The Advancement in Detecting Sepsis and Its Outcome: Usefulness of Procalcitonin in Diagnosing Sepsis and Predicting Fatal Outcomes in Patients Admitted to Intensive Care Unit

The Role of Procalcitonin/Albumin Ratio and CRP/Albumin Ratio in Predicting In-hospital Mortality in COVID-19 Patients

Background

Hospitalized coronavirus disease 2019 (COVID-19) patients have higher mortality rates. Parameters to predict mortality are needed. Therefore, we investigated the power of procalcitonin/albumin ratio (PAR) and C-reactive protein/albumin ratio (CAR) to predict in-hospital mortality in hospitalized COVID-19 patients.

Methods

In this study, 855 patients were included. Patients' PAR and CAR values were recorded from the hospital information management system. The patients were evaluated in two groups according to their in-hospital mortality status.

Results

In-hospital mortality was observed in 163 patients (19.1%). The median PAR and CAR values of patients in the non-survivor group were statistically significantly higher than those of patients in the survivor group, PAR (median: 0.07, interquartile range [IQR]: 0.03-0.33 vs. median: 0.02, IQR: 0.01-0.04, respectively; p < 0.001); CAR (median: 27.60, IQR: 12.49-44.91 vs. median: 7.47, IQR: 2.66-18.93, respectively; p < 0.001). The area under the curve (AUC) and odds ratio (OR) values obtained by PAR to predict in-hospital mortality were higher than the values obtained by procalcitonin, CAR, albumin, and CRP (AUCs of PAR, procalcitonin, CAR, albumin, and CRP: 0.804, 0.792, 0.762, 0.755, and 0.748, respectively; OR: PAR > 0.04, procalcitonin > 0.14, CAR > 20.59, albumin < 4.02, and CRP > 63; 8.215, 7.134, 5.842, 6.073, and 5.07, respectively). Patients with concurrent PAR > 0.04 and CAR > 20.59 had an OR of 15.681 compared to patients with concurrent PAR < 0.04 and CAR < 20.59.

Conclusions

In this study, PAR was found to be more valuable for predicting in-hospital COVID-19 mortality than all other parameters. In addition, concurrent high levels of PAR and CAR were found to be more valuable than a high level of PAR or CAR alone.

A Clinical and Demographic Profile of Elderly (>65 Years) in the Medical Intensive Care Units of a Tertiary Care Center

Background

The elderly population in India is expected to increase to 319 million by 2050. Managing critically ill elderly patients in intensive care units (ICUs) is a difficult task. Proper planning and development of healthcare infrastructure are of prime importance to face this challenge.

Objectives

To study the clinical profile and outcomes of elderly patients admitted to the medical ICUs.

Materials and methods

A time-bound, prospective observational study on elderly patients admitted to medical ICUs for more than 48 hours was conducted from March 2019 to September 2020. The demographic, biochemical, hematologic, and microbiological data on antibiotic susceptibility patterns on various organisms and procalcitonin (PCT) reports were collected. Acute Physiology and Chronic Health Evaluation II (APACHE II) score was calculated. Various treatment modalities, such as mechanical ventilation, inotropes, hemodialysis, antibiotics, culture report in sepsis patients, and length of ICU stay were collected.

Results

The age of the patients and the length of their ICU stay were not significantly associated with outcomes. Sepsis and APACHE II scores are significantly associated with outcomes. Receipt of mechanical ventilation, vasopressor support, and hemodialysis are significantly associated with mortality (p < 0.001).

Conclusion

The patients' ages were not significantly associated with outcomes. The most common cause of death among elderly patients was found to be sepsis, followed by pneumonia. In elderly ICU patients, gram-negative organisms are the most common causative agents in bloodstream infections. The APACHE II score, sepsis, receipt of mechanical ventilation, vasopressor support, and hemodialysis are significantly associated with mortality.

How to cite this article

Prabhudev P, Ramamoorthi K, Acharya RV. A Clinical and Demographic Profile of Elderly (>65 Years) in the Medical Intensive Care Units of a Tertiary Care Center. Indian J Crit Care Med 2023;27(3):166-175.

Comparison of Two Predictive Models of Sepsis in Critically Ill Patients Based on the Combined Use of Inflammatory Markers

Background

Sepsis is a systemic inflammatory response due to infection, resulting in organ dysfunction. Timely targeted interventions can improve prognosis. Inflammation plays a crucial role in the process of sepsis. To identify potential sepsis early, we developed and validated a nomogram model and a simple risk scoring model for predicting sepsis in critically ill patients.

Methods

The medical records of adult patients admitted to our intensive care unit (ICU) from August 2017 to December 2020 were analyzed. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%). A nomogram model was developed through multivariate logistic regression analysis. The continuous variables included in nomogram model were transformed into dichotomous variables. Then, a multivariable logistic regression analysis was performed based on these dichotomous variables, and the odds ratio (OR) for each variable was used to construct a simple risk scoring model. The receiver operating characteristic curves (ROC) were constructed, and the area under the curve (AUC) was calculated.

Results

A total of 2074 patients were enrolled. Finally, white blood cell (WBC), C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin (PCT) and neutrophil-to-lymphocyte ratio (NLR) were included in our models. The AUC of the nomogram model and the simple risk scoring model were 0.854 and 0.842, respectively. The prediction performance of the two models on sepsis is comparable (p = 0.1298).

Conclusion

This study combining five commonly available inflammatory markers (WBC, CRP, IL-6, PCT and NLR) developed a nomogram model and a simple risk scoring model to predict sepsis in critically ill patients. Although the prediction performance of the two models is comparable, the simple risk scoring model may be simpler and more practical for clinicians to identify potential sepsis in critically ill patients at an early stage and strategize treatments.

The Value of a Complete Blood Count (CBC) for Sepsis Diagnosis and Prognosis

Sepsis represents an important global health burden due to its high mortality and morbidity. The rapid detection of sepsis is crucial in order to prevent adverse outcomes and reduce mortality. However, the diagnosis of sepsis is still challenging and many efforts have been made to identify reliable biomarkers. Unfortunately, many investigated biomarkers have several limitations that do not support their introduction in clinical practice, such as moderate diagnostic and prognostic accuracy, long turn-around time, and high-costs. Complete blood count represents instead a precious test that provides a wealth of information on individual health status. It can guide clinicians to early-identify patients at high risk of developing sepsis and to predict adverse outcomes. It has several advantages, being cheap, easy-to-perform, and available in all wards, from the emergency department to the intensive care unit. Noteworthy, it represents a first-level test and an alteration of its parameters must always be considered within the clinical context, and the eventual suspect of sepsis must be confirmed by more specific investigations. In this review, we describe the usefulness of basic and new complete blood count parameters as diagnostic and prognostic biomarkers of sepsis.