Does the level of mobility on ICU discharge impact post-ICU outcomes? A retrospective analysis

PURPOSE

Mobilisation is a common intervention in Intensive Care (ICU). However, few studies have explored the relationship between mobility levels and outcomes. This study assessed the association of the level of mobility on ICU discharge with discharge destination from the hospital and hospital length of stay.

MATERIALS AND METHODS

A retrospective analysis of data from 522 patients admitted to a single UK general ICU who were ventilated for ≥5 days was performed. The level of mobility was assessed using the Manchester Mobility Score (MMS). Multivariable regression analysed demographic and clinical variables for the independence of association with discharge destination and hospital length of stay.

RESULTS

MMS ≥5 on ICU discharge was independently associated with discharge destination and hospital LOS (p < 0.001). Patients achieving MMS ≥5 on ICU discharge were more likely to be discharged home (OR 3.86 95% CI 2.1 to 6.9, p < 0.001), and had an 11.8 day shorter hospital LOS (95% CI -17.6 to -6.1, p < 0.001).

CONCLUSIONS

The ability to step transfer to a chair (MMS ≥5) before ICU discharge was independently associated with discharge to usual residence and hospital LOS, irrespective of preadmission morbidity. Increasing the level of patient mobility at ICU discharge should be a key focus of rehabilitation interventions.

Ventilator-associated pneumonia: pathobiological heterogeneity and diagnostic challenges

Ventilator-associated pneumonia (VAP) affects up to 20% of critically ill patients and induces significant antibiotic prescription pressure, accounting for half of all antibiotic use in the ICU. VAP significantly increases hospital length of stay and healthcare costs yet is also associated with long-term morbidity and mortality. The diagnosis of VAP continues to present challenges and pitfalls for the currently available clinical, radiological and microbiological diagnostic armamentarium. Biomarkers and artificial intelligence offer an innovative potential direction for ongoing future research. In this Review, we summarise the pathobiological heterogeneity and diagnostic challenges associated with VAP.

Synergizing Survival: Uniting Acute Gastrointestinal Injury Grade and Disease Severity Scores in Critical Care Prognostication

How to cite this article: Patnaik RK, Karan N. Synergizing Survival: Uniting Acute Gastrointestinal Injury Grade and Disease Severity Scores in Critical Care Prognostication. Indian J Crit Care Med 2024;28(6):529-530.

Development and internal validation of an algorithm for estimating mortality in patients encountered by physician-staffed helicopter emergency medical services

BACKGROUND

Severity of illness scoring systems are used in intensive care units to enable the calculation of adjusted outcomes for audit and benchmarking purposes. Similar tools are lacking for pre-hospital emergency medicine. Therefore, using a national helicopter emergency medical services database, we developed and internally validated a mortality prediction algorithm.

METHODS

We conducted a multicentre retrospective observational register-based cohort study based on the patients treated by five physician-staffed Finnish helicopter emergency medical service units between 2012 and 2019. Only patients aged 16 and over treated by physician-staffed units were included. We analysed the relationship between 30-day mortality and physiological, patient-related and circumstantial variables. The data were imputed using multiple imputations employing chained equations. We used multivariate logistic regression to estimate the variable effects and performed derivation of multiple multivariable models with different combinations of variables. The models were combined into an algorithm to allow a risk estimation tool that accounts for missing variables. Internal validation was assessed by calculating the optimism of each performance estimate using the von Hippel method with four imputed sets.

RESULTS

After exclusions, 30 186 patients were included in the analysis. 8611 (29%) patients died within the first 30 days after the incident. Eleven predictor variables (systolic blood pressure, heart rate, oxygen saturation, Glasgow Coma Scale, sex, age, emergency medical services vehicle type [helicopter vs ground unit], whether the mission was located in a medical facility or nursing home, cardiac rhythm [asystole, pulseless electrical activity, ventricular fibrillation, ventricular tachycardia vs others], time from emergency call to physician arrival and patient category) were included. Adjusted for optimism after internal validation, the algorithm had an area under the receiver operating characteristic curve of 0.921 (95% CI 0.918 to 0.924), Brier score of 0.097, calibration intercept of 0.000 (95% CI -0.040 to 0.040) and slope of 1.000 (95% CI 0.977 to 1.023).

CONCLUSIONS

Based on 11 demographic, mission-specific, and physiologic variables, we developed and internally validated a novel severity of illness algorithm for use with patients encountered by physician-staffed helicopter emergency medical services, which may help in future quality improvement.

A prospective observational study to correlate lung ultrasound with clinical severity and prognosis score in patients with primary pulmonary pathology on invasive ventilatory support

Background

Lung ultrasound (LUS) is a known imaging modality employed for monitoring patients in an intensive care unit. This study evaluates, LUS in assessing disease severity and prognosis, by correlating its score with the three commonly used clinical severity scoring systems (CSSS), namely, sequential organ failure assessment (SOFA) score, acute physiology and chronic health evaluation (APACHE) II score, and simplified acute physiology score (SAPS) II.

Methods

This single-center prospective observational study included 54 adult patients of primary lung disease-induced acute respiratory distress syndrome (ARDS), on invasive ventilation. The primary objective was to correlate LUS score with SOFA score. Secondary objectives were to correlate LUS score with APACHE II and SAPS II scores. LUS score was also correlated with the estimated mortality derived from the above-mentioned scores. A subgroup analysis on COVID-19-positive cases was also carried out. All scores were calculated on the initiation of mechanical ventilation, daily for 7 days or mortality, whichever was earlier.

Results

A significant positive correlation (P < 0.001) was found between LUS and all three severity scores, as well as their corresponding estimated mortality percentages, for all days of the study period, in both non-COVID-19 ARDS patients and in COVID-19 patients. The merit of all four scores in differentiating between the survivor and mortality group for the duration of study also showed significant (P < 0.05) to very significant (P < 0.001) results.

Conclusion

Point-of-care LUS in conjunction with CSSS is a reliable tool for assessing the severity and progression of primary lung disease.

Clinical characteristics and prognosis of pregnancy-related acute kidney injury: a case series study

OBJECTIVE

Acute kidney injury (AKI) seriously affects the health of both pregnant women and fetuses. This study aimed to investigate the clinical characteristics and prognosis of pregnancy-related AKI (PR-AKI).

METHODS

This case series study enrolled pregnant women with PR-AKI admitted to the surgical intensive care unit of Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine between January 2010 and December 2020.

RESULTS

Thirty-one PR-AKI patients were enrolled with a mean age of 29.16 ± 4.97 years. Seventeen pregnant women (54.84%) had complete recovery of renal function, 5 (16.13%) had partial recovery of renal function, 2 (6.45%) patients had no renal function improvement, and 7 (22.58%) died. Among the 31 patients with 35 fetuses, 25 (80.6%) pregnant women had poor fetal outcomes, including 5 cases of stillbirths, 5 neonatal asphyxia, 18 premature births, 10 low birth weight, and 8 deficient birth weight infants. Compared to cases with good fetal outcomes, cases with poor fetal outcomes had significantly shorter gestational weeks (39.26 ± 1.53 vs. 31.62 ± 5.50, P = 0.002), lower platelet count (217.13 ± 122.87 vs. 90.24 ± 84.88, P = 0.005), lower hemoglobin (94.19 ± 13.21 vs. 74.48 ± 20.78, P = 0.036), higher blood urea nitrogen (11.87 ± 4.28 vs. 19.47 ± 10.98, P = 0.013), and higher uric acid (262.41 ± 167.00 vs. 586.87 ± 144.52, P < 0.001).

CONCLUSIONS

The maternal renal function of women with PR-AKI might improve after treatment, but occurrence rates of adverse fetal outcomes were still high.

Implementing Best Practices for Decreasing Nurse Hesitancy to Call the Rapid Response Team: An Evidence-Based Quality Improvement Initiative

BACKGROUND

Nurses, regardless of experience, sometimes wait for the Modified Early Warning Score to indicate physiological decline before initiating the hospital's Rapid Response Team (RRT). The goal of this quality improvement initiative was to reduce nurse hesitancy to call the RRT, as indicated by the monthly number of proactive calls before the Modified Early Warning Score increased and triggered an automated page.

METHOD

Education, planned handoff communication, debriefing, and good catch emails were the educational strategies used in the implementation of the quality improvement initiatives, encouraging RRT calls sooner. Increasing RRT knowledge and communication is an evidence-based practice strategy to reduce nurse reluctance to call the RRT.

RESULTS

After implementation, monthly proactive RRT calls increased (χ² [1, n = 1,964] = 14.6085, p = .000159). Bedside interventions, unscheduled transfers, and Acute Physiologic and Chronic Health Evaluation scores did not differ.

CONCLUSION

Education, structured handoff communication, and acknowledging good catches reduced nurses' reluctance to call the RRT proactively. [J Contin Educ Nurs. 2023;54(6):281-288.].

The Relationship between Delirium and Statin Use According to Disease Severity in Patients in the Intensive Care Unit

Objective

The aim of this study was to investigate the association between the use of statins and the occurrence of delirium in a large cohort of patients in the intensive care unit (ICU), considering disease severity and statin properties.

Methods

We obtained clinical and demographical information from 3,604 patients admitted to the ICU from January 2013 to April 2020. This included information on daily statin use and delirium state, as assessed by the Confusion Assessment Method for ICU. We used inverse probability of treatment weighting and categorized the patients into four groups based on the Acute Physiology and Chronic Health Evaluation II score (group 1: 0-10 - mild; group 2: 11-20 - mild to moderate; group 3: 21-30 - moderate to severe; group 4: > 30 - severe). We analyzed the association between the use of statin and the occurrence of delirium in each group, while taking into account the properties of statins.

Results

Comparisons between statin and non-statin patient groups revealed that only in group 2, patients who were administered statin showed significantly higher occurrence of delirium (p = 0.004, odds ratio [OR] = 1.58) compared to the patients who did not receive statin. Regardless of whether statins were lipophilic (p = 0.036, OR = 1.47) or hydrophilic (p = 0.032, OR = 1.84), the occurrence of delirium was higher only in patients from group 2.

Conclusion

The use of statins may be associated with the increases in the risk of delirium occurrence in patients with mild to moderate disease severity, irrespective of statin properties.

Association between Fibrinogen-to-Albumin Ratio and Prognosis in Patients Admitted to an Intensive Care Unit

The objective of this study was to investigate the usefulness of fibrinogen-to-albumin ratio (FAR) as a prognostic marker in patients admitted to an intensive care unit (ICU) compared with Sequential Organ Failure Assessment (SOFA) score, a widely used prognostic scoring system. An inverse probability weighting (IPW) was used to control for selection bias and confounding factors. After IPW adjustment, the high FAR group showed significantly higher risk of 1-year compared with low FAR group (36.4% vs. 12.4%, adjust hazard ratio = 1.72; 95% confidence interval (CI): 1.59-1.86; p < 0.001). In the receiver-operating characteristic curve analysis associated with the prediction of 1-year mortality, there was no significant difference between the area under the curve of FAR on ICU admission (C-statistic: 0.684, 95% CI: 0.673-0.694) and that of SOFA score on ICU admission (C-statistic: 0.679, 95% CI: 0.669-0.688) (p = 0.532). In this study, FAR and SOFA score at ICU admission were associated with 1-year mortality in patients admitted to an ICU. Especially, FAR was easier to obtain in critically ill patients than SOFA score. Therefore, FAR is feasible and might help predict long-term mortality in these patients.

Institutional performance and validation of severity of illness score for children with acute hematogenous osteomyelitis

A scoring system has recently been published that uses parameters within the first 4-5 days of hospitalization to determine the severity of illness (SOI) in children with acute hematogenous osteomyelitis (AHO). To our knowledge, no additional studies to date have examined the validity of the SOI score outside of the institution of origin. This study evaluates the performance of the SOI score in a retrospective cohort of cases at our institution. Patients admitted to our institution over the past 5 years with AHO who met inclusion and exclusion criteria were analyzed. Parameters including C reactive protein over the first 96 h of hospitalization, febrile days on antibiotics, ICU admission, and presence of disseminated disease were used to calculate the SOI score for each patient. Pearson and Spearman correlations were used when appropriate. SOI score comparison between groups was achieved with the Kruskal-Wallis and Wilcoxon two-sample tests. Seventy-four patients were analyzed. Significantly higher SOI scores were noted for patients with bacteremia, ICU admission, fever for two or more days on presentation, multiple surgeries, and any complication. Markers of disease severity that significantly correlated with SOI score were total length of stay, LOS, duration of antibiotic course, number of surgical procedures, and case mix index. The SOI score functioned well as higher scores were associated with sicker patients. The SOI score is helpful for determining which patients will require longer hospitalizations and more intense treatment in a setting other than the institution of origin.

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.

In-ICU Outcomes of Critically Ill Patients in a Reference Cameroonian Intensive Care Unit: A Retrospective Cohort Study

Introduction

Mortality rate amongst critically ill patients admitted to the intensive care unit (ICU) is disproportionately high in sub-Saharan African countries such as Cameroon. Identifying factors associated with higher in-ICU mortality guides more aggressive resuscitative measures to curb mortality, but the dearth of data on predictors of in-ICU mortality precludes this action. We aimed to determine predictors of in-ICU mortality in a major referral ICU in Cameroon. Methodology. This was a retrospective cohort study of all patients admitted to the ICU of Douala Laquintinie Hospital from 1st of March 2021 to 28th February 2022. We performed a multivariable analysis of sociodemographic, vital signs on admission, and other clinical and laboratory variables of patients discharged alive and dead from the ICU to control for confounding factors. Significance level was set at p < 0.05.

Results

Overall, the in-ICU mortality rate was 59.4% out of 662 ICU admissions. Factors independently associated with in-ICU mortality were deep coma (aOR = 0.48 (0.23-0.96), 95% CI, p = 0.043), and hypernatremia (>145 meq/L) (aOR = 0.39 (0.17-0.84) 95% CI, p = 0.022).

Conclusion

The in-ICU mortality rate in this major referral Cameroonian ICU is high. Six in 10 patients admitted to the ICU die. Patients were more likely to die if admitted with deep coma and high sodium levels in the blood.

Comparison Between Physiological Scores SIPF, CURB-65, and APACHE II as Predictors of Prognosis and Mortality in Hospitalized Patients with COVID-19 Pneumonia: A Multicenter Study, Saudi Arabia

Background

A coronavirus pandemic (COVID-19) is associated with catastrophic effects on the world with high morbidity and mortality. We aimed to evaluate the accuracy of physiological shock index (SIPF) (shock index and hypoxemia), CURB -65, acute physiology, and chronic health assessment II (APACHE II) as predictors of prognosis and in-hospital mortality in patients with COVID-19 pneumonia.

Methods

In Saudi Arabia, a multicenter retrospective study was conducted on hospitalized adult patients confirmed to have COVID-19 pneumonia. Information needed to calculate SIPF, CURB-65, and APACHE II scores were obtained from medical records within 24 hours of admission.

Results

The study included 1131 COVID-19 patients who met the inclusion criteria. They were divided into two groups: (A) the ICU group (n=340; 30.1%) and (B) the ward group (n=791; 69.9%). The most common concomitant diseases of patients at initial ICU admission were hypertension (71.5%) and diabetes (62.4%), and most of them were men (63.8%). The overall mortality was 18.7%, and the mortality rate was higher in the ICU group than in the ward group (39.4% vs 9.6%; p < 0.001). The SIPF score showed a significantly higher ability to predict both ICU admission and mortality in patients with COVID-19 pneumonia compared with APACHE II and CURB -65; (AUC 0.89 vs 0.87; p < 0.001) and (AUC 0.89 vs 0.84; p < 0.001) for ICU admission and (AUC 0.90 vs 0.65; p < 0.001) and (AUC 0.90 vs 0.80; p < 0.001) for mortality, respectively.

Conclusion

The ability of the SIPF score to predict ICU admission and mortality in COVID-19 pneumonia is higher than that of APACHE II and CURB-65. The overall mortality was 18.7%, and the mortality rate was higher in the ICU group than in the ward group (39.4% vs 9.6%; p < 0.001).

Applying bio-impedance vector analysis (BIVA) to adjust ultrafiltration rate in critically ill patients on continuous renal replacement therapy: A randomized controlled trial

BACKGROUND

Bioimpedance vector analysis (BIVA) has been suggested as a valuable tool in assessing volume status in critically ill patients. However, its effectiveness in guiding fluid removal by continuous renal replacement therapy (CRRT) has not been evaluated.

METHODS

In this randomized controlled trial, 65 critically ill patients receiving CRRT were allocated on a 1:1 ratio to have UF prescribed and adjusted using BIVA fluid assessment in the intervention group (32 patients) or conventional clinical parameters (33 patients). The primary outcome was the lean body mass (LBM) water content at CRRT discontinuation, and the secondary outcomes included the mortality rate, urinary output, the duration of ventilation support, and ICU stay.

RESULTS

The study group was associated with a lower water content of LBM (80.7 ± 9.4 vs. 85.9 ± 10.4%; p < 0.05), and a higher mean UF-rate and urinary output (1.5 ± 0.8 vs. 1.2 ± 0.5 ml/kg/h and 0.9 ± 0.9 vs 0.5 ± 0.6 ml/kg/h, both: p < 0.05). The mortality rate, the length of ICU stay, and ventilation support duration were similar.

CONCLUSION

BIVA guided UF prescription may be associated with a lower rate of fluid overload. Larger studies are required to evaluate its impact on patients' outcomes.

Do Interventions Improve Symptoms Among ICU Surrogates Facing End-of-Life Decisions? A Prognostically-Enriched Systematic Review and Meta-Analysis

OBJECTIVES

Evaluate the efficacy of interventions to improve symptoms for ICU surrogates at highest risk of developing psychologic distress: those facing end-of-life care decisions.

DATA SOURCES

MEDLINE, CINAHL, PsycInfo, Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov were searched through April 16, 2022.

STUDY SELECTION

Following an a priori protocol, randomized trials of interventions delivered to surrogates of adult ICU patients who died or had high likelihood of mortality evaluating surrogate symptoms were identified.

DATA EXTRACTION

Two reviewers performed screening and data extraction and assessed risk of bias (Cochrane Risk of Bias [RoB] 2 tool). Trials were eligible for meta-analysis if group mean symptom scores were provided at 3 or 6 months. Pooled effects were estimated using a random effects model. Heterogeneity was assessed (Cochrane Q, I2 ). Certainty of evidence was assessed (Grading of Recommendations Assessment, Development and Evaluation).

DATA SYNTHESIS

Of 1,660 records, 10 trials met inclusion criteria representing 3,824 surrogates; eight were included in the meta-analysis. Overall RoB was rated Some Concerns. Most ( n = 8) interventions focused on improving communication and enhancing psychologic support in the ICU. All trials measured anxiety, depression, and posttraumatic stress. Significant improvement was seen at 3 months (depression, mean difference [MD], -0.68; 95% CI, -1.14 to -0.22, moderate certainty; posttraumatic stress, standardized MD, -0.25; 95% CI, -0.49 to -0.01, very low certainty) and 6 months (anxiety, MD, -0.70; 95% CI, -1.18 to -0.22, moderate certainty). Sensitivity analyses suggest significant findings may be unstable. Subgroup analyses demonstrated differences in effect by trial location, interventionist, and intervention dose.

CONCLUSIONS

Communication and psychological support interventions in the ICU yielded small but significant improvement in psychological symptoms with moderate to very low certainty evidence in a prognostically-enriched sample of ICU surrogates facing end-of-life care decisions. A new approach to interventions that extend beyond the ICU may be needed.

High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms

We aimed to construct a prediction model based on computed tomography (CT) radiomics features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 patients were studied from a publicly available dataset with 4-class severity scoring performed by a radiologist (based on CT images and clinical features). The entire lungs were segmented and followed by resizing, bin discretization and radiomic features extraction. We utilized two feature selection algorithms, namely bagging random forest (BRF) and multivariate adaptive regression splines (MARS), each coupled to a classifier, namely multinomial logistic regression (MLR), to construct multiclass classification models. The dataset was divided into 50% (555 samples), 20% (223 samples), and 30% (332 samples) for training, validation, and untouched test datasets, respectively. Subsequently, nested cross-validation was performed on train/validation to select the features and tune the models. All predictive power indices were reported based on the testing set. The performance of multi-class models was assessed using precision, recall, F1-score, and accuracy based on the 4 × 4 confusion matrices. In addition, the areas under the receiver operating characteristic curves (AUCs) for multi-class classifications were calculated and compared for both models. Using BRF, 23 radiomic features were selected, 11 from first-order, 9 from GLCM, 1 GLRLM, 1 from GLDM, and 1 from shape. Ten features were selected using the MARS algorithm, namely 3 from first-order, 1 from GLDM, 1 from GLRLM, 1 from GLSZM, 1 from shape, and 3 from GLCM features. The mean absolute deviation, skewness, and variance from first-order and flatness from shape, and cluster prominence from GLCM features and Gray Level Non Uniformity Normalize from GLRLM were selected by both BRF and MARS algorithms. All selected features by BRF or MARS were significantly associated with four-class outcomes as assessed within MLR (All p values < 0.05). BRF + MLR and MARS + MLR resulted in pseudo-R2 prediction performances of 0.305 and 0.253, respectively. Meanwhile, there was a significant difference between the feature selection models when using a likelihood ratio test (p value = 0.046). Based on confusion matrices for BRF + MLR and MARS + MLR algorithms, the precision was 0.856 and 0.728, the recall was 0.852 and 0.722, whereas the accuracy was 0.921 and 0.861, respectively. AUCs (95% CI) for multi-class classification were 0.846 (0.805-0.887) and 0.807 (0.752-0.861) for BRF + MLR and MARS + MLR algorithms, respectively. Our models based on the utilization of radiomic features, coupled with machine learning were able to accurately classify patients according to the severity of pneumonia, thus highlighting the potential of this emerging paradigm in the prognostication and management of COVID-19 patients.

Body mass index and clinical outcome of severe COVID-19 patients with acute hypoxic respiratory failure: Unravelling the “obesity paradox” phenomenon

Background and aims

Although obesity have been generally shown to be an independent risk factor for poor outcomes in COVID-19 infection, some studies demonstrate a paradoxical protective effect (“obesity paradox”). This study examines the influence of obesity categories on clinical outcomes of severe COVID-19 patients admitted to an intensive care unit with acute hypoxic respiratory failure requiring either non-invasive or invasive mechanical ventilation.

Methods

This is a single centre, retrospective study of consecutive COVID-19 patients admitted to the intensive care unit between 03/2020 to 03/2021. Patients were grouped according to the NICE Body Mass Index (BMI) category. Admission variables including age, sex, comorbidities, and ICU severity indices (APACHE-II, SOFA and PaO2/FiO2) were collected. Data were compared between BMI groups for outcomes such as need for invasive mechanical ventilation (IMV), renal replacement therapy (RRT) and 28-day and overall hospital mortality.

Results

340 patients were identified and of those 333 patients had their BMI documented. Just over half of patients (53%) had obesity. Those with extreme obesity (obesity groups II and III) were younger with fewer comorbidities, but were more hypoxaemic at presentation, than the healthy BMI group. Although non-significant, obesity groups II and III paradoxically showed a lower in-hospital mortality than the healthy weight group. However, adjusted (age, sex, APACHE-II and CCI) competing risk regression analysis showed three-times higher mortality in obese category I (sub-distribution hazard ratio = 3.32 (95% CI 1.30–8.46), p = 0.01) and a trend to higher mortality across all obesity groups compared to the healthy weight group.

Conclusions

In this cohort, those with obesity were at higher risk of mortality after adjustment for confounders. We did not identify an “obesity paradox” in this cohort. The obesity paradox may be explained by confounding factors such as younger age, fewer comorbidities, and less severe organ failures. The impact of obesity on indicators of morbidity including likelihood of requirement for organ support measures was not conclusively demonstrated and requires further scrutiny.

Artificial intelligence and machine learning in precision and genomic medicine

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Artificial intelligence and machine learning in precision and genomic medicine

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units

This paper targets a major challenge of how to effectively allocate medical resources in intensive care units (ICUs). We trained multiple regression models using the Medical Information Mart for Intensive Care III (MIMIC III) database recorded in the period between 2001 and 2012. The training and validation dataset included pneumonia, sepsis, congestive heart failure, hypotension, chest pain, coronary artery disease, fever, respiratory failure, acute coronary syndrome, shortness of breath, seizure and transient ischemic attack, and aortic stenosis patients’ recorded data. Then we tested the models on the unseen data of patients diagnosed with coronary artery disease, congestive heart failure or acute coronary syndrome. We included the admission characteristics, clinical prescriptions, physiological measurements, and discharge characteristics of those patients. We assessed the models’ performance using mean residuals and running times as metrics. We ran multiple experiments to study the data partition’s impact on the learning phase. The total running time of our best-evaluated model is 123,450.9 mS. The best model gives an average accuracy of 98%, highlighting the location of discharge, initial diagnosis, location of admission, drug therapy, length of stay and internal transfers as the most influencing patterns to decide a patient’s readiness for discharge.

The effect of treatment and clinical course during Emergency Department stay on severity scoring and predicted mortality risk in Intensive Care patients

Background

Treatment and the clinical course during Emergency Department (ED) stay before Intensive Care Unit (ICU) admission may affect predicted mortality risk calculated by the Acute Physiology and Chronic Health Evaluation (APACHE)-IV, causing lead-time bias. As a result, comparing standardized mortality ratios (SMRs) among hospitals may be difficult if they differ in the location where initial stabilization takes place. The aim of this study was to assess to what extent predicted mortality risk would be affected if the APACHE-IV score was recalculated with the initial physiological variables from the ED. Secondly, to evaluate whether ED Length of Stay (LOS) was associated with a change (delta) in these APACHE-IV scores.

Methods

An observational multicenter cohort study including ICU patients admitted from the ED. Data from two Dutch quality registries were linked: the Netherlands Emergency department Evaluation Database (NEED) and the National Intensive Care Evaluation (NICE) registry. The ICU APACHE-IV, predicted mortality, and SMR based on data of the first 24 h of ICU admission were compared with an ED APACHE-IV model, using the most deviating physiological variables from the ED or ICU.

Results

A total of 1398 patients were included. The predicted mortality from the ICU APACHE-IV (median 0.10; IQR 0.03–0.30) was significantly lower compared to the ED APACHE-IV model (median 0.13; 0.04–0.36; p < 0.01). The SMR changed from 0.63 (95%CI 0.54–0.72) to 0.55 (95%CI 0.47–0.63) based on ED APACHE-IV. Predicted mortality risk changed more than 5% in 321 (23.2%) patients by using the ED APACHE-IV. ED LOS > 3.9 h was associated with a slight increase in delta APACHE-IV of 1.6 (95% CI 0.4–2.8) compared to ED LOS < 1.7 h.

Conclusion

Predicted mortality risks and SMRs calculated by the APACHE IV scores are not directly comparable in patients admitted from the ED if hospitals differ in their policy to stabilize patients in the ED before ICU admission. Future research should focus on developing models to adjust for these differences.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-03986-2.

Efficacy and Safety of Neostigmine Adjunctive Therapy in Patients With Sepsis or Septic Shock: A Randomized Controlled Trial

Background: Neostigmine has been found to improve survival in animal models of sepsis. However, its feasibility, efficacy, and safety in patients with sepsis or septic shock have not been investigated. Aim: This parallel randomized controlled double-blinded design aimed to investigate the efficacy and safety of neostigmine as an adjunctive therapy in patients with sepsis or septic shock. Patients and Methods: A total of 167 adult patients with sepsis or septic shock were assessed for eligibility; 50 patients were randomized to receive a continuous infusion of neostigmine (0.2 mg/h for 120 h; neostigmine arm) or 0.9% saline (control arm) in addition to standard therapy. The primary outcome was the change in Sequential Organ Failure Assessment (SOFA) scores 120 h after therapy initiation. Secondary outcomes included mortality rates and changes in procalcitonin level. Results: The median (interquartile range) change in SOFA scores improved significantly in the neostigmine arm [-2 (-5, 1)] as compared with the control arm [1.5 (0, 2.8); p = 0.007]. Progression from sepsis to septic shock was more frequent in the control arm (p = 0.01). The incidence of shock reversal in patients with septic shock was significantly lower in the control arm than in the neostigmine arm (p = 0.04). Differences in 28-days mortality rates did not reach statistical significance between the control and neostigmine arms (p = 0.36). Percentage change in procalcitonin levels was similar in both arms (p = 0.74). Conclusion: Neostigmine adjunctive therapy may be safe and effective when administered in patients with sepsis or septic shock. Clinical Trial Registration: NCT04130230.

Assessment of APACHE II Score to Predict ICU Outcomes of Patients with AKI: A Single Center Experience from Haryana, North India

Background

The acute physiology and chronic health evaluation (APACHE) II severity score has shown a good calibration and discriminatory value across a range of disease processes and remains the most widely used source of prognostic information on the risk for death in patients admitted to intensive care units (ICUs).

Objectives

To study APACHE II scores in patients of acute kidney injury (AKI) admitted in the ICU and to find its association with outcome.

Materials and methods

One hundred patients with AKI aged 18 years or above were admitted in the ICU, department of general medicine, of a tertiary care institute in Haryana, North India, from October 2019 to September 2020, were studied. Patients who had known causes of chronic kidney disease (CKD), on maintenance hemodialysis, and those who underwent renal replacement therapy (RRT) or nephrectomy were excluded. All required investigations were performed, and data were collected. The patients were followed till discharge or in-hospital mortality.

Results

The mean age of the patients was 55.92 ± 18.18 years. Male–female ratio was 1.5:1. Thirty-five percent of the admitted patients had an in-hospital mortality. Sepsis (47%) was the most common cause of AKI, and 83% of the patients had underlying comorbid conditions. The mean APACHE II score of the expired patients on admission, i.e., 24.80 ± 13.65, was found to be significantly higher compared to the mean APACHE II score (17.25 ± 10.12) of the discharged patients (p-value <0.001). APACHE II score was found to have 57.14% sensitivity, 86.15% specificity, 69% PPV, 78.9% NPV, and 76% diagnostic accuracy to predict mortality among the AKI patients.

Conclusion

APACHE II scoring system has a good discrimination and calibration when applied to ICU-admitted AKI patients and is a good predictor of prognosis in them.

How to cite this article

Patel P, Gupta S, Patel H, Bashar MDA. Assessment of APACHE II Score to Predict ICU Outcomes of Patients with AKI: A Single-center Experience from Haryana, North India. Indian J Crit Care Med 2022;26(3):276–281.

Prediction Model of Acute Respiratory Failure in Patients with Acute Pesticide Poisoning by Intentional Ingestion: Prediction of Respiratory Failure in Pesticide Intoxication (PREP) Scores in Cohort Study

Acute respiratory failure is the primary cause of mortality in patients with acute pesticide poisoning. The aim of the present study was to develop a new and efficient score system for predicting acute respiratory failure in patients with acute pesticide poisoning. This study was a retrospective observational cohort study comprised of 679 patients with acute pesticide poisoning by intentional poisoning. We divided this population into a ratio of 3:1; training set (n = 509) and test set (n = 170) for model development and validation. Multivariable logistic regression models were used in developing a score-based prediction model. The Prediction of Respiratory failure in Pesticide intoxication (PREP) scoring system included a summation of the integer scores of the following five variables; age, pesticide category, amount of ingestion, Glasgow Coma Scale, and arterial pH. The PREP scoring system developed accurately predicted respiratory failure (AUC 0.911 [0.849−0.974], positive predictive value 0.773, accuracy 0.873 in test set). We came up with four risk categories (A, B, C and D) using PREP scores 20, 40 and 60 as the cut-off for mechanical ventilation requirement risk. The PREP scoring system developed in the present study could predict respiratory failure in patients with pesticide poisoning, which can be easily implemented in clinical situations. Further prospective studies are needed to validate the PREP scoring system.

General Severity of Illness Scoring Systems and COVID-19 Mortality Predictions: Is "Old Still Gold?"

How to cite this article: Siddiqui SS, Patnaik R, Kulkarni AP. General Severity of Illness Scoring Systems and COVID-19 Mortality Predictions: Is "Old Still Gold?" Indian J Crit Care Med 2022;26(4):416-418.

Cohort study of the APACHE II score and mortality for different types of intensive care unit patients

OBJECTIVES

Find the discriminant and calibration of APACHE II (Acute Physiology And Chronic Health Evaluation) score to predict mortality for different type of intensive care unit (ICU) patients.

METHODS

This is a cohort retrospective study using secondary data of ICU patients admitted to Siloam Hospital of Lippo Village from 2014 to 2018 with minimum age ≥17 years. The analysis uses the receiver operating characteristic curve, student t-test and logistic regression to find significant variables needed to predict mortality.

RESULTS

A total of 2181 ICU patients: men (55.52%) and women (44.48%) with an average age of 53.8 years old and length of stay 3.92 days were included in this study. Patients were admitted from medical emergency (30.5%), neurosurgical (52.1%) and surgical (17.4%) departments, with 10% of mortality proportion. Patients admitted from the medical emergency had the highest average APACHE score, 23.14±8.5, compared with patients admitted from neurosurgery 15.3±6.6 and surgical 15.8±6.8. The mortality rate of patients from medical emergency (24.5%) was higher than patients from neurosurgery (3.5%) or surgical (5.3%) departments. Area under curve of APACHE II score showed 0.8536 (95% CI 0.827 to 0.879). The goodness of fit Hosmer-Lemeshow show p=0.000 with all ICU patients' mortality; p=0.641 with medical emergency, p=0.0001 with neurosurgical and p=0.000 with surgical patients.

CONCLUSION

APACHE II has a good discriminant for predicting mortality among ICU patients in Siloam Hospital but poor calibration score. However, it demonstrates poor calibration in neurosurgical and surgical patients while demonstrating adequate calibration in medical emergency patients.

Predictive Score Model of Clinical Outcomes Sepsis in Intensive Care Unit Tertier Referral Hospital of Eastern Indonesia

AIM: This study aimed to design a predictive score model of clinical outcome sepsis and bacterial profiles of blood and sputum cultures in the intensive care unit (ICU) of a tertiary referral hospital. METHODS: An observational retrospective study was conducted in 2017–2020 using medical record data in the ICU of Dr. Soetomo Hospital as tertiary referral hospital. The predictor of sepsis prognosis was Acute Physiology and Chronic Health Evaluation II (APACHE II), blood and sputum culture results, procalcitonin (PCT) levels, and antimicrobial resistance in blood and sputum cultures. The model was prepared by logistic regression analysis and receiver operating characteristic (ROC) curves. RESULTS: Data from 355 subjects showed that predictor score was APACHE II, blood and sputum culture results; besides PCT levels were found to contribute significantly to predictive score of sepsis clinical output (p<0.05), while the predictor test of antimicrobial resistance in blood and sputum cultures was not significant to predictive score of sepsis clinical output (p > 0.05). The resulting scores to predict sepsis clinical outcomes include PCT level >2 ng/mL (1.61), APACHE score >20 (1), sputum culture as true pathogen (1.1), and blood culture as true pathogen (1.35). When the total score ≥3, the patient will die, while when the score <3, the patient will survive. ROC curves analysis obtained area under curve 0.859 (p < 0.05) which indicates that the equation is statistically significant in predicting the sepsis clinical outcome. Probability scores and death outcomes indicate that the higher the predictive score, the higher the probability of dying, with a score >3 the probability of dying is above 95.27%, whereas if the score is 5, the probability of dying is above 99%. The bacterial profile of blood cultures leading to mortality is predominately Gram-positive (34.4%), consisting of coagulase-negative Staphylococcus (22.9%), and Staphylococcus aureus (4.3%), while Gram-negative is only 14.7%, which consists of Enterobacteriaceae group (8.7%), Acinetobacter baumannii (4%), polymicrobial infection (2%), Burkholderia cepacia (0.8%), and Pseudomonas aeruginosa (0.4%). Sputum culture profile of patients with sepsis who died in the ICU of a tertiary referral RSUD Soetomo is dominated by Gram-negative, namely, A. baumannii (22.1%), Enterobacteriaceae group (20.6%), P. aeruginosa (11.1%), while Gram-positive is S. aureus (22.9%). CONCLUSION: The predictive score model for sepsis clinical outcomes in the ICU of a tertiary referral hospitals can be used as a basis for determining of patient management and the profile of the bacteria that causes sepsis that results in death.

Understanding the complexity of sepsis mortality prediction via rule discovery and analysis: a pilot study

BACKGROUND

Sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, has become one of the major causes of death in Intensive Care Units (ICUs). The heterogeneity and complexity of this syndrome lead to the absence of golden standards for its diagnosis, treatment, and prognosis. The early prediction of in-hospital mortality for sepsis patients is not only meaningful to medical decision making, but more importantly, relates to the well-being of patients.

METHODS

In this paper, a rule discovery and analysis (rule-based) method is used to predict the in-hospital death events of 2021 ICU patients diagnosed with sepsis using the MIMIC-III database. The method mainly includes two phases: rule discovery phase and rule analysis phase. In the rule discovery phase, the RuleFit method is employed to mine multiple hidden rules which are capable to predict individual in-hospital death events. In the rule analysis phase, survival analysis and decomposition analysis are carried out to test and justify the risk prediction ability of these rules. Then by leveraging a subset of these rules, we establish a prediction model that is both more accurate at the in-hospital death prediction task and more interpretable than most comparable methods.

RESULTS

In our experiment, RuleFit generates 77 risk prediction rules, and the average area under the curve (AUC) of the prediction model based on 62 of these rules reaches 0.781 ([Formula: see text]) which is comparable to or even better than the AUC of existing methods (i.e., commonly used medical scoring system and benchmark machine learning models). External validation of the prediction power of these 62 rules on another 1468 sepsis patients not included in MIMIC-III in ICU provides further supporting evidence for the superiority of the rule-based method. In addition, we discuss and explain in detail the rules with better risk prediction ability. Glasgow Coma Scale (GCS), serum potassium, and serum bilirubin are found to be the most important risk factors for predicting patient death.

CONCLUSION

Our study demonstrates that, with the rule-based method, we could not only make accurate prediction on in-hospital death events of sepsis patients, but also reveal the complex relationship between sepsis-related risk factors through the rules themselves, so as to improve our understanding of the complexity of sepsis as well as its population.

The effect of supplementation with vitamins A, B, C, D, and E on disease severity and inflammatory responses in patients with COVID-19: a randomized clinical trial

BACKGROUND AND OBJECTIVE

Because of the effect of vitamins on modulating the immune system function, we have evaluated the effect of supplementation with vitamins A, B, C, D, and E in ICU-admitted patients with COVID-19.

METHODS

This study was a randomized and single-blinded clinical trial in which 60 subjects were randomly assigned to two groups. The intervention group (n=30) received vitamins, and the control group did not receive any vitamin or placebo. The intervention was included 25,000 IU daily of vitamins A, 600,000 IU once during the study of D, 300 IU twice daily of E, 500 mg four times daily of C, and one amp daily of B complex for 7 days. At baseline and after the 7-day intervention, the serum levels of inflammatory markers, vitamins, and the SOFA score were assessed. In addition, the mortality rate and duration of hospitalization were evaluated after the intervention (IRCT registration number: IRCT20200319046819N1/registration date: 2020-04-04, https://www.irct.ir/trial/46838 ).

RESULTS

Significant changes were detected in serum levels of vitamins (p < 0.001 for all vitamins), ESR (p < 0.001), CRP (p = 0.001), IL6 (p = 0.003), TNF-a (p = 0.001), and SOFA score (p < 0.001) after intervention compared with the control group. The effect of vitamins on the mortality rate was not statistically significant (p=0.112). The prolonged hospitalization rate to more than 7 days was significantly lower in the intervention group than the control group (p=0.001). Regarding the effect size, there was a significant and inverse association between receiving the intervention and prolonged hospitalization (OR = 0.135, 95% CI 0.038-0.481; p=0.002); however, after adjusting for confounders, it was not significant (OR=0.402, 95% CI 0.086-1.883; p=0.247).

CONCLUSION

Supplementation with vitamins A, B, C, D, and E could improve the inflammatory response and decrease the severity of disease in ICU-admitted patients with COVID-19.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Comparison of prognosis predictive value of 4 disease severity scoring systems in patients with acute respiratory failure in intensive care unit: A STROBE report

Various disease severity scoring systems were currently used in critically ill patients with acute respiratory failure, while their performances were not well investigated.The study aimed to investigate the difference in prognosis predictive value of 4 different disease severity scoring systems in patients with acute respiratory failure.With a retrospective cohort study design, adult patients admitted to intensive care unit (ICU) with acute respiratory failure were screened and relevant data were extracted from an open-access American intensive care database to calculate the following disease severity scores on ICU admission: acute physiology score (APS) III, Sequential Organ Failure Assessment score (SOFA), quick SOFA (qSOFA), and Oxford Acute Severity of Illness Score (OASIS). Hospital mortality was chosen as the primary outcome. Multivariable logistic regression analyses were performed to analyze the association of each scoring system with the outcome. Receiver operating characteristic curve analyses were conducted to evaluate the prognosis predictive performance of each scoring system.A total of 4828 patients with acute respiratory failure were enrolled with a hospital mortality rate of 16.78%. APS III (odds ratio [OR] 1.03, 95% confidence interval [CI] 1.02-1.03), SOFA (OR 1.15, 95% CI 1.12-1.18), qSOFA (OR 1.26, 95% CI 1.11-1.42), and OASIS (OR 1.06, 95% CI 1.05-1.08) were all significantly associated with hospital mortality after adjustment for age and comorbidities. Receiver operating characteristic analyses showed that APS III had the highest area under the curve (AUC) (0.703, 95% CI 0.683-0.722), and SOFA and OASIS shared similar predictive performance (area under the curve 0.653 [95% CI 0.631-0.675] and 0.664 [95% CI 0.644-0.685], respectively), while qSOFA had the worst predictive performance for predicting hospital mortality (0.553, 95% CI 0.535-0.572).These results suggested the prognosis predictive value varied among the 4 different disease severity scores for patients admitted to ICU with acute respiratory failure.

Intensive Care Unit Scoring Systems

BACKGROUND

Illness severity scoring systems are commonly used in critical care. When applied to the populations for whom they were developed and validated, these tools can facilitate mortality prediction and risk stratification, optimize resource use, and improve patient outcomes.

OBJECTIVE

To describe the characteristics and applications of the scoring systems most frequently applied to critically ill patients.

METHODS

A literature search was performed using MEDLINE to identify original articles on intensive care unit scoring systems published in the English language from 1980 to 2020. Search terms associated with critical care scoring systems were used alone or in combination to find relevant publications.

RESULTS

Two types of scoring systems are most frequently applied to critically ill patients: those that predict risk of in-hospital mortality at the time of intensive care unit admission (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score, and Mortality Probability Models) and those that assess and characterize current degree of organ dysfunction (Multiple Organ Dysfunction Score, Sequential Organ Failure Assessment, and Logistic Organ Dysfunction System). This article details these systems' differing features and timing of use, score calculation, patient populations, and comparative performance data.

CONCLUSION

Critical care nurses must be aware of the strengths, limitations, and specific characteristics of severity scoring systems commonly used in intensive care unit patients to effectively employ these tools in clinical practice and critically appraise research findings based on their use.

Psychological distress among the family members of Intensive Care Unit (ICU) patients: A scoping review

AIMS AND OBJECTIVES

To map research-based psychological distress among the family members with patients in the intensive care unit (ICU).

BACKGROUND

Having a loved one in the ICU is a stressful experience, which may cause psychological distress for family members. Depression, anxiety and stress are the common forms of psychological distress associated with ICU patient's family members. Directly or indirectly, psychological distress may have behavioural or physiological impacts on the family members and ICU patient's recovery.

DESIGN

The study was based on the five-stage methodological framework by Arksey and O'Malley (International Journal of Social Research Methodology, 2005, 8, 19) and were guided by the PRISMA-ScR Checklist.

METHODS

A comprehensive and systematic search was performed in five electronic databases, namely the Scopus, Web of Sciences, CINAHL^® Complete @EBSCOhost, ScienceDirect and MEDLINE. Reference lists from the screened full-text articles were reviewed.

RESULTS

From a total of 1252 literature screened, 22 studies published between 2010-2019 were included in the review. From those articles, four key themes were identified: (a) Prevalence of psychological distress; (b) Factors affecting family members; (c) Symptoms of psychological distress; and (d) Impact of psychological distress.

CONCLUSIONS

Family members with a critically ill patient in ICU show high levels of anxiety, depression and stress. They had moderate to major symptoms of psychological distress that negatively impacted both the patient and family members.

RELEVANCE TO CLINICAL PRACTICE

The review contributed further insights on psychological distress among ICU patient's family members and proposed psychological interventions that could positively impact the family well-being and improve the patients' recovery.

The association between serum levels of micronutrients and the severity of disease in patients with COVID-19

Objectives

This study aimed to compare the serum level of micronutrients with normal amounts, and assess their association with the severity of disease and inflammatory cytokines in patients with coronavirus disease 2019 (COVID-19).

Methods

The present cross-sectional study included 60 patients admitted to the intensive care unit with COVID-19. We recorded data on demographic characteristics, anthropometric information, and medical history. Serum levels of inflammatory markers (erythrocyte sedimentation rate, C-reactive protein, interferon-gamma, tumor necrosis factor-alpha, interleukin-6), vitamins (A, B₉, B₁₂, C, D, E), and minerals (magnesium, zinc, iron) were measured. A radiologist assessed the severity of lung involvement according to patient computed tomography scans. The severity of illness was evaluated with the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE) score, oxygen saturation, and body temperature. Independent associations among the serum levels of micronutrients with the severity of COVID-19 were measured.

Results

Median patient age was 53.50 years (interquartile range, 12.75 years). Except for vitamin A and zinc, serum levels of other micronutrients were lower than the minimum normal. Patients with APACHE score ≥25 had a higher body mass index (P = 0.044), body temperature (P = 0.003), erythrocyte sedimentation rate (P = 0.008), C-reactive protein (P = 0.003), and lower oxygen saturation (P = 0.005), serum levels of vitamin D (P = < 0.001), and zinc (P = < 0.001) compared with patients with APACHE score <25. We found that lower serum levels of vitamin D, magnesium, and zinc were significantly and independently associated with higher APACHE scores (P = 0.001, 0.028, and < 0.001, respectively) and higher lung involvement (P = 0.002, 0.045, and < 0.001, respectively).

Conclusions

Lower serum levels of vitamin D, zinc, and magnesium were involved in severe COVID-19.

OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality

Background

Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called “weights” or “subscores”) into a final score (or probability) for quantifying the severity of critical illness (or the likelihood of in-hospital mortality). Although these simple additive models are human readable and interpretable, their predictive performance needs to be further improved.

Methods

We present OASIS +, a variant of the Oxford Acute Severity of Illness Score (OASIS) in which an ensemble of 200 decision trees is used to predict in-hospital mortality based on the 10 same clinical variables in OASIS.

Results

Using a test set of 9566 admissions extracted from the MIMIC-III database, we show that OASIS + outperforms nine previously developed severity scoring methods (including OASIS) in predicting in-hospital mortality. Furthermore, our results show that the supervised learning algorithms considered in our experiments demonstrated higher predictive performance when trained using the observed clinical variables as opposed to OASIS subscores.

Conclusions

Our results suggest that there is room for improving the prognostic accuracy of the OASIS severity scores by replacing the simple linear additive scoring function with more sophisticated non-linear machine learning models such as RF and XGB.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01517-7.

Strategic air medical evacuation of critically ill patients involving an intensive care physician: A retrospective analysis of 16 years of mission data

BACKGROUND

Strategic medical evacuation (MEDEVAC) allows airborne repatriation of soldiers injured or sick on missions to their national territory. The aim of this study was to describe the epidemiology of strategic MEDEVAC performed by intensive care physicians (ICP) and to analyze the role of the ICP in the management of critical care situations in flight.

METHODS

All soldiers who had high or medium dependency conditions and who benefited from a strategic MEDEVAC with an ICP on board between 1 January 2001 and 30 November 2017 were included in this epidemiological retrospective study.

RESULTS

A total of 452 soldiers were repatriated; the causes of repatriation were either trauma (n = 245; 54%) or medical pathologies (n = 207; 46%). Two hundred and seventy-six (61%) evacuations were performed within 48 h. The median annual number of strategic MEDEVAC with an ICP was 26 [20-32]. One hundred and fifty-five (34%) patients were mechanically ventilated and 103 (23%) received catecholamines. The median SAPS II score was 13 [8-24]. One hundred and seventy-eight adverse events were identified, of which 123 (69%) related to a worsening of the patient's clinical condition and 30 (20%) related to a technical problem. Forty-seven (20%) patients who initially appeared stable worsened during the flight. No deaths occurred on board, however, and no flights had to be diverted due to an uncontrolled care situation.

CONCLUSION

The results suggested that the presence of an ICP ensured a continued high-level care for patients with serious trauma and medical injuries, due to the medical and aeronautical expertise that resulted from the theoretical and practical training of the personnel on board. Based on these results, lessons regarding future MEDEVAC flights could be learned in order to continue to improve patient outcome.

Clinical features and prognostic factors of critically ill patients with COVID-19 in Daegu, South Korea: A multi-center retrospective study

To describe the clinical and demographic characteristics of critically ill patients with COVID-19 in Daegu, South Korea, and to explore the risk factors for in-hospital mortality in these patients.Retrospective cohort study of 110 critically ill patients with COVID-19 admitted to the ICU in Daegu, South Korea, between February 18 and April 5, 2020. The final date of follow-up was April 20, 2020.A total of 110 patient medical records were reviewed. The median age was 71 years (interquartile range [IQR] = 63-78 years). During the study period, 47 patients (42.7%) died in the hospital. The most common SARS-CoV-2 infection related complication was acute respiratory distress syndrome (ARDS) in 95 patients (86.4%). Of the 79 patients (71.8%) who received invasive mechanical ventilation, 46 (58.2%) received neuromuscular blockade injection, and 19 (24.1%) received ECMO treatment. All patients received antibiotic injection, 99 patients (90%) received hydroxychloroquine, 96 patients (87.3%) received lopinavir-ritonavir antiviral medication, and 14 patients (12.7%) received other antiviral agents, including darunavir-cobicistat and emtricitabine-tenofovir. In the multivariable logistic regression model, the odds ratio of in-hospital death was higher with APACHE II score (OR = 1.126; 95% CI = 1.014-1.252; P  = .027).The in-hospital mortality rate of critically ill patients with COVID-19 was approximately 40%. Higher APACHE II score at admission was an independent risk factor for death in these patients.

The 4-Hour Cairns Sepsis Model: A novel approach to predicting sepsis mortality at intensive care unit admission

BACKGROUND

Sepsis commonly causes intensive care unit (ICU) mortality, yet early identification of adults with sepsis at risk of dying in the ICU remains a challenge.

OBJECTIVE

The aim of the study was to derive a mortality prediction model (MPM) to assist ICU clinicians and researchers as a clinical decision support tool for adults with sepsis within 4 h of ICU admission.

METHODS

A cohort study was performed using 500 consecutive admissions between 2014 and 2018 to an Australian tertiary ICU, who were aged ≥18 years and had sepsis. A total of 106 independent variables were assessed against ICU episode-of-care mortality. Multivariable backward stepwise logistic regression derived an MPM, which was assessed on discrimination, calibration, fit, sensitivity, specificity, and predictive values and bootstrapped.

RESULTS

The average cohort age was 58 years, the Acute Physiology and Chronic Health Evaluation III-j severity score was 72, and the case fatality rate was 12%. The 4-Hour Cairns Sepsis Model (CSM-4) consists of age, history of renal disease, number of vasopressors, Glasgow Coma Scale, lactate, bicarbonate, aspartate aminotransferase, lactate dehydrogenase, albumin, and magnesium with an area under the receiver operating characteristic curve of 0.90 (95% confidence interval = 0.84-0.95, p < 0.00001), a Nagelkerke R² of 0.51, specificity of 0.94, a negative predictive value of 0.98, and almost identical odds ratios during bootstrapping. The CSM-4 outperformed existing MPMs tested on our data set. The CSM-4 also performed similar to existing MPMs in their derivation papers whilst using fewer, routinely collected, and inexpensive variables.

CONCLUSIONS

The CSM-4 is a newly derived MPM for adults with sepsis at ICU admission. It displays excellent discrimination, calibration, fit, specificity, negative predictive value, and bootstrapping values whilst being easy to use and inexpensive. External validation is required.

Rapid clinical evolution for COVID-19 translates into early hospital admission and unfavourable outcome: a preliminary report

BACKGROUND

A wide range of mortality rates has been reported in COVID-19 patients on the intensive care unit. We wanted to describe the clinical course and determine the mortality rate in our institution's intensive care units.

METHODS

To this end, we performed a retrospective cohort study of 50 COVID-19 patients admitted to the ICU at a large German tertiary university hospital. Clinical features are reported with a focus on ICU interventions, such as mechanical ventilation, prone positioning and extracorporeal organ support. Outcome is presented using a 7-point ordinal scale on day 28 and 60 following ICU admission.

RESULTS

The median age was 64 years, 78% were male. LDH and D-Dimers were elevated, and patients were low on Vitamin D. ARDS incidence was 75%, and 43/50 patients needed invasive ventilation. 22/50 patients intermittently needed prone positioning, and 7/50 required ECMO. The interval from onset of the first symptoms to admission to the hospital and to the ICU was shorter in non-survivors than in survivors. By day 60 after ICU admission, 52% of the patients had been discharged. 60-day mortality rate was 32%; 37% for ventilated patients, and 42% for those requiring both: ventilation and renal replacement therapy.

CONCLUSIONS

Early deterioration might be seen as a warning signal for unfavourable outcome. Lung-protective ventilation including prone positioning remain the mainstay of the treatment.

Systematic review on the definition and predictors of severe Clostridiodes difficile infection

Clostridiodes difficile infection (CDI) is one of the most common hospital-acquired infections with high mortality rates. Optimal management of CDI depends on early recognition of severity. However, currently, there is no acceptable standard of prediction. We reviewed severe CDI predictors in published literature and its definition according to clinical guidelines. We systematically reviewed studies describing clinical predictors for severe CDI in medical databases (Cochrane, EMBASE, Global Health Library, and MEDLINE/PubMed). They were independently evaluated by two reviewers. Six hundred thirty-three titles and abstracts were screened, and 31 studies were included. We excluded studies that were restricted to a specific patient population. There were 16 articles that examined mortality in CDI, as compared with 15 articles investigating non-mortality outcomes of CDI. The commonest risk factors identified were comorbidities, white blood cell count, serum albumin level, age, serum creatinine level and intensive care unit admission. Generally, the studies had small patient populations, were retrospective in nature, and mostly from Western centers. The commonest severe CDI criteria in clinical guidelines were raised white blood cell count, followed by low serum albumin and raised serum creatinine levels. There was no commonly agreed upon definition of severe CDI severity in the literature. Current clinical guidelines' definitions for severe CDI are heterogeneous. Hence, there is a need for prospective multi-center studies using standardized protocol for biospecimen investigation collection and shared data on outcomes of patients in order to devise a universally accepted definition for severe CDI.

Can Platelet Count and Mean Platelet Volume and Red Cell Distribution Width Be Used as a Prognostic Factor for Mortality in Intensive Care Unit?

Introduction

Critical patients are those patients who have psychological unstability that can cause morbidity and mortality in a short period of time. These patients need to be intensively monitored for organ function like cardiovascular, respiratory and neurological system. The most critical patients are transferred to intensive care unit (ICU) for close watch. It is not rare that hematological system of critical patient is affected from strong inflammation.

The main purpose of this study is to be able to determine platelet count (PLT), mean platelet volume (MPV) and red cell distribution width (RDW) admission value to predict mortality in ICU. Secondary purpose is to present a view about clinical use of these blood parameters.

Material and methods

In this study, RDW, MPV and PLT values of the patients in the first intensive care admission were evaluated and were compared with the last hemogram values before death. Glasgow Coma Score (GCS) and other risk factors for mortality were tried to be determined to show determinants of scoring systems on mortality in patients admitted to ICU.

Results

When compared with ICU entry in all patient groups and laboratory markers prior to exitus, the value of the input RDW was 14.66 ± 3.08 and the output RDW was 15.94 ± 9.59. Admission value of MPV was 8.180 ± 2.09, and before death the value of MPV was 9.199 ± 2.24. Statistically, it was significantly high (p < 0.001). The MPV values increased in all groups and cerebrovascular disease (CVD), respiratory failure, cardiac causes, head trauma and malignancies were statistically significantly high (p < 0.05). Admission value of PLT was 215.46 ± 116.8, and before death the value of PLT was 154.73 ± 101.32. Statistically, it was significantly low (p < 0.001).

Conclusions

The study showed that the difference between PLT, MPV and RDW values in the ICU and values before death, and decrease in PLT and increase in MPV and RDW in all patients were statistically significant. We believe that decrease in PLT, increase in MPV and RDW is a prognostic factor for mortality.

The applicability of commonly used predictive scoring systems in Indigenous Australians with sepsis: An observational study

Background

Indigenous Australians suffer a disproportionate burden of sepsis, however, the performance of scoring systems that predict mortality in Indigenous patients with critical illness is incompletely defined.

Materials and methods

The study was performed at an Australian tertiary-referral hospital between January 2014 and June 2017, and enrolled consecutive Indigenous and non-Indigenous adults admitted to ICU with sepsis. The ability of the ANZROD, APACHE-II, APACHE-III, SAPS-II, SOFA and qSOFA scores to predict death before ICU discharge in the two populations was compared.

Results

There were 442 individuals enrolled in the study, 145 (33%) identified as Indigenous. Indigenous patients were younger than non-Indigenous patients (median (interquartile range (IQR) 53 (43–60) versus 65 (52–73) years, p = 0.0001) and comorbidity was more common (118/145 (81%) versus 204/297 (69%), p = 0.005). Comorbidities that were more common in the Indigenous patients included diabetes mellitus (84/145 (58%) versus 67/297 (23%), p<0.0001), renal disease (56/145 (39%) versus 29/297 (10%), p<0.0001) and cardiovascular disease (58/145 (40%) versus 83/297 (28%), p = 0.01). The use of supportive care (including vasopressors, mechanical ventilation and renal replacement therapy) was similar in Indigenous and non-Indigenous patients, and the two populations had an overall case-fatality rate that was comparable (17/145 (12%) and 38/297 (13%) (p = 0.75)), although Indigenous patients died at a younger age (median (IQR): 54 (50–60) versus 70 (61–76) years, p = 0.0001). There was no significant difference in the ability of any the scores to predict mortality in the two populations.

Conclusions

Although the crude case-fatality rates of Indigenous and non-Indigenous Australians admitted to ICU with sepsis is comparable, Indigenous patients die at a much younger age. Despite this, the ability of commonly used scoring systems to predict outcome in Indigenous Australians is similar to that of non-Indigenous Australians, supporting their use in ICUs with a significant Indigenous patient population and in clinical trials that enrol Indigenous Australians.

Nonparametric estimation of broad sense agreement between ordinal and censored continuous outcomes

The concept of broad sense agreement (BSA) has recently been proposed for studying the relationship between a continuous measurement and an ordinal measurement. They developed a nonparametric procedure for estimating the BSA index, which is only applicable to completely observed data. In this work, we consider the problem of evaluating BSA index when the continuous measurement is subject to censoring. We propose a nonparametric estimation method built upon a derivation of a new functional representation of the BSA index, which allows for accommodating censoring by plugging in the nonparametric survival function estimators. We establish the consistency and asymptotic normality for the proposed BSA estimator. We also investigate an alternative approach based on the strategy of multiple imputation, which is shown to have better empirical performance with small sample sizes than the plug-in method. Extensive simulation studies are conducted to evaluate our proposals. We illustrate our methods via an application to a Surgical Intensive Care Unit study.