University of Groningen
Pharmacoeconomics of prophylactic, empirical, and diagnostic-based antibiotic treatments
Purba, Abdul
DOI:
10.33612/diss.128518764
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.
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Publication date: 2020
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Purba, A. (2020). Pharmacoeconomics of prophylactic, empirical, and diagnostic-based antibiotic
treatments: Focus on surgical site infection and hospitalized community-acquired pneumonia. University of Groningen. https://doi.org/10.33612/diss.128518764
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CHAPTER 8
Appendix
Laboratory Findings as Predictors of Sepsis Mortality Among Adult Patients in a General Hospital in Indonesia
Ahmad Lukman Hakim Rahmat Sayyid Zharfan Abdul Khairul Rizki Purba
This chapter is based on the published article:
Zarfan RS, Hakim AL, Purba AKR, Sulistiawan SS, Soemedi BP. Albumin, Leukosit, and Protombin as Predictors of Sepsis Mortality among Adult Patients in Soetomo General Hospital, Surabaya, Indonesia. Indonesian Journal of Anaesthesiology and Reanimation, 2019; 1(1): 8-12. doi: 10.20473/ ijar.V1I12019.8-12.
ABSTRACT
Introduction: Sepsis is a complex, multifactorial syndrome that is globally associated with high morbidity and mortality rates. However, there is a paucity of conclusive evidence regarding early predictive factors for sepsis-related mortality and morbidity.
Objective: We aimed to identify and analyze prominent predictors of sepsis-related mortality determined from patients’ laboratory reports.
Methods: The study was designed as an analytic observational study, entailing a case-control approach. The data for the study were extracted from the medical records of 50 patients with sepsis admitted to Dr. Soetomo General Hospital in Surabaya, Indonesia between 2014 and 2015. We assessed levels of blood urea nitrogen, creatinine serum, albumin, sodium, potassium, and chloride in the patients’ blood reports along with leukocyte, hemoglobin, hematocrit, and platelet counts, prothrombin time (PT), and activated partial thromboplastin time (APTT). We applied logistic regression to estimate the frequency of sepsis-related mortality and the relationship between the laboratory results and less than 28-days mortality.
Results: A total of 22 out of 50 patients (44%) succumbed. We initially conducted the regression model using all three biomarkers as covariates and subsequently eliminated the covariate with the highest p-value through a process of backward elimination. This process was repeated until only statistically significant covariates remained. Multivariate analysis revealed that albumin levels, leukocyte counts, and PT were associated with high mortality rates. We obtained the following independent predictors of mortality, identified through further multivariate regression analyses: an albumin level lower than 3.5 g/dL, a leukocyte count above 12,000/µL, and prolonged (>14 seconds) PT, with p-values below 0.05 (0.029, 0.049, and 0.027, respectively).
Conclusions: Low albumin levels, elevated leukocyte counts, and prolonged PT are clinically considered as independent predictors of mortality among adult patients with sepsis.
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INTRODUCTION
Sepsis is a complex, multifactorial disease associated with high rates of morbidity and mortality worldwide.1It continues to pose considerable challenges within intensive care units because of
the associated high mortality rate despite the provision of optimal care. The introduction and use of serum biomarkers have significantly enhanced the abilities of doctors to diagnose and predict sepsis prognoses.1
Within the clinical practice, the number of leukocytes is an extensively used biomarker that is deemed sufficient for assessing the clinical progress of patients with sepsis along with the use of other laboratory parameters, such as lactate acid, procalcitonin, and c-reactive protein. However, the limited availability of facilities for assessing these parameters, particularly in remote areas, significantly impacts on hospital costs. A previous study conducted in Indonesia reported that of the patients in the sample diagnosed with sepsis, 27.08% had severe sepsis, 14.58% were in septic shock, and the remaining 58.33% were in a state of sepsis. In the case of patients with severe sepsis, the mortality rate ranged between 40% and 60%.2
Given the complex pathophysiology of sepsis, more than one biomarker is required to enable a comprehensive description of host responses to the disease. A combination of several biomarkers in conjunction with certain classification rules would improve accuracy and applicability. Given a paucity of conclusive evidence on early predictive factors for sepsis-related mortality and morbidity, we aimed to identify prominent predictors based on values extracted from laboratory reports, entailing a combination of several biomarkers associated with less than 28-days mortality in sepsis patients. These values relate, for example, to leukocyte counts, albumin levels, and coagulation factors.
MATERIALS AND METHODS
The study was designed as an analytic observational study using a case-control approach. The data were extracted from the medical records of 50 patients with sepsis admitted to Soetomo General Hospital in Surabaya, Indonesia, between 2014 and 2015.
The records of adult patients who fulfilled the criteria required for a sepsis diagnosis were collected. Patients who had received antibiotics for a period exceeding 24 hours prior to blood sampling were excluded. Baseline and demographic data, such as the sex, age, admission category, main site of infection, and comorbidities of the patients, were collected.
Levels of blood urea nitrogen, creatinine serum, albumin, sodium, potassium, and chloride along with counts of leukocytes, hemoglobin, hematocrit, and platelets, activated partial thromboplastin time, and prothrombin time (PT) were compiled from the patients’ blood reports. Multivariate logistic regression was conducted to estimate the correlation between covariates in the laboratory findings and less than 28-days of sepsis-related mortality.
RESULTS AND DISCUSSION
We performed multivariate logistic regression to model biomarker capabilities, with the aim of identifying patients with a mortality outcome of less than 28 days. We initially constructed the regression model using the whole biomarkers provided as covariates. The covariate with the highest p-value was removed using a backward elimination method and the model was applied to the remaining three biomarkers. This process was repeated until only statistically significant biomarkers remained.
Table 8.1. Predictive Values for Leukocyte Counts, Prothrombin Time (PT), and Albumin Levels
Associated with Mortality in Sepsis Patients
Biomarker All patients N = 50 Survivors n = 28 Non- Survivors
n = 22 p-value AUROC Cut-off
Leukocytes 13,370 12,463.57 14,523.63 0.049 0.606 12,800
Albumin 2.92 3.21 2.55 0.029 0.750 2.217
PT 19.81 18.19 21.86 0.027 0.649 14.2
Note. The descriptions of the two groups were derived from the results of the Compare Means test. AUROC = area under the
receiver operating characteristic curve; PT = prothrombin time
We calculated the probability of under 28-days sepsis-related mortality, which represents the final predictor of sepsis-related mortality based on the results of the regression equation. As shown in Table 1, 22 out of 50 patients (44%) died. The results of our multivariate analysis showed that low albumin levels, high leukocytes counts, and extended PT were associated with high mortality rates. We identified the following independent predictors of mortality, determined through further multivariate regression analysis: a level of albumin lower than 3.5 g/dL (p-value = 0.029); a leukocyte count above 12,000/µL (p-value = 0,049), and a prolonged PT (>14 seconds) (p-value = 0.027). Figure 8.1 shows the area under the receiver operating characteristic curve (AUROC) model used for predicting sepsis-related mortality along with each of the constituent predictive biomarkers of mortality within 28 days. We found that the AUROC is from the model: the values for the leukocyte count, PT, and albumin levels were 0.606, 0.649, and 0.750 (95% CI), respectively, indicating reasonably good model discrimination. The sepsis mortality scores outperformed those of the individual biomarkers in predicting mortality within 28 days. These biomarkers, when applied, revealed moderate to good performance levels.
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Figure 8.1. Receiver Operating Characteristic (ROC) Curve depicting leukocyte counts,
prothrombin time (PT), and albumin levels
For this analytic observational study, we collected historical data for 50 patients with sepsis and assessed three biomarkers used to predict the risk of less than 28-days mortality for these patients following their hospital admissions. The use of baseline leukocyte counts, PT, and albumin levels as sepsis-related mortality predictors could provide reasonable predictions of under 28-days mortality outcomes.
Biomarkers, especially when used in combination, can be reliable for predicting sepsis-related mortality outcomes. The findings of recent studies3,4 also indicated that a combination of
biomarkers performed better than other clinical scores used routinely for predicting sepsis-related mortality.
Sepsis itself often disrupts the coagulation function, leading to conditions that range from mild changes to severe disseminated intravascular coagulation (DIC). Sepsis patients with severe DIC may experience symptoms of thromboembolic diseases, such as purpura fulminans or clinically obscure microvascular fibrin deposition, both of which are strongly indicative of multiple organ dysfunction. Severe bleeding or bleeding and thrombosis may be the main symptoms.5
The disrupted coagulation mechanism, specifically DIC, is an important predictor and possible clinical outcome for patients with severe sepsis.6
The onset of coagulation activated by proinflammatory cytokines, such as IL-6, is contingent on tissue factors (TF). Increased thrombin formation is caused by the tumor necrosis factor (TNF-α), which breaks down the damaged physiological anticoagulant mechanism, whereas the spread of fibrin deposition within the microvasculature is caused by inadequate fibrin degradation resulting from an inhibited fibrinolytic system.6 The complex TF-VIIa factor catalyzes the activation of the IX
and X factors, increasing the activation of factor X and prothrombin, respectively.8
Our results entailed the combined use of leukocyte counts, PT, and albumin levels as predictors of sepsis-related mortality. Although the results are encouraging, this study had some limitations.
Our predictions of sepsis deaths are based on data from a single case; whether or not they can be generalized to an external population remains to be determined. Clinical outcomes are dependent on the quality of patient management, which can vary across health centers. This lack of standardization may have affected our results. However, the 44% mortality rate within the sample population approximates conditions observed more widely in Indonesia.2 We
attempted to control confounders posed by other clinical variables by modeling sepsis death scores within our logistic regression model. Nevertheless, we found it difficult to explain these other unmeasured confounding factors. Moreover, the selection bias associated with the use of a convenience sample in this study may have led to a less representative population. Consequently, further research is needed to improve and validate the clinical applicableness of this sepsis-related mortality predictor of the clinical outcomes of sepsis treatment.
CONCLUSIONS
Our findings indicated that from a clinical perspective, low albumin levels, elevated leukocyte counts, and prolonged PT are independent predictors of mortality among adult patients with sepsis. More research is needed to explore and develop these findings and to assess whether these predictors of sepsis-related mortality, derived from biomarkers, into certain classification and score, can be successfully integrated with physicians’ clinical practice to enhance predictions and decision making relating to patients’ clinical settings.
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REFERENCES
1. Wan Fadzlina Wan Muhd Shukeri, Azrina MdRalib, Nor Zamzila Abdulah, Mohd BasriMat-Nor. Sepsis mortality score for the prediction of mortality in septic patients. Journal of Critical Care, Volume 43, February 2018, Pages 163-168.
2. Rheza N. Tambajong, Diana C. Lalenoh, Lucky Kumaat. Profil Penderita Sepsis di ICU RSUP Prof. Dr. R. D. Kandou Manado Periode Desember 2014 – November 2015. Thesis. Fakultas Kedokteran Universitas Sam Ratulangi Manado. 2016. 3. Farzanegan B, Zangi M. Predictive factors for sepsis diagnosis, length of intensive care unit (ICU) stay and mortality in ICU.
J Cell Mol Anesth. 2017;2(2):55-62.
4. Bárbara Magalhães Menezes, Fábio Ferreira Amorim, Adriell Ramalho Santana, Felipe Bozi Soares, Fernanda Vilas Bôas Araújo, Jacqueline Rodrigues de Carvalho, Mariana Pinheiro Barbosa de Araújo, Louise Cristhine de Carvalho Santos, Pedro Henrique Gomes Rocha, Jaqueline Lima de Souza, Mateus Gonçalves Gomes, Pedro Nery Ferreira Júnior, Alethea Patrícia Pontes Amorim, Rodrigo Santos Biondi and Rubens Antônio Bento Ribeiro. Platelet/leukocyte ratio as a predictor of mortality in patients with sepsis. Critical Care 201317 (Suppl 4) :52. Available on: https://doi.org/10.1186/cc12952. 5. Aziz Kallikunnel Sayed Mohamed, Asmita Anilkumar Mehta, &Ponneduthamkuzhy James. Predictors of mortality of
severe sepsis among adult patients in the medical Intensive Care Unit. Lung India. 2017 JulAug; 34(4): 330–335. 6. Miriam Sanderson, Marc Chikhani, Esme Blyth, Sally Wood, Iain K Moppett, Tricia McKeever, & Mark JR Simmonds.
Predicting 30-day mortality in patients with sepsis: An exploratory analysis of process of care and patient characteristics. Journal of the Intensive Care Society 0(0) 1–6The Intensive Care Society 2018.
7. Alison Woodworth.Sepsis and the Clinical Laboratory. Department of Pathology, Microbiology, and Immunology Vanderbilt University Medical Center Nashville, TN. 2013.
8. Shapiro NI, Trzeciak S, Hollander JE, et al. prospective, multicenter derivation of a biomarker panel to assess risk of organ dysfunction, shock, and death in emergency department patients with suspected sepsis. Crit Care Med, 2009; 37:96–104.
