Which Factors Are Predicting the Mortality in Patients With Covid 19 in the Intensive Care Unit?

Background: COVID 19 infection is a global health problem; clinical and laboratory parameters have been developed to predict this disease-related mortality/morbidity. Some of these parameters are clinical parameters, while some are laboratory parameters. This study aims to determine whether APACHE II, GCS, age, presence of comorbidity, and absolute lymphocyte count effectively predict mortality in patients admitted to intensive care due to COVID 19. Method: We have included 108 PCR-positive COVID 19 patients admitted to the intensive care between 1 October and 31 November 2020 in our research. Demographic characteristics of all patients, APACHE II values within the rst 24 hours of admission to intensive care, the Glasgow Coma Scale, the presence of comorbidity, lymphocyte count during intensive care admission, duration of intensive care stay, and the mortality rates were recorded. Results: The average age of 108 individuals evaluated in the study was 67 ± 13.61 years, and 56.5% of the patient group consisted of the geriatric age range. Seventy (64.8%) of the patients were female, eighty-nine (82.4%) patients had at least one comorbidity. In the multivariate analysis, it was determined that lymphocyte value, APACHE II score, and the presence of any comorbidity are independent prognostic factors for mortality when accepted to ICU. Conclusion: In our study, we have determined that age, APACHE II value, presence of comorbidity, and baseline lymphocyte counts are independent predictors of mortality. mean standard deviation (min-max) for continuous variables number/percentage for categorical Chi-square test for the categorical parameters, and Annova Table for continuous parameters. a p-value 0.05 the univariate analysis in the multivariate analysis after the correlation between the factors was determined for exitus. Multivariate Backward Stepwise Cox Proportional Hazard Regression Analysis used to determine the effects of variables on P-value < 0.05 statistically signicant for the


Introduction
The SARS CoV 2 epidemic that emerged in Wuhan, China, is a member of the coronavirus family and has affected all world countries and caused a pandemic throughout 2020 (1). According to this infection spread table generated by the SARS CoV 2 virus, 10-20% of the patients require intensive care (2). While the disease-related hospital mortality rate is 4.3-11%, the mortality rate among the patients admitted to intensive care varies between centers and is around 30-60% (3). In COVID 19 patients admitted to intensive care with respiratory failure, clinical deterioration may rapidly deteriorate. Patients are lost due to severe ARDS and subsequent multiple organ dysfunction (4). COVID 19 infection is a global health problem, and an effective scoring system established with clinical and laboratory parameters to predict this disease-related mortality/morbidity has not been de ned. Scoring systems developed to predict patients' prognosis during admission to intensive care and evaluate the treatment's effectiveness is widely used in non-COVID patients. APACHE II, one of the most commonly used scoring systems, is known to be a successful scoring system in terms of mortality prediction (5). There is insu cient information that these scoring systems effectively predict mortality in COVID 19 patients, and research on this subject continues. Again, the decrease in the number of lymphocytes in  infection is thought to be the result of direct binding of the virus with ACE2 receptors on lymphocytes and apopytosis caused by a cytokine storm. In a recent meta-analysis published by Lui et al., 35-75% of the patients developed lymphopenia, and it states that mortality increased signi cantly in these patients (6).
Fan et al. reported that a lymphocyte count of < 0.6 x10 / L was associated with a poor prognosis in an analysis of 67 COVID 19 patients during their admission to an intensive care unit (7,8).
In this study, we aim to determine which factors effectively predict mortality in patients admitted to intensive care due to COVID 19. October − 31 November 2020 were included in our study. After our hospital's ethics committee's approval, all patient data were obtained from electronic medical records and patient follow-up forms.

Methods
The demographic characteristics of all patients, APACHE II values within the rst 24 hours of admission to intensive care, Glasgow Coma Scale, presence of comorbidity, lymphocyte count during intensive care admission, length of stay in intensive care, and presence of mortality were recorded.
In this study, while 65 years and over were accepted as advanced age, the average APACHE value for APACHE II was obtained by univariate analysis. As a result of this analysis, it was planned to compare those with APACHE II values of 19 and above and those below. Again, patients with a value of 10 and below for the Glasgow coma scale were accepted as patients in the precoma state. The cut-off value for the baseline lymphocyte count was accepted as 0.8 x10 /L, based on the national COVID − 19 science committee guidelines.

Statistical Analysis
Statistical analyses were performed using Statistical Package for Social Sciences (IBM SPSS Inc, Chicago, IL, USA) version 20.0. Descriptive statistics were expressed as mean ± standard deviation or median (min-max) for continuous variables and number/percentage for categorical variables. Chi-square test was used for the categorical parameters, and Annova Table Test was used for continuous parameters. Variables with a p-value < 0.05 in the univariate analysis were included in the multivariate analysis after the correlation between the factors was determined for exitus. Multivariate Backward Stepwise Cox Proportional Hazard Regression Analysis was used to determine the effects of variables effective on mortality. P-value < 0.05 was considered statistically signi cant for the results.
When admitted to ICU, the median lymphocyte value was 605 x10 /L and varied between 80 x10 /L and 3910 x10 /L. It was observed that the patients stayed in the ICU for an average of 9.56 ± 7.43 days, 46 (42.6%) patients were discharged from the ICU, and 62 (57.4%) patients died (Table 1).
In the univariate analysis, it was seen that age, APACHE II score, Glasgow Coma Scale Score, lymphocyte value when accepted to ICU, presence of any comorbidity determined mortality (Table 2). Factors associated with mortality were evaluated by correlation in univariate analysis, and it was found that there was only a correlation between the presence of any comorbidity and diabetes mellitus. Therefore, excluding diabetes mellitus, age (≤ 65 years vs. <65 years), lymphocyte value (≤ 800 x10 /L vs.> 800 x10 /L), APACHE II score (> 19 vs. ≤19) when admitted to ICU ), the presence of any comorbidity (present vs. absent), and the Glasgow Coma Scale Score (≤ 10 vs.> 10) for multivariate analysis to determine death. In the multivariate analysis, it was determined that lymphocyte value, APACHE II score, and the presence of any comorbidity are independent prognostic factors for mortality when accepted to ICU. Death increased 76 times in those with APACHE II score > 19 (95% Con dence Interval: 10.851-533.783; p < 0.001). This ratio was 7 (95% Con dence Interval: 1.520-33.827; p = 0.013) for those with a lymphocyte count of ≤ 800 x10 /L and 8 for those with any comorbidity (95% Con dence Interval: 1.015-64.151; p = 0.048) ( Table 3).

Discussion
Our primary aim in this study was to evaluate whether age, APACHE II value, presence of comorbidity, GCS, and baseline lymphocyte count were successful in predicting mortality in COVID 19 patients. Our study determined that the baseline lymphocyte value, APACHE II score, and the presence of any comorbidity are independent prognostic factors for mortality.
For many years, many scoring systems have been used in intensive care units to predict mortality and morbidity. Among these systems, APACHE II is accepted as the most successful scoring system in predicting mortality in all intensive care types and different patient groups (9,10 ). In some articles, it has been reported to be successful in COVID 19 patients (11,12). A study conducted by Zou et al., they stated that the APACHE II value of 17 or above in COVID 19 patients was an independent predictor for hospital mortality (13). Our study determined that the APACHE II value of > 19 and above is an independent risk factor, and death is 76 times more common in these cases. APACHE II scoring, which is calculated by taking the worst values during admission to intensive care, acts as an early warning system for physicians' high scores following these cases. These scoring systems are widely used in centers where intensive patient admissions are made during the pandemic, and they are instrumental in planning the treatment process of cases with high APACHE II values.
Although there is not enough information about using the Glasgow coma scale, which is frequently preferred in neurological examination in intensive care, in COVID − 19 cases, the GCS data can be based on studies using APACHE II since it is a parameter of APACHE II scoring. Our study showed that GCS being 10 or less has a signi cant relationship with mortality. In 28.7% of the cases, the GCS was 10 or less, and the mortality was 90.3%.
In the COVID 19 outbreak, it was observed that mortality rates were different in different age groups. It is observed that pulmonary physiology, pathology, and functions change in the presence of lung infection with aging. Therefore, in elderly individuals, response to the disease and tolerability deteriorate, and the mortality rate increases (14). Studies on advanced age COVID 19 patients have shown an increased risk of death (15)(16)(17)(18). In our study, 56.5% of the cases were 65 years old and above, and the mortality rate in these cases was signi cantly higher compared to patients aged 65 years or younger.
The presence of concomitant diseases in COVID 19 cases complicates the clinical picture. In their study by Chen et al., in which they evaluated the epidemiological and clinical characteristics of the cases they followed up with COVID 19 viral infection, they determined the presence of chronic disease in 51% of the cases and stated that cardiovascular, cerebrovascular disease, and diabetes mellitus were the most common accompanying diseases. They indicate in their research that mortality is higher in cases with comorbidity (19). In our research, we observed that 82.4% of our cases had at least one concomitant disease. The most common accompanying disease was hypertension (42.6%), followed by diabetes mellitus (36.1%). In our study, unlike Chen et al., the accompanying cardiovascular disease rate was in the 3rd rank with 28.7%. While any comorbidity's presence increased the risk of death eight times, mortality was found to be statistically signi cantly higher in patients with diabetes mellitus compared to those without diabetes mellitus.
The absolute value of lymphocytes decreases in COVID 19 associated viral infection. The reason for this decrease is related to the effect of 2019-nCoV on SARS-CoV lymphocytes, especially T lymphocytes. Virus particles spread to the respiratory mucosa and initiate a cytokine storm in the body. This situation stimulates the immune system and causes changes in peripheral white blood cells and immune cells such as lymphocytes. Some patients progress rapidly and pass away by developing ARDS, septic shock, and multiple organ failure. For this reason, early detection and timely treatment of critical cases are vital.
The decrease in the absolute lymphocyte count during admission to intensive care is a laboratory parameter that supports clinicians' diagnosis during the diagnosis of COVID 19. In comparison, Huang (20) and colleagues stated that their absolute lymphocyte count was < 1.0 × 10 / L in 63% of their patients, Fan et al. showed that absolute lymphocyte count < 0.6 × 10 / L had a signi cant correlation with mortality (7). In our study, when we evaluated the mortality relation of absolute lymphocyte count < 0.8 × 10 / L, we determined that lymphocyte count < 0.8 × 10 / L was an independent predictor for mortality. Absolute lymphocyte count < 0.8 × 10 / L increased the risk of death seven times.
Frater et al. state that there is some geographic variation in the percentage of COVID-19 patients presenting with lymphopenia in their article evaluating COVID 19 and clinical, hematological laboratory ndings (21). For example, an article from Singapore reporting several COVID-19 patients describes a much lower percentage of lymphopenia patients, as in a retrospective analysis of COVID-19 patients from Zhejiang Province, located ~ 450 miles from Wuhan (7,22). In contrast, in studies reported from Italy, lymphopenia is common in most patients admitted to the emergency room (23). The reasons for these and similar discrepancies are unclear, although they are probably multifactorial. Due to viral genomic mutations, it is possible that the immunological response to the virus will change as the pandemic spreads to other countries. Another possibility is that testing patients is not uniform, and the degree of lymphopenia can vary depending on the time of admittance. In our study, we have observed that 74 of 108 patients had an absolute lymphocyte value < 0.8 × 10 / L.
There were some limitations in our study. The rst is that it is a retrospective study, and the second is that there is no long-term (28 days or 6 months) data when determining intensive care and hospital mortality. Besides, we think that further studies should be conducted that comparative studies of baseline absolute lymphocyte count with data from different countries may help determine the cut-off value for lymphopenia.

Conclusion
In our study, we determined that age, APACHE II value, presence of comorbidity, and initial lymphocyte count are independent predictors of mortality. We concluded that studies with more patients and other clinical / laboratory data related to COVID-19 would be bene cial.

Declarations
Ethics approval and consent to participate: No consent form since this study is retrospective research study Availability of data and materials : All data is available our hospital medical data bank Competing interest: