that nurse staffing affects patient outcomes, some potentially important factors have not been accounted for in tools to assess relationships between staffing and outcomes. The aim of this project was to develop and test a Nursing Intensity of Care Index using electronically available data from 152 072 patient discharges from three hospitals. Initially, 1765 procedure codes were reviewed; 69 were confirmed as directly increasing nursing workload by at least 15 minutes per shift. Two research staff independently reviewed a random sample of 5 patient days to assess interrater reliability with complete scoring agreement. To assess face validity, eight nurse clinician experts reviewed factors included in the Nursing Intensity of Care Index to assess the accuracy of the nursing time estimates in the tool. To examine concurrent validity, Nursing Intensity of Care Index scores for a random sample of 28 patients from four clinical units were compared with assessments made by a unit-based clinical nurse (low/medium/high intensity) for the same patients on the same day with a Spearman correlation of 0.94. In preliminary testing, data for the Nursing Intensity of Care Index, which accurately reflect nursing care intensity, can be obtained electronically in real time. The next steps will be a discrete-event simulation model and large-scale field trials.