Prognostic utilization of models based on the APACHE II, APACHE IV, and SAPS II scores for predicting in-hospital mortality in emergency department

Development of a severity score based on the International Classification of Disease-10 for general patients visiting emergency centers

BACKGROUND

When comparing mortality, the severity of illness or injury should be considered; therefore, scoring systems that represent severity have been developed and used. Given that diagnosis codes in the International Classification of Disease (ICD) and vital signs are part of routine data used in medical care, a severity scoring system based on these routine data would allow for the comparison of severity-adjusted treatment outcomes without substantial additional efforts.

METHODS

This study was based on the National Emergency Department Information System database of the Republic of Korea. Patients aged 15 years or older were included. Data from between 2016 and 2018 were used to develop the scoring system, and data from 2019 were used for testing. We calculated the products of the number of disease-specific survival probabilities (DSPs) to reflect the severity of the patients with multiple diagnoses. A logistic regression model was developed using DSPs, age, and physiological parameters to develop a more accurate mortality prediction model.

RESULTS

The newly developed model showed predictive ability, as indicated by an area under the receiver-operating characteristic curve of 0.975 (95% CI: 0.974-0.977). When a threshold value of -5.869 was used for determining mortality, the overall accuracy was 0.958 (0.958-0.958).

CONCLUSION

We developed a scoring system based on ICD codes, age, and vital signs to predict the in-hospital mortality of emergency patients, and it achieved good performance. The scoring system would be useful for standardizing the severity of emergency patients and comparing treatment results.

Deep learning‑based prediction of in‑hospital mortality for acute kidney injury

Acute kidney injury (AKI) is a prevalent clinical syndrome that causes over one-fifth of hospitalized patients worldwide to suffer from AKI. We proposed the GCAT, which aims to identify high-risk AKI patients in the hospital settings using the MIMIC-III dataset. Firstly, it fully explores the similarity of attribute features among a large number of patients and calculates the attribute similarity values between patients to generate a node similarity matrix. Then, it selects nodes with high similarity to construct a patient feature similarity network (PFSN). Experiments demonstrate that the GCAT achieves an accuracy of 88.57%, its effectiveness is superior to state-of-the-art methods.

Relationship between atherogenic index of plasma and length of stay in critically ill patients with atherosclerotic cardiovascular disease: a retrospective cohort study and predictive modeling based on machine learning

BACKGROUND

The atherogenic index of plasma (AIP) is considered an important marker of atherosclerosis and cardiovascular risk. However, its potential role in predicting length of stay (LOS), especially in patients with atherosclerotic cardiovascular disease (ASCVD), remains to be explored. We investigated the effect of AIP on hospital LOS in critically ill ASCVD patients and explored the risk factors affecting LOS in conjunction with machine learning.

METHODS

Using data from the Medical Information Mart for Intensive Care (MIMIC)-IV. AIP was calculated as the logarithmic ratio of TG to HDL-C, and patients were stratified into four groups based on AIP values. We investigated the association between AIP and two key clinical outcomes: ICU LOS and total hospital LOS. Multivariate logistic regression models were used to evaluate these associations, while restricted cubic spline (RCS) regressions assessed potential nonlinear relationships. Additionally, machine learning (ML) techniques, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGB), were applied, with the Shapley additive explanation (SHAP) method used to determine feature importance.

RESULTS

The study enrolled a total of 2423 patients with critically ill ASCVD, predominantly male (54.91%), and revealed that higher AIP values were independently associated with longer ICU and hospital stays. Specifically, for each unit increase in AIP, the odds of prolonged ICU and hospital stays were significantly higher, with adjusted odds ratios (OR) of 1.42 (95% CI, 1.11-1.81; P = 0.006) and 1.73 (95% CI, 1.34-2.24; P < 0.001), respectively. The RCS regression demonstrated a linear relationship between increasing AIP and both ICU LOS and hospital LOS. ML models, specifically LGB (ROC:0.740) and LR (ROC:0.832) demonstrated superior predictive accuracy for these endpoints, identifying AIP as a vital component of hospitalization duration.

CONCLUSION

AIP is a significant predictor of ICU and hospital LOS in patients with critically ill ASCVD. AIP could serve as an early prognostic tool for guiding clinical decision-making and managing patient outcomes.

A nomogram for predicting short-term mortality in ICU patients with coexisting chronic obstructive pulmonary disease and congestive heart failure

OBJECTIVE

This study aimed to develop and validate a nomogram for predicting 28-day and 90-day mortality in intensive care unit (ICU) patients who have chronic obstructive pulmonary disease (COPD) coexisting with congestive heart failure (CHF).

METHODS

An extensive analysis was conducted on clinical data from the Medical Information Mart for Intensive Care IV database, covering patients over 18 years old with both COPD and CHF, who were were first-time ICU admissions between 2008 and 2019. The least absolute shrinkage and selection operator (LASSO) regression method was employed to screen clinical features, with the final model being optimized using backward stepwise regression guided by the Akaike Information Criterion (AIC) to construct the nomogram. The predictive model's discrimination and clinical applicability were evaluated via receiver operating characteristic (ROC) curves, calibration curves, the C-index, and decision curve analysi s (DCA).

RESULTS

This analysis was comprised of a total of 1948 patients. Patients were separated into developing and validation cohorts in a 7:3 ratio, with similar baseline characteristics between the two groups. The ICU mortality rates for the developing and verification cohorts were 20.8 % and 19.5 % at 28 days, respectively, and 29.4 % and 28.3 % at 90 days, respectively. The clinical characteristics retained by the backward stepwise regression include age, weight, systolic blood pressure (SBP), respiratory rate (RR), oxygen saturation (SpO2), red blood cell distribution width (RDW), lactate, partial thrombosis time (PTT), race, marital status, type 2 diabetes mellitus (T2DM), malignant cancer, acute kidney failure (AKF), pneumonia, immunosuppressive drugs, antiplatelet agents, vasoactive agents, acute physiology score III (APS III), Oxford acute severity of illness score (OASIS), and Charlson comorbidity index (CCI). We developed two separate models by assigning weighted scores to each independent risk factor: nomogram A excludes CCI but includes age, T2DM, and malignant cancer, while nomogram B includes only CCI, without age, T2DM, and malignant cancer. Based on the results of the AUC and C-index, this study selected nomogram A, which demonstrated better predictive performance, for subsequent validation. The calibration curve, C-index, and DCA results indicate that nomogram A has good accuracy in predicting short-term mortality and demonstrates better discriminative ability than commonly used clinical scoring systems, making it more suitable for clinical application.

CONCLUSION

The nomogram developed in this study offers an effective assessment of short-term mortality risk for ICU patients with COPD and CHF, proving to be a superior tool for predicting their short-term prognosis.

Can We Improve Mortality Prediction in Patients with Sepsis in the Emergency Department?

Background and Objectives: Sepsis represents a global health challenge and requires advanced diagnostic and prognostic approaches due to its elevated rate of morbidity and fatality. Our study aimed to assess the value of a novel set of six biomarkers combined with severity scores in predicting 28 day mortality among patients presenting with sepsis in the Emergency Department (ED). Materials and Methods: This single-center, observational, prospective cohort included sixty-seven consecutive patients with septic shock and sepsis enrolled from November 2020 to December 2022, categorized into survival and non-survival groups based on outcomes. The following were assessed: procalcitonin (PCT), soluble Triggering Receptor Expressed on Myeloid Cells-1 (sTREM-1), the soluble form of the urokinase plasminogen activator receptor (suPAR), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and azurocidin 1 (AZU1), alongside clinical scores such as the Quick Sequential Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS), the Sequential Organ Failure Assessment (SOFA), the Acute Physiology and Chronic Health Evaluation II (APACHE II), the Simplified Acute Physiology Score II and III (SAPS II/III), the National Early Warning Score (NEWS), Mortality in Emergency Department Sepsis (MEDS), the Charlson Comorbidity Index (CCI), and the Glasgow Coma Scale (GCS). The ability of each biomarker and clinical score and their combinations to predict 28 day mortality were evaluated. Results: The overall mortality was 49.25%. Mechanical ventilation was associated with a higher mortality rate. The levels of IL-6 were significantly higher in the non-survival group and had higher AUC values compared to the other biomarkers. The GCS, SOFA, APACHEII, and SAPS II/III showed superior predictive ability. Combining IL-6 with suPAR, AZU1, and clinical scores SOFA, APACHE II, and SAPS II enhanced prediction accuracy compared with individual biomarkers. Conclusion: In our study, IL-6 and SAPS II/III were the most accurate predictors of 28 day mortality for sepsis patients in the ED.

Predictive model of risk factors for 28-day mortality in patients with sepsis or sepsis-associated delirium based on the MIMIC-IV database

Research on the severity and prognosis of sepsis with or without progressive delirium is relatively insufficient. We constructed a prediction model of the risk factors for 28-day mortality in patients who developed sepsis or sepsis-associated delirium. The modeling group of patients diagnosed with Sepsis-3 and patients with progressive delirium of related indicators were selected from the MIMIC-IV database. Relevant independent risk factors were determined and integrated into the prediction model. Receiver operating characteristic (ROC) curves and the Hosmer-Lemeshow (HL) test were used to evaluate the prediction accuracy and goodness-of-fit of the model. Relevant indicators of patients with sepsis or progressive delirium admitted to the intensive care unit (ICU) of a 3A hospital in Xinjiang were collected and included in the verification group for comparative analysis and clinical validation of the prediction model. The total length of stay in the ICU, hemoglobin levels, albumin levels, activated partial thrombin time, and total bilirubin level were the five independent risk factors in constructing a prediction model. The area under the ROC curve of the predictive model (0.904) and the HL test result (χ2 = 8.518) indicate a good fit. This model is valuable for clinical diagnosis and treatment and auxiliary clinical decision-making.

The association between the change in severity score from baseline and the outcomes of critically ill patients was enhanced by integration of bioimpedance analysis parameters

The study of the outcomes of critically ill patients has been a hard stuff in the field of intensive care. To explore the relationship between changes of severity scores, bioelectrical impedance analysis (BIA) and outcomes of critically ill patients, we enrolled patients (n = 206) admitted to intensive care unit (ICU) in Jinling Hospital from 2018 to 2021 with records of BIA on the days 1- and 3- ICU. Collected BIA and clinical data including simplified acute physiology score II (SAPS II) and sequential organ failure assessment. According to the baseline and change of severity scores or phase angle (PA) values, the patients were divided into: G-G, baseline good status, 3rd day unchanged; G-B, baseline good status, 3rd day deteriorated; B-G, baseline bad status, 3rd day improved; and B-B, baseline bad status, 3rd day unchanged. According to PA, the mortality of group G-G was 8.6%, and it was greater than 50% in group B-B for severity scores. The new score combining PA and severity scores established. Multivariate logistic regression analysis revealed that PA-SAPS II score was the only independent factor for 90-day mortality (P < 0.05). A linear correlation was found between mortality and PA-SAPS II score (prediction equation: Y ( % ) = 16.97 × X - 9.67 , R2 = 0.96, P < 0.05).

Construction and validation of a nomogram prediction model for the progression to septic shock in elderly patients with urosepsis

Background

Septic shock is a clinical syndrome characterized by the progression of sepsis to a severe stage. Elderly patients with urosepsis in the intensive care unit (ICU) are more likely to progress to septic shock. This study aimed to establish and validate a nomogram model for predicting the risk of progression to septic shock in elderly patients with urosepsis.

Methods

We extracted data from the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV dataset was split into a training set for model development and an internal validation set to assess model performance. Further external validation was performed using a distinct dataset sourced from the eICU-CRD. Predictors were screened using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses. The evaluation of model performance included discrimination, calibration, and clinical usefulness.

Results

The study demonstrated that the Glasgow Coma Scale (GCS), white blood count (WBC), platelet, blood urea nitrogen (BUN), calcium, albumin, congestive heart failure (CHF), and invasive ventilation were closely associated with septic shock in the training cohort. Nomogram prediction, utilizing eight parameters, demonstrated strong predictive accuracy with area under the curve (AUC) values of 0.809 (95 % CI 0.786-0.834), 0.794 (95 % CI 0.756-0.831), and 0.723 (95 % CI 0.647-0.801) in the training, internal validation, and external validation sets, respectively. Additionally, the nomogram demonstrated a promising calibration performance and significant clinical usefulness in both the training and validation sets.

Conclusion

The constructed nomogram is a reliable and practical tool for predicting the risk of progression to septic shock in elderly patients with urosepsis. Its implementation in clinical practice may enhance the early identification of high-risk patients, facilitate timely and targeted interventions to mitigate the risk of septic shock, and improve patient outcomes.

Development and validation of early prediction models for new-onset functional impairment in patients after being transferred from the ICU

A significant number of intensive care unit (ICU) survivors experience new-onset functional impairments that impede their activities of daily living (ADL). Currently, no effective assessment tools are available to identify these high-risk patients. This study aims to develop an interpretable machine learning (ML) model for predicting the onset of functional impairment in critically ill patients. Data for this study were sourced from a comprehensive hospital in China, focusing on adult patients admitted to the ICU from August 2022 to August 2023 without prior functional impairments. A least absolute shrinkage and selection operator (LASSO) model was utilized to select predictors for inclusion in the model. Four models, logistic regression, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were constructed and validated. Model performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Additionally, the DALEX package was employed to enhance the interpretability of the final models. The study ultimately included 1,380 patients, with 684 (49.6%) exhibiting new-onset functional impairment on the seventh day after leaving the ICU. Among the four models evaluated, the SVM model demonstrated the best performance, with an AUC of 0.909, accuracy of 0.838, sensitivity of 0.902, specificity of 0.772, PPV of 0.802, and NPV of 0.886. ML models are reliable tools for predicting new-onset functional impairments in critically ill patients. Notably, the SVM model emerged as the most effective, enabling early identification of patients at high risk and facilitating the implementation of timely interventions to improve ADL.

Nomogram predictive model for in-hospital mortality risk in elderly ICU patients with urosepsis

BACKGROUND

Urinary tract infection (UTI) is a common cause of sepsis. Elderly patients with urosepsis in intensive care unit (ICU) have more severe conditions and higher mortality rates owing to factors such as advanced age, immunosenescence, and persistent host inflammatory responses. However, comprehensive studies on nomograms to predict the in-hospital mortality risk in elderly patients with urosepsis are lacking. This study aimed to construct a nomogram predictive model to accurately assess the prognosis of elderly patients with urosepsis and provide therapeutic recommendations.

METHODS

Data of elderly patients with urosepsis were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV 2.2 database. Patients were randomly divided into training and validation cohorts. A predictive nomogram model was constructed from the training set using logistic regression analysis, followed by internal validation and sensitivity analysis.

RESULTS

This study included 1,251 patients. LASSO regression analysis revealed that the Glasgow Coma Scale (GCS) score, red cell distribution width (RDW), white blood count (WBC), and invasive ventilation were independent risk factors identified from a total of 43 variables studied. We then created and verified a nomogram. The area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) of the nomogram were superior to those of the traditional SAPS-II, APACHE-II, and SOFA scoring systems. The Hosmer-Lemeshow test results and calibration curves suggested good nomogram calibration. The IDI and NRI values showed that our nomogram scoring tool performed better than the other scoring systems. The DCA curves showed good clinical applicability of the nomogram.

CONCLUSIONS

The nomogram constructed in this study is a convenient tool for accurately predicting in-hospital mortality in elderly patients with urosepsis in ICU. Improving the treatment strategies for factors related to the model could improve the in-hospital survival rates of these patients.

A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department

This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision-recall curve (AUCPR), Hosmer-Lemeshow (H-L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital's ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802-0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787-0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality.

The Physician Surprise Question in the Emergency Department: prospective cohort study

OBJECTIVES

This study aims to test the ability of the surprise question (SQ), when asked to emergency physicians (EPs), to predict in-hospital mortality among adults admitted to an emergency room (ER).

METHODS

This prospective cohort study at an academic medical centre included consecutive patients 18 years or older who received care in the ER and were subsequently admitted to the hospital from 20 April 2018 to 20 October 2018. EPs were required to answer the SQ for all patients who were being admitted to hospital. The primary outcome was in-hospital mortality.

RESULTS

The cohort included 725 adults (mean (SD) age, 60 (17) years, 51% men) from 58 128 emergency department (ED) visits. The mortality rates were 20.6% for 30-day all-cause in-hospital mortality and 23.6% for in-hospital mortality. The diagnostic test characteristics of the SQ have a sensitivity of 53.7% and specificity of 87.1%, and a relative risk of 4.02 (95% CI 3.15 to 5.13), p<0.01). The positive and negative predictive values were 57% and 86%, respectively; the positive likelihood ratio was 4.1 and negative likelihood ratio was 0.53; and the accuracy was 79.2%.

CONCLUSIONS

We found that asking the SQ to EPs may be a useful tool to identify patients in the ED with a high risk of in-hospital mortality.

Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department

Background

The study aimed to compare the prognostic accuracy of six different severity-of-illness scoring systems for predicting in-hospital mortality among patients with confirmed SARS-COV2 who presented to the emergency department (ED). The scoring systems assessed were worthing physiological score (WPS), early warning score (EWS), rapid acute physiology score (RAPS), rapid emergency medicine score (REMS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA).

Materials and methods

A cohort study was conducted using data obtained from electronic medical records of 6,429 confirmed SARS-COV2 patients presenting to the ED. Logistic regression models were fitted on the original severity-of-illness scores to assess the models' performance using the Area Under the Curve for ROC (AUC-ROC) and Precision-Recall curves (AUC-PR), Brier Score (BS), and calibration plots were used to assess the models' performance. Bootstrap samples with multiple imputations were used for internal validation.

Results

The mean age of the patients was 64 years (IQR:50-76) and 57.5% were male. The WPS, REMS, and NEWS models had AUROC of 0.714, 0.705, and 0.701, respectively. The poorest performance was observed in the RAPS model, with an AUROC of 0.601. The BS for the NEWS, qSOFA, EWS, WPS, RAPS, and REMS was 0.18, 0.09, 0.03, 0.14, 0.15, and 0.11 respectively. Excellent calibration was obtained for the NEWS, while the other models had proper calibration.

Conclusion

The WPS, REMS, and NEWS have a fair discriminatory performance and may assist in risk stratification for SARS-COV2 patients presenting to the ED. Generally, underlying diseases and most vital signs are positively associated with mortality and were different between the survivors and non-survivors.

How to cite this article

Rahmatinejad Z, Hoseini B, Reihani H, Hanna AA, Pourmand A, Tabatabaei SM, et al. Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department. Indian J Crit Care Med 2023;27(6):416-425.

Added value of chest CT images to a personalized prognostic model in acute respiratory distress syndrome: a retrospective study

Background

Acute respiratory distress syndrome (ARDS) is a critical disease in the intensive care unit (ICU) with high morbidity and mortality. The accuracy for predicting ARDS patients' outcome with mechanical ventilation is limited, and most based on clinical information.

Methods

The patients diagnosed with ARDS between January 2014 and June 2019 were retrospectively recruited. Radiomics features were extracted from the upper, middle, and lower levels of the lung, and were further analyzed with the primary outcome (28-day mortality after ARDS onset). The univariate and multivariate logistic regression analyses were applied to figure out risk factors. Various predictive models were constructed and compared.

Results

Of 366 ARDS patients recruited in this study, 276 (median age, 64 years [interquartile range, 54-75 years]; 208 male) survive on the Day 28. Among all factors, the APACHE II Score (OR 2.607, 95% CI 1.896-3.584, P < 0.001), the Radiomics_Score of the middle lung (OR 2.230, 95% CI 1.387-3.583, P = 0.01), the Radiomics_Score of the lower lung (OR 1.633, 95% CI 1.143-2.333, P = 0.01) were associated with the 28-day mortality. The clinical_radiomics predictive model (AUC 0.813, 95% CI 0.767-0.850) show the best performance compared with the clinical model (AUC 0.758, 95% CI 0.710-0.802), the radiomics model (AUC 0.692, 95% CI 0.641-0.739) and the various ventilator parameter-based models (highest AUC 0.773, 95% CI 0.726-0.815).

Conclusions

The radiomics features of chest CT images have incremental values in predicting the 28-day mortality in ARDS patients with mechanical ventilation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42058-023-00116-x.

Host Gene Expression to Predict Sepsis Progression

OBJECTIVES

Sepsis causes significant mortality. However, most patients who die of sepsis do not present with severe infection, hampering efforts to deliver early, aggressive therapy. It is also known that the host gene expression response to infection precedes clinical illness. This study seeks to develop transcriptomic models to predict progression to sepsis or shock within 72 hours of hospitalization and to validate previously identified transcriptomic signatures in the prediction of 28-day mortality.

DESIGN

Retrospective differential gene expression analysis and predictive modeling using RNA sequencing data.

PATIENTS

Two hundred seventy-seven patients enrolled at four large academic medical centers; all with clinically adjudicated infection were considered for inclusion in this study.

MEASUREMENTS AND MAIN RESULTS

Sepsis progression was defined as an increase in Sepsis 3 category within 72 hours. Transcriptomic data were generated using RNAseq of whole blood. Least absolute shrinkage and selection operator modeling was used to identify predictive signatures for various measures of disease progression. Four previously identified gene signatures were tested for their ability to predict 28-day mortality. There were no significant differentially expressed genes in 136 subjects with worsened Sepsis 3 category compared with 141 nonprogressor controls. There were 1,178 differentially expressed genes identified when sepsis progression was defined as ICU admission or 28-day mortality. A model based on these genes predicted progression with an area under the curve of 0.71. Validation of previously identified gene signatures to predict sepsis mortality revealed area under the receiver operating characteristic values of 0.70-0.75 and no significant difference between signatures.

CONCLUSIONS

Host gene expression was unable to predict sepsis progression when defined by an increase in Sepsis-3 category, suggesting this definition is not a useful framework for transcriptomic prediction methods. However, there was a differential response when progression was defined as ICU admission or death. Validation of previously described signatures predicted 28-day mortality with insufficient accuracy to offer meaningful clinical utility.

The importance of serum LMAN2 level in septic shock and prognosis prediction in sepsis patients

Objectives

To study the importance of LMAN2 in septic shock and prognosis prediction in sepsis patients.

Methods

Serum LMAN2 was measured by ELISA in 109 sepsis patients within 24 h after their admission to ICU. We also collected clinical and laboratory variables.

Results

Compared with sepsis group (1.21 (1.05) ng/ml), serum LMAN2 level was significantly higher in patients with septic shock (1.75 (2.04) ng/ml) on the day of admission to the ICU (P < 0.001), and serum LMAN2 level were significantly higher in the sepsis non-survival group (1.91 (1.66) ng/ml) than in the survival group (1.15 (1.17) ng/ml). COX regression analysis showed that high serum LMAN2 level (>1.28 ng/ml) was a predictor of 28-day mortality in sepsis patients.

Conclusions

This study shows that high serum LMAN2 level may indicate septic shock and is associated with an unfavorable prognosis for sepsis patients.

Effect of the national early warning score on monitoring the vital signs of patients in the emergency room

OBJECTIVE

To verify the effect of using the National Early Warning Score (NEWS) system on the compliance of the vital signs monitoring interval with those recommended for patients in the emergency room.

METHODS

This is a quasi-experimental, before-and-after study, performed in an emergency room with 280 adult patients selected by convenience. The effect of NEWS on the compliance of the vital signs monitoring interval with those recommended by the system was analyzed by linear regression.

RESULTS

In the Pre-NEWS phase, 143 patients were analyzed (mean age ± standard deviation: 54.4 ± 20.5; male: 56.6%) and, in the Post-NEWS phase, 137 patients (mean age ± standard deviation: 55.5 ± 20.8; male: 50.4%). There was compliance of the vital signs monitoring interval with what is recommended by NEWS in 92.6% of vital signs records after adopting this instrument. This compliance was 9% (p < 0.001) higher in the Post-NEWS phase.

CONCLUSION

The use of the NEWS system increased the compliance of the vital signs monitoring intervals with the ones recommended, but this compliance decreased when the NEWS score pointed to a shorter interval in the monitoring of vital signs.

E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database

Improving the Intensive Care Unit (ICU) management network and building cost-effective and well-managed healthcare systems are high priorities for healthcare units. Creating accurate and explainable mortality prediction models helps identify the most critical risk factors in the patients' survival/death status and early detect the most in-need patients. This study proposes a highly accurate and efficient machine learning model for predicting ICU mortality status upon discharge using the information available during the first 24 hours of admission. The most important features in mortality prediction are identified, and the effects of changing each feature on the prediction are studied. We used supervised machine learning models and illness severity scoring systems to benchmark the mortality prediction. We also implemented a combination of SHAP, LIME, partial dependence, and individual conditional expectation plots to explain the predictions made by the best-performing model (CatBoost). We proposed E-CatBoost, an optimized and efficient patient mortality prediction model, which can accurately predict the patients' discharge status using only ten input features. We used eICU-CRD v2.0 to train and validate the models; the dataset contains information on over 200,000 ICU admissions. The patients were divided into twelve disease groups, and models were fitted and tuned for each group. The models' predictive performance was evaluated using the area under a receiver operating curve (AUROC). The AUROC scores were 0.86 [std:0.02] to 0.92 [std:0.02] for CatBoost and 0.83 [std:0.02] to 0.91 [std:0.03] for E-CatBoost models across the defined disease groups; if measured over the entire patient population, their AUROC scores were 7 to 18 and 2 to 12 percent higher than the baseline models, respectively. Based on SHAP explanations, we found age, heart rate, respiratory rate, blood urine nitrogen, and creatinine level as the most critical cross-disease features in mortality predictions.

Internal Validation of the Predictive Performance of Models Based on Three ED and ICU Scoring Systems to Predict Inhospital Mortality for Intensive Care Patients Referred from the Emergency Department

Background.A variety of scoring systems have been introduced for use in both the emergency department (ED) such as WPS, REMS, and MEWS and the intensive care unit (ICU) such as APACHE II, SAPS II, and SOFA for risk stratification and mortality prediction. However, the performance of these models in the ICU remains unclear and we aimed to evaluate and compare their performance in the ICU. Methods. This multicenter retrospective cohort study was conducted on severely ill patients admitted to the ICU directly from the ED in seven tertiary hospitals in Iran from August 2018 to August 2020. We evaluated all models in terms of discrimination (AUROC), the balance between positive predictive value and sensitivity (AUPRC), calibration (Hosmer-Lemeshow test and calibration plots), and overall performance using the Brier score (BS). The endpoint was considered inhospital mortality. Results. Among the 3,455 patients included in the study, 54.4% of individuals were male ( $N=\mathrm{1,879}$ ) and 26.5% deceased ( $N=916$ ). The BS for the WPS, REMS, MEWS, APACHE II, SAPS II, and SOFA were 0.178, 0.165, 0.183, 0.157, 0.170, and 0.182, respectively. The AUROC of these models were 0.728 (0.71-0.75), 0.761 (0.74-0.78), 0.682 (0.66-0.70), 0.810 (0.79-0.83), 0.767 (0.75-0.79), and 0.785 (0.77-0.80), respectively. The AUPRC was 0.517 (0.50-0.53) for WPS, 0.547 (0.53-0.56) for REMS, 0.445 (0.42-0.46) for MEWS, 0.630 (0.61-0.65) for APACHE II, 0.559 (0.54-0.58) for SAPS II, and 0.564 (0.54-0.57) for SOFA. All models except the MEWS and SOFA had good calibration. The most accurate model belonged to APACHE II with lowest BS. Conclusion. The APACHE II outperformed all the ED and ICU models and was found to be the most appropriate model in predicting inhospital mortality of patients in the ICU in terms of discrimination, calibration, and accuracy of predicted probability. Except for MEWS, the rest of the models had fair discrimination and partially good calibration. Interestingly, although the REMS is less complicated than the SAPS II, both models exhibited similar performance. Clinicians can utilize the REMS as part of a larger clinical assessment to manage patients more effectively.

Internal validation and evaluation of the predictive performance of models based on the PRISM-3 (Pediatric Risk of Mortality) and PIM-3 (Pediatric Index of Mortality) scoring systems for predicting mortality in Pediatric Intensive Care Units (PICUs)

PURPOSE

The study was aimed to assess the prognostic power The Pediatric Risk of Mortality-3 (PRISM-3) and the Pediatric Index of Mortality-3 (PIM-3) to predict in-hospital mortality in a sample of patients admitted to the PICUs.

DESIGN AND METHODS

The study was performed to include all children younger than 18 years of age admitted to receive critical care in two hospitals, Mashhad, northeast of Iran from December 2017 to November 2018. The predictive performance was quantified in terms of the overall performance by measuring the Brier Score (BS) and standardized mortality ratio (SMR), discrimination by assessing the AUC, and calibration by applying the Hosmer-Lemeshow test.

RESULTS

A total of 2446 patients with the median age of 4.2 months (56% male) were included in the study. The PICU and in-hospital mortality were 12.4 and 16.14%, respectively. The BS of the PRISM-3 and PIM-3 was 0.088 and 0.093 for PICU mortality and 0.108 and 0.113 for in-hospital mortality. For the entire sample, the SMR of the PRISM-3 and PIM-3 were 1.34 and 1.37 for PICU mortality and 1.73 and 1.78 for in-hospital mortality, respectively. The PRISM-3 demonstrated significantly higher discrimination power in comparison with the PIM-3 (AUC = 0.829 vs 0.745) for in-hospital mortality. (AUC = 0.779 vs 0.739) for in-hospital mortality. The HL test revealed poor calibration for both models in both outcomes.

CONCLUSIONS

The performance measures of PRISM-3 were better than PIM-3 in both PICU and in-hospital mortality. However, further recalibration and modification studies are required to improve the predictive power to a clinically acceptable level before daily clinical use.

PRACTICE IMPLICATIONS

The calibration of the PRISM-3 model is more satisfactory than PIM-3, however both models have fair discrimination power.

Association between the Predicted Value of APACHE IV Scores and Intensive Care Unit Mortality: A Secondary Analysis Based on EICU Dataset

Objective

The evidence regarding the relationship between Acute Physiological and Chronic Health Assessment (APACHE) IV scores and emergency intensive care unit (EICU) mortality in patients following organ transplantation remains controversial. The purpose of this study was to investigate the relationship between APACHE IV score and EICU mortality.

Methods

Data from 391 American men and women admitted to the EICU after undergoing organ transplants including heart, bone marrow, liver, kidney, lung, and pancreas in the United States. We used this data to analyze the relationship between APACHE IV scores and in-hospital mortality in the postoperative EICU. The primary endpoint was ICU hospitalization mortality after organ transplantation. The entire study data was extracted from the EICU database and uploaded to the DataDryad website.

Results

Interaction tests indicate age, respiratory failure, and hormone use can modify the association between APACHE IV and EICU mortality. A stronger association of APACHE and mortality can be observed at <60 years old, no respiratory failure, and no use of hormones. In contrast, there was no association between respiratory failure, hormone use, APACHE, and ICU mortality in patients over 60 years of age.

Conclusion

When using the APACHE score for risk stratification of critically ill patients after transplantation, the patient's age, respiratory failure, and use of hormones should be taken into account.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Efeito do national early warning score no monitoramento dos sinais vitais de pacientes no pronto-socorro

RESUMO Objetivo: Verificar o efeito do uso do sistema National Early Warning Score (NEWS) na conformidade do intervalo de monitoramento dos sinais vitais com o recomendado em pacientes no pronto-socorro. Método: Estudo quasi-experimental, do tipo antes e depois, realizado em um pronto-socorro com 280 pacientes adultos selecionados por conveniência. O efeito do NEWS na conformidade do intervalo de monitoramento dos sinais vitais com o recomendado pelo sistema foi analisado por regressão linear. Resultados: Na fase Pré-NEWS, foram analisados 143 pacientes (idade média ± desvio-padrão: 54,4 ± 20,5; sexo masculino: 56,6%) e, na fase Pós-NEWS, 137 pacientes (idade média ± desvio-padrão: 55,5 ± 20,8; sexo masculino: 50,4%). Houve conformidade do intervalo de monitoramento dos sinais vitais com o recomendo pelo NEWS em 92,6% dos registros de sinais vitais após adoção desse instrumento. Essa conformidade foi maior na fase Pós-NEWS em 9% (p < 0,001). Conclusão: O uso do sistema NEWS aumentou a conformidade dos intervalos de monitorização dos sinais vitais com o recomendado, porém essa conformidade diminuiu quando o escore NEWS apontou para intervalo menor no monitoramento dos sinais vitais.

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.

Internal validation and comparison of the prognostic performance of models based on six emergency scoring systems to predict in-hospital mortality in the emergency department

Background

Medical scoring systems are potentially useful to make optimal use of available resources. A variety of models have been developed for illness measurement and stratification of patients in Emergency Departments (EDs). This study was aimed to compare the predictive performance of the following six scoring systems: Simple Clinical Score (SCS), Worthing physiological Score (WPS), Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), Modified Early Warning Score (MEWS), and Routine Laboratory Data (RLD) to predict in-hospital mortality.

Methods

A prospective single-center observational study was conducted from March 2016 to March 2017 in Edalatian ED in Emam Reza Hospital, located in the northeast of Iran. All variables needed to calculate the models were recorded at the time of admission and logistic regression was used to develop the models’ prediction probabilities. The Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR), Brier Score (BS), and calibration plots were used to assess the models’ performance. Internal validation was obtained by 1000 bootstrap samples. Pairwise comparison of AUC-ROC was based on the DeLong test.

Results

A total of 2205 patients participated in this study with a mean age of 61.8 ± 18.5 years. About 19% of the patients died in the hospital. Approximately 53% of the participants were male. The discrimination ability of SCS, WPS, RAPS, REMS, MEWS, and RLD methods were 0.714, 0.727, 0.661, 0.678, 0.698, and 0.656, respectively. Additionally, the AUC-PR of SCS, WPS, RAPS, REMS, EWS, and RLD were 0.39, 0.42, 0.35, 0.34, 0.36, and 0.33 respectively. Moreover, BS was 0.1459 for SCS, 0.1713 for WPS, 0.0908 for RAPS, 0.1044 for REMS, 0.1158 for MEWS, and 0.073 for RLD. Results of pairwise comparison which was performed for all models revealed that there was no significant difference between the SCS and WPS. The calibration plots demonstrated a relatively good concordance between the actual and predicted probability of non-survival for the SCS and WPS models.

Conclusion

Both SCS and WPS demonstrated fair discrimination and good calibration, which were superior to the other models. Further recalibration is however still required to improve the predictive performance of all available models and their use in clinical practice is still unwarranted.

Effects of Emergency Care-related Health Policies during the COVID-19 Pandemic in Korea: a Quasi-Experimental Study

BACKGROUND

The purpose of this study was to review the nationwide emergency care-related health policies during the coronavirus disease 2019 (COVID-19) pandemic disaster in Korea and to analyze the effects of the policies on the safety of patients who visit emergency departments (EDs) during this period.

METHODS

This study is a quasi-experiment study. The study population was patients who visited all 402 EDs in Korea between December 31, 2019 and May 13, 2020, using the National Emergency Department Information System (NEDIS) database. The study period was classified into 5 phases according to the level of national crisis warning of infectious disease and the implementation of emergency care-related health policies, and all study phases were 27 days. The primary outcome was in-hospital mortality, and the secondary outcome was length of stay (LOS) in the ED during the COVID-19 outbreak.

RESULTS

The number of ED visits during the study period was 2,636,341, and the in-hospital mortality rate was 1.4%. The number of ED visits decreased from 803,160 in phase 1 to 496,619 in phase 5 during the study period. For in-hospital mortality, the adjusted odds ratio (OR) (95% confidence interval) was 0.77 (0.74-0.79) in phase 5 compared to phase 3. Additionally, by subgroup, the ORs were 0.69 (0.57-0.83) for the patients with acute myocardial infarction and 0.76 (0.67-0.87) for severe trauma in phase 5 compared to phase 3. The ED LOS increased while the number of ED visits decreased as the COVID-19 pandemic progressed, and the ED LOS declined after policy implementation (beta coefficient: -5.3 [-6.5 to -4.2] minutes in phase 5 compared to phase 3).

CONCLUSION

Implementing appropriate emergency care policies in the COVID-19 pandemic would have contributed to improving the safety of all emergency patients and reducing in-hospital mortality by preventing excessive deaths.

Interaction Effects between COVID-19 Outbreak and Community Income Levels on Excess Mortality among Patients Visiting Emergency Departments

BACKGROUND

The objective of this study was to examine the effect of the coronavirus disease 2019 (COVID-19) outbreak on excess in-hospital mortality among patients who visited emergency departments (EDs) and to assess whether the excess mortality during the COVID-19 pandemic varies by community income level.

METHODS

This is a cross-sectional study using the National Emergency Department Information System (NEDIS) database in Korea. The study population was defined as patients who visited all 402 EDs with medical conditions other than injuries between January 27 and May 31, 2020 (after-COVID) and for the corresponding time period in 2019 (before-COVID). The primary outcome was in-hospital mortality. The main exposure was the COVID-19 outbreak, and the interaction variable was county per capita income tax. We calculated the risk-adjusted in-hospital mortality rates by COVID-19 outbreak, as well as the difference-in-difference of risk-adjusted rates between the before-COVID and after-COVID groups according to the county income tax using a multilevel linear regression model with the interaction term.

RESULTS

A total of 11,662,167 patients (6,765,717 in before-COVID and 4,896,450 in after-COVID) were included in the study with a 1.6% crude in-hospital mortality rate. The risk-adjusted mortality rate in the after-COVID group was higher than that in the before-COVID group (1.82% vs. 1.50%, difference: 0.31% [0.30 to 0.33]; adjusted odds ratio: 1.22 [1.18 to 1.25]). The excess in-hospital mortality rate of the after-COVID in the lowest quartile group of county income tax was significantly higher than that in the highest quartile group (difference-in-difference: 0.18% (0.14 to 0.23); P-for-interaction: < 0.01).

CONCLUSION

During the COVID-19 pandemic, there was excess in-hospital mortality among patients who visited EDs, and there were disparities in excess mortality depending on community socioeconomic positions.

Role of qSOFA and SOFA Scoring Systems for Predicting In-Hospital Risk of Deterioration in the Emergency Department

The objective of this study was to analyze and compare the usefulness of quick sequential organ failure assessment score (qSOFA) and sequential organ failure assessment (SOFA) scores for the detection of early (two-day) mortality in patients transported by emergency medical services (EMSs) to the emergency department (ED) (infectious and non-infectious). We performed a multicentric, prospective and blinded end-point study in adults transported with high priority by ambulance from the scene to the ED with the participation of five hospitals. For each score, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated. We included 870 patients in the final cohort. The median age was 70 years (IQR 54–81 years), and 338 (38.8%) of the participants were women. Two-day mortality was 8.3% (73 cases), and 20.9% of cases were of an infectious pathology. For two-day mortality, the qSOFA presented an AUC of 0.812 (95% CI: 0.75–0.87; p < 0.001) globally with a sensitivity of 84.9 (95% CI: 75.0–91.4) and a specificity of 69.4 (95% CI: 66.1–72.5), and a SOFA of 0.909 (95% CI: 0.86–0.95; p < 0.001) with sensitivity of 87.7 (95% CI: 78.2–93.4) and specificity of 80.7 (95% CI: 77.4–83.3). The qSOFA score can serve as a simple initial assessment to detect high-risk patients, and the SOFA score can be used as an advanced tool to confirm organ dysfunction.