Prognosis of patients in a medical intensive care unit

Iron and ferritin effects on intensive care unit mortality: A meta-analysis

BACKGROUND

The effect of serum iron or ferritin parameters on mortality among critically ill patients is not well characterized.

AIM

To determine the association between serum iron or ferritin parameters and mortality among critically ill patients.

METHODS

Web of Science, Embase, PubMed, and Cochrane Library databases were searched for studies on serum iron or ferritin parameters and mortality among critically ill patients. Two reviewers independently assessed, selected, and abstracted data from studies reporting on serum iron or ferritin parameters and mortality among critically ill patients. Data on serum iron or ferritin levels, mortality, and demographics were extracted.

RESULTS

Nineteen studies comprising 125490 patients were eligible for inclusion. We observed a slight negative effect of serum ferritin on mortality in the United States population [relative risk (RR) 1.002; 95%CI: 1.002-1.004). In patients with sepsis, serum iron had a significant negative effect on mortality (RR = 1.567; 95%CI: 1.208-1.925).

CONCLUSION

This systematic review presents evidence of a negative correlation between serum iron levels and mortality among patients with sepsis. Furthermore, it reveals a minor yet adverse impact of serum ferritin on mortality among the United States population.

Characteristics and outcomes of patients with do-not-resuscitate and physician orders for life-sustaining treatment in a medical intensive care unit: a retrospective cohort study

BACKGROUND

In the intensive care unit (ICU), we may encounter patients who have completed a Do-Not-Resuscitate (DNR) or a Physician Orders to Stop Life-Sustaining Treatment (POLST) document. However, the characteristics of ICU patients who choose DNR/POLST are not well understood.

METHODS

We retrospectively analyzed the electronic medical records of 577 patients admitted to a medical ICU from October 2019 to November 2020, focusing on the characteristics of patients according to whether they completed DNR/POLST documents. Patients were categorized into DNR/POLST group and no DNR/POLST group according to whether they completed DNR/POLST documents, and logistic regression analysis was used to evaluate factors influencing DNR/POLST document completion.

RESULTS

A total of 577 patients were admitted to the ICU. Of these, 211 patients (36.6%) had DNR or POLST records. DNR and/or POLST were completed prior to ICU admission in 48 (22.7%) patients. The DNR/POLST group was older (72.9 ± 13.5 vs. 67.6 ± 13.8 years, p < 0.001) and had higher Acute Physiology and Chronic Health Evaluation (APACHE) II score (26.1 ± 9.2 vs. 20.3 ± 7.7, p < 0.001) and clinical frailty scale (5.1 ± 1.4 vs. 4.4 ± 1.4, p < 0.001) than the other groups. Solid tumors, hematologic malignancies, and chronic lung disease were the most common comorbidities in the DNR/POLST groups. The DNR/POLST group had higher ICU and in-hospital mortality and more invasive treatments (arterial line, central line, renal replacement therapy, invasive mechanical ventilation) than the other groups. Body mass index, APAHCE II score, hematologic malignancy, DNR/POLST were factors associated with in-hospital mortality.

CONCLUSIONS

Among ICU patients, 36.6% had DNR or POLST orders and received more invasive treatments. This is contrary to the common belief that DNR/POLST patients would receive less invasive treatment and underscores the need to better understand and include end-of-life care as an important ongoing aspect of patient care, along with communication with patients and families.

Hospital readiness for the provision of care to critically ill patients in Tanzania- an in-depth cross-sectional study

BACKGROUND

Critical illness is a state of ill health with vital organ dysfunction, a high risk of imminent death if care is not provided and potential for reversibility. The burden of critical illness is high, especially in low- and middle-income countries. Critical care can be provided as Essential Emergency and Critical Care (EECC)- the effective, low-cost, basic care that all critically ill patients should receive in all parts of all hospitals in the world- and advanced critical care- complex, resource-intensive care usually provided in an intensive care unit. The required resources may be available in the hospital and yet not be ready in the wards for immediate use for critically ill patients. The ward readiness of these resources, although harder to evaluate, is likely more important than their availability in the hospital. This study aimed to assess the ward readiness for EECC and the hospital availability of resources for EECC and for advanced critical care in hospitals in Tanzania.

METHODS

An in-depth, cross-sectional study was conducted in five purposively selected hospitals by visiting all wards to collect data on all the required 66 EECC and 161 advanced critical care resources. We defined hospital-availability as a resource present in the hospital and ward-readiness as a resource available, functioning, and present in the right place, time and amounts for critically ill patient care in the wards. Data were analyzed to calculate availability and readiness scores as proportions of the resources that were available at hospital level, and ready at ward level respectively.

RESULTS

Availability of EECC resources in hospitals was 84% and readiness in the wards was 56%. District hospitals had lower readiness scores (less than 50%) than regional and tertiary hospitals. Equipment readiness was highest (65%) while that of guidelines lowest (3%). Availability of advanced critical care resources was 31%.

CONCLUSION

Hospitals in Tanzania lack readiness for the provision of EECC- the low-cost, life-saving care for critically ill patients. The resources for EECC were available in hospitals, but were not ready for the immediate needs of critically ill patients in the wards. To provide effective EECC to all patients, improvements are needed around the essential, low-cost resources in hospital wards that are essential for decreasing preventable deaths.

Prognostic Value of Antithrombin Activity Levels in the Early Phase of Intensive Care: A 2-Center Retrospective Cohort Study

To investigate the relationship between antithrombin (AT) activity level and prognosis in patients requiring intensive care. Patients whose AT activity was measured within 24 h of intensive care unit (ICU) admission were enrolled for analysis. The primary endpoint was mortality at discharge. Prognostic accuracy was examined using receiver operating characteristic (ROC) curves and cox hazard regression analysis. Patients were divided into 6 groups based on predicted mortality, and a χ2 independence test was performed on the prognostic value of AT activity for each predicted mortality; P < .05 was considered significant. A total of 281 cases were analyzed. AT activity was associated with mortality at discharge (AT% [interquartile range, IQR]): survivor group, 69 (56-86) versus nonsurvivor group, 56 (44-73), P = .0003). We found an increasing risk for mortality in both the lowest level of AT activity (<50%; hazard ratio [HR] 2.43, 95% confidence interval [CI] 1.20-4.89, P = .01) and the middle-low level of AT activity (≥ 50% and < 70%; HR 2.06, 95% CI 1.06-4.02, P = .03), compared with the normal AT activity level (≥ 70%). ROC curve analysis showed that the prediction accuracy of AT was an area under the curve (AUC) of 0.66 (cutoff 58%, sensitivity 61.4%, specificity 68.2%, P = .0003). AT activity was significantly prognostic in the group with 20% to 50% predicted mortality (AUC 0.74, sensitivity: 24.0%-55.5%, specificity: 83.3%-93.0%). An early decrease in AT activity level in ICU patients may be a predictor of mortality at discharge.

Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches

Background

A disease severity classification system is widely used to predict the survival of patients admitted to the intensive care unit with different diagnoses. In the present study, conventional severity classification systems were compared with artificial intelligence predictive models (Artificial Neural Network and Decision Tree) in terms of the prediction of the survival rate of the patients admitted to the intensive care unit.

Methods

This retrospective cohort study was performed on the data of the patients admitted to the ICU of Ghaemshahr’s Razi Teaching Care Center from March 20th, 2017, to September 22nd, 2019. The required data for calculating conventional severity classification models (SOFA, SAPS II, APACHE II, and APACHE IV) were collected from the patients’ medical records. Subsequently, the score of each model was calculated. Artificial intelligence predictive models (Artificial Neural Network and Decision Tree) were developed in the next step. Lastly, the performance of each model in predicting the survival of the patients admitted to the intensive care unit was evaluated using the criteria of sensitivity, specificity, accuracy, F-measure, and area under the ROC curve. Also, each model was validated externally. The R program, version 4.1, was used to create the artificial intelligence models, and SPSS Statistics Software, version 21, was utilized to perform statistical analysis.

Results

The area under the ROC curve of SOFA, SAPS II, APACHE II, APACHE IV, multilayer perceptron artificial neural network, and CART decision tree were 76.0, 77.1, 80.3, 78.5, 84.1, and 80.0, respectively.

Conclusion

The results showed that although the APACHE II model had better results than other conventional models in predicting the survival rate of the patients admitted to the intensive care unit, the other conventional models provided acceptable results too. Moreover, the findings showed that the artificial neural network model had the best performance among all the studied models, indicating the discrimination power of this model in predicting patient survival compared to the other models.

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.

Influence of transfusions, hemodialysis and extracorporeal life support on hyperferritinemia in critically ill patients

BACKGROUND

Ferritin is the major iron storage protein and an acute phase reactant. Hyperferritinemia is frequently seen in the critically ill where it has been hypothesized that not only underlying conditions but also factors such as transfusions, hemodialysis and extracorporeal life support (ECLS) lead to hyperferritinemia. This study aims to investigate the influence of transfusions, hemodialysis, and ECLS on hyperferritinemia in a multidisciplinary ICU cohort.

METHODS

This is a post-hoc analysis of a retrospective observational study including patients aged ≥ 18 years who were admitted to at least one adult ICU between January 2006 and August 2018 with hyperferritinemia ≥ 500 μg/L and of ≥ 14 days between two ICU ferritin measurements. Patients with hemophagocytic lymphohistiocytosis (HLH) were excluded. To identify the influence of transfusions, hemodialysis, and ECLS on ferritin change, multivariable linear regression analysis with ferritin change between two measurements as dependent variable was performed.

RESULTS

A total of 268 patients was analyzed. Median duration between measurements was 36 days (22-57). Over all patients, ferritin significantly increased between the first and last measurement (p = 0.006). Multivariable linear regression analysis showed no effect of transfusions, hemodialysis, or ECLS on ferritin change. Changes in aspartate aminotransferase (ASAT) and sequential organ failure assessment (SOFA) score were identified as influencing factors on ferritin change [unstandardized regression coefficient (B) = 2.6; (95% confidence interval (CI) 1.9, 3.3); p < 0.001 and B = 376.5; (95% CI 113.8, 639.1); p = 0.005, respectively]. Using the same model for subgroups of SOFA score, we found SOFA platelet count to be associated with ferritin change [B = 1729.3; (95% CI 466.8, 2991.9); p = 0.007]. No association of ferritin change and in-hospital mortality was seen in multivariable analysis.

CONCLUSIONS

The present study demonstrates that transfusions, hemodialysis, and ECLS had no influence on ferritin increases in critically ill patients. Hyperferritinemia appears to be less the result of iatrogenic influences in the ICU thereby underscoring its unskewed diagnostic value.

TRIAL REGISTRATION

The study was registered with www.ClinicalTrials.gov (NCT02854943) on August 1, 2016.

Use of intensive care unit priority model in directing intensive care unit admission in Sudan: A prospective cross-sectional study

Background:

The shortage of specialized intensive care beds is one of the principal factors that limit intensive care unit (ICU) admissions. This study explores the utilization of priority criteria in directing ICU admission and predicting outcomes.

Methods:

This was a prospective cross-sectional study conducted in two ICUs in Sudan from April to December 2018. Patients were assessed for ICU admission and were ranked by priority into Groups 1, 2, 3, and 4 (1 highest priority and 4 lowest priority), and these groups were compared using independent t-test, Chi-square, and ANOVA.

Results:

A total of 180 ICU admitted patients were enrolled, 53% were male. The prioritization categories showed that 86 (47.8%), 50 (27.8%), 13 (7.2%), and 31 (17.2%) were categorized as priority 1, 2, 3, and 4, respectively. Patients in priority groups 3 and 4had significantly higher ICU mortality rates compared to those in groups 1 and 2 (P < 0.001), were likely to be older (P < 0.001), had significantly more comorbidities (P = 0.001), were more likely to be dependent (P < 0.001), and had longer ICU length of stay (P = 0.028).

Conclusion:

Patients classified as priority 3 and 4 were predominantly older and had many comorbidities. They were likely to be dependent, stay longer in ICU, and exhibit mortality.

Physician-related factors associated with unscheduled revisits to the emergency department and admission to the intensive care unit within 72 h

Investigation of physician-related causes of unscheduled revisits to the emergency department (ED) within 72 h with subsequent admission to the intensive care unit (ICU) is an important parameter of emergency care quality. Between 2012 and 2017, medical records of all adult patients who visited the ED and returned within 72 h with subsequent ICU admission were retrospectively reviewed by three experienced emergency physicians. Study parameters were categorized into "input" (Patient characteristics), "throughput" (Time spent on first ED visit and seniority of emergency physicians, and "output" (Charlson Comorbidity Index). Of the 147 patients reviewed for the causes of ICU admission, 35 were physician-related (23.8%). Eight belonged to more urgent categories, whereas the majority (n = 27) were less urgent. Patients who spent less time on their first ED visits before discharge (< 2 h) were significantly associated with physician-related causes of ICU admission, whereas there was no significant difference in other "input," "throughput," and "output" parameters between the "physician-related" and "non-physician-related" groups. Short initial management time was associated with physician-related causes of ICU admission in patients with initial less urgent presentations, highlighting failure of the conventional triage system to identify potentially life-threatening conditions and possibility of misjudgement because of the patients' apparently minor initial presentations.