Is APACHE II a useful tool for clinical research?

Sudden Decompensation of Patients Admitted to Non-ICU Settings Within 24 h of Emergency Department Admission

BACKGROUND

Patients admitted to the hospital floor (non-intensive care (ICU) settings) from the emergency department (ED) are generally stable. Unfortunately, some will unexpectedly decompensate rapidly. This study explores these patients and their characteristics.

METHODS

This retrospective, observational study examined patients admitted to non-ICU settings at a community hospital. Patients were identified by rapid response team (RRT) activation, triggered by acute decompensation. ED chief complaint, reason for activation, and vital signs were compared between patients transferred to a higher level of care versus those who were not.

RESULTS

Throughout 2019, 424 episodes of acute decompensation were identified, 118 occurring within 24 h of admission. A higher rate of ICU transfers was seen in patients with initial ED chief complaints of general malaise (87.5% vs 12.5%, p  =  0.023) and dyspnea (70.6% vs 29.4%, p  =  0.050). Patients with sudden decompensation were more likely to need ICU transfer if the RRT reason was respiratory issues (47% vs 24%, p  =  0.010) or hypertension (9.1% vs 0%, p  =  0.019). Patients with syncope as a reason for decompensation were less likely to need transfer (0% vs 10.3%, p  =  0.014). Patients requiring ICU transfer were significantly older (74.4 vs 71.8 years, p  =  0.016). No differences in admission vital signs, APACHE score, or qSOFA score were found.

CONCLUSIONS

Patients admitted to the floor with chief complaint of general malaise or dyspnea should be considered at higher risk of having a sudden decompensation requiring transfer to a higher level of care. Therefore, greater attention should be taken with disposition of these patients at the time of admission.

Association of APACHE-II Scores With 30-Day Mortality After Tracheostomy: A Retrospective Study

OBJECTIVE

The objective of this study was to assess whether the Acute Physiology, Age, Chronic Health Evaluation II (APACHE-II) score is a reliable predictor of 30-day mortality in the setting of adult patients with ventilator-dependent respiratory failure (VDRF) who undergo tracheostomy.

METHODS

This is a retrospective, single-institution study. Potential subjects were identified using the current procedural terminology codes for the tracheostomy procedure and International Classification of Diseases, 10th Revision, codes for VDRF. APACHE-II scores were retrospectively calculated. Tracheostomies were performed in our population over an 18-month period (November 2018 through April 2020). Our study population did not include patients with novel coronavirus. The primary outcome was mortality at 30 days after tracheostomy.

RESULTS

A total of 238 patients with VDRF who had a tracheostomy were included in this study. Twenty-eight (11.8%) patients died within 30 days of tracheostomy. The mean (standard deviation) APACHE-II score was 22.5 (10.2) for patients who died within 30 days of tracheostomy and 19.8 (7.4) for patients living within 30 days of tracheostomy (p = 0.30). Patients with APACHE-II scores greater than or equal to 30 showed higher odds of death within 30 days of tracheostomy (odds ratio, 3.0; 95% CI, 1.14-7.89, p = 0.03).

CONCLUSION

An APACHE-II score of 30 and above is associated with mortality within 30 days of tracheostomy in patients with VDRF. APACHE-II scores may be a promising tool for assessing risk of mortality in patients with VDRF after tracheostomy.

LEVEL OF EVIDENCE

3 Laryngoscope, 133:273-278, 2023.

Development and validation of an online nomogram for predicting the outcome of open tracheotomy decannulation: a two-center retrospective analysis

BACKGROUND

Tracheotomy decannulation is critical for patients in the intensive care unit (ICU) to recover. In this study, we developed and validated an intuitive nomogram to predict the success rate of tracheotomy decannulation.

METHODS

We collected the data of 627 ICU patients before open tracheotomy decannulation from two medical institutions, including 466 patients (135 success and 331 failure) from the First Affiliated Hospital of Anhui Medical University as a training cohort, and 161 patients (57 success and 104 failure) from the Second Affiliated Hospital of Anhui Medical University as an external validation cohort. A least absolute shrinkage and multivariate logistic regression analysis were performed to determine the independent risk factors and construct the nomogram. The area under the receiver operating characteristic curve (AUC) was used to assess discrimination and the calibration plots were used to assess consistency. The clinical application was assessed using decision curve analysis and the clinical impact curve.

RESULTS

7 independent risk factors were eventually included in the prediction model. The AUC of the training cohort, internal validation and external validation were 0.932, 0.926, and 0.915, showing good discrimination. The model performed well in terms of calibration, decision curve analysis, and clinical impact curves. The superior performance of the model was also confirmed by external validation.

CONCLUSION

This nomogram can help ICU physicians identify high-risk patients for decannulation and plan their pre-decannulation treatment accordingly.

Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction

Cardiovascular disease is currently one of the most important diseases causing death in China and the world, and acute myocardial infarction is a major cause of cardiovascular disease. This study provides an analytical technique for predicting the prognosis of patients with severe acute myocardial infarction using a support vector machine (SVM) technique based on information gleaned from electronic medical records in the Medical Information Marketplace for Intensive Care (MIMIC)-III database. The MIMIC-III database provided 4785 electronic medical records data for inclusion in the model development after screening 7070 electronic medical records of patients admitted to the intensive care unit for treatment of acute myocardial infarction. Adopting the APS-III score as the criterion for identifying anticipated risk, the dimensions of data information incorporated into the mathematical model design were found using correlation coefficient matrix heatmaps and ordered logistic analysis. An automated prognostic risk-prediction model was developed using SVM, and the fit was evaluated by 5× cross-validation. We used a grid search method to further optimize the parameters and improve the model fit. The excellent generalization ability of SVM was fully verified by calculating the 95% confidence interval of the area under the receiver operating characteristic curve (AUC) for six algorithms (linear discriminant, tree, Kernel Naive Bayes, RUSBoost, KNN, and SVM). Compared to the remaining five models, its confidence interval was the narrowest with higher fitting accuracy and better performance. The patient prognostic risk prediction model constructed using SVM had a relatively impressive accuracy (92.2%) and AUC value (0.98). In this study, a model was designed for fitting that can maximize the potential information to be gleaned in the electronic medical records data. It was demonstrated that SVM models based on electronic medical records data can offer an effective solution for clinical disease prognostic risk assessment and improved clinical outcomes and have great potential for clinical application in the clinical treatment of myocardial infarction.

Good tolerance and benefits should make early exercises a routine in patients with acute brain injury

Abstract Introduction: The negative impact of prolonged immobilization results a physical decline during hospitalization in patients with acute brain injury. Objective: To investigate the benefits of early exercises on the mobility of patients with acute brain injury assisted at an Intensive Care Unit (ICU). Methods: This is a prospective, single-blind, controlled clinical trial. A total of 303 patients were assessed. Due to eligibility criteria, exercise protocol was applied in 58 participants, 32 with brain injury caused by traumatic event and 26 with brain injury caused by cerebrovascular event. Exercise began 24 hours after patients’ admission at the ICU. Participants were submitted to passive and active mobilization protocols, performed according to level of sedation, consciousness and collaboration. Statistical analysis was conducted with repeated measures analysis of variance. Significance was set at 5%. Results: The group of patients with traumatic brain injuries was younger (p = 0.001) and with more men (p = 0.025) than the group of patients with clinical events. Most exercise sessions were performed in sedated patients. By the end of the protocol, participants with traumatic and clinical brain injury were able to do sitting and standing exercises. Both groups were similar on ICU discharge (p = 0.290). The clinical group presented better improvement on level of consciousness than the traumatic group (p = 0.005). Conclusion: Participants with an acute brain injury presented at the time of discharge from the ICU good mobility and improvement in the level of consciousness.

The Applicability of Commonly Used Severity of Illness Scores to Tropical Infections in Australia

Many patients with leptospirosis, melioidosis, and rickettsial infection require intensive care unit (ICU) admission in tropical Australia every year. The multi-organ dysfunction associated with these infections results in significantly elevated severity of illness (SOI) scores. However, the accuracy of these SOI scores in predicting death from these tropical infections is incompletely defined. This retrospective study was performed at Cairns Hospital, a tertiary-referral hospital in tropical Australia. All patients admitted to ICU with laboratory-confirmed leptospirosis, melioidosis, and rickettsial disease between January 1, 1999 and June 30, 2020, were eligible for the study. The ability of Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE III, Simplified Acute Physiology Scores (SAPS) II, and Sequential Organ Failure Assessment (SOFA) scores to predict death before ICU discharge was evaluated. Overall, 18 (12.1%) of the 149 included patients died: 15/74 (20.3%) with melioidosis, 2/54 (3.7%) with leptospirosis and 1/21 (4.8%) with rickettsial disease. However, the APACHE II, APACHE III, SAPS II, and SOFA scores significantly overestimated the case-fatality rate of all the infections; the disparity between the predicted and observed mortality was most marked in the cases of leptospirosis and rickettsial disease. Commonly used SOI scores significantly overestimate the case-fatality rate of melioidosis, leptospirosis, and rickettsial infections in Australian ICU patients. This may be at least partly explained by the unique pathophysiology of these infections, particularly leptospirosis and rickettsial disease. However, SOI scores may still be useful in facilitating the comparison of disease severity in clinical trials that examine patients with these pathogens.

Improvement of APACHE II score system for disease severity based on XGBoost algorithm

BACKGROUND

Prognostication is an essential tool for risk adjustment and decision making in the intensive care units (ICUs). In order to improve patient outcomes, we have been trying to develop a more effective model than Acute Physiology and Chronic Health Evaluation (APACHE) II to measure the severity of the patients in ICUs. The aim of the present study was to provide a mortality prediction model for ICUs patients, and to assess its performance relative to prediction based on the APACHE II scoring system.

METHODS

We used the Medical Information Mart for Intensive Care version III (MIMIC-III) database to build our model. After comparing the APACHE II with 6 typical machine learning (ML) methods, the best performing model was screened for external validation on anther independent dataset. Performance measures were calculated using cross-validation to avoid making biased assessments. The primary outcome was hospital mortality. Finally, we used TreeSHAP algorithm to explain the variable relationships in the extreme gradient boosting algorithm (XGBoost) model.

RESULTS

We picked out 14 variables with 24,777 cases to form our basic data set. When the variables were the same as those contained in the APACHE II, the accuracy of XGBoost (accuracy: 0.858) was higher than that of APACHE II (accuracy: 0.742) and other algorithms. In addition, it exhibited better calibration properties than other methods, the result in the area under the ROC curve (AUC: 0.76). we then expand the variable set by adding five new variables to improve the performance of our model. The accuracy, precision, recall, F1, and AUC of the XGBoost model increased, and were still higher than other models (0.866, 0.853, 0.870, 0.845, and 0.81, respectively). On the external validation dataset, the AUC was 0.79 and calibration properties were good.

CONCLUSIONS

As compared to conventional severity scores APACHE II, our XGBoost proposal offers improved performance for predicting hospital mortality in ICUs patients. Furthermore, the TreeSHAP can help to enhance the understanding of our model by providing detailed insights into the impact of different features on the disease risk. In sum, our model could help clinicians determine prognosis and improve patient outcomes.

Einsatz eines ECLS bei Patienten im kardiogenen und septischen Schock: Untersuchung zur Indikation und zum Outcome

Einleitung Das Klinikum Weiden ist das größte Klinikum der nördlichen Oberpfalz (Einzugsgebiet 250 000 Einwohnern auf 5300 km2) und Primärversorger für Patienten im Schockgeschehen (WHIN: Weidener Herzinfarktnetz). Es werden 2 Herzkatheterlabore (24/7-Bereitschaft) und 1 Extrakorporales Life-Support System (ECMO Cardiohelp, Maquet) vorgehalten. Das Ziel dieser retrospektiven Studie war es, Indikation und Outcome nach ECLS-Implantation zu analysieren.

Methoden Im Zeitraum vom 01.01.2008 bis zum 31.12.2017 wurde im Klinikum Weiden an 91 Patienten (68 ♂, 23 ♀; 64 ± 13 Jahren) ein ECLS implantiert. 64% des Gesamt-Patientenkollektivs wurden notfallmäßig vorstellig, die restlichen Patienten erhielten eine ECMO supportiv aufgrund einer High-Risk PTCA. 37 Patienten wurden vor Systemimplantation reanimiert, 17 mit einem mechanischen Thoraxkompressionsgerät (LUCAS). Die folgenden Scoring-Systeme wurden verwendet, um die Schwere des Schocks zu bewerten: APACHE II, SOFA und SAPS II.

Ergebnisse Das Überleben (30 d/12 m) nach Systemexplantation betrug bei VA-ECMO 59% bzw. 49% und bei VV-ECMO 70% bzw. 70%. Die Mortalität war abhängig von der Anzahl der applizierten Katecholamine (KA), 45 (49%) Patienten erhielten mehrere KA (1-Jahres-Überleben: ohne KA 89%; 1 KA 55%; 2 KA 31%; 3 KA 30%). Weitere Einflussfaktoren auf die Mortalität waren eine Sepsis und eine Herz-Lungen-Wiederbelebung (CPR) vor Systemimplantation – die Länge der Reanimation, kombiniert externe und interne Reanimation und LUCAS-CPR verschlechterten das Outcome.

Diskussion Bei Patienten im Schockgeschehen, die nach medikamentöser Maximaltherapie weiterhin hämodynamisch und/oder respiratorisch instabil bleiben, kann durch die Implantation eines ECLS das Schockgeschehen durchbrochen werden. Ein primär versorgendes Klinikum kann mit ECMO eine Therapieoption mit vertretbaren Risiken und nachweislichem Nutzen – wenigstens in kleiner Fallzahl belegt – anbieten und Patienten können davon profitieren. So kann es für Landkreise mit größerer Fahrzeit zu einem Klinikum der Maximalversorgung eine in Teilen maximalmedizinische Therapieoption bieten.

Clinical Characteristics and Risk Factors for Critically Ill Patients with Carbapenem-Resistant Klebsiella pneumonia e (CrKP): A Cohort Study from Developing Country

BACKGROUND

Increasing evidence indicates carbapenem-resistant Klebsiella pneumoniae (CrKP) is increasingly prevalent in intensive care unit (ICU), but its clinical characteristics and risk factors remain unknown.

AIM

The aim of the present study was to evaluate clinical characteristics, risk factors in critically ill patients with CrKP infection.

METHODS

A retrospective study was included in patients from January 2013 to October 2019. Clinical data were collected from CrKP patients on the day of specimen collection admitted to ICU. Multivariable logistic regression was used for risk factors. Receiver operating curve (ROC) and the area under the curve (AUC) with DeLong method of MedCalc software were used. Two-way repeated-measures ANOVA analysis was used to analyze the characteristics of independent risk factors over time.

FINDINGS

A total of 147 adult patients with CrKP were screened, among them, 89 (median age 64.0 years, 66 (74.15%) males) patients with CrKP were finally included, of which 38 patients (42.7%) were non-survival group. Multivariate logistic regression analysis indicated that lactic acid (OR3.04 95% CI 1.38-6.68, P = 0.006), APACHE II score (OR 1.20, 95% CI 1.09-1.33, P < 0.001), tigecycline combined with fosfomycin treatment (OR0.15, 95% CI 0.04-0.65, P = 0.011) are independent risk factors for 28-day mortality in patients with CRKP infection (P<0.05). Combined lactic acid with APACHE II score could predict 28-day mortality, of which AUC value was 0.916 (95% CI, 0.847-0.985), with sensitivity 0.76 and specificity 0.98. ANOVA analysis showed that APACHE II score and lactic acid between the two groups at three-time points were statistically significant, which interactive with time and showed an upward and downward trend with time (P < 0.05).

CONCLUSION

Therapeutic strategy based on improving lactic acid and APACHE II would contribute to the outcome in patients with CrKP infection. Tigecycline combined with fosfomycin could reduce the 28-day mortality in patients with CrKP infection in developing country.

Serum iron levels are an independent predictor of in-hospital mortality of critically ill patients: a retrospective, single-institution study

Objective

This study aimed to examine the relationship between serum iron levels and in-hospital mortality in critically ill patients.

Methods

We retrospectively studied 250 critically ill patients who received treatment at the intensive care unit between June 2015 and May 2017. Blood chemistry and hepatic and renal function were measured. Kaplan–Meier survival curves were plotted according to serum iron levels. Correlations between serum iron levels and other variables were analyzed.

Results

A total of 165 (66.0%) patients had abnormally low serum iron levels (<10.6 μmol/L). Patients who died during hospitalization had markedly higher Acute Physiology and Chronic Health Evaluation II scores and significantly lower serum iron levels compared with those who survived. Cumulative survival was significantly lower in patients with low serum iron levels than in those with normal serum iron levels in subgroup analysis of older patients (n = 192). Multivariate regression analysis showed that, after adjusting for relevant factors, low serum iron levels remained an independent risk for in-hospital mortality (odds ratio 2.014; 95% confidence interval 1.089, 3.725).

Conclusions

Low serum iron levels are present in a significant proportion of critically ill patients and are associated with higher in-hospital mortality, particularly in older patients.