key: cord-1050638-g8jpqsxe
authors: Sevinc, Can; Demirci, Recep; Timur, Ozge
title: Predicting hospital mortality in COVID‐19 hemodialysis patients with developed scores
date: 2021-07-28
journal: Semin Dial
DOI: 10.1111/sdi.13004
sha: 12e5b8d5701a09b6d1a71c9b5f588d34d9496c49
doc_id: 1050638
cord_uid: g8jpqsxe

Various risk scores such as COVID‐GRAM Critical Illness Risk Score (COVID‐GRAM), quick COVID‐19 Severity Index (qCSI), and systemic immune‐inflammation index (SII) have been developed to determine critical illness in hospitalized patients. None of these risk scoring systems was evaluated in HD patients who indeed carry the highest risk of developing critical illnesses. We aimed to evaluate, in hemodialysis (HD) patients with COVID‐19, the performance of these scoring systems for the need of intensive care unit (ICU) and mortality. The qCSI, COVID‐GRAM, and SII scores of the patients at admission to hospital were calculated and grouped according to the scoring results. The primary outcome of the study was mortality and need of ICU. Critical illness was described as a composition of admission to the ICU, invasive ventilation, or death. It was determined that when the qCSI is over 6.5, the need for ICU increased 13.8 times and mortality increased 21.3 times. When the COVID‐GRAM score is >157, the ICU need increased 14.7 times and the mortality increased 33.7 times. We found that the need for ICU increased 4.2 times and mortality increased 3.1 times when the SII score was >1145. These tests, which can be easily calculated, could be used to estimate the risk of developing critical illness among COVID‐19 HD patients. Estimating the risk of critical illness could help to reduce mortality in HD patients.

Coronavirus Disease 2019 (COVID-19) caused by a novel coronavirus was declared as a pandemic by the World Health Organization (WHO) on March 2020. 1 Elderly population and patients with multiple comorbid diseases were more seriously affected by COVID-19. Hemodialysis (HD) patients due to weaker immune system, comorbid diseases, and older age are one of the most susceptible populations to COVID-19. HD patients visit the hospital or dialysis center routinely and stay in the same indoor environment with other patients and staff for 3-4 h each session, so it is difficult to prevent and control COVID-19. HD patients have a less efficient immune system that can alter their response to . Therefore, it is not surprising that HD patients have increased mortality. [2] [3] [4] Studies have shown that mortality rate in HD patients is quite high. [5] [6] [7] Early detection of patients who are likely to develop critical illness is of great importance to reduce mortality. Various risk scores such as COVID-GRAM Critical Illness Risk Score (COVID -GRAM), quick COVID-19 Severity Index (qCSI), and systemic immune-inflammation index (SII) have been developed to determine critical illness in hospitalized patients. The success of these scores in predicting critical illness and mortality in hospitalized patients was found to be high. Easy application at the bedside is also the advantage of these scores. [8] [9] [10] [11] None of these risk scoring systems are evaluated in HD patients who indeed carry the highest risk of developing critical illnesses.

In this study, we aimed to evaluate, in HD patients with COVID-19, the performance of these scoring systems including COVID-GRAM, qCSI, and SII for the need of intensive care unit (ICU) and mortality.

This study comprised 117 maintenance HD patients, hospitalized in Erzurum Regional Training and Research Hospital, the pandemic hospital in our region, between 22 March to 31 December 2020, and who were diagnosed COVID-19 based on positive real-time reverse transcription-polymerase chain reaction (rRT-PCR) assay of a specimen collected on a nasopharyngeal swab or chest computed tomography (CT) compatible in terms of COVID-19. The study was designed as a retrospective cohort study.

Demographic characteristics (age, gender), chronic diseases, complaints during hospitalization, vital signs at the time of admission, chest CT findings during hospitalization, discharge status, COVID-19 PCR test results, laboratory values such as white blood cell count (WBC, 10 3 /μl), neutrophil count (10 3 /μl), lymphocyte count (10 3 /μl), neutrophil lymphocyte ratio (NLR), hemoglobin (g/dl), platelet count (10 3 /μl), platelet lymphocyte ratio (PLR), mean platelet volume (MPV, fl), pH, lactate (mmol/L), alanine aminotransferase (ALT) (U/L), aspartate aminotransferase (AST, U/L), total bilirubin (mg/dl), lactate dehydrogenase (LDH, U/L), creatinine kinase (CK, U/L), blood urea nitrogen (BUN, mg/dl), creatinine (mg/dl), corrected calcium (mg/dl), phosphorus (mg/dl), albumin (g/L), total protein (gr/L), uric acid (mg/dl), international normalized ratio (INR), D-dimer (ng/ml), ferritin (ng/ml), C-reactive protein (CRP, mg/L), and procalcitonin (PCT, ng/ml) of the patients included in the study were recorded. Each record was checked independently by two clinicians.

Treatments given to patients during hospitalization (such as highdose vitamin C, immune plasma, favipiravir, hydroxychloroquine, tocilizumab, antibiotic, and steroid use), intensive care unit needs, high flow oxygen needs, noninvasive mechanical ventilation needs, and intubation needs were retrospectively analyzed and recorded through the hospital's electronic recording system.

The NLR value was calculated by dividing the absolute neutrophil count by the number of lymphocytes, the PLR value by dividing the platelet count by the number of lymphocytes, and CRP/albumin value by dividing the CRP value by the albumin value.

Risk scores were calculated using baseline clinical data collected retrospectively from the patient cohort. The qCSI is a test predicting the risk of 24-h critical respiratory disease in hospitalized COVID-19 patients. The qCSI is a 12-point scale that uses only three variables available at the bedside: nasal cannula oxygen flow rate, respiratory rate, and minimum documented pulse oximetry. Patients are evaluated over 12 points and then assigned to four risk strata based on the following scores: 0-3 low risk, 4-6 low-intermediate risk, [7] [8] [9] high-intermediate risk, and >10 high risk. 11 COVID-GRAM is a scoring system that predicts the risk of critical illness in hospitalized COVID-19 patients and can be easily applied. Age, chest radiography (CXR) abnormality, dyspnea, hemoptysis and confusion, number of comorbid diseases, cancer history, NLR, LDH, and direct bilirubin levels are used to calculate the risk score. Patients are divided into three risk groups according to the score obtained, defined as low risk (<1.7%), medium risk (1.7% to 40.4%), high risk (≥40.4%). SII is calculated by (N Â P)/L (N, P, and L represent neutrophil counts, platelet counts, and lymphocyte counts, respectively). The qCSI, COVID-GRAM, and SII scores of the patients at admission to hospital were calculated and grouped as described above according to the scoring results. The primary outcome of the study was mortality and need of ICU. Critical illness was described as a composition of admission to the ICU, invasive ventilation, or death.

All of the statistical analysis were performed by SPSS software Independent group t test (Student's t test) was used to compare two groups, and Mann-Whitney U test was used when the conditions were not met. One-way analysis of variance and Tukey HSD test, one of the multiple comparison tests, were used for comparison of three or more groups. When the conditions were not met, the Kruskal-Wallis test and the multiple comparison Bonferroni-Dunn test were used. Chi-square and Fisher's exact test methods were used in the analysis of categorical data. p value less than 0.05 was considered to have statistical significance. Receiver operating system (ROC) curve analysis was performed for diagnostic decision-making features. The ratio closest to the sum value of maximum sensitivity and specificity was regarded as the optimal cutoff value. Then, logistic regression analysis of these cutoff values was performed.

A total of 117 patients, 60 women (51.3%) and 57 men (48.7%), were included in the study. The demographic and laboratory data of the patients are presented in Table 1 .

The need for ICU was found to be significantly higher in patients with DM (p = 0.007); there was no difference in terms of other comorbid diseases. The need for ICU was also higher in patients with complaints of shortness of breath at admission (p < 0.001) and

patients with abnormal chest CT (p = 0.011). COVID-GRAM, qCSI,

and SII values were found to be significantly higher in patients who needed ICU. The comparison of patients who need and do not need to be transferred to the ICU is given in Table 2 .

The mortality rate in our patient cohort was 24.7%. Nonsurvival patients were older (p = 0.017). DM and CHF were the most common comorbid conditions in nonsurvival patients (p = 0.021, p = 0.012).

The complaint of all 29 nonsurvival patients at the time of admission was shortness of breath. COVID-GRAM, qCSI, and SII values were found to be significantly higher in nonsurvival patients. Comparison of the patients is given in Table 3 .

The rate of DM and CHF was found to be significantly higher in COVID-GRAM high-risk group patients. The need for ICU and chest CT abnormality were higher in patients in the high-risk group.

In addition, the need for HF, NIMV, and MV was significantly higher in these patients. Patients in the high-risk group have signifi- and SII risk scores were significantly higher. Comparative data of these four groups are given in Table 5 . Table 6 and Figure 1 .

ROC analysis was performed for the diagnostic decision-making features of qCSI, COVID-GRAM, and SII in predicting mortality. AUC was 0.899 (95% CI, 0.838-0.961) for the qCSI score, 0.927 (95% CI, 0.879-0.976) for the COVID-GRAM score, and 0.714 (95% CI, 0.596-0.832) for the SII score (p < 0.001). The cutoff value for ICU need was 6.5 for the qCSI score, 157 for the COVID-GRAM score, and 1145 for the SII score. The cutoff value for mortality was 6.5 for the qCSI score, 157 for the COVID-GRAM score, and 1145 for the SII score. ROC analysis curves and results for mortality are given in Table 7 and Figure 1 . Logistic regression analysis was performed with these cutoff values for both ICU need and mortality. It was determined that when the qCSI is over 6.5, the need for ICU increased 13.8 times and mortality increased 21.3 times. When the COVID-GRAM score is >157, the ICU need increased 14.7 times and the mortality increased 33.7 times. We found that the need for ICU increased 4.2 times and mortality increased 3.1 times when the SII score was >1145 (Tables 8 and 9 ).

Despite all global efforts, the COVID-19 outbreak has not yet been fully controlled, and single-day confirmed cases are on the rise.

Although vaccines developed against the disease, which does not have an effective treatment, have started to be applied, it is still too early to comment on its effectiveness. Mortality is very high in severe diseases. 12 In order to reduce mortality and control the disease, it is important to recognize the serious disease at an early stage and to identify patients who may develop a serious disease. It is possible to distinguish patients who may develop serious diseases, allowing mortality to be prevented by using laboratory data at the time of hospitalization. HD patients have a high risk of COVID 19 infection due to both the CKD itself and its existing comorbidities. 13, 14 In this study involving 117 patients, the most common symptoms at the time of admission to hospital were cough, myalgia fever, and shortness of breath. Fever is the most common symptom in studies conducted with the general population. 15, 16 In HD patients, fever was not a common symptom due to the decreased inflammatory response.

In our study, in accordance with the literature, fever was less observed compared with the other symptoms. 17 In this study, conducted with 117 HD patients diagnosed with Hematological parameters such as lymphopenia, increased neutrophil count, and leukocytosis increased NLR, and thrombocytopenia are the most common findings observed and are positively correlated with disease severity. [28] [29] [30] In our study, NLR and neutrophil counts were found to be significantly higher in patients with high risk for critical illness according to qCSI compared to other groups (p < 0.001).

Parameters previously determined and correlated with disease severity are also compatible with this risk score.

SII including neutrophil, platelet, and lymphocyte counts, which shows the balance between the immune system of the host and the inflammatory state, is a prognostic marker in patients with sepsis. 31 It is also used as a poor prognosis indicator for small cell lung, hepatocellular, colorectal, and gastric cancers. [32] [33] [34] [35] In the study of Usul et al., in which they examined 282 patients, SII was found to be higher in COVID-19 patients compared to healthy controls, and it was suggested that it plays a diagnostic role for SARS-CoV-2 infection. 36 In the study of Fois et al., AUC of SII for mortality was found 0.628 for mortality. 10 In our study, AUC of SII was 0.752 for ICU need and 0.714 for mortality. Therefore, SII can be used to predict mortality and ICU need for hospitalized COVID-19 HD patients.

We found the qCSI was over 6.5, the need for ICU increased 13.8 times, and mortality increased 21.3 times. When the COVID-GRAM score is >157, the ICU need increased 14.7 times and the mortality increased 33.7 times. We found that the need for ICU increased 4.2 times and mortality increased 3.1 times when the SII score was >1145.

Although vaccines developed against SARS-CoV-2 are met with great hope all over the world, time is needed for the vaccination of all individuals and the development of social immunity, and the virus continues to spread in this period. Therefore, it is important to identify patients who may develop critical illness at the time of diagnosis to reduce mortality. Patients in the high-risk group for COVID-19, such as HD patients, should be evaluated in terms of ICU need and mortality at admission to hospital with easily accessible tests.

In our study, it has been shown that COVID-GRAM, qCSI, and SII can also be used in HD patients. Although it has been shown in the literature that these tests can be used separately in the general population to identify patients who may develop critical diseases, there are no studies conducted in HD patients. Risk scoring tests have advantages and disadvantages compared to each other. While COVID-GRAM also evaluates comorbid diseases, qCSI can be calculated with less information. Our study is the first study in the literature that examined three tests together and conducted in a special population such as HD patients.

As for any retrospective study, some limitations are worth considering. Our sample size is limited and therefore the global accuracy of our ROC curve estimation could be reduced, still keeping a good reliability in ROC curve comparison. The data are entirely from a single center in Turkey, which could potentially limit the generalizability of the risk scores in other areas of the world.

As a result, we think that these tests, which can be easily calcu- 

World Health Organization. WHO Coronavirus Disease (COVID-19) Dashboard. World Health Organization

Mortality analysis of COVID-19 infection in chronic kidney disease, haemodialysis and renal transplant patients compared with patients without kidney disease: a nationwide analysis from Turkey

Middle east respiratory syndrome clinical practice guideline for hemodialysis facilities

Urgent: stop preventable infections now

Clinical characteristics of and medical interventions for COVID-19 in hemodialysis patients in Wuhan

Covid-19 and its impact on nephropathic patients: the experience at Ospedale "Guglielmo da Saliceto" in Piacenza

COVID-19: clinical course and outcomes of 36 hemodialysis patients in Spain

Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19

Predicting in-hospital mortality in COVID-19 older patients with specifically developed scores

The systemic inflammation index on admission predicts in-hospital mortality in COVID-19 patients

Development and validation of the quick COVID-19 Severity Index: a prognostic tool for early clinical decompensation

Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review

Chronic kidney disease is associated with severe coronavirus disease 2019 (COVID-19) infection

Excerpts from the US Renal Data System 2009 Annual Data Report

Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China

Clinical features, laboratory characteristics, and outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19): early report from the United States

COVID-19 in hemodialysis (HD) patients: Report from one HD center in Wuhan

Risk factors for mortality and respiratory support in elderly patients hospitalized with COVID-19 in Korea

Clinical observations in very elderly patients with COVID-19 in Wuhan

Arterial hypertension and risk of death in patients with COVID-19 infection: systematic review and meta-analysis

SARS-CoV-2 infection in hospitalized patients with kidney disease

Clinical characteristics of coronavirus disease 2019 in China

Clinical features of patients infected with 2019 novel coronavirus in Wuhan

Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region, Italy

Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China

Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China

Performance of the quick COVID-19 severity index and the Brescia-COVID respiratory severity scale in hospitalized patients with COVID-19 in a community hospital setting

Clinical characteristics of 82 death cases with COVID-19

Haematological characteristics and risk factors in the classification and prognosis evaluation of COVID-19: a retrospective cohort study

Characteristics of disease progress in patients with coronavirus disease 2019 in Wuhan

Systemic immune inflammatory index in sepsis

Systemic immuneinflammation index, based on platelet counts and neutrophillymphocyte ratio, is useful for predicting prognosis in small cell lung cancer

Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma

Prognostic and clinicopathological significance of systemic immune-inflammation index in colorectal cancer: a meta-analysis

Comparison of the prognostic value of immunoinflammation-based biomarkers in patients with gastric cancer

Role of hematological parameters in COVID-19 patients in the emergency room

Predicting hospital mortality in COVID-19 hemodialysis patients with developed scores