Prediction of adverse drug reactions in geriatric patients admitted to intensive care units

INTRODUCTION

Intensive care units (ICUs) pose challenges in managing critically ill patients with polypharmacy, potentially leading to adverse drug reactions (ADRs), particularly in the elderly.

OBJECTIVE

To evaluate whether the severity and clinical prognosis scores used in ICUs correlate with the prediction of ADRs in aged patients admitted to an ICU.

METHODS

A cohort study was conducted in a Brazilian University Hospital ICU. APACHE II and SAPS 3 assessed clinical prognosis, while GerontoNet ADR Risk Score and BADRI evaluated ADR risk at ICU admission. Severity of the patients' clinical conditions was evaluated daily based on the SOFA score. ADR screening was performed daily through the identification of ADR triggers.

RESULTS

1295 triggers were identified (median 30 per patient, IQR=28), with 15 suspected ADRs. No correlation was observed between patient severity and ADRs at admission (p=0.26), during hospitalization (p=0.91), or at follow-up (p=0.77). There was also no association between death and ADRs (p=0.28) or worse prognosis and ADRs (p>0.05). Higher BADRI scores correlated with more ADRs (p=0.001).

CONCLUSIONS

These data suggest that employing the severity and clinical prognosis scores used in ICUs is not sufficient to direct active pharmacovigilance efforts, which are therefore indicated for critically ill patients.

Optimized diagnosis-based comorbidity measures for all-cause mortality prediction in a national population-based ICU population

BACKGROUND

We aimed to optimize prediction of long-term all-cause mortality of intensive care unit (ICU) patients, using quantitative register-based comorbidity information assessed from hospital discharge diagnoses prior to intensive care treatment.

MATERIAL AND METHODS

Adult ICU admissions during 2006 to 2012 in the Swedish intensive care register were followed for at least 4 years. The performance of quantitative comorbidity measures based on the 5-year history of number of hospital admissions, length of stay, and time since latest admission in 36 comorbidity categories was compared in time-to-event analyses with the Charlson comorbidity index (CCI) and the Simplified Acute Physiology Score (SAPS3).

RESULTS

During a 7-year period, there were 230,056 ICU admissions and 62,225 deaths among 188,965 unique individuals. The time interval from the most recent hospital stays and total length of stay within each comorbidity category optimized mortality prediction and provided clear separation of risk categories also within strata of age and CCI, with hazard ratios (HRs) comparing lowest to highest quartile ranging from 1.17 (95% CI: 0.52-2.64) to 6.41 (95% CI: 5.19-7.92). Risk separation was also observed within SAPS deciles with HR ranging from 1.07 (95% CI: 0.83-1.38) to 3.58 (95% CI: 2.12-6.03).

CONCLUSION

Baseline comorbidity measures that included the time interval from the most recent hospital stay in 36 different comorbidity categories substantially improved long-term mortality prediction after ICU admission compared to the Charlson index and the SAPS score. Trial registration ClinicalTrials.gov ID NCT04109001, date of registration 2019-09-26 retrospectively.

Critically ill COVID-19 patients in northeast Brazil: mortality predictors during the first and second waves including SAPS 3

BACKGROUND

The Simplified Acute Physiology Score (SAPS) 3 is a reliable score to predict mortality. This study aims to investigate the predictive values of SAPS 3 and other clinical parameters for death in critically ill coronavirus disease 2019 (COVID-19) patients.

METHODS

This is a prospective study in a tertiary hospital for patients who required intensive care due to COVID-19 infection in northeast Brazil. Two distinct groups were constructed according to the epidemiological data: first wave and second wave. The severity of patients admitted was estimated using the SAPS 3 score.

RESULTS

A total of 767 patients were included: 290 were enrolled in the first wave and 477 in the second wave. Patients in the first wave had more comorbidities, were put on mechanical ventilation and required dialysis and vasopressors more frequently (p<0.05). During the second wave, non-invasive ventilation was more often required (p<0.05). In both periods, older patients and higher SAPS 3 scores on admission were associated with death (p<0.05). Non-invasive ventilation use showed a negative association with death only in the second wave period. In the first wave, the SAPS 3 score was more useful (area under the curve [AUC] 0.897) in predicting death in critically ill COVID-19 patients than in the second wave (AUC 0.810).

CONCLUSION

The SAPS 3 showed very reliable predictive values for death during the waves of the COVID-19 pandemic, mostly together with kidney and pulmonary dysfunction.

Comparison of Mortality Prediction Scores in Intermediate-Care Patients with Liver Cirrhosis at a German University Transplant Centre: A Prospective Study

BACKGROUND AND AIMS

Mortality prediction models help to extract and relate patient data upon admission to intensive or intermediate care units (ImCUs). Considering technical and economic healthcare developments, re-evaluations of score performances are required to warrant their validity. This study validates and compares established scoring systems in cirrhotic ImCU patients.

METHODS

Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 2 and 3, Sepsis Organ Failure Assessment (SOFA), Mortality Probability Model at ICU admission (MPMo) II and III, Model for End stage Liver Disease (MELD), CLIF-Consortium Acute-on-Chronic Liver Failure (CLIF-C ACLF), CLIF-Consortium Acute Decompensation (CLIF-C AD), and Intermediate Care Unit Severity Score (ImCUSS) were calculated in patients with cirrhosis (n = 98) at ImCU admission. Discrimination performances were evaluated by area under the receiver operating characteristic curves (AUROCs), calibration performances with calibration belt plots, and their corresponding p values.

RESULTS

Overall, SAPS 3 and CLIF-C ACLF have shown the best 90-day mortality prediction outcomes with AUROCs of 0.825 and 0.783 along with calibration belt p values of 0.128 and 0.061, respectively. In a subgroup analysis of patients with acute-on-chronic liver failure (ACLF), expanded SAPS 2, SOFA, and SAPS 3 reached the best AUROCs, i.e., 0.760, 0.750, and 0.714, but none of the tested scores reached an acceptable calibration.

CONCLUSION

Ninety-day mortality risk prediction of the SAPS 3 and CLIF-C ACLF was accurate in our cohort of patients with liver cirrhosis admitted to ImCUs. A particular challenge remains that is the mortality prediction in patients with ACLF requiring ImCU-level care; here, further developments are needed to generate scores with acceptable predictive performances.

Performance of APACHE IV in Medical Intensive Care Unit Patients: Comparisons with APACHE II, SAPS 3, and MPM0 III

Background

In this study, we analyze the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE IV, Simplified Acute Physiology Score (SAPS) 3, and Mortality Probability Model (MPM)₀ III in order to determine which system best implements data related to the severity of medical intensive care unit (ICU) patients.

Methods

The present study was a retrospective investigation analyzing the discrimination and calibration of APACHE II, APACHE IV, SAPS 3, and MPM₀ III when used to evaluate medical ICU patients. Data were collected for 788 patients admitted to the ICU from January 1, 2015 to December 31, 2015. All patients were aged 18 years or older with ICU stays of at least 24 hours. The discrimination abilities of the three systems were evaluated using c-statistics, while calibration was evaluated by the Hosmer-Lemeshow test. A severity correction model was created using logistics regression analysis.

Results

For the APACHE IV, SAPS 3, MPM₀ III, and APACHE II systems, the area under the receiver operating characteristic curves was 0.745 for APACHE IV, resulting in the highest discrimination among all four scoring systems. The value was 0.729 for APACHE II, 0.700 for SAP 3, and 0.670 for MPM₀ III. All severity scoring systems showed good calibrations: APACHE II (chi-square, 12.540; P=0.129), APACHE IV (chi-square, 6.959; P=0.541), SAPS 3 (chi-square, 9.290; P=0.318), and MPM₀ III (chi-square, 11.128; P=0.133).

Conclusions

APACHE IV provided the best discrimination and calibration abilities and was useful for quality assessment and predicting mortality in medical ICU patients.

Haemodialysis, nutritional disorders and hypoglycaemia in critical care

This study aimed to determine hypoglycemia incidence and associated factors in critically ill patients. It looked at a retrospective cohort with 106 critically ill adult patients with 48 hours of glycaemic control and 72 hours of follow up. The dependent variable, hypoglycaemia (≤70 mg/dl), was assessed with respect to independent variables: age, diet, insulin, catecholamines, haemodialysis, nursing workload and the Simplified Acute Physiology Score. Statistical analysis was performed using Student's t-test, Fisher's exact test and logistic regression at 5% significance level. Incidence of hypoglycaemia was 14.2%. Hypoglycaemia was higher in the group of patients on catecholamines (p=0.040), with higher glycaemic variability (p<0.001) and death in the intensive care unit (p=0.008). Risk factors were identified as absence of oral diet (OR 5.11; 95% CI 1.04-25.10) and haemodialysis (OR 4.28; 95% CI 1.16-15.76). Patients on haemodialysis and with no oral diet should have their glycaemic control intensified in order to prevent and/or manage hypoglycaemic episodes.

Can we predict pneumococcal bacteremia in patients with severe community-acquired pneumonia?

PURPOSE

This study aimed to evaluate the role of biomarkers as markers of pneumococcal bacteremia in severe community-acquired pneumonia (SCAP).

MATERIALS AND METHODS

A prospective, single-center, observational cohort study of 108 patients with SCAP admitted to the intensive care department of a university hospital in Portugal was conducted. Leucocytes, C-reactive protein (CRP), lactate, procalcitonin (PCT), d-dimer, brain natriuretic peptide (BNP), and cortisol were measured within 12 hours after the first antibiotic dose.

RESULTS

Fifteen patients (14%) had bacteremic pneumococcal pneumonia (BPP). They had significantly higher levels of median CRP (301 [interquartile range, or IQR], 230-350] mg/L vs 201 [IQR, 103-299] mg/L; P = .023), PCT (40 [IQR, 25-102] ng/mL vs 8 [IQR, 2-26] ng/mL; P < .001), BNP (568 [IQR, 478-2841] pg/mL vs 407 [IQR, 175-989] pg/mL; P = .027), and lactate (5.5 [IQR, 4.5-9.8] mmol/L vs 3.1 [IQR, 1.9-6.2] mmol/L; P = .009) than did patients without BPP. The discriminatory power evaluated by the area under the receiver operating characteristic curve (aROC) for PCT (aROC, 0.79) was superior to lactate (aROC, 0.71), BNP (aROC, 0.67), and CRP (aROC, 0.70). At a cutoff point of 17 ng/mL, PCT showed a sensitivity of 87%, a specificity of 67%, a positive predictive value of 30% and a negative predictive value of 97%, as a marker of pneumococcal bacteremia.

CONCLUSIONS

In this cohort, significantly higher PCT, BNP, lactate, and CRP levels were found in BPP, and PCT presented the best ability to identify pneumococcal bacteremia. A PCT serum level lower than 17 ng/mL could identify patients with SCAP unlikely to have pneumococcal bacteremia.

The role of soluble thrombomodulin in the risk stratification and prognosis evaluation of septic patients in the emergency department

INTRODUCTION

Soluble thrombomodulin (sTM) is a sensitive marker of endothelial damage. In this study we investigated the role of sTM in the evaluation of the severity and prognosis of septic patients in the emergency department (ED).

MATERIALS AND METHODS

A prospective, observational cohort study was performed in the ED of an urban, university hospital. Patients who had suspected infection with two or more criteria of systemic inflammatory response syndrome were consecutively enrolled. sTM, D-Dimer and procalcitonin levels were measured on enrollment, and the Mortality in Emergency Department Sepsis (MEDS) score was calculated. A 30-day follow-up was performed for all patients.

RESULTS

A total of 372 patients with sepsis, 210 patients with severe sepsis and 98 patients with septic shock were enrolled in this study. According to the disease severity, patients were divided into sepsis subgroup and severe sepsis subgroup (including septic shock). In addition, patients were divided into survivors subgroup and non-survivors subgroup according to the 30-day mortality. Plasma sTM levels in patients with severe sepsis were higher than those with sepsis (P<0.001). Compared with survivors, non-survivors has higher plasma sTM levels (P<0.001). Multivariate logistic regression analysis showed that sTM was an independent predictor of severe sepsis (odds ratio 1.11) and 30-day mortality (odds ratio 1.059). Receiver operating characteristic curve analysis showed that sTM was a useful parameter in prediction of severe sepsis (0.859) and 30-day mortality (0.78). Compared with the MEDS score alone, combination of sTM and the MEDS score can improve the accuracy in prediction of severe sepsis and 30-day mortality.

CONCLUSIONS

sTM is a valuable biomarker in the risk stratification and prognosis evaluation of ED sepsis. Furthermore, sTM can enhance the ability of the MEDS score in prediction of severe sepsis and 30-day mortality.

Serum concentrations of A Proliferation-Inducing Ligand (APRIL) are elevated in sepsis and predict mortality in critically ill patients

INTRODUCTION

Inflammatory and autoimmune diseases have been associated with the tumor necrosis factor superfamily member "A PRoliferation Inducing Ligand" (APRIL). However, up to now, APRIL has not been investigated in critical illness or sepsis. We therefore analyzed APRIL serum concentrations in a large cohort of well-characterized intensive care unit patients.

METHODS

Serum concentrations of APRIL were measured in 246 critically ill patients, of which 157 fulfilled sepsis criteria in comparison with 81 healthy controls. Clinical data were recorded and correlated with APRIL serum levels.

RESULTS

We detected strongly elevated serum levels of APRIL in critically ill patients compared with healthy controls. Levels of APRIL were further elevated in sepsis and significantly correlated with classical markers of inflammation, bacterial infection, or multiorgan failure. Consequently, high APRIL levels were associated with an unfavorable prognosis and predicted mortality with higher diagnostic accuracy than established prognostic scoring systems such as the Acute Physiology and Chronic Health Evaluation II score.

CONCLUSION

Serum levels of APRIL were significantly elevated in intensive care unit patients, with the highest concentrations in septic patients, and associated with unfavorable outcome. Besides being used as a single marker, APRIL may be implemented into established scoring systems to further improve their sensitivity and specificity in predicting patient's prognosis.